diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/AvifImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/AvifImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..366e0c864bf6ece4c401fe827430e07fd7fc4a09 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/AvifImagePlugin.py @@ -0,0 +1,291 @@ +from __future__ import annotations + +import os +from io import BytesIO +from typing import IO + +from . import ExifTags, Image, ImageFile + +try: + from . import _avif + + SUPPORTED = True +except ImportError: + SUPPORTED = False + +# Decoder options as module globals, until there is a way to pass parameters +# to Image.open (see https://github.com/python-pillow/Pillow/issues/569) +DECODE_CODEC_CHOICE = "auto" +DEFAULT_MAX_THREADS = 0 + + +def get_codec_version(codec_name: str) -> str | None: + versions = _avif.codec_versions() + for version in versions.split(", "): + if version.split(" [")[0] == codec_name: + return version.split(":")[-1].split(" ")[0] + return None + + +def _accept(prefix: bytes) -> bool | str: + if prefix[4:8] != b"ftyp": + return False + major_brand = prefix[8:12] + if major_brand in ( + # coding brands + b"avif", + b"avis", + # We accept files with AVIF container brands; we can't yet know if + # the ftyp box has the correct compatible brands, but if it doesn't + # then the plugin will raise a SyntaxError which Pillow will catch + # before moving on to the next plugin that accepts the file. + # + # Also, because this file might not actually be an AVIF file, we + # don't raise an error if AVIF support isn't properly compiled. + b"mif1", + b"msf1", + ): + if not SUPPORTED: + return ( + "image file could not be identified because AVIF support not installed" + ) + return True + return False + + +def _get_default_max_threads() -> int: + if DEFAULT_MAX_THREADS: + return DEFAULT_MAX_THREADS + if hasattr(os, "sched_getaffinity"): + return len(os.sched_getaffinity(0)) + else: + return os.cpu_count() or 1 + + +class AvifImageFile(ImageFile.ImageFile): + format = "AVIF" + format_description = "AVIF image" + __frame = -1 + + def _open(self) -> None: + if not SUPPORTED: + msg = "image file could not be opened because AVIF support not installed" + raise SyntaxError(msg) + + if DECODE_CODEC_CHOICE != "auto" and not _avif.decoder_codec_available( + DECODE_CODEC_CHOICE + ): + msg = "Invalid opening codec" + raise ValueError(msg) + self._decoder = _avif.AvifDecoder( + self.fp.read(), + DECODE_CODEC_CHOICE, + _get_default_max_threads(), + ) + + # Get info from decoder + self._size, self.n_frames, self._mode, icc, exif, exif_orientation, xmp = ( + self._decoder.get_info() + ) + self.is_animated = self.n_frames > 1 + + if icc: + self.info["icc_profile"] = icc + if xmp: + self.info["xmp"] = xmp + + if exif_orientation != 1 or exif: + exif_data = Image.Exif() + if exif: + exif_data.load(exif) + original_orientation = exif_data.get(ExifTags.Base.Orientation, 1) + else: + original_orientation = 1 + if exif_orientation != original_orientation: + exif_data[ExifTags.Base.Orientation] = exif_orientation + exif = exif_data.tobytes() + if exif: + self.info["exif"] = exif + self.seek(0) + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + + # Set tile + self.__frame = frame + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, 0, self.mode)] + + def load(self) -> Image.core.PixelAccess | None: + if self.tile: + # We need to load the image data for this frame + data, timescale, pts_in_timescales, duration_in_timescales = ( + self._decoder.get_frame(self.__frame) + ) + self.info["timestamp"] = round(1000 * (pts_in_timescales / timescale)) + self.info["duration"] = round(1000 * (duration_in_timescales / timescale)) + + if self.fp and self._exclusive_fp: + self.fp.close() + self.fp = BytesIO(data) + + return super().load() + + def load_seek(self, pos: int) -> None: + pass + + def tell(self) -> int: + return self.__frame + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, save_all=True) + + +def _save( + im: Image.Image, fp: IO[bytes], filename: str | bytes, save_all: bool = False +) -> None: + info = im.encoderinfo.copy() + if save_all: + append_images = list(info.get("append_images", [])) + else: + append_images = [] + + total = 0 + for ims in [im] + append_images: + total += getattr(ims, "n_frames", 1) + + quality = info.get("quality", 75) + if not isinstance(quality, int) or quality < 0 or quality > 100: + msg = "Invalid quality setting" + raise ValueError(msg) + + duration = info.get("duration", 0) + subsampling = info.get("subsampling", "4:2:0") + speed = info.get("speed", 6) + max_threads = info.get("max_threads", _get_default_max_threads()) + codec = info.get("codec", "auto") + if codec != "auto" and not _avif.encoder_codec_available(codec): + msg = "Invalid saving codec" + raise ValueError(msg) + range_ = info.get("range", "full") + tile_rows_log2 = info.get("tile_rows", 0) + tile_cols_log2 = info.get("tile_cols", 0) + alpha_premultiplied = bool(info.get("alpha_premultiplied", False)) + autotiling = bool(info.get("autotiling", tile_rows_log2 == tile_cols_log2 == 0)) + + icc_profile = info.get("icc_profile", im.info.get("icc_profile")) + exif_orientation = 1 + if exif := info.get("exif"): + if isinstance(exif, Image.Exif): + exif_data = exif + else: + exif_data = Image.Exif() + exif_data.load(exif) + if ExifTags.Base.Orientation in exif_data: + exif_orientation = exif_data.pop(ExifTags.Base.Orientation) + exif = exif_data.tobytes() if exif_data else b"" + elif isinstance(exif, Image.Exif): + exif = exif_data.tobytes() + + xmp = info.get("xmp") + + if isinstance(xmp, str): + xmp = xmp.encode("utf-8") + + advanced = info.get("advanced") + if advanced is not None: + if isinstance(advanced, dict): + advanced = advanced.items() + try: + advanced = tuple(advanced) + except TypeError: + invalid = True + else: + invalid = any(not isinstance(v, tuple) or len(v) != 2 for v in advanced) + if invalid: + msg = ( + "advanced codec options must be a dict of key-value string " + "pairs or a series of key-value two-tuples" + ) + raise ValueError(msg) + + # Setup the AVIF encoder + enc = _avif.AvifEncoder( + im.size, + subsampling, + quality, + speed, + max_threads, + codec, + range_, + tile_rows_log2, + tile_cols_log2, + alpha_premultiplied, + autotiling, + icc_profile or b"", + exif or b"", + exif_orientation, + xmp or b"", + advanced, + ) + + # Add each frame + frame_idx = 0 + frame_duration = 0 + cur_idx = im.tell() + is_single_frame = total == 1 + try: + for ims in [im] + append_images: + # Get number of frames in this image + nfr = getattr(ims, "n_frames", 1) + + for idx in range(nfr): + ims.seek(idx) + + # Make sure image mode is supported + frame = ims + rawmode = ims.mode + if ims.mode not in {"RGB", "RGBA"}: + rawmode = "RGBA" if ims.has_transparency_data else "RGB" + frame = ims.convert(rawmode) + + # Update frame duration + if isinstance(duration, (list, tuple)): + frame_duration = duration[frame_idx] + else: + frame_duration = duration + + # Append the frame to the animation encoder + enc.add( + frame.tobytes("raw", rawmode), + frame_duration, + frame.size, + rawmode, + is_single_frame, + ) + + # Update frame index + frame_idx += 1 + + if not save_all: + break + + finally: + im.seek(cur_idx) + + # Get the final output from the encoder + data = enc.finish() + if data is None: + msg = "cannot write file as AVIF (encoder returned None)" + raise OSError(msg) + + fp.write(data) + + +Image.register_open(AvifImageFile.format, AvifImageFile, _accept) +if SUPPORTED: + Image.register_save(AvifImageFile.format, _save) + Image.register_save_all(AvifImageFile.format, _save_all) + Image.register_extensions(AvifImageFile.format, [".avif", ".avifs"]) + Image.register_mime(AvifImageFile.format, "image/avif") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/BdfFontFile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/BdfFontFile.py new file mode 100644 index 0000000000000000000000000000000000000000..f175e2f4f80b1b232d79f15a6db0667296917c97 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/BdfFontFile.py @@ -0,0 +1,122 @@ +# +# The Python Imaging Library +# $Id$ +# +# bitmap distribution font (bdf) file parser +# +# history: +# 1996-05-16 fl created (as bdf2pil) +# 1997-08-25 fl converted to FontFile driver +# 2001-05-25 fl removed bogus __init__ call +# 2002-11-20 fl robustification (from Kevin Cazabon, Dmitry Vasiliev) +# 2003-04-22 fl more robustification (from Graham Dumpleton) +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1997-2003 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +""" +Parse X Bitmap Distribution Format (BDF) +""" +from __future__ import annotations + +from typing import BinaryIO + +from . import FontFile, Image + + +def bdf_char( + f: BinaryIO, +) -> ( + tuple[ + str, + int, + tuple[tuple[int, int], tuple[int, int, int, int], tuple[int, int, int, int]], + Image.Image, + ] + | None +): + # skip to STARTCHAR + while True: + s = f.readline() + if not s: + return None + if s.startswith(b"STARTCHAR"): + break + id = s[9:].strip().decode("ascii") + + # load symbol properties + props = {} + while True: + s = f.readline() + if not s or s.startswith(b"BITMAP"): + break + i = s.find(b" ") + props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii") + + # load bitmap + bitmap = bytearray() + while True: + s = f.readline() + if not s or s.startswith(b"ENDCHAR"): + break + bitmap += s[:-1] + + # The word BBX + # followed by the width in x (BBw), height in y (BBh), + # and x and y displacement (BBxoff0, BByoff0) + # of the lower left corner from the origin of the character. + width, height, x_disp, y_disp = (int(p) for p in props["BBX"].split()) + + # The word DWIDTH + # followed by the width in x and y of the character in device pixels. + dwx, dwy = (int(p) for p in props["DWIDTH"].split()) + + bbox = ( + (dwx, dwy), + (x_disp, -y_disp - height, width + x_disp, -y_disp), + (0, 0, width, height), + ) + + try: + im = Image.frombytes("1", (width, height), bitmap, "hex", "1") + except ValueError: + # deal with zero-width characters + im = Image.new("1", (width, height)) + + return id, int(props["ENCODING"]), bbox, im + + +class BdfFontFile(FontFile.FontFile): + """Font file plugin for the X11 BDF format.""" + + def __init__(self, fp: BinaryIO) -> None: + super().__init__() + + s = fp.readline() + if not s.startswith(b"STARTFONT 2.1"): + msg = "not a valid BDF file" + raise SyntaxError(msg) + + props = {} + comments = [] + + while True: + s = fp.readline() + if not s or s.startswith(b"ENDPROPERTIES"): + break + i = s.find(b" ") + props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii") + if s[:i] in [b"COMMENT", b"COPYRIGHT"]: + if s.find(b"LogicalFontDescription") < 0: + comments.append(s[i + 1 : -1].decode("ascii")) + + while True: + c = bdf_char(fp) + if not c: + break + id, ch, (xy, dst, src), im = c + if 0 <= ch < len(self.glyph): + self.glyph[ch] = xy, dst, src, im diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/BlpImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/BlpImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..f7be7746d84bbc076e0a41124a903a8b2b05ae61 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/BlpImagePlugin.py @@ -0,0 +1,497 @@ +""" +Blizzard Mipmap Format (.blp) +Jerome Leclanche + +The contents of this file are hereby released in the public domain (CC0) +Full text of the CC0 license: + https://creativecommons.org/publicdomain/zero/1.0/ + +BLP1 files, used mostly in Warcraft III, are not fully supported. +All types of BLP2 files used in World of Warcraft are supported. + +The BLP file structure consists of a header, up to 16 mipmaps of the +texture + +Texture sizes must be powers of two, though the two dimensions do +not have to be equal; 512x256 is valid, but 512x200 is not. +The first mipmap (mipmap #0) is the full size image; each subsequent +mipmap halves both dimensions. The final mipmap should be 1x1. + +BLP files come in many different flavours: +* JPEG-compressed (type == 0) - only supported for BLP1. +* RAW images (type == 1, encoding == 1). Each mipmap is stored as an + array of 8-bit values, one per pixel, left to right, top to bottom. + Each value is an index to the palette. +* DXT-compressed (type == 1, encoding == 2): +- DXT1 compression is used if alpha_encoding == 0. + - An additional alpha bit is used if alpha_depth == 1. + - DXT3 compression is used if alpha_encoding == 1. + - DXT5 compression is used if alpha_encoding == 7. +""" + +from __future__ import annotations + +import abc +import os +import struct +from enum import IntEnum +from io import BytesIO +from typing import IO + +from . import Image, ImageFile + + +class Format(IntEnum): + JPEG = 0 + + +class Encoding(IntEnum): + UNCOMPRESSED = 1 + DXT = 2 + UNCOMPRESSED_RAW_BGRA = 3 + + +class AlphaEncoding(IntEnum): + DXT1 = 0 + DXT3 = 1 + DXT5 = 7 + + +def unpack_565(i: int) -> tuple[int, int, int]: + return ((i >> 11) & 0x1F) << 3, ((i >> 5) & 0x3F) << 2, (i & 0x1F) << 3 + + +def decode_dxt1( + data: bytes, alpha: bool = False +) -> tuple[bytearray, bytearray, bytearray, bytearray]: + """ + input: one "row" of data (i.e. will produce 4*width pixels) + """ + + blocks = len(data) // 8 # number of blocks in row + ret = (bytearray(), bytearray(), bytearray(), bytearray()) + + for block_index in range(blocks): + # Decode next 8-byte block. + idx = block_index * 8 + color0, color1, bits = struct.unpack_from("> 2 + + a = 0xFF + if control == 0: + r, g, b = r0, g0, b0 + elif control == 1: + r, g, b = r1, g1, b1 + elif control == 2: + if color0 > color1: + r = (2 * r0 + r1) // 3 + g = (2 * g0 + g1) // 3 + b = (2 * b0 + b1) // 3 + else: + r = (r0 + r1) // 2 + g = (g0 + g1) // 2 + b = (b0 + b1) // 2 + elif control == 3: + if color0 > color1: + r = (2 * r1 + r0) // 3 + g = (2 * g1 + g0) // 3 + b = (2 * b1 + b0) // 3 + else: + r, g, b, a = 0, 0, 0, 0 + + if alpha: + ret[j].extend([r, g, b, a]) + else: + ret[j].extend([r, g, b]) + + return ret + + +def decode_dxt3(data: bytes) -> tuple[bytearray, bytearray, bytearray, bytearray]: + """ + input: one "row" of data (i.e. will produce 4*width pixels) + """ + + blocks = len(data) // 16 # number of blocks in row + ret = (bytearray(), bytearray(), bytearray(), bytearray()) + + for block_index in range(blocks): + idx = block_index * 16 + block = data[idx : idx + 16] + # Decode next 16-byte block. + bits = struct.unpack_from("<8B", block) + color0, color1 = struct.unpack_from(">= 4 + else: + high = True + a &= 0xF + a *= 17 # We get a value between 0 and 15 + + color_code = (code >> 2 * (4 * j + i)) & 0x03 + + if color_code == 0: + r, g, b = r0, g0, b0 + elif color_code == 1: + r, g, b = r1, g1, b1 + elif color_code == 2: + r = (2 * r0 + r1) // 3 + g = (2 * g0 + g1) // 3 + b = (2 * b0 + b1) // 3 + elif color_code == 3: + r = (2 * r1 + r0) // 3 + g = (2 * g1 + g0) // 3 + b = (2 * b1 + b0) // 3 + + ret[j].extend([r, g, b, a]) + + return ret + + +def decode_dxt5(data: bytes) -> tuple[bytearray, bytearray, bytearray, bytearray]: + """ + input: one "row" of data (i.e. will produce 4 * width pixels) + """ + + blocks = len(data) // 16 # number of blocks in row + ret = (bytearray(), bytearray(), bytearray(), bytearray()) + + for block_index in range(blocks): + idx = block_index * 16 + block = data[idx : idx + 16] + # Decode next 16-byte block. + a0, a1 = struct.unpack_from("> alphacode_index) & 0x07 + elif alphacode_index == 15: + alphacode = (alphacode2 >> 15) | ((alphacode1 << 1) & 0x06) + else: # alphacode_index >= 18 and alphacode_index <= 45 + alphacode = (alphacode1 >> (alphacode_index - 16)) & 0x07 + + if alphacode == 0: + a = a0 + elif alphacode == 1: + a = a1 + elif a0 > a1: + a = ((8 - alphacode) * a0 + (alphacode - 1) * a1) // 7 + elif alphacode == 6: + a = 0 + elif alphacode == 7: + a = 255 + else: + a = ((6 - alphacode) * a0 + (alphacode - 1) * a1) // 5 + + color_code = (code >> 2 * (4 * j + i)) & 0x03 + + if color_code == 0: + r, g, b = r0, g0, b0 + elif color_code == 1: + r, g, b = r1, g1, b1 + elif color_code == 2: + r = (2 * r0 + r1) // 3 + g = (2 * g0 + g1) // 3 + b = (2 * b0 + b1) // 3 + elif color_code == 3: + r = (2 * r1 + r0) // 3 + g = (2 * g1 + g0) // 3 + b = (2 * b1 + b0) // 3 + + ret[j].extend([r, g, b, a]) + + return ret + + +class BLPFormatError(NotImplementedError): + pass + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"BLP1", b"BLP2")) + + +class BlpImageFile(ImageFile.ImageFile): + """ + Blizzard Mipmap Format + """ + + format = "BLP" + format_description = "Blizzard Mipmap Format" + + def _open(self) -> None: + self.magic = self.fp.read(4) + if not _accept(self.magic): + msg = f"Bad BLP magic {repr(self.magic)}" + raise BLPFormatError(msg) + + compression = struct.unpack(" tuple[int, int]: + try: + self._read_header() + self._load() + except struct.error as e: + msg = "Truncated BLP file" + raise OSError(msg) from e + return -1, 0 + + @abc.abstractmethod + def _load(self) -> None: + pass + + def _read_header(self) -> None: + self._offsets = struct.unpack("<16I", self._safe_read(16 * 4)) + self._lengths = struct.unpack("<16I", self._safe_read(16 * 4)) + + def _safe_read(self, length: int) -> bytes: + assert self.fd is not None + return ImageFile._safe_read(self.fd, length) + + def _read_palette(self) -> list[tuple[int, int, int, int]]: + ret = [] + for i in range(256): + try: + b, g, r, a = struct.unpack("<4B", self._safe_read(4)) + except struct.error: + break + ret.append((b, g, r, a)) + return ret + + def _read_bgra( + self, palette: list[tuple[int, int, int, int]], alpha: bool + ) -> bytearray: + data = bytearray() + _data = BytesIO(self._safe_read(self._lengths[0])) + while True: + try: + (offset,) = struct.unpack(" None: + self._compression, self._encoding, alpha = self.args + + if self._compression == Format.JPEG: + self._decode_jpeg_stream() + + elif self._compression == 1: + if self._encoding in (4, 5): + palette = self._read_palette() + data = self._read_bgra(palette, alpha) + self.set_as_raw(data) + else: + msg = f"Unsupported BLP encoding {repr(self._encoding)}" + raise BLPFormatError(msg) + else: + msg = f"Unsupported BLP compression {repr(self._encoding)}" + raise BLPFormatError(msg) + + def _decode_jpeg_stream(self) -> None: + from .JpegImagePlugin import JpegImageFile + + (jpeg_header_size,) = struct.unpack(" None: + self._compression, self._encoding, alpha, self._alpha_encoding = self.args + + palette = self._read_palette() + + assert self.fd is not None + self.fd.seek(self._offsets[0]) + + if self._compression == 1: + # Uncompressed or DirectX compression + + if self._encoding == Encoding.UNCOMPRESSED: + data = self._read_bgra(palette, alpha) + + elif self._encoding == Encoding.DXT: + data = bytearray() + if self._alpha_encoding == AlphaEncoding.DXT1: + linesize = (self.state.xsize + 3) // 4 * 8 + for yb in range((self.state.ysize + 3) // 4): + for d in decode_dxt1(self._safe_read(linesize), alpha): + data += d + + elif self._alpha_encoding == AlphaEncoding.DXT3: + linesize = (self.state.xsize + 3) // 4 * 16 + for yb in range((self.state.ysize + 3) // 4): + for d in decode_dxt3(self._safe_read(linesize)): + data += d + + elif self._alpha_encoding == AlphaEncoding.DXT5: + linesize = (self.state.xsize + 3) // 4 * 16 + for yb in range((self.state.ysize + 3) // 4): + for d in decode_dxt5(self._safe_read(linesize)): + data += d + else: + msg = f"Unsupported alpha encoding {repr(self._alpha_encoding)}" + raise BLPFormatError(msg) + else: + msg = f"Unknown BLP encoding {repr(self._encoding)}" + raise BLPFormatError(msg) + + else: + msg = f"Unknown BLP compression {repr(self._compression)}" + raise BLPFormatError(msg) + + self.set_as_raw(data) + + +class BLPEncoder(ImageFile.PyEncoder): + _pushes_fd = True + + def _write_palette(self) -> bytes: + data = b"" + assert self.im is not None + palette = self.im.getpalette("RGBA", "RGBA") + for i in range(len(palette) // 4): + r, g, b, a = palette[i * 4 : (i + 1) * 4] + data += struct.pack("<4B", b, g, r, a) + while len(data) < 256 * 4: + data += b"\x00" * 4 + return data + + def encode(self, bufsize: int) -> tuple[int, int, bytes]: + palette_data = self._write_palette() + + offset = 20 + 16 * 4 * 2 + len(palette_data) + data = struct.pack("<16I", offset, *((0,) * 15)) + + assert self.im is not None + w, h = self.im.size + data += struct.pack("<16I", w * h, *((0,) * 15)) + + data += palette_data + + for y in range(h): + for x in range(w): + data += struct.pack(" None: + if im.mode != "P": + msg = "Unsupported BLP image mode" + raise ValueError(msg) + + magic = b"BLP1" if im.encoderinfo.get("blp_version") == "BLP1" else b"BLP2" + fp.write(magic) + + assert im.palette is not None + fp.write(struct.pack(" mode, rawmode + 1: ("P", "P;1"), + 4: ("P", "P;4"), + 8: ("P", "P"), + 16: ("RGB", "BGR;15"), + 24: ("RGB", "BGR"), + 32: ("RGB", "BGRX"), +} + +USE_RAW_ALPHA = False + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"BM") + + +def _dib_accept(prefix: bytes) -> bool: + return i32(prefix) in [12, 40, 52, 56, 64, 108, 124] + + +# ============================================================================= +# Image plugin for the Windows BMP format. +# ============================================================================= +class BmpImageFile(ImageFile.ImageFile): + """Image plugin for the Windows Bitmap format (BMP)""" + + # ------------------------------------------------------------- Description + format_description = "Windows Bitmap" + format = "BMP" + + # -------------------------------------------------- BMP Compression values + COMPRESSIONS = {"RAW": 0, "RLE8": 1, "RLE4": 2, "BITFIELDS": 3, "JPEG": 4, "PNG": 5} + for k, v in COMPRESSIONS.items(): + vars()[k] = v + + def _bitmap(self, header: int = 0, offset: int = 0) -> None: + """Read relevant info about the BMP""" + read, seek = self.fp.read, self.fp.seek + if header: + seek(header) + # read bmp header size @offset 14 (this is part of the header size) + file_info: dict[str, bool | int | tuple[int, ...]] = { + "header_size": i32(read(4)), + "direction": -1, + } + + # -------------------- If requested, read header at a specific position + # read the rest of the bmp header, without its size + assert isinstance(file_info["header_size"], int) + header_data = ImageFile._safe_read(self.fp, file_info["header_size"] - 4) + + # ------------------------------- Windows Bitmap v2, IBM OS/2 Bitmap v1 + # ----- This format has different offsets because of width/height types + # 12: BITMAPCOREHEADER/OS21XBITMAPHEADER + if file_info["header_size"] == 12: + file_info["width"] = i16(header_data, 0) + file_info["height"] = i16(header_data, 2) + file_info["planes"] = i16(header_data, 4) + file_info["bits"] = i16(header_data, 6) + file_info["compression"] = self.COMPRESSIONS["RAW"] + file_info["palette_padding"] = 3 + + # --------------------------------------------- Windows Bitmap v3 to v5 + # 40: BITMAPINFOHEADER + # 52: BITMAPV2HEADER + # 56: BITMAPV3HEADER + # 64: BITMAPCOREHEADER2/OS22XBITMAPHEADER + # 108: BITMAPV4HEADER + # 124: BITMAPV5HEADER + elif file_info["header_size"] in (40, 52, 56, 64, 108, 124): + file_info["y_flip"] = header_data[7] == 0xFF + file_info["direction"] = 1 if file_info["y_flip"] else -1 + file_info["width"] = i32(header_data, 0) + file_info["height"] = ( + i32(header_data, 4) + if not file_info["y_flip"] + else 2**32 - i32(header_data, 4) + ) + file_info["planes"] = i16(header_data, 8) + file_info["bits"] = i16(header_data, 10) + file_info["compression"] = i32(header_data, 12) + # byte size of pixel data + file_info["data_size"] = i32(header_data, 16) + file_info["pixels_per_meter"] = ( + i32(header_data, 20), + i32(header_data, 24), + ) + file_info["colors"] = i32(header_data, 28) + file_info["palette_padding"] = 4 + assert isinstance(file_info["pixels_per_meter"], tuple) + self.info["dpi"] = tuple(x / 39.3701 for x in file_info["pixels_per_meter"]) + if file_info["compression"] == self.COMPRESSIONS["BITFIELDS"]: + masks = ["r_mask", "g_mask", "b_mask"] + if len(header_data) >= 48: + if len(header_data) >= 52: + masks.append("a_mask") + else: + file_info["a_mask"] = 0x0 + for idx, mask in enumerate(masks): + file_info[mask] = i32(header_data, 36 + idx * 4) + else: + # 40 byte headers only have the three components in the + # bitfields masks, ref: + # https://msdn.microsoft.com/en-us/library/windows/desktop/dd183376(v=vs.85).aspx + # See also + # https://github.com/python-pillow/Pillow/issues/1293 + # There is a 4th component in the RGBQuad, in the alpha + # location, but it is listed as a reserved component, + # and it is not generally an alpha channel + file_info["a_mask"] = 0x0 + for mask in masks: + file_info[mask] = i32(read(4)) + assert isinstance(file_info["r_mask"], int) + assert isinstance(file_info["g_mask"], int) + assert isinstance(file_info["b_mask"], int) + assert isinstance(file_info["a_mask"], int) + file_info["rgb_mask"] = ( + file_info["r_mask"], + file_info["g_mask"], + file_info["b_mask"], + ) + file_info["rgba_mask"] = ( + file_info["r_mask"], + file_info["g_mask"], + file_info["b_mask"], + file_info["a_mask"], + ) + else: + msg = f"Unsupported BMP header type ({file_info['header_size']})" + raise OSError(msg) + + # ------------------ Special case : header is reported 40, which + # ---------------------- is shorter than real size for bpp >= 16 + assert isinstance(file_info["width"], int) + assert isinstance(file_info["height"], int) + self._size = file_info["width"], file_info["height"] + + # ------- If color count was not found in the header, compute from bits + assert isinstance(file_info["bits"], int) + file_info["colors"] = ( + file_info["colors"] + if file_info.get("colors", 0) + else (1 << file_info["bits"]) + ) + assert isinstance(file_info["colors"], int) + if offset == 14 + file_info["header_size"] and file_info["bits"] <= 8: + offset += 4 * file_info["colors"] + + # ---------------------- Check bit depth for unusual unsupported values + self._mode, raw_mode = BIT2MODE.get(file_info["bits"], ("", "")) + if not self.mode: + msg = f"Unsupported BMP pixel depth ({file_info['bits']})" + raise OSError(msg) + + # ---------------- Process BMP with Bitfields compression (not palette) + decoder_name = "raw" + if file_info["compression"] == self.COMPRESSIONS["BITFIELDS"]: + SUPPORTED: dict[int, list[tuple[int, ...]]] = { + 32: [ + (0xFF0000, 0xFF00, 0xFF, 0x0), + (0xFF000000, 0xFF0000, 0xFF00, 0x0), + (0xFF000000, 0xFF00, 0xFF, 0x0), + (0xFF000000, 0xFF0000, 0xFF00, 0xFF), + (0xFF, 0xFF00, 0xFF0000, 0xFF000000), + (0xFF0000, 0xFF00, 0xFF, 0xFF000000), + (0xFF000000, 0xFF00, 0xFF, 0xFF0000), + (0x0, 0x0, 0x0, 0x0), + ], + 24: [(0xFF0000, 0xFF00, 0xFF)], + 16: [(0xF800, 0x7E0, 0x1F), (0x7C00, 0x3E0, 0x1F)], + } + MASK_MODES = { + (32, (0xFF0000, 0xFF00, 0xFF, 0x0)): "BGRX", + (32, (0xFF000000, 0xFF0000, 0xFF00, 0x0)): "XBGR", + (32, (0xFF000000, 0xFF00, 0xFF, 0x0)): "BGXR", + (32, (0xFF000000, 0xFF0000, 0xFF00, 0xFF)): "ABGR", + (32, (0xFF, 0xFF00, 0xFF0000, 0xFF000000)): "RGBA", + (32, (0xFF0000, 0xFF00, 0xFF, 0xFF000000)): "BGRA", + (32, (0xFF000000, 0xFF00, 0xFF, 0xFF0000)): "BGAR", + (32, (0x0, 0x0, 0x0, 0x0)): "BGRA", + (24, (0xFF0000, 0xFF00, 0xFF)): "BGR", + (16, (0xF800, 0x7E0, 0x1F)): "BGR;16", + (16, (0x7C00, 0x3E0, 0x1F)): "BGR;15", + } + if file_info["bits"] in SUPPORTED: + if ( + file_info["bits"] == 32 + and file_info["rgba_mask"] in SUPPORTED[file_info["bits"]] + ): + assert isinstance(file_info["rgba_mask"], tuple) + raw_mode = MASK_MODES[(file_info["bits"], file_info["rgba_mask"])] + self._mode = "RGBA" if "A" in raw_mode else self.mode + elif ( + file_info["bits"] in (24, 16) + and file_info["rgb_mask"] in SUPPORTED[file_info["bits"]] + ): + assert isinstance(file_info["rgb_mask"], tuple) + raw_mode = MASK_MODES[(file_info["bits"], file_info["rgb_mask"])] + else: + msg = "Unsupported BMP bitfields layout" + raise OSError(msg) + else: + msg = "Unsupported BMP bitfields layout" + raise OSError(msg) + elif file_info["compression"] == self.COMPRESSIONS["RAW"]: + if file_info["bits"] == 32 and ( + header == 22 or USE_RAW_ALPHA # 32-bit .cur offset + ): + raw_mode, self._mode = "BGRA", "RGBA" + elif file_info["compression"] in ( + self.COMPRESSIONS["RLE8"], + self.COMPRESSIONS["RLE4"], + ): + decoder_name = "bmp_rle" + else: + msg = f"Unsupported BMP compression ({file_info['compression']})" + raise OSError(msg) + + # --------------- Once the header is processed, process the palette/LUT + if self.mode == "P": # Paletted for 1, 4 and 8 bit images + # ---------------------------------------------------- 1-bit images + if not (0 < file_info["colors"] <= 65536): + msg = f"Unsupported BMP Palette size ({file_info['colors']})" + raise OSError(msg) + else: + assert isinstance(file_info["palette_padding"], int) + padding = file_info["palette_padding"] + palette = read(padding * file_info["colors"]) + grayscale = True + indices = ( + (0, 255) + if file_info["colors"] == 2 + else list(range(file_info["colors"])) + ) + + # ----------------- Check if grayscale and ignore palette if so + for ind, val in enumerate(indices): + rgb = palette[ind * padding : ind * padding + 3] + if rgb != o8(val) * 3: + grayscale = False + + # ------- If all colors are gray, white or black, ditch palette + if grayscale: + self._mode = "1" if file_info["colors"] == 2 else "L" + raw_mode = self.mode + else: + self._mode = "P" + self.palette = ImagePalette.raw( + "BGRX" if padding == 4 else "BGR", palette + ) + + # ---------------------------- Finally set the tile data for the plugin + self.info["compression"] = file_info["compression"] + args: list[Any] = [raw_mode] + if decoder_name == "bmp_rle": + args.append(file_info["compression"] == self.COMPRESSIONS["RLE4"]) + else: + assert isinstance(file_info["width"], int) + args.append(((file_info["width"] * file_info["bits"] + 31) >> 3) & (~3)) + args.append(file_info["direction"]) + self.tile = [ + ImageFile._Tile( + decoder_name, + (0, 0, file_info["width"], file_info["height"]), + offset or self.fp.tell(), + tuple(args), + ) + ] + + def _open(self) -> None: + """Open file, check magic number and read header""" + # read 14 bytes: magic number, filesize, reserved, header final offset + head_data = self.fp.read(14) + # choke if the file does not have the required magic bytes + if not _accept(head_data): + msg = "Not a BMP file" + raise SyntaxError(msg) + # read the start position of the BMP image data (u32) + offset = i32(head_data, 10) + # load bitmap information (offset=raster info) + self._bitmap(offset=offset) + + +class BmpRleDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + rle4 = self.args[1] + data = bytearray() + x = 0 + dest_length = self.state.xsize * self.state.ysize + while len(data) < dest_length: + pixels = self.fd.read(1) + byte = self.fd.read(1) + if not pixels or not byte: + break + num_pixels = pixels[0] + if num_pixels: + # encoded mode + if x + num_pixels > self.state.xsize: + # Too much data for row + num_pixels = max(0, self.state.xsize - x) + if rle4: + first_pixel = o8(byte[0] >> 4) + second_pixel = o8(byte[0] & 0x0F) + for index in range(num_pixels): + if index % 2 == 0: + data += first_pixel + else: + data += second_pixel + else: + data += byte * num_pixels + x += num_pixels + else: + if byte[0] == 0: + # end of line + while len(data) % self.state.xsize != 0: + data += b"\x00" + x = 0 + elif byte[0] == 1: + # end of bitmap + break + elif byte[0] == 2: + # delta + bytes_read = self.fd.read(2) + if len(bytes_read) < 2: + break + right, up = self.fd.read(2) + data += b"\x00" * (right + up * self.state.xsize) + x = len(data) % self.state.xsize + else: + # absolute mode + if rle4: + # 2 pixels per byte + byte_count = byte[0] // 2 + bytes_read = self.fd.read(byte_count) + for byte_read in bytes_read: + data += o8(byte_read >> 4) + data += o8(byte_read & 0x0F) + else: + byte_count = byte[0] + bytes_read = self.fd.read(byte_count) + data += bytes_read + if len(bytes_read) < byte_count: + break + x += byte[0] + + # align to 16-bit word boundary + if self.fd.tell() % 2 != 0: + self.fd.seek(1, os.SEEK_CUR) + rawmode = "L" if self.mode == "L" else "P" + self.set_as_raw(bytes(data), rawmode, (0, self.args[-1])) + return -1, 0 + + +# ============================================================================= +# Image plugin for the DIB format (BMP alias) +# ============================================================================= +class DibImageFile(BmpImageFile): + format = "DIB" + format_description = "Windows Bitmap" + + def _open(self) -> None: + self._bitmap() + + +# +# -------------------------------------------------------------------- +# Write BMP file + + +SAVE = { + "1": ("1", 1, 2), + "L": ("L", 8, 256), + "P": ("P", 8, 256), + "RGB": ("BGR", 24, 0), + "RGBA": ("BGRA", 32, 0), +} + + +def _dib_save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, False) + + +def _save( + im: Image.Image, fp: IO[bytes], filename: str | bytes, bitmap_header: bool = True +) -> None: + try: + rawmode, bits, colors = SAVE[im.mode] + except KeyError as e: + msg = f"cannot write mode {im.mode} as BMP" + raise OSError(msg) from e + + info = im.encoderinfo + + dpi = info.get("dpi", (96, 96)) + + # 1 meter == 39.3701 inches + ppm = tuple(int(x * 39.3701 + 0.5) for x in dpi) + + stride = ((im.size[0] * bits + 7) // 8 + 3) & (~3) + header = 40 # or 64 for OS/2 version 2 + image = stride * im.size[1] + + if im.mode == "1": + palette = b"".join(o8(i) * 3 + b"\x00" for i in (0, 255)) + elif im.mode == "L": + palette = b"".join(o8(i) * 3 + b"\x00" for i in range(256)) + elif im.mode == "P": + palette = im.im.getpalette("RGB", "BGRX") + colors = len(palette) // 4 + else: + palette = None + + # bitmap header + if bitmap_header: + offset = 14 + header + colors * 4 + file_size = offset + image + if file_size > 2**32 - 1: + msg = "File size is too large for the BMP format" + raise ValueError(msg) + fp.write( + b"BM" # file type (magic) + + o32(file_size) # file size + + o32(0) # reserved + + o32(offset) # image data offset + ) + + # bitmap info header + fp.write( + o32(header) # info header size + + o32(im.size[0]) # width + + o32(im.size[1]) # height + + o16(1) # planes + + o16(bits) # depth + + o32(0) # compression (0=uncompressed) + + o32(image) # size of bitmap + + o32(ppm[0]) # resolution + + o32(ppm[1]) # resolution + + o32(colors) # colors used + + o32(colors) # colors important + ) + + fp.write(b"\0" * (header - 40)) # padding (for OS/2 format) + + if palette: + fp.write(palette) + + ImageFile._save( + im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, stride, -1))] + ) + + +# +# -------------------------------------------------------------------- +# Registry + + +Image.register_open(BmpImageFile.format, BmpImageFile, _accept) +Image.register_save(BmpImageFile.format, _save) + +Image.register_extension(BmpImageFile.format, ".bmp") + +Image.register_mime(BmpImageFile.format, "image/bmp") + +Image.register_decoder("bmp_rle", BmpRleDecoder) + +Image.register_open(DibImageFile.format, DibImageFile, _dib_accept) +Image.register_save(DibImageFile.format, _dib_save) + +Image.register_extension(DibImageFile.format, ".dib") + +Image.register_mime(DibImageFile.format, "image/bmp") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/BufrStubImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/BufrStubImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..8c5da14f5f6769127e0ed23d36fa8f3dd0b086c2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/BufrStubImagePlugin.py @@ -0,0 +1,75 @@ +# +# The Python Imaging Library +# $Id$ +# +# BUFR stub adapter +# +# Copyright (c) 1996-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import IO + +from . import Image, ImageFile + +_handler = None + + +def register_handler(handler: ImageFile.StubHandler | None) -> None: + """ + Install application-specific BUFR image handler. + + :param handler: Handler object. + """ + global _handler + _handler = handler + + +# -------------------------------------------------------------------- +# Image adapter + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"BUFR", b"ZCZC")) + + +class BufrStubImageFile(ImageFile.StubImageFile): + format = "BUFR" + format_description = "BUFR" + + def _open(self) -> None: + if not _accept(self.fp.read(4)): + msg = "Not a BUFR file" + raise SyntaxError(msg) + + self.fp.seek(-4, os.SEEK_CUR) + + # make something up + self._mode = "F" + self._size = 1, 1 + + loader = self._load() + if loader: + loader.open(self) + + def _load(self) -> ImageFile.StubHandler | None: + return _handler + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if _handler is None or not hasattr(_handler, "save"): + msg = "BUFR save handler not installed" + raise OSError(msg) + _handler.save(im, fp, filename) + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(BufrStubImageFile.format, BufrStubImageFile, _accept) +Image.register_save(BufrStubImageFile.format, _save) + +Image.register_extension(BufrStubImageFile.format, ".bufr") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ContainerIO.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ContainerIO.py new file mode 100644 index 0000000000000000000000000000000000000000..ec9e66c714fbbfec8c597f6127e5d932b0da521f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ContainerIO.py @@ -0,0 +1,173 @@ +# +# The Python Imaging Library. +# $Id$ +# +# a class to read from a container file +# +# History: +# 1995-06-18 fl Created +# 1995-09-07 fl Added readline(), readlines() +# +# Copyright (c) 1997-2001 by Secret Labs AB +# Copyright (c) 1995 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +from collections.abc import Iterable +from typing import IO, AnyStr, NoReturn + + +class ContainerIO(IO[AnyStr]): + """ + A file object that provides read access to a part of an existing + file (for example a TAR file). + """ + + def __init__(self, file: IO[AnyStr], offset: int, length: int) -> None: + """ + Create file object. + + :param file: Existing file. + :param offset: Start of region, in bytes. + :param length: Size of region, in bytes. + """ + self.fh: IO[AnyStr] = file + self.pos = 0 + self.offset = offset + self.length = length + self.fh.seek(offset) + + ## + # Always false. + + def isatty(self) -> bool: + return False + + def seekable(self) -> bool: + return True + + def seek(self, offset: int, mode: int = io.SEEK_SET) -> int: + """ + Move file pointer. + + :param offset: Offset in bytes. + :param mode: Starting position. Use 0 for beginning of region, 1 + for current offset, and 2 for end of region. You cannot move + the pointer outside the defined region. + :returns: Offset from start of region, in bytes. + """ + if mode == 1: + self.pos = self.pos + offset + elif mode == 2: + self.pos = self.length + offset + else: + self.pos = offset + # clamp + self.pos = max(0, min(self.pos, self.length)) + self.fh.seek(self.offset + self.pos) + return self.pos + + def tell(self) -> int: + """ + Get current file pointer. + + :returns: Offset from start of region, in bytes. + """ + return self.pos + + def readable(self) -> bool: + return True + + def read(self, n: int = -1) -> AnyStr: + """ + Read data. + + :param n: Number of bytes to read. If omitted, zero or negative, + read until end of region. + :returns: An 8-bit string. + """ + if n > 0: + n = min(n, self.length - self.pos) + else: + n = self.length - self.pos + if n <= 0: # EOF + return b"" if "b" in self.fh.mode else "" # type: ignore[return-value] + self.pos = self.pos + n + return self.fh.read(n) + + def readline(self, n: int = -1) -> AnyStr: + """ + Read a line of text. + + :param n: Number of bytes to read. If omitted, zero or negative, + read until end of line. + :returns: An 8-bit string. + """ + s: AnyStr = b"" if "b" in self.fh.mode else "" # type: ignore[assignment] + newline_character = b"\n" if "b" in self.fh.mode else "\n" + while True: + c = self.read(1) + if not c: + break + s = s + c + if c == newline_character or len(s) == n: + break + return s + + def readlines(self, n: int | None = -1) -> list[AnyStr]: + """ + Read multiple lines of text. + + :param n: Number of lines to read. If omitted, zero, negative or None, + read until end of region. + :returns: A list of 8-bit strings. + """ + lines = [] + while True: + s = self.readline() + if not s: + break + lines.append(s) + if len(lines) == n: + break + return lines + + def writable(self) -> bool: + return False + + def write(self, b: AnyStr) -> NoReturn: + raise NotImplementedError() + + def writelines(self, lines: Iterable[AnyStr]) -> NoReturn: + raise NotImplementedError() + + def truncate(self, size: int | None = None) -> int: + raise NotImplementedError() + + def __enter__(self) -> ContainerIO[AnyStr]: + return self + + def __exit__(self, *args: object) -> None: + self.close() + + def __iter__(self) -> ContainerIO[AnyStr]: + return self + + def __next__(self) -> AnyStr: + line = self.readline() + if not line: + msg = "end of region" + raise StopIteration(msg) + return line + + def fileno(self) -> int: + return self.fh.fileno() + + def flush(self) -> None: + self.fh.flush() + + def close(self) -> None: + self.fh.close() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/CurImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/CurImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..b817dbc87b8c291d4bffd5f0b83d5e63a5e6ecbb --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/CurImagePlugin.py @@ -0,0 +1,75 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Windows Cursor support for PIL +# +# notes: +# uses BmpImagePlugin.py to read the bitmap data. +# +# history: +# 96-05-27 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import BmpImagePlugin, Image, ImageFile +from ._binary import i16le as i16 +from ._binary import i32le as i32 + +# +# -------------------------------------------------------------------- + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\0\0\2\0") + + +## +# Image plugin for Windows Cursor files. + + +class CurImageFile(BmpImagePlugin.BmpImageFile): + format = "CUR" + format_description = "Windows Cursor" + + def _open(self) -> None: + offset = self.fp.tell() + + # check magic + s = self.fp.read(6) + if not _accept(s): + msg = "not a CUR file" + raise SyntaxError(msg) + + # pick the largest cursor in the file + m = b"" + for i in range(i16(s, 4)): + s = self.fp.read(16) + if not m: + m = s + elif s[0] > m[0] and s[1] > m[1]: + m = s + if not m: + msg = "No cursors were found" + raise TypeError(msg) + + # load as bitmap + self._bitmap(i32(m, 12) + offset) + + # patch up the bitmap height + self._size = self.size[0], self.size[1] // 2 + d, e, o, a = self.tile[0] + self.tile[0] = ImageFile._Tile(d, (0, 0) + self.size, o, a) + + +# +# -------------------------------------------------------------------- + +Image.register_open(CurImageFile.format, CurImageFile, _accept) + +Image.register_extension(CurImageFile.format, ".cur") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/DcxImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/DcxImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..aea661b9cb6eef184696e377678ee69f66c5f772 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/DcxImagePlugin.py @@ -0,0 +1,83 @@ +# +# The Python Imaging Library. +# $Id$ +# +# DCX file handling +# +# DCX is a container file format defined by Intel, commonly used +# for fax applications. Each DCX file consists of a directory +# (a list of file offsets) followed by a set of (usually 1-bit) +# PCX files. +# +# History: +# 1995-09-09 fl Created +# 1996-03-20 fl Properly derived from PcxImageFile. +# 1998-07-15 fl Renamed offset attribute to avoid name clash +# 2002-07-30 fl Fixed file handling +# +# Copyright (c) 1997-98 by Secret Labs AB. +# Copyright (c) 1995-96 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image +from ._binary import i32le as i32 +from ._util import DeferredError +from .PcxImagePlugin import PcxImageFile + +MAGIC = 0x3ADE68B1 # QUIZ: what's this value, then? + + +def _accept(prefix: bytes) -> bool: + return len(prefix) >= 4 and i32(prefix) == MAGIC + + +## +# Image plugin for the Intel DCX format. + + +class DcxImageFile(PcxImageFile): + format = "DCX" + format_description = "Intel DCX" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # Header + s = self.fp.read(4) + if not _accept(s): + msg = "not a DCX file" + raise SyntaxError(msg) + + # Component directory + self._offset = [] + for i in range(1024): + offset = i32(self.fp.read(4)) + if not offset: + break + self._offset.append(offset) + + self._fp = self.fp + self.frame = -1 + self.n_frames = len(self._offset) + self.is_animated = self.n_frames > 1 + self.seek(0) + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self.frame = frame + self.fp = self._fp + self.fp.seek(self._offset[frame]) + PcxImageFile._open(self) + + def tell(self) -> int: + return self.frame + + +Image.register_open(DcxImageFile.format, DcxImageFile, _accept) + +Image.register_extension(DcxImageFile.format, ".dcx") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/DdsImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/DdsImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..f9ade18f9a1edf431524dd86a238f6b0445e6ab0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/DdsImagePlugin.py @@ -0,0 +1,624 @@ +""" +A Pillow plugin for .dds files (S3TC-compressed aka DXTC) +Jerome Leclanche + +Documentation: +https://web.archive.org/web/20170802060935/http://oss.sgi.com/projects/ogl-sample/registry/EXT/texture_compression_s3tc.txt + +The contents of this file are hereby released in the public domain (CC0) +Full text of the CC0 license: +https://creativecommons.org/publicdomain/zero/1.0/ +""" + +from __future__ import annotations + +import io +import struct +import sys +from enum import IntEnum, IntFlag +from typing import IO + +from . import Image, ImageFile, ImagePalette +from ._binary import i32le as i32 +from ._binary import o8 +from ._binary import o32le as o32 + +# Magic ("DDS ") +DDS_MAGIC = 0x20534444 + + +# DDS flags +class DDSD(IntFlag): + CAPS = 0x1 + HEIGHT = 0x2 + WIDTH = 0x4 + PITCH = 0x8 + PIXELFORMAT = 0x1000 + MIPMAPCOUNT = 0x20000 + LINEARSIZE = 0x80000 + DEPTH = 0x800000 + + +# DDS caps +class DDSCAPS(IntFlag): + COMPLEX = 0x8 + TEXTURE = 0x1000 + MIPMAP = 0x400000 + + +class DDSCAPS2(IntFlag): + CUBEMAP = 0x200 + CUBEMAP_POSITIVEX = 0x400 + CUBEMAP_NEGATIVEX = 0x800 + CUBEMAP_POSITIVEY = 0x1000 + CUBEMAP_NEGATIVEY = 0x2000 + CUBEMAP_POSITIVEZ = 0x4000 + CUBEMAP_NEGATIVEZ = 0x8000 + VOLUME = 0x200000 + + +# Pixel Format +class DDPF(IntFlag): + ALPHAPIXELS = 0x1 + ALPHA = 0x2 + FOURCC = 0x4 + PALETTEINDEXED8 = 0x20 + RGB = 0x40 + LUMINANCE = 0x20000 + + +# dxgiformat.h +class DXGI_FORMAT(IntEnum): + UNKNOWN = 0 + R32G32B32A32_TYPELESS = 1 + R32G32B32A32_FLOAT = 2 + R32G32B32A32_UINT = 3 + R32G32B32A32_SINT = 4 + R32G32B32_TYPELESS = 5 + R32G32B32_FLOAT = 6 + R32G32B32_UINT = 7 + R32G32B32_SINT = 8 + R16G16B16A16_TYPELESS = 9 + R16G16B16A16_FLOAT = 10 + R16G16B16A16_UNORM = 11 + R16G16B16A16_UINT = 12 + R16G16B16A16_SNORM = 13 + R16G16B16A16_SINT = 14 + R32G32_TYPELESS = 15 + R32G32_FLOAT = 16 + R32G32_UINT = 17 + R32G32_SINT = 18 + R32G8X24_TYPELESS = 19 + D32_FLOAT_S8X24_UINT = 20 + R32_FLOAT_X8X24_TYPELESS = 21 + X32_TYPELESS_G8X24_UINT = 22 + R10G10B10A2_TYPELESS = 23 + R10G10B10A2_UNORM = 24 + R10G10B10A2_UINT = 25 + R11G11B10_FLOAT = 26 + R8G8B8A8_TYPELESS = 27 + R8G8B8A8_UNORM = 28 + R8G8B8A8_UNORM_SRGB = 29 + R8G8B8A8_UINT = 30 + R8G8B8A8_SNORM = 31 + R8G8B8A8_SINT = 32 + R16G16_TYPELESS = 33 + R16G16_FLOAT = 34 + R16G16_UNORM = 35 + R16G16_UINT = 36 + R16G16_SNORM = 37 + R16G16_SINT = 38 + R32_TYPELESS = 39 + D32_FLOAT = 40 + R32_FLOAT = 41 + R32_UINT = 42 + R32_SINT = 43 + R24G8_TYPELESS = 44 + D24_UNORM_S8_UINT = 45 + R24_UNORM_X8_TYPELESS = 46 + X24_TYPELESS_G8_UINT = 47 + R8G8_TYPELESS = 48 + R8G8_UNORM = 49 + R8G8_UINT = 50 + R8G8_SNORM = 51 + R8G8_SINT = 52 + R16_TYPELESS = 53 + R16_FLOAT = 54 + D16_UNORM = 55 + R16_UNORM = 56 + R16_UINT = 57 + R16_SNORM = 58 + R16_SINT = 59 + R8_TYPELESS = 60 + R8_UNORM = 61 + R8_UINT = 62 + R8_SNORM = 63 + R8_SINT = 64 + A8_UNORM = 65 + R1_UNORM = 66 + R9G9B9E5_SHAREDEXP = 67 + R8G8_B8G8_UNORM = 68 + G8R8_G8B8_UNORM = 69 + BC1_TYPELESS = 70 + BC1_UNORM = 71 + BC1_UNORM_SRGB = 72 + BC2_TYPELESS = 73 + BC2_UNORM = 74 + BC2_UNORM_SRGB = 75 + BC3_TYPELESS = 76 + BC3_UNORM = 77 + BC3_UNORM_SRGB = 78 + BC4_TYPELESS = 79 + BC4_UNORM = 80 + BC4_SNORM = 81 + BC5_TYPELESS = 82 + BC5_UNORM = 83 + BC5_SNORM = 84 + B5G6R5_UNORM = 85 + B5G5R5A1_UNORM = 86 + B8G8R8A8_UNORM = 87 + B8G8R8X8_UNORM = 88 + R10G10B10_XR_BIAS_A2_UNORM = 89 + B8G8R8A8_TYPELESS = 90 + B8G8R8A8_UNORM_SRGB = 91 + B8G8R8X8_TYPELESS = 92 + B8G8R8X8_UNORM_SRGB = 93 + BC6H_TYPELESS = 94 + BC6H_UF16 = 95 + BC6H_SF16 = 96 + BC7_TYPELESS = 97 + BC7_UNORM = 98 + BC7_UNORM_SRGB = 99 + AYUV = 100 + Y410 = 101 + Y416 = 102 + NV12 = 103 + P010 = 104 + P016 = 105 + OPAQUE_420 = 106 + YUY2 = 107 + Y210 = 108 + Y216 = 109 + NV11 = 110 + AI44 = 111 + IA44 = 112 + P8 = 113 + A8P8 = 114 + B4G4R4A4_UNORM = 115 + P208 = 130 + V208 = 131 + V408 = 132 + SAMPLER_FEEDBACK_MIN_MIP_OPAQUE = 189 + SAMPLER_FEEDBACK_MIP_REGION_USED_OPAQUE = 190 + + +class D3DFMT(IntEnum): + UNKNOWN = 0 + R8G8B8 = 20 + A8R8G8B8 = 21 + X8R8G8B8 = 22 + R5G6B5 = 23 + X1R5G5B5 = 24 + A1R5G5B5 = 25 + A4R4G4B4 = 26 + R3G3B2 = 27 + A8 = 28 + A8R3G3B2 = 29 + X4R4G4B4 = 30 + A2B10G10R10 = 31 + A8B8G8R8 = 32 + X8B8G8R8 = 33 + G16R16 = 34 + A2R10G10B10 = 35 + A16B16G16R16 = 36 + A8P8 = 40 + P8 = 41 + L8 = 50 + A8L8 = 51 + A4L4 = 52 + V8U8 = 60 + L6V5U5 = 61 + X8L8V8U8 = 62 + Q8W8V8U8 = 63 + V16U16 = 64 + A2W10V10U10 = 67 + D16_LOCKABLE = 70 + D32 = 71 + D15S1 = 73 + D24S8 = 75 + D24X8 = 77 + D24X4S4 = 79 + D16 = 80 + D32F_LOCKABLE = 82 + D24FS8 = 83 + D32_LOCKABLE = 84 + S8_LOCKABLE = 85 + L16 = 81 + VERTEXDATA = 100 + INDEX16 = 101 + INDEX32 = 102 + Q16W16V16U16 = 110 + R16F = 111 + G16R16F = 112 + A16B16G16R16F = 113 + R32F = 114 + G32R32F = 115 + A32B32G32R32F = 116 + CxV8U8 = 117 + A1 = 118 + A2B10G10R10_XR_BIAS = 119 + BINARYBUFFER = 199 + + UYVY = i32(b"UYVY") + R8G8_B8G8 = i32(b"RGBG") + YUY2 = i32(b"YUY2") + G8R8_G8B8 = i32(b"GRGB") + DXT1 = i32(b"DXT1") + DXT2 = i32(b"DXT2") + DXT3 = i32(b"DXT3") + DXT4 = i32(b"DXT4") + DXT5 = i32(b"DXT5") + DX10 = i32(b"DX10") + BC4S = i32(b"BC4S") + BC4U = i32(b"BC4U") + BC5S = i32(b"BC5S") + BC5U = i32(b"BC5U") + ATI1 = i32(b"ATI1") + ATI2 = i32(b"ATI2") + MULTI2_ARGB8 = i32(b"MET1") + + +# Backward compatibility layer +module = sys.modules[__name__] +for item in DDSD: + assert item.name is not None + setattr(module, f"DDSD_{item.name}", item.value) +for item1 in DDSCAPS: + assert item1.name is not None + setattr(module, f"DDSCAPS_{item1.name}", item1.value) +for item2 in DDSCAPS2: + assert item2.name is not None + setattr(module, f"DDSCAPS2_{item2.name}", item2.value) +for item3 in DDPF: + assert item3.name is not None + setattr(module, f"DDPF_{item3.name}", item3.value) + +DDS_FOURCC = DDPF.FOURCC +DDS_RGB = DDPF.RGB +DDS_RGBA = DDPF.RGB | DDPF.ALPHAPIXELS +DDS_LUMINANCE = DDPF.LUMINANCE +DDS_LUMINANCEA = DDPF.LUMINANCE | DDPF.ALPHAPIXELS +DDS_ALPHA = DDPF.ALPHA +DDS_PAL8 = DDPF.PALETTEINDEXED8 + +DDS_HEADER_FLAGS_TEXTURE = DDSD.CAPS | DDSD.HEIGHT | DDSD.WIDTH | DDSD.PIXELFORMAT +DDS_HEADER_FLAGS_MIPMAP = DDSD.MIPMAPCOUNT +DDS_HEADER_FLAGS_VOLUME = DDSD.DEPTH +DDS_HEADER_FLAGS_PITCH = DDSD.PITCH +DDS_HEADER_FLAGS_LINEARSIZE = DDSD.LINEARSIZE + +DDS_HEIGHT = DDSD.HEIGHT +DDS_WIDTH = DDSD.WIDTH + +DDS_SURFACE_FLAGS_TEXTURE = DDSCAPS.TEXTURE +DDS_SURFACE_FLAGS_MIPMAP = DDSCAPS.COMPLEX | DDSCAPS.MIPMAP +DDS_SURFACE_FLAGS_CUBEMAP = DDSCAPS.COMPLEX + +DDS_CUBEMAP_POSITIVEX = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_POSITIVEX +DDS_CUBEMAP_NEGATIVEX = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_NEGATIVEX +DDS_CUBEMAP_POSITIVEY = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_POSITIVEY +DDS_CUBEMAP_NEGATIVEY = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_NEGATIVEY +DDS_CUBEMAP_POSITIVEZ = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_POSITIVEZ +DDS_CUBEMAP_NEGATIVEZ = DDSCAPS2.CUBEMAP | DDSCAPS2.CUBEMAP_NEGATIVEZ + +DXT1_FOURCC = D3DFMT.DXT1 +DXT3_FOURCC = D3DFMT.DXT3 +DXT5_FOURCC = D3DFMT.DXT5 + +DXGI_FORMAT_R8G8B8A8_TYPELESS = DXGI_FORMAT.R8G8B8A8_TYPELESS +DXGI_FORMAT_R8G8B8A8_UNORM = DXGI_FORMAT.R8G8B8A8_UNORM +DXGI_FORMAT_R8G8B8A8_UNORM_SRGB = DXGI_FORMAT.R8G8B8A8_UNORM_SRGB +DXGI_FORMAT_BC5_TYPELESS = DXGI_FORMAT.BC5_TYPELESS +DXGI_FORMAT_BC5_UNORM = DXGI_FORMAT.BC5_UNORM +DXGI_FORMAT_BC5_SNORM = DXGI_FORMAT.BC5_SNORM +DXGI_FORMAT_BC6H_UF16 = DXGI_FORMAT.BC6H_UF16 +DXGI_FORMAT_BC6H_SF16 = DXGI_FORMAT.BC6H_SF16 +DXGI_FORMAT_BC7_TYPELESS = DXGI_FORMAT.BC7_TYPELESS +DXGI_FORMAT_BC7_UNORM = DXGI_FORMAT.BC7_UNORM +DXGI_FORMAT_BC7_UNORM_SRGB = DXGI_FORMAT.BC7_UNORM_SRGB + + +class DdsImageFile(ImageFile.ImageFile): + format = "DDS" + format_description = "DirectDraw Surface" + + def _open(self) -> None: + if not _accept(self.fp.read(4)): + msg = "not a DDS file" + raise SyntaxError(msg) + (header_size,) = struct.unpack(" None: + pass + + +class DdsRgbDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + bitcount, masks = self.args + + # Some masks will be padded with zeros, e.g. R 0b11 G 0b1100 + # Calculate how many zeros each mask is padded with + mask_offsets = [] + # And the maximum value of each channel without the padding + mask_totals = [] + for mask in masks: + offset = 0 + if mask != 0: + while mask >> (offset + 1) << (offset + 1) == mask: + offset += 1 + mask_offsets.append(offset) + mask_totals.append(mask >> offset) + + data = bytearray() + bytecount = bitcount // 8 + dest_length = self.state.xsize * self.state.ysize * len(masks) + while len(data) < dest_length: + value = int.from_bytes(self.fd.read(bytecount), "little") + for i, mask in enumerate(masks): + masked_value = value & mask + # Remove the zero padding, and scale it to 8 bits + data += o8( + int(((masked_value >> mask_offsets[i]) / mask_totals[i]) * 255) + ) + self.set_as_raw(data) + return -1, 0 + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode not in ("RGB", "RGBA", "L", "LA"): + msg = f"cannot write mode {im.mode} as DDS" + raise OSError(msg) + + flags = DDSD.CAPS | DDSD.HEIGHT | DDSD.WIDTH | DDSD.PIXELFORMAT + bitcount = len(im.getbands()) * 8 + pixel_format = im.encoderinfo.get("pixel_format") + args: tuple[int] | str + if pixel_format: + codec_name = "bcn" + flags |= DDSD.LINEARSIZE + pitch = (im.width + 3) * 4 + rgba_mask = [0, 0, 0, 0] + pixel_flags = DDPF.FOURCC + if pixel_format == "DXT1": + fourcc = D3DFMT.DXT1 + args = (1,) + elif pixel_format == "DXT3": + fourcc = D3DFMT.DXT3 + args = (2,) + elif pixel_format == "DXT5": + fourcc = D3DFMT.DXT5 + args = (3,) + else: + fourcc = D3DFMT.DX10 + if pixel_format == "BC2": + args = (2,) + dxgi_format = DXGI_FORMAT.BC2_TYPELESS + elif pixel_format == "BC3": + args = (3,) + dxgi_format = DXGI_FORMAT.BC3_TYPELESS + elif pixel_format == "BC5": + args = (5,) + dxgi_format = DXGI_FORMAT.BC5_TYPELESS + if im.mode != "RGB": + msg = "only RGB mode can be written as BC5" + raise OSError(msg) + else: + msg = f"cannot write pixel format {pixel_format}" + raise OSError(msg) + else: + codec_name = "raw" + flags |= DDSD.PITCH + pitch = (im.width * bitcount + 7) // 8 + + alpha = im.mode[-1] == "A" + if im.mode[0] == "L": + pixel_flags = DDPF.LUMINANCE + args = im.mode + if alpha: + rgba_mask = [0x000000FF, 0x000000FF, 0x000000FF] + else: + rgba_mask = [0xFF000000, 0xFF000000, 0xFF000000] + else: + pixel_flags = DDPF.RGB + args = im.mode[::-1] + rgba_mask = [0x00FF0000, 0x0000FF00, 0x000000FF] + + if alpha: + r, g, b, a = im.split() + im = Image.merge("RGBA", (a, r, g, b)) + if alpha: + pixel_flags |= DDPF.ALPHAPIXELS + rgba_mask.append(0xFF000000 if alpha else 0) + + fourcc = D3DFMT.UNKNOWN + fp.write( + o32(DDS_MAGIC) + + struct.pack( + "<7I", + 124, # header size + flags, # flags + im.height, + im.width, + pitch, + 0, # depth + 0, # mipmaps + ) + + struct.pack("11I", *((0,) * 11)) # reserved + # pfsize, pfflags, fourcc, bitcount + + struct.pack("<4I", 32, pixel_flags, fourcc, bitcount) + + struct.pack("<4I", *rgba_mask) # dwRGBABitMask + + struct.pack("<5I", DDSCAPS.TEXTURE, 0, 0, 0, 0) + ) + if fourcc == D3DFMT.DX10: + fp.write( + # dxgi_format, 2D resource, misc, array size, straight alpha + struct.pack("<5I", dxgi_format, 3, 0, 0, 1) + ) + ImageFile._save(im, fp, [ImageFile._Tile(codec_name, (0, 0) + im.size, 0, args)]) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"DDS ") + + +Image.register_open(DdsImageFile.format, DdsImageFile, _accept) +Image.register_decoder("dds_rgb", DdsRgbDecoder) +Image.register_save(DdsImageFile.format, _save) +Image.register_extension(DdsImageFile.format, ".dds") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/EpsImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/EpsImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..5e2ddad99e99565fee795d49c9563f5c9ba5dac1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/EpsImagePlugin.py @@ -0,0 +1,476 @@ +# +# The Python Imaging Library. +# $Id$ +# +# EPS file handling +# +# History: +# 1995-09-01 fl Created (0.1) +# 1996-05-18 fl Don't choke on "atend" fields, Ghostscript interface (0.2) +# 1996-08-22 fl Don't choke on floating point BoundingBox values +# 1996-08-23 fl Handle files from Macintosh (0.3) +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.4) +# 2003-09-07 fl Check gs.close status (from Federico Di Gregorio) (0.5) +# 2014-05-07 e Handling of EPS with binary preview and fixed resolution +# resizing +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import os +import re +import subprocess +import sys +import tempfile +from typing import IO + +from . import Image, ImageFile +from ._binary import i32le as i32 + +# -------------------------------------------------------------------- + + +split = re.compile(r"^%%([^:]*):[ \t]*(.*)[ \t]*$") +field = re.compile(r"^%[%!\w]([^:]*)[ \t]*$") + +gs_binary: str | bool | None = None +gs_windows_binary = None + + +def has_ghostscript() -> bool: + global gs_binary, gs_windows_binary + if gs_binary is None: + if sys.platform.startswith("win"): + if gs_windows_binary is None: + import shutil + + for binary in ("gswin32c", "gswin64c", "gs"): + if shutil.which(binary) is not None: + gs_windows_binary = binary + break + else: + gs_windows_binary = False + gs_binary = gs_windows_binary + else: + try: + subprocess.check_call(["gs", "--version"], stdout=subprocess.DEVNULL) + gs_binary = "gs" + except OSError: + gs_binary = False + return gs_binary is not False + + +def Ghostscript( + tile: list[ImageFile._Tile], + size: tuple[int, int], + fp: IO[bytes], + scale: int = 1, + transparency: bool = False, +) -> Image.core.ImagingCore: + """Render an image using Ghostscript""" + global gs_binary + if not has_ghostscript(): + msg = "Unable to locate Ghostscript on paths" + raise OSError(msg) + assert isinstance(gs_binary, str) + + # Unpack decoder tile + args = tile[0].args + assert isinstance(args, tuple) + length, bbox = args + + # Hack to support hi-res rendering + scale = int(scale) or 1 + width = size[0] * scale + height = size[1] * scale + # resolution is dependent on bbox and size + res_x = 72.0 * width / (bbox[2] - bbox[0]) + res_y = 72.0 * height / (bbox[3] - bbox[1]) + + out_fd, outfile = tempfile.mkstemp() + os.close(out_fd) + + infile_temp = None + if hasattr(fp, "name") and os.path.exists(fp.name): + infile = fp.name + else: + in_fd, infile_temp = tempfile.mkstemp() + os.close(in_fd) + infile = infile_temp + + # Ignore length and offset! + # Ghostscript can read it + # Copy whole file to read in Ghostscript + with open(infile_temp, "wb") as f: + # fetch length of fp + fp.seek(0, io.SEEK_END) + fsize = fp.tell() + # ensure start position + # go back + fp.seek(0) + lengthfile = fsize + while lengthfile > 0: + s = fp.read(min(lengthfile, 100 * 1024)) + if not s: + break + lengthfile -= len(s) + f.write(s) + + if transparency: + # "RGBA" + device = "pngalpha" + else: + # "pnmraw" automatically chooses between + # PBM ("1"), PGM ("L"), and PPM ("RGB"). + device = "pnmraw" + + # Build Ghostscript command + command = [ + gs_binary, + "-q", # quiet mode + f"-g{width:d}x{height:d}", # set output geometry (pixels) + f"-r{res_x:f}x{res_y:f}", # set input DPI (dots per inch) + "-dBATCH", # exit after processing + "-dNOPAUSE", # don't pause between pages + "-dSAFER", # safe mode + f"-sDEVICE={device}", + f"-sOutputFile={outfile}", # output file + # adjust for image origin + "-c", + f"{-bbox[0]} {-bbox[1]} translate", + "-f", + infile, # input file + # showpage (see https://bugs.ghostscript.com/show_bug.cgi?id=698272) + "-c", + "showpage", + ] + + # push data through Ghostscript + try: + startupinfo = None + if sys.platform.startswith("win"): + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + subprocess.check_call(command, startupinfo=startupinfo) + with Image.open(outfile) as out_im: + out_im.load() + return out_im.im.copy() + finally: + try: + os.unlink(outfile) + if infile_temp: + os.unlink(infile_temp) + except OSError: + pass + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"%!PS") or ( + len(prefix) >= 4 and i32(prefix) == 0xC6D3D0C5 + ) + + +## +# Image plugin for Encapsulated PostScript. This plugin supports only +# a few variants of this format. + + +class EpsImageFile(ImageFile.ImageFile): + """EPS File Parser for the Python Imaging Library""" + + format = "EPS" + format_description = "Encapsulated Postscript" + + mode_map = {1: "L", 2: "LAB", 3: "RGB", 4: "CMYK"} + + def _open(self) -> None: + (length, offset) = self._find_offset(self.fp) + + # go to offset - start of "%!PS" + self.fp.seek(offset) + + self._mode = "RGB" + + # When reading header comments, the first comment is used. + # When reading trailer comments, the last comment is used. + bounding_box: list[int] | None = None + imagedata_size: tuple[int, int] | None = None + + byte_arr = bytearray(255) + bytes_mv = memoryview(byte_arr) + bytes_read = 0 + reading_header_comments = True + reading_trailer_comments = False + trailer_reached = False + + def check_required_header_comments() -> None: + """ + The EPS specification requires that some headers exist. + This should be checked when the header comments formally end, + when image data starts, or when the file ends, whichever comes first. + """ + if "PS-Adobe" not in self.info: + msg = 'EPS header missing "%!PS-Adobe" comment' + raise SyntaxError(msg) + if "BoundingBox" not in self.info: + msg = 'EPS header missing "%%BoundingBox" comment' + raise SyntaxError(msg) + + def read_comment(s: str) -> bool: + nonlocal bounding_box, reading_trailer_comments + try: + m = split.match(s) + except re.error as e: + msg = "not an EPS file" + raise SyntaxError(msg) from e + + if not m: + return False + + k, v = m.group(1, 2) + self.info[k] = v + if k == "BoundingBox": + if v == "(atend)": + reading_trailer_comments = True + elif not bounding_box or (trailer_reached and reading_trailer_comments): + try: + # Note: The DSC spec says that BoundingBox + # fields should be integers, but some drivers + # put floating point values there anyway. + bounding_box = [int(float(i)) for i in v.split()] + except Exception: + pass + return True + + while True: + byte = self.fp.read(1) + if byte == b"": + # if we didn't read a byte we must be at the end of the file + if bytes_read == 0: + if reading_header_comments: + check_required_header_comments() + break + elif byte in b"\r\n": + # if we read a line ending character, ignore it and parse what + # we have already read. if we haven't read any other characters, + # continue reading + if bytes_read == 0: + continue + else: + # ASCII/hexadecimal lines in an EPS file must not exceed + # 255 characters, not including line ending characters + if bytes_read >= 255: + # only enforce this for lines starting with a "%", + # otherwise assume it's binary data + if byte_arr[0] == ord("%"): + msg = "not an EPS file" + raise SyntaxError(msg) + else: + if reading_header_comments: + check_required_header_comments() + reading_header_comments = False + # reset bytes_read so we can keep reading + # data until the end of the line + bytes_read = 0 + byte_arr[bytes_read] = byte[0] + bytes_read += 1 + continue + + if reading_header_comments: + # Load EPS header + + # if this line doesn't start with a "%", + # or does start with "%%EndComments", + # then we've reached the end of the header/comments + if byte_arr[0] != ord("%") or bytes_mv[:13] == b"%%EndComments": + check_required_header_comments() + reading_header_comments = False + continue + + s = str(bytes_mv[:bytes_read], "latin-1") + if not read_comment(s): + m = field.match(s) + if m: + k = m.group(1) + if k.startswith("PS-Adobe"): + self.info["PS-Adobe"] = k[9:] + else: + self.info[k] = "" + elif s[0] == "%": + # handle non-DSC PostScript comments that some + # tools mistakenly put in the Comments section + pass + else: + msg = "bad EPS header" + raise OSError(msg) + elif bytes_mv[:11] == b"%ImageData:": + # Check for an "ImageData" descriptor + # https://www.adobe.com/devnet-apps/photoshop/fileformatashtml/#50577413_pgfId-1035096 + + # If we've already read an "ImageData" descriptor, + # don't read another one. + if imagedata_size: + bytes_read = 0 + continue + + # Values: + # columns + # rows + # bit depth (1 or 8) + # mode (1: L, 2: LAB, 3: RGB, 4: CMYK) + # number of padding channels + # block size (number of bytes per row per channel) + # binary/ascii (1: binary, 2: ascii) + # data start identifier (the image data follows after a single line + # consisting only of this quoted value) + image_data_values = byte_arr[11:bytes_read].split(None, 7) + columns, rows, bit_depth, mode_id = ( + int(value) for value in image_data_values[:4] + ) + + if bit_depth == 1: + self._mode = "1" + elif bit_depth == 8: + try: + self._mode = self.mode_map[mode_id] + except ValueError: + break + else: + break + + # Parse the columns and rows after checking the bit depth and mode + # in case the bit depth and/or mode are invalid. + imagedata_size = columns, rows + elif bytes_mv[:5] == b"%%EOF": + break + elif trailer_reached and reading_trailer_comments: + # Load EPS trailer + s = str(bytes_mv[:bytes_read], "latin-1") + read_comment(s) + elif bytes_mv[:9] == b"%%Trailer": + trailer_reached = True + bytes_read = 0 + + # A "BoundingBox" is always required, + # even if an "ImageData" descriptor size exists. + if not bounding_box: + msg = "cannot determine EPS bounding box" + raise OSError(msg) + + # An "ImageData" size takes precedence over the "BoundingBox". + self._size = imagedata_size or ( + bounding_box[2] - bounding_box[0], + bounding_box[3] - bounding_box[1], + ) + + self.tile = [ + ImageFile._Tile("eps", (0, 0) + self.size, offset, (length, bounding_box)) + ] + + def _find_offset(self, fp: IO[bytes]) -> tuple[int, int]: + s = fp.read(4) + + if s == b"%!PS": + # for HEAD without binary preview + fp.seek(0, io.SEEK_END) + length = fp.tell() + offset = 0 + elif i32(s) == 0xC6D3D0C5: + # FIX for: Some EPS file not handled correctly / issue #302 + # EPS can contain binary data + # or start directly with latin coding + # more info see: + # https://web.archive.org/web/20160528181353/http://partners.adobe.com/public/developer/en/ps/5002.EPSF_Spec.pdf + s = fp.read(8) + offset = i32(s) + length = i32(s, 4) + else: + msg = "not an EPS file" + raise SyntaxError(msg) + + return length, offset + + def load( + self, scale: int = 1, transparency: bool = False + ) -> Image.core.PixelAccess | None: + # Load EPS via Ghostscript + if self.tile: + self.im = Ghostscript(self.tile, self.size, self.fp, scale, transparency) + self._mode = self.im.mode + self._size = self.im.size + self.tile = [] + return Image.Image.load(self) + + def load_seek(self, pos: int) -> None: + # we can't incrementally load, so force ImageFile.parser to + # use our custom load method by defining this method. + pass + + +# -------------------------------------------------------------------- + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes, eps: int = 1) -> None: + """EPS Writer for the Python Imaging Library.""" + + # make sure image data is available + im.load() + + # determine PostScript image mode + if im.mode == "L": + operator = (8, 1, b"image") + elif im.mode == "RGB": + operator = (8, 3, b"false 3 colorimage") + elif im.mode == "CMYK": + operator = (8, 4, b"false 4 colorimage") + else: + msg = "image mode is not supported" + raise ValueError(msg) + + if eps: + # write EPS header + fp.write(b"%!PS-Adobe-3.0 EPSF-3.0\n") + fp.write(b"%%Creator: PIL 0.1 EpsEncode\n") + # fp.write("%%CreationDate: %s"...) + fp.write(b"%%%%BoundingBox: 0 0 %d %d\n" % im.size) + fp.write(b"%%Pages: 1\n") + fp.write(b"%%EndComments\n") + fp.write(b"%%Page: 1 1\n") + fp.write(b"%%ImageData: %d %d " % im.size) + fp.write(b'%d %d 0 1 1 "%s"\n' % operator) + + # image header + fp.write(b"gsave\n") + fp.write(b"10 dict begin\n") + fp.write(b"/buf %d string def\n" % (im.size[0] * operator[1])) + fp.write(b"%d %d scale\n" % im.size) + fp.write(b"%d %d 8\n" % im.size) # <= bits + fp.write(b"[%d 0 0 -%d 0 %d]\n" % (im.size[0], im.size[1], im.size[1])) + fp.write(b"{ currentfile buf readhexstring pop } bind\n") + fp.write(operator[2] + b"\n") + if hasattr(fp, "flush"): + fp.flush() + + ImageFile._save(im, fp, [ImageFile._Tile("eps", (0, 0) + im.size)]) + + fp.write(b"\n%%%%EndBinary\n") + fp.write(b"grestore end\n") + if hasattr(fp, "flush"): + fp.flush() + + +# -------------------------------------------------------------------- + + +Image.register_open(EpsImageFile.format, EpsImageFile, _accept) + +Image.register_save(EpsImageFile.format, _save) + +Image.register_extensions(EpsImageFile.format, [".ps", ".eps"]) + +Image.register_mime(EpsImageFile.format, "application/postscript") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ExifTags.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ExifTags.py new file mode 100644 index 0000000000000000000000000000000000000000..2280d5ce84b9badabe16cfb0db37f739d50d51c6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ExifTags.py @@ -0,0 +1,382 @@ +# +# The Python Imaging Library. +# $Id$ +# +# EXIF tags +# +# Copyright (c) 2003 by Secret Labs AB +# +# See the README file for information on usage and redistribution. +# + +""" +This module provides constants and clear-text names for various +well-known EXIF tags. +""" +from __future__ import annotations + +from enum import IntEnum + + +class Base(IntEnum): + # possibly incomplete + InteropIndex = 0x0001 + ProcessingSoftware = 0x000B + NewSubfileType = 0x00FE + SubfileType = 0x00FF + ImageWidth = 0x0100 + ImageLength = 0x0101 + BitsPerSample = 0x0102 + Compression = 0x0103 + PhotometricInterpretation = 0x0106 + Thresholding = 0x0107 + CellWidth = 0x0108 + CellLength = 0x0109 + FillOrder = 0x010A + DocumentName = 0x010D + ImageDescription = 0x010E + Make = 0x010F + Model = 0x0110 + StripOffsets = 0x0111 + Orientation = 0x0112 + SamplesPerPixel = 0x0115 + RowsPerStrip = 0x0116 + StripByteCounts = 0x0117 + MinSampleValue = 0x0118 + MaxSampleValue = 0x0119 + XResolution = 0x011A + YResolution = 0x011B + PlanarConfiguration = 0x011C + PageName = 0x011D + FreeOffsets = 0x0120 + FreeByteCounts = 0x0121 + GrayResponseUnit = 0x0122 + GrayResponseCurve = 0x0123 + T4Options = 0x0124 + T6Options = 0x0125 + ResolutionUnit = 0x0128 + PageNumber = 0x0129 + TransferFunction = 0x012D + Software = 0x0131 + DateTime = 0x0132 + Artist = 0x013B + HostComputer = 0x013C + Predictor = 0x013D + WhitePoint = 0x013E + PrimaryChromaticities = 0x013F + ColorMap = 0x0140 + HalftoneHints = 0x0141 + TileWidth = 0x0142 + TileLength = 0x0143 + TileOffsets = 0x0144 + TileByteCounts = 0x0145 + SubIFDs = 0x014A + InkSet = 0x014C + InkNames = 0x014D + NumberOfInks = 0x014E + DotRange = 0x0150 + TargetPrinter = 0x0151 + ExtraSamples = 0x0152 + SampleFormat = 0x0153 + SMinSampleValue = 0x0154 + SMaxSampleValue = 0x0155 + TransferRange = 0x0156 + ClipPath = 0x0157 + XClipPathUnits = 0x0158 + YClipPathUnits = 0x0159 + Indexed = 0x015A + JPEGTables = 0x015B + OPIProxy = 0x015F + JPEGProc = 0x0200 + JpegIFOffset = 0x0201 + JpegIFByteCount = 0x0202 + JpegRestartInterval = 0x0203 + JpegLosslessPredictors = 0x0205 + JpegPointTransforms = 0x0206 + JpegQTables = 0x0207 + JpegDCTables = 0x0208 + JpegACTables = 0x0209 + YCbCrCoefficients = 0x0211 + YCbCrSubSampling = 0x0212 + YCbCrPositioning = 0x0213 + ReferenceBlackWhite = 0x0214 + XMLPacket = 0x02BC + RelatedImageFileFormat = 0x1000 + RelatedImageWidth = 0x1001 + RelatedImageLength = 0x1002 + Rating = 0x4746 + RatingPercent = 0x4749 + ImageID = 0x800D + CFARepeatPatternDim = 0x828D + BatteryLevel = 0x828F + Copyright = 0x8298 + ExposureTime = 0x829A + FNumber = 0x829D + IPTCNAA = 0x83BB + ImageResources = 0x8649 + ExifOffset = 0x8769 + InterColorProfile = 0x8773 + ExposureProgram = 0x8822 + SpectralSensitivity = 0x8824 + GPSInfo = 0x8825 + ISOSpeedRatings = 0x8827 + OECF = 0x8828 + Interlace = 0x8829 + TimeZoneOffset = 0x882A + SelfTimerMode = 0x882B + SensitivityType = 0x8830 + StandardOutputSensitivity = 0x8831 + RecommendedExposureIndex = 0x8832 + ISOSpeed = 0x8833 + ISOSpeedLatitudeyyy = 0x8834 + ISOSpeedLatitudezzz = 0x8835 + ExifVersion = 0x9000 + DateTimeOriginal = 0x9003 + DateTimeDigitized = 0x9004 + OffsetTime = 0x9010 + OffsetTimeOriginal = 0x9011 + OffsetTimeDigitized = 0x9012 + ComponentsConfiguration = 0x9101 + CompressedBitsPerPixel = 0x9102 + ShutterSpeedValue = 0x9201 + ApertureValue = 0x9202 + BrightnessValue = 0x9203 + ExposureBiasValue = 0x9204 + MaxApertureValue = 0x9205 + SubjectDistance = 0x9206 + MeteringMode = 0x9207 + LightSource = 0x9208 + Flash = 0x9209 + FocalLength = 0x920A + Noise = 0x920D + ImageNumber = 0x9211 + SecurityClassification = 0x9212 + ImageHistory = 0x9213 + TIFFEPStandardID = 0x9216 + MakerNote = 0x927C + UserComment = 0x9286 + SubsecTime = 0x9290 + SubsecTimeOriginal = 0x9291 + SubsecTimeDigitized = 0x9292 + AmbientTemperature = 0x9400 + Humidity = 0x9401 + Pressure = 0x9402 + WaterDepth = 0x9403 + Acceleration = 0x9404 + CameraElevationAngle = 0x9405 + XPTitle = 0x9C9B + XPComment = 0x9C9C + XPAuthor = 0x9C9D + XPKeywords = 0x9C9E + XPSubject = 0x9C9F + FlashPixVersion = 0xA000 + ColorSpace = 0xA001 + ExifImageWidth = 0xA002 + ExifImageHeight = 0xA003 + RelatedSoundFile = 0xA004 + ExifInteroperabilityOffset = 0xA005 + FlashEnergy = 0xA20B + SpatialFrequencyResponse = 0xA20C + FocalPlaneXResolution = 0xA20E + FocalPlaneYResolution = 0xA20F + FocalPlaneResolutionUnit = 0xA210 + SubjectLocation = 0xA214 + ExposureIndex = 0xA215 + SensingMethod = 0xA217 + FileSource = 0xA300 + SceneType = 0xA301 + CFAPattern = 0xA302 + CustomRendered = 0xA401 + ExposureMode = 0xA402 + WhiteBalance = 0xA403 + DigitalZoomRatio = 0xA404 + FocalLengthIn35mmFilm = 0xA405 + SceneCaptureType = 0xA406 + GainControl = 0xA407 + Contrast = 0xA408 + Saturation = 0xA409 + Sharpness = 0xA40A + DeviceSettingDescription = 0xA40B + SubjectDistanceRange = 0xA40C + ImageUniqueID = 0xA420 + CameraOwnerName = 0xA430 + BodySerialNumber = 0xA431 + LensSpecification = 0xA432 + LensMake = 0xA433 + LensModel = 0xA434 + LensSerialNumber = 0xA435 + CompositeImage = 0xA460 + CompositeImageCount = 0xA461 + CompositeImageExposureTimes = 0xA462 + Gamma = 0xA500 + PrintImageMatching = 0xC4A5 + DNGVersion = 0xC612 + DNGBackwardVersion = 0xC613 + UniqueCameraModel = 0xC614 + LocalizedCameraModel = 0xC615 + CFAPlaneColor = 0xC616 + CFALayout = 0xC617 + LinearizationTable = 0xC618 + BlackLevelRepeatDim = 0xC619 + BlackLevel = 0xC61A + BlackLevelDeltaH = 0xC61B + BlackLevelDeltaV = 0xC61C + WhiteLevel = 0xC61D + DefaultScale = 0xC61E + DefaultCropOrigin = 0xC61F + DefaultCropSize = 0xC620 + ColorMatrix1 = 0xC621 + ColorMatrix2 = 0xC622 + CameraCalibration1 = 0xC623 + CameraCalibration2 = 0xC624 + ReductionMatrix1 = 0xC625 + ReductionMatrix2 = 0xC626 + AnalogBalance = 0xC627 + AsShotNeutral = 0xC628 + AsShotWhiteXY = 0xC629 + BaselineExposure = 0xC62A + BaselineNoise = 0xC62B + BaselineSharpness = 0xC62C + BayerGreenSplit = 0xC62D + LinearResponseLimit = 0xC62E + CameraSerialNumber = 0xC62F + LensInfo = 0xC630 + ChromaBlurRadius = 0xC631 + AntiAliasStrength = 0xC632 + ShadowScale = 0xC633 + DNGPrivateData = 0xC634 + MakerNoteSafety = 0xC635 + CalibrationIlluminant1 = 0xC65A + CalibrationIlluminant2 = 0xC65B + BestQualityScale = 0xC65C + RawDataUniqueID = 0xC65D + OriginalRawFileName = 0xC68B + OriginalRawFileData = 0xC68C + ActiveArea = 0xC68D + MaskedAreas = 0xC68E + AsShotICCProfile = 0xC68F + AsShotPreProfileMatrix = 0xC690 + CurrentICCProfile = 0xC691 + CurrentPreProfileMatrix = 0xC692 + ColorimetricReference = 0xC6BF + CameraCalibrationSignature = 0xC6F3 + ProfileCalibrationSignature = 0xC6F4 + AsShotProfileName = 0xC6F6 + NoiseReductionApplied = 0xC6F7 + ProfileName = 0xC6F8 + ProfileHueSatMapDims = 0xC6F9 + ProfileHueSatMapData1 = 0xC6FA + ProfileHueSatMapData2 = 0xC6FB + ProfileToneCurve = 0xC6FC + ProfileEmbedPolicy = 0xC6FD + ProfileCopyright = 0xC6FE + ForwardMatrix1 = 0xC714 + ForwardMatrix2 = 0xC715 + PreviewApplicationName = 0xC716 + PreviewApplicationVersion = 0xC717 + PreviewSettingsName = 0xC718 + PreviewSettingsDigest = 0xC719 + PreviewColorSpace = 0xC71A + PreviewDateTime = 0xC71B + RawImageDigest = 0xC71C + OriginalRawFileDigest = 0xC71D + SubTileBlockSize = 0xC71E + RowInterleaveFactor = 0xC71F + ProfileLookTableDims = 0xC725 + ProfileLookTableData = 0xC726 + OpcodeList1 = 0xC740 + OpcodeList2 = 0xC741 + OpcodeList3 = 0xC74E + NoiseProfile = 0xC761 + + +"""Maps EXIF tags to tag names.""" +TAGS = { + **{i.value: i.name for i in Base}, + 0x920C: "SpatialFrequencyResponse", + 0x9214: "SubjectLocation", + 0x9215: "ExposureIndex", + 0x828E: "CFAPattern", + 0x920B: "FlashEnergy", + 0x9216: "TIFF/EPStandardID", +} + + +class GPS(IntEnum): + GPSVersionID = 0x00 + GPSLatitudeRef = 0x01 + GPSLatitude = 0x02 + GPSLongitudeRef = 0x03 + GPSLongitude = 0x04 + GPSAltitudeRef = 0x05 + GPSAltitude = 0x06 + GPSTimeStamp = 0x07 + GPSSatellites = 0x08 + GPSStatus = 0x09 + GPSMeasureMode = 0x0A + GPSDOP = 0x0B + GPSSpeedRef = 0x0C + GPSSpeed = 0x0D + GPSTrackRef = 0x0E + GPSTrack = 0x0F + GPSImgDirectionRef = 0x10 + GPSImgDirection = 0x11 + GPSMapDatum = 0x12 + GPSDestLatitudeRef = 0x13 + GPSDestLatitude = 0x14 + GPSDestLongitudeRef = 0x15 + GPSDestLongitude = 0x16 + GPSDestBearingRef = 0x17 + GPSDestBearing = 0x18 + GPSDestDistanceRef = 0x19 + GPSDestDistance = 0x1A + GPSProcessingMethod = 0x1B + GPSAreaInformation = 0x1C + GPSDateStamp = 0x1D + GPSDifferential = 0x1E + GPSHPositioningError = 0x1F + + +"""Maps EXIF GPS tags to tag names.""" +GPSTAGS = {i.value: i.name for i in GPS} + + +class Interop(IntEnum): + InteropIndex = 0x0001 + InteropVersion = 0x0002 + RelatedImageFileFormat = 0x1000 + RelatedImageWidth = 0x1001 + RelatedImageHeight = 0x1002 + + +class IFD(IntEnum): + Exif = 0x8769 + GPSInfo = 0x8825 + MakerNote = 0x927C + Makernote = 0x927C # Deprecated + Interop = 0xA005 + IFD1 = -1 + + +class LightSource(IntEnum): + Unknown = 0x00 + Daylight = 0x01 + Fluorescent = 0x02 + Tungsten = 0x03 + Flash = 0x04 + Fine = 0x09 + Cloudy = 0x0A + Shade = 0x0B + DaylightFluorescent = 0x0C + DayWhiteFluorescent = 0x0D + CoolWhiteFluorescent = 0x0E + WhiteFluorescent = 0x0F + StandardLightA = 0x11 + StandardLightB = 0x12 + StandardLightC = 0x13 + D55 = 0x14 + D65 = 0x15 + D75 = 0x16 + D50 = 0x17 + ISO = 0x18 + Other = 0xFF diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FitsImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FitsImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..a3fdc0efeec6f7ec195112ded41d8ff1e248a6a0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FitsImagePlugin.py @@ -0,0 +1,152 @@ +# +# The Python Imaging Library +# $Id$ +# +# FITS file handling +# +# Copyright (c) 1998-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import gzip +import math + +from . import Image, ImageFile + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"SIMPLE") + + +class FitsImageFile(ImageFile.ImageFile): + format = "FITS" + format_description = "FITS" + + def _open(self) -> None: + assert self.fp is not None + + headers: dict[bytes, bytes] = {} + header_in_progress = False + decoder_name = "" + while True: + header = self.fp.read(80) + if not header: + msg = "Truncated FITS file" + raise OSError(msg) + keyword = header[:8].strip() + if keyword in (b"SIMPLE", b"XTENSION"): + header_in_progress = True + elif headers and not header_in_progress: + # This is now a data unit + break + elif keyword == b"END": + # Seek to the end of the header unit + self.fp.seek(math.ceil(self.fp.tell() / 2880) * 2880) + if not decoder_name: + decoder_name, offset, args = self._parse_headers(headers) + + header_in_progress = False + continue + + if decoder_name: + # Keep going to read past the headers + continue + + value = header[8:].split(b"/")[0].strip() + if value.startswith(b"="): + value = value[1:].strip() + if not headers and (not _accept(keyword) or value != b"T"): + msg = "Not a FITS file" + raise SyntaxError(msg) + headers[keyword] = value + + if not decoder_name: + msg = "No image data" + raise ValueError(msg) + + offset += self.fp.tell() - 80 + self.tile = [ImageFile._Tile(decoder_name, (0, 0) + self.size, offset, args)] + + def _get_size( + self, headers: dict[bytes, bytes], prefix: bytes + ) -> tuple[int, int] | None: + naxis = int(headers[prefix + b"NAXIS"]) + if naxis == 0: + return None + + if naxis == 1: + return 1, int(headers[prefix + b"NAXIS1"]) + else: + return int(headers[prefix + b"NAXIS1"]), int(headers[prefix + b"NAXIS2"]) + + def _parse_headers( + self, headers: dict[bytes, bytes] + ) -> tuple[str, int, tuple[str | int, ...]]: + prefix = b"" + decoder_name = "raw" + offset = 0 + if ( + headers.get(b"XTENSION") == b"'BINTABLE'" + and headers.get(b"ZIMAGE") == b"T" + and headers[b"ZCMPTYPE"] == b"'GZIP_1 '" + ): + no_prefix_size = self._get_size(headers, prefix) or (0, 0) + number_of_bits = int(headers[b"BITPIX"]) + offset = no_prefix_size[0] * no_prefix_size[1] * (number_of_bits // 8) + + prefix = b"Z" + decoder_name = "fits_gzip" + + size = self._get_size(headers, prefix) + if not size: + return "", 0, () + + self._size = size + + number_of_bits = int(headers[prefix + b"BITPIX"]) + if number_of_bits == 8: + self._mode = "L" + elif number_of_bits == 16: + self._mode = "I;16" + elif number_of_bits == 32: + self._mode = "I" + elif number_of_bits in (-32, -64): + self._mode = "F" + + args: tuple[str | int, ...] + if decoder_name == "raw": + args = (self.mode, 0, -1) + else: + args = (number_of_bits,) + return decoder_name, offset, args + + +class FitsGzipDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + value = gzip.decompress(self.fd.read()) + + rows = [] + offset = 0 + number_of_bits = min(self.args[0] // 8, 4) + for y in range(self.state.ysize): + row = bytearray() + for x in range(self.state.xsize): + row += value[offset + (4 - number_of_bits) : offset + 4] + offset += 4 + rows.append(row) + self.set_as_raw(bytes([pixel for row in rows[::-1] for pixel in row])) + return -1, 0 + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(FitsImageFile.format, FitsImageFile, _accept) +Image.register_decoder("fits_gzip", FitsGzipDecoder) + +Image.register_extensions(FitsImageFile.format, [".fit", ".fits"]) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FliImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FliImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..7c5bfeefa1bf24ee536e13e26ce6ca8aa4ba319d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FliImagePlugin.py @@ -0,0 +1,178 @@ +# +# The Python Imaging Library. +# $Id$ +# +# FLI/FLC file handling. +# +# History: +# 95-09-01 fl Created +# 97-01-03 fl Fixed parser, setup decoder tile +# 98-07-15 fl Renamed offset attribute to avoid name clash +# +# Copyright (c) Secret Labs AB 1997-98. +# Copyright (c) Fredrik Lundh 1995-97. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os + +from . import Image, ImageFile, ImagePalette +from ._binary import i16le as i16 +from ._binary import i32le as i32 +from ._binary import o8 +from ._util import DeferredError + +# +# decoder + + +def _accept(prefix: bytes) -> bool: + return ( + len(prefix) >= 6 + and i16(prefix, 4) in [0xAF11, 0xAF12] + and i16(prefix, 14) in [0, 3] # flags + ) + + +## +# Image plugin for the FLI/FLC animation format. Use the seek +# method to load individual frames. + + +class FliImageFile(ImageFile.ImageFile): + format = "FLI" + format_description = "Autodesk FLI/FLC Animation" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # HEAD + s = self.fp.read(128) + if not (_accept(s) and s[20:22] == b"\x00\x00"): + msg = "not an FLI/FLC file" + raise SyntaxError(msg) + + # frames + self.n_frames = i16(s, 6) + self.is_animated = self.n_frames > 1 + + # image characteristics + self._mode = "P" + self._size = i16(s, 8), i16(s, 10) + + # animation speed + duration = i32(s, 16) + magic = i16(s, 4) + if magic == 0xAF11: + duration = (duration * 1000) // 70 + self.info["duration"] = duration + + # look for palette + palette = [(a, a, a) for a in range(256)] + + s = self.fp.read(16) + + self.__offset = 128 + + if i16(s, 4) == 0xF100: + # prefix chunk; ignore it + self.__offset = self.__offset + i32(s) + self.fp.seek(self.__offset) + s = self.fp.read(16) + + if i16(s, 4) == 0xF1FA: + # look for palette chunk + number_of_subchunks = i16(s, 6) + chunk_size: int | None = None + for _ in range(number_of_subchunks): + if chunk_size is not None: + self.fp.seek(chunk_size - 6, os.SEEK_CUR) + s = self.fp.read(6) + chunk_type = i16(s, 4) + if chunk_type in (4, 11): + self._palette(palette, 2 if chunk_type == 11 else 0) + break + chunk_size = i32(s) + if not chunk_size: + break + + self.palette = ImagePalette.raw( + "RGB", b"".join(o8(r) + o8(g) + o8(b) for (r, g, b) in palette) + ) + + # set things up to decode first frame + self.__frame = -1 + self._fp = self.fp + self.__rewind = self.fp.tell() + self.seek(0) + + def _palette(self, palette: list[tuple[int, int, int]], shift: int) -> None: + # load palette + + i = 0 + for e in range(i16(self.fp.read(2))): + s = self.fp.read(2) + i = i + s[0] + n = s[1] + if n == 0: + n = 256 + s = self.fp.read(n * 3) + for n in range(0, len(s), 3): + r = s[n] << shift + g = s[n + 1] << shift + b = s[n + 2] << shift + palette[i] = (r, g, b) + i += 1 + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if frame < self.__frame: + self._seek(0) + + for f in range(self.__frame + 1, frame + 1): + self._seek(f) + + def _seek(self, frame: int) -> None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + if frame == 0: + self.__frame = -1 + self._fp.seek(self.__rewind) + self.__offset = 128 + else: + # ensure that the previous frame was loaded + self.load() + + if frame != self.__frame + 1: + msg = f"cannot seek to frame {frame}" + raise ValueError(msg) + self.__frame = frame + + # move to next frame + self.fp = self._fp + self.fp.seek(self.__offset) + + s = self.fp.read(4) + if not s: + msg = "missing frame size" + raise EOFError(msg) + + framesize = i32(s) + + self.decodermaxblock = framesize + self.tile = [ImageFile._Tile("fli", (0, 0) + self.size, self.__offset)] + + self.__offset += framesize + + def tell(self) -> int: + return self.__frame + + +# +# registry + +Image.register_open(FliImageFile.format, FliImageFile, _accept) + +Image.register_extensions(FliImageFile.format, [".fli", ".flc"]) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FontFile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FontFile.py new file mode 100644 index 0000000000000000000000000000000000000000..1e0c1c166b5932a7621e510eba047586465e03d8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FontFile.py @@ -0,0 +1,134 @@ +# +# The Python Imaging Library +# $Id$ +# +# base class for raster font file parsers +# +# history: +# 1997-06-05 fl created +# 1997-08-19 fl restrict image width +# +# Copyright (c) 1997-1998 by Secret Labs AB +# Copyright (c) 1997-1998 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import BinaryIO + +from . import Image, _binary + +WIDTH = 800 + + +def puti16( + fp: BinaryIO, values: tuple[int, int, int, int, int, int, int, int, int, int] +) -> None: + """Write network order (big-endian) 16-bit sequence""" + for v in values: + if v < 0: + v += 65536 + fp.write(_binary.o16be(v)) + + +class FontFile: + """Base class for raster font file handlers.""" + + bitmap: Image.Image | None = None + + def __init__(self) -> None: + self.info: dict[bytes, bytes | int] = {} + self.glyph: list[ + tuple[ + tuple[int, int], + tuple[int, int, int, int], + tuple[int, int, int, int], + Image.Image, + ] + | None + ] = [None] * 256 + + def __getitem__(self, ix: int) -> ( + tuple[ + tuple[int, int], + tuple[int, int, int, int], + tuple[int, int, int, int], + Image.Image, + ] + | None + ): + return self.glyph[ix] + + def compile(self) -> None: + """Create metrics and bitmap""" + + if self.bitmap: + return + + # create bitmap large enough to hold all data + h = w = maxwidth = 0 + lines = 1 + for glyph in self.glyph: + if glyph: + d, dst, src, im = glyph + h = max(h, src[3] - src[1]) + w = w + (src[2] - src[0]) + if w > WIDTH: + lines += 1 + w = src[2] - src[0] + maxwidth = max(maxwidth, w) + + xsize = maxwidth + ysize = lines * h + + if xsize == 0 and ysize == 0: + return + + self.ysize = h + + # paste glyphs into bitmap + self.bitmap = Image.new("1", (xsize, ysize)) + self.metrics: list[ + tuple[tuple[int, int], tuple[int, int, int, int], tuple[int, int, int, int]] + | None + ] = [None] * 256 + x = y = 0 + for i in range(256): + glyph = self[i] + if glyph: + d, dst, src, im = glyph + xx = src[2] - src[0] + x0, y0 = x, y + x = x + xx + if x > WIDTH: + x, y = 0, y + h + x0, y0 = x, y + x = xx + s = src[0] + x0, src[1] + y0, src[2] + x0, src[3] + y0 + self.bitmap.paste(im.crop(src), s) + self.metrics[i] = d, dst, s + + def save(self, filename: str) -> None: + """Save font""" + + self.compile() + + # font data + if not self.bitmap: + msg = "No bitmap created" + raise ValueError(msg) + self.bitmap.save(os.path.splitext(filename)[0] + ".pbm", "PNG") + + # font metrics + with open(os.path.splitext(filename)[0] + ".pil", "wb") as fp: + fp.write(b"PILfont\n") + fp.write(f";;;;;;{self.ysize};\n".encode("ascii")) # HACK!!! + fp.write(b"DATA\n") + for id in range(256): + m = self.metrics[id] + if not m: + puti16(fp, (0,) * 10) + else: + puti16(fp, m[0] + m[1] + m[2]) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FpxImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FpxImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..fd992cd9e20eb7cf0c6de347ac0a76f928ffc238 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FpxImagePlugin.py @@ -0,0 +1,257 @@ +# +# THIS IS WORK IN PROGRESS +# +# The Python Imaging Library. +# $Id$ +# +# FlashPix support for PIL +# +# History: +# 97-01-25 fl Created (reads uncompressed RGB images only) +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import olefile + +from . import Image, ImageFile +from ._binary import i32le as i32 + +# we map from colour field tuples to (mode, rawmode) descriptors +MODES = { + # opacity + (0x00007FFE,): ("A", "L"), + # monochrome + (0x00010000,): ("L", "L"), + (0x00018000, 0x00017FFE): ("RGBA", "LA"), + # photo YCC + (0x00020000, 0x00020001, 0x00020002): ("RGB", "YCC;P"), + (0x00028000, 0x00028001, 0x00028002, 0x00027FFE): ("RGBA", "YCCA;P"), + # standard RGB (NIFRGB) + (0x00030000, 0x00030001, 0x00030002): ("RGB", "RGB"), + (0x00038000, 0x00038001, 0x00038002, 0x00037FFE): ("RGBA", "RGBA"), +} + + +# +# -------------------------------------------------------------------- + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(olefile.MAGIC) + + +## +# Image plugin for the FlashPix images. + + +class FpxImageFile(ImageFile.ImageFile): + format = "FPX" + format_description = "FlashPix" + + def _open(self) -> None: + # + # read the OLE directory and see if this is a likely + # to be a FlashPix file + + try: + self.ole = olefile.OleFileIO(self.fp) + except OSError as e: + msg = "not an FPX file; invalid OLE file" + raise SyntaxError(msg) from e + + root = self.ole.root + if not root or root.clsid != "56616700-C154-11CE-8553-00AA00A1F95B": + msg = "not an FPX file; bad root CLSID" + raise SyntaxError(msg) + + self._open_index(1) + + def _open_index(self, index: int = 1) -> None: + # + # get the Image Contents Property Set + + prop = self.ole.getproperties( + [f"Data Object Store {index:06d}", "\005Image Contents"] + ) + + # size (highest resolution) + + assert isinstance(prop[0x1000002], int) + assert isinstance(prop[0x1000003], int) + self._size = prop[0x1000002], prop[0x1000003] + + size = max(self.size) + i = 1 + while size > 64: + size = size // 2 + i += 1 + self.maxid = i - 1 + + # mode. instead of using a single field for this, flashpix + # requires you to specify the mode for each channel in each + # resolution subimage, and leaves it to the decoder to make + # sure that they all match. for now, we'll cheat and assume + # that this is always the case. + + id = self.maxid << 16 + + s = prop[0x2000002 | id] + + if not isinstance(s, bytes) or (bands := i32(s, 4)) > 4: + msg = "Invalid number of bands" + raise OSError(msg) + + # note: for now, we ignore the "uncalibrated" flag + colors = tuple(i32(s, 8 + i * 4) & 0x7FFFFFFF for i in range(bands)) + + self._mode, self.rawmode = MODES[colors] + + # load JPEG tables, if any + self.jpeg = {} + for i in range(256): + id = 0x3000001 | (i << 16) + if id in prop: + self.jpeg[i] = prop[id] + + self._open_subimage(1, self.maxid) + + def _open_subimage(self, index: int = 1, subimage: int = 0) -> None: + # + # setup tile descriptors for a given subimage + + stream = [ + f"Data Object Store {index:06d}", + f"Resolution {subimage:04d}", + "Subimage 0000 Header", + ] + + fp = self.ole.openstream(stream) + + # skip prefix + fp.read(28) + + # header stream + s = fp.read(36) + + size = i32(s, 4), i32(s, 8) + # tilecount = i32(s, 12) + tilesize = i32(s, 16), i32(s, 20) + # channels = i32(s, 24) + offset = i32(s, 28) + length = i32(s, 32) + + if size != self.size: + msg = "subimage mismatch" + raise OSError(msg) + + # get tile descriptors + fp.seek(28 + offset) + s = fp.read(i32(s, 12) * length) + + x = y = 0 + xsize, ysize = size + xtile, ytile = tilesize + self.tile = [] + + for i in range(0, len(s), length): + x1 = min(xsize, x + xtile) + y1 = min(ysize, y + ytile) + + compression = i32(s, i + 8) + + if compression == 0: + self.tile.append( + ImageFile._Tile( + "raw", + (x, y, x1, y1), + i32(s, i) + 28, + self.rawmode, + ) + ) + + elif compression == 1: + # FIXME: the fill decoder is not implemented + self.tile.append( + ImageFile._Tile( + "fill", + (x, y, x1, y1), + i32(s, i) + 28, + (self.rawmode, s[12:16]), + ) + ) + + elif compression == 2: + internal_color_conversion = s[14] + jpeg_tables = s[15] + rawmode = self.rawmode + + if internal_color_conversion: + # The image is stored as usual (usually YCbCr). + if rawmode == "RGBA": + # For "RGBA", data is stored as YCbCrA based on + # negative RGB. The following trick works around + # this problem : + jpegmode, rawmode = "YCbCrK", "CMYK" + else: + jpegmode = None # let the decoder decide + + else: + # The image is stored as defined by rawmode + jpegmode = rawmode + + self.tile.append( + ImageFile._Tile( + "jpeg", + (x, y, x1, y1), + i32(s, i) + 28, + (rawmode, jpegmode), + ) + ) + + # FIXME: jpeg tables are tile dependent; the prefix + # data must be placed in the tile descriptor itself! + + if jpeg_tables: + self.tile_prefix = self.jpeg[jpeg_tables] + + else: + msg = "unknown/invalid compression" + raise OSError(msg) + + x = x + xtile + if x >= xsize: + x, y = 0, y + ytile + if y >= ysize: + break # isn't really required + + self.stream = stream + self._fp = self.fp + self.fp = None + + def load(self) -> Image.core.PixelAccess | None: + if not self.fp: + self.fp = self.ole.openstream(self.stream[:2] + ["Subimage 0000 Data"]) + + return ImageFile.ImageFile.load(self) + + def close(self) -> None: + self.ole.close() + super().close() + + def __exit__(self, *args: object) -> None: + self.ole.close() + super().__exit__() + + +# +# -------------------------------------------------------------------- + + +Image.register_open(FpxImageFile.format, FpxImageFile, _accept) + +Image.register_extension(FpxImageFile.format, ".fpx") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FtexImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FtexImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..d60e75bb60bdb5113c7cb3c48840918207ced694 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/FtexImagePlugin.py @@ -0,0 +1,114 @@ +""" +A Pillow loader for .ftc and .ftu files (FTEX) +Jerome Leclanche + +The contents of this file are hereby released in the public domain (CC0) +Full text of the CC0 license: + https://creativecommons.org/publicdomain/zero/1.0/ + +Independence War 2: Edge Of Chaos - Texture File Format - 16 October 2001 + +The textures used for 3D objects in Independence War 2: Edge Of Chaos are in a +packed custom format called FTEX. This file format uses file extensions FTC +and FTU. +* FTC files are compressed textures (using standard texture compression). +* FTU files are not compressed. +Texture File Format +The FTC and FTU texture files both use the same format. This +has the following structure: +{header} +{format_directory} +{data} +Where: +{header} = { + u32:magic, + u32:version, + u32:width, + u32:height, + u32:mipmap_count, + u32:format_count +} + +* The "magic" number is "FTEX". +* "width" and "height" are the dimensions of the texture. +* "mipmap_count" is the number of mipmaps in the texture. +* "format_count" is the number of texture formats (different versions of the +same texture) in this file. + +{format_directory} = format_count * { u32:format, u32:where } + +The format value is 0 for DXT1 compressed textures and 1 for 24-bit RGB +uncompressed textures. +The texture data for a format starts at the position "where" in the file. + +Each set of texture data in the file has the following structure: +{data} = format_count * { u32:mipmap_size, mipmap_size * { u8 } } +* "mipmap_size" is the number of bytes in that mip level. For compressed +textures this is the size of the texture data compressed with DXT1. For 24 bit +uncompressed textures, this is 3 * width * height. Following this are the image +bytes for that mipmap level. + +Note: All data is stored in little-Endian (Intel) byte order. +""" + +from __future__ import annotations + +import struct +from enum import IntEnum +from io import BytesIO + +from . import Image, ImageFile + +MAGIC = b"FTEX" + + +class Format(IntEnum): + DXT1 = 0 + UNCOMPRESSED = 1 + + +class FtexImageFile(ImageFile.ImageFile): + format = "FTEX" + format_description = "Texture File Format (IW2:EOC)" + + def _open(self) -> None: + if not _accept(self.fp.read(4)): + msg = "not an FTEX file" + raise SyntaxError(msg) + struct.unpack(" None: + pass + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(MAGIC) + + +Image.register_open(FtexImageFile.format, FtexImageFile, _accept) +Image.register_extensions(FtexImageFile.format, [".ftc", ".ftu"]) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GbrImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GbrImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..f319d7e846e4c7ecb32a43751204bd1fbee168c0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GbrImagePlugin.py @@ -0,0 +1,103 @@ +# +# The Python Imaging Library +# +# load a GIMP brush file +# +# History: +# 96-03-14 fl Created +# 16-01-08 es Version 2 +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# Copyright (c) Eric Soroos 2016. +# +# See the README file for information on usage and redistribution. +# +# +# See https://github.com/GNOME/gimp/blob/mainline/devel-docs/gbr.txt for +# format documentation. +# +# This code Interprets version 1 and 2 .gbr files. +# Version 1 files are obsolete, and should not be used for new +# brushes. +# Version 2 files are saved by GIMP v2.8 (at least) +# Version 3 files have a format specifier of 18 for 16bit floats in +# the color depth field. This is currently unsupported by Pillow. +from __future__ import annotations + +from . import Image, ImageFile +from ._binary import i32be as i32 + + +def _accept(prefix: bytes) -> bool: + return len(prefix) >= 8 and i32(prefix, 0) >= 20 and i32(prefix, 4) in (1, 2) + + +## +# Image plugin for the GIMP brush format. + + +class GbrImageFile(ImageFile.ImageFile): + format = "GBR" + format_description = "GIMP brush file" + + def _open(self) -> None: + header_size = i32(self.fp.read(4)) + if header_size < 20: + msg = "not a GIMP brush" + raise SyntaxError(msg) + version = i32(self.fp.read(4)) + if version not in (1, 2): + msg = f"Unsupported GIMP brush version: {version}" + raise SyntaxError(msg) + + width = i32(self.fp.read(4)) + height = i32(self.fp.read(4)) + color_depth = i32(self.fp.read(4)) + if width <= 0 or height <= 0: + msg = "not a GIMP brush" + raise SyntaxError(msg) + if color_depth not in (1, 4): + msg = f"Unsupported GIMP brush color depth: {color_depth}" + raise SyntaxError(msg) + + if version == 1: + comment_length = header_size - 20 + else: + comment_length = header_size - 28 + magic_number = self.fp.read(4) + if magic_number != b"GIMP": + msg = "not a GIMP brush, bad magic number" + raise SyntaxError(msg) + self.info["spacing"] = i32(self.fp.read(4)) + + comment = self.fp.read(comment_length)[:-1] + + if color_depth == 1: + self._mode = "L" + else: + self._mode = "RGBA" + + self._size = width, height + + self.info["comment"] = comment + + # Image might not be small + Image._decompression_bomb_check(self.size) + + # Data is an uncompressed block of w * h * bytes/pixel + self._data_size = width * height * color_depth + + def load(self) -> Image.core.PixelAccess | None: + if self._im is None: + self.im = Image.core.new(self.mode, self.size) + self.frombytes(self.fp.read(self._data_size)) + return Image.Image.load(self) + + +# +# registry + + +Image.register_open(GbrImageFile.format, GbrImageFile, _accept) +Image.register_extension(GbrImageFile.format, ".gbr") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GdImageFile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GdImageFile.py new file mode 100644 index 0000000000000000000000000000000000000000..891225ce2fd034a11963bb64212cfa7311190441 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GdImageFile.py @@ -0,0 +1,102 @@ +# +# The Python Imaging Library. +# $Id$ +# +# GD file handling +# +# History: +# 1996-04-12 fl Created +# +# Copyright (c) 1997 by Secret Labs AB. +# Copyright (c) 1996 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + + +""" +.. note:: + This format cannot be automatically recognized, so the + class is not registered for use with :py:func:`PIL.Image.open()`. To open a + gd file, use the :py:func:`PIL.GdImageFile.open()` function instead. + +.. warning:: + THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE. This + implementation is provided for convenience and demonstrational + purposes only. +""" +from __future__ import annotations + +from typing import IO + +from . import ImageFile, ImagePalette, UnidentifiedImageError +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._typing import StrOrBytesPath + + +class GdImageFile(ImageFile.ImageFile): + """ + Image plugin for the GD uncompressed format. Note that this format + is not supported by the standard :py:func:`PIL.Image.open()` function. To use + this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and + use the :py:func:`PIL.GdImageFile.open()` function. + """ + + format = "GD" + format_description = "GD uncompressed images" + + def _open(self) -> None: + # Header + assert self.fp is not None + + s = self.fp.read(1037) + + if i16(s) not in [65534, 65535]: + msg = "Not a valid GD 2.x .gd file" + raise SyntaxError(msg) + + self._mode = "P" + self._size = i16(s, 2), i16(s, 4) + + true_color = s[6] + true_color_offset = 2 if true_color else 0 + + # transparency index + tindex = i32(s, 7 + true_color_offset) + if tindex < 256: + self.info["transparency"] = tindex + + self.palette = ImagePalette.raw( + "RGBX", s[7 + true_color_offset + 6 : 7 + true_color_offset + 6 + 256 * 4] + ) + + self.tile = [ + ImageFile._Tile( + "raw", + (0, 0) + self.size, + 7 + true_color_offset + 6 + 256 * 4, + "L", + ) + ] + + +def open(fp: StrOrBytesPath | IO[bytes], mode: str = "r") -> GdImageFile: + """ + Load texture from a GD image file. + + :param fp: GD file name, or an opened file handle. + :param mode: Optional mode. In this version, if the mode argument + is given, it must be "r". + :returns: An image instance. + :raises OSError: If the image could not be read. + """ + if mode != "r": + msg = "bad mode" + raise ValueError(msg) + + try: + return GdImageFile(fp) + except SyntaxError as e: + msg = "cannot identify this image file" + raise UnidentifiedImageError(msg) from e diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GifImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GifImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..b03aa7f1505e8624b1a50551adbc4488ac3bd1fa --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GifImagePlugin.py @@ -0,0 +1,1213 @@ +# +# The Python Imaging Library. +# $Id$ +# +# GIF file handling +# +# History: +# 1995-09-01 fl Created +# 1996-12-14 fl Added interlace support +# 1996-12-30 fl Added animation support +# 1997-01-05 fl Added write support, fixed local colour map bug +# 1997-02-23 fl Make sure to load raster data in getdata() +# 1997-07-05 fl Support external decoder (0.4) +# 1998-07-09 fl Handle all modes when saving (0.5) +# 1998-07-15 fl Renamed offset attribute to avoid name clash +# 2001-04-16 fl Added rewind support (seek to frame 0) (0.6) +# 2001-04-17 fl Added palette optimization (0.7) +# 2002-06-06 fl Added transparency support for save (0.8) +# 2004-02-24 fl Disable interlacing for small images +# +# Copyright (c) 1997-2004 by Secret Labs AB +# Copyright (c) 1995-2004 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import itertools +import math +import os +import subprocess +from enum import IntEnum +from functools import cached_property +from typing import IO, Any, Literal, NamedTuple, Union, cast + +from . import ( + Image, + ImageChops, + ImageFile, + ImageMath, + ImageOps, + ImagePalette, + ImageSequence, +) +from ._binary import i16le as i16 +from ._binary import o8 +from ._binary import o16le as o16 +from ._util import DeferredError + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import _imaging + from ._typing import Buffer + + +class LoadingStrategy(IntEnum): + """.. versionadded:: 9.1.0""" + + RGB_AFTER_FIRST = 0 + RGB_AFTER_DIFFERENT_PALETTE_ONLY = 1 + RGB_ALWAYS = 2 + + +#: .. versionadded:: 9.1.0 +LOADING_STRATEGY = LoadingStrategy.RGB_AFTER_FIRST + +# -------------------------------------------------------------------- +# Identify/read GIF files + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"GIF87a", b"GIF89a")) + + +## +# Image plugin for GIF images. This plugin supports both GIF87 and +# GIF89 images. + + +class GifImageFile(ImageFile.ImageFile): + format = "GIF" + format_description = "Compuserve GIF" + _close_exclusive_fp_after_loading = False + + global_palette = None + + def data(self) -> bytes | None: + s = self.fp.read(1) + if s and s[0]: + return self.fp.read(s[0]) + return None + + def _is_palette_needed(self, p: bytes) -> bool: + for i in range(0, len(p), 3): + if not (i // 3 == p[i] == p[i + 1] == p[i + 2]): + return True + return False + + def _open(self) -> None: + # Screen + s = self.fp.read(13) + if not _accept(s): + msg = "not a GIF file" + raise SyntaxError(msg) + + self.info["version"] = s[:6] + self._size = i16(s, 6), i16(s, 8) + flags = s[10] + bits = (flags & 7) + 1 + + if flags & 128: + # get global palette + self.info["background"] = s[11] + # check if palette contains colour indices + p = self.fp.read(3 << bits) + if self._is_palette_needed(p): + p = ImagePalette.raw("RGB", p) + self.global_palette = self.palette = p + + self._fp = self.fp # FIXME: hack + self.__rewind = self.fp.tell() + self._n_frames: int | None = None + self._seek(0) # get ready to read first frame + + @property + def n_frames(self) -> int: + if self._n_frames is None: + current = self.tell() + try: + while True: + self._seek(self.tell() + 1, False) + except EOFError: + self._n_frames = self.tell() + 1 + self.seek(current) + return self._n_frames + + @cached_property + def is_animated(self) -> bool: + if self._n_frames is not None: + return self._n_frames != 1 + + current = self.tell() + if current: + return True + + try: + self._seek(1, False) + is_animated = True + except EOFError: + is_animated = False + + self.seek(current) + return is_animated + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if frame < self.__frame: + self._im = None + self._seek(0) + + last_frame = self.__frame + for f in range(self.__frame + 1, frame + 1): + try: + self._seek(f) + except EOFError as e: + self.seek(last_frame) + msg = "no more images in GIF file" + raise EOFError(msg) from e + + def _seek(self, frame: int, update_image: bool = True) -> None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + if frame == 0: + # rewind + self.__offset = 0 + self.dispose: _imaging.ImagingCore | None = None + self.__frame = -1 + self._fp.seek(self.__rewind) + self.disposal_method = 0 + if "comment" in self.info: + del self.info["comment"] + else: + # ensure that the previous frame was loaded + if self.tile and update_image: + self.load() + + if frame != self.__frame + 1: + msg = f"cannot seek to frame {frame}" + raise ValueError(msg) + + self.fp = self._fp + if self.__offset: + # backup to last frame + self.fp.seek(self.__offset) + while self.data(): + pass + self.__offset = 0 + + s = self.fp.read(1) + if not s or s == b";": + msg = "no more images in GIF file" + raise EOFError(msg) + + palette: ImagePalette.ImagePalette | Literal[False] | None = None + + info: dict[str, Any] = {} + frame_transparency = None + interlace = None + frame_dispose_extent = None + while True: + if not s: + s = self.fp.read(1) + if not s or s == b";": + break + + elif s == b"!": + # + # extensions + # + s = self.fp.read(1) + block = self.data() + if s[0] == 249 and block is not None: + # + # graphic control extension + # + flags = block[0] + if flags & 1: + frame_transparency = block[3] + info["duration"] = i16(block, 1) * 10 + + # disposal method - find the value of bits 4 - 6 + dispose_bits = 0b00011100 & flags + dispose_bits = dispose_bits >> 2 + if dispose_bits: + # only set the dispose if it is not + # unspecified. I'm not sure if this is + # correct, but it seems to prevent the last + # frame from looking odd for some animations + self.disposal_method = dispose_bits + elif s[0] == 254: + # + # comment extension + # + comment = b"" + + # Read this comment block + while block: + comment += block + block = self.data() + + if "comment" in info: + # If multiple comment blocks in frame, separate with \n + info["comment"] += b"\n" + comment + else: + info["comment"] = comment + s = None + continue + elif s[0] == 255 and frame == 0 and block is not None: + # + # application extension + # + info["extension"] = block, self.fp.tell() + if block.startswith(b"NETSCAPE2.0"): + block = self.data() + if block and len(block) >= 3 and block[0] == 1: + self.info["loop"] = i16(block, 1) + while self.data(): + pass + + elif s == b",": + # + # local image + # + s = self.fp.read(9) + + # extent + x0, y0 = i16(s, 0), i16(s, 2) + x1, y1 = x0 + i16(s, 4), y0 + i16(s, 6) + if (x1 > self.size[0] or y1 > self.size[1]) and update_image: + self._size = max(x1, self.size[0]), max(y1, self.size[1]) + Image._decompression_bomb_check(self._size) + frame_dispose_extent = x0, y0, x1, y1 + flags = s[8] + + interlace = (flags & 64) != 0 + + if flags & 128: + bits = (flags & 7) + 1 + p = self.fp.read(3 << bits) + if self._is_palette_needed(p): + palette = ImagePalette.raw("RGB", p) + else: + palette = False + + # image data + bits = self.fp.read(1)[0] + self.__offset = self.fp.tell() + break + s = None + + if interlace is None: + msg = "image not found in GIF frame" + raise EOFError(msg) + + self.__frame = frame + if not update_image: + return + + self.tile = [] + + if self.dispose: + self.im.paste(self.dispose, self.dispose_extent) + + self._frame_palette = palette if palette is not None else self.global_palette + self._frame_transparency = frame_transparency + if frame == 0: + if self._frame_palette: + if LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS: + self._mode = "RGBA" if frame_transparency is not None else "RGB" + else: + self._mode = "P" + else: + self._mode = "L" + + if palette: + self.palette = palette + elif self.global_palette: + from copy import copy + + self.palette = copy(self.global_palette) + else: + self.palette = None + else: + if self.mode == "P": + if ( + LOADING_STRATEGY != LoadingStrategy.RGB_AFTER_DIFFERENT_PALETTE_ONLY + or palette + ): + if "transparency" in self.info: + self.im.putpalettealpha(self.info["transparency"], 0) + self.im = self.im.convert("RGBA", Image.Dither.FLOYDSTEINBERG) + self._mode = "RGBA" + del self.info["transparency"] + else: + self._mode = "RGB" + self.im = self.im.convert("RGB", Image.Dither.FLOYDSTEINBERG) + + def _rgb(color: int) -> tuple[int, int, int]: + if self._frame_palette: + if color * 3 + 3 > len(self._frame_palette.palette): + color = 0 + return cast( + tuple[int, int, int], + tuple(self._frame_palette.palette[color * 3 : color * 3 + 3]), + ) + else: + return (color, color, color) + + self.dispose = None + self.dispose_extent: tuple[int, int, int, int] | None = frame_dispose_extent + if self.dispose_extent and self.disposal_method >= 2: + try: + if self.disposal_method == 2: + # replace with background colour + + # only dispose the extent in this frame + x0, y0, x1, y1 = self.dispose_extent + dispose_size = (x1 - x0, y1 - y0) + + Image._decompression_bomb_check(dispose_size) + + # by convention, attempt to use transparency first + dispose_mode = "P" + color = self.info.get("transparency", frame_transparency) + if color is not None: + if self.mode in ("RGB", "RGBA"): + dispose_mode = "RGBA" + color = _rgb(color) + (0,) + else: + color = self.info.get("background", 0) + if self.mode in ("RGB", "RGBA"): + dispose_mode = "RGB" + color = _rgb(color) + self.dispose = Image.core.fill(dispose_mode, dispose_size, color) + else: + # replace with previous contents + if self._im is not None: + # only dispose the extent in this frame + self.dispose = self._crop(self.im, self.dispose_extent) + elif frame_transparency is not None: + x0, y0, x1, y1 = self.dispose_extent + dispose_size = (x1 - x0, y1 - y0) + + Image._decompression_bomb_check(dispose_size) + dispose_mode = "P" + color = frame_transparency + if self.mode in ("RGB", "RGBA"): + dispose_mode = "RGBA" + color = _rgb(frame_transparency) + (0,) + self.dispose = Image.core.fill( + dispose_mode, dispose_size, color + ) + except AttributeError: + pass + + if interlace is not None: + transparency = -1 + if frame_transparency is not None: + if frame == 0: + if LOADING_STRATEGY != LoadingStrategy.RGB_ALWAYS: + self.info["transparency"] = frame_transparency + elif self.mode not in ("RGB", "RGBA"): + transparency = frame_transparency + self.tile = [ + ImageFile._Tile( + "gif", + (x0, y0, x1, y1), + self.__offset, + (bits, interlace, transparency), + ) + ] + + if info.get("comment"): + self.info["comment"] = info["comment"] + for k in ["duration", "extension"]: + if k in info: + self.info[k] = info[k] + elif k in self.info: + del self.info[k] + + def load_prepare(self) -> None: + temp_mode = "P" if self._frame_palette else "L" + self._prev_im = None + if self.__frame == 0: + if self._frame_transparency is not None: + self.im = Image.core.fill( + temp_mode, self.size, self._frame_transparency + ) + elif self.mode in ("RGB", "RGBA"): + self._prev_im = self.im + if self._frame_palette: + self.im = Image.core.fill("P", self.size, self._frame_transparency or 0) + self.im.putpalette("RGB", *self._frame_palette.getdata()) + else: + self._im = None + if not self._prev_im and self._im is not None and self.size != self.im.size: + expanded_im = Image.core.fill(self.im.mode, self.size) + if self._frame_palette: + expanded_im.putpalette("RGB", *self._frame_palette.getdata()) + expanded_im.paste(self.im, (0, 0) + self.im.size) + + self.im = expanded_im + self._mode = temp_mode + self._frame_palette = None + + super().load_prepare() + + def load_end(self) -> None: + if self.__frame == 0: + if self.mode == "P" and LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS: + if self._frame_transparency is not None: + self.im.putpalettealpha(self._frame_transparency, 0) + self._mode = "RGBA" + else: + self._mode = "RGB" + self.im = self.im.convert(self.mode, Image.Dither.FLOYDSTEINBERG) + return + if not self._prev_im: + return + if self.size != self._prev_im.size: + if self._frame_transparency is not None: + expanded_im = Image.core.fill("RGBA", self.size) + else: + expanded_im = Image.core.fill("P", self.size) + expanded_im.putpalette("RGB", "RGB", self.im.getpalette()) + expanded_im = expanded_im.convert("RGB") + expanded_im.paste(self._prev_im, (0, 0) + self._prev_im.size) + + self._prev_im = expanded_im + assert self._prev_im is not None + if self._frame_transparency is not None: + if self.mode == "L": + frame_im = self.im.convert_transparent("LA", self._frame_transparency) + else: + self.im.putpalettealpha(self._frame_transparency, 0) + frame_im = self.im.convert("RGBA") + else: + frame_im = self.im.convert("RGB") + + assert self.dispose_extent is not None + frame_im = self._crop(frame_im, self.dispose_extent) + + self.im = self._prev_im + self._mode = self.im.mode + if frame_im.mode in ("LA", "RGBA"): + self.im.paste(frame_im, self.dispose_extent, frame_im) + else: + self.im.paste(frame_im, self.dispose_extent) + + def tell(self) -> int: + return self.__frame + + +# -------------------------------------------------------------------- +# Write GIF files + + +RAWMODE = {"1": "L", "L": "L", "P": "P"} + + +def _normalize_mode(im: Image.Image) -> Image.Image: + """ + Takes an image (or frame), returns an image in a mode that is appropriate + for saving in a Gif. + + It may return the original image, or it may return an image converted to + palette or 'L' mode. + + :param im: Image object + :returns: Image object + """ + if im.mode in RAWMODE: + im.load() + return im + if Image.getmodebase(im.mode) == "RGB": + im = im.convert("P", palette=Image.Palette.ADAPTIVE) + assert im.palette is not None + if im.palette.mode == "RGBA": + for rgba in im.palette.colors: + if rgba[3] == 0: + im.info["transparency"] = im.palette.colors[rgba] + break + return im + return im.convert("L") + + +_Palette = Union[bytes, bytearray, list[int], ImagePalette.ImagePalette] + + +def _normalize_palette( + im: Image.Image, palette: _Palette | None, info: dict[str, Any] +) -> Image.Image: + """ + Normalizes the palette for image. + - Sets the palette to the incoming palette, if provided. + - Ensures that there's a palette for L mode images + - Optimizes the palette if necessary/desired. + + :param im: Image object + :param palette: bytes object containing the source palette, or .... + :param info: encoderinfo + :returns: Image object + """ + source_palette = None + if palette: + # a bytes palette + if isinstance(palette, (bytes, bytearray, list)): + source_palette = bytearray(palette[:768]) + if isinstance(palette, ImagePalette.ImagePalette): + source_palette = bytearray(palette.palette) + + if im.mode == "P": + if not source_palette: + im_palette = im.getpalette(None) + assert im_palette is not None + source_palette = bytearray(im_palette) + else: # L-mode + if not source_palette: + source_palette = bytearray(i // 3 for i in range(768)) + im.palette = ImagePalette.ImagePalette("RGB", palette=source_palette) + assert source_palette is not None + + if palette: + used_palette_colors: list[int | None] = [] + assert im.palette is not None + for i in range(0, len(source_palette), 3): + source_color = tuple(source_palette[i : i + 3]) + index = im.palette.colors.get(source_color) + if index in used_palette_colors: + index = None + used_palette_colors.append(index) + for i, index in enumerate(used_palette_colors): + if index is None: + for j in range(len(used_palette_colors)): + if j not in used_palette_colors: + used_palette_colors[i] = j + break + dest_map: list[int] = [] + for index in used_palette_colors: + assert index is not None + dest_map.append(index) + im = im.remap_palette(dest_map) + else: + optimized_palette_colors = _get_optimize(im, info) + if optimized_palette_colors is not None: + im = im.remap_palette(optimized_palette_colors, source_palette) + if "transparency" in info: + try: + info["transparency"] = optimized_palette_colors.index( + info["transparency"] + ) + except ValueError: + del info["transparency"] + return im + + assert im.palette is not None + im.palette.palette = source_palette + return im + + +def _write_single_frame( + im: Image.Image, + fp: IO[bytes], + palette: _Palette | None, +) -> None: + im_out = _normalize_mode(im) + for k, v in im_out.info.items(): + if isinstance(k, str): + im.encoderinfo.setdefault(k, v) + im_out = _normalize_palette(im_out, palette, im.encoderinfo) + + for s in _get_global_header(im_out, im.encoderinfo): + fp.write(s) + + # local image header + flags = 0 + if get_interlace(im): + flags = flags | 64 + _write_local_header(fp, im, (0, 0), flags) + + im_out.encoderconfig = (8, get_interlace(im)) + ImageFile._save( + im_out, fp, [ImageFile._Tile("gif", (0, 0) + im.size, 0, RAWMODE[im_out.mode])] + ) + + fp.write(b"\0") # end of image data + + +def _getbbox( + base_im: Image.Image, im_frame: Image.Image +) -> tuple[Image.Image, tuple[int, int, int, int] | None]: + palette_bytes = [ + bytes(im.palette.palette) if im.palette else b"" for im in (base_im, im_frame) + ] + if palette_bytes[0] != palette_bytes[1]: + im_frame = im_frame.convert("RGBA") + base_im = base_im.convert("RGBA") + delta = ImageChops.subtract_modulo(im_frame, base_im) + return delta, delta.getbbox(alpha_only=False) + + +class _Frame(NamedTuple): + im: Image.Image + bbox: tuple[int, int, int, int] | None + encoderinfo: dict[str, Any] + + +def _write_multiple_frames( + im: Image.Image, fp: IO[bytes], palette: _Palette | None +) -> bool: + duration = im.encoderinfo.get("duration") + disposal = im.encoderinfo.get("disposal", im.info.get("disposal")) + + im_frames: list[_Frame] = [] + previous_im: Image.Image | None = None + frame_count = 0 + background_im = None + for imSequence in itertools.chain([im], im.encoderinfo.get("append_images", [])): + for im_frame in ImageSequence.Iterator(imSequence): + # a copy is required here since seek can still mutate the image + im_frame = _normalize_mode(im_frame.copy()) + if frame_count == 0: + for k, v in im_frame.info.items(): + if k == "transparency": + continue + if isinstance(k, str): + im.encoderinfo.setdefault(k, v) + + encoderinfo = im.encoderinfo.copy() + if "transparency" in im_frame.info: + encoderinfo.setdefault("transparency", im_frame.info["transparency"]) + im_frame = _normalize_palette(im_frame, palette, encoderinfo) + if isinstance(duration, (list, tuple)): + encoderinfo["duration"] = duration[frame_count] + elif duration is None and "duration" in im_frame.info: + encoderinfo["duration"] = im_frame.info["duration"] + if isinstance(disposal, (list, tuple)): + encoderinfo["disposal"] = disposal[frame_count] + frame_count += 1 + + diff_frame = None + if im_frames and previous_im: + # delta frame + delta, bbox = _getbbox(previous_im, im_frame) + if not bbox: + # This frame is identical to the previous frame + if encoderinfo.get("duration"): + im_frames[-1].encoderinfo["duration"] += encoderinfo["duration"] + continue + if im_frames[-1].encoderinfo.get("disposal") == 2: + # To appear correctly in viewers using a convention, + # only consider transparency, and not background color + color = im.encoderinfo.get( + "transparency", im.info.get("transparency") + ) + if color is not None: + if background_im is None: + background = _get_background(im_frame, color) + background_im = Image.new("P", im_frame.size, background) + first_palette = im_frames[0].im.palette + assert first_palette is not None + background_im.putpalette(first_palette, first_palette.mode) + bbox = _getbbox(background_im, im_frame)[1] + else: + bbox = (0, 0) + im_frame.size + elif encoderinfo.get("optimize") and im_frame.mode != "1": + if "transparency" not in encoderinfo: + assert im_frame.palette is not None + try: + encoderinfo["transparency"] = ( + im_frame.palette._new_color_index(im_frame) + ) + except ValueError: + pass + if "transparency" in encoderinfo: + # When the delta is zero, fill the image with transparency + diff_frame = im_frame.copy() + fill = Image.new("P", delta.size, encoderinfo["transparency"]) + if delta.mode == "RGBA": + r, g, b, a = delta.split() + mask = ImageMath.lambda_eval( + lambda args: args["convert"]( + args["max"]( + args["max"]( + args["max"](args["r"], args["g"]), args["b"] + ), + args["a"], + ) + * 255, + "1", + ), + r=r, + g=g, + b=b, + a=a, + ) + else: + if delta.mode == "P": + # Convert to L without considering palette + delta_l = Image.new("L", delta.size) + delta_l.putdata(delta.getdata()) + delta = delta_l + mask = ImageMath.lambda_eval( + lambda args: args["convert"](args["im"] * 255, "1"), + im=delta, + ) + diff_frame.paste(fill, mask=ImageOps.invert(mask)) + else: + bbox = None + previous_im = im_frame + im_frames.append(_Frame(diff_frame or im_frame, bbox, encoderinfo)) + + if len(im_frames) == 1: + if "duration" in im.encoderinfo: + # Since multiple frames will not be written, use the combined duration + im.encoderinfo["duration"] = im_frames[0].encoderinfo["duration"] + return False + + for frame_data in im_frames: + im_frame = frame_data.im + if not frame_data.bbox: + # global header + for s in _get_global_header(im_frame, frame_data.encoderinfo): + fp.write(s) + offset = (0, 0) + else: + # compress difference + if not palette: + frame_data.encoderinfo["include_color_table"] = True + + if frame_data.bbox != (0, 0) + im_frame.size: + im_frame = im_frame.crop(frame_data.bbox) + offset = frame_data.bbox[:2] + _write_frame_data(fp, im_frame, offset, frame_data.encoderinfo) + return True + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, save_all=True) + + +def _save( + im: Image.Image, fp: IO[bytes], filename: str | bytes, save_all: bool = False +) -> None: + # header + if "palette" in im.encoderinfo or "palette" in im.info: + palette = im.encoderinfo.get("palette", im.info.get("palette")) + else: + palette = None + im.encoderinfo.setdefault("optimize", True) + + if not save_all or not _write_multiple_frames(im, fp, palette): + _write_single_frame(im, fp, palette) + + fp.write(b";") # end of file + + if hasattr(fp, "flush"): + fp.flush() + + +def get_interlace(im: Image.Image) -> int: + interlace = im.encoderinfo.get("interlace", 1) + + # workaround for @PIL153 + if min(im.size) < 16: + interlace = 0 + + return interlace + + +def _write_local_header( + fp: IO[bytes], im: Image.Image, offset: tuple[int, int], flags: int +) -> None: + try: + transparency = im.encoderinfo["transparency"] + except KeyError: + transparency = None + + if "duration" in im.encoderinfo: + duration = int(im.encoderinfo["duration"] / 10) + else: + duration = 0 + + disposal = int(im.encoderinfo.get("disposal", 0)) + + if transparency is not None or duration != 0 or disposal: + packed_flag = 1 if transparency is not None else 0 + packed_flag |= disposal << 2 + + fp.write( + b"!" + + o8(249) # extension intro + + o8(4) # length + + o8(packed_flag) # packed fields + + o16(duration) # duration + + o8(transparency or 0) # transparency index + + o8(0) + ) + + include_color_table = im.encoderinfo.get("include_color_table") + if include_color_table: + palette_bytes = _get_palette_bytes(im) + color_table_size = _get_color_table_size(palette_bytes) + if color_table_size: + flags = flags | 128 # local color table flag + flags = flags | color_table_size + + fp.write( + b"," + + o16(offset[0]) # offset + + o16(offset[1]) + + o16(im.size[0]) # size + + o16(im.size[1]) + + o8(flags) # flags + ) + if include_color_table and color_table_size: + fp.write(_get_header_palette(palette_bytes)) + fp.write(o8(8)) # bits + + +def _save_netpbm(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + # Unused by default. + # To use, uncomment the register_save call at the end of the file. + # + # If you need real GIF compression and/or RGB quantization, you + # can use the external NETPBM/PBMPLUS utilities. See comments + # below for information on how to enable this. + tempfile = im._dump() + + try: + with open(filename, "wb") as f: + if im.mode != "RGB": + subprocess.check_call( + ["ppmtogif", tempfile], stdout=f, stderr=subprocess.DEVNULL + ) + else: + # Pipe ppmquant output into ppmtogif + # "ppmquant 256 %s | ppmtogif > %s" % (tempfile, filename) + quant_cmd = ["ppmquant", "256", tempfile] + togif_cmd = ["ppmtogif"] + quant_proc = subprocess.Popen( + quant_cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL + ) + togif_proc = subprocess.Popen( + togif_cmd, + stdin=quant_proc.stdout, + stdout=f, + stderr=subprocess.DEVNULL, + ) + + # Allow ppmquant to receive SIGPIPE if ppmtogif exits + assert quant_proc.stdout is not None + quant_proc.stdout.close() + + retcode = quant_proc.wait() + if retcode: + raise subprocess.CalledProcessError(retcode, quant_cmd) + + retcode = togif_proc.wait() + if retcode: + raise subprocess.CalledProcessError(retcode, togif_cmd) + finally: + try: + os.unlink(tempfile) + except OSError: + pass + + +# Force optimization so that we can test performance against +# cases where it took lots of memory and time previously. +_FORCE_OPTIMIZE = False + + +def _get_optimize(im: Image.Image, info: dict[str, Any]) -> list[int] | None: + """ + Palette optimization is a potentially expensive operation. + + This function determines if the palette should be optimized using + some heuristics, then returns the list of palette entries in use. + + :param im: Image object + :param info: encoderinfo + :returns: list of indexes of palette entries in use, or None + """ + if im.mode in ("P", "L") and info and info.get("optimize"): + # Potentially expensive operation. + + # The palette saves 3 bytes per color not used, but palette + # lengths are restricted to 3*(2**N) bytes. Max saving would + # be 768 -> 6 bytes if we went all the way down to 2 colors. + # * If we're over 128 colors, we can't save any space. + # * If there aren't any holes, it's not worth collapsing. + # * If we have a 'large' image, the palette is in the noise. + + # create the new palette if not every color is used + optimise = _FORCE_OPTIMIZE or im.mode == "L" + if optimise or im.width * im.height < 512 * 512: + # check which colors are used + used_palette_colors = [] + for i, count in enumerate(im.histogram()): + if count: + used_palette_colors.append(i) + + if optimise or max(used_palette_colors) >= len(used_palette_colors): + return used_palette_colors + + assert im.palette is not None + num_palette_colors = len(im.palette.palette) // Image.getmodebands( + im.palette.mode + ) + current_palette_size = 1 << (num_palette_colors - 1).bit_length() + if ( + # check that the palette would become smaller when saved + len(used_palette_colors) <= current_palette_size // 2 + # check that the palette is not already the smallest possible size + and current_palette_size > 2 + ): + return used_palette_colors + return None + + +def _get_color_table_size(palette_bytes: bytes) -> int: + # calculate the palette size for the header + if not palette_bytes: + return 0 + elif len(palette_bytes) < 9: + return 1 + else: + return math.ceil(math.log(len(palette_bytes) // 3, 2)) - 1 + + +def _get_header_palette(palette_bytes: bytes) -> bytes: + """ + Returns the palette, null padded to the next power of 2 (*3) bytes + suitable for direct inclusion in the GIF header + + :param palette_bytes: Unpadded palette bytes, in RGBRGB form + :returns: Null padded palette + """ + color_table_size = _get_color_table_size(palette_bytes) + + # add the missing amount of bytes + # the palette has to be 2< 0: + palette_bytes += o8(0) * 3 * actual_target_size_diff + return palette_bytes + + +def _get_palette_bytes(im: Image.Image) -> bytes: + """ + Gets the palette for inclusion in the gif header + + :param im: Image object + :returns: Bytes, len<=768 suitable for inclusion in gif header + """ + if not im.palette: + return b"" + + palette = bytes(im.palette.palette) + if im.palette.mode == "RGBA": + palette = b"".join(palette[i * 4 : i * 4 + 3] for i in range(len(palette) // 3)) + return palette + + +def _get_background( + im: Image.Image, + info_background: int | tuple[int, int, int] | tuple[int, int, int, int] | None, +) -> int: + background = 0 + if info_background: + if isinstance(info_background, tuple): + # WebPImagePlugin stores an RGBA value in info["background"] + # So it must be converted to the same format as GifImagePlugin's + # info["background"] - a global color table index + assert im.palette is not None + try: + background = im.palette.getcolor(info_background, im) + except ValueError as e: + if str(e) not in ( + # If all 256 colors are in use, + # then there is no need for the background color + "cannot allocate more than 256 colors", + # Ignore non-opaque WebP background + "cannot add non-opaque RGBA color to RGB palette", + ): + raise + else: + background = info_background + return background + + +def _get_global_header(im: Image.Image, info: dict[str, Any]) -> list[bytes]: + """Return a list of strings representing a GIF header""" + + # Header Block + # https://www.matthewflickinger.com/lab/whatsinagif/bits_and_bytes.asp + + version = b"87a" + if im.info.get("version") == b"89a" or ( + info + and ( + "transparency" in info + or info.get("loop") is not None + or info.get("duration") + or info.get("comment") + ) + ): + version = b"89a" + + background = _get_background(im, info.get("background")) + + palette_bytes = _get_palette_bytes(im) + color_table_size = _get_color_table_size(palette_bytes) + + header = [ + b"GIF" # signature + + version # version + + o16(im.size[0]) # canvas width + + o16(im.size[1]), # canvas height + # Logical Screen Descriptor + # size of global color table + global color table flag + o8(color_table_size + 128), # packed fields + # background + reserved/aspect + o8(background) + o8(0), + # Global Color Table + _get_header_palette(palette_bytes), + ] + if info.get("loop") is not None: + header.append( + b"!" + + o8(255) # extension intro + + o8(11) + + b"NETSCAPE2.0" + + o8(3) + + o8(1) + + o16(info["loop"]) # number of loops + + o8(0) + ) + if info.get("comment"): + comment_block = b"!" + o8(254) # extension intro + + comment = info["comment"] + if isinstance(comment, str): + comment = comment.encode() + for i in range(0, len(comment), 255): + subblock = comment[i : i + 255] + comment_block += o8(len(subblock)) + subblock + + comment_block += o8(0) + header.append(comment_block) + return header + + +def _write_frame_data( + fp: IO[bytes], + im_frame: Image.Image, + offset: tuple[int, int], + params: dict[str, Any], +) -> None: + try: + im_frame.encoderinfo = params + + # local image header + _write_local_header(fp, im_frame, offset, 0) + + ImageFile._save( + im_frame, + fp, + [ImageFile._Tile("gif", (0, 0) + im_frame.size, 0, RAWMODE[im_frame.mode])], + ) + + fp.write(b"\0") # end of image data + finally: + del im_frame.encoderinfo + + +# -------------------------------------------------------------------- +# Legacy GIF utilities + + +def getheader( + im: Image.Image, palette: _Palette | None = None, info: dict[str, Any] | None = None +) -> tuple[list[bytes], list[int] | None]: + """ + Legacy Method to get Gif data from image. + + Warning:: May modify image data. + + :param im: Image object + :param palette: bytes object containing the source palette, or .... + :param info: encoderinfo + :returns: tuple of(list of header items, optimized palette) + + """ + if info is None: + info = {} + + used_palette_colors = _get_optimize(im, info) + + if "background" not in info and "background" in im.info: + info["background"] = im.info["background"] + + im_mod = _normalize_palette(im, palette, info) + im.palette = im_mod.palette + im.im = im_mod.im + header = _get_global_header(im, info) + + return header, used_palette_colors + + +def getdata( + im: Image.Image, offset: tuple[int, int] = (0, 0), **params: Any +) -> list[bytes]: + """ + Legacy Method + + Return a list of strings representing this image. + The first string is a local image header, the rest contains + encoded image data. + + To specify duration, add the time in milliseconds, + e.g. ``getdata(im_frame, duration=1000)`` + + :param im: Image object + :param offset: Tuple of (x, y) pixels. Defaults to (0, 0) + :param \\**params: e.g. duration or other encoder info parameters + :returns: List of bytes containing GIF encoded frame data + + """ + from io import BytesIO + + class Collector(BytesIO): + data = [] + + def write(self, data: Buffer) -> int: + self.data.append(data) + return len(data) + + im.load() # make sure raster data is available + + fp = Collector() + + _write_frame_data(fp, im, offset, params) + + return fp.data + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(GifImageFile.format, GifImageFile, _accept) +Image.register_save(GifImageFile.format, _save) +Image.register_save_all(GifImageFile.format, _save_all) +Image.register_extension(GifImageFile.format, ".gif") +Image.register_mime(GifImageFile.format, "image/gif") + +# +# Uncomment the following line if you wish to use NETPBM/PBMPLUS +# instead of the built-in "uncompressed" GIF encoder + +# Image.register_save(GifImageFile.format, _save_netpbm) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GimpGradientFile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GimpGradientFile.py new file mode 100644 index 0000000000000000000000000000000000000000..ec62f8e4ebc37d3aef9b171a0d03b7deeab702c4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GimpGradientFile.py @@ -0,0 +1,149 @@ +# +# Python Imaging Library +# $Id$ +# +# stuff to read (and render) GIMP gradient files +# +# History: +# 97-08-23 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# + +""" +Stuff to translate curve segments to palette values (derived from +the corresponding code in GIMP, written by Federico Mena Quintero. +See the GIMP distribution for more information.) +""" +from __future__ import annotations + +from math import log, pi, sin, sqrt +from typing import IO, Callable + +from ._binary import o8 + +EPSILON = 1e-10 +"""""" # Enable auto-doc for data member + + +def linear(middle: float, pos: float) -> float: + if pos <= middle: + if middle < EPSILON: + return 0.0 + else: + return 0.5 * pos / middle + else: + pos = pos - middle + middle = 1.0 - middle + if middle < EPSILON: + return 1.0 + else: + return 0.5 + 0.5 * pos / middle + + +def curved(middle: float, pos: float) -> float: + return pos ** (log(0.5) / log(max(middle, EPSILON))) + + +def sine(middle: float, pos: float) -> float: + return (sin((-pi / 2.0) + pi * linear(middle, pos)) + 1.0) / 2.0 + + +def sphere_increasing(middle: float, pos: float) -> float: + return sqrt(1.0 - (linear(middle, pos) - 1.0) ** 2) + + +def sphere_decreasing(middle: float, pos: float) -> float: + return 1.0 - sqrt(1.0 - linear(middle, pos) ** 2) + + +SEGMENTS = [linear, curved, sine, sphere_increasing, sphere_decreasing] +"""""" # Enable auto-doc for data member + + +class GradientFile: + gradient: ( + list[ + tuple[ + float, + float, + float, + list[float], + list[float], + Callable[[float, float], float], + ] + ] + | None + ) = None + + def getpalette(self, entries: int = 256) -> tuple[bytes, str]: + assert self.gradient is not None + palette = [] + + ix = 0 + x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix] + + for i in range(entries): + x = i / (entries - 1) + + while x1 < x: + ix += 1 + x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix] + + w = x1 - x0 + + if w < EPSILON: + scale = segment(0.5, 0.5) + else: + scale = segment((xm - x0) / w, (x - x0) / w) + + # expand to RGBA + r = o8(int(255 * ((rgb1[0] - rgb0[0]) * scale + rgb0[0]) + 0.5)) + g = o8(int(255 * ((rgb1[1] - rgb0[1]) * scale + rgb0[1]) + 0.5)) + b = o8(int(255 * ((rgb1[2] - rgb0[2]) * scale + rgb0[2]) + 0.5)) + a = o8(int(255 * ((rgb1[3] - rgb0[3]) * scale + rgb0[3]) + 0.5)) + + # add to palette + palette.append(r + g + b + a) + + return b"".join(palette), "RGBA" + + +class GimpGradientFile(GradientFile): + """File handler for GIMP's gradient format.""" + + def __init__(self, fp: IO[bytes]) -> None: + if not fp.readline().startswith(b"GIMP Gradient"): + msg = "not a GIMP gradient file" + raise SyntaxError(msg) + + line = fp.readline() + + # GIMP 1.2 gradient files don't contain a name, but GIMP 1.3 files do + if line.startswith(b"Name: "): + line = fp.readline().strip() + + count = int(line) + + self.gradient = [] + + for i in range(count): + s = fp.readline().split() + w = [float(x) for x in s[:11]] + + x0, x1 = w[0], w[2] + xm = w[1] + rgb0 = w[3:7] + rgb1 = w[7:11] + + segment = SEGMENTS[int(s[11])] + cspace = int(s[12]) + + if cspace != 0: + msg = "cannot handle HSV colour space" + raise OSError(msg) + + self.gradient.append((x0, x1, xm, rgb0, rgb1, segment)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GimpPaletteFile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GimpPaletteFile.py new file mode 100644 index 0000000000000000000000000000000000000000..379ffd739182c4caaad3bce92e0e8344ced2eef4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GimpPaletteFile.py @@ -0,0 +1,72 @@ +# +# Python Imaging Library +# $Id$ +# +# stuff to read GIMP palette files +# +# History: +# 1997-08-23 fl Created +# 2004-09-07 fl Support GIMP 2.0 palette files. +# +# Copyright (c) Secret Labs AB 1997-2004. All rights reserved. +# Copyright (c) Fredrik Lundh 1997-2004. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re +from io import BytesIO +from typing import IO + + +class GimpPaletteFile: + """File handler for GIMP's palette format.""" + + rawmode = "RGB" + + def _read(self, fp: IO[bytes], limit: bool = True) -> None: + if not fp.readline().startswith(b"GIMP Palette"): + msg = "not a GIMP palette file" + raise SyntaxError(msg) + + palette: list[int] = [] + i = 0 + while True: + if limit and i == 256 + 3: + break + + i += 1 + s = fp.readline() + if not s: + break + + # skip fields and comment lines + if re.match(rb"\w+:|#", s): + continue + if limit and len(s) > 100: + msg = "bad palette file" + raise SyntaxError(msg) + + v = s.split(maxsplit=3) + if len(v) < 3: + msg = "bad palette entry" + raise ValueError(msg) + + palette += (int(v[i]) for i in range(3)) + if limit and len(palette) == 768: + break + + self.palette = bytes(palette) + + def __init__(self, fp: IO[bytes]) -> None: + self._read(fp) + + @classmethod + def frombytes(cls, data: bytes) -> GimpPaletteFile: + self = cls.__new__(cls) + self._read(BytesIO(data), False) + return self + + def getpalette(self) -> tuple[bytes, str]: + return self.palette, self.rawmode diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GribStubImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GribStubImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..439fc5a3eda8414add95d53660eca8d11bf6ab8f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/GribStubImagePlugin.py @@ -0,0 +1,75 @@ +# +# The Python Imaging Library +# $Id$ +# +# GRIB stub adapter +# +# Copyright (c) 1996-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import IO + +from . import Image, ImageFile + +_handler = None + + +def register_handler(handler: ImageFile.StubHandler | None) -> None: + """ + Install application-specific GRIB image handler. + + :param handler: Handler object. + """ + global _handler + _handler = handler + + +# -------------------------------------------------------------------- +# Image adapter + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"GRIB") and prefix[7] == 1 + + +class GribStubImageFile(ImageFile.StubImageFile): + format = "GRIB" + format_description = "GRIB" + + def _open(self) -> None: + if not _accept(self.fp.read(8)): + msg = "Not a GRIB file" + raise SyntaxError(msg) + + self.fp.seek(-8, os.SEEK_CUR) + + # make something up + self._mode = "F" + self._size = 1, 1 + + loader = self._load() + if loader: + loader.open(self) + + def _load(self) -> ImageFile.StubHandler | None: + return _handler + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if _handler is None or not hasattr(_handler, "save"): + msg = "GRIB save handler not installed" + raise OSError(msg) + _handler.save(im, fp, filename) + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(GribStubImageFile.format, GribStubImageFile, _accept) +Image.register_save(GribStubImageFile.format, _save) + +Image.register_extension(GribStubImageFile.format, ".grib") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/Hdf5StubImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/Hdf5StubImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..76e640f15abfe60a56a571380133a0463c104035 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/Hdf5StubImagePlugin.py @@ -0,0 +1,75 @@ +# +# The Python Imaging Library +# $Id$ +# +# HDF5 stub adapter +# +# Copyright (c) 2000-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import IO + +from . import Image, ImageFile + +_handler = None + + +def register_handler(handler: ImageFile.StubHandler | None) -> None: + """ + Install application-specific HDF5 image handler. + + :param handler: Handler object. + """ + global _handler + _handler = handler + + +# -------------------------------------------------------------------- +# Image adapter + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\x89HDF\r\n\x1a\n") + + +class HDF5StubImageFile(ImageFile.StubImageFile): + format = "HDF5" + format_description = "HDF5" + + def _open(self) -> None: + if not _accept(self.fp.read(8)): + msg = "Not an HDF file" + raise SyntaxError(msg) + + self.fp.seek(-8, os.SEEK_CUR) + + # make something up + self._mode = "F" + self._size = 1, 1 + + loader = self._load() + if loader: + loader.open(self) + + def _load(self) -> ImageFile.StubHandler | None: + return _handler + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if _handler is None or not hasattr(_handler, "save"): + msg = "HDF5 save handler not installed" + raise OSError(msg) + _handler.save(im, fp, filename) + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(HDF5StubImageFile.format, HDF5StubImageFile, _accept) +Image.register_save(HDF5StubImageFile.format, _save) + +Image.register_extensions(HDF5StubImageFile.format, [".h5", ".hdf"]) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/IcnsImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/IcnsImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..5a88429e5e4b3be4e57ce85b70fdaa4c7927fe09 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/IcnsImagePlugin.py @@ -0,0 +1,411 @@ +# +# The Python Imaging Library. +# $Id$ +# +# macOS icns file decoder, based on icns.py by Bob Ippolito. +# +# history: +# 2004-10-09 fl Turned into a PIL plugin; removed 2.3 dependencies. +# 2020-04-04 Allow saving on all operating systems. +# +# Copyright (c) 2004 by Bob Ippolito. +# Copyright (c) 2004 by Secret Labs. +# Copyright (c) 2004 by Fredrik Lundh. +# Copyright (c) 2014 by Alastair Houghton. +# Copyright (c) 2020 by Pan Jing. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import os +import struct +import sys +from typing import IO + +from . import Image, ImageFile, PngImagePlugin, features +from ._deprecate import deprecate + +enable_jpeg2k = features.check_codec("jpg_2000") +if enable_jpeg2k: + from . import Jpeg2KImagePlugin + +MAGIC = b"icns" +HEADERSIZE = 8 + + +def nextheader(fobj: IO[bytes]) -> tuple[bytes, int]: + return struct.unpack(">4sI", fobj.read(HEADERSIZE)) + + +def read_32t( + fobj: IO[bytes], start_length: tuple[int, int], size: tuple[int, int, int] +) -> dict[str, Image.Image]: + # The 128x128 icon seems to have an extra header for some reason. + (start, length) = start_length + fobj.seek(start) + sig = fobj.read(4) + if sig != b"\x00\x00\x00\x00": + msg = "Unknown signature, expecting 0x00000000" + raise SyntaxError(msg) + return read_32(fobj, (start + 4, length - 4), size) + + +def read_32( + fobj: IO[bytes], start_length: tuple[int, int], size: tuple[int, int, int] +) -> dict[str, Image.Image]: + """ + Read a 32bit RGB icon resource. Seems to be either uncompressed or + an RLE packbits-like scheme. + """ + (start, length) = start_length + fobj.seek(start) + pixel_size = (size[0] * size[2], size[1] * size[2]) + sizesq = pixel_size[0] * pixel_size[1] + if length == sizesq * 3: + # uncompressed ("RGBRGBGB") + indata = fobj.read(length) + im = Image.frombuffer("RGB", pixel_size, indata, "raw", "RGB", 0, 1) + else: + # decode image + im = Image.new("RGB", pixel_size, None) + for band_ix in range(3): + data = [] + bytesleft = sizesq + while bytesleft > 0: + byte = fobj.read(1) + if not byte: + break + byte_int = byte[0] + if byte_int & 0x80: + blocksize = byte_int - 125 + byte = fobj.read(1) + for i in range(blocksize): + data.append(byte) + else: + blocksize = byte_int + 1 + data.append(fobj.read(blocksize)) + bytesleft -= blocksize + if bytesleft <= 0: + break + if bytesleft != 0: + msg = f"Error reading channel [{repr(bytesleft)} left]" + raise SyntaxError(msg) + band = Image.frombuffer("L", pixel_size, b"".join(data), "raw", "L", 0, 1) + im.im.putband(band.im, band_ix) + return {"RGB": im} + + +def read_mk( + fobj: IO[bytes], start_length: tuple[int, int], size: tuple[int, int, int] +) -> dict[str, Image.Image]: + # Alpha masks seem to be uncompressed + start = start_length[0] + fobj.seek(start) + pixel_size = (size[0] * size[2], size[1] * size[2]) + sizesq = pixel_size[0] * pixel_size[1] + band = Image.frombuffer("L", pixel_size, fobj.read(sizesq), "raw", "L", 0, 1) + return {"A": band} + + +def read_png_or_jpeg2000( + fobj: IO[bytes], start_length: tuple[int, int], size: tuple[int, int, int] +) -> dict[str, Image.Image]: + (start, length) = start_length + fobj.seek(start) + sig = fobj.read(12) + + im: Image.Image + if sig.startswith(b"\x89PNG\x0d\x0a\x1a\x0a"): + fobj.seek(start) + im = PngImagePlugin.PngImageFile(fobj) + Image._decompression_bomb_check(im.size) + return {"RGBA": im} + elif ( + sig.startswith((b"\xff\x4f\xff\x51", b"\x0d\x0a\x87\x0a")) + or sig == b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a" + ): + if not enable_jpeg2k: + msg = ( + "Unsupported icon subimage format (rebuild PIL " + "with JPEG 2000 support to fix this)" + ) + raise ValueError(msg) + # j2k, jpc or j2c + fobj.seek(start) + jp2kstream = fobj.read(length) + f = io.BytesIO(jp2kstream) + im = Jpeg2KImagePlugin.Jpeg2KImageFile(f) + Image._decompression_bomb_check(im.size) + if im.mode != "RGBA": + im = im.convert("RGBA") + return {"RGBA": im} + else: + msg = "Unsupported icon subimage format" + raise ValueError(msg) + + +class IcnsFile: + SIZES = { + (512, 512, 2): [(b"ic10", read_png_or_jpeg2000)], + (512, 512, 1): [(b"ic09", read_png_or_jpeg2000)], + (256, 256, 2): [(b"ic14", read_png_or_jpeg2000)], + (256, 256, 1): [(b"ic08", read_png_or_jpeg2000)], + (128, 128, 2): [(b"ic13", read_png_or_jpeg2000)], + (128, 128, 1): [ + (b"ic07", read_png_or_jpeg2000), + (b"it32", read_32t), + (b"t8mk", read_mk), + ], + (64, 64, 1): [(b"icp6", read_png_or_jpeg2000)], + (32, 32, 2): [(b"ic12", read_png_or_jpeg2000)], + (48, 48, 1): [(b"ih32", read_32), (b"h8mk", read_mk)], + (32, 32, 1): [ + (b"icp5", read_png_or_jpeg2000), + (b"il32", read_32), + (b"l8mk", read_mk), + ], + (16, 16, 2): [(b"ic11", read_png_or_jpeg2000)], + (16, 16, 1): [ + (b"icp4", read_png_or_jpeg2000), + (b"is32", read_32), + (b"s8mk", read_mk), + ], + } + + def __init__(self, fobj: IO[bytes]) -> None: + """ + fobj is a file-like object as an icns resource + """ + # signature : (start, length) + self.dct = {} + self.fobj = fobj + sig, filesize = nextheader(fobj) + if not _accept(sig): + msg = "not an icns file" + raise SyntaxError(msg) + i = HEADERSIZE + while i < filesize: + sig, blocksize = nextheader(fobj) + if blocksize <= 0: + msg = "invalid block header" + raise SyntaxError(msg) + i += HEADERSIZE + blocksize -= HEADERSIZE + self.dct[sig] = (i, blocksize) + fobj.seek(blocksize, io.SEEK_CUR) + i += blocksize + + def itersizes(self) -> list[tuple[int, int, int]]: + sizes = [] + for size, fmts in self.SIZES.items(): + for fmt, reader in fmts: + if fmt in self.dct: + sizes.append(size) + break + return sizes + + def bestsize(self) -> tuple[int, int, int]: + sizes = self.itersizes() + if not sizes: + msg = "No 32bit icon resources found" + raise SyntaxError(msg) + return max(sizes) + + def dataforsize(self, size: tuple[int, int, int]) -> dict[str, Image.Image]: + """ + Get an icon resource as {channel: array}. Note that + the arrays are bottom-up like windows bitmaps and will likely + need to be flipped or transposed in some way. + """ + dct = {} + for code, reader in self.SIZES[size]: + desc = self.dct.get(code) + if desc is not None: + dct.update(reader(self.fobj, desc, size)) + return dct + + def getimage( + self, size: tuple[int, int] | tuple[int, int, int] | None = None + ) -> Image.Image: + if size is None: + size = self.bestsize() + elif len(size) == 2: + size = (size[0], size[1], 1) + channels = self.dataforsize(size) + + im = channels.get("RGBA") + if im: + return im + + im = channels["RGB"].copy() + try: + im.putalpha(channels["A"]) + except KeyError: + pass + return im + + +## +# Image plugin for Mac OS icons. + + +class IcnsImageFile(ImageFile.ImageFile): + """ + PIL image support for Mac OS .icns files. + Chooses the best resolution, but will possibly load + a different size image if you mutate the size attribute + before calling 'load'. + + The info dictionary has a key 'sizes' that is a list + of sizes that the icns file has. + """ + + format = "ICNS" + format_description = "Mac OS icns resource" + + def _open(self) -> None: + self.icns = IcnsFile(self.fp) + self._mode = "RGBA" + self.info["sizes"] = self.icns.itersizes() + self.best_size = self.icns.bestsize() + self.size = ( + self.best_size[0] * self.best_size[2], + self.best_size[1] * self.best_size[2], + ) + + @property # type: ignore[override] + def size(self) -> tuple[int, int] | tuple[int, int, int]: + return self._size + + @size.setter + def size(self, value: tuple[int, int] | tuple[int, int, int]) -> None: + if len(value) == 3: + deprecate("Setting size to (width, height, scale)", 12, "load(scale)") + if value in self.info["sizes"]: + self._size = value # type: ignore[assignment] + return + else: + # Check that a matching size exists, + # or that there is a scale that would create a size that matches + for size in self.info["sizes"]: + simple_size = size[0] * size[2], size[1] * size[2] + scale = simple_size[0] // value[0] + if simple_size[1] / value[1] == scale: + self._size = value + return + msg = "This is not one of the allowed sizes of this image" + raise ValueError(msg) + + def load(self, scale: int | None = None) -> Image.core.PixelAccess | None: + if scale is not None or len(self.size) == 3: + if scale is None and len(self.size) == 3: + scale = self.size[2] + assert scale is not None + width, height = self.size[:2] + self.size = width * scale, height * scale + self.best_size = width, height, scale + + px = Image.Image.load(self) + if self._im is not None and self.im.size == self.size: + # Already loaded + return px + self.load_prepare() + # This is likely NOT the best way to do it, but whatever. + im = self.icns.getimage(self.best_size) + + # If this is a PNG or JPEG 2000, it won't be loaded yet + px = im.load() + + self.im = im.im + self._mode = im.mode + self.size = im.size + + return px + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + """ + Saves the image as a series of PNG files, + that are then combined into a .icns file. + """ + if hasattr(fp, "flush"): + fp.flush() + + sizes = { + b"ic07": 128, + b"ic08": 256, + b"ic09": 512, + b"ic10": 1024, + b"ic11": 32, + b"ic12": 64, + b"ic13": 256, + b"ic14": 512, + } + provided_images = {im.width: im for im in im.encoderinfo.get("append_images", [])} + size_streams = {} + for size in set(sizes.values()): + image = ( + provided_images[size] + if size in provided_images + else im.resize((size, size)) + ) + + temp = io.BytesIO() + image.save(temp, "png") + size_streams[size] = temp.getvalue() + + entries = [] + for type, size in sizes.items(): + stream = size_streams[size] + entries.append((type, HEADERSIZE + len(stream), stream)) + + # Header + fp.write(MAGIC) + file_length = HEADERSIZE # Header + file_length += HEADERSIZE + 8 * len(entries) # TOC + file_length += sum(entry[1] for entry in entries) + fp.write(struct.pack(">i", file_length)) + + # TOC + fp.write(b"TOC ") + fp.write(struct.pack(">i", HEADERSIZE + len(entries) * HEADERSIZE)) + for entry in entries: + fp.write(entry[0]) + fp.write(struct.pack(">i", entry[1])) + + # Data + for entry in entries: + fp.write(entry[0]) + fp.write(struct.pack(">i", entry[1])) + fp.write(entry[2]) + + if hasattr(fp, "flush"): + fp.flush() + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(MAGIC) + + +Image.register_open(IcnsImageFile.format, IcnsImageFile, _accept) +Image.register_extension(IcnsImageFile.format, ".icns") + +Image.register_save(IcnsImageFile.format, _save) +Image.register_mime(IcnsImageFile.format, "image/icns") + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Syntax: python3 IcnsImagePlugin.py [file]") + sys.exit() + + with open(sys.argv[1], "rb") as fp: + imf = IcnsImageFile(fp) + for size in imf.info["sizes"]: + width, height, scale = imf.size = size + imf.save(f"out-{width}-{height}-{scale}.png") + with Image.open(sys.argv[1]) as im: + im.save("out.png") + if sys.platform == "windows": + os.startfile("out.png") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/IcoImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/IcoImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..bd35ac890e6cf824e9c890404416d871e5b94f7c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/IcoImagePlugin.py @@ -0,0 +1,381 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Windows Icon support for PIL +# +# History: +# 96-05-27 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# + +# This plugin is a refactored version of Win32IconImagePlugin by Bryan Davis +# . +# https://code.google.com/archive/p/casadebender/wikis/Win32IconImagePlugin.wiki +# +# Icon format references: +# * https://en.wikipedia.org/wiki/ICO_(file_format) +# * https://msdn.microsoft.com/en-us/library/ms997538.aspx +from __future__ import annotations + +import warnings +from io import BytesIO +from math import ceil, log +from typing import IO, NamedTuple + +from . import BmpImagePlugin, Image, ImageFile, PngImagePlugin +from ._binary import i16le as i16 +from ._binary import i32le as i32 +from ._binary import o8 +from ._binary import o16le as o16 +from ._binary import o32le as o32 + +# +# -------------------------------------------------------------------- + +_MAGIC = b"\0\0\1\0" + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + fp.write(_MAGIC) # (2+2) + bmp = im.encoderinfo.get("bitmap_format") == "bmp" + sizes = im.encoderinfo.get( + "sizes", + [(16, 16), (24, 24), (32, 32), (48, 48), (64, 64), (128, 128), (256, 256)], + ) + frames = [] + provided_ims = [im] + im.encoderinfo.get("append_images", []) + width, height = im.size + for size in sorted(set(sizes)): + if size[0] > width or size[1] > height or size[0] > 256 or size[1] > 256: + continue + + for provided_im in provided_ims: + if provided_im.size != size: + continue + frames.append(provided_im) + if bmp: + bits = BmpImagePlugin.SAVE[provided_im.mode][1] + bits_used = [bits] + for other_im in provided_ims: + if other_im.size != size: + continue + bits = BmpImagePlugin.SAVE[other_im.mode][1] + if bits not in bits_used: + # Another image has been supplied for this size + # with a different bit depth + frames.append(other_im) + bits_used.append(bits) + break + else: + # TODO: invent a more convenient method for proportional scalings + frame = provided_im.copy() + frame.thumbnail(size, Image.Resampling.LANCZOS, reducing_gap=None) + frames.append(frame) + fp.write(o16(len(frames))) # idCount(2) + offset = fp.tell() + len(frames) * 16 + for frame in frames: + width, height = frame.size + # 0 means 256 + fp.write(o8(width if width < 256 else 0)) # bWidth(1) + fp.write(o8(height if height < 256 else 0)) # bHeight(1) + + bits, colors = BmpImagePlugin.SAVE[frame.mode][1:] if bmp else (32, 0) + fp.write(o8(colors)) # bColorCount(1) + fp.write(b"\0") # bReserved(1) + fp.write(b"\0\0") # wPlanes(2) + fp.write(o16(bits)) # wBitCount(2) + + image_io = BytesIO() + if bmp: + frame.save(image_io, "dib") + + if bits != 32: + and_mask = Image.new("1", size) + ImageFile._save( + and_mask, + image_io, + [ImageFile._Tile("raw", (0, 0) + size, 0, ("1", 0, -1))], + ) + else: + frame.save(image_io, "png") + image_io.seek(0) + image_bytes = image_io.read() + if bmp: + image_bytes = image_bytes[:8] + o32(height * 2) + image_bytes[12:] + bytes_len = len(image_bytes) + fp.write(o32(bytes_len)) # dwBytesInRes(4) + fp.write(o32(offset)) # dwImageOffset(4) + current = fp.tell() + fp.seek(offset) + fp.write(image_bytes) + offset = offset + bytes_len + fp.seek(current) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(_MAGIC) + + +class IconHeader(NamedTuple): + width: int + height: int + nb_color: int + reserved: int + planes: int + bpp: int + size: int + offset: int + dim: tuple[int, int] + square: int + color_depth: int + + +class IcoFile: + def __init__(self, buf: IO[bytes]) -> None: + """ + Parse image from file-like object containing ico file data + """ + + # check magic + s = buf.read(6) + if not _accept(s): + msg = "not an ICO file" + raise SyntaxError(msg) + + self.buf = buf + self.entry = [] + + # Number of items in file + self.nb_items = i16(s, 4) + + # Get headers for each item + for i in range(self.nb_items): + s = buf.read(16) + + # See Wikipedia + width = s[0] or 256 + height = s[1] or 256 + + # No. of colors in image (0 if >=8bpp) + nb_color = s[2] + bpp = i16(s, 6) + icon_header = IconHeader( + width=width, + height=height, + nb_color=nb_color, + reserved=s[3], + planes=i16(s, 4), + bpp=i16(s, 6), + size=i32(s, 8), + offset=i32(s, 12), + dim=(width, height), + square=width * height, + # See Wikipedia notes about color depth. + # We need this just to differ images with equal sizes + color_depth=bpp or (nb_color != 0 and ceil(log(nb_color, 2))) or 256, + ) + + self.entry.append(icon_header) + + self.entry = sorted(self.entry, key=lambda x: x.color_depth) + # ICO images are usually squares + self.entry = sorted(self.entry, key=lambda x: x.square, reverse=True) + + def sizes(self) -> set[tuple[int, int]]: + """ + Get a set of all available icon sizes and color depths. + """ + return {(h.width, h.height) for h in self.entry} + + def getentryindex(self, size: tuple[int, int], bpp: int | bool = False) -> int: + for i, h in enumerate(self.entry): + if size == h.dim and (bpp is False or bpp == h.color_depth): + return i + return 0 + + def getimage(self, size: tuple[int, int], bpp: int | bool = False) -> Image.Image: + """ + Get an image from the icon + """ + return self.frame(self.getentryindex(size, bpp)) + + def frame(self, idx: int) -> Image.Image: + """ + Get an image from frame idx + """ + + header = self.entry[idx] + + self.buf.seek(header.offset) + data = self.buf.read(8) + self.buf.seek(header.offset) + + im: Image.Image + if data[:8] == PngImagePlugin._MAGIC: + # png frame + im = PngImagePlugin.PngImageFile(self.buf) + Image._decompression_bomb_check(im.size) + else: + # XOR + AND mask bmp frame + im = BmpImagePlugin.DibImageFile(self.buf) + Image._decompression_bomb_check(im.size) + + # change tile dimension to only encompass XOR image + im._size = (im.size[0], int(im.size[1] / 2)) + d, e, o, a = im.tile[0] + im.tile[0] = ImageFile._Tile(d, (0, 0) + im.size, o, a) + + # figure out where AND mask image starts + if header.bpp == 32: + # 32-bit color depth icon image allows semitransparent areas + # PIL's DIB format ignores transparency bits, recover them. + # The DIB is packed in BGRX byte order where X is the alpha + # channel. + + # Back up to start of bmp data + self.buf.seek(o) + # extract every 4th byte (eg. 3,7,11,15,...) + alpha_bytes = self.buf.read(im.size[0] * im.size[1] * 4)[3::4] + + # convert to an 8bpp grayscale image + try: + mask = Image.frombuffer( + "L", # 8bpp + im.size, # (w, h) + alpha_bytes, # source chars + "raw", # raw decoder + ("L", 0, -1), # 8bpp inverted, unpadded, reversed + ) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + mask = None + else: + raise + else: + # get AND image from end of bitmap + w = im.size[0] + if (w % 32) > 0: + # bitmap row data is aligned to word boundaries + w += 32 - (im.size[0] % 32) + + # the total mask data is + # padded row size * height / bits per char + + total_bytes = int((w * im.size[1]) / 8) + and_mask_offset = header.offset + header.size - total_bytes + + self.buf.seek(and_mask_offset) + mask_data = self.buf.read(total_bytes) + + # convert raw data to image + try: + mask = Image.frombuffer( + "1", # 1 bpp + im.size, # (w, h) + mask_data, # source chars + "raw", # raw decoder + ("1;I", int(w / 8), -1), # 1bpp inverted, padded, reversed + ) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + mask = None + else: + raise + + # now we have two images, im is XOR image and mask is AND image + + # apply mask image as alpha channel + if mask: + im = im.convert("RGBA") + im.putalpha(mask) + + return im + + +## +# Image plugin for Windows Icon files. + + +class IcoImageFile(ImageFile.ImageFile): + """ + PIL read-only image support for Microsoft Windows .ico files. + + By default the largest resolution image in the file will be loaded. This + can be changed by altering the 'size' attribute before calling 'load'. + + The info dictionary has a key 'sizes' that is a list of the sizes available + in the icon file. + + Handles classic, XP and Vista icon formats. + + When saving, PNG compression is used. Support for this was only added in + Windows Vista. If you are unable to view the icon in Windows, convert the + image to "RGBA" mode before saving. + + This plugin is a refactored version of Win32IconImagePlugin by Bryan Davis + . + https://code.google.com/archive/p/casadebender/wikis/Win32IconImagePlugin.wiki + """ + + format = "ICO" + format_description = "Windows Icon" + + def _open(self) -> None: + self.ico = IcoFile(self.fp) + self.info["sizes"] = self.ico.sizes() + self.size = self.ico.entry[0].dim + self.load() + + @property + def size(self) -> tuple[int, int]: + return self._size + + @size.setter + def size(self, value: tuple[int, int]) -> None: + if value not in self.info["sizes"]: + msg = "This is not one of the allowed sizes of this image" + raise ValueError(msg) + self._size = value + + def load(self) -> Image.core.PixelAccess | None: + if self._im is not None and self.im.size == self.size: + # Already loaded + return Image.Image.load(self) + im = self.ico.getimage(self.size) + # if tile is PNG, it won't really be loaded yet + im.load() + self.im = im.im + self._mode = im.mode + if im.palette: + self.palette = im.palette + if im.size != self.size: + warnings.warn("Image was not the expected size") + + index = self.ico.getentryindex(self.size) + sizes = list(self.info["sizes"]) + sizes[index] = im.size + self.info["sizes"] = set(sizes) + + self.size = im.size + return Image.Image.load(self) + + def load_seek(self, pos: int) -> None: + # Flag the ImageFile.Parser so that it + # just does all the decode at the end. + pass + + +# +# -------------------------------------------------------------------- + + +Image.register_open(IcoImageFile.format, IcoImageFile, _accept) +Image.register_save(IcoImageFile.format, _save) +Image.register_extension(IcoImageFile.format, ".ico") + +Image.register_mime(IcoImageFile.format, "image/x-icon") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..71b9996780ce8dfc420670b5732216f934a1f677 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImImagePlugin.py @@ -0,0 +1,389 @@ +# +# The Python Imaging Library. +# $Id$ +# +# IFUNC IM file handling for PIL +# +# history: +# 1995-09-01 fl Created. +# 1997-01-03 fl Save palette images +# 1997-01-08 fl Added sequence support +# 1997-01-23 fl Added P and RGB save support +# 1997-05-31 fl Read floating point images +# 1997-06-22 fl Save floating point images +# 1997-08-27 fl Read and save 1-bit images +# 1998-06-25 fl Added support for RGB+LUT images +# 1998-07-02 fl Added support for YCC images +# 1998-07-15 fl Renamed offset attribute to avoid name clash +# 1998-12-29 fl Added I;16 support +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.7) +# 2003-09-26 fl Added LA/PA support +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-2001 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +import re +from typing import IO, Any + +from . import Image, ImageFile, ImagePalette +from ._util import DeferredError + +# -------------------------------------------------------------------- +# Standard tags + +COMMENT = "Comment" +DATE = "Date" +EQUIPMENT = "Digitalization equipment" +FRAMES = "File size (no of images)" +LUT = "Lut" +NAME = "Name" +SCALE = "Scale (x,y)" +SIZE = "Image size (x*y)" +MODE = "Image type" + +TAGS = { + COMMENT: 0, + DATE: 0, + EQUIPMENT: 0, + FRAMES: 0, + LUT: 0, + NAME: 0, + SCALE: 0, + SIZE: 0, + MODE: 0, +} + +OPEN = { + # ifunc93/p3cfunc formats + "0 1 image": ("1", "1"), + "L 1 image": ("1", "1"), + "Greyscale image": ("L", "L"), + "Grayscale image": ("L", "L"), + "RGB image": ("RGB", "RGB;L"), + "RLB image": ("RGB", "RLB"), + "RYB image": ("RGB", "RLB"), + "B1 image": ("1", "1"), + "B2 image": ("P", "P;2"), + "B4 image": ("P", "P;4"), + "X 24 image": ("RGB", "RGB"), + "L 32 S image": ("I", "I;32"), + "L 32 F image": ("F", "F;32"), + # old p3cfunc formats + "RGB3 image": ("RGB", "RGB;T"), + "RYB3 image": ("RGB", "RYB;T"), + # extensions + "LA image": ("LA", "LA;L"), + "PA image": ("LA", "PA;L"), + "RGBA image": ("RGBA", "RGBA;L"), + "RGBX image": ("RGB", "RGBX;L"), + "CMYK image": ("CMYK", "CMYK;L"), + "YCC image": ("YCbCr", "YCbCr;L"), +} + +# ifunc95 extensions +for i in ["8", "8S", "16", "16S", "32", "32F"]: + OPEN[f"L {i} image"] = ("F", f"F;{i}") + OPEN[f"L*{i} image"] = ("F", f"F;{i}") +for i in ["16", "16L", "16B"]: + OPEN[f"L {i} image"] = (f"I;{i}", f"I;{i}") + OPEN[f"L*{i} image"] = (f"I;{i}", f"I;{i}") +for i in ["32S"]: + OPEN[f"L {i} image"] = ("I", f"I;{i}") + OPEN[f"L*{i} image"] = ("I", f"I;{i}") +for j in range(2, 33): + OPEN[f"L*{j} image"] = ("F", f"F;{j}") + + +# -------------------------------------------------------------------- +# Read IM directory + +split = re.compile(rb"^([A-Za-z][^:]*):[ \t]*(.*)[ \t]*$") + + +def number(s: Any) -> float: + try: + return int(s) + except ValueError: + return float(s) + + +## +# Image plugin for the IFUNC IM file format. + + +class ImImageFile(ImageFile.ImageFile): + format = "IM" + format_description = "IFUNC Image Memory" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # Quick rejection: if there's not an LF among the first + # 100 bytes, this is (probably) not a text header. + + if b"\n" not in self.fp.read(100): + msg = "not an IM file" + raise SyntaxError(msg) + self.fp.seek(0) + + n = 0 + + # Default values + self.info[MODE] = "L" + self.info[SIZE] = (512, 512) + self.info[FRAMES] = 1 + + self.rawmode = "L" + + while True: + s = self.fp.read(1) + + # Some versions of IFUNC uses \n\r instead of \r\n... + if s == b"\r": + continue + + if not s or s == b"\0" or s == b"\x1a": + break + + # FIXME: this may read whole file if not a text file + s = s + self.fp.readline() + + if len(s) > 100: + msg = "not an IM file" + raise SyntaxError(msg) + + if s.endswith(b"\r\n"): + s = s[:-2] + elif s.endswith(b"\n"): + s = s[:-1] + + try: + m = split.match(s) + except re.error as e: + msg = "not an IM file" + raise SyntaxError(msg) from e + + if m: + k, v = m.group(1, 2) + + # Don't know if this is the correct encoding, + # but a decent guess (I guess) + k = k.decode("latin-1", "replace") + v = v.decode("latin-1", "replace") + + # Convert value as appropriate + if k in [FRAMES, SCALE, SIZE]: + v = v.replace("*", ",") + v = tuple(map(number, v.split(","))) + if len(v) == 1: + v = v[0] + elif k == MODE and v in OPEN: + v, self.rawmode = OPEN[v] + + # Add to dictionary. Note that COMMENT tags are + # combined into a list of strings. + if k == COMMENT: + if k in self.info: + self.info[k].append(v) + else: + self.info[k] = [v] + else: + self.info[k] = v + + if k in TAGS: + n += 1 + + else: + msg = f"Syntax error in IM header: {s.decode('ascii', 'replace')}" + raise SyntaxError(msg) + + if not n: + msg = "Not an IM file" + raise SyntaxError(msg) + + # Basic attributes + self._size = self.info[SIZE] + self._mode = self.info[MODE] + + # Skip forward to start of image data + while s and not s.startswith(b"\x1a"): + s = self.fp.read(1) + if not s: + msg = "File truncated" + raise SyntaxError(msg) + + if LUT in self.info: + # convert lookup table to palette or lut attribute + palette = self.fp.read(768) + greyscale = 1 # greyscale palette + linear = 1 # linear greyscale palette + for i in range(256): + if palette[i] == palette[i + 256] == palette[i + 512]: + if palette[i] != i: + linear = 0 + else: + greyscale = 0 + if self.mode in ["L", "LA", "P", "PA"]: + if greyscale: + if not linear: + self.lut = list(palette[:256]) + else: + if self.mode in ["L", "P"]: + self._mode = self.rawmode = "P" + elif self.mode in ["LA", "PA"]: + self._mode = "PA" + self.rawmode = "PA;L" + self.palette = ImagePalette.raw("RGB;L", palette) + elif self.mode == "RGB": + if not greyscale or not linear: + self.lut = list(palette) + + self.frame = 0 + + self.__offset = offs = self.fp.tell() + + self._fp = self.fp # FIXME: hack + + if self.rawmode.startswith("F;"): + # ifunc95 formats + try: + # use bit decoder (if necessary) + bits = int(self.rawmode[2:]) + if bits not in [8, 16, 32]: + self.tile = [ + ImageFile._Tile( + "bit", (0, 0) + self.size, offs, (bits, 8, 3, 0, -1) + ) + ] + return + except ValueError: + pass + + if self.rawmode in ["RGB;T", "RYB;T"]: + # Old LabEye/3PC files. Would be very surprised if anyone + # ever stumbled upon such a file ;-) + size = self.size[0] * self.size[1] + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offs, ("G", 0, -1)), + ImageFile._Tile("raw", (0, 0) + self.size, offs + size, ("R", 0, -1)), + ImageFile._Tile( + "raw", (0, 0) + self.size, offs + 2 * size, ("B", 0, -1) + ), + ] + else: + # LabEye/IFUNC files + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offs, (self.rawmode, 0, -1)) + ] + + @property + def n_frames(self) -> int: + return self.info[FRAMES] + + @property + def is_animated(self) -> bool: + return self.info[FRAMES] > 1 + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + + self.frame = frame + + if self.mode == "1": + bits = 1 + else: + bits = 8 * len(self.mode) + + size = ((self.size[0] * bits + 7) // 8) * self.size[1] + offs = self.__offset + frame * size + + self.fp = self._fp + + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offs, (self.rawmode, 0, -1)) + ] + + def tell(self) -> int: + return self.frame + + +# +# -------------------------------------------------------------------- +# Save IM files + + +SAVE = { + # mode: (im type, raw mode) + "1": ("0 1", "1"), + "L": ("Greyscale", "L"), + "LA": ("LA", "LA;L"), + "P": ("Greyscale", "P"), + "PA": ("LA", "PA;L"), + "I": ("L 32S", "I;32S"), + "I;16": ("L 16", "I;16"), + "I;16L": ("L 16L", "I;16L"), + "I;16B": ("L 16B", "I;16B"), + "F": ("L 32F", "F;32F"), + "RGB": ("RGB", "RGB;L"), + "RGBA": ("RGBA", "RGBA;L"), + "RGBX": ("RGBX", "RGBX;L"), + "CMYK": ("CMYK", "CMYK;L"), + "YCbCr": ("YCC", "YCbCr;L"), +} + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + try: + image_type, rawmode = SAVE[im.mode] + except KeyError as e: + msg = f"Cannot save {im.mode} images as IM" + raise ValueError(msg) from e + + frames = im.encoderinfo.get("frames", 1) + + fp.write(f"Image type: {image_type} image\r\n".encode("ascii")) + if filename: + # Each line must be 100 characters or less, + # or: SyntaxError("not an IM file") + # 8 characters are used for "Name: " and "\r\n" + # Keep just the filename, ditch the potentially overlong path + if isinstance(filename, bytes): + filename = filename.decode("ascii") + name, ext = os.path.splitext(os.path.basename(filename)) + name = "".join([name[: 92 - len(ext)], ext]) + + fp.write(f"Name: {name}\r\n".encode("ascii")) + fp.write(f"Image size (x*y): {im.size[0]}*{im.size[1]}\r\n".encode("ascii")) + fp.write(f"File size (no of images): {frames}\r\n".encode("ascii")) + if im.mode in ["P", "PA"]: + fp.write(b"Lut: 1\r\n") + fp.write(b"\000" * (511 - fp.tell()) + b"\032") + if im.mode in ["P", "PA"]: + im_palette = im.im.getpalette("RGB", "RGB;L") + colors = len(im_palette) // 3 + palette = b"" + for i in range(3): + palette += im_palette[colors * i : colors * (i + 1)] + palette += b"\x00" * (256 - colors) + fp.write(palette) # 768 bytes + ImageFile._save( + im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, 0, -1))] + ) + + +# +# -------------------------------------------------------------------- +# Registry + + +Image.register_open(ImImageFile.format, ImImageFile) +Image.register_save(ImImageFile.format, _save) + +Image.register_extension(ImImageFile.format, ".im") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/Image.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/Image.py new file mode 100644 index 0000000000000000000000000000000000000000..d209405c4c5e3c00179aa82c706730bca7bc7b28 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/Image.py @@ -0,0 +1,4245 @@ +# +# The Python Imaging Library. +# $Id$ +# +# the Image class wrapper +# +# partial release history: +# 1995-09-09 fl Created +# 1996-03-11 fl PIL release 0.0 (proof of concept) +# 1996-04-30 fl PIL release 0.1b1 +# 1999-07-28 fl PIL release 1.0 final +# 2000-06-07 fl PIL release 1.1 +# 2000-10-20 fl PIL release 1.1.1 +# 2001-05-07 fl PIL release 1.1.2 +# 2002-03-15 fl PIL release 1.1.3 +# 2003-05-10 fl PIL release 1.1.4 +# 2005-03-28 fl PIL release 1.1.5 +# 2006-12-02 fl PIL release 1.1.6 +# 2009-11-15 fl PIL release 1.1.7 +# +# Copyright (c) 1997-2009 by Secret Labs AB. All rights reserved. +# Copyright (c) 1995-2009 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +from __future__ import annotations + +import abc +import atexit +import builtins +import io +import logging +import math +import os +import re +import struct +import sys +import tempfile +import warnings +from collections.abc import Callable, Iterator, MutableMapping, Sequence +from enum import IntEnum +from types import ModuleType +from typing import IO, Any, Literal, Protocol, cast + +# VERSION was removed in Pillow 6.0.0. +# PILLOW_VERSION was removed in Pillow 9.0.0. +# Use __version__ instead. +from . import ( + ExifTags, + ImageMode, + TiffTags, + UnidentifiedImageError, + __version__, + _plugins, +) +from ._binary import i32le, o32be, o32le +from ._deprecate import deprecate +from ._util import DeferredError, is_path + +ElementTree: ModuleType | None +try: + from defusedxml import ElementTree +except ImportError: + ElementTree = None + +logger = logging.getLogger(__name__) + + +class DecompressionBombWarning(RuntimeWarning): + pass + + +class DecompressionBombError(Exception): + pass + + +WARN_POSSIBLE_FORMATS: bool = False + +# Limit to around a quarter gigabyte for a 24-bit (3 bpp) image +MAX_IMAGE_PIXELS: int | None = int(1024 * 1024 * 1024 // 4 // 3) + + +try: + # If the _imaging C module is not present, Pillow will not load. + # Note that other modules should not refer to _imaging directly; + # import Image and use the Image.core variable instead. + # Also note that Image.core is not a publicly documented interface, + # and should be considered private and subject to change. + from . import _imaging as core + + if __version__ != getattr(core, "PILLOW_VERSION", None): + msg = ( + "The _imaging extension was built for another version of Pillow or PIL:\n" + f"Core version: {getattr(core, 'PILLOW_VERSION', None)}\n" + f"Pillow version: {__version__}" + ) + raise ImportError(msg) + +except ImportError as v: + core = DeferredError.new(ImportError("The _imaging C module is not installed.")) + # Explanations for ways that we know we might have an import error + if str(v).startswith("Module use of python"): + # The _imaging C module is present, but not compiled for + # the right version (windows only). Print a warning, if + # possible. + warnings.warn( + "The _imaging extension was built for another version of Python.", + RuntimeWarning, + ) + elif str(v).startswith("The _imaging extension"): + warnings.warn(str(v), RuntimeWarning) + # Fail here anyway. Don't let people run with a mostly broken Pillow. + # see docs/porting.rst + raise + + +def isImageType(t: Any) -> TypeGuard[Image]: + """ + Checks if an object is an image object. + + .. warning:: + + This function is for internal use only. + + :param t: object to check if it's an image + :returns: True if the object is an image + """ + deprecate("Image.isImageType(im)", 12, "isinstance(im, Image.Image)") + return hasattr(t, "im") + + +# +# Constants + + +# transpose +class Transpose(IntEnum): + FLIP_LEFT_RIGHT = 0 + FLIP_TOP_BOTTOM = 1 + ROTATE_90 = 2 + ROTATE_180 = 3 + ROTATE_270 = 4 + TRANSPOSE = 5 + TRANSVERSE = 6 + + +# transforms (also defined in Imaging.h) +class Transform(IntEnum): + AFFINE = 0 + EXTENT = 1 + PERSPECTIVE = 2 + QUAD = 3 + MESH = 4 + + +# resampling filters (also defined in Imaging.h) +class Resampling(IntEnum): + NEAREST = 0 + BOX = 4 + BILINEAR = 2 + HAMMING = 5 + BICUBIC = 3 + LANCZOS = 1 + + +_filters_support = { + Resampling.BOX: 0.5, + Resampling.BILINEAR: 1.0, + Resampling.HAMMING: 1.0, + Resampling.BICUBIC: 2.0, + Resampling.LANCZOS: 3.0, +} + + +# dithers +class Dither(IntEnum): + NONE = 0 + ORDERED = 1 # Not yet implemented + RASTERIZE = 2 # Not yet implemented + FLOYDSTEINBERG = 3 # default + + +# palettes/quantizers +class Palette(IntEnum): + WEB = 0 + ADAPTIVE = 1 + + +class Quantize(IntEnum): + MEDIANCUT = 0 + MAXCOVERAGE = 1 + FASTOCTREE = 2 + LIBIMAGEQUANT = 3 + + +module = sys.modules[__name__] +for enum in (Transpose, Transform, Resampling, Dither, Palette, Quantize): + for item in enum: + setattr(module, item.name, item.value) + + +if hasattr(core, "DEFAULT_STRATEGY"): + DEFAULT_STRATEGY = core.DEFAULT_STRATEGY + FILTERED = core.FILTERED + HUFFMAN_ONLY = core.HUFFMAN_ONLY + RLE = core.RLE + FIXED = core.FIXED + + +# -------------------------------------------------------------------- +# Registries + +TYPE_CHECKING = False +if TYPE_CHECKING: + import mmap + from xml.etree.ElementTree import Element + + from IPython.lib.pretty import PrettyPrinter + + from . import ImageFile, ImageFilter, ImagePalette, ImageQt, TiffImagePlugin + from ._typing import CapsuleType, NumpyArray, StrOrBytesPath, TypeGuard +ID: list[str] = [] +OPEN: dict[ + str, + tuple[ + Callable[[IO[bytes], str | bytes], ImageFile.ImageFile], + Callable[[bytes], bool | str] | None, + ], +] = {} +MIME: dict[str, str] = {} +SAVE: dict[str, Callable[[Image, IO[bytes], str | bytes], None]] = {} +SAVE_ALL: dict[str, Callable[[Image, IO[bytes], str | bytes], None]] = {} +EXTENSION: dict[str, str] = {} +DECODERS: dict[str, type[ImageFile.PyDecoder]] = {} +ENCODERS: dict[str, type[ImageFile.PyEncoder]] = {} + +# -------------------------------------------------------------------- +# Modes + +_ENDIAN = "<" if sys.byteorder == "little" else ">" + + +def _conv_type_shape(im: Image) -> tuple[tuple[int, ...], str]: + m = ImageMode.getmode(im.mode) + shape: tuple[int, ...] = (im.height, im.width) + extra = len(m.bands) + if extra != 1: + shape += (extra,) + return shape, m.typestr + + +MODES = [ + "1", + "CMYK", + "F", + "HSV", + "I", + "I;16", + "I;16B", + "I;16L", + "I;16N", + "L", + "LA", + "La", + "LAB", + "P", + "PA", + "RGB", + "RGBA", + "RGBa", + "RGBX", + "YCbCr", +] + +# raw modes that may be memory mapped. NOTE: if you change this, you +# may have to modify the stride calculation in map.c too! +_MAPMODES = ("L", "P", "RGBX", "RGBA", "CMYK", "I;16", "I;16L", "I;16B") + + +def getmodebase(mode: str) -> str: + """ + Gets the "base" mode for given mode. This function returns "L" for + images that contain grayscale data, and "RGB" for images that + contain color data. + + :param mode: Input mode. + :returns: "L" or "RGB". + :exception KeyError: If the input mode was not a standard mode. + """ + return ImageMode.getmode(mode).basemode + + +def getmodetype(mode: str) -> str: + """ + Gets the storage type mode. Given a mode, this function returns a + single-layer mode suitable for storing individual bands. + + :param mode: Input mode. + :returns: "L", "I", or "F". + :exception KeyError: If the input mode was not a standard mode. + """ + return ImageMode.getmode(mode).basetype + + +def getmodebandnames(mode: str) -> tuple[str, ...]: + """ + Gets a list of individual band names. Given a mode, this function returns + a tuple containing the names of individual bands (use + :py:method:`~PIL.Image.getmodetype` to get the mode used to store each + individual band. + + :param mode: Input mode. + :returns: A tuple containing band names. The length of the tuple + gives the number of bands in an image of the given mode. + :exception KeyError: If the input mode was not a standard mode. + """ + return ImageMode.getmode(mode).bands + + +def getmodebands(mode: str) -> int: + """ + Gets the number of individual bands for this mode. + + :param mode: Input mode. + :returns: The number of bands in this mode. + :exception KeyError: If the input mode was not a standard mode. + """ + return len(ImageMode.getmode(mode).bands) + + +# -------------------------------------------------------------------- +# Helpers + +_initialized = 0 + + +def preinit() -> None: + """ + Explicitly loads BMP, GIF, JPEG, PPM and PPM file format drivers. + + It is called when opening or saving images. + """ + + global _initialized + if _initialized >= 1: + return + + try: + from . import BmpImagePlugin + + assert BmpImagePlugin + except ImportError: + pass + try: + from . import GifImagePlugin + + assert GifImagePlugin + except ImportError: + pass + try: + from . import JpegImagePlugin + + assert JpegImagePlugin + except ImportError: + pass + try: + from . import PpmImagePlugin + + assert PpmImagePlugin + except ImportError: + pass + try: + from . import PngImagePlugin + + assert PngImagePlugin + except ImportError: + pass + + _initialized = 1 + + +def init() -> bool: + """ + Explicitly initializes the Python Imaging Library. This function + loads all available file format drivers. + + It is called when opening or saving images if :py:meth:`~preinit()` is + insufficient, and by :py:meth:`~PIL.features.pilinfo`. + """ + + global _initialized + if _initialized >= 2: + return False + + parent_name = __name__.rpartition(".")[0] + for plugin in _plugins: + try: + logger.debug("Importing %s", plugin) + __import__(f"{parent_name}.{plugin}", globals(), locals(), []) + except ImportError as e: + logger.debug("Image: failed to import %s: %s", plugin, e) + + if OPEN or SAVE: + _initialized = 2 + return True + return False + + +# -------------------------------------------------------------------- +# Codec factories (used by tobytes/frombytes and ImageFile.load) + + +def _getdecoder( + mode: str, decoder_name: str, args: Any, extra: tuple[Any, ...] = () +) -> core.ImagingDecoder | ImageFile.PyDecoder: + # tweak arguments + if args is None: + args = () + elif not isinstance(args, tuple): + args = (args,) + + try: + decoder = DECODERS[decoder_name] + except KeyError: + pass + else: + return decoder(mode, *args + extra) + + try: + # get decoder + decoder = getattr(core, f"{decoder_name}_decoder") + except AttributeError as e: + msg = f"decoder {decoder_name} not available" + raise OSError(msg) from e + return decoder(mode, *args + extra) + + +def _getencoder( + mode: str, encoder_name: str, args: Any, extra: tuple[Any, ...] = () +) -> core.ImagingEncoder | ImageFile.PyEncoder: + # tweak arguments + if args is None: + args = () + elif not isinstance(args, tuple): + args = (args,) + + try: + encoder = ENCODERS[encoder_name] + except KeyError: + pass + else: + return encoder(mode, *args + extra) + + try: + # get encoder + encoder = getattr(core, f"{encoder_name}_encoder") + except AttributeError as e: + msg = f"encoder {encoder_name} not available" + raise OSError(msg) from e + return encoder(mode, *args + extra) + + +# -------------------------------------------------------------------- +# Simple expression analyzer + + +class ImagePointTransform: + """ + Used with :py:meth:`~PIL.Image.Image.point` for single band images with more than + 8 bits, this represents an affine transformation, where the value is multiplied by + ``scale`` and ``offset`` is added. + """ + + def __init__(self, scale: float, offset: float) -> None: + self.scale = scale + self.offset = offset + + def __neg__(self) -> ImagePointTransform: + return ImagePointTransform(-self.scale, -self.offset) + + def __add__(self, other: ImagePointTransform | float) -> ImagePointTransform: + if isinstance(other, ImagePointTransform): + return ImagePointTransform( + self.scale + other.scale, self.offset + other.offset + ) + return ImagePointTransform(self.scale, self.offset + other) + + __radd__ = __add__ + + def __sub__(self, other: ImagePointTransform | float) -> ImagePointTransform: + return self + -other + + def __rsub__(self, other: ImagePointTransform | float) -> ImagePointTransform: + return other + -self + + def __mul__(self, other: ImagePointTransform | float) -> ImagePointTransform: + if isinstance(other, ImagePointTransform): + return NotImplemented + return ImagePointTransform(self.scale * other, self.offset * other) + + __rmul__ = __mul__ + + def __truediv__(self, other: ImagePointTransform | float) -> ImagePointTransform: + if isinstance(other, ImagePointTransform): + return NotImplemented + return ImagePointTransform(self.scale / other, self.offset / other) + + +def _getscaleoffset( + expr: Callable[[ImagePointTransform], ImagePointTransform | float], +) -> tuple[float, float]: + a = expr(ImagePointTransform(1, 0)) + return (a.scale, a.offset) if isinstance(a, ImagePointTransform) else (0, a) + + +# -------------------------------------------------------------------- +# Implementation wrapper + + +class SupportsGetData(Protocol): + def getdata( + self, + ) -> tuple[Transform, Sequence[int]]: ... + + +class Image: + """ + This class represents an image object. To create + :py:class:`~PIL.Image.Image` objects, use the appropriate factory + functions. There's hardly ever any reason to call the Image constructor + directly. + + * :py:func:`~PIL.Image.open` + * :py:func:`~PIL.Image.new` + * :py:func:`~PIL.Image.frombytes` + """ + + format: str | None = None + format_description: str | None = None + _close_exclusive_fp_after_loading = True + + def __init__(self) -> None: + # FIXME: take "new" parameters / other image? + self._im: core.ImagingCore | DeferredError | None = None + self._mode = "" + self._size = (0, 0) + self.palette: ImagePalette.ImagePalette | None = None + self.info: dict[str | tuple[int, int], Any] = {} + self.readonly = 0 + self._exif: Exif | None = None + + @property + def im(self) -> core.ImagingCore: + if isinstance(self._im, DeferredError): + raise self._im.ex + assert self._im is not None + return self._im + + @im.setter + def im(self, im: core.ImagingCore) -> None: + self._im = im + + @property + def width(self) -> int: + return self.size[0] + + @property + def height(self) -> int: + return self.size[1] + + @property + def size(self) -> tuple[int, int]: + return self._size + + @property + def mode(self) -> str: + return self._mode + + @property + def readonly(self) -> int: + return (self._im and self._im.readonly) or self._readonly + + @readonly.setter + def readonly(self, readonly: int) -> None: + self._readonly = readonly + + def _new(self, im: core.ImagingCore) -> Image: + new = Image() + new.im = im + new._mode = im.mode + new._size = im.size + if im.mode in ("P", "PA"): + if self.palette: + new.palette = self.palette.copy() + else: + from . import ImagePalette + + new.palette = ImagePalette.ImagePalette() + new.info = self.info.copy() + return new + + # Context manager support + def __enter__(self): + return self + + def __exit__(self, *args): + from . import ImageFile + + if isinstance(self, ImageFile.ImageFile): + if getattr(self, "_exclusive_fp", False): + self._close_fp() + self.fp = None + + def close(self) -> None: + """ + This operation will destroy the image core and release its memory. + The image data will be unusable afterward. + + This function is required to close images that have multiple frames or + have not had their file read and closed by the + :py:meth:`~PIL.Image.Image.load` method. See :ref:`file-handling` for + more information. + """ + if getattr(self, "map", None): + if sys.platform == "win32" and hasattr(sys, "pypy_version_info"): + self.map.close() + self.map: mmap.mmap | None = None + + # Instead of simply setting to None, we're setting up a + # deferred error that will better explain that the core image + # object is gone. + self._im = DeferredError(ValueError("Operation on closed image")) + + def _copy(self) -> None: + self.load() + self.im = self.im.copy() + self.readonly = 0 + + def _ensure_mutable(self) -> None: + if self.readonly: + self._copy() + else: + self.load() + + def _dump( + self, file: str | None = None, format: str | None = None, **options: Any + ) -> str: + suffix = "" + if format: + suffix = f".{format}" + + if not file: + f, filename = tempfile.mkstemp(suffix) + os.close(f) + else: + filename = file + if not filename.endswith(suffix): + filename = filename + suffix + + self.load() + + if not format or format == "PPM": + self.im.save_ppm(filename) + else: + self.save(filename, format, **options) + + return filename + + def __eq__(self, other: object) -> bool: + if self.__class__ is not other.__class__: + return False + assert isinstance(other, Image) + return ( + self.mode == other.mode + and self.size == other.size + and self.info == other.info + and self.getpalette() == other.getpalette() + and self.tobytes() == other.tobytes() + ) + + def __repr__(self) -> str: + return ( + f"<{self.__class__.__module__}.{self.__class__.__name__} " + f"image mode={self.mode} size={self.size[0]}x{self.size[1]} " + f"at 0x{id(self):X}>" + ) + + def _repr_pretty_(self, p: PrettyPrinter, cycle: bool) -> None: + """IPython plain text display support""" + + # Same as __repr__ but without unpredictable id(self), + # to keep Jupyter notebook `text/plain` output stable. + p.text( + f"<{self.__class__.__module__}.{self.__class__.__name__} " + f"image mode={self.mode} size={self.size[0]}x{self.size[1]}>" + ) + + def _repr_image(self, image_format: str, **kwargs: Any) -> bytes | None: + """Helper function for iPython display hook. + + :param image_format: Image format. + :returns: image as bytes, saved into the given format. + """ + b = io.BytesIO() + try: + self.save(b, image_format, **kwargs) + except Exception: + return None + return b.getvalue() + + def _repr_png_(self) -> bytes | None: + """iPython display hook support for PNG format. + + :returns: PNG version of the image as bytes + """ + return self._repr_image("PNG", compress_level=1) + + def _repr_jpeg_(self) -> bytes | None: + """iPython display hook support for JPEG format. + + :returns: JPEG version of the image as bytes + """ + return self._repr_image("JPEG") + + @property + def __array_interface__(self) -> dict[str, str | bytes | int | tuple[int, ...]]: + # numpy array interface support + new: dict[str, str | bytes | int | tuple[int, ...]] = {"version": 3} + if self.mode == "1": + # Binary images need to be extended from bits to bytes + # See: https://github.com/python-pillow/Pillow/issues/350 + new["data"] = self.tobytes("raw", "L") + else: + new["data"] = self.tobytes() + new["shape"], new["typestr"] = _conv_type_shape(self) + return new + + def __arrow_c_schema__(self) -> object: + self.load() + return self.im.__arrow_c_schema__() + + def __arrow_c_array__( + self, requested_schema: object | None = None + ) -> tuple[object, object]: + self.load() + return (self.im.__arrow_c_schema__(), self.im.__arrow_c_array__()) + + def __getstate__(self) -> list[Any]: + im_data = self.tobytes() # load image first + return [self.info, self.mode, self.size, self.getpalette(), im_data] + + def __setstate__(self, state: list[Any]) -> None: + Image.__init__(self) + info, mode, size, palette, data = state[:5] + self.info = info + self._mode = mode + self._size = size + self.im = core.new(mode, size) + if mode in ("L", "LA", "P", "PA") and palette: + self.putpalette(palette) + self.frombytes(data) + + def tobytes(self, encoder_name: str = "raw", *args: Any) -> bytes: + """ + Return image as a bytes object. + + .. warning:: + + This method returns raw image data derived from Pillow's internal + storage. For compressed image data (e.g. PNG, JPEG) use + :meth:`~.save`, with a BytesIO parameter for in-memory data. + + :param encoder_name: What encoder to use. + + The default is to use the standard "raw" encoder. + To see how this packs pixel data into the returned + bytes, see :file:`libImaging/Pack.c`. + + A list of C encoders can be seen under codecs + section of the function array in + :file:`_imaging.c`. Python encoders are registered + within the relevant plugins. + :param args: Extra arguments to the encoder. + :returns: A :py:class:`bytes` object. + """ + + encoder_args: Any = args + if len(encoder_args) == 1 and isinstance(encoder_args[0], tuple): + # may pass tuple instead of argument list + encoder_args = encoder_args[0] + + if encoder_name == "raw" and encoder_args == (): + encoder_args = self.mode + + self.load() + + if self.width == 0 or self.height == 0: + return b"" + + # unpack data + e = _getencoder(self.mode, encoder_name, encoder_args) + e.setimage(self.im) + + from . import ImageFile + + bufsize = max(ImageFile.MAXBLOCK, self.size[0] * 4) # see RawEncode.c + + output = [] + while True: + bytes_consumed, errcode, data = e.encode(bufsize) + output.append(data) + if errcode: + break + if errcode < 0: + msg = f"encoder error {errcode} in tobytes" + raise RuntimeError(msg) + + return b"".join(output) + + def tobitmap(self, name: str = "image") -> bytes: + """ + Returns the image converted to an X11 bitmap. + + .. note:: This method only works for mode "1" images. + + :param name: The name prefix to use for the bitmap variables. + :returns: A string containing an X11 bitmap. + :raises ValueError: If the mode is not "1" + """ + + self.load() + if self.mode != "1": + msg = "not a bitmap" + raise ValueError(msg) + data = self.tobytes("xbm") + return b"".join( + [ + f"#define {name}_width {self.size[0]}\n".encode("ascii"), + f"#define {name}_height {self.size[1]}\n".encode("ascii"), + f"static char {name}_bits[] = {{\n".encode("ascii"), + data, + b"};", + ] + ) + + def frombytes( + self, + data: bytes | bytearray | SupportsArrayInterface, + decoder_name: str = "raw", + *args: Any, + ) -> None: + """ + Loads this image with pixel data from a bytes object. + + This method is similar to the :py:func:`~PIL.Image.frombytes` function, + but loads data into this image instead of creating a new image object. + """ + + if self.width == 0 or self.height == 0: + return + + decoder_args: Any = args + if len(decoder_args) == 1 and isinstance(decoder_args[0], tuple): + # may pass tuple instead of argument list + decoder_args = decoder_args[0] + + # default format + if decoder_name == "raw" and decoder_args == (): + decoder_args = self.mode + + # unpack data + d = _getdecoder(self.mode, decoder_name, decoder_args) + d.setimage(self.im) + s = d.decode(data) + + if s[0] >= 0: + msg = "not enough image data" + raise ValueError(msg) + if s[1] != 0: + msg = "cannot decode image data" + raise ValueError(msg) + + def load(self) -> core.PixelAccess | None: + """ + Allocates storage for the image and loads the pixel data. In + normal cases, you don't need to call this method, since the + Image class automatically loads an opened image when it is + accessed for the first time. + + If the file associated with the image was opened by Pillow, then this + method will close it. The exception to this is if the image has + multiple frames, in which case the file will be left open for seek + operations. See :ref:`file-handling` for more information. + + :returns: An image access object. + :rtype: :py:class:`.PixelAccess` + """ + if self._im is not None and self.palette and self.palette.dirty: + # realize palette + mode, arr = self.palette.getdata() + self.im.putpalette(self.palette.mode, mode, arr) + self.palette.dirty = 0 + self.palette.rawmode = None + if "transparency" in self.info and mode in ("LA", "PA"): + if isinstance(self.info["transparency"], int): + self.im.putpalettealpha(self.info["transparency"], 0) + else: + self.im.putpalettealphas(self.info["transparency"]) + self.palette.mode = "RGBA" + else: + self.palette.palette = self.im.getpalette( + self.palette.mode, self.palette.mode + ) + + if self._im is not None: + return self.im.pixel_access(self.readonly) + return None + + def verify(self) -> None: + """ + Verifies the contents of a file. For data read from a file, this + method attempts to determine if the file is broken, without + actually decoding the image data. If this method finds any + problems, it raises suitable exceptions. If you need to load + the image after using this method, you must reopen the image + file. + """ + pass + + def convert( + self, + mode: str | None = None, + matrix: tuple[float, ...] | None = None, + dither: Dither | None = None, + palette: Palette = Palette.WEB, + colors: int = 256, + ) -> Image: + """ + Returns a converted copy of this image. For the "P" mode, this + method translates pixels through the palette. If mode is + omitted, a mode is chosen so that all information in the image + and the palette can be represented without a palette. + + This supports all possible conversions between "L", "RGB" and "CMYK". The + ``matrix`` argument only supports "L" and "RGB". + + When translating a color image to grayscale (mode "L"), + the library uses the ITU-R 601-2 luma transform:: + + L = R * 299/1000 + G * 587/1000 + B * 114/1000 + + The default method of converting a grayscale ("L") or "RGB" + image into a bilevel (mode "1") image uses Floyd-Steinberg + dither to approximate the original image luminosity levels. If + dither is ``None``, all values larger than 127 are set to 255 (white), + all other values to 0 (black). To use other thresholds, use the + :py:meth:`~PIL.Image.Image.point` method. + + When converting from "RGBA" to "P" without a ``matrix`` argument, + this passes the operation to :py:meth:`~PIL.Image.Image.quantize`, + and ``dither`` and ``palette`` are ignored. + + When converting from "PA", if an "RGBA" palette is present, the alpha + channel from the image will be used instead of the values from the palette. + + :param mode: The requested mode. See: :ref:`concept-modes`. + :param matrix: An optional conversion matrix. If given, this + should be 4- or 12-tuple containing floating point values. + :param dither: Dithering method, used when converting from + mode "RGB" to "P" or from "RGB" or "L" to "1". + Available methods are :data:`Dither.NONE` or :data:`Dither.FLOYDSTEINBERG` + (default). Note that this is not used when ``matrix`` is supplied. + :param palette: Palette to use when converting from mode "RGB" + to "P". Available palettes are :data:`Palette.WEB` or + :data:`Palette.ADAPTIVE`. + :param colors: Number of colors to use for the :data:`Palette.ADAPTIVE` + palette. Defaults to 256. + :rtype: :py:class:`~PIL.Image.Image` + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if mode in ("BGR;15", "BGR;16", "BGR;24"): + deprecate(mode, 12) + + self.load() + + has_transparency = "transparency" in self.info + if not mode and self.mode == "P": + # determine default mode + if self.palette: + mode = self.palette.mode + else: + mode = "RGB" + if mode == "RGB" and has_transparency: + mode = "RGBA" + if not mode or (mode == self.mode and not matrix): + return self.copy() + + if matrix: + # matrix conversion + if mode not in ("L", "RGB"): + msg = "illegal conversion" + raise ValueError(msg) + im = self.im.convert_matrix(mode, matrix) + new_im = self._new(im) + if has_transparency and self.im.bands == 3: + transparency = new_im.info["transparency"] + + def convert_transparency( + m: tuple[float, ...], v: tuple[int, int, int] + ) -> int: + value = m[0] * v[0] + m[1] * v[1] + m[2] * v[2] + m[3] * 0.5 + return max(0, min(255, int(value))) + + if mode == "L": + transparency = convert_transparency(matrix, transparency) + elif len(mode) == 3: + transparency = tuple( + convert_transparency(matrix[i * 4 : i * 4 + 4], transparency) + for i in range(len(transparency)) + ) + new_im.info["transparency"] = transparency + return new_im + + if mode == "P" and self.mode == "RGBA": + return self.quantize(colors) + + trns = None + delete_trns = False + # transparency handling + if has_transparency: + if (self.mode in ("1", "L", "I", "I;16") and mode in ("LA", "RGBA")) or ( + self.mode == "RGB" and mode in ("La", "LA", "RGBa", "RGBA") + ): + # Use transparent conversion to promote from transparent + # color to an alpha channel. + new_im = self._new( + self.im.convert_transparent(mode, self.info["transparency"]) + ) + del new_im.info["transparency"] + return new_im + elif self.mode in ("L", "RGB", "P") and mode in ("L", "RGB", "P"): + t = self.info["transparency"] + if isinstance(t, bytes): + # Dragons. This can't be represented by a single color + warnings.warn( + "Palette images with Transparency expressed in bytes should be " + "converted to RGBA images" + ) + delete_trns = True + else: + # get the new transparency color. + # use existing conversions + trns_im = new(self.mode, (1, 1)) + if self.mode == "P": + assert self.palette is not None + trns_im.putpalette(self.palette, self.palette.mode) + if isinstance(t, tuple): + err = "Couldn't allocate a palette color for transparency" + assert trns_im.palette is not None + try: + t = trns_im.palette.getcolor(t, self) + except ValueError as e: + if str(e) == "cannot allocate more than 256 colors": + # If all 256 colors are in use, + # then there is no need for transparency + t = None + else: + raise ValueError(err) from e + if t is None: + trns = None + else: + trns_im.putpixel((0, 0), t) + + if mode in ("L", "RGB"): + trns_im = trns_im.convert(mode) + else: + # can't just retrieve the palette number, got to do it + # after quantization. + trns_im = trns_im.convert("RGB") + trns = trns_im.getpixel((0, 0)) + + elif self.mode == "P" and mode in ("LA", "PA", "RGBA"): + t = self.info["transparency"] + delete_trns = True + + if isinstance(t, bytes): + self.im.putpalettealphas(t) + elif isinstance(t, int): + self.im.putpalettealpha(t, 0) + else: + msg = "Transparency for P mode should be bytes or int" + raise ValueError(msg) + + if mode == "P" and palette == Palette.ADAPTIVE: + im = self.im.quantize(colors) + new_im = self._new(im) + from . import ImagePalette + + new_im.palette = ImagePalette.ImagePalette( + "RGB", new_im.im.getpalette("RGB") + ) + if delete_trns: + # This could possibly happen if we requantize to fewer colors. + # The transparency would be totally off in that case. + del new_im.info["transparency"] + if trns is not None: + try: + new_im.info["transparency"] = new_im.palette.getcolor( + cast(tuple[int, ...], trns), # trns was converted to RGB + new_im, + ) + except Exception: + # if we can't make a transparent color, don't leave the old + # transparency hanging around to mess us up. + del new_im.info["transparency"] + warnings.warn("Couldn't allocate palette entry for transparency") + return new_im + + if "LAB" in (self.mode, mode): + im = self + if mode == "LAB": + if im.mode not in ("RGB", "RGBA", "RGBX"): + im = im.convert("RGBA") + other_mode = im.mode + else: + other_mode = mode + if other_mode in ("RGB", "RGBA", "RGBX"): + from . import ImageCms + + srgb = ImageCms.createProfile("sRGB") + lab = ImageCms.createProfile("LAB") + profiles = [lab, srgb] if im.mode == "LAB" else [srgb, lab] + transform = ImageCms.buildTransform( + profiles[0], profiles[1], im.mode, mode + ) + return transform.apply(im) + + # colorspace conversion + if dither is None: + dither = Dither.FLOYDSTEINBERG + + try: + im = self.im.convert(mode, dither) + except ValueError: + try: + # normalize source image and try again + modebase = getmodebase(self.mode) + if modebase == self.mode: + raise + im = self.im.convert(modebase) + im = im.convert(mode, dither) + except KeyError as e: + msg = "illegal conversion" + raise ValueError(msg) from e + + new_im = self._new(im) + if mode == "P" and palette != Palette.ADAPTIVE: + from . import ImagePalette + + new_im.palette = ImagePalette.ImagePalette("RGB", im.getpalette("RGB")) + if delete_trns: + # crash fail if we leave a bytes transparency in an rgb/l mode. + del new_im.info["transparency"] + if trns is not None: + if new_im.mode == "P" and new_im.palette: + try: + new_im.info["transparency"] = new_im.palette.getcolor( + cast(tuple[int, ...], trns), new_im # trns was converted to RGB + ) + except ValueError as e: + del new_im.info["transparency"] + if str(e) != "cannot allocate more than 256 colors": + # If all 256 colors are in use, + # then there is no need for transparency + warnings.warn( + "Couldn't allocate palette entry for transparency" + ) + else: + new_im.info["transparency"] = trns + return new_im + + def quantize( + self, + colors: int = 256, + method: int | None = None, + kmeans: int = 0, + palette: Image | None = None, + dither: Dither = Dither.FLOYDSTEINBERG, + ) -> Image: + """ + Convert the image to 'P' mode with the specified number + of colors. + + :param colors: The desired number of colors, <= 256 + :param method: :data:`Quantize.MEDIANCUT` (median cut), + :data:`Quantize.MAXCOVERAGE` (maximum coverage), + :data:`Quantize.FASTOCTREE` (fast octree), + :data:`Quantize.LIBIMAGEQUANT` (libimagequant; check support + using :py:func:`PIL.features.check_feature` with + ``feature="libimagequant"``). + + By default, :data:`Quantize.MEDIANCUT` will be used. + + The exception to this is RGBA images. :data:`Quantize.MEDIANCUT` + and :data:`Quantize.MAXCOVERAGE` do not support RGBA images, so + :data:`Quantize.FASTOCTREE` is used by default instead. + :param kmeans: Integer greater than or equal to zero. + :param palette: Quantize to the palette of given + :py:class:`PIL.Image.Image`. + :param dither: Dithering method, used when converting from + mode "RGB" to "P" or from "RGB" or "L" to "1". + Available methods are :data:`Dither.NONE` or :data:`Dither.FLOYDSTEINBERG` + (default). + :returns: A new image + """ + + self.load() + + if method is None: + # defaults: + method = Quantize.MEDIANCUT + if self.mode == "RGBA": + method = Quantize.FASTOCTREE + + if self.mode == "RGBA" and method not in ( + Quantize.FASTOCTREE, + Quantize.LIBIMAGEQUANT, + ): + # Caller specified an invalid mode. + msg = ( + "Fast Octree (method == 2) and libimagequant (method == 3) " + "are the only valid methods for quantizing RGBA images" + ) + raise ValueError(msg) + + if palette: + # use palette from reference image + palette.load() + if palette.mode != "P": + msg = "bad mode for palette image" + raise ValueError(msg) + if self.mode not in {"RGB", "L"}: + msg = "only RGB or L mode images can be quantized to a palette" + raise ValueError(msg) + im = self.im.convert("P", dither, palette.im) + new_im = self._new(im) + assert palette.palette is not None + new_im.palette = palette.palette.copy() + return new_im + + if kmeans < 0: + msg = "kmeans must not be negative" + raise ValueError(msg) + + im = self._new(self.im.quantize(colors, method, kmeans)) + + from . import ImagePalette + + mode = im.im.getpalettemode() + palette_data = im.im.getpalette(mode, mode)[: colors * len(mode)] + im.palette = ImagePalette.ImagePalette(mode, palette_data) + + return im + + def copy(self) -> Image: + """ + Copies this image. Use this method if you wish to paste things + into an image, but still retain the original. + + :rtype: :py:class:`~PIL.Image.Image` + :returns: An :py:class:`~PIL.Image.Image` object. + """ + self.load() + return self._new(self.im.copy()) + + __copy__ = copy + + def crop(self, box: tuple[float, float, float, float] | None = None) -> Image: + """ + Returns a rectangular region from this image. The box is a + 4-tuple defining the left, upper, right, and lower pixel + coordinate. See :ref:`coordinate-system`. + + Note: Prior to Pillow 3.4.0, this was a lazy operation. + + :param box: The crop rectangle, as a (left, upper, right, lower)-tuple. + :rtype: :py:class:`~PIL.Image.Image` + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if box is None: + return self.copy() + + if box[2] < box[0]: + msg = "Coordinate 'right' is less than 'left'" + raise ValueError(msg) + elif box[3] < box[1]: + msg = "Coordinate 'lower' is less than 'upper'" + raise ValueError(msg) + + self.load() + return self._new(self._crop(self.im, box)) + + def _crop( + self, im: core.ImagingCore, box: tuple[float, float, float, float] + ) -> core.ImagingCore: + """ + Returns a rectangular region from the core image object im. + + This is equivalent to calling im.crop((x0, y0, x1, y1)), but + includes additional sanity checks. + + :param im: a core image object + :param box: The crop rectangle, as a (left, upper, right, lower)-tuple. + :returns: A core image object. + """ + + x0, y0, x1, y1 = map(int, map(round, box)) + + absolute_values = (abs(x1 - x0), abs(y1 - y0)) + + _decompression_bomb_check(absolute_values) + + return im.crop((x0, y0, x1, y1)) + + def draft( + self, mode: str | None, size: tuple[int, int] | None + ) -> tuple[str, tuple[int, int, float, float]] | None: + """ + Configures the image file loader so it returns a version of the + image that as closely as possible matches the given mode and + size. For example, you can use this method to convert a color + JPEG to grayscale while loading it. + + If any changes are made, returns a tuple with the chosen ``mode`` and + ``box`` with coordinates of the original image within the altered one. + + Note that this method modifies the :py:class:`~PIL.Image.Image` object + in place. If the image has already been loaded, this method has no + effect. + + Note: This method is not implemented for most images. It is + currently implemented only for JPEG and MPO images. + + :param mode: The requested mode. + :param size: The requested size in pixels, as a 2-tuple: + (width, height). + """ + pass + + def _expand(self, xmargin: int, ymargin: int | None = None) -> Image: + if ymargin is None: + ymargin = xmargin + self.load() + return self._new(self.im.expand(xmargin, ymargin)) + + def filter(self, filter: ImageFilter.Filter | type[ImageFilter.Filter]) -> Image: + """ + Filters this image using the given filter. For a list of + available filters, see the :py:mod:`~PIL.ImageFilter` module. + + :param filter: Filter kernel. + :returns: An :py:class:`~PIL.Image.Image` object.""" + + from . import ImageFilter + + self.load() + + if callable(filter): + filter = filter() + if not hasattr(filter, "filter"): + msg = "filter argument should be ImageFilter.Filter instance or class" + raise TypeError(msg) + + multiband = isinstance(filter, ImageFilter.MultibandFilter) + if self.im.bands == 1 or multiband: + return self._new(filter.filter(self.im)) + + ims = [ + self._new(filter.filter(self.im.getband(c))) for c in range(self.im.bands) + ] + return merge(self.mode, ims) + + def getbands(self) -> tuple[str, ...]: + """ + Returns a tuple containing the name of each band in this image. + For example, ``getbands`` on an RGB image returns ("R", "G", "B"). + + :returns: A tuple containing band names. + :rtype: tuple + """ + return ImageMode.getmode(self.mode).bands + + def getbbox(self, *, alpha_only: bool = True) -> tuple[int, int, int, int] | None: + """ + Calculates the bounding box of the non-zero regions in the + image. + + :param alpha_only: Optional flag, defaulting to ``True``. + If ``True`` and the image has an alpha channel, trim transparent pixels. + Otherwise, trim pixels when all channels are zero. + Keyword-only argument. + :returns: The bounding box is returned as a 4-tuple defining the + left, upper, right, and lower pixel coordinate. See + :ref:`coordinate-system`. If the image is completely empty, this + method returns None. + + """ + + self.load() + return self.im.getbbox(alpha_only) + + def getcolors( + self, maxcolors: int = 256 + ) -> list[tuple[int, tuple[int, ...]]] | list[tuple[int, float]] | None: + """ + Returns a list of colors used in this image. + + The colors will be in the image's mode. For example, an RGB image will + return a tuple of (red, green, blue) color values, and a P image will + return the index of the color in the palette. + + :param maxcolors: Maximum number of colors. If this number is + exceeded, this method returns None. The default limit is + 256 colors. + :returns: An unsorted list of (count, pixel) values. + """ + + self.load() + if self.mode in ("1", "L", "P"): + h = self.im.histogram() + out: list[tuple[int, float]] = [(h[i], i) for i in range(256) if h[i]] + if len(out) > maxcolors: + return None + return out + return self.im.getcolors(maxcolors) + + def getdata(self, band: int | None = None) -> core.ImagingCore: + """ + Returns the contents of this image as a sequence object + containing pixel values. The sequence object is flattened, so + that values for line one follow directly after the values of + line zero, and so on. + + Note that the sequence object returned by this method is an + internal PIL data type, which only supports certain sequence + operations. To convert it to an ordinary sequence (e.g. for + printing), use ``list(im.getdata())``. + + :param band: What band to return. The default is to return + all bands. To return a single band, pass in the index + value (e.g. 0 to get the "R" band from an "RGB" image). + :returns: A sequence-like object. + """ + + self.load() + if band is not None: + return self.im.getband(band) + return self.im # could be abused + + def getextrema(self) -> tuple[float, float] | tuple[tuple[int, int], ...]: + """ + Gets the minimum and maximum pixel values for each band in + the image. + + :returns: For a single-band image, a 2-tuple containing the + minimum and maximum pixel value. For a multi-band image, + a tuple containing one 2-tuple for each band. + """ + + self.load() + if self.im.bands > 1: + return tuple(self.im.getband(i).getextrema() for i in range(self.im.bands)) + return self.im.getextrema() + + def getxmp(self) -> dict[str, Any]: + """ + Returns a dictionary containing the XMP tags. + Requires defusedxml to be installed. + + :returns: XMP tags in a dictionary. + """ + + def get_name(tag: str) -> str: + return re.sub("^{[^}]+}", "", tag) + + def get_value(element: Element) -> str | dict[str, Any] | None: + value: dict[str, Any] = {get_name(k): v for k, v in element.attrib.items()} + children = list(element) + if children: + for child in children: + name = get_name(child.tag) + child_value = get_value(child) + if name in value: + if not isinstance(value[name], list): + value[name] = [value[name]] + value[name].append(child_value) + else: + value[name] = child_value + elif value: + if element.text: + value["text"] = element.text + else: + return element.text + return value + + if ElementTree is None: + warnings.warn("XMP data cannot be read without defusedxml dependency") + return {} + if "xmp" not in self.info: + return {} + root = ElementTree.fromstring(self.info["xmp"].rstrip(b"\x00 ")) + return {get_name(root.tag): get_value(root)} + + def getexif(self) -> Exif: + """ + Gets EXIF data from the image. + + :returns: an :py:class:`~PIL.Image.Exif` object. + """ + if self._exif is None: + self._exif = Exif() + elif self._exif._loaded: + return self._exif + self._exif._loaded = True + + exif_info = self.info.get("exif") + if exif_info is None: + if "Raw profile type exif" in self.info: + exif_info = bytes.fromhex( + "".join(self.info["Raw profile type exif"].split("\n")[3:]) + ) + elif hasattr(self, "tag_v2"): + self._exif.bigtiff = self.tag_v2._bigtiff + self._exif.endian = self.tag_v2._endian + self._exif.load_from_fp(self.fp, self.tag_v2._offset) + if exif_info is not None: + self._exif.load(exif_info) + + # XMP tags + if ExifTags.Base.Orientation not in self._exif: + xmp_tags = self.info.get("XML:com.adobe.xmp") + pattern: str | bytes = r'tiff:Orientation(="|>)([0-9])' + if not xmp_tags and (xmp_tags := self.info.get("xmp")): + pattern = rb'tiff:Orientation(="|>)([0-9])' + if xmp_tags: + match = re.search(pattern, xmp_tags) + if match: + self._exif[ExifTags.Base.Orientation] = int(match[2]) + + return self._exif + + def _reload_exif(self) -> None: + if self._exif is None or not self._exif._loaded: + return + self._exif._loaded = False + self.getexif() + + def get_child_images(self) -> list[ImageFile.ImageFile]: + from . import ImageFile + + deprecate("Image.Image.get_child_images", 13) + return ImageFile.ImageFile.get_child_images(self) # type: ignore[arg-type] + + def getim(self) -> CapsuleType: + """ + Returns a capsule that points to the internal image memory. + + :returns: A capsule object. + """ + + self.load() + return self.im.ptr + + def getpalette(self, rawmode: str | None = "RGB") -> list[int] | None: + """ + Returns the image palette as a list. + + :param rawmode: The mode in which to return the palette. ``None`` will + return the palette in its current mode. + + .. versionadded:: 9.1.0 + + :returns: A list of color values [r, g, b, ...], or None if the + image has no palette. + """ + + self.load() + try: + mode = self.im.getpalettemode() + except ValueError: + return None # no palette + if rawmode is None: + rawmode = mode + return list(self.im.getpalette(mode, rawmode)) + + @property + def has_transparency_data(self) -> bool: + """ + Determine if an image has transparency data, whether in the form of an + alpha channel, a palette with an alpha channel, or a "transparency" key + in the info dictionary. + + Note the image might still appear solid, if all of the values shown + within are opaque. + + :returns: A boolean. + """ + if ( + self.mode in ("LA", "La", "PA", "RGBA", "RGBa") + or "transparency" in self.info + ): + return True + if self.mode == "P": + assert self.palette is not None + return self.palette.mode.endswith("A") + return False + + def apply_transparency(self) -> None: + """ + If a P mode image has a "transparency" key in the info dictionary, + remove the key and instead apply the transparency to the palette. + Otherwise, the image is unchanged. + """ + if self.mode != "P" or "transparency" not in self.info: + return + + from . import ImagePalette + + palette = self.getpalette("RGBA") + assert palette is not None + transparency = self.info["transparency"] + if isinstance(transparency, bytes): + for i, alpha in enumerate(transparency): + palette[i * 4 + 3] = alpha + else: + palette[transparency * 4 + 3] = 0 + self.palette = ImagePalette.ImagePalette("RGBA", bytes(palette)) + self.palette.dirty = 1 + + del self.info["transparency"] + + def getpixel( + self, xy: tuple[int, int] | list[int] + ) -> float | tuple[int, ...] | None: + """ + Returns the pixel value at a given position. + + :param xy: The coordinate, given as (x, y). See + :ref:`coordinate-system`. + :returns: The pixel value. If the image is a multi-layer image, + this method returns a tuple. + """ + + self.load() + return self.im.getpixel(tuple(xy)) + + def getprojection(self) -> tuple[list[int], list[int]]: + """ + Get projection to x and y axes + + :returns: Two sequences, indicating where there are non-zero + pixels along the X-axis and the Y-axis, respectively. + """ + + self.load() + x, y = self.im.getprojection() + return list(x), list(y) + + def histogram( + self, mask: Image | None = None, extrema: tuple[float, float] | None = None + ) -> list[int]: + """ + Returns a histogram for the image. The histogram is returned as a + list of pixel counts, one for each pixel value in the source + image. Counts are grouped into 256 bins for each band, even if + the image has more than 8 bits per band. If the image has more + than one band, the histograms for all bands are concatenated (for + example, the histogram for an "RGB" image contains 768 values). + + A bilevel image (mode "1") is treated as a grayscale ("L") image + by this method. + + If a mask is provided, the method returns a histogram for those + parts of the image where the mask image is non-zero. The mask + image must have the same size as the image, and be either a + bi-level image (mode "1") or a grayscale image ("L"). + + :param mask: An optional mask. + :param extrema: An optional tuple of manually-specified extrema. + :returns: A list containing pixel counts. + """ + self.load() + if mask: + mask.load() + return self.im.histogram((0, 0), mask.im) + if self.mode in ("I", "F"): + return self.im.histogram( + extrema if extrema is not None else self.getextrema() + ) + return self.im.histogram() + + def entropy( + self, mask: Image | None = None, extrema: tuple[float, float] | None = None + ) -> float: + """ + Calculates and returns the entropy for the image. + + A bilevel image (mode "1") is treated as a grayscale ("L") + image by this method. + + If a mask is provided, the method employs the histogram for + those parts of the image where the mask image is non-zero. + The mask image must have the same size as the image, and be + either a bi-level image (mode "1") or a grayscale image ("L"). + + :param mask: An optional mask. + :param extrema: An optional tuple of manually-specified extrema. + :returns: A float value representing the image entropy + """ + self.load() + if mask: + mask.load() + return self.im.entropy((0, 0), mask.im) + if self.mode in ("I", "F"): + return self.im.entropy( + extrema if extrema is not None else self.getextrema() + ) + return self.im.entropy() + + def paste( + self, + im: Image | str | float | tuple[float, ...], + box: Image | tuple[int, int, int, int] | tuple[int, int] | None = None, + mask: Image | None = None, + ) -> None: + """ + Pastes another image into this image. The box argument is either + a 2-tuple giving the upper left corner, a 4-tuple defining the + left, upper, right, and lower pixel coordinate, or None (same as + (0, 0)). See :ref:`coordinate-system`. If a 4-tuple is given, the size + of the pasted image must match the size of the region. + + If the modes don't match, the pasted image is converted to the mode of + this image (see the :py:meth:`~PIL.Image.Image.convert` method for + details). + + Instead of an image, the source can be a integer or tuple + containing pixel values. The method then fills the region + with the given color. When creating RGB images, you can + also use color strings as supported by the ImageColor module. + + If a mask is given, this method updates only the regions + indicated by the mask. You can use either "1", "L", "LA", "RGBA" + or "RGBa" images (if present, the alpha band is used as mask). + Where the mask is 255, the given image is copied as is. Where + the mask is 0, the current value is preserved. Intermediate + values will mix the two images together, including their alpha + channels if they have them. + + See :py:meth:`~PIL.Image.Image.alpha_composite` if you want to + combine images with respect to their alpha channels. + + :param im: Source image or pixel value (integer, float or tuple). + :param box: An optional 4-tuple giving the region to paste into. + If a 2-tuple is used instead, it's treated as the upper left + corner. If omitted or None, the source is pasted into the + upper left corner. + + If an image is given as the second argument and there is no + third, the box defaults to (0, 0), and the second argument + is interpreted as a mask image. + :param mask: An optional mask image. + """ + + if isinstance(box, Image): + if mask is not None: + msg = "If using second argument as mask, third argument must be None" + raise ValueError(msg) + # abbreviated paste(im, mask) syntax + mask = box + box = None + + if box is None: + box = (0, 0) + + if len(box) == 2: + # upper left corner given; get size from image or mask + if isinstance(im, Image): + size = im.size + elif isinstance(mask, Image): + size = mask.size + else: + # FIXME: use self.size here? + msg = "cannot determine region size; use 4-item box" + raise ValueError(msg) + box += (box[0] + size[0], box[1] + size[1]) + + source: core.ImagingCore | str | float | tuple[float, ...] + if isinstance(im, str): + from . import ImageColor + + source = ImageColor.getcolor(im, self.mode) + elif isinstance(im, Image): + im.load() + if self.mode != im.mode: + if self.mode != "RGB" or im.mode not in ("LA", "RGBA", "RGBa"): + # should use an adapter for this! + im = im.convert(self.mode) + source = im.im + else: + source = im + + self._ensure_mutable() + + if mask: + mask.load() + self.im.paste(source, box, mask.im) + else: + self.im.paste(source, box) + + def alpha_composite( + self, im: Image, dest: Sequence[int] = (0, 0), source: Sequence[int] = (0, 0) + ) -> None: + """'In-place' analog of Image.alpha_composite. Composites an image + onto this image. + + :param im: image to composite over this one + :param dest: Optional 2 tuple (left, top) specifying the upper + left corner in this (destination) image. + :param source: Optional 2 (left, top) tuple for the upper left + corner in the overlay source image, or 4 tuple (left, top, right, + bottom) for the bounds of the source rectangle + + Performance Note: Not currently implemented in-place in the core layer. + """ + + if not isinstance(source, (list, tuple)): + msg = "Source must be a list or tuple" + raise ValueError(msg) + if not isinstance(dest, (list, tuple)): + msg = "Destination must be a list or tuple" + raise ValueError(msg) + + if len(source) == 4: + overlay_crop_box = tuple(source) + elif len(source) == 2: + overlay_crop_box = tuple(source) + im.size + else: + msg = "Source must be a sequence of length 2 or 4" + raise ValueError(msg) + + if not len(dest) == 2: + msg = "Destination must be a sequence of length 2" + raise ValueError(msg) + if min(source) < 0: + msg = "Source must be non-negative" + raise ValueError(msg) + + # over image, crop if it's not the whole image. + if overlay_crop_box == (0, 0) + im.size: + overlay = im + else: + overlay = im.crop(overlay_crop_box) + + # target for the paste + box = tuple(dest) + (dest[0] + overlay.width, dest[1] + overlay.height) + + # destination image. don't copy if we're using the whole image. + if box == (0, 0) + self.size: + background = self + else: + background = self.crop(box) + + result = alpha_composite(background, overlay) + self.paste(result, box) + + def point( + self, + lut: ( + Sequence[float] + | NumpyArray + | Callable[[int], float] + | Callable[[ImagePointTransform], ImagePointTransform | float] + | ImagePointHandler + ), + mode: str | None = None, + ) -> Image: + """ + Maps this image through a lookup table or function. + + :param lut: A lookup table, containing 256 (or 65536 if + self.mode=="I" and mode == "L") values per band in the + image. A function can be used instead, it should take a + single argument. The function is called once for each + possible pixel value, and the resulting table is applied to + all bands of the image. + + It may also be an :py:class:`~PIL.Image.ImagePointHandler` + object:: + + class Example(Image.ImagePointHandler): + def point(self, im: Image) -> Image: + # Return result + :param mode: Output mode (default is same as input). This can only be used if + the source image has mode "L" or "P", and the output has mode "1" or the + source image mode is "I" and the output mode is "L". + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + self.load() + + if isinstance(lut, ImagePointHandler): + return lut.point(self) + + if callable(lut): + # if it isn't a list, it should be a function + if self.mode in ("I", "I;16", "F"): + # check if the function can be used with point_transform + # UNDONE wiredfool -- I think this prevents us from ever doing + # a gamma function point transform on > 8bit images. + scale, offset = _getscaleoffset(lut) # type: ignore[arg-type] + return self._new(self.im.point_transform(scale, offset)) + # for other modes, convert the function to a table + flatLut = [lut(i) for i in range(256)] * self.im.bands # type: ignore[arg-type] + else: + flatLut = lut + + if self.mode == "F": + # FIXME: _imaging returns a confusing error message for this case + msg = "point operation not supported for this mode" + raise ValueError(msg) + + if mode != "F": + flatLut = [round(i) for i in flatLut] + return self._new(self.im.point(flatLut, mode)) + + def putalpha(self, alpha: Image | int) -> None: + """ + Adds or replaces the alpha layer in this image. If the image + does not have an alpha layer, it's converted to "LA" or "RGBA". + The new layer must be either "L" or "1". + + :param alpha: The new alpha layer. This can either be an "L" or "1" + image having the same size as this image, or an integer. + """ + + self._ensure_mutable() + + if self.mode not in ("LA", "PA", "RGBA"): + # attempt to promote self to a matching alpha mode + try: + mode = getmodebase(self.mode) + "A" + try: + self.im.setmode(mode) + except (AttributeError, ValueError) as e: + # do things the hard way + im = self.im.convert(mode) + if im.mode not in ("LA", "PA", "RGBA"): + msg = "alpha channel could not be added" + raise ValueError(msg) from e # sanity check + self.im = im + self._mode = self.im.mode + except KeyError as e: + msg = "illegal image mode" + raise ValueError(msg) from e + + if self.mode in ("LA", "PA"): + band = 1 + else: + band = 3 + + if isinstance(alpha, Image): + # alpha layer + if alpha.mode not in ("1", "L"): + msg = "illegal image mode" + raise ValueError(msg) + alpha.load() + if alpha.mode == "1": + alpha = alpha.convert("L") + else: + # constant alpha + try: + self.im.fillband(band, alpha) + except (AttributeError, ValueError): + # do things the hard way + alpha = new("L", self.size, alpha) + else: + return + + self.im.putband(alpha.im, band) + + def putdata( + self, + data: Sequence[float] | Sequence[Sequence[int]] | core.ImagingCore | NumpyArray, + scale: float = 1.0, + offset: float = 0.0, + ) -> None: + """ + Copies pixel data from a flattened sequence object into the image. The + values should start at the upper left corner (0, 0), continue to the + end of the line, followed directly by the first value of the second + line, and so on. Data will be read until either the image or the + sequence ends. The scale and offset values are used to adjust the + sequence values: **pixel = value*scale + offset**. + + :param data: A flattened sequence object. + :param scale: An optional scale value. The default is 1.0. + :param offset: An optional offset value. The default is 0.0. + """ + + self._ensure_mutable() + + self.im.putdata(data, scale, offset) + + def putpalette( + self, + data: ImagePalette.ImagePalette | bytes | Sequence[int], + rawmode: str = "RGB", + ) -> None: + """ + Attaches a palette to this image. The image must be a "P", "PA", "L" + or "LA" image. + + The palette sequence must contain at most 256 colors, made up of one + integer value for each channel in the raw mode. + For example, if the raw mode is "RGB", then it can contain at most 768 + values, made up of red, green and blue values for the corresponding pixel + index in the 256 colors. + If the raw mode is "RGBA", then it can contain at most 1024 values, + containing red, green, blue and alpha values. + + Alternatively, an 8-bit string may be used instead of an integer sequence. + + :param data: A palette sequence (either a list or a string). + :param rawmode: The raw mode of the palette. Either "RGB", "RGBA", or a mode + that can be transformed to "RGB" or "RGBA" (e.g. "R", "BGR;15", "RGBA;L"). + """ + from . import ImagePalette + + if self.mode not in ("L", "LA", "P", "PA"): + msg = "illegal image mode" + raise ValueError(msg) + if isinstance(data, ImagePalette.ImagePalette): + if data.rawmode is not None: + palette = ImagePalette.raw(data.rawmode, data.palette) + else: + palette = ImagePalette.ImagePalette(palette=data.palette) + palette.dirty = 1 + else: + if not isinstance(data, bytes): + data = bytes(data) + palette = ImagePalette.raw(rawmode, data) + self._mode = "PA" if "A" in self.mode else "P" + self.palette = palette + self.palette.mode = "RGBA" if "A" in rawmode else "RGB" + self.load() # install new palette + + def putpixel( + self, xy: tuple[int, int], value: float | tuple[int, ...] | list[int] + ) -> None: + """ + Modifies the pixel at the given position. The color is given as + a single numerical value for single-band images, and a tuple for + multi-band images. In addition to this, RGB and RGBA tuples are + accepted for P and PA images. + + Note that this method is relatively slow. For more extensive changes, + use :py:meth:`~PIL.Image.Image.paste` or the :py:mod:`~PIL.ImageDraw` + module instead. + + See: + + * :py:meth:`~PIL.Image.Image.paste` + * :py:meth:`~PIL.Image.Image.putdata` + * :py:mod:`~PIL.ImageDraw` + + :param xy: The pixel coordinate, given as (x, y). See + :ref:`coordinate-system`. + :param value: The pixel value. + """ + + if self.readonly: + self._copy() + self.load() + + if ( + self.mode in ("P", "PA") + and isinstance(value, (list, tuple)) + and len(value) in [3, 4] + ): + # RGB or RGBA value for a P or PA image + if self.mode == "PA": + alpha = value[3] if len(value) == 4 else 255 + value = value[:3] + assert self.palette is not None + palette_index = self.palette.getcolor(tuple(value), self) + value = (palette_index, alpha) if self.mode == "PA" else palette_index + return self.im.putpixel(xy, value) + + def remap_palette( + self, dest_map: list[int], source_palette: bytes | bytearray | None = None + ) -> Image: + """ + Rewrites the image to reorder the palette. + + :param dest_map: A list of indexes into the original palette. + e.g. ``[1,0]`` would swap a two item palette, and ``list(range(256))`` + is the identity transform. + :param source_palette: Bytes or None. + :returns: An :py:class:`~PIL.Image.Image` object. + + """ + from . import ImagePalette + + if self.mode not in ("L", "P"): + msg = "illegal image mode" + raise ValueError(msg) + + bands = 3 + palette_mode = "RGB" + if source_palette is None: + if self.mode == "P": + self.load() + palette_mode = self.im.getpalettemode() + if palette_mode == "RGBA": + bands = 4 + source_palette = self.im.getpalette(palette_mode, palette_mode) + else: # L-mode + source_palette = bytearray(i // 3 for i in range(768)) + elif len(source_palette) > 768: + bands = 4 + palette_mode = "RGBA" + + palette_bytes = b"" + new_positions = [0] * 256 + + # pick only the used colors from the palette + for i, oldPosition in enumerate(dest_map): + palette_bytes += source_palette[ + oldPosition * bands : oldPosition * bands + bands + ] + new_positions[oldPosition] = i + + # replace the palette color id of all pixel with the new id + + # Palette images are [0..255], mapped through a 1 or 3 + # byte/color map. We need to remap the whole image + # from palette 1 to palette 2. New_positions is + # an array of indexes into palette 1. Palette 2 is + # palette 1 with any holes removed. + + # We're going to leverage the convert mechanism to use the + # C code to remap the image from palette 1 to palette 2, + # by forcing the source image into 'L' mode and adding a + # mapping 'L' mode palette, then converting back to 'L' + # sans palette thus converting the image bytes, then + # assigning the optimized RGB palette. + + # perf reference, 9500x4000 gif, w/~135 colors + # 14 sec prepatch, 1 sec postpatch with optimization forced. + + mapping_palette = bytearray(new_positions) + + m_im = self.copy() + m_im._mode = "P" + + m_im.palette = ImagePalette.ImagePalette( + palette_mode, palette=mapping_palette * bands + ) + # possibly set palette dirty, then + # m_im.putpalette(mapping_palette, 'L') # converts to 'P' + # or just force it. + # UNDONE -- this is part of the general issue with palettes + m_im.im.putpalette(palette_mode, palette_mode + ";L", m_im.palette.tobytes()) + + m_im = m_im.convert("L") + + m_im.putpalette(palette_bytes, palette_mode) + m_im.palette = ImagePalette.ImagePalette(palette_mode, palette=palette_bytes) + + if "transparency" in self.info: + try: + m_im.info["transparency"] = dest_map.index(self.info["transparency"]) + except ValueError: + if "transparency" in m_im.info: + del m_im.info["transparency"] + + return m_im + + def _get_safe_box( + self, + size: tuple[int, int], + resample: Resampling, + box: tuple[float, float, float, float], + ) -> tuple[int, int, int, int]: + """Expands the box so it includes adjacent pixels + that may be used by resampling with the given resampling filter. + """ + filter_support = _filters_support[resample] - 0.5 + scale_x = (box[2] - box[0]) / size[0] + scale_y = (box[3] - box[1]) / size[1] + support_x = filter_support * scale_x + support_y = filter_support * scale_y + + return ( + max(0, int(box[0] - support_x)), + max(0, int(box[1] - support_y)), + min(self.size[0], math.ceil(box[2] + support_x)), + min(self.size[1], math.ceil(box[3] + support_y)), + ) + + def resize( + self, + size: tuple[int, int] | list[int] | NumpyArray, + resample: int | None = None, + box: tuple[float, float, float, float] | None = None, + reducing_gap: float | None = None, + ) -> Image: + """ + Returns a resized copy of this image. + + :param size: The requested size in pixels, as a tuple or array: + (width, height). + :param resample: An optional resampling filter. This can be + one of :py:data:`Resampling.NEAREST`, :py:data:`Resampling.BOX`, + :py:data:`Resampling.BILINEAR`, :py:data:`Resampling.HAMMING`, + :py:data:`Resampling.BICUBIC` or :py:data:`Resampling.LANCZOS`. + If the image has mode "1" or "P", it is always set to + :py:data:`Resampling.NEAREST`. If the image mode is "BGR;15", + "BGR;16" or "BGR;24", then the default filter is + :py:data:`Resampling.NEAREST`. Otherwise, the default filter is + :py:data:`Resampling.BICUBIC`. See: :ref:`concept-filters`. + :param box: An optional 4-tuple of floats providing + the source image region to be scaled. + The values must be within (0, 0, width, height) rectangle. + If omitted or None, the entire source is used. + :param reducing_gap: Apply optimization by resizing the image + in two steps. First, reducing the image by integer times + using :py:meth:`~PIL.Image.Image.reduce`. + Second, resizing using regular resampling. The last step + changes size no less than by ``reducing_gap`` times. + ``reducing_gap`` may be None (no first step is performed) + or should be greater than 1.0. The bigger ``reducing_gap``, + the closer the result to the fair resampling. + The smaller ``reducing_gap``, the faster resizing. + With ``reducing_gap`` greater or equal to 3.0, the result is + indistinguishable from fair resampling in most cases. + The default value is None (no optimization). + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if resample is None: + bgr = self.mode.startswith("BGR;") + resample = Resampling.NEAREST if bgr else Resampling.BICUBIC + elif resample not in ( + Resampling.NEAREST, + Resampling.BILINEAR, + Resampling.BICUBIC, + Resampling.LANCZOS, + Resampling.BOX, + Resampling.HAMMING, + ): + msg = f"Unknown resampling filter ({resample})." + + filters = [ + f"{filter[1]} ({filter[0]})" + for filter in ( + (Resampling.NEAREST, "Image.Resampling.NEAREST"), + (Resampling.LANCZOS, "Image.Resampling.LANCZOS"), + (Resampling.BILINEAR, "Image.Resampling.BILINEAR"), + (Resampling.BICUBIC, "Image.Resampling.BICUBIC"), + (Resampling.BOX, "Image.Resampling.BOX"), + (Resampling.HAMMING, "Image.Resampling.HAMMING"), + ) + ] + msg += f" Use {', '.join(filters[:-1])} or {filters[-1]}" + raise ValueError(msg) + + if reducing_gap is not None and reducing_gap < 1.0: + msg = "reducing_gap must be 1.0 or greater" + raise ValueError(msg) + + if box is None: + box = (0, 0) + self.size + + size = tuple(size) + if self.size == size and box == (0, 0) + self.size: + return self.copy() + + if self.mode in ("1", "P"): + resample = Resampling.NEAREST + + if self.mode in ["LA", "RGBA"] and resample != Resampling.NEAREST: + im = self.convert({"LA": "La", "RGBA": "RGBa"}[self.mode]) + im = im.resize(size, resample, box) + return im.convert(self.mode) + + self.load() + + if reducing_gap is not None and resample != Resampling.NEAREST: + factor_x = int((box[2] - box[0]) / size[0] / reducing_gap) or 1 + factor_y = int((box[3] - box[1]) / size[1] / reducing_gap) or 1 + if factor_x > 1 or factor_y > 1: + reduce_box = self._get_safe_box(size, cast(Resampling, resample), box) + factor = (factor_x, factor_y) + self = ( + self.reduce(factor, box=reduce_box) + if callable(self.reduce) + else Image.reduce(self, factor, box=reduce_box) + ) + box = ( + (box[0] - reduce_box[0]) / factor_x, + (box[1] - reduce_box[1]) / factor_y, + (box[2] - reduce_box[0]) / factor_x, + (box[3] - reduce_box[1]) / factor_y, + ) + + return self._new(self.im.resize(size, resample, box)) + + def reduce( + self, + factor: int | tuple[int, int], + box: tuple[int, int, int, int] | None = None, + ) -> Image: + """ + Returns a copy of the image reduced ``factor`` times. + If the size of the image is not dividable by ``factor``, + the resulting size will be rounded up. + + :param factor: A greater than 0 integer or tuple of two integers + for width and height separately. + :param box: An optional 4-tuple of ints providing + the source image region to be reduced. + The values must be within ``(0, 0, width, height)`` rectangle. + If omitted or ``None``, the entire source is used. + """ + if not isinstance(factor, (list, tuple)): + factor = (factor, factor) + + if box is None: + box = (0, 0) + self.size + + if factor == (1, 1) and box == (0, 0) + self.size: + return self.copy() + + if self.mode in ["LA", "RGBA"]: + im = self.convert({"LA": "La", "RGBA": "RGBa"}[self.mode]) + im = im.reduce(factor, box) + return im.convert(self.mode) + + self.load() + + return self._new(self.im.reduce(factor, box)) + + def rotate( + self, + angle: float, + resample: Resampling = Resampling.NEAREST, + expand: int | bool = False, + center: tuple[float, float] | None = None, + translate: tuple[int, int] | None = None, + fillcolor: float | tuple[float, ...] | str | None = None, + ) -> Image: + """ + Returns a rotated copy of this image. This method returns a + copy of this image, rotated the given number of degrees counter + clockwise around its centre. + + :param angle: In degrees counter clockwise. + :param resample: An optional resampling filter. This can be + one of :py:data:`Resampling.NEAREST` (use nearest neighbour), + :py:data:`Resampling.BILINEAR` (linear interpolation in a 2x2 + environment), or :py:data:`Resampling.BICUBIC` (cubic spline + interpolation in a 4x4 environment). If omitted, or if the image has + mode "1" or "P", it is set to :py:data:`Resampling.NEAREST`. + See :ref:`concept-filters`. + :param expand: Optional expansion flag. If true, expands the output + image to make it large enough to hold the entire rotated image. + If false or omitted, make the output image the same size as the + input image. Note that the expand flag assumes rotation around + the center and no translation. + :param center: Optional center of rotation (a 2-tuple). Origin is + the upper left corner. Default is the center of the image. + :param translate: An optional post-rotate translation (a 2-tuple). + :param fillcolor: An optional color for area outside the rotated image. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + angle = angle % 360.0 + + # Fast paths regardless of filter, as long as we're not + # translating or changing the center. + if not (center or translate): + if angle == 0: + return self.copy() + if angle == 180: + return self.transpose(Transpose.ROTATE_180) + if angle in (90, 270) and (expand or self.width == self.height): + return self.transpose( + Transpose.ROTATE_90 if angle == 90 else Transpose.ROTATE_270 + ) + + # Calculate the affine matrix. Note that this is the reverse + # transformation (from destination image to source) because we + # want to interpolate the (discrete) destination pixel from + # the local area around the (floating) source pixel. + + # The matrix we actually want (note that it operates from the right): + # (1, 0, tx) (1, 0, cx) ( cos a, sin a, 0) (1, 0, -cx) + # (0, 1, ty) * (0, 1, cy) * (-sin a, cos a, 0) * (0, 1, -cy) + # (0, 0, 1) (0, 0, 1) ( 0, 0, 1) (0, 0, 1) + + # The reverse matrix is thus: + # (1, 0, cx) ( cos -a, sin -a, 0) (1, 0, -cx) (1, 0, -tx) + # (0, 1, cy) * (-sin -a, cos -a, 0) * (0, 1, -cy) * (0, 1, -ty) + # (0, 0, 1) ( 0, 0, 1) (0, 0, 1) (0, 0, 1) + + # In any case, the final translation may be updated at the end to + # compensate for the expand flag. + + w, h = self.size + + if translate is None: + post_trans = (0, 0) + else: + post_trans = translate + if center is None: + center = (w / 2, h / 2) + + angle = -math.radians(angle) + matrix = [ + round(math.cos(angle), 15), + round(math.sin(angle), 15), + 0.0, + round(-math.sin(angle), 15), + round(math.cos(angle), 15), + 0.0, + ] + + def transform(x: float, y: float, matrix: list[float]) -> tuple[float, float]: + (a, b, c, d, e, f) = matrix + return a * x + b * y + c, d * x + e * y + f + + matrix[2], matrix[5] = transform( + -center[0] - post_trans[0], -center[1] - post_trans[1], matrix + ) + matrix[2] += center[0] + matrix[5] += center[1] + + if expand: + # calculate output size + xx = [] + yy = [] + for x, y in ((0, 0), (w, 0), (w, h), (0, h)): + transformed_x, transformed_y = transform(x, y, matrix) + xx.append(transformed_x) + yy.append(transformed_y) + nw = math.ceil(max(xx)) - math.floor(min(xx)) + nh = math.ceil(max(yy)) - math.floor(min(yy)) + + # We multiply a translation matrix from the right. Because of its + # special form, this is the same as taking the image of the + # translation vector as new translation vector. + matrix[2], matrix[5] = transform(-(nw - w) / 2.0, -(nh - h) / 2.0, matrix) + w, h = nw, nh + + return self.transform( + (w, h), Transform.AFFINE, matrix, resample, fillcolor=fillcolor + ) + + def save( + self, fp: StrOrBytesPath | IO[bytes], format: str | None = None, **params: Any + ) -> None: + """ + Saves this image under the given filename. If no format is + specified, the format to use is determined from the filename + extension, if possible. + + Keyword options can be used to provide additional instructions + to the writer. If a writer doesn't recognise an option, it is + silently ignored. The available options are described in the + :doc:`image format documentation + <../handbook/image-file-formats>` for each writer. + + You can use a file object instead of a filename. In this case, + you must always specify the format. The file object must + implement the ``seek``, ``tell``, and ``write`` + methods, and be opened in binary mode. + + :param fp: A filename (string), os.PathLike object or file object. + :param format: Optional format override. If omitted, the + format to use is determined from the filename extension. + If a file object was used instead of a filename, this + parameter should always be used. + :param params: Extra parameters to the image writer. These can also be + set on the image itself through ``encoderinfo``. This is useful when + saving multiple images:: + + # Saving XMP data to a single image + from PIL import Image + red = Image.new("RGB", (1, 1), "#f00") + red.save("out.mpo", xmp=b"test") + + # Saving XMP data to the second frame of an image + from PIL import Image + black = Image.new("RGB", (1, 1)) + red = Image.new("RGB", (1, 1), "#f00") + red.encoderinfo = {"xmp": b"test"} + black.save("out.mpo", save_all=True, append_images=[red]) + :returns: None + :exception ValueError: If the output format could not be determined + from the file name. Use the format option to solve this. + :exception OSError: If the file could not be written. The file + may have been created, and may contain partial data. + """ + + filename: str | bytes = "" + open_fp = False + if is_path(fp): + filename = os.fspath(fp) + open_fp = True + elif fp == sys.stdout: + try: + fp = sys.stdout.buffer + except AttributeError: + pass + if not filename and hasattr(fp, "name") and is_path(fp.name): + # only set the name for metadata purposes + filename = os.fspath(fp.name) + + preinit() + + filename_ext = os.path.splitext(filename)[1].lower() + ext = filename_ext.decode() if isinstance(filename_ext, bytes) else filename_ext + + if not format: + if ext not in EXTENSION: + init() + try: + format = EXTENSION[ext] + except KeyError as e: + msg = f"unknown file extension: {ext}" + raise ValueError(msg) from e + + from . import ImageFile + + # may mutate self! + if isinstance(self, ImageFile.ImageFile) and os.path.abspath( + filename + ) == os.path.abspath(self.filename): + self._ensure_mutable() + else: + self.load() + + save_all = params.pop("save_all", None) + self._default_encoderinfo = params + encoderinfo = getattr(self, "encoderinfo", {}) + self._attach_default_encoderinfo(self) + self.encoderconfig: tuple[Any, ...] = () + + if format.upper() not in SAVE: + init() + if save_all or ( + save_all is None + and params.get("append_images") + and format.upper() in SAVE_ALL + ): + save_handler = SAVE_ALL[format.upper()] + else: + save_handler = SAVE[format.upper()] + + created = False + if open_fp: + created = not os.path.exists(filename) + if params.get("append", False): + # Open also for reading ("+"), because TIFF save_all + # writer needs to go back and edit the written data. + fp = builtins.open(filename, "r+b") + else: + fp = builtins.open(filename, "w+b") + else: + fp = cast(IO[bytes], fp) + + try: + save_handler(self, fp, filename) + except Exception: + if open_fp: + fp.close() + if created: + try: + os.remove(filename) + except PermissionError: + pass + raise + finally: + self.encoderinfo = encoderinfo + if open_fp: + fp.close() + + def _attach_default_encoderinfo(self, im: Image) -> dict[str, Any]: + encoderinfo = getattr(self, "encoderinfo", {}) + self.encoderinfo = {**im._default_encoderinfo, **encoderinfo} + return encoderinfo + + def seek(self, frame: int) -> None: + """ + Seeks to the given frame in this sequence file. If you seek + beyond the end of the sequence, the method raises an + ``EOFError`` exception. When a sequence file is opened, the + library automatically seeks to frame 0. + + See :py:meth:`~PIL.Image.Image.tell`. + + If defined, :attr:`~PIL.Image.Image.n_frames` refers to the + number of available frames. + + :param frame: Frame number, starting at 0. + :exception EOFError: If the call attempts to seek beyond the end + of the sequence. + """ + + # overridden by file handlers + if frame != 0: + msg = "no more images in file" + raise EOFError(msg) + + def show(self, title: str | None = None) -> None: + """ + Displays this image. This method is mainly intended for debugging purposes. + + This method calls :py:func:`PIL.ImageShow.show` internally. You can use + :py:func:`PIL.ImageShow.register` to override its default behaviour. + + The image is first saved to a temporary file. By default, it will be in + PNG format. + + On Unix, the image is then opened using the **xdg-open**, **display**, + **gm**, **eog** or **xv** utility, depending on which one can be found. + + On macOS, the image is opened with the native Preview application. + + On Windows, the image is opened with the standard PNG display utility. + + :param title: Optional title to use for the image window, where possible. + """ + + _show(self, title=title) + + def split(self) -> tuple[Image, ...]: + """ + Split this image into individual bands. This method returns a + tuple of individual image bands from an image. For example, + splitting an "RGB" image creates three new images each + containing a copy of one of the original bands (red, green, + blue). + + If you need only one band, :py:meth:`~PIL.Image.Image.getchannel` + method can be more convenient and faster. + + :returns: A tuple containing bands. + """ + + self.load() + if self.im.bands == 1: + return (self.copy(),) + return tuple(map(self._new, self.im.split())) + + def getchannel(self, channel: int | str) -> Image: + """ + Returns an image containing a single channel of the source image. + + :param channel: What channel to return. Could be index + (0 for "R" channel of "RGB") or channel name + ("A" for alpha channel of "RGBA"). + :returns: An image in "L" mode. + + .. versionadded:: 4.3.0 + """ + self.load() + + if isinstance(channel, str): + try: + channel = self.getbands().index(channel) + except ValueError as e: + msg = f'The image has no channel "{channel}"' + raise ValueError(msg) from e + + return self._new(self.im.getband(channel)) + + def tell(self) -> int: + """ + Returns the current frame number. See :py:meth:`~PIL.Image.Image.seek`. + + If defined, :attr:`~PIL.Image.Image.n_frames` refers to the + number of available frames. + + :returns: Frame number, starting with 0. + """ + return 0 + + def thumbnail( + self, + size: tuple[float, float], + resample: Resampling = Resampling.BICUBIC, + reducing_gap: float | None = 2.0, + ) -> None: + """ + Make this image into a thumbnail. This method modifies the + image to contain a thumbnail version of itself, no larger than + the given size. This method calculates an appropriate thumbnail + size to preserve the aspect of the image, calls the + :py:meth:`~PIL.Image.Image.draft` method to configure the file reader + (where applicable), and finally resizes the image. + + Note that this function modifies the :py:class:`~PIL.Image.Image` + object in place. If you need to use the full resolution image as well, + apply this method to a :py:meth:`~PIL.Image.Image.copy` of the original + image. + + :param size: The requested size in pixels, as a 2-tuple: + (width, height). + :param resample: Optional resampling filter. This can be one + of :py:data:`Resampling.NEAREST`, :py:data:`Resampling.BOX`, + :py:data:`Resampling.BILINEAR`, :py:data:`Resampling.HAMMING`, + :py:data:`Resampling.BICUBIC` or :py:data:`Resampling.LANCZOS`. + If omitted, it defaults to :py:data:`Resampling.BICUBIC`. + (was :py:data:`Resampling.NEAREST` prior to version 2.5.0). + See: :ref:`concept-filters`. + :param reducing_gap: Apply optimization by resizing the image + in two steps. First, reducing the image by integer times + using :py:meth:`~PIL.Image.Image.reduce` or + :py:meth:`~PIL.Image.Image.draft` for JPEG images. + Second, resizing using regular resampling. The last step + changes size no less than by ``reducing_gap`` times. + ``reducing_gap`` may be None (no first step is performed) + or should be greater than 1.0. The bigger ``reducing_gap``, + the closer the result to the fair resampling. + The smaller ``reducing_gap``, the faster resizing. + With ``reducing_gap`` greater or equal to 3.0, the result is + indistinguishable from fair resampling in most cases. + The default value is 2.0 (very close to fair resampling + while still being faster in many cases). + :returns: None + """ + + provided_size = tuple(map(math.floor, size)) + + def preserve_aspect_ratio() -> tuple[int, int] | None: + def round_aspect(number: float, key: Callable[[int], float]) -> int: + return max(min(math.floor(number), math.ceil(number), key=key), 1) + + x, y = provided_size + if x >= self.width and y >= self.height: + return None + + aspect = self.width / self.height + if x / y >= aspect: + x = round_aspect(y * aspect, key=lambda n: abs(aspect - n / y)) + else: + y = round_aspect( + x / aspect, key=lambda n: 0 if n == 0 else abs(aspect - x / n) + ) + return x, y + + preserved_size = preserve_aspect_ratio() + if preserved_size is None: + return + final_size = preserved_size + + box = None + if reducing_gap is not None: + res = self.draft( + None, (int(size[0] * reducing_gap), int(size[1] * reducing_gap)) + ) + if res is not None: + box = res[1] + + if self.size != final_size: + im = self.resize(final_size, resample, box=box, reducing_gap=reducing_gap) + + self.im = im.im + self._size = final_size + self._mode = self.im.mode + + self.readonly = 0 + + # FIXME: the different transform methods need further explanation + # instead of bloating the method docs, add a separate chapter. + def transform( + self, + size: tuple[int, int], + method: Transform | ImageTransformHandler | SupportsGetData, + data: Sequence[Any] | None = None, + resample: int = Resampling.NEAREST, + fill: int = 1, + fillcolor: float | tuple[float, ...] | str | None = None, + ) -> Image: + """ + Transforms this image. This method creates a new image with the + given size, and the same mode as the original, and copies data + to the new image using the given transform. + + :param size: The output size in pixels, as a 2-tuple: + (width, height). + :param method: The transformation method. This is one of + :py:data:`Transform.EXTENT` (cut out a rectangular subregion), + :py:data:`Transform.AFFINE` (affine transform), + :py:data:`Transform.PERSPECTIVE` (perspective transform), + :py:data:`Transform.QUAD` (map a quadrilateral to a rectangle), or + :py:data:`Transform.MESH` (map a number of source quadrilaterals + in one operation). + + It may also be an :py:class:`~PIL.Image.ImageTransformHandler` + object:: + + class Example(Image.ImageTransformHandler): + def transform(self, size, data, resample, fill=1): + # Return result + + Implementations of :py:class:`~PIL.Image.ImageTransformHandler` + for some of the :py:class:`Transform` methods are provided + in :py:mod:`~PIL.ImageTransform`. + + It may also be an object with a ``method.getdata`` method + that returns a tuple supplying new ``method`` and ``data`` values:: + + class Example: + def getdata(self): + method = Image.Transform.EXTENT + data = (0, 0, 100, 100) + return method, data + :param data: Extra data to the transformation method. + :param resample: Optional resampling filter. It can be one of + :py:data:`Resampling.NEAREST` (use nearest neighbour), + :py:data:`Resampling.BILINEAR` (linear interpolation in a 2x2 + environment), or :py:data:`Resampling.BICUBIC` (cubic spline + interpolation in a 4x4 environment). If omitted, or if the image + has mode "1" or "P", it is set to :py:data:`Resampling.NEAREST`. + See: :ref:`concept-filters`. + :param fill: If ``method`` is an + :py:class:`~PIL.Image.ImageTransformHandler` object, this is one of + the arguments passed to it. Otherwise, it is unused. + :param fillcolor: Optional fill color for the area outside the + transform in the output image. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if self.mode in ("LA", "RGBA") and resample != Resampling.NEAREST: + return ( + self.convert({"LA": "La", "RGBA": "RGBa"}[self.mode]) + .transform(size, method, data, resample, fill, fillcolor) + .convert(self.mode) + ) + + if isinstance(method, ImageTransformHandler): + return method.transform(size, self, resample=resample, fill=fill) + + if hasattr(method, "getdata"): + # compatibility w. old-style transform objects + method, data = method.getdata() + + if data is None: + msg = "missing method data" + raise ValueError(msg) + + im = new(self.mode, size, fillcolor) + if self.mode == "P" and self.palette: + im.palette = self.palette.copy() + im.info = self.info.copy() + if method == Transform.MESH: + # list of quads + for box, quad in data: + im.__transformer( + box, self, Transform.QUAD, quad, resample, fillcolor is None + ) + else: + im.__transformer( + (0, 0) + size, self, method, data, resample, fillcolor is None + ) + + return im + + def __transformer( + self, + box: tuple[int, int, int, int], + image: Image, + method: Transform, + data: Sequence[float], + resample: int = Resampling.NEAREST, + fill: bool = True, + ) -> None: + w = box[2] - box[0] + h = box[3] - box[1] + + if method == Transform.AFFINE: + data = data[:6] + + elif method == Transform.EXTENT: + # convert extent to an affine transform + x0, y0, x1, y1 = data + xs = (x1 - x0) / w + ys = (y1 - y0) / h + method = Transform.AFFINE + data = (xs, 0, x0, 0, ys, y0) + + elif method == Transform.PERSPECTIVE: + data = data[:8] + + elif method == Transform.QUAD: + # quadrilateral warp. data specifies the four corners + # given as NW, SW, SE, and NE. + nw = data[:2] + sw = data[2:4] + se = data[4:6] + ne = data[6:8] + x0, y0 = nw + As = 1.0 / w + At = 1.0 / h + data = ( + x0, + (ne[0] - x0) * As, + (sw[0] - x0) * At, + (se[0] - sw[0] - ne[0] + x0) * As * At, + y0, + (ne[1] - y0) * As, + (sw[1] - y0) * At, + (se[1] - sw[1] - ne[1] + y0) * As * At, + ) + + else: + msg = "unknown transformation method" + raise ValueError(msg) + + if resample not in ( + Resampling.NEAREST, + Resampling.BILINEAR, + Resampling.BICUBIC, + ): + if resample in (Resampling.BOX, Resampling.HAMMING, Resampling.LANCZOS): + unusable: dict[int, str] = { + Resampling.BOX: "Image.Resampling.BOX", + Resampling.HAMMING: "Image.Resampling.HAMMING", + Resampling.LANCZOS: "Image.Resampling.LANCZOS", + } + msg = unusable[resample] + f" ({resample}) cannot be used." + else: + msg = f"Unknown resampling filter ({resample})." + + filters = [ + f"{filter[1]} ({filter[0]})" + for filter in ( + (Resampling.NEAREST, "Image.Resampling.NEAREST"), + (Resampling.BILINEAR, "Image.Resampling.BILINEAR"), + (Resampling.BICUBIC, "Image.Resampling.BICUBIC"), + ) + ] + msg += f" Use {', '.join(filters[:-1])} or {filters[-1]}" + raise ValueError(msg) + + image.load() + + self.load() + + if image.mode in ("1", "P"): + resample = Resampling.NEAREST + + self.im.transform(box, image.im, method, data, resample, fill) + + def transpose(self, method: Transpose) -> Image: + """ + Transpose image (flip or rotate in 90 degree steps) + + :param method: One of :py:data:`Transpose.FLIP_LEFT_RIGHT`, + :py:data:`Transpose.FLIP_TOP_BOTTOM`, :py:data:`Transpose.ROTATE_90`, + :py:data:`Transpose.ROTATE_180`, :py:data:`Transpose.ROTATE_270`, + :py:data:`Transpose.TRANSPOSE` or :py:data:`Transpose.TRANSVERSE`. + :returns: Returns a flipped or rotated copy of this image. + """ + + self.load() + return self._new(self.im.transpose(method)) + + def effect_spread(self, distance: int) -> Image: + """ + Randomly spread pixels in an image. + + :param distance: Distance to spread pixels. + """ + self.load() + return self._new(self.im.effect_spread(distance)) + + def toqimage(self) -> ImageQt.ImageQt: + """Returns a QImage copy of this image""" + from . import ImageQt + + if not ImageQt.qt_is_installed: + msg = "Qt bindings are not installed" + raise ImportError(msg) + return ImageQt.toqimage(self) + + def toqpixmap(self) -> ImageQt.QPixmap: + """Returns a QPixmap copy of this image""" + from . import ImageQt + + if not ImageQt.qt_is_installed: + msg = "Qt bindings are not installed" + raise ImportError(msg) + return ImageQt.toqpixmap(self) + + +# -------------------------------------------------------------------- +# Abstract handlers. + + +class ImagePointHandler(abc.ABC): + """ + Used as a mixin by point transforms + (for use with :py:meth:`~PIL.Image.Image.point`) + """ + + @abc.abstractmethod + def point(self, im: Image) -> Image: + pass + + +class ImageTransformHandler(abc.ABC): + """ + Used as a mixin by geometry transforms + (for use with :py:meth:`~PIL.Image.Image.transform`) + """ + + @abc.abstractmethod + def transform( + self, + size: tuple[int, int], + image: Image, + **options: Any, + ) -> Image: + pass + + +# -------------------------------------------------------------------- +# Factories + + +def _check_size(size: Any) -> None: + """ + Common check to enforce type and sanity check on size tuples + + :param size: Should be a 2 tuple of (width, height) + :returns: None, or raises a ValueError + """ + + if not isinstance(size, (list, tuple)): + msg = "Size must be a list or tuple" + raise ValueError(msg) + if len(size) != 2: + msg = "Size must be a sequence of length 2" + raise ValueError(msg) + if size[0] < 0 or size[1] < 0: + msg = "Width and height must be >= 0" + raise ValueError(msg) + + +def new( + mode: str, + size: tuple[int, int] | list[int], + color: float | tuple[float, ...] | str | None = 0, +) -> Image: + """ + Creates a new image with the given mode and size. + + :param mode: The mode to use for the new image. See: + :ref:`concept-modes`. + :param size: A 2-tuple, containing (width, height) in pixels. + :param color: What color to use for the image. Default is black. + If given, this should be a single integer or floating point value + for single-band modes, and a tuple for multi-band modes (one value + per band). When creating RGB or HSV images, you can also use color + strings as supported by the ImageColor module. If the color is + None, the image is not initialised. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if mode in ("BGR;15", "BGR;16", "BGR;24"): + deprecate(mode, 12) + + _check_size(size) + + if color is None: + # don't initialize + return Image()._new(core.new(mode, size)) + + if isinstance(color, str): + # css3-style specifier + + from . import ImageColor + + color = ImageColor.getcolor(color, mode) + + im = Image() + if ( + mode == "P" + and isinstance(color, (list, tuple)) + and all(isinstance(i, int) for i in color) + ): + color_ints: tuple[int, ...] = cast(tuple[int, ...], tuple(color)) + if len(color_ints) == 3 or len(color_ints) == 4: + # RGB or RGBA value for a P image + from . import ImagePalette + + im.palette = ImagePalette.ImagePalette() + color = im.palette.getcolor(color_ints) + return im._new(core.fill(mode, size, color)) + + +def frombytes( + mode: str, + size: tuple[int, int], + data: bytes | bytearray | SupportsArrayInterface, + decoder_name: str = "raw", + *args: Any, +) -> Image: + """ + Creates a copy of an image memory from pixel data in a buffer. + + In its simplest form, this function takes three arguments + (mode, size, and unpacked pixel data). + + You can also use any pixel decoder supported by PIL. For more + information on available decoders, see the section + :ref:`Writing Your Own File Codec `. + + Note that this function decodes pixel data only, not entire images. + If you have an entire image in a string, wrap it in a + :py:class:`~io.BytesIO` object, and use :py:func:`~PIL.Image.open` to load + it. + + :param mode: The image mode. See: :ref:`concept-modes`. + :param size: The image size. + :param data: A byte buffer containing raw data for the given mode. + :param decoder_name: What decoder to use. + :param args: Additional parameters for the given decoder. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + _check_size(size) + + im = new(mode, size) + if im.width != 0 and im.height != 0: + decoder_args: Any = args + if len(decoder_args) == 1 and isinstance(decoder_args[0], tuple): + # may pass tuple instead of argument list + decoder_args = decoder_args[0] + + if decoder_name == "raw" and decoder_args == (): + decoder_args = mode + + im.frombytes(data, decoder_name, decoder_args) + return im + + +def frombuffer( + mode: str, + size: tuple[int, int], + data: bytes | SupportsArrayInterface, + decoder_name: str = "raw", + *args: Any, +) -> Image: + """ + Creates an image memory referencing pixel data in a byte buffer. + + This function is similar to :py:func:`~PIL.Image.frombytes`, but uses data + in the byte buffer, where possible. This means that changes to the + original buffer object are reflected in this image). Not all modes can + share memory; supported modes include "L", "RGBX", "RGBA", and "CMYK". + + Note that this function decodes pixel data only, not entire images. + If you have an entire image file in a string, wrap it in a + :py:class:`~io.BytesIO` object, and use :py:func:`~PIL.Image.open` to load it. + + The default parameters used for the "raw" decoder differs from that used for + :py:func:`~PIL.Image.frombytes`. This is a bug, and will probably be fixed in a + future release. The current release issues a warning if you do this; to disable + the warning, you should provide the full set of parameters. See below for details. + + :param mode: The image mode. See: :ref:`concept-modes`. + :param size: The image size. + :param data: A bytes or other buffer object containing raw + data for the given mode. + :param decoder_name: What decoder to use. + :param args: Additional parameters for the given decoder. For the + default encoder ("raw"), it's recommended that you provide the + full set of parameters:: + + frombuffer(mode, size, data, "raw", mode, 0, 1) + + :returns: An :py:class:`~PIL.Image.Image` object. + + .. versionadded:: 1.1.4 + """ + + _check_size(size) + + # may pass tuple instead of argument list + if len(args) == 1 and isinstance(args[0], tuple): + args = args[0] + + if decoder_name == "raw": + if args == (): + args = mode, 0, 1 + if args[0] in _MAPMODES: + im = new(mode, (0, 0)) + im = im._new(core.map_buffer(data, size, decoder_name, 0, args)) + if mode == "P": + from . import ImagePalette + + im.palette = ImagePalette.ImagePalette("RGB", im.im.getpalette("RGB")) + im.readonly = 1 + return im + + return frombytes(mode, size, data, decoder_name, args) + + +class SupportsArrayInterface(Protocol): + """ + An object that has an ``__array_interface__`` dictionary. + """ + + @property + def __array_interface__(self) -> dict[str, Any]: + raise NotImplementedError() + + +class SupportsArrowArrayInterface(Protocol): + """ + An object that has an ``__arrow_c_array__`` method corresponding to the arrow c + data interface. + """ + + def __arrow_c_array__( + self, requested_schema: "PyCapsule" = None # type: ignore[name-defined] # noqa: F821, UP037 + ) -> tuple["PyCapsule", "PyCapsule"]: # type: ignore[name-defined] # noqa: F821, UP037 + raise NotImplementedError() + + +def fromarray(obj: SupportsArrayInterface, mode: str | None = None) -> Image: + """ + Creates an image memory from an object exporting the array interface + (using the buffer protocol):: + + from PIL import Image + import numpy as np + a = np.zeros((5, 5)) + im = Image.fromarray(a) + + If ``obj`` is not contiguous, then the ``tobytes`` method is called + and :py:func:`~PIL.Image.frombuffer` is used. + + In the case of NumPy, be aware that Pillow modes do not always correspond + to NumPy dtypes. Pillow modes only offer 1-bit pixels, 8-bit pixels, + 32-bit signed integer pixels, and 32-bit floating point pixels. + + Pillow images can also be converted to arrays:: + + from PIL import Image + import numpy as np + im = Image.open("hopper.jpg") + a = np.asarray(im) + + When converting Pillow images to arrays however, only pixel values are + transferred. This means that P and PA mode images will lose their palette. + + :param obj: Object with array interface + :param mode: Optional mode to use when reading ``obj``. Will be determined from + type if ``None``. Deprecated. + + This will not be used to convert the data after reading, but will be used to + change how the data is read:: + + from PIL import Image + import numpy as np + a = np.full((1, 1), 300) + im = Image.fromarray(a, mode="L") + im.getpixel((0, 0)) # 44 + im = Image.fromarray(a, mode="RGB") + im.getpixel((0, 0)) # (44, 1, 0) + + See: :ref:`concept-modes` for general information about modes. + :returns: An image object. + + .. versionadded:: 1.1.6 + """ + arr = obj.__array_interface__ + shape = arr["shape"] + ndim = len(shape) + strides = arr.get("strides", None) + if mode is None: + try: + typekey = (1, 1) + shape[2:], arr["typestr"] + except KeyError as e: + msg = "Cannot handle this data type" + raise TypeError(msg) from e + try: + mode, rawmode = _fromarray_typemap[typekey] + except KeyError as e: + typekey_shape, typestr = typekey + msg = f"Cannot handle this data type: {typekey_shape}, {typestr}" + raise TypeError(msg) from e + else: + deprecate("'mode' parameter", 13) + rawmode = mode + if mode in ["1", "L", "I", "P", "F"]: + ndmax = 2 + elif mode == "RGB": + ndmax = 3 + else: + ndmax = 4 + if ndim > ndmax: + msg = f"Too many dimensions: {ndim} > {ndmax}." + raise ValueError(msg) + + size = 1 if ndim == 1 else shape[1], shape[0] + if strides is not None: + if hasattr(obj, "tobytes"): + obj = obj.tobytes() + elif hasattr(obj, "tostring"): + obj = obj.tostring() + else: + msg = "'strides' requires either tobytes() or tostring()" + raise ValueError(msg) + + return frombuffer(mode, size, obj, "raw", rawmode, 0, 1) + + +def fromarrow( + obj: SupportsArrowArrayInterface, mode: str, size: tuple[int, int] +) -> Image: + """Creates an image with zero-copy shared memory from an object exporting + the arrow_c_array interface protocol:: + + from PIL import Image + import pyarrow as pa + arr = pa.array([0]*(5*5*4), type=pa.uint8()) + im = Image.fromarrow(arr, 'RGBA', (5, 5)) + + If the data representation of the ``obj`` is not compatible with + Pillow internal storage, a ValueError is raised. + + Pillow images can also be converted to Arrow objects:: + + from PIL import Image + import pyarrow as pa + im = Image.open('hopper.jpg') + arr = pa.array(im) + + As with array support, when converting Pillow images to arrays, + only pixel values are transferred. This means that P and PA mode + images will lose their palette. + + :param obj: Object with an arrow_c_array interface + :param mode: Image mode. + :param size: Image size. This must match the storage of the arrow object. + :returns: An Image object + + Note that according to the Arrow spec, both the producer and the + consumer should consider the exported array to be immutable, as + unsynchronized updates will potentially cause inconsistent data. + + See: :ref:`arrow-support` for more detailed information + + .. versionadded:: 11.2.1 + + """ + if not hasattr(obj, "__arrow_c_array__"): + msg = "arrow_c_array interface not found" + raise ValueError(msg) + + (schema_capsule, array_capsule) = obj.__arrow_c_array__() + _im = core.new_arrow(mode, size, schema_capsule, array_capsule) + if _im: + return Image()._new(_im) + + msg = "new_arrow returned None without an exception" + raise ValueError(msg) + + +def fromqimage(im: ImageQt.QImage) -> ImageFile.ImageFile: + """Creates an image instance from a QImage image""" + from . import ImageQt + + if not ImageQt.qt_is_installed: + msg = "Qt bindings are not installed" + raise ImportError(msg) + return ImageQt.fromqimage(im) + + +def fromqpixmap(im: ImageQt.QPixmap) -> ImageFile.ImageFile: + """Creates an image instance from a QPixmap image""" + from . import ImageQt + + if not ImageQt.qt_is_installed: + msg = "Qt bindings are not installed" + raise ImportError(msg) + return ImageQt.fromqpixmap(im) + + +_fromarray_typemap = { + # (shape, typestr) => mode, rawmode + # first two members of shape are set to one + ((1, 1), "|b1"): ("1", "1;8"), + ((1, 1), "|u1"): ("L", "L"), + ((1, 1), "|i1"): ("I", "I;8"), + ((1, 1), "u2"): ("I", "I;16B"), + ((1, 1), "i2"): ("I", "I;16BS"), + ((1, 1), "u4"): ("I", "I;32B"), + ((1, 1), "i4"): ("I", "I;32BS"), + ((1, 1), "f4"): ("F", "F;32BF"), + ((1, 1), "f8"): ("F", "F;64BF"), + ((1, 1, 2), "|u1"): ("LA", "LA"), + ((1, 1, 3), "|u1"): ("RGB", "RGB"), + ((1, 1, 4), "|u1"): ("RGBA", "RGBA"), + # shortcuts: + ((1, 1), f"{_ENDIAN}i4"): ("I", "I"), + ((1, 1), f"{_ENDIAN}f4"): ("F", "F"), +} + + +def _decompression_bomb_check(size: tuple[int, int]) -> None: + if MAX_IMAGE_PIXELS is None: + return + + pixels = max(1, size[0]) * max(1, size[1]) + + if pixels > 2 * MAX_IMAGE_PIXELS: + msg = ( + f"Image size ({pixels} pixels) exceeds limit of {2 * MAX_IMAGE_PIXELS} " + "pixels, could be decompression bomb DOS attack." + ) + raise DecompressionBombError(msg) + + if pixels > MAX_IMAGE_PIXELS: + warnings.warn( + f"Image size ({pixels} pixels) exceeds limit of {MAX_IMAGE_PIXELS} pixels, " + "could be decompression bomb DOS attack.", + DecompressionBombWarning, + ) + + +def open( + fp: StrOrBytesPath | IO[bytes], + mode: Literal["r"] = "r", + formats: list[str] | tuple[str, ...] | None = None, +) -> ImageFile.ImageFile: + """ + Opens and identifies the given image file. + + This is a lazy operation; this function identifies the file, but + the file remains open and the actual image data is not read from + the file until you try to process the data (or call the + :py:meth:`~PIL.Image.Image.load` method). See + :py:func:`~PIL.Image.new`. See :ref:`file-handling`. + + :param fp: A filename (string), os.PathLike object or a file object. + The file object must implement ``file.read``, + ``file.seek``, and ``file.tell`` methods, + and be opened in binary mode. The file object will also seek to zero + before reading. + :param mode: The mode. If given, this argument must be "r". + :param formats: A list or tuple of formats to attempt to load the file in. + This can be used to restrict the set of formats checked. + Pass ``None`` to try all supported formats. You can print the set of + available formats by running ``python3 -m PIL`` or using + the :py:func:`PIL.features.pilinfo` function. + :returns: An :py:class:`~PIL.Image.Image` object. + :exception FileNotFoundError: If the file cannot be found. + :exception PIL.UnidentifiedImageError: If the image cannot be opened and + identified. + :exception ValueError: If the ``mode`` is not "r", or if a ``StringIO`` + instance is used for ``fp``. + :exception TypeError: If ``formats`` is not ``None``, a list or a tuple. + """ + + if mode != "r": + msg = f"bad mode {repr(mode)}" # type: ignore[unreachable] + raise ValueError(msg) + elif isinstance(fp, io.StringIO): + msg = ( # type: ignore[unreachable] + "StringIO cannot be used to open an image. " + "Binary data must be used instead." + ) + raise ValueError(msg) + + if formats is None: + formats = ID + elif not isinstance(formats, (list, tuple)): + msg = "formats must be a list or tuple" # type: ignore[unreachable] + raise TypeError(msg) + + exclusive_fp = False + filename: str | bytes = "" + if is_path(fp): + filename = os.fspath(fp) + fp = builtins.open(filename, "rb") + exclusive_fp = True + else: + fp = cast(IO[bytes], fp) + + try: + fp.seek(0) + except (AttributeError, io.UnsupportedOperation): + fp = io.BytesIO(fp.read()) + exclusive_fp = True + + prefix = fp.read(16) + + preinit() + + warning_messages: list[str] = [] + + def _open_core( + fp: IO[bytes], + filename: str | bytes, + prefix: bytes, + formats: list[str] | tuple[str, ...], + ) -> ImageFile.ImageFile | None: + for i in formats: + i = i.upper() + if i not in OPEN: + init() + try: + factory, accept = OPEN[i] + result = not accept or accept(prefix) + if isinstance(result, str): + warning_messages.append(result) + elif result: + fp.seek(0) + im = factory(fp, filename) + _decompression_bomb_check(im.size) + return im + except (SyntaxError, IndexError, TypeError, struct.error) as e: + if WARN_POSSIBLE_FORMATS: + warning_messages.append(i + " opening failed. " + str(e)) + except BaseException: + if exclusive_fp: + fp.close() + raise + return None + + im = _open_core(fp, filename, prefix, formats) + + if im is None and formats is ID: + checked_formats = ID.copy() + if init(): + im = _open_core( + fp, + filename, + prefix, + tuple(format for format in formats if format not in checked_formats), + ) + + if im: + im._exclusive_fp = exclusive_fp + return im + + if exclusive_fp: + fp.close() + for message in warning_messages: + warnings.warn(message) + msg = "cannot identify image file %r" % (filename if filename else fp) + raise UnidentifiedImageError(msg) + + +# +# Image processing. + + +def alpha_composite(im1: Image, im2: Image) -> Image: + """ + Alpha composite im2 over im1. + + :param im1: The first image. Must have mode RGBA. + :param im2: The second image. Must have mode RGBA, and the same size as + the first image. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + im1.load() + im2.load() + return im1._new(core.alpha_composite(im1.im, im2.im)) + + +def blend(im1: Image, im2: Image, alpha: float) -> Image: + """ + Creates a new image by interpolating between two input images, using + a constant alpha:: + + out = image1 * (1.0 - alpha) + image2 * alpha + + :param im1: The first image. + :param im2: The second image. Must have the same mode and size as + the first image. + :param alpha: The interpolation alpha factor. If alpha is 0.0, a + copy of the first image is returned. If alpha is 1.0, a copy of + the second image is returned. There are no restrictions on the + alpha value. If necessary, the result is clipped to fit into + the allowed output range. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + im1.load() + im2.load() + return im1._new(core.blend(im1.im, im2.im, alpha)) + + +def composite(image1: Image, image2: Image, mask: Image) -> Image: + """ + Create composite image by blending images using a transparency mask. + + :param image1: The first image. + :param image2: The second image. Must have the same mode and + size as the first image. + :param mask: A mask image. This image can have mode + "1", "L", or "RGBA", and must have the same size as the + other two images. + """ + + image = image2.copy() + image.paste(image1, None, mask) + return image + + +def eval(image: Image, *args: Callable[[int], float]) -> Image: + """ + Applies the function (which should take one argument) to each pixel + in the given image. If the image has more than one band, the same + function is applied to each band. Note that the function is + evaluated once for each possible pixel value, so you cannot use + random components or other generators. + + :param image: The input image. + :param function: A function object, taking one integer argument. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + return image.point(args[0]) + + +def merge(mode: str, bands: Sequence[Image]) -> Image: + """ + Merge a set of single band images into a new multiband image. + + :param mode: The mode to use for the output image. See: + :ref:`concept-modes`. + :param bands: A sequence containing one single-band image for + each band in the output image. All bands must have the + same size. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + + if getmodebands(mode) != len(bands) or "*" in mode: + msg = "wrong number of bands" + raise ValueError(msg) + for band in bands[1:]: + if band.mode != getmodetype(mode): + msg = "mode mismatch" + raise ValueError(msg) + if band.size != bands[0].size: + msg = "size mismatch" + raise ValueError(msg) + for band in bands: + band.load() + return bands[0]._new(core.merge(mode, *[b.im for b in bands])) + + +# -------------------------------------------------------------------- +# Plugin registry + + +def register_open( + id: str, + factory: ( + Callable[[IO[bytes], str | bytes], ImageFile.ImageFile] + | type[ImageFile.ImageFile] + ), + accept: Callable[[bytes], bool | str] | None = None, +) -> None: + """ + Register an image file plugin. This function should not be used + in application code. + + :param id: An image format identifier. + :param factory: An image file factory method. + :param accept: An optional function that can be used to quickly + reject images having another format. + """ + id = id.upper() + if id not in ID: + ID.append(id) + OPEN[id] = factory, accept + + +def register_mime(id: str, mimetype: str) -> None: + """ + Registers an image MIME type by populating ``Image.MIME``. This function + should not be used in application code. + + ``Image.MIME`` provides a mapping from image format identifiers to mime + formats, but :py:meth:`~PIL.ImageFile.ImageFile.get_format_mimetype` can + provide a different result for specific images. + + :param id: An image format identifier. + :param mimetype: The image MIME type for this format. + """ + MIME[id.upper()] = mimetype + + +def register_save( + id: str, driver: Callable[[Image, IO[bytes], str | bytes], None] +) -> None: + """ + Registers an image save function. This function should not be + used in application code. + + :param id: An image format identifier. + :param driver: A function to save images in this format. + """ + SAVE[id.upper()] = driver + + +def register_save_all( + id: str, driver: Callable[[Image, IO[bytes], str | bytes], None] +) -> None: + """ + Registers an image function to save all the frames + of a multiframe format. This function should not be + used in application code. + + :param id: An image format identifier. + :param driver: A function to save images in this format. + """ + SAVE_ALL[id.upper()] = driver + + +def register_extension(id: str, extension: str) -> None: + """ + Registers an image extension. This function should not be + used in application code. + + :param id: An image format identifier. + :param extension: An extension used for this format. + """ + EXTENSION[extension.lower()] = id.upper() + + +def register_extensions(id: str, extensions: list[str]) -> None: + """ + Registers image extensions. This function should not be + used in application code. + + :param id: An image format identifier. + :param extensions: A list of extensions used for this format. + """ + for extension in extensions: + register_extension(id, extension) + + +def registered_extensions() -> dict[str, str]: + """ + Returns a dictionary containing all file extensions belonging + to registered plugins + """ + init() + return EXTENSION + + +def register_decoder(name: str, decoder: type[ImageFile.PyDecoder]) -> None: + """ + Registers an image decoder. This function should not be + used in application code. + + :param name: The name of the decoder + :param decoder: An ImageFile.PyDecoder object + + .. versionadded:: 4.1.0 + """ + DECODERS[name] = decoder + + +def register_encoder(name: str, encoder: type[ImageFile.PyEncoder]) -> None: + """ + Registers an image encoder. This function should not be + used in application code. + + :param name: The name of the encoder + :param encoder: An ImageFile.PyEncoder object + + .. versionadded:: 4.1.0 + """ + ENCODERS[name] = encoder + + +# -------------------------------------------------------------------- +# Simple display support. + + +def _show(image: Image, **options: Any) -> None: + from . import ImageShow + + ImageShow.show(image, **options) + + +# -------------------------------------------------------------------- +# Effects + + +def effect_mandelbrot( + size: tuple[int, int], extent: tuple[float, float, float, float], quality: int +) -> Image: + """ + Generate a Mandelbrot set covering the given extent. + + :param size: The requested size in pixels, as a 2-tuple: + (width, height). + :param extent: The extent to cover, as a 4-tuple: + (x0, y0, x1, y1). + :param quality: Quality. + """ + return Image()._new(core.effect_mandelbrot(size, extent, quality)) + + +def effect_noise(size: tuple[int, int], sigma: float) -> Image: + """ + Generate Gaussian noise centered around 128. + + :param size: The requested size in pixels, as a 2-tuple: + (width, height). + :param sigma: Standard deviation of noise. + """ + return Image()._new(core.effect_noise(size, sigma)) + + +def linear_gradient(mode: str) -> Image: + """ + Generate 256x256 linear gradient from black to white, top to bottom. + + :param mode: Input mode. + """ + return Image()._new(core.linear_gradient(mode)) + + +def radial_gradient(mode: str) -> Image: + """ + Generate 256x256 radial gradient from black to white, centre to edge. + + :param mode: Input mode. + """ + return Image()._new(core.radial_gradient(mode)) + + +# -------------------------------------------------------------------- +# Resources + + +def _apply_env_variables(env: dict[str, str] | None = None) -> None: + env_dict = env if env is not None else os.environ + + for var_name, setter in [ + ("PILLOW_ALIGNMENT", core.set_alignment), + ("PILLOW_BLOCK_SIZE", core.set_block_size), + ("PILLOW_BLOCKS_MAX", core.set_blocks_max), + ]: + if var_name not in env_dict: + continue + + var = env_dict[var_name].lower() + + units = 1 + for postfix, mul in [("k", 1024), ("m", 1024 * 1024)]: + if var.endswith(postfix): + units = mul + var = var[: -len(postfix)] + + try: + var_int = int(var) * units + except ValueError: + warnings.warn(f"{var_name} is not int") + continue + + try: + setter(var_int) + except ValueError as e: + warnings.warn(f"{var_name}: {e}") + + +_apply_env_variables() +atexit.register(core.clear_cache) + + +if TYPE_CHECKING: + _ExifBase = MutableMapping[int, Any] +else: + _ExifBase = MutableMapping + + +class Exif(_ExifBase): + """ + This class provides read and write access to EXIF image data:: + + from PIL import Image + im = Image.open("exif.png") + exif = im.getexif() # Returns an instance of this class + + Information can be read and written, iterated over or deleted:: + + print(exif[274]) # 1 + exif[274] = 2 + for k, v in exif.items(): + print("Tag", k, "Value", v) # Tag 274 Value 2 + del exif[274] + + To access information beyond IFD0, :py:meth:`~PIL.Image.Exif.get_ifd` + returns a dictionary:: + + from PIL import ExifTags + im = Image.open("exif_gps.jpg") + exif = im.getexif() + gps_ifd = exif.get_ifd(ExifTags.IFD.GPSInfo) + print(gps_ifd) + + Other IFDs include ``ExifTags.IFD.Exif``, ``ExifTags.IFD.MakerNote``, + ``ExifTags.IFD.Interop`` and ``ExifTags.IFD.IFD1``. + + :py:mod:`~PIL.ExifTags` also has enum classes to provide names for data:: + + print(exif[ExifTags.Base.Software]) # PIL + print(gps_ifd[ExifTags.GPS.GPSDateStamp]) # 1999:99:99 99:99:99 + """ + + endian: str | None = None + bigtiff = False + _loaded = False + + def __init__(self) -> None: + self._data: dict[int, Any] = {} + self._hidden_data: dict[int, Any] = {} + self._ifds: dict[int, dict[int, Any]] = {} + self._info: TiffImagePlugin.ImageFileDirectory_v2 | None = None + self._loaded_exif: bytes | None = None + + def _fixup(self, value: Any) -> Any: + try: + if len(value) == 1 and isinstance(value, tuple): + return value[0] + except Exception: + pass + return value + + def _fixup_dict(self, src_dict: dict[int, Any]) -> dict[int, Any]: + # Helper function + # returns a dict with any single item tuples/lists as individual values + return {k: self._fixup(v) for k, v in src_dict.items()} + + def _get_ifd_dict( + self, offset: int, group: int | None = None + ) -> dict[int, Any] | None: + try: + # an offset pointer to the location of the nested embedded IFD. + # It should be a long, but may be corrupted. + self.fp.seek(offset) + except (KeyError, TypeError): + return None + else: + from . import TiffImagePlugin + + info = TiffImagePlugin.ImageFileDirectory_v2(self.head, group=group) + info.load(self.fp) + return self._fixup_dict(dict(info)) + + def _get_head(self) -> bytes: + version = b"\x2b" if self.bigtiff else b"\x2a" + if self.endian == "<": + head = b"II" + version + b"\x00" + o32le(8) + else: + head = b"MM\x00" + version + o32be(8) + if self.bigtiff: + head += o32le(8) if self.endian == "<" else o32be(8) + head += b"\x00\x00\x00\x00" + return head + + def load(self, data: bytes) -> None: + # Extract EXIF information. This is highly experimental, + # and is likely to be replaced with something better in a future + # version. + + # The EXIF record consists of a TIFF file embedded in a JPEG + # application marker (!). + if data == self._loaded_exif: + return + self._loaded_exif = data + self._data.clear() + self._hidden_data.clear() + self._ifds.clear() + while data and data.startswith(b"Exif\x00\x00"): + data = data[6:] + if not data: + self._info = None + return + + self.fp: IO[bytes] = io.BytesIO(data) + self.head = self.fp.read(8) + # process dictionary + from . import TiffImagePlugin + + self._info = TiffImagePlugin.ImageFileDirectory_v2(self.head) + self.endian = self._info._endian + self.fp.seek(self._info.next) + self._info.load(self.fp) + + def load_from_fp(self, fp: IO[bytes], offset: int | None = None) -> None: + self._loaded_exif = None + self._data.clear() + self._hidden_data.clear() + self._ifds.clear() + + # process dictionary + from . import TiffImagePlugin + + self.fp = fp + if offset is not None: + self.head = self._get_head() + else: + self.head = self.fp.read(8) + self._info = TiffImagePlugin.ImageFileDirectory_v2(self.head) + if self.endian is None: + self.endian = self._info._endian + if offset is None: + offset = self._info.next + self.fp.tell() + self.fp.seek(offset) + self._info.load(self.fp) + + def _get_merged_dict(self) -> dict[int, Any]: + merged_dict = dict(self) + + # get EXIF extension + if ExifTags.IFD.Exif in self: + ifd = self._get_ifd_dict(self[ExifTags.IFD.Exif], ExifTags.IFD.Exif) + if ifd: + merged_dict.update(ifd) + + # GPS + if ExifTags.IFD.GPSInfo in self: + merged_dict[ExifTags.IFD.GPSInfo] = self._get_ifd_dict( + self[ExifTags.IFD.GPSInfo], ExifTags.IFD.GPSInfo + ) + + return merged_dict + + def tobytes(self, offset: int = 8) -> bytes: + from . import TiffImagePlugin + + head = self._get_head() + ifd = TiffImagePlugin.ImageFileDirectory_v2(ifh=head) + for tag, ifd_dict in self._ifds.items(): + if tag not in self: + ifd[tag] = ifd_dict + for tag, value in self.items(): + if tag in [ + ExifTags.IFD.Exif, + ExifTags.IFD.GPSInfo, + ] and not isinstance(value, dict): + value = self.get_ifd(tag) + if ( + tag == ExifTags.IFD.Exif + and ExifTags.IFD.Interop in value + and not isinstance(value[ExifTags.IFD.Interop], dict) + ): + value = value.copy() + value[ExifTags.IFD.Interop] = self.get_ifd(ExifTags.IFD.Interop) + ifd[tag] = value + return b"Exif\x00\x00" + head + ifd.tobytes(offset) + + def get_ifd(self, tag: int) -> dict[int, Any]: + if tag not in self._ifds: + if tag == ExifTags.IFD.IFD1: + if self._info is not None and self._info.next != 0: + ifd = self._get_ifd_dict(self._info.next) + if ifd is not None: + self._ifds[tag] = ifd + elif tag in [ExifTags.IFD.Exif, ExifTags.IFD.GPSInfo]: + offset = self._hidden_data.get(tag, self.get(tag)) + if offset is not None: + ifd = self._get_ifd_dict(offset, tag) + if ifd is not None: + self._ifds[tag] = ifd + elif tag in [ExifTags.IFD.Interop, ExifTags.IFD.MakerNote]: + if ExifTags.IFD.Exif not in self._ifds: + self.get_ifd(ExifTags.IFD.Exif) + tag_data = self._ifds[ExifTags.IFD.Exif][tag] + if tag == ExifTags.IFD.MakerNote: + from .TiffImagePlugin import ImageFileDirectory_v2 + + if tag_data.startswith(b"FUJIFILM"): + ifd_offset = i32le(tag_data, 8) + ifd_data = tag_data[ifd_offset:] + + makernote = {} + for i in range(struct.unpack(" 4: + (offset,) = struct.unpack("H", tag_data[:2])[0]): + ifd_tag, typ, count, data = struct.unpack( + ">HHL4s", tag_data[i * 12 + 2 : (i + 1) * 12 + 2] + ) + if ifd_tag == 0x1101: + # CameraInfo + (offset,) = struct.unpack(">L", data) + self.fp.seek(offset) + + camerainfo: dict[str, int | bytes] = { + "ModelID": self.fp.read(4) + } + + self.fp.read(4) + # Seconds since 2000 + camerainfo["TimeStamp"] = i32le(self.fp.read(12)) + + self.fp.read(4) + camerainfo["InternalSerialNumber"] = self.fp.read(4) + + self.fp.read(12) + parallax = self.fp.read(4) + handler = ImageFileDirectory_v2._load_dispatch[ + TiffTags.FLOAT + ][1] + camerainfo["Parallax"] = handler( + ImageFileDirectory_v2(), parallax, False + )[0] + + self.fp.read(4) + camerainfo["Category"] = self.fp.read(2) + + makernote = {0x1101: camerainfo} + self._ifds[tag] = makernote + else: + # Interop + ifd = self._get_ifd_dict(tag_data, tag) + if ifd is not None: + self._ifds[tag] = ifd + ifd = self._ifds.setdefault(tag, {}) + if tag == ExifTags.IFD.Exif and self._hidden_data: + ifd = { + k: v + for (k, v) in ifd.items() + if k not in (ExifTags.IFD.Interop, ExifTags.IFD.MakerNote) + } + return ifd + + def hide_offsets(self) -> None: + for tag in (ExifTags.IFD.Exif, ExifTags.IFD.GPSInfo): + if tag in self: + self._hidden_data[tag] = self[tag] + del self[tag] + + def __str__(self) -> str: + if self._info is not None: + # Load all keys into self._data + for tag in self._info: + self[tag] + + return str(self._data) + + def __len__(self) -> int: + keys = set(self._data) + if self._info is not None: + keys.update(self._info) + return len(keys) + + def __getitem__(self, tag: int) -> Any: + if self._info is not None and tag not in self._data and tag in self._info: + self._data[tag] = self._fixup(self._info[tag]) + del self._info[tag] + return self._data[tag] + + def __contains__(self, tag: object) -> bool: + return tag in self._data or (self._info is not None and tag in self._info) + + def __setitem__(self, tag: int, value: Any) -> None: + if self._info is not None and tag in self._info: + del self._info[tag] + self._data[tag] = value + + def __delitem__(self, tag: int) -> None: + if self._info is not None and tag in self._info: + del self._info[tag] + else: + del self._data[tag] + + def __iter__(self) -> Iterator[int]: + keys = set(self._data) + if self._info is not None: + keys.update(self._info) + return iter(keys) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageChops.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageChops.py new file mode 100644 index 0000000000000000000000000000000000000000..29a5c995fd802c9be16784f80707cfecb88b2002 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageChops.py @@ -0,0 +1,311 @@ +# +# The Python Imaging Library. +# $Id$ +# +# standard channel operations +# +# History: +# 1996-03-24 fl Created +# 1996-08-13 fl Added logical operations (for "1" images) +# 2000-10-12 fl Added offset method (from Image.py) +# +# Copyright (c) 1997-2000 by Secret Labs AB +# Copyright (c) 1996-2000 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# + +from __future__ import annotations + +from . import Image + + +def constant(image: Image.Image, value: int) -> Image.Image: + """Fill a channel with a given gray level. + + :rtype: :py:class:`~PIL.Image.Image` + """ + + return Image.new("L", image.size, value) + + +def duplicate(image: Image.Image) -> Image.Image: + """Copy a channel. Alias for :py:meth:`PIL.Image.Image.copy`. + + :rtype: :py:class:`~PIL.Image.Image` + """ + + return image.copy() + + +def invert(image: Image.Image) -> Image.Image: + """ + Invert an image (channel). :: + + out = MAX - image + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image.load() + return image._new(image.im.chop_invert()) + + +def lighter(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Compares the two images, pixel by pixel, and returns a new image containing + the lighter values. :: + + out = max(image1, image2) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_lighter(image2.im)) + + +def darker(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Compares the two images, pixel by pixel, and returns a new image containing + the darker values. :: + + out = min(image1, image2) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_darker(image2.im)) + + +def difference(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Returns the absolute value of the pixel-by-pixel difference between the two + images. :: + + out = abs(image1 - image2) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_difference(image2.im)) + + +def multiply(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two images on top of each other. + + If you multiply an image with a solid black image, the result is black. If + you multiply with a solid white image, the image is unaffected. :: + + out = image1 * image2 / MAX + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_multiply(image2.im)) + + +def screen(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two inverted images on top of each other. :: + + out = MAX - ((MAX - image1) * (MAX - image2) / MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_screen(image2.im)) + + +def soft_light(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two images on top of each other using the Soft Light algorithm + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_soft_light(image2.im)) + + +def hard_light(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two images on top of each other using the Hard Light algorithm + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_hard_light(image2.im)) + + +def overlay(image1: Image.Image, image2: Image.Image) -> Image.Image: + """ + Superimposes two images on top of each other using the Overlay algorithm + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_overlay(image2.im)) + + +def add( + image1: Image.Image, image2: Image.Image, scale: float = 1.0, offset: float = 0 +) -> Image.Image: + """ + Adds two images, dividing the result by scale and adding the + offset. If omitted, scale defaults to 1.0, and offset to 0.0. :: + + out = ((image1 + image2) / scale + offset) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_add(image2.im, scale, offset)) + + +def subtract( + image1: Image.Image, image2: Image.Image, scale: float = 1.0, offset: float = 0 +) -> Image.Image: + """ + Subtracts two images, dividing the result by scale and adding the offset. + If omitted, scale defaults to 1.0, and offset to 0.0. :: + + out = ((image1 - image2) / scale + offset) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_subtract(image2.im, scale, offset)) + + +def add_modulo(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Add two images, without clipping the result. :: + + out = ((image1 + image2) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_add_modulo(image2.im)) + + +def subtract_modulo(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Subtract two images, without clipping the result. :: + + out = ((image1 - image2) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_subtract_modulo(image2.im)) + + +def logical_and(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Logical AND between two images. + + Both of the images must have mode "1". If you would like to perform a + logical AND on an image with a mode other than "1", try + :py:meth:`~PIL.ImageChops.multiply` instead, using a black-and-white mask + as the second image. :: + + out = ((image1 and image2) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_and(image2.im)) + + +def logical_or(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Logical OR between two images. + + Both of the images must have mode "1". :: + + out = ((image1 or image2) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_or(image2.im)) + + +def logical_xor(image1: Image.Image, image2: Image.Image) -> Image.Image: + """Logical XOR between two images. + + Both of the images must have mode "1". :: + + out = ((bool(image1) != bool(image2)) % MAX) + + :rtype: :py:class:`~PIL.Image.Image` + """ + + image1.load() + image2.load() + return image1._new(image1.im.chop_xor(image2.im)) + + +def blend(image1: Image.Image, image2: Image.Image, alpha: float) -> Image.Image: + """Blend images using constant transparency weight. Alias for + :py:func:`PIL.Image.blend`. + + :rtype: :py:class:`~PIL.Image.Image` + """ + + return Image.blend(image1, image2, alpha) + + +def composite( + image1: Image.Image, image2: Image.Image, mask: Image.Image +) -> Image.Image: + """Create composite using transparency mask. Alias for + :py:func:`PIL.Image.composite`. + + :rtype: :py:class:`~PIL.Image.Image` + """ + + return Image.composite(image1, image2, mask) + + +def offset(image: Image.Image, xoffset: int, yoffset: int | None = None) -> Image.Image: + """Returns a copy of the image where data has been offset by the given + distances. Data wraps around the edges. If ``yoffset`` is omitted, it + is assumed to be equal to ``xoffset``. + + :param image: Input image. + :param xoffset: The horizontal distance. + :param yoffset: The vertical distance. If omitted, both + distances are set to the same value. + :rtype: :py:class:`~PIL.Image.Image` + """ + + if yoffset is None: + yoffset = xoffset + image.load() + return image._new(image.im.offset(xoffset, yoffset)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageCms.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageCms.py new file mode 100644 index 0000000000000000000000000000000000000000..a1584f111d4a6c33a62dcb94ae583a539138f0c3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageCms.py @@ -0,0 +1,1123 @@ +# The Python Imaging Library. +# $Id$ + +# Optional color management support, based on Kevin Cazabon's PyCMS +# library. + +# Originally released under LGPL. Graciously donated to PIL in +# March 2009, for distribution under the standard PIL license + +# History: + +# 2009-03-08 fl Added to PIL. + +# Copyright (C) 2002-2003 Kevin Cazabon +# Copyright (c) 2009 by Fredrik Lundh +# Copyright (c) 2013 by Eric Soroos + +# See the README file for information on usage and redistribution. See +# below for the original description. +from __future__ import annotations + +import operator +import sys +from enum import IntEnum, IntFlag +from functools import reduce +from typing import Any, Literal, SupportsFloat, SupportsInt, Union + +from . import Image, __version__ +from ._deprecate import deprecate +from ._typing import SupportsRead + +try: + from . import _imagingcms as core + + _CmsProfileCompatible = Union[ + str, SupportsRead[bytes], core.CmsProfile, "ImageCmsProfile" + ] +except ImportError as ex: + # Allow error import for doc purposes, but error out when accessing + # anything in core. + from ._util import DeferredError + + core = DeferredError.new(ex) + +_DESCRIPTION = """ +pyCMS + + a Python / PIL interface to the littleCMS ICC Color Management System + Copyright (C) 2002-2003 Kevin Cazabon + kevin@cazabon.com + https://www.cazabon.com + + pyCMS home page: https://www.cazabon.com/pyCMS + littleCMS home page: https://www.littlecms.com + (littleCMS is Copyright (C) 1998-2001 Marti Maria) + + Originally released under LGPL. Graciously donated to PIL in + March 2009, for distribution under the standard PIL license + + The pyCMS.py module provides a "clean" interface between Python/PIL and + pyCMSdll, taking care of some of the more complex handling of the direct + pyCMSdll functions, as well as error-checking and making sure that all + relevant data is kept together. + + While it is possible to call pyCMSdll functions directly, it's not highly + recommended. + + Version History: + + 1.0.0 pil Oct 2013 Port to LCMS 2. + + 0.1.0 pil mod March 10, 2009 + + Renamed display profile to proof profile. The proof + profile is the profile of the device that is being + simulated, not the profile of the device which is + actually used to display/print the final simulation + (that'd be the output profile) - also see LCMSAPI.txt + input colorspace -> using 'renderingIntent' -> proof + colorspace -> using 'proofRenderingIntent' -> output + colorspace + + Added LCMS FLAGS support. + Added FLAGS["SOFTPROOFING"] as default flag for + buildProofTransform (otherwise the proof profile/intent + would be ignored). + + 0.1.0 pil March 2009 - added to PIL, as PIL.ImageCms + + 0.0.2 alpha Jan 6, 2002 + + Added try/except statements around type() checks of + potential CObjects... Python won't let you use type() + on them, and raises a TypeError (stupid, if you ask + me!) + + Added buildProofTransformFromOpenProfiles() function. + Additional fixes in DLL, see DLL code for details. + + 0.0.1 alpha first public release, Dec. 26, 2002 + + Known to-do list with current version (of Python interface, not pyCMSdll): + + none + +""" + +_VERSION = "1.0.0 pil" + + +def __getattr__(name: str) -> Any: + if name == "DESCRIPTION": + deprecate("PIL.ImageCms.DESCRIPTION", 12) + return _DESCRIPTION + elif name == "VERSION": + deprecate("PIL.ImageCms.VERSION", 12) + return _VERSION + elif name == "FLAGS": + deprecate("PIL.ImageCms.FLAGS", 12, "PIL.ImageCms.Flags") + return _FLAGS + msg = f"module '{__name__}' has no attribute '{name}'" + raise AttributeError(msg) + + +# --------------------------------------------------------------------. + + +# +# intent/direction values + + +class Intent(IntEnum): + PERCEPTUAL = 0 + RELATIVE_COLORIMETRIC = 1 + SATURATION = 2 + ABSOLUTE_COLORIMETRIC = 3 + + +class Direction(IntEnum): + INPUT = 0 + OUTPUT = 1 + PROOF = 2 + + +# +# flags + + +class Flags(IntFlag): + """Flags and documentation are taken from ``lcms2.h``.""" + + NONE = 0 + NOCACHE = 0x0040 + """Inhibit 1-pixel cache""" + NOOPTIMIZE = 0x0100 + """Inhibit optimizations""" + NULLTRANSFORM = 0x0200 + """Don't transform anyway""" + GAMUTCHECK = 0x1000 + """Out of Gamut alarm""" + SOFTPROOFING = 0x4000 + """Do softproofing""" + BLACKPOINTCOMPENSATION = 0x2000 + NOWHITEONWHITEFIXUP = 0x0004 + """Don't fix scum dot""" + HIGHRESPRECALC = 0x0400 + """Use more memory to give better accuracy""" + LOWRESPRECALC = 0x0800 + """Use less memory to minimize resources""" + # this should be 8BITS_DEVICELINK, but that is not a valid name in Python: + USE_8BITS_DEVICELINK = 0x0008 + """Create 8 bits devicelinks""" + GUESSDEVICECLASS = 0x0020 + """Guess device class (for ``transform2devicelink``)""" + KEEP_SEQUENCE = 0x0080 + """Keep profile sequence for devicelink creation""" + FORCE_CLUT = 0x0002 + """Force CLUT optimization""" + CLUT_POST_LINEARIZATION = 0x0001 + """create postlinearization tables if possible""" + CLUT_PRE_LINEARIZATION = 0x0010 + """create prelinearization tables if possible""" + NONEGATIVES = 0x8000 + """Prevent negative numbers in floating point transforms""" + COPY_ALPHA = 0x04000000 + """Alpha channels are copied on ``cmsDoTransform()``""" + NODEFAULTRESOURCEDEF = 0x01000000 + + _GRIDPOINTS_1 = 1 << 16 + _GRIDPOINTS_2 = 2 << 16 + _GRIDPOINTS_4 = 4 << 16 + _GRIDPOINTS_8 = 8 << 16 + _GRIDPOINTS_16 = 16 << 16 + _GRIDPOINTS_32 = 32 << 16 + _GRIDPOINTS_64 = 64 << 16 + _GRIDPOINTS_128 = 128 << 16 + + @staticmethod + def GRIDPOINTS(n: int) -> Flags: + """ + Fine-tune control over number of gridpoints + + :param n: :py:class:`int` in range ``0 <= n <= 255`` + """ + return Flags.NONE | ((n & 0xFF) << 16) + + +_MAX_FLAG = reduce(operator.or_, Flags) + + +_FLAGS = { + "MATRIXINPUT": 1, + "MATRIXOUTPUT": 2, + "MATRIXONLY": (1 | 2), + "NOWHITEONWHITEFIXUP": 4, # Don't hot fix scum dot + # Don't create prelinearization tables on precalculated transforms + # (internal use): + "NOPRELINEARIZATION": 16, + "GUESSDEVICECLASS": 32, # Guess device class (for transform2devicelink) + "NOTCACHE": 64, # Inhibit 1-pixel cache + "NOTPRECALC": 256, + "NULLTRANSFORM": 512, # Don't transform anyway + "HIGHRESPRECALC": 1024, # Use more memory to give better accuracy + "LOWRESPRECALC": 2048, # Use less memory to minimize resources + "WHITEBLACKCOMPENSATION": 8192, + "BLACKPOINTCOMPENSATION": 8192, + "GAMUTCHECK": 4096, # Out of Gamut alarm + "SOFTPROOFING": 16384, # Do softproofing + "PRESERVEBLACK": 32768, # Black preservation + "NODEFAULTRESOURCEDEF": 16777216, # CRD special + "GRIDPOINTS": lambda n: (n & 0xFF) << 16, # Gridpoints +} + + +# --------------------------------------------------------------------. +# Experimental PIL-level API +# --------------------------------------------------------------------. + +## +# Profile. + + +class ImageCmsProfile: + def __init__(self, profile: str | SupportsRead[bytes] | core.CmsProfile) -> None: + """ + :param profile: Either a string representing a filename, + a file like object containing a profile or a + low-level profile object + + """ + self.filename = None + self.product_name = None # profile.product_name + self.product_info = None # profile.product_info + + if isinstance(profile, str): + if sys.platform == "win32": + profile_bytes_path = profile.encode() + try: + profile_bytes_path.decode("ascii") + except UnicodeDecodeError: + with open(profile, "rb") as f: + self.profile = core.profile_frombytes(f.read()) + return + self.filename = profile + self.profile = core.profile_open(profile) + elif hasattr(profile, "read"): + self.profile = core.profile_frombytes(profile.read()) + elif isinstance(profile, core.CmsProfile): + self.profile = profile + else: + msg = "Invalid type for Profile" # type: ignore[unreachable] + raise TypeError(msg) + + def tobytes(self) -> bytes: + """ + Returns the profile in a format suitable for embedding in + saved images. + + :returns: a bytes object containing the ICC profile. + """ + + return core.profile_tobytes(self.profile) + + +class ImageCmsTransform(Image.ImagePointHandler): + """ + Transform. This can be used with the procedural API, or with the standard + :py:func:`~PIL.Image.Image.point` method. + + Will return the output profile in the ``output.info['icc_profile']``. + """ + + def __init__( + self, + input: ImageCmsProfile, + output: ImageCmsProfile, + input_mode: str, + output_mode: str, + intent: Intent = Intent.PERCEPTUAL, + proof: ImageCmsProfile | None = None, + proof_intent: Intent = Intent.ABSOLUTE_COLORIMETRIC, + flags: Flags = Flags.NONE, + ): + supported_modes = ( + "RGB", + "RGBA", + "RGBX", + "CMYK", + "I;16", + "I;16L", + "I;16B", + "YCbCr", + "LAB", + "L", + "1", + ) + for mode in (input_mode, output_mode): + if mode not in supported_modes: + deprecate( + mode, + 12, + { + "L;16": "I;16 or I;16L", + "L:16B": "I;16B", + "YCCA": "YCbCr", + "YCC": "YCbCr", + }.get(mode), + ) + if proof is None: + self.transform = core.buildTransform( + input.profile, output.profile, input_mode, output_mode, intent, flags + ) + else: + self.transform = core.buildProofTransform( + input.profile, + output.profile, + proof.profile, + input_mode, + output_mode, + intent, + proof_intent, + flags, + ) + # Note: inputMode and outputMode are for pyCMS compatibility only + self.input_mode = self.inputMode = input_mode + self.output_mode = self.outputMode = output_mode + + self.output_profile = output + + def point(self, im: Image.Image) -> Image.Image: + return self.apply(im) + + def apply(self, im: Image.Image, imOut: Image.Image | None = None) -> Image.Image: + if imOut is None: + imOut = Image.new(self.output_mode, im.size, None) + self.transform.apply(im.getim(), imOut.getim()) + imOut.info["icc_profile"] = self.output_profile.tobytes() + return imOut + + def apply_in_place(self, im: Image.Image) -> Image.Image: + if im.mode != self.output_mode: + msg = "mode mismatch" + raise ValueError(msg) # wrong output mode + self.transform.apply(im.getim(), im.getim()) + im.info["icc_profile"] = self.output_profile.tobytes() + return im + + +def get_display_profile(handle: SupportsInt | None = None) -> ImageCmsProfile | None: + """ + (experimental) Fetches the profile for the current display device. + + :returns: ``None`` if the profile is not known. + """ + + if sys.platform != "win32": + return None + + from . import ImageWin # type: ignore[unused-ignore, unreachable] + + if isinstance(handle, ImageWin.HDC): + profile = core.get_display_profile_win32(int(handle), 1) + else: + profile = core.get_display_profile_win32(int(handle or 0)) + if profile is None: + return None + return ImageCmsProfile(profile) + + +# --------------------------------------------------------------------. +# pyCMS compatible layer +# --------------------------------------------------------------------. + + +class PyCMSError(Exception): + """(pyCMS) Exception class. + This is used for all errors in the pyCMS API.""" + + pass + + +def profileToProfile( + im: Image.Image, + inputProfile: _CmsProfileCompatible, + outputProfile: _CmsProfileCompatible, + renderingIntent: Intent = Intent.PERCEPTUAL, + outputMode: str | None = None, + inPlace: bool = False, + flags: Flags = Flags.NONE, +) -> Image.Image | None: + """ + (pyCMS) Applies an ICC transformation to a given image, mapping from + ``inputProfile`` to ``outputProfile``. + + If the input or output profiles specified are not valid filenames, a + :exc:`PyCMSError` will be raised. If ``inPlace`` is ``True`` and + ``outputMode != im.mode``, a :exc:`PyCMSError` will be raised. + If an error occurs during application of the profiles, + a :exc:`PyCMSError` will be raised. + If ``outputMode`` is not a mode supported by the ``outputProfile`` (or by pyCMS), + a :exc:`PyCMSError` will be raised. + + This function applies an ICC transformation to im from ``inputProfile``'s + color space to ``outputProfile``'s color space using the specified rendering + intent to decide how to handle out-of-gamut colors. + + ``outputMode`` can be used to specify that a color mode conversion is to + be done using these profiles, but the specified profiles must be able + to handle that mode. I.e., if converting im from RGB to CMYK using + profiles, the input profile must handle RGB data, and the output + profile must handle CMYK data. + + :param im: An open :py:class:`~PIL.Image.Image` object (i.e. Image.new(...) + or Image.open(...), etc.) + :param inputProfile: String, as a valid filename path to the ICC input + profile you wish to use for this image, or a profile object + :param outputProfile: String, as a valid filename path to the ICC output + profile you wish to use for this image, or a profile object + :param renderingIntent: Integer (0-3) specifying the rendering intent you + wish to use for the transform + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param outputMode: A valid PIL mode for the output image (i.e. "RGB", + "CMYK", etc.). Note: if rendering the image "inPlace", outputMode + MUST be the same mode as the input, or omitted completely. If + omitted, the outputMode will be the same as the mode of the input + image (im.mode) + :param inPlace: Boolean. If ``True``, the original image is modified in-place, + and ``None`` is returned. If ``False`` (default), a new + :py:class:`~PIL.Image.Image` object is returned with the transform applied. + :param flags: Integer (0-...) specifying additional flags + :returns: Either None or a new :py:class:`~PIL.Image.Image` object, depending on + the value of ``inPlace`` + :exception PyCMSError: + """ + + if outputMode is None: + outputMode = im.mode + + if not isinstance(renderingIntent, int) or not (0 <= renderingIntent <= 3): + msg = "renderingIntent must be an integer between 0 and 3" + raise PyCMSError(msg) + + if not isinstance(flags, int) or not (0 <= flags <= _MAX_FLAG): + msg = f"flags must be an integer between 0 and {_MAX_FLAG}" + raise PyCMSError(msg) + + try: + if not isinstance(inputProfile, ImageCmsProfile): + inputProfile = ImageCmsProfile(inputProfile) + if not isinstance(outputProfile, ImageCmsProfile): + outputProfile = ImageCmsProfile(outputProfile) + transform = ImageCmsTransform( + inputProfile, + outputProfile, + im.mode, + outputMode, + renderingIntent, + flags=flags, + ) + if inPlace: + transform.apply_in_place(im) + imOut = None + else: + imOut = transform.apply(im) + except (OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + return imOut + + +def getOpenProfile( + profileFilename: str | SupportsRead[bytes] | core.CmsProfile, +) -> ImageCmsProfile: + """ + (pyCMS) Opens an ICC profile file. + + The PyCMSProfile object can be passed back into pyCMS for use in creating + transforms and such (as in ImageCms.buildTransformFromOpenProfiles()). + + If ``profileFilename`` is not a valid filename for an ICC profile, + a :exc:`PyCMSError` will be raised. + + :param profileFilename: String, as a valid filename path to the ICC profile + you wish to open, or a file-like object. + :returns: A CmsProfile class object. + :exception PyCMSError: + """ + + try: + return ImageCmsProfile(profileFilename) + except (OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def buildTransform( + inputProfile: _CmsProfileCompatible, + outputProfile: _CmsProfileCompatible, + inMode: str, + outMode: str, + renderingIntent: Intent = Intent.PERCEPTUAL, + flags: Flags = Flags.NONE, +) -> ImageCmsTransform: + """ + (pyCMS) Builds an ICC transform mapping from the ``inputProfile`` to the + ``outputProfile``. Use applyTransform to apply the transform to a given + image. + + If the input or output profiles specified are not valid filenames, a + :exc:`PyCMSError` will be raised. If an error occurs during creation + of the transform, a :exc:`PyCMSError` will be raised. + + If ``inMode`` or ``outMode`` are not a mode supported by the ``outputProfile`` + (or by pyCMS), a :exc:`PyCMSError` will be raised. + + This function builds and returns an ICC transform from the ``inputProfile`` + to the ``outputProfile`` using the ``renderingIntent`` to determine what to do + with out-of-gamut colors. It will ONLY work for converting images that + are in ``inMode`` to images that are in ``outMode`` color format (PIL mode, + i.e. "RGB", "RGBA", "CMYK", etc.). + + Building the transform is a fair part of the overhead in + ImageCms.profileToProfile(), so if you're planning on converting multiple + images using the same input/output settings, this can save you time. + Once you have a transform object, it can be used with + ImageCms.applyProfile() to convert images without the need to re-compute + the lookup table for the transform. + + The reason pyCMS returns a class object rather than a handle directly + to the transform is that it needs to keep track of the PIL input/output + modes that the transform is meant for. These attributes are stored in + the ``inMode`` and ``outMode`` attributes of the object (which can be + manually overridden if you really want to, but I don't know of any + time that would be of use, or would even work). + + :param inputProfile: String, as a valid filename path to the ICC input + profile you wish to use for this transform, or a profile object + :param outputProfile: String, as a valid filename path to the ICC output + profile you wish to use for this transform, or a profile object + :param inMode: String, as a valid PIL mode that the appropriate profile + also supports (i.e. "RGB", "RGBA", "CMYK", etc.) + :param outMode: String, as a valid PIL mode that the appropriate profile + also supports (i.e. "RGB", "RGBA", "CMYK", etc.) + :param renderingIntent: Integer (0-3) specifying the rendering intent you + wish to use for the transform + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param flags: Integer (0-...) specifying additional flags + :returns: A CmsTransform class object. + :exception PyCMSError: + """ + + if not isinstance(renderingIntent, int) or not (0 <= renderingIntent <= 3): + msg = "renderingIntent must be an integer between 0 and 3" + raise PyCMSError(msg) + + if not isinstance(flags, int) or not (0 <= flags <= _MAX_FLAG): + msg = f"flags must be an integer between 0 and {_MAX_FLAG}" + raise PyCMSError(msg) + + try: + if not isinstance(inputProfile, ImageCmsProfile): + inputProfile = ImageCmsProfile(inputProfile) + if not isinstance(outputProfile, ImageCmsProfile): + outputProfile = ImageCmsProfile(outputProfile) + return ImageCmsTransform( + inputProfile, outputProfile, inMode, outMode, renderingIntent, flags=flags + ) + except (OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def buildProofTransform( + inputProfile: _CmsProfileCompatible, + outputProfile: _CmsProfileCompatible, + proofProfile: _CmsProfileCompatible, + inMode: str, + outMode: str, + renderingIntent: Intent = Intent.PERCEPTUAL, + proofRenderingIntent: Intent = Intent.ABSOLUTE_COLORIMETRIC, + flags: Flags = Flags.SOFTPROOFING, +) -> ImageCmsTransform: + """ + (pyCMS) Builds an ICC transform mapping from the ``inputProfile`` to the + ``outputProfile``, but tries to simulate the result that would be + obtained on the ``proofProfile`` device. + + If the input, output, or proof profiles specified are not valid + filenames, a :exc:`PyCMSError` will be raised. + + If an error occurs during creation of the transform, + a :exc:`PyCMSError` will be raised. + + If ``inMode`` or ``outMode`` are not a mode supported by the ``outputProfile`` + (or by pyCMS), a :exc:`PyCMSError` will be raised. + + This function builds and returns an ICC transform from the ``inputProfile`` + to the ``outputProfile``, but tries to simulate the result that would be + obtained on the ``proofProfile`` device using ``renderingIntent`` and + ``proofRenderingIntent`` to determine what to do with out-of-gamut + colors. This is known as "soft-proofing". It will ONLY work for + converting images that are in ``inMode`` to images that are in outMode + color format (PIL mode, i.e. "RGB", "RGBA", "CMYK", etc.). + + Usage of the resulting transform object is exactly the same as with + ImageCms.buildTransform(). + + Proof profiling is generally used when using an output device to get a + good idea of what the final printed/displayed image would look like on + the ``proofProfile`` device when it's quicker and easier to use the + output device for judging color. Generally, this means that the + output device is a monitor, or a dye-sub printer (etc.), and the simulated + device is something more expensive, complicated, or time consuming + (making it difficult to make a real print for color judgement purposes). + + Soft-proofing basically functions by adjusting the colors on the + output device to match the colors of the device being simulated. However, + when the simulated device has a much wider gamut than the output + device, you may obtain marginal results. + + :param inputProfile: String, as a valid filename path to the ICC input + profile you wish to use for this transform, or a profile object + :param outputProfile: String, as a valid filename path to the ICC output + (monitor, usually) profile you wish to use for this transform, or a + profile object + :param proofProfile: String, as a valid filename path to the ICC proof + profile you wish to use for this transform, or a profile object + :param inMode: String, as a valid PIL mode that the appropriate profile + also supports (i.e. "RGB", "RGBA", "CMYK", etc.) + :param outMode: String, as a valid PIL mode that the appropriate profile + also supports (i.e. "RGB", "RGBA", "CMYK", etc.) + :param renderingIntent: Integer (0-3) specifying the rendering intent you + wish to use for the input->proof (simulated) transform + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param proofRenderingIntent: Integer (0-3) specifying the rendering intent + you wish to use for proof->output transform + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param flags: Integer (0-...) specifying additional flags + :returns: A CmsTransform class object. + :exception PyCMSError: + """ + + if not isinstance(renderingIntent, int) or not (0 <= renderingIntent <= 3): + msg = "renderingIntent must be an integer between 0 and 3" + raise PyCMSError(msg) + + if not isinstance(flags, int) or not (0 <= flags <= _MAX_FLAG): + msg = f"flags must be an integer between 0 and {_MAX_FLAG}" + raise PyCMSError(msg) + + try: + if not isinstance(inputProfile, ImageCmsProfile): + inputProfile = ImageCmsProfile(inputProfile) + if not isinstance(outputProfile, ImageCmsProfile): + outputProfile = ImageCmsProfile(outputProfile) + if not isinstance(proofProfile, ImageCmsProfile): + proofProfile = ImageCmsProfile(proofProfile) + return ImageCmsTransform( + inputProfile, + outputProfile, + inMode, + outMode, + renderingIntent, + proofProfile, + proofRenderingIntent, + flags, + ) + except (OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +buildTransformFromOpenProfiles = buildTransform +buildProofTransformFromOpenProfiles = buildProofTransform + + +def applyTransform( + im: Image.Image, transform: ImageCmsTransform, inPlace: bool = False +) -> Image.Image | None: + """ + (pyCMS) Applies a transform to a given image. + + If ``im.mode != transform.input_mode``, a :exc:`PyCMSError` is raised. + + If ``inPlace`` is ``True`` and ``transform.input_mode != transform.output_mode``, a + :exc:`PyCMSError` is raised. + + If ``im.mode``, ``transform.input_mode`` or ``transform.output_mode`` is not + supported by pyCMSdll or the profiles you used for the transform, a + :exc:`PyCMSError` is raised. + + If an error occurs while the transform is being applied, + a :exc:`PyCMSError` is raised. + + This function applies a pre-calculated transform (from + ImageCms.buildTransform() or ImageCms.buildTransformFromOpenProfiles()) + to an image. The transform can be used for multiple images, saving + considerable calculation time if doing the same conversion multiple times. + + If you want to modify im in-place instead of receiving a new image as + the return value, set ``inPlace`` to ``True``. This can only be done if + ``transform.input_mode`` and ``transform.output_mode`` are the same, because we + can't change the mode in-place (the buffer sizes for some modes are + different). The default behavior is to return a new :py:class:`~PIL.Image.Image` + object of the same dimensions in mode ``transform.output_mode``. + + :param im: An :py:class:`~PIL.Image.Image` object, and ``im.mode`` must be the same + as the ``input_mode`` supported by the transform. + :param transform: A valid CmsTransform class object + :param inPlace: Bool. If ``True``, ``im`` is modified in place and ``None`` is + returned, if ``False``, a new :py:class:`~PIL.Image.Image` object with the + transform applied is returned (and ``im`` is not changed). The default is + ``False``. + :returns: Either ``None``, or a new :py:class:`~PIL.Image.Image` object, + depending on the value of ``inPlace``. The profile will be returned in + the image's ``info['icc_profile']``. + :exception PyCMSError: + """ + + try: + if inPlace: + transform.apply_in_place(im) + imOut = None + else: + imOut = transform.apply(im) + except (TypeError, ValueError) as v: + raise PyCMSError(v) from v + + return imOut + + +def createProfile( + colorSpace: Literal["LAB", "XYZ", "sRGB"], colorTemp: SupportsFloat = 0 +) -> core.CmsProfile: + """ + (pyCMS) Creates a profile. + + If colorSpace not in ``["LAB", "XYZ", "sRGB"]``, + a :exc:`PyCMSError` is raised. + + If using LAB and ``colorTemp`` is not a positive integer, + a :exc:`PyCMSError` is raised. + + If an error occurs while creating the profile, + a :exc:`PyCMSError` is raised. + + Use this function to create common profiles on-the-fly instead of + having to supply a profile on disk and knowing the path to it. It + returns a normal CmsProfile object that can be passed to + ImageCms.buildTransformFromOpenProfiles() to create a transform to apply + to images. + + :param colorSpace: String, the color space of the profile you wish to + create. + Currently only "LAB", "XYZ", and "sRGB" are supported. + :param colorTemp: Positive number for the white point for the profile, in + degrees Kelvin (i.e. 5000, 6500, 9600, etc.). The default is for D50 + illuminant if omitted (5000k). colorTemp is ONLY applied to LAB + profiles, and is ignored for XYZ and sRGB. + :returns: A CmsProfile class object + :exception PyCMSError: + """ + + if colorSpace not in ["LAB", "XYZ", "sRGB"]: + msg = ( + f"Color space not supported for on-the-fly profile creation ({colorSpace})" + ) + raise PyCMSError(msg) + + if colorSpace == "LAB": + try: + colorTemp = float(colorTemp) + except (TypeError, ValueError) as e: + msg = f'Color temperature must be numeric, "{colorTemp}" not valid' + raise PyCMSError(msg) from e + + try: + return core.createProfile(colorSpace, colorTemp) + except (TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileName(profile: _CmsProfileCompatible) -> str: + """ + + (pyCMS) Gets the internal product name for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, + a :exc:`PyCMSError` is raised If an error occurs while trying + to obtain the name tag, a :exc:`PyCMSError` is raised. + + Use this function to obtain the INTERNAL name of the profile (stored + in an ICC tag in the profile itself), usually the one used when the + profile was originally created. Sometimes this tag also contains + additional information supplied by the creator. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal name of the profile as stored + in an ICC tag. + :exception PyCMSError: + """ + + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + # do it in python, not c. + # // name was "%s - %s" (model, manufacturer) || Description , + # // but if the Model and Manufacturer were the same or the model + # // was long, Just the model, in 1.x + model = profile.profile.model + manufacturer = profile.profile.manufacturer + + if not (model or manufacturer): + return (profile.profile.profile_description or "") + "\n" + if not manufacturer or (model and len(model) > 30): + return f"{model}\n" + return f"{model} - {manufacturer}\n" + + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileInfo(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the internal product information for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, + a :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the info tag, + a :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + info tag. This often contains details about the profile, and how it + was created, as supplied by the creator. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in + an ICC tag. + :exception PyCMSError: + """ + + try: + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + # add an extra newline to preserve pyCMS compatibility + # Python, not C. the white point bits weren't working well, + # so skipping. + # info was description \r\n\r\n copyright \r\n\r\n K007 tag \r\n\r\n whitepoint + description = profile.profile.profile_description + cpright = profile.profile.copyright + elements = [element for element in (description, cpright) if element] + return "\r\n\r\n".join(elements) + "\r\n\r\n" + + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileCopyright(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the copyright for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the copyright tag, + a :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + copyright tag. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in + an ICC tag. + :exception PyCMSError: + """ + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return (profile.profile.copyright or "") + "\n" + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileManufacturer(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the manufacturer for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the manufacturer tag, a + :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + manufacturer tag. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in + an ICC tag. + :exception PyCMSError: + """ + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return (profile.profile.manufacturer or "") + "\n" + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileModel(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the model for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the model tag, + a :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + model tag. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in + an ICC tag. + :exception PyCMSError: + """ + + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return (profile.profile.model or "") + "\n" + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getProfileDescription(profile: _CmsProfileCompatible) -> str: + """ + (pyCMS) Gets the description for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the description tag, + a :exc:`PyCMSError` is raised. + + Use this function to obtain the information stored in the profile's + description tag. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: A string containing the internal profile information stored in an + ICC tag. + :exception PyCMSError: + """ + + try: + # add an extra newline to preserve pyCMS compatibility + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return (profile.profile.profile_description or "") + "\n" + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def getDefaultIntent(profile: _CmsProfileCompatible) -> int: + """ + (pyCMS) Gets the default intent name for the given profile. + + If ``profile`` isn't a valid CmsProfile object or filename to a profile, a + :exc:`PyCMSError` is raised. + + If an error occurs while trying to obtain the default intent, a + :exc:`PyCMSError` is raised. + + Use this function to determine the default (and usually best optimized) + rendering intent for this profile. Most profiles support multiple + rendering intents, but are intended mostly for one type of conversion. + If you wish to use a different intent than returned, use + ImageCms.isIntentSupported() to verify it will work first. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :returns: Integer 0-3 specifying the default rendering intent for this + profile. + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :exception PyCMSError: + """ + + try: + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + return profile.profile.rendering_intent + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def isIntentSupported( + profile: _CmsProfileCompatible, intent: Intent, direction: Direction +) -> Literal[-1, 1]: + """ + (pyCMS) Checks if a given intent is supported. + + Use this function to verify that you can use your desired + ``intent`` with ``profile``, and that ``profile`` can be used for the + input/output/proof profile as you desire. + + Some profiles are created specifically for one "direction", can cannot + be used for others. Some profiles can only be used for certain + rendering intents, so it's best to either verify this before trying + to create a transform with them (using this function), or catch the + potential :exc:`PyCMSError` that will occur if they don't + support the modes you select. + + :param profile: EITHER a valid CmsProfile object, OR a string of the + filename of an ICC profile. + :param intent: Integer (0-3) specifying the rendering intent you wish to + use with this profile + + ImageCms.Intent.PERCEPTUAL = 0 (DEFAULT) + ImageCms.Intent.RELATIVE_COLORIMETRIC = 1 + ImageCms.Intent.SATURATION = 2 + ImageCms.Intent.ABSOLUTE_COLORIMETRIC = 3 + + see the pyCMS documentation for details on rendering intents and what + they do. + :param direction: Integer specifying if the profile is to be used for + input, output, or proof + + INPUT = 0 (or use ImageCms.Direction.INPUT) + OUTPUT = 1 (or use ImageCms.Direction.OUTPUT) + PROOF = 2 (or use ImageCms.Direction.PROOF) + + :returns: 1 if the intent/direction are supported, -1 if they are not. + :exception PyCMSError: + """ + + try: + if not isinstance(profile, ImageCmsProfile): + profile = ImageCmsProfile(profile) + # FIXME: I get different results for the same data w. different + # compilers. Bug in LittleCMS or in the binding? + if profile.profile.is_intent_supported(intent, direction): + return 1 + else: + return -1 + except (AttributeError, OSError, TypeError, ValueError) as v: + raise PyCMSError(v) from v + + +def versions() -> tuple[str, str | None, str, str]: + """ + (pyCMS) Fetches versions. + """ + + deprecate( + "PIL.ImageCms.versions()", + 12, + '(PIL.features.version("littlecms2"), sys.version, PIL.__version__)', + ) + return _VERSION, core.littlecms_version, sys.version.split()[0], __version__ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageColor.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageColor.py new file mode 100644 index 0000000000000000000000000000000000000000..9a15a8eb7597998f1bc9a01e8eae3588c087838b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageColor.py @@ -0,0 +1,320 @@ +# +# The Python Imaging Library +# $Id$ +# +# map CSS3-style colour description strings to RGB +# +# History: +# 2002-10-24 fl Added support for CSS-style color strings +# 2002-12-15 fl Added RGBA support +# 2004-03-27 fl Fixed remaining int() problems for Python 1.5.2 +# 2004-07-19 fl Fixed gray/grey spelling issues +# 2009-03-05 fl Fixed rounding error in grayscale calculation +# +# Copyright (c) 2002-2004 by Secret Labs AB +# Copyright (c) 2002-2004 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re +from functools import lru_cache + +from . import Image + + +@lru_cache +def getrgb(color: str) -> tuple[int, int, int] | tuple[int, int, int, int]: + """ + Convert a color string to an RGB or RGBA tuple. If the string cannot be + parsed, this function raises a :py:exc:`ValueError` exception. + + .. versionadded:: 1.1.4 + + :param color: A color string + :return: ``(red, green, blue[, alpha])`` + """ + if len(color) > 100: + msg = "color specifier is too long" + raise ValueError(msg) + color = color.lower() + + rgb = colormap.get(color, None) + if rgb: + if isinstance(rgb, tuple): + return rgb + rgb_tuple = getrgb(rgb) + assert len(rgb_tuple) == 3 + colormap[color] = rgb_tuple + return rgb_tuple + + # check for known string formats + if re.match("#[a-f0-9]{3}$", color): + return int(color[1] * 2, 16), int(color[2] * 2, 16), int(color[3] * 2, 16) + + if re.match("#[a-f0-9]{4}$", color): + return ( + int(color[1] * 2, 16), + int(color[2] * 2, 16), + int(color[3] * 2, 16), + int(color[4] * 2, 16), + ) + + if re.match("#[a-f0-9]{6}$", color): + return int(color[1:3], 16), int(color[3:5], 16), int(color[5:7], 16) + + if re.match("#[a-f0-9]{8}$", color): + return ( + int(color[1:3], 16), + int(color[3:5], 16), + int(color[5:7], 16), + int(color[7:9], 16), + ) + + m = re.match(r"rgb\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$", color) + if m: + return int(m.group(1)), int(m.group(2)), int(m.group(3)) + + m = re.match(r"rgb\(\s*(\d+)%\s*,\s*(\d+)%\s*,\s*(\d+)%\s*\)$", color) + if m: + return ( + int((int(m.group(1)) * 255) / 100.0 + 0.5), + int((int(m.group(2)) * 255) / 100.0 + 0.5), + int((int(m.group(3)) * 255) / 100.0 + 0.5), + ) + + m = re.match( + r"hsl\(\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)%\s*,\s*(\d+\.?\d*)%\s*\)$", color + ) + if m: + from colorsys import hls_to_rgb + + rgb_floats = hls_to_rgb( + float(m.group(1)) / 360.0, + float(m.group(3)) / 100.0, + float(m.group(2)) / 100.0, + ) + return ( + int(rgb_floats[0] * 255 + 0.5), + int(rgb_floats[1] * 255 + 0.5), + int(rgb_floats[2] * 255 + 0.5), + ) + + m = re.match( + r"hs[bv]\(\s*(\d+\.?\d*)\s*,\s*(\d+\.?\d*)%\s*,\s*(\d+\.?\d*)%\s*\)$", color + ) + if m: + from colorsys import hsv_to_rgb + + rgb_floats = hsv_to_rgb( + float(m.group(1)) / 360.0, + float(m.group(2)) / 100.0, + float(m.group(3)) / 100.0, + ) + return ( + int(rgb_floats[0] * 255 + 0.5), + int(rgb_floats[1] * 255 + 0.5), + int(rgb_floats[2] * 255 + 0.5), + ) + + m = re.match(r"rgba\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)$", color) + if m: + return int(m.group(1)), int(m.group(2)), int(m.group(3)), int(m.group(4)) + msg = f"unknown color specifier: {repr(color)}" + raise ValueError(msg) + + +@lru_cache +def getcolor(color: str, mode: str) -> int | tuple[int, ...]: + """ + Same as :py:func:`~PIL.ImageColor.getrgb` for most modes. However, if + ``mode`` is HSV, converts the RGB value to a HSV value, or if ``mode`` is + not color or a palette image, converts the RGB value to a grayscale value. + If the string cannot be parsed, this function raises a :py:exc:`ValueError` + exception. + + .. versionadded:: 1.1.4 + + :param color: A color string + :param mode: Convert result to this mode + :return: ``graylevel, (graylevel, alpha) or (red, green, blue[, alpha])`` + """ + # same as getrgb, but converts the result to the given mode + rgb, alpha = getrgb(color), 255 + if len(rgb) == 4: + alpha = rgb[3] + rgb = rgb[:3] + + if mode == "HSV": + from colorsys import rgb_to_hsv + + r, g, b = rgb + h, s, v = rgb_to_hsv(r / 255, g / 255, b / 255) + return int(h * 255), int(s * 255), int(v * 255) + elif Image.getmodebase(mode) == "L": + r, g, b = rgb + # ITU-R Recommendation 601-2 for nonlinear RGB + # scaled to 24 bits to match the convert's implementation. + graylevel = (r * 19595 + g * 38470 + b * 7471 + 0x8000) >> 16 + if mode[-1] == "A": + return graylevel, alpha + return graylevel + elif mode[-1] == "A": + return rgb + (alpha,) + return rgb + + +colormap: dict[str, str | tuple[int, int, int]] = { + # X11 colour table from https://drafts.csswg.org/css-color-4/, with + # gray/grey spelling issues fixed. This is a superset of HTML 4.0 + # colour names used in CSS 1. + "aliceblue": "#f0f8ff", + "antiquewhite": "#faebd7", + "aqua": "#00ffff", + "aquamarine": "#7fffd4", + "azure": "#f0ffff", + "beige": "#f5f5dc", + "bisque": "#ffe4c4", + "black": "#000000", + "blanchedalmond": "#ffebcd", + "blue": "#0000ff", + "blueviolet": "#8a2be2", + "brown": "#a52a2a", + "burlywood": "#deb887", + "cadetblue": "#5f9ea0", + "chartreuse": "#7fff00", + "chocolate": "#d2691e", + "coral": "#ff7f50", + "cornflowerblue": "#6495ed", + "cornsilk": "#fff8dc", + "crimson": "#dc143c", + "cyan": "#00ffff", + "darkblue": "#00008b", + "darkcyan": "#008b8b", + "darkgoldenrod": "#b8860b", + "darkgray": "#a9a9a9", + "darkgrey": "#a9a9a9", + "darkgreen": "#006400", + "darkkhaki": "#bdb76b", + "darkmagenta": "#8b008b", + "darkolivegreen": "#556b2f", + "darkorange": "#ff8c00", + "darkorchid": "#9932cc", + "darkred": "#8b0000", + "darksalmon": "#e9967a", + "darkseagreen": "#8fbc8f", + "darkslateblue": "#483d8b", + "darkslategray": "#2f4f4f", + "darkslategrey": "#2f4f4f", + "darkturquoise": "#00ced1", + "darkviolet": "#9400d3", + "deeppink": "#ff1493", + "deepskyblue": "#00bfff", + "dimgray": "#696969", + "dimgrey": "#696969", + "dodgerblue": "#1e90ff", + "firebrick": "#b22222", + "floralwhite": "#fffaf0", + "forestgreen": "#228b22", + "fuchsia": "#ff00ff", + "gainsboro": "#dcdcdc", + "ghostwhite": "#f8f8ff", + "gold": "#ffd700", + "goldenrod": "#daa520", + "gray": "#808080", + "grey": "#808080", + "green": "#008000", + "greenyellow": "#adff2f", + "honeydew": "#f0fff0", + "hotpink": "#ff69b4", + "indianred": "#cd5c5c", + "indigo": "#4b0082", + "ivory": "#fffff0", + "khaki": "#f0e68c", + "lavender": "#e6e6fa", + "lavenderblush": "#fff0f5", + "lawngreen": "#7cfc00", + "lemonchiffon": "#fffacd", + "lightblue": "#add8e6", + "lightcoral": "#f08080", + "lightcyan": "#e0ffff", + "lightgoldenrodyellow": "#fafad2", + "lightgreen": "#90ee90", + "lightgray": "#d3d3d3", + "lightgrey": "#d3d3d3", + "lightpink": "#ffb6c1", + "lightsalmon": "#ffa07a", + "lightseagreen": "#20b2aa", + "lightskyblue": "#87cefa", + "lightslategray": "#778899", + "lightslategrey": "#778899", + "lightsteelblue": "#b0c4de", + "lightyellow": "#ffffe0", + "lime": "#00ff00", + "limegreen": "#32cd32", + "linen": "#faf0e6", + "magenta": "#ff00ff", + "maroon": "#800000", + "mediumaquamarine": "#66cdaa", + "mediumblue": "#0000cd", + "mediumorchid": "#ba55d3", + "mediumpurple": "#9370db", + "mediumseagreen": "#3cb371", + "mediumslateblue": "#7b68ee", + "mediumspringgreen": "#00fa9a", + "mediumturquoise": "#48d1cc", + "mediumvioletred": "#c71585", + "midnightblue": "#191970", + "mintcream": "#f5fffa", + "mistyrose": "#ffe4e1", + "moccasin": "#ffe4b5", + "navajowhite": "#ffdead", + "navy": "#000080", + "oldlace": "#fdf5e6", + "olive": "#808000", + "olivedrab": "#6b8e23", + "orange": "#ffa500", + "orangered": "#ff4500", + "orchid": "#da70d6", + "palegoldenrod": "#eee8aa", + "palegreen": "#98fb98", + "paleturquoise": "#afeeee", + "palevioletred": "#db7093", + "papayawhip": "#ffefd5", + "peachpuff": "#ffdab9", + "peru": "#cd853f", + "pink": "#ffc0cb", + "plum": "#dda0dd", + "powderblue": "#b0e0e6", + "purple": "#800080", + "rebeccapurple": "#663399", + "red": "#ff0000", + "rosybrown": "#bc8f8f", + "royalblue": "#4169e1", + "saddlebrown": "#8b4513", + "salmon": "#fa8072", + "sandybrown": "#f4a460", + "seagreen": "#2e8b57", + "seashell": "#fff5ee", + "sienna": "#a0522d", + "silver": "#c0c0c0", + "skyblue": "#87ceeb", + "slateblue": "#6a5acd", + "slategray": "#708090", + "slategrey": "#708090", + "snow": "#fffafa", + "springgreen": "#00ff7f", + "steelblue": "#4682b4", + "tan": "#d2b48c", + "teal": "#008080", + "thistle": "#d8bfd8", + "tomato": "#ff6347", + "turquoise": "#40e0d0", + "violet": "#ee82ee", + "wheat": "#f5deb3", + "white": "#ffffff", + "whitesmoke": "#f5f5f5", + "yellow": "#ffff00", + "yellowgreen": "#9acd32", +} diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageDraw.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageDraw.py new file mode 100644 index 0000000000000000000000000000000000000000..6cf1ee62659f07b21f257a37565a6f81022d9910 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageDraw.py @@ -0,0 +1,1232 @@ +# +# The Python Imaging Library +# $Id$ +# +# drawing interface operations +# +# History: +# 1996-04-13 fl Created (experimental) +# 1996-08-07 fl Filled polygons, ellipses. +# 1996-08-13 fl Added text support +# 1998-06-28 fl Handle I and F images +# 1998-12-29 fl Added arc; use arc primitive to draw ellipses +# 1999-01-10 fl Added shape stuff (experimental) +# 1999-02-06 fl Added bitmap support +# 1999-02-11 fl Changed all primitives to take options +# 1999-02-20 fl Fixed backwards compatibility +# 2000-10-12 fl Copy on write, when necessary +# 2001-02-18 fl Use default ink for bitmap/text also in fill mode +# 2002-10-24 fl Added support for CSS-style color strings +# 2002-12-10 fl Added experimental support for RGBA-on-RGB drawing +# 2002-12-11 fl Refactored low-level drawing API (work in progress) +# 2004-08-26 fl Made Draw() a factory function, added getdraw() support +# 2004-09-04 fl Added width support to line primitive +# 2004-09-10 fl Added font mode handling +# 2006-06-19 fl Added font bearing support (getmask2) +# +# Copyright (c) 1997-2006 by Secret Labs AB +# Copyright (c) 1996-2006 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import math +import struct +from collections.abc import Sequence +from types import ModuleType +from typing import Any, AnyStr, Callable, Union, cast + +from . import Image, ImageColor +from ._deprecate import deprecate +from ._typing import Coords + +# experimental access to the outline API +Outline: Callable[[], Image.core._Outline] = Image.core.outline + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import ImageDraw2, ImageFont + +_Ink = Union[float, tuple[int, ...], str] + +""" +A simple 2D drawing interface for PIL images. +

+Application code should use the Draw factory, instead of +directly. +""" + + +class ImageDraw: + font: ( + ImageFont.ImageFont | ImageFont.FreeTypeFont | ImageFont.TransposedFont | None + ) = None + + def __init__(self, im: Image.Image, mode: str | None = None) -> None: + """ + Create a drawing instance. + + :param im: The image to draw in. + :param mode: Optional mode to use for color values. For RGB + images, this argument can be RGB or RGBA (to blend the + drawing into the image). For all other modes, this argument + must be the same as the image mode. If omitted, the mode + defaults to the mode of the image. + """ + im.load() + if im.readonly: + im._copy() # make it writeable + blend = 0 + if mode is None: + mode = im.mode + if mode != im.mode: + if mode == "RGBA" and im.mode == "RGB": + blend = 1 + else: + msg = "mode mismatch" + raise ValueError(msg) + if mode == "P": + self.palette = im.palette + else: + self.palette = None + self._image = im + self.im = im.im + self.draw = Image.core.draw(self.im, blend) + self.mode = mode + if mode in ("I", "F"): + self.ink = self.draw.draw_ink(1) + else: + self.ink = self.draw.draw_ink(-1) + if mode in ("1", "P", "I", "F"): + # FIXME: fix Fill2 to properly support matte for I+F images + self.fontmode = "1" + else: + self.fontmode = "L" # aliasing is okay for other modes + self.fill = False + + def getfont( + self, + ) -> ImageFont.ImageFont | ImageFont.FreeTypeFont | ImageFont.TransposedFont: + """ + Get the current default font. + + To set the default font for this ImageDraw instance:: + + from PIL import ImageDraw, ImageFont + draw.font = ImageFont.truetype("Tests/fonts/FreeMono.ttf") + + To set the default font for all future ImageDraw instances:: + + from PIL import ImageDraw, ImageFont + ImageDraw.ImageDraw.font = ImageFont.truetype("Tests/fonts/FreeMono.ttf") + + If the current default font is ``None``, + it is initialized with ``ImageFont.load_default()``. + + :returns: An image font.""" + if not self.font: + # FIXME: should add a font repository + from . import ImageFont + + self.font = ImageFont.load_default() + return self.font + + def _getfont( + self, font_size: float | None + ) -> ImageFont.ImageFont | ImageFont.FreeTypeFont | ImageFont.TransposedFont: + if font_size is not None: + from . import ImageFont + + return ImageFont.load_default(font_size) + else: + return self.getfont() + + def _getink( + self, ink: _Ink | None, fill: _Ink | None = None + ) -> tuple[int | None, int | None]: + result_ink = None + result_fill = None + if ink is None and fill is None: + if self.fill: + result_fill = self.ink + else: + result_ink = self.ink + else: + if ink is not None: + if isinstance(ink, str): + ink = ImageColor.getcolor(ink, self.mode) + if self.palette and isinstance(ink, tuple): + ink = self.palette.getcolor(ink, self._image) + result_ink = self.draw.draw_ink(ink) + if fill is not None: + if isinstance(fill, str): + fill = ImageColor.getcolor(fill, self.mode) + if self.palette and isinstance(fill, tuple): + fill = self.palette.getcolor(fill, self._image) + result_fill = self.draw.draw_ink(fill) + return result_ink, result_fill + + def arc( + self, + xy: Coords, + start: float, + end: float, + fill: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw an arc.""" + ink, fill = self._getink(fill) + if ink is not None: + self.draw.draw_arc(xy, start, end, ink, width) + + def bitmap( + self, xy: Sequence[int], bitmap: Image.Image, fill: _Ink | None = None + ) -> None: + """Draw a bitmap.""" + bitmap.load() + ink, fill = self._getink(fill) + if ink is None: + ink = fill + if ink is not None: + self.draw.draw_bitmap(xy, bitmap.im, ink) + + def chord( + self, + xy: Coords, + start: float, + end: float, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a chord.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_chord(xy, start, end, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + self.draw.draw_chord(xy, start, end, ink, 0, width) + + def ellipse( + self, + xy: Coords, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw an ellipse.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_ellipse(xy, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + self.draw.draw_ellipse(xy, ink, 0, width) + + def circle( + self, + xy: Sequence[float], + radius: float, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a circle given center coordinates and a radius.""" + ellipse_xy = (xy[0] - radius, xy[1] - radius, xy[0] + radius, xy[1] + radius) + self.ellipse(ellipse_xy, fill, outline, width) + + def line( + self, + xy: Coords, + fill: _Ink | None = None, + width: int = 0, + joint: str | None = None, + ) -> None: + """Draw a line, or a connected sequence of line segments.""" + ink = self._getink(fill)[0] + if ink is not None: + self.draw.draw_lines(xy, ink, width) + if joint == "curve" and width > 4: + points: Sequence[Sequence[float]] + if isinstance(xy[0], (list, tuple)): + points = cast(Sequence[Sequence[float]], xy) + else: + points = [ + cast(Sequence[float], tuple(xy[i : i + 2])) + for i in range(0, len(xy), 2) + ] + for i in range(1, len(points) - 1): + point = points[i] + angles = [ + math.degrees(math.atan2(end[0] - start[0], start[1] - end[1])) + % 360 + for start, end in ( + (points[i - 1], point), + (point, points[i + 1]), + ) + ] + if angles[0] == angles[1]: + # This is a straight line, so no joint is required + continue + + def coord_at_angle( + coord: Sequence[float], angle: float + ) -> tuple[float, ...]: + x, y = coord + angle -= 90 + distance = width / 2 - 1 + return tuple( + p + (math.floor(p_d) if p_d > 0 else math.ceil(p_d)) + for p, p_d in ( + (x, distance * math.cos(math.radians(angle))), + (y, distance * math.sin(math.radians(angle))), + ) + ) + + flipped = ( + angles[1] > angles[0] and angles[1] - 180 > angles[0] + ) or (angles[1] < angles[0] and angles[1] + 180 > angles[0]) + coords = [ + (point[0] - width / 2 + 1, point[1] - width / 2 + 1), + (point[0] + width / 2 - 1, point[1] + width / 2 - 1), + ] + if flipped: + start, end = (angles[1] + 90, angles[0] + 90) + else: + start, end = (angles[0] - 90, angles[1] - 90) + self.pieslice(coords, start - 90, end - 90, fill) + + if width > 8: + # Cover potential gaps between the line and the joint + if flipped: + gap_coords = [ + coord_at_angle(point, angles[0] + 90), + point, + coord_at_angle(point, angles[1] + 90), + ] + else: + gap_coords = [ + coord_at_angle(point, angles[0] - 90), + point, + coord_at_angle(point, angles[1] - 90), + ] + self.line(gap_coords, fill, width=3) + + def shape( + self, + shape: Image.core._Outline, + fill: _Ink | None = None, + outline: _Ink | None = None, + ) -> None: + """(Experimental) Draw a shape.""" + shape.close() + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_outline(shape, fill_ink, 1) + if ink is not None and ink != fill_ink: + self.draw.draw_outline(shape, ink, 0) + + def pieslice( + self, + xy: Coords, + start: float, + end: float, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a pieslice.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_pieslice(xy, start, end, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + self.draw.draw_pieslice(xy, start, end, ink, 0, width) + + def point(self, xy: Coords, fill: _Ink | None = None) -> None: + """Draw one or more individual pixels.""" + ink, fill = self._getink(fill) + if ink is not None: + self.draw.draw_points(xy, ink) + + def polygon( + self, + xy: Coords, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a polygon.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_polygon(xy, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + if width == 1: + self.draw.draw_polygon(xy, ink, 0, width) + elif self.im is not None: + # To avoid expanding the polygon outwards, + # use the fill as a mask + mask = Image.new("1", self.im.size) + mask_ink = self._getink(1)[0] + draw = Draw(mask) + draw.draw.draw_polygon(xy, mask_ink, 1) + + self.draw.draw_polygon(xy, ink, 0, width * 2 - 1, mask.im) + + def regular_polygon( + self, + bounding_circle: Sequence[Sequence[float] | float], + n_sides: int, + rotation: float = 0, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a regular polygon.""" + xy = _compute_regular_polygon_vertices(bounding_circle, n_sides, rotation) + self.polygon(xy, fill, outline, width) + + def rectangle( + self, + xy: Coords, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + ) -> None: + """Draw a rectangle.""" + ink, fill_ink = self._getink(outline, fill) + if fill_ink is not None: + self.draw.draw_rectangle(xy, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + self.draw.draw_rectangle(xy, ink, 0, width) + + def rounded_rectangle( + self, + xy: Coords, + radius: float = 0, + fill: _Ink | None = None, + outline: _Ink | None = None, + width: int = 1, + *, + corners: tuple[bool, bool, bool, bool] | None = None, + ) -> None: + """Draw a rounded rectangle.""" + if isinstance(xy[0], (list, tuple)): + (x0, y0), (x1, y1) = cast(Sequence[Sequence[float]], xy) + else: + x0, y0, x1, y1 = cast(Sequence[float], xy) + if x1 < x0: + msg = "x1 must be greater than or equal to x0" + raise ValueError(msg) + if y1 < y0: + msg = "y1 must be greater than or equal to y0" + raise ValueError(msg) + if corners is None: + corners = (True, True, True, True) + + d = radius * 2 + + x0 = round(x0) + y0 = round(y0) + x1 = round(x1) + y1 = round(y1) + full_x, full_y = False, False + if all(corners): + full_x = d >= x1 - x0 - 1 + if full_x: + # The two left and two right corners are joined + d = x1 - x0 + full_y = d >= y1 - y0 - 1 + if full_y: + # The two top and two bottom corners are joined + d = y1 - y0 + if full_x and full_y: + # If all corners are joined, that is a circle + return self.ellipse(xy, fill, outline, width) + + if d == 0 or not any(corners): + # If the corners have no curve, + # or there are no corners, + # that is a rectangle + return self.rectangle(xy, fill, outline, width) + + r = int(d // 2) + ink, fill_ink = self._getink(outline, fill) + + def draw_corners(pieslice: bool) -> None: + parts: tuple[tuple[tuple[float, float, float, float], int, int], ...] + if full_x: + # Draw top and bottom halves + parts = ( + ((x0, y0, x0 + d, y0 + d), 180, 360), + ((x0, y1 - d, x0 + d, y1), 0, 180), + ) + elif full_y: + # Draw left and right halves + parts = ( + ((x0, y0, x0 + d, y0 + d), 90, 270), + ((x1 - d, y0, x1, y0 + d), 270, 90), + ) + else: + # Draw four separate corners + parts = tuple( + part + for i, part in enumerate( + ( + ((x0, y0, x0 + d, y0 + d), 180, 270), + ((x1 - d, y0, x1, y0 + d), 270, 360), + ((x1 - d, y1 - d, x1, y1), 0, 90), + ((x0, y1 - d, x0 + d, y1), 90, 180), + ) + ) + if corners[i] + ) + for part in parts: + if pieslice: + self.draw.draw_pieslice(*(part + (fill_ink, 1))) + else: + self.draw.draw_arc(*(part + (ink, width))) + + if fill_ink is not None: + draw_corners(True) + + if full_x: + self.draw.draw_rectangle((x0, y0 + r + 1, x1, y1 - r - 1), fill_ink, 1) + elif x1 - r - 1 > x0 + r + 1: + self.draw.draw_rectangle((x0 + r + 1, y0, x1 - r - 1, y1), fill_ink, 1) + if not full_x and not full_y: + left = [x0, y0, x0 + r, y1] + if corners[0]: + left[1] += r + 1 + if corners[3]: + left[3] -= r + 1 + self.draw.draw_rectangle(left, fill_ink, 1) + + right = [x1 - r, y0, x1, y1] + if corners[1]: + right[1] += r + 1 + if corners[2]: + right[3] -= r + 1 + self.draw.draw_rectangle(right, fill_ink, 1) + if ink is not None and ink != fill_ink and width != 0: + draw_corners(False) + + if not full_x: + top = [x0, y0, x1, y0 + width - 1] + if corners[0]: + top[0] += r + 1 + if corners[1]: + top[2] -= r + 1 + self.draw.draw_rectangle(top, ink, 1) + + bottom = [x0, y1 - width + 1, x1, y1] + if corners[3]: + bottom[0] += r + 1 + if corners[2]: + bottom[2] -= r + 1 + self.draw.draw_rectangle(bottom, ink, 1) + if not full_y: + left = [x0, y0, x0 + width - 1, y1] + if corners[0]: + left[1] += r + 1 + if corners[3]: + left[3] -= r + 1 + self.draw.draw_rectangle(left, ink, 1) + + right = [x1 - width + 1, y0, x1, y1] + if corners[1]: + right[1] += r + 1 + if corners[2]: + right[3] -= r + 1 + self.draw.draw_rectangle(right, ink, 1) + + def _multiline_check(self, text: AnyStr) -> bool: + split_character = "\n" if isinstance(text, str) else b"\n" + + return split_character in text + + def text( + self, + xy: tuple[float, float], + text: AnyStr, + fill: _Ink | None = None, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + anchor: str | None = None, + spacing: float = 4, + align: str = "left", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + stroke_fill: _Ink | None = None, + embedded_color: bool = False, + *args: Any, + **kwargs: Any, + ) -> None: + """Draw text.""" + if embedded_color and self.mode not in ("RGB", "RGBA"): + msg = "Embedded color supported only in RGB and RGBA modes" + raise ValueError(msg) + + if font is None: + font = self._getfont(kwargs.get("font_size")) + + if self._multiline_check(text): + return self.multiline_text( + xy, + text, + fill, + font, + anchor, + spacing, + align, + direction, + features, + language, + stroke_width, + stroke_fill, + embedded_color, + ) + + def getink(fill: _Ink | None) -> int: + ink, fill_ink = self._getink(fill) + if ink is None: + assert fill_ink is not None + return fill_ink + return ink + + def draw_text(ink: int, stroke_width: float = 0) -> None: + mode = self.fontmode + if stroke_width == 0 and embedded_color: + mode = "RGBA" + coord = [] + for i in range(2): + coord.append(int(xy[i])) + start = (math.modf(xy[0])[0], math.modf(xy[1])[0]) + try: + mask, offset = font.getmask2( # type: ignore[union-attr,misc] + text, + mode, + direction=direction, + features=features, + language=language, + stroke_width=stroke_width, + stroke_filled=True, + anchor=anchor, + ink=ink, + start=start, + *args, + **kwargs, + ) + coord = [coord[0] + offset[0], coord[1] + offset[1]] + except AttributeError: + try: + mask = font.getmask( # type: ignore[misc] + text, + mode, + direction, + features, + language, + stroke_width, + anchor, + ink, + start=start, + *args, + **kwargs, + ) + except TypeError: + mask = font.getmask(text) + if mode == "RGBA": + # font.getmask2(mode="RGBA") returns color in RGB bands and mask in A + # extract mask and set text alpha + color, mask = mask, mask.getband(3) + ink_alpha = struct.pack("i", ink)[3] + color.fillband(3, ink_alpha) + x, y = coord + if self.im is not None: + self.im.paste( + color, (x, y, x + mask.size[0], y + mask.size[1]), mask + ) + else: + self.draw.draw_bitmap(coord, mask, ink) + + ink = getink(fill) + if ink is not None: + stroke_ink = None + if stroke_width: + stroke_ink = getink(stroke_fill) if stroke_fill is not None else ink + + if stroke_ink is not None: + # Draw stroked text + draw_text(stroke_ink, stroke_width) + + # Draw normal text + if ink != stroke_ink: + draw_text(ink) + else: + # Only draw normal text + draw_text(ink) + + def _prepare_multiline_text( + self, + xy: tuple[float, float], + text: AnyStr, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ), + anchor: str | None, + spacing: float, + align: str, + direction: str | None, + features: list[str] | None, + language: str | None, + stroke_width: float, + embedded_color: bool, + font_size: float | None, + ) -> tuple[ + ImageFont.ImageFont | ImageFont.FreeTypeFont | ImageFont.TransposedFont, + list[tuple[tuple[float, float], str, AnyStr]], + ]: + if anchor is None: + anchor = "lt" if direction == "ttb" else "la" + elif len(anchor) != 2: + msg = "anchor must be a 2 character string" + raise ValueError(msg) + elif anchor[1] in "tb" and direction != "ttb": + msg = "anchor not supported for multiline text" + raise ValueError(msg) + + if font is None: + font = self._getfont(font_size) + + lines = text.split("\n" if isinstance(text, str) else b"\n") + line_spacing = ( + self.textbbox((0, 0), "A", font, stroke_width=stroke_width)[3] + + stroke_width + + spacing + ) + + top = xy[1] + parts = [] + if direction == "ttb": + left = xy[0] + for line in lines: + parts.append(((left, top), anchor, line)) + left += line_spacing + else: + widths = [] + max_width: float = 0 + for line in lines: + line_width = self.textlength( + line, + font, + direction=direction, + features=features, + language=language, + embedded_color=embedded_color, + ) + widths.append(line_width) + max_width = max(max_width, line_width) + + if anchor[1] == "m": + top -= (len(lines) - 1) * line_spacing / 2.0 + elif anchor[1] == "d": + top -= (len(lines) - 1) * line_spacing + + for idx, line in enumerate(lines): + left = xy[0] + width_difference = max_width - widths[idx] + + # align by align parameter + if align in ("left", "justify"): + pass + elif align == "center": + left += width_difference / 2.0 + elif align == "right": + left += width_difference + else: + msg = 'align must be "left", "center", "right" or "justify"' + raise ValueError(msg) + + if ( + align == "justify" + and width_difference != 0 + and idx != len(lines) - 1 + ): + words = line.split(" " if isinstance(text, str) else b" ") + if len(words) > 1: + # align left by anchor + if anchor[0] == "m": + left -= max_width / 2.0 + elif anchor[0] == "r": + left -= max_width + + word_widths = [ + self.textlength( + word, + font, + direction=direction, + features=features, + language=language, + embedded_color=embedded_color, + ) + for word in words + ] + word_anchor = "l" + anchor[1] + width_difference = max_width - sum(word_widths) + for i, word in enumerate(words): + parts.append(((left, top), word_anchor, word)) + left += word_widths[i] + width_difference / (len(words) - 1) + top += line_spacing + continue + + # align left by anchor + if anchor[0] == "m": + left -= width_difference / 2.0 + elif anchor[0] == "r": + left -= width_difference + parts.append(((left, top), anchor, line)) + top += line_spacing + + return font, parts + + def multiline_text( + self, + xy: tuple[float, float], + text: AnyStr, + fill: _Ink | None = None, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + anchor: str | None = None, + spacing: float = 4, + align: str = "left", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + stroke_fill: _Ink | None = None, + embedded_color: bool = False, + *, + font_size: float | None = None, + ) -> None: + font, lines = self._prepare_multiline_text( + xy, + text, + font, + anchor, + spacing, + align, + direction, + features, + language, + stroke_width, + embedded_color, + font_size, + ) + + for xy, anchor, line in lines: + self.text( + xy, + line, + fill, + font, + anchor, + direction=direction, + features=features, + language=language, + stroke_width=stroke_width, + stroke_fill=stroke_fill, + embedded_color=embedded_color, + ) + + def textlength( + self, + text: AnyStr, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + embedded_color: bool = False, + *, + font_size: float | None = None, + ) -> float: + """Get the length of a given string, in pixels with 1/64 precision.""" + if self._multiline_check(text): + msg = "can't measure length of multiline text" + raise ValueError(msg) + if embedded_color and self.mode not in ("RGB", "RGBA"): + msg = "Embedded color supported only in RGB and RGBA modes" + raise ValueError(msg) + + if font is None: + font = self._getfont(font_size) + mode = "RGBA" if embedded_color else self.fontmode + return font.getlength(text, mode, direction, features, language) + + def textbbox( + self, + xy: tuple[float, float], + text: AnyStr, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + anchor: str | None = None, + spacing: float = 4, + align: str = "left", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + embedded_color: bool = False, + *, + font_size: float | None = None, + ) -> tuple[float, float, float, float]: + """Get the bounding box of a given string, in pixels.""" + if embedded_color and self.mode not in ("RGB", "RGBA"): + msg = "Embedded color supported only in RGB and RGBA modes" + raise ValueError(msg) + + if font is None: + font = self._getfont(font_size) + + if self._multiline_check(text): + return self.multiline_textbbox( + xy, + text, + font, + anchor, + spacing, + align, + direction, + features, + language, + stroke_width, + embedded_color, + ) + + mode = "RGBA" if embedded_color else self.fontmode + bbox = font.getbbox( + text, mode, direction, features, language, stroke_width, anchor + ) + return bbox[0] + xy[0], bbox[1] + xy[1], bbox[2] + xy[0], bbox[3] + xy[1] + + def multiline_textbbox( + self, + xy: tuple[float, float], + text: AnyStr, + font: ( + ImageFont.ImageFont + | ImageFont.FreeTypeFont + | ImageFont.TransposedFont + | None + ) = None, + anchor: str | None = None, + spacing: float = 4, + align: str = "left", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + embedded_color: bool = False, + *, + font_size: float | None = None, + ) -> tuple[float, float, float, float]: + font, lines = self._prepare_multiline_text( + xy, + text, + font, + anchor, + spacing, + align, + direction, + features, + language, + stroke_width, + embedded_color, + font_size, + ) + + bbox: tuple[float, float, float, float] | None = None + + for xy, anchor, line in lines: + bbox_line = self.textbbox( + xy, + line, + font, + anchor, + direction=direction, + features=features, + language=language, + stroke_width=stroke_width, + embedded_color=embedded_color, + ) + if bbox is None: + bbox = bbox_line + else: + bbox = ( + min(bbox[0], bbox_line[0]), + min(bbox[1], bbox_line[1]), + max(bbox[2], bbox_line[2]), + max(bbox[3], bbox_line[3]), + ) + + if bbox is None: + return xy[0], xy[1], xy[0], xy[1] + return bbox + + +def Draw(im: Image.Image, mode: str | None = None) -> ImageDraw: + """ + A simple 2D drawing interface for PIL images. + + :param im: The image to draw in. + :param mode: Optional mode to use for color values. For RGB + images, this argument can be RGB or RGBA (to blend the + drawing into the image). For all other modes, this argument + must be the same as the image mode. If omitted, the mode + defaults to the mode of the image. + """ + try: + return getattr(im, "getdraw")(mode) + except AttributeError: + return ImageDraw(im, mode) + + +def getdraw( + im: Image.Image | None = None, hints: list[str] | None = None +) -> tuple[ImageDraw2.Draw | None, ModuleType]: + """ + :param im: The image to draw in. + :param hints: An optional list of hints. Deprecated. + :returns: A (drawing context, drawing resource factory) tuple. + """ + if hints is not None: + deprecate("'hints' parameter", 12) + from . import ImageDraw2 + + draw = ImageDraw2.Draw(im) if im is not None else None + return draw, ImageDraw2 + + +def floodfill( + image: Image.Image, + xy: tuple[int, int], + value: float | tuple[int, ...], + border: float | tuple[int, ...] | None = None, + thresh: float = 0, +) -> None: + """ + .. warning:: This method is experimental. + + Fills a bounded region with a given color. + + :param image: Target image. + :param xy: Seed position (a 2-item coordinate tuple). See + :ref:`coordinate-system`. + :param value: Fill color. + :param border: Optional border value. If given, the region consists of + pixels with a color different from the border color. If not given, + the region consists of pixels having the same color as the seed + pixel. + :param thresh: Optional threshold value which specifies a maximum + tolerable difference of a pixel value from the 'background' in + order for it to be replaced. Useful for filling regions of + non-homogeneous, but similar, colors. + """ + # based on an implementation by Eric S. Raymond + # amended by yo1995 @20180806 + pixel = image.load() + assert pixel is not None + x, y = xy + try: + background = pixel[x, y] + if _color_diff(value, background) <= thresh: + return # seed point already has fill color + pixel[x, y] = value + except (ValueError, IndexError): + return # seed point outside image + edge = {(x, y)} + # use a set to keep record of current and previous edge pixels + # to reduce memory consumption + full_edge = set() + while edge: + new_edge = set() + for x, y in edge: # 4 adjacent method + for s, t in ((x + 1, y), (x - 1, y), (x, y + 1), (x, y - 1)): + # If already processed, or if a coordinate is negative, skip + if (s, t) in full_edge or s < 0 or t < 0: + continue + try: + p = pixel[s, t] + except (ValueError, IndexError): + pass + else: + full_edge.add((s, t)) + if border is None: + fill = _color_diff(p, background) <= thresh + else: + fill = p not in (value, border) + if fill: + pixel[s, t] = value + new_edge.add((s, t)) + full_edge = edge # discard pixels processed + edge = new_edge + + +def _compute_regular_polygon_vertices( + bounding_circle: Sequence[Sequence[float] | float], n_sides: int, rotation: float +) -> list[tuple[float, float]]: + """ + Generate a list of vertices for a 2D regular polygon. + + :param bounding_circle: The bounding circle is a sequence defined + by a point and radius. The polygon is inscribed in this circle. + (e.g. ``bounding_circle=(x, y, r)`` or ``((x, y), r)``) + :param n_sides: Number of sides + (e.g. ``n_sides=3`` for a triangle, ``6`` for a hexagon) + :param rotation: Apply an arbitrary rotation to the polygon + (e.g. ``rotation=90``, applies a 90 degree rotation) + :return: List of regular polygon vertices + (e.g. ``[(25, 50), (50, 50), (50, 25), (25, 25)]``) + + How are the vertices computed? + 1. Compute the following variables + - theta: Angle between the apothem & the nearest polygon vertex + - side_length: Length of each polygon edge + - centroid: Center of bounding circle (1st, 2nd elements of bounding_circle) + - polygon_radius: Polygon radius (last element of bounding_circle) + - angles: Location of each polygon vertex in polar grid + (e.g. A square with 0 degree rotation => [225.0, 315.0, 45.0, 135.0]) + + 2. For each angle in angles, get the polygon vertex at that angle + The vertex is computed using the equation below. + X= xcos(φ) + ysin(φ) + Y= −xsin(φ) + ycos(φ) + + Note: + φ = angle in degrees + x = 0 + y = polygon_radius + + The formula above assumes rotation around the origin. + In our case, we are rotating around the centroid. + To account for this, we use the formula below + X = xcos(φ) + ysin(φ) + centroid_x + Y = −xsin(φ) + ycos(φ) + centroid_y + """ + # 1. Error Handling + # 1.1 Check `n_sides` has an appropriate value + if not isinstance(n_sides, int): + msg = "n_sides should be an int" # type: ignore[unreachable] + raise TypeError(msg) + if n_sides < 3: + msg = "n_sides should be an int > 2" + raise ValueError(msg) + + # 1.2 Check `bounding_circle` has an appropriate value + if not isinstance(bounding_circle, (list, tuple)): + msg = "bounding_circle should be a sequence" + raise TypeError(msg) + + if len(bounding_circle) == 3: + if not all(isinstance(i, (int, float)) for i in bounding_circle): + msg = "bounding_circle should only contain numeric data" + raise ValueError(msg) + + *centroid, polygon_radius = cast(list[float], list(bounding_circle)) + elif len(bounding_circle) == 2 and isinstance(bounding_circle[0], (list, tuple)): + if not all( + isinstance(i, (int, float)) for i in bounding_circle[0] + ) or not isinstance(bounding_circle[1], (int, float)): + msg = "bounding_circle should only contain numeric data" + raise ValueError(msg) + + if len(bounding_circle[0]) != 2: + msg = "bounding_circle centre should contain 2D coordinates (e.g. (x, y))" + raise ValueError(msg) + + centroid = cast(list[float], list(bounding_circle[0])) + polygon_radius = cast(float, bounding_circle[1]) + else: + msg = ( + "bounding_circle should contain 2D coordinates " + "and a radius (e.g. (x, y, r) or ((x, y), r) )" + ) + raise ValueError(msg) + + if polygon_radius <= 0: + msg = "bounding_circle radius should be > 0" + raise ValueError(msg) + + # 1.3 Check `rotation` has an appropriate value + if not isinstance(rotation, (int, float)): + msg = "rotation should be an int or float" # type: ignore[unreachable] + raise ValueError(msg) + + # 2. Define Helper Functions + def _apply_rotation(point: list[float], degrees: float) -> tuple[float, float]: + return ( + round( + point[0] * math.cos(math.radians(360 - degrees)) + - point[1] * math.sin(math.radians(360 - degrees)) + + centroid[0], + 2, + ), + round( + point[1] * math.cos(math.radians(360 - degrees)) + + point[0] * math.sin(math.radians(360 - degrees)) + + centroid[1], + 2, + ), + ) + + def _compute_polygon_vertex(angle: float) -> tuple[float, float]: + start_point = [polygon_radius, 0] + return _apply_rotation(start_point, angle) + + def _get_angles(n_sides: int, rotation: float) -> list[float]: + angles = [] + degrees = 360 / n_sides + # Start with the bottom left polygon vertex + current_angle = (270 - 0.5 * degrees) + rotation + for _ in range(n_sides): + angles.append(current_angle) + current_angle += degrees + if current_angle > 360: + current_angle -= 360 + return angles + + # 3. Variable Declarations + angles = _get_angles(n_sides, rotation) + + # 4. Compute Vertices + return [_compute_polygon_vertex(angle) for angle in angles] + + +def _color_diff( + color1: float | tuple[int, ...], color2: float | tuple[int, ...] +) -> float: + """ + Uses 1-norm distance to calculate difference between two values. + """ + first = color1 if isinstance(color1, tuple) else (color1,) + second = color2 if isinstance(color2, tuple) else (color2,) + + return sum(abs(first[i] - second[i]) for i in range(len(second))) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageDraw2.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageDraw2.py new file mode 100644 index 0000000000000000000000000000000000000000..3d68658ed5b79a36597e4953b888c41aa82fc7da --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageDraw2.py @@ -0,0 +1,243 @@ +# +# The Python Imaging Library +# $Id$ +# +# WCK-style drawing interface operations +# +# History: +# 2003-12-07 fl created +# 2005-05-15 fl updated; added to PIL as ImageDraw2 +# 2005-05-15 fl added text support +# 2005-05-20 fl added arc/chord/pieslice support +# +# Copyright (c) 2003-2005 by Secret Labs AB +# Copyright (c) 2003-2005 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# + + +""" +(Experimental) WCK-style drawing interface operations + +.. seealso:: :py:mod:`PIL.ImageDraw` +""" +from __future__ import annotations + +from typing import Any, AnyStr, BinaryIO + +from . import Image, ImageColor, ImageDraw, ImageFont, ImagePath +from ._typing import Coords, StrOrBytesPath + + +class Pen: + """Stores an outline color and width.""" + + def __init__(self, color: str, width: int = 1, opacity: int = 255) -> None: + self.color = ImageColor.getrgb(color) + self.width = width + + +class Brush: + """Stores a fill color""" + + def __init__(self, color: str, opacity: int = 255) -> None: + self.color = ImageColor.getrgb(color) + + +class Font: + """Stores a TrueType font and color""" + + def __init__( + self, color: str, file: StrOrBytesPath | BinaryIO, size: float = 12 + ) -> None: + # FIXME: add support for bitmap fonts + self.color = ImageColor.getrgb(color) + self.font = ImageFont.truetype(file, size) + + +class Draw: + """ + (Experimental) WCK-style drawing interface + """ + + def __init__( + self, + image: Image.Image | str, + size: tuple[int, int] | list[int] | None = None, + color: float | tuple[float, ...] | str | None = None, + ) -> None: + if isinstance(image, str): + if size is None: + msg = "If image argument is mode string, size must be a list or tuple" + raise ValueError(msg) + image = Image.new(image, size, color) + self.draw = ImageDraw.Draw(image) + self.image = image + self.transform: tuple[float, float, float, float, float, float] | None = None + + def flush(self) -> Image.Image: + return self.image + + def render( + self, + op: str, + xy: Coords, + pen: Pen | Brush | None, + brush: Brush | Pen | None = None, + **kwargs: Any, + ) -> None: + # handle color arguments + outline = fill = None + width = 1 + if isinstance(pen, Pen): + outline = pen.color + width = pen.width + elif isinstance(brush, Pen): + outline = brush.color + width = brush.width + if isinstance(brush, Brush): + fill = brush.color + elif isinstance(pen, Brush): + fill = pen.color + # handle transformation + if self.transform: + path = ImagePath.Path(xy) + path.transform(self.transform) + xy = path + # render the item + if op in ("arc", "line"): + kwargs.setdefault("fill", outline) + else: + kwargs.setdefault("fill", fill) + kwargs.setdefault("outline", outline) + if op == "line": + kwargs.setdefault("width", width) + getattr(self.draw, op)(xy, **kwargs) + + def settransform(self, offset: tuple[float, float]) -> None: + """Sets a transformation offset.""" + (xoffset, yoffset) = offset + self.transform = (1, 0, xoffset, 0, 1, yoffset) + + def arc( + self, + xy: Coords, + pen: Pen | Brush | None, + start: float, + end: float, + *options: Any, + ) -> None: + """ + Draws an arc (a portion of a circle outline) between the start and end + angles, inside the given bounding box. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.arc` + """ + self.render("arc", xy, pen, *options, start=start, end=end) + + def chord( + self, + xy: Coords, + pen: Pen | Brush | None, + start: float, + end: float, + *options: Any, + ) -> None: + """ + Same as :py:meth:`~PIL.ImageDraw2.Draw.arc`, but connects the end points + with a straight line. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.chord` + """ + self.render("chord", xy, pen, *options, start=start, end=end) + + def ellipse(self, xy: Coords, pen: Pen | Brush | None, *options: Any) -> None: + """ + Draws an ellipse inside the given bounding box. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.ellipse` + """ + self.render("ellipse", xy, pen, *options) + + def line(self, xy: Coords, pen: Pen | Brush | None, *options: Any) -> None: + """ + Draws a line between the coordinates in the ``xy`` list. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.line` + """ + self.render("line", xy, pen, *options) + + def pieslice( + self, + xy: Coords, + pen: Pen | Brush | None, + start: float, + end: float, + *options: Any, + ) -> None: + """ + Same as arc, but also draws straight lines between the end points and the + center of the bounding box. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.pieslice` + """ + self.render("pieslice", xy, pen, *options, start=start, end=end) + + def polygon(self, xy: Coords, pen: Pen | Brush | None, *options: Any) -> None: + """ + Draws a polygon. + + The polygon outline consists of straight lines between the given + coordinates, plus a straight line between the last and the first + coordinate. + + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.polygon` + """ + self.render("polygon", xy, pen, *options) + + def rectangle(self, xy: Coords, pen: Pen | Brush | None, *options: Any) -> None: + """ + Draws a rectangle. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.rectangle` + """ + self.render("rectangle", xy, pen, *options) + + def text(self, xy: tuple[float, float], text: AnyStr, font: Font) -> None: + """ + Draws the string at the given position. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.text` + """ + if self.transform: + path = ImagePath.Path(xy) + path.transform(self.transform) + xy = path + self.draw.text(xy, text, font=font.font, fill=font.color) + + def textbbox( + self, xy: tuple[float, float], text: AnyStr, font: Font + ) -> tuple[float, float, float, float]: + """ + Returns bounding box (in pixels) of given text. + + :return: ``(left, top, right, bottom)`` bounding box + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.textbbox` + """ + if self.transform: + path = ImagePath.Path(xy) + path.transform(self.transform) + xy = path + return self.draw.textbbox(xy, text, font=font.font) + + def textlength(self, text: AnyStr, font: Font) -> float: + """ + Returns length (in pixels) of given text. + This is the amount by which following text should be offset. + + .. seealso:: :py:meth:`PIL.ImageDraw.ImageDraw.textlength` + """ + return self.draw.textlength(text, font=font.font) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageEnhance.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageEnhance.py new file mode 100644 index 0000000000000000000000000000000000000000..0e7e6dd8ae631ad3577bda1d3e823bd2a3227536 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageEnhance.py @@ -0,0 +1,113 @@ +# +# The Python Imaging Library. +# $Id$ +# +# image enhancement classes +# +# For a background, see "Image Processing By Interpolation and +# Extrapolation", Paul Haeberli and Douglas Voorhies. Available +# at http://www.graficaobscura.com/interp/index.html +# +# History: +# 1996-03-23 fl Created +# 2009-06-16 fl Fixed mean calculation +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFilter, ImageStat + + +class _Enhance: + image: Image.Image + degenerate: Image.Image + + def enhance(self, factor: float) -> Image.Image: + """ + Returns an enhanced image. + + :param factor: A floating point value controlling the enhancement. + Factor 1.0 always returns a copy of the original image, + lower factors mean less color (brightness, contrast, + etc), and higher values more. There are no restrictions + on this value. + :rtype: :py:class:`~PIL.Image.Image` + """ + return Image.blend(self.degenerate, self.image, factor) + + +class Color(_Enhance): + """Adjust image color balance. + + This class can be used to adjust the colour balance of an image, in + a manner similar to the controls on a colour TV set. An enhancement + factor of 0.0 gives a black and white image. A factor of 1.0 gives + the original image. + """ + + def __init__(self, image: Image.Image) -> None: + self.image = image + self.intermediate_mode = "L" + if "A" in image.getbands(): + self.intermediate_mode = "LA" + + if self.intermediate_mode != image.mode: + image = image.convert(self.intermediate_mode).convert(image.mode) + self.degenerate = image + + +class Contrast(_Enhance): + """Adjust image contrast. + + This class can be used to control the contrast of an image, similar + to the contrast control on a TV set. An enhancement factor of 0.0 + gives a solid gray image. A factor of 1.0 gives the original image. + """ + + def __init__(self, image: Image.Image) -> None: + self.image = image + if image.mode != "L": + image = image.convert("L") + mean = int(ImageStat.Stat(image).mean[0] + 0.5) + self.degenerate = Image.new("L", image.size, mean) + if self.degenerate.mode != self.image.mode: + self.degenerate = self.degenerate.convert(self.image.mode) + + if "A" in self.image.getbands(): + self.degenerate.putalpha(self.image.getchannel("A")) + + +class Brightness(_Enhance): + """Adjust image brightness. + + This class can be used to control the brightness of an image. An + enhancement factor of 0.0 gives a black image. A factor of 1.0 gives the + original image. + """ + + def __init__(self, image: Image.Image) -> None: + self.image = image + self.degenerate = Image.new(image.mode, image.size, 0) + + if "A" in image.getbands(): + self.degenerate.putalpha(image.getchannel("A")) + + +class Sharpness(_Enhance): + """Adjust image sharpness. + + This class can be used to adjust the sharpness of an image. An + enhancement factor of 0.0 gives a blurred image, a factor of 1.0 gives the + original image, and a factor of 2.0 gives a sharpened image. + """ + + def __init__(self, image: Image.Image) -> None: + self.image = image + self.degenerate = image.filter(ImageFilter.SMOOTH) + + if "A" in image.getbands(): + self.degenerate.putalpha(image.getchannel("A")) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageFile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageFile.py new file mode 100644 index 0000000000000000000000000000000000000000..bf556a2c69036902d4b842cd8f9fa33ee04fa633 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageFile.py @@ -0,0 +1,922 @@ +# +# The Python Imaging Library. +# $Id$ +# +# base class for image file handlers +# +# history: +# 1995-09-09 fl Created +# 1996-03-11 fl Fixed load mechanism. +# 1996-04-15 fl Added pcx/xbm decoders. +# 1996-04-30 fl Added encoders. +# 1996-12-14 fl Added load helpers +# 1997-01-11 fl Use encode_to_file where possible +# 1997-08-27 fl Flush output in _save +# 1998-03-05 fl Use memory mapping for some modes +# 1999-02-04 fl Use memory mapping also for "I;16" and "I;16B" +# 1999-05-31 fl Added image parser +# 2000-10-12 fl Set readonly flag on memory-mapped images +# 2002-03-20 fl Use better messages for common decoder errors +# 2003-04-21 fl Fall back on mmap/map_buffer if map is not available +# 2003-10-30 fl Added StubImageFile class +# 2004-02-25 fl Made incremental parser more robust +# +# Copyright (c) 1997-2004 by Secret Labs AB +# Copyright (c) 1995-2004 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import abc +import io +import itertools +import logging +import os +import struct +from typing import IO, Any, NamedTuple, cast + +from . import ExifTags, Image +from ._deprecate import deprecate +from ._util import DeferredError, is_path + +TYPE_CHECKING = False +if TYPE_CHECKING: + from ._typing import StrOrBytesPath + +logger = logging.getLogger(__name__) + +MAXBLOCK = 65536 + +SAFEBLOCK = 1024 * 1024 + +LOAD_TRUNCATED_IMAGES = False +"""Whether or not to load truncated image files. User code may change this.""" + +ERRORS = { + -1: "image buffer overrun error", + -2: "decoding error", + -3: "unknown error", + -8: "bad configuration", + -9: "out of memory error", +} +""" +Dict of known error codes returned from :meth:`.PyDecoder.decode`, +:meth:`.PyEncoder.encode` :meth:`.PyEncoder.encode_to_pyfd` and +:meth:`.PyEncoder.encode_to_file`. +""" + + +# +# -------------------------------------------------------------------- +# Helpers + + +def _get_oserror(error: int, *, encoder: bool) -> OSError: + try: + msg = Image.core.getcodecstatus(error) + except AttributeError: + msg = ERRORS.get(error) + if not msg: + msg = f"{'encoder' if encoder else 'decoder'} error {error}" + msg += f" when {'writing' if encoder else 'reading'} image file" + return OSError(msg) + + +def raise_oserror(error: int) -> OSError: + deprecate( + "raise_oserror", + 12, + action="It is only useful for translating error codes returned by a codec's " + "decode() method, which ImageFile already does automatically.", + ) + raise _get_oserror(error, encoder=False) + + +def _tilesort(t: _Tile) -> int: + # sort on offset + return t[2] + + +class _Tile(NamedTuple): + codec_name: str + extents: tuple[int, int, int, int] | None + offset: int = 0 + args: tuple[Any, ...] | str | None = None + + +# +# -------------------------------------------------------------------- +# ImageFile base class + + +class ImageFile(Image.Image): + """Base class for image file format handlers.""" + + def __init__( + self, fp: StrOrBytesPath | IO[bytes], filename: str | bytes | None = None + ) -> None: + super().__init__() + + self._min_frame = 0 + + self.custom_mimetype: str | None = None + + self.tile: list[_Tile] = [] + """ A list of tile descriptors """ + + self.readonly = 1 # until we know better + + self.decoderconfig: tuple[Any, ...] = () + self.decodermaxblock = MAXBLOCK + + if is_path(fp): + # filename + self.fp = open(fp, "rb") + self.filename = os.fspath(fp) + self._exclusive_fp = True + else: + # stream + self.fp = cast(IO[bytes], fp) + self.filename = filename if filename is not None else "" + # can be overridden + self._exclusive_fp = False + + try: + try: + self._open() + except ( + IndexError, # end of data + TypeError, # end of data (ord) + KeyError, # unsupported mode + EOFError, # got header but not the first frame + struct.error, + ) as v: + raise SyntaxError(v) from v + + if not self.mode or self.size[0] <= 0 or self.size[1] <= 0: + msg = "not identified by this driver" + raise SyntaxError(msg) + except BaseException: + # close the file only if we have opened it this constructor + if self._exclusive_fp: + self.fp.close() + raise + + def _open(self) -> None: + pass + + def _close_fp(self): + if getattr(self, "_fp", False) and not isinstance(self._fp, DeferredError): + if self._fp != self.fp: + self._fp.close() + self._fp = DeferredError(ValueError("Operation on closed image")) + if self.fp: + self.fp.close() + + def close(self) -> None: + """ + Closes the file pointer, if possible. + + This operation will destroy the image core and release its memory. + The image data will be unusable afterward. + + This function is required to close images that have multiple frames or + have not had their file read and closed by the + :py:meth:`~PIL.Image.Image.load` method. See :ref:`file-handling` for + more information. + """ + try: + self._close_fp() + self.fp = None + except Exception as msg: + logger.debug("Error closing: %s", msg) + + super().close() + + def get_child_images(self) -> list[ImageFile]: + child_images = [] + exif = self.getexif() + ifds = [] + if ExifTags.Base.SubIFDs in exif: + subifd_offsets = exif[ExifTags.Base.SubIFDs] + if subifd_offsets: + if not isinstance(subifd_offsets, tuple): + subifd_offsets = (subifd_offsets,) + for subifd_offset in subifd_offsets: + ifds.append((exif._get_ifd_dict(subifd_offset), subifd_offset)) + ifd1 = exif.get_ifd(ExifTags.IFD.IFD1) + if ifd1 and ifd1.get(ExifTags.Base.JpegIFOffset): + assert exif._info is not None + ifds.append((ifd1, exif._info.next)) + + offset = None + for ifd, ifd_offset in ifds: + assert self.fp is not None + current_offset = self.fp.tell() + if offset is None: + offset = current_offset + + fp = self.fp + if ifd is not None: + thumbnail_offset = ifd.get(ExifTags.Base.JpegIFOffset) + if thumbnail_offset is not None: + thumbnail_offset += getattr(self, "_exif_offset", 0) + self.fp.seek(thumbnail_offset) + + length = ifd.get(ExifTags.Base.JpegIFByteCount) + assert isinstance(length, int) + data = self.fp.read(length) + fp = io.BytesIO(data) + + with Image.open(fp) as im: + from . import TiffImagePlugin + + if thumbnail_offset is None and isinstance( + im, TiffImagePlugin.TiffImageFile + ): + im._frame_pos = [ifd_offset] + im._seek(0) + im.load() + child_images.append(im) + + if offset is not None: + assert self.fp is not None + self.fp.seek(offset) + return child_images + + def get_format_mimetype(self) -> str | None: + if self.custom_mimetype: + return self.custom_mimetype + if self.format is not None: + return Image.MIME.get(self.format.upper()) + return None + + def __getstate__(self) -> list[Any]: + return super().__getstate__() + [self.filename] + + def __setstate__(self, state: list[Any]) -> None: + self.tile = [] + if len(state) > 5: + self.filename = state[5] + super().__setstate__(state) + + def verify(self) -> None: + """Check file integrity""" + + # raise exception if something's wrong. must be called + # directly after open, and closes file when finished. + if self._exclusive_fp: + self.fp.close() + self.fp = None + + def load(self) -> Image.core.PixelAccess | None: + """Load image data based on tile list""" + + if not self.tile and self._im is None: + msg = "cannot load this image" + raise OSError(msg) + + pixel = Image.Image.load(self) + if not self.tile: + return pixel + + self.map: mmap.mmap | None = None + use_mmap = self.filename and len(self.tile) == 1 + + readonly = 0 + + # look for read/seek overrides + if hasattr(self, "load_read"): + read = self.load_read + # don't use mmap if there are custom read/seek functions + use_mmap = False + else: + read = self.fp.read + + if hasattr(self, "load_seek"): + seek = self.load_seek + use_mmap = False + else: + seek = self.fp.seek + + if use_mmap: + # try memory mapping + decoder_name, extents, offset, args = self.tile[0] + if isinstance(args, str): + args = (args, 0, 1) + if ( + decoder_name == "raw" + and isinstance(args, tuple) + and len(args) >= 3 + and args[0] == self.mode + and args[0] in Image._MAPMODES + ): + try: + # use mmap, if possible + import mmap + + with open(self.filename) as fp: + self.map = mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ) + if offset + self.size[1] * args[1] > self.map.size(): + msg = "buffer is not large enough" + raise OSError(msg) + self.im = Image.core.map_buffer( + self.map, self.size, decoder_name, offset, args + ) + readonly = 1 + # After trashing self.im, + # we might need to reload the palette data. + if self.palette: + self.palette.dirty = 1 + except (AttributeError, OSError, ImportError): + self.map = None + + self.load_prepare() + err_code = -3 # initialize to unknown error + if not self.map: + # sort tiles in file order + self.tile.sort(key=_tilesort) + + # FIXME: This is a hack to handle TIFF's JpegTables tag. + prefix = getattr(self, "tile_prefix", b"") + + # Remove consecutive duplicates that only differ by their offset + self.tile = [ + list(tiles)[-1] + for _, tiles in itertools.groupby( + self.tile, lambda tile: (tile[0], tile[1], tile[3]) + ) + ] + for i, (decoder_name, extents, offset, args) in enumerate(self.tile): + seek(offset) + decoder = Image._getdecoder( + self.mode, decoder_name, args, self.decoderconfig + ) + try: + decoder.setimage(self.im, extents) + if decoder.pulls_fd: + decoder.setfd(self.fp) + err_code = decoder.decode(b"")[1] + else: + b = prefix + while True: + read_bytes = self.decodermaxblock + if i + 1 < len(self.tile): + next_offset = self.tile[i + 1].offset + if next_offset > offset: + read_bytes = next_offset - offset + try: + s = read(read_bytes) + except (IndexError, struct.error) as e: + # truncated png/gif + if LOAD_TRUNCATED_IMAGES: + break + else: + msg = "image file is truncated" + raise OSError(msg) from e + + if not s: # truncated jpeg + if LOAD_TRUNCATED_IMAGES: + break + else: + msg = ( + "image file is truncated " + f"({len(b)} bytes not processed)" + ) + raise OSError(msg) + + b = b + s + n, err_code = decoder.decode(b) + if n < 0: + break + b = b[n:] + finally: + # Need to cleanup here to prevent leaks + decoder.cleanup() + + self.tile = [] + self.readonly = readonly + + self.load_end() + + if self._exclusive_fp and self._close_exclusive_fp_after_loading: + self.fp.close() + self.fp = None + + if not self.map and not LOAD_TRUNCATED_IMAGES and err_code < 0: + # still raised if decoder fails to return anything + raise _get_oserror(err_code, encoder=False) + + return Image.Image.load(self) + + def load_prepare(self) -> None: + # create image memory if necessary + if self._im is None: + self.im = Image.core.new(self.mode, self.size) + # create palette (optional) + if self.mode == "P": + Image.Image.load(self) + + def load_end(self) -> None: + # may be overridden + pass + + # may be defined for contained formats + # def load_seek(self, pos: int) -> None: + # pass + + # may be defined for blocked formats (e.g. PNG) + # def load_read(self, read_bytes: int) -> bytes: + # pass + + def _seek_check(self, frame: int) -> bool: + if ( + frame < self._min_frame + # Only check upper limit on frames if additional seek operations + # are not required to do so + or ( + not (hasattr(self, "_n_frames") and self._n_frames is None) + and frame >= getattr(self, "n_frames") + self._min_frame + ) + ): + msg = "attempt to seek outside sequence" + raise EOFError(msg) + + return self.tell() != frame + + +class StubHandler(abc.ABC): + def open(self, im: StubImageFile) -> None: + pass + + @abc.abstractmethod + def load(self, im: StubImageFile) -> Image.Image: + pass + + +class StubImageFile(ImageFile, metaclass=abc.ABCMeta): + """ + Base class for stub image loaders. + + A stub loader is an image loader that can identify files of a + certain format, but relies on external code to load the file. + """ + + @abc.abstractmethod + def _open(self) -> None: + pass + + def load(self) -> Image.core.PixelAccess | None: + loader = self._load() + if loader is None: + msg = f"cannot find loader for this {self.format} file" + raise OSError(msg) + image = loader.load(self) + assert image is not None + # become the other object (!) + self.__class__ = image.__class__ # type: ignore[assignment] + self.__dict__ = image.__dict__ + return image.load() + + @abc.abstractmethod + def _load(self) -> StubHandler | None: + """(Hook) Find actual image loader.""" + pass + + +class Parser: + """ + Incremental image parser. This class implements the standard + feed/close consumer interface. + """ + + incremental = None + image: Image.Image | None = None + data: bytes | None = None + decoder: Image.core.ImagingDecoder | PyDecoder | None = None + offset = 0 + finished = 0 + + def reset(self) -> None: + """ + (Consumer) Reset the parser. Note that you can only call this + method immediately after you've created a parser; parser + instances cannot be reused. + """ + assert self.data is None, "cannot reuse parsers" + + def feed(self, data: bytes) -> None: + """ + (Consumer) Feed data to the parser. + + :param data: A string buffer. + :exception OSError: If the parser failed to parse the image file. + """ + # collect data + + if self.finished: + return + + if self.data is None: + self.data = data + else: + self.data = self.data + data + + # parse what we have + if self.decoder: + if self.offset > 0: + # skip header + skip = min(len(self.data), self.offset) + self.data = self.data[skip:] + self.offset = self.offset - skip + if self.offset > 0 or not self.data: + return + + n, e = self.decoder.decode(self.data) + + if n < 0: + # end of stream + self.data = None + self.finished = 1 + if e < 0: + # decoding error + self.image = None + raise _get_oserror(e, encoder=False) + else: + # end of image + return + self.data = self.data[n:] + + elif self.image: + # if we end up here with no decoder, this file cannot + # be incrementally parsed. wait until we've gotten all + # available data + pass + + else: + # attempt to open this file + try: + with io.BytesIO(self.data) as fp: + im = Image.open(fp) + except OSError: + pass # not enough data + else: + flag = hasattr(im, "load_seek") or hasattr(im, "load_read") + if flag or len(im.tile) != 1: + # custom load code, or multiple tiles + self.decode = None + else: + # initialize decoder + im.load_prepare() + d, e, o, a = im.tile[0] + im.tile = [] + self.decoder = Image._getdecoder(im.mode, d, a, im.decoderconfig) + self.decoder.setimage(im.im, e) + + # calculate decoder offset + self.offset = o + if self.offset <= len(self.data): + self.data = self.data[self.offset :] + self.offset = 0 + + self.image = im + + def __enter__(self) -> Parser: + return self + + def __exit__(self, *args: object) -> None: + self.close() + + def close(self) -> Image.Image: + """ + (Consumer) Close the stream. + + :returns: An image object. + :exception OSError: If the parser failed to parse the image file either + because it cannot be identified or cannot be + decoded. + """ + # finish decoding + if self.decoder: + # get rid of what's left in the buffers + self.feed(b"") + self.data = self.decoder = None + if not self.finished: + msg = "image was incomplete" + raise OSError(msg) + if not self.image: + msg = "cannot parse this image" + raise OSError(msg) + if self.data: + # incremental parsing not possible; reopen the file + # not that we have all data + with io.BytesIO(self.data) as fp: + try: + self.image = Image.open(fp) + finally: + self.image.load() + return self.image + + +# -------------------------------------------------------------------- + + +def _save(im: Image.Image, fp: IO[bytes], tile: list[_Tile], bufsize: int = 0) -> None: + """Helper to save image based on tile list + + :param im: Image object. + :param fp: File object. + :param tile: Tile list. + :param bufsize: Optional buffer size + """ + + im.load() + if not hasattr(im, "encoderconfig"): + im.encoderconfig = () + tile.sort(key=_tilesort) + # FIXME: make MAXBLOCK a configuration parameter + # It would be great if we could have the encoder specify what it needs + # But, it would need at least the image size in most cases. RawEncode is + # a tricky case. + bufsize = max(MAXBLOCK, bufsize, im.size[0] * 4) # see RawEncode.c + try: + fh = fp.fileno() + fp.flush() + _encode_tile(im, fp, tile, bufsize, fh) + except (AttributeError, io.UnsupportedOperation) as exc: + _encode_tile(im, fp, tile, bufsize, None, exc) + if hasattr(fp, "flush"): + fp.flush() + + +def _encode_tile( + im: Image.Image, + fp: IO[bytes], + tile: list[_Tile], + bufsize: int, + fh: int | None, + exc: BaseException | None = None, +) -> None: + for encoder_name, extents, offset, args in tile: + if offset > 0: + fp.seek(offset) + encoder = Image._getencoder(im.mode, encoder_name, args, im.encoderconfig) + try: + encoder.setimage(im.im, extents) + if encoder.pushes_fd: + encoder.setfd(fp) + errcode = encoder.encode_to_pyfd()[1] + else: + if exc: + # compress to Python file-compatible object + while True: + errcode, data = encoder.encode(bufsize)[1:] + fp.write(data) + if errcode: + break + else: + # slight speedup: compress to real file object + assert fh is not None + errcode = encoder.encode_to_file(fh, bufsize) + if errcode < 0: + raise _get_oserror(errcode, encoder=True) from exc + finally: + encoder.cleanup() + + +def _safe_read(fp: IO[bytes], size: int) -> bytes: + """ + Reads large blocks in a safe way. Unlike fp.read(n), this function + doesn't trust the user. If the requested size is larger than + SAFEBLOCK, the file is read block by block. + + :param fp: File handle. Must implement a read method. + :param size: Number of bytes to read. + :returns: A string containing size bytes of data. + + Raises an OSError if the file is truncated and the read cannot be completed + + """ + if size <= 0: + return b"" + if size <= SAFEBLOCK: + data = fp.read(size) + if len(data) < size: + msg = "Truncated File Read" + raise OSError(msg) + return data + blocks: list[bytes] = [] + remaining_size = size + while remaining_size > 0: + block = fp.read(min(remaining_size, SAFEBLOCK)) + if not block: + break + blocks.append(block) + remaining_size -= len(block) + if sum(len(block) for block in blocks) < size: + msg = "Truncated File Read" + raise OSError(msg) + return b"".join(blocks) + + +class PyCodecState: + def __init__(self) -> None: + self.xsize = 0 + self.ysize = 0 + self.xoff = 0 + self.yoff = 0 + + def extents(self) -> tuple[int, int, int, int]: + return self.xoff, self.yoff, self.xoff + self.xsize, self.yoff + self.ysize + + +class PyCodec: + fd: IO[bytes] | None + + def __init__(self, mode: str, *args: Any) -> None: + self.im: Image.core.ImagingCore | None = None + self.state = PyCodecState() + self.fd = None + self.mode = mode + self.init(args) + + def init(self, args: tuple[Any, ...]) -> None: + """ + Override to perform codec specific initialization + + :param args: Tuple of arg items from the tile entry + :returns: None + """ + self.args = args + + def cleanup(self) -> None: + """ + Override to perform codec specific cleanup + + :returns: None + """ + pass + + def setfd(self, fd: IO[bytes]) -> None: + """ + Called from ImageFile to set the Python file-like object + + :param fd: A Python file-like object + :returns: None + """ + self.fd = fd + + def setimage( + self, + im: Image.core.ImagingCore, + extents: tuple[int, int, int, int] | None = None, + ) -> None: + """ + Called from ImageFile to set the core output image for the codec + + :param im: A core image object + :param extents: a 4 tuple of (x0, y0, x1, y1) defining the rectangle + for this tile + :returns: None + """ + + # following c code + self.im = im + + if extents: + (x0, y0, x1, y1) = extents + else: + (x0, y0, x1, y1) = (0, 0, 0, 0) + + if x0 == 0 and x1 == 0: + self.state.xsize, self.state.ysize = self.im.size + else: + self.state.xoff = x0 + self.state.yoff = y0 + self.state.xsize = x1 - x0 + self.state.ysize = y1 - y0 + + if self.state.xsize <= 0 or self.state.ysize <= 0: + msg = "Size cannot be negative" + raise ValueError(msg) + + if ( + self.state.xsize + self.state.xoff > self.im.size[0] + or self.state.ysize + self.state.yoff > self.im.size[1] + ): + msg = "Tile cannot extend outside image" + raise ValueError(msg) + + +class PyDecoder(PyCodec): + """ + Python implementation of a format decoder. Override this class and + add the decoding logic in the :meth:`decode` method. + + See :ref:`Writing Your Own File Codec in Python` + """ + + _pulls_fd = False + + @property + def pulls_fd(self) -> bool: + return self._pulls_fd + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + """ + Override to perform the decoding process. + + :param buffer: A bytes object with the data to be decoded. + :returns: A tuple of ``(bytes consumed, errcode)``. + If finished with decoding return -1 for the bytes consumed. + Err codes are from :data:`.ImageFile.ERRORS`. + """ + msg = "unavailable in base decoder" + raise NotImplementedError(msg) + + def set_as_raw( + self, data: bytes, rawmode: str | None = None, extra: tuple[Any, ...] = () + ) -> None: + """ + Convenience method to set the internal image from a stream of raw data + + :param data: Bytes to be set + :param rawmode: The rawmode to be used for the decoder. + If not specified, it will default to the mode of the image + :param extra: Extra arguments for the decoder. + :returns: None + """ + + if not rawmode: + rawmode = self.mode + d = Image._getdecoder(self.mode, "raw", rawmode, extra) + assert self.im is not None + d.setimage(self.im, self.state.extents()) + s = d.decode(data) + + if s[0] >= 0: + msg = "not enough image data" + raise ValueError(msg) + if s[1] != 0: + msg = "cannot decode image data" + raise ValueError(msg) + + +class PyEncoder(PyCodec): + """ + Python implementation of a format encoder. Override this class and + add the decoding logic in the :meth:`encode` method. + + See :ref:`Writing Your Own File Codec in Python` + """ + + _pushes_fd = False + + @property + def pushes_fd(self) -> bool: + return self._pushes_fd + + def encode(self, bufsize: int) -> tuple[int, int, bytes]: + """ + Override to perform the encoding process. + + :param bufsize: Buffer size. + :returns: A tuple of ``(bytes encoded, errcode, bytes)``. + If finished with encoding return 1 for the error code. + Err codes are from :data:`.ImageFile.ERRORS`. + """ + msg = "unavailable in base encoder" + raise NotImplementedError(msg) + + def encode_to_pyfd(self) -> tuple[int, int]: + """ + If ``pushes_fd`` is ``True``, then this method will be used, + and ``encode()`` will only be called once. + + :returns: A tuple of ``(bytes consumed, errcode)``. + Err codes are from :data:`.ImageFile.ERRORS`. + """ + if not self.pushes_fd: + return 0, -8 # bad configuration + bytes_consumed, errcode, data = self.encode(0) + if data: + assert self.fd is not None + self.fd.write(data) + return bytes_consumed, errcode + + def encode_to_file(self, fh: int, bufsize: int) -> int: + """ + :param fh: File handle. + :param bufsize: Buffer size. + + :returns: If finished successfully, return 0. + Otherwise, return an error code. Err codes are from + :data:`.ImageFile.ERRORS`. + """ + errcode = 0 + while errcode == 0: + status, errcode, buf = self.encode(bufsize) + if status > 0: + os.write(fh, buf[status:]) + return errcode diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageFilter.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageFilter.py new file mode 100644 index 0000000000000000000000000000000000000000..b9ed54ab20a132aa9d2aec894dd846736923f70e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageFilter.py @@ -0,0 +1,604 @@ +# +# The Python Imaging Library. +# $Id$ +# +# standard filters +# +# History: +# 1995-11-27 fl Created +# 2002-06-08 fl Added rank and mode filters +# 2003-09-15 fl Fixed rank calculation in rank filter; added expand call +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-2002 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import abc +import functools +from collections.abc import Sequence +from types import ModuleType +from typing import Any, Callable, cast + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import _imaging + from ._typing import NumpyArray + + +class Filter(abc.ABC): + @abc.abstractmethod + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + pass + + +class MultibandFilter(Filter): + pass + + +class BuiltinFilter(MultibandFilter): + filterargs: tuple[Any, ...] + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + if image.mode == "P": + msg = "cannot filter palette images" + raise ValueError(msg) + return image.filter(*self.filterargs) + + +class Kernel(BuiltinFilter): + """ + Create a convolution kernel. This only supports 3x3 and 5x5 integer and floating + point kernels. + + Kernels can only be applied to "L" and "RGB" images. + + :param size: Kernel size, given as (width, height). This must be (3,3) or (5,5). + :param kernel: A sequence containing kernel weights. The kernel will be flipped + vertically before being applied to the image. + :param scale: Scale factor. If given, the result for each pixel is divided by this + value. The default is the sum of the kernel weights. + :param offset: Offset. If given, this value is added to the result, after it has + been divided by the scale factor. + """ + + name = "Kernel" + + def __init__( + self, + size: tuple[int, int], + kernel: Sequence[float], + scale: float | None = None, + offset: float = 0, + ) -> None: + if scale is None: + # default scale is sum of kernel + scale = functools.reduce(lambda a, b: a + b, kernel) + if size[0] * size[1] != len(kernel): + msg = "not enough coefficients in kernel" + raise ValueError(msg) + self.filterargs = size, scale, offset, kernel + + +class RankFilter(Filter): + """ + Create a rank filter. The rank filter sorts all pixels in + a window of the given size, and returns the ``rank``'th value. + + :param size: The kernel size, in pixels. + :param rank: What pixel value to pick. Use 0 for a min filter, + ``size * size / 2`` for a median filter, ``size * size - 1`` + for a max filter, etc. + """ + + name = "Rank" + + def __init__(self, size: int, rank: int) -> None: + self.size = size + self.rank = rank + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + if image.mode == "P": + msg = "cannot filter palette images" + raise ValueError(msg) + image = image.expand(self.size // 2, self.size // 2) + return image.rankfilter(self.size, self.rank) + + +class MedianFilter(RankFilter): + """ + Create a median filter. Picks the median pixel value in a window with the + given size. + + :param size: The kernel size, in pixels. + """ + + name = "Median" + + def __init__(self, size: int = 3) -> None: + self.size = size + self.rank = size * size // 2 + + +class MinFilter(RankFilter): + """ + Create a min filter. Picks the lowest pixel value in a window with the + given size. + + :param size: The kernel size, in pixels. + """ + + name = "Min" + + def __init__(self, size: int = 3) -> None: + self.size = size + self.rank = 0 + + +class MaxFilter(RankFilter): + """ + Create a max filter. Picks the largest pixel value in a window with the + given size. + + :param size: The kernel size, in pixels. + """ + + name = "Max" + + def __init__(self, size: int = 3) -> None: + self.size = size + self.rank = size * size - 1 + + +class ModeFilter(Filter): + """ + Create a mode filter. Picks the most frequent pixel value in a box with the + given size. Pixel values that occur only once or twice are ignored; if no + pixel value occurs more than twice, the original pixel value is preserved. + + :param size: The kernel size, in pixels. + """ + + name = "Mode" + + def __init__(self, size: int = 3) -> None: + self.size = size + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + return image.modefilter(self.size) + + +class GaussianBlur(MultibandFilter): + """Blurs the image with a sequence of extended box filters, which + approximates a Gaussian kernel. For details on accuracy see + + + :param radius: Standard deviation of the Gaussian kernel. Either a sequence of two + numbers for x and y, or a single number for both. + """ + + name = "GaussianBlur" + + def __init__(self, radius: float | Sequence[float] = 2) -> None: + self.radius = radius + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + xy = self.radius + if isinstance(xy, (int, float)): + xy = (xy, xy) + if xy == (0, 0): + return image.copy() + return image.gaussian_blur(xy) + + +class BoxBlur(MultibandFilter): + """Blurs the image by setting each pixel to the average value of the pixels + in a square box extending radius pixels in each direction. + Supports float radius of arbitrary size. Uses an optimized implementation + which runs in linear time relative to the size of the image + for any radius value. + + :param radius: Size of the box in a direction. Either a sequence of two numbers for + x and y, or a single number for both. + + Radius 0 does not blur, returns an identical image. + Radius 1 takes 1 pixel in each direction, i.e. 9 pixels in total. + """ + + name = "BoxBlur" + + def __init__(self, radius: float | Sequence[float]) -> None: + xy = radius if isinstance(radius, (tuple, list)) else (radius, radius) + if xy[0] < 0 or xy[1] < 0: + msg = "radius must be >= 0" + raise ValueError(msg) + self.radius = radius + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + xy = self.radius + if isinstance(xy, (int, float)): + xy = (xy, xy) + if xy == (0, 0): + return image.copy() + return image.box_blur(xy) + + +class UnsharpMask(MultibandFilter): + """Unsharp mask filter. + + See Wikipedia's entry on `digital unsharp masking`_ for an explanation of + the parameters. + + :param radius: Blur Radius + :param percent: Unsharp strength, in percent + :param threshold: Threshold controls the minimum brightness change that + will be sharpened + + .. _digital unsharp masking: https://en.wikipedia.org/wiki/Unsharp_masking#Digital_unsharp_masking + + """ + + name = "UnsharpMask" + + def __init__( + self, radius: float = 2, percent: int = 150, threshold: int = 3 + ) -> None: + self.radius = radius + self.percent = percent + self.threshold = threshold + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + return image.unsharp_mask(self.radius, self.percent, self.threshold) + + +class BLUR(BuiltinFilter): + name = "Blur" + # fmt: off + filterargs = (5, 5), 16, 0, ( + 1, 1, 1, 1, 1, + 1, 0, 0, 0, 1, + 1, 0, 0, 0, 1, + 1, 0, 0, 0, 1, + 1, 1, 1, 1, 1, + ) + # fmt: on + + +class CONTOUR(BuiltinFilter): + name = "Contour" + # fmt: off + filterargs = (3, 3), 1, 255, ( + -1, -1, -1, + -1, 8, -1, + -1, -1, -1, + ) + # fmt: on + + +class DETAIL(BuiltinFilter): + name = "Detail" + # fmt: off + filterargs = (3, 3), 6, 0, ( + 0, -1, 0, + -1, 10, -1, + 0, -1, 0, + ) + # fmt: on + + +class EDGE_ENHANCE(BuiltinFilter): + name = "Edge-enhance" + # fmt: off + filterargs = (3, 3), 2, 0, ( + -1, -1, -1, + -1, 10, -1, + -1, -1, -1, + ) + # fmt: on + + +class EDGE_ENHANCE_MORE(BuiltinFilter): + name = "Edge-enhance More" + # fmt: off + filterargs = (3, 3), 1, 0, ( + -1, -1, -1, + -1, 9, -1, + -1, -1, -1, + ) + # fmt: on + + +class EMBOSS(BuiltinFilter): + name = "Emboss" + # fmt: off + filterargs = (3, 3), 1, 128, ( + -1, 0, 0, + 0, 1, 0, + 0, 0, 0, + ) + # fmt: on + + +class FIND_EDGES(BuiltinFilter): + name = "Find Edges" + # fmt: off + filterargs = (3, 3), 1, 0, ( + -1, -1, -1, + -1, 8, -1, + -1, -1, -1, + ) + # fmt: on + + +class SHARPEN(BuiltinFilter): + name = "Sharpen" + # fmt: off + filterargs = (3, 3), 16, 0, ( + -2, -2, -2, + -2, 32, -2, + -2, -2, -2, + ) + # fmt: on + + +class SMOOTH(BuiltinFilter): + name = "Smooth" + # fmt: off + filterargs = (3, 3), 13, 0, ( + 1, 1, 1, + 1, 5, 1, + 1, 1, 1, + ) + # fmt: on + + +class SMOOTH_MORE(BuiltinFilter): + name = "Smooth More" + # fmt: off + filterargs = (5, 5), 100, 0, ( + 1, 1, 1, 1, 1, + 1, 5, 5, 5, 1, + 1, 5, 44, 5, 1, + 1, 5, 5, 5, 1, + 1, 1, 1, 1, 1, + ) + # fmt: on + + +class Color3DLUT(MultibandFilter): + """Three-dimensional color lookup table. + + Transforms 3-channel pixels using the values of the channels as coordinates + in the 3D lookup table and interpolating the nearest elements. + + This method allows you to apply almost any color transformation + in constant time by using pre-calculated decimated tables. + + .. versionadded:: 5.2.0 + + :param size: Size of the table. One int or tuple of (int, int, int). + Minimal size in any dimension is 2, maximum is 65. + :param table: Flat lookup table. A list of ``channels * size**3`` + float elements or a list of ``size**3`` channels-sized + tuples with floats. Channels are changed first, + then first dimension, then second, then third. + Value 0.0 corresponds lowest value of output, 1.0 highest. + :param channels: Number of channels in the table. Could be 3 or 4. + Default is 3. + :param target_mode: A mode for the result image. Should have not less + than ``channels`` channels. Default is ``None``, + which means that mode wouldn't be changed. + """ + + name = "Color 3D LUT" + + def __init__( + self, + size: int | tuple[int, int, int], + table: Sequence[float] | Sequence[Sequence[int]] | NumpyArray, + channels: int = 3, + target_mode: str | None = None, + **kwargs: bool, + ) -> None: + if channels not in (3, 4): + msg = "Only 3 or 4 output channels are supported" + raise ValueError(msg) + self.size = size = self._check_size(size) + self.channels = channels + self.mode = target_mode + + # Hidden flag `_copy_table=False` could be used to avoid extra copying + # of the table if the table is specially made for the constructor. + copy_table = kwargs.get("_copy_table", True) + items = size[0] * size[1] * size[2] + wrong_size = False + + numpy: ModuleType | None = None + if hasattr(table, "shape"): + try: + import numpy + except ImportError: + pass + + if numpy and isinstance(table, numpy.ndarray): + numpy_table: NumpyArray = table + if copy_table: + numpy_table = numpy_table.copy() + + if numpy_table.shape in [ + (items * channels,), + (items, channels), + (size[2], size[1], size[0], channels), + ]: + table = numpy_table.reshape(items * channels) + else: + wrong_size = True + + else: + if copy_table: + table = list(table) + + # Convert to a flat list + if table and isinstance(table[0], (list, tuple)): + raw_table = cast(Sequence[Sequence[int]], table) + flat_table: list[int] = [] + for pixel in raw_table: + if len(pixel) != channels: + msg = ( + "The elements of the table should " + f"have a length of {channels}." + ) + raise ValueError(msg) + flat_table.extend(pixel) + table = flat_table + + if wrong_size or len(table) != items * channels: + msg = ( + "The table should have either channels * size**3 float items " + "or size**3 items of channels-sized tuples with floats. " + f"Table should be: {channels}x{size[0]}x{size[1]}x{size[2]}. " + f"Actual length: {len(table)}" + ) + raise ValueError(msg) + self.table = table + + @staticmethod + def _check_size(size: Any) -> tuple[int, int, int]: + try: + _, _, _ = size + except ValueError as e: + msg = "Size should be either an integer or a tuple of three integers." + raise ValueError(msg) from e + except TypeError: + size = (size, size, size) + size = tuple(int(x) for x in size) + for size_1d in size: + if not 2 <= size_1d <= 65: + msg = "Size should be in [2, 65] range." + raise ValueError(msg) + return size + + @classmethod + def generate( + cls, + size: int | tuple[int, int, int], + callback: Callable[[float, float, float], tuple[float, ...]], + channels: int = 3, + target_mode: str | None = None, + ) -> Color3DLUT: + """Generates new LUT using provided callback. + + :param size: Size of the table. Passed to the constructor. + :param callback: Function with three parameters which correspond + three color channels. Will be called ``size**3`` + times with values from 0.0 to 1.0 and should return + a tuple with ``channels`` elements. + :param channels: The number of channels which should return callback. + :param target_mode: Passed to the constructor of the resulting + lookup table. + """ + size_1d, size_2d, size_3d = cls._check_size(size) + if channels not in (3, 4): + msg = "Only 3 or 4 output channels are supported" + raise ValueError(msg) + + table: list[float] = [0] * (size_1d * size_2d * size_3d * channels) + idx_out = 0 + for b in range(size_3d): + for g in range(size_2d): + for r in range(size_1d): + table[idx_out : idx_out + channels] = callback( + r / (size_1d - 1), g / (size_2d - 1), b / (size_3d - 1) + ) + idx_out += channels + + return cls( + (size_1d, size_2d, size_3d), + table, + channels=channels, + target_mode=target_mode, + _copy_table=False, + ) + + def transform( + self, + callback: Callable[..., tuple[float, ...]], + with_normals: bool = False, + channels: int | None = None, + target_mode: str | None = None, + ) -> Color3DLUT: + """Transforms the table values using provided callback and returns + a new LUT with altered values. + + :param callback: A function which takes old lookup table values + and returns a new set of values. The number + of arguments which function should take is + ``self.channels`` or ``3 + self.channels`` + if ``with_normals`` flag is set. + Should return a tuple of ``self.channels`` or + ``channels`` elements if it is set. + :param with_normals: If true, ``callback`` will be called with + coordinates in the color cube as the first + three arguments. Otherwise, ``callback`` + will be called only with actual color values. + :param channels: The number of channels in the resulting lookup table. + :param target_mode: Passed to the constructor of the resulting + lookup table. + """ + if channels not in (None, 3, 4): + msg = "Only 3 or 4 output channels are supported" + raise ValueError(msg) + ch_in = self.channels + ch_out = channels or ch_in + size_1d, size_2d, size_3d = self.size + + table: list[float] = [0] * (size_1d * size_2d * size_3d * ch_out) + idx_in = 0 + idx_out = 0 + for b in range(size_3d): + for g in range(size_2d): + for r in range(size_1d): + values = self.table[idx_in : idx_in + ch_in] + if with_normals: + values = callback( + r / (size_1d - 1), + g / (size_2d - 1), + b / (size_3d - 1), + *values, + ) + else: + values = callback(*values) + table[idx_out : idx_out + ch_out] = values + idx_in += ch_in + idx_out += ch_out + + return type(self)( + self.size, + table, + channels=ch_out, + target_mode=target_mode or self.mode, + _copy_table=False, + ) + + def __repr__(self) -> str: + r = [ + f"{self.__class__.__name__} from {self.table.__class__.__name__}", + "size={:d}x{:d}x{:d}".format(*self.size), + f"channels={self.channels:d}", + ] + if self.mode: + r.append(f"target_mode={self.mode}") + return "<{}>".format(" ".join(r)) + + def filter(self, image: _imaging.ImagingCore) -> _imaging.ImagingCore: + from . import Image + + return image.color_lut_3d( + self.mode or image.mode, + Image.Resampling.BILINEAR, + self.channels, + self.size, + self.table, + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageFont.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageFont.py new file mode 100644 index 0000000000000000000000000000000000000000..329c463ff864191849506bd61f7c0559af671f8f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageFont.py @@ -0,0 +1,1339 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PIL raster font management +# +# History: +# 1996-08-07 fl created (experimental) +# 1997-08-25 fl minor adjustments to handle fonts from pilfont 0.3 +# 1999-02-06 fl rewrote most font management stuff in C +# 1999-03-17 fl take pth files into account in load_path (from Richard Jones) +# 2001-02-17 fl added freetype support +# 2001-05-09 fl added TransposedFont wrapper class +# 2002-03-04 fl make sure we have a "L" or "1" font +# 2002-12-04 fl skip non-directory entries in the system path +# 2003-04-29 fl add embedded default font +# 2003-09-27 fl added support for truetype charmap encodings +# +# Todo: +# Adapt to PILFONT2 format (16-bit fonts, compressed, single file) +# +# Copyright (c) 1997-2003 by Secret Labs AB +# Copyright (c) 1996-2003 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# + +from __future__ import annotations + +import base64 +import os +import sys +import warnings +from enum import IntEnum +from io import BytesIO +from types import ModuleType +from typing import IO, Any, BinaryIO, TypedDict, cast + +from . import Image, features +from ._typing import StrOrBytesPath +from ._util import DeferredError, is_path + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import ImageFile + from ._imaging import ImagingFont + from ._imagingft import Font + + +class Axis(TypedDict): + minimum: int | None + default: int | None + maximum: int | None + name: bytes | None + + +class Layout(IntEnum): + BASIC = 0 + RAQM = 1 + + +MAX_STRING_LENGTH = 1_000_000 + + +core: ModuleType | DeferredError +try: + from . import _imagingft as core +except ImportError as ex: + core = DeferredError.new(ex) + + +def _string_length_check(text: str | bytes | bytearray) -> None: + if MAX_STRING_LENGTH is not None and len(text) > MAX_STRING_LENGTH: + msg = "too many characters in string" + raise ValueError(msg) + + +# FIXME: add support for pilfont2 format (see FontFile.py) + +# -------------------------------------------------------------------- +# Font metrics format: +# "PILfont" LF +# fontdescriptor LF +# (optional) key=value... LF +# "DATA" LF +# binary data: 256*10*2 bytes (dx, dy, dstbox, srcbox) +# +# To place a character, cut out srcbox and paste at dstbox, +# relative to the character position. Then move the character +# position according to dx, dy. +# -------------------------------------------------------------------- + + +class ImageFont: + """PIL font wrapper""" + + font: ImagingFont + + def _load_pilfont(self, filename: str) -> None: + with open(filename, "rb") as fp: + image: ImageFile.ImageFile | None = None + root = os.path.splitext(filename)[0] + + for ext in (".png", ".gif", ".pbm"): + if image: + image.close() + try: + fullname = root + ext + image = Image.open(fullname) + except Exception: + pass + else: + if image and image.mode in ("1", "L"): + break + else: + if image: + image.close() + + msg = f"cannot find glyph data file {root}.{{gif|pbm|png}}" + raise OSError(msg) + + self.file = fullname + + self._load_pilfont_data(fp, image) + image.close() + + def _load_pilfont_data(self, file: IO[bytes], image: Image.Image) -> None: + # read PILfont header + if file.readline() != b"PILfont\n": + msg = "Not a PILfont file" + raise SyntaxError(msg) + file.readline().split(b";") + self.info = [] # FIXME: should be a dictionary + while True: + s = file.readline() + if not s or s == b"DATA\n": + break + self.info.append(s) + + # read PILfont metrics + data = file.read(256 * 20) + + # check image + if image.mode not in ("1", "L"): + msg = "invalid font image mode" + raise TypeError(msg) + + image.load() + + self.font = Image.core.font(image.im, data) + + def getmask( + self, text: str | bytes, mode: str = "", *args: Any, **kwargs: Any + ) -> Image.core.ImagingCore: + """ + Create a bitmap for the text. + + If the font uses antialiasing, the bitmap should have mode ``L`` and use a + maximum value of 255. Otherwise, it should have mode ``1``. + + :param text: Text to render. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + .. versionadded:: 1.1.5 + + :return: An internal PIL storage memory instance as defined by the + :py:mod:`PIL.Image.core` interface module. + """ + _string_length_check(text) + Image._decompression_bomb_check(self.font.getsize(text)) + return self.font.getmask(text, mode) + + def getbbox( + self, text: str | bytes | bytearray, *args: Any, **kwargs: Any + ) -> tuple[int, int, int, int]: + """ + Returns bounding box (in pixels) of given text. + + .. versionadded:: 9.2.0 + + :param text: Text to render. + + :return: ``(left, top, right, bottom)`` bounding box + """ + _string_length_check(text) + width, height = self.font.getsize(text) + return 0, 0, width, height + + def getlength( + self, text: str | bytes | bytearray, *args: Any, **kwargs: Any + ) -> int: + """ + Returns length (in pixels) of given text. + This is the amount by which following text should be offset. + + .. versionadded:: 9.2.0 + """ + _string_length_check(text) + width, height = self.font.getsize(text) + return width + + +## +# Wrapper for FreeType fonts. Application code should use the +# truetype factory function to create font objects. + + +class FreeTypeFont: + """FreeType font wrapper (requires _imagingft service)""" + + font: Font + font_bytes: bytes + + def __init__( + self, + font: StrOrBytesPath | BinaryIO, + size: float = 10, + index: int = 0, + encoding: str = "", + layout_engine: Layout | None = None, + ) -> None: + # FIXME: use service provider instead + + if isinstance(core, DeferredError): + raise core.ex + + if size <= 0: + msg = f"font size must be greater than 0, not {size}" + raise ValueError(msg) + + self.path = font + self.size = size + self.index = index + self.encoding = encoding + + try: + from packaging.version import parse as parse_version + except ImportError: + pass + else: + if freetype_version := features.version_module("freetype2"): + if parse_version(freetype_version) < parse_version("2.9.1"): + warnings.warn( + "Support for FreeType 2.9.0 is deprecated and will be removed " + "in Pillow 12 (2025-10-15). Please upgrade to FreeType 2.9.1 " + "or newer, preferably FreeType 2.10.4 which fixes " + "CVE-2020-15999.", + DeprecationWarning, + ) + + if layout_engine not in (Layout.BASIC, Layout.RAQM): + layout_engine = Layout.BASIC + if core.HAVE_RAQM: + layout_engine = Layout.RAQM + elif layout_engine == Layout.RAQM and not core.HAVE_RAQM: + warnings.warn( + "Raqm layout was requested, but Raqm is not available. " + "Falling back to basic layout." + ) + layout_engine = Layout.BASIC + + self.layout_engine = layout_engine + + def load_from_bytes(f: IO[bytes]) -> None: + self.font_bytes = f.read() + self.font = core.getfont( + "", size, index, encoding, self.font_bytes, layout_engine + ) + + if is_path(font): + font = os.fspath(font) + if sys.platform == "win32": + font_bytes_path = font if isinstance(font, bytes) else font.encode() + try: + font_bytes_path.decode("ascii") + except UnicodeDecodeError: + # FreeType cannot load fonts with non-ASCII characters on Windows + # So load it into memory first + with open(font, "rb") as f: + load_from_bytes(f) + return + self.font = core.getfont( + font, size, index, encoding, layout_engine=layout_engine + ) + else: + load_from_bytes(cast(IO[bytes], font)) + + def __getstate__(self) -> list[Any]: + return [self.path, self.size, self.index, self.encoding, self.layout_engine] + + def __setstate__(self, state: list[Any]) -> None: + path, size, index, encoding, layout_engine = state + FreeTypeFont.__init__(self, path, size, index, encoding, layout_engine) + + def getname(self) -> tuple[str | None, str | None]: + """ + :return: A tuple of the font family (e.g. Helvetica) and the font style + (e.g. Bold) + """ + return self.font.family, self.font.style + + def getmetrics(self) -> tuple[int, int]: + """ + :return: A tuple of the font ascent (the distance from the baseline to + the highest outline point) and descent (the distance from the + baseline to the lowest outline point, a negative value) + """ + return self.font.ascent, self.font.descent + + def getlength( + self, + text: str | bytes, + mode: str = "", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + ) -> float: + """ + Returns length (in pixels with 1/64 precision) of given text when rendered + in font with provided direction, features, and language. + + This is the amount by which following text should be offset. + Text bounding box may extend past the length in some fonts, + e.g. when using italics or accents. + + The result is returned as a float; it is a whole number if using basic layout. + + Note that the sum of two lengths may not equal the length of a concatenated + string due to kerning. If you need to adjust for kerning, include the following + character and subtract its length. + + For example, instead of :: + + hello = font.getlength("Hello") + world = font.getlength("World") + hello_world = hello + world # not adjusted for kerning + assert hello_world == font.getlength("HelloWorld") # may fail + + use :: + + hello = font.getlength("HelloW") - font.getlength("W") # adjusted for kerning + world = font.getlength("World") + hello_world = hello + world # adjusted for kerning + assert hello_world == font.getlength("HelloWorld") # True + + or disable kerning with (requires libraqm) :: + + hello = draw.textlength("Hello", font, features=["-kern"]) + world = draw.textlength("World", font, features=["-kern"]) + hello_world = hello + world # kerning is disabled, no need to adjust + assert hello_world == draw.textlength("HelloWorld", font, features=["-kern"]) + + .. versionadded:: 8.0.0 + + :param text: Text to measure. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + :param direction: Direction of the text. It can be 'rtl' (right to + left), 'ltr' (left to right) or 'ttb' (top to bottom). + Requires libraqm. + + :param features: A list of OpenType font features to be used during text + layout. This is usually used to turn on optional + font features that are not enabled by default, + for example 'dlig' or 'ss01', but can be also + used to turn off default font features for + example '-liga' to disable ligatures or '-kern' + to disable kerning. To get all supported + features, see + https://learn.microsoft.com/en-us/typography/opentype/spec/featurelist + Requires libraqm. + + :param language: Language of the text. Different languages may use + different glyph shapes or ligatures. This parameter tells + the font which language the text is in, and to apply the + correct substitutions as appropriate, if available. + It should be a `BCP 47 language code + `_ + Requires libraqm. + + :return: Either width for horizontal text, or height for vertical text. + """ + _string_length_check(text) + return self.font.getlength(text, mode, direction, features, language) / 64 + + def getbbox( + self, + text: str | bytes, + mode: str = "", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + anchor: str | None = None, + ) -> tuple[float, float, float, float]: + """ + Returns bounding box (in pixels) of given text relative to given anchor + when rendered in font with provided direction, features, and language. + + Use :py:meth:`getlength()` to get the offset of following text with + 1/64 pixel precision. The bounding box includes extra margins for + some fonts, e.g. italics or accents. + + .. versionadded:: 8.0.0 + + :param text: Text to render. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + :param direction: Direction of the text. It can be 'rtl' (right to + left), 'ltr' (left to right) or 'ttb' (top to bottom). + Requires libraqm. + + :param features: A list of OpenType font features to be used during text + layout. This is usually used to turn on optional + font features that are not enabled by default, + for example 'dlig' or 'ss01', but can be also + used to turn off default font features for + example '-liga' to disable ligatures or '-kern' + to disable kerning. To get all supported + features, see + https://learn.microsoft.com/en-us/typography/opentype/spec/featurelist + Requires libraqm. + + :param language: Language of the text. Different languages may use + different glyph shapes or ligatures. This parameter tells + the font which language the text is in, and to apply the + correct substitutions as appropriate, if available. + It should be a `BCP 47 language code + `_ + Requires libraqm. + + :param stroke_width: The width of the text stroke. + + :param anchor: The text anchor alignment. Determines the relative location of + the anchor to the text. The default alignment is top left, + specifically ``la`` for horizontal text and ``lt`` for + vertical text. See :ref:`text-anchors` for details. + + :return: ``(left, top, right, bottom)`` bounding box + """ + _string_length_check(text) + size, offset = self.font.getsize( + text, mode, direction, features, language, anchor + ) + left, top = offset[0] - stroke_width, offset[1] - stroke_width + width, height = size[0] + 2 * stroke_width, size[1] + 2 * stroke_width + return left, top, left + width, top + height + + def getmask( + self, + text: str | bytes, + mode: str = "", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + anchor: str | None = None, + ink: int = 0, + start: tuple[float, float] | None = None, + ) -> Image.core.ImagingCore: + """ + Create a bitmap for the text. + + If the font uses antialiasing, the bitmap should have mode ``L`` and use a + maximum value of 255. If the font has embedded color data, the bitmap + should have mode ``RGBA``. Otherwise, it should have mode ``1``. + + :param text: Text to render. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + .. versionadded:: 1.1.5 + + :param direction: Direction of the text. It can be 'rtl' (right to + left), 'ltr' (left to right) or 'ttb' (top to bottom). + Requires libraqm. + + .. versionadded:: 4.2.0 + + :param features: A list of OpenType font features to be used during text + layout. This is usually used to turn on optional + font features that are not enabled by default, + for example 'dlig' or 'ss01', but can be also + used to turn off default font features for + example '-liga' to disable ligatures or '-kern' + to disable kerning. To get all supported + features, see + https://learn.microsoft.com/en-us/typography/opentype/spec/featurelist + Requires libraqm. + + .. versionadded:: 4.2.0 + + :param language: Language of the text. Different languages may use + different glyph shapes or ligatures. This parameter tells + the font which language the text is in, and to apply the + correct substitutions as appropriate, if available. + It should be a `BCP 47 language code + `_ + Requires libraqm. + + .. versionadded:: 6.0.0 + + :param stroke_width: The width of the text stroke. + + .. versionadded:: 6.2.0 + + :param anchor: The text anchor alignment. Determines the relative location of + the anchor to the text. The default alignment is top left, + specifically ``la`` for horizontal text and ``lt`` for + vertical text. See :ref:`text-anchors` for details. + + .. versionadded:: 8.0.0 + + :param ink: Foreground ink for rendering in RGBA mode. + + .. versionadded:: 8.0.0 + + :param start: Tuple of horizontal and vertical offset, as text may render + differently when starting at fractional coordinates. + + .. versionadded:: 9.4.0 + + :return: An internal PIL storage memory instance as defined by the + :py:mod:`PIL.Image.core` interface module. + """ + return self.getmask2( + text, + mode, + direction=direction, + features=features, + language=language, + stroke_width=stroke_width, + anchor=anchor, + ink=ink, + start=start, + )[0] + + def getmask2( + self, + text: str | bytes, + mode: str = "", + direction: str | None = None, + features: list[str] | None = None, + language: str | None = None, + stroke_width: float = 0, + anchor: str | None = None, + ink: int = 0, + start: tuple[float, float] | None = None, + *args: Any, + **kwargs: Any, + ) -> tuple[Image.core.ImagingCore, tuple[int, int]]: + """ + Create a bitmap for the text. + + If the font uses antialiasing, the bitmap should have mode ``L`` and use a + maximum value of 255. If the font has embedded color data, the bitmap + should have mode ``RGBA``. Otherwise, it should have mode ``1``. + + :param text: Text to render. + :param mode: Used by some graphics drivers to indicate what mode the + driver prefers; if empty, the renderer may return either + mode. Note that the mode is always a string, to simplify + C-level implementations. + + .. versionadded:: 1.1.5 + + :param direction: Direction of the text. It can be 'rtl' (right to + left), 'ltr' (left to right) or 'ttb' (top to bottom). + Requires libraqm. + + .. versionadded:: 4.2.0 + + :param features: A list of OpenType font features to be used during text + layout. This is usually used to turn on optional + font features that are not enabled by default, + for example 'dlig' or 'ss01', but can be also + used to turn off default font features for + example '-liga' to disable ligatures or '-kern' + to disable kerning. To get all supported + features, see + https://learn.microsoft.com/en-us/typography/opentype/spec/featurelist + Requires libraqm. + + .. versionadded:: 4.2.0 + + :param language: Language of the text. Different languages may use + different glyph shapes or ligatures. This parameter tells + the font which language the text is in, and to apply the + correct substitutions as appropriate, if available. + It should be a `BCP 47 language code + `_ + Requires libraqm. + + .. versionadded:: 6.0.0 + + :param stroke_width: The width of the text stroke. + + .. versionadded:: 6.2.0 + + :param anchor: The text anchor alignment. Determines the relative location of + the anchor to the text. The default alignment is top left, + specifically ``la`` for horizontal text and ``lt`` for + vertical text. See :ref:`text-anchors` for details. + + .. versionadded:: 8.0.0 + + :param ink: Foreground ink for rendering in RGBA mode. + + .. versionadded:: 8.0.0 + + :param start: Tuple of horizontal and vertical offset, as text may render + differently when starting at fractional coordinates. + + .. versionadded:: 9.4.0 + + :return: A tuple of an internal PIL storage memory instance as defined by the + :py:mod:`PIL.Image.core` interface module, and the text offset, the + gap between the starting coordinate and the first marking + """ + _string_length_check(text) + if start is None: + start = (0, 0) + + def fill(width: int, height: int) -> Image.core.ImagingCore: + size = (width, height) + Image._decompression_bomb_check(size) + return Image.core.fill("RGBA" if mode == "RGBA" else "L", size) + + return self.font.render( + text, + fill, + mode, + direction, + features, + language, + stroke_width, + kwargs.get("stroke_filled", False), + anchor, + ink, + start, + ) + + def font_variant( + self, + font: StrOrBytesPath | BinaryIO | None = None, + size: float | None = None, + index: int | None = None, + encoding: str | None = None, + layout_engine: Layout | None = None, + ) -> FreeTypeFont: + """ + Create a copy of this FreeTypeFont object, + using any specified arguments to override the settings. + + Parameters are identical to the parameters used to initialize this + object. + + :return: A FreeTypeFont object. + """ + if font is None: + try: + font = BytesIO(self.font_bytes) + except AttributeError: + font = self.path + return FreeTypeFont( + font=font, + size=self.size if size is None else size, + index=self.index if index is None else index, + encoding=self.encoding if encoding is None else encoding, + layout_engine=layout_engine or self.layout_engine, + ) + + def get_variation_names(self) -> list[bytes]: + """ + :returns: A list of the named styles in a variation font. + :exception OSError: If the font is not a variation font. + """ + try: + names = self.font.getvarnames() + except AttributeError as e: + msg = "FreeType 2.9.1 or greater is required" + raise NotImplementedError(msg) from e + return [name.replace(b"\x00", b"") for name in names] + + def set_variation_by_name(self, name: str | bytes) -> None: + """ + :param name: The name of the style. + :exception OSError: If the font is not a variation font. + """ + names = self.get_variation_names() + if not isinstance(name, bytes): + name = name.encode() + index = names.index(name) + 1 + + if index == getattr(self, "_last_variation_index", None): + # When the same name is set twice in a row, + # there is an 'unknown freetype error' + # https://savannah.nongnu.org/bugs/?56186 + return + self._last_variation_index = index + + self.font.setvarname(index) + + def get_variation_axes(self) -> list[Axis]: + """ + :returns: A list of the axes in a variation font. + :exception OSError: If the font is not a variation font. + """ + try: + axes = self.font.getvaraxes() + except AttributeError as e: + msg = "FreeType 2.9.1 or greater is required" + raise NotImplementedError(msg) from e + for axis in axes: + if axis["name"]: + axis["name"] = axis["name"].replace(b"\x00", b"") + return axes + + def set_variation_by_axes(self, axes: list[float]) -> None: + """ + :param axes: A list of values for each axis. + :exception OSError: If the font is not a variation font. + """ + try: + self.font.setvaraxes(axes) + except AttributeError as e: + msg = "FreeType 2.9.1 or greater is required" + raise NotImplementedError(msg) from e + + +class TransposedFont: + """Wrapper for writing rotated or mirrored text""" + + def __init__( + self, font: ImageFont | FreeTypeFont, orientation: Image.Transpose | None = None + ): + """ + Wrapper that creates a transposed font from any existing font + object. + + :param font: A font object. + :param orientation: An optional orientation. If given, this should + be one of Image.Transpose.FLIP_LEFT_RIGHT, Image.Transpose.FLIP_TOP_BOTTOM, + Image.Transpose.ROTATE_90, Image.Transpose.ROTATE_180, or + Image.Transpose.ROTATE_270. + """ + self.font = font + self.orientation = orientation # any 'transpose' argument, or None + + def getmask( + self, text: str | bytes, mode: str = "", *args: Any, **kwargs: Any + ) -> Image.core.ImagingCore: + im = self.font.getmask(text, mode, *args, **kwargs) + if self.orientation is not None: + return im.transpose(self.orientation) + return im + + def getbbox( + self, text: str | bytes, *args: Any, **kwargs: Any + ) -> tuple[int, int, float, float]: + # TransposedFont doesn't support getmask2, move top-left point to (0, 0) + # this has no effect on ImageFont and simulates anchor="lt" for FreeTypeFont + left, top, right, bottom = self.font.getbbox(text, *args, **kwargs) + width = right - left + height = bottom - top + if self.orientation in (Image.Transpose.ROTATE_90, Image.Transpose.ROTATE_270): + return 0, 0, height, width + return 0, 0, width, height + + def getlength(self, text: str | bytes, *args: Any, **kwargs: Any) -> float: + if self.orientation in (Image.Transpose.ROTATE_90, Image.Transpose.ROTATE_270): + msg = "text length is undefined for text rotated by 90 or 270 degrees" + raise ValueError(msg) + return self.font.getlength(text, *args, **kwargs) + + +def load(filename: str) -> ImageFont: + """ + Load a font file. This function loads a font object from the given + bitmap font file, and returns the corresponding font object. For loading TrueType + or OpenType fonts instead, see :py:func:`~PIL.ImageFont.truetype`. + + :param filename: Name of font file. + :return: A font object. + :exception OSError: If the file could not be read. + """ + f = ImageFont() + f._load_pilfont(filename) + return f + + +def truetype( + font: StrOrBytesPath | BinaryIO, + size: float = 10, + index: int = 0, + encoding: str = "", + layout_engine: Layout | None = None, +) -> FreeTypeFont: + """ + Load a TrueType or OpenType font from a file or file-like object, + and create a font object. This function loads a font object from the given + file or file-like object, and creates a font object for a font of the given + size. For loading bitmap fonts instead, see :py:func:`~PIL.ImageFont.load` + and :py:func:`~PIL.ImageFont.load_path`. + + Pillow uses FreeType to open font files. On Windows, be aware that FreeType + will keep the file open as long as the FreeTypeFont object exists. Windows + limits the number of files that can be open in C at once to 512, so if many + fonts are opened simultaneously and that limit is approached, an + ``OSError`` may be thrown, reporting that FreeType "cannot open resource". + A workaround would be to copy the file(s) into memory, and open that instead. + + This function requires the _imagingft service. + + :param font: A filename or file-like object containing a TrueType font. + If the file is not found in this filename, the loader may also + search in other directories, such as: + + * The :file:`fonts/` directory on Windows, + * :file:`/Library/Fonts/`, :file:`/System/Library/Fonts/` + and :file:`~/Library/Fonts/` on macOS. + * :file:`~/.local/share/fonts`, :file:`/usr/local/share/fonts`, + and :file:`/usr/share/fonts` on Linux; or those specified by + the ``XDG_DATA_HOME`` and ``XDG_DATA_DIRS`` environment variables + for user-installed and system-wide fonts, respectively. + + :param size: The requested size, in pixels. + :param index: Which font face to load (default is first available face). + :param encoding: Which font encoding to use (default is Unicode). Possible + encodings include (see the FreeType documentation for more + information): + + * "unic" (Unicode) + * "symb" (Microsoft Symbol) + * "ADOB" (Adobe Standard) + * "ADBE" (Adobe Expert) + * "ADBC" (Adobe Custom) + * "armn" (Apple Roman) + * "sjis" (Shift JIS) + * "gb " (PRC) + * "big5" + * "wans" (Extended Wansung) + * "joha" (Johab) + * "lat1" (Latin-1) + + This specifies the character set to use. It does not alter the + encoding of any text provided in subsequent operations. + :param layout_engine: Which layout engine to use, if available: + :attr:`.ImageFont.Layout.BASIC` or :attr:`.ImageFont.Layout.RAQM`. + If it is available, Raqm layout will be used by default. + Otherwise, basic layout will be used. + + Raqm layout is recommended for all non-English text. If Raqm layout + is not required, basic layout will have better performance. + + You can check support for Raqm layout using + :py:func:`PIL.features.check_feature` with ``feature="raqm"``. + + .. versionadded:: 4.2.0 + :return: A font object. + :exception OSError: If the file could not be read. + :exception ValueError: If the font size is not greater than zero. + """ + + def freetype(font: StrOrBytesPath | BinaryIO) -> FreeTypeFont: + return FreeTypeFont(font, size, index, encoding, layout_engine) + + try: + return freetype(font) + except OSError: + if not is_path(font): + raise + ttf_filename = os.path.basename(font) + + dirs = [] + if sys.platform == "win32": + # check the windows font repository + # NOTE: must use uppercase WINDIR, to work around bugs in + # 1.5.2's os.environ.get() + windir = os.environ.get("WINDIR") + if windir: + dirs.append(os.path.join(windir, "fonts")) + elif sys.platform in ("linux", "linux2"): + data_home = os.environ.get("XDG_DATA_HOME") + if not data_home: + # The freedesktop spec defines the following default directory for + # when XDG_DATA_HOME is unset or empty. This user-level directory + # takes precedence over system-level directories. + data_home = os.path.expanduser("~/.local/share") + xdg_dirs = [data_home] + + data_dirs = os.environ.get("XDG_DATA_DIRS") + if not data_dirs: + # Similarly, defaults are defined for the system-level directories + data_dirs = "/usr/local/share:/usr/share" + xdg_dirs += data_dirs.split(":") + + dirs += [os.path.join(xdg_dir, "fonts") for xdg_dir in xdg_dirs] + elif sys.platform == "darwin": + dirs += [ + "/Library/Fonts", + "/System/Library/Fonts", + os.path.expanduser("~/Library/Fonts"), + ] + + ext = os.path.splitext(ttf_filename)[1] + first_font_with_a_different_extension = None + for directory in dirs: + for walkroot, walkdir, walkfilenames in os.walk(directory): + for walkfilename in walkfilenames: + if ext and walkfilename == ttf_filename: + return freetype(os.path.join(walkroot, walkfilename)) + elif not ext and os.path.splitext(walkfilename)[0] == ttf_filename: + fontpath = os.path.join(walkroot, walkfilename) + if os.path.splitext(fontpath)[1] == ".ttf": + return freetype(fontpath) + if not ext and first_font_with_a_different_extension is None: + first_font_with_a_different_extension = fontpath + if first_font_with_a_different_extension: + return freetype(first_font_with_a_different_extension) + raise + + +def load_path(filename: str | bytes) -> ImageFont: + """ + Load font file. Same as :py:func:`~PIL.ImageFont.load`, but searches for a + bitmap font along the Python path. + + :param filename: Name of font file. + :return: A font object. + :exception OSError: If the file could not be read. + """ + if not isinstance(filename, str): + filename = filename.decode("utf-8") + for directory in sys.path: + try: + return load(os.path.join(directory, filename)) + except OSError: + pass + msg = f'cannot find font file "{filename}" in sys.path' + if os.path.exists(filename): + msg += f', did you mean ImageFont.load("{filename}") instead?' + + raise OSError(msg) + + +def load_default_imagefont() -> ImageFont: + f = ImageFont() + f._load_pilfont_data( + # courB08 + BytesIO( + base64.b64decode( + b""" +UElMZm9udAo7Ozs7OzsxMDsKREFUQQoAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA +AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA 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+ ), + 10 if size is None else size, + layout_engine=Layout.BASIC, + ) + return load_default_imagefont() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageGrab.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageGrab.py new file mode 100644 index 0000000000000000000000000000000000000000..1eb4507344cd412bed5c113d4c52e39f26cecab2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageGrab.py @@ -0,0 +1,196 @@ +# +# The Python Imaging Library +# $Id$ +# +# screen grabber +# +# History: +# 2001-04-26 fl created +# 2001-09-17 fl use builtin driver, if present +# 2002-11-19 fl added grabclipboard support +# +# Copyright (c) 2001-2002 by Secret Labs AB +# Copyright (c) 2001-2002 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import os +import shutil +import subprocess +import sys +import tempfile + +from . import Image + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import ImageWin + + +def grab( + bbox: tuple[int, int, int, int] | None = None, + include_layered_windows: bool = False, + all_screens: bool = False, + xdisplay: str | None = None, + window: int | ImageWin.HWND | None = None, +) -> Image.Image: + im: Image.Image + if xdisplay is None: + if sys.platform == "darwin": + fh, filepath = tempfile.mkstemp(".png") + os.close(fh) + args = ["screencapture"] + if bbox: + left, top, right, bottom = bbox + args += ["-R", f"{left},{top},{right-left},{bottom-top}"] + subprocess.call(args + ["-x", filepath]) + im = Image.open(filepath) + im.load() + os.unlink(filepath) + if bbox: + im_resized = im.resize((right - left, bottom - top)) + im.close() + return im_resized + return im + elif sys.platform == "win32": + if window is not None: + all_screens = -1 + offset, size, data = Image.core.grabscreen_win32( + include_layered_windows, + all_screens, + int(window) if window is not None else 0, + ) + im = Image.frombytes( + "RGB", + size, + data, + # RGB, 32-bit line padding, origin lower left corner + "raw", + "BGR", + (size[0] * 3 + 3) & -4, + -1, + ) + if bbox: + x0, y0 = offset + left, top, right, bottom = bbox + im = im.crop((left - x0, top - y0, right - x0, bottom - y0)) + return im + # Cast to Optional[str] needed for Windows and macOS. + display_name: str | None = xdisplay + try: + if not Image.core.HAVE_XCB: + msg = "Pillow was built without XCB support" + raise OSError(msg) + size, data = Image.core.grabscreen_x11(display_name) + except OSError: + if display_name is None and sys.platform not in ("darwin", "win32"): + if shutil.which("gnome-screenshot"): + args = ["gnome-screenshot", "-f"] + elif shutil.which("grim"): + args = ["grim"] + elif shutil.which("spectacle"): + args = ["spectacle", "-n", "-b", "-f", "-o"] + else: + raise + fh, filepath = tempfile.mkstemp(".png") + os.close(fh) + subprocess.call(args + [filepath]) + im = Image.open(filepath) + im.load() + os.unlink(filepath) + if bbox: + im_cropped = im.crop(bbox) + im.close() + return im_cropped + return im + else: + raise + else: + im = Image.frombytes("RGB", size, data, "raw", "BGRX", size[0] * 4, 1) + if bbox: + im = im.crop(bbox) + return im + + +def grabclipboard() -> Image.Image | list[str] | None: + if sys.platform == "darwin": + p = subprocess.run( + ["osascript", "-e", "get the clipboard as «class PNGf»"], + capture_output=True, + ) + if p.returncode != 0: + return None + + import binascii + + data = io.BytesIO(binascii.unhexlify(p.stdout[11:-3])) + return Image.open(data) + elif sys.platform == "win32": + fmt, data = Image.core.grabclipboard_win32() + if fmt == "file": # CF_HDROP + import struct + + o = struct.unpack_from("I", data)[0] + if data[16] == 0: + files = data[o:].decode("mbcs").split("\0") + else: + files = data[o:].decode("utf-16le").split("\0") + return files[: files.index("")] + if isinstance(data, bytes): + data = io.BytesIO(data) + if fmt == "png": + from . import PngImagePlugin + + return PngImagePlugin.PngImageFile(data) + elif fmt == "DIB": + from . import BmpImagePlugin + + return BmpImagePlugin.DibImageFile(data) + return None + else: + if os.getenv("WAYLAND_DISPLAY"): + session_type = "wayland" + elif os.getenv("DISPLAY"): + session_type = "x11" + else: # Session type check failed + session_type = None + + if shutil.which("wl-paste") and session_type in ("wayland", None): + args = ["wl-paste", "-t", "image"] + elif shutil.which("xclip") and session_type in ("x11", None): + args = ["xclip", "-selection", "clipboard", "-t", "image/png", "-o"] + else: + msg = "wl-paste or xclip is required for ImageGrab.grabclipboard() on Linux" + raise NotImplementedError(msg) + + p = subprocess.run(args, capture_output=True) + if p.returncode != 0: + err = p.stderr + for silent_error in [ + # wl-paste, when the clipboard is empty + b"Nothing is copied", + # Ubuntu/Debian wl-paste, when the clipboard is empty + b"No selection", + # Ubuntu/Debian wl-paste, when an image isn't available + b"No suitable type of content copied", + # wl-paste or Ubuntu/Debian xclip, when an image isn't available + b" not available", + # xclip, when an image isn't available + b"cannot convert ", + # xclip, when the clipboard isn't initialized + b"xclip: Error: There is no owner for the ", + ]: + if silent_error in err: + return None + msg = f"{args[0]} error" + if err: + msg += f": {err.strip().decode()}" + raise ChildProcessError(msg) + + data = io.BytesIO(p.stdout) + im = Image.open(data) + im.load() + return im diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageMath.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageMath.py new file mode 100644 index 0000000000000000000000000000000000000000..c33809ced890e437d10102a6c065d9efe3207685 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageMath.py @@ -0,0 +1,368 @@ +# +# The Python Imaging Library +# $Id$ +# +# a simple math add-on for the Python Imaging Library +# +# History: +# 1999-02-15 fl Original PIL Plus release +# 2005-05-05 fl Simplified and cleaned up for PIL 1.1.6 +# 2005-09-12 fl Fixed int() and float() for Python 2.4.1 +# +# Copyright (c) 1999-2005 by Secret Labs AB +# Copyright (c) 2005 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import builtins +from types import CodeType +from typing import Any, Callable + +from . import Image, _imagingmath +from ._deprecate import deprecate + + +class _Operand: + """Wraps an image operand, providing standard operators""" + + def __init__(self, im: Image.Image): + self.im = im + + def __fixup(self, im1: _Operand | float) -> Image.Image: + # convert image to suitable mode + if isinstance(im1, _Operand): + # argument was an image. + if im1.im.mode in ("1", "L"): + return im1.im.convert("I") + elif im1.im.mode in ("I", "F"): + return im1.im + else: + msg = f"unsupported mode: {im1.im.mode}" + raise ValueError(msg) + else: + # argument was a constant + if isinstance(im1, (int, float)) and self.im.mode in ("1", "L", "I"): + return Image.new("I", self.im.size, im1) + else: + return Image.new("F", self.im.size, im1) + + def apply( + self, + op: str, + im1: _Operand | float, + im2: _Operand | float | None = None, + mode: str | None = None, + ) -> _Operand: + im_1 = self.__fixup(im1) + if im2 is None: + # unary operation + out = Image.new(mode or im_1.mode, im_1.size, None) + try: + op = getattr(_imagingmath, f"{op}_{im_1.mode}") + except AttributeError as e: + msg = f"bad operand type for '{op}'" + raise TypeError(msg) from e + _imagingmath.unop(op, out.getim(), im_1.getim()) + else: + # binary operation + im_2 = self.__fixup(im2) + if im_1.mode != im_2.mode: + # convert both arguments to floating point + if im_1.mode != "F": + im_1 = im_1.convert("F") + if im_2.mode != "F": + im_2 = im_2.convert("F") + if im_1.size != im_2.size: + # crop both arguments to a common size + size = ( + min(im_1.size[0], im_2.size[0]), + min(im_1.size[1], im_2.size[1]), + ) + if im_1.size != size: + im_1 = im_1.crop((0, 0) + size) + if im_2.size != size: + im_2 = im_2.crop((0, 0) + size) + out = Image.new(mode or im_1.mode, im_1.size, None) + try: + op = getattr(_imagingmath, f"{op}_{im_1.mode}") + except AttributeError as e: + msg = f"bad operand type for '{op}'" + raise TypeError(msg) from e + _imagingmath.binop(op, out.getim(), im_1.getim(), im_2.getim()) + return _Operand(out) + + # unary operators + def __bool__(self) -> bool: + # an image is "true" if it contains at least one non-zero pixel + return self.im.getbbox() is not None + + def __abs__(self) -> _Operand: + return self.apply("abs", self) + + def __pos__(self) -> _Operand: + return self + + def __neg__(self) -> _Operand: + return self.apply("neg", self) + + # binary operators + def __add__(self, other: _Operand | float) -> _Operand: + return self.apply("add", self, other) + + def __radd__(self, other: _Operand | float) -> _Operand: + return self.apply("add", other, self) + + def __sub__(self, other: _Operand | float) -> _Operand: + return self.apply("sub", self, other) + + def __rsub__(self, other: _Operand | float) -> _Operand: + return self.apply("sub", other, self) + + def __mul__(self, other: _Operand | float) -> _Operand: + return self.apply("mul", self, other) + + def __rmul__(self, other: _Operand | float) -> _Operand: + return self.apply("mul", other, self) + + def __truediv__(self, other: _Operand | float) -> _Operand: + return self.apply("div", self, other) + + def __rtruediv__(self, other: _Operand | float) -> _Operand: + return self.apply("div", other, self) + + def __mod__(self, other: _Operand | float) -> _Operand: + return self.apply("mod", self, other) + + def __rmod__(self, other: _Operand | float) -> _Operand: + return self.apply("mod", other, self) + + def __pow__(self, other: _Operand | float) -> _Operand: + return self.apply("pow", self, other) + + def __rpow__(self, other: _Operand | float) -> _Operand: + return self.apply("pow", other, self) + + # bitwise + def __invert__(self) -> _Operand: + return self.apply("invert", self) + + def __and__(self, other: _Operand | float) -> _Operand: + return self.apply("and", self, other) + + def __rand__(self, other: _Operand | float) -> _Operand: + return self.apply("and", other, self) + + def __or__(self, other: _Operand | float) -> _Operand: + return self.apply("or", self, other) + + def __ror__(self, other: _Operand | float) -> _Operand: + return self.apply("or", other, self) + + def __xor__(self, other: _Operand | float) -> _Operand: + return self.apply("xor", self, other) + + def __rxor__(self, other: _Operand | float) -> _Operand: + return self.apply("xor", other, self) + + def __lshift__(self, other: _Operand | float) -> _Operand: + return self.apply("lshift", self, other) + + def __rshift__(self, other: _Operand | float) -> _Operand: + return self.apply("rshift", self, other) + + # logical + def __eq__(self, other: _Operand | float) -> _Operand: # type: ignore[override] + return self.apply("eq", self, other) + + def __ne__(self, other: _Operand | float) -> _Operand: # type: ignore[override] + return self.apply("ne", self, other) + + def __lt__(self, other: _Operand | float) -> _Operand: + return self.apply("lt", self, other) + + def __le__(self, other: _Operand | float) -> _Operand: + return self.apply("le", self, other) + + def __gt__(self, other: _Operand | float) -> _Operand: + return self.apply("gt", self, other) + + def __ge__(self, other: _Operand | float) -> _Operand: + return self.apply("ge", self, other) + + +# conversions +def imagemath_int(self: _Operand) -> _Operand: + return _Operand(self.im.convert("I")) + + +def imagemath_float(self: _Operand) -> _Operand: + return _Operand(self.im.convert("F")) + + +# logical +def imagemath_equal(self: _Operand, other: _Operand | float | None) -> _Operand: + return self.apply("eq", self, other, mode="I") + + +def imagemath_notequal(self: _Operand, other: _Operand | float | None) -> _Operand: + return self.apply("ne", self, other, mode="I") + + +def imagemath_min(self: _Operand, other: _Operand | float | None) -> _Operand: + return self.apply("min", self, other) + + +def imagemath_max(self: _Operand, other: _Operand | float | None) -> _Operand: + return self.apply("max", self, other) + + +def imagemath_convert(self: _Operand, mode: str) -> _Operand: + return _Operand(self.im.convert(mode)) + + +ops = { + "int": imagemath_int, + "float": imagemath_float, + "equal": imagemath_equal, + "notequal": imagemath_notequal, + "min": imagemath_min, + "max": imagemath_max, + "convert": imagemath_convert, +} + + +def lambda_eval( + expression: Callable[[dict[str, Any]], Any], + options: dict[str, Any] = {}, + **kw: Any, +) -> Any: + """ + Returns the result of an image function. + + :py:mod:`~PIL.ImageMath` only supports single-layer images. To process multi-band + images, use the :py:meth:`~PIL.Image.Image.split` method or + :py:func:`~PIL.Image.merge` function. + + :param expression: A function that receives a dictionary. + :param options: Values to add to the function's dictionary. Deprecated. + You can instead use one or more keyword arguments. + :param **kw: Values to add to the function's dictionary. + :return: The expression result. This is usually an image object, but can + also be an integer, a floating point value, or a pixel tuple, + depending on the expression. + """ + + if options: + deprecate( + "ImageMath.lambda_eval options", + 12, + "ImageMath.lambda_eval keyword arguments", + ) + + args: dict[str, Any] = ops.copy() + args.update(options) + args.update(kw) + for k, v in args.items(): + if isinstance(v, Image.Image): + args[k] = _Operand(v) + + out = expression(args) + try: + return out.im + except AttributeError: + return out + + +def unsafe_eval( + expression: str, + options: dict[str, Any] = {}, + **kw: Any, +) -> Any: + """ + Evaluates an image expression. This uses Python's ``eval()`` function to process + the expression string, and carries the security risks of doing so. It is not + recommended to process expressions without considering this. + :py:meth:`~lambda_eval` is a more secure alternative. + + :py:mod:`~PIL.ImageMath` only supports single-layer images. To process multi-band + images, use the :py:meth:`~PIL.Image.Image.split` method or + :py:func:`~PIL.Image.merge` function. + + :param expression: A string containing a Python-style expression. + :param options: Values to add to the evaluation context. Deprecated. + You can instead use one or more keyword arguments. + :param **kw: Values to add to the evaluation context. + :return: The evaluated expression. This is usually an image object, but can + also be an integer, a floating point value, or a pixel tuple, + depending on the expression. + """ + + if options: + deprecate( + "ImageMath.unsafe_eval options", + 12, + "ImageMath.unsafe_eval keyword arguments", + ) + + # build execution namespace + args: dict[str, Any] = ops.copy() + for k in [*options, *kw]: + if "__" in k or hasattr(builtins, k): + msg = f"'{k}' not allowed" + raise ValueError(msg) + + args.update(options) + args.update(kw) + for k, v in args.items(): + if isinstance(v, Image.Image): + args[k] = _Operand(v) + + compiled_code = compile(expression, "", "eval") + + def scan(code: CodeType) -> None: + for const in code.co_consts: + if type(const) is type(compiled_code): + scan(const) + + for name in code.co_names: + if name not in args and name != "abs": + msg = f"'{name}' not allowed" + raise ValueError(msg) + + scan(compiled_code) + out = builtins.eval(expression, {"__builtins": {"abs": abs}}, args) + try: + return out.im + except AttributeError: + return out + + +def eval( + expression: str, + _dict: dict[str, Any] = {}, + **kw: Any, +) -> Any: + """ + Evaluates an image expression. + + Deprecated. Use lambda_eval() or unsafe_eval() instead. + + :param expression: A string containing a Python-style expression. + :param _dict: Values to add to the evaluation context. You + can either use a dictionary, or one or more keyword + arguments. + :return: The evaluated expression. This is usually an image object, but can + also be an integer, a floating point value, or a pixel tuple, + depending on the expression. + + .. deprecated:: 10.3.0 + """ + + deprecate( + "ImageMath.eval", + 12, + "ImageMath.lambda_eval or ImageMath.unsafe_eval", + ) + return unsafe_eval(expression, _dict, **kw) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageMode.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageMode.py new file mode 100644 index 0000000000000000000000000000000000000000..92a08d2cbcb4f8f2f7e24a265b83cd6c1012b41f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageMode.py @@ -0,0 +1,92 @@ +# +# The Python Imaging Library. +# $Id$ +# +# standard mode descriptors +# +# History: +# 2006-03-20 fl Added +# +# Copyright (c) 2006 by Secret Labs AB. +# Copyright (c) 2006 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import sys +from functools import lru_cache +from typing import NamedTuple + +from ._deprecate import deprecate + + +class ModeDescriptor(NamedTuple): + """Wrapper for mode strings.""" + + mode: str + bands: tuple[str, ...] + basemode: str + basetype: str + typestr: str + + def __str__(self) -> str: + return self.mode + + +@lru_cache +def getmode(mode: str) -> ModeDescriptor: + """Gets a mode descriptor for the given mode.""" + endian = "<" if sys.byteorder == "little" else ">" + + modes = { + # core modes + # Bits need to be extended to bytes + "1": ("L", "L", ("1",), "|b1"), + "L": ("L", "L", ("L",), "|u1"), + "I": ("L", "I", ("I",), f"{endian}i4"), + "F": ("L", "F", ("F",), f"{endian}f4"), + "P": ("P", "L", ("P",), "|u1"), + "RGB": ("RGB", "L", ("R", "G", "B"), "|u1"), + "RGBX": ("RGB", "L", ("R", "G", "B", "X"), "|u1"), + "RGBA": ("RGB", "L", ("R", "G", "B", "A"), "|u1"), + "CMYK": ("RGB", "L", ("C", "M", "Y", "K"), "|u1"), + "YCbCr": ("RGB", "L", ("Y", "Cb", "Cr"), "|u1"), + # UNDONE - unsigned |u1i1i1 + "LAB": ("RGB", "L", ("L", "A", "B"), "|u1"), + "HSV": ("RGB", "L", ("H", "S", "V"), "|u1"), + # extra experimental modes + "RGBa": ("RGB", "L", ("R", "G", "B", "a"), "|u1"), + "BGR;15": ("RGB", "L", ("B", "G", "R"), "|u1"), + "BGR;16": ("RGB", "L", ("B", "G", "R"), "|u1"), + "BGR;24": ("RGB", "L", ("B", "G", "R"), "|u1"), + "LA": ("L", "L", ("L", "A"), "|u1"), + "La": ("L", "L", ("L", "a"), "|u1"), + "PA": ("RGB", "L", ("P", "A"), "|u1"), + } + if mode in modes: + if mode in ("BGR;15", "BGR;16", "BGR;24"): + deprecate(mode, 12) + base_mode, base_type, bands, type_str = modes[mode] + return ModeDescriptor(mode, bands, base_mode, base_type, type_str) + + mapping_modes = { + # I;16 == I;16L, and I;32 == I;32L + "I;16": "u2", + "I;16BS": ">i2", + "I;16N": f"{endian}u2", + "I;16NS": f"{endian}i2", + "I;32": "u4", + "I;32L": "i4", + "I;32LS": " +from __future__ import annotations + +import re + +from . import Image, _imagingmorph + +LUT_SIZE = 1 << 9 + +# fmt: off +ROTATION_MATRIX = [ + 6, 3, 0, + 7, 4, 1, + 8, 5, 2, +] +MIRROR_MATRIX = [ + 2, 1, 0, + 5, 4, 3, + 8, 7, 6, +] +# fmt: on + + +class LutBuilder: + """A class for building a MorphLut from a descriptive language + + The input patterns is a list of a strings sequences like these:: + + 4:(... + .1. + 111)->1 + + (whitespaces including linebreaks are ignored). The option 4 + describes a series of symmetry operations (in this case a + 4-rotation), the pattern is described by: + + - . or X - Ignore + - 1 - Pixel is on + - 0 - Pixel is off + + The result of the operation is described after "->" string. + + The default is to return the current pixel value, which is + returned if no other match is found. + + Operations: + + - 4 - 4 way rotation + - N - Negate + - 1 - Dummy op for no other operation (an op must always be given) + - M - Mirroring + + Example:: + + lb = LutBuilder(patterns = ["4:(... .1. 111)->1"]) + lut = lb.build_lut() + + """ + + def __init__( + self, patterns: list[str] | None = None, op_name: str | None = None + ) -> None: + if patterns is not None: + self.patterns = patterns + else: + self.patterns = [] + self.lut: bytearray | None = None + if op_name is not None: + known_patterns = { + "corner": ["1:(... ... ...)->0", "4:(00. 01. ...)->1"], + "dilation4": ["4:(... .0. .1.)->1"], + "dilation8": ["4:(... .0. .1.)->1", "4:(... .0. ..1)->1"], + "erosion4": ["4:(... .1. .0.)->0"], + "erosion8": ["4:(... .1. .0.)->0", "4:(... .1. ..0)->0"], + "edge": [ + "1:(... ... ...)->0", + "4:(.0. .1. ...)->1", + "4:(01. .1. ...)->1", + ], + } + if op_name not in known_patterns: + msg = f"Unknown pattern {op_name}!" + raise Exception(msg) + + self.patterns = known_patterns[op_name] + + def add_patterns(self, patterns: list[str]) -> None: + self.patterns += patterns + + def build_default_lut(self) -> None: + symbols = [0, 1] + m = 1 << 4 # pos of current pixel + self.lut = bytearray(symbols[(i & m) > 0] for i in range(LUT_SIZE)) + + def get_lut(self) -> bytearray | None: + return self.lut + + def _string_permute(self, pattern: str, permutation: list[int]) -> str: + """string_permute takes a pattern and a permutation and returns the + string permuted according to the permutation list. + """ + assert len(permutation) == 9 + return "".join(pattern[p] for p in permutation) + + def _pattern_permute( + self, basic_pattern: str, options: str, basic_result: int + ) -> list[tuple[str, int]]: + """pattern_permute takes a basic pattern and its result and clones + the pattern according to the modifications described in the $options + parameter. It returns a list of all cloned patterns.""" + patterns = [(basic_pattern, basic_result)] + + # rotations + if "4" in options: + res = patterns[-1][1] + for i in range(4): + patterns.append( + (self._string_permute(patterns[-1][0], ROTATION_MATRIX), res) + ) + # mirror + if "M" in options: + n = len(patterns) + for pattern, res in patterns[:n]: + patterns.append((self._string_permute(pattern, MIRROR_MATRIX), res)) + + # negate + if "N" in options: + n = len(patterns) + for pattern, res in patterns[:n]: + # Swap 0 and 1 + pattern = pattern.replace("0", "Z").replace("1", "0").replace("Z", "1") + res = 1 - int(res) + patterns.append((pattern, res)) + + return patterns + + def build_lut(self) -> bytearray: + """Compile all patterns into a morphology lut. + + TBD :Build based on (file) morphlut:modify_lut + """ + self.build_default_lut() + assert self.lut is not None + patterns = [] + + # Parse and create symmetries of the patterns strings + for p in self.patterns: + m = re.search(r"(\w*):?\s*\((.+?)\)\s*->\s*(\d)", p.replace("\n", "")) + if not m: + msg = 'Syntax error in pattern "' + p + '"' + raise Exception(msg) + options = m.group(1) + pattern = m.group(2) + result = int(m.group(3)) + + # Get rid of spaces + pattern = pattern.replace(" ", "").replace("\n", "") + + patterns += self._pattern_permute(pattern, options, result) + + # compile the patterns into regular expressions for speed + compiled_patterns = [] + for pattern in patterns: + p = pattern[0].replace(".", "X").replace("X", "[01]") + compiled_patterns.append((re.compile(p), pattern[1])) + + # Step through table and find patterns that match. + # Note that all the patterns are searched. The last one + # caught overrides + for i in range(LUT_SIZE): + # Build the bit pattern + bitpattern = bin(i)[2:] + bitpattern = ("0" * (9 - len(bitpattern)) + bitpattern)[::-1] + + for pattern, r in compiled_patterns: + if pattern.match(bitpattern): + self.lut[i] = [0, 1][r] + + return self.lut + + +class MorphOp: + """A class for binary morphological operators""" + + def __init__( + self, + lut: bytearray | None = None, + op_name: str | None = None, + patterns: list[str] | None = None, + ) -> None: + """Create a binary morphological operator""" + self.lut = lut + if op_name is not None: + self.lut = LutBuilder(op_name=op_name).build_lut() + elif patterns is not None: + self.lut = LutBuilder(patterns=patterns).build_lut() + + def apply(self, image: Image.Image) -> tuple[int, Image.Image]: + """Run a single morphological operation on an image + + Returns a tuple of the number of changed pixels and the + morphed image""" + if self.lut is None: + msg = "No operator loaded" + raise Exception(msg) + + if image.mode != "L": + msg = "Image mode must be L" + raise ValueError(msg) + outimage = Image.new(image.mode, image.size, None) + count = _imagingmorph.apply(bytes(self.lut), image.getim(), outimage.getim()) + return count, outimage + + def match(self, image: Image.Image) -> list[tuple[int, int]]: + """Get a list of coordinates matching the morphological operation on + an image. + + Returns a list of tuples of (x,y) coordinates + of all matching pixels. See :ref:`coordinate-system`.""" + if self.lut is None: + msg = "No operator loaded" + raise Exception(msg) + + if image.mode != "L": + msg = "Image mode must be L" + raise ValueError(msg) + return _imagingmorph.match(bytes(self.lut), image.getim()) + + def get_on_pixels(self, image: Image.Image) -> list[tuple[int, int]]: + """Get a list of all turned on pixels in a binary image + + Returns a list of tuples of (x,y) coordinates + of all matching pixels. See :ref:`coordinate-system`.""" + + if image.mode != "L": + msg = "Image mode must be L" + raise ValueError(msg) + return _imagingmorph.get_on_pixels(image.getim()) + + def load_lut(self, filename: str) -> None: + """Load an operator from an mrl file""" + with open(filename, "rb") as f: + self.lut = bytearray(f.read()) + + if len(self.lut) != LUT_SIZE: + self.lut = None + msg = "Wrong size operator file!" + raise Exception(msg) + + def save_lut(self, filename: str) -> None: + """Save an operator to an mrl file""" + if self.lut is None: + msg = "No operator loaded" + raise Exception(msg) + with open(filename, "wb") as f: + f.write(self.lut) + + def set_lut(self, lut: bytearray | None) -> None: + """Set the lut from an external source""" + self.lut = lut diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageOps.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageOps.py new file mode 100644 index 0000000000000000000000000000000000000000..da28854b5745459cc682beabb53480855092e7a4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageOps.py @@ -0,0 +1,745 @@ +# +# The Python Imaging Library. +# $Id$ +# +# standard image operations +# +# History: +# 2001-10-20 fl Created +# 2001-10-23 fl Added autocontrast operator +# 2001-12-18 fl Added Kevin's fit operator +# 2004-03-14 fl Fixed potential division by zero in equalize +# 2005-05-05 fl Fixed equalize for low number of values +# +# Copyright (c) 2001-2004 by Secret Labs AB +# Copyright (c) 2001-2004 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import functools +import operator +import re +from collections.abc import Sequence +from typing import Literal, Protocol, cast, overload + +from . import ExifTags, Image, ImagePalette + +# +# helpers + + +def _border(border: int | tuple[int, ...]) -> tuple[int, int, int, int]: + if isinstance(border, tuple): + if len(border) == 2: + left, top = right, bottom = border + elif len(border) == 4: + left, top, right, bottom = border + else: + left = top = right = bottom = border + return left, top, right, bottom + + +def _color(color: str | int | tuple[int, ...], mode: str) -> int | tuple[int, ...]: + if isinstance(color, str): + from . import ImageColor + + color = ImageColor.getcolor(color, mode) + return color + + +def _lut(image: Image.Image, lut: list[int]) -> Image.Image: + if image.mode == "P": + # FIXME: apply to lookup table, not image data + msg = "mode P support coming soon" + raise NotImplementedError(msg) + elif image.mode in ("L", "RGB"): + if image.mode == "RGB" and len(lut) == 256: + lut = lut + lut + lut + return image.point(lut) + else: + msg = f"not supported for mode {image.mode}" + raise OSError(msg) + + +# +# actions + + +def autocontrast( + image: Image.Image, + cutoff: float | tuple[float, float] = 0, + ignore: int | Sequence[int] | None = None, + mask: Image.Image | None = None, + preserve_tone: bool = False, +) -> Image.Image: + """ + Maximize (normalize) image contrast. This function calculates a + histogram of the input image (or mask region), removes ``cutoff`` percent of the + lightest and darkest pixels from the histogram, and remaps the image + so that the darkest pixel becomes black (0), and the lightest + becomes white (255). + + :param image: The image to process. + :param cutoff: The percent to cut off from the histogram on the low and + high ends. Either a tuple of (low, high), or a single + number for both. + :param ignore: The background pixel value (use None for no background). + :param mask: Histogram used in contrast operation is computed using pixels + within the mask. If no mask is given the entire image is used + for histogram computation. + :param preserve_tone: Preserve image tone in Photoshop-like style autocontrast. + + .. versionadded:: 8.2.0 + + :return: An image. + """ + if preserve_tone: + histogram = image.convert("L").histogram(mask) + else: + histogram = image.histogram(mask) + + lut = [] + for layer in range(0, len(histogram), 256): + h = histogram[layer : layer + 256] + if ignore is not None: + # get rid of outliers + if isinstance(ignore, int): + h[ignore] = 0 + else: + for ix in ignore: + h[ix] = 0 + if cutoff: + # cut off pixels from both ends of the histogram + if not isinstance(cutoff, tuple): + cutoff = (cutoff, cutoff) + # get number of pixels + n = 0 + for ix in range(256): + n = n + h[ix] + # remove cutoff% pixels from the low end + cut = int(n * cutoff[0] // 100) + for lo in range(256): + if cut > h[lo]: + cut = cut - h[lo] + h[lo] = 0 + else: + h[lo] -= cut + cut = 0 + if cut <= 0: + break + # remove cutoff% samples from the high end + cut = int(n * cutoff[1] // 100) + for hi in range(255, -1, -1): + if cut > h[hi]: + cut = cut - h[hi] + h[hi] = 0 + else: + h[hi] -= cut + cut = 0 + if cut <= 0: + break + # find lowest/highest samples after preprocessing + for lo in range(256): + if h[lo]: + break + for hi in range(255, -1, -1): + if h[hi]: + break + if hi <= lo: + # don't bother + lut.extend(list(range(256))) + else: + scale = 255.0 / (hi - lo) + offset = -lo * scale + for ix in range(256): + ix = int(ix * scale + offset) + if ix < 0: + ix = 0 + elif ix > 255: + ix = 255 + lut.append(ix) + return _lut(image, lut) + + +def colorize( + image: Image.Image, + black: str | tuple[int, ...], + white: str | tuple[int, ...], + mid: str | int | tuple[int, ...] | None = None, + blackpoint: int = 0, + whitepoint: int = 255, + midpoint: int = 127, +) -> Image.Image: + """ + Colorize grayscale image. + This function calculates a color wedge which maps all black pixels in + the source image to the first color and all white pixels to the + second color. If ``mid`` is specified, it uses three-color mapping. + The ``black`` and ``white`` arguments should be RGB tuples or color names; + optionally you can use three-color mapping by also specifying ``mid``. + Mapping positions for any of the colors can be specified + (e.g. ``blackpoint``), where these parameters are the integer + value corresponding to where the corresponding color should be mapped. + These parameters must have logical order, such that + ``blackpoint <= midpoint <= whitepoint`` (if ``mid`` is specified). + + :param image: The image to colorize. + :param black: The color to use for black input pixels. + :param white: The color to use for white input pixels. + :param mid: The color to use for midtone input pixels. + :param blackpoint: an int value [0, 255] for the black mapping. + :param whitepoint: an int value [0, 255] for the white mapping. + :param midpoint: an int value [0, 255] for the midtone mapping. + :return: An image. + """ + + # Initial asserts + assert image.mode == "L" + if mid is None: + assert 0 <= blackpoint <= whitepoint <= 255 + else: + assert 0 <= blackpoint <= midpoint <= whitepoint <= 255 + + # Define colors from arguments + rgb_black = cast(Sequence[int], _color(black, "RGB")) + rgb_white = cast(Sequence[int], _color(white, "RGB")) + rgb_mid = cast(Sequence[int], _color(mid, "RGB")) if mid is not None else None + + # Empty lists for the mapping + red = [] + green = [] + blue = [] + + # Create the low-end values + for i in range(blackpoint): + red.append(rgb_black[0]) + green.append(rgb_black[1]) + blue.append(rgb_black[2]) + + # Create the mapping (2-color) + if rgb_mid is None: + range_map = range(whitepoint - blackpoint) + + for i in range_map: + red.append( + rgb_black[0] + i * (rgb_white[0] - rgb_black[0]) // len(range_map) + ) + green.append( + rgb_black[1] + i * (rgb_white[1] - rgb_black[1]) // len(range_map) + ) + blue.append( + rgb_black[2] + i * (rgb_white[2] - rgb_black[2]) // len(range_map) + ) + + # Create the mapping (3-color) + else: + range_map1 = range(midpoint - blackpoint) + range_map2 = range(whitepoint - midpoint) + + for i in range_map1: + red.append( + rgb_black[0] + i * (rgb_mid[0] - rgb_black[0]) // len(range_map1) + ) + green.append( + rgb_black[1] + i * (rgb_mid[1] - rgb_black[1]) // len(range_map1) + ) + blue.append( + rgb_black[2] + i * (rgb_mid[2] - rgb_black[2]) // len(range_map1) + ) + for i in range_map2: + red.append(rgb_mid[0] + i * (rgb_white[0] - rgb_mid[0]) // len(range_map2)) + green.append( + rgb_mid[1] + i * (rgb_white[1] - rgb_mid[1]) // len(range_map2) + ) + blue.append(rgb_mid[2] + i * (rgb_white[2] - rgb_mid[2]) // len(range_map2)) + + # Create the high-end values + for i in range(256 - whitepoint): + red.append(rgb_white[0]) + green.append(rgb_white[1]) + blue.append(rgb_white[2]) + + # Return converted image + image = image.convert("RGB") + return _lut(image, red + green + blue) + + +def contain( + image: Image.Image, size: tuple[int, int], method: int = Image.Resampling.BICUBIC +) -> Image.Image: + """ + Returns a resized version of the image, set to the maximum width and height + within the requested size, while maintaining the original aspect ratio. + + :param image: The image to resize. + :param size: The requested output size in pixels, given as a + (width, height) tuple. + :param method: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :return: An image. + """ + + im_ratio = image.width / image.height + dest_ratio = size[0] / size[1] + + if im_ratio != dest_ratio: + if im_ratio > dest_ratio: + new_height = round(image.height / image.width * size[0]) + if new_height != size[1]: + size = (size[0], new_height) + else: + new_width = round(image.width / image.height * size[1]) + if new_width != size[0]: + size = (new_width, size[1]) + return image.resize(size, resample=method) + + +def cover( + image: Image.Image, size: tuple[int, int], method: int = Image.Resampling.BICUBIC +) -> Image.Image: + """ + Returns a resized version of the image, so that the requested size is + covered, while maintaining the original aspect ratio. + + :param image: The image to resize. + :param size: The requested output size in pixels, given as a + (width, height) tuple. + :param method: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :return: An image. + """ + + im_ratio = image.width / image.height + dest_ratio = size[0] / size[1] + + if im_ratio != dest_ratio: + if im_ratio < dest_ratio: + new_height = round(image.height / image.width * size[0]) + if new_height != size[1]: + size = (size[0], new_height) + else: + new_width = round(image.width / image.height * size[1]) + if new_width != size[0]: + size = (new_width, size[1]) + return image.resize(size, resample=method) + + +def pad( + image: Image.Image, + size: tuple[int, int], + method: int = Image.Resampling.BICUBIC, + color: str | int | tuple[int, ...] | None = None, + centering: tuple[float, float] = (0.5, 0.5), +) -> Image.Image: + """ + Returns a resized and padded version of the image, expanded to fill the + requested aspect ratio and size. + + :param image: The image to resize and crop. + :param size: The requested output size in pixels, given as a + (width, height) tuple. + :param method: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :param color: The background color of the padded image. + :param centering: Control the position of the original image within the + padded version. + + (0.5, 0.5) will keep the image centered + (0, 0) will keep the image aligned to the top left + (1, 1) will keep the image aligned to the bottom + right + :return: An image. + """ + + resized = contain(image, size, method) + if resized.size == size: + out = resized + else: + out = Image.new(image.mode, size, color) + if resized.palette: + palette = resized.getpalette() + if palette is not None: + out.putpalette(palette) + if resized.width != size[0]: + x = round((size[0] - resized.width) * max(0, min(centering[0], 1))) + out.paste(resized, (x, 0)) + else: + y = round((size[1] - resized.height) * max(0, min(centering[1], 1))) + out.paste(resized, (0, y)) + return out + + +def crop(image: Image.Image, border: int = 0) -> Image.Image: + """ + Remove border from image. The same amount of pixels are removed + from all four sides. This function works on all image modes. + + .. seealso:: :py:meth:`~PIL.Image.Image.crop` + + :param image: The image to crop. + :param border: The number of pixels to remove. + :return: An image. + """ + left, top, right, bottom = _border(border) + return image.crop((left, top, image.size[0] - right, image.size[1] - bottom)) + + +def scale( + image: Image.Image, factor: float, resample: int = Image.Resampling.BICUBIC +) -> Image.Image: + """ + Returns a rescaled image by a specific factor given in parameter. + A factor greater than 1 expands the image, between 0 and 1 contracts the + image. + + :param image: The image to rescale. + :param factor: The expansion factor, as a float. + :param resample: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :returns: An :py:class:`~PIL.Image.Image` object. + """ + if factor == 1: + return image.copy() + elif factor <= 0: + msg = "the factor must be greater than 0" + raise ValueError(msg) + else: + size = (round(factor * image.width), round(factor * image.height)) + return image.resize(size, resample) + + +class SupportsGetMesh(Protocol): + """ + An object that supports the ``getmesh`` method, taking an image as an + argument, and returning a list of tuples. Each tuple contains two tuples, + the source box as a tuple of 4 integers, and a tuple of 8 integers for the + final quadrilateral, in order of top left, bottom left, bottom right, top + right. + """ + + def getmesh( + self, image: Image.Image + ) -> list[ + tuple[tuple[int, int, int, int], tuple[int, int, int, int, int, int, int, int]] + ]: ... + + +def deform( + image: Image.Image, + deformer: SupportsGetMesh, + resample: int = Image.Resampling.BILINEAR, +) -> Image.Image: + """ + Deform the image. + + :param image: The image to deform. + :param deformer: A deformer object. Any object that implements a + ``getmesh`` method can be used. + :param resample: An optional resampling filter. Same values possible as + in the PIL.Image.transform function. + :return: An image. + """ + return image.transform( + image.size, Image.Transform.MESH, deformer.getmesh(image), resample + ) + + +def equalize(image: Image.Image, mask: Image.Image | None = None) -> Image.Image: + """ + Equalize the image histogram. This function applies a non-linear + mapping to the input image, in order to create a uniform + distribution of grayscale values in the output image. + + :param image: The image to equalize. + :param mask: An optional mask. If given, only the pixels selected by + the mask are included in the analysis. + :return: An image. + """ + if image.mode == "P": + image = image.convert("RGB") + h = image.histogram(mask) + lut = [] + for b in range(0, len(h), 256): + histo = [_f for _f in h[b : b + 256] if _f] + if len(histo) <= 1: + lut.extend(list(range(256))) + else: + step = (functools.reduce(operator.add, histo) - histo[-1]) // 255 + if not step: + lut.extend(list(range(256))) + else: + n = step // 2 + for i in range(256): + lut.append(n // step) + n = n + h[i + b] + return _lut(image, lut) + + +def expand( + image: Image.Image, + border: int | tuple[int, ...] = 0, + fill: str | int | tuple[int, ...] = 0, +) -> Image.Image: + """ + Add border to the image + + :param image: The image to expand. + :param border: Border width, in pixels. + :param fill: Pixel fill value (a color value). Default is 0 (black). + :return: An image. + """ + left, top, right, bottom = _border(border) + width = left + image.size[0] + right + height = top + image.size[1] + bottom + color = _color(fill, image.mode) + if image.palette: + palette = ImagePalette.ImagePalette(palette=image.getpalette()) + if isinstance(color, tuple) and (len(color) == 3 or len(color) == 4): + color = palette.getcolor(color) + else: + palette = None + out = Image.new(image.mode, (width, height), color) + if palette: + out.putpalette(palette.palette) + out.paste(image, (left, top)) + return out + + +def fit( + image: Image.Image, + size: tuple[int, int], + method: int = Image.Resampling.BICUBIC, + bleed: float = 0.0, + centering: tuple[float, float] = (0.5, 0.5), +) -> Image.Image: + """ + Returns a resized and cropped version of the image, cropped to the + requested aspect ratio and size. + + This function was contributed by Kevin Cazabon. + + :param image: The image to resize and crop. + :param size: The requested output size in pixels, given as a + (width, height) tuple. + :param method: Resampling method to use. Default is + :py:attr:`~PIL.Image.Resampling.BICUBIC`. + See :ref:`concept-filters`. + :param bleed: Remove a border around the outside of the image from all + four edges. The value is a decimal percentage (use 0.01 for + one percent). The default value is 0 (no border). + Cannot be greater than or equal to 0.5. + :param centering: Control the cropping position. Use (0.5, 0.5) for + center cropping (e.g. if cropping the width, take 50% off + of the left side, and therefore 50% off the right side). + (0.0, 0.0) will crop from the top left corner (i.e. if + cropping the width, take all of the crop off of the right + side, and if cropping the height, take all of it off the + bottom). (1.0, 0.0) will crop from the bottom left + corner, etc. (i.e. if cropping the width, take all of the + crop off the left side, and if cropping the height take + none from the top, and therefore all off the bottom). + :return: An image. + """ + + # by Kevin Cazabon, Feb 17/2000 + # kevin@cazabon.com + # https://www.cazabon.com + + centering_x, centering_y = centering + + if not 0.0 <= centering_x <= 1.0: + centering_x = 0.5 + if not 0.0 <= centering_y <= 1.0: + centering_y = 0.5 + + if not 0.0 <= bleed < 0.5: + bleed = 0.0 + + # calculate the area to use for resizing and cropping, subtracting + # the 'bleed' around the edges + + # number of pixels to trim off on Top and Bottom, Left and Right + bleed_pixels = (bleed * image.size[0], bleed * image.size[1]) + + live_size = ( + image.size[0] - bleed_pixels[0] * 2, + image.size[1] - bleed_pixels[1] * 2, + ) + + # calculate the aspect ratio of the live_size + live_size_ratio = live_size[0] / live_size[1] + + # calculate the aspect ratio of the output image + output_ratio = size[0] / size[1] + + # figure out if the sides or top/bottom will be cropped off + if live_size_ratio == output_ratio: + # live_size is already the needed ratio + crop_width = live_size[0] + crop_height = live_size[1] + elif live_size_ratio >= output_ratio: + # live_size is wider than what's needed, crop the sides + crop_width = output_ratio * live_size[1] + crop_height = live_size[1] + else: + # live_size is taller than what's needed, crop the top and bottom + crop_width = live_size[0] + crop_height = live_size[0] / output_ratio + + # make the crop + crop_left = bleed_pixels[0] + (live_size[0] - crop_width) * centering_x + crop_top = bleed_pixels[1] + (live_size[1] - crop_height) * centering_y + + crop = (crop_left, crop_top, crop_left + crop_width, crop_top + crop_height) + + # resize the image and return it + return image.resize(size, method, box=crop) + + +def flip(image: Image.Image) -> Image.Image: + """ + Flip the image vertically (top to bottom). + + :param image: The image to flip. + :return: An image. + """ + return image.transpose(Image.Transpose.FLIP_TOP_BOTTOM) + + +def grayscale(image: Image.Image) -> Image.Image: + """ + Convert the image to grayscale. + + :param image: The image to convert. + :return: An image. + """ + return image.convert("L") + + +def invert(image: Image.Image) -> Image.Image: + """ + Invert (negate) the image. + + :param image: The image to invert. + :return: An image. + """ + lut = list(range(255, -1, -1)) + return image.point(lut) if image.mode == "1" else _lut(image, lut) + + +def mirror(image: Image.Image) -> Image.Image: + """ + Flip image horizontally (left to right). + + :param image: The image to mirror. + :return: An image. + """ + return image.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + + +def posterize(image: Image.Image, bits: int) -> Image.Image: + """ + Reduce the number of bits for each color channel. + + :param image: The image to posterize. + :param bits: The number of bits to keep for each channel (1-8). + :return: An image. + """ + mask = ~(2 ** (8 - bits) - 1) + lut = [i & mask for i in range(256)] + return _lut(image, lut) + + +def solarize(image: Image.Image, threshold: int = 128) -> Image.Image: + """ + Invert all pixel values above a threshold. + + :param image: The image to solarize. + :param threshold: All pixels above this grayscale level are inverted. + :return: An image. + """ + lut = [] + for i in range(256): + if i < threshold: + lut.append(i) + else: + lut.append(255 - i) + return _lut(image, lut) + + +@overload +def exif_transpose(image: Image.Image, *, in_place: Literal[True]) -> None: ... + + +@overload +def exif_transpose( + image: Image.Image, *, in_place: Literal[False] = False +) -> Image.Image: ... + + +def exif_transpose(image: Image.Image, *, in_place: bool = False) -> Image.Image | None: + """ + If an image has an EXIF Orientation tag, other than 1, transpose the image + accordingly, and remove the orientation data. + + :param image: The image to transpose. + :param in_place: Boolean. Keyword-only argument. + If ``True``, the original image is modified in-place, and ``None`` is returned. + If ``False`` (default), a new :py:class:`~PIL.Image.Image` object is returned + with the transposition applied. If there is no transposition, a copy of the + image will be returned. + """ + image.load() + image_exif = image.getexif() + orientation = image_exif.get(ExifTags.Base.Orientation, 1) + method = { + 2: Image.Transpose.FLIP_LEFT_RIGHT, + 3: Image.Transpose.ROTATE_180, + 4: Image.Transpose.FLIP_TOP_BOTTOM, + 5: Image.Transpose.TRANSPOSE, + 6: Image.Transpose.ROTATE_270, + 7: Image.Transpose.TRANSVERSE, + 8: Image.Transpose.ROTATE_90, + }.get(orientation) + if method is not None: + if in_place: + image.im = image.im.transpose(method) + image._size = image.im.size + else: + transposed_image = image.transpose(method) + exif_image = image if in_place else transposed_image + + exif = exif_image.getexif() + if ExifTags.Base.Orientation in exif: + del exif[ExifTags.Base.Orientation] + if "exif" in exif_image.info: + exif_image.info["exif"] = exif.tobytes() + elif "Raw profile type exif" in exif_image.info: + exif_image.info["Raw profile type exif"] = exif.tobytes().hex() + for key in ("XML:com.adobe.xmp", "xmp"): + if key in exif_image.info: + for pattern in ( + r'tiff:Orientation="([0-9])"', + r"([0-9])", + ): + value = exif_image.info[key] + if isinstance(value, str): + value = re.sub(pattern, "", value) + elif isinstance(value, tuple): + value = tuple( + re.sub(pattern.encode(), b"", v) for v in value + ) + else: + value = re.sub(pattern.encode(), b"", value) + exif_image.info[key] = value + if not in_place: + return transposed_image + elif not in_place: + return image.copy() + return None diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImagePalette.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImagePalette.py new file mode 100644 index 0000000000000000000000000000000000000000..103697117b92a3dca9794fbea5b9c92306b9b198 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImagePalette.py @@ -0,0 +1,286 @@ +# +# The Python Imaging Library. +# $Id$ +# +# image palette object +# +# History: +# 1996-03-11 fl Rewritten. +# 1997-01-03 fl Up and running. +# 1997-08-23 fl Added load hack +# 2001-04-16 fl Fixed randint shadow bug in random() +# +# Copyright (c) 1997-2001 by Secret Labs AB +# Copyright (c) 1996-1997 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import array +from collections.abc import Sequence +from typing import IO + +from . import GimpGradientFile, GimpPaletteFile, ImageColor, PaletteFile + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import Image + + +class ImagePalette: + """ + Color palette for palette mapped images + + :param mode: The mode to use for the palette. See: + :ref:`concept-modes`. Defaults to "RGB" + :param palette: An optional palette. If given, it must be a bytearray, + an array or a list of ints between 0-255. The list must consist of + all channels for one color followed by the next color (e.g. RGBRGBRGB). + Defaults to an empty palette. + """ + + def __init__( + self, + mode: str = "RGB", + palette: Sequence[int] | bytes | bytearray | None = None, + ) -> None: + self.mode = mode + self.rawmode: str | None = None # if set, palette contains raw data + self.palette = palette or bytearray() + self.dirty: int | None = None + + @property + def palette(self) -> Sequence[int] | bytes | bytearray: + return self._palette + + @palette.setter + def palette(self, palette: Sequence[int] | bytes | bytearray) -> None: + self._colors: dict[tuple[int, ...], int] | None = None + self._palette = palette + + @property + def colors(self) -> dict[tuple[int, ...], int]: + if self._colors is None: + mode_len = len(self.mode) + self._colors = {} + for i in range(0, len(self.palette), mode_len): + color = tuple(self.palette[i : i + mode_len]) + if color in self._colors: + continue + self._colors[color] = i // mode_len + return self._colors + + @colors.setter + def colors(self, colors: dict[tuple[int, ...], int]) -> None: + self._colors = colors + + def copy(self) -> ImagePalette: + new = ImagePalette() + + new.mode = self.mode + new.rawmode = self.rawmode + if self.palette is not None: + new.palette = self.palette[:] + new.dirty = self.dirty + + return new + + def getdata(self) -> tuple[str, Sequence[int] | bytes | bytearray]: + """ + Get palette contents in format suitable for the low-level + ``im.putpalette`` primitive. + + .. warning:: This method is experimental. + """ + if self.rawmode: + return self.rawmode, self.palette + return self.mode, self.tobytes() + + def tobytes(self) -> bytes: + """Convert palette to bytes. + + .. warning:: This method is experimental. + """ + if self.rawmode: + msg = "palette contains raw palette data" + raise ValueError(msg) + if isinstance(self.palette, bytes): + return self.palette + arr = array.array("B", self.palette) + return arr.tobytes() + + # Declare tostring as an alias for tobytes + tostring = tobytes + + def _new_color_index( + self, image: Image.Image | None = None, e: Exception | None = None + ) -> int: + if not isinstance(self.palette, bytearray): + self._palette = bytearray(self.palette) + index = len(self.palette) // 3 + special_colors: tuple[int | tuple[int, ...] | None, ...] = () + if image: + special_colors = ( + image.info.get("background"), + image.info.get("transparency"), + ) + while index in special_colors: + index += 1 + if index >= 256: + if image: + # Search for an unused index + for i, count in reversed(list(enumerate(image.histogram()))): + if count == 0 and i not in special_colors: + index = i + break + if index >= 256: + msg = "cannot allocate more than 256 colors" + raise ValueError(msg) from e + return index + + def getcolor( + self, + color: tuple[int, ...], + image: Image.Image | None = None, + ) -> int: + """Given an rgb tuple, allocate palette entry. + + .. warning:: This method is experimental. + """ + if self.rawmode: + msg = "palette contains raw palette data" + raise ValueError(msg) + if isinstance(color, tuple): + if self.mode == "RGB": + if len(color) == 4: + if color[3] != 255: + msg = "cannot add non-opaque RGBA color to RGB palette" + raise ValueError(msg) + color = color[:3] + elif self.mode == "RGBA": + if len(color) == 3: + color += (255,) + try: + return self.colors[color] + except KeyError as e: + # allocate new color slot + index = self._new_color_index(image, e) + assert isinstance(self._palette, bytearray) + self.colors[color] = index + if index * 3 < len(self.palette): + self._palette = ( + self._palette[: index * 3] + + bytes(color) + + self._palette[index * 3 + 3 :] + ) + else: + self._palette += bytes(color) + self.dirty = 1 + return index + else: + msg = f"unknown color specifier: {repr(color)}" # type: ignore[unreachable] + raise ValueError(msg) + + def save(self, fp: str | IO[str]) -> None: + """Save palette to text file. + + .. warning:: This method is experimental. + """ + if self.rawmode: + msg = "palette contains raw palette data" + raise ValueError(msg) + if isinstance(fp, str): + fp = open(fp, "w") + fp.write("# Palette\n") + fp.write(f"# Mode: {self.mode}\n") + for i in range(256): + fp.write(f"{i}") + for j in range(i * len(self.mode), (i + 1) * len(self.mode)): + try: + fp.write(f" {self.palette[j]}") + except IndexError: + fp.write(" 0") + fp.write("\n") + fp.close() + + +# -------------------------------------------------------------------- +# Internal + + +def raw(rawmode: str, data: Sequence[int] | bytes | bytearray) -> ImagePalette: + palette = ImagePalette() + palette.rawmode = rawmode + palette.palette = data + palette.dirty = 1 + return palette + + +# -------------------------------------------------------------------- +# Factories + + +def make_linear_lut(black: int, white: float) -> list[int]: + if black == 0: + return [int(white * i // 255) for i in range(256)] + + msg = "unavailable when black is non-zero" + raise NotImplementedError(msg) # FIXME + + +def make_gamma_lut(exp: float) -> list[int]: + return [int(((i / 255.0) ** exp) * 255.0 + 0.5) for i in range(256)] + + +def negative(mode: str = "RGB") -> ImagePalette: + palette = list(range(256 * len(mode))) + palette.reverse() + return ImagePalette(mode, [i // len(mode) for i in palette]) + + +def random(mode: str = "RGB") -> ImagePalette: + from random import randint + + palette = [randint(0, 255) for _ in range(256 * len(mode))] + return ImagePalette(mode, palette) + + +def sepia(white: str = "#fff0c0") -> ImagePalette: + bands = [make_linear_lut(0, band) for band in ImageColor.getrgb(white)] + return ImagePalette("RGB", [bands[i % 3][i // 3] for i in range(256 * 3)]) + + +def wedge(mode: str = "RGB") -> ImagePalette: + palette = list(range(256 * len(mode))) + return ImagePalette(mode, [i // len(mode) for i in palette]) + + +def load(filename: str) -> tuple[bytes, str]: + # FIXME: supports GIMP gradients only + + with open(filename, "rb") as fp: + paletteHandlers: list[ + type[ + GimpPaletteFile.GimpPaletteFile + | GimpGradientFile.GimpGradientFile + | PaletteFile.PaletteFile + ] + ] = [ + GimpPaletteFile.GimpPaletteFile, + GimpGradientFile.GimpGradientFile, + PaletteFile.PaletteFile, + ] + for paletteHandler in paletteHandlers: + try: + fp.seek(0) + lut = paletteHandler(fp).getpalette() + if lut: + break + except (SyntaxError, ValueError): + pass + else: + msg = "cannot load palette" + raise OSError(msg) + + return lut # data, rawmode diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImagePath.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImagePath.py new file mode 100644 index 0000000000000000000000000000000000000000..77e8a609a552ae7d8c6b87e78a36ecbfc1cdce89 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImagePath.py @@ -0,0 +1,20 @@ +# +# The Python Imaging Library +# $Id$ +# +# path interface +# +# History: +# 1996-11-04 fl Created +# 2002-04-14 fl Added documentation stub class +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image + +Path = Image.core.path diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageQt.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageQt.py new file mode 100644 index 0000000000000000000000000000000000000000..df7a57b652cd18b43c33774ca0006546ff4af274 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageQt.py @@ -0,0 +1,220 @@ +# +# The Python Imaging Library. +# $Id$ +# +# a simple Qt image interface. +# +# history: +# 2006-06-03 fl: created +# 2006-06-04 fl: inherit from QImage instead of wrapping it +# 2006-06-05 fl: removed toimage helper; move string support to ImageQt +# 2013-11-13 fl: add support for Qt5 (aurelien.ballier@cyclonit.com) +# +# Copyright (c) 2006 by Secret Labs AB +# Copyright (c) 2006 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import sys +from io import BytesIO +from typing import Any, Callable, Union + +from . import Image +from ._util import is_path + +TYPE_CHECKING = False +if TYPE_CHECKING: + import PyQt6 + import PySide6 + + from . import ImageFile + + QBuffer: type + QByteArray = Union[PyQt6.QtCore.QByteArray, PySide6.QtCore.QByteArray] + QIODevice = Union[PyQt6.QtCore.QIODevice, PySide6.QtCore.QIODevice] + QImage = Union[PyQt6.QtGui.QImage, PySide6.QtGui.QImage] + QPixmap = Union[PyQt6.QtGui.QPixmap, PySide6.QtGui.QPixmap] + +qt_version: str | None +qt_versions = [ + ["6", "PyQt6"], + ["side6", "PySide6"], +] + +# If a version has already been imported, attempt it first +qt_versions.sort(key=lambda version: version[1] in sys.modules, reverse=True) +for version, qt_module in qt_versions: + try: + qRgba: Callable[[int, int, int, int], int] + if qt_module == "PyQt6": + from PyQt6.QtCore import QBuffer, QIODevice + from PyQt6.QtGui import QImage, QPixmap, qRgba + elif qt_module == "PySide6": + from PySide6.QtCore import QBuffer, QIODevice + from PySide6.QtGui import QImage, QPixmap, qRgba + except (ImportError, RuntimeError): + continue + qt_is_installed = True + qt_version = version + break +else: + qt_is_installed = False + qt_version = None + + +def rgb(r: int, g: int, b: int, a: int = 255) -> int: + """(Internal) Turns an RGB color into a Qt compatible color integer.""" + # use qRgb to pack the colors, and then turn the resulting long + # into a negative integer with the same bitpattern. + return qRgba(r, g, b, a) & 0xFFFFFFFF + + +def fromqimage(im: QImage | QPixmap) -> ImageFile.ImageFile: + """ + :param im: QImage or PIL ImageQt object + """ + buffer = QBuffer() + qt_openmode: object + if qt_version == "6": + try: + qt_openmode = getattr(QIODevice, "OpenModeFlag") + except AttributeError: + qt_openmode = getattr(QIODevice, "OpenMode") + else: + qt_openmode = QIODevice + buffer.open(getattr(qt_openmode, "ReadWrite")) + # preserve alpha channel with png + # otherwise ppm is more friendly with Image.open + if im.hasAlphaChannel(): + im.save(buffer, "png") + else: + im.save(buffer, "ppm") + + b = BytesIO() + b.write(buffer.data()) + buffer.close() + b.seek(0) + + return Image.open(b) + + +def fromqpixmap(im: QPixmap) -> ImageFile.ImageFile: + return fromqimage(im) + + +def align8to32(bytes: bytes, width: int, mode: str) -> bytes: + """ + converts each scanline of data from 8 bit to 32 bit aligned + """ + + bits_per_pixel = {"1": 1, "L": 8, "P": 8, "I;16": 16}[mode] + + # calculate bytes per line and the extra padding if needed + bits_per_line = bits_per_pixel * width + full_bytes_per_line, remaining_bits_per_line = divmod(bits_per_line, 8) + bytes_per_line = full_bytes_per_line + (1 if remaining_bits_per_line else 0) + + extra_padding = -bytes_per_line % 4 + + # already 32 bit aligned by luck + if not extra_padding: + return bytes + + new_data = [ + bytes[i * bytes_per_line : (i + 1) * bytes_per_line] + b"\x00" * extra_padding + for i in range(len(bytes) // bytes_per_line) + ] + + return b"".join(new_data) + + +def _toqclass_helper(im: Image.Image | str | QByteArray) -> dict[str, Any]: + data = None + colortable = None + exclusive_fp = False + + # handle filename, if given instead of image name + if hasattr(im, "toUtf8"): + # FIXME - is this really the best way to do this? + im = str(im.toUtf8(), "utf-8") + if is_path(im): + im = Image.open(im) + exclusive_fp = True + assert isinstance(im, Image.Image) + + qt_format = getattr(QImage, "Format") if qt_version == "6" else QImage + if im.mode == "1": + format = getattr(qt_format, "Format_Mono") + elif im.mode == "L": + format = getattr(qt_format, "Format_Indexed8") + colortable = [rgb(i, i, i) for i in range(256)] + elif im.mode == "P": + format = getattr(qt_format, "Format_Indexed8") + palette = im.getpalette() + assert palette is not None + colortable = [rgb(*palette[i : i + 3]) for i in range(0, len(palette), 3)] + elif im.mode == "RGB": + # Populate the 4th channel with 255 + im = im.convert("RGBA") + + data = im.tobytes("raw", "BGRA") + format = getattr(qt_format, "Format_RGB32") + elif im.mode == "RGBA": + data = im.tobytes("raw", "BGRA") + format = getattr(qt_format, "Format_ARGB32") + elif im.mode == "I;16": + im = im.point(lambda i: i * 256) + + format = getattr(qt_format, "Format_Grayscale16") + else: + if exclusive_fp: + im.close() + msg = f"unsupported image mode {repr(im.mode)}" + raise ValueError(msg) + + size = im.size + __data = data or align8to32(im.tobytes(), size[0], im.mode) + if exclusive_fp: + im.close() + return {"data": __data, "size": size, "format": format, "colortable": colortable} + + +if qt_is_installed: + + class ImageQt(QImage): # type: ignore[misc] + def __init__(self, im: Image.Image | str | QByteArray) -> None: + """ + An PIL image wrapper for Qt. This is a subclass of PyQt's QImage + class. + + :param im: A PIL Image object, or a file name (given either as + Python string or a PyQt string object). + """ + im_data = _toqclass_helper(im) + # must keep a reference, or Qt will crash! + # All QImage constructors that take data operate on an existing + # buffer, so this buffer has to hang on for the life of the image. + # Fixes https://github.com/python-pillow/Pillow/issues/1370 + self.__data = im_data["data"] + super().__init__( + self.__data, + im_data["size"][0], + im_data["size"][1], + im_data["format"], + ) + if im_data["colortable"]: + self.setColorTable(im_data["colortable"]) + + +def toqimage(im: Image.Image | str | QByteArray) -> ImageQt: + return ImageQt(im) + + +def toqpixmap(im: Image.Image | str | QByteArray) -> QPixmap: + qimage = toqimage(im) + pixmap = getattr(QPixmap, "fromImage")(qimage) + if qt_version == "6": + pixmap.detach() + return pixmap diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageSequence.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageSequence.py new file mode 100644 index 0000000000000000000000000000000000000000..a6fc340d55f5516934349b3fa62c1276a204b03b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageSequence.py @@ -0,0 +1,86 @@ +# +# The Python Imaging Library. +# $Id$ +# +# sequence support classes +# +# history: +# 1997-02-20 fl Created +# +# Copyright (c) 1997 by Secret Labs AB. +# Copyright (c) 1997 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +## +from __future__ import annotations + +from typing import Callable + +from . import Image + + +class Iterator: + """ + This class implements an iterator object that can be used to loop + over an image sequence. + + You can use the ``[]`` operator to access elements by index. This operator + will raise an :py:exc:`IndexError` if you try to access a nonexistent + frame. + + :param im: An image object. + """ + + def __init__(self, im: Image.Image) -> None: + if not hasattr(im, "seek"): + msg = "im must have seek method" + raise AttributeError(msg) + self.im = im + self.position = getattr(self.im, "_min_frame", 0) + + def __getitem__(self, ix: int) -> Image.Image: + try: + self.im.seek(ix) + return self.im + except EOFError as e: + msg = "end of sequence" + raise IndexError(msg) from e + + def __iter__(self) -> Iterator: + return self + + def __next__(self) -> Image.Image: + try: + self.im.seek(self.position) + self.position += 1 + return self.im + except EOFError as e: + msg = "end of sequence" + raise StopIteration(msg) from e + + +def all_frames( + im: Image.Image | list[Image.Image], + func: Callable[[Image.Image], Image.Image] | None = None, +) -> list[Image.Image]: + """ + Applies a given function to all frames in an image or a list of images. + The frames are returned as a list of separate images. + + :param im: An image, or a list of images. + :param func: The function to apply to all of the image frames. + :returns: A list of images. + """ + if not isinstance(im, list): + im = [im] + + ims = [] + for imSequence in im: + current = imSequence.tell() + + ims += [im_frame.copy() for im_frame in Iterator(imSequence)] + + imSequence.seek(current) + return [func(im) for im in ims] if func else ims diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageShow.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageShow.py new file mode 100644 index 0000000000000000000000000000000000000000..7705608e3eccd5e82cfca87daa1264df2c81dacd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageShow.py @@ -0,0 +1,362 @@ +# +# The Python Imaging Library. +# $Id$ +# +# im.show() drivers +# +# History: +# 2008-04-06 fl Created +# +# Copyright (c) Secret Labs AB 2008. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import abc +import os +import shutil +import subprocess +import sys +from shlex import quote +from typing import Any + +from . import Image + +_viewers = [] + + +def register(viewer: type[Viewer] | Viewer, order: int = 1) -> None: + """ + The :py:func:`register` function is used to register additional viewers:: + + from PIL import ImageShow + ImageShow.register(MyViewer()) # MyViewer will be used as a last resort + ImageShow.register(MySecondViewer(), 0) # MySecondViewer will be prioritised + ImageShow.register(ImageShow.XVViewer(), 0) # XVViewer will be prioritised + + :param viewer: The viewer to be registered. + :param order: + Zero or a negative integer to prepend this viewer to the list, + a positive integer to append it. + """ + if isinstance(viewer, type) and issubclass(viewer, Viewer): + viewer = viewer() + if order > 0: + _viewers.append(viewer) + else: + _viewers.insert(0, viewer) + + +def show(image: Image.Image, title: str | None = None, **options: Any) -> bool: + r""" + Display a given image. + + :param image: An image object. + :param title: Optional title. Not all viewers can display the title. + :param \**options: Additional viewer options. + :returns: ``True`` if a suitable viewer was found, ``False`` otherwise. + """ + for viewer in _viewers: + if viewer.show(image, title=title, **options): + return True + return False + + +class Viewer: + """Base class for viewers.""" + + # main api + + def show(self, image: Image.Image, **options: Any) -> int: + """ + The main function for displaying an image. + Converts the given image to the target format and displays it. + """ + + if not ( + image.mode in ("1", "RGBA") + or (self.format == "PNG" and image.mode in ("I;16", "LA")) + ): + base = Image.getmodebase(image.mode) + if image.mode != base: + image = image.convert(base) + + return self.show_image(image, **options) + + # hook methods + + format: str | None = None + """The format to convert the image into.""" + options: dict[str, Any] = {} + """Additional options used to convert the image.""" + + def get_format(self, image: Image.Image) -> str | None: + """Return format name, or ``None`` to save as PGM/PPM.""" + return self.format + + def get_command(self, file: str, **options: Any) -> str: + """ + Returns the command used to display the file. + Not implemented in the base class. + """ + msg = "unavailable in base viewer" + raise NotImplementedError(msg) + + def save_image(self, image: Image.Image) -> str: + """Save to temporary file and return filename.""" + return image._dump(format=self.get_format(image), **self.options) + + def show_image(self, image: Image.Image, **options: Any) -> int: + """Display the given image.""" + return self.show_file(self.save_image(image), **options) + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + os.system(self.get_command(path, **options)) # nosec + return 1 + + +# -------------------------------------------------------------------- + + +class WindowsViewer(Viewer): + """The default viewer on Windows is the default system application for PNG files.""" + + format = "PNG" + options = {"compress_level": 1, "save_all": True} + + def get_command(self, file: str, **options: Any) -> str: + return ( + f'start "Pillow" /WAIT "{file}" ' + "&& ping -n 4 127.0.0.1 >NUL " + f'&& del /f "{file}"' + ) + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.Popen( + self.get_command(path, **options), + shell=True, + creationflags=getattr(subprocess, "CREATE_NO_WINDOW"), + ) # nosec + return 1 + + +if sys.platform == "win32": + register(WindowsViewer) + + +class MacViewer(Viewer): + """The default viewer on macOS using ``Preview.app``.""" + + format = "PNG" + options = {"compress_level": 1, "save_all": True} + + def get_command(self, file: str, **options: Any) -> str: + # on darwin open returns immediately resulting in the temp + # file removal while app is opening + command = "open -a Preview.app" + command = f"({command} {quote(file)}; sleep 20; rm -f {quote(file)})&" + return command + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.call(["open", "-a", "Preview.app", path]) + + pyinstaller = getattr(sys, "frozen", False) and hasattr(sys, "_MEIPASS") + executable = (not pyinstaller and sys.executable) or shutil.which("python3") + if executable: + subprocess.Popen( + [ + executable, + "-c", + "import os, sys, time; time.sleep(20); os.remove(sys.argv[1])", + path, + ] + ) + return 1 + + +if sys.platform == "darwin": + register(MacViewer) + + +class UnixViewer(abc.ABC, Viewer): + format = "PNG" + options = {"compress_level": 1, "save_all": True} + + @abc.abstractmethod + def get_command_ex(self, file: str, **options: Any) -> tuple[str, str]: + pass + + def get_command(self, file: str, **options: Any) -> str: + command = self.get_command_ex(file, **options)[0] + return f"{command} {quote(file)}" + + +class XDGViewer(UnixViewer): + """ + The freedesktop.org ``xdg-open`` command. + """ + + def get_command_ex(self, file: str, **options: Any) -> tuple[str, str]: + command = executable = "xdg-open" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.Popen(["xdg-open", path]) + return 1 + + +class DisplayViewer(UnixViewer): + """ + The ImageMagick ``display`` command. + This viewer supports the ``title`` parameter. + """ + + def get_command_ex( + self, file: str, title: str | None = None, **options: Any + ) -> tuple[str, str]: + command = executable = "display" + if title: + command += f" -title {quote(title)}" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + args = ["display"] + title = options.get("title") + if title: + args += ["-title", title] + args.append(path) + + subprocess.Popen(args) + return 1 + + +class GmDisplayViewer(UnixViewer): + """The GraphicsMagick ``gm display`` command.""" + + def get_command_ex(self, file: str, **options: Any) -> tuple[str, str]: + executable = "gm" + command = "gm display" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.Popen(["gm", "display", path]) + return 1 + + +class EogViewer(UnixViewer): + """The GNOME Image Viewer ``eog`` command.""" + + def get_command_ex(self, file: str, **options: Any) -> tuple[str, str]: + executable = "eog" + command = "eog -n" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + subprocess.Popen(["eog", "-n", path]) + return 1 + + +class XVViewer(UnixViewer): + """ + The X Viewer ``xv`` command. + This viewer supports the ``title`` parameter. + """ + + def get_command_ex( + self, file: str, title: str | None = None, **options: Any + ) -> tuple[str, str]: + # note: xv is pretty outdated. most modern systems have + # imagemagick's display command instead. + command = executable = "xv" + if title: + command += f" -name {quote(title)}" + return command, executable + + def show_file(self, path: str, **options: Any) -> int: + """ + Display given file. + """ + if not os.path.exists(path): + raise FileNotFoundError + args = ["xv"] + title = options.get("title") + if title: + args += ["-name", title] + args.append(path) + + subprocess.Popen(args) + return 1 + + +if sys.platform not in ("win32", "darwin"): # unixoids + if shutil.which("xdg-open"): + register(XDGViewer) + if shutil.which("display"): + register(DisplayViewer) + if shutil.which("gm"): + register(GmDisplayViewer) + if shutil.which("eog"): + register(EogViewer) + if shutil.which("xv"): + register(XVViewer) + + +class IPythonViewer(Viewer): + """The viewer for IPython frontends.""" + + def show_image(self, image: Image.Image, **options: Any) -> int: + ipython_display(image) + return 1 + + +try: + from IPython.display import display as ipython_display +except ImportError: + pass +else: + register(IPythonViewer) + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Syntax: python3 ImageShow.py imagefile [title]") + sys.exit() + + with Image.open(sys.argv[1]) as im: + print(show(im, *sys.argv[2:])) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageStat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageStat.py new file mode 100644 index 0000000000000000000000000000000000000000..8bc504526f0a00cde1229798234d4f0c5db95138 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageStat.py @@ -0,0 +1,160 @@ +# +# The Python Imaging Library. +# $Id$ +# +# global image statistics +# +# History: +# 1996-04-05 fl Created +# 1997-05-21 fl Added mask; added rms, var, stddev attributes +# 1997-08-05 fl Added median +# 1998-07-05 hk Fixed integer overflow error +# +# Notes: +# This class shows how to implement delayed evaluation of attributes. +# To get a certain value, simply access the corresponding attribute. +# The __getattr__ dispatcher takes care of the rest. +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996-97. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import math +from functools import cached_property + +from . import Image + + +class Stat: + def __init__( + self, image_or_list: Image.Image | list[int], mask: Image.Image | None = None + ) -> None: + """ + Calculate statistics for the given image. If a mask is included, + only the regions covered by that mask are included in the + statistics. You can also pass in a previously calculated histogram. + + :param image: A PIL image, or a precalculated histogram. + + .. note:: + + For a PIL image, calculations rely on the + :py:meth:`~PIL.Image.Image.histogram` method. The pixel counts are + grouped into 256 bins, even if the image has more than 8 bits per + channel. So ``I`` and ``F`` mode images have a maximum ``mean``, + ``median`` and ``rms`` of 255, and cannot have an ``extrema`` maximum + of more than 255. + + :param mask: An optional mask. + """ + if isinstance(image_or_list, Image.Image): + self.h = image_or_list.histogram(mask) + elif isinstance(image_or_list, list): + self.h = image_or_list + else: + msg = "first argument must be image or list" # type: ignore[unreachable] + raise TypeError(msg) + self.bands = list(range(len(self.h) // 256)) + + @cached_property + def extrema(self) -> list[tuple[int, int]]: + """ + Min/max values for each band in the image. + + .. note:: + This relies on the :py:meth:`~PIL.Image.Image.histogram` method, and + simply returns the low and high bins used. This is correct for + images with 8 bits per channel, but fails for other modes such as + ``I`` or ``F``. Instead, use :py:meth:`~PIL.Image.Image.getextrema` to + return per-band extrema for the image. This is more correct and + efficient because, for non-8-bit modes, the histogram method uses + :py:meth:`~PIL.Image.Image.getextrema` to determine the bins used. + """ + + def minmax(histogram: list[int]) -> tuple[int, int]: + res_min, res_max = 255, 0 + for i in range(256): + if histogram[i]: + res_min = i + break + for i in range(255, -1, -1): + if histogram[i]: + res_max = i + break + return res_min, res_max + + return [minmax(self.h[i:]) for i in range(0, len(self.h), 256)] + + @cached_property + def count(self) -> list[int]: + """Total number of pixels for each band in the image.""" + return [sum(self.h[i : i + 256]) for i in range(0, len(self.h), 256)] + + @cached_property + def sum(self) -> list[float]: + """Sum of all pixels for each band in the image.""" + + v = [] + for i in range(0, len(self.h), 256): + layer_sum = 0.0 + for j in range(256): + layer_sum += j * self.h[i + j] + v.append(layer_sum) + return v + + @cached_property + def sum2(self) -> list[float]: + """Squared sum of all pixels for each band in the image.""" + + v = [] + for i in range(0, len(self.h), 256): + sum2 = 0.0 + for j in range(256): + sum2 += (j**2) * float(self.h[i + j]) + v.append(sum2) + return v + + @cached_property + def mean(self) -> list[float]: + """Average (arithmetic mean) pixel level for each band in the image.""" + return [self.sum[i] / self.count[i] for i in self.bands] + + @cached_property + def median(self) -> list[int]: + """Median pixel level for each band in the image.""" + + v = [] + for i in self.bands: + s = 0 + half = self.count[i] // 2 + b = i * 256 + for j in range(256): + s = s + self.h[b + j] + if s > half: + break + v.append(j) + return v + + @cached_property + def rms(self) -> list[float]: + """RMS (root-mean-square) for each band in the image.""" + return [math.sqrt(self.sum2[i] / self.count[i]) for i in self.bands] + + @cached_property + def var(self) -> list[float]: + """Variance for each band in the image.""" + return [ + (self.sum2[i] - (self.sum[i] ** 2.0) / self.count[i]) / self.count[i] + for i in self.bands + ] + + @cached_property + def stddev(self) -> list[float]: + """Standard deviation for each band in the image.""" + return [math.sqrt(self.var[i]) for i in self.bands] + + +Global = Stat # compatibility diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageTk.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageTk.py new file mode 100644 index 0000000000000000000000000000000000000000..3a4cb81e9ef5ef4abe617d4a364074c2203571ad --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageTk.py @@ -0,0 +1,266 @@ +# +# The Python Imaging Library. +# $Id$ +# +# a Tk display interface +# +# History: +# 96-04-08 fl Created +# 96-09-06 fl Added getimage method +# 96-11-01 fl Rewritten, removed image attribute and crop method +# 97-05-09 fl Use PyImagingPaste method instead of image type +# 97-05-12 fl Minor tweaks to match the IFUNC95 interface +# 97-05-17 fl Support the "pilbitmap" booster patch +# 97-06-05 fl Added file= and data= argument to image constructors +# 98-03-09 fl Added width and height methods to Image classes +# 98-07-02 fl Use default mode for "P" images without palette attribute +# 98-07-02 fl Explicitly destroy Tkinter image objects +# 99-07-24 fl Support multiple Tk interpreters (from Greg Couch) +# 99-07-26 fl Automatically hook into Tkinter (if possible) +# 99-08-15 fl Hook uses _imagingtk instead of _imaging +# +# Copyright (c) 1997-1999 by Secret Labs AB +# Copyright (c) 1996-1997 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import tkinter +from io import BytesIO +from typing import Any + +from . import Image, ImageFile + +TYPE_CHECKING = False +if TYPE_CHECKING: + from ._typing import CapsuleType + +# -------------------------------------------------------------------- +# Check for Tkinter interface hooks + + +def _get_image_from_kw(kw: dict[str, Any]) -> ImageFile.ImageFile | None: + source = None + if "file" in kw: + source = kw.pop("file") + elif "data" in kw: + source = BytesIO(kw.pop("data")) + if not source: + return None + return Image.open(source) + + +def _pyimagingtkcall( + command: str, photo: PhotoImage | tkinter.PhotoImage, ptr: CapsuleType +) -> None: + tk = photo.tk + try: + tk.call(command, photo, repr(ptr)) + except tkinter.TclError: + # activate Tkinter hook + # may raise an error if it cannot attach to Tkinter + from . import _imagingtk + + _imagingtk.tkinit(tk.interpaddr()) + tk.call(command, photo, repr(ptr)) + + +# -------------------------------------------------------------------- +# PhotoImage + + +class PhotoImage: + """ + A Tkinter-compatible photo image. This can be used + everywhere Tkinter expects an image object. If the image is an RGBA + image, pixels having alpha 0 are treated as transparent. + + The constructor takes either a PIL image, or a mode and a size. + Alternatively, you can use the ``file`` or ``data`` options to initialize + the photo image object. + + :param image: Either a PIL image, or a mode string. If a mode string is + used, a size must also be given. + :param size: If the first argument is a mode string, this defines the size + of the image. + :keyword file: A filename to load the image from (using + ``Image.open(file)``). + :keyword data: An 8-bit string containing image data (as loaded from an + image file). + """ + + def __init__( + self, + image: Image.Image | str | None = None, + size: tuple[int, int] | None = None, + **kw: Any, + ) -> None: + # Tk compatibility: file or data + if image is None: + image = _get_image_from_kw(kw) + + if image is None: + msg = "Image is required" + raise ValueError(msg) + elif isinstance(image, str): + mode = image + image = None + + if size is None: + msg = "If first argument is mode, size is required" + raise ValueError(msg) + else: + # got an image instead of a mode + mode = image.mode + if mode == "P": + # palette mapped data + image.apply_transparency() + image.load() + mode = image.palette.mode if image.palette else "RGB" + size = image.size + kw["width"], kw["height"] = size + + if mode not in ["1", "L", "RGB", "RGBA"]: + mode = Image.getmodebase(mode) + + self.__mode = mode + self.__size = size + self.__photo = tkinter.PhotoImage(**kw) + self.tk = self.__photo.tk + if image: + self.paste(image) + + def __del__(self) -> None: + try: + name = self.__photo.name + except AttributeError: + return + self.__photo.name = None + try: + self.__photo.tk.call("image", "delete", name) + except Exception: + pass # ignore internal errors + + def __str__(self) -> str: + """ + Get the Tkinter photo image identifier. This method is automatically + called by Tkinter whenever a PhotoImage object is passed to a Tkinter + method. + + :return: A Tkinter photo image identifier (a string). + """ + return str(self.__photo) + + def width(self) -> int: + """ + Get the width of the image. + + :return: The width, in pixels. + """ + return self.__size[0] + + def height(self) -> int: + """ + Get the height of the image. + + :return: The height, in pixels. + """ + return self.__size[1] + + def paste(self, im: Image.Image) -> None: + """ + Paste a PIL image into the photo image. Note that this can + be very slow if the photo image is displayed. + + :param im: A PIL image. The size must match the target region. If the + mode does not match, the image is converted to the mode of + the bitmap image. + """ + # convert to blittable + ptr = im.getim() + image = im.im + if not image.isblock() or im.mode != self.__mode: + block = Image.core.new_block(self.__mode, im.size) + image.convert2(block, image) # convert directly between buffers + ptr = block.ptr + + _pyimagingtkcall("PyImagingPhoto", self.__photo, ptr) + + +# -------------------------------------------------------------------- +# BitmapImage + + +class BitmapImage: + """ + A Tkinter-compatible bitmap image. This can be used everywhere Tkinter + expects an image object. + + The given image must have mode "1". Pixels having value 0 are treated as + transparent. Options, if any, are passed on to Tkinter. The most commonly + used option is ``foreground``, which is used to specify the color for the + non-transparent parts. See the Tkinter documentation for information on + how to specify colours. + + :param image: A PIL image. + """ + + def __init__(self, image: Image.Image | None = None, **kw: Any) -> None: + # Tk compatibility: file or data + if image is None: + image = _get_image_from_kw(kw) + + if image is None: + msg = "Image is required" + raise ValueError(msg) + self.__mode = image.mode + self.__size = image.size + + self.__photo = tkinter.BitmapImage(data=image.tobitmap(), **kw) + + def __del__(self) -> None: + try: + name = self.__photo.name + except AttributeError: + return + self.__photo.name = None + try: + self.__photo.tk.call("image", "delete", name) + except Exception: + pass # ignore internal errors + + def width(self) -> int: + """ + Get the width of the image. + + :return: The width, in pixels. + """ + return self.__size[0] + + def height(self) -> int: + """ + Get the height of the image. + + :return: The height, in pixels. + """ + return self.__size[1] + + def __str__(self) -> str: + """ + Get the Tkinter bitmap image identifier. This method is automatically + called by Tkinter whenever a BitmapImage object is passed to a Tkinter + method. + + :return: A Tkinter bitmap image identifier (a string). + """ + return str(self.__photo) + + +def getimage(photo: PhotoImage) -> Image.Image: + """Copies the contents of a PhotoImage to a PIL image memory.""" + im = Image.new("RGBA", (photo.width(), photo.height())) + + _pyimagingtkcall("PyImagingPhotoGet", photo, im.getim()) + + return im diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageTransform.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageTransform.py new file mode 100644 index 0000000000000000000000000000000000000000..fb144ff38a1ee7ff77cc01f3b941756a60b2b4cd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageTransform.py @@ -0,0 +1,136 @@ +# +# The Python Imaging Library. +# $Id$ +# +# transform wrappers +# +# History: +# 2002-04-08 fl Created +# +# Copyright (c) 2002 by Secret Labs AB +# Copyright (c) 2002 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +from . import Image + + +class Transform(Image.ImageTransformHandler): + """Base class for other transforms defined in :py:mod:`~PIL.ImageTransform`.""" + + method: Image.Transform + + def __init__(self, data: Sequence[Any]) -> None: + self.data = data + + def getdata(self) -> tuple[Image.Transform, Sequence[int]]: + return self.method, self.data + + def transform( + self, + size: tuple[int, int], + image: Image.Image, + **options: Any, + ) -> Image.Image: + """Perform the transform. Called from :py:meth:`.Image.transform`.""" + # can be overridden + method, data = self.getdata() + return image.transform(size, method, data, **options) + + +class AffineTransform(Transform): + """ + Define an affine image transform. + + This function takes a 6-tuple (a, b, c, d, e, f) which contain the first + two rows from the inverse of an affine transform matrix. For each pixel + (x, y) in the output image, the new value is taken from a position (a x + + b y + c, d x + e y + f) in the input image, rounded to nearest pixel. + + This function can be used to scale, translate, rotate, and shear the + original image. + + See :py:meth:`.Image.transform` + + :param matrix: A 6-tuple (a, b, c, d, e, f) containing the first two rows + from the inverse of an affine transform matrix. + """ + + method = Image.Transform.AFFINE + + +class PerspectiveTransform(Transform): + """ + Define a perspective image transform. + + This function takes an 8-tuple (a, b, c, d, e, f, g, h). For each pixel + (x, y) in the output image, the new value is taken from a position + ((a x + b y + c) / (g x + h y + 1), (d x + e y + f) / (g x + h y + 1)) in + the input image, rounded to nearest pixel. + + This function can be used to scale, translate, rotate, and shear the + original image. + + See :py:meth:`.Image.transform` + + :param matrix: An 8-tuple (a, b, c, d, e, f, g, h). + """ + + method = Image.Transform.PERSPECTIVE + + +class ExtentTransform(Transform): + """ + Define a transform to extract a subregion from an image. + + Maps a rectangle (defined by two corners) from the image to a rectangle of + the given size. The resulting image will contain data sampled from between + the corners, such that (x0, y0) in the input image will end up at (0,0) in + the output image, and (x1, y1) at size. + + This method can be used to crop, stretch, shrink, or mirror an arbitrary + rectangle in the current image. It is slightly slower than crop, but about + as fast as a corresponding resize operation. + + See :py:meth:`.Image.transform` + + :param bbox: A 4-tuple (x0, y0, x1, y1) which specifies two points in the + input image's coordinate system. See :ref:`coordinate-system`. + """ + + method = Image.Transform.EXTENT + + +class QuadTransform(Transform): + """ + Define a quad image transform. + + Maps a quadrilateral (a region defined by four corners) from the image to a + rectangle of the given size. + + See :py:meth:`.Image.transform` + + :param xy: An 8-tuple (x0, y0, x1, y1, x2, y2, x3, y3) which contain the + upper left, lower left, lower right, and upper right corner of the + source quadrilateral. + """ + + method = Image.Transform.QUAD + + +class MeshTransform(Transform): + """ + Define a mesh image transform. A mesh transform consists of one or more + individual quad transforms. + + See :py:meth:`.Image.transform` + + :param data: A list of (bbox, quad) tuples. + """ + + method = Image.Transform.MESH diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageWin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageWin.py new file mode 100644 index 0000000000000000000000000000000000000000..98c28f29f1dbbb069b68dc9359051b6629148f0d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImageWin.py @@ -0,0 +1,247 @@ +# +# The Python Imaging Library. +# $Id$ +# +# a Windows DIB display interface +# +# History: +# 1996-05-20 fl Created +# 1996-09-20 fl Fixed subregion exposure +# 1997-09-21 fl Added draw primitive (for tzPrint) +# 2003-05-21 fl Added experimental Window/ImageWindow classes +# 2003-09-05 fl Added fromstring/tostring methods +# +# Copyright (c) Secret Labs AB 1997-2003. +# Copyright (c) Fredrik Lundh 1996-2003. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image + + +class HDC: + """ + Wraps an HDC integer. The resulting object can be passed to the + :py:meth:`~PIL.ImageWin.Dib.draw` and :py:meth:`~PIL.ImageWin.Dib.expose` + methods. + """ + + def __init__(self, dc: int) -> None: + self.dc = dc + + def __int__(self) -> int: + return self.dc + + +class HWND: + """ + Wraps an HWND integer. The resulting object can be passed to the + :py:meth:`~PIL.ImageWin.Dib.draw` and :py:meth:`~PIL.ImageWin.Dib.expose` + methods, instead of a DC. + """ + + def __init__(self, wnd: int) -> None: + self.wnd = wnd + + def __int__(self) -> int: + return self.wnd + + +class Dib: + """ + A Windows bitmap with the given mode and size. The mode can be one of "1", + "L", "P", or "RGB". + + If the display requires a palette, this constructor creates a suitable + palette and associates it with the image. For an "L" image, 128 graylevels + are allocated. For an "RGB" image, a 6x6x6 colour cube is used, together + with 20 graylevels. + + To make sure that palettes work properly under Windows, you must call the + ``palette`` method upon certain events from Windows. + + :param image: Either a PIL image, or a mode string. If a mode string is + used, a size must also be given. The mode can be one of "1", + "L", "P", or "RGB". + :param size: If the first argument is a mode string, this + defines the size of the image. + """ + + def __init__( + self, image: Image.Image | str, size: tuple[int, int] | None = None + ) -> None: + if isinstance(image, str): + mode = image + image = "" + if size is None: + msg = "If first argument is mode, size is required" + raise ValueError(msg) + else: + mode = image.mode + size = image.size + if mode not in ["1", "L", "P", "RGB"]: + mode = Image.getmodebase(mode) + self.image = Image.core.display(mode, size) + self.mode = mode + self.size = size + if image: + assert not isinstance(image, str) + self.paste(image) + + def expose(self, handle: int | HDC | HWND) -> None: + """ + Copy the bitmap contents to a device context. + + :param handle: Device context (HDC), cast to a Python integer, or an + HDC or HWND instance. In PythonWin, you can use + ``CDC.GetHandleAttrib()`` to get a suitable handle. + """ + handle_int = int(handle) + if isinstance(handle, HWND): + dc = self.image.getdc(handle_int) + try: + self.image.expose(dc) + finally: + self.image.releasedc(handle_int, dc) + else: + self.image.expose(handle_int) + + def draw( + self, + handle: int | HDC | HWND, + dst: tuple[int, int, int, int], + src: tuple[int, int, int, int] | None = None, + ) -> None: + """ + Same as expose, but allows you to specify where to draw the image, and + what part of it to draw. + + The destination and source areas are given as 4-tuple rectangles. If + the source is omitted, the entire image is copied. If the source and + the destination have different sizes, the image is resized as + necessary. + """ + if src is None: + src = (0, 0) + self.size + handle_int = int(handle) + if isinstance(handle, HWND): + dc = self.image.getdc(handle_int) + try: + self.image.draw(dc, dst, src) + finally: + self.image.releasedc(handle_int, dc) + else: + self.image.draw(handle_int, dst, src) + + def query_palette(self, handle: int | HDC | HWND) -> int: + """ + Installs the palette associated with the image in the given device + context. + + This method should be called upon **QUERYNEWPALETTE** and + **PALETTECHANGED** events from Windows. If this method returns a + non-zero value, one or more display palette entries were changed, and + the image should be redrawn. + + :param handle: Device context (HDC), cast to a Python integer, or an + HDC or HWND instance. + :return: The number of entries that were changed (if one or more entries, + this indicates that the image should be redrawn). + """ + handle_int = int(handle) + if isinstance(handle, HWND): + handle = self.image.getdc(handle_int) + try: + result = self.image.query_palette(handle) + finally: + self.image.releasedc(handle, handle) + else: + result = self.image.query_palette(handle_int) + return result + + def paste( + self, im: Image.Image, box: tuple[int, int, int, int] | None = None + ) -> None: + """ + Paste a PIL image into the bitmap image. + + :param im: A PIL image. The size must match the target region. + If the mode does not match, the image is converted to the + mode of the bitmap image. + :param box: A 4-tuple defining the left, upper, right, and + lower pixel coordinate. See :ref:`coordinate-system`. If + None is given instead of a tuple, all of the image is + assumed. + """ + im.load() + if self.mode != im.mode: + im = im.convert(self.mode) + if box: + self.image.paste(im.im, box) + else: + self.image.paste(im.im) + + def frombytes(self, buffer: bytes) -> None: + """ + Load display memory contents from byte data. + + :param buffer: A buffer containing display data (usually + data returned from :py:func:`~PIL.ImageWin.Dib.tobytes`) + """ + self.image.frombytes(buffer) + + def tobytes(self) -> bytes: + """ + Copy display memory contents to bytes object. + + :return: A bytes object containing display data. + """ + return self.image.tobytes() + + +class Window: + """Create a Window with the given title size.""" + + def __init__( + self, title: str = "PIL", width: int | None = None, height: int | None = None + ) -> None: + self.hwnd = Image.core.createwindow( + title, self.__dispatcher, width or 0, height or 0 + ) + + def __dispatcher(self, action: str, *args: int) -> None: + getattr(self, f"ui_handle_{action}")(*args) + + def ui_handle_clear(self, dc: int, x0: int, y0: int, x1: int, y1: int) -> None: + pass + + def ui_handle_damage(self, x0: int, y0: int, x1: int, y1: int) -> None: + pass + + def ui_handle_destroy(self) -> None: + pass + + def ui_handle_repair(self, dc: int, x0: int, y0: int, x1: int, y1: int) -> None: + pass + + def ui_handle_resize(self, width: int, height: int) -> None: + pass + + def mainloop(self) -> None: + Image.core.eventloop() + + +class ImageWindow(Window): + """Create an image window which displays the given image.""" + + def __init__(self, image: Image.Image | Dib, title: str = "PIL") -> None: + if not isinstance(image, Dib): + image = Dib(image) + self.image = image + width, height = image.size + super().__init__(title, width=width, height=height) + + def ui_handle_repair(self, dc: int, x0: int, y0: int, x1: int, y1: int) -> None: + self.image.draw(dc, (x0, y0, x1, y1)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImtImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImtImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..c4eccee3423dc6c273bdc1ea88eda5ef4e17cf7d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/ImtImagePlugin.py @@ -0,0 +1,103 @@ +# +# The Python Imaging Library. +# $Id$ +# +# IM Tools support for PIL +# +# history: +# 1996-05-27 fl Created (read 8-bit images only) +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.2) +# +# Copyright (c) Secret Labs AB 1997-2001. +# Copyright (c) Fredrik Lundh 1996-2001. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re + +from . import Image, ImageFile + +# +# -------------------------------------------------------------------- + +field = re.compile(rb"([a-z]*) ([^ \r\n]*)") + + +## +# Image plugin for IM Tools images. + + +class ImtImageFile(ImageFile.ImageFile): + format = "IMT" + format_description = "IM Tools" + + def _open(self) -> None: + # Quick rejection: if there's not a LF among the first + # 100 bytes, this is (probably) not a text header. + + assert self.fp is not None + + buffer = self.fp.read(100) + if b"\n" not in buffer: + msg = "not an IM file" + raise SyntaxError(msg) + + xsize = ysize = 0 + + while True: + if buffer: + s = buffer[:1] + buffer = buffer[1:] + else: + s = self.fp.read(1) + if not s: + break + + if s == b"\x0c": + # image data begins + self.tile = [ + ImageFile._Tile( + "raw", + (0, 0) + self.size, + self.fp.tell() - len(buffer), + self.mode, + ) + ] + + break + + else: + # read key/value pair + if b"\n" not in buffer: + buffer += self.fp.read(100) + lines = buffer.split(b"\n") + s += lines.pop(0) + buffer = b"\n".join(lines) + if len(s) == 1 or len(s) > 100: + break + if s[0] == ord(b"*"): + continue # comment + + m = field.match(s) + if not m: + break + k, v = m.group(1, 2) + if k == b"width": + xsize = int(v) + self._size = xsize, ysize + elif k == b"height": + ysize = int(v) + self._size = xsize, ysize + elif k == b"pixel" and v == b"n8": + self._mode = "L" + + +# +# -------------------------------------------------------------------- + +Image.register_open(ImtImageFile.format, ImtImageFile) + +# +# no extension registered (".im" is simply too common) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/IptcImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/IptcImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..fc024d668f2f42c4b0ea4c90c702ccc6d7c528f0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/IptcImagePlugin.py @@ -0,0 +1,250 @@ +# +# The Python Imaging Library. +# $Id$ +# +# IPTC/NAA file handling +# +# history: +# 1995-10-01 fl Created +# 1998-03-09 fl Cleaned up and added to PIL +# 2002-06-18 fl Added getiptcinfo helper +# +# Copyright (c) Secret Labs AB 1997-2002. +# Copyright (c) Fredrik Lundh 1995. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from collections.abc import Sequence +from io import BytesIO +from typing import cast + +from . import Image, ImageFile +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._deprecate import deprecate + +COMPRESSION = {1: "raw", 5: "jpeg"} + + +def __getattr__(name: str) -> bytes: + if name == "PAD": + deprecate("IptcImagePlugin.PAD", 12) + return b"\0\0\0\0" + msg = f"module '{__name__}' has no attribute '{name}'" + raise AttributeError(msg) + + +# +# Helpers + + +def _i(c: bytes) -> int: + return i32((b"\0\0\0\0" + c)[-4:]) + + +def _i8(c: int | bytes) -> int: + return c if isinstance(c, int) else c[0] + + +def i(c: bytes) -> int: + """.. deprecated:: 10.2.0""" + deprecate("IptcImagePlugin.i", 12) + return _i(c) + + +def dump(c: Sequence[int | bytes]) -> None: + """.. deprecated:: 10.2.0""" + deprecate("IptcImagePlugin.dump", 12) + for i in c: + print(f"{_i8(i):02x}", end=" ") + print() + + +## +# Image plugin for IPTC/NAA datastreams. To read IPTC/NAA fields +# from TIFF and JPEG files, use the getiptcinfo function. + + +class IptcImageFile(ImageFile.ImageFile): + format = "IPTC" + format_description = "IPTC/NAA" + + def getint(self, key: tuple[int, int]) -> int: + return _i(self.info[key]) + + def field(self) -> tuple[tuple[int, int] | None, int]: + # + # get a IPTC field header + s = self.fp.read(5) + if not s.strip(b"\x00"): + return None, 0 + + tag = s[1], s[2] + + # syntax + if s[0] != 0x1C or tag[0] not in [1, 2, 3, 4, 5, 6, 7, 8, 9, 240]: + msg = "invalid IPTC/NAA file" + raise SyntaxError(msg) + + # field size + size = s[3] + if size > 132: + msg = "illegal field length in IPTC/NAA file" + raise OSError(msg) + elif size == 128: + size = 0 + elif size > 128: + size = _i(self.fp.read(size - 128)) + else: + size = i16(s, 3) + + return tag, size + + def _open(self) -> None: + # load descriptive fields + while True: + offset = self.fp.tell() + tag, size = self.field() + if not tag or tag == (8, 10): + break + if size: + tagdata = self.fp.read(size) + else: + tagdata = None + if tag in self.info: + if isinstance(self.info[tag], list): + self.info[tag].append(tagdata) + else: + self.info[tag] = [self.info[tag], tagdata] + else: + self.info[tag] = tagdata + + # mode + layers = self.info[(3, 60)][0] + component = self.info[(3, 60)][1] + if (3, 65) in self.info: + id = self.info[(3, 65)][0] - 1 + else: + id = 0 + if layers == 1 and not component: + self._mode = "L" + elif layers == 3 and component: + self._mode = "RGB"[id] + elif layers == 4 and component: + self._mode = "CMYK"[id] + + # size + self._size = self.getint((3, 20)), self.getint((3, 30)) + + # compression + try: + compression = COMPRESSION[self.getint((3, 120))] + except KeyError as e: + msg = "Unknown IPTC image compression" + raise OSError(msg) from e + + # tile + if tag == (8, 10): + self.tile = [ + ImageFile._Tile("iptc", (0, 0) + self.size, offset, compression) + ] + + def load(self) -> Image.core.PixelAccess | None: + if len(self.tile) != 1 or self.tile[0][0] != "iptc": + return ImageFile.ImageFile.load(self) + + offset, compression = self.tile[0][2:] + + self.fp.seek(offset) + + # Copy image data to temporary file + o = BytesIO() + if compression == "raw": + # To simplify access to the extracted file, + # prepend a PPM header + o.write(b"P5\n%d %d\n255\n" % self.size) + while True: + type, size = self.field() + if type != (8, 10): + break + while size > 0: + s = self.fp.read(min(size, 8192)) + if not s: + break + o.write(s) + size -= len(s) + + with Image.open(o) as _im: + _im.load() + self.im = _im.im + self.tile = [] + return Image.Image.load(self) + + +Image.register_open(IptcImageFile.format, IptcImageFile) + +Image.register_extension(IptcImageFile.format, ".iim") + + +def getiptcinfo( + im: ImageFile.ImageFile, +) -> dict[tuple[int, int], bytes | list[bytes]] | None: + """ + Get IPTC information from TIFF, JPEG, or IPTC file. + + :param im: An image containing IPTC data. + :returns: A dictionary containing IPTC information, or None if + no IPTC information block was found. + """ + from . import JpegImagePlugin, TiffImagePlugin + + data = None + + info: dict[tuple[int, int], bytes | list[bytes]] = {} + if isinstance(im, IptcImageFile): + # return info dictionary right away + for k, v in im.info.items(): + if isinstance(k, tuple): + info[k] = v + return info + + elif isinstance(im, JpegImagePlugin.JpegImageFile): + # extract the IPTC/NAA resource + photoshop = im.info.get("photoshop") + if photoshop: + data = photoshop.get(0x0404) + + elif isinstance(im, TiffImagePlugin.TiffImageFile): + # get raw data from the IPTC/NAA tag (PhotoShop tags the data + # as 4-byte integers, so we cannot use the get method...) + try: + data = im.tag_v2._tagdata[TiffImagePlugin.IPTC_NAA_CHUNK] + except KeyError: + pass + + if data is None: + return None # no properties + + # create an IptcImagePlugin object without initializing it + class FakeImage: + pass + + fake_im = FakeImage() + fake_im.__class__ = IptcImageFile # type: ignore[assignment] + iptc_im = cast(IptcImageFile, fake_im) + + # parse the IPTC information chunk + iptc_im.info = {} + iptc_im.fp = BytesIO(data) + + try: + iptc_im._open() + except (IndexError, KeyError): + pass # expected failure + + for k, v in iptc_im.info.items(): + if isinstance(k, tuple): + info[k] = v + return info diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/Jpeg2KImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/Jpeg2KImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..e0f4ecae595d3f1aef3f529d91efeefa560c5134 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/Jpeg2KImagePlugin.py @@ -0,0 +1,442 @@ +# +# The Python Imaging Library +# $Id$ +# +# JPEG2000 file handling +# +# History: +# 2014-03-12 ajh Created +# 2021-06-30 rogermb Extract dpi information from the 'resc' header box +# +# Copyright (c) 2014 Coriolis Systems Limited +# Copyright (c) 2014 Alastair Houghton +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import os +import struct +from collections.abc import Callable +from typing import IO, cast + +from . import Image, ImageFile, ImagePalette, _binary + + +class BoxReader: + """ + A small helper class to read fields stored in JPEG2000 header boxes + and to easily step into and read sub-boxes. + """ + + def __init__(self, fp: IO[bytes], length: int = -1) -> None: + self.fp = fp + self.has_length = length >= 0 + self.length = length + self.remaining_in_box = -1 + + def _can_read(self, num_bytes: int) -> bool: + if self.has_length and self.fp.tell() + num_bytes > self.length: + # Outside box: ensure we don't read past the known file length + return False + if self.remaining_in_box >= 0: + # Inside box contents: ensure read does not go past box boundaries + return num_bytes <= self.remaining_in_box + else: + return True # No length known, just read + + def _read_bytes(self, num_bytes: int) -> bytes: + if not self._can_read(num_bytes): + msg = "Not enough data in header" + raise SyntaxError(msg) + + data = self.fp.read(num_bytes) + if len(data) < num_bytes: + msg = f"Expected to read {num_bytes} bytes but only got {len(data)}." + raise OSError(msg) + + if self.remaining_in_box > 0: + self.remaining_in_box -= num_bytes + return data + + def read_fields(self, field_format: str) -> tuple[int | bytes, ...]: + size = struct.calcsize(field_format) + data = self._read_bytes(size) + return struct.unpack(field_format, data) + + def read_boxes(self) -> BoxReader: + size = self.remaining_in_box + data = self._read_bytes(size) + return BoxReader(io.BytesIO(data), size) + + def has_next_box(self) -> bool: + if self.has_length: + return self.fp.tell() + self.remaining_in_box < self.length + else: + return True + + def next_box_type(self) -> bytes: + # Skip the rest of the box if it has not been read + if self.remaining_in_box > 0: + self.fp.seek(self.remaining_in_box, os.SEEK_CUR) + self.remaining_in_box = -1 + + # Read the length and type of the next box + lbox, tbox = cast(tuple[int, bytes], self.read_fields(">I4s")) + if lbox == 1: + lbox = cast(int, self.read_fields(">Q")[0]) + hlen = 16 + else: + hlen = 8 + + if lbox < hlen or not self._can_read(lbox - hlen): + msg = "Invalid header length" + raise SyntaxError(msg) + + self.remaining_in_box = lbox - hlen + return tbox + + +def _parse_codestream(fp: IO[bytes]) -> tuple[tuple[int, int], str]: + """Parse the JPEG 2000 codestream to extract the size and component + count from the SIZ marker segment, returning a PIL (size, mode) tuple.""" + + hdr = fp.read(2) + lsiz = _binary.i16be(hdr) + siz = hdr + fp.read(lsiz - 2) + lsiz, rsiz, xsiz, ysiz, xosiz, yosiz, _, _, _, _, csiz = struct.unpack_from( + ">HHIIIIIIIIH", siz + ) + + size = (xsiz - xosiz, ysiz - yosiz) + if csiz == 1: + ssiz = struct.unpack_from(">B", siz, 38) + if (ssiz[0] & 0x7F) + 1 > 8: + mode = "I;16" + else: + mode = "L" + elif csiz == 2: + mode = "LA" + elif csiz == 3: + mode = "RGB" + elif csiz == 4: + mode = "RGBA" + else: + msg = "unable to determine J2K image mode" + raise SyntaxError(msg) + + return size, mode + + +def _res_to_dpi(num: int, denom: int, exp: int) -> float | None: + """Convert JPEG2000's (numerator, denominator, exponent-base-10) resolution, + calculated as (num / denom) * 10^exp and stored in dots per meter, + to floating-point dots per inch.""" + if denom == 0: + return None + return (254 * num * (10**exp)) / (10000 * denom) + + +def _parse_jp2_header( + fp: IO[bytes], +) -> tuple[ + tuple[int, int], + str, + str | None, + tuple[float, float] | None, + ImagePalette.ImagePalette | None, +]: + """Parse the JP2 header box to extract size, component count, + color space information, and optionally DPI information, + returning a (size, mode, mimetype, dpi) tuple.""" + + # Find the JP2 header box + reader = BoxReader(fp) + header = None + mimetype = None + while reader.has_next_box(): + tbox = reader.next_box_type() + + if tbox == b"jp2h": + header = reader.read_boxes() + break + elif tbox == b"ftyp": + if reader.read_fields(">4s")[0] == b"jpx ": + mimetype = "image/jpx" + assert header is not None + + size = None + mode = None + bpc = None + nc = None + dpi = None # 2-tuple of DPI info, or None + palette = None + + while header.has_next_box(): + tbox = header.next_box_type() + + if tbox == b"ihdr": + height, width, nc, bpc = header.read_fields(">IIHB") + assert isinstance(height, int) + assert isinstance(width, int) + assert isinstance(bpc, int) + size = (width, height) + if nc == 1 and (bpc & 0x7F) > 8: + mode = "I;16" + elif nc == 1: + mode = "L" + elif nc == 2: + mode = "LA" + elif nc == 3: + mode = "RGB" + elif nc == 4: + mode = "RGBA" + elif tbox == b"colr" and nc == 4: + meth, _, _, enumcs = header.read_fields(">BBBI") + if meth == 1 and enumcs == 12: + mode = "CMYK" + elif tbox == b"pclr" and mode in ("L", "LA"): + ne, npc = header.read_fields(">HB") + assert isinstance(ne, int) + assert isinstance(npc, int) + max_bitdepth = 0 + for bitdepth in header.read_fields(">" + ("B" * npc)): + assert isinstance(bitdepth, int) + if bitdepth > max_bitdepth: + max_bitdepth = bitdepth + if max_bitdepth <= 8: + palette = ImagePalette.ImagePalette("RGBA" if npc == 4 else "RGB") + for i in range(ne): + color: list[int] = [] + for value in header.read_fields(">" + ("B" * npc)): + assert isinstance(value, int) + color.append(value) + palette.getcolor(tuple(color)) + mode = "P" if mode == "L" else "PA" + elif tbox == b"res ": + res = header.read_boxes() + while res.has_next_box(): + tres = res.next_box_type() + if tres == b"resc": + vrcn, vrcd, hrcn, hrcd, vrce, hrce = res.read_fields(">HHHHBB") + assert isinstance(vrcn, int) + assert isinstance(vrcd, int) + assert isinstance(hrcn, int) + assert isinstance(hrcd, int) + assert isinstance(vrce, int) + assert isinstance(hrce, int) + hres = _res_to_dpi(hrcn, hrcd, hrce) + vres = _res_to_dpi(vrcn, vrcd, vrce) + if hres is not None and vres is not None: + dpi = (hres, vres) + break + + if size is None or mode is None: + msg = "Malformed JP2 header" + raise SyntaxError(msg) + + return size, mode, mimetype, dpi, palette + + +## +# Image plugin for JPEG2000 images. + + +class Jpeg2KImageFile(ImageFile.ImageFile): + format = "JPEG2000" + format_description = "JPEG 2000 (ISO 15444)" + + def _open(self) -> None: + sig = self.fp.read(4) + if sig == b"\xff\x4f\xff\x51": + self.codec = "j2k" + self._size, self._mode = _parse_codestream(self.fp) + self._parse_comment() + else: + sig = sig + self.fp.read(8) + + if sig == b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a": + self.codec = "jp2" + header = _parse_jp2_header(self.fp) + self._size, self._mode, self.custom_mimetype, dpi, self.palette = header + if dpi is not None: + self.info["dpi"] = dpi + if self.fp.read(12).endswith(b"jp2c\xff\x4f\xff\x51"): + hdr = self.fp.read(2) + length = _binary.i16be(hdr) + self.fp.seek(length - 2, os.SEEK_CUR) + self._parse_comment() + else: + msg = "not a JPEG 2000 file" + raise SyntaxError(msg) + + self._reduce = 0 + self.layers = 0 + + fd = -1 + length = -1 + + try: + fd = self.fp.fileno() + length = os.fstat(fd).st_size + except Exception: + fd = -1 + try: + pos = self.fp.tell() + self.fp.seek(0, io.SEEK_END) + length = self.fp.tell() + self.fp.seek(pos) + except Exception: + length = -1 + + self.tile = [ + ImageFile._Tile( + "jpeg2k", + (0, 0) + self.size, + 0, + (self.codec, self._reduce, self.layers, fd, length), + ) + ] + + def _parse_comment(self) -> None: + while True: + marker = self.fp.read(2) + if not marker: + break + typ = marker[1] + if typ in (0x90, 0xD9): + # Start of tile or end of codestream + break + hdr = self.fp.read(2) + length = _binary.i16be(hdr) + if typ == 0x64: + # Comment + self.info["comment"] = self.fp.read(length - 2)[2:] + break + else: + self.fp.seek(length - 2, os.SEEK_CUR) + + @property # type: ignore[override] + def reduce( + self, + ) -> ( + Callable[[int | tuple[int, int], tuple[int, int, int, int] | None], Image.Image] + | int + ): + # https://github.com/python-pillow/Pillow/issues/4343 found that the + # new Image 'reduce' method was shadowed by this plugin's 'reduce' + # property. This attempts to allow for both scenarios + return self._reduce or super().reduce + + @reduce.setter + def reduce(self, value: int) -> None: + self._reduce = value + + def load(self) -> Image.core.PixelAccess | None: + if self.tile and self._reduce: + power = 1 << self._reduce + adjust = power >> 1 + self._size = ( + int((self.size[0] + adjust) / power), + int((self.size[1] + adjust) / power), + ) + + # Update the reduce and layers settings + t = self.tile[0] + assert isinstance(t[3], tuple) + t3 = (t[3][0], self._reduce, self.layers, t[3][3], t[3][4]) + self.tile = [ImageFile._Tile(t[0], (0, 0) + self.size, t[2], t3)] + + return ImageFile.ImageFile.load(self) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith( + (b"\xff\x4f\xff\x51", b"\x00\x00\x00\x0cjP \x0d\x0a\x87\x0a") + ) + + +# ------------------------------------------------------------ +# Save support + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + # Get the keyword arguments + info = im.encoderinfo + + if isinstance(filename, str): + filename = filename.encode() + if filename.endswith(b".j2k") or info.get("no_jp2", False): + kind = "j2k" + else: + kind = "jp2" + + offset = info.get("offset", None) + tile_offset = info.get("tile_offset", None) + tile_size = info.get("tile_size", None) + quality_mode = info.get("quality_mode", "rates") + quality_layers = info.get("quality_layers", None) + if quality_layers is not None and not ( + isinstance(quality_layers, (list, tuple)) + and all( + isinstance(quality_layer, (int, float)) for quality_layer in quality_layers + ) + ): + msg = "quality_layers must be a sequence of numbers" + raise ValueError(msg) + + num_resolutions = info.get("num_resolutions", 0) + cblk_size = info.get("codeblock_size", None) + precinct_size = info.get("precinct_size", None) + irreversible = info.get("irreversible", False) + progression = info.get("progression", "LRCP") + cinema_mode = info.get("cinema_mode", "no") + mct = info.get("mct", 0) + signed = info.get("signed", False) + comment = info.get("comment") + if isinstance(comment, str): + comment = comment.encode() + plt = info.get("plt", False) + + fd = -1 + if hasattr(fp, "fileno"): + try: + fd = fp.fileno() + except Exception: + fd = -1 + + im.encoderconfig = ( + offset, + tile_offset, + tile_size, + quality_mode, + quality_layers, + num_resolutions, + cblk_size, + precinct_size, + irreversible, + progression, + cinema_mode, + mct, + signed, + fd, + comment, + plt, + ) + + ImageFile._save(im, fp, [ImageFile._Tile("jpeg2k", (0, 0) + im.size, 0, kind)]) + + +# ------------------------------------------------------------ +# Registry stuff + + +Image.register_open(Jpeg2KImageFile.format, Jpeg2KImageFile, _accept) +Image.register_save(Jpeg2KImageFile.format, _save) + +Image.register_extensions( + Jpeg2KImageFile.format, [".jp2", ".j2k", ".jpc", ".jpf", ".jpx", ".j2c"] +) + +Image.register_mime(Jpeg2KImageFile.format, "image/jp2") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/JpegImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/JpegImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..defe9f773f9215c9f5f31f918edfdaeda6474a16 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/JpegImagePlugin.py @@ -0,0 +1,902 @@ +# +# The Python Imaging Library. +# $Id$ +# +# JPEG (JFIF) file handling +# +# See "Digital Compression and Coding of Continuous-Tone Still Images, +# Part 1, Requirements and Guidelines" (CCITT T.81 / ISO 10918-1) +# +# History: +# 1995-09-09 fl Created +# 1995-09-13 fl Added full parser +# 1996-03-25 fl Added hack to use the IJG command line utilities +# 1996-05-05 fl Workaround Photoshop 2.5 CMYK polarity bug +# 1996-05-28 fl Added draft support, JFIF version (0.1) +# 1996-12-30 fl Added encoder options, added progression property (0.2) +# 1997-08-27 fl Save mode 1 images as BW (0.3) +# 1998-07-12 fl Added YCbCr to draft and save methods (0.4) +# 1998-10-19 fl Don't hang on files using 16-bit DQT's (0.4.1) +# 2001-04-16 fl Extract DPI settings from JFIF files (0.4.2) +# 2002-07-01 fl Skip pad bytes before markers; identify Exif files (0.4.3) +# 2003-04-25 fl Added experimental EXIF decoder (0.5) +# 2003-06-06 fl Added experimental EXIF GPSinfo decoder +# 2003-09-13 fl Extract COM markers +# 2009-09-06 fl Added icc_profile support (from Florian Hoech) +# 2009-03-06 fl Changed CMYK handling; always use Adobe polarity (0.6) +# 2009-03-08 fl Added subsampling support (from Justin Huff). +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-1996 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import array +import io +import math +import os +import struct +import subprocess +import sys +import tempfile +import warnings +from typing import IO, Any + +from . import Image, ImageFile +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._binary import o8 +from ._binary import o16be as o16 +from ._deprecate import deprecate +from .JpegPresets import presets + +TYPE_CHECKING = False +if TYPE_CHECKING: + from .MpoImagePlugin import MpoImageFile + +# +# Parser + + +def Skip(self: JpegImageFile, marker: int) -> None: + n = i16(self.fp.read(2)) - 2 + ImageFile._safe_read(self.fp, n) + + +def APP(self: JpegImageFile, marker: int) -> None: + # + # Application marker. Store these in the APP dictionary. + # Also look for well-known application markers. + + n = i16(self.fp.read(2)) - 2 + s = ImageFile._safe_read(self.fp, n) + + app = f"APP{marker & 15}" + + self.app[app] = s # compatibility + self.applist.append((app, s)) + + if marker == 0xFFE0 and s.startswith(b"JFIF"): + # extract JFIF information + self.info["jfif"] = version = i16(s, 5) # version + self.info["jfif_version"] = divmod(version, 256) + # extract JFIF properties + try: + jfif_unit = s[7] + jfif_density = i16(s, 8), i16(s, 10) + except Exception: + pass + else: + if jfif_unit == 1: + self.info["dpi"] = jfif_density + elif jfif_unit == 2: # cm + # 1 dpcm = 2.54 dpi + self.info["dpi"] = tuple(d * 2.54 for d in jfif_density) + self.info["jfif_unit"] = jfif_unit + self.info["jfif_density"] = jfif_density + elif marker == 0xFFE1 and s.startswith(b"Exif\0\0"): + # extract EXIF information + if "exif" in self.info: + self.info["exif"] += s[6:] + else: + self.info["exif"] = s + self._exif_offset = self.fp.tell() - n + 6 + elif marker == 0xFFE1 and s.startswith(b"http://ns.adobe.com/xap/1.0/\x00"): + self.info["xmp"] = s.split(b"\x00", 1)[1] + elif marker == 0xFFE2 and s.startswith(b"FPXR\0"): + # extract FlashPix information (incomplete) + self.info["flashpix"] = s # FIXME: value will change + elif marker == 0xFFE2 and s.startswith(b"ICC_PROFILE\0"): + # Since an ICC profile can be larger than the maximum size of + # a JPEG marker (64K), we need provisions to split it into + # multiple markers. The format defined by the ICC specifies + # one or more APP2 markers containing the following data: + # Identifying string ASCII "ICC_PROFILE\0" (12 bytes) + # Marker sequence number 1, 2, etc (1 byte) + # Number of markers Total of APP2's used (1 byte) + # Profile data (remainder of APP2 data) + # Decoders should use the marker sequence numbers to + # reassemble the profile, rather than assuming that the APP2 + # markers appear in the correct sequence. + self.icclist.append(s) + elif marker == 0xFFED and s.startswith(b"Photoshop 3.0\x00"): + # parse the image resource block + offset = 14 + photoshop = self.info.setdefault("photoshop", {}) + while s[offset : offset + 4] == b"8BIM": + try: + offset += 4 + # resource code + code = i16(s, offset) + offset += 2 + # resource name (usually empty) + name_len = s[offset] + # name = s[offset+1:offset+1+name_len] + offset += 1 + name_len + offset += offset & 1 # align + # resource data block + size = i32(s, offset) + offset += 4 + data = s[offset : offset + size] + if code == 0x03ED: # ResolutionInfo + photoshop[code] = { + "XResolution": i32(data, 0) / 65536, + "DisplayedUnitsX": i16(data, 4), + "YResolution": i32(data, 8) / 65536, + "DisplayedUnitsY": i16(data, 12), + } + else: + photoshop[code] = data + offset += size + offset += offset & 1 # align + except struct.error: + break # insufficient data + + elif marker == 0xFFEE and s.startswith(b"Adobe"): + self.info["adobe"] = i16(s, 5) + # extract Adobe custom properties + try: + adobe_transform = s[11] + except IndexError: + pass + else: + self.info["adobe_transform"] = adobe_transform + elif marker == 0xFFE2 and s.startswith(b"MPF\0"): + # extract MPO information + self.info["mp"] = s[4:] + # offset is current location minus buffer size + # plus constant header size + self.info["mpoffset"] = self.fp.tell() - n + 4 + + +def COM(self: JpegImageFile, marker: int) -> None: + # + # Comment marker. Store these in the APP dictionary. + n = i16(self.fp.read(2)) - 2 + s = ImageFile._safe_read(self.fp, n) + + self.info["comment"] = s + self.app["COM"] = s # compatibility + self.applist.append(("COM", s)) + + +def SOF(self: JpegImageFile, marker: int) -> None: + # + # Start of frame marker. Defines the size and mode of the + # image. JPEG is colour blind, so we use some simple + # heuristics to map the number of layers to an appropriate + # mode. Note that this could be made a bit brighter, by + # looking for JFIF and Adobe APP markers. + + n = i16(self.fp.read(2)) - 2 + s = ImageFile._safe_read(self.fp, n) + self._size = i16(s, 3), i16(s, 1) + + self.bits = s[0] + if self.bits != 8: + msg = f"cannot handle {self.bits}-bit layers" + raise SyntaxError(msg) + + self.layers = s[5] + if self.layers == 1: + self._mode = "L" + elif self.layers == 3: + self._mode = "RGB" + elif self.layers == 4: + self._mode = "CMYK" + else: + msg = f"cannot handle {self.layers}-layer images" + raise SyntaxError(msg) + + if marker in [0xFFC2, 0xFFC6, 0xFFCA, 0xFFCE]: + self.info["progressive"] = self.info["progression"] = 1 + + if self.icclist: + # fixup icc profile + self.icclist.sort() # sort by sequence number + if self.icclist[0][13] == len(self.icclist): + profile = [p[14:] for p in self.icclist] + icc_profile = b"".join(profile) + else: + icc_profile = None # wrong number of fragments + self.info["icc_profile"] = icc_profile + self.icclist = [] + + for i in range(6, len(s), 3): + t = s[i : i + 3] + # 4-tuples: id, vsamp, hsamp, qtable + self.layer.append((t[0], t[1] // 16, t[1] & 15, t[2])) + + +def DQT(self: JpegImageFile, marker: int) -> None: + # + # Define quantization table. Note that there might be more + # than one table in each marker. + + # FIXME: The quantization tables can be used to estimate the + # compression quality. + + n = i16(self.fp.read(2)) - 2 + s = ImageFile._safe_read(self.fp, n) + while len(s): + v = s[0] + precision = 1 if (v // 16 == 0) else 2 # in bytes + qt_length = 1 + precision * 64 + if len(s) < qt_length: + msg = "bad quantization table marker" + raise SyntaxError(msg) + data = array.array("B" if precision == 1 else "H", s[1:qt_length]) + if sys.byteorder == "little" and precision > 1: + data.byteswap() # the values are always big-endian + self.quantization[v & 15] = [data[i] for i in zigzag_index] + s = s[qt_length:] + + +# +# JPEG marker table + +MARKER = { + 0xFFC0: ("SOF0", "Baseline DCT", SOF), + 0xFFC1: ("SOF1", "Extended Sequential DCT", SOF), + 0xFFC2: ("SOF2", "Progressive DCT", SOF), + 0xFFC3: ("SOF3", "Spatial lossless", SOF), + 0xFFC4: ("DHT", "Define Huffman table", Skip), + 0xFFC5: ("SOF5", "Differential sequential DCT", SOF), + 0xFFC6: ("SOF6", "Differential progressive DCT", SOF), + 0xFFC7: ("SOF7", "Differential spatial", SOF), + 0xFFC8: ("JPG", "Extension", None), + 0xFFC9: ("SOF9", "Extended sequential DCT (AC)", SOF), + 0xFFCA: ("SOF10", "Progressive DCT (AC)", SOF), + 0xFFCB: ("SOF11", "Spatial lossless DCT (AC)", SOF), + 0xFFCC: ("DAC", "Define arithmetic coding conditioning", Skip), + 0xFFCD: ("SOF13", "Differential sequential DCT (AC)", SOF), + 0xFFCE: ("SOF14", "Differential progressive DCT (AC)", SOF), + 0xFFCF: ("SOF15", "Differential spatial (AC)", SOF), + 0xFFD0: ("RST0", "Restart 0", None), + 0xFFD1: ("RST1", "Restart 1", None), + 0xFFD2: ("RST2", "Restart 2", None), + 0xFFD3: ("RST3", "Restart 3", None), + 0xFFD4: ("RST4", "Restart 4", None), + 0xFFD5: ("RST5", "Restart 5", None), + 0xFFD6: ("RST6", "Restart 6", None), + 0xFFD7: ("RST7", "Restart 7", None), + 0xFFD8: ("SOI", "Start of image", None), + 0xFFD9: ("EOI", "End of image", None), + 0xFFDA: ("SOS", "Start of scan", Skip), + 0xFFDB: ("DQT", "Define quantization table", DQT), + 0xFFDC: ("DNL", "Define number of lines", Skip), + 0xFFDD: ("DRI", "Define restart interval", Skip), + 0xFFDE: ("DHP", "Define hierarchical progression", SOF), + 0xFFDF: ("EXP", "Expand reference component", Skip), + 0xFFE0: ("APP0", "Application segment 0", APP), + 0xFFE1: ("APP1", "Application segment 1", APP), + 0xFFE2: ("APP2", "Application segment 2", APP), + 0xFFE3: ("APP3", "Application segment 3", APP), + 0xFFE4: ("APP4", "Application segment 4", APP), + 0xFFE5: ("APP5", "Application segment 5", APP), + 0xFFE6: ("APP6", "Application segment 6", APP), + 0xFFE7: ("APP7", "Application segment 7", APP), + 0xFFE8: ("APP8", "Application segment 8", APP), + 0xFFE9: ("APP9", "Application segment 9", APP), + 0xFFEA: ("APP10", "Application segment 10", APP), + 0xFFEB: ("APP11", "Application segment 11", APP), + 0xFFEC: ("APP12", "Application segment 12", APP), + 0xFFED: ("APP13", "Application segment 13", APP), + 0xFFEE: ("APP14", "Application segment 14", APP), + 0xFFEF: ("APP15", "Application segment 15", APP), + 0xFFF0: ("JPG0", "Extension 0", None), + 0xFFF1: ("JPG1", "Extension 1", None), + 0xFFF2: ("JPG2", "Extension 2", None), + 0xFFF3: ("JPG3", "Extension 3", None), + 0xFFF4: ("JPG4", "Extension 4", None), + 0xFFF5: ("JPG5", "Extension 5", None), + 0xFFF6: ("JPG6", "Extension 6", None), + 0xFFF7: ("JPG7", "Extension 7", None), + 0xFFF8: ("JPG8", "Extension 8", None), + 0xFFF9: ("JPG9", "Extension 9", None), + 0xFFFA: ("JPG10", "Extension 10", None), + 0xFFFB: ("JPG11", "Extension 11", None), + 0xFFFC: ("JPG12", "Extension 12", None), + 0xFFFD: ("JPG13", "Extension 13", None), + 0xFFFE: ("COM", "Comment", COM), +} + + +def _accept(prefix: bytes) -> bool: + # Magic number was taken from https://en.wikipedia.org/wiki/JPEG + return prefix.startswith(b"\xff\xd8\xff") + + +## +# Image plugin for JPEG and JFIF images. + + +class JpegImageFile(ImageFile.ImageFile): + format = "JPEG" + format_description = "JPEG (ISO 10918)" + + def _open(self) -> None: + s = self.fp.read(3) + + if not _accept(s): + msg = "not a JPEG file" + raise SyntaxError(msg) + s = b"\xff" + + # Create attributes + self.bits = self.layers = 0 + self._exif_offset = 0 + + # JPEG specifics (internal) + self.layer: list[tuple[int, int, int, int]] = [] + self._huffman_dc: dict[Any, Any] = {} + self._huffman_ac: dict[Any, Any] = {} + self.quantization: dict[int, list[int]] = {} + self.app: dict[str, bytes] = {} # compatibility + self.applist: list[tuple[str, bytes]] = [] + self.icclist: list[bytes] = [] + + while True: + i = s[0] + if i == 0xFF: + s = s + self.fp.read(1) + i = i16(s) + else: + # Skip non-0xFF junk + s = self.fp.read(1) + continue + + if i in MARKER: + name, description, handler = MARKER[i] + if handler is not None: + handler(self, i) + if i == 0xFFDA: # start of scan + rawmode = self.mode + if self.mode == "CMYK": + rawmode = "CMYK;I" # assume adobe conventions + self.tile = [ + ImageFile._Tile("jpeg", (0, 0) + self.size, 0, (rawmode, "")) + ] + # self.__offset = self.fp.tell() + break + s = self.fp.read(1) + elif i in {0, 0xFFFF}: + # padded marker or junk; move on + s = b"\xff" + elif i == 0xFF00: # Skip extraneous data (escaped 0xFF) + s = self.fp.read(1) + else: + msg = "no marker found" + raise SyntaxError(msg) + + self._read_dpi_from_exif() + + def __getattr__(self, name: str) -> Any: + if name in ("huffman_ac", "huffman_dc"): + deprecate(name, 12) + return getattr(self, "_" + name) + raise AttributeError(name) + + def __getstate__(self) -> list[Any]: + return super().__getstate__() + [self.layers, self.layer] + + def __setstate__(self, state: list[Any]) -> None: + self.layers, self.layer = state[6:] + super().__setstate__(state) + + def load_read(self, read_bytes: int) -> bytes: + """ + internal: read more image data + For premature EOF and LOAD_TRUNCATED_IMAGES adds EOI marker + so libjpeg can finish decoding + """ + s = self.fp.read(read_bytes) + + if not s and ImageFile.LOAD_TRUNCATED_IMAGES and not hasattr(self, "_ended"): + # Premature EOF. + # Pretend file is finished adding EOI marker + self._ended = True + return b"\xff\xd9" + + return s + + def draft( + self, mode: str | None, size: tuple[int, int] | None + ) -> tuple[str, tuple[int, int, float, float]] | None: + if len(self.tile) != 1: + return None + + # Protect from second call + if self.decoderconfig: + return None + + d, e, o, a = self.tile[0] + scale = 1 + original_size = self.size + + assert isinstance(a, tuple) + if a[0] == "RGB" and mode in ["L", "YCbCr"]: + self._mode = mode + a = mode, "" + + if size: + scale = min(self.size[0] // size[0], self.size[1] // size[1]) + for s in [8, 4, 2, 1]: + if scale >= s: + break + assert e is not None + e = ( + e[0], + e[1], + (e[2] - e[0] + s - 1) // s + e[0], + (e[3] - e[1] + s - 1) // s + e[1], + ) + self._size = ((self.size[0] + s - 1) // s, (self.size[1] + s - 1) // s) + scale = s + + self.tile = [ImageFile._Tile(d, e, o, a)] + self.decoderconfig = (scale, 0) + + box = (0, 0, original_size[0] / scale, original_size[1] / scale) + return self.mode, box + + def load_djpeg(self) -> None: + # ALTERNATIVE: handle JPEGs via the IJG command line utilities + + f, path = tempfile.mkstemp() + os.close(f) + if os.path.exists(self.filename): + subprocess.check_call(["djpeg", "-outfile", path, self.filename]) + else: + try: + os.unlink(path) + except OSError: + pass + + msg = "Invalid Filename" + raise ValueError(msg) + + try: + with Image.open(path) as _im: + _im.load() + self.im = _im.im + finally: + try: + os.unlink(path) + except OSError: + pass + + self._mode = self.im.mode + self._size = self.im.size + + self.tile = [] + + def _getexif(self) -> dict[int, Any] | None: + return _getexif(self) + + def _read_dpi_from_exif(self) -> None: + # If DPI isn't in JPEG header, fetch from EXIF + if "dpi" in self.info or "exif" not in self.info: + return + try: + exif = self.getexif() + resolution_unit = exif[0x0128] + x_resolution = exif[0x011A] + try: + dpi = float(x_resolution[0]) / x_resolution[1] + except TypeError: + dpi = x_resolution + if math.isnan(dpi): + msg = "DPI is not a number" + raise ValueError(msg) + if resolution_unit == 3: # cm + # 1 dpcm = 2.54 dpi + dpi *= 2.54 + self.info["dpi"] = dpi, dpi + except ( + struct.error, # truncated EXIF + KeyError, # dpi not included + SyntaxError, # invalid/unreadable EXIF + TypeError, # dpi is an invalid float + ValueError, # dpi is an invalid float + ZeroDivisionError, # invalid dpi rational value + ): + self.info["dpi"] = 72, 72 + + def _getmp(self) -> dict[int, Any] | None: + return _getmp(self) + + +def _getexif(self: JpegImageFile) -> dict[int, Any] | None: + if "exif" not in self.info: + return None + return self.getexif()._get_merged_dict() + + +def _getmp(self: JpegImageFile) -> dict[int, Any] | None: + # Extract MP information. This method was inspired by the "highly + # experimental" _getexif version that's been in use for years now, + # itself based on the ImageFileDirectory class in the TIFF plugin. + + # The MP record essentially consists of a TIFF file embedded in a JPEG + # application marker. + try: + data = self.info["mp"] + except KeyError: + return None + file_contents = io.BytesIO(data) + head = file_contents.read(8) + endianness = ">" if head.startswith(b"\x4d\x4d\x00\x2a") else "<" + # process dictionary + from . import TiffImagePlugin + + try: + info = TiffImagePlugin.ImageFileDirectory_v2(head) + file_contents.seek(info.next) + info.load(file_contents) + mp = dict(info) + except Exception as e: + msg = "malformed MP Index (unreadable directory)" + raise SyntaxError(msg) from e + # it's an error not to have a number of images + try: + quant = mp[0xB001] + except KeyError as e: + msg = "malformed MP Index (no number of images)" + raise SyntaxError(msg) from e + # get MP entries + mpentries = [] + try: + rawmpentries = mp[0xB002] + for entrynum in range(quant): + unpackedentry = struct.unpack_from( + f"{endianness}LLLHH", rawmpentries, entrynum * 16 + ) + labels = ("Attribute", "Size", "DataOffset", "EntryNo1", "EntryNo2") + mpentry = dict(zip(labels, unpackedentry)) + mpentryattr = { + "DependentParentImageFlag": bool(mpentry["Attribute"] & (1 << 31)), + "DependentChildImageFlag": bool(mpentry["Attribute"] & (1 << 30)), + "RepresentativeImageFlag": bool(mpentry["Attribute"] & (1 << 29)), + "Reserved": (mpentry["Attribute"] & (3 << 27)) >> 27, + "ImageDataFormat": (mpentry["Attribute"] & (7 << 24)) >> 24, + "MPType": mpentry["Attribute"] & 0x00FFFFFF, + } + if mpentryattr["ImageDataFormat"] == 0: + mpentryattr["ImageDataFormat"] = "JPEG" + else: + msg = "unsupported picture format in MPO" + raise SyntaxError(msg) + mptypemap = { + 0x000000: "Undefined", + 0x010001: "Large Thumbnail (VGA Equivalent)", + 0x010002: "Large Thumbnail (Full HD Equivalent)", + 0x020001: "Multi-Frame Image (Panorama)", + 0x020002: "Multi-Frame Image: (Disparity)", + 0x020003: "Multi-Frame Image: (Multi-Angle)", + 0x030000: "Baseline MP Primary Image", + } + mpentryattr["MPType"] = mptypemap.get(mpentryattr["MPType"], "Unknown") + mpentry["Attribute"] = mpentryattr + mpentries.append(mpentry) + mp[0xB002] = mpentries + except KeyError as e: + msg = "malformed MP Index (bad MP Entry)" + raise SyntaxError(msg) from e + # Next we should try and parse the individual image unique ID list; + # we don't because I've never seen this actually used in a real MPO + # file and so can't test it. + return mp + + +# -------------------------------------------------------------------- +# stuff to save JPEG files + +RAWMODE = { + "1": "L", + "L": "L", + "RGB": "RGB", + "RGBX": "RGB", + "CMYK": "CMYK;I", # assume adobe conventions + "YCbCr": "YCbCr", +} + +# fmt: off +zigzag_index = ( + 0, 1, 5, 6, 14, 15, 27, 28, + 2, 4, 7, 13, 16, 26, 29, 42, + 3, 8, 12, 17, 25, 30, 41, 43, + 9, 11, 18, 24, 31, 40, 44, 53, + 10, 19, 23, 32, 39, 45, 52, 54, + 20, 22, 33, 38, 46, 51, 55, 60, + 21, 34, 37, 47, 50, 56, 59, 61, + 35, 36, 48, 49, 57, 58, 62, 63, +) + +samplings = { + (1, 1, 1, 1, 1, 1): 0, + (2, 1, 1, 1, 1, 1): 1, + (2, 2, 1, 1, 1, 1): 2, +} +# fmt: on + + +def get_sampling(im: Image.Image) -> int: + # There's no subsampling when images have only 1 layer + # (grayscale images) or when they are CMYK (4 layers), + # so set subsampling to the default value. + # + # NOTE: currently Pillow can't encode JPEG to YCCK format. + # If YCCK support is added in the future, subsampling code will have + # to be updated (here and in JpegEncode.c) to deal with 4 layers. + if not isinstance(im, JpegImageFile) or im.layers in (1, 4): + return -1 + sampling = im.layer[0][1:3] + im.layer[1][1:3] + im.layer[2][1:3] + return samplings.get(sampling, -1) + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.width == 0 or im.height == 0: + msg = "cannot write empty image as JPEG" + raise ValueError(msg) + + try: + rawmode = RAWMODE[im.mode] + except KeyError as e: + msg = f"cannot write mode {im.mode} as JPEG" + raise OSError(msg) from e + + info = im.encoderinfo + + dpi = [round(x) for x in info.get("dpi", (0, 0))] + + quality = info.get("quality", -1) + subsampling = info.get("subsampling", -1) + qtables = info.get("qtables") + + if quality == "keep": + quality = -1 + subsampling = "keep" + qtables = "keep" + elif quality in presets: + preset = presets[quality] + quality = -1 + subsampling = preset.get("subsampling", -1) + qtables = preset.get("quantization") + elif not isinstance(quality, int): + msg = "Invalid quality setting" + raise ValueError(msg) + else: + if subsampling in presets: + subsampling = presets[subsampling].get("subsampling", -1) + if isinstance(qtables, str) and qtables in presets: + qtables = presets[qtables].get("quantization") + + if subsampling == "4:4:4": + subsampling = 0 + elif subsampling == "4:2:2": + subsampling = 1 + elif subsampling == "4:2:0": + subsampling = 2 + elif subsampling == "4:1:1": + # For compatibility. Before Pillow 4.3, 4:1:1 actually meant 4:2:0. + # Set 4:2:0 if someone is still using that value. + subsampling = 2 + elif subsampling == "keep": + if im.format != "JPEG": + msg = "Cannot use 'keep' when original image is not a JPEG" + raise ValueError(msg) + subsampling = get_sampling(im) + + def validate_qtables( + qtables: ( + str | tuple[list[int], ...] | list[list[int]] | dict[int, list[int]] | None + ), + ) -> list[list[int]] | None: + if qtables is None: + return qtables + if isinstance(qtables, str): + try: + lines = [ + int(num) + for line in qtables.splitlines() + for num in line.split("#", 1)[0].split() + ] + except ValueError as e: + msg = "Invalid quantization table" + raise ValueError(msg) from e + else: + qtables = [lines[s : s + 64] for s in range(0, len(lines), 64)] + if isinstance(qtables, (tuple, list, dict)): + if isinstance(qtables, dict): + qtables = [ + qtables[key] for key in range(len(qtables)) if key in qtables + ] + elif isinstance(qtables, tuple): + qtables = list(qtables) + if not (0 < len(qtables) < 5): + msg = "None or too many quantization tables" + raise ValueError(msg) + for idx, table in enumerate(qtables): + try: + if len(table) != 64: + msg = "Invalid quantization table" + raise TypeError(msg) + table_array = array.array("H", table) + except TypeError as e: + msg = "Invalid quantization table" + raise ValueError(msg) from e + else: + qtables[idx] = list(table_array) + return qtables + + if qtables == "keep": + if im.format != "JPEG": + msg = "Cannot use 'keep' when original image is not a JPEG" + raise ValueError(msg) + qtables = getattr(im, "quantization", None) + qtables = validate_qtables(qtables) + + extra = info.get("extra", b"") + + MAX_BYTES_IN_MARKER = 65533 + if xmp := info.get("xmp"): + overhead_len = 29 # b"http://ns.adobe.com/xap/1.0/\x00" + max_data_bytes_in_marker = MAX_BYTES_IN_MARKER - overhead_len + if len(xmp) > max_data_bytes_in_marker: + msg = "XMP data is too long" + raise ValueError(msg) + size = o16(2 + overhead_len + len(xmp)) + extra += b"\xff\xe1" + size + b"http://ns.adobe.com/xap/1.0/\x00" + xmp + + if icc_profile := info.get("icc_profile"): + overhead_len = 14 # b"ICC_PROFILE\0" + o8(i) + o8(len(markers)) + max_data_bytes_in_marker = MAX_BYTES_IN_MARKER - overhead_len + markers = [] + while icc_profile: + markers.append(icc_profile[:max_data_bytes_in_marker]) + icc_profile = icc_profile[max_data_bytes_in_marker:] + i = 1 + for marker in markers: + size = o16(2 + overhead_len + len(marker)) + extra += ( + b"\xff\xe2" + + size + + b"ICC_PROFILE\0" + + o8(i) + + o8(len(markers)) + + marker + ) + i += 1 + + comment = info.get("comment", im.info.get("comment")) + + # "progressive" is the official name, but older documentation + # says "progression" + # FIXME: issue a warning if the wrong form is used (post-1.1.7) + progressive = info.get("progressive", False) or info.get("progression", False) + + optimize = info.get("optimize", False) + + exif = info.get("exif", b"") + if isinstance(exif, Image.Exif): + exif = exif.tobytes() + if len(exif) > MAX_BYTES_IN_MARKER: + msg = "EXIF data is too long" + raise ValueError(msg) + + # get keyword arguments + im.encoderconfig = ( + quality, + progressive, + info.get("smooth", 0), + optimize, + info.get("keep_rgb", False), + info.get("streamtype", 0), + dpi, + subsampling, + info.get("restart_marker_blocks", 0), + info.get("restart_marker_rows", 0), + qtables, + comment, + extra, + exif, + ) + + # if we optimize, libjpeg needs a buffer big enough to hold the whole image + # in a shot. Guessing on the size, at im.size bytes. (raw pixel size is + # channels*size, this is a value that's been used in a django patch. + # https://github.com/matthewwithanm/django-imagekit/issues/50 + if optimize or progressive: + # CMYK can be bigger + if im.mode == "CMYK": + bufsize = 4 * im.size[0] * im.size[1] + # keep sets quality to -1, but the actual value may be high. + elif quality >= 95 or quality == -1: + bufsize = 2 * im.size[0] * im.size[1] + else: + bufsize = im.size[0] * im.size[1] + if exif: + bufsize += len(exif) + 5 + if extra: + bufsize += len(extra) + 1 + else: + # The EXIF info needs to be written as one block, + APP1, + one spare byte. + # Ensure that our buffer is big enough. Same with the icc_profile block. + bufsize = max(len(exif) + 5, len(extra) + 1) + + ImageFile._save( + im, fp, [ImageFile._Tile("jpeg", (0, 0) + im.size, 0, rawmode)], bufsize + ) + + +def _save_cjpeg(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + # ALTERNATIVE: handle JPEGs via the IJG command line utilities. + tempfile = im._dump() + subprocess.check_call(["cjpeg", "-outfile", filename, tempfile]) + try: + os.unlink(tempfile) + except OSError: + pass + + +## +# Factory for making JPEG and MPO instances +def jpeg_factory( + fp: IO[bytes], filename: str | bytes | None = None +) -> JpegImageFile | MpoImageFile: + im = JpegImageFile(fp, filename) + try: + mpheader = im._getmp() + if mpheader is not None and mpheader[45057] > 1: + for segment, content in im.applist: + if segment == "APP1" and b' hdrgm:Version="' in content: + # Ultra HDR images are not yet supported + return im + # It's actually an MPO + from .MpoImagePlugin import MpoImageFile + + # Don't reload everything, just convert it. + im = MpoImageFile.adopt(im, mpheader) + except (TypeError, IndexError): + # It is really a JPEG + pass + except SyntaxError: + warnings.warn( + "Image appears to be a malformed MPO file, it will be " + "interpreted as a base JPEG file" + ) + return im + + +# --------------------------------------------------------------------- +# Registry stuff + +Image.register_open(JpegImageFile.format, jpeg_factory, _accept) +Image.register_save(JpegImageFile.format, _save) + +Image.register_extensions(JpegImageFile.format, [".jfif", ".jpe", ".jpg", ".jpeg"]) + +Image.register_mime(JpegImageFile.format, "image/jpeg") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/JpegPresets.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/JpegPresets.py new file mode 100644 index 0000000000000000000000000000000000000000..d0e64a35ee1b6fe3ac6da792682a3129253993bb --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/JpegPresets.py @@ -0,0 +1,242 @@ +""" +JPEG quality settings equivalent to the Photoshop settings. +Can be used when saving JPEG files. + +The following presets are available by default: +``web_low``, ``web_medium``, ``web_high``, ``web_very_high``, ``web_maximum``, +``low``, ``medium``, ``high``, ``maximum``. +More presets can be added to the :py:data:`presets` dict if needed. + +To apply the preset, specify:: + + quality="preset_name" + +To apply only the quantization table:: + + qtables="preset_name" + +To apply only the subsampling setting:: + + subsampling="preset_name" + +Example:: + + im.save("image_name.jpg", quality="web_high") + +Subsampling +----------- + +Subsampling is the practice of encoding images by implementing less resolution +for chroma information than for luma information. +(ref.: https://en.wikipedia.org/wiki/Chroma_subsampling) + +Possible subsampling values are 0, 1 and 2 that correspond to 4:4:4, 4:2:2 and +4:2:0. + +You can get the subsampling of a JPEG with the +:func:`.JpegImagePlugin.get_sampling` function. + +In JPEG compressed data a JPEG marker is used instead of an EXIF tag. +(ref.: https://exiv2.org/tags.html) + + +Quantization tables +------------------- + +They are values use by the DCT (Discrete cosine transform) to remove +*unnecessary* information from the image (the lossy part of the compression). +(ref.: https://en.wikipedia.org/wiki/Quantization_matrix#Quantization_matrices, +https://en.wikipedia.org/wiki/JPEG#Quantization) + +You can get the quantization tables of a JPEG with:: + + im.quantization + +This will return a dict with a number of lists. You can pass this dict +directly as the qtables argument when saving a JPEG. + +The quantization table format in presets is a list with sublists. These formats +are interchangeable. + +Libjpeg ref.: +https://web.archive.org/web/20120328125543/http://www.jpegcameras.com/libjpeg/libjpeg-3.html + +""" + +from __future__ import annotations + +# fmt: off +presets = { + 'web_low': {'subsampling': 2, # "4:2:0" + 'quantization': [ + [20, 16, 25, 39, 50, 46, 62, 68, + 16, 18, 23, 38, 38, 53, 65, 68, + 25, 23, 31, 38, 53, 65, 68, 68, + 39, 38, 38, 53, 65, 68, 68, 68, + 50, 38, 53, 65, 68, 68, 68, 68, + 46, 53, 65, 68, 68, 68, 68, 68, + 62, 65, 68, 68, 68, 68, 68, 68, + 68, 68, 68, 68, 68, 68, 68, 68], + [21, 25, 32, 38, 54, 68, 68, 68, + 25, 28, 24, 38, 54, 68, 68, 68, + 32, 24, 32, 43, 66, 68, 68, 68, + 38, 38, 43, 53, 68, 68, 68, 68, + 54, 54, 66, 68, 68, 68, 68, 68, + 68, 68, 68, 68, 68, 68, 68, 68, + 68, 68, 68, 68, 68, 68, 68, 68, + 68, 68, 68, 68, 68, 68, 68, 68] + ]}, + 'web_medium': {'subsampling': 2, # "4:2:0" + 'quantization': [ + [16, 11, 11, 16, 23, 27, 31, 30, + 11, 12, 12, 15, 20, 23, 23, 30, + 11, 12, 13, 16, 23, 26, 35, 47, + 16, 15, 16, 23, 26, 37, 47, 64, + 23, 20, 23, 26, 39, 51, 64, 64, + 27, 23, 26, 37, 51, 64, 64, 64, + 31, 23, 35, 47, 64, 64, 64, 64, + 30, 30, 47, 64, 64, 64, 64, 64], + [17, 15, 17, 21, 20, 26, 38, 48, + 15, 19, 18, 17, 20, 26, 35, 43, + 17, 18, 20, 22, 26, 30, 46, 53, + 21, 17, 22, 28, 30, 39, 53, 64, + 20, 20, 26, 30, 39, 48, 64, 64, + 26, 26, 30, 39, 48, 63, 64, 64, + 38, 35, 46, 53, 64, 64, 64, 64, + 48, 43, 53, 64, 64, 64, 64, 64] + ]}, + 'web_high': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [6, 4, 4, 6, 9, 11, 12, 16, + 4, 5, 5, 6, 8, 10, 12, 12, + 4, 5, 5, 6, 10, 12, 14, 19, + 6, 6, 6, 11, 12, 15, 19, 28, + 9, 8, 10, 12, 16, 20, 27, 31, + 11, 10, 12, 15, 20, 27, 31, 31, + 12, 12, 14, 19, 27, 31, 31, 31, + 16, 12, 19, 28, 31, 31, 31, 31], + [7, 7, 13, 24, 26, 31, 31, 31, + 7, 12, 16, 21, 31, 31, 31, 31, + 13, 16, 17, 31, 31, 31, 31, 31, + 24, 21, 31, 31, 31, 31, 31, 31, + 26, 31, 31, 31, 31, 31, 31, 31, + 31, 31, 31, 31, 31, 31, 31, 31, + 31, 31, 31, 31, 31, 31, 31, 31, + 31, 31, 31, 31, 31, 31, 31, 31] + ]}, + 'web_very_high': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [2, 2, 2, 2, 3, 4, 5, 6, + 2, 2, 2, 2, 3, 4, 5, 6, + 2, 2, 2, 2, 4, 5, 7, 9, + 2, 2, 2, 4, 5, 7, 9, 12, + 3, 3, 4, 5, 8, 10, 12, 12, + 4, 4, 5, 7, 10, 12, 12, 12, + 5, 5, 7, 9, 12, 12, 12, 12, + 6, 6, 9, 12, 12, 12, 12, 12], + [3, 3, 5, 9, 13, 15, 15, 15, + 3, 4, 6, 11, 14, 12, 12, 12, + 5, 6, 9, 14, 12, 12, 12, 12, + 9, 11, 14, 12, 12, 12, 12, 12, + 13, 14, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12] + ]}, + 'web_maximum': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 2, + 1, 1, 1, 1, 1, 1, 2, 2, + 1, 1, 1, 1, 1, 2, 2, 3, + 1, 1, 1, 1, 2, 2, 3, 3, + 1, 1, 1, 2, 2, 3, 3, 3, + 1, 1, 2, 2, 3, 3, 3, 3], + [1, 1, 1, 2, 2, 3, 3, 3, + 1, 1, 1, 2, 3, 3, 3, 3, + 1, 1, 1, 3, 3, 3, 3, 3, + 2, 2, 3, 3, 3, 3, 3, 3, + 2, 3, 3, 3, 3, 3, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 3, + 3, 3, 3, 3, 3, 3, 3, 3] + ]}, + 'low': {'subsampling': 2, # "4:2:0" + 'quantization': [ + [18, 14, 14, 21, 30, 35, 34, 17, + 14, 16, 16, 19, 26, 23, 12, 12, + 14, 16, 17, 21, 23, 12, 12, 12, + 21, 19, 21, 23, 12, 12, 12, 12, + 30, 26, 23, 12, 12, 12, 12, 12, + 35, 23, 12, 12, 12, 12, 12, 12, + 34, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12], + [20, 19, 22, 27, 20, 20, 17, 17, + 19, 25, 23, 14, 14, 12, 12, 12, + 22, 23, 14, 14, 12, 12, 12, 12, + 27, 14, 14, 12, 12, 12, 12, 12, + 20, 14, 12, 12, 12, 12, 12, 12, + 20, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12] + ]}, + 'medium': {'subsampling': 2, # "4:2:0" + 'quantization': [ + [12, 8, 8, 12, 17, 21, 24, 17, + 8, 9, 9, 11, 15, 19, 12, 12, + 8, 9, 10, 12, 19, 12, 12, 12, + 12, 11, 12, 21, 12, 12, 12, 12, + 17, 15, 19, 12, 12, 12, 12, 12, + 21, 19, 12, 12, 12, 12, 12, 12, + 24, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12], + [13, 11, 13, 16, 20, 20, 17, 17, + 11, 14, 14, 14, 14, 12, 12, 12, + 13, 14, 14, 14, 12, 12, 12, 12, + 16, 14, 14, 12, 12, 12, 12, 12, + 20, 14, 12, 12, 12, 12, 12, 12, + 20, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12] + ]}, + 'high': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [6, 4, 4, 6, 9, 11, 12, 16, + 4, 5, 5, 6, 8, 10, 12, 12, + 4, 5, 5, 6, 10, 12, 12, 12, + 6, 6, 6, 11, 12, 12, 12, 12, + 9, 8, 10, 12, 12, 12, 12, 12, + 11, 10, 12, 12, 12, 12, 12, 12, + 12, 12, 12, 12, 12, 12, 12, 12, + 16, 12, 12, 12, 12, 12, 12, 12], + [7, 7, 13, 24, 20, 20, 17, 17, + 7, 12, 16, 14, 14, 12, 12, 12, + 13, 16, 14, 14, 12, 12, 12, 12, + 24, 14, 14, 12, 12, 12, 12, 12, + 20, 14, 12, 12, 12, 12, 12, 12, + 20, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12, + 17, 12, 12, 12, 12, 12, 12, 12] + ]}, + 'maximum': {'subsampling': 0, # "4:4:4" + 'quantization': [ + [2, 2, 2, 2, 3, 4, 5, 6, + 2, 2, 2, 2, 3, 4, 5, 6, + 2, 2, 2, 2, 4, 5, 7, 9, + 2, 2, 2, 4, 5, 7, 9, 12, + 3, 3, 4, 5, 8, 10, 12, 12, + 4, 4, 5, 7, 10, 12, 12, 12, + 5, 5, 7, 9, 12, 12, 12, 12, + 6, 6, 9, 12, 12, 12, 12, 12], + [3, 3, 5, 9, 13, 15, 15, 15, + 3, 4, 6, 10, 14, 12, 12, 12, + 5, 6, 9, 14, 12, 12, 12, 12, + 9, 10, 14, 12, 12, 12, 12, 12, + 13, 14, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12, + 15, 12, 12, 12, 12, 12, 12, 12] + ]}, +} +# fmt: on diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/McIdasImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/McIdasImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..9a47933b69cbdc628faafb67b2fca8de703abfc1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/McIdasImagePlugin.py @@ -0,0 +1,78 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Basic McIdas support for PIL +# +# History: +# 1997-05-05 fl Created (8-bit images only) +# 2009-03-08 fl Added 16/32-bit support. +# +# Thanks to Richard Jones and Craig Swank for specs and samples. +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import struct + +from . import Image, ImageFile + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\x00\x00\x00\x00\x00\x00\x00\x04") + + +## +# Image plugin for McIdas area images. + + +class McIdasImageFile(ImageFile.ImageFile): + format = "MCIDAS" + format_description = "McIdas area file" + + def _open(self) -> None: + # parse area file directory + assert self.fp is not None + + s = self.fp.read(256) + if not _accept(s) or len(s) != 256: + msg = "not an McIdas area file" + raise SyntaxError(msg) + + self.area_descriptor_raw = s + self.area_descriptor = w = [0, *struct.unpack("!64i", s)] + + # get mode + if w[11] == 1: + mode = rawmode = "L" + elif w[11] == 2: + mode = rawmode = "I;16B" + elif w[11] == 4: + # FIXME: add memory map support + mode = "I" + rawmode = "I;32B" + else: + msg = "unsupported McIdas format" + raise SyntaxError(msg) + + self._mode = mode + self._size = w[10], w[9] + + offset = w[34] + w[15] + stride = w[15] + w[10] * w[11] * w[14] + + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offset, (rawmode, stride, 1)) + ] + + +# -------------------------------------------------------------------- +# registry + +Image.register_open(McIdasImageFile.format, McIdasImageFile, _accept) + +# no default extension diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MicImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MicImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..9ce38c427b6c19be9e0c5092181a54b936a7a2f3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MicImagePlugin.py @@ -0,0 +1,102 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Microsoft Image Composer support for PIL +# +# Notes: +# uses TiffImagePlugin.py to read the actual image streams +# +# History: +# 97-01-20 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import olefile + +from . import Image, TiffImagePlugin + +# +# -------------------------------------------------------------------- + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(olefile.MAGIC) + + +## +# Image plugin for Microsoft's Image Composer file format. + + +class MicImageFile(TiffImagePlugin.TiffImageFile): + format = "MIC" + format_description = "Microsoft Image Composer" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # read the OLE directory and see if this is a likely + # to be a Microsoft Image Composer file + + try: + self.ole = olefile.OleFileIO(self.fp) + except OSError as e: + msg = "not an MIC file; invalid OLE file" + raise SyntaxError(msg) from e + + # find ACI subfiles with Image members (maybe not the + # best way to identify MIC files, but what the... ;-) + + self.images = [ + path + for path in self.ole.listdir() + if path[1:] and path[0].endswith(".ACI") and path[1] == "Image" + ] + + # if we didn't find any images, this is probably not + # an MIC file. + if not self.images: + msg = "not an MIC file; no image entries" + raise SyntaxError(msg) + + self.frame = -1 + self._n_frames = len(self.images) + self.is_animated = self._n_frames > 1 + + self.__fp = self.fp + self.seek(0) + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + filename = self.images[frame] + self.fp = self.ole.openstream(filename) + + TiffImagePlugin.TiffImageFile._open(self) + + self.frame = frame + + def tell(self) -> int: + return self.frame + + def close(self) -> None: + self.__fp.close() + self.ole.close() + super().close() + + def __exit__(self, *args: object) -> None: + self.__fp.close() + self.ole.close() + super().__exit__() + + +# +# -------------------------------------------------------------------- + +Image.register_open(MicImageFile.format, MicImageFile, _accept) + +Image.register_extension(MicImageFile.format, ".mic") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MpegImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MpegImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..47ebe9d62c4edd3b5e97f760ff7e9b0417e5b5ab --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MpegImagePlugin.py @@ -0,0 +1,84 @@ +# +# The Python Imaging Library. +# $Id$ +# +# MPEG file handling +# +# History: +# 95-09-09 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1995. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFile +from ._binary import i8 +from ._typing import SupportsRead + +# +# Bitstream parser + + +class BitStream: + def __init__(self, fp: SupportsRead[bytes]) -> None: + self.fp = fp + self.bits = 0 + self.bitbuffer = 0 + + def next(self) -> int: + return i8(self.fp.read(1)) + + def peek(self, bits: int) -> int: + while self.bits < bits: + self.bitbuffer = (self.bitbuffer << 8) + self.next() + self.bits += 8 + return self.bitbuffer >> (self.bits - bits) & (1 << bits) - 1 + + def skip(self, bits: int) -> None: + while self.bits < bits: + self.bitbuffer = (self.bitbuffer << 8) + i8(self.fp.read(1)) + self.bits += 8 + self.bits = self.bits - bits + + def read(self, bits: int) -> int: + v = self.peek(bits) + self.bits = self.bits - bits + return v + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\x00\x00\x01\xb3") + + +## +# Image plugin for MPEG streams. This plugin can identify a stream, +# but it cannot read it. + + +class MpegImageFile(ImageFile.ImageFile): + format = "MPEG" + format_description = "MPEG" + + def _open(self) -> None: + assert self.fp is not None + + s = BitStream(self.fp) + if s.read(32) != 0x1B3: + msg = "not an MPEG file" + raise SyntaxError(msg) + + self._mode = "RGB" + self._size = s.read(12), s.read(12) + + +# -------------------------------------------------------------------- +# Registry stuff + +Image.register_open(MpegImageFile.format, MpegImageFile, _accept) + +Image.register_extensions(MpegImageFile.format, [".mpg", ".mpeg"]) + +Image.register_mime(MpegImageFile.format, "video/mpeg") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MpoImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MpoImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..b1ae07873ac215b7abeeed9fe32d0f17db45d124 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MpoImagePlugin.py @@ -0,0 +1,202 @@ +# +# The Python Imaging Library. +# $Id$ +# +# MPO file handling +# +# See "Multi-Picture Format" (CIPA DC-007-Translation 2009, Standard of the +# Camera & Imaging Products Association) +# +# The multi-picture object combines multiple JPEG images (with a modified EXIF +# data format) into a single file. While it can theoretically be used much like +# a GIF animation, it is commonly used to represent 3D photographs and is (as +# of this writing) the most commonly used format by 3D cameras. +# +# History: +# 2014-03-13 Feneric Created +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +import struct +from typing import IO, Any, cast + +from . import ( + Image, + ImageFile, + ImageSequence, + JpegImagePlugin, + TiffImagePlugin, +) +from ._binary import o32le +from ._util import DeferredError + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + JpegImagePlugin._save(im, fp, filename) + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + append_images = im.encoderinfo.get("append_images", []) + if not append_images and not getattr(im, "is_animated", False): + _save(im, fp, filename) + return + + mpf_offset = 28 + offsets: list[int] = [] + im_sequences = [im, *append_images] + total = sum(getattr(seq, "n_frames", 1) for seq in im_sequences) + for im_sequence in im_sequences: + for im_frame in ImageSequence.Iterator(im_sequence): + if not offsets: + # APP2 marker + ifd_length = 66 + 16 * total + im_frame.encoderinfo["extra"] = ( + b"\xff\xe2" + + struct.pack(">H", 6 + ifd_length) + + b"MPF\0" + + b" " * ifd_length + ) + exif = im_frame.encoderinfo.get("exif") + if isinstance(exif, Image.Exif): + exif = exif.tobytes() + im_frame.encoderinfo["exif"] = exif + if exif: + mpf_offset += 4 + len(exif) + + JpegImagePlugin._save(im_frame, fp, filename) + offsets.append(fp.tell()) + else: + encoderinfo = im_frame._attach_default_encoderinfo(im) + im_frame.save(fp, "JPEG") + im_frame.encoderinfo = encoderinfo + offsets.append(fp.tell() - offsets[-1]) + + ifd = TiffImagePlugin.ImageFileDirectory_v2() + ifd[0xB000] = b"0100" + ifd[0xB001] = len(offsets) + + mpentries = b"" + data_offset = 0 + for i, size in enumerate(offsets): + if i == 0: + mptype = 0x030000 # Baseline MP Primary Image + else: + mptype = 0x000000 # Undefined + mpentries += struct.pack(" None: + self.fp.seek(0) # prep the fp in order to pass the JPEG test + JpegImagePlugin.JpegImageFile._open(self) + self._after_jpeg_open() + + def _after_jpeg_open(self, mpheader: dict[int, Any] | None = None) -> None: + self.mpinfo = mpheader if mpheader is not None else self._getmp() + if self.mpinfo is None: + msg = "Image appears to be a malformed MPO file" + raise ValueError(msg) + self.n_frames = self.mpinfo[0xB001] + self.__mpoffsets = [ + mpent["DataOffset"] + self.info["mpoffset"] for mpent in self.mpinfo[0xB002] + ] + self.__mpoffsets[0] = 0 + # Note that the following assertion will only be invalid if something + # gets broken within JpegImagePlugin. + assert self.n_frames == len(self.__mpoffsets) + del self.info["mpoffset"] # no longer needed + self.is_animated = self.n_frames > 1 + self._fp = self.fp # FIXME: hack + self._fp.seek(self.__mpoffsets[0]) # get ready to read first frame + self.__frame = 0 + self.offset = 0 + # for now we can only handle reading and individual frame extraction + self.readonly = 1 + + def load_seek(self, pos: int) -> None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self._fp.seek(pos) + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self.fp = self._fp + self.offset = self.__mpoffsets[frame] + + original_exif = self.info.get("exif") + if "exif" in self.info: + del self.info["exif"] + + self.fp.seek(self.offset + 2) # skip SOI marker + if not self.fp.read(2): + msg = "No data found for frame" + raise ValueError(msg) + self.fp.seek(self.offset) + JpegImagePlugin.JpegImageFile._open(self) + if self.info.get("exif") != original_exif: + self._reload_exif() + + self.tile = [ + ImageFile._Tile("jpeg", (0, 0) + self.size, self.offset, self.tile[0][-1]) + ] + self.__frame = frame + + def tell(self) -> int: + return self.__frame + + @staticmethod + def adopt( + jpeg_instance: JpegImagePlugin.JpegImageFile, + mpheader: dict[int, Any] | None = None, + ) -> MpoImageFile: + """ + Transform the instance of JpegImageFile into + an instance of MpoImageFile. + After the call, the JpegImageFile is extended + to be an MpoImageFile. + + This is essentially useful when opening a JPEG + file that reveals itself as an MPO, to avoid + double call to _open. + """ + jpeg_instance.__class__ = MpoImageFile + mpo_instance = cast(MpoImageFile, jpeg_instance) + mpo_instance._after_jpeg_open(mpheader) + return mpo_instance + + +# --------------------------------------------------------------------- +# Registry stuff + +# Note that since MPO shares a factory with JPEG, we do not need to do a +# separate registration for it here. +# Image.register_open(MpoImageFile.format, +# JpegImagePlugin.jpeg_factory, _accept) +Image.register_save(MpoImageFile.format, _save) +Image.register_save_all(MpoImageFile.format, _save_all) + +Image.register_extension(MpoImageFile.format, ".mpo") + +Image.register_mime(MpoImageFile.format, "image/mpo") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MspImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MspImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..277087a8677708a3a5fe21a3f6d2c3b27f880d03 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/MspImagePlugin.py @@ -0,0 +1,200 @@ +# +# The Python Imaging Library. +# +# MSP file handling +# +# This is the format used by the Paint program in Windows 1 and 2. +# +# History: +# 95-09-05 fl Created +# 97-01-03 fl Read/write MSP images +# 17-02-21 es Fixed RLE interpretation +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1995-97. +# Copyright (c) Eric Soroos 2017. +# +# See the README file for information on usage and redistribution. +# +# More info on this format: https://archive.org/details/gg243631 +# Page 313: +# Figure 205. Windows Paint Version 1: "DanM" Format +# Figure 206. Windows Paint Version 2: "LinS" Format. Used in Windows V2.03 +# +# See also: https://www.fileformat.info/format/mspaint/egff.htm +from __future__ import annotations + +import io +import struct +from typing import IO + +from . import Image, ImageFile +from ._binary import i16le as i16 +from ._binary import o16le as o16 + +# +# read MSP files + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"DanM", b"LinS")) + + +## +# Image plugin for Windows MSP images. This plugin supports both +# uncompressed (Windows 1.0). + + +class MspImageFile(ImageFile.ImageFile): + format = "MSP" + format_description = "Windows Paint" + + def _open(self) -> None: + # Header + assert self.fp is not None + + s = self.fp.read(32) + if not _accept(s): + msg = "not an MSP file" + raise SyntaxError(msg) + + # Header checksum + checksum = 0 + for i in range(0, 32, 2): + checksum = checksum ^ i16(s, i) + if checksum != 0: + msg = "bad MSP checksum" + raise SyntaxError(msg) + + self._mode = "1" + self._size = i16(s, 4), i16(s, 6) + + if s.startswith(b"DanM"): + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, 32, "1")] + else: + self.tile = [ImageFile._Tile("MSP", (0, 0) + self.size, 32)] + + +class MspDecoder(ImageFile.PyDecoder): + # The algo for the MSP decoder is from + # https://www.fileformat.info/format/mspaint/egff.htm + # cc-by-attribution -- That page references is taken from the + # Encyclopedia of Graphics File Formats and is licensed by + # O'Reilly under the Creative Common/Attribution license + # + # For RLE encoded files, the 32byte header is followed by a scan + # line map, encoded as one 16bit word of encoded byte length per + # line. + # + # NOTE: the encoded length of the line can be 0. This was not + # handled in the previous version of this encoder, and there's no + # mention of how to handle it in the documentation. From the few + # examples I've seen, I've assumed that it is a fill of the + # background color, in this case, white. + # + # + # Pseudocode of the decoder: + # Read a BYTE value as the RunType + # If the RunType value is zero + # Read next byte as the RunCount + # Read the next byte as the RunValue + # Write the RunValue byte RunCount times + # If the RunType value is non-zero + # Use this value as the RunCount + # Read and write the next RunCount bytes literally + # + # e.g.: + # 0x00 03 ff 05 00 01 02 03 04 + # would yield the bytes: + # 0xff ff ff 00 01 02 03 04 + # + # which are then interpreted as a bit packed mode '1' image + + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + + img = io.BytesIO() + blank_line = bytearray((0xFF,) * ((self.state.xsize + 7) // 8)) + try: + self.fd.seek(32) + rowmap = struct.unpack_from( + f"<{self.state.ysize}H", self.fd.read(self.state.ysize * 2) + ) + except struct.error as e: + msg = "Truncated MSP file in row map" + raise OSError(msg) from e + + for x, rowlen in enumerate(rowmap): + try: + if rowlen == 0: + img.write(blank_line) + continue + row = self.fd.read(rowlen) + if len(row) != rowlen: + msg = f"Truncated MSP file, expected {rowlen} bytes on row {x}" + raise OSError(msg) + idx = 0 + while idx < rowlen: + runtype = row[idx] + idx += 1 + if runtype == 0: + (runcount, runval) = struct.unpack_from("Bc", row, idx) + img.write(runval * runcount) + idx += 2 + else: + runcount = runtype + img.write(row[idx : idx + runcount]) + idx += runcount + + except struct.error as e: + msg = f"Corrupted MSP file in row {x}" + raise OSError(msg) from e + + self.set_as_raw(img.getvalue(), "1") + + return -1, 0 + + +Image.register_decoder("MSP", MspDecoder) + + +# +# write MSP files (uncompressed only) + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode != "1": + msg = f"cannot write mode {im.mode} as MSP" + raise OSError(msg) + + # create MSP header + header = [0] * 16 + + header[0], header[1] = i16(b"Da"), i16(b"nM") # version 1 + header[2], header[3] = im.size + header[4], header[5] = 1, 1 + header[6], header[7] = 1, 1 + header[8], header[9] = im.size + + checksum = 0 + for h in header: + checksum = checksum ^ h + header[12] = checksum # FIXME: is this the right field? + + # header + for h in header: + fp.write(o16(h)) + + # image body + ImageFile._save(im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 32, "1")]) + + +# +# registry + +Image.register_open(MspImageFile.format, MspImageFile, _accept) +Image.register_save(MspImageFile.format, _save) + +Image.register_extension(MspImageFile.format, ".msp") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PSDraw.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PSDraw.py new file mode 100644 index 0000000000000000000000000000000000000000..7fd4c5c94cfa7ec46332f4da78f3e402fd5b311b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PSDraw.py @@ -0,0 +1,237 @@ +# +# The Python Imaging Library +# $Id$ +# +# Simple PostScript graphics interface +# +# History: +# 1996-04-20 fl Created +# 1999-01-10 fl Added gsave/grestore to image method +# 2005-05-04 fl Fixed floating point issue in image (from Eric Etheridge) +# +# Copyright (c) 1997-2005 by Secret Labs AB. All rights reserved. +# Copyright (c) 1996 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import sys +from typing import IO + +from . import EpsImagePlugin + +TYPE_CHECKING = False + + +## +# Simple PostScript graphics interface. + + +class PSDraw: + """ + Sets up printing to the given file. If ``fp`` is omitted, + ``sys.stdout.buffer`` is assumed. + """ + + def __init__(self, fp: IO[bytes] | None = None) -> None: + if not fp: + fp = sys.stdout.buffer + self.fp = fp + + def begin_document(self, id: str | None = None) -> None: + """Set up printing of a document. (Write PostScript DSC header.)""" + # FIXME: incomplete + self.fp.write( + b"%!PS-Adobe-3.0\n" + b"save\n" + b"/showpage { } def\n" + b"%%EndComments\n" + b"%%BeginDocument\n" + ) + # self.fp.write(ERROR_PS) # debugging! + self.fp.write(EDROFF_PS) + self.fp.write(VDI_PS) + self.fp.write(b"%%EndProlog\n") + self.isofont: dict[bytes, int] = {} + + def end_document(self) -> None: + """Ends printing. (Write PostScript DSC footer.)""" + self.fp.write(b"%%EndDocument\nrestore showpage\n%%End\n") + if hasattr(self.fp, "flush"): + self.fp.flush() + + def setfont(self, font: str, size: int) -> None: + """ + Selects which font to use. + + :param font: A PostScript font name + :param size: Size in points. + """ + font_bytes = bytes(font, "UTF-8") + if font_bytes not in self.isofont: + # reencode font + self.fp.write( + b"/PSDraw-%s ISOLatin1Encoding /%s E\n" % (font_bytes, font_bytes) + ) + self.isofont[font_bytes] = 1 + # rough + self.fp.write(b"/F0 %d /PSDraw-%s F\n" % (size, font_bytes)) + + def line(self, xy0: tuple[int, int], xy1: tuple[int, int]) -> None: + """ + Draws a line between the two points. Coordinates are given in + PostScript point coordinates (72 points per inch, (0, 0) is the lower + left corner of the page). + """ + self.fp.write(b"%d %d %d %d Vl\n" % (*xy0, *xy1)) + + def rectangle(self, box: tuple[int, int, int, int]) -> None: + """ + Draws a rectangle. + + :param box: A tuple of four integers, specifying left, bottom, width and + height. + """ + self.fp.write(b"%d %d M 0 %d %d Vr\n" % box) + + def text(self, xy: tuple[int, int], text: str) -> None: + """ + Draws text at the given position. You must use + :py:meth:`~PIL.PSDraw.PSDraw.setfont` before calling this method. + """ + text_bytes = bytes(text, "UTF-8") + text_bytes = b"\\(".join(text_bytes.split(b"(")) + text_bytes = b"\\)".join(text_bytes.split(b")")) + self.fp.write(b"%d %d M (%s) S\n" % (xy + (text_bytes,))) + + if TYPE_CHECKING: + from . import Image + + def image( + self, box: tuple[int, int, int, int], im: Image.Image, dpi: int | None = None + ) -> None: + """Draw a PIL image, centered in the given box.""" + # default resolution depends on mode + if not dpi: + if im.mode == "1": + dpi = 200 # fax + else: + dpi = 100 # grayscale + # image size (on paper) + x = im.size[0] * 72 / dpi + y = im.size[1] * 72 / dpi + # max allowed size + xmax = float(box[2] - box[0]) + ymax = float(box[3] - box[1]) + if x > xmax: + y = y * xmax / x + x = xmax + if y > ymax: + x = x * ymax / y + y = ymax + dx = (xmax - x) / 2 + box[0] + dy = (ymax - y) / 2 + box[1] + self.fp.write(b"gsave\n%f %f translate\n" % (dx, dy)) + if (x, y) != im.size: + # EpsImagePlugin._save prints the image at (0,0,xsize,ysize) + sx = x / im.size[0] + sy = y / im.size[1] + self.fp.write(b"%f %f scale\n" % (sx, sy)) + EpsImagePlugin._save(im, self.fp, "", 0) + self.fp.write(b"\ngrestore\n") + + +# -------------------------------------------------------------------- +# PostScript driver + +# +# EDROFF.PS -- PostScript driver for Edroff 2 +# +# History: +# 94-01-25 fl: created (edroff 2.04) +# +# Copyright (c) Fredrik Lundh 1994. +# + + +EDROFF_PS = b"""\ +/S { show } bind def +/P { moveto show } bind def +/M { moveto } bind def +/X { 0 rmoveto } bind def +/Y { 0 exch rmoveto } bind def +/E { findfont + dup maxlength dict begin + { + 1 index /FID ne { def } { pop pop } ifelse + } forall + /Encoding exch def + dup /FontName exch def + currentdict end definefont pop +} bind def +/F { findfont exch scalefont dup setfont + [ exch /setfont cvx ] cvx bind def +} bind def +""" + +# +# VDI.PS -- PostScript driver for VDI meta commands +# +# History: +# 94-01-25 fl: created (edroff 2.04) +# +# Copyright (c) Fredrik Lundh 1994. +# + +VDI_PS = b"""\ +/Vm { moveto } bind def +/Va { newpath arcn stroke } bind def +/Vl { moveto lineto stroke } bind def +/Vc { newpath 0 360 arc closepath } bind def +/Vr { exch dup 0 rlineto + exch dup 0 exch rlineto + exch neg 0 rlineto + 0 exch neg rlineto + setgray fill } bind def +/Tm matrix def +/Ve { Tm currentmatrix pop + translate scale newpath 0 0 .5 0 360 arc closepath + Tm setmatrix +} bind def +/Vf { currentgray exch setgray fill setgray } bind def +""" + +# +# ERROR.PS -- Error handler +# +# History: +# 89-11-21 fl: created (pslist 1.10) +# + +ERROR_PS = b"""\ +/landscape false def +/errorBUF 200 string def +/errorNL { currentpoint 10 sub exch pop 72 exch moveto } def +errordict begin /handleerror { + initmatrix /Courier findfont 10 scalefont setfont + newpath 72 720 moveto $error begin /newerror false def + (PostScript Error) show errorNL errorNL + (Error: ) show + /errorname load errorBUF cvs show errorNL errorNL + (Command: ) show + /command load dup type /stringtype ne { errorBUF cvs } if show + errorNL errorNL + (VMstatus: ) show + vmstatus errorBUF cvs show ( bytes available, ) show + errorBUF cvs show ( bytes used at level ) show + errorBUF cvs show errorNL errorNL + (Operand stargck: ) show errorNL /ostargck load { + dup type /stringtype ne { errorBUF cvs } if 72 0 rmoveto show errorNL + } forall errorNL + (Execution stargck: ) show errorNL /estargck load { + dup type /stringtype ne { errorBUF cvs } if 72 0 rmoveto show errorNL + } forall + end showpage +} def end +""" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PaletteFile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PaletteFile.py new file mode 100644 index 0000000000000000000000000000000000000000..2a26e5d4e223ba0bc80ad1bfb37b4c3927e222ac --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PaletteFile.py @@ -0,0 +1,54 @@ +# +# Python Imaging Library +# $Id$ +# +# stuff to read simple, teragon-style palette files +# +# History: +# 97-08-23 fl Created +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from typing import IO + +from ._binary import o8 + + +class PaletteFile: + """File handler for Teragon-style palette files.""" + + rawmode = "RGB" + + def __init__(self, fp: IO[bytes]) -> None: + palette = [o8(i) * 3 for i in range(256)] + + while True: + s = fp.readline() + + if not s: + break + if s.startswith(b"#"): + continue + if len(s) > 100: + msg = "bad palette file" + raise SyntaxError(msg) + + v = [int(x) for x in s.split()] + try: + [i, r, g, b] = v + except ValueError: + [i, r] = v + g = b = r + + if 0 <= i <= 255: + palette[i] = o8(r) + o8(g) + o8(b) + + self.palette = b"".join(palette) + + def getpalette(self) -> tuple[bytes, str]: + return self.palette, self.rawmode diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PalmImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PalmImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..15f71290816c5fa6a5178842260a1520eb0b372f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PalmImagePlugin.py @@ -0,0 +1,217 @@ +# +# The Python Imaging Library. +# $Id$ +# + +## +# Image plugin for Palm pixmap images (output only). +## +from __future__ import annotations + +from typing import IO + +from . import Image, ImageFile +from ._binary import o8 +from ._binary import o16be as o16b + +# fmt: off +_Palm8BitColormapValues = ( + (255, 255, 255), (255, 204, 255), (255, 153, 255), (255, 102, 255), + (255, 51, 255), (255, 0, 255), (255, 255, 204), (255, 204, 204), + (255, 153, 204), (255, 102, 204), (255, 51, 204), (255, 0, 204), + (255, 255, 153), (255, 204, 153), (255, 153, 153), (255, 102, 153), + (255, 51, 153), (255, 0, 153), (204, 255, 255), (204, 204, 255), + (204, 153, 255), (204, 102, 255), (204, 51, 255), (204, 0, 255), + (204, 255, 204), (204, 204, 204), (204, 153, 204), (204, 102, 204), + (204, 51, 204), (204, 0, 204), (204, 255, 153), (204, 204, 153), + (204, 153, 153), (204, 102, 153), (204, 51, 153), (204, 0, 153), + (153, 255, 255), (153, 204, 255), (153, 153, 255), (153, 102, 255), + (153, 51, 255), (153, 0, 255), (153, 255, 204), (153, 204, 204), + (153, 153, 204), (153, 102, 204), (153, 51, 204), (153, 0, 204), + (153, 255, 153), (153, 204, 153), (153, 153, 153), (153, 102, 153), + (153, 51, 153), (153, 0, 153), (102, 255, 255), (102, 204, 255), + (102, 153, 255), (102, 102, 255), (102, 51, 255), (102, 0, 255), + (102, 255, 204), (102, 204, 204), (102, 153, 204), (102, 102, 204), + (102, 51, 204), (102, 0, 204), (102, 255, 153), (102, 204, 153), + (102, 153, 153), (102, 102, 153), (102, 51, 153), (102, 0, 153), + (51, 255, 255), (51, 204, 255), (51, 153, 255), (51, 102, 255), + (51, 51, 255), (51, 0, 255), (51, 255, 204), (51, 204, 204), + (51, 153, 204), (51, 102, 204), (51, 51, 204), (51, 0, 204), + (51, 255, 153), (51, 204, 153), (51, 153, 153), (51, 102, 153), + (51, 51, 153), (51, 0, 153), (0, 255, 255), (0, 204, 255), + (0, 153, 255), (0, 102, 255), (0, 51, 255), (0, 0, 255), + (0, 255, 204), (0, 204, 204), (0, 153, 204), (0, 102, 204), + (0, 51, 204), (0, 0, 204), (0, 255, 153), (0, 204, 153), + (0, 153, 153), (0, 102, 153), (0, 51, 153), (0, 0, 153), + (255, 255, 102), (255, 204, 102), (255, 153, 102), (255, 102, 102), + (255, 51, 102), (255, 0, 102), (255, 255, 51), (255, 204, 51), + (255, 153, 51), (255, 102, 51), (255, 51, 51), (255, 0, 51), + (255, 255, 0), (255, 204, 0), (255, 153, 0), (255, 102, 0), + (255, 51, 0), (255, 0, 0), (204, 255, 102), (204, 204, 102), + (204, 153, 102), (204, 102, 102), (204, 51, 102), (204, 0, 102), + (204, 255, 51), (204, 204, 51), (204, 153, 51), (204, 102, 51), + (204, 51, 51), (204, 0, 51), (204, 255, 0), (204, 204, 0), + (204, 153, 0), (204, 102, 0), (204, 51, 0), (204, 0, 0), + (153, 255, 102), (153, 204, 102), (153, 153, 102), (153, 102, 102), + (153, 51, 102), (153, 0, 102), (153, 255, 51), (153, 204, 51), + (153, 153, 51), (153, 102, 51), (153, 51, 51), (153, 0, 51), + (153, 255, 0), (153, 204, 0), (153, 153, 0), (153, 102, 0), + (153, 51, 0), (153, 0, 0), (102, 255, 102), (102, 204, 102), + (102, 153, 102), (102, 102, 102), (102, 51, 102), (102, 0, 102), + (102, 255, 51), (102, 204, 51), (102, 153, 51), (102, 102, 51), + (102, 51, 51), (102, 0, 51), (102, 255, 0), (102, 204, 0), + (102, 153, 0), (102, 102, 0), (102, 51, 0), (102, 0, 0), + (51, 255, 102), (51, 204, 102), (51, 153, 102), (51, 102, 102), + (51, 51, 102), (51, 0, 102), (51, 255, 51), (51, 204, 51), + (51, 153, 51), (51, 102, 51), (51, 51, 51), (51, 0, 51), + (51, 255, 0), (51, 204, 0), (51, 153, 0), (51, 102, 0), + (51, 51, 0), (51, 0, 0), (0, 255, 102), (0, 204, 102), + (0, 153, 102), (0, 102, 102), (0, 51, 102), (0, 0, 102), + (0, 255, 51), (0, 204, 51), (0, 153, 51), (0, 102, 51), + (0, 51, 51), (0, 0, 51), (0, 255, 0), (0, 204, 0), + (0, 153, 0), (0, 102, 0), (0, 51, 0), (17, 17, 17), + (34, 34, 34), (68, 68, 68), (85, 85, 85), (119, 119, 119), + (136, 136, 136), (170, 170, 170), (187, 187, 187), (221, 221, 221), + (238, 238, 238), (192, 192, 192), (128, 0, 0), (128, 0, 128), + (0, 128, 0), (0, 128, 128), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), + (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0)) +# fmt: on + + +# so build a prototype image to be used for palette resampling +def build_prototype_image() -> Image.Image: + image = Image.new("L", (1, len(_Palm8BitColormapValues))) + image.putdata(list(range(len(_Palm8BitColormapValues)))) + palettedata: tuple[int, ...] = () + for colormapValue in _Palm8BitColormapValues: + palettedata += colormapValue + palettedata += (0, 0, 0) * (256 - len(_Palm8BitColormapValues)) + image.putpalette(palettedata) + return image + + +Palm8BitColormapImage = build_prototype_image() + +# OK, we now have in Palm8BitColormapImage, +# a "P"-mode image with the right palette +# +# -------------------------------------------------------------------- + +_FLAGS = {"custom-colormap": 0x4000, "is-compressed": 0x8000, "has-transparent": 0x2000} + +_COMPRESSION_TYPES = {"none": 0xFF, "rle": 0x01, "scanline": 0x00} + + +# +# -------------------------------------------------------------------- + +## +# (Internal) Image save plugin for the Palm format. + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode == "P": + rawmode = "P" + bpp = 8 + version = 1 + + elif im.mode == "L": + if im.encoderinfo.get("bpp") in (1, 2, 4): + # this is 8-bit grayscale, so we shift it to get the high-order bits, + # and invert it because + # Palm does grayscale from white (0) to black (1) + bpp = im.encoderinfo["bpp"] + maxval = (1 << bpp) - 1 + shift = 8 - bpp + im = im.point(lambda x: maxval - (x >> shift)) + elif im.info.get("bpp") in (1, 2, 4): + # here we assume that even though the inherent mode is 8-bit grayscale, + # only the lower bpp bits are significant. + # We invert them to match the Palm. + bpp = im.info["bpp"] + maxval = (1 << bpp) - 1 + im = im.point(lambda x: maxval - (x & maxval)) + else: + msg = f"cannot write mode {im.mode} as Palm" + raise OSError(msg) + + # we ignore the palette here + im._mode = "P" + rawmode = f"P;{bpp}" + version = 1 + + elif im.mode == "1": + # monochrome -- write it inverted, as is the Palm standard + rawmode = "1;I" + bpp = 1 + version = 0 + + else: + msg = f"cannot write mode {im.mode} as Palm" + raise OSError(msg) + + # + # make sure image data is available + im.load() + + # write header + + cols = im.size[0] + rows = im.size[1] + + rowbytes = int((cols + (16 // bpp - 1)) / (16 // bpp)) * 2 + transparent_index = 0 + compression_type = _COMPRESSION_TYPES["none"] + + flags = 0 + if im.mode == "P": + flags |= _FLAGS["custom-colormap"] + colormap = im.im.getpalette() + colors = len(colormap) // 3 + colormapsize = 4 * colors + 2 + else: + colormapsize = 0 + + if "offset" in im.info: + offset = (rowbytes * rows + 16 + 3 + colormapsize) // 4 + else: + offset = 0 + + fp.write(o16b(cols) + o16b(rows) + o16b(rowbytes) + o16b(flags)) + fp.write(o8(bpp)) + fp.write(o8(version)) + fp.write(o16b(offset)) + fp.write(o8(transparent_index)) + fp.write(o8(compression_type)) + fp.write(o16b(0)) # reserved by Palm + + # now write colormap if necessary + + if colormapsize: + fp.write(o16b(colors)) + for i in range(colors): + fp.write(o8(i)) + fp.write(colormap[3 * i : 3 * i + 3]) + + # now convert data to raw form + ImageFile._save( + im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, rowbytes, 1))] + ) + + if hasattr(fp, "flush"): + fp.flush() + + +# +# -------------------------------------------------------------------- + +Image.register_save("Palm", _save) + +Image.register_extension("Palm", ".palm") + +Image.register_mime("Palm", "image/palm") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PcdImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PcdImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..3aa249988c8b035822ab994866e405990d1cc96e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PcdImagePlugin.py @@ -0,0 +1,64 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PCD file handling +# +# History: +# 96-05-10 fl Created +# 96-05-27 fl Added draft mode (128x192, 256x384) +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFile + +## +# Image plugin for PhotoCD images. This plugin only reads the 768x512 +# image from the file; higher resolutions are encoded in a proprietary +# encoding. + + +class PcdImageFile(ImageFile.ImageFile): + format = "PCD" + format_description = "Kodak PhotoCD" + + def _open(self) -> None: + # rough + assert self.fp is not None + + self.fp.seek(2048) + s = self.fp.read(2048) + + if not s.startswith(b"PCD_"): + msg = "not a PCD file" + raise SyntaxError(msg) + + orientation = s[1538] & 3 + self.tile_post_rotate = None + if orientation == 1: + self.tile_post_rotate = 90 + elif orientation == 3: + self.tile_post_rotate = -90 + + self._mode = "RGB" + self._size = 768, 512 # FIXME: not correct for rotated images! + self.tile = [ImageFile._Tile("pcd", (0, 0) + self.size, 96 * 2048)] + + def load_end(self) -> None: + if self.tile_post_rotate: + # Handle rotated PCDs + self.im = self.im.rotate(self.tile_post_rotate) + self._size = self.im.size + + +# +# registry + +Image.register_open(PcdImageFile.format, PcdImageFile) + +Image.register_extension(PcdImageFile.format, ".pcd") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PcfFontFile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PcfFontFile.py new file mode 100644 index 0000000000000000000000000000000000000000..0d1968b140a93fb3d1a026d2fd9186e8696e2d1b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PcfFontFile.py @@ -0,0 +1,254 @@ +# +# THIS IS WORK IN PROGRESS +# +# The Python Imaging Library +# $Id$ +# +# portable compiled font file parser +# +# history: +# 1997-08-19 fl created +# 2003-09-13 fl fixed loading of unicode fonts +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1997-2003 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +from typing import BinaryIO, Callable + +from . import FontFile, Image +from ._binary import i8 +from ._binary import i16be as b16 +from ._binary import i16le as l16 +from ._binary import i32be as b32 +from ._binary import i32le as l32 + +# -------------------------------------------------------------------- +# declarations + +PCF_MAGIC = 0x70636601 # "\x01fcp" + +PCF_PROPERTIES = 1 << 0 +PCF_ACCELERATORS = 1 << 1 +PCF_METRICS = 1 << 2 +PCF_BITMAPS = 1 << 3 +PCF_INK_METRICS = 1 << 4 +PCF_BDF_ENCODINGS = 1 << 5 +PCF_SWIDTHS = 1 << 6 +PCF_GLYPH_NAMES = 1 << 7 +PCF_BDF_ACCELERATORS = 1 << 8 + +BYTES_PER_ROW: list[Callable[[int], int]] = [ + lambda bits: ((bits + 7) >> 3), + lambda bits: ((bits + 15) >> 3) & ~1, + lambda bits: ((bits + 31) >> 3) & ~3, + lambda bits: ((bits + 63) >> 3) & ~7, +] + + +def sz(s: bytes, o: int) -> bytes: + return s[o : s.index(b"\0", o)] + + +class PcfFontFile(FontFile.FontFile): + """Font file plugin for the X11 PCF format.""" + + name = "name" + + def __init__(self, fp: BinaryIO, charset_encoding: str = "iso8859-1"): + self.charset_encoding = charset_encoding + + magic = l32(fp.read(4)) + if magic != PCF_MAGIC: + msg = "not a PCF file" + raise SyntaxError(msg) + + super().__init__() + + count = l32(fp.read(4)) + self.toc = {} + for i in range(count): + type = l32(fp.read(4)) + self.toc[type] = l32(fp.read(4)), l32(fp.read(4)), l32(fp.read(4)) + + self.fp = fp + + self.info = self._load_properties() + + metrics = self._load_metrics() + bitmaps = self._load_bitmaps(metrics) + encoding = self._load_encoding() + + # + # create glyph structure + + for ch, ix in enumerate(encoding): + if ix is not None: + ( + xsize, + ysize, + left, + right, + width, + ascent, + descent, + attributes, + ) = metrics[ix] + self.glyph[ch] = ( + (width, 0), + (left, descent - ysize, xsize + left, descent), + (0, 0, xsize, ysize), + bitmaps[ix], + ) + + def _getformat( + self, tag: int + ) -> tuple[BinaryIO, int, Callable[[bytes], int], Callable[[bytes], int]]: + format, size, offset = self.toc[tag] + + fp = self.fp + fp.seek(offset) + + format = l32(fp.read(4)) + + if format & 4: + i16, i32 = b16, b32 + else: + i16, i32 = l16, l32 + + return fp, format, i16, i32 + + def _load_properties(self) -> dict[bytes, bytes | int]: + # + # font properties + + properties = {} + + fp, format, i16, i32 = self._getformat(PCF_PROPERTIES) + + nprops = i32(fp.read(4)) + + # read property description + p = [(i32(fp.read(4)), i8(fp.read(1)), i32(fp.read(4))) for _ in range(nprops)] + + if nprops & 3: + fp.seek(4 - (nprops & 3), io.SEEK_CUR) # pad + + data = fp.read(i32(fp.read(4))) + + for k, s, v in p: + property_value: bytes | int = sz(data, v) if s else v + properties[sz(data, k)] = property_value + + return properties + + def _load_metrics(self) -> list[tuple[int, int, int, int, int, int, int, int]]: + # + # font metrics + + metrics: list[tuple[int, int, int, int, int, int, int, int]] = [] + + fp, format, i16, i32 = self._getformat(PCF_METRICS) + + append = metrics.append + + if (format & 0xFF00) == 0x100: + # "compressed" metrics + for i in range(i16(fp.read(2))): + left = i8(fp.read(1)) - 128 + right = i8(fp.read(1)) - 128 + width = i8(fp.read(1)) - 128 + ascent = i8(fp.read(1)) - 128 + descent = i8(fp.read(1)) - 128 + xsize = right - left + ysize = ascent + descent + append((xsize, ysize, left, right, width, ascent, descent, 0)) + + else: + # "jumbo" metrics + for i in range(i32(fp.read(4))): + left = i16(fp.read(2)) + right = i16(fp.read(2)) + width = i16(fp.read(2)) + ascent = i16(fp.read(2)) + descent = i16(fp.read(2)) + attributes = i16(fp.read(2)) + xsize = right - left + ysize = ascent + descent + append((xsize, ysize, left, right, width, ascent, descent, attributes)) + + return metrics + + def _load_bitmaps( + self, metrics: list[tuple[int, int, int, int, int, int, int, int]] + ) -> list[Image.Image]: + # + # bitmap data + + fp, format, i16, i32 = self._getformat(PCF_BITMAPS) + + nbitmaps = i32(fp.read(4)) + + if nbitmaps != len(metrics): + msg = "Wrong number of bitmaps" + raise OSError(msg) + + offsets = [i32(fp.read(4)) for _ in range(nbitmaps)] + + bitmap_sizes = [i32(fp.read(4)) for _ in range(4)] + + # byteorder = format & 4 # non-zero => MSB + bitorder = format & 8 # non-zero => MSB + padindex = format & 3 + + bitmapsize = bitmap_sizes[padindex] + offsets.append(bitmapsize) + + data = fp.read(bitmapsize) + + pad = BYTES_PER_ROW[padindex] + mode = "1;R" + if bitorder: + mode = "1" + + bitmaps = [] + for i in range(nbitmaps): + xsize, ysize = metrics[i][:2] + b, e = offsets[i : i + 2] + bitmaps.append( + Image.frombytes("1", (xsize, ysize), data[b:e], "raw", mode, pad(xsize)) + ) + + return bitmaps + + def _load_encoding(self) -> list[int | None]: + fp, format, i16, i32 = self._getformat(PCF_BDF_ENCODINGS) + + first_col, last_col = i16(fp.read(2)), i16(fp.read(2)) + first_row, last_row = i16(fp.read(2)), i16(fp.read(2)) + + i16(fp.read(2)) # default + + nencoding = (last_col - first_col + 1) * (last_row - first_row + 1) + + # map character code to bitmap index + encoding: list[int | None] = [None] * min(256, nencoding) + + encoding_offsets = [i16(fp.read(2)) for _ in range(nencoding)] + + for i in range(first_col, len(encoding)): + try: + encoding_offset = encoding_offsets[ + ord(bytearray([i]).decode(self.charset_encoding)) + ] + if encoding_offset != 0xFFFF: + encoding[i] = encoding_offset + except UnicodeDecodeError: + # character is not supported in selected encoding + pass + + return encoding diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PcxImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PcxImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..458d586c463ce5355d807f2a2dd583545ccc8229 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PcxImagePlugin.py @@ -0,0 +1,228 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PCX file handling +# +# This format was originally used by ZSoft's popular PaintBrush +# program for the IBM PC. It is also supported by many MS-DOS and +# Windows applications, including the Windows PaintBrush program in +# Windows 3. +# +# history: +# 1995-09-01 fl Created +# 1996-05-20 fl Fixed RGB support +# 1997-01-03 fl Fixed 2-bit and 4-bit support +# 1999-02-03 fl Fixed 8-bit support (broken in 1.0b1) +# 1999-02-07 fl Added write support +# 2002-06-09 fl Made 2-bit and 4-bit support a bit more robust +# 2002-07-30 fl Seek from to current position, not beginning of file +# 2003-06-03 fl Extract DPI settings (info["dpi"]) +# +# Copyright (c) 1997-2003 by Secret Labs AB. +# Copyright (c) 1995-2003 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import logging +from typing import IO + +from . import Image, ImageFile, ImagePalette +from ._binary import i16le as i16 +from ._binary import o8 +from ._binary import o16le as o16 + +logger = logging.getLogger(__name__) + + +def _accept(prefix: bytes) -> bool: + return prefix[0] == 10 and prefix[1] in [0, 2, 3, 5] + + +## +# Image plugin for Paintbrush images. + + +class PcxImageFile(ImageFile.ImageFile): + format = "PCX" + format_description = "Paintbrush" + + def _open(self) -> None: + # header + assert self.fp is not None + + s = self.fp.read(68) + if not _accept(s): + msg = "not a PCX file" + raise SyntaxError(msg) + + # image + bbox = i16(s, 4), i16(s, 6), i16(s, 8) + 1, i16(s, 10) + 1 + if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]: + msg = "bad PCX image size" + raise SyntaxError(msg) + logger.debug("BBox: %s %s %s %s", *bbox) + + offset = self.fp.tell() + 60 + + # format + version = s[1] + bits = s[3] + planes = s[65] + provided_stride = i16(s, 66) + logger.debug( + "PCX version %s, bits %s, planes %s, stride %s", + version, + bits, + planes, + provided_stride, + ) + + self.info["dpi"] = i16(s, 12), i16(s, 14) + + if bits == 1 and planes == 1: + mode = rawmode = "1" + + elif bits == 1 and planes in (2, 4): + mode = "P" + rawmode = f"P;{planes}L" + self.palette = ImagePalette.raw("RGB", s[16:64]) + + elif version == 5 and bits == 8 and planes == 1: + mode = rawmode = "L" + # FIXME: hey, this doesn't work with the incremental loader !!! + self.fp.seek(-769, io.SEEK_END) + s = self.fp.read(769) + if len(s) == 769 and s[0] == 12: + # check if the palette is linear grayscale + for i in range(256): + if s[i * 3 + 1 : i * 3 + 4] != o8(i) * 3: + mode = rawmode = "P" + break + if mode == "P": + self.palette = ImagePalette.raw("RGB", s[1:]) + + elif version == 5 and bits == 8 and planes == 3: + mode = "RGB" + rawmode = "RGB;L" + + else: + msg = "unknown PCX mode" + raise OSError(msg) + + self._mode = mode + self._size = bbox[2] - bbox[0], bbox[3] - bbox[1] + + # Don't trust the passed in stride. + # Calculate the approximate position for ourselves. + # CVE-2020-35653 + stride = (self._size[0] * bits + 7) // 8 + + # While the specification states that this must be even, + # not all images follow this + if provided_stride != stride: + stride += stride % 2 + + bbox = (0, 0) + self.size + logger.debug("size: %sx%s", *self.size) + + self.tile = [ImageFile._Tile("pcx", bbox, offset, (rawmode, planes * stride))] + + +# -------------------------------------------------------------------- +# save PCX files + + +SAVE = { + # mode: (version, bits, planes, raw mode) + "1": (2, 1, 1, "1"), + "L": (5, 8, 1, "L"), + "P": (5, 8, 1, "P"), + "RGB": (5, 8, 3, "RGB;L"), +} + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + try: + version, bits, planes, rawmode = SAVE[im.mode] + except KeyError as e: + msg = f"Cannot save {im.mode} images as PCX" + raise ValueError(msg) from e + + # bytes per plane + stride = (im.size[0] * bits + 7) // 8 + # stride should be even + stride += stride % 2 + # Stride needs to be kept in sync with the PcxEncode.c version. + # Ideally it should be passed in in the state, but the bytes value + # gets overwritten. + + logger.debug( + "PcxImagePlugin._save: xwidth: %d, bits: %d, stride: %d", + im.size[0], + bits, + stride, + ) + + # under windows, we could determine the current screen size with + # "Image.core.display_mode()[1]", but I think that's overkill... + + screen = im.size + + dpi = 100, 100 + + # PCX header + fp.write( + o8(10) + + o8(version) + + o8(1) + + o8(bits) + + o16(0) + + o16(0) + + o16(im.size[0] - 1) + + o16(im.size[1] - 1) + + o16(dpi[0]) + + o16(dpi[1]) + + b"\0" * 24 + + b"\xff" * 24 + + b"\0" + + o8(planes) + + o16(stride) + + o16(1) + + o16(screen[0]) + + o16(screen[1]) + + b"\0" * 54 + ) + + assert fp.tell() == 128 + + ImageFile._save( + im, fp, [ImageFile._Tile("pcx", (0, 0) + im.size, 0, (rawmode, bits * planes))] + ) + + if im.mode == "P": + # colour palette + fp.write(o8(12)) + palette = im.im.getpalette("RGB", "RGB") + palette += b"\x00" * (768 - len(palette)) + fp.write(palette) # 768 bytes + elif im.mode == "L": + # grayscale palette + fp.write(o8(12)) + for i in range(256): + fp.write(o8(i) * 3) + + +# -------------------------------------------------------------------- +# registry + + +Image.register_open(PcxImageFile.format, PcxImageFile, _accept) +Image.register_save(PcxImageFile.format, _save) + +Image.register_extension(PcxImageFile.format, ".pcx") + +Image.register_mime(PcxImageFile.format, "image/x-pcx") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PdfImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PdfImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..e9c20ddc159e150676ead01cf314420008f05232 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PdfImagePlugin.py @@ -0,0 +1,311 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PDF (Acrobat) file handling +# +# History: +# 1996-07-16 fl Created +# 1997-01-18 fl Fixed header +# 2004-02-21 fl Fixes for 1/L/CMYK images, etc. +# 2004-02-24 fl Fixes for 1 and P images. +# +# Copyright (c) 1997-2004 by Secret Labs AB. All rights reserved. +# Copyright (c) 1996-1997 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +## +# Image plugin for PDF images (output only). +## +from __future__ import annotations + +import io +import math +import os +import time +from typing import IO, Any + +from . import Image, ImageFile, ImageSequence, PdfParser, __version__, features + +# +# -------------------------------------------------------------------- + +# object ids: +# 1. catalogue +# 2. pages +# 3. image +# 4. page +# 5. page contents + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, save_all=True) + + +## +# (Internal) Image save plugin for the PDF format. + + +def _write_image( + im: Image.Image, + filename: str | bytes, + existing_pdf: PdfParser.PdfParser, + image_refs: list[PdfParser.IndirectReference], +) -> tuple[PdfParser.IndirectReference, str]: + # FIXME: Should replace ASCIIHexDecode with RunLengthDecode + # (packbits) or LZWDecode (tiff/lzw compression). Note that + # PDF 1.2 also supports Flatedecode (zip compression). + + params = None + decode = None + + # + # Get image characteristics + + width, height = im.size + + dict_obj: dict[str, Any] = {"BitsPerComponent": 8} + if im.mode == "1": + if features.check("libtiff"): + decode_filter = "CCITTFaxDecode" + dict_obj["BitsPerComponent"] = 1 + params = PdfParser.PdfArray( + [ + PdfParser.PdfDict( + { + "K": -1, + "BlackIs1": True, + "Columns": width, + "Rows": height, + } + ) + ] + ) + else: + decode_filter = "DCTDecode" + dict_obj["ColorSpace"] = PdfParser.PdfName("DeviceGray") + procset = "ImageB" # grayscale + elif im.mode == "L": + decode_filter = "DCTDecode" + # params = f"<< /Predictor 15 /Columns {width-2} >>" + dict_obj["ColorSpace"] = PdfParser.PdfName("DeviceGray") + procset = "ImageB" # grayscale + elif im.mode == "LA": + decode_filter = "JPXDecode" + # params = f"<< /Predictor 15 /Columns {width-2} >>" + procset = "ImageB" # grayscale + dict_obj["SMaskInData"] = 1 + elif im.mode == "P": + decode_filter = "ASCIIHexDecode" + palette = im.getpalette() + assert palette is not None + dict_obj["ColorSpace"] = [ + PdfParser.PdfName("Indexed"), + PdfParser.PdfName("DeviceRGB"), + len(palette) // 3 - 1, + PdfParser.PdfBinary(palette), + ] + procset = "ImageI" # indexed color + + if "transparency" in im.info: + smask = im.convert("LA").getchannel("A") + smask.encoderinfo = {} + + image_ref = _write_image(smask, filename, existing_pdf, image_refs)[0] + dict_obj["SMask"] = image_ref + elif im.mode == "RGB": + decode_filter = "DCTDecode" + dict_obj["ColorSpace"] = PdfParser.PdfName("DeviceRGB") + procset = "ImageC" # color images + elif im.mode == "RGBA": + decode_filter = "JPXDecode" + procset = "ImageC" # color images + dict_obj["SMaskInData"] = 1 + elif im.mode == "CMYK": + decode_filter = "DCTDecode" + dict_obj["ColorSpace"] = PdfParser.PdfName("DeviceCMYK") + procset = "ImageC" # color images + decode = [1, 0, 1, 0, 1, 0, 1, 0] + else: + msg = f"cannot save mode {im.mode}" + raise ValueError(msg) + + # + # image + + op = io.BytesIO() + + if decode_filter == "ASCIIHexDecode": + ImageFile._save(im, op, [ImageFile._Tile("hex", (0, 0) + im.size, 0, im.mode)]) + elif decode_filter == "CCITTFaxDecode": + im.save( + op, + "TIFF", + compression="group4", + # use a single strip + strip_size=math.ceil(width / 8) * height, + ) + elif decode_filter == "DCTDecode": + Image.SAVE["JPEG"](im, op, filename) + elif decode_filter == "JPXDecode": + del dict_obj["BitsPerComponent"] + Image.SAVE["JPEG2000"](im, op, filename) + else: + msg = f"unsupported PDF filter ({decode_filter})" + raise ValueError(msg) + + stream = op.getvalue() + filter: PdfParser.PdfArray | PdfParser.PdfName + if decode_filter == "CCITTFaxDecode": + stream = stream[8:] + filter = PdfParser.PdfArray([PdfParser.PdfName(decode_filter)]) + else: + filter = PdfParser.PdfName(decode_filter) + + image_ref = image_refs.pop(0) + existing_pdf.write_obj( + image_ref, + stream=stream, + Type=PdfParser.PdfName("XObject"), + Subtype=PdfParser.PdfName("Image"), + Width=width, # * 72.0 / x_resolution, + Height=height, # * 72.0 / y_resolution, + Filter=filter, + Decode=decode, + DecodeParms=params, + **dict_obj, + ) + + return image_ref, procset + + +def _save( + im: Image.Image, fp: IO[bytes], filename: str | bytes, save_all: bool = False +) -> None: + is_appending = im.encoderinfo.get("append", False) + filename_str = filename.decode() if isinstance(filename, bytes) else filename + if is_appending: + existing_pdf = PdfParser.PdfParser(f=fp, filename=filename_str, mode="r+b") + else: + existing_pdf = PdfParser.PdfParser(f=fp, filename=filename_str, mode="w+b") + + dpi = im.encoderinfo.get("dpi") + if dpi: + x_resolution = dpi[0] + y_resolution = dpi[1] + else: + x_resolution = y_resolution = im.encoderinfo.get("resolution", 72.0) + + info = { + "title": ( + None if is_appending else os.path.splitext(os.path.basename(filename))[0] + ), + "author": None, + "subject": None, + "keywords": None, + "creator": None, + "producer": None, + "creationDate": None if is_appending else time.gmtime(), + "modDate": None if is_appending else time.gmtime(), + } + for k, default in info.items(): + v = im.encoderinfo.get(k) if k in im.encoderinfo else default + if v: + existing_pdf.info[k[0].upper() + k[1:]] = v + + # + # make sure image data is available + im.load() + + existing_pdf.start_writing() + existing_pdf.write_header() + existing_pdf.write_comment(f"created by Pillow {__version__} PDF driver") + + # + # pages + ims = [im] + if save_all: + append_images = im.encoderinfo.get("append_images", []) + for append_im in append_images: + append_im.encoderinfo = im.encoderinfo.copy() + ims.append(append_im) + number_of_pages = 0 + image_refs = [] + page_refs = [] + contents_refs = [] + for im in ims: + im_number_of_pages = 1 + if save_all: + im_number_of_pages = getattr(im, "n_frames", 1) + number_of_pages += im_number_of_pages + for i in range(im_number_of_pages): + image_refs.append(existing_pdf.next_object_id(0)) + if im.mode == "P" and "transparency" in im.info: + image_refs.append(existing_pdf.next_object_id(0)) + + page_refs.append(existing_pdf.next_object_id(0)) + contents_refs.append(existing_pdf.next_object_id(0)) + existing_pdf.pages.append(page_refs[-1]) + + # + # catalog and list of pages + existing_pdf.write_catalog() + + page_number = 0 + for im_sequence in ims: + im_pages: ImageSequence.Iterator | list[Image.Image] = ( + ImageSequence.Iterator(im_sequence) if save_all else [im_sequence] + ) + for im in im_pages: + image_ref, procset = _write_image(im, filename, existing_pdf, image_refs) + + # + # page + + existing_pdf.write_page( + page_refs[page_number], + Resources=PdfParser.PdfDict( + ProcSet=[PdfParser.PdfName("PDF"), PdfParser.PdfName(procset)], + XObject=PdfParser.PdfDict(image=image_ref), + ), + MediaBox=[ + 0, + 0, + im.width * 72.0 / x_resolution, + im.height * 72.0 / y_resolution, + ], + Contents=contents_refs[page_number], + ) + + # + # page contents + + page_contents = b"q %f 0 0 %f 0 0 cm /image Do Q\n" % ( + im.width * 72.0 / x_resolution, + im.height * 72.0 / y_resolution, + ) + + existing_pdf.write_obj(contents_refs[page_number], stream=page_contents) + + page_number += 1 + + # + # trailer + existing_pdf.write_xref_and_trailer() + if hasattr(fp, "flush"): + fp.flush() + existing_pdf.close() + + +# +# -------------------------------------------------------------------- + + +Image.register_save("PDF", _save) +Image.register_save_all("PDF", _save_all) + +Image.register_extension("PDF", ".pdf") + +Image.register_mime("PDF", "application/pdf") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PdfParser.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PdfParser.py new file mode 100644 index 0000000000000000000000000000000000000000..73d8c21c023c95304c6fddfc2e5fce6d331401b1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PdfParser.py @@ -0,0 +1,1074 @@ +from __future__ import annotations + +import calendar +import codecs +import collections +import mmap +import os +import re +import time +import zlib +from typing import IO, Any, NamedTuple, Union + + +# see 7.9.2.2 Text String Type on page 86 and D.3 PDFDocEncoding Character Set +# on page 656 +def encode_text(s: str) -> bytes: + return codecs.BOM_UTF16_BE + s.encode("utf_16_be") + + +PDFDocEncoding = { + 0x16: "\u0017", + 0x18: "\u02d8", + 0x19: "\u02c7", + 0x1A: "\u02c6", + 0x1B: "\u02d9", + 0x1C: "\u02dd", + 0x1D: "\u02db", + 0x1E: "\u02da", + 0x1F: "\u02dc", + 0x80: "\u2022", + 0x81: "\u2020", + 0x82: "\u2021", + 0x83: "\u2026", + 0x84: "\u2014", + 0x85: "\u2013", + 0x86: "\u0192", + 0x87: "\u2044", + 0x88: "\u2039", + 0x89: "\u203a", + 0x8A: "\u2212", + 0x8B: "\u2030", + 0x8C: "\u201e", + 0x8D: "\u201c", + 0x8E: "\u201d", + 0x8F: "\u2018", + 0x90: "\u2019", + 0x91: "\u201a", + 0x92: "\u2122", + 0x93: "\ufb01", + 0x94: "\ufb02", + 0x95: "\u0141", + 0x96: "\u0152", + 0x97: "\u0160", + 0x98: "\u0178", + 0x99: "\u017d", + 0x9A: "\u0131", + 0x9B: "\u0142", + 0x9C: "\u0153", + 0x9D: "\u0161", + 0x9E: "\u017e", + 0xA0: "\u20ac", +} + + +def decode_text(b: bytes) -> str: + if b[: len(codecs.BOM_UTF16_BE)] == codecs.BOM_UTF16_BE: + return b[len(codecs.BOM_UTF16_BE) :].decode("utf_16_be") + else: + return "".join(PDFDocEncoding.get(byte, chr(byte)) for byte in b) + + +class PdfFormatError(RuntimeError): + """An error that probably indicates a syntactic or semantic error in the + PDF file structure""" + + pass + + +def check_format_condition(condition: bool, error_message: str) -> None: + if not condition: + raise PdfFormatError(error_message) + + +class IndirectReferenceTuple(NamedTuple): + object_id: int + generation: int + + +class IndirectReference(IndirectReferenceTuple): + def __str__(self) -> str: + return f"{self.object_id} {self.generation} R" + + def __bytes__(self) -> bytes: + return self.__str__().encode("us-ascii") + + def __eq__(self, other: object) -> bool: + if self.__class__ is not other.__class__: + return False + assert isinstance(other, IndirectReference) + return other.object_id == self.object_id and other.generation == self.generation + + def __ne__(self, other: object) -> bool: + return not (self == other) + + def __hash__(self) -> int: + return hash((self.object_id, self.generation)) + + +class IndirectObjectDef(IndirectReference): + def __str__(self) -> str: + return f"{self.object_id} {self.generation} obj" + + +class XrefTable: + def __init__(self) -> None: + self.existing_entries: dict[int, tuple[int, int]] = ( + {} + ) # object ID => (offset, generation) + self.new_entries: dict[int, tuple[int, int]] = ( + {} + ) # object ID => (offset, generation) + self.deleted_entries = {0: 65536} # object ID => generation + self.reading_finished = False + + def __setitem__(self, key: int, value: tuple[int, int]) -> None: + if self.reading_finished: + self.new_entries[key] = value + else: + self.existing_entries[key] = value + if key in self.deleted_entries: + del self.deleted_entries[key] + + def __getitem__(self, key: int) -> tuple[int, int]: + try: + return self.new_entries[key] + except KeyError: + return self.existing_entries[key] + + def __delitem__(self, key: int) -> None: + if key in self.new_entries: + generation = self.new_entries[key][1] + 1 + del self.new_entries[key] + self.deleted_entries[key] = generation + elif key in self.existing_entries: + generation = self.existing_entries[key][1] + 1 + self.deleted_entries[key] = generation + elif key in self.deleted_entries: + generation = self.deleted_entries[key] + else: + msg = f"object ID {key} cannot be deleted because it doesn't exist" + raise IndexError(msg) + + def __contains__(self, key: int) -> bool: + return key in self.existing_entries or key in self.new_entries + + def __len__(self) -> int: + return len( + set(self.existing_entries.keys()) + | set(self.new_entries.keys()) + | set(self.deleted_entries.keys()) + ) + + def keys(self) -> set[int]: + return ( + set(self.existing_entries.keys()) - set(self.deleted_entries.keys()) + ) | set(self.new_entries.keys()) + + def write(self, f: IO[bytes]) -> int: + keys = sorted(set(self.new_entries.keys()) | set(self.deleted_entries.keys())) + deleted_keys = sorted(set(self.deleted_entries.keys())) + startxref = f.tell() + f.write(b"xref\n") + while keys: + # find a contiguous sequence of object IDs + prev: int | None = None + for index, key in enumerate(keys): + if prev is None or prev + 1 == key: + prev = key + else: + contiguous_keys = keys[:index] + keys = keys[index:] + break + else: + contiguous_keys = keys + keys = [] + f.write(b"%d %d\n" % (contiguous_keys[0], len(contiguous_keys))) + for object_id in contiguous_keys: + if object_id in self.new_entries: + f.write(b"%010d %05d n \n" % self.new_entries[object_id]) + else: + this_deleted_object_id = deleted_keys.pop(0) + check_format_condition( + object_id == this_deleted_object_id, + f"expected the next deleted object ID to be {object_id}, " + f"instead found {this_deleted_object_id}", + ) + try: + next_in_linked_list = deleted_keys[0] + except IndexError: + next_in_linked_list = 0 + f.write( + b"%010d %05d f \n" + % (next_in_linked_list, self.deleted_entries[object_id]) + ) + return startxref + + +class PdfName: + name: bytes + + def __init__(self, name: PdfName | bytes | str) -> None: + if isinstance(name, PdfName): + self.name = name.name + elif isinstance(name, bytes): + self.name = name + else: + self.name = name.encode("us-ascii") + + def name_as_str(self) -> str: + return self.name.decode("us-ascii") + + def __eq__(self, other: object) -> bool: + return ( + isinstance(other, PdfName) and other.name == self.name + ) or other == self.name + + def __hash__(self) -> int: + return hash(self.name) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({repr(self.name)})" + + @classmethod + def from_pdf_stream(cls, data: bytes) -> PdfName: + return cls(PdfParser.interpret_name(data)) + + allowed_chars = set(range(33, 127)) - {ord(c) for c in "#%/()<>[]{}"} + + def __bytes__(self) -> bytes: + result = bytearray(b"/") + for b in self.name: + if b in self.allowed_chars: + result.append(b) + else: + result.extend(b"#%02X" % b) + return bytes(result) + + +class PdfArray(list[Any]): + def __bytes__(self) -> bytes: + return b"[ " + b" ".join(pdf_repr(x) for x in self) + b" ]" + + +TYPE_CHECKING = False +if TYPE_CHECKING: + _DictBase = collections.UserDict[Union[str, bytes], Any] +else: + _DictBase = collections.UserDict + + +class PdfDict(_DictBase): + def __setattr__(self, key: str, value: Any) -> None: + if key == "data": + collections.UserDict.__setattr__(self, key, value) + else: + self[key.encode("us-ascii")] = value + + def __getattr__(self, key: str) -> str | time.struct_time: + try: + value = self[key.encode("us-ascii")] + except KeyError as e: + raise AttributeError(key) from e + if isinstance(value, bytes): + value = decode_text(value) + if key.endswith("Date"): + if value.startswith("D:"): + value = value[2:] + + relationship = "Z" + if len(value) > 17: + relationship = value[14] + offset = int(value[15:17]) * 60 + if len(value) > 20: + offset += int(value[18:20]) + + format = "%Y%m%d%H%M%S"[: len(value) - 2] + value = time.strptime(value[: len(format) + 2], format) + if relationship in ["+", "-"]: + offset *= 60 + if relationship == "+": + offset *= -1 + value = time.gmtime(calendar.timegm(value) + offset) + return value + + def __bytes__(self) -> bytes: + out = bytearray(b"<<") + for key, value in self.items(): + if value is None: + continue + value = pdf_repr(value) + out.extend(b"\n") + out.extend(bytes(PdfName(key))) + out.extend(b" ") + out.extend(value) + out.extend(b"\n>>") + return bytes(out) + + +class PdfBinary: + def __init__(self, data: list[int] | bytes) -> None: + self.data = data + + def __bytes__(self) -> bytes: + return b"<%s>" % b"".join(b"%02X" % b for b in self.data) + + +class PdfStream: + def __init__(self, dictionary: PdfDict, buf: bytes) -> None: + self.dictionary = dictionary + self.buf = buf + + def decode(self) -> bytes: + try: + filter = self.dictionary[b"Filter"] + except KeyError: + return self.buf + if filter == b"FlateDecode": + try: + expected_length = self.dictionary[b"DL"] + except KeyError: + expected_length = self.dictionary[b"Length"] + return zlib.decompress(self.buf, bufsize=int(expected_length)) + else: + msg = f"stream filter {repr(filter)} unknown/unsupported" + raise NotImplementedError(msg) + + +def pdf_repr(x: Any) -> bytes: + if x is True: + return b"true" + elif x is False: + return b"false" + elif x is None: + return b"null" + elif isinstance(x, (PdfName, PdfDict, PdfArray, PdfBinary)): + return bytes(x) + elif isinstance(x, (int, float)): + return str(x).encode("us-ascii") + elif isinstance(x, time.struct_time): + return b"(D:" + time.strftime("%Y%m%d%H%M%SZ", x).encode("us-ascii") + b")" + elif isinstance(x, dict): + return bytes(PdfDict(x)) + elif isinstance(x, list): + return bytes(PdfArray(x)) + elif isinstance(x, str): + return pdf_repr(encode_text(x)) + elif isinstance(x, bytes): + # XXX escape more chars? handle binary garbage + x = x.replace(b"\\", b"\\\\") + x = x.replace(b"(", b"\\(") + x = x.replace(b")", b"\\)") + return b"(" + x + b")" + else: + return bytes(x) + + +class PdfParser: + """Based on + https://www.adobe.com/content/dam/acom/en/devnet/acrobat/pdfs/PDF32000_2008.pdf + Supports PDF up to 1.4 + """ + + def __init__( + self, + filename: str | None = None, + f: IO[bytes] | None = None, + buf: bytes | bytearray | None = None, + start_offset: int = 0, + mode: str = "rb", + ) -> None: + if buf and f: + msg = "specify buf or f or filename, but not both buf and f" + raise RuntimeError(msg) + self.filename = filename + self.buf: bytes | bytearray | mmap.mmap | None = buf + self.f = f + self.start_offset = start_offset + self.should_close_buf = False + self.should_close_file = False + if filename is not None and f is None: + self.f = f = open(filename, mode) + self.should_close_file = True + if f is not None: + self.buf = self.get_buf_from_file(f) + self.should_close_buf = True + if not filename and hasattr(f, "name"): + self.filename = f.name + self.cached_objects: dict[IndirectReference, Any] = {} + self.root_ref: IndirectReference | None + self.info_ref: IndirectReference | None + self.pages_ref: IndirectReference | None + self.last_xref_section_offset: int | None + if self.buf: + self.read_pdf_info() + else: + self.file_size_total = self.file_size_this = 0 + self.root = PdfDict() + self.root_ref = None + self.info = PdfDict() + self.info_ref = None + self.page_tree_root = PdfDict() + self.pages: list[IndirectReference] = [] + self.orig_pages: list[IndirectReference] = [] + self.pages_ref = None + self.last_xref_section_offset = None + self.trailer_dict: dict[bytes, Any] = {} + self.xref_table = XrefTable() + self.xref_table.reading_finished = True + if f: + self.seek_end() + + def __enter__(self) -> PdfParser: + return self + + def __exit__(self, *args: object) -> None: + self.close() + + def start_writing(self) -> None: + self.close_buf() + self.seek_end() + + def close_buf(self) -> None: + if isinstance(self.buf, mmap.mmap): + self.buf.close() + self.buf = None + + def close(self) -> None: + if self.should_close_buf: + self.close_buf() + if self.f is not None and self.should_close_file: + self.f.close() + self.f = None + + def seek_end(self) -> None: + assert self.f is not None + self.f.seek(0, os.SEEK_END) + + def write_header(self) -> None: + assert self.f is not None + self.f.write(b"%PDF-1.4\n") + + def write_comment(self, s: str) -> None: + assert self.f is not None + self.f.write(f"% {s}\n".encode()) + + def write_catalog(self) -> IndirectReference: + assert self.f is not None + self.del_root() + self.root_ref = self.next_object_id(self.f.tell()) + self.pages_ref = self.next_object_id(0) + self.rewrite_pages() + self.write_obj(self.root_ref, Type=PdfName(b"Catalog"), Pages=self.pages_ref) + self.write_obj( + self.pages_ref, + Type=PdfName(b"Pages"), + Count=len(self.pages), + Kids=self.pages, + ) + return self.root_ref + + def rewrite_pages(self) -> None: + pages_tree_nodes_to_delete = [] + for i, page_ref in enumerate(self.orig_pages): + page_info = self.cached_objects[page_ref] + del self.xref_table[page_ref.object_id] + pages_tree_nodes_to_delete.append(page_info[PdfName(b"Parent")]) + if page_ref not in self.pages: + # the page has been deleted + continue + # make dict keys into strings for passing to write_page + stringified_page_info = {} + for key, value in page_info.items(): + # key should be a PdfName + stringified_page_info[key.name_as_str()] = value + stringified_page_info["Parent"] = self.pages_ref + new_page_ref = self.write_page(None, **stringified_page_info) + for j, cur_page_ref in enumerate(self.pages): + if cur_page_ref == page_ref: + # replace the page reference with the new one + self.pages[j] = new_page_ref + # delete redundant Pages tree nodes from xref table + for pages_tree_node_ref in pages_tree_nodes_to_delete: + while pages_tree_node_ref: + pages_tree_node = self.cached_objects[pages_tree_node_ref] + if pages_tree_node_ref.object_id in self.xref_table: + del self.xref_table[pages_tree_node_ref.object_id] + pages_tree_node_ref = pages_tree_node.get(b"Parent", None) + self.orig_pages = [] + + def write_xref_and_trailer( + self, new_root_ref: IndirectReference | None = None + ) -> None: + assert self.f is not None + if new_root_ref: + self.del_root() + self.root_ref = new_root_ref + if self.info: + self.info_ref = self.write_obj(None, self.info) + start_xref = self.xref_table.write(self.f) + num_entries = len(self.xref_table) + trailer_dict: dict[str | bytes, Any] = { + b"Root": self.root_ref, + b"Size": num_entries, + } + if self.last_xref_section_offset is not None: + trailer_dict[b"Prev"] = self.last_xref_section_offset + if self.info: + trailer_dict[b"Info"] = self.info_ref + self.last_xref_section_offset = start_xref + self.f.write( + b"trailer\n" + + bytes(PdfDict(trailer_dict)) + + b"\nstartxref\n%d\n%%%%EOF" % start_xref + ) + + def write_page( + self, ref: int | IndirectReference | None, *objs: Any, **dict_obj: Any + ) -> IndirectReference: + obj_ref = self.pages[ref] if isinstance(ref, int) else ref + if "Type" not in dict_obj: + dict_obj["Type"] = PdfName(b"Page") + if "Parent" not in dict_obj: + dict_obj["Parent"] = self.pages_ref + return self.write_obj(obj_ref, *objs, **dict_obj) + + def write_obj( + self, ref: IndirectReference | None, *objs: Any, **dict_obj: Any + ) -> IndirectReference: + assert self.f is not None + f = self.f + if ref is None: + ref = self.next_object_id(f.tell()) + else: + self.xref_table[ref.object_id] = (f.tell(), ref.generation) + f.write(bytes(IndirectObjectDef(*ref))) + stream = dict_obj.pop("stream", None) + if stream is not None: + dict_obj["Length"] = len(stream) + if dict_obj: + f.write(pdf_repr(dict_obj)) + for obj in objs: + f.write(pdf_repr(obj)) + if stream is not None: + f.write(b"stream\n") + f.write(stream) + f.write(b"\nendstream\n") + f.write(b"endobj\n") + return ref + + def del_root(self) -> None: + if self.root_ref is None: + return + del self.xref_table[self.root_ref.object_id] + del self.xref_table[self.root[b"Pages"].object_id] + + @staticmethod + def get_buf_from_file(f: IO[bytes]) -> bytes | mmap.mmap: + if hasattr(f, "getbuffer"): + return f.getbuffer() + elif hasattr(f, "getvalue"): + return f.getvalue() + else: + try: + return mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) + except ValueError: # cannot mmap an empty file + return b"" + + def read_pdf_info(self) -> None: + assert self.buf is not None + self.file_size_total = len(self.buf) + self.file_size_this = self.file_size_total - self.start_offset + self.read_trailer() + check_format_condition( + self.trailer_dict.get(b"Root") is not None, "Root is missing" + ) + self.root_ref = self.trailer_dict[b"Root"] + assert self.root_ref is not None + self.info_ref = self.trailer_dict.get(b"Info", None) + self.root = PdfDict(self.read_indirect(self.root_ref)) + if self.info_ref is None: + self.info = PdfDict() + else: + self.info = PdfDict(self.read_indirect(self.info_ref)) + check_format_condition(b"Type" in self.root, "/Type missing in Root") + check_format_condition( + self.root[b"Type"] == b"Catalog", "/Type in Root is not /Catalog" + ) + check_format_condition( + self.root.get(b"Pages") is not None, "/Pages missing in Root" + ) + check_format_condition( + isinstance(self.root[b"Pages"], IndirectReference), + "/Pages in Root is not an indirect reference", + ) + self.pages_ref = self.root[b"Pages"] + assert self.pages_ref is not None + self.page_tree_root = self.read_indirect(self.pages_ref) + self.pages = self.linearize_page_tree(self.page_tree_root) + # save the original list of page references + # in case the user modifies, adds or deletes some pages + # and we need to rewrite the pages and their list + self.orig_pages = self.pages[:] + + def next_object_id(self, offset: int | None = None) -> IndirectReference: + try: + # TODO: support reuse of deleted objects + reference = IndirectReference(max(self.xref_table.keys()) + 1, 0) + except ValueError: + reference = IndirectReference(1, 0) + if offset is not None: + self.xref_table[reference.object_id] = (offset, 0) + return reference + + delimiter = rb"[][()<>{}/%]" + delimiter_or_ws = rb"[][()<>{}/%\000\011\012\014\015\040]" + whitespace = rb"[\000\011\012\014\015\040]" + whitespace_or_hex = rb"[\000\011\012\014\015\0400-9a-fA-F]" + whitespace_optional = whitespace + b"*" + whitespace_mandatory = whitespace + b"+" + # No "\012" aka "\n" or "\015" aka "\r": + whitespace_optional_no_nl = rb"[\000\011\014\040]*" + newline_only = rb"[\r\n]+" + newline = whitespace_optional_no_nl + newline_only + whitespace_optional_no_nl + re_trailer_end = re.compile( + whitespace_mandatory + + rb"trailer" + + whitespace_optional + + rb"<<(.*>>)" + + newline + + rb"startxref" + + newline + + rb"([0-9]+)" + + newline + + rb"%%EOF" + + whitespace_optional + + rb"$", + re.DOTALL, + ) + re_trailer_prev = re.compile( + whitespace_optional + + rb"trailer" + + whitespace_optional + + rb"<<(.*?>>)" + + newline + + rb"startxref" + + newline + + rb"([0-9]+)" + + newline + + rb"%%EOF" + + whitespace_optional, + re.DOTALL, + ) + + def read_trailer(self) -> None: + assert self.buf is not None + search_start_offset = len(self.buf) - 16384 + if search_start_offset < self.start_offset: + search_start_offset = self.start_offset + m = self.re_trailer_end.search(self.buf, search_start_offset) + check_format_condition(m is not None, "trailer end not found") + # make sure we found the LAST trailer + last_match = m + while m: + last_match = m + m = self.re_trailer_end.search(self.buf, m.start() + 16) + if not m: + m = last_match + assert m is not None + trailer_data = m.group(1) + self.last_xref_section_offset = int(m.group(2)) + self.trailer_dict = self.interpret_trailer(trailer_data) + self.xref_table = XrefTable() + self.read_xref_table(xref_section_offset=self.last_xref_section_offset) + if b"Prev" in self.trailer_dict: + self.read_prev_trailer(self.trailer_dict[b"Prev"]) + + def read_prev_trailer(self, xref_section_offset: int) -> None: + assert self.buf is not None + trailer_offset = self.read_xref_table(xref_section_offset=xref_section_offset) + m = self.re_trailer_prev.search( + self.buf[trailer_offset : trailer_offset + 16384] + ) + check_format_condition(m is not None, "previous trailer not found") + assert m is not None + trailer_data = m.group(1) + check_format_condition( + int(m.group(2)) == xref_section_offset, + "xref section offset in previous trailer doesn't match what was expected", + ) + trailer_dict = self.interpret_trailer(trailer_data) + if b"Prev" in trailer_dict: + self.read_prev_trailer(trailer_dict[b"Prev"]) + + re_whitespace_optional = re.compile(whitespace_optional) + re_name = re.compile( + whitespace_optional + + rb"/([!-$&'*-.0-;=?-Z\\^-z|~]+)(?=" + + delimiter_or_ws + + rb")" + ) + re_dict_start = re.compile(whitespace_optional + rb"<<") + re_dict_end = re.compile(whitespace_optional + rb">>" + whitespace_optional) + + @classmethod + def interpret_trailer(cls, trailer_data: bytes) -> dict[bytes, Any]: + trailer = {} + offset = 0 + while True: + m = cls.re_name.match(trailer_data, offset) + if not m: + m = cls.re_dict_end.match(trailer_data, offset) + check_format_condition( + m is not None and m.end() == len(trailer_data), + "name not found in trailer, remaining data: " + + repr(trailer_data[offset:]), + ) + break + key = cls.interpret_name(m.group(1)) + assert isinstance(key, bytes) + value, value_offset = cls.get_value(trailer_data, m.end()) + trailer[key] = value + if value_offset is None: + break + offset = value_offset + check_format_condition( + b"Size" in trailer and isinstance(trailer[b"Size"], int), + "/Size not in trailer or not an integer", + ) + check_format_condition( + b"Root" in trailer and isinstance(trailer[b"Root"], IndirectReference), + "/Root not in trailer or not an indirect reference", + ) + return trailer + + re_hashes_in_name = re.compile(rb"([^#]*)(#([0-9a-fA-F]{2}))?") + + @classmethod + def interpret_name(cls, raw: bytes, as_text: bool = False) -> str | bytes: + name = b"" + for m in cls.re_hashes_in_name.finditer(raw): + if m.group(3): + name += m.group(1) + bytearray.fromhex(m.group(3).decode("us-ascii")) + else: + name += m.group(1) + if as_text: + return name.decode("utf-8") + else: + return bytes(name) + + re_null = re.compile(whitespace_optional + rb"null(?=" + delimiter_or_ws + rb")") + re_true = re.compile(whitespace_optional + rb"true(?=" + delimiter_or_ws + rb")") + re_false = re.compile(whitespace_optional + rb"false(?=" + delimiter_or_ws + rb")") + re_int = re.compile( + whitespace_optional + rb"([-+]?[0-9]+)(?=" + delimiter_or_ws + rb")" + ) + re_real = re.compile( + whitespace_optional + + rb"([-+]?([0-9]+\.[0-9]*|[0-9]*\.[0-9]+))(?=" + + delimiter_or_ws + + rb")" + ) + re_array_start = re.compile(whitespace_optional + rb"\[") + re_array_end = re.compile(whitespace_optional + rb"]") + re_string_hex = re.compile( + whitespace_optional + rb"<(" + whitespace_or_hex + rb"*)>" + ) + re_string_lit = re.compile(whitespace_optional + rb"\(") + re_indirect_reference = re.compile( + whitespace_optional + + rb"([-+]?[0-9]+)" + + whitespace_mandatory + + rb"([-+]?[0-9]+)" + + whitespace_mandatory + + rb"R(?=" + + delimiter_or_ws + + rb")" + ) + re_indirect_def_start = re.compile( + whitespace_optional + + rb"([-+]?[0-9]+)" + + whitespace_mandatory + + rb"([-+]?[0-9]+)" + + whitespace_mandatory + + rb"obj(?=" + + delimiter_or_ws + + rb")" + ) + re_indirect_def_end = re.compile( + whitespace_optional + rb"endobj(?=" + delimiter_or_ws + rb")" + ) + re_comment = re.compile( + rb"(" + whitespace_optional + rb"%[^\r\n]*" + newline + rb")*" + ) + re_stream_start = re.compile(whitespace_optional + rb"stream\r?\n") + re_stream_end = re.compile( + whitespace_optional + rb"endstream(?=" + delimiter_or_ws + rb")" + ) + + @classmethod + def get_value( + cls, + data: bytes | bytearray | mmap.mmap, + offset: int, + expect_indirect: IndirectReference | None = None, + max_nesting: int = -1, + ) -> tuple[Any, int | None]: + if max_nesting == 0: + return None, None + m = cls.re_comment.match(data, offset) + if m: + offset = m.end() + m = cls.re_indirect_def_start.match(data, offset) + if m: + check_format_condition( + int(m.group(1)) > 0, + "indirect object definition: object ID must be greater than 0", + ) + check_format_condition( + int(m.group(2)) >= 0, + "indirect object definition: generation must be non-negative", + ) + check_format_condition( + expect_indirect is None + or expect_indirect + == IndirectReference(int(m.group(1)), int(m.group(2))), + "indirect object definition different than expected", + ) + object, object_offset = cls.get_value( + data, m.end(), max_nesting=max_nesting - 1 + ) + if object_offset is None: + return object, None + m = cls.re_indirect_def_end.match(data, object_offset) + check_format_condition( + m is not None, "indirect object definition end not found" + ) + assert m is not None + return object, m.end() + check_format_condition( + not expect_indirect, "indirect object definition not found" + ) + m = cls.re_indirect_reference.match(data, offset) + if m: + check_format_condition( + int(m.group(1)) > 0, + "indirect object reference: object ID must be greater than 0", + ) + check_format_condition( + int(m.group(2)) >= 0, + "indirect object reference: generation must be non-negative", + ) + return IndirectReference(int(m.group(1)), int(m.group(2))), m.end() + m = cls.re_dict_start.match(data, offset) + if m: + offset = m.end() + result: dict[Any, Any] = {} + m = cls.re_dict_end.match(data, offset) + current_offset: int | None = offset + while not m: + assert current_offset is not None + key, current_offset = cls.get_value( + data, current_offset, max_nesting=max_nesting - 1 + ) + if current_offset is None: + return result, None + value, current_offset = cls.get_value( + data, current_offset, max_nesting=max_nesting - 1 + ) + result[key] = value + if current_offset is None: + return result, None + m = cls.re_dict_end.match(data, current_offset) + current_offset = m.end() + m = cls.re_stream_start.match(data, current_offset) + if m: + stream_len = result.get(b"Length") + if stream_len is None or not isinstance(stream_len, int): + msg = f"bad or missing Length in stream dict ({stream_len})" + raise PdfFormatError(msg) + stream_data = data[m.end() : m.end() + stream_len] + m = cls.re_stream_end.match(data, m.end() + stream_len) + check_format_condition(m is not None, "stream end not found") + assert m is not None + current_offset = m.end() + return PdfStream(PdfDict(result), stream_data), current_offset + return PdfDict(result), current_offset + m = cls.re_array_start.match(data, offset) + if m: + offset = m.end() + results = [] + m = cls.re_array_end.match(data, offset) + current_offset = offset + while not m: + assert current_offset is not None + value, current_offset = cls.get_value( + data, current_offset, max_nesting=max_nesting - 1 + ) + results.append(value) + if current_offset is None: + return results, None + m = cls.re_array_end.match(data, current_offset) + return results, m.end() + m = cls.re_null.match(data, offset) + if m: + return None, m.end() + m = cls.re_true.match(data, offset) + if m: + return True, m.end() + m = cls.re_false.match(data, offset) + if m: + return False, m.end() + m = cls.re_name.match(data, offset) + if m: + return PdfName(cls.interpret_name(m.group(1))), m.end() + m = cls.re_int.match(data, offset) + if m: + return int(m.group(1)), m.end() + m = cls.re_real.match(data, offset) + if m: + # XXX Decimal instead of float??? + return float(m.group(1)), m.end() + m = cls.re_string_hex.match(data, offset) + if m: + # filter out whitespace + hex_string = bytearray( + b for b in m.group(1) if b in b"0123456789abcdefABCDEF" + ) + if len(hex_string) % 2 == 1: + # append a 0 if the length is not even - yes, at the end + hex_string.append(ord(b"0")) + return bytearray.fromhex(hex_string.decode("us-ascii")), m.end() + m = cls.re_string_lit.match(data, offset) + if m: + return cls.get_literal_string(data, m.end()) + # return None, offset # fallback (only for debugging) + msg = f"unrecognized object: {repr(data[offset : offset + 32])}" + raise PdfFormatError(msg) + + re_lit_str_token = re.compile( + rb"(\\[nrtbf()\\])|(\\[0-9]{1,3})|(\\(\r\n|\r|\n))|(\r\n|\r|\n)|(\()|(\))" + ) + escaped_chars = { + b"n": b"\n", + b"r": b"\r", + b"t": b"\t", + b"b": b"\b", + b"f": b"\f", + b"(": b"(", + b")": b")", + b"\\": b"\\", + ord(b"n"): b"\n", + ord(b"r"): b"\r", + ord(b"t"): b"\t", + ord(b"b"): b"\b", + ord(b"f"): b"\f", + ord(b"("): b"(", + ord(b")"): b")", + ord(b"\\"): b"\\", + } + + @classmethod + def get_literal_string( + cls, data: bytes | bytearray | mmap.mmap, offset: int + ) -> tuple[bytes, int]: + nesting_depth = 0 + result = bytearray() + for m in cls.re_lit_str_token.finditer(data, offset): + result.extend(data[offset : m.start()]) + if m.group(1): + result.extend(cls.escaped_chars[m.group(1)[1]]) + elif m.group(2): + result.append(int(m.group(2)[1:], 8)) + elif m.group(3): + pass + elif m.group(5): + result.extend(b"\n") + elif m.group(6): + result.extend(b"(") + nesting_depth += 1 + elif m.group(7): + if nesting_depth == 0: + return bytes(result), m.end() + result.extend(b")") + nesting_depth -= 1 + offset = m.end() + msg = "unfinished literal string" + raise PdfFormatError(msg) + + re_xref_section_start = re.compile(whitespace_optional + rb"xref" + newline) + re_xref_subsection_start = re.compile( + whitespace_optional + + rb"([0-9]+)" + + whitespace_mandatory + + rb"([0-9]+)" + + whitespace_optional + + newline_only + ) + re_xref_entry = re.compile(rb"([0-9]{10}) ([0-9]{5}) ([fn])( \r| \n|\r\n)") + + def read_xref_table(self, xref_section_offset: int) -> int: + assert self.buf is not None + subsection_found = False + m = self.re_xref_section_start.match( + self.buf, xref_section_offset + self.start_offset + ) + check_format_condition(m is not None, "xref section start not found") + assert m is not None + offset = m.end() + while True: + m = self.re_xref_subsection_start.match(self.buf, offset) + if not m: + check_format_condition( + subsection_found, "xref subsection start not found" + ) + break + subsection_found = True + offset = m.end() + first_object = int(m.group(1)) + num_objects = int(m.group(2)) + for i in range(first_object, first_object + num_objects): + m = self.re_xref_entry.match(self.buf, offset) + check_format_condition(m is not None, "xref entry not found") + assert m is not None + offset = m.end() + is_free = m.group(3) == b"f" + if not is_free: + generation = int(m.group(2)) + new_entry = (int(m.group(1)), generation) + if i not in self.xref_table: + self.xref_table[i] = new_entry + return offset + + def read_indirect(self, ref: IndirectReference, max_nesting: int = -1) -> Any: + offset, generation = self.xref_table[ref[0]] + check_format_condition( + generation == ref[1], + f"expected to find generation {ref[1]} for object ID {ref[0]} in xref " + f"table, instead found generation {generation} at offset {offset}", + ) + assert self.buf is not None + value = self.get_value( + self.buf, + offset + self.start_offset, + expect_indirect=IndirectReference(*ref), + max_nesting=max_nesting, + )[0] + self.cached_objects[ref] = value + return value + + def linearize_page_tree( + self, node: PdfDict | None = None + ) -> list[IndirectReference]: + page_node = node if node is not None else self.page_tree_root + check_format_condition( + page_node[b"Type"] == b"Pages", "/Type of page tree node is not /Pages" + ) + pages = [] + for kid in page_node[b"Kids"]: + kid_object = self.read_indirect(kid) + if kid_object[b"Type"] == b"Page": + pages.append(kid) + else: + pages.extend(self.linearize_page_tree(node=kid_object)) + return pages diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PixarImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PixarImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..d2b6d0a97e4bd230134d4741fc997baca5b4507f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PixarImagePlugin.py @@ -0,0 +1,72 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PIXAR raster support for PIL +# +# history: +# 97-01-29 fl Created +# +# notes: +# This is incomplete; it is based on a few samples created with +# Photoshop 2.5 and 3.0, and a summary description provided by +# Greg Coats . Hopefully, "L" and +# "RGBA" support will be added in future versions. +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1997. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFile +from ._binary import i16le as i16 + +# +# helpers + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"\200\350\000\000") + + +## +# Image plugin for PIXAR raster images. + + +class PixarImageFile(ImageFile.ImageFile): + format = "PIXAR" + format_description = "PIXAR raster image" + + def _open(self) -> None: + # assuming a 4-byte magic label + assert self.fp is not None + + s = self.fp.read(4) + if not _accept(s): + msg = "not a PIXAR file" + raise SyntaxError(msg) + + # read rest of header + s = s + self.fp.read(508) + + self._size = i16(s, 418), i16(s, 416) + + # get channel/depth descriptions + mode = i16(s, 424), i16(s, 426) + + if mode == (14, 2): + self._mode = "RGB" + # FIXME: to be continued... + + # create tile descriptor (assuming "dumped") + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, 1024, self.mode)] + + +# +# -------------------------------------------------------------------- + +Image.register_open(PixarImageFile.format, PixarImageFile, _accept) + +Image.register_extension(PixarImageFile.format, ".pxr") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PngImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PngImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..1b9a89aef0dd15a292b5e2e55b6e56369dc31b62 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PngImagePlugin.py @@ -0,0 +1,1551 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PNG support code +# +# See "PNG (Portable Network Graphics) Specification, version 1.0; +# W3C Recommendation", 1996-10-01, Thomas Boutell (ed.). +# +# history: +# 1996-05-06 fl Created (couldn't resist it) +# 1996-12-14 fl Upgraded, added read and verify support (0.2) +# 1996-12-15 fl Separate PNG stream parser +# 1996-12-29 fl Added write support, added getchunks +# 1996-12-30 fl Eliminated circular references in decoder (0.3) +# 1998-07-12 fl Read/write 16-bit images as mode I (0.4) +# 2001-02-08 fl Added transparency support (from Zircon) (0.5) +# 2001-04-16 fl Don't close data source in "open" method (0.6) +# 2004-02-24 fl Don't even pretend to support interlaced files (0.7) +# 2004-08-31 fl Do basic sanity check on chunk identifiers (0.8) +# 2004-09-20 fl Added PngInfo chunk container +# 2004-12-18 fl Added DPI read support (based on code by Niki Spahiev) +# 2008-08-13 fl Added tRNS support for RGB images +# 2009-03-06 fl Support for preserving ICC profiles (by Florian Hoech) +# 2009-03-08 fl Added zTXT support (from Lowell Alleman) +# 2009-03-29 fl Read interlaced PNG files (from Conrado Porto Lopes Gouvua) +# +# Copyright (c) 1997-2009 by Secret Labs AB +# Copyright (c) 1996 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import itertools +import logging +import re +import struct +import warnings +import zlib +from collections.abc import Callable +from enum import IntEnum +from typing import IO, Any, NamedTuple, NoReturn, cast + +from . import Image, ImageChops, ImageFile, ImagePalette, ImageSequence +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._binary import o8 +from ._binary import o16be as o16 +from ._binary import o32be as o32 +from ._deprecate import deprecate +from ._util import DeferredError + +TYPE_CHECKING = False +if TYPE_CHECKING: + from . import _imaging + +logger = logging.getLogger(__name__) + +is_cid = re.compile(rb"\w\w\w\w").match + + +_MAGIC = b"\211PNG\r\n\032\n" + + +_MODES = { + # supported bits/color combinations, and corresponding modes/rawmodes + # Grayscale + (1, 0): ("1", "1"), + (2, 0): ("L", "L;2"), + (4, 0): ("L", "L;4"), + (8, 0): ("L", "L"), + (16, 0): ("I;16", "I;16B"), + # Truecolour + (8, 2): ("RGB", "RGB"), + (16, 2): ("RGB", "RGB;16B"), + # Indexed-colour + (1, 3): ("P", "P;1"), + (2, 3): ("P", "P;2"), + (4, 3): ("P", "P;4"), + (8, 3): ("P", "P"), + # Grayscale with alpha + (8, 4): ("LA", "LA"), + (16, 4): ("RGBA", "LA;16B"), # LA;16B->LA not yet available + # Truecolour with alpha + (8, 6): ("RGBA", "RGBA"), + (16, 6): ("RGBA", "RGBA;16B"), +} + + +_simple_palette = re.compile(b"^\xff*\x00\xff*$") + +MAX_TEXT_CHUNK = ImageFile.SAFEBLOCK +""" +Maximum decompressed size for a iTXt or zTXt chunk. +Eliminates decompression bombs where compressed chunks can expand 1000x. +See :ref:`Text in PNG File Format`. +""" +MAX_TEXT_MEMORY = 64 * MAX_TEXT_CHUNK +""" +Set the maximum total text chunk size. +See :ref:`Text in PNG File Format`. +""" + + +# APNG frame disposal modes +class Disposal(IntEnum): + OP_NONE = 0 + """ + No disposal is done on this frame before rendering the next frame. + See :ref:`Saving APNG sequences`. + """ + OP_BACKGROUND = 1 + """ + This frame’s modified region is cleared to fully transparent black before rendering + the next frame. + See :ref:`Saving APNG sequences`. + """ + OP_PREVIOUS = 2 + """ + This frame’s modified region is reverted to the previous frame’s contents before + rendering the next frame. + See :ref:`Saving APNG sequences`. + """ + + +# APNG frame blend modes +class Blend(IntEnum): + OP_SOURCE = 0 + """ + All color components of this frame, including alpha, overwrite the previous output + image contents. + See :ref:`Saving APNG sequences`. + """ + OP_OVER = 1 + """ + This frame should be alpha composited with the previous output image contents. + See :ref:`Saving APNG sequences`. + """ + + +def _safe_zlib_decompress(s: bytes) -> bytes: + dobj = zlib.decompressobj() + plaintext = dobj.decompress(s, MAX_TEXT_CHUNK) + if dobj.unconsumed_tail: + msg = "Decompressed data too large for PngImagePlugin.MAX_TEXT_CHUNK" + raise ValueError(msg) + return plaintext + + +def _crc32(data: bytes, seed: int = 0) -> int: + return zlib.crc32(data, seed) & 0xFFFFFFFF + + +# -------------------------------------------------------------------- +# Support classes. Suitable for PNG and related formats like MNG etc. + + +class ChunkStream: + def __init__(self, fp: IO[bytes]) -> None: + self.fp: IO[bytes] | None = fp + self.queue: list[tuple[bytes, int, int]] | None = [] + + def read(self) -> tuple[bytes, int, int]: + """Fetch a new chunk. Returns header information.""" + cid = None + + assert self.fp is not None + if self.queue: + cid, pos, length = self.queue.pop() + self.fp.seek(pos) + else: + s = self.fp.read(8) + cid = s[4:] + pos = self.fp.tell() + length = i32(s) + + if not is_cid(cid): + if not ImageFile.LOAD_TRUNCATED_IMAGES: + msg = f"broken PNG file (chunk {repr(cid)})" + raise SyntaxError(msg) + + return cid, pos, length + + def __enter__(self) -> ChunkStream: + return self + + def __exit__(self, *args: object) -> None: + self.close() + + def close(self) -> None: + self.queue = self.fp = None + + def push(self, cid: bytes, pos: int, length: int) -> None: + assert self.queue is not None + self.queue.append((cid, pos, length)) + + def call(self, cid: bytes, pos: int, length: int) -> bytes: + """Call the appropriate chunk handler""" + + logger.debug("STREAM %r %s %s", cid, pos, length) + return getattr(self, f"chunk_{cid.decode('ascii')}")(pos, length) + + def crc(self, cid: bytes, data: bytes) -> None: + """Read and verify checksum""" + + # Skip CRC checks for ancillary chunks if allowed to load truncated + # images + # 5th byte of first char is 1 [specs, section 5.4] + if ImageFile.LOAD_TRUNCATED_IMAGES and (cid[0] >> 5 & 1): + self.crc_skip(cid, data) + return + + assert self.fp is not None + try: + crc1 = _crc32(data, _crc32(cid)) + crc2 = i32(self.fp.read(4)) + if crc1 != crc2: + msg = f"broken PNG file (bad header checksum in {repr(cid)})" + raise SyntaxError(msg) + except struct.error as e: + msg = f"broken PNG file (incomplete checksum in {repr(cid)})" + raise SyntaxError(msg) from e + + def crc_skip(self, cid: bytes, data: bytes) -> None: + """Read checksum""" + + assert self.fp is not None + self.fp.read(4) + + def verify(self, endchunk: bytes = b"IEND") -> list[bytes]: + # Simple approach; just calculate checksum for all remaining + # blocks. Must be called directly after open. + + cids = [] + + assert self.fp is not None + while True: + try: + cid, pos, length = self.read() + except struct.error as e: + msg = "truncated PNG file" + raise OSError(msg) from e + + if cid == endchunk: + break + self.crc(cid, ImageFile._safe_read(self.fp, length)) + cids.append(cid) + + return cids + + +class iTXt(str): + """ + Subclass of string to allow iTXt chunks to look like strings while + keeping their extra information + + """ + + lang: str | bytes | None + tkey: str | bytes | None + + @staticmethod + def __new__( + cls, text: str, lang: str | None = None, tkey: str | None = None + ) -> iTXt: + """ + :param cls: the class to use when creating the instance + :param text: value for this key + :param lang: language code + :param tkey: UTF-8 version of the key name + """ + + self = str.__new__(cls, text) + self.lang = lang + self.tkey = tkey + return self + + +class PngInfo: + """ + PNG chunk container (for use with save(pnginfo=)) + + """ + + def __init__(self) -> None: + self.chunks: list[tuple[bytes, bytes, bool]] = [] + + def add(self, cid: bytes, data: bytes, after_idat: bool = False) -> None: + """Appends an arbitrary chunk. Use with caution. + + :param cid: a byte string, 4 bytes long. + :param data: a byte string of the encoded data + :param after_idat: for use with private chunks. Whether the chunk + should be written after IDAT + + """ + + self.chunks.append((cid, data, after_idat)) + + def add_itxt( + self, + key: str | bytes, + value: str | bytes, + lang: str | bytes = "", + tkey: str | bytes = "", + zip: bool = False, + ) -> None: + """Appends an iTXt chunk. + + :param key: latin-1 encodable text key name + :param value: value for this key + :param lang: language code + :param tkey: UTF-8 version of the key name + :param zip: compression flag + + """ + + if not isinstance(key, bytes): + key = key.encode("latin-1", "strict") + if not isinstance(value, bytes): + value = value.encode("utf-8", "strict") + if not isinstance(lang, bytes): + lang = lang.encode("utf-8", "strict") + if not isinstance(tkey, bytes): + tkey = tkey.encode("utf-8", "strict") + + if zip: + self.add( + b"iTXt", + key + b"\0\x01\0" + lang + b"\0" + tkey + b"\0" + zlib.compress(value), + ) + else: + self.add(b"iTXt", key + b"\0\0\0" + lang + b"\0" + tkey + b"\0" + value) + + def add_text( + self, key: str | bytes, value: str | bytes | iTXt, zip: bool = False + ) -> None: + """Appends a text chunk. + + :param key: latin-1 encodable text key name + :param value: value for this key, text or an + :py:class:`PIL.PngImagePlugin.iTXt` instance + :param zip: compression flag + + """ + if isinstance(value, iTXt): + return self.add_itxt( + key, + value, + value.lang if value.lang is not None else b"", + value.tkey if value.tkey is not None else b"", + zip=zip, + ) + + # The tEXt chunk stores latin-1 text + if not isinstance(value, bytes): + try: + value = value.encode("latin-1", "strict") + except UnicodeError: + return self.add_itxt(key, value, zip=zip) + + if not isinstance(key, bytes): + key = key.encode("latin-1", "strict") + + if zip: + self.add(b"zTXt", key + b"\0\0" + zlib.compress(value)) + else: + self.add(b"tEXt", key + b"\0" + value) + + +# -------------------------------------------------------------------- +# PNG image stream (IHDR/IEND) + + +class _RewindState(NamedTuple): + info: dict[str | tuple[int, int], Any] + tile: list[ImageFile._Tile] + seq_num: int | None + + +class PngStream(ChunkStream): + def __init__(self, fp: IO[bytes]) -> None: + super().__init__(fp) + + # local copies of Image attributes + self.im_info: dict[str | tuple[int, int], Any] = {} + self.im_text: dict[str, str | iTXt] = {} + self.im_size = (0, 0) + self.im_mode = "" + self.im_tile: list[ImageFile._Tile] = [] + self.im_palette: tuple[str, bytes] | None = None + self.im_custom_mimetype: str | None = None + self.im_n_frames: int | None = None + self._seq_num: int | None = None + self.rewind_state = _RewindState({}, [], None) + + self.text_memory = 0 + + def check_text_memory(self, chunklen: int) -> None: + self.text_memory += chunklen + if self.text_memory > MAX_TEXT_MEMORY: + msg = ( + "Too much memory used in text chunks: " + f"{self.text_memory}>MAX_TEXT_MEMORY" + ) + raise ValueError(msg) + + def save_rewind(self) -> None: + self.rewind_state = _RewindState( + self.im_info.copy(), + self.im_tile, + self._seq_num, + ) + + def rewind(self) -> None: + self.im_info = self.rewind_state.info.copy() + self.im_tile = self.rewind_state.tile + self._seq_num = self.rewind_state.seq_num + + def chunk_iCCP(self, pos: int, length: int) -> bytes: + # ICC profile + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + # according to PNG spec, the iCCP chunk contains: + # Profile name 1-79 bytes (character string) + # Null separator 1 byte (null character) + # Compression method 1 byte (0) + # Compressed profile n bytes (zlib with deflate compression) + i = s.find(b"\0") + logger.debug("iCCP profile name %r", s[:i]) + comp_method = s[i + 1] + logger.debug("Compression method %s", comp_method) + if comp_method != 0: + msg = f"Unknown compression method {comp_method} in iCCP chunk" + raise SyntaxError(msg) + try: + icc_profile = _safe_zlib_decompress(s[i + 2 :]) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + icc_profile = None + else: + raise + except zlib.error: + icc_profile = None # FIXME + self.im_info["icc_profile"] = icc_profile + return s + + def chunk_IHDR(self, pos: int, length: int) -> bytes: + # image header + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 13: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "Truncated IHDR chunk" + raise ValueError(msg) + self.im_size = i32(s, 0), i32(s, 4) + try: + self.im_mode, self.im_rawmode = _MODES[(s[8], s[9])] + except Exception: + pass + if s[12]: + self.im_info["interlace"] = 1 + if s[11]: + msg = "unknown filter category" + raise SyntaxError(msg) + return s + + def chunk_IDAT(self, pos: int, length: int) -> NoReturn: + # image data + if "bbox" in self.im_info: + tile = [ImageFile._Tile("zip", self.im_info["bbox"], pos, self.im_rawmode)] + else: + if self.im_n_frames is not None: + self.im_info["default_image"] = True + tile = [ImageFile._Tile("zip", (0, 0) + self.im_size, pos, self.im_rawmode)] + self.im_tile = tile + self.im_idat = length + msg = "image data found" + raise EOFError(msg) + + def chunk_IEND(self, pos: int, length: int) -> NoReturn: + msg = "end of PNG image" + raise EOFError(msg) + + def chunk_PLTE(self, pos: int, length: int) -> bytes: + # palette + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if self.im_mode == "P": + self.im_palette = "RGB", s + return s + + def chunk_tRNS(self, pos: int, length: int) -> bytes: + # transparency + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if self.im_mode == "P": + if _simple_palette.match(s): + # tRNS contains only one full-transparent entry, + # other entries are full opaque + i = s.find(b"\0") + if i >= 0: + self.im_info["transparency"] = i + else: + # otherwise, we have a byte string with one alpha value + # for each palette entry + self.im_info["transparency"] = s + elif self.im_mode in ("1", "L", "I;16"): + self.im_info["transparency"] = i16(s) + elif self.im_mode == "RGB": + self.im_info["transparency"] = i16(s), i16(s, 2), i16(s, 4) + return s + + def chunk_gAMA(self, pos: int, length: int) -> bytes: + # gamma setting + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + self.im_info["gamma"] = i32(s) / 100000.0 + return s + + def chunk_cHRM(self, pos: int, length: int) -> bytes: + # chromaticity, 8 unsigned ints, actual value is scaled by 100,000 + # WP x,y, Red x,y, Green x,y Blue x,y + + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + raw_vals = struct.unpack(f">{len(s) // 4}I", s) + self.im_info["chromaticity"] = tuple(elt / 100000.0 for elt in raw_vals) + return s + + def chunk_sRGB(self, pos: int, length: int) -> bytes: + # srgb rendering intent, 1 byte + # 0 perceptual + # 1 relative colorimetric + # 2 saturation + # 3 absolute colorimetric + + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 1: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "Truncated sRGB chunk" + raise ValueError(msg) + self.im_info["srgb"] = s[0] + return s + + def chunk_pHYs(self, pos: int, length: int) -> bytes: + # pixels per unit + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 9: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "Truncated pHYs chunk" + raise ValueError(msg) + px, py = i32(s, 0), i32(s, 4) + unit = s[8] + if unit == 1: # meter + dpi = px * 0.0254, py * 0.0254 + self.im_info["dpi"] = dpi + elif unit == 0: + self.im_info["aspect"] = px, py + return s + + def chunk_tEXt(self, pos: int, length: int) -> bytes: + # text + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + try: + k, v = s.split(b"\0", 1) + except ValueError: + # fallback for broken tEXt tags + k = s + v = b"" + if k: + k_str = k.decode("latin-1", "strict") + v_str = v.decode("latin-1", "replace") + + self.im_info[k_str] = v if k == b"exif" else v_str + self.im_text[k_str] = v_str + self.check_text_memory(len(v_str)) + + return s + + def chunk_zTXt(self, pos: int, length: int) -> bytes: + # compressed text + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + try: + k, v = s.split(b"\0", 1) + except ValueError: + k = s + v = b"" + if v: + comp_method = v[0] + else: + comp_method = 0 + if comp_method != 0: + msg = f"Unknown compression method {comp_method} in zTXt chunk" + raise SyntaxError(msg) + try: + v = _safe_zlib_decompress(v[1:]) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + v = b"" + else: + raise + except zlib.error: + v = b"" + + if k: + k_str = k.decode("latin-1", "strict") + v_str = v.decode("latin-1", "replace") + + self.im_info[k_str] = self.im_text[k_str] = v_str + self.check_text_memory(len(v_str)) + + return s + + def chunk_iTXt(self, pos: int, length: int) -> bytes: + # international text + assert self.fp is not None + r = s = ImageFile._safe_read(self.fp, length) + try: + k, r = r.split(b"\0", 1) + except ValueError: + return s + if len(r) < 2: + return s + cf, cm, r = r[0], r[1], r[2:] + try: + lang, tk, v = r.split(b"\0", 2) + except ValueError: + return s + if cf != 0: + if cm == 0: + try: + v = _safe_zlib_decompress(v) + except ValueError: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + else: + raise + except zlib.error: + return s + else: + return s + if k == b"XML:com.adobe.xmp": + self.im_info["xmp"] = v + try: + k_str = k.decode("latin-1", "strict") + lang_str = lang.decode("utf-8", "strict") + tk_str = tk.decode("utf-8", "strict") + v_str = v.decode("utf-8", "strict") + except UnicodeError: + return s + + self.im_info[k_str] = self.im_text[k_str] = iTXt(v_str, lang_str, tk_str) + self.check_text_memory(len(v_str)) + + return s + + def chunk_eXIf(self, pos: int, length: int) -> bytes: + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + self.im_info["exif"] = b"Exif\x00\x00" + s + return s + + # APNG chunks + def chunk_acTL(self, pos: int, length: int) -> bytes: + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 8: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "APNG contains truncated acTL chunk" + raise ValueError(msg) + if self.im_n_frames is not None: + self.im_n_frames = None + warnings.warn("Invalid APNG, will use default PNG image if possible") + return s + n_frames = i32(s) + if n_frames == 0 or n_frames > 0x80000000: + warnings.warn("Invalid APNG, will use default PNG image if possible") + return s + self.im_n_frames = n_frames + self.im_info["loop"] = i32(s, 4) + self.im_custom_mimetype = "image/apng" + return s + + def chunk_fcTL(self, pos: int, length: int) -> bytes: + assert self.fp is not None + s = ImageFile._safe_read(self.fp, length) + if length < 26: + if ImageFile.LOAD_TRUNCATED_IMAGES: + return s + msg = "APNG contains truncated fcTL chunk" + raise ValueError(msg) + seq = i32(s) + if (self._seq_num is None and seq != 0) or ( + self._seq_num is not None and self._seq_num != seq - 1 + ): + msg = "APNG contains frame sequence errors" + raise SyntaxError(msg) + self._seq_num = seq + width, height = i32(s, 4), i32(s, 8) + px, py = i32(s, 12), i32(s, 16) + im_w, im_h = self.im_size + if px + width > im_w or py + height > im_h: + msg = "APNG contains invalid frames" + raise SyntaxError(msg) + self.im_info["bbox"] = (px, py, px + width, py + height) + delay_num, delay_den = i16(s, 20), i16(s, 22) + if delay_den == 0: + delay_den = 100 + self.im_info["duration"] = float(delay_num) / float(delay_den) * 1000 + self.im_info["disposal"] = s[24] + self.im_info["blend"] = s[25] + return s + + def chunk_fdAT(self, pos: int, length: int) -> bytes: + assert self.fp is not None + if length < 4: + if ImageFile.LOAD_TRUNCATED_IMAGES: + s = ImageFile._safe_read(self.fp, length) + return s + msg = "APNG contains truncated fDAT chunk" + raise ValueError(msg) + s = ImageFile._safe_read(self.fp, 4) + seq = i32(s) + if self._seq_num != seq - 1: + msg = "APNG contains frame sequence errors" + raise SyntaxError(msg) + self._seq_num = seq + return self.chunk_IDAT(pos + 4, length - 4) + + +# -------------------------------------------------------------------- +# PNG reader + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(_MAGIC) + + +## +# Image plugin for PNG images. + + +class PngImageFile(ImageFile.ImageFile): + format = "PNG" + format_description = "Portable network graphics" + + def _open(self) -> None: + if not _accept(self.fp.read(8)): + msg = "not a PNG file" + raise SyntaxError(msg) + self._fp = self.fp + self.__frame = 0 + + # + # Parse headers up to the first IDAT or fDAT chunk + + self.private_chunks: list[tuple[bytes, bytes] | tuple[bytes, bytes, bool]] = [] + self.png: PngStream | None = PngStream(self.fp) + + while True: + # + # get next chunk + + cid, pos, length = self.png.read() + + try: + s = self.png.call(cid, pos, length) + except EOFError: + break + except AttributeError: + logger.debug("%r %s %s (unknown)", cid, pos, length) + s = ImageFile._safe_read(self.fp, length) + if cid[1:2].islower(): + self.private_chunks.append((cid, s)) + + self.png.crc(cid, s) + + # + # Copy relevant attributes from the PngStream. An alternative + # would be to let the PngStream class modify these attributes + # directly, but that introduces circular references which are + # difficult to break if things go wrong in the decoder... + # (believe me, I've tried ;-) + + self._mode = self.png.im_mode + self._size = self.png.im_size + self.info = self.png.im_info + self._text: dict[str, str | iTXt] | None = None + self.tile = self.png.im_tile + self.custom_mimetype = self.png.im_custom_mimetype + self.n_frames = self.png.im_n_frames or 1 + self.default_image = self.info.get("default_image", False) + + if self.png.im_palette: + rawmode, data = self.png.im_palette + self.palette = ImagePalette.raw(rawmode, data) + + if cid == b"fdAT": + self.__prepare_idat = length - 4 + else: + self.__prepare_idat = length # used by load_prepare() + + if self.png.im_n_frames is not None: + self._close_exclusive_fp_after_loading = False + self.png.save_rewind() + self.__rewind_idat = self.__prepare_idat + self.__rewind = self._fp.tell() + if self.default_image: + # IDAT chunk contains default image and not first animation frame + self.n_frames += 1 + self._seek(0) + self.is_animated = self.n_frames > 1 + + @property + def text(self) -> dict[str, str | iTXt]: + # experimental + if self._text is None: + # iTxt, tEXt and zTXt chunks may appear at the end of the file + # So load the file to ensure that they are read + if self.is_animated: + frame = self.__frame + # for APNG, seek to the final frame before loading + self.seek(self.n_frames - 1) + self.load() + if self.is_animated: + self.seek(frame) + assert self._text is not None + return self._text + + def verify(self) -> None: + """Verify PNG file""" + + if self.fp is None: + msg = "verify must be called directly after open" + raise RuntimeError(msg) + + # back up to beginning of IDAT block + self.fp.seek(self.tile[0][2] - 8) + + assert self.png is not None + self.png.verify() + self.png.close() + + if self._exclusive_fp: + self.fp.close() + self.fp = None + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + if frame < self.__frame: + self._seek(0, True) + + last_frame = self.__frame + for f in range(self.__frame + 1, frame + 1): + try: + self._seek(f) + except EOFError as e: + self.seek(last_frame) + msg = "no more images in APNG file" + raise EOFError(msg) from e + + def _seek(self, frame: int, rewind: bool = False) -> None: + assert self.png is not None + if isinstance(self._fp, DeferredError): + raise self._fp.ex + + self.dispose: _imaging.ImagingCore | None + dispose_extent = None + if frame == 0: + if rewind: + self._fp.seek(self.__rewind) + self.png.rewind() + self.__prepare_idat = self.__rewind_idat + self._im = None + self.info = self.png.im_info + self.tile = self.png.im_tile + self.fp = self._fp + self._prev_im = None + self.dispose = None + self.default_image = self.info.get("default_image", False) + self.dispose_op = self.info.get("disposal") + self.blend_op = self.info.get("blend") + dispose_extent = self.info.get("bbox") + self.__frame = 0 + else: + if frame != self.__frame + 1: + msg = f"cannot seek to frame {frame}" + raise ValueError(msg) + + # ensure previous frame was loaded + self.load() + + if self.dispose: + self.im.paste(self.dispose, self.dispose_extent) + self._prev_im = self.im.copy() + + self.fp = self._fp + + # advance to the next frame + if self.__prepare_idat: + ImageFile._safe_read(self.fp, self.__prepare_idat) + self.__prepare_idat = 0 + frame_start = False + while True: + self.fp.read(4) # CRC + + try: + cid, pos, length = self.png.read() + except (struct.error, SyntaxError): + break + + if cid == b"IEND": + msg = "No more images in APNG file" + raise EOFError(msg) + if cid == b"fcTL": + if frame_start: + # there must be at least one fdAT chunk between fcTL chunks + msg = "APNG missing frame data" + raise SyntaxError(msg) + frame_start = True + + try: + self.png.call(cid, pos, length) + except UnicodeDecodeError: + break + except EOFError: + if cid == b"fdAT": + length -= 4 + if frame_start: + self.__prepare_idat = length + break + ImageFile._safe_read(self.fp, length) + except AttributeError: + logger.debug("%r %s %s (unknown)", cid, pos, length) + ImageFile._safe_read(self.fp, length) + + self.__frame = frame + self.tile = self.png.im_tile + self.dispose_op = self.info.get("disposal") + self.blend_op = self.info.get("blend") + dispose_extent = self.info.get("bbox") + + if not self.tile: + msg = "image not found in APNG frame" + raise EOFError(msg) + if dispose_extent: + self.dispose_extent: tuple[float, float, float, float] = dispose_extent + + # setup frame disposal (actual disposal done when needed in the next _seek()) + if self._prev_im is None and self.dispose_op == Disposal.OP_PREVIOUS: + self.dispose_op = Disposal.OP_BACKGROUND + + self.dispose = None + if self.dispose_op == Disposal.OP_PREVIOUS: + if self._prev_im: + self.dispose = self._prev_im.copy() + self.dispose = self._crop(self.dispose, self.dispose_extent) + elif self.dispose_op == Disposal.OP_BACKGROUND: + self.dispose = Image.core.fill(self.mode, self.size) + self.dispose = self._crop(self.dispose, self.dispose_extent) + + def tell(self) -> int: + return self.__frame + + def load_prepare(self) -> None: + """internal: prepare to read PNG file""" + + if self.info.get("interlace"): + self.decoderconfig = self.decoderconfig + (1,) + + self.__idat = self.__prepare_idat # used by load_read() + ImageFile.ImageFile.load_prepare(self) + + def load_read(self, read_bytes: int) -> bytes: + """internal: read more image data""" + + assert self.png is not None + while self.__idat == 0: + # end of chunk, skip forward to next one + + self.fp.read(4) # CRC + + cid, pos, length = self.png.read() + + if cid not in [b"IDAT", b"DDAT", b"fdAT"]: + self.png.push(cid, pos, length) + return b"" + + if cid == b"fdAT": + try: + self.png.call(cid, pos, length) + except EOFError: + pass + self.__idat = length - 4 # sequence_num has already been read + else: + self.__idat = length # empty chunks are allowed + + # read more data from this chunk + if read_bytes <= 0: + read_bytes = self.__idat + else: + read_bytes = min(read_bytes, self.__idat) + + self.__idat = self.__idat - read_bytes + + return self.fp.read(read_bytes) + + def load_end(self) -> None: + """internal: finished reading image data""" + assert self.png is not None + if self.__idat != 0: + self.fp.read(self.__idat) + while True: + self.fp.read(4) # CRC + + try: + cid, pos, length = self.png.read() + except (struct.error, SyntaxError): + break + + if cid == b"IEND": + break + elif cid == b"fcTL" and self.is_animated: + # start of the next frame, stop reading + self.__prepare_idat = 0 + self.png.push(cid, pos, length) + break + + try: + self.png.call(cid, pos, length) + except UnicodeDecodeError: + break + except EOFError: + if cid == b"fdAT": + length -= 4 + try: + ImageFile._safe_read(self.fp, length) + except OSError as e: + if ImageFile.LOAD_TRUNCATED_IMAGES: + break + else: + raise e + except AttributeError: + logger.debug("%r %s %s (unknown)", cid, pos, length) + s = ImageFile._safe_read(self.fp, length) + if cid[1:2].islower(): + self.private_chunks.append((cid, s, True)) + self._text = self.png.im_text + if not self.is_animated: + self.png.close() + self.png = None + else: + if self._prev_im and self.blend_op == Blend.OP_OVER: + updated = self._crop(self.im, self.dispose_extent) + if self.im.mode == "RGB" and "transparency" in self.info: + mask = updated.convert_transparent( + "RGBA", self.info["transparency"] + ) + else: + if self.im.mode == "P" and "transparency" in self.info: + t = self.info["transparency"] + if isinstance(t, bytes): + updated.putpalettealphas(t) + elif isinstance(t, int): + updated.putpalettealpha(t) + mask = updated.convert("RGBA") + self._prev_im.paste(updated, self.dispose_extent, mask) + self.im = self._prev_im + + def _getexif(self) -> dict[int, Any] | None: + if "exif" not in self.info: + self.load() + if "exif" not in self.info and "Raw profile type exif" not in self.info: + return None + return self.getexif()._get_merged_dict() + + def getexif(self) -> Image.Exif: + if "exif" not in self.info: + self.load() + + return super().getexif() + + +# -------------------------------------------------------------------- +# PNG writer + +_OUTMODES = { + # supported PIL modes, and corresponding rawmode, bit depth and color type + "1": ("1", b"\x01", b"\x00"), + "L;1": ("L;1", b"\x01", b"\x00"), + "L;2": ("L;2", b"\x02", b"\x00"), + "L;4": ("L;4", b"\x04", b"\x00"), + "L": ("L", b"\x08", b"\x00"), + "LA": ("LA", b"\x08", b"\x04"), + "I": ("I;16B", b"\x10", b"\x00"), + "I;16": ("I;16B", b"\x10", b"\x00"), + "I;16B": ("I;16B", b"\x10", b"\x00"), + "P;1": ("P;1", b"\x01", b"\x03"), + "P;2": ("P;2", b"\x02", b"\x03"), + "P;4": ("P;4", b"\x04", b"\x03"), + "P": ("P", b"\x08", b"\x03"), + "RGB": ("RGB", b"\x08", b"\x02"), + "RGBA": ("RGBA", b"\x08", b"\x06"), +} + + +def putchunk(fp: IO[bytes], cid: bytes, *data: bytes) -> None: + """Write a PNG chunk (including CRC field)""" + + byte_data = b"".join(data) + + fp.write(o32(len(byte_data)) + cid) + fp.write(byte_data) + crc = _crc32(byte_data, _crc32(cid)) + fp.write(o32(crc)) + + +class _idat: + # wrap output from the encoder in IDAT chunks + + def __init__(self, fp: IO[bytes], chunk: Callable[..., None]) -> None: + self.fp = fp + self.chunk = chunk + + def write(self, data: bytes) -> None: + self.chunk(self.fp, b"IDAT", data) + + +class _fdat: + # wrap encoder output in fdAT chunks + + def __init__(self, fp: IO[bytes], chunk: Callable[..., None], seq_num: int) -> None: + self.fp = fp + self.chunk = chunk + self.seq_num = seq_num + + def write(self, data: bytes) -> None: + self.chunk(self.fp, b"fdAT", o32(self.seq_num), data) + self.seq_num += 1 + + +class _Frame(NamedTuple): + im: Image.Image + bbox: tuple[int, int, int, int] | None + encoderinfo: dict[str, Any] + + +def _write_multiple_frames( + im: Image.Image, + fp: IO[bytes], + chunk: Callable[..., None], + mode: str, + rawmode: str, + default_image: Image.Image | None, + append_images: list[Image.Image], +) -> Image.Image | None: + duration = im.encoderinfo.get("duration") + loop = im.encoderinfo.get("loop", im.info.get("loop", 0)) + disposal = im.encoderinfo.get("disposal", im.info.get("disposal", Disposal.OP_NONE)) + blend = im.encoderinfo.get("blend", im.info.get("blend", Blend.OP_SOURCE)) + + if default_image: + chain = itertools.chain(append_images) + else: + chain = itertools.chain([im], append_images) + + im_frames: list[_Frame] = [] + frame_count = 0 + for im_seq in chain: + for im_frame in ImageSequence.Iterator(im_seq): + if im_frame.mode == mode: + im_frame = im_frame.copy() + else: + im_frame = im_frame.convert(mode) + encoderinfo = im.encoderinfo.copy() + if isinstance(duration, (list, tuple)): + encoderinfo["duration"] = duration[frame_count] + elif duration is None and "duration" in im_frame.info: + encoderinfo["duration"] = im_frame.info["duration"] + if isinstance(disposal, (list, tuple)): + encoderinfo["disposal"] = disposal[frame_count] + if isinstance(blend, (list, tuple)): + encoderinfo["blend"] = blend[frame_count] + frame_count += 1 + + if im_frames: + previous = im_frames[-1] + prev_disposal = previous.encoderinfo.get("disposal") + prev_blend = previous.encoderinfo.get("blend") + if prev_disposal == Disposal.OP_PREVIOUS and len(im_frames) < 2: + prev_disposal = Disposal.OP_BACKGROUND + + if prev_disposal == Disposal.OP_BACKGROUND: + base_im = previous.im.copy() + dispose = Image.core.fill("RGBA", im.size, (0, 0, 0, 0)) + bbox = previous.bbox + if bbox: + dispose = dispose.crop(bbox) + else: + bbox = (0, 0) + im.size + base_im.paste(dispose, bbox) + elif prev_disposal == Disposal.OP_PREVIOUS: + base_im = im_frames[-2].im + else: + base_im = previous.im + delta = ImageChops.subtract_modulo( + im_frame.convert("RGBA"), base_im.convert("RGBA") + ) + bbox = delta.getbbox(alpha_only=False) + if ( + not bbox + and prev_disposal == encoderinfo.get("disposal") + and prev_blend == encoderinfo.get("blend") + and "duration" in encoderinfo + ): + previous.encoderinfo["duration"] += encoderinfo["duration"] + continue + else: + bbox = None + im_frames.append(_Frame(im_frame, bbox, encoderinfo)) + + if len(im_frames) == 1 and not default_image: + return im_frames[0].im + + # animation control + chunk( + fp, + b"acTL", + o32(len(im_frames)), # 0: num_frames + o32(loop), # 4: num_plays + ) + + # default image IDAT (if it exists) + if default_image: + if im.mode != mode: + im = im.convert(mode) + ImageFile._save( + im, + cast(IO[bytes], _idat(fp, chunk)), + [ImageFile._Tile("zip", (0, 0) + im.size, 0, rawmode)], + ) + + seq_num = 0 + for frame, frame_data in enumerate(im_frames): + im_frame = frame_data.im + if not frame_data.bbox: + bbox = (0, 0) + im_frame.size + else: + bbox = frame_data.bbox + im_frame = im_frame.crop(bbox) + size = im_frame.size + encoderinfo = frame_data.encoderinfo + frame_duration = int(round(encoderinfo.get("duration", 0))) + frame_disposal = encoderinfo.get("disposal", disposal) + frame_blend = encoderinfo.get("blend", blend) + # frame control + chunk( + fp, + b"fcTL", + o32(seq_num), # sequence_number + o32(size[0]), # width + o32(size[1]), # height + o32(bbox[0]), # x_offset + o32(bbox[1]), # y_offset + o16(frame_duration), # delay_numerator + o16(1000), # delay_denominator + o8(frame_disposal), # dispose_op + o8(frame_blend), # blend_op + ) + seq_num += 1 + # frame data + if frame == 0 and not default_image: + # first frame must be in IDAT chunks for backwards compatibility + ImageFile._save( + im_frame, + cast(IO[bytes], _idat(fp, chunk)), + [ImageFile._Tile("zip", (0, 0) + im_frame.size, 0, rawmode)], + ) + else: + fdat_chunks = _fdat(fp, chunk, seq_num) + ImageFile._save( + im_frame, + cast(IO[bytes], fdat_chunks), + [ImageFile._Tile("zip", (0, 0) + im_frame.size, 0, rawmode)], + ) + seq_num = fdat_chunks.seq_num + return None + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + _save(im, fp, filename, save_all=True) + + +def _save( + im: Image.Image, + fp: IO[bytes], + filename: str | bytes, + chunk: Callable[..., None] = putchunk, + save_all: bool = False, +) -> None: + # save an image to disk (called by the save method) + + if save_all: + default_image = im.encoderinfo.get( + "default_image", im.info.get("default_image") + ) + modes = set() + sizes = set() + append_images = im.encoderinfo.get("append_images", []) + for im_seq in itertools.chain([im], append_images): + for im_frame in ImageSequence.Iterator(im_seq): + modes.add(im_frame.mode) + sizes.add(im_frame.size) + for mode in ("RGBA", "RGB", "P"): + if mode in modes: + break + else: + mode = modes.pop() + size = tuple(max(frame_size[i] for frame_size in sizes) for i in range(2)) + else: + size = im.size + mode = im.mode + + outmode = mode + if mode == "P": + # + # attempt to minimize storage requirements for palette images + if "bits" in im.encoderinfo: + # number of bits specified by user + colors = min(1 << im.encoderinfo["bits"], 256) + else: + # check palette contents + if im.palette: + colors = max(min(len(im.palette.getdata()[1]) // 3, 256), 1) + else: + colors = 256 + + if colors <= 16: + if colors <= 2: + bits = 1 + elif colors <= 4: + bits = 2 + else: + bits = 4 + outmode += f";{bits}" + + # encoder options + im.encoderconfig = ( + im.encoderinfo.get("optimize", False), + im.encoderinfo.get("compress_level", -1), + im.encoderinfo.get("compress_type", -1), + im.encoderinfo.get("dictionary", b""), + ) + + # get the corresponding PNG mode + try: + rawmode, bit_depth, color_type = _OUTMODES[outmode] + except KeyError as e: + msg = f"cannot write mode {mode} as PNG" + raise OSError(msg) from e + if outmode == "I": + deprecate("Saving I mode images as PNG", 13, stacklevel=4) + + # + # write minimal PNG file + + fp.write(_MAGIC) + + chunk( + fp, + b"IHDR", + o32(size[0]), # 0: size + o32(size[1]), + bit_depth, + color_type, + b"\0", # 10: compression + b"\0", # 11: filter category + b"\0", # 12: interlace flag + ) + + chunks = [b"cHRM", b"cICP", b"gAMA", b"sBIT", b"sRGB", b"tIME"] + + icc = im.encoderinfo.get("icc_profile", im.info.get("icc_profile")) + if icc: + # ICC profile + # according to PNG spec, the iCCP chunk contains: + # Profile name 1-79 bytes (character string) + # Null separator 1 byte (null character) + # Compression method 1 byte (0) + # Compressed profile n bytes (zlib with deflate compression) + name = b"ICC Profile" + data = name + b"\0\0" + zlib.compress(icc) + chunk(fp, b"iCCP", data) + + # You must either have sRGB or iCCP. + # Disallow sRGB chunks when an iCCP-chunk has been emitted. + chunks.remove(b"sRGB") + + info = im.encoderinfo.get("pnginfo") + if info: + chunks_multiple_allowed = [b"sPLT", b"iTXt", b"tEXt", b"zTXt"] + for info_chunk in info.chunks: + cid, data = info_chunk[:2] + if cid in chunks: + chunks.remove(cid) + chunk(fp, cid, data) + elif cid in chunks_multiple_allowed: + chunk(fp, cid, data) + elif cid[1:2].islower(): + # Private chunk + after_idat = len(info_chunk) == 3 and info_chunk[2] + if not after_idat: + chunk(fp, cid, data) + + if im.mode == "P": + palette_byte_number = colors * 3 + palette_bytes = im.im.getpalette("RGB")[:palette_byte_number] + while len(palette_bytes) < palette_byte_number: + palette_bytes += b"\0" + chunk(fp, b"PLTE", palette_bytes) + + transparency = im.encoderinfo.get("transparency", im.info.get("transparency", None)) + + if transparency or transparency == 0: + if im.mode == "P": + # limit to actual palette size + alpha_bytes = colors + if isinstance(transparency, bytes): + chunk(fp, b"tRNS", transparency[:alpha_bytes]) + else: + transparency = max(0, min(255, transparency)) + alpha = b"\xff" * transparency + b"\0" + chunk(fp, b"tRNS", alpha[:alpha_bytes]) + elif im.mode in ("1", "L", "I", "I;16"): + transparency = max(0, min(65535, transparency)) + chunk(fp, b"tRNS", o16(transparency)) + elif im.mode == "RGB": + red, green, blue = transparency + chunk(fp, b"tRNS", o16(red) + o16(green) + o16(blue)) + else: + if "transparency" in im.encoderinfo: + # don't bother with transparency if it's an RGBA + # and it's in the info dict. It's probably just stale. + msg = "cannot use transparency for this mode" + raise OSError(msg) + else: + if im.mode == "P" and im.im.getpalettemode() == "RGBA": + alpha = im.im.getpalette("RGBA", "A") + alpha_bytes = colors + chunk(fp, b"tRNS", alpha[:alpha_bytes]) + + dpi = im.encoderinfo.get("dpi") + if dpi: + chunk( + fp, + b"pHYs", + o32(int(dpi[0] / 0.0254 + 0.5)), + o32(int(dpi[1] / 0.0254 + 0.5)), + b"\x01", + ) + + if info: + chunks = [b"bKGD", b"hIST"] + for info_chunk in info.chunks: + cid, data = info_chunk[:2] + if cid in chunks: + chunks.remove(cid) + chunk(fp, cid, data) + + exif = im.encoderinfo.get("exif") + if exif: + if isinstance(exif, Image.Exif): + exif = exif.tobytes(8) + if exif.startswith(b"Exif\x00\x00"): + exif = exif[6:] + chunk(fp, b"eXIf", exif) + + single_im: Image.Image | None = im + if save_all: + single_im = _write_multiple_frames( + im, fp, chunk, mode, rawmode, default_image, append_images + ) + if single_im: + ImageFile._save( + single_im, + cast(IO[bytes], _idat(fp, chunk)), + [ImageFile._Tile("zip", (0, 0) + single_im.size, 0, rawmode)], + ) + + if info: + for info_chunk in info.chunks: + cid, data = info_chunk[:2] + if cid[1:2].islower(): + # Private chunk + after_idat = len(info_chunk) == 3 and info_chunk[2] + if after_idat: + chunk(fp, cid, data) + + chunk(fp, b"IEND", b"") + + if hasattr(fp, "flush"): + fp.flush() + + +# -------------------------------------------------------------------- +# PNG chunk converter + + +def getchunks(im: Image.Image, **params: Any) -> list[tuple[bytes, bytes, bytes]]: + """Return a list of PNG chunks representing this image.""" + from io import BytesIO + + chunks = [] + + def append(fp: IO[bytes], cid: bytes, *data: bytes) -> None: + byte_data = b"".join(data) + crc = o32(_crc32(byte_data, _crc32(cid))) + chunks.append((cid, byte_data, crc)) + + fp = BytesIO() + + try: + im.encoderinfo = params + _save(im, fp, "", append) + finally: + del im.encoderinfo + + return chunks + + +# -------------------------------------------------------------------- +# Registry + +Image.register_open(PngImageFile.format, PngImageFile, _accept) +Image.register_save(PngImageFile.format, _save) +Image.register_save_all(PngImageFile.format, _save_all) + +Image.register_extensions(PngImageFile.format, [".png", ".apng"]) + +Image.register_mime(PngImageFile.format, "image/png") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PpmImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PpmImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..db34d107a4f4a9a7a27fe332e211133d88870fce --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PpmImagePlugin.py @@ -0,0 +1,375 @@ +# +# The Python Imaging Library. +# $Id$ +# +# PPM support for PIL +# +# History: +# 96-03-24 fl Created +# 98-03-06 fl Write RGBA images (as RGB, that is) +# +# Copyright (c) Secret Labs AB 1997-98. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import math +from typing import IO + +from . import Image, ImageFile +from ._binary import i16be as i16 +from ._binary import o8 +from ._binary import o32le as o32 + +# +# -------------------------------------------------------------------- + +b_whitespace = b"\x20\x09\x0a\x0b\x0c\x0d" + +MODES = { + # standard + b"P1": "1", + b"P2": "L", + b"P3": "RGB", + b"P4": "1", + b"P5": "L", + b"P6": "RGB", + # extensions + b"P0CMYK": "CMYK", + b"Pf": "F", + # PIL extensions (for test purposes only) + b"PyP": "P", + b"PyRGBA": "RGBA", + b"PyCMYK": "CMYK", +} + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"P") and prefix[1] in b"0123456fy" + + +## +# Image plugin for PBM, PGM, and PPM images. + + +class PpmImageFile(ImageFile.ImageFile): + format = "PPM" + format_description = "Pbmplus image" + + def _read_magic(self) -> bytes: + assert self.fp is not None + + magic = b"" + # read until whitespace or longest available magic number + for _ in range(6): + c = self.fp.read(1) + if not c or c in b_whitespace: + break + magic += c + return magic + + def _read_token(self) -> bytes: + assert self.fp is not None + + token = b"" + while len(token) <= 10: # read until next whitespace or limit of 10 characters + c = self.fp.read(1) + if not c: + break + elif c in b_whitespace: # token ended + if not token: + # skip whitespace at start + continue + break + elif c == b"#": + # ignores rest of the line; stops at CR, LF or EOF + while self.fp.read(1) not in b"\r\n": + pass + continue + token += c + if not token: + # Token was not even 1 byte + msg = "Reached EOF while reading header" + raise ValueError(msg) + elif len(token) > 10: + msg_too_long = b"Token too long in file header: %s" % token + raise ValueError(msg_too_long) + return token + + def _open(self) -> None: + assert self.fp is not None + + magic_number = self._read_magic() + try: + mode = MODES[magic_number] + except KeyError: + msg = "not a PPM file" + raise SyntaxError(msg) + self._mode = mode + + if magic_number in (b"P1", b"P4"): + self.custom_mimetype = "image/x-portable-bitmap" + elif magic_number in (b"P2", b"P5"): + self.custom_mimetype = "image/x-portable-graymap" + elif magic_number in (b"P3", b"P6"): + self.custom_mimetype = "image/x-portable-pixmap" + + self._size = int(self._read_token()), int(self._read_token()) + + decoder_name = "raw" + if magic_number in (b"P1", b"P2", b"P3"): + decoder_name = "ppm_plain" + + args: str | tuple[str | int, ...] + if mode == "1": + args = "1;I" + elif mode == "F": + scale = float(self._read_token()) + if scale == 0.0 or not math.isfinite(scale): + msg = "scale must be finite and non-zero" + raise ValueError(msg) + self.info["scale"] = abs(scale) + + rawmode = "F;32F" if scale < 0 else "F;32BF" + args = (rawmode, 0, -1) + else: + maxval = int(self._read_token()) + if not 0 < maxval < 65536: + msg = "maxval must be greater than 0 and less than 65536" + raise ValueError(msg) + if maxval > 255 and mode == "L": + self._mode = "I" + + rawmode = mode + if decoder_name != "ppm_plain": + # If maxval matches a bit depth, use the raw decoder directly + if maxval == 65535 and mode == "L": + rawmode = "I;16B" + elif maxval != 255: + decoder_name = "ppm" + + args = rawmode if decoder_name == "raw" else (rawmode, maxval) + self.tile = [ + ImageFile._Tile(decoder_name, (0, 0) + self.size, self.fp.tell(), args) + ] + + +# +# -------------------------------------------------------------------- + + +class PpmPlainDecoder(ImageFile.PyDecoder): + _pulls_fd = True + _comment_spans: bool + + def _read_block(self) -> bytes: + assert self.fd is not None + + return self.fd.read(ImageFile.SAFEBLOCK) + + def _find_comment_end(self, block: bytes, start: int = 0) -> int: + a = block.find(b"\n", start) + b = block.find(b"\r", start) + return min(a, b) if a * b > 0 else max(a, b) # lowest nonnegative index (or -1) + + def _ignore_comments(self, block: bytes) -> bytes: + if self._comment_spans: + # Finish current comment + while block: + comment_end = self._find_comment_end(block) + if comment_end != -1: + # Comment ends in this block + # Delete tail of comment + block = block[comment_end + 1 :] + break + else: + # Comment spans whole block + # So read the next block, looking for the end + block = self._read_block() + + # Search for any further comments + self._comment_spans = False + while True: + comment_start = block.find(b"#") + if comment_start == -1: + # No comment found + break + comment_end = self._find_comment_end(block, comment_start) + if comment_end != -1: + # Comment ends in this block + # Delete comment + block = block[:comment_start] + block[comment_end + 1 :] + else: + # Comment continues to next block(s) + block = block[:comment_start] + self._comment_spans = True + break + return block + + def _decode_bitonal(self) -> bytearray: + """ + This is a separate method because in the plain PBM format, all data tokens are + exactly one byte, so the inter-token whitespace is optional. + """ + data = bytearray() + total_bytes = self.state.xsize * self.state.ysize + + while len(data) != total_bytes: + block = self._read_block() # read next block + if not block: + # eof + break + + block = self._ignore_comments(block) + + tokens = b"".join(block.split()) + for token in tokens: + if token not in (48, 49): + msg = b"Invalid token for this mode: %s" % bytes([token]) + raise ValueError(msg) + data = (data + tokens)[:total_bytes] + invert = bytes.maketrans(b"01", b"\xff\x00") + return data.translate(invert) + + def _decode_blocks(self, maxval: int) -> bytearray: + data = bytearray() + max_len = 10 + out_byte_count = 4 if self.mode == "I" else 1 + out_max = 65535 if self.mode == "I" else 255 + bands = Image.getmodebands(self.mode) + total_bytes = self.state.xsize * self.state.ysize * bands * out_byte_count + + half_token = b"" + while len(data) != total_bytes: + block = self._read_block() # read next block + if not block: + if half_token: + block = bytearray(b" ") # flush half_token + else: + # eof + break + + block = self._ignore_comments(block) + + if half_token: + block = half_token + block # stitch half_token to new block + half_token = b"" + + tokens = block.split() + + if block and not block[-1:].isspace(): # block might split token + half_token = tokens.pop() # save half token for later + if len(half_token) > max_len: # prevent buildup of half_token + msg = ( + b"Token too long found in data: %s" % half_token[: max_len + 1] + ) + raise ValueError(msg) + + for token in tokens: + if len(token) > max_len: + msg = b"Token too long found in data: %s" % token[: max_len + 1] + raise ValueError(msg) + value = int(token) + if value < 0: + msg_str = f"Channel value is negative: {value}" + raise ValueError(msg_str) + if value > maxval: + msg_str = f"Channel value too large for this mode: {value}" + raise ValueError(msg_str) + value = round(value / maxval * out_max) + data += o32(value) if self.mode == "I" else o8(value) + if len(data) == total_bytes: # finished! + break + return data + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + self._comment_spans = False + if self.mode == "1": + data = self._decode_bitonal() + rawmode = "1;8" + else: + maxval = self.args[-1] + data = self._decode_blocks(maxval) + rawmode = "I;32" if self.mode == "I" else self.mode + self.set_as_raw(bytes(data), rawmode) + return -1, 0 + + +class PpmDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + + data = bytearray() + maxval = self.args[-1] + in_byte_count = 1 if maxval < 256 else 2 + out_byte_count = 4 if self.mode == "I" else 1 + out_max = 65535 if self.mode == "I" else 255 + bands = Image.getmodebands(self.mode) + dest_length = self.state.xsize * self.state.ysize * bands * out_byte_count + while len(data) < dest_length: + pixels = self.fd.read(in_byte_count * bands) + if len(pixels) < in_byte_count * bands: + # eof + break + for b in range(bands): + value = ( + pixels[b] if in_byte_count == 1 else i16(pixels, b * in_byte_count) + ) + value = min(out_max, round(value / maxval * out_max)) + data += o32(value) if self.mode == "I" else o8(value) + rawmode = "I;32" if self.mode == "I" else self.mode + self.set_as_raw(bytes(data), rawmode) + return -1, 0 + + +# +# -------------------------------------------------------------------- + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode == "1": + rawmode, head = "1;I", b"P4" + elif im.mode == "L": + rawmode, head = "L", b"P5" + elif im.mode in ("I", "I;16"): + rawmode, head = "I;16B", b"P5" + elif im.mode in ("RGB", "RGBA"): + rawmode, head = "RGB", b"P6" + elif im.mode == "F": + rawmode, head = "F;32F", b"Pf" + else: + msg = f"cannot write mode {im.mode} as PPM" + raise OSError(msg) + fp.write(head + b"\n%d %d\n" % im.size) + if head == b"P6": + fp.write(b"255\n") + elif head == b"P5": + if rawmode == "L": + fp.write(b"255\n") + else: + fp.write(b"65535\n") + elif head == b"Pf": + fp.write(b"-1.0\n") + row_order = -1 if im.mode == "F" else 1 + ImageFile._save( + im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, 0, row_order))] + ) + + +# +# -------------------------------------------------------------------- + + +Image.register_open(PpmImageFile.format, PpmImageFile, _accept) +Image.register_save(PpmImageFile.format, _save) + +Image.register_decoder("ppm", PpmDecoder) +Image.register_decoder("ppm_plain", PpmPlainDecoder) + +Image.register_extensions(PpmImageFile.format, [".pbm", ".pgm", ".ppm", ".pnm", ".pfm"]) + +Image.register_mime(PpmImageFile.format, "image/x-portable-anymap") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PsdImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PsdImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..f49aaeeb1f55cd2b6b17d9aa10f68876384fd410 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/PsdImagePlugin.py @@ -0,0 +1,333 @@ +# +# The Python Imaging Library +# $Id$ +# +# Adobe PSD 2.5/3.0 file handling +# +# History: +# 1995-09-01 fl Created +# 1997-01-03 fl Read most PSD images +# 1997-01-18 fl Fixed P and CMYK support +# 2001-10-21 fl Added seek/tell support (for layers) +# +# Copyright (c) 1997-2001 by Secret Labs AB. +# Copyright (c) 1995-2001 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +from functools import cached_property +from typing import IO + +from . import Image, ImageFile, ImagePalette +from ._binary import i8 +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._binary import si16be as si16 +from ._binary import si32be as si32 +from ._util import DeferredError + +MODES = { + # (photoshop mode, bits) -> (pil mode, required channels) + (0, 1): ("1", 1), + (0, 8): ("L", 1), + (1, 8): ("L", 1), + (2, 8): ("P", 1), + (3, 8): ("RGB", 3), + (4, 8): ("CMYK", 4), + (7, 8): ("L", 1), # FIXME: multilayer + (8, 8): ("L", 1), # duotone + (9, 8): ("LAB", 3), +} + + +# --------------------------------------------------------------------. +# read PSD images + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"8BPS") + + +## +# Image plugin for Photoshop images. + + +class PsdImageFile(ImageFile.ImageFile): + format = "PSD" + format_description = "Adobe Photoshop" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + read = self.fp.read + + # + # header + + s = read(26) + if not _accept(s) or i16(s, 4) != 1: + msg = "not a PSD file" + raise SyntaxError(msg) + + psd_bits = i16(s, 22) + psd_channels = i16(s, 12) + psd_mode = i16(s, 24) + + mode, channels = MODES[(psd_mode, psd_bits)] + + if channels > psd_channels: + msg = "not enough channels" + raise OSError(msg) + if mode == "RGB" and psd_channels == 4: + mode = "RGBA" + channels = 4 + + self._mode = mode + self._size = i32(s, 18), i32(s, 14) + + # + # color mode data + + size = i32(read(4)) + if size: + data = read(size) + if mode == "P" and size == 768: + self.palette = ImagePalette.raw("RGB;L", data) + + # + # image resources + + self.resources = [] + + size = i32(read(4)) + if size: + # load resources + end = self.fp.tell() + size + while self.fp.tell() < end: + read(4) # signature + id = i16(read(2)) + name = read(i8(read(1))) + if not (len(name) & 1): + read(1) # padding + data = read(i32(read(4))) + if len(data) & 1: + read(1) # padding + self.resources.append((id, name, data)) + if id == 1039: # ICC profile + self.info["icc_profile"] = data + + # + # layer and mask information + + self._layers_position = None + + size = i32(read(4)) + if size: + end = self.fp.tell() + size + size = i32(read(4)) + if size: + self._layers_position = self.fp.tell() + self._layers_size = size + self.fp.seek(end) + self._n_frames: int | None = None + + # + # image descriptor + + self.tile = _maketile(self.fp, mode, (0, 0) + self.size, channels) + + # keep the file open + self._fp = self.fp + self.frame = 1 + self._min_frame = 1 + + @cached_property + def layers( + self, + ) -> list[tuple[str, str, tuple[int, int, int, int], list[ImageFile._Tile]]]: + layers = [] + if self._layers_position is not None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self._fp.seek(self._layers_position) + _layer_data = io.BytesIO(ImageFile._safe_read(self._fp, self._layers_size)) + layers = _layerinfo(_layer_data, self._layers_size) + self._n_frames = len(layers) + return layers + + @property + def n_frames(self) -> int: + if self._n_frames is None: + self._n_frames = len(self.layers) + return self._n_frames + + @property + def is_animated(self) -> bool: + return len(self.layers) > 1 + + def seek(self, layer: int) -> None: + if not self._seek_check(layer): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + + # seek to given layer (1..max) + _, mode, _, tile = self.layers[layer - 1] + self._mode = mode + self.tile = tile + self.frame = layer + self.fp = self._fp + + def tell(self) -> int: + # return layer number (0=image, 1..max=layers) + return self.frame + + +def _layerinfo( + fp: IO[bytes], ct_bytes: int +) -> list[tuple[str, str, tuple[int, int, int, int], list[ImageFile._Tile]]]: + # read layerinfo block + layers = [] + + def read(size: int) -> bytes: + return ImageFile._safe_read(fp, size) + + ct = si16(read(2)) + + # sanity check + if ct_bytes < (abs(ct) * 20): + msg = "Layer block too short for number of layers requested" + raise SyntaxError(msg) + + for _ in range(abs(ct)): + # bounding box + y0 = si32(read(4)) + x0 = si32(read(4)) + y1 = si32(read(4)) + x1 = si32(read(4)) + + # image info + bands = [] + ct_types = i16(read(2)) + if ct_types > 4: + fp.seek(ct_types * 6 + 12, io.SEEK_CUR) + size = i32(read(4)) + fp.seek(size, io.SEEK_CUR) + continue + + for _ in range(ct_types): + type = i16(read(2)) + + if type == 65535: + b = "A" + else: + b = "RGBA"[type] + + bands.append(b) + read(4) # size + + # figure out the image mode + bands.sort() + if bands == ["R"]: + mode = "L" + elif bands == ["B", "G", "R"]: + mode = "RGB" + elif bands == ["A", "B", "G", "R"]: + mode = "RGBA" + else: + mode = "" # unknown + + # skip over blend flags and extra information + read(12) # filler + name = "" + size = i32(read(4)) # length of the extra data field + if size: + data_end = fp.tell() + size + + length = i32(read(4)) + if length: + fp.seek(length - 16, io.SEEK_CUR) + + length = i32(read(4)) + if length: + fp.seek(length, io.SEEK_CUR) + + length = i8(read(1)) + if length: + # Don't know the proper encoding, + # Latin-1 should be a good guess + name = read(length).decode("latin-1", "replace") + + fp.seek(data_end) + layers.append((name, mode, (x0, y0, x1, y1))) + + # get tiles + layerinfo = [] + for i, (name, mode, bbox) in enumerate(layers): + tile = [] + for m in mode: + t = _maketile(fp, m, bbox, 1) + if t: + tile.extend(t) + layerinfo.append((name, mode, bbox, tile)) + + return layerinfo + + +def _maketile( + file: IO[bytes], mode: str, bbox: tuple[int, int, int, int], channels: int +) -> list[ImageFile._Tile]: + tiles = [] + read = file.read + + compression = i16(read(2)) + + xsize = bbox[2] - bbox[0] + ysize = bbox[3] - bbox[1] + + offset = file.tell() + + if compression == 0: + # + # raw compression + for channel in range(channels): + layer = mode[channel] + if mode == "CMYK": + layer += ";I" + tiles.append(ImageFile._Tile("raw", bbox, offset, layer)) + offset = offset + xsize * ysize + + elif compression == 1: + # + # packbits compression + i = 0 + bytecount = read(channels * ysize * 2) + offset = file.tell() + for channel in range(channels): + layer = mode[channel] + if mode == "CMYK": + layer += ";I" + tiles.append(ImageFile._Tile("packbits", bbox, offset, layer)) + for y in range(ysize): + offset = offset + i16(bytecount, i) + i += 2 + + file.seek(offset) + + if offset & 1: + read(1) # padding + + return tiles + + +# -------------------------------------------------------------------- +# registry + + +Image.register_open(PsdImageFile.format, PsdImageFile, _accept) + +Image.register_extension(PsdImageFile.format, ".psd") + +Image.register_mime(PsdImageFile.format, "image/vnd.adobe.photoshop") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/QoiImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/QoiImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..dba5d809fef75e281ac10f92f1868c58b1b4508c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/QoiImagePlugin.py @@ -0,0 +1,234 @@ +# +# The Python Imaging Library. +# +# QOI support for PIL +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +from typing import IO + +from . import Image, ImageFile +from ._binary import i32be as i32 +from ._binary import o8 +from ._binary import o32be as o32 + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"qoif") + + +class QoiImageFile(ImageFile.ImageFile): + format = "QOI" + format_description = "Quite OK Image" + + def _open(self) -> None: + if not _accept(self.fp.read(4)): + msg = "not a QOI file" + raise SyntaxError(msg) + + self._size = i32(self.fp.read(4)), i32(self.fp.read(4)) + + channels = self.fp.read(1)[0] + self._mode = "RGB" if channels == 3 else "RGBA" + + self.fp.seek(1, os.SEEK_CUR) # colorspace + self.tile = [ImageFile._Tile("qoi", (0, 0) + self._size, self.fp.tell())] + + +class QoiDecoder(ImageFile.PyDecoder): + _pulls_fd = True + _previous_pixel: bytes | bytearray | None = None + _previously_seen_pixels: dict[int, bytes | bytearray] = {} + + def _add_to_previous_pixels(self, value: bytes | bytearray) -> None: + self._previous_pixel = value + + r, g, b, a = value + hash_value = (r * 3 + g * 5 + b * 7 + a * 11) % 64 + self._previously_seen_pixels[hash_value] = value + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + + self._previously_seen_pixels = {} + self._previous_pixel = bytearray((0, 0, 0, 255)) + + data = bytearray() + bands = Image.getmodebands(self.mode) + dest_length = self.state.xsize * self.state.ysize * bands + while len(data) < dest_length: + byte = self.fd.read(1)[0] + value: bytes | bytearray + if byte == 0b11111110 and self._previous_pixel: # QOI_OP_RGB + value = bytearray(self.fd.read(3)) + self._previous_pixel[3:] + elif byte == 0b11111111: # QOI_OP_RGBA + value = self.fd.read(4) + else: + op = byte >> 6 + if op == 0: # QOI_OP_INDEX + op_index = byte & 0b00111111 + value = self._previously_seen_pixels.get( + op_index, bytearray((0, 0, 0, 0)) + ) + elif op == 1 and self._previous_pixel: # QOI_OP_DIFF + value = bytearray( + ( + (self._previous_pixel[0] + ((byte & 0b00110000) >> 4) - 2) + % 256, + (self._previous_pixel[1] + ((byte & 0b00001100) >> 2) - 2) + % 256, + (self._previous_pixel[2] + (byte & 0b00000011) - 2) % 256, + self._previous_pixel[3], + ) + ) + elif op == 2 and self._previous_pixel: # QOI_OP_LUMA + second_byte = self.fd.read(1)[0] + diff_green = (byte & 0b00111111) - 32 + diff_red = ((second_byte & 0b11110000) >> 4) - 8 + diff_blue = (second_byte & 0b00001111) - 8 + + value = bytearray( + tuple( + (self._previous_pixel[i] + diff_green + diff) % 256 + for i, diff in enumerate((diff_red, 0, diff_blue)) + ) + ) + value += self._previous_pixel[3:] + elif op == 3 and self._previous_pixel: # QOI_OP_RUN + run_length = (byte & 0b00111111) + 1 + value = self._previous_pixel + if bands == 3: + value = value[:3] + data += value * run_length + continue + self._add_to_previous_pixels(value) + + if bands == 3: + value = value[:3] + data += value + self.set_as_raw(data) + return -1, 0 + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode == "RGB": + channels = 3 + elif im.mode == "RGBA": + channels = 4 + else: + msg = "Unsupported QOI image mode" + raise ValueError(msg) + + colorspace = 0 if im.encoderinfo.get("colorspace") == "sRGB" else 1 + + fp.write(b"qoif") + fp.write(o32(im.size[0])) + fp.write(o32(im.size[1])) + fp.write(o8(channels)) + fp.write(o8(colorspace)) + + ImageFile._save(im, fp, [ImageFile._Tile("qoi", (0, 0) + im.size)]) + + +class QoiEncoder(ImageFile.PyEncoder): + _pushes_fd = True + _previous_pixel: tuple[int, int, int, int] | None = None + _previously_seen_pixels: dict[int, tuple[int, int, int, int]] = {} + _run = 0 + + def _write_run(self) -> bytes: + data = o8(0b11000000 | (self._run - 1)) # QOI_OP_RUN + self._run = 0 + return data + + def _delta(self, left: int, right: int) -> int: + result = (left - right) & 255 + if result >= 128: + result -= 256 + return result + + def encode(self, bufsize: int) -> tuple[int, int, bytes]: + assert self.im is not None + + self._previously_seen_pixels = {0: (0, 0, 0, 0)} + self._previous_pixel = (0, 0, 0, 255) + + data = bytearray() + w, h = self.im.size + bands = Image.getmodebands(self.mode) + + for y in range(h): + for x in range(w): + pixel = self.im.getpixel((x, y)) + if bands == 3: + pixel = (*pixel, 255) + + if pixel == self._previous_pixel: + self._run += 1 + if self._run == 62: + data += self._write_run() + else: + if self._run: + data += self._write_run() + + r, g, b, a = pixel + hash_value = (r * 3 + g * 5 + b * 7 + a * 11) % 64 + if self._previously_seen_pixels.get(hash_value) == pixel: + data += o8(hash_value) # QOI_OP_INDEX + elif self._previous_pixel: + self._previously_seen_pixels[hash_value] = pixel + + prev_r, prev_g, prev_b, prev_a = self._previous_pixel + if prev_a == a: + delta_r = self._delta(r, prev_r) + delta_g = self._delta(g, prev_g) + delta_b = self._delta(b, prev_b) + + if ( + -2 <= delta_r < 2 + and -2 <= delta_g < 2 + and -2 <= delta_b < 2 + ): + data += o8( + 0b01000000 + | (delta_r + 2) << 4 + | (delta_g + 2) << 2 + | (delta_b + 2) + ) # QOI_OP_DIFF + else: + delta_gr = self._delta(delta_r, delta_g) + delta_gb = self._delta(delta_b, delta_g) + if ( + -8 <= delta_gr < 8 + and -32 <= delta_g < 32 + and -8 <= delta_gb < 8 + ): + data += o8( + 0b10000000 | (delta_g + 32) + ) # QOI_OP_LUMA + data += o8((delta_gr + 8) << 4 | (delta_gb + 8)) + else: + data += o8(0b11111110) # QOI_OP_RGB + data += bytes(pixel[:3]) + else: + data += o8(0b11111111) # QOI_OP_RGBA + data += bytes(pixel) + + self._previous_pixel = pixel + + if self._run: + data += self._write_run() + data += bytes((0, 0, 0, 0, 0, 0, 0, 1)) # padding + + return len(data), 0, data + + +Image.register_open(QoiImageFile.format, QoiImageFile, _accept) +Image.register_decoder("qoi", QoiDecoder) +Image.register_extension(QoiImageFile.format, ".qoi") + +Image.register_save(QoiImageFile.format, _save) +Image.register_encoder("qoi", QoiEncoder) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/SgiImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/SgiImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..853022150ae849e490378e41e831897050c207a2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/SgiImagePlugin.py @@ -0,0 +1,231 @@ +# +# The Python Imaging Library. +# $Id$ +# +# SGI image file handling +# +# See "The SGI Image File Format (Draft version 0.97)", Paul Haeberli. +# +# +# +# History: +# 2017-22-07 mb Add RLE decompression +# 2016-16-10 mb Add save method without compression +# 1995-09-10 fl Created +# +# Copyright (c) 2016 by Mickael Bonfill. +# Copyright (c) 2008 by Karsten Hiddemann. +# Copyright (c) 1997 by Secret Labs AB. +# Copyright (c) 1995 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import os +import struct +from typing import IO + +from . import Image, ImageFile +from ._binary import i16be as i16 +from ._binary import o8 + + +def _accept(prefix: bytes) -> bool: + return len(prefix) >= 2 and i16(prefix) == 474 + + +MODES = { + (1, 1, 1): "L", + (1, 2, 1): "L", + (2, 1, 1): "L;16B", + (2, 2, 1): "L;16B", + (1, 3, 3): "RGB", + (2, 3, 3): "RGB;16B", + (1, 3, 4): "RGBA", + (2, 3, 4): "RGBA;16B", +} + + +## +# Image plugin for SGI images. +class SgiImageFile(ImageFile.ImageFile): + format = "SGI" + format_description = "SGI Image File Format" + + def _open(self) -> None: + # HEAD + assert self.fp is not None + + headlen = 512 + s = self.fp.read(headlen) + + if not _accept(s): + msg = "Not an SGI image file" + raise ValueError(msg) + + # compression : verbatim or RLE + compression = s[2] + + # bpc : 1 or 2 bytes (8bits or 16bits) + bpc = s[3] + + # dimension : 1, 2 or 3 (depending on xsize, ysize and zsize) + dimension = i16(s, 4) + + # xsize : width + xsize = i16(s, 6) + + # ysize : height + ysize = i16(s, 8) + + # zsize : channels count + zsize = i16(s, 10) + + # determine mode from bits/zsize + try: + rawmode = MODES[(bpc, dimension, zsize)] + except KeyError: + msg = "Unsupported SGI image mode" + raise ValueError(msg) + + self._size = xsize, ysize + self._mode = rawmode.split(";")[0] + if self.mode == "RGB": + self.custom_mimetype = "image/rgb" + + # orientation -1 : scanlines begins at the bottom-left corner + orientation = -1 + + # decoder info + if compression == 0: + pagesize = xsize * ysize * bpc + if bpc == 2: + self.tile = [ + ImageFile._Tile( + "SGI16", + (0, 0) + self.size, + headlen, + (self.mode, 0, orientation), + ) + ] + else: + self.tile = [] + offset = headlen + for layer in self.mode: + self.tile.append( + ImageFile._Tile( + "raw", (0, 0) + self.size, offset, (layer, 0, orientation) + ) + ) + offset += pagesize + elif compression == 1: + self.tile = [ + ImageFile._Tile( + "sgi_rle", (0, 0) + self.size, headlen, (rawmode, orientation, bpc) + ) + ] + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode not in {"RGB", "RGBA", "L"}: + msg = "Unsupported SGI image mode" + raise ValueError(msg) + + # Get the keyword arguments + info = im.encoderinfo + + # Byte-per-pixel precision, 1 = 8bits per pixel + bpc = info.get("bpc", 1) + + if bpc not in (1, 2): + msg = "Unsupported number of bytes per pixel" + raise ValueError(msg) + + # Flip the image, since the origin of SGI file is the bottom-left corner + orientation = -1 + # Define the file as SGI File Format + magic_number = 474 + # Run-Length Encoding Compression - Unsupported at this time + rle = 0 + + # X Dimension = width / Y Dimension = height + x, y = im.size + # Z Dimension: Number of channels + z = len(im.mode) + # Number of dimensions (x,y,z) + if im.mode == "L": + dimension = 1 if y == 1 else 2 + else: + dimension = 3 + + # Minimum Byte value + pinmin = 0 + # Maximum Byte value (255 = 8bits per pixel) + pinmax = 255 + # Image name (79 characters max, truncated below in write) + img_name = os.path.splitext(os.path.basename(filename))[0] + if isinstance(img_name, str): + img_name = img_name.encode("ascii", "ignore") + # Standard representation of pixel in the file + colormap = 0 + fp.write(struct.pack(">h", magic_number)) + fp.write(o8(rle)) + fp.write(o8(bpc)) + fp.write(struct.pack(">H", dimension)) + fp.write(struct.pack(">H", x)) + fp.write(struct.pack(">H", y)) + fp.write(struct.pack(">H", z)) + fp.write(struct.pack(">l", pinmin)) + fp.write(struct.pack(">l", pinmax)) + fp.write(struct.pack("4s", b"")) # dummy + fp.write(struct.pack("79s", img_name)) # truncates to 79 chars + fp.write(struct.pack("s", b"")) # force null byte after img_name + fp.write(struct.pack(">l", colormap)) + fp.write(struct.pack("404s", b"")) # dummy + + rawmode = "L" + if bpc == 2: + rawmode = "L;16B" + + for channel in im.split(): + fp.write(channel.tobytes("raw", rawmode, 0, orientation)) + + if hasattr(fp, "flush"): + fp.flush() + + +class SGI16Decoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + assert self.im is not None + + rawmode, stride, orientation = self.args + pagesize = self.state.xsize * self.state.ysize + zsize = len(self.mode) + self.fd.seek(512) + + for band in range(zsize): + channel = Image.new("L", (self.state.xsize, self.state.ysize)) + channel.frombytes( + self.fd.read(2 * pagesize), "raw", "L;16B", stride, orientation + ) + self.im.putband(channel.im, band) + + return -1, 0 + + +# +# registry + + +Image.register_decoder("SGI16", SGI16Decoder) +Image.register_open(SgiImageFile.format, SgiImageFile, _accept) +Image.register_save(SgiImageFile.format, _save) +Image.register_mime(SgiImageFile.format, "image/sgi") + +Image.register_extensions(SgiImageFile.format, [".bw", ".rgb", ".rgba", ".sgi"]) + +# End of file diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/SpiderImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/SpiderImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..868019e80a80cffc5e9f193ddbf96a0ba64ad9ea --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/SpiderImagePlugin.py @@ -0,0 +1,331 @@ +# +# The Python Imaging Library. +# +# SPIDER image file handling +# +# History: +# 2004-08-02 Created BB +# 2006-03-02 added save method +# 2006-03-13 added support for stack images +# +# Copyright (c) 2004 by Health Research Inc. (HRI) RENSSELAER, NY 12144. +# Copyright (c) 2004 by William Baxter. +# Copyright (c) 2004 by Secret Labs AB. +# Copyright (c) 2004 by Fredrik Lundh. +# + +## +# Image plugin for the Spider image format. This format is used +# by the SPIDER software, in processing image data from electron +# microscopy and tomography. +## + +# +# SpiderImagePlugin.py +# +# The Spider image format is used by SPIDER software, in processing +# image data from electron microscopy and tomography. +# +# Spider home page: +# https://spider.wadsworth.org/spider_doc/spider/docs/spider.html +# +# Details about the Spider image format: +# https://spider.wadsworth.org/spider_doc/spider/docs/image_doc.html +# +from __future__ import annotations + +import os +import struct +import sys +from typing import IO, Any, cast + +from . import Image, ImageFile +from ._util import DeferredError + +TYPE_CHECKING = False + + +def isInt(f: Any) -> int: + try: + i = int(f) + if f - i == 0: + return 1 + else: + return 0 + except (ValueError, OverflowError): + return 0 + + +iforms = [1, 3, -11, -12, -21, -22] + + +# There is no magic number to identify Spider files, so just check a +# series of header locations to see if they have reasonable values. +# Returns no. of bytes in the header, if it is a valid Spider header, +# otherwise returns 0 + + +def isSpiderHeader(t: tuple[float, ...]) -> int: + h = (99,) + t # add 1 value so can use spider header index start=1 + # header values 1,2,5,12,13,22,23 should be integers + for i in [1, 2, 5, 12, 13, 22, 23]: + if not isInt(h[i]): + return 0 + # check iform + iform = int(h[5]) + if iform not in iforms: + return 0 + # check other header values + labrec = int(h[13]) # no. records in file header + labbyt = int(h[22]) # total no. of bytes in header + lenbyt = int(h[23]) # record length in bytes + if labbyt != (labrec * lenbyt): + return 0 + # looks like a valid header + return labbyt + + +def isSpiderImage(filename: str) -> int: + with open(filename, "rb") as fp: + f = fp.read(92) # read 23 * 4 bytes + t = struct.unpack(">23f", f) # try big-endian first + hdrlen = isSpiderHeader(t) + if hdrlen == 0: + t = struct.unpack("<23f", f) # little-endian + hdrlen = isSpiderHeader(t) + return hdrlen + + +class SpiderImageFile(ImageFile.ImageFile): + format = "SPIDER" + format_description = "Spider 2D image" + _close_exclusive_fp_after_loading = False + + def _open(self) -> None: + # check header + n = 27 * 4 # read 27 float values + f = self.fp.read(n) + + try: + self.bigendian = 1 + t = struct.unpack(">27f", f) # try big-endian first + hdrlen = isSpiderHeader(t) + if hdrlen == 0: + self.bigendian = 0 + t = struct.unpack("<27f", f) # little-endian + hdrlen = isSpiderHeader(t) + if hdrlen == 0: + msg = "not a valid Spider file" + raise SyntaxError(msg) + except struct.error as e: + msg = "not a valid Spider file" + raise SyntaxError(msg) from e + + h = (99,) + t # add 1 value : spider header index starts at 1 + iform = int(h[5]) + if iform != 1: + msg = "not a Spider 2D image" + raise SyntaxError(msg) + + self._size = int(h[12]), int(h[2]) # size in pixels (width, height) + self.istack = int(h[24]) + self.imgnumber = int(h[27]) + + if self.istack == 0 and self.imgnumber == 0: + # stk=0, img=0: a regular 2D image + offset = hdrlen + self._nimages = 1 + elif self.istack > 0 and self.imgnumber == 0: + # stk>0, img=0: Opening the stack for the first time + self.imgbytes = int(h[12]) * int(h[2]) * 4 + self.hdrlen = hdrlen + self._nimages = int(h[26]) + # Point to the first image in the stack + offset = hdrlen * 2 + self.imgnumber = 1 + elif self.istack == 0 and self.imgnumber > 0: + # stk=0, img>0: an image within the stack + offset = hdrlen + self.stkoffset + self.istack = 2 # So Image knows it's still a stack + else: + msg = "inconsistent stack header values" + raise SyntaxError(msg) + + if self.bigendian: + self.rawmode = "F;32BF" + else: + self.rawmode = "F;32F" + self._mode = "F" + + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, offset, self.rawmode)] + self._fp = self.fp # FIXME: hack + + @property + def n_frames(self) -> int: + return self._nimages + + @property + def is_animated(self) -> bool: + return self._nimages > 1 + + # 1st image index is zero (although SPIDER imgnumber starts at 1) + def tell(self) -> int: + if self.imgnumber < 1: + return 0 + else: + return self.imgnumber - 1 + + def seek(self, frame: int) -> None: + if self.istack == 0: + msg = "attempt to seek in a non-stack file" + raise EOFError(msg) + if not self._seek_check(frame): + return + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self.stkoffset = self.hdrlen + frame * (self.hdrlen + self.imgbytes) + self.fp = self._fp + self.fp.seek(self.stkoffset) + self._open() + + # returns a byte image after rescaling to 0..255 + def convert2byte(self, depth: int = 255) -> Image.Image: + extrema = self.getextrema() + assert isinstance(extrema[0], float) + minimum, maximum = cast(tuple[float, float], extrema) + m: float = 1 + if maximum != minimum: + m = depth / (maximum - minimum) + b = -m * minimum + return self.point(lambda i: i * m + b).convert("L") + + if TYPE_CHECKING: + from . import ImageTk + + # returns a ImageTk.PhotoImage object, after rescaling to 0..255 + def tkPhotoImage(self) -> ImageTk.PhotoImage: + from . import ImageTk + + return ImageTk.PhotoImage(self.convert2byte(), palette=256) + + +# -------------------------------------------------------------------- +# Image series + + +# given a list of filenames, return a list of images +def loadImageSeries(filelist: list[str] | None = None) -> list[Image.Image] | None: + """create a list of :py:class:`~PIL.Image.Image` objects for use in a montage""" + if filelist is None or len(filelist) < 1: + return None + + byte_imgs = [] + for img in filelist: + if not os.path.exists(img): + print(f"unable to find {img}") + continue + try: + with Image.open(img) as im: + assert isinstance(im, SpiderImageFile) + byte_im = im.convert2byte() + except Exception: + if not isSpiderImage(img): + print(f"{img} is not a Spider image file") + continue + byte_im.info["filename"] = img + byte_imgs.append(byte_im) + return byte_imgs + + +# -------------------------------------------------------------------- +# For saving images in Spider format + + +def makeSpiderHeader(im: Image.Image) -> list[bytes]: + nsam, nrow = im.size + lenbyt = nsam * 4 # There are labrec records in the header + labrec = int(1024 / lenbyt) + if 1024 % lenbyt != 0: + labrec += 1 + labbyt = labrec * lenbyt + nvalues = int(labbyt / 4) + if nvalues < 23: + return [] + + hdr = [0.0] * nvalues + + # NB these are Fortran indices + hdr[1] = 1.0 # nslice (=1 for an image) + hdr[2] = float(nrow) # number of rows per slice + hdr[3] = float(nrow) # number of records in the image + hdr[5] = 1.0 # iform for 2D image + hdr[12] = float(nsam) # number of pixels per line + hdr[13] = float(labrec) # number of records in file header + hdr[22] = float(labbyt) # total number of bytes in header + hdr[23] = float(lenbyt) # record length in bytes + + # adjust for Fortran indexing + hdr = hdr[1:] + hdr.append(0.0) + # pack binary data into a string + return [struct.pack("f", v) for v in hdr] + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode != "F": + im = im.convert("F") + + hdr = makeSpiderHeader(im) + if len(hdr) < 256: + msg = "Error creating Spider header" + raise OSError(msg) + + # write the SPIDER header + fp.writelines(hdr) + + rawmode = "F;32NF" # 32-bit native floating point + ImageFile._save(im, fp, [ImageFile._Tile("raw", (0, 0) + im.size, 0, rawmode)]) + + +def _save_spider(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + # get the filename extension and register it with Image + filename_ext = os.path.splitext(filename)[1] + ext = filename_ext.decode() if isinstance(filename_ext, bytes) else filename_ext + Image.register_extension(SpiderImageFile.format, ext) + _save(im, fp, filename) + + +# -------------------------------------------------------------------- + + +Image.register_open(SpiderImageFile.format, SpiderImageFile) +Image.register_save(SpiderImageFile.format, _save_spider) + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("Syntax: python3 SpiderImagePlugin.py [infile] [outfile]") + sys.exit() + + filename = sys.argv[1] + if not isSpiderImage(filename): + print("input image must be in Spider format") + sys.exit() + + with Image.open(filename) as im: + print(f"image: {im}") + print(f"format: {im.format}") + print(f"size: {im.size}") + print(f"mode: {im.mode}") + print("max, min: ", end=" ") + print(im.getextrema()) + + if len(sys.argv) > 2: + outfile = sys.argv[2] + + # perform some image operation + im = im.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + print( + f"saving a flipped version of {os.path.basename(filename)} " + f"as {outfile} " + ) + im.save(outfile, SpiderImageFile.format) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/SunImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/SunImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..8912379ea3e7801cdac9a557d2bc0c557bce8991 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/SunImagePlugin.py @@ -0,0 +1,145 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Sun image file handling +# +# History: +# 1995-09-10 fl Created +# 1996-05-28 fl Fixed 32-bit alignment +# 1998-12-29 fl Import ImagePalette module +# 2001-12-18 fl Fixed palette loading (from Jean-Claude Rimbault) +# +# Copyright (c) 1997-2001 by Secret Labs AB +# Copyright (c) 1995-1996 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image, ImageFile, ImagePalette +from ._binary import i32be as i32 + + +def _accept(prefix: bytes) -> bool: + return len(prefix) >= 4 and i32(prefix) == 0x59A66A95 + + +## +# Image plugin for Sun raster files. + + +class SunImageFile(ImageFile.ImageFile): + format = "SUN" + format_description = "Sun Raster File" + + def _open(self) -> None: + # The Sun Raster file header is 32 bytes in length + # and has the following format: + + # typedef struct _SunRaster + # { + # DWORD MagicNumber; /* Magic (identification) number */ + # DWORD Width; /* Width of image in pixels */ + # DWORD Height; /* Height of image in pixels */ + # DWORD Depth; /* Number of bits per pixel */ + # DWORD Length; /* Size of image data in bytes */ + # DWORD Type; /* Type of raster file */ + # DWORD ColorMapType; /* Type of color map */ + # DWORD ColorMapLength; /* Size of the color map in bytes */ + # } SUNRASTER; + + assert self.fp is not None + + # HEAD + s = self.fp.read(32) + if not _accept(s): + msg = "not an SUN raster file" + raise SyntaxError(msg) + + offset = 32 + + self._size = i32(s, 4), i32(s, 8) + + depth = i32(s, 12) + # data_length = i32(s, 16) # unreliable, ignore. + file_type = i32(s, 20) + palette_type = i32(s, 24) # 0: None, 1: RGB, 2: Raw/arbitrary + palette_length = i32(s, 28) + + if depth == 1: + self._mode, rawmode = "1", "1;I" + elif depth == 4: + self._mode, rawmode = "L", "L;4" + elif depth == 8: + self._mode = rawmode = "L" + elif depth == 24: + if file_type == 3: + self._mode, rawmode = "RGB", "RGB" + else: + self._mode, rawmode = "RGB", "BGR" + elif depth == 32: + if file_type == 3: + self._mode, rawmode = "RGB", "RGBX" + else: + self._mode, rawmode = "RGB", "BGRX" + else: + msg = "Unsupported Mode/Bit Depth" + raise SyntaxError(msg) + + if palette_length: + if palette_length > 1024: + msg = "Unsupported Color Palette Length" + raise SyntaxError(msg) + + if palette_type != 1: + msg = "Unsupported Palette Type" + raise SyntaxError(msg) + + offset = offset + palette_length + self.palette = ImagePalette.raw("RGB;L", self.fp.read(palette_length)) + if self.mode == "L": + self._mode = "P" + rawmode = rawmode.replace("L", "P") + + # 16 bit boundaries on stride + stride = ((self.size[0] * depth + 15) // 16) * 2 + + # file type: Type is the version (or flavor) of the bitmap + # file. The following values are typically found in the Type + # field: + # 0000h Old + # 0001h Standard + # 0002h Byte-encoded + # 0003h RGB format + # 0004h TIFF format + # 0005h IFF format + # FFFFh Experimental + + # Old and standard are the same, except for the length tag. + # byte-encoded is run-length-encoded + # RGB looks similar to standard, but RGB byte order + # TIFF and IFF mean that they were converted from T/IFF + # Experimental means that it's something else. + # (https://www.fileformat.info/format/sunraster/egff.htm) + + if file_type in (0, 1, 3, 4, 5): + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, offset, (rawmode, stride)) + ] + elif file_type == 2: + self.tile = [ + ImageFile._Tile("sun_rle", (0, 0) + self.size, offset, rawmode) + ] + else: + msg = "Unsupported Sun Raster file type" + raise SyntaxError(msg) + + +# +# registry + + +Image.register_open(SunImageFile.format, SunImageFile, _accept) + +Image.register_extension(SunImageFile.format, ".ras") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TarIO.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TarIO.py new file mode 100644 index 0000000000000000000000000000000000000000..86490a496f3f106fcc042c03fb235ed5fb41f3a7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TarIO.py @@ -0,0 +1,61 @@ +# +# The Python Imaging Library. +# $Id$ +# +# read files from within a tar file +# +# History: +# 95-06-18 fl Created +# 96-05-28 fl Open files in binary mode +# +# Copyright (c) Secret Labs AB 1997. +# Copyright (c) Fredrik Lundh 1995-96. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io + +from . import ContainerIO + + +class TarIO(ContainerIO.ContainerIO[bytes]): + """A file object that provides read access to a given member of a TAR file.""" + + def __init__(self, tarfile: str, file: str) -> None: + """ + Create file object. + + :param tarfile: Name of TAR file. + :param file: Name of member file. + """ + self.fh = open(tarfile, "rb") + + while True: + s = self.fh.read(512) + if len(s) != 512: + self.fh.close() + + msg = "unexpected end of tar file" + raise OSError(msg) + + name = s[:100].decode("utf-8") + i = name.find("\0") + if i == 0: + self.fh.close() + + msg = "cannot find subfile" + raise OSError(msg) + if i > 0: + name = name[:i] + + size = int(s[124:135], 8) + + if file == name: + break + + self.fh.seek((size + 511) & (~511), io.SEEK_CUR) + + # Open region + super().__init__(self.fh, self.fh.tell(), size) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TgaImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TgaImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..90d5b5cf4ee17fc050784bf591adae3247407b56 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TgaImagePlugin.py @@ -0,0 +1,264 @@ +# +# The Python Imaging Library. +# $Id$ +# +# TGA file handling +# +# History: +# 95-09-01 fl created (reads 24-bit files only) +# 97-01-04 fl support more TGA versions, including compressed images +# 98-07-04 fl fixed orientation and alpha layer bugs +# 98-09-11 fl fixed orientation for runlength decoder +# +# Copyright (c) Secret Labs AB 1997-98. +# Copyright (c) Fredrik Lundh 1995-97. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import warnings +from typing import IO + +from . import Image, ImageFile, ImagePalette +from ._binary import i16le as i16 +from ._binary import o8 +from ._binary import o16le as o16 + +# +# -------------------------------------------------------------------- +# Read RGA file + + +MODES = { + # map imagetype/depth to rawmode + (1, 8): "P", + (3, 1): "1", + (3, 8): "L", + (3, 16): "LA", + (2, 16): "BGRA;15Z", + (2, 24): "BGR", + (2, 32): "BGRA", +} + + +## +# Image plugin for Targa files. + + +class TgaImageFile(ImageFile.ImageFile): + format = "TGA" + format_description = "Targa" + + def _open(self) -> None: + # process header + assert self.fp is not None + + s = self.fp.read(18) + + id_len = s[0] + + colormaptype = s[1] + imagetype = s[2] + + depth = s[16] + + flags = s[17] + + self._size = i16(s, 12), i16(s, 14) + + # validate header fields + if ( + colormaptype not in (0, 1) + or self.size[0] <= 0 + or self.size[1] <= 0 + or depth not in (1, 8, 16, 24, 32) + ): + msg = "not a TGA file" + raise SyntaxError(msg) + + # image mode + if imagetype in (3, 11): + self._mode = "L" + if depth == 1: + self._mode = "1" # ??? + elif depth == 16: + self._mode = "LA" + elif imagetype in (1, 9): + self._mode = "P" if colormaptype else "L" + elif imagetype in (2, 10): + self._mode = "RGB" if depth == 24 else "RGBA" + else: + msg = "unknown TGA mode" + raise SyntaxError(msg) + + # orientation + orientation = flags & 0x30 + self._flip_horizontally = orientation in [0x10, 0x30] + if orientation in [0x20, 0x30]: + orientation = 1 + elif orientation in [0, 0x10]: + orientation = -1 + else: + msg = "unknown TGA orientation" + raise SyntaxError(msg) + + self.info["orientation"] = orientation + + if imagetype & 8: + self.info["compression"] = "tga_rle" + + if id_len: + self.info["id_section"] = self.fp.read(id_len) + + if colormaptype: + # read palette + start, size, mapdepth = i16(s, 3), i16(s, 5), s[7] + if mapdepth == 16: + self.palette = ImagePalette.raw( + "BGRA;15Z", bytes(2 * start) + self.fp.read(2 * size) + ) + self.palette.mode = "RGBA" + elif mapdepth == 24: + self.palette = ImagePalette.raw( + "BGR", bytes(3 * start) + self.fp.read(3 * size) + ) + elif mapdepth == 32: + self.palette = ImagePalette.raw( + "BGRA", bytes(4 * start) + self.fp.read(4 * size) + ) + else: + msg = "unknown TGA map depth" + raise SyntaxError(msg) + + # setup tile descriptor + try: + rawmode = MODES[(imagetype & 7, depth)] + if imagetype & 8: + # compressed + self.tile = [ + ImageFile._Tile( + "tga_rle", + (0, 0) + self.size, + self.fp.tell(), + (rawmode, orientation, depth), + ) + ] + else: + self.tile = [ + ImageFile._Tile( + "raw", + (0, 0) + self.size, + self.fp.tell(), + (rawmode, 0, orientation), + ) + ] + except KeyError: + pass # cannot decode + + def load_end(self) -> None: + if self._flip_horizontally: + self.im = self.im.transpose(Image.Transpose.FLIP_LEFT_RIGHT) + + +# +# -------------------------------------------------------------------- +# Write TGA file + + +SAVE = { + "1": ("1", 1, 0, 3), + "L": ("L", 8, 0, 3), + "LA": ("LA", 16, 0, 3), + "P": ("P", 8, 1, 1), + "RGB": ("BGR", 24, 0, 2), + "RGBA": ("BGRA", 32, 0, 2), +} + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + try: + rawmode, bits, colormaptype, imagetype = SAVE[im.mode] + except KeyError as e: + msg = f"cannot write mode {im.mode} as TGA" + raise OSError(msg) from e + + if "rle" in im.encoderinfo: + rle = im.encoderinfo["rle"] + else: + compression = im.encoderinfo.get("compression", im.info.get("compression")) + rle = compression == "tga_rle" + if rle: + imagetype += 8 + + id_section = im.encoderinfo.get("id_section", im.info.get("id_section", "")) + id_len = len(id_section) + if id_len > 255: + id_len = 255 + id_section = id_section[:255] + warnings.warn("id_section has been trimmed to 255 characters") + + if colormaptype: + palette = im.im.getpalette("RGB", "BGR") + colormaplength, colormapentry = len(palette) // 3, 24 + else: + colormaplength, colormapentry = 0, 0 + + if im.mode in ("LA", "RGBA"): + flags = 8 + else: + flags = 0 + + orientation = im.encoderinfo.get("orientation", im.info.get("orientation", -1)) + if orientation > 0: + flags = flags | 0x20 + + fp.write( + o8(id_len) + + o8(colormaptype) + + o8(imagetype) + + o16(0) # colormapfirst + + o16(colormaplength) + + o8(colormapentry) + + o16(0) + + o16(0) + + o16(im.size[0]) + + o16(im.size[1]) + + o8(bits) + + o8(flags) + ) + + if id_section: + fp.write(id_section) + + if colormaptype: + fp.write(palette) + + if rle: + ImageFile._save( + im, + fp, + [ImageFile._Tile("tga_rle", (0, 0) + im.size, 0, (rawmode, orientation))], + ) + else: + ImageFile._save( + im, + fp, + [ImageFile._Tile("raw", (0, 0) + im.size, 0, (rawmode, 0, orientation))], + ) + + # write targa version 2 footer + fp.write(b"\000" * 8 + b"TRUEVISION-XFILE." + b"\000") + + +# +# -------------------------------------------------------------------- +# Registry + + +Image.register_open(TgaImageFile.format, TgaImageFile) +Image.register_save(TgaImageFile.format, _save) + +Image.register_extensions(TgaImageFile.format, [".tga", ".icb", ".vda", ".vst"]) + +Image.register_mime(TgaImageFile.format, "image/x-tga") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TiffImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TiffImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..daf20f2e899608c28905272de7b5c0620ef5938c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TiffImagePlugin.py @@ -0,0 +1,2339 @@ +# +# The Python Imaging Library. +# $Id$ +# +# TIFF file handling +# +# TIFF is a flexible, if somewhat aged, image file format originally +# defined by Aldus. Although TIFF supports a wide variety of pixel +# layouts and compression methods, the name doesn't really stand for +# "thousands of incompatible file formats," it just feels that way. +# +# To read TIFF data from a stream, the stream must be seekable. For +# progressive decoding, make sure to use TIFF files where the tag +# directory is placed first in the file. +# +# History: +# 1995-09-01 fl Created +# 1996-05-04 fl Handle JPEGTABLES tag +# 1996-05-18 fl Fixed COLORMAP support +# 1997-01-05 fl Fixed PREDICTOR support +# 1997-08-27 fl Added support for rational tags (from Perry Stoll) +# 1998-01-10 fl Fixed seek/tell (from Jan Blom) +# 1998-07-15 fl Use private names for internal variables +# 1999-06-13 fl Rewritten for PIL 1.0 (1.0) +# 2000-10-11 fl Additional fixes for Python 2.0 (1.1) +# 2001-04-17 fl Fixed rewind support (seek to frame 0) (1.2) +# 2001-05-12 fl Added write support for more tags (from Greg Couch) (1.3) +# 2001-12-18 fl Added workaround for broken Matrox library +# 2002-01-18 fl Don't mess up if photometric tag is missing (D. Alan Stewart) +# 2003-05-19 fl Check FILLORDER tag +# 2003-09-26 fl Added RGBa support +# 2004-02-24 fl Added DPI support; fixed rational write support +# 2005-02-07 fl Added workaround for broken Corel Draw 10 files +# 2006-01-09 fl Added support for float/double tags (from Russell Nelson) +# +# Copyright (c) 1997-2006 by Secret Labs AB. All rights reserved. +# Copyright (c) 1995-1997 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import io +import itertools +import logging +import math +import os +import struct +import warnings +from collections.abc import Iterator, MutableMapping +from fractions import Fraction +from numbers import Number, Rational +from typing import IO, Any, Callable, NoReturn, cast + +from . import ExifTags, Image, ImageFile, ImageOps, ImagePalette, TiffTags +from ._binary import i16be as i16 +from ._binary import i32be as i32 +from ._binary import o8 +from ._deprecate import deprecate +from ._typing import StrOrBytesPath +from ._util import DeferredError, is_path +from .TiffTags import TYPES + +TYPE_CHECKING = False +if TYPE_CHECKING: + from ._typing import Buffer, IntegralLike + +logger = logging.getLogger(__name__) + +# Set these to true to force use of libtiff for reading or writing. +READ_LIBTIFF = False +WRITE_LIBTIFF = False +STRIP_SIZE = 65536 + +II = b"II" # little-endian (Intel style) +MM = b"MM" # big-endian (Motorola style) + +# +# -------------------------------------------------------------------- +# Read TIFF files + +# a few tag names, just to make the code below a bit more readable +OSUBFILETYPE = 255 +IMAGEWIDTH = 256 +IMAGELENGTH = 257 +BITSPERSAMPLE = 258 +COMPRESSION = 259 +PHOTOMETRIC_INTERPRETATION = 262 +FILLORDER = 266 +IMAGEDESCRIPTION = 270 +STRIPOFFSETS = 273 +SAMPLESPERPIXEL = 277 +ROWSPERSTRIP = 278 +STRIPBYTECOUNTS = 279 +X_RESOLUTION = 282 +Y_RESOLUTION = 283 +PLANAR_CONFIGURATION = 284 +RESOLUTION_UNIT = 296 +TRANSFERFUNCTION = 301 +SOFTWARE = 305 +DATE_TIME = 306 +ARTIST = 315 +PREDICTOR = 317 +COLORMAP = 320 +TILEWIDTH = 322 +TILELENGTH = 323 +TILEOFFSETS = 324 +TILEBYTECOUNTS = 325 +SUBIFD = 330 +EXTRASAMPLES = 338 +SAMPLEFORMAT = 339 +JPEGTABLES = 347 +YCBCRSUBSAMPLING = 530 +REFERENCEBLACKWHITE = 532 +COPYRIGHT = 33432 +IPTC_NAA_CHUNK = 33723 # newsphoto properties +PHOTOSHOP_CHUNK = 34377 # photoshop properties +ICCPROFILE = 34675 +EXIFIFD = 34665 +XMP = 700 +JPEGQUALITY = 65537 # pseudo-tag by libtiff + +# https://github.com/imagej/ImageJA/blob/master/src/main/java/ij/io/TiffDecoder.java +IMAGEJ_META_DATA_BYTE_COUNTS = 50838 +IMAGEJ_META_DATA = 50839 + +COMPRESSION_INFO = { + # Compression => pil compression name + 1: "raw", + 2: "tiff_ccitt", + 3: "group3", + 4: "group4", + 5: "tiff_lzw", + 6: "tiff_jpeg", # obsolete + 7: "jpeg", + 8: "tiff_adobe_deflate", + 32771: "tiff_raw_16", # 16-bit padding + 32773: "packbits", + 32809: "tiff_thunderscan", + 32946: "tiff_deflate", + 34676: "tiff_sgilog", + 34677: "tiff_sgilog24", + 34925: "lzma", + 50000: "zstd", + 50001: "webp", +} + +COMPRESSION_INFO_REV = {v: k for k, v in COMPRESSION_INFO.items()} + +OPEN_INFO = { + # (ByteOrder, PhotoInterpretation, SampleFormat, FillOrder, BitsPerSample, + # ExtraSamples) => mode, rawmode + (II, 0, (1,), 1, (1,), ()): ("1", "1;I"), + (MM, 0, (1,), 1, (1,), ()): ("1", "1;I"), + (II, 0, (1,), 2, (1,), ()): ("1", "1;IR"), + (MM, 0, (1,), 2, (1,), ()): ("1", "1;IR"), + (II, 1, (1,), 1, (1,), ()): ("1", "1"), + (MM, 1, (1,), 1, (1,), ()): ("1", "1"), + (II, 1, (1,), 2, (1,), ()): ("1", "1;R"), + (MM, 1, (1,), 2, (1,), ()): ("1", "1;R"), + (II, 0, (1,), 1, (2,), ()): ("L", "L;2I"), + (MM, 0, (1,), 1, (2,), ()): ("L", "L;2I"), + (II, 0, (1,), 2, (2,), ()): ("L", "L;2IR"), + (MM, 0, (1,), 2, (2,), ()): ("L", "L;2IR"), + (II, 1, (1,), 1, (2,), ()): ("L", "L;2"), + (MM, 1, (1,), 1, (2,), ()): ("L", "L;2"), + (II, 1, (1,), 2, (2,), ()): ("L", "L;2R"), + (MM, 1, (1,), 2, (2,), ()): ("L", "L;2R"), + (II, 0, (1,), 1, (4,), ()): ("L", "L;4I"), + (MM, 0, (1,), 1, (4,), ()): ("L", "L;4I"), + (II, 0, (1,), 2, (4,), ()): ("L", "L;4IR"), + (MM, 0, (1,), 2, (4,), ()): ("L", "L;4IR"), + (II, 1, (1,), 1, (4,), ()): ("L", "L;4"), + (MM, 1, (1,), 1, (4,), ()): ("L", "L;4"), + (II, 1, (1,), 2, (4,), ()): ("L", "L;4R"), + (MM, 1, (1,), 2, (4,), ()): ("L", "L;4R"), + (II, 0, (1,), 1, (8,), ()): ("L", "L;I"), + (MM, 0, (1,), 1, (8,), ()): ("L", "L;I"), + (II, 0, (1,), 2, (8,), ()): ("L", "L;IR"), + (MM, 0, (1,), 2, (8,), ()): ("L", "L;IR"), + (II, 1, (1,), 1, (8,), ()): ("L", "L"), + (MM, 1, (1,), 1, (8,), ()): ("L", "L"), + (II, 1, (2,), 1, (8,), ()): ("L", "L"), + (MM, 1, (2,), 1, (8,), ()): ("L", "L"), + (II, 1, (1,), 2, (8,), ()): ("L", "L;R"), + (MM, 1, (1,), 2, (8,), ()): ("L", "L;R"), + (II, 1, (1,), 1, (12,), ()): ("I;16", "I;12"), + (II, 0, (1,), 1, (16,), ()): ("I;16", "I;16"), + (II, 1, (1,), 1, (16,), ()): ("I;16", "I;16"), + (MM, 1, (1,), 1, (16,), ()): ("I;16B", "I;16B"), + (II, 1, (1,), 2, (16,), ()): ("I;16", "I;16R"), + (II, 1, (2,), 1, (16,), ()): ("I", "I;16S"), + (MM, 1, (2,), 1, (16,), ()): ("I", "I;16BS"), + (II, 0, (3,), 1, (32,), ()): ("F", "F;32F"), + (MM, 0, (3,), 1, (32,), ()): ("F", "F;32BF"), + (II, 1, (1,), 1, (32,), ()): ("I", "I;32N"), + (II, 1, (2,), 1, (32,), ()): ("I", "I;32S"), + (MM, 1, (2,), 1, (32,), ()): ("I", "I;32BS"), + (II, 1, (3,), 1, (32,), ()): ("F", "F;32F"), + (MM, 1, (3,), 1, (32,), ()): ("F", "F;32BF"), + (II, 1, (1,), 1, (8, 8), (2,)): ("LA", "LA"), + (MM, 1, (1,), 1, (8, 8), (2,)): ("LA", "LA"), + (II, 2, (1,), 1, (8, 8, 8), ()): ("RGB", "RGB"), + (MM, 2, (1,), 1, (8, 8, 8), ()): ("RGB", "RGB"), + (II, 2, (1,), 2, (8, 8, 8), ()): ("RGB", "RGB;R"), + (MM, 2, (1,), 2, (8, 8, 8), ()): ("RGB", "RGB;R"), + (II, 2, (1,), 1, (8, 8, 8, 8), ()): ("RGBA", "RGBA"), # missing ExtraSamples + (MM, 2, (1,), 1, (8, 8, 8, 8), ()): ("RGBA", "RGBA"), # missing ExtraSamples + (II, 2, (1,), 1, (8, 8, 8, 8), (0,)): ("RGB", "RGBX"), + (MM, 2, (1,), 1, (8, 8, 8, 8), (0,)): ("RGB", "RGBX"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8), (0, 0)): ("RGB", "RGBXX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8), (0, 0)): ("RGB", "RGBXX"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0, 0)): ("RGB", "RGBXXX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0, 0)): ("RGB", "RGBXXX"), + (II, 2, (1,), 1, (8, 8, 8, 8), (1,)): ("RGBA", "RGBa"), + (MM, 2, (1,), 1, (8, 8, 8, 8), (1,)): ("RGBA", "RGBa"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8), (1, 0)): ("RGBA", "RGBaX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8), (1, 0)): ("RGBA", "RGBaX"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (1, 0, 0)): ("RGBA", "RGBaXX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (1, 0, 0)): ("RGBA", "RGBaXX"), + (II, 2, (1,), 1, (8, 8, 8, 8), (2,)): ("RGBA", "RGBA"), + (MM, 2, (1,), 1, (8, 8, 8, 8), (2,)): ("RGBA", "RGBA"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8), (2, 0)): ("RGBA", "RGBAX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8), (2, 0)): ("RGBA", "RGBAX"), + (II, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (2, 0, 0)): ("RGBA", "RGBAXX"), + (MM, 2, (1,), 1, (8, 8, 8, 8, 8, 8), (2, 0, 0)): ("RGBA", "RGBAXX"), + (II, 2, (1,), 1, (8, 8, 8, 8), (999,)): ("RGBA", "RGBA"), # Corel Draw 10 + (MM, 2, (1,), 1, (8, 8, 8, 8), (999,)): ("RGBA", "RGBA"), # Corel Draw 10 + (II, 2, (1,), 1, (16, 16, 16), ()): ("RGB", "RGB;16L"), + (MM, 2, (1,), 1, (16, 16, 16), ()): ("RGB", "RGB;16B"), + (II, 2, (1,), 1, (16, 16, 16, 16), ()): ("RGBA", "RGBA;16L"), + (MM, 2, (1,), 1, (16, 16, 16, 16), ()): ("RGBA", "RGBA;16B"), + (II, 2, (1,), 1, (16, 16, 16, 16), (0,)): ("RGB", "RGBX;16L"), + (MM, 2, (1,), 1, (16, 16, 16, 16), (0,)): ("RGB", "RGBX;16B"), + (II, 2, (1,), 1, (16, 16, 16, 16), (1,)): ("RGBA", "RGBa;16L"), + (MM, 2, (1,), 1, (16, 16, 16, 16), (1,)): ("RGBA", "RGBa;16B"), + (II, 2, (1,), 1, (16, 16, 16, 16), (2,)): ("RGBA", "RGBA;16L"), + (MM, 2, (1,), 1, (16, 16, 16, 16), (2,)): ("RGBA", "RGBA;16B"), + (II, 3, (1,), 1, (1,), ()): ("P", "P;1"), + (MM, 3, (1,), 1, (1,), ()): ("P", "P;1"), + (II, 3, (1,), 2, (1,), ()): ("P", "P;1R"), + (MM, 3, (1,), 2, (1,), ()): ("P", "P;1R"), + (II, 3, (1,), 1, (2,), ()): ("P", "P;2"), + (MM, 3, (1,), 1, (2,), ()): ("P", "P;2"), + (II, 3, (1,), 2, (2,), ()): ("P", "P;2R"), + (MM, 3, (1,), 2, (2,), ()): ("P", "P;2R"), + (II, 3, (1,), 1, (4,), ()): ("P", "P;4"), + (MM, 3, (1,), 1, (4,), ()): ("P", "P;4"), + (II, 3, (1,), 2, (4,), ()): ("P", "P;4R"), + (MM, 3, (1,), 2, (4,), ()): ("P", "P;4R"), + (II, 3, (1,), 1, (8,), ()): ("P", "P"), + (MM, 3, (1,), 1, (8,), ()): ("P", "P"), + (II, 3, (1,), 1, (8, 8), (0,)): ("P", "PX"), + (II, 3, (1,), 1, (8, 8), (2,)): ("PA", "PA"), + (MM, 3, (1,), 1, (8, 8), (2,)): ("PA", "PA"), + (II, 3, (1,), 2, (8,), ()): ("P", "P;R"), + (MM, 3, (1,), 2, (8,), ()): ("P", "P;R"), + (II, 5, (1,), 1, (8, 8, 8, 8), ()): ("CMYK", "CMYK"), + (MM, 5, (1,), 1, (8, 8, 8, 8), ()): ("CMYK", "CMYK"), + (II, 5, (1,), 1, (8, 8, 8, 8, 8), (0,)): ("CMYK", "CMYKX"), + (MM, 5, (1,), 1, (8, 8, 8, 8, 8), (0,)): ("CMYK", "CMYKX"), + (II, 5, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0)): ("CMYK", "CMYKXX"), + (MM, 5, (1,), 1, (8, 8, 8, 8, 8, 8), (0, 0)): ("CMYK", "CMYKXX"), + (II, 5, (1,), 1, (16, 16, 16, 16), ()): ("CMYK", "CMYK;16L"), + (MM, 5, (1,), 1, (16, 16, 16, 16), ()): ("CMYK", "CMYK;16B"), + (II, 6, (1,), 1, (8,), ()): ("L", "L"), + (MM, 6, (1,), 1, (8,), ()): ("L", "L"), + # JPEG compressed images handled by LibTiff and auto-converted to RGBX + # Minimal Baseline TIFF requires YCbCr images to have 3 SamplesPerPixel + (II, 6, (1,), 1, (8, 8, 8), ()): ("RGB", "RGBX"), + (MM, 6, (1,), 1, (8, 8, 8), ()): ("RGB", "RGBX"), + (II, 8, (1,), 1, (8, 8, 8), ()): ("LAB", "LAB"), + (MM, 8, (1,), 1, (8, 8, 8), ()): ("LAB", "LAB"), +} + +MAX_SAMPLESPERPIXEL = max(len(key_tp[4]) for key_tp in OPEN_INFO) + +PREFIXES = [ + b"MM\x00\x2a", # Valid TIFF header with big-endian byte order + b"II\x2a\x00", # Valid TIFF header with little-endian byte order + b"MM\x2a\x00", # Invalid TIFF header, assume big-endian + b"II\x00\x2a", # Invalid TIFF header, assume little-endian + b"MM\x00\x2b", # BigTIFF with big-endian byte order + b"II\x2b\x00", # BigTIFF with little-endian byte order +] + +if not getattr(Image.core, "libtiff_support_custom_tags", True): + deprecate("Support for LibTIFF earlier than version 4", 12) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(tuple(PREFIXES)) + + +def _limit_rational( + val: float | Fraction | IFDRational, max_val: int +) -> tuple[IntegralLike, IntegralLike]: + inv = abs(val) > 1 + n_d = IFDRational(1 / val if inv else val).limit_rational(max_val) + return n_d[::-1] if inv else n_d + + +def _limit_signed_rational( + val: IFDRational, max_val: int, min_val: int +) -> tuple[IntegralLike, IntegralLike]: + frac = Fraction(val) + n_d: tuple[IntegralLike, IntegralLike] = frac.numerator, frac.denominator + + if min(float(i) for i in n_d) < min_val: + n_d = _limit_rational(val, abs(min_val)) + + n_d_float = tuple(float(i) for i in n_d) + if max(n_d_float) > max_val: + n_d = _limit_rational(n_d_float[0] / n_d_float[1], max_val) + + return n_d + + +## +# Wrapper for TIFF IFDs. + +_load_dispatch = {} +_write_dispatch = {} + + +def _delegate(op: str) -> Any: + def delegate( + self: IFDRational, *args: tuple[float, ...] + ) -> bool | float | Fraction: + return getattr(self._val, op)(*args) + + return delegate + + +class IFDRational(Rational): + """Implements a rational class where 0/0 is a legal value to match + the in the wild use of exif rationals. + + e.g., DigitalZoomRatio - 0.00/0.00 indicates that no digital zoom was used + """ + + """ If the denominator is 0, store this as a float('nan'), otherwise store + as a fractions.Fraction(). Delegate as appropriate + + """ + + __slots__ = ("_numerator", "_denominator", "_val") + + def __init__( + self, value: float | Fraction | IFDRational, denominator: int = 1 + ) -> None: + """ + :param value: either an integer numerator, a + float/rational/other number, or an IFDRational + :param denominator: Optional integer denominator + """ + self._val: Fraction | float + if isinstance(value, IFDRational): + self._numerator = value.numerator + self._denominator = value.denominator + self._val = value._val + return + + if isinstance(value, Fraction): + self._numerator = value.numerator + self._denominator = value.denominator + else: + if TYPE_CHECKING: + self._numerator = cast(IntegralLike, value) + else: + self._numerator = value + self._denominator = denominator + + if denominator == 0: + self._val = float("nan") + elif denominator == 1: + self._val = Fraction(value) + elif int(value) == value: + self._val = Fraction(int(value), denominator) + else: + self._val = Fraction(value / denominator) + + @property + def numerator(self) -> IntegralLike: + return self._numerator + + @property + def denominator(self) -> int: + return self._denominator + + def limit_rational(self, max_denominator: int) -> tuple[IntegralLike, int]: + """ + + :param max_denominator: Integer, the maximum denominator value + :returns: Tuple of (numerator, denominator) + """ + + if self.denominator == 0: + return self.numerator, self.denominator + + assert isinstance(self._val, Fraction) + f = self._val.limit_denominator(max_denominator) + return f.numerator, f.denominator + + def __repr__(self) -> str: + return str(float(self._val)) + + def __hash__(self) -> int: # type: ignore[override] + return self._val.__hash__() + + def __eq__(self, other: object) -> bool: + val = self._val + if isinstance(other, IFDRational): + other = other._val + if isinstance(other, float): + val = float(val) + return val == other + + def __getstate__(self) -> list[float | Fraction | IntegralLike]: + return [self._val, self._numerator, self._denominator] + + def __setstate__(self, state: list[float | Fraction | IntegralLike]) -> None: + IFDRational.__init__(self, 0) + _val, _numerator, _denominator = state + assert isinstance(_val, (float, Fraction)) + self._val = _val + if TYPE_CHECKING: + self._numerator = cast(IntegralLike, _numerator) + else: + self._numerator = _numerator + assert isinstance(_denominator, int) + self._denominator = _denominator + + """ a = ['add','radd', 'sub', 'rsub', 'mul', 'rmul', + 'truediv', 'rtruediv', 'floordiv', 'rfloordiv', + 'mod','rmod', 'pow','rpow', 'pos', 'neg', + 'abs', 'trunc', 'lt', 'gt', 'le', 'ge', 'bool', + 'ceil', 'floor', 'round'] + print("\n".join("__%s__ = _delegate('__%s__')" % (s,s) for s in a)) + """ + + __add__ = _delegate("__add__") + __radd__ = _delegate("__radd__") + __sub__ = _delegate("__sub__") + __rsub__ = _delegate("__rsub__") + __mul__ = _delegate("__mul__") + __rmul__ = _delegate("__rmul__") + __truediv__ = _delegate("__truediv__") + __rtruediv__ = _delegate("__rtruediv__") + __floordiv__ = _delegate("__floordiv__") + __rfloordiv__ = _delegate("__rfloordiv__") + __mod__ = _delegate("__mod__") + __rmod__ = _delegate("__rmod__") + __pow__ = _delegate("__pow__") + __rpow__ = _delegate("__rpow__") + __pos__ = _delegate("__pos__") + __neg__ = _delegate("__neg__") + __abs__ = _delegate("__abs__") + __trunc__ = _delegate("__trunc__") + __lt__ = _delegate("__lt__") + __gt__ = _delegate("__gt__") + __le__ = _delegate("__le__") + __ge__ = _delegate("__ge__") + __bool__ = _delegate("__bool__") + __ceil__ = _delegate("__ceil__") + __floor__ = _delegate("__floor__") + __round__ = _delegate("__round__") + # Python >= 3.11 + if hasattr(Fraction, "__int__"): + __int__ = _delegate("__int__") + + +_LoaderFunc = Callable[["ImageFileDirectory_v2", bytes, bool], Any] + + +def _register_loader(idx: int, size: int) -> Callable[[_LoaderFunc], _LoaderFunc]: + def decorator(func: _LoaderFunc) -> _LoaderFunc: + from .TiffTags import TYPES + + if func.__name__.startswith("load_"): + TYPES[idx] = func.__name__[5:].replace("_", " ") + _load_dispatch[idx] = size, func # noqa: F821 + return func + + return decorator + + +def _register_writer(idx: int) -> Callable[[Callable[..., Any]], Callable[..., Any]]: + def decorator(func: Callable[..., Any]) -> Callable[..., Any]: + _write_dispatch[idx] = func # noqa: F821 + return func + + return decorator + + +def _register_basic(idx_fmt_name: tuple[int, str, str]) -> None: + from .TiffTags import TYPES + + idx, fmt, name = idx_fmt_name + TYPES[idx] = name + size = struct.calcsize(f"={fmt}") + + def basic_handler( + self: ImageFileDirectory_v2, data: bytes, legacy_api: bool = True + ) -> tuple[Any, ...]: + return self._unpack(f"{len(data) // size}{fmt}", data) + + _load_dispatch[idx] = size, basic_handler # noqa: F821 + _write_dispatch[idx] = lambda self, *values: ( # noqa: F821 + b"".join(self._pack(fmt, value) for value in values) + ) + + +if TYPE_CHECKING: + _IFDv2Base = MutableMapping[int, Any] +else: + _IFDv2Base = MutableMapping + + +class ImageFileDirectory_v2(_IFDv2Base): + """This class represents a TIFF tag directory. To speed things up, we + don't decode tags unless they're asked for. + + Exposes a dictionary interface of the tags in the directory:: + + ifd = ImageFileDirectory_v2() + ifd[key] = 'Some Data' + ifd.tagtype[key] = TiffTags.ASCII + print(ifd[key]) + 'Some Data' + + Individual values are returned as the strings or numbers, sequences are + returned as tuples of the values. + + The tiff metadata type of each item is stored in a dictionary of + tag types in + :attr:`~PIL.TiffImagePlugin.ImageFileDirectory_v2.tagtype`. The types + are read from a tiff file, guessed from the type added, or added + manually. + + Data Structures: + + * ``self.tagtype = {}`` + + * Key: numerical TIFF tag number + * Value: integer corresponding to the data type from + :py:data:`.TiffTags.TYPES` + + .. versionadded:: 3.0.0 + + 'Internal' data structures: + + * ``self._tags_v2 = {}`` + + * Key: numerical TIFF tag number + * Value: decoded data, as tuple for multiple values + + * ``self._tagdata = {}`` + + * Key: numerical TIFF tag number + * Value: undecoded byte string from file + + * ``self._tags_v1 = {}`` + + * Key: numerical TIFF tag number + * Value: decoded data in the v1 format + + Tags will be found in the private attributes ``self._tagdata``, and in + ``self._tags_v2`` once decoded. + + ``self.legacy_api`` is a value for internal use, and shouldn't be changed + from outside code. In cooperation with + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1`, if ``legacy_api`` + is true, then decoded tags will be populated into both ``_tags_v1`` and + ``_tags_v2``. ``_tags_v2`` will be used if this IFD is used in the TIFF + save routine. Tags should be read from ``_tags_v1`` if + ``legacy_api == true``. + + """ + + _load_dispatch: dict[int, tuple[int, _LoaderFunc]] = {} + _write_dispatch: dict[int, Callable[..., Any]] = {} + + def __init__( + self, + ifh: bytes = b"II\x2a\x00\x00\x00\x00\x00", + prefix: bytes | None = None, + group: int | None = None, + ) -> None: + """Initialize an ImageFileDirectory. + + To construct an ImageFileDirectory from a real file, pass the 8-byte + magic header to the constructor. To only set the endianness, pass it + as the 'prefix' keyword argument. + + :param ifh: One of the accepted magic headers (cf. PREFIXES); also sets + endianness. + :param prefix: Override the endianness of the file. + """ + if not _accept(ifh): + msg = f"not a TIFF file (header {repr(ifh)} not valid)" + raise SyntaxError(msg) + self._prefix = prefix if prefix is not None else ifh[:2] + if self._prefix == MM: + self._endian = ">" + elif self._prefix == II: + self._endian = "<" + else: + msg = "not a TIFF IFD" + raise SyntaxError(msg) + self._bigtiff = ifh[2] == 43 + self.group = group + self.tagtype: dict[int, int] = {} + """ Dictionary of tag types """ + self.reset() + self.next = ( + self._unpack("Q", ifh[8:])[0] + if self._bigtiff + else self._unpack("L", ifh[4:])[0] + ) + self._legacy_api = False + + prefix = property(lambda self: self._prefix) + offset = property(lambda self: self._offset) + + @property + def legacy_api(self) -> bool: + return self._legacy_api + + @legacy_api.setter + def legacy_api(self, value: bool) -> NoReturn: + msg = "Not allowing setting of legacy api" + raise Exception(msg) + + def reset(self) -> None: + self._tags_v1: dict[int, Any] = {} # will remain empty if legacy_api is false + self._tags_v2: dict[int, Any] = {} # main tag storage + self._tagdata: dict[int, bytes] = {} + self.tagtype = {} # added 2008-06-05 by Florian Hoech + self._next = None + self._offset: int | None = None + + def __str__(self) -> str: + return str(dict(self)) + + def named(self) -> dict[str, Any]: + """ + :returns: dict of name|key: value + + Returns the complete tag dictionary, with named tags where possible. + """ + return { + TiffTags.lookup(code, self.group).name: value + for code, value in self.items() + } + + def __len__(self) -> int: + return len(set(self._tagdata) | set(self._tags_v2)) + + def __getitem__(self, tag: int) -> Any: + if tag not in self._tags_v2: # unpack on the fly + data = self._tagdata[tag] + typ = self.tagtype[tag] + size, handler = self._load_dispatch[typ] + self[tag] = handler(self, data, self.legacy_api) # check type + val = self._tags_v2[tag] + if self.legacy_api and not isinstance(val, (tuple, bytes)): + val = (val,) + return val + + def __contains__(self, tag: object) -> bool: + return tag in self._tags_v2 or tag in self._tagdata + + def __setitem__(self, tag: int, value: Any) -> None: + self._setitem(tag, value, self.legacy_api) + + def _setitem(self, tag: int, value: Any, legacy_api: bool) -> None: + basetypes = (Number, bytes, str) + + info = TiffTags.lookup(tag, self.group) + values = [value] if isinstance(value, basetypes) else value + + if tag not in self.tagtype: + if info.type: + self.tagtype[tag] = info.type + else: + self.tagtype[tag] = TiffTags.UNDEFINED + if all(isinstance(v, IFDRational) for v in values): + for v in values: + assert isinstance(v, IFDRational) + if v < 0: + self.tagtype[tag] = TiffTags.SIGNED_RATIONAL + break + else: + self.tagtype[tag] = TiffTags.RATIONAL + elif all(isinstance(v, int) for v in values): + short = True + signed_short = True + long = True + for v in values: + assert isinstance(v, int) + if short and not (0 <= v < 2**16): + short = False + if signed_short and not (-(2**15) < v < 2**15): + signed_short = False + if long and v < 0: + long = False + if short: + self.tagtype[tag] = TiffTags.SHORT + elif signed_short: + self.tagtype[tag] = TiffTags.SIGNED_SHORT + elif long: + self.tagtype[tag] = TiffTags.LONG + else: + self.tagtype[tag] = TiffTags.SIGNED_LONG + elif all(isinstance(v, float) for v in values): + self.tagtype[tag] = TiffTags.DOUBLE + elif all(isinstance(v, str) for v in values): + self.tagtype[tag] = TiffTags.ASCII + elif all(isinstance(v, bytes) for v in values): + self.tagtype[tag] = TiffTags.BYTE + + if self.tagtype[tag] == TiffTags.UNDEFINED: + values = [ + v.encode("ascii", "replace") if isinstance(v, str) else v + for v in values + ] + elif self.tagtype[tag] == TiffTags.RATIONAL: + values = [float(v) if isinstance(v, int) else v for v in values] + + is_ifd = self.tagtype[tag] == TiffTags.LONG and isinstance(values, dict) + if not is_ifd: + values = tuple( + info.cvt_enum(value) if isinstance(value, str) else value + for value in values + ) + + dest = self._tags_v1 if legacy_api else self._tags_v2 + + # Three branches: + # Spec'd length == 1, Actual length 1, store as element + # Spec'd length == 1, Actual > 1, Warn and truncate. Formerly barfed. + # No Spec, Actual length 1, Formerly (<4.2) returned a 1 element tuple. + # Don't mess with the legacy api, since it's frozen. + if not is_ifd and ( + (info.length == 1) + or self.tagtype[tag] == TiffTags.BYTE + or (info.length is None and len(values) == 1 and not legacy_api) + ): + # Don't mess with the legacy api, since it's frozen. + if legacy_api and self.tagtype[tag] in [ + TiffTags.RATIONAL, + TiffTags.SIGNED_RATIONAL, + ]: # rationals + values = (values,) + try: + (dest[tag],) = values + except ValueError: + # We've got a builtin tag with 1 expected entry + warnings.warn( + f"Metadata Warning, tag {tag} had too many entries: " + f"{len(values)}, expected 1" + ) + dest[tag] = values[0] + + else: + # Spec'd length > 1 or undefined + # Unspec'd, and length > 1 + dest[tag] = values + + def __delitem__(self, tag: int) -> None: + self._tags_v2.pop(tag, None) + self._tags_v1.pop(tag, None) + self._tagdata.pop(tag, None) + + def __iter__(self) -> Iterator[int]: + return iter(set(self._tagdata) | set(self._tags_v2)) + + def _unpack(self, fmt: str, data: bytes) -> tuple[Any, ...]: + return struct.unpack(self._endian + fmt, data) + + def _pack(self, fmt: str, *values: Any) -> bytes: + return struct.pack(self._endian + fmt, *values) + + list( + map( + _register_basic, + [ + (TiffTags.SHORT, "H", "short"), + (TiffTags.LONG, "L", "long"), + (TiffTags.SIGNED_BYTE, "b", "signed byte"), + (TiffTags.SIGNED_SHORT, "h", "signed short"), + (TiffTags.SIGNED_LONG, "l", "signed long"), + (TiffTags.FLOAT, "f", "float"), + (TiffTags.DOUBLE, "d", "double"), + (TiffTags.IFD, "L", "long"), + (TiffTags.LONG8, "Q", "long8"), + ], + ) + ) + + @_register_loader(1, 1) # Basic type, except for the legacy API. + def load_byte(self, data: bytes, legacy_api: bool = True) -> bytes: + return data + + @_register_writer(1) # Basic type, except for the legacy API. + def write_byte(self, data: bytes | int | IFDRational) -> bytes: + if isinstance(data, IFDRational): + data = int(data) + if isinstance(data, int): + data = bytes((data,)) + return data + + @_register_loader(2, 1) + def load_string(self, data: bytes, legacy_api: bool = True) -> str: + if data.endswith(b"\0"): + data = data[:-1] + return data.decode("latin-1", "replace") + + @_register_writer(2) + def write_string(self, value: str | bytes | int) -> bytes: + # remerge of https://github.com/python-pillow/Pillow/pull/1416 + if isinstance(value, int): + value = str(value) + if not isinstance(value, bytes): + value = value.encode("ascii", "replace") + return value + b"\0" + + @_register_loader(5, 8) + def load_rational( + self, data: bytes, legacy_api: bool = True + ) -> tuple[tuple[int, int] | IFDRational, ...]: + vals = self._unpack(f"{len(data) // 4}L", data) + + def combine(a: int, b: int) -> tuple[int, int] | IFDRational: + return (a, b) if legacy_api else IFDRational(a, b) + + return tuple(combine(num, denom) for num, denom in zip(vals[::2], vals[1::2])) + + @_register_writer(5) + def write_rational(self, *values: IFDRational) -> bytes: + return b"".join( + self._pack("2L", *_limit_rational(frac, 2**32 - 1)) for frac in values + ) + + @_register_loader(7, 1) + def load_undefined(self, data: bytes, legacy_api: bool = True) -> bytes: + return data + + @_register_writer(7) + def write_undefined(self, value: bytes | int | IFDRational) -> bytes: + if isinstance(value, IFDRational): + value = int(value) + if isinstance(value, int): + value = str(value).encode("ascii", "replace") + return value + + @_register_loader(10, 8) + def load_signed_rational( + self, data: bytes, legacy_api: bool = True + ) -> tuple[tuple[int, int] | IFDRational, ...]: + vals = self._unpack(f"{len(data) // 4}l", data) + + def combine(a: int, b: int) -> tuple[int, int] | IFDRational: + return (a, b) if legacy_api else IFDRational(a, b) + + return tuple(combine(num, denom) for num, denom in zip(vals[::2], vals[1::2])) + + @_register_writer(10) + def write_signed_rational(self, *values: IFDRational) -> bytes: + return b"".join( + self._pack("2l", *_limit_signed_rational(frac, 2**31 - 1, -(2**31))) + for frac in values + ) + + def _ensure_read(self, fp: IO[bytes], size: int) -> bytes: + ret = fp.read(size) + if len(ret) != size: + msg = ( + "Corrupt EXIF data. " + f"Expecting to read {size} bytes but only got {len(ret)}. " + ) + raise OSError(msg) + return ret + + def load(self, fp: IO[bytes]) -> None: + self.reset() + self._offset = fp.tell() + + try: + tag_count = ( + self._unpack("Q", self._ensure_read(fp, 8)) + if self._bigtiff + else self._unpack("H", self._ensure_read(fp, 2)) + )[0] + for i in range(tag_count): + tag, typ, count, data = ( + self._unpack("HHQ8s", self._ensure_read(fp, 20)) + if self._bigtiff + else self._unpack("HHL4s", self._ensure_read(fp, 12)) + ) + + tagname = TiffTags.lookup(tag, self.group).name + typname = TYPES.get(typ, "unknown") + msg = f"tag: {tagname} ({tag}) - type: {typname} ({typ})" + + try: + unit_size, handler = self._load_dispatch[typ] + except KeyError: + logger.debug("%s - unsupported type %s", msg, typ) + continue # ignore unsupported type + size = count * unit_size + if size > (8 if self._bigtiff else 4): + here = fp.tell() + (offset,) = self._unpack("Q" if self._bigtiff else "L", data) + msg += f" Tag Location: {here} - Data Location: {offset}" + fp.seek(offset) + data = ImageFile._safe_read(fp, size) + fp.seek(here) + else: + data = data[:size] + + if len(data) != size: + warnings.warn( + "Possibly corrupt EXIF data. " + f"Expecting to read {size} bytes but only got {len(data)}." + f" Skipping tag {tag}" + ) + logger.debug(msg) + continue + + if not data: + logger.debug(msg) + continue + + self._tagdata[tag] = data + self.tagtype[tag] = typ + + msg += " - value: " + msg += f"" if size > 32 else repr(data) + + logger.debug(msg) + + (self.next,) = ( + self._unpack("Q", self._ensure_read(fp, 8)) + if self._bigtiff + else self._unpack("L", self._ensure_read(fp, 4)) + ) + except OSError as msg: + warnings.warn(str(msg)) + return + + def _get_ifh(self) -> bytes: + ifh = self._prefix + self._pack("H", 43 if self._bigtiff else 42) + if self._bigtiff: + ifh += self._pack("HH", 8, 0) + ifh += self._pack("Q", 16) if self._bigtiff else self._pack("L", 8) + + return ifh + + def tobytes(self, offset: int = 0) -> bytes: + # FIXME What about tagdata? + result = self._pack("Q" if self._bigtiff else "H", len(self._tags_v2)) + + entries: list[tuple[int, int, int, bytes, bytes]] = [] + + fmt = "Q" if self._bigtiff else "L" + fmt_size = 8 if self._bigtiff else 4 + offset += ( + len(result) + len(self._tags_v2) * (20 if self._bigtiff else 12) + fmt_size + ) + stripoffsets = None + + # pass 1: convert tags to binary format + # always write tags in ascending order + for tag, value in sorted(self._tags_v2.items()): + if tag == STRIPOFFSETS: + stripoffsets = len(entries) + typ = self.tagtype[tag] + logger.debug("Tag %s, Type: %s, Value: %s", tag, typ, repr(value)) + is_ifd = typ == TiffTags.LONG and isinstance(value, dict) + if is_ifd: + ifd = ImageFileDirectory_v2(self._get_ifh(), group=tag) + values = self._tags_v2[tag] + for ifd_tag, ifd_value in values.items(): + ifd[ifd_tag] = ifd_value + data = ifd.tobytes(offset) + else: + values = value if isinstance(value, tuple) else (value,) + data = self._write_dispatch[typ](self, *values) + + tagname = TiffTags.lookup(tag, self.group).name + typname = "ifd" if is_ifd else TYPES.get(typ, "unknown") + msg = f"save: {tagname} ({tag}) - type: {typname} ({typ}) - value: " + msg += f"" if len(data) >= 16 else str(values) + logger.debug(msg) + + # count is sum of lengths for string and arbitrary data + if is_ifd: + count = 1 + elif typ in [TiffTags.BYTE, TiffTags.ASCII, TiffTags.UNDEFINED]: + count = len(data) + else: + count = len(values) + # figure out if data fits into the entry + if len(data) <= fmt_size: + entries.append((tag, typ, count, data.ljust(fmt_size, b"\0"), b"")) + else: + entries.append((tag, typ, count, self._pack(fmt, offset), data)) + offset += (len(data) + 1) // 2 * 2 # pad to word + + # update strip offset data to point beyond auxiliary data + if stripoffsets is not None: + tag, typ, count, value, data = entries[stripoffsets] + if data: + size, handler = self._load_dispatch[typ] + values = [val + offset for val in handler(self, data, self.legacy_api)] + data = self._write_dispatch[typ](self, *values) + else: + value = self._pack(fmt, self._unpack(fmt, value)[0] + offset) + entries[stripoffsets] = tag, typ, count, value, data + + # pass 2: write entries to file + for tag, typ, count, value, data in entries: + logger.debug("%s %s %s %s %s", tag, typ, count, repr(value), repr(data)) + result += self._pack( + "HHQ8s" if self._bigtiff else "HHL4s", tag, typ, count, value + ) + + # -- overwrite here for multi-page -- + result += self._pack(fmt, 0) # end of entries + + # pass 3: write auxiliary data to file + for tag, typ, count, value, data in entries: + result += data + if len(data) & 1: + result += b"\0" + + return result + + def save(self, fp: IO[bytes]) -> int: + if fp.tell() == 0: # skip TIFF header on subsequent pages + fp.write(self._get_ifh()) + + offset = fp.tell() + result = self.tobytes(offset) + fp.write(result) + return offset + len(result) + + +ImageFileDirectory_v2._load_dispatch = _load_dispatch +ImageFileDirectory_v2._write_dispatch = _write_dispatch +for idx, name in TYPES.items(): + name = name.replace(" ", "_") + setattr(ImageFileDirectory_v2, f"load_{name}", _load_dispatch[idx][1]) + setattr(ImageFileDirectory_v2, f"write_{name}", _write_dispatch[idx]) +del _load_dispatch, _write_dispatch, idx, name + + +# Legacy ImageFileDirectory support. +class ImageFileDirectory_v1(ImageFileDirectory_v2): + """This class represents the **legacy** interface to a TIFF tag directory. + + Exposes a dictionary interface of the tags in the directory:: + + ifd = ImageFileDirectory_v1() + ifd[key] = 'Some Data' + ifd.tagtype[key] = TiffTags.ASCII + print(ifd[key]) + ('Some Data',) + + Also contains a dictionary of tag types as read from the tiff image file, + :attr:`~PIL.TiffImagePlugin.ImageFileDirectory_v1.tagtype`. + + Values are returned as a tuple. + + .. deprecated:: 3.0.0 + """ + + def __init__(self, *args: Any, **kwargs: Any) -> None: + super().__init__(*args, **kwargs) + self._legacy_api = True + + tags = property(lambda self: self._tags_v1) + tagdata = property(lambda self: self._tagdata) + + # defined in ImageFileDirectory_v2 + tagtype: dict[int, int] + """Dictionary of tag types""" + + @classmethod + def from_v2(cls, original: ImageFileDirectory_v2) -> ImageFileDirectory_v1: + """Returns an + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1` + instance with the same data as is contained in the original + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v2` + instance. + + :returns: :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1` + + """ + + ifd = cls(prefix=original.prefix) + ifd._tagdata = original._tagdata + ifd.tagtype = original.tagtype + ifd.next = original.next # an indicator for multipage tiffs + return ifd + + def to_v2(self) -> ImageFileDirectory_v2: + """Returns an + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v2` + instance with the same data as is contained in the original + :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v1` + instance. + + :returns: :py:class:`~PIL.TiffImagePlugin.ImageFileDirectory_v2` + + """ + + ifd = ImageFileDirectory_v2(prefix=self.prefix) + ifd._tagdata = dict(self._tagdata) + ifd.tagtype = dict(self.tagtype) + ifd._tags_v2 = dict(self._tags_v2) + return ifd + + def __contains__(self, tag: object) -> bool: + return tag in self._tags_v1 or tag in self._tagdata + + def __len__(self) -> int: + return len(set(self._tagdata) | set(self._tags_v1)) + + def __iter__(self) -> Iterator[int]: + return iter(set(self._tagdata) | set(self._tags_v1)) + + def __setitem__(self, tag: int, value: Any) -> None: + for legacy_api in (False, True): + self._setitem(tag, value, legacy_api) + + def __getitem__(self, tag: int) -> Any: + if tag not in self._tags_v1: # unpack on the fly + data = self._tagdata[tag] + typ = self.tagtype[tag] + size, handler = self._load_dispatch[typ] + for legacy in (False, True): + self._setitem(tag, handler(self, data, legacy), legacy) + val = self._tags_v1[tag] + if not isinstance(val, (tuple, bytes)): + val = (val,) + return val + + +# undone -- switch this pointer +ImageFileDirectory = ImageFileDirectory_v1 + + +## +# Image plugin for TIFF files. + + +class TiffImageFile(ImageFile.ImageFile): + format = "TIFF" + format_description = "Adobe TIFF" + _close_exclusive_fp_after_loading = False + + def __init__( + self, + fp: StrOrBytesPath | IO[bytes], + filename: str | bytes | None = None, + ) -> None: + self.tag_v2: ImageFileDirectory_v2 + """ Image file directory (tag dictionary) """ + + self.tag: ImageFileDirectory_v1 + """ Legacy tag entries """ + + super().__init__(fp, filename) + + def _open(self) -> None: + """Open the first image in a TIFF file""" + + # Header + ifh = self.fp.read(8) + if ifh[2] == 43: + ifh += self.fp.read(8) + + self.tag_v2 = ImageFileDirectory_v2(ifh) + + # setup frame pointers + self.__first = self.__next = self.tag_v2.next + self.__frame = -1 + self._fp = self.fp + self._frame_pos: list[int] = [] + self._n_frames: int | None = None + + logger.debug("*** TiffImageFile._open ***") + logger.debug("- __first: %s", self.__first) + logger.debug("- ifh: %s", repr(ifh)) # Use repr to avoid str(bytes) + + # and load the first frame + self._seek(0) + + @property + def n_frames(self) -> int: + current_n_frames = self._n_frames + if current_n_frames is None: + current = self.tell() + self._seek(len(self._frame_pos)) + while self._n_frames is None: + self._seek(self.tell() + 1) + self.seek(current) + assert self._n_frames is not None + return self._n_frames + + def seek(self, frame: int) -> None: + """Select a given frame as current image""" + if not self._seek_check(frame): + return + self._seek(frame) + if self._im is not None and ( + self.im.size != self._tile_size + or self.im.mode != self.mode + or self.readonly + ): + self._im = None + + def _seek(self, frame: int) -> None: + if isinstance(self._fp, DeferredError): + raise self._fp.ex + self.fp = self._fp + + while len(self._frame_pos) <= frame: + if not self.__next: + msg = "no more images in TIFF file" + raise EOFError(msg) + logger.debug( + "Seeking to frame %s, on frame %s, __next %s, location: %s", + frame, + self.__frame, + self.__next, + self.fp.tell(), + ) + if self.__next >= 2**63: + msg = "Unable to seek to frame" + raise ValueError(msg) + self.fp.seek(self.__next) + self._frame_pos.append(self.__next) + logger.debug("Loading tags, location: %s", self.fp.tell()) + self.tag_v2.load(self.fp) + if self.tag_v2.next in self._frame_pos: + # This IFD has already been processed + # Declare this to be the end of the image + self.__next = 0 + else: + self.__next = self.tag_v2.next + if self.__next == 0: + self._n_frames = frame + 1 + if len(self._frame_pos) == 1: + self.is_animated = self.__next != 0 + self.__frame += 1 + self.fp.seek(self._frame_pos[frame]) + self.tag_v2.load(self.fp) + if XMP in self.tag_v2: + xmp = self.tag_v2[XMP] + if isinstance(xmp, tuple) and len(xmp) == 1: + xmp = xmp[0] + self.info["xmp"] = xmp + elif "xmp" in self.info: + del self.info["xmp"] + self._reload_exif() + # fill the legacy tag/ifd entries + self.tag = self.ifd = ImageFileDirectory_v1.from_v2(self.tag_v2) + self.__frame = frame + self._setup() + + def tell(self) -> int: + """Return the current frame number""" + return self.__frame + + def get_photoshop_blocks(self) -> dict[int, dict[str, bytes]]: + """ + Returns a dictionary of Photoshop "Image Resource Blocks". + The keys are the image resource ID. For more information, see + https://www.adobe.com/devnet-apps/photoshop/fileformatashtml/#50577409_pgfId-1037727 + + :returns: Photoshop "Image Resource Blocks" in a dictionary. + """ + blocks = {} + val = self.tag_v2.get(ExifTags.Base.ImageResources) + if val: + while val.startswith(b"8BIM"): + id = i16(val[4:6]) + n = math.ceil((val[6] + 1) / 2) * 2 + size = i32(val[6 + n : 10 + n]) + data = val[10 + n : 10 + n + size] + blocks[id] = {"data": data} + + val = val[math.ceil((10 + n + size) / 2) * 2 :] + return blocks + + def load(self) -> Image.core.PixelAccess | None: + if self.tile and self.use_load_libtiff: + return self._load_libtiff() + return super().load() + + def load_prepare(self) -> None: + if self._im is None: + Image._decompression_bomb_check(self._tile_size) + self.im = Image.core.new(self.mode, self._tile_size) + ImageFile.ImageFile.load_prepare(self) + + def load_end(self) -> None: + # allow closing if we're on the first frame, there's no next + # This is the ImageFile.load path only, libtiff specific below. + if not self.is_animated: + self._close_exclusive_fp_after_loading = True + + # load IFD data from fp before it is closed + exif = self.getexif() + for key in TiffTags.TAGS_V2_GROUPS: + if key not in exif: + continue + exif.get_ifd(key) + + ImageOps.exif_transpose(self, in_place=True) + if ExifTags.Base.Orientation in self.tag_v2: + del self.tag_v2[ExifTags.Base.Orientation] + + def _load_libtiff(self) -> Image.core.PixelAccess | None: + """Overload method triggered when we detect a compressed tiff + Calls out to libtiff""" + + Image.Image.load(self) + + self.load_prepare() + + if not len(self.tile) == 1: + msg = "Not exactly one tile" + raise OSError(msg) + + # (self._compression, (extents tuple), + # 0, (rawmode, self._compression, fp)) + extents = self.tile[0][1] + args = self.tile[0][3] + + # To be nice on memory footprint, if there's a + # file descriptor, use that instead of reading + # into a string in python. + try: + fp = hasattr(self.fp, "fileno") and self.fp.fileno() + # flush the file descriptor, prevents error on pypy 2.4+ + # should also eliminate the need for fp.tell + # in _seek + if hasattr(self.fp, "flush"): + self.fp.flush() + except OSError: + # io.BytesIO have a fileno, but returns an OSError if + # it doesn't use a file descriptor. + fp = False + + if fp: + assert isinstance(args, tuple) + args_list = list(args) + args_list[2] = fp + args = tuple(args_list) + + decoder = Image._getdecoder(self.mode, "libtiff", args, self.decoderconfig) + try: + decoder.setimage(self.im, extents) + except ValueError as e: + msg = "Couldn't set the image" + raise OSError(msg) from e + + close_self_fp = self._exclusive_fp and not self.is_animated + if hasattr(self.fp, "getvalue"): + # We've got a stringio like thing passed in. Yay for all in memory. + # The decoder needs the entire file in one shot, so there's not + # a lot we can do here other than give it the entire file. + # unless we could do something like get the address of the + # underlying string for stringio. + # + # Rearranging for supporting byteio items, since they have a fileno + # that returns an OSError if there's no underlying fp. Easier to + # deal with here by reordering. + logger.debug("have getvalue. just sending in a string from getvalue") + n, err = decoder.decode(self.fp.getvalue()) + elif fp: + # we've got a actual file on disk, pass in the fp. + logger.debug("have fileno, calling fileno version of the decoder.") + if not close_self_fp: + self.fp.seek(0) + # Save and restore the file position, because libtiff will move it + # outside of the Python runtime, and that will confuse + # io.BufferedReader and possible others. + # NOTE: This must use os.lseek(), and not fp.tell()/fp.seek(), + # because the buffer read head already may not equal the actual + # file position, and fp.seek() may just adjust it's internal + # pointer and not actually seek the OS file handle. + pos = os.lseek(fp, 0, os.SEEK_CUR) + # 4 bytes, otherwise the trace might error out + n, err = decoder.decode(b"fpfp") + os.lseek(fp, pos, os.SEEK_SET) + else: + # we have something else. + logger.debug("don't have fileno or getvalue. just reading") + self.fp.seek(0) + # UNDONE -- so much for that buffer size thing. + n, err = decoder.decode(self.fp.read()) + + self.tile = [] + self.readonly = 0 + + self.load_end() + + if close_self_fp: + self.fp.close() + self.fp = None # might be shared + + if err < 0: + msg = f"decoder error {err}" + raise OSError(msg) + + return Image.Image.load(self) + + def _setup(self) -> None: + """Setup this image object based on current tags""" + + if 0xBC01 in self.tag_v2: + msg = "Windows Media Photo files not yet supported" + raise OSError(msg) + + # extract relevant tags + self._compression = COMPRESSION_INFO[self.tag_v2.get(COMPRESSION, 1)] + self._planar_configuration = self.tag_v2.get(PLANAR_CONFIGURATION, 1) + + # photometric is a required tag, but not everyone is reading + # the specification + photo = self.tag_v2.get(PHOTOMETRIC_INTERPRETATION, 0) + + # old style jpeg compression images most certainly are YCbCr + if self._compression == "tiff_jpeg": + photo = 6 + + fillorder = self.tag_v2.get(FILLORDER, 1) + + logger.debug("*** Summary ***") + logger.debug("- compression: %s", self._compression) + logger.debug("- photometric_interpretation: %s", photo) + logger.debug("- planar_configuration: %s", self._planar_configuration) + logger.debug("- fill_order: %s", fillorder) + logger.debug("- YCbCr subsampling: %s", self.tag_v2.get(YCBCRSUBSAMPLING)) + + # size + try: + xsize = self.tag_v2[IMAGEWIDTH] + ysize = self.tag_v2[IMAGELENGTH] + except KeyError as e: + msg = "Missing dimensions" + raise TypeError(msg) from e + if not isinstance(xsize, int) or not isinstance(ysize, int): + msg = "Invalid dimensions" + raise ValueError(msg) + self._tile_size = xsize, ysize + orientation = self.tag_v2.get(ExifTags.Base.Orientation) + if orientation in (5, 6, 7, 8): + self._size = ysize, xsize + else: + self._size = xsize, ysize + + logger.debug("- size: %s", self.size) + + sample_format = self.tag_v2.get(SAMPLEFORMAT, (1,)) + if len(sample_format) > 1 and max(sample_format) == min(sample_format) == 1: + # SAMPLEFORMAT is properly per band, so an RGB image will + # be (1,1,1). But, we don't support per band pixel types, + # and anything more than one band is a uint8. So, just + # take the first element. Revisit this if adding support + # for more exotic images. + sample_format = (1,) + + bps_tuple = self.tag_v2.get(BITSPERSAMPLE, (1,)) + extra_tuple = self.tag_v2.get(EXTRASAMPLES, ()) + if photo in (2, 6, 8): # RGB, YCbCr, LAB + bps_count = 3 + elif photo == 5: # CMYK + bps_count = 4 + else: + bps_count = 1 + bps_count += len(extra_tuple) + bps_actual_count = len(bps_tuple) + samples_per_pixel = self.tag_v2.get( + SAMPLESPERPIXEL, + 3 if self._compression == "tiff_jpeg" and photo in (2, 6) else 1, + ) + + if samples_per_pixel > MAX_SAMPLESPERPIXEL: + # DOS check, samples_per_pixel can be a Long, and we extend the tuple below + logger.error( + "More samples per pixel than can be decoded: %s", samples_per_pixel + ) + msg = "Invalid value for samples per pixel" + raise SyntaxError(msg) + + if samples_per_pixel < bps_actual_count: + # If a file has more values in bps_tuple than expected, + # remove the excess. + bps_tuple = bps_tuple[:samples_per_pixel] + elif samples_per_pixel > bps_actual_count and bps_actual_count == 1: + # If a file has only one value in bps_tuple, when it should have more, + # presume it is the same number of bits for all of the samples. + bps_tuple = bps_tuple * samples_per_pixel + + if len(bps_tuple) != samples_per_pixel: + msg = "unknown data organization" + raise SyntaxError(msg) + + # mode: check photometric interpretation and bits per pixel + key = ( + self.tag_v2.prefix, + photo, + sample_format, + fillorder, + bps_tuple, + extra_tuple, + ) + logger.debug("format key: %s", key) + try: + self._mode, rawmode = OPEN_INFO[key] + except KeyError as e: + logger.debug("- unsupported format") + msg = "unknown pixel mode" + raise SyntaxError(msg) from e + + logger.debug("- raw mode: %s", rawmode) + logger.debug("- pil mode: %s", self.mode) + + self.info["compression"] = self._compression + + xres = self.tag_v2.get(X_RESOLUTION, 1) + yres = self.tag_v2.get(Y_RESOLUTION, 1) + + if xres and yres: + resunit = self.tag_v2.get(RESOLUTION_UNIT) + if resunit == 2: # dots per inch + self.info["dpi"] = (xres, yres) + elif resunit == 3: # dots per centimeter. convert to dpi + self.info["dpi"] = (xres * 2.54, yres * 2.54) + elif resunit is None: # used to default to 1, but now 2) + self.info["dpi"] = (xres, yres) + # For backward compatibility, + # we also preserve the old behavior + self.info["resolution"] = xres, yres + else: # No absolute unit of measurement + self.info["resolution"] = xres, yres + + # build tile descriptors + x = y = layer = 0 + self.tile = [] + self.use_load_libtiff = READ_LIBTIFF or self._compression != "raw" + if self.use_load_libtiff: + # Decoder expects entire file as one tile. + # There's a buffer size limit in load (64k) + # so large g4 images will fail if we use that + # function. + # + # Setup the one tile for the whole image, then + # use the _load_libtiff function. + + # libtiff handles the fillmode for us, so 1;IR should + # actually be 1;I. Including the R double reverses the + # bits, so stripes of the image are reversed. See + # https://github.com/python-pillow/Pillow/issues/279 + if fillorder == 2: + # Replace fillorder with fillorder=1 + key = key[:3] + (1,) + key[4:] + logger.debug("format key: %s", key) + # this should always work, since all the + # fillorder==2 modes have a corresponding + # fillorder=1 mode + self._mode, rawmode = OPEN_INFO[key] + # YCbCr images with new jpeg compression with pixels in one plane + # unpacked straight into RGB values + if ( + photo == 6 + and self._compression == "jpeg" + and self._planar_configuration == 1 + ): + rawmode = "RGB" + # libtiff always returns the bytes in native order. + # we're expecting image byte order. So, if the rawmode + # contains I;16, we need to convert from native to image + # byte order. + elif rawmode == "I;16": + rawmode = "I;16N" + elif rawmode.endswith((";16B", ";16L")): + rawmode = rawmode[:-1] + "N" + + # Offset in the tile tuple is 0, we go from 0,0 to + # w,h, and we only do this once -- eds + a = (rawmode, self._compression, False, self.tag_v2.offset) + self.tile.append(ImageFile._Tile("libtiff", (0, 0, xsize, ysize), 0, a)) + + elif STRIPOFFSETS in self.tag_v2 or TILEOFFSETS in self.tag_v2: + # striped image + if STRIPOFFSETS in self.tag_v2: + offsets = self.tag_v2[STRIPOFFSETS] + h = self.tag_v2.get(ROWSPERSTRIP, ysize) + w = xsize + else: + # tiled image + offsets = self.tag_v2[TILEOFFSETS] + tilewidth = self.tag_v2.get(TILEWIDTH) + h = self.tag_v2.get(TILELENGTH) + if not isinstance(tilewidth, int) or not isinstance(h, int): + msg = "Invalid tile dimensions" + raise ValueError(msg) + w = tilewidth + + if w == xsize and h == ysize and self._planar_configuration != 2: + # Every tile covers the image. Only use the last offset + offsets = offsets[-1:] + + for offset in offsets: + if x + w > xsize: + stride = w * sum(bps_tuple) / 8 # bytes per line + else: + stride = 0 + + tile_rawmode = rawmode + if self._planar_configuration == 2: + # each band on it's own layer + tile_rawmode = rawmode[layer] + # adjust stride width accordingly + stride /= bps_count + + args = (tile_rawmode, int(stride), 1) + self.tile.append( + ImageFile._Tile( + self._compression, + (x, y, min(x + w, xsize), min(y + h, ysize)), + offset, + args, + ) + ) + x += w + if x >= xsize: + x, y = 0, y + h + if y >= ysize: + y = 0 + layer += 1 + else: + logger.debug("- unsupported data organization") + msg = "unknown data organization" + raise SyntaxError(msg) + + # Fix up info. + if ICCPROFILE in self.tag_v2: + self.info["icc_profile"] = self.tag_v2[ICCPROFILE] + + # fixup palette descriptor + + if self.mode in ["P", "PA"]: + palette = [o8(b // 256) for b in self.tag_v2[COLORMAP]] + self.palette = ImagePalette.raw("RGB;L", b"".join(palette)) + + +# +# -------------------------------------------------------------------- +# Write TIFF files + +# little endian is default except for image modes with +# explicit big endian byte-order + +SAVE_INFO = { + # mode => rawmode, byteorder, photometrics, + # sampleformat, bitspersample, extra + "1": ("1", II, 1, 1, (1,), None), + "L": ("L", II, 1, 1, (8,), None), + "LA": ("LA", II, 1, 1, (8, 8), 2), + "P": ("P", II, 3, 1, (8,), None), + "PA": ("PA", II, 3, 1, (8, 8), 2), + "I": ("I;32S", II, 1, 2, (32,), None), + "I;16": ("I;16", II, 1, 1, (16,), None), + "I;16L": ("I;16L", II, 1, 1, (16,), None), + "F": ("F;32F", II, 1, 3, (32,), None), + "RGB": ("RGB", II, 2, 1, (8, 8, 8), None), + "RGBX": ("RGBX", II, 2, 1, (8, 8, 8, 8), 0), + "RGBA": ("RGBA", II, 2, 1, (8, 8, 8, 8), 2), + "CMYK": ("CMYK", II, 5, 1, (8, 8, 8, 8), None), + "YCbCr": ("YCbCr", II, 6, 1, (8, 8, 8), None), + "LAB": ("LAB", II, 8, 1, (8, 8, 8), None), + "I;16B": ("I;16B", MM, 1, 1, (16,), None), +} + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + try: + rawmode, prefix, photo, format, bits, extra = SAVE_INFO[im.mode] + except KeyError as e: + msg = f"cannot write mode {im.mode} as TIFF" + raise OSError(msg) from e + + encoderinfo = im.encoderinfo + encoderconfig = im.encoderconfig + + ifd = ImageFileDirectory_v2(prefix=prefix) + if encoderinfo.get("big_tiff"): + ifd._bigtiff = True + + try: + compression = encoderinfo["compression"] + except KeyError: + compression = im.info.get("compression") + if isinstance(compression, int): + # compression value may be from BMP. Ignore it + compression = None + if compression is None: + compression = "raw" + elif compression == "tiff_jpeg": + # OJPEG is obsolete, so use new-style JPEG compression instead + compression = "jpeg" + elif compression == "tiff_deflate": + compression = "tiff_adobe_deflate" + + libtiff = WRITE_LIBTIFF or compression != "raw" + + # required for color libtiff images + ifd[PLANAR_CONFIGURATION] = 1 + + ifd[IMAGEWIDTH] = im.size[0] + ifd[IMAGELENGTH] = im.size[1] + + # write any arbitrary tags passed in as an ImageFileDirectory + if "tiffinfo" in encoderinfo: + info = encoderinfo["tiffinfo"] + elif "exif" in encoderinfo: + info = encoderinfo["exif"] + if isinstance(info, bytes): + exif = Image.Exif() + exif.load(info) + info = exif + else: + info = {} + logger.debug("Tiffinfo Keys: %s", list(info)) + if isinstance(info, ImageFileDirectory_v1): + info = info.to_v2() + for key in info: + if isinstance(info, Image.Exif) and key in TiffTags.TAGS_V2_GROUPS: + ifd[key] = info.get_ifd(key) + else: + ifd[key] = info.get(key) + try: + ifd.tagtype[key] = info.tagtype[key] + except Exception: + pass # might not be an IFD. Might not have populated type + + legacy_ifd = {} + if hasattr(im, "tag"): + legacy_ifd = im.tag.to_v2() + + supplied_tags = {**legacy_ifd, **getattr(im, "tag_v2", {})} + for tag in ( + # IFD offset that may not be correct in the saved image + EXIFIFD, + # Determined by the image format and should not be copied from legacy_ifd. + SAMPLEFORMAT, + ): + if tag in supplied_tags: + del supplied_tags[tag] + + # additions written by Greg Couch, gregc@cgl.ucsf.edu + # inspired by image-sig posting from Kevin Cazabon, kcazabon@home.com + if hasattr(im, "tag_v2"): + # preserve tags from original TIFF image file + for key in ( + RESOLUTION_UNIT, + X_RESOLUTION, + Y_RESOLUTION, + IPTC_NAA_CHUNK, + PHOTOSHOP_CHUNK, + XMP, + ): + if key in im.tag_v2: + if key == IPTC_NAA_CHUNK and im.tag_v2.tagtype[key] not in ( + TiffTags.BYTE, + TiffTags.UNDEFINED, + ): + del supplied_tags[key] + else: + ifd[key] = im.tag_v2[key] + ifd.tagtype[key] = im.tag_v2.tagtype[key] + + # preserve ICC profile (should also work when saving other formats + # which support profiles as TIFF) -- 2008-06-06 Florian Hoech + icc = encoderinfo.get("icc_profile", im.info.get("icc_profile")) + if icc: + ifd[ICCPROFILE] = icc + + for key, name in [ + (IMAGEDESCRIPTION, "description"), + (X_RESOLUTION, "resolution"), + (Y_RESOLUTION, "resolution"), + (X_RESOLUTION, "x_resolution"), + (Y_RESOLUTION, "y_resolution"), + (RESOLUTION_UNIT, "resolution_unit"), + (SOFTWARE, "software"), + (DATE_TIME, "date_time"), + (ARTIST, "artist"), + (COPYRIGHT, "copyright"), + ]: + if name in encoderinfo: + ifd[key] = encoderinfo[name] + + dpi = encoderinfo.get("dpi") + if dpi: + ifd[RESOLUTION_UNIT] = 2 + ifd[X_RESOLUTION] = dpi[0] + ifd[Y_RESOLUTION] = dpi[1] + + if bits != (1,): + ifd[BITSPERSAMPLE] = bits + if len(bits) != 1: + ifd[SAMPLESPERPIXEL] = len(bits) + if extra is not None: + ifd[EXTRASAMPLES] = extra + if format != 1: + ifd[SAMPLEFORMAT] = format + + if PHOTOMETRIC_INTERPRETATION not in ifd: + ifd[PHOTOMETRIC_INTERPRETATION] = photo + elif im.mode in ("1", "L") and ifd[PHOTOMETRIC_INTERPRETATION] == 0: + if im.mode == "1": + inverted_im = im.copy() + px = inverted_im.load() + if px is not None: + for y in range(inverted_im.height): + for x in range(inverted_im.width): + px[x, y] = 0 if px[x, y] == 255 else 255 + im = inverted_im + else: + im = ImageOps.invert(im) + + if im.mode in ["P", "PA"]: + lut = im.im.getpalette("RGB", "RGB;L") + colormap = [] + colors = len(lut) // 3 + for i in range(3): + colormap += [v * 256 for v in lut[colors * i : colors * (i + 1)]] + colormap += [0] * (256 - colors) + ifd[COLORMAP] = colormap + # data orientation + w, h = ifd[IMAGEWIDTH], ifd[IMAGELENGTH] + stride = len(bits) * ((w * bits[0] + 7) // 8) + if ROWSPERSTRIP not in ifd: + # aim for given strip size (64 KB by default) when using libtiff writer + if libtiff: + im_strip_size = encoderinfo.get("strip_size", STRIP_SIZE) + rows_per_strip = 1 if stride == 0 else min(im_strip_size // stride, h) + # JPEG encoder expects multiple of 8 rows + if compression == "jpeg": + rows_per_strip = min(((rows_per_strip + 7) // 8) * 8, h) + else: + rows_per_strip = h + if rows_per_strip == 0: + rows_per_strip = 1 + ifd[ROWSPERSTRIP] = rows_per_strip + strip_byte_counts = 1 if stride == 0 else stride * ifd[ROWSPERSTRIP] + strips_per_image = (h + ifd[ROWSPERSTRIP] - 1) // ifd[ROWSPERSTRIP] + if strip_byte_counts >= 2**16: + ifd.tagtype[STRIPBYTECOUNTS] = TiffTags.LONG + ifd[STRIPBYTECOUNTS] = (strip_byte_counts,) * (strips_per_image - 1) + ( + stride * h - strip_byte_counts * (strips_per_image - 1), + ) + ifd[STRIPOFFSETS] = tuple( + range(0, strip_byte_counts * strips_per_image, strip_byte_counts) + ) # this is adjusted by IFD writer + # no compression by default: + ifd[COMPRESSION] = COMPRESSION_INFO_REV.get(compression, 1) + + if im.mode == "YCbCr": + for tag, default_value in { + YCBCRSUBSAMPLING: (1, 1), + REFERENCEBLACKWHITE: (0, 255, 128, 255, 128, 255), + }.items(): + ifd.setdefault(tag, default_value) + + blocklist = [TILEWIDTH, TILELENGTH, TILEOFFSETS, TILEBYTECOUNTS] + if libtiff: + if "quality" in encoderinfo: + quality = encoderinfo["quality"] + if not isinstance(quality, int) or quality < 0 or quality > 100: + msg = "Invalid quality setting" + raise ValueError(msg) + if compression != "jpeg": + msg = "quality setting only supported for 'jpeg' compression" + raise ValueError(msg) + ifd[JPEGQUALITY] = quality + + logger.debug("Saving using libtiff encoder") + logger.debug("Items: %s", sorted(ifd.items())) + _fp = 0 + if hasattr(fp, "fileno"): + try: + fp.seek(0) + _fp = fp.fileno() + except io.UnsupportedOperation: + pass + + # optional types for non core tags + types = {} + # STRIPOFFSETS and STRIPBYTECOUNTS are added by the library + # based on the data in the strip. + # OSUBFILETYPE is deprecated. + # The other tags expect arrays with a certain length (fixed or depending on + # BITSPERSAMPLE, etc), passing arrays with a different length will result in + # segfaults. Block these tags until we add extra validation. + # SUBIFD may also cause a segfault. + blocklist += [ + OSUBFILETYPE, + REFERENCEBLACKWHITE, + STRIPBYTECOUNTS, + STRIPOFFSETS, + TRANSFERFUNCTION, + SUBIFD, + ] + + # bits per sample is a single short in the tiff directory, not a list. + atts: dict[int, Any] = {BITSPERSAMPLE: bits[0]} + # Merge the ones that we have with (optional) more bits from + # the original file, e.g x,y resolution so that we can + # save(load('')) == original file. + for tag, value in itertools.chain(ifd.items(), supplied_tags.items()): + # Libtiff can only process certain core items without adding + # them to the custom dictionary. + # Custom items are supported for int, float, unicode, string and byte + # values. Other types and tuples require a tagtype. + if tag not in TiffTags.LIBTIFF_CORE: + if not getattr(Image.core, "libtiff_support_custom_tags", False): + continue + + if tag in TiffTags.TAGS_V2_GROUPS: + types[tag] = TiffTags.LONG8 + elif tag in ifd.tagtype: + types[tag] = ifd.tagtype[tag] + elif not (isinstance(value, (int, float, str, bytes))): + continue + else: + type = TiffTags.lookup(tag).type + if type: + types[tag] = type + if tag not in atts and tag not in blocklist: + if isinstance(value, str): + atts[tag] = value.encode("ascii", "replace") + b"\0" + elif isinstance(value, IFDRational): + atts[tag] = float(value) + else: + atts[tag] = value + + if SAMPLEFORMAT in atts and len(atts[SAMPLEFORMAT]) == 1: + atts[SAMPLEFORMAT] = atts[SAMPLEFORMAT][0] + + logger.debug("Converted items: %s", sorted(atts.items())) + + # libtiff always expects the bytes in native order. + # we're storing image byte order. So, if the rawmode + # contains I;16, we need to convert from native to image + # byte order. + if im.mode in ("I;16", "I;16B", "I;16L"): + rawmode = "I;16N" + + # Pass tags as sorted list so that the tags are set in a fixed order. + # This is required by libtiff for some tags. For example, the JPEGQUALITY + # pseudo tag requires that the COMPRESS tag was already set. + tags = list(atts.items()) + tags.sort() + a = (rawmode, compression, _fp, filename, tags, types) + encoder = Image._getencoder(im.mode, "libtiff", a, encoderconfig) + encoder.setimage(im.im, (0, 0) + im.size) + while True: + errcode, data = encoder.encode(ImageFile.MAXBLOCK)[1:] + if not _fp: + fp.write(data) + if errcode: + break + if errcode < 0: + msg = f"encoder error {errcode} when writing image file" + raise OSError(msg) + + else: + for tag in blocklist: + del ifd[tag] + offset = ifd.save(fp) + + ImageFile._save( + im, + fp, + [ImageFile._Tile("raw", (0, 0) + im.size, offset, (rawmode, stride, 1))], + ) + + # -- helper for multi-page save -- + if "_debug_multipage" in encoderinfo: + # just to access o32 and o16 (using correct byte order) + setattr(im, "_debug_multipage", ifd) + + +class AppendingTiffWriter(io.BytesIO): + fieldSizes = [ + 0, # None + 1, # byte + 1, # ascii + 2, # short + 4, # long + 8, # rational + 1, # sbyte + 1, # undefined + 2, # sshort + 4, # slong + 8, # srational + 4, # float + 8, # double + 4, # ifd + 2, # unicode + 4, # complex + 8, # long8 + ] + + Tags = { + 273, # StripOffsets + 288, # FreeOffsets + 324, # TileOffsets + 519, # JPEGQTables + 520, # JPEGDCTables + 521, # JPEGACTables + } + + def __init__(self, fn: StrOrBytesPath | IO[bytes], new: bool = False) -> None: + self.f: IO[bytes] + if is_path(fn): + self.name = fn + self.close_fp = True + try: + self.f = open(fn, "w+b" if new else "r+b") + except OSError: + self.f = open(fn, "w+b") + else: + self.f = cast(IO[bytes], fn) + self.close_fp = False + self.beginning = self.f.tell() + self.setup() + + def setup(self) -> None: + # Reset everything. + self.f.seek(self.beginning, os.SEEK_SET) + + self.whereToWriteNewIFDOffset: int | None = None + self.offsetOfNewPage = 0 + + self.IIMM = iimm = self.f.read(4) + self._bigtiff = b"\x2b" in iimm + if not iimm: + # empty file - first page + self.isFirst = True + return + + self.isFirst = False + if iimm not in PREFIXES: + msg = "Invalid TIFF file header" + raise RuntimeError(msg) + + self.setEndian("<" if iimm.startswith(II) else ">") + + if self._bigtiff: + self.f.seek(4, os.SEEK_CUR) + self.skipIFDs() + self.goToEnd() + + def finalize(self) -> None: + if self.isFirst: + return + + # fix offsets + self.f.seek(self.offsetOfNewPage) + + iimm = self.f.read(4) + if not iimm: + # Make it easy to finish a frame without committing to a new one. + return + + if iimm != self.IIMM: + msg = "IIMM of new page doesn't match IIMM of first page" + raise RuntimeError(msg) + + if self._bigtiff: + self.f.seek(4, os.SEEK_CUR) + ifd_offset = self._read(8 if self._bigtiff else 4) + ifd_offset += self.offsetOfNewPage + assert self.whereToWriteNewIFDOffset is not None + self.f.seek(self.whereToWriteNewIFDOffset) + self._write(ifd_offset, 8 if self._bigtiff else 4) + self.f.seek(ifd_offset) + self.fixIFD() + + def newFrame(self) -> None: + # Call this to finish a frame. + self.finalize() + self.setup() + + def __enter__(self) -> AppendingTiffWriter: + return self + + def __exit__(self, *args: object) -> None: + if self.close_fp: + self.close() + + def tell(self) -> int: + return self.f.tell() - self.offsetOfNewPage + + def seek(self, offset: int, whence: int = io.SEEK_SET) -> int: + """ + :param offset: Distance to seek. + :param whence: Whether the distance is relative to the start, + end or current position. + :returns: The resulting position, relative to the start. + """ + if whence == os.SEEK_SET: + offset += self.offsetOfNewPage + + self.f.seek(offset, whence) + return self.tell() + + def goToEnd(self) -> None: + self.f.seek(0, os.SEEK_END) + pos = self.f.tell() + + # pad to 16 byte boundary + pad_bytes = 16 - pos % 16 + if 0 < pad_bytes < 16: + self.f.write(bytes(pad_bytes)) + self.offsetOfNewPage = self.f.tell() + + def setEndian(self, endian: str) -> None: + self.endian = endian + self.longFmt = f"{self.endian}L" + self.shortFmt = f"{self.endian}H" + self.tagFormat = f"{self.endian}HH" + ("Q" if self._bigtiff else "L") + + def skipIFDs(self) -> None: + while True: + ifd_offset = self._read(8 if self._bigtiff else 4) + if ifd_offset == 0: + self.whereToWriteNewIFDOffset = self.f.tell() - ( + 8 if self._bigtiff else 4 + ) + break + + self.f.seek(ifd_offset) + num_tags = self._read(8 if self._bigtiff else 2) + self.f.seek(num_tags * (20 if self._bigtiff else 12), os.SEEK_CUR) + + def write(self, data: Buffer, /) -> int: + return self.f.write(data) + + def _fmt(self, field_size: int) -> str: + try: + return {2: "H", 4: "L", 8: "Q"}[field_size] + except KeyError: + msg = "offset is not supported" + raise RuntimeError(msg) + + def _read(self, field_size: int) -> int: + (value,) = struct.unpack( + self.endian + self._fmt(field_size), self.f.read(field_size) + ) + return value + + def readShort(self) -> int: + return self._read(2) + + def readLong(self) -> int: + return self._read(4) + + @staticmethod + def _verify_bytes_written(bytes_written: int | None, expected: int) -> None: + if bytes_written is not None and bytes_written != expected: + msg = f"wrote only {bytes_written} bytes but wanted {expected}" + raise RuntimeError(msg) + + def _rewriteLast( + self, value: int, field_size: int, new_field_size: int = 0 + ) -> None: + self.f.seek(-field_size, os.SEEK_CUR) + if not new_field_size: + new_field_size = field_size + bytes_written = self.f.write( + struct.pack(self.endian + self._fmt(new_field_size), value) + ) + self._verify_bytes_written(bytes_written, new_field_size) + + def rewriteLastShortToLong(self, value: int) -> None: + self._rewriteLast(value, 2, 4) + + def rewriteLastShort(self, value: int) -> None: + return self._rewriteLast(value, 2) + + def rewriteLastLong(self, value: int) -> None: + return self._rewriteLast(value, 4) + + def _write(self, value: int, field_size: int) -> None: + bytes_written = self.f.write( + struct.pack(self.endian + self._fmt(field_size), value) + ) + self._verify_bytes_written(bytes_written, field_size) + + def writeShort(self, value: int) -> None: + self._write(value, 2) + + def writeLong(self, value: int) -> None: + self._write(value, 4) + + def close(self) -> None: + self.finalize() + if self.close_fp: + self.f.close() + + def fixIFD(self) -> None: + num_tags = self._read(8 if self._bigtiff else 2) + + for i in range(num_tags): + tag, field_type, count = struct.unpack( + self.tagFormat, self.f.read(12 if self._bigtiff else 8) + ) + + field_size = self.fieldSizes[field_type] + total_size = field_size * count + fmt_size = 8 if self._bigtiff else 4 + is_local = total_size <= fmt_size + if not is_local: + offset = self._read(fmt_size) + self.offsetOfNewPage + self._rewriteLast(offset, fmt_size) + + if tag in self.Tags: + cur_pos = self.f.tell() + + logger.debug( + "fixIFD: %s (%d) - type: %s (%d) - type size: %d - count: %d", + TiffTags.lookup(tag).name, + tag, + TYPES.get(field_type, "unknown"), + field_type, + field_size, + count, + ) + + if is_local: + self._fixOffsets(count, field_size) + self.f.seek(cur_pos + fmt_size) + else: + self.f.seek(offset) + self._fixOffsets(count, field_size) + self.f.seek(cur_pos) + + elif is_local: + # skip the locally stored value that is not an offset + self.f.seek(fmt_size, os.SEEK_CUR) + + def _fixOffsets(self, count: int, field_size: int) -> None: + for i in range(count): + offset = self._read(field_size) + offset += self.offsetOfNewPage + + new_field_size = 0 + if self._bigtiff and field_size in (2, 4) and offset >= 2**32: + # offset is now too large - we must convert long to long8 + new_field_size = 8 + elif field_size == 2 and offset >= 2**16: + # offset is now too large - we must convert short to long + new_field_size = 4 + if new_field_size: + if count != 1: + msg = "not implemented" + raise RuntimeError(msg) # XXX TODO + + # simple case - the offset is just one and therefore it is + # local (not referenced with another offset) + self._rewriteLast(offset, field_size, new_field_size) + # Move back past the new offset, past 'count', and before 'field_type' + rewind = -new_field_size - 4 - 2 + self.f.seek(rewind, os.SEEK_CUR) + self.writeShort(new_field_size) # rewrite the type + self.f.seek(2 - rewind, os.SEEK_CUR) + else: + self._rewriteLast(offset, field_size) + + def fixOffsets( + self, count: int, isShort: bool = False, isLong: bool = False + ) -> None: + if isShort: + field_size = 2 + elif isLong: + field_size = 4 + else: + field_size = 0 + return self._fixOffsets(count, field_size) + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + append_images = list(im.encoderinfo.get("append_images", [])) + if not hasattr(im, "n_frames") and not append_images: + return _save(im, fp, filename) + + cur_idx = im.tell() + try: + with AppendingTiffWriter(fp) as tf: + for ims in [im] + append_images: + encoderinfo = ims._attach_default_encoderinfo(im) + if not hasattr(ims, "encoderconfig"): + ims.encoderconfig = () + nfr = getattr(ims, "n_frames", 1) + + for idx in range(nfr): + ims.seek(idx) + ims.load() + _save(ims, tf, filename) + tf.newFrame() + ims.encoderinfo = encoderinfo + finally: + im.seek(cur_idx) + + +# +# -------------------------------------------------------------------- +# Register + +Image.register_open(TiffImageFile.format, TiffImageFile, _accept) +Image.register_save(TiffImageFile.format, _save) +Image.register_save_all(TiffImageFile.format, _save_all) + +Image.register_extensions(TiffImageFile.format, [".tif", ".tiff"]) + +Image.register_mime(TiffImageFile.format, "image/tiff") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TiffTags.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TiffTags.py new file mode 100644 index 0000000000000000000000000000000000000000..86adaa45857338f9fa8864296077393a3dc3350f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/TiffTags.py @@ -0,0 +1,562 @@ +# +# The Python Imaging Library. +# $Id$ +# +# TIFF tags +# +# This module provides clear-text names for various well-known +# TIFF tags. the TIFF codec works just fine without it. +# +# Copyright (c) Secret Labs AB 1999. +# +# See the README file for information on usage and redistribution. +# + +## +# This module provides constants and clear-text names for various +# well-known TIFF tags. +## +from __future__ import annotations + +from typing import NamedTuple + + +class _TagInfo(NamedTuple): + value: int | None + name: str + type: int | None + length: int | None + enum: dict[str, int] + + +class TagInfo(_TagInfo): + __slots__: list[str] = [] + + def __new__( + cls, + value: int | None = None, + name: str = "unknown", + type: int | None = None, + length: int | None = None, + enum: dict[str, int] | None = None, + ) -> TagInfo: + return super().__new__(cls, value, name, type, length, enum or {}) + + def cvt_enum(self, value: str) -> int | str: + # Using get will call hash(value), which can be expensive + # for some types (e.g. Fraction). Since self.enum is rarely + # used, it's usually better to test it first. + return self.enum.get(value, value) if self.enum else value + + +def lookup(tag: int, group: int | None = None) -> TagInfo: + """ + :param tag: Integer tag number + :param group: Which :py:data:`~PIL.TiffTags.TAGS_V2_GROUPS` to look in + + .. versionadded:: 8.3.0 + + :returns: Taginfo namedtuple, From the ``TAGS_V2`` info if possible, + otherwise just populating the value and name from ``TAGS``. + If the tag is not recognized, "unknown" is returned for the name + + """ + + if group is not None: + info = TAGS_V2_GROUPS[group].get(tag) if group in TAGS_V2_GROUPS else None + else: + info = TAGS_V2.get(tag) + return info or TagInfo(tag, TAGS.get(tag, "unknown")) + + +## +# Map tag numbers to tag info. +# +# id: (Name, Type, Length[, enum_values]) +# +# The length here differs from the length in the tiff spec. For +# numbers, the tiff spec is for the number of fields returned. We +# agree here. For string-like types, the tiff spec uses the length of +# field in bytes. In Pillow, we are using the number of expected +# fields, in general 1 for string-like types. + + +BYTE = 1 +ASCII = 2 +SHORT = 3 +LONG = 4 +RATIONAL = 5 +SIGNED_BYTE = 6 +UNDEFINED = 7 +SIGNED_SHORT = 8 +SIGNED_LONG = 9 +SIGNED_RATIONAL = 10 +FLOAT = 11 +DOUBLE = 12 +IFD = 13 +LONG8 = 16 + +_tags_v2: dict[int, tuple[str, int, int] | tuple[str, int, int, dict[str, int]]] = { + 254: ("NewSubfileType", LONG, 1), + 255: ("SubfileType", SHORT, 1), + 256: ("ImageWidth", LONG, 1), + 257: ("ImageLength", LONG, 1), + 258: ("BitsPerSample", SHORT, 0), + 259: ( + "Compression", + SHORT, + 1, + { + "Uncompressed": 1, + "CCITT 1d": 2, + "Group 3 Fax": 3, + "Group 4 Fax": 4, + "LZW": 5, + "JPEG": 6, + "PackBits": 32773, + }, + ), + 262: ( + "PhotometricInterpretation", + SHORT, + 1, + { + "WhiteIsZero": 0, + "BlackIsZero": 1, + "RGB": 2, + "RGB Palette": 3, + "Transparency Mask": 4, + "CMYK": 5, + "YCbCr": 6, + "CieLAB": 8, + "CFA": 32803, # TIFF/EP, Adobe DNG + "LinearRaw": 32892, # Adobe DNG + }, + ), + 263: ("Threshholding", SHORT, 1), + 264: ("CellWidth", SHORT, 1), + 265: ("CellLength", SHORT, 1), + 266: ("FillOrder", SHORT, 1), + 269: ("DocumentName", ASCII, 1), + 270: ("ImageDescription", ASCII, 1), + 271: ("Make", ASCII, 1), + 272: ("Model", ASCII, 1), + 273: ("StripOffsets", LONG, 0), + 274: ("Orientation", SHORT, 1), + 277: ("SamplesPerPixel", SHORT, 1), + 278: ("RowsPerStrip", LONG, 1), + 279: ("StripByteCounts", LONG, 0), + 280: ("MinSampleValue", SHORT, 0), + 281: ("MaxSampleValue", SHORT, 0), + 282: ("XResolution", RATIONAL, 1), + 283: ("YResolution", RATIONAL, 1), + 284: ("PlanarConfiguration", SHORT, 1, {"Contiguous": 1, "Separate": 2}), + 285: ("PageName", ASCII, 1), + 286: ("XPosition", RATIONAL, 1), + 287: ("YPosition", RATIONAL, 1), + 288: ("FreeOffsets", LONG, 1), + 289: ("FreeByteCounts", LONG, 1), + 290: ("GrayResponseUnit", SHORT, 1), + 291: ("GrayResponseCurve", SHORT, 0), + 292: ("T4Options", LONG, 1), + 293: ("T6Options", LONG, 1), + 296: ("ResolutionUnit", SHORT, 1, {"none": 1, "inch": 2, "cm": 3}), + 297: ("PageNumber", SHORT, 2), + 301: ("TransferFunction", SHORT, 0), + 305: ("Software", ASCII, 1), + 306: ("DateTime", ASCII, 1), + 315: ("Artist", ASCII, 1), + 316: ("HostComputer", ASCII, 1), + 317: ("Predictor", SHORT, 1, {"none": 1, "Horizontal Differencing": 2}), + 318: ("WhitePoint", RATIONAL, 2), + 319: ("PrimaryChromaticities", RATIONAL, 6), + 320: ("ColorMap", SHORT, 0), + 321: ("HalftoneHints", SHORT, 2), + 322: ("TileWidth", LONG, 1), + 323: ("TileLength", LONG, 1), + 324: ("TileOffsets", LONG, 0), + 325: ("TileByteCounts", LONG, 0), + 330: ("SubIFDs", LONG, 0), + 332: ("InkSet", SHORT, 1), + 333: ("InkNames", ASCII, 1), + 334: ("NumberOfInks", SHORT, 1), + 336: ("DotRange", SHORT, 0), + 337: ("TargetPrinter", ASCII, 1), + 338: ("ExtraSamples", SHORT, 0), + 339: ("SampleFormat", SHORT, 0), + 340: ("SMinSampleValue", DOUBLE, 0), + 341: ("SMaxSampleValue", DOUBLE, 0), + 342: ("TransferRange", SHORT, 6), + 347: ("JPEGTables", UNDEFINED, 1), + # obsolete JPEG tags + 512: ("JPEGProc", SHORT, 1), + 513: ("JPEGInterchangeFormat", LONG, 1), + 514: ("JPEGInterchangeFormatLength", LONG, 1), + 515: ("JPEGRestartInterval", SHORT, 1), + 517: ("JPEGLosslessPredictors", SHORT, 0), + 518: ("JPEGPointTransforms", SHORT, 0), + 519: ("JPEGQTables", LONG, 0), + 520: ("JPEGDCTables", LONG, 0), + 521: ("JPEGACTables", LONG, 0), + 529: ("YCbCrCoefficients", RATIONAL, 3), + 530: ("YCbCrSubSampling", SHORT, 2), + 531: ("YCbCrPositioning", SHORT, 1), + 532: ("ReferenceBlackWhite", RATIONAL, 6), + 700: ("XMP", BYTE, 0), + 33432: ("Copyright", ASCII, 1), + 33723: ("IptcNaaInfo", UNDEFINED, 1), + 34377: ("PhotoshopInfo", BYTE, 0), + # FIXME add more tags here + 34665: ("ExifIFD", LONG, 1), + 34675: ("ICCProfile", UNDEFINED, 1), + 34853: ("GPSInfoIFD", LONG, 1), + 36864: ("ExifVersion", UNDEFINED, 1), + 37724: ("ImageSourceData", UNDEFINED, 1), + 40965: ("InteroperabilityIFD", LONG, 1), + 41730: ("CFAPattern", UNDEFINED, 1), + # MPInfo + 45056: ("MPFVersion", UNDEFINED, 1), + 45057: ("NumberOfImages", LONG, 1), + 45058: ("MPEntry", UNDEFINED, 1), + 45059: ("ImageUIDList", UNDEFINED, 0), # UNDONE, check + 45060: ("TotalFrames", LONG, 1), + 45313: ("MPIndividualNum", LONG, 1), + 45569: ("PanOrientation", LONG, 1), + 45570: ("PanOverlap_H", RATIONAL, 1), + 45571: ("PanOverlap_V", RATIONAL, 1), + 45572: ("BaseViewpointNum", LONG, 1), + 45573: ("ConvergenceAngle", SIGNED_RATIONAL, 1), + 45574: ("BaselineLength", RATIONAL, 1), + 45575: ("VerticalDivergence", SIGNED_RATIONAL, 1), + 45576: ("AxisDistance_X", SIGNED_RATIONAL, 1), + 45577: ("AxisDistance_Y", SIGNED_RATIONAL, 1), + 45578: ("AxisDistance_Z", SIGNED_RATIONAL, 1), + 45579: ("YawAngle", SIGNED_RATIONAL, 1), + 45580: ("PitchAngle", SIGNED_RATIONAL, 1), + 45581: ("RollAngle", SIGNED_RATIONAL, 1), + 40960: ("FlashPixVersion", UNDEFINED, 1), + 50741: ("MakerNoteSafety", SHORT, 1, {"Unsafe": 0, "Safe": 1}), + 50780: ("BestQualityScale", RATIONAL, 1), + 50838: ("ImageJMetaDataByteCounts", LONG, 0), # Can be more than one + 50839: ("ImageJMetaData", UNDEFINED, 1), # see Issue #2006 +} +_tags_v2_groups = { + # ExifIFD + 34665: { + 36864: ("ExifVersion", UNDEFINED, 1), + 40960: ("FlashPixVersion", UNDEFINED, 1), + 40965: ("InteroperabilityIFD", LONG, 1), + 41730: ("CFAPattern", UNDEFINED, 1), + }, + # GPSInfoIFD + 34853: { + 0: ("GPSVersionID", BYTE, 4), + 1: ("GPSLatitudeRef", ASCII, 2), + 2: ("GPSLatitude", RATIONAL, 3), + 3: ("GPSLongitudeRef", ASCII, 2), + 4: ("GPSLongitude", RATIONAL, 3), + 5: ("GPSAltitudeRef", BYTE, 1), + 6: ("GPSAltitude", RATIONAL, 1), + 7: ("GPSTimeStamp", RATIONAL, 3), + 8: ("GPSSatellites", ASCII, 0), + 9: ("GPSStatus", ASCII, 2), + 10: ("GPSMeasureMode", ASCII, 2), + 11: ("GPSDOP", RATIONAL, 1), + 12: ("GPSSpeedRef", ASCII, 2), + 13: ("GPSSpeed", RATIONAL, 1), + 14: ("GPSTrackRef", ASCII, 2), + 15: ("GPSTrack", RATIONAL, 1), + 16: ("GPSImgDirectionRef", ASCII, 2), + 17: ("GPSImgDirection", RATIONAL, 1), + 18: ("GPSMapDatum", ASCII, 0), + 19: ("GPSDestLatitudeRef", ASCII, 2), + 20: ("GPSDestLatitude", RATIONAL, 3), + 21: ("GPSDestLongitudeRef", ASCII, 2), + 22: ("GPSDestLongitude", RATIONAL, 3), + 23: ("GPSDestBearingRef", ASCII, 2), + 24: ("GPSDestBearing", RATIONAL, 1), + 25: ("GPSDestDistanceRef", ASCII, 2), + 26: ("GPSDestDistance", RATIONAL, 1), + 27: ("GPSProcessingMethod", UNDEFINED, 0), + 28: ("GPSAreaInformation", UNDEFINED, 0), + 29: ("GPSDateStamp", ASCII, 11), + 30: ("GPSDifferential", SHORT, 1), + }, + # InteroperabilityIFD + 40965: {1: ("InteropIndex", ASCII, 1), 2: ("InteropVersion", UNDEFINED, 1)}, +} + +# Legacy Tags structure +# these tags aren't included above, but were in the previous versions +TAGS: dict[int | tuple[int, int], str] = { + 347: "JPEGTables", + 700: "XMP", + # Additional Exif Info + 32932: "Wang Annotation", + 33434: "ExposureTime", + 33437: "FNumber", + 33445: "MD FileTag", + 33446: "MD ScalePixel", + 33447: "MD ColorTable", + 33448: "MD LabName", + 33449: "MD SampleInfo", + 33450: "MD PrepDate", + 33451: "MD PrepTime", + 33452: "MD FileUnits", + 33550: "ModelPixelScaleTag", + 33723: "IptcNaaInfo", + 33918: "INGR Packet Data Tag", + 33919: "INGR Flag Registers", + 33920: "IrasB Transformation Matrix", + 33922: "ModelTiepointTag", + 34264: "ModelTransformationTag", + 34377: "PhotoshopInfo", + 34735: "GeoKeyDirectoryTag", + 34736: "GeoDoubleParamsTag", + 34737: "GeoAsciiParamsTag", + 34850: "ExposureProgram", + 34852: "SpectralSensitivity", + 34855: "ISOSpeedRatings", + 34856: "OECF", + 34864: "SensitivityType", + 34865: "StandardOutputSensitivity", + 34866: "RecommendedExposureIndex", + 34867: "ISOSpeed", + 34868: "ISOSpeedLatitudeyyy", + 34869: "ISOSpeedLatitudezzz", + 34908: "HylaFAX FaxRecvParams", + 34909: "HylaFAX FaxSubAddress", + 34910: "HylaFAX FaxRecvTime", + 36864: "ExifVersion", + 36867: "DateTimeOriginal", + 36868: "DateTimeDigitized", + 37121: "ComponentsConfiguration", + 37122: "CompressedBitsPerPixel", + 37724: "ImageSourceData", + 37377: "ShutterSpeedValue", + 37378: "ApertureValue", + 37379: "BrightnessValue", + 37380: "ExposureBiasValue", + 37381: "MaxApertureValue", + 37382: "SubjectDistance", + 37383: "MeteringMode", + 37384: "LightSource", + 37385: "Flash", + 37386: "FocalLength", + 37396: "SubjectArea", + 37500: "MakerNote", + 37510: "UserComment", + 37520: "SubSec", + 37521: "SubSecTimeOriginal", + 37522: "SubsecTimeDigitized", + 40960: "FlashPixVersion", + 40961: "ColorSpace", + 40962: "PixelXDimension", + 40963: "PixelYDimension", + 40964: "RelatedSoundFile", + 40965: "InteroperabilityIFD", + 41483: "FlashEnergy", + 41484: "SpatialFrequencyResponse", + 41486: "FocalPlaneXResolution", + 41487: "FocalPlaneYResolution", + 41488: "FocalPlaneResolutionUnit", + 41492: "SubjectLocation", + 41493: "ExposureIndex", + 41495: "SensingMethod", + 41728: "FileSource", + 41729: "SceneType", + 41730: "CFAPattern", + 41985: "CustomRendered", + 41986: "ExposureMode", + 41987: "WhiteBalance", + 41988: "DigitalZoomRatio", + 41989: "FocalLengthIn35mmFilm", + 41990: "SceneCaptureType", + 41991: "GainControl", + 41992: "Contrast", + 41993: "Saturation", + 41994: "Sharpness", + 41995: "DeviceSettingDescription", + 41996: "SubjectDistanceRange", + 42016: "ImageUniqueID", + 42032: "CameraOwnerName", + 42033: "BodySerialNumber", + 42034: "LensSpecification", + 42035: "LensMake", + 42036: "LensModel", + 42037: "LensSerialNumber", + 42112: "GDAL_METADATA", + 42113: "GDAL_NODATA", + 42240: "Gamma", + 50215: "Oce Scanjob Description", + 50216: "Oce Application Selector", + 50217: "Oce Identification Number", + 50218: "Oce ImageLogic Characteristics", + # Adobe DNG + 50706: "DNGVersion", + 50707: "DNGBackwardVersion", + 50708: "UniqueCameraModel", + 50709: "LocalizedCameraModel", + 50710: "CFAPlaneColor", + 50711: "CFALayout", + 50712: "LinearizationTable", + 50713: "BlackLevelRepeatDim", + 50714: "BlackLevel", + 50715: "BlackLevelDeltaH", + 50716: "BlackLevelDeltaV", + 50717: "WhiteLevel", + 50718: "DefaultScale", + 50719: "DefaultCropOrigin", + 50720: "DefaultCropSize", + 50721: "ColorMatrix1", + 50722: "ColorMatrix2", + 50723: "CameraCalibration1", + 50724: "CameraCalibration2", + 50725: "ReductionMatrix1", + 50726: "ReductionMatrix2", + 50727: "AnalogBalance", + 50728: "AsShotNeutral", + 50729: "AsShotWhiteXY", + 50730: "BaselineExposure", + 50731: "BaselineNoise", + 50732: "BaselineSharpness", + 50733: "BayerGreenSplit", + 50734: "LinearResponseLimit", + 50735: "CameraSerialNumber", + 50736: "LensInfo", + 50737: "ChromaBlurRadius", + 50738: "AntiAliasStrength", + 50740: "DNGPrivateData", + 50778: "CalibrationIlluminant1", + 50779: "CalibrationIlluminant2", + 50784: "Alias Layer Metadata", +} + +TAGS_V2: dict[int, TagInfo] = {} +TAGS_V2_GROUPS: dict[int, dict[int, TagInfo]] = {} + + +def _populate() -> None: + for k, v in _tags_v2.items(): + # Populate legacy structure. + TAGS[k] = v[0] + if len(v) == 4: + for sk, sv in v[3].items(): + TAGS[(k, sv)] = sk + + TAGS_V2[k] = TagInfo(k, *v) + + for group, tags in _tags_v2_groups.items(): + TAGS_V2_GROUPS[group] = {k: TagInfo(k, *v) for k, v in tags.items()} + + +_populate() +## +# Map type numbers to type names -- defined in ImageFileDirectory. + +TYPES: dict[int, str] = {} + +# +# These tags are handled by default in libtiff, without +# adding to the custom dictionary. From tif_dir.c, searching for +# case TIFFTAG in the _TIFFVSetField function: +# Line: item. +# 148: case TIFFTAG_SUBFILETYPE: +# 151: case TIFFTAG_IMAGEWIDTH: +# 154: case TIFFTAG_IMAGELENGTH: +# 157: case TIFFTAG_BITSPERSAMPLE: +# 181: case TIFFTAG_COMPRESSION: +# 202: case TIFFTAG_PHOTOMETRIC: +# 205: case TIFFTAG_THRESHHOLDING: +# 208: case TIFFTAG_FILLORDER: +# 214: case TIFFTAG_ORIENTATION: +# 221: case TIFFTAG_SAMPLESPERPIXEL: +# 228: case TIFFTAG_ROWSPERSTRIP: +# 238: case TIFFTAG_MINSAMPLEVALUE: +# 241: case TIFFTAG_MAXSAMPLEVALUE: +# 244: case TIFFTAG_SMINSAMPLEVALUE: +# 247: case TIFFTAG_SMAXSAMPLEVALUE: +# 250: case TIFFTAG_XRESOLUTION: +# 256: case TIFFTAG_YRESOLUTION: +# 262: case TIFFTAG_PLANARCONFIG: +# 268: case TIFFTAG_XPOSITION: +# 271: case TIFFTAG_YPOSITION: +# 274: case TIFFTAG_RESOLUTIONUNIT: +# 280: case TIFFTAG_PAGENUMBER: +# 284: case TIFFTAG_HALFTONEHINTS: +# 288: case TIFFTAG_COLORMAP: +# 294: case TIFFTAG_EXTRASAMPLES: +# 298: case TIFFTAG_MATTEING: +# 305: case TIFFTAG_TILEWIDTH: +# 316: case TIFFTAG_TILELENGTH: +# 327: case TIFFTAG_TILEDEPTH: +# 333: case TIFFTAG_DATATYPE: +# 344: case TIFFTAG_SAMPLEFORMAT: +# 361: case TIFFTAG_IMAGEDEPTH: +# 364: case TIFFTAG_SUBIFD: +# 376: case TIFFTAG_YCBCRPOSITIONING: +# 379: case TIFFTAG_YCBCRSUBSAMPLING: +# 383: case TIFFTAG_TRANSFERFUNCTION: +# 389: case TIFFTAG_REFERENCEBLACKWHITE: +# 393: case TIFFTAG_INKNAMES: + +# Following pseudo-tags are also handled by default in libtiff: +# TIFFTAG_JPEGQUALITY 65537 + +# some of these are not in our TAGS_V2 dict and were included from tiff.h + +# This list also exists in encode.c +LIBTIFF_CORE = { + 255, + 256, + 257, + 258, + 259, + 262, + 263, + 266, + 274, + 277, + 278, + 280, + 281, + 340, + 341, + 282, + 283, + 284, + 286, + 287, + 296, + 297, + 321, + 320, + 338, + 32995, + 322, + 323, + 32998, + 32996, + 339, + 32997, + 330, + 531, + 530, + 301, + 532, + 333, + # as above + 269, # this has been in our tests forever, and works + 65537, +} + +LIBTIFF_CORE.remove(255) # We don't have support for subfiletypes +LIBTIFF_CORE.remove(322) # We don't have support for writing tiled images with libtiff +LIBTIFF_CORE.remove(323) # Tiled images +LIBTIFF_CORE.remove(333) # Ink Names either + +# Note to advanced users: There may be combinations of these +# parameters and values that when added properly, will work and +# produce valid tiff images that may work in your application. +# It is safe to add and remove tags from this set from Pillow's point +# of view so long as you test against libtiff. diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/WalImageFile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/WalImageFile.py new file mode 100644 index 0000000000000000000000000000000000000000..87e32878b1970876913e88fd5e8480d6813392c4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/WalImageFile.py @@ -0,0 +1,127 @@ +# +# The Python Imaging Library. +# $Id$ +# +# WAL file handling +# +# History: +# 2003-04-23 fl created +# +# Copyright (c) 2003 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# + +""" +This reader is based on the specification available from: +https://www.flipcode.com/archives/Quake_2_BSP_File_Format.shtml +and has been tested with a few sample files found using google. + +.. note:: + This format cannot be automatically recognized, so the reader + is not registered for use with :py:func:`PIL.Image.open()`. + To open a WAL file, use the :py:func:`PIL.WalImageFile.open()` function instead. +""" +from __future__ import annotations + +from typing import IO + +from . import Image, ImageFile +from ._binary import i32le as i32 +from ._typing import StrOrBytesPath + + +class WalImageFile(ImageFile.ImageFile): + format = "WAL" + format_description = "Quake2 Texture" + + def _open(self) -> None: + self._mode = "P" + + # read header fields + header = self.fp.read(32 + 24 + 32 + 12) + self._size = i32(header, 32), i32(header, 36) + Image._decompression_bomb_check(self.size) + + # load pixel data + offset = i32(header, 40) + self.fp.seek(offset) + + # strings are null-terminated + self.info["name"] = header[:32].split(b"\0", 1)[0] + next_name = header[56 : 56 + 32].split(b"\0", 1)[0] + if next_name: + self.info["next_name"] = next_name + + def load(self) -> Image.core.PixelAccess | None: + if self._im is None: + self.im = Image.core.new(self.mode, self.size) + self.frombytes(self.fp.read(self.size[0] * self.size[1])) + self.putpalette(quake2palette) + return Image.Image.load(self) + + +def open(filename: StrOrBytesPath | IO[bytes]) -> WalImageFile: + """ + Load texture from a Quake2 WAL texture file. + + By default, a Quake2 standard palette is attached to the texture. + To override the palette, use the :py:func:`PIL.Image.Image.putpalette()` method. + + :param filename: WAL file name, or an opened file handle. + :returns: An image instance. + """ + return WalImageFile(filename) + + +quake2palette = ( + # default palette taken from piffo 0.93 by Hans Häggström + b"\x01\x01\x01\x0b\x0b\x0b\x12\x12\x12\x17\x17\x17\x1b\x1b\x1b\x1e" + b"\x1e\x1e\x22\x22\x22\x26\x26\x26\x29\x29\x29\x2c\x2c\x2c\x2f\x2f" + b"\x2f\x32\x32\x32\x35\x35\x35\x37\x37\x37\x3a\x3a\x3a\x3c\x3c\x3c" + b"\x24\x1e\x13\x22\x1c\x12\x20\x1b\x12\x1f\x1a\x10\x1d\x19\x10\x1b" + b"\x17\x0f\x1a\x16\x0f\x18\x14\x0d\x17\x13\x0d\x16\x12\x0d\x14\x10" + b"\x0b\x13\x0f\x0b\x10\x0d\x0a\x0f\x0b\x0a\x0d\x0b\x07\x0b\x0a\x07" + b"\x23\x23\x26\x22\x22\x25\x22\x20\x23\x21\x1f\x22\x20\x1e\x20\x1f" + b"\x1d\x1e\x1d\x1b\x1c\x1b\x1a\x1a\x1a\x19\x19\x18\x17\x17\x17\x16" + b"\x16\x14\x14\x14\x13\x13\x13\x10\x10\x10\x0f\x0f\x0f\x0d\x0d\x0d" + b"\x2d\x28\x20\x29\x24\x1c\x27\x22\x1a\x25\x1f\x17\x38\x2e\x1e\x31" + b"\x29\x1a\x2c\x25\x17\x26\x20\x14\x3c\x30\x14\x37\x2c\x13\x33\x28" + b"\x12\x2d\x24\x10\x28\x1f\x0f\x22\x1a\x0b\x1b\x14\x0a\x13\x0f\x07" + b"\x31\x1a\x16\x30\x17\x13\x2e\x16\x10\x2c\x14\x0d\x2a\x12\x0b\x27" + b"\x0f\x0a\x25\x0f\x07\x21\x0d\x01\x1e\x0b\x01\x1c\x0b\x01\x1a\x0b" + b"\x01\x18\x0a\x01\x16\x0a\x01\x13\x0a\x01\x10\x07\x01\x0d\x07\x01" + b"\x29\x23\x1e\x27\x21\x1c\x26\x20\x1b\x25\x1f\x1a\x23\x1d\x19\x21" + b"\x1c\x18\x20\x1b\x17\x1e\x19\x16\x1c\x18\x14\x1b\x17\x13\x19\x14" + b"\x10\x17\x13\x0f\x14\x10\x0d\x12\x0f\x0b\x0f\x0b\x0a\x0b\x0a\x07" + b"\x26\x1a\x0f\x23\x19\x0f\x20\x17\x0f\x1c\x16\x0f\x19\x13\x0d\x14" + b"\x10\x0b\x10\x0d\x0a\x0b\x0a\x07\x33\x22\x1f\x35\x29\x26\x37\x2f" + b"\x2d\x39\x35\x34\x37\x39\x3a\x33\x37\x39\x30\x34\x36\x2b\x31\x34" + b"\x27\x2e\x31\x22\x2b\x2f\x1d\x28\x2c\x17\x25\x2a\x0f\x20\x26\x0d" + b"\x1e\x25\x0b\x1c\x22\x0a\x1b\x20\x07\x19\x1e\x07\x17\x1b\x07\x14" + b"\x18\x01\x12\x16\x01\x0f\x12\x01\x0b\x0d\x01\x07\x0a\x01\x01\x01" + b"\x2c\x21\x21\x2a\x1f\x1f\x29\x1d\x1d\x27\x1c\x1c\x26\x1a\x1a\x24" + b"\x18\x18\x22\x17\x17\x21\x16\x16\x1e\x13\x13\x1b\x12\x12\x18\x10" + b"\x10\x16\x0d\x0d\x12\x0b\x0b\x0d\x0a\x0a\x0a\x07\x07\x01\x01\x01" + b"\x2e\x30\x29\x2d\x2e\x27\x2b\x2c\x26\x2a\x2a\x24\x28\x29\x23\x27" + b"\x27\x21\x26\x26\x1f\x24\x24\x1d\x22\x22\x1c\x1f\x1f\x1a\x1c\x1c" + b"\x18\x19\x19\x16\x17\x17\x13\x13\x13\x10\x0f\x0f\x0d\x0b\x0b\x0a" + b"\x30\x1e\x1b\x2d\x1c\x19\x2c\x1a\x17\x2a\x19\x14\x28\x17\x13\x26" + b"\x16\x10\x24\x13\x0f\x21\x12\x0d\x1f\x10\x0b\x1c\x0f\x0a\x19\x0d" + b"\x0a\x16\x0b\x07\x12\x0a\x07\x0f\x07\x01\x0a\x01\x01\x01\x01\x01" + b"\x28\x29\x38\x26\x27\x36\x25\x26\x34\x24\x24\x31\x22\x22\x2f\x20" + b"\x21\x2d\x1e\x1f\x2a\x1d\x1d\x27\x1b\x1b\x25\x19\x19\x21\x17\x17" + b"\x1e\x14\x14\x1b\x13\x12\x17\x10\x0f\x13\x0d\x0b\x0f\x0a\x07\x07" + b"\x2f\x32\x29\x2d\x30\x26\x2b\x2e\x24\x29\x2c\x21\x27\x2a\x1e\x25" + b"\x28\x1c\x23\x26\x1a\x21\x25\x18\x1e\x22\x14\x1b\x1f\x10\x19\x1c" + b"\x0d\x17\x1a\x0a\x13\x17\x07\x10\x13\x01\x0d\x0f\x01\x0a\x0b\x01" + b"\x01\x3f\x01\x13\x3c\x0b\x1b\x39\x10\x20\x35\x14\x23\x31\x17\x23" + b"\x2d\x18\x23\x29\x18\x3f\x3f\x3f\x3f\x3f\x39\x3f\x3f\x31\x3f\x3f" + b"\x2a\x3f\x3f\x20\x3f\x3f\x14\x3f\x3c\x12\x3f\x39\x0f\x3f\x35\x0b" + b"\x3f\x32\x07\x3f\x2d\x01\x3d\x2a\x01\x3b\x26\x01\x39\x21\x01\x37" + b"\x1d\x01\x34\x1a\x01\x32\x16\x01\x2f\x12\x01\x2d\x0f\x01\x2a\x0b" + b"\x01\x27\x07\x01\x23\x01\x01\x1d\x01\x01\x17\x01\x01\x10\x01\x01" + b"\x3d\x01\x01\x19\x19\x3f\x3f\x01\x01\x01\x01\x3f\x16\x16\x13\x10" + b"\x10\x0f\x0d\x0d\x0b\x3c\x2e\x2a\x36\x27\x20\x30\x21\x18\x29\x1b" + b"\x10\x3c\x39\x37\x37\x32\x2f\x31\x2c\x28\x2b\x26\x21\x30\x22\x20" +) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/WebPImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/WebPImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..1716a18ccda48ea47c6176c22914ab1f8b035e7c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/WebPImagePlugin.py @@ -0,0 +1,320 @@ +from __future__ import annotations + +from io import BytesIO +from typing import IO, Any + +from . import Image, ImageFile + +try: + from . import _webp + + SUPPORTED = True +except ImportError: + SUPPORTED = False + + +_VP8_MODES_BY_IDENTIFIER = { + b"VP8 ": "RGB", + b"VP8X": "RGBA", + b"VP8L": "RGBA", # lossless +} + + +def _accept(prefix: bytes) -> bool | str: + is_riff_file_format = prefix.startswith(b"RIFF") + is_webp_file = prefix[8:12] == b"WEBP" + is_valid_vp8_mode = prefix[12:16] in _VP8_MODES_BY_IDENTIFIER + + if is_riff_file_format and is_webp_file and is_valid_vp8_mode: + if not SUPPORTED: + return ( + "image file could not be identified because WEBP support not installed" + ) + return True + return False + + +class WebPImageFile(ImageFile.ImageFile): + format = "WEBP" + format_description = "WebP image" + __loaded = 0 + __logical_frame = 0 + + def _open(self) -> None: + # Use the newer AnimDecoder API to parse the (possibly) animated file, + # and access muxed chunks like ICC/EXIF/XMP. + self._decoder = _webp.WebPAnimDecoder(self.fp.read()) + + # Get info from decoder + self._size, loop_count, bgcolor, frame_count, mode = self._decoder.get_info() + self.info["loop"] = loop_count + bg_a, bg_r, bg_g, bg_b = ( + (bgcolor >> 24) & 0xFF, + (bgcolor >> 16) & 0xFF, + (bgcolor >> 8) & 0xFF, + bgcolor & 0xFF, + ) + self.info["background"] = (bg_r, bg_g, bg_b, bg_a) + self.n_frames = frame_count + self.is_animated = self.n_frames > 1 + self._mode = "RGB" if mode == "RGBX" else mode + self.rawmode = mode + + # Attempt to read ICC / EXIF / XMP chunks from file + icc_profile = self._decoder.get_chunk("ICCP") + exif = self._decoder.get_chunk("EXIF") + xmp = self._decoder.get_chunk("XMP ") + if icc_profile: + self.info["icc_profile"] = icc_profile + if exif: + self.info["exif"] = exif + if xmp: + self.info["xmp"] = xmp + + # Initialize seek state + self._reset(reset=False) + + def _getexif(self) -> dict[int, Any] | None: + if "exif" not in self.info: + return None + return self.getexif()._get_merged_dict() + + def seek(self, frame: int) -> None: + if not self._seek_check(frame): + return + + # Set logical frame to requested position + self.__logical_frame = frame + + def _reset(self, reset: bool = True) -> None: + if reset: + self._decoder.reset() + self.__physical_frame = 0 + self.__loaded = -1 + self.__timestamp = 0 + + def _get_next(self) -> tuple[bytes, int, int]: + # Get next frame + ret = self._decoder.get_next() + self.__physical_frame += 1 + + # Check if an error occurred + if ret is None: + self._reset() # Reset just to be safe + self.seek(0) + msg = "failed to decode next frame in WebP file" + raise EOFError(msg) + + # Compute duration + data, timestamp = ret + duration = timestamp - self.__timestamp + self.__timestamp = timestamp + + # libwebp gives frame end, adjust to start of frame + timestamp -= duration + return data, timestamp, duration + + def _seek(self, frame: int) -> None: + if self.__physical_frame == frame: + return # Nothing to do + if frame < self.__physical_frame: + self._reset() # Rewind to beginning + while self.__physical_frame < frame: + self._get_next() # Advance to the requested frame + + def load(self) -> Image.core.PixelAccess | None: + if self.__loaded != self.__logical_frame: + self._seek(self.__logical_frame) + + # We need to load the image data for this frame + data, timestamp, duration = self._get_next() + self.info["timestamp"] = timestamp + self.info["duration"] = duration + self.__loaded = self.__logical_frame + + # Set tile + if self.fp and self._exclusive_fp: + self.fp.close() + self.fp = BytesIO(data) + self.tile = [ImageFile._Tile("raw", (0, 0) + self.size, 0, self.rawmode)] + + return super().load() + + def load_seek(self, pos: int) -> None: + pass + + def tell(self) -> int: + return self.__logical_frame + + +def _convert_frame(im: Image.Image) -> Image.Image: + # Make sure image mode is supported + if im.mode not in ("RGBX", "RGBA", "RGB"): + im = im.convert("RGBA" if im.has_transparency_data else "RGB") + return im + + +def _save_all(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + encoderinfo = im.encoderinfo.copy() + append_images = list(encoderinfo.get("append_images", [])) + + # If total frame count is 1, then save using the legacy API, which + # will preserve non-alpha modes + total = 0 + for ims in [im] + append_images: + total += getattr(ims, "n_frames", 1) + if total == 1: + _save(im, fp, filename) + return + + background: int | tuple[int, ...] = (0, 0, 0, 0) + if "background" in encoderinfo: + background = encoderinfo["background"] + elif "background" in im.info: + background = im.info["background"] + if isinstance(background, int): + # GifImagePlugin stores a global color table index in + # info["background"]. So it must be converted to an RGBA value + palette = im.getpalette() + if palette: + r, g, b = palette[background * 3 : (background + 1) * 3] + background = (r, g, b, 255) + else: + background = (background, background, background, 255) + + duration = im.encoderinfo.get("duration", im.info.get("duration", 0)) + loop = im.encoderinfo.get("loop", 0) + minimize_size = im.encoderinfo.get("minimize_size", False) + kmin = im.encoderinfo.get("kmin", None) + kmax = im.encoderinfo.get("kmax", None) + allow_mixed = im.encoderinfo.get("allow_mixed", False) + verbose = False + lossless = im.encoderinfo.get("lossless", False) + quality = im.encoderinfo.get("quality", 80) + alpha_quality = im.encoderinfo.get("alpha_quality", 100) + method = im.encoderinfo.get("method", 0) + icc_profile = im.encoderinfo.get("icc_profile") or "" + exif = im.encoderinfo.get("exif", "") + if isinstance(exif, Image.Exif): + exif = exif.tobytes() + xmp = im.encoderinfo.get("xmp", "") + if allow_mixed: + lossless = False + + # Sensible keyframe defaults are from gif2webp.c script + if kmin is None: + kmin = 9 if lossless else 3 + if kmax is None: + kmax = 17 if lossless else 5 + + # Validate background color + if ( + not isinstance(background, (list, tuple)) + or len(background) != 4 + or not all(0 <= v < 256 for v in background) + ): + msg = f"Background color is not an RGBA tuple clamped to (0-255): {background}" + raise OSError(msg) + + # Convert to packed uint + bg_r, bg_g, bg_b, bg_a = background + background = (bg_a << 24) | (bg_r << 16) | (bg_g << 8) | (bg_b << 0) + + # Setup the WebP animation encoder + enc = _webp.WebPAnimEncoder( + im.size, + background, + loop, + minimize_size, + kmin, + kmax, + allow_mixed, + verbose, + ) + + # Add each frame + frame_idx = 0 + timestamp = 0 + cur_idx = im.tell() + try: + for ims in [im] + append_images: + # Get number of frames in this image + nfr = getattr(ims, "n_frames", 1) + + for idx in range(nfr): + ims.seek(idx) + + frame = _convert_frame(ims) + + # Append the frame to the animation encoder + enc.add( + frame.getim(), + round(timestamp), + lossless, + quality, + alpha_quality, + method, + ) + + # Update timestamp and frame index + if isinstance(duration, (list, tuple)): + timestamp += duration[frame_idx] + else: + timestamp += duration + frame_idx += 1 + + finally: + im.seek(cur_idx) + + # Force encoder to flush frames + enc.add(None, round(timestamp), lossless, quality, alpha_quality, 0) + + # Get the final output from the encoder + data = enc.assemble(icc_profile, exif, xmp) + if data is None: + msg = "cannot write file as WebP (encoder returned None)" + raise OSError(msg) + + fp.write(data) + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + lossless = im.encoderinfo.get("lossless", False) + quality = im.encoderinfo.get("quality", 80) + alpha_quality = im.encoderinfo.get("alpha_quality", 100) + icc_profile = im.encoderinfo.get("icc_profile") or "" + exif = im.encoderinfo.get("exif", b"") + if isinstance(exif, Image.Exif): + exif = exif.tobytes() + if exif.startswith(b"Exif\x00\x00"): + exif = exif[6:] + xmp = im.encoderinfo.get("xmp", "") + method = im.encoderinfo.get("method", 4) + exact = 1 if im.encoderinfo.get("exact") else 0 + + im = _convert_frame(im) + + data = _webp.WebPEncode( + im.getim(), + lossless, + float(quality), + float(alpha_quality), + icc_profile, + method, + exact, + exif, + xmp, + ) + if data is None: + msg = "cannot write file as WebP (encoder returned None)" + raise OSError(msg) + + fp.write(data) + + +Image.register_open(WebPImageFile.format, WebPImageFile, _accept) +if SUPPORTED: + Image.register_save(WebPImageFile.format, _save) + Image.register_save_all(WebPImageFile.format, _save_all) + Image.register_extension(WebPImageFile.format, ".webp") + Image.register_mime(WebPImageFile.format, "image/webp") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/WmfImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/WmfImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..d569cb4b819db35966c67fbe75518c719973ef59 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/WmfImagePlugin.py @@ -0,0 +1,186 @@ +# +# The Python Imaging Library +# $Id$ +# +# WMF stub codec +# +# history: +# 1996-12-14 fl Created +# 2004-02-22 fl Turned into a stub driver +# 2004-02-23 fl Added EMF support +# +# Copyright (c) Secret Labs AB 1997-2004. All rights reserved. +# Copyright (c) Fredrik Lundh 1996. +# +# See the README file for information on usage and redistribution. +# +# WMF/EMF reference documentation: +# https://winprotocoldoc.blob.core.windows.net/productionwindowsarchives/MS-WMF/[MS-WMF].pdf +# http://wvware.sourceforge.net/caolan/index.html +# http://wvware.sourceforge.net/caolan/ora-wmf.html +from __future__ import annotations + +from typing import IO + +from . import Image, ImageFile +from ._binary import i16le as word +from ._binary import si16le as short +from ._binary import si32le as _long + +_handler = None + + +def register_handler(handler: ImageFile.StubHandler | None) -> None: + """ + Install application-specific WMF image handler. + + :param handler: Handler object. + """ + global _handler + _handler = handler + + +if hasattr(Image.core, "drawwmf"): + # install default handler (windows only) + + class WmfHandler(ImageFile.StubHandler): + def open(self, im: ImageFile.StubImageFile) -> None: + im._mode = "RGB" + self.bbox = im.info["wmf_bbox"] + + def load(self, im: ImageFile.StubImageFile) -> Image.Image: + im.fp.seek(0) # rewind + return Image.frombytes( + "RGB", + im.size, + Image.core.drawwmf(im.fp.read(), im.size, self.bbox), + "raw", + "BGR", + (im.size[0] * 3 + 3) & -4, + -1, + ) + + register_handler(WmfHandler()) + +# +# -------------------------------------------------------------------- +# Read WMF file + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith((b"\xd7\xcd\xc6\x9a\x00\x00", b"\x01\x00\x00\x00")) + + +## +# Image plugin for Windows metafiles. + + +class WmfStubImageFile(ImageFile.StubImageFile): + format = "WMF" + format_description = "Windows Metafile" + + def _open(self) -> None: + # check placable header + s = self.fp.read(44) + + if s.startswith(b"\xd7\xcd\xc6\x9a\x00\x00"): + # placeable windows metafile + + # get units per inch + inch = word(s, 14) + if inch == 0: + msg = "Invalid inch" + raise ValueError(msg) + self._inch: tuple[float, float] = inch, inch + + # get bounding box + x0 = short(s, 6) + y0 = short(s, 8) + x1 = short(s, 10) + y1 = short(s, 12) + + # normalize size to 72 dots per inch + self.info["dpi"] = 72 + size = ( + (x1 - x0) * self.info["dpi"] // inch, + (y1 - y0) * self.info["dpi"] // inch, + ) + + self.info["wmf_bbox"] = x0, y0, x1, y1 + + # sanity check (standard metafile header) + if s[22:26] != b"\x01\x00\t\x00": + msg = "Unsupported WMF file format" + raise SyntaxError(msg) + + elif s.startswith(b"\x01\x00\x00\x00") and s[40:44] == b" EMF": + # enhanced metafile + + # get bounding box + x0 = _long(s, 8) + y0 = _long(s, 12) + x1 = _long(s, 16) + y1 = _long(s, 20) + + # get frame (in 0.01 millimeter units) + frame = _long(s, 24), _long(s, 28), _long(s, 32), _long(s, 36) + + size = x1 - x0, y1 - y0 + + # calculate dots per inch from bbox and frame + xdpi = 2540.0 * (x1 - x0) / (frame[2] - frame[0]) + ydpi = 2540.0 * (y1 - y0) / (frame[3] - frame[1]) + + self.info["wmf_bbox"] = x0, y0, x1, y1 + + if xdpi == ydpi: + self.info["dpi"] = xdpi + else: + self.info["dpi"] = xdpi, ydpi + self._inch = xdpi, ydpi + + else: + msg = "Unsupported file format" + raise SyntaxError(msg) + + self._mode = "RGB" + self._size = size + + loader = self._load() + if loader: + loader.open(self) + + def _load(self) -> ImageFile.StubHandler | None: + return _handler + + def load( + self, dpi: float | tuple[float, float] | None = None + ) -> Image.core.PixelAccess | None: + if dpi is not None: + self.info["dpi"] = dpi + x0, y0, x1, y1 = self.info["wmf_bbox"] + if not isinstance(dpi, tuple): + dpi = dpi, dpi + self._size = ( + int((x1 - x0) * dpi[0] / self._inch[0]), + int((y1 - y0) * dpi[1] / self._inch[1]), + ) + return super().load() + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if _handler is None or not hasattr(_handler, "save"): + msg = "WMF save handler not installed" + raise OSError(msg) + _handler.save(im, fp, filename) + + +# +# -------------------------------------------------------------------- +# Registry stuff + + +Image.register_open(WmfStubImageFile.format, WmfStubImageFile, _accept) +Image.register_save(WmfStubImageFile.format, _save) + +Image.register_extensions(WmfStubImageFile.format, [".wmf", ".emf"]) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/XVThumbImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/XVThumbImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..cde28388ff0535262770dd0336ee2b48db2178ba --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/XVThumbImagePlugin.py @@ -0,0 +1,83 @@ +# +# The Python Imaging Library. +# $Id$ +# +# XV Thumbnail file handler by Charles E. "Gene" Cash +# (gcash@magicnet.net) +# +# see xvcolor.c and xvbrowse.c in the sources to John Bradley's XV, +# available from ftp://ftp.cis.upenn.edu/pub/xv/ +# +# history: +# 98-08-15 cec created (b/w only) +# 98-12-09 cec added color palette +# 98-12-28 fl added to PIL (with only a few very minor modifications) +# +# To do: +# FIXME: make save work (this requires quantization support) +# +from __future__ import annotations + +from . import Image, ImageFile, ImagePalette +from ._binary import o8 + +_MAGIC = b"P7 332" + +# standard color palette for thumbnails (RGB332) +PALETTE = b"" +for r in range(8): + for g in range(8): + for b in range(4): + PALETTE = PALETTE + ( + o8((r * 255) // 7) + o8((g * 255) // 7) + o8((b * 255) // 3) + ) + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(_MAGIC) + + +## +# Image plugin for XV thumbnail images. + + +class XVThumbImageFile(ImageFile.ImageFile): + format = "XVThumb" + format_description = "XV thumbnail image" + + def _open(self) -> None: + # check magic + assert self.fp is not None + + if not _accept(self.fp.read(6)): + msg = "not an XV thumbnail file" + raise SyntaxError(msg) + + # Skip to beginning of next line + self.fp.readline() + + # skip info comments + while True: + s = self.fp.readline() + if not s: + msg = "Unexpected EOF reading XV thumbnail file" + raise SyntaxError(msg) + if s[0] != 35: # ie. when not a comment: '#' + break + + # parse header line (already read) + s = s.strip().split() + + self._mode = "P" + self._size = int(s[0]), int(s[1]) + + self.palette = ImagePalette.raw("RGB", PALETTE) + + self.tile = [ + ImageFile._Tile("raw", (0, 0) + self.size, self.fp.tell(), self.mode) + ] + + +# -------------------------------------------------------------------- + +Image.register_open(XVThumbImageFile.format, XVThumbImageFile, _accept) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/XbmImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/XbmImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..1e57aa162ea4f8618dac66cf042352f73d2199c8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/XbmImagePlugin.py @@ -0,0 +1,98 @@ +# +# The Python Imaging Library. +# $Id$ +# +# XBM File handling +# +# History: +# 1995-09-08 fl Created +# 1996-11-01 fl Added save support +# 1997-07-07 fl Made header parser more tolerant +# 1997-07-22 fl Fixed yet another parser bug +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.4) +# 2001-05-13 fl Added hotspot handling (based on code from Bernhard Herzog) +# 2004-02-24 fl Allow some whitespace before first #define +# +# Copyright (c) 1997-2004 by Secret Labs AB +# Copyright (c) 1996-1997 by Fredrik Lundh +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re +from typing import IO + +from . import Image, ImageFile + +# XBM header +xbm_head = re.compile( + rb"\s*#define[ \t]+.*_width[ \t]+(?P[0-9]+)[\r\n]+" + b"#define[ \t]+.*_height[ \t]+(?P[0-9]+)[\r\n]+" + b"(?P" + b"#define[ \t]+[^_]*_x_hot[ \t]+(?P[0-9]+)[\r\n]+" + b"#define[ \t]+[^_]*_y_hot[ \t]+(?P[0-9]+)[\r\n]+" + b")?" + rb"[\000-\377]*_bits\[]" +) + + +def _accept(prefix: bytes) -> bool: + return prefix.lstrip().startswith(b"#define") + + +## +# Image plugin for X11 bitmaps. + + +class XbmImageFile(ImageFile.ImageFile): + format = "XBM" + format_description = "X11 Bitmap" + + def _open(self) -> None: + assert self.fp is not None + + m = xbm_head.match(self.fp.read(512)) + + if not m: + msg = "not a XBM file" + raise SyntaxError(msg) + + xsize = int(m.group("width")) + ysize = int(m.group("height")) + + if m.group("hotspot"): + self.info["hotspot"] = (int(m.group("xhot")), int(m.group("yhot"))) + + self._mode = "1" + self._size = xsize, ysize + + self.tile = [ImageFile._Tile("xbm", (0, 0) + self.size, m.end())] + + +def _save(im: Image.Image, fp: IO[bytes], filename: str | bytes) -> None: + if im.mode != "1": + msg = f"cannot write mode {im.mode} as XBM" + raise OSError(msg) + + fp.write(f"#define im_width {im.size[0]}\n".encode("ascii")) + fp.write(f"#define im_height {im.size[1]}\n".encode("ascii")) + + hotspot = im.encoderinfo.get("hotspot") + if hotspot: + fp.write(f"#define im_x_hot {hotspot[0]}\n".encode("ascii")) + fp.write(f"#define im_y_hot {hotspot[1]}\n".encode("ascii")) + + fp.write(b"static char im_bits[] = {\n") + + ImageFile._save(im, fp, [ImageFile._Tile("xbm", (0, 0) + im.size)]) + + fp.write(b"};\n") + + +Image.register_open(XbmImageFile.format, XbmImageFile, _accept) +Image.register_save(XbmImageFile.format, _save) + +Image.register_extension(XbmImageFile.format, ".xbm") + +Image.register_mime(XbmImageFile.format, "image/xbm") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/XpmImagePlugin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/XpmImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..3be240fbc1aeb7660de46fbd4f99f309ce9915dd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/XpmImagePlugin.py @@ -0,0 +1,157 @@ +# +# The Python Imaging Library. +# $Id$ +# +# XPM File handling +# +# History: +# 1996-12-29 fl Created +# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.7) +# +# Copyright (c) Secret Labs AB 1997-2001. +# Copyright (c) Fredrik Lundh 1996-2001. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +import re + +from . import Image, ImageFile, ImagePalette +from ._binary import o8 + +# XPM header +xpm_head = re.compile(b'"([0-9]*) ([0-9]*) ([0-9]*) ([0-9]*)') + + +def _accept(prefix: bytes) -> bool: + return prefix.startswith(b"/* XPM */") + + +## +# Image plugin for X11 pixel maps. + + +class XpmImageFile(ImageFile.ImageFile): + format = "XPM" + format_description = "X11 Pixel Map" + + def _open(self) -> None: + assert self.fp is not None + if not _accept(self.fp.read(9)): + msg = "not an XPM file" + raise SyntaxError(msg) + + # skip forward to next string + while True: + line = self.fp.readline() + if not line: + msg = "broken XPM file" + raise SyntaxError(msg) + m = xpm_head.match(line) + if m: + break + + self._size = int(m.group(1)), int(m.group(2)) + + palette_length = int(m.group(3)) + bpp = int(m.group(4)) + + # + # load palette description + + palette = {} + + for _ in range(palette_length): + line = self.fp.readline().rstrip() + + c = line[1 : bpp + 1] + s = line[bpp + 1 : -2].split() + + for i in range(0, len(s), 2): + if s[i] == b"c": + # process colour key + rgb = s[i + 1] + if rgb == b"None": + self.info["transparency"] = c + elif rgb.startswith(b"#"): + rgb_int = int(rgb[1:], 16) + palette[c] = ( + o8((rgb_int >> 16) & 255) + + o8((rgb_int >> 8) & 255) + + o8(rgb_int & 255) + ) + else: + # unknown colour + msg = "cannot read this XPM file" + raise ValueError(msg) + break + + else: + # missing colour key + msg = "cannot read this XPM file" + raise ValueError(msg) + + args: tuple[int, dict[bytes, bytes] | tuple[bytes, ...]] + if palette_length > 256: + self._mode = "RGB" + args = (bpp, palette) + else: + self._mode = "P" + self.palette = ImagePalette.raw("RGB", b"".join(palette.values())) + args = (bpp, tuple(palette.keys())) + + self.tile = [ImageFile._Tile("xpm", (0, 0) + self.size, self.fp.tell(), args)] + + def load_read(self, read_bytes: int) -> bytes: + # + # load all image data in one chunk + + xsize, ysize = self.size + + assert self.fp is not None + s = [self.fp.readline()[1 : xsize + 1].ljust(xsize) for i in range(ysize)] + + return b"".join(s) + + +class XpmDecoder(ImageFile.PyDecoder): + _pulls_fd = True + + def decode(self, buffer: bytes | Image.SupportsArrayInterface) -> tuple[int, int]: + assert self.fd is not None + + data = bytearray() + bpp, palette = self.args + dest_length = self.state.xsize * self.state.ysize + if self.mode == "RGB": + dest_length *= 3 + pixel_header = False + while len(data) < dest_length: + line = self.fd.readline() + if not line: + break + if line.rstrip() == b"/* pixels */" and not pixel_header: + pixel_header = True + continue + line = b'"'.join(line.split(b'"')[1:-1]) + for i in range(0, len(line), bpp): + key = line[i : i + bpp] + if self.mode == "RGB": + data += palette[key] + else: + data += o8(palette.index(key)) + self.set_as_raw(bytes(data)) + return -1, 0 + + +# +# Registry + + +Image.register_open(XpmImageFile.format, XpmImageFile, _accept) +Image.register_decoder("xpm", XpmDecoder) + +Image.register_extension(XpmImageFile.format, ".xpm") + +Image.register_mime(XpmImageFile.format, "image/xpm") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6e4c23f897f83ef72fc10070bd22e9dc70614cf9 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/__init__.py @@ -0,0 +1,87 @@ +"""Pillow (Fork of the Python Imaging Library) + +Pillow is the friendly PIL fork by Jeffrey A. Clark and contributors. + https://github.com/python-pillow/Pillow/ + +Pillow is forked from PIL 1.1.7. + +PIL is the Python Imaging Library by Fredrik Lundh and contributors. +Copyright (c) 1999 by Secret Labs AB. + +Use PIL.__version__ for this Pillow version. + +;-) +""" + +from __future__ import annotations + +from . import _version + +# VERSION was removed in Pillow 6.0.0. +# PILLOW_VERSION was removed in Pillow 9.0.0. +# Use __version__ instead. +__version__ = _version.__version__ +del _version + + +_plugins = [ + "AvifImagePlugin", + "BlpImagePlugin", + "BmpImagePlugin", + "BufrStubImagePlugin", + "CurImagePlugin", + "DcxImagePlugin", + "DdsImagePlugin", + "EpsImagePlugin", + "FitsImagePlugin", + "FliImagePlugin", + "FpxImagePlugin", + "FtexImagePlugin", + "GbrImagePlugin", + "GifImagePlugin", + "GribStubImagePlugin", + "Hdf5StubImagePlugin", + "IcnsImagePlugin", + "IcoImagePlugin", + "ImImagePlugin", + "ImtImagePlugin", + "IptcImagePlugin", + "JpegImagePlugin", + "Jpeg2KImagePlugin", + "McIdasImagePlugin", + "MicImagePlugin", + "MpegImagePlugin", + "MpoImagePlugin", + "MspImagePlugin", + "PalmImagePlugin", + "PcdImagePlugin", + "PcxImagePlugin", + "PdfImagePlugin", + "PixarImagePlugin", + "PngImagePlugin", + "PpmImagePlugin", + "PsdImagePlugin", + "QoiImagePlugin", + "SgiImagePlugin", + "SpiderImagePlugin", + "SunImagePlugin", + "TgaImagePlugin", + "TiffImagePlugin", + "WebPImagePlugin", + "WmfImagePlugin", + "XbmImagePlugin", + "XpmImagePlugin", + "XVThumbImagePlugin", +] + + +class UnidentifiedImageError(OSError): + """ + Raised in :py:meth:`PIL.Image.open` if an image cannot be opened and identified. + + If a PNG image raises this error, setting :data:`.ImageFile.LOAD_TRUNCATED_IMAGES` + to true may allow the image to be opened after all. The setting will ignore missing + data and checksum failures. + """ + + pass diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/__main__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..043156e892dadc4fb1222b33f5eda33251cd15aa --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/__main__.py @@ -0,0 +1,7 @@ +from __future__ import annotations + +import sys + +from .features import pilinfo + +pilinfo(supported_formats="--report" not in sys.argv) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/__pycache__/BmpImagePlugin.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/__pycache__/BmpImagePlugin.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2dcd6c03e8836326239e7496263ec4bd7af370c2 Binary files /dev/null and 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-> Any: ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_binary.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_binary.py new file mode 100644 index 0000000000000000000000000000000000000000..4594ccce361168cf77e630cb88ffb09bb4362831 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_binary.py @@ -0,0 +1,112 @@ +# +# The Python Imaging Library. +# $Id$ +# +# Binary input/output support routines. +# +# Copyright (c) 1997-2003 by Secret Labs AB +# Copyright (c) 1995-2003 by Fredrik Lundh +# Copyright (c) 2012 by Brian Crowell +# +# See the README file for information on usage and redistribution. +# + + +"""Binary input/output support routines.""" +from __future__ import annotations + +from struct import pack, unpack_from + + +def i8(c: bytes) -> int: + return c[0] + + +def o8(i: int) -> bytes: + return bytes((i & 255,)) + + +# Input, le = little endian, be = big endian +def i16le(c: bytes, o: int = 0) -> int: + """ + Converts a 2-bytes (16 bits) string to an unsigned integer. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(" int: + """ + Converts a 2-bytes (16 bits) string to a signed integer. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(" int: + """ + Converts a 2-bytes (16 bits) string to a signed integer, big endian. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(">h", c, o)[0] + + +def i32le(c: bytes, o: int = 0) -> int: + """ + Converts a 4-bytes (32 bits) string to an unsigned integer. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(" int: + """ + Converts a 4-bytes (32 bits) string to a signed integer. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(" int: + """ + Converts a 4-bytes (32 bits) string to a signed integer, big endian. + + :param c: string containing bytes to convert + :param o: offset of bytes to convert in string + """ + return unpack_from(">i", c, o)[0] + + +def i16be(c: bytes, o: int = 0) -> int: + return unpack_from(">H", c, o)[0] + + +def i32be(c: bytes, o: int = 0) -> int: + return unpack_from(">I", c, o)[0] + + +# Output, le = little endian, be = big endian +def o16le(i: int) -> bytes: + return pack(" bytes: + return pack(" bytes: + return pack(">H", i) + + +def o32be(i: int) -> bytes: + return pack(">I", i) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_deprecate.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_deprecate.py new file mode 100644 index 0000000000000000000000000000000000000000..170d444904996cac753bf33deedcd13e442c9027 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_deprecate.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +import warnings + +from . import __version__ + + +def deprecate( + deprecated: str, + when: int | None, + replacement: str | None = None, + *, + action: str | None = None, + plural: bool = False, + stacklevel: int = 3, +) -> None: + """ + Deprecations helper. + + :param deprecated: Name of thing to be deprecated. + :param when: Pillow major version to be removed in. + :param replacement: Name of replacement. + :param action: Instead of "replacement", give a custom call to action + e.g. "Upgrade to new thing". + :param plural: if the deprecated thing is plural, needing "are" instead of "is". + + Usually of the form: + + "[deprecated] is deprecated and will be removed in Pillow [when] (yyyy-mm-dd). + Use [replacement] instead." + + You can leave out the replacement sentence: + + "[deprecated] is deprecated and will be removed in Pillow [when] (yyyy-mm-dd)" + + Or with another call to action: + + "[deprecated] is deprecated and will be removed in Pillow [when] (yyyy-mm-dd). + [action]." + """ + + is_ = "are" if plural else "is" + + if when is None: + removed = "a future version" + elif when <= int(__version__.split(".")[0]): + msg = f"{deprecated} {is_} deprecated and should be removed." + raise RuntimeError(msg) + elif when == 12: + removed = "Pillow 12 (2025-10-15)" + elif when == 13: + removed = "Pillow 13 (2026-10-15)" + else: + msg = f"Unknown removal version: {when}. Update {__name__}?" + raise ValueError(msg) + + if replacement and action: + msg = "Use only one of 'replacement' and 'action'" + raise ValueError(msg) + + if replacement: + action = f". Use {replacement} instead." + elif action: + action = f". {action.rstrip('.')}." + else: + action = "" + + warnings.warn( + f"{deprecated} {is_} deprecated and will be removed in {removed}{action}", + DeprecationWarning, + stacklevel=stacklevel, + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imaging.pyi b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imaging.pyi new file mode 100644 index 0000000000000000000000000000000000000000..998bc52eb8a73b5ee5868cd2c8e5c87c4e6d3037 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imaging.pyi @@ -0,0 +1,31 @@ +from typing import Any + +class ImagingCore: + def __getitem__(self, index: int) -> float: ... + def __getattr__(self, name: str) -> Any: ... + +class ImagingFont: + def __getattr__(self, name: str) -> Any: ... + +class ImagingDraw: + def __getattr__(self, name: str) -> Any: ... + +class PixelAccess: + def __getitem__(self, xy: tuple[int, int]) -> float | tuple[int, ...]: ... + def __setitem__( + self, xy: tuple[int, int], color: float | tuple[int, ...] + ) -> None: ... + +class ImagingDecoder: + def __getattr__(self, name: str) -> Any: ... + +class ImagingEncoder: + def __getattr__(self, name: str) -> Any: ... + +class _Outline: + def close(self) -> None: ... + def __getattr__(self, name: str) -> Any: ... + +def font(image: ImagingCore, glyphdata: bytes) -> ImagingFont: ... +def outline() -> _Outline: ... +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingcms.pyi b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingcms.pyi new file mode 100644 index 0000000000000000000000000000000000000000..ddcf93ab1ebd51947f900ad2dba1aee9392338bb --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingcms.pyi @@ -0,0 +1,143 @@ +import datetime +import sys +from typing import Literal, SupportsFloat, TypedDict + +from ._typing import CapsuleType + +littlecms_version: str | None + +_Tuple3f = tuple[float, float, float] +_Tuple2x3f = tuple[_Tuple3f, _Tuple3f] +_Tuple3x3f = tuple[_Tuple3f, _Tuple3f, _Tuple3f] + +class _IccMeasurementCondition(TypedDict): + observer: int + backing: _Tuple3f + geo: str + flare: float + illuminant_type: str + +class _IccViewingCondition(TypedDict): + illuminant: _Tuple3f + surround: _Tuple3f + illuminant_type: str + +class CmsProfile: + @property + def rendering_intent(self) -> int: ... + @property + def creation_date(self) -> datetime.datetime | None: ... + @property + def copyright(self) -> str | None: ... + @property + def target(self) -> str | None: ... + @property + def manufacturer(self) -> str | None: ... + @property + def model(self) -> str | None: ... + @property + def profile_description(self) -> str | None: ... + @property + def screening_description(self) -> str | None: ... + @property + def viewing_condition(self) -> str | None: ... + @property + def version(self) -> float: ... + @property + def icc_version(self) -> int: ... + @property + def attributes(self) -> int: ... + @property + def header_flags(self) -> int: ... + @property + def header_manufacturer(self) -> str: ... + @property + def header_model(self) -> str: ... + @property + def device_class(self) -> str: ... + @property + def connection_space(self) -> str: ... + @property + def xcolor_space(self) -> str: ... + @property + def profile_id(self) -> bytes: ... + @property + def is_matrix_shaper(self) -> bool: ... + @property + def technology(self) -> str | None: ... + @property + def colorimetric_intent(self) -> str | None: ... + @property + def perceptual_rendering_intent_gamut(self) -> str | None: ... + @property + def saturation_rendering_intent_gamut(self) -> str | None: ... + @property + def red_colorant(self) -> _Tuple2x3f | None: ... + @property + def green_colorant(self) -> _Tuple2x3f | None: ... + @property + def blue_colorant(self) -> _Tuple2x3f | None: ... + @property + def red_primary(self) -> _Tuple2x3f | None: ... + @property + def green_primary(self) -> _Tuple2x3f | None: ... + @property + def blue_primary(self) -> _Tuple2x3f | None: ... + @property + def media_white_point_temperature(self) -> float | None: ... + @property + def media_white_point(self) -> _Tuple2x3f | None: ... + @property + def media_black_point(self) -> _Tuple2x3f | None: ... + @property + def luminance(self) -> _Tuple2x3f | None: ... + @property + def chromatic_adaptation(self) -> tuple[_Tuple3x3f, _Tuple3x3f] | None: ... + @property + def chromaticity(self) -> _Tuple3x3f | None: ... + @property + def colorant_table(self) -> list[str] | None: ... + @property + def colorant_table_out(self) -> list[str] | None: ... + @property + def intent_supported(self) -> dict[int, tuple[bool, bool, bool]] | None: ... + @property + def clut(self) -> dict[int, tuple[bool, bool, bool]] | None: ... + @property + def icc_measurement_condition(self) -> _IccMeasurementCondition | None: ... + @property + def icc_viewing_condition(self) -> _IccViewingCondition | None: ... + def is_intent_supported(self, intent: int, direction: int, /) -> int: ... + +class CmsTransform: + def apply(self, id_in: CapsuleType, id_out: CapsuleType) -> int: ... + +def profile_open(profile: str, /) -> CmsProfile: ... +def profile_frombytes(profile: bytes, /) -> CmsProfile: ... +def profile_tobytes(profile: CmsProfile, /) -> bytes: ... +def buildTransform( + input_profile: CmsProfile, + output_profile: CmsProfile, + in_mode: str, + out_mode: str, + rendering_intent: int = 0, + cms_flags: int = 0, + /, +) -> CmsTransform: ... +def buildProofTransform( + input_profile: CmsProfile, + output_profile: CmsProfile, + proof_profile: CmsProfile, + in_mode: str, + out_mode: str, + rendering_intent: int = 0, + proof_intent: int = 0, + cms_flags: int = 0, + /, +) -> CmsTransform: ... +def createProfile( + color_space: Literal["LAB", "XYZ", "sRGB"], color_temp: SupportsFloat = 0.0, / +) -> CmsProfile: ... + +if sys.platform == "win32": + def get_display_profile_win32(handle: int = 0, is_dc: int = 0, /) -> str | None: ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingft.pyi b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingft.pyi new file mode 100644 index 0000000000000000000000000000000000000000..1cb1429d6cf6f29432fbd7f56d3aa0e45f0722f3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingft.pyi @@ -0,0 +1,69 @@ +from typing import Any, Callable + +from . import ImageFont, _imaging + +class Font: + @property + def family(self) -> str | None: ... + @property + def style(self) -> str | None: ... + @property + def ascent(self) -> int: ... + @property + def descent(self) -> int: ... + @property + def height(self) -> int: ... + @property + def x_ppem(self) -> int: ... + @property + def y_ppem(self) -> int: ... + @property + def glyphs(self) -> int: ... + def render( + self, + string: str | bytes, + fill: Callable[[int, int], _imaging.ImagingCore], + mode: str, + dir: str | None, + features: list[str] | None, + lang: str | None, + stroke_width: float, + stroke_filled: bool, + anchor: str | None, + foreground_ink_long: int, + start: tuple[float, float], + /, + ) -> tuple[_imaging.ImagingCore, tuple[int, int]]: ... + def getsize( + self, + string: str | bytes | bytearray, + mode: str, + dir: str | None, + features: list[str] | None, + lang: str | None, + anchor: str | None, + /, + ) -> tuple[tuple[int, int], tuple[int, int]]: ... + def getlength( + self, + string: str | bytes, + mode: str, + dir: str | None, + features: list[str] | None, + lang: str | None, + /, + ) -> float: ... + def getvarnames(self) -> list[bytes]: ... + def getvaraxes(self) -> list[ImageFont.Axis]: ... + def setvarname(self, instance_index: int, /) -> None: ... + def setvaraxes(self, axes: list[float], /) -> None: ... + +def getfont( + filename: str | bytes, + size: float, + index: int, + encoding: str, + font_bytes: bytes, + layout_engine: int, +) -> Font: ... +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingmath.pyi b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingmath.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e27843e5338213713e26973127c738c14313ff98 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingmath.pyi @@ -0,0 +1,3 @@ +from typing import Any + +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..ea41a9e27b3914e7a4f192da35e3be1cc5b8ada9 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.pyi b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e27843e5338213713e26973127c738c14313ff98 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingmorph.pyi @@ -0,0 +1,3 @@ +from typing import Any + +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingtk.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingtk.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..d4302f4bc93d136d0af56fe4797beb93dd5f9556 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingtk.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingtk.pyi b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingtk.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e27843e5338213713e26973127c738c14313ff98 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_imagingtk.pyi @@ -0,0 +1,3 @@ +from typing import Any + +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_tkinter_finder.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_tkinter_finder.py new file mode 100644 index 0000000000000000000000000000000000000000..9c0143003a7320dd475cfcd168168b82e4f64964 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_tkinter_finder.py @@ -0,0 +1,20 @@ +"""Find compiled module linking to Tcl / Tk libraries""" + +from __future__ import annotations + +import sys +import tkinter + +tk = getattr(tkinter, "_tkinter") + +try: + if hasattr(sys, "pypy_find_executable"): + TKINTER_LIB = tk.tklib_cffi.__file__ + else: + TKINTER_LIB = tk.__file__ +except AttributeError: + # _tkinter may be compiled directly into Python, in which case __file__ is + # not available. load_tkinter_funcs will check the binary first in any case. + TKINTER_LIB = None + +tk_version = str(tkinter.TkVersion) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_typing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_typing.py new file mode 100644 index 0000000000000000000000000000000000000000..373938e71e0331ed5d9ce2517dcf5f5098ffca72 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_typing.py @@ -0,0 +1,54 @@ +from __future__ import annotations + +import os +import sys +from collections.abc import Sequence +from typing import Any, Protocol, TypeVar, Union + +TYPE_CHECKING = False +if TYPE_CHECKING: + from numbers import _IntegralLike as IntegralLike + + try: + import numpy.typing as npt + + NumpyArray = npt.NDArray[Any] # requires numpy>=1.21 + except (ImportError, AttributeError): + pass + +if sys.version_info >= (3, 13): + from types import CapsuleType +else: + CapsuleType = object + +if sys.version_info >= (3, 12): + from collections.abc import Buffer +else: + Buffer = Any + +if sys.version_info >= (3, 10): + from typing import TypeGuard +else: + try: + from typing_extensions import TypeGuard + except ImportError: + + class TypeGuard: # type: ignore[no-redef] + def __class_getitem__(cls, item: Any) -> type[bool]: + return bool + + +Coords = Union[Sequence[float], Sequence[Sequence[float]]] + + +_T_co = TypeVar("_T_co", covariant=True) + + +class SupportsRead(Protocol[_T_co]): + def read(self, length: int = ..., /) -> _T_co: ... + + +StrOrBytesPath = Union[str, bytes, os.PathLike[str], os.PathLike[bytes]] + + +__all__ = ["Buffer", "IntegralLike", "StrOrBytesPath", "SupportsRead", "TypeGuard"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_util.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_util.py new file mode 100644 index 0000000000000000000000000000000000000000..8ef0d36f7545a85e57829e036818bc4fa2eb72c7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_util.py @@ -0,0 +1,26 @@ +from __future__ import annotations + +import os +from typing import Any, NoReturn + +from ._typing import StrOrBytesPath, TypeGuard + + +def is_path(f: Any) -> TypeGuard[StrOrBytesPath]: + return isinstance(f, (bytes, str, os.PathLike)) + + +class DeferredError: + def __init__(self, ex: BaseException): + self.ex = ex + + def __getattr__(self, elt: str) -> NoReturn: + raise self.ex + + @staticmethod + def new(ex: BaseException) -> Any: + """ + Creates an object that raises the wrapped exception ``ex`` when used, + and casts it to :py:obj:`~typing.Any` type. + """ + return DeferredError(ex) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_version.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..74e63356c95519ff405d0fdd29ce997d3dd5c8f5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_version.py @@ -0,0 +1,4 @@ +# Master version for Pillow +from __future__ import annotations + +__version__ = "11.3.0" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_webp.cpython-310-x86_64-linux-gnu.so b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_webp.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..6e1e542a9ae8d05efb85a3344a27a527490af79a Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_webp.cpython-310-x86_64-linux-gnu.so differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_webp.pyi b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_webp.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e27843e5338213713e26973127c738c14313ff98 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/_webp.pyi @@ -0,0 +1,3 @@ +from typing import Any + +def __getattr__(name: str) -> Any: ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/features.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/features.py new file mode 100644 index 0000000000000000000000000000000000000000..573f1d41256012ccf1a021055222c1f5c3c23339 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/features.py @@ -0,0 +1,361 @@ +from __future__ import annotations + +import collections +import os +import sys +import warnings +from typing import IO + +import PIL + +from . import Image +from ._deprecate import deprecate + +modules = { + "pil": ("PIL._imaging", "PILLOW_VERSION"), + "tkinter": ("PIL._tkinter_finder", "tk_version"), + "freetype2": ("PIL._imagingft", "freetype2_version"), + "littlecms2": ("PIL._imagingcms", "littlecms_version"), + "webp": ("PIL._webp", "webpdecoder_version"), + "avif": ("PIL._avif", "libavif_version"), +} + + +def check_module(feature: str) -> bool: + """ + Checks if a module is available. + + :param feature: The module to check for. + :returns: ``True`` if available, ``False`` otherwise. + :raises ValueError: If the module is not defined in this version of Pillow. + """ + if feature not in modules: + msg = f"Unknown module {feature}" + raise ValueError(msg) + + module, ver = modules[feature] + + try: + __import__(module) + return True + except ModuleNotFoundError: + return False + except ImportError as ex: + warnings.warn(str(ex)) + return False + + +def version_module(feature: str) -> str | None: + """ + :param feature: The module to check for. + :returns: + The loaded version number as a string, or ``None`` if unknown or not available. + :raises ValueError: If the module is not defined in this version of Pillow. + """ + if not check_module(feature): + return None + + module, ver = modules[feature] + + return getattr(__import__(module, fromlist=[ver]), ver) + + +def get_supported_modules() -> list[str]: + """ + :returns: A list of all supported modules. + """ + return [f for f in modules if check_module(f)] + + +codecs = { + "jpg": ("jpeg", "jpeglib"), + "jpg_2000": ("jpeg2k", "jp2klib"), + "zlib": ("zip", "zlib"), + "libtiff": ("libtiff", "libtiff"), +} + + +def check_codec(feature: str) -> bool: + """ + Checks if a codec is available. + + :param feature: The codec to check for. + :returns: ``True`` if available, ``False`` otherwise. + :raises ValueError: If the codec is not defined in this version of Pillow. + """ + if feature not in codecs: + msg = f"Unknown codec {feature}" + raise ValueError(msg) + + codec, lib = codecs[feature] + + return f"{codec}_encoder" in dir(Image.core) + + +def version_codec(feature: str) -> str | None: + """ + :param feature: The codec to check for. + :returns: + The version number as a string, or ``None`` if not available. + Checked at compile time for ``jpg``, run-time otherwise. + :raises ValueError: If the codec is not defined in this version of Pillow. + """ + if not check_codec(feature): + return None + + codec, lib = codecs[feature] + + version = getattr(Image.core, f"{lib}_version") + + if feature == "libtiff": + return version.split("\n")[0].split("Version ")[1] + + return version + + +def get_supported_codecs() -> list[str]: + """ + :returns: A list of all supported codecs. + """ + return [f for f in codecs if check_codec(f)] + + +features: dict[str, tuple[str, str | bool, str | None]] = { + "webp_anim": ("PIL._webp", True, None), + "webp_mux": ("PIL._webp", True, None), + "transp_webp": ("PIL._webp", True, None), + "raqm": ("PIL._imagingft", "HAVE_RAQM", "raqm_version"), + "fribidi": ("PIL._imagingft", "HAVE_FRIBIDI", "fribidi_version"), + "harfbuzz": ("PIL._imagingft", "HAVE_HARFBUZZ", "harfbuzz_version"), + "libjpeg_turbo": ("PIL._imaging", "HAVE_LIBJPEGTURBO", "libjpeg_turbo_version"), + "mozjpeg": ("PIL._imaging", "HAVE_MOZJPEG", "libjpeg_turbo_version"), + "zlib_ng": ("PIL._imaging", "HAVE_ZLIBNG", "zlib_ng_version"), + "libimagequant": ("PIL._imaging", "HAVE_LIBIMAGEQUANT", "imagequant_version"), + "xcb": ("PIL._imaging", "HAVE_XCB", None), +} + + +def check_feature(feature: str) -> bool | None: + """ + Checks if a feature is available. + + :param feature: The feature to check for. + :returns: ``True`` if available, ``False`` if unavailable, ``None`` if unknown. + :raises ValueError: If the feature is not defined in this version of Pillow. + """ + if feature not in features: + msg = f"Unknown feature {feature}" + raise ValueError(msg) + + module, flag, ver = features[feature] + + if isinstance(flag, bool): + deprecate(f'check_feature("{feature}")', 12) + try: + imported_module = __import__(module, fromlist=["PIL"]) + if isinstance(flag, bool): + return flag + return getattr(imported_module, flag) + except ModuleNotFoundError: + return None + except ImportError as ex: + warnings.warn(str(ex)) + return None + + +def version_feature(feature: str) -> str | None: + """ + :param feature: The feature to check for. + :returns: The version number as a string, or ``None`` if not available. + :raises ValueError: If the feature is not defined in this version of Pillow. + """ + if not check_feature(feature): + return None + + module, flag, ver = features[feature] + + if ver is None: + return None + + return getattr(__import__(module, fromlist=[ver]), ver) + + +def get_supported_features() -> list[str]: + """ + :returns: A list of all supported features. + """ + supported_features = [] + for f, (module, flag, _) in features.items(): + if flag is True: + for feature, (feature_module, _) in modules.items(): + if feature_module == module: + if check_module(feature): + supported_features.append(f) + break + elif check_feature(f): + supported_features.append(f) + return supported_features + + +def check(feature: str) -> bool | None: + """ + :param feature: A module, codec, or feature name. + :returns: + ``True`` if the module, codec, or feature is available, + ``False`` or ``None`` otherwise. + """ + + if feature in modules: + return check_module(feature) + if feature in codecs: + return check_codec(feature) + if feature in features: + return check_feature(feature) + warnings.warn(f"Unknown feature '{feature}'.", stacklevel=2) + return False + + +def version(feature: str) -> str | None: + """ + :param feature: + The module, codec, or feature to check for. + :returns: + The version number as a string, or ``None`` if unknown or not available. + """ + if feature in modules: + return version_module(feature) + if feature in codecs: + return version_codec(feature) + if feature in features: + return version_feature(feature) + return None + + +def get_supported() -> list[str]: + """ + :returns: A list of all supported modules, features, and codecs. + """ + + ret = get_supported_modules() + ret.extend(get_supported_features()) + ret.extend(get_supported_codecs()) + return ret + + +def pilinfo(out: IO[str] | None = None, supported_formats: bool = True) -> None: + """ + Prints information about this installation of Pillow. + This function can be called with ``python3 -m PIL``. + It can also be called with ``python3 -m PIL.report`` or ``python3 -m PIL --report`` + to have "supported_formats" set to ``False``, omitting the list of all supported + image file formats. + + :param out: + The output stream to print to. Defaults to ``sys.stdout`` if ``None``. + :param supported_formats: + If ``True``, a list of all supported image file formats will be printed. + """ + + if out is None: + out = sys.stdout + + Image.init() + + print("-" * 68, file=out) + print(f"Pillow {PIL.__version__}", file=out) + py_version_lines = sys.version.splitlines() + print(f"Python {py_version_lines[0].strip()}", file=out) + for py_version in py_version_lines[1:]: + print(f" {py_version.strip()}", file=out) + print("-" * 68, file=out) + print(f"Python executable is {sys.executable or 'unknown'}", file=out) + if sys.prefix != sys.base_prefix: + print(f"Environment Python files loaded from {sys.prefix}", file=out) + print(f"System Python files loaded from {sys.base_prefix}", file=out) + print("-" * 68, file=out) + print( + f"Python Pillow modules loaded from {os.path.dirname(Image.__file__)}", + file=out, + ) + print( + f"Binary Pillow modules loaded from {os.path.dirname(Image.core.__file__)}", + file=out, + ) + print("-" * 68, file=out) + + for name, feature in [ + ("pil", "PIL CORE"), + ("tkinter", "TKINTER"), + ("freetype2", "FREETYPE2"), + ("littlecms2", "LITTLECMS2"), + ("webp", "WEBP"), + ("avif", "AVIF"), + ("jpg", "JPEG"), + ("jpg_2000", "OPENJPEG (JPEG2000)"), + ("zlib", "ZLIB (PNG/ZIP)"), + ("libtiff", "LIBTIFF"), + ("raqm", "RAQM (Bidirectional Text)"), + ("libimagequant", "LIBIMAGEQUANT (Quantization method)"), + ("xcb", "XCB (X protocol)"), + ]: + if check(name): + v: str | None = None + if name == "jpg": + libjpeg_turbo_version = version_feature("libjpeg_turbo") + if libjpeg_turbo_version is not None: + v = "mozjpeg" if check_feature("mozjpeg") else "libjpeg-turbo" + v += " " + libjpeg_turbo_version + if v is None: + v = version(name) + if v is not None: + version_static = name in ("pil", "jpg") + if name == "littlecms2": + # this check is also in src/_imagingcms.c:setup_module() + version_static = tuple(int(x) for x in v.split(".")) < (2, 7) + t = "compiled for" if version_static else "loaded" + if name == "zlib": + zlib_ng_version = version_feature("zlib_ng") + if zlib_ng_version is not None: + v += ", compiled for zlib-ng " + zlib_ng_version + elif name == "raqm": + for f in ("fribidi", "harfbuzz"): + v2 = version_feature(f) + if v2 is not None: + v += f", {f} {v2}" + print("---", feature, "support ok,", t, v, file=out) + else: + print("---", feature, "support ok", file=out) + else: + print("***", feature, "support not installed", file=out) + print("-" * 68, file=out) + + if supported_formats: + extensions = collections.defaultdict(list) + for ext, i in Image.EXTENSION.items(): + extensions[i].append(ext) + + for i in sorted(Image.ID): + line = f"{i}" + if i in Image.MIME: + line = f"{line} {Image.MIME[i]}" + print(line, file=out) + + if i in extensions: + print( + "Extensions: {}".format(", ".join(sorted(extensions[i]))), file=out + ) + + features = [] + if i in Image.OPEN: + features.append("open") + if i in Image.SAVE: + features.append("save") + if i in Image.SAVE_ALL: + features.append("save_all") + if i in Image.DECODERS: + features.append("decode") + if i in Image.ENCODERS: + features.append("encode") + + print("Features: {}".format(", ".join(features)), file=out) + print("-" * 68, file=out) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/py.typed b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/report.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/report.py new file mode 100644 index 0000000000000000000000000000000000000000..d2815e8455e2ead803de4417314987ce7e9b7598 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/PIL/report.py @@ -0,0 +1,5 @@ +from __future__ import annotations + +from .features import pilinfo + +pilinfo(supported_formats=False) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/INSTALLER b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..5c69047b2eb8235994febeeae1da4a82365a240a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/INSTALLER @@ -0,0 +1 @@ +uv \ No newline at end of file diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/LICENSE b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..ef3747bad8bc502664a46c8971e1ac6962ac60a8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/LICENSE @@ -0,0 +1,20 @@ +The MIT License (MIT) + +Copyright (c) 2012-2021 Sam Clements + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/METADATA b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..2ea89044262cb4953178757597558a3ea001c6d0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/METADATA @@ -0,0 +1,310 @@ +Metadata-Version: 2.1 +Name: colorlog +Version: 6.9.0 +Summary: Add colours to the output of Python's logging module. +Home-page: https://github.com/borntyping/python-colorlog +Author: Sam Clements +Author-email: sam@borntyping.co.uk +License: MIT License +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Console +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Topic :: Terminals +Classifier: Topic :: Utilities +Requires-Python: >=3.6 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: colorama; sys_platform == "win32" +Provides-Extra: development +Requires-Dist: black; extra == "development" +Requires-Dist: flake8; extra == "development" +Requires-Dist: mypy; extra == "development" +Requires-Dist: pytest; extra == "development" +Requires-Dist: types-colorama; extra == "development" + +# Log formatting with colors! + +[![](https://img.shields.io/pypi/v/colorlog.svg)](https://pypi.org/project/colorlog/) +[![](https://img.shields.io/pypi/l/colorlog.svg)](https://pypi.org/project/colorlog/) + +Add colours to the output of Python's `logging` module. + +* [Source on GitHub](https://github.com/borntyping/python-colorlog) +* [Packages on PyPI](https://pypi.org/pypi/colorlog/) + +## Status + +colorlog currently requires Python 3.6 or higher. Older versions (below 5.x.x) +support Python 2.6 and above. + +* colorlog 6.x requires Python 3.6 or higher. +* colorlog 5.x is an interim version that will warn Python 2 users to downgrade. +* colorlog 4.x is the final version supporting Python 2. + +[colorama] is included as a required dependency and initialised when using +colorlog on Windows. + +This library is over a decade old and supported a wide set of Python versions +for most of its life, which has made it a difficult library to add new features +to. colorlog 6 may break backwards compatibility so that newer features +can be added more easily, but may still not accept all changes or feature +requests. colorlog 4 might accept essential bugfixes but should not be +considered actively maintained and will not accept any major changes or new +features. + +## Installation + +Install from PyPI with: + +```bash +pip install colorlog +``` + +Several Linux distributions provide official packages ([Debian], [Arch], [Fedora], +[Gentoo], [OpenSuse] and [Ubuntu]), and others have user provided packages +([BSD ports], [Conda]). + +## Usage + +```python +import colorlog + +handler = colorlog.StreamHandler() +handler.setFormatter(colorlog.ColoredFormatter( + '%(log_color)s%(levelname)s:%(name)s:%(message)s')) + +logger = colorlog.getLogger('example') +logger.addHandler(handler) +``` + +The `ColoredFormatter` class takes several arguments: + +- `format`: The format string used to output the message (required). +- `datefmt`: An optional date format passed to the base class. See [`logging.Formatter`][Formatter]. +- `reset`: Implicitly adds a color reset code to the message output, unless the output already ends with one. Defaults to `True`. +- `log_colors`: A mapping of record level names to color names. The defaults can be found in `colorlog.default_log_colors`, or the below example. +- `secondary_log_colors`: A mapping of names to `log_colors` style mappings, defining additional colors that can be used in format strings. See below for an example. +- `style`: Available on Python 3.2 and above. See [`logging.Formatter`][Formatter]. + +Color escape codes can be selected based on the log records level, by adding +parameters to the format string: + +- `log_color`: Return the color associated with the records level. +- `_log_color`: Return another color based on the records level if the formatter has secondary colors configured (see `secondary_log_colors` below). + +Multiple escape codes can be used at once by joining them with commas when +configuring the color for a log level (but can't be used directly in the format +string). For example, `black,bg_white` would use the escape codes for black +text on a white background. + +The following escape codes are made available for use in the format string: + +- `{color}`, `fg_{color}`, `bg_{color}`: Foreground and background colors. +- `bold`, `bold_{color}`, `fg_bold_{color}`, `bg_bold_{color}`: Bold/bright colors. +- `thin`, `thin_{color}`, `fg_thin_{color}`: Thin colors (terminal dependent). +- `reset`: Clear all formatting (both foreground and background colors). + +The available color names are: + +- `black` +- `red` +- `green` +- `yellow` +- `blue`, +- `purple` +- `cyan` +- `white` + +You can also use "bright" colors. These aren't standard ANSI codes, and +support for these varies wildly across different terminals. + +- `light_black` +- `light_red` +- `light_green` +- `light_yellow` +- `light_blue` +- `light_purple` +- `light_cyan` +- `light_white` + +## Examples + +![Example output](docs/example.png) + +The following code creates a `ColoredFormatter` for use in a logging setup, +using the default values for each argument. + +```python +from colorlog import ColoredFormatter + +formatter = ColoredFormatter( + "%(log_color)s%(levelname)-8s%(reset)s %(blue)s%(message)s", + datefmt=None, + reset=True, + log_colors={ + 'DEBUG': 'cyan', + 'INFO': 'green', + 'WARNING': 'yellow', + 'ERROR': 'red', + 'CRITICAL': 'red,bg_white', + }, + secondary_log_colors={}, + style='%' +) +``` + +### Using `secondary_log_colors` + +Secondary log colors are a way to have more than one color that is selected +based on the log level. Each key in `secondary_log_colors` adds an attribute +that can be used in format strings (`message` becomes `message_log_color`), and +has a corresponding value that is identical in format to the `log_colors` +argument. + +The following example highlights the level name using the default log colors, +and highlights the message in red for `error` and `critical` level log messages. + +```python +from colorlog import ColoredFormatter + +formatter = ColoredFormatter( + "%(log_color)s%(levelname)-8s%(reset)s %(message_log_color)s%(message)s", + secondary_log_colors={ + 'message': { + 'ERROR': 'red', + 'CRITICAL': 'red' + } + } +) +``` + +### With [`dictConfig`][dictConfig] + +```python +logging.config.dictConfig({ + 'formatters': { + 'colored': { + '()': 'colorlog.ColoredFormatter', + 'format': "%(log_color)s%(levelname)-8s%(reset)s %(blue)s%(message)s" + } + } +}) +``` + +A full example dictionary can be found in `tests/test_colorlog.py`. + +### With [`fileConfig`][fileConfig] + +```ini +... + +[formatters] +keys=color + +[formatter_color] +class=colorlog.ColoredFormatter +format=%(log_color)s%(levelname)-8s%(reset)s %(bg_blue)s[%(name)s]%(reset)s %(message)s from fileConfig +datefmt=%m-%d %H:%M:%S +``` + +An instance of ColoredFormatter created with those arguments will then be used +by any handlers that are configured to use the `color` formatter. + +A full example configuration can be found in `tests/test_config.ini`. + +### With custom log levels + +ColoredFormatter will work with custom log levels added with +[`logging.addLevelName`][addLevelName]: + +```python +import logging, colorlog +TRACE = 5 +logging.addLevelName(TRACE, 'TRACE') +formatter = colorlog.ColoredFormatter(log_colors={'TRACE': 'yellow'}) +handler = logging.StreamHandler() +handler.setFormatter(formatter) +logger = logging.getLogger('example') +logger.addHandler(handler) +logger.setLevel('TRACE') +logger.log(TRACE, 'a message using a custom level') +``` + +## Tests + +Tests similar to the above examples are found in `tests/test_colorlog.py`. + +## Status + +colorlog is in maintenance mode. I try and ensure bugfixes are published, +but compatibility with Python 2.6+ and Python 3+ makes this a difficult +codebase to add features to. Any changes that might break backwards +compatibility for existing users will not be considered. + +## Alternatives + +There are some more modern libraries for improving Python logging you may +find useful. + +- [structlog] +- [jsonlog] + +## Projects using colorlog + +GitHub provides [a list of projects that depend on colorlog][dependents]. + +Some early adopters included [Errbot], [Pythran], and [zenlog]. + +## Licence + +Copyright (c) 2012-2021 Sam Clements + +Permission is hereby granted, free of charge, to any person obtaining a copy of +this software and associated documentation files (the "Software"), to deal in +the Software without restriction, including without limitation the rights to +use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of +the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS +FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR +COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +[dictConfig]: http://docs.python.org/3/library/logging.config.html#logging.config.dictConfig +[fileConfig]: http://docs.python.org/3/library/logging.config.html#logging.config.fileConfig +[addLevelName]: https://docs.python.org/3/library/logging.html#logging.addLevelName +[Formatter]: http://docs.python.org/3/library/logging.html#logging.Formatter +[tox]: http://tox.readthedocs.org/ +[Arch]: https://archlinux.org/packages/extra/any/python-colorlog/ +[BSD ports]: https://www.freshports.org/devel/py-colorlog/ +[colorama]: https://pypi.python.org/pypi/colorama +[Conda]: https://anaconda.org/conda-forge/colorlog +[Debian]: [https://packages.debian.org/buster/python3-colorlog](https://packages.debian.org/buster/python3-colorlog) +[Errbot]: http://errbot.io/ +[Fedora]: https://src.fedoraproject.org/rpms/python-colorlog +[Gentoo]: https://packages.gentoo.org/packages/dev-python/colorlog +[OpenSuse]: http://rpm.pbone.net/index.php3?stat=3&search=python-colorlog&srodzaj=3 +[Pythran]: https://github.com/serge-sans-paille/pythran +[Ubuntu]: https://launchpad.net/python-colorlog +[zenlog]: https://github.com/ManufacturaInd/python-zenlog +[structlog]: https://www.structlog.org/en/stable/ +[jsonlog]: https://github.com/borntyping/jsonlog +[dependents]: https://github.com/borntyping/python-colorlog/network/dependents?package_id=UGFja2FnZS01MDk3NDcyMQ%3D%3D diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/RECORD b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/colorlog-6.9.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..04d5fa9c5b3d6efcabdc52630c3a2f354208ed87 --- /dev/null +++ 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+Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Requires-Python: >=3.9 +Provides-Extra: abfs +Requires-Dist: adlfs; extra == 'abfs' +Provides-Extra: adl +Requires-Dist: adlfs; extra == 'adl' +Provides-Extra: arrow +Requires-Dist: pyarrow>=1; extra == 'arrow' +Provides-Extra: dask +Requires-Dist: dask; extra == 'dask' +Requires-Dist: distributed; extra == 'dask' +Provides-Extra: dev +Requires-Dist: pre-commit; extra == 'dev' +Requires-Dist: ruff>=0.5; extra == 'dev' +Provides-Extra: doc +Requires-Dist: numpydoc; extra == 'doc' +Requires-Dist: sphinx; extra == 'doc' +Requires-Dist: sphinx-design; extra == 'doc' +Requires-Dist: sphinx-rtd-theme; extra == 'doc' +Requires-Dist: yarl; extra == 'doc' +Provides-Extra: dropbox +Requires-Dist: dropbox; extra == 'dropbox' +Requires-Dist: 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extra == 'gcs' +Provides-Extra: git +Requires-Dist: pygit2; extra == 'git' +Provides-Extra: github +Requires-Dist: requests; extra == 'github' +Provides-Extra: gs +Requires-Dist: gcsfs; extra == 'gs' +Provides-Extra: gui +Requires-Dist: panel; extra == 'gui' +Provides-Extra: hdfs +Requires-Dist: pyarrow>=1; extra == 'hdfs' +Provides-Extra: http +Requires-Dist: aiohttp!=4.0.0a0,!=4.0.0a1; extra == 'http' +Provides-Extra: libarchive +Requires-Dist: libarchive-c; extra == 'libarchive' +Provides-Extra: oci +Requires-Dist: ocifs; extra == 'oci' +Provides-Extra: s3 +Requires-Dist: s3fs; extra == 's3' +Provides-Extra: sftp +Requires-Dist: paramiko; extra == 'sftp' +Provides-Extra: smb +Requires-Dist: smbprotocol; extra == 'smb' +Provides-Extra: ssh +Requires-Dist: paramiko; extra == 'ssh' +Provides-Extra: test +Requires-Dist: aiohttp!=4.0.0a0,!=4.0.0a1; extra == 'test' +Requires-Dist: numpy; extra == 'test' +Requires-Dist: pytest; extra == 'test' +Requires-Dist: pytest-asyncio!=0.22.0; extra == 'test' +Requires-Dist: pytest-benchmark; extra == 'test' +Requires-Dist: pytest-cov; extra == 'test' +Requires-Dist: pytest-mock; extra == 'test' +Requires-Dist: pytest-recording; extra == 'test' +Requires-Dist: pytest-rerunfailures; extra == 'test' +Requires-Dist: requests; extra == 'test' +Provides-Extra: test-downstream +Requires-Dist: aiobotocore<3.0.0,>=2.5.4; extra == 'test-downstream' +Requires-Dist: dask[dataframe,test]; extra == 'test-downstream' +Requires-Dist: moto[server]<5,>4; extra == 'test-downstream' +Requires-Dist: pytest-timeout; extra == 'test-downstream' +Requires-Dist: xarray; extra == 'test-downstream' +Provides-Extra: test-full +Requires-Dist: adlfs; extra == 'test-full' +Requires-Dist: aiohttp!=4.0.0a0,!=4.0.0a1; extra == 'test-full' +Requires-Dist: cloudpickle; extra == 'test-full' +Requires-Dist: dask; extra == 'test-full' +Requires-Dist: distributed; extra == 'test-full' +Requires-Dist: dropbox; extra == 'test-full' +Requires-Dist: dropboxdrivefs; extra == 'test-full' +Requires-Dist: fastparquet; extra == 'test-full' +Requires-Dist: fusepy; extra == 'test-full' +Requires-Dist: gcsfs; extra == 'test-full' +Requires-Dist: jinja2; extra == 'test-full' +Requires-Dist: kerchunk; extra == 'test-full' +Requires-Dist: libarchive-c; extra == 'test-full' +Requires-Dist: lz4; extra == 'test-full' +Requires-Dist: notebook; extra == 'test-full' +Requires-Dist: numpy; extra == 'test-full' +Requires-Dist: ocifs; extra == 'test-full' +Requires-Dist: pandas; extra == 'test-full' +Requires-Dist: panel; extra == 'test-full' +Requires-Dist: paramiko; extra == 'test-full' +Requires-Dist: pyarrow; extra == 'test-full' +Requires-Dist: pyarrow>=1; extra == 'test-full' +Requires-Dist: pyftpdlib; extra == 'test-full' +Requires-Dist: pygit2; extra == 'test-full' +Requires-Dist: pytest; extra == 'test-full' +Requires-Dist: pytest-asyncio!=0.22.0; extra == 'test-full' +Requires-Dist: pytest-benchmark; extra == 'test-full' +Requires-Dist: pytest-cov; extra == 'test-full' +Requires-Dist: pytest-mock; extra == 'test-full' +Requires-Dist: pytest-recording; extra == 'test-full' +Requires-Dist: pytest-rerunfailures; extra == 'test-full' +Requires-Dist: python-snappy; extra == 'test-full' +Requires-Dist: requests; extra == 'test-full' +Requires-Dist: smbprotocol; extra == 'test-full' +Requires-Dist: tqdm; extra == 'test-full' +Requires-Dist: urllib3; extra == 'test-full' +Requires-Dist: zarr; extra == 'test-full' +Requires-Dist: zstandard; (python_version < '3.14') and extra == 'test-full' +Provides-Extra: tqdm +Requires-Dist: tqdm; extra == 'tqdm' +Description-Content-Type: text/markdown + +# filesystem_spec + +[![PyPI version](https://badge.fury.io/py/fsspec.svg)](https://pypi.python.org/pypi/fsspec/) +[![Anaconda-Server Badge](https://anaconda.org/conda-forge/fsspec/badges/version.svg)](https://anaconda.org/conda-forge/fsspec) +![Build](https://github.com/fsspec/filesystem_spec/workflows/CI/badge.svg) +[![Docs](https://readthedocs.org/projects/filesystem-spec/badge/?version=latest)](https://filesystem-spec.readthedocs.io/en/latest/?badge=latest) + +A specification for pythonic filesystems. + +## Install + +```bash +pip install fsspec +``` + +would install the base fsspec. Various optionally supported features might require specification of custom +extra require, e.g. `pip install fsspec[ssh]` will install dependencies for `ssh` backends support. +Use `pip install fsspec[full]` for installation of all known extra dependencies. + +Up-to-date package also provided through conda-forge distribution: + +```bash +conda install -c conda-forge fsspec +``` + + +## Purpose + +To produce a template or specification for a file-system interface, that specific implementations should follow, +so that applications making use of them can rely on a common behaviour and not have to worry about the specific +internal implementation decisions with any given backend. Many such implementations are included in this package, +or in sister projects such as `s3fs` and `gcsfs`. + +In addition, if this is well-designed, then additional functionality, such as a key-value store or FUSE +mounting of the file-system implementation may be available for all implementations "for free". + +## Documentation + +Please refer to [RTD](https://filesystem-spec.readthedocs.io/en/latest/?badge=latest) + +## Develop + +fsspec uses GitHub Actions for CI. Environment files can be found +in the "ci/" directory. Note that the main environment is called "py38", +but it is expected that the version of python installed be adjustable at +CI runtime. For local use, pick a version suitable for you. + +```bash +# For a new environment (mamba / conda). +mamba create -n fsspec -c conda-forge python=3.9 -y +conda activate fsspec + +# Standard dev install with docs and tests. +pip install -e ".[dev,doc,test]" + +# Full tests except for downstream +pip install s3fs +pip uninstall s3fs +pip install -e .[dev,doc,test_full] +pip install s3fs --no-deps +pytest -v + +# Downstream tests. +sh install_s3fs.sh +# Windows powershell. +install_s3fs.sh +``` + +### Testing + +Tests can be run in the dev environment, if activated, via ``pytest fsspec``. + +The full fsspec suite requires a system-level docker, docker-compose, and fuse +installation. If only making changes to one backend implementation, it is +not generally necessary to run all tests locally. + +It is expected that contributors ensure that any change to fsspec does not +cause issues or regressions for either other fsspec-related packages such +as gcsfs and s3fs, nor for downstream users of fsspec. The "downstream" CI +run and corresponding environment file run a set of tests from the dask +test suite, and very minimal tests against pandas and zarr from the +test_downstream.py module in this repo. + +### Code Formatting + +fsspec uses [Black](https://black.readthedocs.io/en/stable) to ensure +a consistent code format throughout the project. +Run ``black fsspec`` from the root of the filesystem_spec repository to +auto-format your code. Additionally, many editors have plugins that will apply +``black`` as you edit files. ``black`` is included in the ``tox`` environments. + +Optionally, you may wish to setup [pre-commit hooks](https://pre-commit.com) to +automatically run ``black`` when you make a git commit. +Run ``pre-commit install --install-hooks`` from the root of the +filesystem_spec repository to setup pre-commit hooks. ``black`` will now be run +before you commit, reformatting any changed files. You can format without +committing via ``pre-commit run`` or skip these checks with ``git commit +--no-verify``. + +## Support + +Work on this repository is supported in part by: + +"Anaconda, Inc. - Advancing AI through open source." + +anaconda logo diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/fsspec-2025.9.0.dist-info/RECORD b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/fsspec-2025.9.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..332a9e835e4fe62cb09ab4a85d2aaf9f0facf98e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/fsspec-2025.9.0.dist-info/RECORD @@ -0,0 +1,62 @@ +fsspec-2025.9.0.dist-info/INSTALLER,sha256=5hhM4Q4mYTT9z6QB6PGpUAW81PGNFrYrdXMj4oM_6ak,2 +fsspec-2025.9.0.dist-info/METADATA,sha256=VmMoGluoRhQXlQigYs9kzwlXfPIg1KBkRL7V2F5O2B0,10397 +fsspec-2025.9.0.dist-info/RECORD,, +fsspec-2025.9.0.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 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a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/fsspec-2025.9.0.dist-info/licenses/LICENSE b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/fsspec-2025.9.0.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..67590a5e5be5a5a2dde3fe53a7512e404a896c22 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/fsspec-2025.9.0.dist-info/licenses/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2018, Martin Durant +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c55302485e3fd570f55a255314318c274adc9098 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/__init__.py @@ -0,0 +1,10 @@ +from . import axes_size as Size +from .axes_divider import Divider, SubplotDivider, make_axes_locatable +from .axes_grid import AxesGrid, Grid, ImageGrid + +from .parasite_axes import host_subplot, host_axes + +__all__ = ["Size", + "Divider", "SubplotDivider", "make_axes_locatable", + "AxesGrid", "Grid", "ImageGrid", + "host_subplot", "host_axes"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/anchored_artists.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/anchored_artists.py new file mode 100644 index 0000000000000000000000000000000000000000..214b15843ebfaf2e2ee26e100a2194dfe092382d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/anchored_artists.py @@ -0,0 +1,414 @@ +from matplotlib import transforms +from matplotlib.offsetbox import (AnchoredOffsetbox, AuxTransformBox, + DrawingArea, TextArea, VPacker) +from matplotlib.patches import (Rectangle, ArrowStyle, + FancyArrowPatch, PathPatch) +from matplotlib.text import TextPath + +__all__ = ['AnchoredDrawingArea', 'AnchoredAuxTransformBox', + 'AnchoredSizeBar', 'AnchoredDirectionArrows'] + + +class AnchoredDrawingArea(AnchoredOffsetbox): + def __init__(self, width, height, xdescent, ydescent, + loc, pad=0.4, borderpad=0.5, prop=None, frameon=True, + **kwargs): + """ + An anchored container with a fixed size and fillable `.DrawingArea`. + + Artists added to the *drawing_area* will have their coordinates + interpreted as pixels. Any transformations set on the artists will be + overridden. + + Parameters + ---------- + width, height : float + Width and height of the container, in pixels. + xdescent, ydescent : float + Descent of the container in the x- and y- direction, in pixels. + loc : str + Location of this artist. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + pad : float, default: 0.4 + Padding around the child objects, in fraction of the font size. + borderpad : float, default: 0.5 + Border padding, in fraction of the font size. + prop : `~matplotlib.font_manager.FontProperties`, optional + Font property used as a reference for paddings. + frameon : bool, default: True + If True, draw a box around this artist. + **kwargs + Keyword arguments forwarded to `.AnchoredOffsetbox`. + + Attributes + ---------- + drawing_area : `~matplotlib.offsetbox.DrawingArea` + A container for artists to display. + + Examples + -------- + To display blue and red circles of different sizes in the upper right + of an Axes *ax*: + + >>> ada = AnchoredDrawingArea(20, 20, 0, 0, + ... loc='upper right', frameon=False) + >>> ada.drawing_area.add_artist(Circle((10, 10), 10, fc="b")) + >>> ada.drawing_area.add_artist(Circle((30, 10), 5, fc="r")) + >>> ax.add_artist(ada) + """ + self.da = DrawingArea(width, height, xdescent, ydescent) + self.drawing_area = self.da + + super().__init__( + loc, pad=pad, borderpad=borderpad, child=self.da, prop=None, + frameon=frameon, **kwargs + ) + + +class AnchoredAuxTransformBox(AnchoredOffsetbox): + def __init__(self, transform, loc, + pad=0.4, borderpad=0.5, prop=None, frameon=True, **kwargs): + """ + An anchored container with transformed coordinates. + + Artists added to the *drawing_area* are scaled according to the + coordinates of the transformation used. The dimensions of this artist + will scale to contain the artists added. + + Parameters + ---------- + transform : `~matplotlib.transforms.Transform` + The transformation object for the coordinate system in use, i.e., + :attr:`matplotlib.axes.Axes.transData`. + loc : str + Location of this artist. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + pad : float, default: 0.4 + Padding around the child objects, in fraction of the font size. + borderpad : float, default: 0.5 + Border padding, in fraction of the font size. + prop : `~matplotlib.font_manager.FontProperties`, optional + Font property used as a reference for paddings. + frameon : bool, default: True + If True, draw a box around this artist. + **kwargs + Keyword arguments forwarded to `.AnchoredOffsetbox`. + + Attributes + ---------- + drawing_area : `~matplotlib.offsetbox.AuxTransformBox` + A container for artists to display. + + Examples + -------- + To display an ellipse in the upper left, with a width of 0.1 and + height of 0.4 in data coordinates: + + >>> box = AnchoredAuxTransformBox(ax.transData, loc='upper left') + >>> el = Ellipse((0, 0), width=0.1, height=0.4, angle=30) + >>> box.drawing_area.add_artist(el) + >>> ax.add_artist(box) + """ + self.drawing_area = AuxTransformBox(transform) + + super().__init__(loc, pad=pad, borderpad=borderpad, + child=self.drawing_area, prop=prop, frameon=frameon, + **kwargs) + + +class AnchoredSizeBar(AnchoredOffsetbox): + def __init__(self, transform, size, label, loc, + pad=0.1, borderpad=0.1, sep=2, + frameon=True, size_vertical=0, color='black', + label_top=False, fontproperties=None, fill_bar=None, + **kwargs): + """ + Draw a horizontal scale bar with a center-aligned label underneath. + + Parameters + ---------- + transform : `~matplotlib.transforms.Transform` + The transformation object for the coordinate system in use, i.e., + :attr:`matplotlib.axes.Axes.transData`. + size : float + Horizontal length of the size bar, given in coordinates of + *transform*. + label : str + Label to display. + loc : str + Location of the size bar. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + pad : float, default: 0.1 + Padding around the label and size bar, in fraction of the font + size. + borderpad : float, default: 0.1 + Border padding, in fraction of the font size. + sep : float, default: 2 + Separation between the label and the size bar, in points. + frameon : bool, default: True + If True, draw a box around the horizontal bar and label. + size_vertical : float, default: 0 + Vertical length of the size bar, given in coordinates of + *transform*. + color : str, default: 'black' + Color for the size bar and label. + label_top : bool, default: False + If True, the label will be over the size bar. + fontproperties : `~matplotlib.font_manager.FontProperties`, optional + Font properties for the label text. + fill_bar : bool, optional + If True and if *size_vertical* is nonzero, the size bar will + be filled in with the color specified by the size bar. + Defaults to True if *size_vertical* is greater than + zero and False otherwise. + **kwargs + Keyword arguments forwarded to `.AnchoredOffsetbox`. + + Attributes + ---------- + size_bar : `~matplotlib.offsetbox.AuxTransformBox` + Container for the size bar. + txt_label : `~matplotlib.offsetbox.TextArea` + Container for the label of the size bar. + + Notes + ----- + If *prop* is passed as a keyword argument, but *fontproperties* is + not, then *prop* is assumed to be the intended *fontproperties*. + Using both *prop* and *fontproperties* is not supported. + + Examples + -------- + >>> import matplotlib.pyplot as plt + >>> import numpy as np + >>> from mpl_toolkits.axes_grid1.anchored_artists import ( + ... AnchoredSizeBar) + >>> fig, ax = plt.subplots() + >>> ax.imshow(np.random.random((10, 10))) + >>> bar = AnchoredSizeBar(ax.transData, 3, '3 data units', 4) + >>> ax.add_artist(bar) + >>> fig.show() + + Using all the optional parameters + + >>> import matplotlib.font_manager as fm + >>> fontprops = fm.FontProperties(size=14, family='monospace') + >>> bar = AnchoredSizeBar(ax.transData, 3, '3 units', 4, pad=0.5, + ... sep=5, borderpad=0.5, frameon=False, + ... size_vertical=0.5, color='white', + ... fontproperties=fontprops) + """ + if fill_bar is None: + fill_bar = size_vertical > 0 + + self.size_bar = AuxTransformBox(transform) + self.size_bar.add_artist(Rectangle((0, 0), size, size_vertical, + fill=fill_bar, facecolor=color, + edgecolor=color)) + + if fontproperties is None and 'prop' in kwargs: + fontproperties = kwargs.pop('prop') + + if fontproperties is None: + textprops = {'color': color} + else: + textprops = {'color': color, 'fontproperties': fontproperties} + + self.txt_label = TextArea(label, textprops=textprops) + + if label_top: + _box_children = [self.txt_label, self.size_bar] + else: + _box_children = [self.size_bar, self.txt_label] + + self._box = VPacker(children=_box_children, + align="center", + pad=0, sep=sep) + + super().__init__(loc, pad=pad, borderpad=borderpad, child=self._box, + prop=fontproperties, frameon=frameon, **kwargs) + + +class AnchoredDirectionArrows(AnchoredOffsetbox): + def __init__(self, transform, label_x, label_y, length=0.15, + fontsize=0.08, loc='upper left', angle=0, aspect_ratio=1, + pad=0.4, borderpad=0.4, frameon=False, color='w', alpha=1, + sep_x=0.01, sep_y=0, fontproperties=None, back_length=0.15, + head_width=10, head_length=15, tail_width=2, + text_props=None, arrow_props=None, + **kwargs): + """ + Draw two perpendicular arrows to indicate directions. + + Parameters + ---------- + transform : `~matplotlib.transforms.Transform` + The transformation object for the coordinate system in use, i.e., + :attr:`matplotlib.axes.Axes.transAxes`. + label_x, label_y : str + Label text for the x and y arrows + length : float, default: 0.15 + Length of the arrow, given in coordinates of *transform*. + fontsize : float, default: 0.08 + Size of label strings, given in coordinates of *transform*. + loc : str, default: 'upper left' + Location of the arrow. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + angle : float, default: 0 + The angle of the arrows in degrees. + aspect_ratio : float, default: 1 + The ratio of the length of arrow_x and arrow_y. + Negative numbers can be used to change the direction. + pad : float, default: 0.4 + Padding around the labels and arrows, in fraction of the font size. + borderpad : float, default: 0.4 + Border padding, in fraction of the font size. + frameon : bool, default: False + If True, draw a box around the arrows and labels. + color : str, default: 'white' + Color for the arrows and labels. + alpha : float, default: 1 + Alpha values of the arrows and labels + sep_x, sep_y : float, default: 0.01 and 0 respectively + Separation between the arrows and labels in coordinates of + *transform*. + fontproperties : `~matplotlib.font_manager.FontProperties`, optional + Font properties for the label text. + back_length : float, default: 0.15 + Fraction of the arrow behind the arrow crossing. + head_width : float, default: 10 + Width of arrow head, sent to `.ArrowStyle`. + head_length : float, default: 15 + Length of arrow head, sent to `.ArrowStyle`. + tail_width : float, default: 2 + Width of arrow tail, sent to `.ArrowStyle`. + text_props, arrow_props : dict + Properties of the text and arrows, passed to `.TextPath` and + `.FancyArrowPatch`. + **kwargs + Keyword arguments forwarded to `.AnchoredOffsetbox`. + + Attributes + ---------- + arrow_x, arrow_y : `~matplotlib.patches.FancyArrowPatch` + Arrow x and y + text_path_x, text_path_y : `~matplotlib.text.TextPath` + Path for arrow labels + p_x, p_y : `~matplotlib.patches.PathPatch` + Patch for arrow labels + box : `~matplotlib.offsetbox.AuxTransformBox` + Container for the arrows and labels. + + Notes + ----- + If *prop* is passed as a keyword argument, but *fontproperties* is + not, then *prop* is assumed to be the intended *fontproperties*. + Using both *prop* and *fontproperties* is not supported. + + Examples + -------- + >>> import matplotlib.pyplot as plt + >>> import numpy as np + >>> from mpl_toolkits.axes_grid1.anchored_artists import ( + ... AnchoredDirectionArrows) + >>> fig, ax = plt.subplots() + >>> ax.imshow(np.random.random((10, 10))) + >>> arrows = AnchoredDirectionArrows(ax.transAxes, '111', '110') + >>> ax.add_artist(arrows) + >>> fig.show() + + Using several of the optional parameters, creating downward pointing + arrow and high contrast text labels. + + >>> import matplotlib.font_manager as fm + >>> fontprops = fm.FontProperties(family='monospace') + >>> arrows = AnchoredDirectionArrows(ax.transAxes, 'East', 'South', + ... loc='lower left', color='k', + ... aspect_ratio=-1, sep_x=0.02, + ... sep_y=-0.01, + ... text_props={'ec':'w', 'fc':'k'}, + ... fontproperties=fontprops) + """ + if arrow_props is None: + arrow_props = {} + + if text_props is None: + text_props = {} + + arrowstyle = ArrowStyle("Simple", + head_width=head_width, + head_length=head_length, + tail_width=tail_width) + + if fontproperties is None and 'prop' in kwargs: + fontproperties = kwargs.pop('prop') + + if 'color' not in arrow_props: + arrow_props['color'] = color + + if 'alpha' not in arrow_props: + arrow_props['alpha'] = alpha + + if 'color' not in text_props: + text_props['color'] = color + + if 'alpha' not in text_props: + text_props['alpha'] = alpha + + t_start = transform + t_end = t_start + transforms.Affine2D().rotate_deg(angle) + + self.box = AuxTransformBox(t_end) + + length_x = length + length_y = length*aspect_ratio + + self.arrow_x = FancyArrowPatch( + (0, back_length*length_y), + (length_x, back_length*length_y), + arrowstyle=arrowstyle, + shrinkA=0.0, + shrinkB=0.0, + **arrow_props) + + self.arrow_y = FancyArrowPatch( + (back_length*length_x, 0), + (back_length*length_x, length_y), + arrowstyle=arrowstyle, + shrinkA=0.0, + shrinkB=0.0, + **arrow_props) + + self.box.add_artist(self.arrow_x) + self.box.add_artist(self.arrow_y) + + text_path_x = TextPath(( + length_x+sep_x, back_length*length_y+sep_y), label_x, + size=fontsize, prop=fontproperties) + self.p_x = PathPatch(text_path_x, transform=t_start, **text_props) + self.box.add_artist(self.p_x) + + text_path_y = TextPath(( + length_x*back_length+sep_x, length_y*(1-back_length)+sep_y), + label_y, size=fontsize, prop=fontproperties) + self.p_y = PathPatch(text_path_y, **text_props) + self.box.add_artist(self.p_y) + + super().__init__(loc, pad=pad, borderpad=borderpad, child=self.box, + frameon=frameon, **kwargs) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_divider.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_divider.py new file mode 100644 index 0000000000000000000000000000000000000000..50365f482b725d3be435ddd14d9729d30b343124 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_divider.py @@ -0,0 +1,618 @@ +""" +Helper classes to adjust the positions of multiple axes at drawing time. +""" + +import functools + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api +from matplotlib.gridspec import SubplotSpec +import matplotlib.transforms as mtransforms +from . import axes_size as Size + + +class Divider: + """ + An Axes positioning class. + + The divider is initialized with lists of horizontal and vertical sizes + (:mod:`mpl_toolkits.axes_grid1.axes_size`) based on which a given + rectangular area will be divided. + + The `new_locator` method then creates a callable object + that can be used as the *axes_locator* of the axes. + """ + + def __init__(self, fig, pos, horizontal, vertical, + aspect=None, anchor="C"): + """ + Parameters + ---------- + fig : Figure + pos : tuple of 4 floats + Position of the rectangle that will be divided. + horizontal : list of :mod:`~mpl_toolkits.axes_grid1.axes_size` + Sizes for horizontal division. + vertical : list of :mod:`~mpl_toolkits.axes_grid1.axes_size` + Sizes for vertical division. + aspect : bool, optional + Whether overall rectangular area is reduced so that the relative + part of the horizontal and vertical scales have the same scale. + anchor : (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', 'N', \ +'NW', 'W'}, default: 'C' + Placement of the reduced rectangle, when *aspect* is True. + """ + + self._fig = fig + self._pos = pos + self._horizontal = horizontal + self._vertical = vertical + self._anchor = anchor + self.set_anchor(anchor) + self._aspect = aspect + self._xrefindex = 0 + self._yrefindex = 0 + self._locator = None + + def get_horizontal_sizes(self, renderer): + return np.array([s.get_size(renderer) for s in self.get_horizontal()]) + + def get_vertical_sizes(self, renderer): + return np.array([s.get_size(renderer) for s in self.get_vertical()]) + + def set_position(self, pos): + """ + Set the position of the rectangle. + + Parameters + ---------- + pos : tuple of 4 floats + position of the rectangle that will be divided + """ + self._pos = pos + + def get_position(self): + """Return the position of the rectangle.""" + return self._pos + + def set_anchor(self, anchor): + """ + Parameters + ---------- + anchor : (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', 'N', \ +'NW', 'W'} + Either an (*x*, *y*) pair of relative coordinates (0 is left or + bottom, 1 is right or top), 'C' (center), or a cardinal direction + ('SW', southwest, is bottom left, etc.). + + See Also + -------- + .Axes.set_anchor + """ + if isinstance(anchor, str): + _api.check_in_list(mtransforms.Bbox.coefs, anchor=anchor) + elif not isinstance(anchor, (tuple, list)) or len(anchor) != 2: + raise TypeError("anchor must be str or 2-tuple") + self._anchor = anchor + + def get_anchor(self): + """Return the anchor.""" + return self._anchor + + def get_subplotspec(self): + return None + + def set_horizontal(self, h): + """ + Parameters + ---------- + h : list of :mod:`~mpl_toolkits.axes_grid1.axes_size` + sizes for horizontal division + """ + self._horizontal = h + + def get_horizontal(self): + """Return horizontal sizes.""" + return self._horizontal + + def set_vertical(self, v): + """ + Parameters + ---------- + v : list of :mod:`~mpl_toolkits.axes_grid1.axes_size` + sizes for vertical division + """ + self._vertical = v + + def get_vertical(self): + """Return vertical sizes.""" + return self._vertical + + def set_aspect(self, aspect=False): + """ + Parameters + ---------- + aspect : bool + """ + self._aspect = aspect + + def get_aspect(self): + """Return aspect.""" + return self._aspect + + def set_locator(self, _locator): + self._locator = _locator + + def get_locator(self): + return self._locator + + def get_position_runtime(self, ax, renderer): + if self._locator is None: + return self.get_position() + else: + return self._locator(ax, renderer).bounds + + @staticmethod + def _calc_k(sizes, total): + # sizes is a (n, 2) array of (rel_size, abs_size); this method finds + # the k factor such that sum(rel_size * k + abs_size) == total. + rel_sum, abs_sum = sizes.sum(0) + return (total - abs_sum) / rel_sum if rel_sum else 0 + + @staticmethod + def _calc_offsets(sizes, k): + # Apply k factors to (n, 2) sizes array of (rel_size, abs_size); return + # the resulting cumulative offset positions. + return np.cumsum([0, *(sizes @ [k, 1])]) + + def new_locator(self, nx, ny, nx1=None, ny1=None): + """ + Return an axes locator callable for the specified cell. + + Parameters + ---------- + nx, nx1 : int + Integers specifying the column-position of the + cell. When *nx1* is None, a single *nx*-th column is + specified. Otherwise, location of columns spanning between *nx* + to *nx1* (but excluding *nx1*-th column) is specified. + ny, ny1 : int + Same as *nx* and *nx1*, but for row positions. + """ + if nx1 is None: + nx1 = nx + 1 + if ny1 is None: + ny1 = ny + 1 + # append_size("left") adds a new size at the beginning of the + # horizontal size lists; this shift transforms e.g. + # new_locator(nx=2, ...) into effectively new_locator(nx=3, ...). To + # take that into account, instead of recording nx, we record + # nx-self._xrefindex, where _xrefindex is shifted by 1 by each + # append_size("left"), and re-add self._xrefindex back to nx in + # _locate, when the actual axes position is computed. Ditto for y. + xref = self._xrefindex + yref = self._yrefindex + locator = functools.partial( + self._locate, nx - xref, ny - yref, nx1 - xref, ny1 - yref) + locator.get_subplotspec = self.get_subplotspec + return locator + + def _locate(self, nx, ny, nx1, ny1, axes, renderer): + """ + Implementation of ``divider.new_locator().__call__``. + + The axes locator callable returned by ``new_locator()`` is created as + a `functools.partial` of this method with *nx*, *ny*, *nx1*, and *ny1* + specifying the requested cell. + """ + nx += self._xrefindex + nx1 += self._xrefindex + ny += self._yrefindex + ny1 += self._yrefindex + + fig_w, fig_h = self._fig.bbox.size / self._fig.dpi + x, y, w, h = self.get_position_runtime(axes, renderer) + + hsizes = self.get_horizontal_sizes(renderer) + vsizes = self.get_vertical_sizes(renderer) + k_h = self._calc_k(hsizes, fig_w * w) + k_v = self._calc_k(vsizes, fig_h * h) + + if self.get_aspect(): + k = min(k_h, k_v) + ox = self._calc_offsets(hsizes, k) + oy = self._calc_offsets(vsizes, k) + + ww = (ox[-1] - ox[0]) / fig_w + hh = (oy[-1] - oy[0]) / fig_h + pb = mtransforms.Bbox.from_bounds(x, y, w, h) + pb1 = mtransforms.Bbox.from_bounds(x, y, ww, hh) + x0, y0 = pb1.anchored(self.get_anchor(), pb).p0 + + else: + ox = self._calc_offsets(hsizes, k_h) + oy = self._calc_offsets(vsizes, k_v) + x0, y0 = x, y + + if nx1 is None: + nx1 = -1 + if ny1 is None: + ny1 = -1 + + x1, w1 = x0 + ox[nx] / fig_w, (ox[nx1] - ox[nx]) / fig_w + y1, h1 = y0 + oy[ny] / fig_h, (oy[ny1] - oy[ny]) / fig_h + + return mtransforms.Bbox.from_bounds(x1, y1, w1, h1) + + def append_size(self, position, size): + _api.check_in_list(["left", "right", "bottom", "top"], + position=position) + if position == "left": + self._horizontal.insert(0, size) + self._xrefindex += 1 + elif position == "right": + self._horizontal.append(size) + elif position == "bottom": + self._vertical.insert(0, size) + self._yrefindex += 1 + else: # 'top' + self._vertical.append(size) + + def add_auto_adjustable_area(self, use_axes, pad=0.1, adjust_dirs=None): + """ + Add auto-adjustable padding around *use_axes* to take their decorations + (title, labels, ticks, ticklabels) into account during layout. + + Parameters + ---------- + use_axes : `~matplotlib.axes.Axes` or list of `~matplotlib.axes.Axes` + The Axes whose decorations are taken into account. + pad : float, default: 0.1 + Additional padding in inches. + adjust_dirs : list of {"left", "right", "bottom", "top"}, optional + The sides where padding is added; defaults to all four sides. + """ + if adjust_dirs is None: + adjust_dirs = ["left", "right", "bottom", "top"] + for d in adjust_dirs: + self.append_size(d, Size._AxesDecorationsSize(use_axes, d) + pad) + + +class SubplotDivider(Divider): + """ + The Divider class whose rectangle area is specified as a subplot geometry. + """ + + def __init__(self, fig, *args, horizontal=None, vertical=None, + aspect=None, anchor='C'): + """ + Parameters + ---------- + fig : `~matplotlib.figure.Figure` + + *args : tuple (*nrows*, *ncols*, *index*) or int + The array of subplots in the figure has dimensions ``(nrows, + ncols)``, and *index* is the index of the subplot being created. + *index* starts at 1 in the upper left corner and increases to the + right. + + If *nrows*, *ncols*, and *index* are all single digit numbers, then + *args* can be passed as a single 3-digit number (e.g. 234 for + (2, 3, 4)). + horizontal : list of :mod:`~mpl_toolkits.axes_grid1.axes_size`, optional + Sizes for horizontal division. + vertical : list of :mod:`~mpl_toolkits.axes_grid1.axes_size`, optional + Sizes for vertical division. + aspect : bool, optional + Whether overall rectangular area is reduced so that the relative + part of the horizontal and vertical scales have the same scale. + anchor : (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', 'N', \ +'NW', 'W'}, default: 'C' + Placement of the reduced rectangle, when *aspect* is True. + """ + self.figure = fig + super().__init__(fig, [0, 0, 1, 1], + horizontal=horizontal or [], vertical=vertical or [], + aspect=aspect, anchor=anchor) + self.set_subplotspec(SubplotSpec._from_subplot_args(fig, args)) + + def get_position(self): + """Return the bounds of the subplot box.""" + return self.get_subplotspec().get_position(self.figure).bounds + + def get_subplotspec(self): + """Get the SubplotSpec instance.""" + return self._subplotspec + + def set_subplotspec(self, subplotspec): + """Set the SubplotSpec instance.""" + self._subplotspec = subplotspec + self.set_position(subplotspec.get_position(self.figure)) + + +class AxesDivider(Divider): + """ + Divider based on the preexisting axes. + """ + + def __init__(self, axes, xref=None, yref=None): + """ + Parameters + ---------- + axes : :class:`~matplotlib.axes.Axes` + xref + yref + """ + self._axes = axes + if xref is None: + self._xref = Size.AxesX(axes) + else: + self._xref = xref + if yref is None: + self._yref = Size.AxesY(axes) + else: + self._yref = yref + + super().__init__(fig=axes.get_figure(), pos=None, + horizontal=[self._xref], vertical=[self._yref], + aspect=None, anchor="C") + + def _get_new_axes(self, *, axes_class=None, **kwargs): + axes = self._axes + if axes_class is None: + axes_class = type(axes) + return axes_class(axes.get_figure(), axes.get_position(original=True), + **kwargs) + + def new_horizontal(self, size, pad=None, pack_start=False, **kwargs): + """ + Helper method for ``append_axes("left")`` and ``append_axes("right")``. + + See the documentation of `append_axes` for more details. + + :meta private: + """ + if pad is None: + pad = mpl.rcParams["figure.subplot.wspace"] * self._xref + pos = "left" if pack_start else "right" + if pad: + if not isinstance(pad, Size._Base): + pad = Size.from_any(pad, fraction_ref=self._xref) + self.append_size(pos, pad) + if not isinstance(size, Size._Base): + size = Size.from_any(size, fraction_ref=self._xref) + self.append_size(pos, size) + locator = self.new_locator( + nx=0 if pack_start else len(self._horizontal) - 1, + ny=self._yrefindex) + ax = self._get_new_axes(**kwargs) + ax.set_axes_locator(locator) + return ax + + def new_vertical(self, size, pad=None, pack_start=False, **kwargs): + """ + Helper method for ``append_axes("top")`` and ``append_axes("bottom")``. + + See the documentation of `append_axes` for more details. + + :meta private: + """ + if pad is None: + pad = mpl.rcParams["figure.subplot.hspace"] * self._yref + pos = "bottom" if pack_start else "top" + if pad: + if not isinstance(pad, Size._Base): + pad = Size.from_any(pad, fraction_ref=self._yref) + self.append_size(pos, pad) + if not isinstance(size, Size._Base): + size = Size.from_any(size, fraction_ref=self._yref) + self.append_size(pos, size) + locator = self.new_locator( + nx=self._xrefindex, + ny=0 if pack_start else len(self._vertical) - 1) + ax = self._get_new_axes(**kwargs) + ax.set_axes_locator(locator) + return ax + + def append_axes(self, position, size, pad=None, *, axes_class=None, + **kwargs): + """ + Add a new axes on a given side of the main axes. + + Parameters + ---------- + position : {"left", "right", "bottom", "top"} + Where the new axes is positioned relative to the main axes. + size : :mod:`~mpl_toolkits.axes_grid1.axes_size` or float or str + The axes width or height. float or str arguments are interpreted + as ``axes_size.from_any(size, AxesX())`` for left or + right axes, and likewise with ``AxesY`` for bottom or top axes. + pad : :mod:`~mpl_toolkits.axes_grid1.axes_size` or float or str + Padding between the axes. float or str arguments are interpreted + as for *size*. Defaults to :rc:`figure.subplot.wspace` times the + main Axes width (left or right axes) or :rc:`figure.subplot.hspace` + times the main Axes height (bottom or top axes). + axes_class : subclass type of `~.axes.Axes`, optional + The type of the new axes. Defaults to the type of the main axes. + **kwargs + All extra keywords arguments are passed to the created axes. + """ + create_axes, pack_start = _api.check_getitem({ + "left": (self.new_horizontal, True), + "right": (self.new_horizontal, False), + "bottom": (self.new_vertical, True), + "top": (self.new_vertical, False), + }, position=position) + ax = create_axes( + size, pad, pack_start=pack_start, axes_class=axes_class, **kwargs) + self._fig.add_axes(ax) + return ax + + def get_aspect(self): + if self._aspect is None: + aspect = self._axes.get_aspect() + if aspect == "auto": + return False + else: + return True + else: + return self._aspect + + def get_position(self): + if self._pos is None: + bbox = self._axes.get_position(original=True) + return bbox.bounds + else: + return self._pos + + def get_anchor(self): + if self._anchor is None: + return self._axes.get_anchor() + else: + return self._anchor + + def get_subplotspec(self): + return self._axes.get_subplotspec() + + +# Helper for HBoxDivider/VBoxDivider. +# The variable names are written for a horizontal layout, but the calculations +# work identically for vertical layouts. +def _locate(x, y, w, h, summed_widths, equal_heights, fig_w, fig_h, anchor): + + total_width = fig_w * w + max_height = fig_h * h + + # Determine the k factors. + n = len(equal_heights) + eq_rels, eq_abss = equal_heights.T + sm_rels, sm_abss = summed_widths.T + A = np.diag([*eq_rels, 0]) + A[:n, -1] = -1 + A[-1, :-1] = sm_rels + B = [*(-eq_abss), total_width - sm_abss.sum()] + # A @ K = B: This finds factors {k_0, ..., k_{N-1}, H} so that + # eq_rel_i * k_i + eq_abs_i = H for all i: all axes have the same height + # sum(sm_rel_i * k_i + sm_abs_i) = total_width: fixed total width + # (foo_rel_i * k_i + foo_abs_i will end up being the size of foo.) + *karray, height = np.linalg.solve(A, B) + if height > max_height: # Additionally, upper-bound the height. + karray = (max_height - eq_abss) / eq_rels + + # Compute the offsets corresponding to these factors. + ox = np.cumsum([0, *(sm_rels * karray + sm_abss)]) + ww = (ox[-1] - ox[0]) / fig_w + h0_rel, h0_abs = equal_heights[0] + hh = (karray[0]*h0_rel + h0_abs) / fig_h + pb = mtransforms.Bbox.from_bounds(x, y, w, h) + pb1 = mtransforms.Bbox.from_bounds(x, y, ww, hh) + x0, y0 = pb1.anchored(anchor, pb).p0 + + return x0, y0, ox, hh + + +class HBoxDivider(SubplotDivider): + """ + A `.SubplotDivider` for laying out axes horizontally, while ensuring that + they have equal heights. + + Examples + -------- + .. plot:: gallery/axes_grid1/demo_axes_hbox_divider.py + """ + + def new_locator(self, nx, nx1=None): + """ + Create an axes locator callable for the specified cell. + + Parameters + ---------- + nx, nx1 : int + Integers specifying the column-position of the + cell. When *nx1* is None, a single *nx*-th column is + specified. Otherwise, location of columns spanning between *nx* + to *nx1* (but excluding *nx1*-th column) is specified. + """ + return super().new_locator(nx, 0, nx1, 0) + + def _locate(self, nx, ny, nx1, ny1, axes, renderer): + # docstring inherited + nx += self._xrefindex + nx1 += self._xrefindex + fig_w, fig_h = self._fig.bbox.size / self._fig.dpi + x, y, w, h = self.get_position_runtime(axes, renderer) + summed_ws = self.get_horizontal_sizes(renderer) + equal_hs = self.get_vertical_sizes(renderer) + x0, y0, ox, hh = _locate( + x, y, w, h, summed_ws, equal_hs, fig_w, fig_h, self.get_anchor()) + if nx1 is None: + nx1 = -1 + x1, w1 = x0 + ox[nx] / fig_w, (ox[nx1] - ox[nx]) / fig_w + y1, h1 = y0, hh + return mtransforms.Bbox.from_bounds(x1, y1, w1, h1) + + +class VBoxDivider(SubplotDivider): + """ + A `.SubplotDivider` for laying out axes vertically, while ensuring that + they have equal widths. + """ + + def new_locator(self, ny, ny1=None): + """ + Create an axes locator callable for the specified cell. + + Parameters + ---------- + ny, ny1 : int + Integers specifying the row-position of the + cell. When *ny1* is None, a single *ny*-th row is + specified. Otherwise, location of rows spanning between *ny* + to *ny1* (but excluding *ny1*-th row) is specified. + """ + return super().new_locator(0, ny, 0, ny1) + + def _locate(self, nx, ny, nx1, ny1, axes, renderer): + # docstring inherited + ny += self._yrefindex + ny1 += self._yrefindex + fig_w, fig_h = self._fig.bbox.size / self._fig.dpi + x, y, w, h = self.get_position_runtime(axes, renderer) + summed_hs = self.get_vertical_sizes(renderer) + equal_ws = self.get_horizontal_sizes(renderer) + y0, x0, oy, ww = _locate( + y, x, h, w, summed_hs, equal_ws, fig_h, fig_w, self.get_anchor()) + if ny1 is None: + ny1 = -1 + x1, w1 = x0, ww + y1, h1 = y0 + oy[ny] / fig_h, (oy[ny1] - oy[ny]) / fig_h + return mtransforms.Bbox.from_bounds(x1, y1, w1, h1) + + +def make_axes_locatable(axes): + divider = AxesDivider(axes) + locator = divider.new_locator(nx=0, ny=0) + axes.set_axes_locator(locator) + + return divider + + +def make_axes_area_auto_adjustable( + ax, use_axes=None, pad=0.1, adjust_dirs=None): + """ + Add auto-adjustable padding around *ax* to take its decorations (title, + labels, ticks, ticklabels) into account during layout, using + `.Divider.add_auto_adjustable_area`. + + By default, padding is determined from the decorations of *ax*. + Pass *use_axes* to consider the decorations of other Axes instead. + """ + if adjust_dirs is None: + adjust_dirs = ["left", "right", "bottom", "top"] + divider = make_axes_locatable(ax) + if use_axes is None: + use_axes = ax + divider.add_auto_adjustable_area(use_axes=use_axes, pad=pad, + adjust_dirs=adjust_dirs) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_grid.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..20abf18ea79c32ccdfe678d81ad975e83fe4bc0b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_grid.py @@ -0,0 +1,563 @@ +from numbers import Number +import functools +from types import MethodType + +import numpy as np + +from matplotlib import _api, cbook +from matplotlib.gridspec import SubplotSpec + +from .axes_divider import Size, SubplotDivider, Divider +from .mpl_axes import Axes, SimpleAxisArtist + + +class CbarAxesBase: + def __init__(self, *args, orientation, **kwargs): + self.orientation = orientation + super().__init__(*args, **kwargs) + + def colorbar(self, mappable, **kwargs): + return self.get_figure(root=False).colorbar( + mappable, cax=self, location=self.orientation, **kwargs) + + +_cbaraxes_class_factory = cbook._make_class_factory(CbarAxesBase, "Cbar{}") + + +class Grid: + """ + A grid of Axes. + + In Matplotlib, the Axes location (and size) is specified in normalized + figure coordinates. This may not be ideal for images that needs to be + displayed with a given aspect ratio; for example, it is difficult to + display multiple images of a same size with some fixed padding between + them. AxesGrid can be used in such case. + + Attributes + ---------- + axes_all : list of Axes + A flat list of Axes. Note that you can also access this directly + from the grid. The following is equivalent :: + + grid[i] == grid.axes_all[i] + len(grid) == len(grid.axes_all) + + axes_column : list of list of Axes + A 2D list of Axes where the first index is the column. This results + in the usage pattern ``grid.axes_column[col][row]``. + axes_row : list of list of Axes + A 2D list of Axes where the first index is the row. This results + in the usage pattern ``grid.axes_row[row][col]``. + axes_llc : Axes + The Axes in the lower left corner. + ngrids : int + Number of Axes in the grid. + """ + + _defaultAxesClass = Axes + + def __init__(self, fig, + rect, + nrows_ncols, + ngrids=None, + direction="row", + axes_pad=0.02, + *, + share_all=False, + share_x=True, + share_y=True, + label_mode="L", + axes_class=None, + aspect=False, + ): + """ + Parameters + ---------- + fig : `.Figure` + The parent figure. + rect : (float, float, float, float), (int, int, int), int, or \ + `~.SubplotSpec` + The axes position, as a ``(left, bottom, width, height)`` tuple, + as a three-digit subplot position code (e.g., ``(1, 2, 1)`` or + ``121``), or as a `~.SubplotSpec`. + nrows_ncols : (int, int) + Number of rows and columns in the grid. + ngrids : int or None, default: None + If not None, only the first *ngrids* axes in the grid are created. + direction : {"row", "column"}, default: "row" + Whether axes are created in row-major ("row by row") or + column-major order ("column by column"). This also affects the + order in which axes are accessed using indexing (``grid[index]``). + axes_pad : float or (float, float), default: 0.02 + Padding or (horizontal padding, vertical padding) between axes, in + inches. + share_all : bool, default: False + Whether all axes share their x- and y-axis. Overrides *share_x* + and *share_y*. + share_x : bool, default: True + Whether all axes of a column share their x-axis. + share_y : bool, default: True + Whether all axes of a row share their y-axis. + label_mode : {"L", "1", "all", "keep"}, default: "L" + Determines which axes will get tick labels: + + - "L": All axes on the left column get vertical tick labels; + all axes on the bottom row get horizontal tick labels. + - "1": Only the bottom left axes is labelled. + - "all": All axes are labelled. + - "keep": Do not do anything. + + axes_class : subclass of `matplotlib.axes.Axes`, default: `.mpl_axes.Axes` + The type of Axes to create. + aspect : bool, default: False + Whether the axes aspect ratio follows the aspect ratio of the data + limits. + """ + self._nrows, self._ncols = nrows_ncols + + if ngrids is None: + ngrids = self._nrows * self._ncols + else: + if not 0 < ngrids <= self._nrows * self._ncols: + raise ValueError( + "ngrids must be positive and not larger than nrows*ncols") + + self.ngrids = ngrids + + self._horiz_pad_size, self._vert_pad_size = map( + Size.Fixed, np.broadcast_to(axes_pad, 2)) + + _api.check_in_list(["column", "row"], direction=direction) + self._direction = direction + + if axes_class is None: + axes_class = self._defaultAxesClass + elif isinstance(axes_class, (list, tuple)): + cls, kwargs = axes_class + axes_class = functools.partial(cls, **kwargs) + + kw = dict(horizontal=[], vertical=[], aspect=aspect) + if isinstance(rect, (Number, SubplotSpec)): + self._divider = SubplotDivider(fig, rect, **kw) + elif len(rect) == 3: + self._divider = SubplotDivider(fig, *rect, **kw) + elif len(rect) == 4: + self._divider = Divider(fig, rect, **kw) + else: + raise TypeError("Incorrect rect format") + + rect = self._divider.get_position() + + axes_array = np.full((self._nrows, self._ncols), None, dtype=object) + for i in range(self.ngrids): + col, row = self._get_col_row(i) + if share_all: + sharex = sharey = axes_array[0, 0] + else: + sharex = axes_array[0, col] if share_x else None + sharey = axes_array[row, 0] if share_y else None + axes_array[row, col] = axes_class( + fig, rect, sharex=sharex, sharey=sharey) + self.axes_all = axes_array.ravel( + order="C" if self._direction == "row" else "F").tolist() + self.axes_column = axes_array.T.tolist() + self.axes_row = axes_array.tolist() + self.axes_llc = self.axes_column[0][-1] + + self._init_locators() + + for ax in self.axes_all: + fig.add_axes(ax) + + self.set_label_mode(label_mode) + + def _init_locators(self): + self._divider.set_horizontal( + [Size.Scaled(1), self._horiz_pad_size] * (self._ncols-1) + [Size.Scaled(1)]) + self._divider.set_vertical( + [Size.Scaled(1), self._vert_pad_size] * (self._nrows-1) + [Size.Scaled(1)]) + for i in range(self.ngrids): + col, row = self._get_col_row(i) + self.axes_all[i].set_axes_locator( + self._divider.new_locator(nx=2 * col, ny=2 * (self._nrows - 1 - row))) + + def _get_col_row(self, n): + if self._direction == "column": + col, row = divmod(n, self._nrows) + else: + row, col = divmod(n, self._ncols) + + return col, row + + # Good to propagate __len__ if we have __getitem__ + def __len__(self): + return len(self.axes_all) + + def __getitem__(self, i): + return self.axes_all[i] + + def get_geometry(self): + """ + Return the number of rows and columns of the grid as (nrows, ncols). + """ + return self._nrows, self._ncols + + def set_axes_pad(self, axes_pad): + """ + Set the padding between the axes. + + Parameters + ---------- + axes_pad : (float, float) + The padding (horizontal pad, vertical pad) in inches. + """ + self._horiz_pad_size.fixed_size = axes_pad[0] + self._vert_pad_size.fixed_size = axes_pad[1] + + def get_axes_pad(self): + """ + Return the axes padding. + + Returns + ------- + hpad, vpad + Padding (horizontal pad, vertical pad) in inches. + """ + return (self._horiz_pad_size.fixed_size, + self._vert_pad_size.fixed_size) + + def set_aspect(self, aspect): + """Set the aspect of the SubplotDivider.""" + self._divider.set_aspect(aspect) + + def get_aspect(self): + """Return the aspect of the SubplotDivider.""" + return self._divider.get_aspect() + + def set_label_mode(self, mode): + """ + Define which axes have tick labels. + + Parameters + ---------- + mode : {"L", "1", "all", "keep"} + The label mode: + + - "L": All axes on the left column get vertical tick labels; + all axes on the bottom row get horizontal tick labels. + - "1": Only the bottom left axes is labelled. + - "all": All axes are labelled. + - "keep": Do not do anything. + """ + _api.check_in_list(["all", "L", "1", "keep"], mode=mode) + is_last_row, is_first_col = ( + np.mgrid[:self._nrows, :self._ncols] == [[[self._nrows - 1]], [[0]]]) + if mode == "all": + bottom = left = np.full((self._nrows, self._ncols), True) + elif mode == "L": + bottom = is_last_row + left = is_first_col + elif mode == "1": + bottom = left = is_last_row & is_first_col + else: + return + for i in range(self._nrows): + for j in range(self._ncols): + ax = self.axes_row[i][j] + if isinstance(ax.axis, MethodType): + bottom_axis = SimpleAxisArtist(ax.xaxis, 1, ax.spines["bottom"]) + left_axis = SimpleAxisArtist(ax.yaxis, 1, ax.spines["left"]) + else: + bottom_axis = ax.axis["bottom"] + left_axis = ax.axis["left"] + bottom_axis.toggle(ticklabels=bottom[i, j], label=bottom[i, j]) + left_axis.toggle(ticklabels=left[i, j], label=left[i, j]) + + def get_divider(self): + return self._divider + + def set_axes_locator(self, locator): + self._divider.set_locator(locator) + + def get_axes_locator(self): + return self._divider.get_locator() + + +class ImageGrid(Grid): + """ + A grid of Axes for Image display. + + This class is a specialization of `~.axes_grid1.axes_grid.Grid` for displaying a + grid of images. In particular, it forces all axes in a column to share their x-axis + and all axes in a row to share their y-axis. It further provides helpers to add + colorbars to some or all axes. + """ + + def __init__(self, fig, + rect, + nrows_ncols, + ngrids=None, + direction="row", + axes_pad=0.02, + *, + share_all=False, + aspect=True, + label_mode="L", + cbar_mode=None, + cbar_location="right", + cbar_pad=None, + cbar_size="5%", + cbar_set_cax=True, + axes_class=None, + ): + """ + Parameters + ---------- + fig : `.Figure` + The parent figure. + rect : (float, float, float, float) or int + The axes position, as a ``(left, bottom, width, height)`` tuple or + as a three-digit subplot position code (e.g., "121"). + nrows_ncols : (int, int) + Number of rows and columns in the grid. + ngrids : int or None, default: None + If not None, only the first *ngrids* axes in the grid are created. + direction : {"row", "column"}, default: "row" + Whether axes are created in row-major ("row by row") or + column-major order ("column by column"). This also affects the + order in which axes are accessed using indexing (``grid[index]``). + axes_pad : float or (float, float), default: 0.02in + Padding or (horizontal padding, vertical padding) between axes, in + inches. + share_all : bool, default: False + Whether all axes share their x- and y-axis. Note that in any case, + all axes in a column share their x-axis and all axes in a row share + their y-axis. + aspect : bool, default: True + Whether the axes aspect ratio follows the aspect ratio of the data + limits. + label_mode : {"L", "1", "all"}, default: "L" + Determines which axes will get tick labels: + + - "L": All axes on the left column get vertical tick labels; + all axes on the bottom row get horizontal tick labels. + - "1": Only the bottom left axes is labelled. + - "all": all axes are labelled. + + cbar_mode : {"each", "single", "edge", None}, default: None + Whether to create a colorbar for "each" axes, a "single" colorbar + for the entire grid, colorbars only for axes on the "edge" + determined by *cbar_location*, or no colorbars. The colorbars are + stored in the :attr:`cbar_axes` attribute. + cbar_location : {"left", "right", "bottom", "top"}, default: "right" + cbar_pad : float, default: None + Padding between the image axes and the colorbar axes. + + .. versionchanged:: 3.10 + ``cbar_mode="single"`` no longer adds *axes_pad* between the axes + and the colorbar if the *cbar_location* is "left" or "bottom". + + cbar_size : size specification (see `.Size.from_any`), default: "5%" + Colorbar size. + cbar_set_cax : bool, default: True + If True, each axes in the grid has a *cax* attribute that is bound + to associated *cbar_axes*. + axes_class : subclass of `matplotlib.axes.Axes`, default: None + """ + _api.check_in_list(["each", "single", "edge", None], + cbar_mode=cbar_mode) + _api.check_in_list(["left", "right", "bottom", "top"], + cbar_location=cbar_location) + self._colorbar_mode = cbar_mode + self._colorbar_location = cbar_location + self._colorbar_pad = cbar_pad + self._colorbar_size = cbar_size + # The colorbar axes are created in _init_locators(). + + super().__init__( + fig, rect, nrows_ncols, ngrids, + direction=direction, axes_pad=axes_pad, + share_all=share_all, share_x=True, share_y=True, aspect=aspect, + label_mode=label_mode, axes_class=axes_class) + + for ax in self.cbar_axes: + fig.add_axes(ax) + + if cbar_set_cax: + if self._colorbar_mode == "single": + for ax in self.axes_all: + ax.cax = self.cbar_axes[0] + elif self._colorbar_mode == "edge": + for index, ax in enumerate(self.axes_all): + col, row = self._get_col_row(index) + if self._colorbar_location in ("left", "right"): + ax.cax = self.cbar_axes[row] + else: + ax.cax = self.cbar_axes[col] + else: + for ax, cax in zip(self.axes_all, self.cbar_axes): + ax.cax = cax + + def _init_locators(self): + # Slightly abusing this method to inject colorbar creation into init. + + if self._colorbar_pad is None: + # horizontal or vertical arrangement? + if self._colorbar_location in ("left", "right"): + self._colorbar_pad = self._horiz_pad_size.fixed_size + else: + self._colorbar_pad = self._vert_pad_size.fixed_size + self.cbar_axes = [ + _cbaraxes_class_factory(self._defaultAxesClass)( + self.axes_all[0].get_figure(root=False), self._divider.get_position(), + orientation=self._colorbar_location) + for _ in range(self.ngrids)] + + cb_mode = self._colorbar_mode + cb_location = self._colorbar_location + + h = [] + v = [] + + h_ax_pos = [] + h_cb_pos = [] + if cb_mode == "single" and cb_location in ("left", "bottom"): + if cb_location == "left": + sz = self._nrows * Size.AxesX(self.axes_llc) + h.append(Size.from_any(self._colorbar_size, sz)) + h.append(Size.from_any(self._colorbar_pad, sz)) + locator = self._divider.new_locator(nx=0, ny=0, ny1=-1) + elif cb_location == "bottom": + sz = self._ncols * Size.AxesY(self.axes_llc) + v.append(Size.from_any(self._colorbar_size, sz)) + v.append(Size.from_any(self._colorbar_pad, sz)) + locator = self._divider.new_locator(nx=0, nx1=-1, ny=0) + for i in range(self.ngrids): + self.cbar_axes[i].set_visible(False) + self.cbar_axes[0].set_axes_locator(locator) + self.cbar_axes[0].set_visible(True) + + for col, ax in enumerate(self.axes_row[0]): + if col != 0: + h.append(self._horiz_pad_size) + + if ax: + sz = Size.AxesX(ax, aspect="axes", ref_ax=self.axes_all[0]) + else: + sz = Size.AxesX(self.axes_all[0], + aspect="axes", ref_ax=self.axes_all[0]) + + if (cb_location == "left" + and (cb_mode == "each" + or (cb_mode == "edge" and col == 0))): + h_cb_pos.append(len(h)) + h.append(Size.from_any(self._colorbar_size, sz)) + h.append(Size.from_any(self._colorbar_pad, sz)) + + h_ax_pos.append(len(h)) + h.append(sz) + + if (cb_location == "right" + and (cb_mode == "each" + or (cb_mode == "edge" and col == self._ncols - 1))): + h.append(Size.from_any(self._colorbar_pad, sz)) + h_cb_pos.append(len(h)) + h.append(Size.from_any(self._colorbar_size, sz)) + + v_ax_pos = [] + v_cb_pos = [] + for row, ax in enumerate(self.axes_column[0][::-1]): + if row != 0: + v.append(self._vert_pad_size) + + if ax: + sz = Size.AxesY(ax, aspect="axes", ref_ax=self.axes_all[0]) + else: + sz = Size.AxesY(self.axes_all[0], + aspect="axes", ref_ax=self.axes_all[0]) + + if (cb_location == "bottom" + and (cb_mode == "each" + or (cb_mode == "edge" and row == 0))): + v_cb_pos.append(len(v)) + v.append(Size.from_any(self._colorbar_size, sz)) + v.append(Size.from_any(self._colorbar_pad, sz)) + + v_ax_pos.append(len(v)) + v.append(sz) + + if (cb_location == "top" + and (cb_mode == "each" + or (cb_mode == "edge" and row == self._nrows - 1))): + v.append(Size.from_any(self._colorbar_pad, sz)) + v_cb_pos.append(len(v)) + v.append(Size.from_any(self._colorbar_size, sz)) + + for i in range(self.ngrids): + col, row = self._get_col_row(i) + locator = self._divider.new_locator(nx=h_ax_pos[col], + ny=v_ax_pos[self._nrows-1-row]) + self.axes_all[i].set_axes_locator(locator) + + if cb_mode == "each": + if cb_location in ("right", "left"): + locator = self._divider.new_locator( + nx=h_cb_pos[col], ny=v_ax_pos[self._nrows - 1 - row]) + + elif cb_location in ("top", "bottom"): + locator = self._divider.new_locator( + nx=h_ax_pos[col], ny=v_cb_pos[self._nrows - 1 - row]) + + self.cbar_axes[i].set_axes_locator(locator) + elif cb_mode == "edge": + if (cb_location == "left" and col == 0 + or cb_location == "right" and col == self._ncols - 1): + locator = self._divider.new_locator( + nx=h_cb_pos[0], ny=v_ax_pos[self._nrows - 1 - row]) + self.cbar_axes[row].set_axes_locator(locator) + elif (cb_location == "bottom" and row == self._nrows - 1 + or cb_location == "top" and row == 0): + locator = self._divider.new_locator(nx=h_ax_pos[col], + ny=v_cb_pos[0]) + self.cbar_axes[col].set_axes_locator(locator) + + if cb_mode == "single": + if cb_location == "right": + sz = self._nrows * Size.AxesX(self.axes_llc) + h.append(Size.from_any(self._colorbar_pad, sz)) + h.append(Size.from_any(self._colorbar_size, sz)) + locator = self._divider.new_locator(nx=-2, ny=0, ny1=-1) + elif cb_location == "top": + sz = self._ncols * Size.AxesY(self.axes_llc) + v.append(Size.from_any(self._colorbar_pad, sz)) + v.append(Size.from_any(self._colorbar_size, sz)) + locator = self._divider.new_locator(nx=0, nx1=-1, ny=-2) + if cb_location in ("right", "top"): + for i in range(self.ngrids): + self.cbar_axes[i].set_visible(False) + self.cbar_axes[0].set_axes_locator(locator) + self.cbar_axes[0].set_visible(True) + elif cb_mode == "each": + for i in range(self.ngrids): + self.cbar_axes[i].set_visible(True) + elif cb_mode == "edge": + if cb_location in ("right", "left"): + count = self._nrows + else: + count = self._ncols + for i in range(count): + self.cbar_axes[i].set_visible(True) + for j in range(i + 1, self.ngrids): + self.cbar_axes[j].set_visible(False) + else: + for i in range(self.ngrids): + self.cbar_axes[i].set_visible(False) + self.cbar_axes[i].set_position([1., 1., 0.001, 0.001], + which="active") + + self._divider.set_horizontal(h) + self._divider.set_vertical(v) + + +AxesGrid = ImageGrid diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_rgb.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_rgb.py new file mode 100644 index 0000000000000000000000000000000000000000..52fd707e87043eb17929a84c53da2741722ce002 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_rgb.py @@ -0,0 +1,157 @@ +from types import MethodType + +import numpy as np + +from .axes_divider import make_axes_locatable, Size +from .mpl_axes import Axes, SimpleAxisArtist + + +def make_rgb_axes(ax, pad=0.01, axes_class=None, **kwargs): + """ + Parameters + ---------- + ax : `~matplotlib.axes.Axes` + Axes instance to create the RGB Axes in. + pad : float, optional + Fraction of the Axes height to pad. + axes_class : `matplotlib.axes.Axes` or None, optional + Axes class to use for the R, G, and B Axes. If None, use + the same class as *ax*. + **kwargs + Forwarded to *axes_class* init for the R, G, and B Axes. + """ + + divider = make_axes_locatable(ax) + + pad_size = pad * Size.AxesY(ax) + + xsize = ((1-2*pad)/3) * Size.AxesX(ax) + ysize = ((1-2*pad)/3) * Size.AxesY(ax) + + divider.set_horizontal([Size.AxesX(ax), pad_size, xsize]) + divider.set_vertical([ysize, pad_size, ysize, pad_size, ysize]) + + ax.set_axes_locator(divider.new_locator(0, 0, ny1=-1)) + + ax_rgb = [] + if axes_class is None: + axes_class = type(ax) + + for ny in [4, 2, 0]: + ax1 = axes_class(ax.get_figure(), ax.get_position(original=True), + sharex=ax, sharey=ax, **kwargs) + locator = divider.new_locator(nx=2, ny=ny) + ax1.set_axes_locator(locator) + for t in ax1.yaxis.get_ticklabels() + ax1.xaxis.get_ticklabels(): + t.set_visible(False) + try: + for axis in ax1.axis.values(): + axis.major_ticklabels.set_visible(False) + except AttributeError: + pass + + ax_rgb.append(ax1) + + fig = ax.get_figure() + for ax1 in ax_rgb: + fig.add_axes(ax1) + + return ax_rgb + + +class RGBAxes: + """ + 4-panel `~.Axes.imshow` (RGB, R, G, B). + + Layout:: + + ┌───────────────┬─────┐ + │ │ R │ + │ ├─────┤ + │ RGB │ G │ + │ ├─────┤ + │ │ B │ + └───────────────┴─────┘ + + Subclasses can override the ``_defaultAxesClass`` attribute. + By default RGBAxes uses `.mpl_axes.Axes`. + + Attributes + ---------- + RGB : ``_defaultAxesClass`` + The Axes object for the three-channel `~.Axes.imshow`. + R : ``_defaultAxesClass`` + The Axes object for the red channel `~.Axes.imshow`. + G : ``_defaultAxesClass`` + The Axes object for the green channel `~.Axes.imshow`. + B : ``_defaultAxesClass`` + The Axes object for the blue channel `~.Axes.imshow`. + """ + + _defaultAxesClass = Axes + + def __init__(self, *args, pad=0, **kwargs): + """ + Parameters + ---------- + pad : float, default: 0 + Fraction of the Axes height to put as padding. + axes_class : `~matplotlib.axes.Axes` + Axes class to use. If not provided, ``_defaultAxesClass`` is used. + *args + Forwarded to *axes_class* init for the RGB Axes + **kwargs + Forwarded to *axes_class* init for the RGB, R, G, and B Axes + """ + axes_class = kwargs.pop("axes_class", self._defaultAxesClass) + self.RGB = ax = axes_class(*args, **kwargs) + ax.get_figure().add_axes(ax) + self.R, self.G, self.B = make_rgb_axes( + ax, pad=pad, axes_class=axes_class, **kwargs) + # Set the line color and ticks for the axes. + for ax1 in [self.RGB, self.R, self.G, self.B]: + if isinstance(ax1.axis, MethodType): + ad = Axes.AxisDict(self) + ad.update( + bottom=SimpleAxisArtist(ax1.xaxis, 1, ax1.spines["bottom"]), + top=SimpleAxisArtist(ax1.xaxis, 2, ax1.spines["top"]), + left=SimpleAxisArtist(ax1.yaxis, 1, ax1.spines["left"]), + right=SimpleAxisArtist(ax1.yaxis, 2, ax1.spines["right"])) + else: + ad = ax1.axis + ad[:].line.set_color("w") + ad[:].major_ticks.set_markeredgecolor("w") + + def imshow_rgb(self, r, g, b, **kwargs): + """ + Create the four images {rgb, r, g, b}. + + Parameters + ---------- + r, g, b : array-like + The red, green, and blue arrays. + **kwargs + Forwarded to `~.Axes.imshow` calls for the four images. + + Returns + ------- + rgb : `~matplotlib.image.AxesImage` + r : `~matplotlib.image.AxesImage` + g : `~matplotlib.image.AxesImage` + b : `~matplotlib.image.AxesImage` + """ + if not (r.shape == g.shape == b.shape): + raise ValueError( + f'Input shapes ({r.shape}, {g.shape}, {b.shape}) do not match') + RGB = np.dstack([r, g, b]) + R = np.zeros_like(RGB) + R[:, :, 0] = r + G = np.zeros_like(RGB) + G[:, :, 1] = g + B = np.zeros_like(RGB) + B[:, :, 2] = b + im_rgb = self.RGB.imshow(RGB, **kwargs) + im_r = self.R.imshow(R, **kwargs) + im_g = self.G.imshow(G, **kwargs) + im_b = self.B.imshow(B, **kwargs) + return im_rgb, im_r, im_g, im_b diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_size.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_size.py new file mode 100644 index 0000000000000000000000000000000000000000..86e5f70d9824392a8c49bce9a18aaf45cf6f0232 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/axes_size.py @@ -0,0 +1,271 @@ +""" +Provides classes of simple units that will be used with `.AxesDivider` +class (or others) to determine the size of each Axes. The unit +classes define `get_size` method that returns a tuple of two floats, +meaning relative and absolute sizes, respectively. + +Note that this class is nothing more than a simple tuple of two +floats. Take a look at the Divider class to see how these two +values are used. + +Once created, the unit classes can be modified by simple arithmetic +operations: addition /subtraction with another unit type or a real number and scaling +(multiplication or division) by a real number. +""" + +from numbers import Real + +from matplotlib import _api +from matplotlib.axes import Axes + + +class _Base: + def __rmul__(self, other): + return self * other + + def __mul__(self, other): + if not isinstance(other, Real): + return NotImplemented + return Fraction(other, self) + + def __div__(self, other): + return (1 / other) * self + + def __add__(self, other): + if isinstance(other, _Base): + return Add(self, other) + else: + return Add(self, Fixed(other)) + + def __neg__(self): + return -1 * self + + def __radd__(self, other): + # other cannot be a _Base instance, because A + B would trigger + # A.__add__(B) first. + return Add(self, Fixed(other)) + + def __sub__(self, other): + return self + (-other) + + def get_size(self, renderer): + """ + Return two-float tuple with relative and absolute sizes. + """ + raise NotImplementedError("Subclasses must implement") + + +class Add(_Base): + """ + Sum of two sizes. + """ + + def __init__(self, a, b): + self._a = a + self._b = b + + def get_size(self, renderer): + a_rel_size, a_abs_size = self._a.get_size(renderer) + b_rel_size, b_abs_size = self._b.get_size(renderer) + return a_rel_size + b_rel_size, a_abs_size + b_abs_size + + +class Fixed(_Base): + """ + Simple fixed size with absolute part = *fixed_size* and relative part = 0. + """ + + def __init__(self, fixed_size): + _api.check_isinstance(Real, fixed_size=fixed_size) + self.fixed_size = fixed_size + + def get_size(self, renderer): + rel_size = 0. + abs_size = self.fixed_size + return rel_size, abs_size + + +class Scaled(_Base): + """ + Simple scaled(?) size with absolute part = 0 and + relative part = *scalable_size*. + """ + + def __init__(self, scalable_size): + self._scalable_size = scalable_size + + def get_size(self, renderer): + rel_size = self._scalable_size + abs_size = 0. + return rel_size, abs_size + +Scalable = Scaled + + +def _get_axes_aspect(ax): + aspect = ax.get_aspect() + if aspect == "auto": + aspect = 1. + return aspect + + +class AxesX(_Base): + """ + Scaled size whose relative part corresponds to the data width + of the *axes* multiplied by the *aspect*. + """ + + def __init__(self, axes, aspect=1., ref_ax=None): + self._axes = axes + self._aspect = aspect + if aspect == "axes" and ref_ax is None: + raise ValueError("ref_ax must be set when aspect='axes'") + self._ref_ax = ref_ax + + def get_size(self, renderer): + l1, l2 = self._axes.get_xlim() + if self._aspect == "axes": + ref_aspect = _get_axes_aspect(self._ref_ax) + aspect = ref_aspect / _get_axes_aspect(self._axes) + else: + aspect = self._aspect + + rel_size = abs(l2-l1)*aspect + abs_size = 0. + return rel_size, abs_size + + +class AxesY(_Base): + """ + Scaled size whose relative part corresponds to the data height + of the *axes* multiplied by the *aspect*. + """ + + def __init__(self, axes, aspect=1., ref_ax=None): + self._axes = axes + self._aspect = aspect + if aspect == "axes" and ref_ax is None: + raise ValueError("ref_ax must be set when aspect='axes'") + self._ref_ax = ref_ax + + def get_size(self, renderer): + l1, l2 = self._axes.get_ylim() + + if self._aspect == "axes": + ref_aspect = _get_axes_aspect(self._ref_ax) + aspect = _get_axes_aspect(self._axes) + else: + aspect = self._aspect + + rel_size = abs(l2-l1)*aspect + abs_size = 0. + return rel_size, abs_size + + +class MaxExtent(_Base): + """ + Size whose absolute part is either the largest width or the largest height + of the given *artist_list*. + """ + + def __init__(self, artist_list, w_or_h): + self._artist_list = artist_list + _api.check_in_list(["width", "height"], w_or_h=w_or_h) + self._w_or_h = w_or_h + + def add_artist(self, a): + self._artist_list.append(a) + + def get_size(self, renderer): + rel_size = 0. + extent_list = [ + getattr(a.get_window_extent(renderer), self._w_or_h) / a.figure.dpi + for a in self._artist_list] + abs_size = max(extent_list, default=0) + return rel_size, abs_size + + +class MaxWidth(MaxExtent): + """ + Size whose absolute part is the largest width of the given *artist_list*. + """ + + def __init__(self, artist_list): + super().__init__(artist_list, "width") + + +class MaxHeight(MaxExtent): + """ + Size whose absolute part is the largest height of the given *artist_list*. + """ + + def __init__(self, artist_list): + super().__init__(artist_list, "height") + + +class Fraction(_Base): + """ + An instance whose size is a *fraction* of the *ref_size*. + + >>> s = Fraction(0.3, AxesX(ax)) + """ + + def __init__(self, fraction, ref_size): + _api.check_isinstance(Real, fraction=fraction) + self._fraction_ref = ref_size + self._fraction = fraction + + def get_size(self, renderer): + if self._fraction_ref is None: + return self._fraction, 0. + else: + r, a = self._fraction_ref.get_size(renderer) + rel_size = r*self._fraction + abs_size = a*self._fraction + return rel_size, abs_size + + +def from_any(size, fraction_ref=None): + """ + Create a Fixed unit when the first argument is a float, or a + Fraction unit if that is a string that ends with %. The second + argument is only meaningful when Fraction unit is created. + + >>> from mpl_toolkits.axes_grid1.axes_size import from_any + >>> a = from_any(1.2) # => Fixed(1.2) + >>> from_any("50%", a) # => Fraction(0.5, a) + """ + if isinstance(size, Real): + return Fixed(size) + elif isinstance(size, str): + if size[-1] == "%": + return Fraction(float(size[:-1]) / 100, fraction_ref) + raise ValueError("Unknown format") + + +class _AxesDecorationsSize(_Base): + """ + Fixed size, corresponding to the size of decorations on a given Axes side. + """ + + _get_size_map = { + "left": lambda tight_bb, axes_bb: axes_bb.xmin - tight_bb.xmin, + "right": lambda tight_bb, axes_bb: tight_bb.xmax - axes_bb.xmax, + "bottom": lambda tight_bb, axes_bb: axes_bb.ymin - tight_bb.ymin, + "top": lambda tight_bb, axes_bb: tight_bb.ymax - axes_bb.ymax, + } + + def __init__(self, ax, direction): + _api.check_in_list(self._get_size_map, direction=direction) + self._direction = direction + self._ax_list = [ax] if isinstance(ax, Axes) else ax + + def get_size(self, renderer): + sz = max([ + self._get_size_map[self._direction]( + ax.get_tightbbox(renderer, call_axes_locator=False), ax.bbox) + for ax in self._ax_list]) + dpi = renderer.points_to_pixels(72) + abs_size = sz / dpi + rel_size = 0 + return rel_size, abs_size diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/inset_locator.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/inset_locator.py new file mode 100644 index 0000000000000000000000000000000000000000..52fe6efc061856bd33939c31efe404f619faf033 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/inset_locator.py @@ -0,0 +1,519 @@ +""" +A collection of functions and objects for creating or placing inset axes. +""" + +from matplotlib import _api, _docstring +from matplotlib.offsetbox import AnchoredOffsetbox +from matplotlib.patches import Patch, Rectangle +from matplotlib.path import Path +from matplotlib.transforms import Bbox +from matplotlib.transforms import IdentityTransform, TransformedBbox + +from . import axes_size as Size +from .parasite_axes import HostAxes + + +class AnchoredLocatorBase(AnchoredOffsetbox): + def __init__(self, bbox_to_anchor, offsetbox, loc, + borderpad=0.5, bbox_transform=None): + super().__init__( + loc, pad=0., child=None, borderpad=borderpad, + bbox_to_anchor=bbox_to_anchor, bbox_transform=bbox_transform + ) + + def draw(self, renderer): + raise RuntimeError("No draw method should be called") + + def __call__(self, ax, renderer): + fig = ax.get_figure(root=False) + if renderer is None: + renderer = fig._get_renderer() + self.axes = ax + bbox = self.get_window_extent(renderer) + px, py = self.get_offset(bbox.width, bbox.height, 0, 0, renderer) + bbox_canvas = Bbox.from_bounds(px, py, bbox.width, bbox.height) + tr = fig.transSubfigure.inverted() + return TransformedBbox(bbox_canvas, tr) + + +class AnchoredSizeLocator(AnchoredLocatorBase): + def __init__(self, bbox_to_anchor, x_size, y_size, loc, + borderpad=0.5, bbox_transform=None): + super().__init__( + bbox_to_anchor, None, loc, + borderpad=borderpad, bbox_transform=bbox_transform + ) + + self.x_size = Size.from_any(x_size) + self.y_size = Size.from_any(y_size) + + def get_bbox(self, renderer): + bbox = self.get_bbox_to_anchor() + dpi = renderer.points_to_pixels(72.) + + r, a = self.x_size.get_size(renderer) + width = bbox.width * r + a * dpi + r, a = self.y_size.get_size(renderer) + height = bbox.height * r + a * dpi + + fontsize = renderer.points_to_pixels(self.prop.get_size_in_points()) + pad = self.pad * fontsize + + return Bbox.from_bounds(0, 0, width, height).padded(pad) + + +class AnchoredZoomLocator(AnchoredLocatorBase): + def __init__(self, parent_axes, zoom, loc, + borderpad=0.5, + bbox_to_anchor=None, + bbox_transform=None): + self.parent_axes = parent_axes + self.zoom = zoom + if bbox_to_anchor is None: + bbox_to_anchor = parent_axes.bbox + super().__init__( + bbox_to_anchor, None, loc, borderpad=borderpad, + bbox_transform=bbox_transform) + + def get_bbox(self, renderer): + bb = self.parent_axes.transData.transform_bbox(self.axes.viewLim) + fontsize = renderer.points_to_pixels(self.prop.get_size_in_points()) + pad = self.pad * fontsize + return ( + Bbox.from_bounds( + 0, 0, abs(bb.width * self.zoom), abs(bb.height * self.zoom)) + .padded(pad)) + + +class BboxPatch(Patch): + @_docstring.interpd + def __init__(self, bbox, **kwargs): + """ + Patch showing the shape bounded by a Bbox. + + Parameters + ---------- + bbox : `~matplotlib.transforms.Bbox` + Bbox to use for the extents of this patch. + + **kwargs + Patch properties. Valid arguments include: + + %(Patch:kwdoc)s + """ + if "transform" in kwargs: + raise ValueError("transform should not be set") + + kwargs["transform"] = IdentityTransform() + super().__init__(**kwargs) + self.bbox = bbox + + def get_path(self): + # docstring inherited + x0, y0, x1, y1 = self.bbox.extents + return Path._create_closed([(x0, y0), (x1, y0), (x1, y1), (x0, y1)]) + + +class BboxConnector(Patch): + @staticmethod + def get_bbox_edge_pos(bbox, loc): + """ + Return the ``(x, y)`` coordinates of corner *loc* of *bbox*; parameters + behave as documented for the `.BboxConnector` constructor. + """ + x0, y0, x1, y1 = bbox.extents + if loc == 1: + return x1, y1 + elif loc == 2: + return x0, y1 + elif loc == 3: + return x0, y0 + elif loc == 4: + return x1, y0 + + @staticmethod + def connect_bbox(bbox1, bbox2, loc1, loc2=None): + """ + Construct a `.Path` connecting corner *loc1* of *bbox1* to corner + *loc2* of *bbox2*, where parameters behave as documented as for the + `.BboxConnector` constructor. + """ + if isinstance(bbox1, Rectangle): + bbox1 = TransformedBbox(Bbox.unit(), bbox1.get_transform()) + if isinstance(bbox2, Rectangle): + bbox2 = TransformedBbox(Bbox.unit(), bbox2.get_transform()) + if loc2 is None: + loc2 = loc1 + x1, y1 = BboxConnector.get_bbox_edge_pos(bbox1, loc1) + x2, y2 = BboxConnector.get_bbox_edge_pos(bbox2, loc2) + return Path([[x1, y1], [x2, y2]]) + + @_docstring.interpd + def __init__(self, bbox1, bbox2, loc1, loc2=None, **kwargs): + """ + Connect two bboxes with a straight line. + + Parameters + ---------- + bbox1, bbox2 : `~matplotlib.transforms.Bbox` + Bounding boxes to connect. + + loc1, loc2 : {1, 2, 3, 4} + Corner of *bbox1* and *bbox2* to draw the line. Valid values are:: + + 'upper right' : 1, + 'upper left' : 2, + 'lower left' : 3, + 'lower right' : 4 + + *loc2* is optional and defaults to *loc1*. + + **kwargs + Patch properties for the line drawn. Valid arguments include: + + %(Patch:kwdoc)s + """ + if "transform" in kwargs: + raise ValueError("transform should not be set") + + kwargs["transform"] = IdentityTransform() + kwargs.setdefault( + "fill", bool({'fc', 'facecolor', 'color'}.intersection(kwargs))) + super().__init__(**kwargs) + self.bbox1 = bbox1 + self.bbox2 = bbox2 + self.loc1 = loc1 + self.loc2 = loc2 + + def get_path(self): + # docstring inherited + return self.connect_bbox(self.bbox1, self.bbox2, + self.loc1, self.loc2) + + +class BboxConnectorPatch(BboxConnector): + @_docstring.interpd + def __init__(self, bbox1, bbox2, loc1a, loc2a, loc1b, loc2b, **kwargs): + """ + Connect two bboxes with a quadrilateral. + + The quadrilateral is specified by two lines that start and end at + corners of the bboxes. The four sides of the quadrilateral are defined + by the two lines given, the line between the two corners specified in + *bbox1* and the line between the two corners specified in *bbox2*. + + Parameters + ---------- + bbox1, bbox2 : `~matplotlib.transforms.Bbox` + Bounding boxes to connect. + + loc1a, loc2a, loc1b, loc2b : {1, 2, 3, 4} + The first line connects corners *loc1a* of *bbox1* and *loc2a* of + *bbox2*; the second line connects corners *loc1b* of *bbox1* and + *loc2b* of *bbox2*. Valid values are:: + + 'upper right' : 1, + 'upper left' : 2, + 'lower left' : 3, + 'lower right' : 4 + + **kwargs + Patch properties for the line drawn: + + %(Patch:kwdoc)s + """ + if "transform" in kwargs: + raise ValueError("transform should not be set") + super().__init__(bbox1, bbox2, loc1a, loc2a, **kwargs) + self.loc1b = loc1b + self.loc2b = loc2b + + def get_path(self): + # docstring inherited + path1 = self.connect_bbox(self.bbox1, self.bbox2, self.loc1, self.loc2) + path2 = self.connect_bbox(self.bbox2, self.bbox1, + self.loc2b, self.loc1b) + path_merged = [*path1.vertices, *path2.vertices, path1.vertices[0]] + return Path(path_merged) + + +def _add_inset_axes(parent_axes, axes_class, axes_kwargs, axes_locator): + """Helper function to add an inset axes and disable navigation in it.""" + if axes_class is None: + axes_class = HostAxes + if axes_kwargs is None: + axes_kwargs = {} + fig = parent_axes.get_figure(root=False) + inset_axes = axes_class( + fig, parent_axes.get_position(), + **{"navigate": False, **axes_kwargs, "axes_locator": axes_locator}) + return fig.add_axes(inset_axes) + + +@_docstring.interpd +def inset_axes(parent_axes, width, height, loc='upper right', + bbox_to_anchor=None, bbox_transform=None, + axes_class=None, axes_kwargs=None, + borderpad=0.5): + """ + Create an inset axes with a given width and height. + + Both sizes used can be specified either in inches or percentage. + For example,:: + + inset_axes(parent_axes, width='40%%', height='30%%', loc='lower left') + + creates in inset axes in the lower left corner of *parent_axes* which spans + over 30%% in height and 40%% in width of the *parent_axes*. Since the usage + of `.inset_axes` may become slightly tricky when exceeding such standard + cases, it is recommended to read :doc:`the examples + `. + + Notes + ----- + The meaning of *bbox_to_anchor* and *bbox_to_transform* is interpreted + differently from that of legend. The value of bbox_to_anchor + (or the return value of its get_points method; the default is + *parent_axes.bbox*) is transformed by the bbox_transform (the default + is Identity transform) and then interpreted as points in the pixel + coordinate (which is dpi dependent). + + Thus, following three calls are identical and creates an inset axes + with respect to the *parent_axes*:: + + axins = inset_axes(parent_axes, "30%%", "40%%") + axins = inset_axes(parent_axes, "30%%", "40%%", + bbox_to_anchor=parent_axes.bbox) + axins = inset_axes(parent_axes, "30%%", "40%%", + bbox_to_anchor=(0, 0, 1, 1), + bbox_transform=parent_axes.transAxes) + + Parameters + ---------- + parent_axes : `matplotlib.axes.Axes` + Axes to place the inset axes. + + width, height : float or str + Size of the inset axes to create. If a float is provided, it is + the size in inches, e.g. *width=1.3*. If a string is provided, it is + the size in relative units, e.g. *width='40%%'*. By default, i.e. if + neither *bbox_to_anchor* nor *bbox_transform* are specified, those + are relative to the parent_axes. Otherwise, they are to be understood + relative to the bounding box provided via *bbox_to_anchor*. + + loc : str, default: 'upper right' + Location to place the inset axes. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + + bbox_to_anchor : tuple or `~matplotlib.transforms.BboxBase`, optional + Bbox that the inset axes will be anchored to. If None, + a tuple of (0, 0, 1, 1) is used if *bbox_transform* is set + to *parent_axes.transAxes* or *parent_axes.figure.transFigure*. + Otherwise, *parent_axes.bbox* is used. If a tuple, can be either + [left, bottom, width, height], or [left, bottom]. + If the kwargs *width* and/or *height* are specified in relative units, + the 2-tuple [left, bottom] cannot be used. Note that, + unless *bbox_transform* is set, the units of the bounding box + are interpreted in the pixel coordinate. When using *bbox_to_anchor* + with tuple, it almost always makes sense to also specify + a *bbox_transform*. This might often be the axes transform + *parent_axes.transAxes*. + + bbox_transform : `~matplotlib.transforms.Transform`, optional + Transformation for the bbox that contains the inset axes. + If None, a `.transforms.IdentityTransform` is used. The value + of *bbox_to_anchor* (or the return value of its get_points method) + is transformed by the *bbox_transform* and then interpreted + as points in the pixel coordinate (which is dpi dependent). + You may provide *bbox_to_anchor* in some normalized coordinate, + and give an appropriate transform (e.g., *parent_axes.transAxes*). + + axes_class : `~matplotlib.axes.Axes` type, default: `.HostAxes` + The type of the newly created inset axes. + + axes_kwargs : dict, optional + Keyword arguments to pass to the constructor of the inset axes. + Valid arguments include: + + %(Axes:kwdoc)s + + borderpad : float, default: 0.5 + Padding between inset axes and the bbox_to_anchor. + The units are axes font size, i.e. for a default font size of 10 points + *borderpad = 0.5* is equivalent to a padding of 5 points. + + Returns + ------- + inset_axes : *axes_class* + Inset axes object created. + """ + + if (bbox_transform in [parent_axes.transAxes, + parent_axes.get_figure(root=False).transFigure] + and bbox_to_anchor is None): + _api.warn_external("Using the axes or figure transform requires a " + "bounding box in the respective coordinates. " + "Using bbox_to_anchor=(0, 0, 1, 1) now.") + bbox_to_anchor = (0, 0, 1, 1) + if bbox_to_anchor is None: + bbox_to_anchor = parent_axes.bbox + if (isinstance(bbox_to_anchor, tuple) and + (isinstance(width, str) or isinstance(height, str))): + if len(bbox_to_anchor) != 4: + raise ValueError("Using relative units for width or height " + "requires to provide a 4-tuple or a " + "`Bbox` instance to `bbox_to_anchor.") + return _add_inset_axes( + parent_axes, axes_class, axes_kwargs, + AnchoredSizeLocator( + bbox_to_anchor, width, height, loc=loc, + bbox_transform=bbox_transform, borderpad=borderpad)) + + +@_docstring.interpd +def zoomed_inset_axes(parent_axes, zoom, loc='upper right', + bbox_to_anchor=None, bbox_transform=None, + axes_class=None, axes_kwargs=None, + borderpad=0.5): + """ + Create an anchored inset axes by scaling a parent axes. For usage, also see + :doc:`the examples `. + + Parameters + ---------- + parent_axes : `~matplotlib.axes.Axes` + Axes to place the inset axes. + + zoom : float + Scaling factor of the data axes. *zoom* > 1 will enlarge the + coordinates (i.e., "zoomed in"), while *zoom* < 1 will shrink the + coordinates (i.e., "zoomed out"). + + loc : str, default: 'upper right' + Location to place the inset axes. Valid locations are + 'upper left', 'upper center', 'upper right', + 'center left', 'center', 'center right', + 'lower left', 'lower center', 'lower right'. + For backward compatibility, numeric values are accepted as well. + See the parameter *loc* of `.Legend` for details. + + bbox_to_anchor : tuple or `~matplotlib.transforms.BboxBase`, optional + Bbox that the inset axes will be anchored to. If None, + *parent_axes.bbox* is used. If a tuple, can be either + [left, bottom, width, height], or [left, bottom]. + If the kwargs *width* and/or *height* are specified in relative units, + the 2-tuple [left, bottom] cannot be used. Note that + the units of the bounding box are determined through the transform + in use. When using *bbox_to_anchor* it almost always makes sense to + also specify a *bbox_transform*. This might often be the axes transform + *parent_axes.transAxes*. + + bbox_transform : `~matplotlib.transforms.Transform`, optional + Transformation for the bbox that contains the inset axes. + If None, a `.transforms.IdentityTransform` is used (i.e. pixel + coordinates). This is useful when not providing any argument to + *bbox_to_anchor*. When using *bbox_to_anchor* it almost always makes + sense to also specify a *bbox_transform*. This might often be the + axes transform *parent_axes.transAxes*. Inversely, when specifying + the axes- or figure-transform here, be aware that not specifying + *bbox_to_anchor* will use *parent_axes.bbox*, the units of which are + in display (pixel) coordinates. + + axes_class : `~matplotlib.axes.Axes` type, default: `.HostAxes` + The type of the newly created inset axes. + + axes_kwargs : dict, optional + Keyword arguments to pass to the constructor of the inset axes. + Valid arguments include: + + %(Axes:kwdoc)s + + borderpad : float, default: 0.5 + Padding between inset axes and the bbox_to_anchor. + The units are axes font size, i.e. for a default font size of 10 points + *borderpad = 0.5* is equivalent to a padding of 5 points. + + Returns + ------- + inset_axes : *axes_class* + Inset axes object created. + """ + + return _add_inset_axes( + parent_axes, axes_class, axes_kwargs, + AnchoredZoomLocator( + parent_axes, zoom=zoom, loc=loc, + bbox_to_anchor=bbox_to_anchor, bbox_transform=bbox_transform, + borderpad=borderpad)) + + +class _TransformedBboxWithCallback(TransformedBbox): + """ + Variant of `.TransformBbox` which calls *callback* before returning points. + + Used by `.mark_inset` to unstale the parent axes' viewlim as needed. + """ + + def __init__(self, *args, callback, **kwargs): + super().__init__(*args, **kwargs) + self._callback = callback + + def get_points(self): + self._callback() + return super().get_points() + + +@_docstring.interpd +def mark_inset(parent_axes, inset_axes, loc1, loc2, **kwargs): + """ + Draw a box to mark the location of an area represented by an inset axes. + + This function draws a box in *parent_axes* at the bounding box of + *inset_axes*, and shows a connection with the inset axes by drawing lines + at the corners, giving a "zoomed in" effect. + + Parameters + ---------- + parent_axes : `~matplotlib.axes.Axes` + Axes which contains the area of the inset axes. + + inset_axes : `~matplotlib.axes.Axes` + The inset axes. + + loc1, loc2 : {1, 2, 3, 4} + Corners to use for connecting the inset axes and the area in the + parent axes. + + **kwargs + Patch properties for the lines and box drawn: + + %(Patch:kwdoc)s + + Returns + ------- + pp : `~matplotlib.patches.Patch` + The patch drawn to represent the area of the inset axes. + + p1, p2 : `~matplotlib.patches.Patch` + The patches connecting two corners of the inset axes and its area. + """ + rect = _TransformedBboxWithCallback( + inset_axes.viewLim, parent_axes.transData, + callback=parent_axes._unstale_viewLim) + + kwargs.setdefault("fill", bool({'fc', 'facecolor', 'color'}.intersection(kwargs))) + pp = BboxPatch(rect, **kwargs) + parent_axes.add_patch(pp) + + p1 = BboxConnector(inset_axes.bbox, rect, loc1=loc1, **kwargs) + inset_axes.add_patch(p1) + p1.set_clip_on(False) + p2 = BboxConnector(inset_axes.bbox, rect, loc1=loc2, **kwargs) + inset_axes.add_patch(p2) + p2.set_clip_on(False) + + return pp, p1, p2 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/mpl_axes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/mpl_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..51c8748758cb6da3052f0e8b05ceba427d77a3f6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/mpl_axes.py @@ -0,0 +1,128 @@ +import matplotlib.axes as maxes +from matplotlib.artist import Artist +from matplotlib.axis import XAxis, YAxis + + +class SimpleChainedObjects: + def __init__(self, objects): + self._objects = objects + + def __getattr__(self, k): + _a = SimpleChainedObjects([getattr(a, k) for a in self._objects]) + return _a + + def __call__(self, *args, **kwargs): + for m in self._objects: + m(*args, **kwargs) + + +class Axes(maxes.Axes): + + class AxisDict(dict): + def __init__(self, axes): + self.axes = axes + super().__init__() + + def __getitem__(self, k): + if isinstance(k, tuple): + r = SimpleChainedObjects( + # super() within a list comprehension needs explicit args. + [super(Axes.AxisDict, self).__getitem__(k1) for k1 in k]) + return r + elif isinstance(k, slice): + if k.start is None and k.stop is None and k.step is None: + return SimpleChainedObjects(list(self.values())) + else: + raise ValueError("Unsupported slice") + else: + return dict.__getitem__(self, k) + + def __call__(self, *v, **kwargs): + return maxes.Axes.axis(self.axes, *v, **kwargs) + + @property + def axis(self): + return self._axislines + + def clear(self): + # docstring inherited + super().clear() + # Init axis artists. + self._axislines = self.AxisDict(self) + self._axislines.update( + bottom=SimpleAxisArtist(self.xaxis, 1, self.spines["bottom"]), + top=SimpleAxisArtist(self.xaxis, 2, self.spines["top"]), + left=SimpleAxisArtist(self.yaxis, 1, self.spines["left"]), + right=SimpleAxisArtist(self.yaxis, 2, self.spines["right"])) + + +class SimpleAxisArtist(Artist): + def __init__(self, axis, axisnum, spine): + self._axis = axis + self._axisnum = axisnum + self.line = spine + + if isinstance(axis, XAxis): + self._axis_direction = ["bottom", "top"][axisnum-1] + elif isinstance(axis, YAxis): + self._axis_direction = ["left", "right"][axisnum-1] + else: + raise ValueError( + f"axis must be instance of XAxis or YAxis, but got {axis}") + super().__init__() + + @property + def major_ticks(self): + tickline = "tick%dline" % self._axisnum + return SimpleChainedObjects([getattr(tick, tickline) + for tick in self._axis.get_major_ticks()]) + + @property + def major_ticklabels(self): + label = "label%d" % self._axisnum + return SimpleChainedObjects([getattr(tick, label) + for tick in self._axis.get_major_ticks()]) + + @property + def label(self): + return self._axis.label + + def set_visible(self, b): + self.toggle(all=b) + self.line.set_visible(b) + self._axis.set_visible(True) + super().set_visible(b) + + def set_label(self, txt): + self._axis.set_label_text(txt) + + def toggle(self, all=None, ticks=None, ticklabels=None, label=None): + + if all: + _ticks, _ticklabels, _label = True, True, True + elif all is not None: + _ticks, _ticklabels, _label = False, False, False + else: + _ticks, _ticklabels, _label = None, None, None + + if ticks is not None: + _ticks = ticks + if ticklabels is not None: + _ticklabels = ticklabels + if label is not None: + _label = label + + if _ticks is not None: + tickparam = {f"tick{self._axisnum}On": _ticks} + self._axis.set_tick_params(**tickparam) + if _ticklabels is not None: + tickparam = {f"label{self._axisnum}On": _ticklabels} + self._axis.set_tick_params(**tickparam) + + if _label is not None: + pos = self._axis.get_label_position() + if (pos == self._axis_direction) and not _label: + self._axis.label.set_visible(False) + elif _label: + self._axis.label.set_visible(True) + self._axis.set_label_position(self._axis_direction) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..f7bc2df6d7e01141254114065316a70df5f35e33 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/parasite_axes.py @@ -0,0 +1,257 @@ +from matplotlib import _api, cbook +import matplotlib.artist as martist +import matplotlib.transforms as mtransforms +from matplotlib.transforms import Bbox +from .mpl_axes import Axes + + +class ParasiteAxesBase: + + def __init__(self, parent_axes, aux_transform=None, + *, viewlim_mode=None, **kwargs): + self._parent_axes = parent_axes + self.transAux = aux_transform + self.set_viewlim_mode(viewlim_mode) + kwargs["frameon"] = False + super().__init__(parent_axes.get_figure(root=False), + parent_axes._position, **kwargs) + + def clear(self): + super().clear() + martist.setp(self.get_children(), visible=False) + self._get_lines = self._parent_axes._get_lines + self._parent_axes.callbacks._connect_picklable( + "xlim_changed", self._sync_lims) + self._parent_axes.callbacks._connect_picklable( + "ylim_changed", self._sync_lims) + + def pick(self, mouseevent): + # This most likely goes to Artist.pick (depending on axes_class given + # to the factory), which only handles pick events registered on the + # axes associated with each child: + super().pick(mouseevent) + # But parasite axes are additionally given pick events from their host + # axes (cf. HostAxesBase.pick), which we handle here: + for a in self.get_children(): + if (hasattr(mouseevent.inaxes, "parasites") + and self in mouseevent.inaxes.parasites): + a.pick(mouseevent) + + # aux_transform support + + def _set_lim_and_transforms(self): + if self.transAux is not None: + self.transAxes = self._parent_axes.transAxes + self.transData = self.transAux + self._parent_axes.transData + self._xaxis_transform = mtransforms.blended_transform_factory( + self.transData, self.transAxes) + self._yaxis_transform = mtransforms.blended_transform_factory( + self.transAxes, self.transData) + else: + super()._set_lim_and_transforms() + + def set_viewlim_mode(self, mode): + _api.check_in_list([None, "equal", "transform"], mode=mode) + self._viewlim_mode = mode + + def get_viewlim_mode(self): + return self._viewlim_mode + + def _sync_lims(self, parent): + viewlim = parent.viewLim.frozen() + mode = self.get_viewlim_mode() + if mode is None: + pass + elif mode == "equal": + self.viewLim.set(viewlim) + elif mode == "transform": + self.viewLim.set(viewlim.transformed(self.transAux.inverted())) + else: + _api.check_in_list([None, "equal", "transform"], mode=mode) + + # end of aux_transform support + + +parasite_axes_class_factory = cbook._make_class_factory( + ParasiteAxesBase, "{}Parasite") +ParasiteAxes = parasite_axes_class_factory(Axes) + + +class HostAxesBase: + def __init__(self, *args, **kwargs): + self.parasites = [] + super().__init__(*args, **kwargs) + + def get_aux_axes( + self, tr=None, viewlim_mode="equal", axes_class=None, **kwargs): + """ + Add a parasite axes to this host. + + Despite this method's name, this should actually be thought of as an + ``add_parasite_axes`` method. + + .. versionchanged:: 3.7 + Defaults to same base axes class as host axes. + + Parameters + ---------- + tr : `~matplotlib.transforms.Transform` or None, default: None + If a `.Transform`, the following relation will hold: + ``parasite.transData = tr + host.transData``. + If None, the parasite's and the host's ``transData`` are unrelated. + viewlim_mode : {"equal", "transform", None}, default: "equal" + How the parasite's view limits are set: directly equal to the + parent axes ("equal"), equal after application of *tr* + ("transform"), or independently (None). + axes_class : subclass type of `~matplotlib.axes.Axes`, optional + The `~.axes.Axes` subclass that is instantiated. If None, the base + class of the host axes is used. + **kwargs + Other parameters are forwarded to the parasite axes constructor. + """ + if axes_class is None: + axes_class = self._base_axes_class + parasite_axes_class = parasite_axes_class_factory(axes_class) + ax2 = parasite_axes_class( + self, tr, viewlim_mode=viewlim_mode, **kwargs) + # note that ax2.transData == tr + ax1.transData + # Anything you draw in ax2 will match the ticks and grids of ax1. + self.parasites.append(ax2) + ax2._remove_method = self.parasites.remove + return ax2 + + def draw(self, renderer): + orig_children_len = len(self._children) + + locator = self.get_axes_locator() + if locator: + pos = locator(self, renderer) + self.set_position(pos, which="active") + self.apply_aspect(pos) + else: + self.apply_aspect() + + rect = self.get_position() + for ax in self.parasites: + ax.apply_aspect(rect) + self._children.extend(ax.get_children()) + + super().draw(renderer) + del self._children[orig_children_len:] + + def clear(self): + super().clear() + for ax in self.parasites: + ax.clear() + + def pick(self, mouseevent): + super().pick(mouseevent) + # Also pass pick events on to parasite axes and, in turn, their + # children (cf. ParasiteAxesBase.pick) + for a in self.parasites: + a.pick(mouseevent) + + def twinx(self, axes_class=None): + """ + Create a twin of Axes with a shared x-axis but independent y-axis. + + The y-axis of self will have ticks on the left and the returned axes + will have ticks on the right. + """ + ax = self._add_twin_axes(axes_class, sharex=self) + self.axis["right"].set_visible(False) + ax.axis["right"].set_visible(True) + ax.axis["left", "top", "bottom"].set_visible(False) + return ax + + def twiny(self, axes_class=None): + """ + Create a twin of Axes with a shared y-axis but independent x-axis. + + The x-axis of self will have ticks on the bottom and the returned axes + will have ticks on the top. + """ + ax = self._add_twin_axes(axes_class, sharey=self) + self.axis["top"].set_visible(False) + ax.axis["top"].set_visible(True) + ax.axis["left", "right", "bottom"].set_visible(False) + return ax + + def twin(self, aux_trans=None, axes_class=None): + """ + Create a twin of Axes with no shared axis. + + While self will have ticks on the left and bottom axis, the returned + axes will have ticks on the top and right axis. + """ + if aux_trans is None: + aux_trans = mtransforms.IdentityTransform() + ax = self._add_twin_axes( + axes_class, aux_transform=aux_trans, viewlim_mode="transform") + self.axis["top", "right"].set_visible(False) + ax.axis["top", "right"].set_visible(True) + ax.axis["left", "bottom"].set_visible(False) + return ax + + def _add_twin_axes(self, axes_class, **kwargs): + """ + Helper for `.twinx`/`.twiny`/`.twin`. + + *kwargs* are forwarded to the parasite axes constructor. + """ + if axes_class is None: + axes_class = self._base_axes_class + ax = parasite_axes_class_factory(axes_class)(self, **kwargs) + self.parasites.append(ax) + ax._remove_method = self._remove_any_twin + return ax + + def _remove_any_twin(self, ax): + self.parasites.remove(ax) + restore = ["top", "right"] + if ax._sharex: + restore.remove("top") + if ax._sharey: + restore.remove("right") + self.axis[tuple(restore)].set_visible(True) + self.axis[tuple(restore)].toggle(ticklabels=False, label=False) + + def get_tightbbox(self, renderer=None, *, call_axes_locator=True, + bbox_extra_artists=None): + bbs = [ + *[ax.get_tightbbox(renderer, call_axes_locator=call_axes_locator) + for ax in self.parasites], + super().get_tightbbox(renderer, + call_axes_locator=call_axes_locator, + bbox_extra_artists=bbox_extra_artists)] + return Bbox.union([b for b in bbs if b.width != 0 or b.height != 0]) + + +host_axes_class_factory = host_subplot_class_factory = \ + cbook._make_class_factory(HostAxesBase, "{}HostAxes", "_base_axes_class") +HostAxes = SubplotHost = host_axes_class_factory(Axes) + + +def host_axes(*args, axes_class=Axes, figure=None, **kwargs): + """ + Create axes that can act as a hosts to parasitic axes. + + Parameters + ---------- + figure : `~matplotlib.figure.Figure` + Figure to which the axes will be added. Defaults to the current figure + `.pyplot.gcf()`. + + *args, **kwargs + Will be passed on to the underlying `~.axes.Axes` object creation. + """ + import matplotlib.pyplot as plt + host_axes_class = host_axes_class_factory(axes_class) + if figure is None: + figure = plt.gcf() + ax = host_axes_class(figure, *args, **kwargs) + figure.add_axes(ax) + return ax + + +host_subplot = host_axes diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ea4d8ed16a6a24a8c15ab2956ef678a7f256cd80 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/__init__.py @@ -0,0 +1,10 @@ +from pathlib import Path + + +# Check that the test directories exist +if not (Path(__file__).parent / "baseline_images").exists(): + raise OSError( + 'The baseline image directory does not exist. ' + 'This is most likely because the test data is not installed. ' + 'You may need to install matplotlib from source to get the ' + 'test data.') diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/conftest.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..61c2de3e07bac4db323f8704961264d123e01544 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/conftest.py @@ -0,0 +1,2 @@ +from matplotlib.testing.conftest import (mpl_test_settings, # noqa + pytest_configure, pytest_unconfigure) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/test_axes_grid1.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/test_axes_grid1.py new file mode 100644 index 0000000000000000000000000000000000000000..b045e8dc87569b0ea5653cf8b0c6307135b69c75 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axes_grid1/tests/test_axes_grid1.py @@ -0,0 +1,782 @@ +from itertools import product +import io +import platform + +import matplotlib as mpl +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +from matplotlib import cbook +from matplotlib.backend_bases import MouseEvent +from matplotlib.colors import LogNorm +from matplotlib.patches import Circle, Ellipse +from matplotlib.transforms import Bbox, TransformedBbox +from matplotlib.testing.decorators import ( + check_figures_equal, image_comparison, remove_ticks_and_titles) + +from mpl_toolkits.axes_grid1 import ( + axes_size as Size, + host_subplot, make_axes_locatable, + Grid, AxesGrid, ImageGrid) +from mpl_toolkits.axes_grid1.anchored_artists import ( + AnchoredAuxTransformBox, AnchoredDrawingArea, + AnchoredDirectionArrows, AnchoredSizeBar) +from mpl_toolkits.axes_grid1.axes_divider import ( + Divider, HBoxDivider, make_axes_area_auto_adjustable, SubplotDivider, + VBoxDivider) +from mpl_toolkits.axes_grid1.axes_rgb import RGBAxes +from mpl_toolkits.axes_grid1.inset_locator import ( + zoomed_inset_axes, mark_inset, inset_axes, BboxConnectorPatch) +import mpl_toolkits.axes_grid1.mpl_axes +import pytest + +import numpy as np +from numpy.testing import assert_array_equal, assert_array_almost_equal + + +def test_divider_append_axes(): + fig, ax = plt.subplots() + divider = make_axes_locatable(ax) + axs = { + "main": ax, + "top": divider.append_axes("top", 1.2, pad=0.1, sharex=ax), + "bottom": divider.append_axes("bottom", 1.2, pad=0.1, sharex=ax), + "left": divider.append_axes("left", 1.2, pad=0.1, sharey=ax), + "right": divider.append_axes("right", 1.2, pad=0.1, sharey=ax), + } + fig.canvas.draw() + bboxes = {k: axs[k].get_window_extent() for k in axs} + dpi = fig.dpi + assert bboxes["top"].height == pytest.approx(1.2 * dpi) + assert bboxes["bottom"].height == pytest.approx(1.2 * dpi) + assert bboxes["left"].width == pytest.approx(1.2 * dpi) + assert bboxes["right"].width == pytest.approx(1.2 * dpi) + assert bboxes["top"].y0 - bboxes["main"].y1 == pytest.approx(0.1 * dpi) + assert bboxes["main"].y0 - bboxes["bottom"].y1 == pytest.approx(0.1 * dpi) + assert bboxes["main"].x0 - bboxes["left"].x1 == pytest.approx(0.1 * dpi) + assert bboxes["right"].x0 - bboxes["main"].x1 == pytest.approx(0.1 * dpi) + assert bboxes["left"].y0 == bboxes["main"].y0 == bboxes["right"].y0 + assert bboxes["left"].y1 == bboxes["main"].y1 == bboxes["right"].y1 + assert bboxes["top"].x0 == bboxes["main"].x0 == bboxes["bottom"].x0 + assert bboxes["top"].x1 == bboxes["main"].x1 == bboxes["bottom"].x1 + + +# Update style when regenerating the test image +@image_comparison(['twin_axes_empty_and_removed'], extensions=["png"], tol=1, + style=('classic', '_classic_test_patch')) +def test_twin_axes_empty_and_removed(): + # Purely cosmetic font changes (avoid overlap) + mpl.rcParams.update( + {"font.size": 8, "xtick.labelsize": 8, "ytick.labelsize": 8}) + generators = ["twinx", "twiny", "twin"] + modifiers = ["", "host invisible", "twin removed", "twin invisible", + "twin removed\nhost invisible"] + # Unmodified host subplot at the beginning for reference + h = host_subplot(len(modifiers)+1, len(generators), 2) + h.text(0.5, 0.5, "host_subplot", + horizontalalignment="center", verticalalignment="center") + # Host subplots with various modifications (twin*, visibility) applied + for i, (mod, gen) in enumerate(product(modifiers, generators), + len(generators) + 1): + h = host_subplot(len(modifiers)+1, len(generators), i) + t = getattr(h, gen)() + if "twin invisible" in mod: + t.axis[:].set_visible(False) + if "twin removed" in mod: + t.remove() + if "host invisible" in mod: + h.axis[:].set_visible(False) + h.text(0.5, 0.5, gen + ("\n" + mod if mod else ""), + horizontalalignment="center", verticalalignment="center") + plt.subplots_adjust(wspace=0.5, hspace=1) + + +def test_twin_axes_both_with_units(): + host = host_subplot(111) + with pytest.warns(mpl.MatplotlibDeprecationWarning): + host.plot_date([0, 1, 2], [0, 1, 2], xdate=False, ydate=True) + twin = host.twinx() + twin.plot(["a", "b", "c"]) + assert host.get_yticklabels()[0].get_text() == "00:00:00" + assert twin.get_yticklabels()[0].get_text() == "a" + + +def test_axesgrid_colorbar_log_smoketest(): + fig = plt.figure() + grid = AxesGrid(fig, 111, # modified to be only subplot + nrows_ncols=(1, 1), + ngrids=1, + label_mode="L", + cbar_location="top", + cbar_mode="single", + ) + + Z = 10000 * np.random.rand(10, 10) + im = grid[0].imshow(Z, interpolation="nearest", norm=LogNorm()) + + grid.cbar_axes[0].colorbar(im) + + +def test_inset_colorbar_tight_layout_smoketest(): + fig, ax = plt.subplots(1, 1) + pts = ax.scatter([0, 1], [0, 1], c=[1, 5]) + + cax = inset_axes(ax, width="3%", height="70%") + plt.colorbar(pts, cax=cax) + + with pytest.warns(UserWarning, match="This figure includes Axes"): + # Will warn, but not raise an error + plt.tight_layout() + + +@image_comparison(['inset_locator.png'], style='default', remove_text=True) +def test_inset_locator(): + fig, ax = plt.subplots(figsize=[5, 4]) + + # prepare the demo image + # Z is a 15x15 array + Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy") + extent = (-3, 4, -4, 3) + Z2 = np.zeros((150, 150)) + ny, nx = Z.shape + Z2[30:30+ny, 30:30+nx] = Z + + ax.imshow(Z2, extent=extent, interpolation="nearest", + origin="lower") + + axins = zoomed_inset_axes(ax, zoom=6, loc='upper right') + axins.imshow(Z2, extent=extent, interpolation="nearest", + origin="lower") + axins.yaxis.get_major_locator().set_params(nbins=7) + axins.xaxis.get_major_locator().set_params(nbins=7) + # sub region of the original image + x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9 + axins.set_xlim(x1, x2) + axins.set_ylim(y1, y2) + + plt.xticks(visible=False) + plt.yticks(visible=False) + + # draw a bbox of the region of the inset axes in the parent axes and + # connecting lines between the bbox and the inset axes area + mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") + + asb = AnchoredSizeBar(ax.transData, + 0.5, + '0.5', + loc='lower center', + pad=0.1, borderpad=0.5, sep=5, + frameon=False) + ax.add_artist(asb) + + +@image_comparison(['inset_axes.png'], style='default', remove_text=True) +def test_inset_axes(): + fig, ax = plt.subplots(figsize=[5, 4]) + + # prepare the demo image + # Z is a 15x15 array + Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy") + extent = (-3, 4, -4, 3) + Z2 = np.zeros((150, 150)) + ny, nx = Z.shape + Z2[30:30+ny, 30:30+nx] = Z + + ax.imshow(Z2, extent=extent, interpolation="nearest", + origin="lower") + + # creating our inset axes with a bbox_transform parameter + axins = inset_axes(ax, width=1., height=1., bbox_to_anchor=(1, 1), + bbox_transform=ax.transAxes) + + axins.imshow(Z2, extent=extent, interpolation="nearest", + origin="lower") + axins.yaxis.get_major_locator().set_params(nbins=7) + axins.xaxis.get_major_locator().set_params(nbins=7) + # sub region of the original image + x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9 + axins.set_xlim(x1, x2) + axins.set_ylim(y1, y2) + + plt.xticks(visible=False) + plt.yticks(visible=False) + + # draw a bbox of the region of the inset axes in the parent axes and + # connecting lines between the bbox and the inset axes area + mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") + + asb = AnchoredSizeBar(ax.transData, + 0.5, + '0.5', + loc='lower center', + pad=0.1, borderpad=0.5, sep=5, + frameon=False) + ax.add_artist(asb) + + +def test_inset_axes_complete(): + dpi = 100 + figsize = (6, 5) + fig, ax = plt.subplots(figsize=figsize, dpi=dpi) + fig.subplots_adjust(.1, .1, .9, .9) + + ins = inset_axes(ax, width=2., height=2., borderpad=0) + fig.canvas.draw() + assert_array_almost_equal( + ins.get_position().extents, + [(0.9*figsize[0]-2.)/figsize[0], (0.9*figsize[1]-2.)/figsize[1], + 0.9, 0.9]) + + ins = inset_axes(ax, width="40%", height="30%", borderpad=0) + fig.canvas.draw() + assert_array_almost_equal( + ins.get_position().extents, [.9-.8*.4, .9-.8*.3, 0.9, 0.9]) + + ins = inset_axes(ax, width=1., height=1.2, bbox_to_anchor=(200, 100), + loc=3, borderpad=0) + fig.canvas.draw() + assert_array_almost_equal( + ins.get_position().extents, + [200/dpi/figsize[0], 100/dpi/figsize[1], + (200/dpi+1)/figsize[0], (100/dpi+1.2)/figsize[1]]) + + ins1 = inset_axes(ax, width="35%", height="60%", loc=3, borderpad=1) + ins2 = inset_axes(ax, width="100%", height="100%", + bbox_to_anchor=(0, 0, .35, .60), + bbox_transform=ax.transAxes, loc=3, borderpad=1) + fig.canvas.draw() + assert_array_equal(ins1.get_position().extents, + ins2.get_position().extents) + + with pytest.raises(ValueError): + ins = inset_axes(ax, width="40%", height="30%", + bbox_to_anchor=(0.4, 0.5)) + + with pytest.warns(UserWarning): + ins = inset_axes(ax, width="40%", height="30%", + bbox_transform=ax.transAxes) + + +def test_inset_axes_tight(): + # gh-26287 found that inset_axes raised with bbox_inches=tight + fig, ax = plt.subplots() + inset_axes(ax, width=1.3, height=0.9) + + f = io.BytesIO() + fig.savefig(f, bbox_inches="tight") + + +@image_comparison(['fill_facecolor.png'], remove_text=True, style='mpl20') +def test_fill_facecolor(): + fig, ax = plt.subplots(1, 5) + fig.set_size_inches(5, 5) + for i in range(1, 4): + ax[i].yaxis.set_visible(False) + ax[4].yaxis.tick_right() + bbox = Bbox.from_extents(0, 0.4, 1, 0.6) + + # fill with blue by setting 'fc' field + bbox1 = TransformedBbox(bbox, ax[0].transData) + bbox2 = TransformedBbox(bbox, ax[1].transData) + # set color to BboxConnectorPatch + p = BboxConnectorPatch( + bbox1, bbox2, loc1a=1, loc2a=2, loc1b=4, loc2b=3, + ec="r", fc="b") + p.set_clip_on(False) + ax[0].add_patch(p) + # set color to marked area + axins = zoomed_inset_axes(ax[0], 1, loc='upper right') + axins.set_xlim(0, 0.2) + axins.set_ylim(0, 0.2) + plt.gca().axes.xaxis.set_ticks([]) + plt.gca().axes.yaxis.set_ticks([]) + mark_inset(ax[0], axins, loc1=2, loc2=4, fc="b", ec="0.5") + + # fill with yellow by setting 'facecolor' field + bbox3 = TransformedBbox(bbox, ax[1].transData) + bbox4 = TransformedBbox(bbox, ax[2].transData) + # set color to BboxConnectorPatch + p = BboxConnectorPatch( + bbox3, bbox4, loc1a=1, loc2a=2, loc1b=4, loc2b=3, + ec="r", facecolor="y") + p.set_clip_on(False) + ax[1].add_patch(p) + # set color to marked area + axins = zoomed_inset_axes(ax[1], 1, loc='upper right') + axins.set_xlim(0, 0.2) + axins.set_ylim(0, 0.2) + plt.gca().axes.xaxis.set_ticks([]) + plt.gca().axes.yaxis.set_ticks([]) + mark_inset(ax[1], axins, loc1=2, loc2=4, facecolor="y", ec="0.5") + + # fill with green by setting 'color' field + bbox5 = TransformedBbox(bbox, ax[2].transData) + bbox6 = TransformedBbox(bbox, ax[3].transData) + # set color to BboxConnectorPatch + p = BboxConnectorPatch( + bbox5, bbox6, loc1a=1, loc2a=2, loc1b=4, loc2b=3, + ec="r", color="g") + p.set_clip_on(False) + ax[2].add_patch(p) + # set color to marked area + axins = zoomed_inset_axes(ax[2], 1, loc='upper right') + axins.set_xlim(0, 0.2) + axins.set_ylim(0, 0.2) + plt.gca().axes.xaxis.set_ticks([]) + plt.gca().axes.yaxis.set_ticks([]) + mark_inset(ax[2], axins, loc1=2, loc2=4, color="g", ec="0.5") + + # fill with green but color won't show if set fill to False + bbox7 = TransformedBbox(bbox, ax[3].transData) + bbox8 = TransformedBbox(bbox, ax[4].transData) + # BboxConnectorPatch won't show green + p = BboxConnectorPatch( + bbox7, bbox8, loc1a=1, loc2a=2, loc1b=4, loc2b=3, + ec="r", fc="g", fill=False) + p.set_clip_on(False) + ax[3].add_patch(p) + # marked area won't show green + axins = zoomed_inset_axes(ax[3], 1, loc='upper right') + axins.set_xlim(0, 0.2) + axins.set_ylim(0, 0.2) + axins.xaxis.set_ticks([]) + axins.yaxis.set_ticks([]) + mark_inset(ax[3], axins, loc1=2, loc2=4, fc="g", ec="0.5", fill=False) + + +# Update style when regenerating the test image +@image_comparison(['zoomed_axes.png', 'inverted_zoomed_axes.png'], + style=('classic', '_classic_test_patch'), + tol=0 if platform.machine() == 'x86_64' else 0.02) +def test_zooming_with_inverted_axes(): + fig, ax = plt.subplots() + ax.plot([1, 2, 3], [1, 2, 3]) + ax.axis([1, 3, 1, 3]) + inset_ax = zoomed_inset_axes(ax, zoom=2.5, loc='lower right') + inset_ax.axis([1.1, 1.4, 1.1, 1.4]) + + fig, ax = plt.subplots() + ax.plot([1, 2, 3], [1, 2, 3]) + ax.axis([3, 1, 3, 1]) + inset_ax = zoomed_inset_axes(ax, zoom=2.5, loc='lower right') + inset_ax.axis([1.4, 1.1, 1.4, 1.1]) + + +# Update style when regenerating the test image +@image_comparison(['anchored_direction_arrows.png'], + tol=0 if platform.machine() == 'x86_64' else 0.01, + style=('classic', '_classic_test_patch')) +def test_anchored_direction_arrows(): + fig, ax = plt.subplots() + ax.imshow(np.zeros((10, 10)), interpolation='nearest') + + simple_arrow = AnchoredDirectionArrows(ax.transAxes, 'X', 'Y') + ax.add_artist(simple_arrow) + + +# Update style when regenerating the test image +@image_comparison(['anchored_direction_arrows_many_args.png'], + style=('classic', '_classic_test_patch')) +def test_anchored_direction_arrows_many_args(): + fig, ax = plt.subplots() + ax.imshow(np.ones((10, 10))) + + direction_arrows = AnchoredDirectionArrows( + ax.transAxes, 'A', 'B', loc='upper right', color='red', + aspect_ratio=-0.5, pad=0.6, borderpad=2, frameon=True, alpha=0.7, + sep_x=-0.06, sep_y=-0.08, back_length=0.1, head_width=9, + head_length=10, tail_width=5) + ax.add_artist(direction_arrows) + + +def test_axes_locatable_position(): + fig, ax = plt.subplots() + divider = make_axes_locatable(ax) + with mpl.rc_context({"figure.subplot.wspace": 0.02}): + cax = divider.append_axes('right', size='5%') + fig.canvas.draw() + assert np.isclose(cax.get_position(original=False).width, + 0.03621495327102808) + + +@image_comparison(['image_grid_each_left_label_mode_all.png'], style='mpl20', + savefig_kwarg={'bbox_inches': 'tight'}) +def test_image_grid_each_left_label_mode_all(): + imdata = np.arange(100).reshape((10, 10)) + + fig = plt.figure(1, (3, 3)) + grid = ImageGrid(fig, (1, 1, 1), nrows_ncols=(3, 2), axes_pad=(0.5, 0.3), + cbar_mode="each", cbar_location="left", cbar_size="15%", + label_mode="all") + # 3-tuple rect => SubplotDivider + assert isinstance(grid.get_divider(), SubplotDivider) + assert grid.get_axes_pad() == (0.5, 0.3) + assert grid.get_aspect() # True by default for ImageGrid + for ax, cax in zip(grid, grid.cbar_axes): + im = ax.imshow(imdata, interpolation='none') + cax.colorbar(im) + + +@image_comparison(['image_grid_single_bottom_label_mode_1.png'], style='mpl20', + savefig_kwarg={'bbox_inches': 'tight'}) +def test_image_grid_single_bottom(): + imdata = np.arange(100).reshape((10, 10)) + + fig = plt.figure(1, (2.5, 1.5)) + grid = ImageGrid(fig, (0, 0, 1, 1), nrows_ncols=(1, 3), + axes_pad=(0.2, 0.15), cbar_mode="single", cbar_pad=0.3, + cbar_location="bottom", cbar_size="10%", label_mode="1") + # 4-tuple rect => Divider, isinstance will give True for SubplotDivider + assert type(grid.get_divider()) is Divider + for i in range(3): + im = grid[i].imshow(imdata, interpolation='none') + grid.cbar_axes[0].colorbar(im) + + +def test_image_grid_label_mode_invalid(): + fig = plt.figure() + with pytest.raises(ValueError, match="'foo' is not a valid value for mode"): + ImageGrid(fig, (0, 0, 1, 1), (2, 1), label_mode="foo") + + +@image_comparison(['image_grid.png'], + remove_text=True, style='mpl20', + savefig_kwarg={'bbox_inches': 'tight'}) +def test_image_grid(): + # test that image grid works with bbox_inches=tight. + im = np.arange(100).reshape((10, 10)) + + fig = plt.figure(1, (4, 4)) + grid = ImageGrid(fig, 111, nrows_ncols=(2, 2), axes_pad=0.1) + assert grid.get_axes_pad() == (0.1, 0.1) + for i in range(4): + grid[i].imshow(im, interpolation='nearest') + + +def test_gettightbbox(): + fig, ax = plt.subplots(figsize=(8, 6)) + + l, = ax.plot([1, 2, 3], [0, 1, 0]) + + ax_zoom = zoomed_inset_axes(ax, 4) + ax_zoom.plot([1, 2, 3], [0, 1, 0]) + + mark_inset(ax, ax_zoom, loc1=1, loc2=3, fc="none", ec='0.3') + + remove_ticks_and_titles(fig) + bbox = fig.get_tightbbox(fig.canvas.get_renderer()) + np.testing.assert_array_almost_equal(bbox.extents, + [-17.7, -13.9, 7.2, 5.4]) + + +@pytest.mark.parametrize("click_on", ["big", "small"]) +@pytest.mark.parametrize("big_on_axes,small_on_axes", [ + ("gca", "gca"), + ("host", "host"), + ("host", "parasite"), + ("parasite", "host"), + ("parasite", "parasite") +]) +def test_picking_callbacks_overlap(big_on_axes, small_on_axes, click_on): + """Test pick events on normal, host or parasite axes.""" + # Two rectangles are drawn and "clicked on", a small one and a big one + # enclosing the small one. The axis on which they are drawn as well as the + # rectangle that is clicked on are varied. + # In each case we expect that both rectangles are picked if we click on the + # small one and only the big one is picked if we click on the big one. + # Also tests picking on normal axes ("gca") as a control. + big = plt.Rectangle((0.25, 0.25), 0.5, 0.5, picker=5) + small = plt.Rectangle((0.4, 0.4), 0.2, 0.2, facecolor="r", picker=5) + # Machinery for "receiving" events + received_events = [] + def on_pick(event): + received_events.append(event) + plt.gcf().canvas.mpl_connect('pick_event', on_pick) + # Shortcut + rectangles_on_axes = (big_on_axes, small_on_axes) + # Axes setup + axes = {"gca": None, "host": None, "parasite": None} + if "gca" in rectangles_on_axes: + axes["gca"] = plt.gca() + if "host" in rectangles_on_axes or "parasite" in rectangles_on_axes: + axes["host"] = host_subplot(111) + axes["parasite"] = axes["host"].twin() + # Add rectangles to axes + axes[big_on_axes].add_patch(big) + axes[small_on_axes].add_patch(small) + # Simulate picking with click mouse event + if click_on == "big": + click_axes = axes[big_on_axes] + axes_coords = (0.3, 0.3) + else: + click_axes = axes[small_on_axes] + axes_coords = (0.5, 0.5) + # In reality mouse events never happen on parasite axes, only host axes + if click_axes is axes["parasite"]: + click_axes = axes["host"] + (x, y) = click_axes.transAxes.transform(axes_coords) + m = MouseEvent("button_press_event", click_axes.get_figure(root=True).canvas, x, y, + button=1) + click_axes.pick(m) + # Checks + expected_n_events = 2 if click_on == "small" else 1 + assert len(received_events) == expected_n_events + event_rects = [event.artist for event in received_events] + assert big in event_rects + if click_on == "small": + assert small in event_rects + + +@image_comparison(['anchored_artists.png'], remove_text=True, style='mpl20') +def test_anchored_artists(): + fig, ax = plt.subplots(figsize=(3, 3)) + ada = AnchoredDrawingArea(40, 20, 0, 0, loc='upper right', pad=0., + frameon=False) + p1 = Circle((10, 10), 10) + ada.drawing_area.add_artist(p1) + p2 = Circle((30, 10), 5, fc="r") + ada.drawing_area.add_artist(p2) + ax.add_artist(ada) + + box = AnchoredAuxTransformBox(ax.transData, loc='upper left') + el = Ellipse((0, 0), width=0.1, height=0.4, angle=30, color='cyan') + box.drawing_area.add_artist(el) + ax.add_artist(box) + + # This block used to test the AnchoredEllipse class, but that was removed. The block + # remains, though it duplicates the above ellipse, so that the test image doesn't + # need to be regenerated. + box = AnchoredAuxTransformBox(ax.transData, loc='lower left', frameon=True, + pad=0.5, borderpad=0.4) + el = Ellipse((0, 0), width=0.1, height=0.25, angle=-60) + box.drawing_area.add_artist(el) + ax.add_artist(box) + + asb = AnchoredSizeBar(ax.transData, 0.2, r"0.2 units", loc='lower right', + pad=0.3, borderpad=0.4, sep=4, fill_bar=True, + frameon=False, label_top=True, prop={'size': 20}, + size_vertical=0.05, color='green') + ax.add_artist(asb) + + +def test_hbox_divider(): + arr1 = np.arange(20).reshape((4, 5)) + arr2 = np.arange(20).reshape((5, 4)) + + fig, (ax1, ax2) = plt.subplots(1, 2) + ax1.imshow(arr1) + ax2.imshow(arr2) + + pad = 0.5 # inches. + divider = HBoxDivider( + fig, 111, # Position of combined axes. + horizontal=[Size.AxesX(ax1), Size.Fixed(pad), Size.AxesX(ax2)], + vertical=[Size.AxesY(ax1), Size.Scaled(1), Size.AxesY(ax2)]) + ax1.set_axes_locator(divider.new_locator(0)) + ax2.set_axes_locator(divider.new_locator(2)) + + fig.canvas.draw() + p1 = ax1.get_position() + p2 = ax2.get_position() + assert p1.height == p2.height + assert p2.width / p1.width == pytest.approx((4 / 5) ** 2) + + +def test_vbox_divider(): + arr1 = np.arange(20).reshape((4, 5)) + arr2 = np.arange(20).reshape((5, 4)) + + fig, (ax1, ax2) = plt.subplots(1, 2) + ax1.imshow(arr1) + ax2.imshow(arr2) + + pad = 0.5 # inches. + divider = VBoxDivider( + fig, 111, # Position of combined axes. + horizontal=[Size.AxesX(ax1), Size.Scaled(1), Size.AxesX(ax2)], + vertical=[Size.AxesY(ax1), Size.Fixed(pad), Size.AxesY(ax2)]) + ax1.set_axes_locator(divider.new_locator(0)) + ax2.set_axes_locator(divider.new_locator(2)) + + fig.canvas.draw() + p1 = ax1.get_position() + p2 = ax2.get_position() + assert p1.width == p2.width + assert p1.height / p2.height == pytest.approx((4 / 5) ** 2) + + +def test_axes_class_tuple(): + fig = plt.figure() + axes_class = (mpl_toolkits.axes_grid1.mpl_axes.Axes, {}) + gr = AxesGrid(fig, 111, nrows_ncols=(1, 1), axes_class=axes_class) + + +def test_grid_axes_lists(): + """Test Grid axes_all, axes_row and axes_column relationship.""" + fig = plt.figure() + grid = Grid(fig, 111, (2, 3), direction="row") + assert_array_equal(grid, grid.axes_all) + assert_array_equal(grid.axes_row, np.transpose(grid.axes_column)) + assert_array_equal(grid, np.ravel(grid.axes_row), "row") + assert grid.get_geometry() == (2, 3) + grid = Grid(fig, 111, (2, 3), direction="column") + assert_array_equal(grid, np.ravel(grid.axes_column), "column") + + +@pytest.mark.parametrize('direction', ('row', 'column')) +def test_grid_axes_position(direction): + """Test positioning of the axes in Grid.""" + fig = plt.figure() + grid = Grid(fig, 111, (2, 2), direction=direction) + loc = [ax.get_axes_locator() for ax in np.ravel(grid.axes_row)] + # Test nx. + assert loc[1].args[0] > loc[0].args[0] + assert loc[0].args[0] == loc[2].args[0] + assert loc[3].args[0] == loc[1].args[0] + # Test ny. + assert loc[2].args[1] < loc[0].args[1] + assert loc[0].args[1] == loc[1].args[1] + assert loc[3].args[1] == loc[2].args[1] + + +@pytest.mark.parametrize('rect, ngrids, error, message', ( + ((1, 1), None, TypeError, "Incorrect rect format"), + (111, -1, ValueError, "ngrids must be positive"), + (111, 7, ValueError, "ngrids must be positive"), +)) +def test_grid_errors(rect, ngrids, error, message): + fig = plt.figure() + with pytest.raises(error, match=message): + Grid(fig, rect, (2, 3), ngrids=ngrids) + + +@pytest.mark.parametrize('anchor, error, message', ( + (None, TypeError, "anchor must be str"), + ("CC", ValueError, "'CC' is not a valid value for anchor"), + ((1, 1, 1), TypeError, "anchor must be str"), +)) +def test_divider_errors(anchor, error, message): + fig = plt.figure() + with pytest.raises(error, match=message): + Divider(fig, [0, 0, 1, 1], [Size.Fixed(1)], [Size.Fixed(1)], + anchor=anchor) + + +@check_figures_equal(extensions=["png"]) +def test_mark_inset_unstales_viewlim(fig_test, fig_ref): + inset, full = fig_test.subplots(1, 2) + full.plot([0, 5], [0, 5]) + inset.set(xlim=(1, 2), ylim=(1, 2)) + # Check that mark_inset unstales full's viewLim before drawing the marks. + mark_inset(full, inset, 1, 4) + + inset, full = fig_ref.subplots(1, 2) + full.plot([0, 5], [0, 5]) + inset.set(xlim=(1, 2), ylim=(1, 2)) + mark_inset(full, inset, 1, 4) + # Manually unstale the full's viewLim. + fig_ref.canvas.draw() + + +def test_auto_adjustable(): + fig = plt.figure() + ax = fig.add_axes([0, 0, 1, 1]) + pad = 0.1 + make_axes_area_auto_adjustable(ax, pad=pad) + fig.canvas.draw() + tbb = ax.get_tightbbox() + assert tbb.x0 == pytest.approx(pad * fig.dpi) + assert tbb.x1 == pytest.approx(fig.bbox.width - pad * fig.dpi) + assert tbb.y0 == pytest.approx(pad * fig.dpi) + assert tbb.y1 == pytest.approx(fig.bbox.height - pad * fig.dpi) + + +# Update style when regenerating the test image +@image_comparison(['rgb_axes.png'], remove_text=True, + style=('classic', '_classic_test_patch')) +def test_rgb_axes(): + fig = plt.figure() + ax = RGBAxes(fig, (0.1, 0.1, 0.8, 0.8), pad=0.1) + rng = np.random.default_rng(19680801) + r = rng.random((5, 5)) + g = rng.random((5, 5)) + b = rng.random((5, 5)) + ax.imshow_rgb(r, g, b, interpolation='none') + + +# The original version of this test relied on mpl_toolkits's slightly different +# colorbar implementation; moving to matplotlib's own colorbar implementation +# caused the small image comparison error. +@image_comparison(['imagegrid_cbar_mode.png'], + remove_text=True, style='mpl20', tol=0.3) +def test_imagegrid_cbar_mode_edge(): + arr = np.arange(16).reshape((4, 4)) + + fig = plt.figure(figsize=(18, 9)) + + positions = (241, 242, 243, 244, 245, 246, 247, 248) + directions = ['row']*4 + ['column']*4 + cbar_locations = ['left', 'right', 'top', 'bottom']*2 + + for position, direction, location in zip( + positions, directions, cbar_locations): + grid = ImageGrid(fig, position, + nrows_ncols=(2, 2), + direction=direction, + cbar_location=location, + cbar_size='20%', + cbar_mode='edge') + ax1, ax2, ax3, ax4 = grid + + ax1.imshow(arr, cmap='nipy_spectral') + ax2.imshow(arr.T, cmap='hot') + ax3.imshow(np.hypot(arr, arr.T), cmap='jet') + ax4.imshow(np.arctan2(arr, arr.T), cmap='hsv') + + # In each row/column, the "first" colorbars must be overwritten by the + # "second" ones. To achieve this, clear out the axes first. + for ax in grid: + ax.cax.cla() + cb = ax.cax.colorbar(ax.images[0]) + + +def test_imagegrid(): + fig = plt.figure() + grid = ImageGrid(fig, 111, nrows_ncols=(1, 1)) + ax = grid[0] + im = ax.imshow([[1, 2]], norm=mpl.colors.LogNorm()) + cb = ax.cax.colorbar(im) + assert isinstance(cb.locator, mticker.LogLocator) + + +def test_removal(): + import matplotlib.pyplot as plt + import mpl_toolkits.axisartist as AA + fig = plt.figure() + ax = host_subplot(111, axes_class=AA.Axes, figure=fig) + col = ax.fill_between(range(5), 0, range(5)) + fig.canvas.draw() + col.remove() + fig.canvas.draw() + + +@image_comparison(['anchored_locator_base_call.png'], style="mpl20") +def test_anchored_locator_base_call(): + fig = plt.figure(figsize=(3, 3)) + fig1, fig2 = fig.subfigures(nrows=2, ncols=1) + + ax = fig1.subplots() + ax.set(aspect=1, xlim=(-15, 15), ylim=(-20, 5)) + ax.set(xticks=[], yticks=[]) + + Z = cbook.get_sample_data("axes_grid/bivariate_normal.npy") + extent = (-3, 4, -4, 3) + + axins = zoomed_inset_axes(ax, zoom=2, loc="upper left") + axins.set(xticks=[], yticks=[]) + + axins.imshow(Z, extent=extent, origin="lower") + + +def test_grid_with_axes_class_not_overriding_axis(): + Grid(plt.figure(), 111, (2, 2), axes_class=mpl.axes.Axes) + RGBAxes(plt.figure(), 111, axes_class=mpl.axes.Axes) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7b8d8c08ac226d105a5a54c5f21040cd25107ae6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/__init__.py @@ -0,0 +1,14 @@ +from .axislines import Axes +from .axislines import ( # noqa: F401 + AxesZero, AxisArtistHelper, AxisArtistHelperRectlinear, + GridHelperBase, GridHelperRectlinear, Subplot, SubplotZero) +from .axis_artist import AxisArtist, GridlinesCollection # noqa: F401 +from .grid_helper_curvelinear import GridHelperCurveLinear # noqa: F401 +from .floating_axes import FloatingAxes, FloatingSubplot # noqa: F401 +from mpl_toolkits.axes_grid1.parasite_axes import ( + host_axes_class_factory, parasite_axes_class_factory) + + +ParasiteAxes = parasite_axes_class_factory(Axes) +HostAxes = host_axes_class_factory(Axes) +SubplotHost = HostAxes diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/angle_helper.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/angle_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..1786cd70bcdb297de6c17374353cc2a49dfd0ae1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/angle_helper.py @@ -0,0 +1,394 @@ +import numpy as np +import math + +from mpl_toolkits.axisartist.grid_finder import ExtremeFinderSimple + + +def select_step_degree(dv): + + degree_limits_ = [1.5, 3, 7, 13, 20, 40, 70, 120, 270, 520] + degree_steps_ = [1, 2, 5, 10, 15, 30, 45, 90, 180, 360] + degree_factors = [1.] * len(degree_steps_) + + minsec_limits_ = [1.5, 2.5, 3.5, 8, 11, 18, 25, 45] + minsec_steps_ = [1, 2, 3, 5, 10, 15, 20, 30] + + minute_limits_ = np.array(minsec_limits_) / 60 + minute_factors = [60.] * len(minute_limits_) + + second_limits_ = np.array(minsec_limits_) / 3600 + second_factors = [3600.] * len(second_limits_) + + degree_limits = [*second_limits_, *minute_limits_, *degree_limits_] + degree_steps = [*minsec_steps_, *minsec_steps_, *degree_steps_] + degree_factors = [*second_factors, *minute_factors, *degree_factors] + + n = np.searchsorted(degree_limits, dv) + step = degree_steps[n] + factor = degree_factors[n] + + return step, factor + + +def select_step_hour(dv): + + hour_limits_ = [1.5, 2.5, 3.5, 5, 7, 10, 15, 21, 36] + hour_steps_ = [1, 2, 3, 4, 6, 8, 12, 18, 24] + hour_factors = [1.] * len(hour_steps_) + + minsec_limits_ = [1.5, 2.5, 3.5, 4.5, 5.5, 8, 11, 14, 18, 25, 45] + minsec_steps_ = [1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30] + + minute_limits_ = np.array(minsec_limits_) / 60 + minute_factors = [60.] * len(minute_limits_) + + second_limits_ = np.array(minsec_limits_) / 3600 + second_factors = [3600.] * len(second_limits_) + + hour_limits = [*second_limits_, *minute_limits_, *hour_limits_] + hour_steps = [*minsec_steps_, *minsec_steps_, *hour_steps_] + hour_factors = [*second_factors, *minute_factors, *hour_factors] + + n = np.searchsorted(hour_limits, dv) + step = hour_steps[n] + factor = hour_factors[n] + + return step, factor + + +def select_step_sub(dv): + + # subarcsec or degree + tmp = 10.**(int(math.log10(dv))-1.) + + factor = 1./tmp + + if 1.5*tmp >= dv: + step = 1 + elif 3.*tmp >= dv: + step = 2 + elif 7.*tmp >= dv: + step = 5 + else: + step = 1 + factor = 0.1*factor + + return step, factor + + +def select_step(v1, v2, nv, hour=False, include_last=True, + threshold_factor=3600.): + + if v1 > v2: + v1, v2 = v2, v1 + + dv = (v2 - v1) / nv + + if hour: + _select_step = select_step_hour + cycle = 24. + else: + _select_step = select_step_degree + cycle = 360. + + # for degree + if dv > 1 / threshold_factor: + step, factor = _select_step(dv) + else: + step, factor = select_step_sub(dv*threshold_factor) + + factor = factor * threshold_factor + + levs = np.arange(np.floor(v1 * factor / step), + np.ceil(v2 * factor / step) + 0.5, + dtype=int) * step + + # n : number of valid levels. If there is a cycle, e.g., [0, 90, 180, + # 270, 360], the grid line needs to be extended from 0 to 360, so + # we need to return the whole array. However, the last level (360) + # needs to be ignored often. In this case, so we return n=4. + + n = len(levs) + + # we need to check the range of values + # for example, -90 to 90, 0 to 360, + + if factor == 1. and levs[-1] >= levs[0] + cycle: # check for cycle + nv = int(cycle / step) + if include_last: + levs = levs[0] + np.arange(0, nv+1, 1) * step + else: + levs = levs[0] + np.arange(0, nv, 1) * step + + n = len(levs) + + return np.array(levs), n, factor + + +def select_step24(v1, v2, nv, include_last=True, threshold_factor=3600): + v1, v2 = v1 / 15, v2 / 15 + levs, n, factor = select_step(v1, v2, nv, hour=True, + include_last=include_last, + threshold_factor=threshold_factor) + return levs * 15, n, factor + + +def select_step360(v1, v2, nv, include_last=True, threshold_factor=3600): + return select_step(v1, v2, nv, hour=False, + include_last=include_last, + threshold_factor=threshold_factor) + + +class LocatorBase: + def __init__(self, nbins, include_last=True): + self.nbins = nbins + self._include_last = include_last + + def set_params(self, nbins=None): + if nbins is not None: + self.nbins = int(nbins) + + +class LocatorHMS(LocatorBase): + def __call__(self, v1, v2): + return select_step24(v1, v2, self.nbins, self._include_last) + + +class LocatorHM(LocatorBase): + def __call__(self, v1, v2): + return select_step24(v1, v2, self.nbins, self._include_last, + threshold_factor=60) + + +class LocatorH(LocatorBase): + def __call__(self, v1, v2): + return select_step24(v1, v2, self.nbins, self._include_last, + threshold_factor=1) + + +class LocatorDMS(LocatorBase): + def __call__(self, v1, v2): + return select_step360(v1, v2, self.nbins, self._include_last) + + +class LocatorDM(LocatorBase): + def __call__(self, v1, v2): + return select_step360(v1, v2, self.nbins, self._include_last, + threshold_factor=60) + + +class LocatorD(LocatorBase): + def __call__(self, v1, v2): + return select_step360(v1, v2, self.nbins, self._include_last, + threshold_factor=1) + + +class FormatterDMS: + deg_mark = r"^{\circ}" + min_mark = r"^{\prime}" + sec_mark = r"^{\prime\prime}" + + fmt_d = "$%d" + deg_mark + "$" + fmt_ds = r"$%d.%s" + deg_mark + "$" + + # %s for sign + fmt_d_m = r"$%s%d" + deg_mark + r"\,%02d" + min_mark + "$" + fmt_d_ms = r"$%s%d" + deg_mark + r"\,%02d.%s" + min_mark + "$" + + fmt_d_m_partial = "$%s%d" + deg_mark + r"\,%02d" + min_mark + r"\," + fmt_s_partial = "%02d" + sec_mark + "$" + fmt_ss_partial = "%02d.%s" + sec_mark + "$" + + def _get_number_fraction(self, factor): + ## check for fractional numbers + number_fraction = None + # check for 60 + + for threshold in [1, 60, 3600]: + if factor <= threshold: + break + + d = factor // threshold + int_log_d = int(np.floor(np.log10(d))) + if 10**int_log_d == d and d != 1: + number_fraction = int_log_d + factor = factor // 10**int_log_d + return factor, number_fraction + + return factor, number_fraction + + def __call__(self, direction, factor, values): + if len(values) == 0: + return [] + + ss = np.sign(values) + signs = ["-" if v < 0 else "" for v in values] + + factor, number_fraction = self._get_number_fraction(factor) + + values = np.abs(values) + + if number_fraction is not None: + values, frac_part = divmod(values, 10 ** number_fraction) + frac_fmt = "%%0%dd" % (number_fraction,) + frac_str = [frac_fmt % (f1,) for f1 in frac_part] + + if factor == 1: + if number_fraction is None: + return [self.fmt_d % (s * int(v),) for s, v in zip(ss, values)] + else: + return [self.fmt_ds % (s * int(v), f1) + for s, v, f1 in zip(ss, values, frac_str)] + elif factor == 60: + deg_part, min_part = divmod(values, 60) + if number_fraction is None: + return [self.fmt_d_m % (s1, d1, m1) + for s1, d1, m1 in zip(signs, deg_part, min_part)] + else: + return [self.fmt_d_ms % (s, d1, m1, f1) + for s, d1, m1, f1 + in zip(signs, deg_part, min_part, frac_str)] + + elif factor == 3600: + if ss[-1] == -1: + inverse_order = True + values = values[::-1] + signs = signs[::-1] + else: + inverse_order = False + + l_hm_old = "" + r = [] + + deg_part, min_part_ = divmod(values, 3600) + min_part, sec_part = divmod(min_part_, 60) + + if number_fraction is None: + sec_str = [self.fmt_s_partial % (s1,) for s1 in sec_part] + else: + sec_str = [self.fmt_ss_partial % (s1, f1) + for s1, f1 in zip(sec_part, frac_str)] + + for s, d1, m1, s1 in zip(signs, deg_part, min_part, sec_str): + l_hm = self.fmt_d_m_partial % (s, d1, m1) + if l_hm != l_hm_old: + l_hm_old = l_hm + l = l_hm + s1 + else: + l = "$" + s + s1 + r.append(l) + + if inverse_order: + return r[::-1] + else: + return r + + else: # factor > 3600. + return [r"$%s^{\circ}$" % v for v in ss*values] + + +class FormatterHMS(FormatterDMS): + deg_mark = r"^\mathrm{h}" + min_mark = r"^\mathrm{m}" + sec_mark = r"^\mathrm{s}" + + fmt_d = "$%d" + deg_mark + "$" + fmt_ds = r"$%d.%s" + deg_mark + "$" + + # %s for sign + fmt_d_m = r"$%s%d" + deg_mark + r"\,%02d" + min_mark+"$" + fmt_d_ms = r"$%s%d" + deg_mark + r"\,%02d.%s" + min_mark+"$" + + fmt_d_m_partial = "$%s%d" + deg_mark + r"\,%02d" + min_mark + r"\," + fmt_s_partial = "%02d" + sec_mark + "$" + fmt_ss_partial = "%02d.%s" + sec_mark + "$" + + def __call__(self, direction, factor, values): # hour + return super().__call__(direction, factor, np.asarray(values) / 15) + + +class ExtremeFinderCycle(ExtremeFinderSimple): + # docstring inherited + + def __init__(self, nx, ny, + lon_cycle=360., lat_cycle=None, + lon_minmax=None, lat_minmax=(-90, 90)): + """ + This subclass handles the case where one or both coordinates should be + taken modulo 360, or be restricted to not exceed a specific range. + + Parameters + ---------- + nx, ny : int + The number of samples in each direction. + + lon_cycle, lat_cycle : 360 or None + If not None, values in the corresponding direction are taken modulo + *lon_cycle* or *lat_cycle*; in theory this can be any number but + the implementation actually assumes that it is 360 (if not None); + other values give nonsensical results. + + This is done by "unwrapping" the transformed grid coordinates so + that jumps are less than a half-cycle; then normalizing the span to + no more than a full cycle. + + For example, if values are in the union of the [0, 2] and + [358, 360] intervals (typically, angles measured modulo 360), the + values in the second interval are normalized to [-2, 0] instead so + that the values now cover [-2, 2]. If values are in a range of + [5, 1000], this gets normalized to [5, 365]. + + lon_minmax, lat_minmax : (float, float) or None + If not None, the computed bounding box is clipped to the given + range in the corresponding direction. + """ + self.nx, self.ny = nx, ny + self.lon_cycle, self.lat_cycle = lon_cycle, lat_cycle + self.lon_minmax = lon_minmax + self.lat_minmax = lat_minmax + + def __call__(self, transform_xy, x1, y1, x2, y2): + # docstring inherited + x, y = np.meshgrid( + np.linspace(x1, x2, self.nx), np.linspace(y1, y2, self.ny)) + lon, lat = transform_xy(np.ravel(x), np.ravel(y)) + + # iron out jumps, but algorithm should be improved. + # This is just naive way of doing and my fail for some cases. + # Consider replacing this with numpy.unwrap + # We are ignoring invalid warnings. They are triggered when + # comparing arrays with NaNs using > We are already handling + # that correctly using np.nanmin and np.nanmax + with np.errstate(invalid='ignore'): + if self.lon_cycle is not None: + lon0 = np.nanmin(lon) + lon -= 360. * ((lon - lon0) > 180.) + if self.lat_cycle is not None: + lat0 = np.nanmin(lat) + lat -= 360. * ((lat - lat0) > 180.) + + lon_min, lon_max = np.nanmin(lon), np.nanmax(lon) + lat_min, lat_max = np.nanmin(lat), np.nanmax(lat) + + lon_min, lon_max, lat_min, lat_max = \ + self._add_pad(lon_min, lon_max, lat_min, lat_max) + + # check cycle + if self.lon_cycle: + lon_max = min(lon_max, lon_min + self.lon_cycle) + if self.lat_cycle: + lat_max = min(lat_max, lat_min + self.lat_cycle) + + if self.lon_minmax is not None: + min0 = self.lon_minmax[0] + lon_min = max(min0, lon_min) + max0 = self.lon_minmax[1] + lon_max = min(max0, lon_max) + + if self.lat_minmax is not None: + min0 = self.lat_minmax[0] + lat_min = max(min0, lat_min) + max0 = self.lat_minmax[1] + lat_max = min(max0, lat_max) + + return lon_min, lon_max, lat_min, lat_max diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axes_divider.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axes_divider.py new file mode 100644 index 0000000000000000000000000000000000000000..d0392be782d9c06404c3d83c2b8ca271bbb8fa72 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axes_divider.py @@ -0,0 +1,2 @@ +from mpl_toolkits.axes_grid1.axes_divider import ( # noqa + Divider, SubplotDivider, AxesDivider, make_axes_locatable) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axis_artist.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axis_artist.py new file mode 100644 index 0000000000000000000000000000000000000000..725c665d818ade7772b427114975a0b1bfcec3b0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axis_artist.py @@ -0,0 +1,1116 @@ +""" +The :mod:`.axis_artist` module implements custom artists to draw axis elements +(axis lines and labels, tick lines and labels, grid lines). + +Axis lines and labels and tick lines and labels are managed by the `AxisArtist` +class; grid lines are managed by the `GridlinesCollection` class. + +There is one `AxisArtist` per Axis; it can be accessed through +the ``axis`` dictionary of the parent Axes (which should be a +`mpl_toolkits.axislines.Axes`), e.g. ``ax.axis["bottom"]``. + +Children of the AxisArtist are accessed as attributes: ``.line`` and ``.label`` +for the axis line and label, ``.major_ticks``, ``.major_ticklabels``, +``.minor_ticks``, ``.minor_ticklabels`` for the tick lines and labels (e.g. +``ax.axis["bottom"].line``). + +Children properties (colors, fonts, line widths, etc.) can be set using +setters, e.g. :: + + # Make the major ticks of the bottom axis red. + ax.axis["bottom"].major_ticks.set_color("red") + +However, things like the locations of ticks, and their ticklabels need to be +changed from the side of the grid_helper. + +axis_direction +-------------- + +`AxisArtist`, `AxisLabel`, `TickLabels` have an *axis_direction* attribute, +which adjusts the location, angle, etc. The *axis_direction* must be one of +"left", "right", "bottom", "top", and follows the Matplotlib convention for +rectangular axis. + +For example, for the *bottom* axis (the left and right is relative to the +direction of the increasing coordinate), + +* ticklabels and axislabel are on the right +* ticklabels and axislabel have text angle of 0 +* ticklabels are baseline, center-aligned +* axislabel is top, center-aligned + +The text angles are actually relative to (90 + angle of the direction to the +ticklabel), which gives 0 for bottom axis. + +=================== ====== ======== ====== ======== +Property left bottom right top +=================== ====== ======== ====== ======== +ticklabel location left right right left +axislabel location left right right left +ticklabel angle 90 0 -90 180 +axislabel angle 180 0 0 180 +ticklabel va center baseline center baseline +axislabel va center top center bottom +ticklabel ha right center right center +axislabel ha right center right center +=================== ====== ======== ====== ======== + +Ticks are by default direct opposite side of the ticklabels. To make ticks to +the same side of the ticklabels, :: + + ax.axis["bottom"].major_ticks.set_tick_out(True) + +The following attributes can be customized (use the ``set_xxx`` methods): + +* `Ticks`: ticksize, tick_out +* `TickLabels`: pad +* `AxisLabel`: pad +""" + +# FIXME : +# angles are given in data coordinate - need to convert it to canvas coordinate + + +from operator import methodcaller + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api, cbook +import matplotlib.artist as martist +import matplotlib.colors as mcolors +import matplotlib.text as mtext +from matplotlib.collections import LineCollection +from matplotlib.lines import Line2D +from matplotlib.patches import PathPatch +from matplotlib.path import Path +from matplotlib.transforms import ( + Affine2D, Bbox, IdentityTransform, ScaledTranslation) + +from .axisline_style import AxislineStyle + + +class AttributeCopier: + def get_ref_artist(self): + """ + Return the underlying artist that actually defines some properties + (e.g., color) of this artist. + """ + raise RuntimeError("get_ref_artist must overridden") + + def get_attribute_from_ref_artist(self, attr_name): + getter = methodcaller("get_" + attr_name) + prop = getter(super()) + return getter(self.get_ref_artist()) if prop == "auto" else prop + + +class Ticks(AttributeCopier, Line2D): + """ + Ticks are derived from `.Line2D`, and note that ticks themselves + are markers. Thus, you should use set_mec, set_mew, etc. + + To change the tick size (length), you need to use + `set_ticksize`. To change the direction of the ticks (ticks are + in opposite direction of ticklabels by default), use + ``set_tick_out(False)`` + """ + + def __init__(self, ticksize, tick_out=False, *, axis=None, **kwargs): + self._ticksize = ticksize + self.locs_angles_labels = [] + + self.set_tick_out(tick_out) + + self._axis = axis + if self._axis is not None: + if "color" not in kwargs: + kwargs["color"] = "auto" + if "mew" not in kwargs and "markeredgewidth" not in kwargs: + kwargs["markeredgewidth"] = "auto" + + Line2D.__init__(self, [0.], [0.], **kwargs) + self.set_snap(True) + + def get_ref_artist(self): + # docstring inherited + return self._axis.majorTicks[0].tick1line + + def set_color(self, color): + # docstring inherited + # Unlike the base Line2D.set_color, this also supports "auto". + if not cbook._str_equal(color, "auto"): + mcolors._check_color_like(color=color) + self._color = color + self.stale = True + + def get_color(self): + return self.get_attribute_from_ref_artist("color") + + def get_markeredgecolor(self): + return self.get_attribute_from_ref_artist("markeredgecolor") + + def get_markeredgewidth(self): + return self.get_attribute_from_ref_artist("markeredgewidth") + + def set_tick_out(self, b): + """Set whether ticks are drawn inside or outside the axes.""" + self._tick_out = b + + def get_tick_out(self): + """Return whether ticks are drawn inside or outside the axes.""" + return self._tick_out + + def set_ticksize(self, ticksize): + """Set length of the ticks in points.""" + self._ticksize = ticksize + + def get_ticksize(self): + """Return length of the ticks in points.""" + return self._ticksize + + def set_locs_angles(self, locs_angles): + self.locs_angles = locs_angles + + _tickvert_path = Path([[0., 0.], [1., 0.]]) + + def draw(self, renderer): + if not self.get_visible(): + return + + gc = renderer.new_gc() + gc.set_foreground(self.get_markeredgecolor()) + gc.set_linewidth(self.get_markeredgewidth()) + gc.set_alpha(self._alpha) + + path_trans = self.get_transform() + marker_transform = (Affine2D() + .scale(renderer.points_to_pixels(self._ticksize))) + if self.get_tick_out(): + marker_transform.rotate_deg(180) + + for loc, angle in self.locs_angles: + locs = path_trans.transform_non_affine(np.array([loc])) + if self.axes and not self.axes.viewLim.contains(*locs[0]): + continue + renderer.draw_markers( + gc, self._tickvert_path, + marker_transform + Affine2D().rotate_deg(angle), + Path(locs), path_trans.get_affine()) + + gc.restore() + + +class LabelBase(mtext.Text): + """ + A base class for `.AxisLabel` and `.TickLabels`. The position and + angle of the text are calculated by the offset_ref_angle, + text_ref_angle, and offset_radius attributes. + """ + + def __init__(self, *args, **kwargs): + self.locs_angles_labels = [] + self._ref_angle = 0 + self._offset_radius = 0. + + super().__init__(*args, **kwargs) + + self.set_rotation_mode("anchor") + self._text_follow_ref_angle = True + + @property + def _text_ref_angle(self): + if self._text_follow_ref_angle: + return self._ref_angle + 90 + else: + return 0 + + @property + def _offset_ref_angle(self): + return self._ref_angle + + _get_opposite_direction = {"left": "right", + "right": "left", + "top": "bottom", + "bottom": "top"}.__getitem__ + + def draw(self, renderer): + if not self.get_visible(): + return + + # save original and adjust some properties + tr = self.get_transform() + angle_orig = self.get_rotation() + theta = np.deg2rad(self._offset_ref_angle) + dd = self._offset_radius + dx, dy = dd * np.cos(theta), dd * np.sin(theta) + + self.set_transform(tr + Affine2D().translate(dx, dy)) + self.set_rotation(self._text_ref_angle + angle_orig) + super().draw(renderer) + # restore original properties + self.set_transform(tr) + self.set_rotation(angle_orig) + + def get_window_extent(self, renderer=None): + if renderer is None: + renderer = self.get_figure(root=True)._get_renderer() + + # save original and adjust some properties + tr = self.get_transform() + angle_orig = self.get_rotation() + theta = np.deg2rad(self._offset_ref_angle) + dd = self._offset_radius + dx, dy = dd * np.cos(theta), dd * np.sin(theta) + + self.set_transform(tr + Affine2D().translate(dx, dy)) + self.set_rotation(self._text_ref_angle + angle_orig) + bbox = super().get_window_extent(renderer).frozen() + # restore original properties + self.set_transform(tr) + self.set_rotation(angle_orig) + + return bbox + + +class AxisLabel(AttributeCopier, LabelBase): + """ + Axis label. Derived from `.Text`. The position of the text is updated + in the fly, so changing text position has no effect. Otherwise, the + properties can be changed as a normal `.Text`. + + To change the pad between tick labels and axis label, use `set_pad`. + """ + + def __init__(self, *args, axis_direction="bottom", axis=None, **kwargs): + self._axis = axis + self._pad = 5 + self._external_pad = 0 # in pixels + LabelBase.__init__(self, *args, **kwargs) + self.set_axis_direction(axis_direction) + + def set_pad(self, pad): + """ + Set the internal pad in points. + + The actual pad will be the sum of the internal pad and the + external pad (the latter is set automatically by the `.AxisArtist`). + + Parameters + ---------- + pad : float + The internal pad in points. + """ + self._pad = pad + + def get_pad(self): + """ + Return the internal pad in points. + + See `.set_pad` for more details. + """ + return self._pad + + def get_ref_artist(self): + # docstring inherited + return self._axis.label + + def get_text(self): + # docstring inherited + t = super().get_text() + if t == "__from_axes__": + return self._axis.label.get_text() + return self._text + + _default_alignments = dict(left=("bottom", "center"), + right=("top", "center"), + bottom=("top", "center"), + top=("bottom", "center")) + + def set_default_alignment(self, d): + """ + Set the default alignment. See `set_axis_direction` for details. + + Parameters + ---------- + d : {"left", "bottom", "right", "top"} + """ + va, ha = _api.check_getitem(self._default_alignments, d=d) + self.set_va(va) + self.set_ha(ha) + + _default_angles = dict(left=180, + right=0, + bottom=0, + top=180) + + def set_default_angle(self, d): + """ + Set the default angle. See `set_axis_direction` for details. + + Parameters + ---------- + d : {"left", "bottom", "right", "top"} + """ + self.set_rotation(_api.check_getitem(self._default_angles, d=d)) + + def set_axis_direction(self, d): + """ + Adjust the text angle and text alignment of axis label + according to the matplotlib convention. + + ===================== ========== ========= ========== ========== + Property left bottom right top + ===================== ========== ========= ========== ========== + axislabel angle 180 0 0 180 + axislabel va center top center bottom + axislabel ha right center right center + ===================== ========== ========= ========== ========== + + Note that the text angles are actually relative to (90 + angle + of the direction to the ticklabel), which gives 0 for bottom + axis. + + Parameters + ---------- + d : {"left", "bottom", "right", "top"} + """ + self.set_default_alignment(d) + self.set_default_angle(d) + + def get_color(self): + return self.get_attribute_from_ref_artist("color") + + def draw(self, renderer): + if not self.get_visible(): + return + + self._offset_radius = \ + self._external_pad + renderer.points_to_pixels(self.get_pad()) + + super().draw(renderer) + + def get_window_extent(self, renderer=None): + if renderer is None: + renderer = self.get_figure(root=True)._get_renderer() + if not self.get_visible(): + return + + r = self._external_pad + renderer.points_to_pixels(self.get_pad()) + self._offset_radius = r + + bb = super().get_window_extent(renderer) + + return bb + + +class TickLabels(AxisLabel): # mtext.Text + """ + Tick labels. While derived from `.Text`, this single artist draws all + ticklabels. As in `.AxisLabel`, the position of the text is updated + in the fly, so changing text position has no effect. Otherwise, + the properties can be changed as a normal `.Text`. Unlike the + ticklabels of the mainline Matplotlib, properties of a single + ticklabel alone cannot be modified. + + To change the pad between ticks and ticklabels, use `~.AxisLabel.set_pad`. + """ + + def __init__(self, *, axis_direction="bottom", **kwargs): + super().__init__(**kwargs) + self.set_axis_direction(axis_direction) + self._axislabel_pad = 0 + + def get_ref_artist(self): + # docstring inherited + return self._axis.get_ticklabels()[0] + + def set_axis_direction(self, label_direction): + """ + Adjust the text angle and text alignment of ticklabels + according to the Matplotlib convention. + + The *label_direction* must be one of [left, right, bottom, top]. + + ===================== ========== ========= ========== ========== + Property left bottom right top + ===================== ========== ========= ========== ========== + ticklabel angle 90 0 -90 180 + ticklabel va center baseline center baseline + ticklabel ha right center right center + ===================== ========== ========= ========== ========== + + Note that the text angles are actually relative to (90 + angle + of the direction to the ticklabel), which gives 0 for bottom + axis. + + Parameters + ---------- + label_direction : {"left", "bottom", "right", "top"} + + """ + self.set_default_alignment(label_direction) + self.set_default_angle(label_direction) + self._axis_direction = label_direction + + def invert_axis_direction(self): + label_direction = self._get_opposite_direction(self._axis_direction) + self.set_axis_direction(label_direction) + + def _get_ticklabels_offsets(self, renderer, label_direction): + """ + Calculate the ticklabel offsets from the tick and their total heights. + + The offset only takes account the offset due to the vertical alignment + of the ticklabels: if axis direction is bottom and va is 'top', it will + return 0; if va is 'baseline', it will return (height-descent). + """ + whd_list = self.get_texts_widths_heights_descents(renderer) + + if not whd_list: + return 0, 0 + + r = 0 + va, ha = self.get_va(), self.get_ha() + + if label_direction == "left": + pad = max(w for w, h, d in whd_list) + if ha == "left": + r = pad + elif ha == "center": + r = .5 * pad + elif label_direction == "right": + pad = max(w for w, h, d in whd_list) + if ha == "right": + r = pad + elif ha == "center": + r = .5 * pad + elif label_direction == "bottom": + pad = max(h for w, h, d in whd_list) + if va == "bottom": + r = pad + elif va == "center": + r = .5 * pad + elif va == "baseline": + max_ascent = max(h - d for w, h, d in whd_list) + max_descent = max(d for w, h, d in whd_list) + r = max_ascent + pad = max_ascent + max_descent + elif label_direction == "top": + pad = max(h for w, h, d in whd_list) + if va == "top": + r = pad + elif va == "center": + r = .5 * pad + elif va == "baseline": + max_ascent = max(h - d for w, h, d in whd_list) + max_descent = max(d for w, h, d in whd_list) + r = max_descent + pad = max_ascent + max_descent + + # r : offset + # pad : total height of the ticklabels. This will be used to + # calculate the pad for the axislabel. + return r, pad + + _default_alignments = dict(left=("center", "right"), + right=("center", "left"), + bottom=("baseline", "center"), + top=("baseline", "center")) + + _default_angles = dict(left=90, + right=-90, + bottom=0, + top=180) + + def draw(self, renderer): + if not self.get_visible(): + self._axislabel_pad = self._external_pad + return + + r, total_width = self._get_ticklabels_offsets(renderer, + self._axis_direction) + + pad = self._external_pad + renderer.points_to_pixels(self.get_pad()) + self._offset_radius = r + pad + + for (x, y), a, l in self._locs_angles_labels: + if not l.strip(): + continue + self._ref_angle = a + self.set_x(x) + self.set_y(y) + self.set_text(l) + LabelBase.draw(self, renderer) + + # the value saved will be used to draw axislabel. + self._axislabel_pad = total_width + pad + + def set_locs_angles_labels(self, locs_angles_labels): + self._locs_angles_labels = locs_angles_labels + + def get_window_extents(self, renderer=None): + if renderer is None: + renderer = self.get_figure(root=True)._get_renderer() + + if not self.get_visible(): + self._axislabel_pad = self._external_pad + return [] + + bboxes = [] + + r, total_width = self._get_ticklabels_offsets(renderer, + self._axis_direction) + + pad = self._external_pad + renderer.points_to_pixels(self.get_pad()) + self._offset_radius = r + pad + + for (x, y), a, l in self._locs_angles_labels: + self._ref_angle = a + self.set_x(x) + self.set_y(y) + self.set_text(l) + bb = LabelBase.get_window_extent(self, renderer) + bboxes.append(bb) + + # the value saved will be used to draw axislabel. + self._axislabel_pad = total_width + pad + + return bboxes + + def get_texts_widths_heights_descents(self, renderer): + """ + Return a list of ``(width, height, descent)`` tuples for ticklabels. + + Empty labels are left out. + """ + whd_list = [] + for _loc, _angle, label in self._locs_angles_labels: + if not label.strip(): + continue + clean_line, ismath = self._preprocess_math(label) + whd = mtext._get_text_metrics_with_cache( + renderer, clean_line, self._fontproperties, ismath=ismath, + dpi=self.get_figure(root=True).dpi) + whd_list.append(whd) + return whd_list + + +class GridlinesCollection(LineCollection): + def __init__(self, *args, which="major", axis="both", **kwargs): + """ + Collection of grid lines. + + Parameters + ---------- + which : {"major", "minor"} + Which grid to consider. + axis : {"both", "x", "y"} + Which axis to consider. + *args, **kwargs + Passed to `.LineCollection`. + """ + self._which = which + self._axis = axis + super().__init__(*args, **kwargs) + self.set_grid_helper(None) + + def set_which(self, which): + """ + Select major or minor grid lines. + + Parameters + ---------- + which : {"major", "minor"} + """ + self._which = which + + def set_axis(self, axis): + """ + Select axis. + + Parameters + ---------- + axis : {"both", "x", "y"} + """ + self._axis = axis + + def set_grid_helper(self, grid_helper): + """ + Set grid helper. + + Parameters + ---------- + grid_helper : `.GridHelperBase` subclass + """ + self._grid_helper = grid_helper + + def draw(self, renderer): + if self._grid_helper is not None: + self._grid_helper.update_lim(self.axes) + gl = self._grid_helper.get_gridlines(self._which, self._axis) + self.set_segments([np.transpose(l) for l in gl]) + super().draw(renderer) + + +class AxisArtist(martist.Artist): + """ + An artist which draws axis (a line along which the n-th axes coord + is constant) line, ticks, tick labels, and axis label. + """ + + zorder = 2.5 + + @property + def LABELPAD(self): + return self.label.get_pad() + + @LABELPAD.setter + def LABELPAD(self, v): + self.label.set_pad(v) + + def __init__(self, axes, + helper, + offset=None, + axis_direction="bottom", + **kwargs): + """ + Parameters + ---------- + axes : `mpl_toolkits.axisartist.axislines.Axes` + helper : `~mpl_toolkits.axisartist.axislines.AxisArtistHelper` + """ + # axes is also used to follow the axis attribute (tick color, etc). + + super().__init__(**kwargs) + + self.axes = axes + + self._axis_artist_helper = helper + + if offset is None: + offset = (0, 0) + self.offset_transform = ScaledTranslation( + *offset, + Affine2D().scale(1 / 72) # points to inches. + + self.axes.get_figure(root=False).dpi_scale_trans) + + if axis_direction in ["left", "right"]: + self.axis = axes.yaxis + else: + self.axis = axes.xaxis + + self._axisline_style = None + self._axis_direction = axis_direction + + self._init_line() + self._init_ticks(**kwargs) + self._init_offsetText(axis_direction) + self._init_label() + + # axis direction + self._ticklabel_add_angle = 0. + self._axislabel_add_angle = 0. + self.set_axis_direction(axis_direction) + + # axis direction + + def set_axis_direction(self, axis_direction): + """ + Adjust the direction, text angle, and text alignment of tick labels + and axis labels following the Matplotlib convention for the rectangle + axes. + + The *axis_direction* must be one of [left, right, bottom, top]. + + ===================== ========== ========= ========== ========== + Property left bottom right top + ===================== ========== ========= ========== ========== + ticklabel direction "-" "+" "+" "-" + axislabel direction "-" "+" "+" "-" + ticklabel angle 90 0 -90 180 + ticklabel va center baseline center baseline + ticklabel ha right center right center + axislabel angle 180 0 0 180 + axislabel va center top center bottom + axislabel ha right center right center + ===================== ========== ========= ========== ========== + + Note that the direction "+" and "-" are relative to the direction of + the increasing coordinate. Also, the text angles are actually + relative to (90 + angle of the direction to the ticklabel), + which gives 0 for bottom axis. + + Parameters + ---------- + axis_direction : {"left", "bottom", "right", "top"} + """ + self.major_ticklabels.set_axis_direction(axis_direction) + self.label.set_axis_direction(axis_direction) + self._axis_direction = axis_direction + if axis_direction in ["left", "top"]: + self.set_ticklabel_direction("-") + self.set_axislabel_direction("-") + else: + self.set_ticklabel_direction("+") + self.set_axislabel_direction("+") + + def set_ticklabel_direction(self, tick_direction): + r""" + Adjust the direction of the tick labels. + + Note that the *tick_direction*\s '+' and '-' are relative to the + direction of the increasing coordinate. + + Parameters + ---------- + tick_direction : {"+", "-"} + """ + self._ticklabel_add_angle = _api.check_getitem( + {"+": 0, "-": 180}, tick_direction=tick_direction) + + def invert_ticklabel_direction(self): + self._ticklabel_add_angle = (self._ticklabel_add_angle + 180) % 360 + self.major_ticklabels.invert_axis_direction() + self.minor_ticklabels.invert_axis_direction() + + def set_axislabel_direction(self, label_direction): + r""" + Adjust the direction of the axis label. + + Note that the *label_direction*\s '+' and '-' are relative to the + direction of the increasing coordinate. + + Parameters + ---------- + label_direction : {"+", "-"} + """ + self._axislabel_add_angle = _api.check_getitem( + {"+": 0, "-": 180}, label_direction=label_direction) + + def get_transform(self): + return self.axes.transAxes + self.offset_transform + + def get_helper(self): + """ + Return axis artist helper instance. + """ + return self._axis_artist_helper + + def set_axisline_style(self, axisline_style=None, **kwargs): + """ + Set the axisline style. + + The new style is completely defined by the passed attributes. Existing + style attributes are forgotten. + + Parameters + ---------- + axisline_style : str or None + The line style, e.g. '->', optionally followed by a comma-separated + list of attributes. Alternatively, the attributes can be provided + as keywords. + + If *None* this returns a string containing the available styles. + + Examples + -------- + The following two commands are equal: + + >>> set_axisline_style("->,size=1.5") + >>> set_axisline_style("->", size=1.5) + """ + if axisline_style is None: + return AxislineStyle.pprint_styles() + + if isinstance(axisline_style, AxislineStyle._Base): + self._axisline_style = axisline_style + else: + self._axisline_style = AxislineStyle(axisline_style, **kwargs) + + self._init_line() + + def get_axisline_style(self): + """Return the current axisline style.""" + return self._axisline_style + + def _init_line(self): + """ + Initialize the *line* artist that is responsible to draw the axis line. + """ + tran = (self._axis_artist_helper.get_line_transform(self.axes) + + self.offset_transform) + + axisline_style = self.get_axisline_style() + if axisline_style is None: + self.line = PathPatch( + self._axis_artist_helper.get_line(self.axes), + color=mpl.rcParams['axes.edgecolor'], + fill=False, + linewidth=mpl.rcParams['axes.linewidth'], + capstyle=mpl.rcParams['lines.solid_capstyle'], + joinstyle=mpl.rcParams['lines.solid_joinstyle'], + transform=tran) + else: + self.line = axisline_style(self, transform=tran) + + def _draw_line(self, renderer): + self.line.set_path(self._axis_artist_helper.get_line(self.axes)) + if self.get_axisline_style() is not None: + self.line.set_line_mutation_scale(self.major_ticklabels.get_size()) + self.line.draw(renderer) + + def _init_ticks(self, **kwargs): + axis_name = self.axis.axis_name + + trans = (self._axis_artist_helper.get_tick_transform(self.axes) + + self.offset_transform) + + self.major_ticks = Ticks( + kwargs.get( + "major_tick_size", + mpl.rcParams[f"{axis_name}tick.major.size"]), + axis=self.axis, transform=trans) + self.minor_ticks = Ticks( + kwargs.get( + "minor_tick_size", + mpl.rcParams[f"{axis_name}tick.minor.size"]), + axis=self.axis, transform=trans) + + size = mpl.rcParams[f"{axis_name}tick.labelsize"] + self.major_ticklabels = TickLabels( + axis=self.axis, + axis_direction=self._axis_direction, + figure=self.axes.get_figure(root=False), + transform=trans, + fontsize=size, + pad=kwargs.get( + "major_tick_pad", mpl.rcParams[f"{axis_name}tick.major.pad"]), + ) + self.minor_ticklabels = TickLabels( + axis=self.axis, + axis_direction=self._axis_direction, + figure=self.axes.get_figure(root=False), + transform=trans, + fontsize=size, + pad=kwargs.get( + "minor_tick_pad", mpl.rcParams[f"{axis_name}tick.minor.pad"]), + ) + + def _get_tick_info(self, tick_iter): + """ + Return a pair of: + + - list of locs and angles for ticks + - list of locs, angles and labels for ticklabels. + """ + ticks_loc_angle = [] + ticklabels_loc_angle_label = [] + + ticklabel_add_angle = self._ticklabel_add_angle + + for loc, angle_normal, angle_tangent, label in tick_iter: + angle_label = angle_tangent - 90 + ticklabel_add_angle + angle_tick = (angle_normal + if 90 <= (angle_label - angle_normal) % 360 <= 270 + else angle_normal + 180) + ticks_loc_angle.append([loc, angle_tick]) + ticklabels_loc_angle_label.append([loc, angle_label, label]) + + return ticks_loc_angle, ticklabels_loc_angle_label + + def _update_ticks(self, renderer=None): + # set extra pad for major and minor ticklabels: use ticksize of + # majorticks even for minor ticks. not clear what is best. + + if renderer is None: + renderer = self.get_figure(root=True)._get_renderer() + + dpi_cor = renderer.points_to_pixels(1.) + if self.major_ticks.get_visible() and self.major_ticks.get_tick_out(): + ticklabel_pad = self.major_ticks._ticksize * dpi_cor + self.major_ticklabels._external_pad = ticklabel_pad + self.minor_ticklabels._external_pad = ticklabel_pad + else: + self.major_ticklabels._external_pad = 0 + self.minor_ticklabels._external_pad = 0 + + majortick_iter, minortick_iter = \ + self._axis_artist_helper.get_tick_iterators(self.axes) + + tick_loc_angle, ticklabel_loc_angle_label = \ + self._get_tick_info(majortick_iter) + self.major_ticks.set_locs_angles(tick_loc_angle) + self.major_ticklabels.set_locs_angles_labels(ticklabel_loc_angle_label) + + tick_loc_angle, ticklabel_loc_angle_label = \ + self._get_tick_info(minortick_iter) + self.minor_ticks.set_locs_angles(tick_loc_angle) + self.minor_ticklabels.set_locs_angles_labels(ticklabel_loc_angle_label) + + def _draw_ticks(self, renderer): + self._update_ticks(renderer) + self.major_ticks.draw(renderer) + self.major_ticklabels.draw(renderer) + self.minor_ticks.draw(renderer) + self.minor_ticklabels.draw(renderer) + if (self.major_ticklabels.get_visible() + or self.minor_ticklabels.get_visible()): + self._draw_offsetText(renderer) + + _offsetText_pos = dict(left=(0, 1, "bottom", "right"), + right=(1, 1, "bottom", "left"), + bottom=(1, 0, "top", "right"), + top=(1, 1, "bottom", "right")) + + def _init_offsetText(self, direction): + x, y, va, ha = self._offsetText_pos[direction] + self.offsetText = mtext.Annotation( + "", + xy=(x, y), xycoords="axes fraction", + xytext=(0, 0), textcoords="offset points", + color=mpl.rcParams['xtick.color'], + horizontalalignment=ha, verticalalignment=va, + ) + self.offsetText.set_transform(IdentityTransform()) + self.axes._set_artist_props(self.offsetText) + + def _update_offsetText(self): + self.offsetText.set_text(self.axis.major.formatter.get_offset()) + self.offsetText.set_size(self.major_ticklabels.get_size()) + offset = (self.major_ticklabels.get_pad() + + self.major_ticklabels.get_size() + + 2) + self.offsetText.xyann = (0, offset) + + def _draw_offsetText(self, renderer): + self._update_offsetText() + self.offsetText.draw(renderer) + + def _init_label(self, **kwargs): + tr = (self._axis_artist_helper.get_axislabel_transform(self.axes) + + self.offset_transform) + self.label = AxisLabel( + 0, 0, "__from_axes__", + color="auto", + fontsize=kwargs.get("labelsize", mpl.rcParams['axes.labelsize']), + fontweight=mpl.rcParams['axes.labelweight'], + axis=self.axis, + transform=tr, + axis_direction=self._axis_direction, + ) + self.label.set_figure(self.axes.get_figure(root=False)) + labelpad = kwargs.get("labelpad", 5) + self.label.set_pad(labelpad) + + def _update_label(self, renderer): + if not self.label.get_visible(): + return + + if self._ticklabel_add_angle != self._axislabel_add_angle: + if ((self.major_ticks.get_visible() + and not self.major_ticks.get_tick_out()) + or (self.minor_ticks.get_visible() + and not self.major_ticks.get_tick_out())): + axislabel_pad = self.major_ticks._ticksize + else: + axislabel_pad = 0 + else: + axislabel_pad = max(self.major_ticklabels._axislabel_pad, + self.minor_ticklabels._axislabel_pad) + + self.label._external_pad = axislabel_pad + + xy, angle_tangent = \ + self._axis_artist_helper.get_axislabel_pos_angle(self.axes) + if xy is None: + return + + angle_label = angle_tangent - 90 + + x, y = xy + self.label._ref_angle = angle_label + self._axislabel_add_angle + self.label.set(x=x, y=y) + + def _draw_label(self, renderer): + self._update_label(renderer) + self.label.draw(renderer) + + def set_label(self, s): + # docstring inherited + self.label.set_text(s) + + def get_tightbbox(self, renderer=None): + if not self.get_visible(): + return + self._axis_artist_helper.update_lim(self.axes) + self._update_ticks(renderer) + self._update_label(renderer) + + self.line.set_path(self._axis_artist_helper.get_line(self.axes)) + if self.get_axisline_style() is not None: + self.line.set_line_mutation_scale(self.major_ticklabels.get_size()) + + bb = [ + *self.major_ticklabels.get_window_extents(renderer), + *self.minor_ticklabels.get_window_extents(renderer), + self.label.get_window_extent(renderer), + self.offsetText.get_window_extent(renderer), + self.line.get_window_extent(renderer), + ] + bb = [b for b in bb if b and (b.width != 0 or b.height != 0)] + if bb: + _bbox = Bbox.union(bb) + return _bbox + else: + return None + + @martist.allow_rasterization + def draw(self, renderer): + # docstring inherited + if not self.get_visible(): + return + renderer.open_group(__name__, gid=self.get_gid()) + self._axis_artist_helper.update_lim(self.axes) + self._draw_ticks(renderer) + self._draw_line(renderer) + self._draw_label(renderer) + renderer.close_group(__name__) + + def toggle(self, all=None, ticks=None, ticklabels=None, label=None): + """ + Toggle visibility of ticks, ticklabels, and (axis) label. + To turn all off, :: + + axis.toggle(all=False) + + To turn all off but ticks on :: + + axis.toggle(all=False, ticks=True) + + To turn all on but (axis) label off :: + + axis.toggle(all=True, label=False) + + """ + if all: + _ticks, _ticklabels, _label = True, True, True + elif all is not None: + _ticks, _ticklabels, _label = False, False, False + else: + _ticks, _ticklabels, _label = None, None, None + + if ticks is not None: + _ticks = ticks + if ticklabels is not None: + _ticklabels = ticklabels + if label is not None: + _label = label + + if _ticks is not None: + self.major_ticks.set_visible(_ticks) + self.minor_ticks.set_visible(_ticks) + if _ticklabels is not None: + self.major_ticklabels.set_visible(_ticklabels) + self.minor_ticklabels.set_visible(_ticklabels) + if _label is not None: + self.label.set_visible(_label) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axisline_style.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axisline_style.py new file mode 100644 index 0000000000000000000000000000000000000000..7f25b98082ef52b9f162542a3eafaec3b9750fd0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axisline_style.py @@ -0,0 +1,193 @@ +""" +Provides classes to style the axis lines. +""" +import math + +import numpy as np + +import matplotlib as mpl +from matplotlib.patches import _Style, FancyArrowPatch +from matplotlib.path import Path +from matplotlib.transforms import IdentityTransform + + +class _FancyAxislineStyle: + class SimpleArrow(FancyArrowPatch): + """The artist class that will be returned for SimpleArrow style.""" + _ARROW_STYLE = "->" + + def __init__(self, axis_artist, line_path, transform, + line_mutation_scale): + self._axis_artist = axis_artist + self._line_transform = transform + self._line_path = line_path + self._line_mutation_scale = line_mutation_scale + + FancyArrowPatch.__init__(self, + path=self._line_path, + arrowstyle=self._ARROW_STYLE, + patchA=None, + patchB=None, + shrinkA=0., + shrinkB=0., + mutation_scale=line_mutation_scale, + mutation_aspect=None, + transform=IdentityTransform(), + ) + + def set_line_mutation_scale(self, scale): + self.set_mutation_scale(scale*self._line_mutation_scale) + + def _extend_path(self, path, mutation_size=10): + """ + Extend the path to make a room for drawing arrow. + """ + (x0, y0), (x1, y1) = path.vertices[-2:] + theta = math.atan2(y1 - y0, x1 - x0) + x2 = x1 + math.cos(theta) * mutation_size + y2 = y1 + math.sin(theta) * mutation_size + if path.codes is None: + return Path(np.concatenate([path.vertices, [[x2, y2]]])) + else: + return Path(np.concatenate([path.vertices, [[x2, y2]]]), + np.concatenate([path.codes, [Path.LINETO]])) + + def set_path(self, path): + self._line_path = path + + def draw(self, renderer): + """ + Draw the axis line. + 1) Transform the path to the display coordinate. + 2) Extend the path to make a room for arrow. + 3) Update the path of the FancyArrowPatch. + 4) Draw. + """ + path_in_disp = self._line_transform.transform_path(self._line_path) + mutation_size = self.get_mutation_scale() # line_mutation_scale() + extended_path = self._extend_path(path_in_disp, + mutation_size=mutation_size) + self._path_original = extended_path + FancyArrowPatch.draw(self, renderer) + + def get_window_extent(self, renderer=None): + + path_in_disp = self._line_transform.transform_path(self._line_path) + mutation_size = self.get_mutation_scale() # line_mutation_scale() + extended_path = self._extend_path(path_in_disp, + mutation_size=mutation_size) + self._path_original = extended_path + return FancyArrowPatch.get_window_extent(self, renderer) + + class FilledArrow(SimpleArrow): + """The artist class that will be returned for FilledArrow style.""" + _ARROW_STYLE = "-|>" + + def __init__(self, axis_artist, line_path, transform, + line_mutation_scale, facecolor): + super().__init__(axis_artist, line_path, transform, + line_mutation_scale) + self.set_facecolor(facecolor) + + +class AxislineStyle(_Style): + """ + A container class which defines style classes for AxisArtists. + + An instance of any axisline style class is a callable object, + whose call signature is :: + + __call__(self, axis_artist, path, transform) + + When called, this should return an `.Artist` with the following methods:: + + def set_path(self, path): + # set the path for axisline. + + def set_line_mutation_scale(self, scale): + # set the scale + + def draw(self, renderer): + # draw + """ + + _style_list = {} + + class _Base: + # The derived classes are required to be able to be initialized + # w/o arguments, i.e., all its argument (except self) must have + # the default values. + + def __init__(self): + """ + initialization. + """ + super().__init__() + + def __call__(self, axis_artist, transform): + """ + Given the AxisArtist instance, and transform for the path (set_path + method), return the Matplotlib artist for drawing the axis line. + """ + return self.new_line(axis_artist, transform) + + class SimpleArrow(_Base): + """ + A simple arrow. + """ + + ArrowAxisClass = _FancyAxislineStyle.SimpleArrow + + def __init__(self, size=1): + """ + Parameters + ---------- + size : float + Size of the arrow as a fraction of the ticklabel size. + """ + + self.size = size + super().__init__() + + def new_line(self, axis_artist, transform): + + linepath = Path([(0, 0), (0, 1)]) + axisline = self.ArrowAxisClass(axis_artist, linepath, transform, + line_mutation_scale=self.size) + return axisline + + _style_list["->"] = SimpleArrow + + class FilledArrow(SimpleArrow): + """ + An arrow with a filled head. + """ + + ArrowAxisClass = _FancyAxislineStyle.FilledArrow + + def __init__(self, size=1, facecolor=None): + """ + Parameters + ---------- + size : float + Size of the arrow as a fraction of the ticklabel size. + facecolor : :mpltype:`color`, default: :rc:`axes.edgecolor` + Fill color. + + .. versionadded:: 3.7 + """ + + if facecolor is None: + facecolor = mpl.rcParams['axes.edgecolor'] + self.size = size + self._facecolor = facecolor + super().__init__(size=size) + + def new_line(self, axis_artist, transform): + linepath = Path([(0, 0), (0, 1)]) + axisline = self.ArrowAxisClass(axis_artist, linepath, transform, + line_mutation_scale=self.size, + facecolor=self._facecolor) + return axisline + + _style_list["-|>"] = FilledArrow diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axislines.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axislines.py new file mode 100644 index 0000000000000000000000000000000000000000..8d06cb236269d00ed20a27297b59fa816338e79a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/axislines.py @@ -0,0 +1,479 @@ +""" +Axislines includes modified implementation of the Axes class. The +biggest difference is that the artists responsible for drawing the axis spine, +ticks, ticklabels and axis labels are separated out from Matplotlib's Axis +class. Originally, this change was motivated to support curvilinear +grid. Here are a few reasons that I came up with a new axes class: + +* "top" and "bottom" x-axis (or "left" and "right" y-axis) can have + different ticks (tick locations and labels). This is not possible + with the current Matplotlib, although some twin axes trick can help. + +* Curvilinear grid. + +* angled ticks. + +In the new axes class, xaxis and yaxis is set to not visible by +default, and new set of artist (AxisArtist) are defined to draw axis +line, ticks, ticklabels and axis label. Axes.axis attribute serves as +a dictionary of these artists, i.e., ax.axis["left"] is a AxisArtist +instance responsible to draw left y-axis. The default Axes.axis contains +"bottom", "left", "top" and "right". + +AxisArtist can be considered as a container artist and has the following +children artists which will draw ticks, labels, etc. + +* line +* major_ticks, major_ticklabels +* minor_ticks, minor_ticklabels +* offsetText +* label + +Note that these are separate artists from `matplotlib.axis.Axis`, thus most +tick-related functions in Matplotlib won't work. For example, color and +markerwidth of the ``ax.axis["bottom"].major_ticks`` will follow those of +Axes.xaxis unless explicitly specified. + +In addition to AxisArtist, the Axes will have *gridlines* attribute, +which obviously draws grid lines. The gridlines needs to be separated +from the axis as some gridlines can never pass any axis. +""" + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api +import matplotlib.axes as maxes +from matplotlib.path import Path +from mpl_toolkits.axes_grid1 import mpl_axes +from .axisline_style import AxislineStyle # noqa +from .axis_artist import AxisArtist, GridlinesCollection + + +class _AxisArtistHelperBase: + """ + Base class for axis helper. + + Subclasses should define the methods listed below. The *axes* + argument will be the ``.axes`` attribute of the caller artist. :: + + # Construct the spine. + + def get_line_transform(self, axes): + return transform + + def get_line(self, axes): + return path + + # Construct the label. + + def get_axislabel_transform(self, axes): + return transform + + def get_axislabel_pos_angle(self, axes): + return (x, y), angle + + # Construct the ticks. + + def get_tick_transform(self, axes): + return transform + + def get_tick_iterators(self, axes): + # A pair of iterables (one for major ticks, one for minor ticks) + # that yield (tick_position, tick_angle, tick_label). + return iter_major, iter_minor + """ + + def __init__(self, nth_coord): + self.nth_coord = nth_coord + + def update_lim(self, axes): + pass + + def get_nth_coord(self): + return self.nth_coord + + def _to_xy(self, values, const): + """ + Create a (*values.shape, 2)-shape array representing (x, y) pairs. + + The other coordinate is filled with the constant *const*. + + Example:: + + >>> self.nth_coord = 0 + >>> self._to_xy([1, 2, 3], const=0) + array([[1, 0], + [2, 0], + [3, 0]]) + """ + if self.nth_coord == 0: + return np.stack(np.broadcast_arrays(values, const), axis=-1) + elif self.nth_coord == 1: + return np.stack(np.broadcast_arrays(const, values), axis=-1) + else: + raise ValueError("Unexpected nth_coord") + + +class _FixedAxisArtistHelperBase(_AxisArtistHelperBase): + """Helper class for a fixed (in the axes coordinate) axis.""" + + @_api.delete_parameter("3.9", "nth_coord") + def __init__(self, loc, nth_coord=None): + """``nth_coord = 0``: x-axis; ``nth_coord = 1``: y-axis.""" + super().__init__(_api.check_getitem( + {"bottom": 0, "top": 0, "left": 1, "right": 1}, loc=loc)) + self._loc = loc + self._pos = {"bottom": 0, "top": 1, "left": 0, "right": 1}[loc] + # axis line in transAxes + self._path = Path(self._to_xy((0, 1), const=self._pos)) + + # LINE + + def get_line(self, axes): + return self._path + + def get_line_transform(self, axes): + return axes.transAxes + + # LABEL + + def get_axislabel_transform(self, axes): + return axes.transAxes + + def get_axislabel_pos_angle(self, axes): + """ + Return the label reference position in transAxes. + + get_label_transform() returns a transform of (transAxes+offset) + """ + return dict(left=((0., 0.5), 90), # (position, angle_tangent) + right=((1., 0.5), 90), + bottom=((0.5, 0.), 0), + top=((0.5, 1.), 0))[self._loc] + + # TICK + + def get_tick_transform(self, axes): + return [axes.get_xaxis_transform(), axes.get_yaxis_transform()][self.nth_coord] + + +class _FloatingAxisArtistHelperBase(_AxisArtistHelperBase): + def __init__(self, nth_coord, value): + self._value = value + super().__init__(nth_coord) + + def get_line(self, axes): + raise RuntimeError("get_line method should be defined by the derived class") + + +class FixedAxisArtistHelperRectilinear(_FixedAxisArtistHelperBase): + + @_api.delete_parameter("3.9", "nth_coord") + def __init__(self, axes, loc, nth_coord=None): + """ + nth_coord = along which coordinate value varies + in 2D, nth_coord = 0 -> x axis, nth_coord = 1 -> y axis + """ + super().__init__(loc) + self.axis = [axes.xaxis, axes.yaxis][self.nth_coord] + + # TICK + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label""" + angle_normal, angle_tangent = {0: (90, 0), 1: (0, 90)}[self.nth_coord] + + major = self.axis.major + major_locs = major.locator() + major_labels = major.formatter.format_ticks(major_locs) + + minor = self.axis.minor + minor_locs = minor.locator() + minor_labels = minor.formatter.format_ticks(minor_locs) + + tick_to_axes = self.get_tick_transform(axes) - axes.transAxes + + def _f(locs, labels): + for loc, label in zip(locs, labels): + c = self._to_xy(loc, const=self._pos) + # check if the tick point is inside axes + c2 = tick_to_axes.transform(c) + if mpl.transforms._interval_contains_close((0, 1), c2[self.nth_coord]): + yield c, angle_normal, angle_tangent, label + + return _f(major_locs, major_labels), _f(minor_locs, minor_labels) + + +class FloatingAxisArtistHelperRectilinear(_FloatingAxisArtistHelperBase): + + def __init__(self, axes, nth_coord, + passingthrough_point, axis_direction="bottom"): + super().__init__(nth_coord, passingthrough_point) + self._axis_direction = axis_direction + self.axis = [axes.xaxis, axes.yaxis][self.nth_coord] + + def get_line(self, axes): + fixed_coord = 1 - self.nth_coord + data_to_axes = axes.transData - axes.transAxes + p = data_to_axes.transform([self._value, self._value]) + return Path(self._to_xy((0, 1), const=p[fixed_coord])) + + def get_line_transform(self, axes): + return axes.transAxes + + def get_axislabel_transform(self, axes): + return axes.transAxes + + def get_axislabel_pos_angle(self, axes): + """ + Return the label reference position in transAxes. + + get_label_transform() returns a transform of (transAxes+offset) + """ + angle = [0, 90][self.nth_coord] + fixed_coord = 1 - self.nth_coord + data_to_axes = axes.transData - axes.transAxes + p = data_to_axes.transform([self._value, self._value]) + verts = self._to_xy(0.5, const=p[fixed_coord]) + return (verts, angle) if 0 <= verts[fixed_coord] <= 1 else (None, None) + + def get_tick_transform(self, axes): + return axes.transData + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label""" + angle_normal, angle_tangent = {0: (90, 0), 1: (0, 90)}[self.nth_coord] + + major = self.axis.major + major_locs = major.locator() + major_labels = major.formatter.format_ticks(major_locs) + + minor = self.axis.minor + minor_locs = minor.locator() + minor_labels = minor.formatter.format_ticks(minor_locs) + + data_to_axes = axes.transData - axes.transAxes + + def _f(locs, labels): + for loc, label in zip(locs, labels): + c = self._to_xy(loc, const=self._value) + c1, c2 = data_to_axes.transform(c) + if 0 <= c1 <= 1 and 0 <= c2 <= 1: + yield c, angle_normal, angle_tangent, label + + return _f(major_locs, major_labels), _f(minor_locs, minor_labels) + + +class AxisArtistHelper: # Backcompat. + Fixed = _FixedAxisArtistHelperBase + Floating = _FloatingAxisArtistHelperBase + + +class AxisArtistHelperRectlinear: # Backcompat. + Fixed = FixedAxisArtistHelperRectilinear + Floating = FloatingAxisArtistHelperRectilinear + + +class GridHelperBase: + + def __init__(self): + self._old_limits = None + super().__init__() + + def update_lim(self, axes): + x1, x2 = axes.get_xlim() + y1, y2 = axes.get_ylim() + if self._old_limits != (x1, x2, y1, y2): + self._update_grid(x1, y1, x2, y2) + self._old_limits = (x1, x2, y1, y2) + + def _update_grid(self, x1, y1, x2, y2): + """Cache relevant computations when the axes limits have changed.""" + + def get_gridlines(self, which, axis): + """ + Return list of grid lines as a list of paths (list of points). + + Parameters + ---------- + which : {"both", "major", "minor"} + axis : {"both", "x", "y"} + """ + return [] + + +class GridHelperRectlinear(GridHelperBase): + + def __init__(self, axes): + super().__init__() + self.axes = axes + + @_api.delete_parameter( + "3.9", "nth_coord", addendum="'nth_coord' is now inferred from 'loc'.") + def new_fixed_axis( + self, loc, nth_coord=None, axis_direction=None, offset=None, axes=None): + if axes is None: + _api.warn_external( + "'new_fixed_axis' explicitly requires the axes keyword.") + axes = self.axes + if axis_direction is None: + axis_direction = loc + return AxisArtist(axes, FixedAxisArtistHelperRectilinear(axes, loc), + offset=offset, axis_direction=axis_direction) + + def new_floating_axis(self, nth_coord, value, axis_direction="bottom", axes=None): + if axes is None: + _api.warn_external( + "'new_floating_axis' explicitly requires the axes keyword.") + axes = self.axes + helper = FloatingAxisArtistHelperRectilinear( + axes, nth_coord, value, axis_direction) + axisline = AxisArtist(axes, helper, axis_direction=axis_direction) + axisline.line.set_clip_on(True) + axisline.line.set_clip_box(axisline.axes.bbox) + return axisline + + def get_gridlines(self, which="major", axis="both"): + """ + Return list of gridline coordinates in data coordinates. + + Parameters + ---------- + which : {"both", "major", "minor"} + axis : {"both", "x", "y"} + """ + _api.check_in_list(["both", "major", "minor"], which=which) + _api.check_in_list(["both", "x", "y"], axis=axis) + gridlines = [] + + if axis in ("both", "x"): + locs = [] + y1, y2 = self.axes.get_ylim() + if which in ("both", "major"): + locs.extend(self.axes.xaxis.major.locator()) + if which in ("both", "minor"): + locs.extend(self.axes.xaxis.minor.locator()) + gridlines.extend([[x, x], [y1, y2]] for x in locs) + + if axis in ("both", "y"): + x1, x2 = self.axes.get_xlim() + locs = [] + if self.axes.yaxis._major_tick_kw["gridOn"]: + locs.extend(self.axes.yaxis.major.locator()) + if self.axes.yaxis._minor_tick_kw["gridOn"]: + locs.extend(self.axes.yaxis.minor.locator()) + gridlines.extend([[x1, x2], [y, y]] for y in locs) + + return gridlines + + +class Axes(maxes.Axes): + + def __init__(self, *args, grid_helper=None, **kwargs): + self._axisline_on = True + self._grid_helper = grid_helper if grid_helper else GridHelperRectlinear(self) + super().__init__(*args, **kwargs) + self.toggle_axisline(True) + + def toggle_axisline(self, b=None): + if b is None: + b = not self._axisline_on + if b: + self._axisline_on = True + self.spines[:].set_visible(False) + self.xaxis.set_visible(False) + self.yaxis.set_visible(False) + else: + self._axisline_on = False + self.spines[:].set_visible(True) + self.xaxis.set_visible(True) + self.yaxis.set_visible(True) + + @property + def axis(self): + return self._axislines + + def clear(self): + # docstring inherited + + # Init gridlines before clear() as clear() calls grid(). + self.gridlines = gridlines = GridlinesCollection( + [], + colors=mpl.rcParams['grid.color'], + linestyles=mpl.rcParams['grid.linestyle'], + linewidths=mpl.rcParams['grid.linewidth']) + self._set_artist_props(gridlines) + gridlines.set_grid_helper(self.get_grid_helper()) + + super().clear() + + # clip_path is set after Axes.clear(): that's when a patch is created. + gridlines.set_clip_path(self.axes.patch) + + # Init axis artists. + self._axislines = mpl_axes.Axes.AxisDict(self) + new_fixed_axis = self.get_grid_helper().new_fixed_axis + self._axislines.update({ + loc: new_fixed_axis(loc=loc, axes=self, axis_direction=loc) + for loc in ["bottom", "top", "left", "right"]}) + for axisline in [self._axislines["top"], self._axislines["right"]]: + axisline.label.set_visible(False) + axisline.major_ticklabels.set_visible(False) + axisline.minor_ticklabels.set_visible(False) + + def get_grid_helper(self): + return self._grid_helper + + def grid(self, visible=None, which='major', axis="both", **kwargs): + """ + Toggle the gridlines, and optionally set the properties of the lines. + """ + # There are some discrepancies in the behavior of grid() between + # axes_grid and Matplotlib, because axes_grid explicitly sets the + # visibility of the gridlines. + super().grid(visible, which=which, axis=axis, **kwargs) + if not self._axisline_on: + return + if visible is None: + visible = (self.axes.xaxis._minor_tick_kw["gridOn"] + or self.axes.xaxis._major_tick_kw["gridOn"] + or self.axes.yaxis._minor_tick_kw["gridOn"] + or self.axes.yaxis._major_tick_kw["gridOn"]) + self.gridlines.set(which=which, axis=axis, visible=visible) + self.gridlines.set(**kwargs) + + def get_children(self): + if self._axisline_on: + children = [*self._axislines.values(), self.gridlines] + else: + children = [] + children.extend(super().get_children()) + return children + + def new_fixed_axis(self, loc, offset=None): + return self.get_grid_helper().new_fixed_axis(loc, offset=offset, axes=self) + + def new_floating_axis(self, nth_coord, value, axis_direction="bottom"): + return self.get_grid_helper().new_floating_axis( + nth_coord, value, axis_direction=axis_direction, axes=self) + + +class AxesZero(Axes): + + def clear(self): + super().clear() + new_floating_axis = self.get_grid_helper().new_floating_axis + self._axislines.update( + xzero=new_floating_axis( + nth_coord=0, value=0., axis_direction="bottom", axes=self), + yzero=new_floating_axis( + nth_coord=1, value=0., axis_direction="left", axes=self), + ) + for k in ["xzero", "yzero"]: + self._axislines[k].line.set_clip_path(self.patch) + self._axislines[k].set_visible(False) + + +Subplot = Axes +SubplotZero = AxesZero diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/floating_axes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/floating_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..74e4c941879babca194cec7d60fe949e648cb4cf --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/floating_axes.py @@ -0,0 +1,275 @@ +""" +An experimental support for curvilinear grid. +""" + +# TODO : +# see if tick_iterator method can be simplified by reusing the parent method. + +import functools + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api, cbook +import matplotlib.patches as mpatches +from matplotlib.path import Path + +from mpl_toolkits.axes_grid1.parasite_axes import host_axes_class_factory + +from . import axislines, grid_helper_curvelinear +from .axis_artist import AxisArtist +from .grid_finder import ExtremeFinderSimple + + +class FloatingAxisArtistHelper( + grid_helper_curvelinear.FloatingAxisArtistHelper): + pass + + +class FixedAxisArtistHelper(grid_helper_curvelinear.FloatingAxisArtistHelper): + + def __init__(self, grid_helper, side, nth_coord_ticks=None): + """ + nth_coord = along which coordinate value varies. + nth_coord = 0 -> x axis, nth_coord = 1 -> y axis + """ + lon1, lon2, lat1, lat2 = grid_helper.grid_finder.extreme_finder(*[None] * 5) + value, nth_coord = _api.check_getitem( + dict(left=(lon1, 0), right=(lon2, 0), bottom=(lat1, 1), top=(lat2, 1)), + side=side) + super().__init__(grid_helper, nth_coord, value, axis_direction=side) + if nth_coord_ticks is None: + nth_coord_ticks = nth_coord + self.nth_coord_ticks = nth_coord_ticks + + self.value = value + self.grid_helper = grid_helper + self._side = side + + def update_lim(self, axes): + self.grid_helper.update_lim(axes) + self._grid_info = self.grid_helper._grid_info + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label, (optionally) tick_label""" + + grid_finder = self.grid_helper.grid_finder + + lat_levs, lat_n, lat_factor = self._grid_info["lat_info"] + yy0 = lat_levs / lat_factor + + lon_levs, lon_n, lon_factor = self._grid_info["lon_info"] + xx0 = lon_levs / lon_factor + + extremes = self.grid_helper.grid_finder.extreme_finder(*[None] * 5) + xmin, xmax = sorted(extremes[:2]) + ymin, ymax = sorted(extremes[2:]) + + def trf_xy(x, y): + trf = grid_finder.get_transform() + axes.transData + return trf.transform(np.column_stack(np.broadcast_arrays(x, y))).T + + if self.nth_coord == 0: + mask = (ymin <= yy0) & (yy0 <= ymax) + (xx1, yy1), (dxx1, dyy1), (dxx2, dyy2) = \ + grid_helper_curvelinear._value_and_jacobian( + trf_xy, self.value, yy0[mask], (xmin, xmax), (ymin, ymax)) + labels = self._grid_info["lat_labels"] + + elif self.nth_coord == 1: + mask = (xmin <= xx0) & (xx0 <= xmax) + (xx1, yy1), (dxx2, dyy2), (dxx1, dyy1) = \ + grid_helper_curvelinear._value_and_jacobian( + trf_xy, xx0[mask], self.value, (xmin, xmax), (ymin, ymax)) + labels = self._grid_info["lon_labels"] + + labels = [l for l, m in zip(labels, mask) if m] + + angle_normal = np.arctan2(dyy1, dxx1) + angle_tangent = np.arctan2(dyy2, dxx2) + mm = (dyy1 == 0) & (dxx1 == 0) # points with degenerate normal + angle_normal[mm] = angle_tangent[mm] + np.pi / 2 + + tick_to_axes = self.get_tick_transform(axes) - axes.transAxes + in_01 = functools.partial( + mpl.transforms._interval_contains_close, (0, 1)) + + def f1(): + for x, y, normal, tangent, lab \ + in zip(xx1, yy1, angle_normal, angle_tangent, labels): + c2 = tick_to_axes.transform((x, y)) + if in_01(c2[0]) and in_01(c2[1]): + yield [x, y], *np.rad2deg([normal, tangent]), lab + + return f1(), iter([]) + + def get_line(self, axes): + self.update_lim(axes) + k, v = dict(left=("lon_lines0", 0), + right=("lon_lines0", 1), + bottom=("lat_lines0", 0), + top=("lat_lines0", 1))[self._side] + xx, yy = self._grid_info[k][v] + return Path(np.column_stack([xx, yy])) + + +class ExtremeFinderFixed(ExtremeFinderSimple): + # docstring inherited + + def __init__(self, extremes): + """ + This subclass always returns the same bounding box. + + Parameters + ---------- + extremes : (float, float, float, float) + The bounding box that this helper always returns. + """ + self._extremes = extremes + + def __call__(self, transform_xy, x1, y1, x2, y2): + # docstring inherited + return self._extremes + + +class GridHelperCurveLinear(grid_helper_curvelinear.GridHelperCurveLinear): + + def __init__(self, aux_trans, extremes, + grid_locator1=None, + grid_locator2=None, + tick_formatter1=None, + tick_formatter2=None): + # docstring inherited + super().__init__(aux_trans, + extreme_finder=ExtremeFinderFixed(extremes), + grid_locator1=grid_locator1, + grid_locator2=grid_locator2, + tick_formatter1=tick_formatter1, + tick_formatter2=tick_formatter2) + + def new_fixed_axis( + self, loc, nth_coord=None, axis_direction=None, offset=None, axes=None): + if axes is None: + axes = self.axes + if axis_direction is None: + axis_direction = loc + # This is not the same as the FixedAxisArtistHelper class used by + # grid_helper_curvelinear.GridHelperCurveLinear.new_fixed_axis! + helper = FixedAxisArtistHelper( + self, loc, nth_coord_ticks=nth_coord) + axisline = AxisArtist(axes, helper, axis_direction=axis_direction) + # Perhaps should be moved to the base class? + axisline.line.set_clip_on(True) + axisline.line.set_clip_box(axisline.axes.bbox) + return axisline + + # new_floating_axis will inherit the grid_helper's extremes. + + # def new_floating_axis(self, nth_coord, value, axes=None, axis_direction="bottom"): + # axis = super(GridHelperCurveLinear, + # self).new_floating_axis(nth_coord, + # value, axes=axes, + # axis_direction=axis_direction) + # # set extreme values of the axis helper + # if nth_coord == 1: + # axis.get_helper().set_extremes(*self._extremes[:2]) + # elif nth_coord == 0: + # axis.get_helper().set_extremes(*self._extremes[2:]) + # return axis + + def _update_grid(self, x1, y1, x2, y2): + if self._grid_info is None: + self._grid_info = dict() + + grid_info = self._grid_info + + grid_finder = self.grid_finder + extremes = grid_finder.extreme_finder(grid_finder.inv_transform_xy, + x1, y1, x2, y2) + + lon_min, lon_max = sorted(extremes[:2]) + lat_min, lat_max = sorted(extremes[2:]) + grid_info["extremes"] = lon_min, lon_max, lat_min, lat_max # extremes + + lon_levs, lon_n, lon_factor = \ + grid_finder.grid_locator1(lon_min, lon_max) + lon_levs = np.asarray(lon_levs) + lat_levs, lat_n, lat_factor = \ + grid_finder.grid_locator2(lat_min, lat_max) + lat_levs = np.asarray(lat_levs) + + grid_info["lon_info"] = lon_levs, lon_n, lon_factor + grid_info["lat_info"] = lat_levs, lat_n, lat_factor + + grid_info["lon_labels"] = grid_finder._format_ticks( + 1, "bottom", lon_factor, lon_levs) + grid_info["lat_labels"] = grid_finder._format_ticks( + 2, "bottom", lat_factor, lat_levs) + + lon_values = lon_levs[:lon_n] / lon_factor + lat_values = lat_levs[:lat_n] / lat_factor + + lon_lines, lat_lines = grid_finder._get_raw_grid_lines( + lon_values[(lon_min < lon_values) & (lon_values < lon_max)], + lat_values[(lat_min < lat_values) & (lat_values < lat_max)], + lon_min, lon_max, lat_min, lat_max) + + grid_info["lon_lines"] = lon_lines + grid_info["lat_lines"] = lat_lines + + lon_lines, lat_lines = grid_finder._get_raw_grid_lines( + # lon_min, lon_max, lat_min, lat_max) + extremes[:2], extremes[2:], *extremes) + + grid_info["lon_lines0"] = lon_lines + grid_info["lat_lines0"] = lat_lines + + def get_gridlines(self, which="major", axis="both"): + grid_lines = [] + if axis in ["both", "x"]: + grid_lines.extend(self._grid_info["lon_lines"]) + if axis in ["both", "y"]: + grid_lines.extend(self._grid_info["lat_lines"]) + return grid_lines + + +class FloatingAxesBase: + + def __init__(self, *args, grid_helper, **kwargs): + _api.check_isinstance(GridHelperCurveLinear, grid_helper=grid_helper) + super().__init__(*args, grid_helper=grid_helper, **kwargs) + self.set_aspect(1.) + + def _gen_axes_patch(self): + # docstring inherited + x0, x1, y0, y1 = self.get_grid_helper().grid_finder.extreme_finder(*[None] * 5) + patch = mpatches.Polygon([(x0, y0), (x1, y0), (x1, y1), (x0, y1)]) + patch.get_path()._interpolation_steps = 100 + return patch + + def clear(self): + super().clear() + self.patch.set_transform( + self.get_grid_helper().grid_finder.get_transform() + + self.transData) + # The original patch is not in the draw tree; it is only used for + # clipping purposes. + orig_patch = super()._gen_axes_patch() + orig_patch.set_figure(self.get_figure(root=False)) + orig_patch.set_transform(self.transAxes) + self.patch.set_clip_path(orig_patch) + self.gridlines.set_clip_path(orig_patch) + self.adjust_axes_lim() + + def adjust_axes_lim(self): + bbox = self.patch.get_path().get_extents( + # First transform to pixel coords, then to parent data coords. + self.patch.get_transform() - self.transData) + bbox = bbox.expanded(1.02, 1.02) + self.set_xlim(bbox.xmin, bbox.xmax) + self.set_ylim(bbox.ymin, bbox.ymax) + + +floatingaxes_class_factory = cbook._make_class_factory(FloatingAxesBase, "Floating{}") +FloatingAxes = floatingaxes_class_factory(host_axes_class_factory(axislines.Axes)) +FloatingSubplot = FloatingAxes diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_finder.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_finder.py new file mode 100644 index 0000000000000000000000000000000000000000..ff67aa6e872044bb74e8f2e3882a4d3719cbd55d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_finder.py @@ -0,0 +1,326 @@ +import numpy as np + +from matplotlib import ticker as mticker, _api +from matplotlib.transforms import Bbox, Transform + + +def _find_line_box_crossings(xys, bbox): + """ + Find the points where a polyline crosses a bbox, and the crossing angles. + + Parameters + ---------- + xys : (N, 2) array + The polyline coordinates. + bbox : `.Bbox` + The bounding box. + + Returns + ------- + list of ((float, float), float) + Four separate lists of crossings, for the left, right, bottom, and top + sides of the bbox, respectively. For each list, the entries are the + ``((x, y), ccw_angle_in_degrees)`` of the crossing, where an angle of 0 + means that the polyline is moving to the right at the crossing point. + + The entries are computed by linearly interpolating at each crossing + between the nearest points on either side of the bbox edges. + """ + crossings = [] + dxys = xys[1:] - xys[:-1] + for sl in [slice(None), slice(None, None, -1)]: + us, vs = xys.T[sl] # "this" coord, "other" coord + dus, dvs = dxys.T[sl] + umin, vmin = bbox.min[sl] + umax, vmax = bbox.max[sl] + for u0, inside in [(umin, us > umin), (umax, us < umax)]: + cross = [] + idxs, = (inside[:-1] ^ inside[1:]).nonzero() + for idx in idxs: + v = vs[idx] + (u0 - us[idx]) * dvs[idx] / dus[idx] + if not vmin <= v <= vmax: + continue + crossing = (u0, v)[sl] + theta = np.degrees(np.arctan2(*dxys[idx][::-1])) + cross.append((crossing, theta)) + crossings.append(cross) + return crossings + + +class ExtremeFinderSimple: + """ + A helper class to figure out the range of grid lines that need to be drawn. + """ + + def __init__(self, nx, ny): + """ + Parameters + ---------- + nx, ny : int + The number of samples in each direction. + """ + self.nx = nx + self.ny = ny + + def __call__(self, transform_xy, x1, y1, x2, y2): + """ + Compute an approximation of the bounding box obtained by applying + *transform_xy* to the box delimited by ``(x1, y1, x2, y2)``. + + The intended use is to have ``(x1, y1, x2, y2)`` in axes coordinates, + and have *transform_xy* be the transform from axes coordinates to data + coordinates; this method then returns the range of data coordinates + that span the actual axes. + + The computation is done by sampling ``nx * ny`` equispaced points in + the ``(x1, y1, x2, y2)`` box and finding the resulting points with + extremal coordinates; then adding some padding to take into account the + finite sampling. + + As each sampling step covers a relative range of *1/nx* or *1/ny*, + the padding is computed by expanding the span covered by the extremal + coordinates by these fractions. + """ + x, y = np.meshgrid( + np.linspace(x1, x2, self.nx), np.linspace(y1, y2, self.ny)) + xt, yt = transform_xy(np.ravel(x), np.ravel(y)) + return self._add_pad(xt.min(), xt.max(), yt.min(), yt.max()) + + def _add_pad(self, x_min, x_max, y_min, y_max): + """Perform the padding mentioned in `__call__`.""" + dx = (x_max - x_min) / self.nx + dy = (y_max - y_min) / self.ny + return x_min - dx, x_max + dx, y_min - dy, y_max + dy + + +class _User2DTransform(Transform): + """A transform defined by two user-set functions.""" + + input_dims = output_dims = 2 + + def __init__(self, forward, backward): + """ + Parameters + ---------- + forward, backward : callable + The forward and backward transforms, taking ``x`` and ``y`` as + separate arguments and returning ``(tr_x, tr_y)``. + """ + # The normal Matplotlib convention would be to take and return an + # (N, 2) array but axisartist uses the transposed version. + super().__init__() + self._forward = forward + self._backward = backward + + def transform_non_affine(self, values): + # docstring inherited + return np.transpose(self._forward(*np.transpose(values))) + + def inverted(self): + # docstring inherited + return type(self)(self._backward, self._forward) + + +class GridFinder: + """ + Internal helper for `~.grid_helper_curvelinear.GridHelperCurveLinear`, with + the same constructor parameters; should not be directly instantiated. + """ + + def __init__(self, + transform, + extreme_finder=None, + grid_locator1=None, + grid_locator2=None, + tick_formatter1=None, + tick_formatter2=None): + if extreme_finder is None: + extreme_finder = ExtremeFinderSimple(20, 20) + if grid_locator1 is None: + grid_locator1 = MaxNLocator() + if grid_locator2 is None: + grid_locator2 = MaxNLocator() + if tick_formatter1 is None: + tick_formatter1 = FormatterPrettyPrint() + if tick_formatter2 is None: + tick_formatter2 = FormatterPrettyPrint() + self.extreme_finder = extreme_finder + self.grid_locator1 = grid_locator1 + self.grid_locator2 = grid_locator2 + self.tick_formatter1 = tick_formatter1 + self.tick_formatter2 = tick_formatter2 + self.set_transform(transform) + + def _format_ticks(self, idx, direction, factor, levels): + """ + Helper to support both standard formatters (inheriting from + `.mticker.Formatter`) and axisartist-specific ones; should be called instead of + directly calling ``self.tick_formatter1`` and ``self.tick_formatter2``. This + method should be considered as a temporary workaround which will be removed in + the future at the same time as axisartist-specific formatters. + """ + fmt = _api.check_getitem( + {1: self.tick_formatter1, 2: self.tick_formatter2}, idx=idx) + return (fmt.format_ticks(levels) if isinstance(fmt, mticker.Formatter) + else fmt(direction, factor, levels)) + + def get_grid_info(self, x1, y1, x2, y2): + """ + lon_values, lat_values : list of grid values. if integer is given, + rough number of grids in each direction. + """ + + extremes = self.extreme_finder(self.inv_transform_xy, x1, y1, x2, y2) + + # min & max rage of lat (or lon) for each grid line will be drawn. + # i.e., gridline of lon=0 will be drawn from lat_min to lat_max. + + lon_min, lon_max, lat_min, lat_max = extremes + lon_levs, lon_n, lon_factor = self.grid_locator1(lon_min, lon_max) + lon_levs = np.asarray(lon_levs) + lat_levs, lat_n, lat_factor = self.grid_locator2(lat_min, lat_max) + lat_levs = np.asarray(lat_levs) + + lon_values = lon_levs[:lon_n] / lon_factor + lat_values = lat_levs[:lat_n] / lat_factor + + lon_lines, lat_lines = self._get_raw_grid_lines(lon_values, + lat_values, + lon_min, lon_max, + lat_min, lat_max) + + bb = Bbox.from_extents(x1, y1, x2, y2).expanded(1 + 2e-10, 1 + 2e-10) + + grid_info = { + "extremes": extremes, + # "lon", "lat", filled below. + } + + for idx, lon_or_lat, levs, factor, values, lines in [ + (1, "lon", lon_levs, lon_factor, lon_values, lon_lines), + (2, "lat", lat_levs, lat_factor, lat_values, lat_lines), + ]: + grid_info[lon_or_lat] = gi = { + "lines": [[l] for l in lines], + "ticks": {"left": [], "right": [], "bottom": [], "top": []}, + } + for (lx, ly), v, level in zip(lines, values, levs): + all_crossings = _find_line_box_crossings(np.column_stack([lx, ly]), bb) + for side, crossings in zip( + ["left", "right", "bottom", "top"], all_crossings): + for crossing in crossings: + gi["ticks"][side].append({"level": level, "loc": crossing}) + for side in gi["ticks"]: + levs = [tick["level"] for tick in gi["ticks"][side]] + labels = self._format_ticks(idx, side, factor, levs) + for tick, label in zip(gi["ticks"][side], labels): + tick["label"] = label + + return grid_info + + def _get_raw_grid_lines(self, + lon_values, lat_values, + lon_min, lon_max, lat_min, lat_max): + + lons_i = np.linspace(lon_min, lon_max, 100) # for interpolation + lats_i = np.linspace(lat_min, lat_max, 100) + + lon_lines = [self.transform_xy(np.full_like(lats_i, lon), lats_i) + for lon in lon_values] + lat_lines = [self.transform_xy(lons_i, np.full_like(lons_i, lat)) + for lat in lat_values] + + return lon_lines, lat_lines + + def set_transform(self, aux_trans): + if isinstance(aux_trans, Transform): + self._aux_transform = aux_trans + elif len(aux_trans) == 2 and all(map(callable, aux_trans)): + self._aux_transform = _User2DTransform(*aux_trans) + else: + raise TypeError("'aux_trans' must be either a Transform " + "instance or a pair of callables") + + def get_transform(self): + return self._aux_transform + + update_transform = set_transform # backcompat alias. + + def transform_xy(self, x, y): + return self._aux_transform.transform(np.column_stack([x, y])).T + + def inv_transform_xy(self, x, y): + return self._aux_transform.inverted().transform( + np.column_stack([x, y])).T + + def update(self, **kwargs): + for k, v in kwargs.items(): + if k in ["extreme_finder", + "grid_locator1", + "grid_locator2", + "tick_formatter1", + "tick_formatter2"]: + setattr(self, k, v) + else: + raise ValueError(f"Unknown update property {k!r}") + + +class MaxNLocator(mticker.MaxNLocator): + def __init__(self, nbins=10, steps=None, + trim=True, + integer=False, + symmetric=False, + prune=None): + # trim argument has no effect. It has been left for API compatibility + super().__init__(nbins, steps=steps, integer=integer, + symmetric=symmetric, prune=prune) + self.create_dummy_axis() + + def __call__(self, v1, v2): + locs = super().tick_values(v1, v2) + return np.array(locs), len(locs), 1 # 1: factor (see angle_helper) + + +class FixedLocator: + def __init__(self, locs): + self._locs = locs + + def __call__(self, v1, v2): + v1, v2 = sorted([v1, v2]) + locs = np.array([l for l in self._locs if v1 <= l <= v2]) + return locs, len(locs), 1 # 1: factor (see angle_helper) + + +# Tick Formatter + +class FormatterPrettyPrint: + def __init__(self, useMathText=True): + self._fmt = mticker.ScalarFormatter( + useMathText=useMathText, useOffset=False) + self._fmt.create_dummy_axis() + + def __call__(self, direction, factor, values): + return self._fmt.format_ticks(values) + + +class DictFormatter: + def __init__(self, format_dict, formatter=None): + """ + format_dict : dictionary for format strings to be used. + formatter : fall-back formatter + """ + super().__init__() + self._format_dict = format_dict + self._fallback_formatter = formatter + + def __call__(self, direction, factor, values): + """ + factor is ignored if value is found in the dictionary + """ + if self._fallback_formatter: + fallback_strings = self._fallback_formatter( + direction, factor, values) + else: + fallback_strings = [""] * len(values) + return [self._format_dict.get(k, v) + for k, v in zip(values, fallback_strings)] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py new file mode 100644 index 0000000000000000000000000000000000000000..a7eb9d5cfe2108c3691e72f527e98b7b73e95a65 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/grid_helper_curvelinear.py @@ -0,0 +1,328 @@ +""" +An experimental support for curvilinear grid. +""" + +import functools + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api +from matplotlib.path import Path +from matplotlib.transforms import Affine2D, IdentityTransform +from .axislines import ( + _FixedAxisArtistHelperBase, _FloatingAxisArtistHelperBase, GridHelperBase) +from .axis_artist import AxisArtist +from .grid_finder import GridFinder + + +def _value_and_jacobian(func, xs, ys, xlims, ylims): + """ + Compute *func* and its derivatives along x and y at positions *xs*, *ys*, + while ensuring that finite difference calculations don't try to evaluate + values outside of *xlims*, *ylims*. + """ + eps = np.finfo(float).eps ** (1/2) # see e.g. scipy.optimize.approx_fprime + val = func(xs, ys) + # Take the finite difference step in the direction where the bound is the + # furthest; the step size is min of epsilon and distance to that bound. + xlo, xhi = sorted(xlims) + dxlo = xs - xlo + dxhi = xhi - xs + xeps = (np.take([-1, 1], dxhi >= dxlo) + * np.minimum(eps, np.maximum(dxlo, dxhi))) + val_dx = func(xs + xeps, ys) + ylo, yhi = sorted(ylims) + dylo = ys - ylo + dyhi = yhi - ys + yeps = (np.take([-1, 1], dyhi >= dylo) + * np.minimum(eps, np.maximum(dylo, dyhi))) + val_dy = func(xs, ys + yeps) + return (val, (val_dx - val) / xeps, (val_dy - val) / yeps) + + +class FixedAxisArtistHelper(_FixedAxisArtistHelperBase): + """ + Helper class for a fixed axis. + """ + + def __init__(self, grid_helper, side, nth_coord_ticks=None): + """ + nth_coord = along which coordinate value varies. + nth_coord = 0 -> x axis, nth_coord = 1 -> y axis + """ + + super().__init__(loc=side) + + self.grid_helper = grid_helper + if nth_coord_ticks is None: + nth_coord_ticks = self.nth_coord + self.nth_coord_ticks = nth_coord_ticks + + self.side = side + + def update_lim(self, axes): + self.grid_helper.update_lim(axes) + + def get_tick_transform(self, axes): + return axes.transData + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label""" + v1, v2 = axes.get_ylim() if self.nth_coord == 0 else axes.get_xlim() + if v1 > v2: # Inverted limits. + side = {"left": "right", "right": "left", + "top": "bottom", "bottom": "top"}[self.side] + else: + side = self.side + + angle_tangent = dict(left=90, right=90, bottom=0, top=0)[side] + + def iter_major(): + for nth_coord, show_labels in [ + (self.nth_coord_ticks, True), (1 - self.nth_coord_ticks, False)]: + gi = self.grid_helper._grid_info[["lon", "lat"][nth_coord]] + for tick in gi["ticks"][side]: + yield (*tick["loc"], angle_tangent, + (tick["label"] if show_labels else "")) + + return iter_major(), iter([]) + + +class FloatingAxisArtistHelper(_FloatingAxisArtistHelperBase): + + def __init__(self, grid_helper, nth_coord, value, axis_direction=None): + """ + nth_coord = along which coordinate value varies. + nth_coord = 0 -> x axis, nth_coord = 1 -> y axis + """ + super().__init__(nth_coord, value) + self.value = value + self.grid_helper = grid_helper + self._extremes = -np.inf, np.inf + self._line_num_points = 100 # number of points to create a line + + def set_extremes(self, e1, e2): + if e1 is None: + e1 = -np.inf + if e2 is None: + e2 = np.inf + self._extremes = e1, e2 + + def update_lim(self, axes): + self.grid_helper.update_lim(axes) + + x1, x2 = axes.get_xlim() + y1, y2 = axes.get_ylim() + grid_finder = self.grid_helper.grid_finder + extremes = grid_finder.extreme_finder(grid_finder.inv_transform_xy, + x1, y1, x2, y2) + + lon_min, lon_max, lat_min, lat_max = extremes + e_min, e_max = self._extremes # ranges of other coordinates + if self.nth_coord == 0: + lat_min = max(e_min, lat_min) + lat_max = min(e_max, lat_max) + elif self.nth_coord == 1: + lon_min = max(e_min, lon_min) + lon_max = min(e_max, lon_max) + + lon_levs, lon_n, lon_factor = \ + grid_finder.grid_locator1(lon_min, lon_max) + lat_levs, lat_n, lat_factor = \ + grid_finder.grid_locator2(lat_min, lat_max) + + if self.nth_coord == 0: + xx0 = np.full(self._line_num_points, self.value) + yy0 = np.linspace(lat_min, lat_max, self._line_num_points) + xx, yy = grid_finder.transform_xy(xx0, yy0) + elif self.nth_coord == 1: + xx0 = np.linspace(lon_min, lon_max, self._line_num_points) + yy0 = np.full(self._line_num_points, self.value) + xx, yy = grid_finder.transform_xy(xx0, yy0) + + self._grid_info = { + "extremes": (lon_min, lon_max, lat_min, lat_max), + "lon_info": (lon_levs, lon_n, np.asarray(lon_factor)), + "lat_info": (lat_levs, lat_n, np.asarray(lat_factor)), + "lon_labels": grid_finder._format_ticks( + 1, "bottom", lon_factor, lon_levs), + "lat_labels": grid_finder._format_ticks( + 2, "bottom", lat_factor, lat_levs), + "line_xy": (xx, yy), + } + + def get_axislabel_transform(self, axes): + return Affine2D() # axes.transData + + def get_axislabel_pos_angle(self, axes): + def trf_xy(x, y): + trf = self.grid_helper.grid_finder.get_transform() + axes.transData + return trf.transform([x, y]).T + + xmin, xmax, ymin, ymax = self._grid_info["extremes"] + if self.nth_coord == 0: + xx0 = self.value + yy0 = (ymin + ymax) / 2 + elif self.nth_coord == 1: + xx0 = (xmin + xmax) / 2 + yy0 = self.value + xy1, dxy1_dx, dxy1_dy = _value_and_jacobian( + trf_xy, xx0, yy0, (xmin, xmax), (ymin, ymax)) + p = axes.transAxes.inverted().transform(xy1) + if 0 <= p[0] <= 1 and 0 <= p[1] <= 1: + d = [dxy1_dy, dxy1_dx][self.nth_coord] + return xy1, np.rad2deg(np.arctan2(*d[::-1])) + else: + return None, None + + def get_tick_transform(self, axes): + return IdentityTransform() # axes.transData + + def get_tick_iterators(self, axes): + """tick_loc, tick_angle, tick_label, (optionally) tick_label""" + + lat_levs, lat_n, lat_factor = self._grid_info["lat_info"] + yy0 = lat_levs / lat_factor + + lon_levs, lon_n, lon_factor = self._grid_info["lon_info"] + xx0 = lon_levs / lon_factor + + e0, e1 = self._extremes + + def trf_xy(x, y): + trf = self.grid_helper.grid_finder.get_transform() + axes.transData + return trf.transform(np.column_stack(np.broadcast_arrays(x, y))).T + + # find angles + if self.nth_coord == 0: + mask = (e0 <= yy0) & (yy0 <= e1) + (xx1, yy1), (dxx1, dyy1), (dxx2, dyy2) = _value_and_jacobian( + trf_xy, self.value, yy0[mask], (-np.inf, np.inf), (e0, e1)) + labels = self._grid_info["lat_labels"] + + elif self.nth_coord == 1: + mask = (e0 <= xx0) & (xx0 <= e1) + (xx1, yy1), (dxx2, dyy2), (dxx1, dyy1) = _value_and_jacobian( + trf_xy, xx0[mask], self.value, (-np.inf, np.inf), (e0, e1)) + labels = self._grid_info["lon_labels"] + + labels = [l for l, m in zip(labels, mask) if m] + + angle_normal = np.arctan2(dyy1, dxx1) + angle_tangent = np.arctan2(dyy2, dxx2) + mm = (dyy1 == 0) & (dxx1 == 0) # points with degenerate normal + angle_normal[mm] = angle_tangent[mm] + np.pi / 2 + + tick_to_axes = self.get_tick_transform(axes) - axes.transAxes + in_01 = functools.partial( + mpl.transforms._interval_contains_close, (0, 1)) + + def iter_major(): + for x, y, normal, tangent, lab \ + in zip(xx1, yy1, angle_normal, angle_tangent, labels): + c2 = tick_to_axes.transform((x, y)) + if in_01(c2[0]) and in_01(c2[1]): + yield [x, y], *np.rad2deg([normal, tangent]), lab + + return iter_major(), iter([]) + + def get_line_transform(self, axes): + return axes.transData + + def get_line(self, axes): + self.update_lim(axes) + x, y = self._grid_info["line_xy"] + return Path(np.column_stack([x, y])) + + +class GridHelperCurveLinear(GridHelperBase): + def __init__(self, aux_trans, + extreme_finder=None, + grid_locator1=None, + grid_locator2=None, + tick_formatter1=None, + tick_formatter2=None): + """ + Parameters + ---------- + aux_trans : `.Transform` or tuple[Callable, Callable] + The transform from curved coordinates to rectilinear coordinate: + either a `.Transform` instance (which provides also its inverse), + or a pair of callables ``(trans, inv_trans)`` that define the + transform and its inverse. The callables should have signature:: + + x_rect, y_rect = trans(x_curved, y_curved) + x_curved, y_curved = inv_trans(x_rect, y_rect) + + extreme_finder + + grid_locator1, grid_locator2 + Grid locators for each axis. + + tick_formatter1, tick_formatter2 + Tick formatters for each axis. + """ + super().__init__() + self._grid_info = None + self.grid_finder = GridFinder(aux_trans, + extreme_finder, + grid_locator1, + grid_locator2, + tick_formatter1, + tick_formatter2) + + def update_grid_finder(self, aux_trans=None, **kwargs): + if aux_trans is not None: + self.grid_finder.update_transform(aux_trans) + self.grid_finder.update(**kwargs) + self._old_limits = None # Force revalidation. + + @_api.make_keyword_only("3.9", "nth_coord") + def new_fixed_axis( + self, loc, nth_coord=None, axis_direction=None, offset=None, axes=None): + if axes is None: + axes = self.axes + if axis_direction is None: + axis_direction = loc + helper = FixedAxisArtistHelper(self, loc, nth_coord_ticks=nth_coord) + axisline = AxisArtist(axes, helper, axis_direction=axis_direction) + # Why is clip not set on axisline, unlike in new_floating_axis or in + # the floating_axig.GridHelperCurveLinear subclass? + return axisline + + def new_floating_axis(self, nth_coord, value, axes=None, axis_direction="bottom"): + if axes is None: + axes = self.axes + helper = FloatingAxisArtistHelper( + self, nth_coord, value, axis_direction) + axisline = AxisArtist(axes, helper) + axisline.line.set_clip_on(True) + axisline.line.set_clip_box(axisline.axes.bbox) + # axisline.major_ticklabels.set_visible(True) + # axisline.minor_ticklabels.set_visible(False) + return axisline + + def _update_grid(self, x1, y1, x2, y2): + self._grid_info = self.grid_finder.get_grid_info(x1, y1, x2, y2) + + def get_gridlines(self, which="major", axis="both"): + grid_lines = [] + if axis in ["both", "x"]: + for gl in self._grid_info["lon"]["lines"]: + grid_lines.extend(gl) + if axis in ["both", "y"]: + for gl in self._grid_info["lat"]["lines"]: + grid_lines.extend(gl) + return grid_lines + + @_api.deprecated("3.9") + def get_tick_iterator(self, nth_coord, axis_side, minor=False): + angle_tangent = dict(left=90, right=90, bottom=0, top=0)[axis_side] + lon_or_lat = ["lon", "lat"][nth_coord] + if not minor: # major ticks + for tick in self._grid_info[lon_or_lat]["ticks"][axis_side]: + yield *tick["loc"], angle_tangent, tick["label"] + else: + for tick in self._grid_info[lon_or_lat]["ticks"][axis_side]: + yield *tick["loc"], angle_tangent, "" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/parasite_axes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/parasite_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..4ebd6acc03be2dbb0f5c3360ede2a6a36a3be01b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/parasite_axes.py @@ -0,0 +1,7 @@ +from mpl_toolkits.axes_grid1.parasite_axes import ( + host_axes_class_factory, parasite_axes_class_factory) +from .axislines import Axes + + +ParasiteAxes = parasite_axes_class_factory(Axes) +HostAxes = SubplotHost = host_axes_class_factory(Axes) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ea4d8ed16a6a24a8c15ab2956ef678a7f256cd80 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/__init__.py @@ -0,0 +1,10 @@ +from pathlib import Path + + +# Check that the test directories exist +if not (Path(__file__).parent / "baseline_images").exists(): + raise OSError( + 'The baseline image directory does not exist. ' + 'This is most likely because the test data is not installed. ' + 'You may need to install matplotlib from source to get the ' + 'test data.') diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/conftest.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..61c2de3e07bac4db323f8704961264d123e01544 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/conftest.py @@ -0,0 +1,2 @@ +from matplotlib.testing.conftest import (mpl_test_settings, # noqa + pytest_configure, pytest_unconfigure) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_angle_helper.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_angle_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..3156b33b69c4f59418f2dc78f700ba105547155a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_angle_helper.py @@ -0,0 +1,141 @@ +import re + +import numpy as np +import pytest + +from mpl_toolkits.axisartist.angle_helper import ( + FormatterDMS, FormatterHMS, select_step, select_step24, select_step360) + + +_MS_RE = ( + r'''\$ # Mathtext + ( + # The sign sometimes appears on a 0 when a fraction is shown. + # Check later that there's only one. + (?P-)? + (?P[0-9.]+) # Degrees value + {degree} # Degree symbol (to be replaced by format.) + )? + ( + (?(degree)\\,) # Separator if degrees are also visible. + (?P-)? + (?P[0-9.]+) # Minutes value + {minute} # Minute symbol (to be replaced by format.) + )? + ( + (?(minute)\\,) # Separator if minutes are also visible. + (?P-)? + (?P[0-9.]+) # Seconds value + {second} # Second symbol (to be replaced by format.) + )? + \$ # Mathtext + ''' +) +DMS_RE = re.compile(_MS_RE.format(degree=re.escape(FormatterDMS.deg_mark), + minute=re.escape(FormatterDMS.min_mark), + second=re.escape(FormatterDMS.sec_mark)), + re.VERBOSE) +HMS_RE = re.compile(_MS_RE.format(degree=re.escape(FormatterHMS.deg_mark), + minute=re.escape(FormatterHMS.min_mark), + second=re.escape(FormatterHMS.sec_mark)), + re.VERBOSE) + + +def dms2float(degrees, minutes=0, seconds=0): + return degrees + minutes / 60.0 + seconds / 3600.0 + + +@pytest.mark.parametrize('args, kwargs, expected_levels, expected_factor', [ + ((-180, 180, 10), {'hour': False}, np.arange(-180, 181, 30), 1.0), + ((-12, 12, 10), {'hour': True}, np.arange(-12, 13, 2), 1.0) +]) +def test_select_step(args, kwargs, expected_levels, expected_factor): + levels, n, factor = select_step(*args, **kwargs) + + assert n == len(levels) + np.testing.assert_array_equal(levels, expected_levels) + assert factor == expected_factor + + +@pytest.mark.parametrize('args, kwargs, expected_levels, expected_factor', [ + ((-180, 180, 10), {}, np.arange(-180, 181, 30), 1.0), + ((-12, 12, 10), {}, np.arange(-750, 751, 150), 60.0) +]) +def test_select_step24(args, kwargs, expected_levels, expected_factor): + levels, n, factor = select_step24(*args, **kwargs) + + assert n == len(levels) + np.testing.assert_array_equal(levels, expected_levels) + assert factor == expected_factor + + +@pytest.mark.parametrize('args, kwargs, expected_levels, expected_factor', [ + ((dms2float(20, 21.2), dms2float(21, 33.3), 5), {}, + np.arange(1215, 1306, 15), 60.0), + ((dms2float(20.5, seconds=21.2), dms2float(20.5, seconds=33.3), 5), {}, + np.arange(73820, 73835, 2), 3600.0), + ((dms2float(20, 21.2), dms2float(20, 53.3), 5), {}, + np.arange(1220, 1256, 5), 60.0), + ((21.2, 33.3, 5), {}, + np.arange(20, 35, 2), 1.0), + ((dms2float(20, 21.2), dms2float(21, 33.3), 5), {}, + np.arange(1215, 1306, 15), 60.0), + ((dms2float(20.5, seconds=21.2), dms2float(20.5, seconds=33.3), 5), {}, + np.arange(73820, 73835, 2), 3600.0), + ((dms2float(20.5, seconds=21.2), dms2float(20.5, seconds=21.4), 5), {}, + np.arange(7382120, 7382141, 5), 360000.0), + # test threshold factor + ((dms2float(20.5, seconds=11.2), dms2float(20.5, seconds=53.3), 5), + {'threshold_factor': 60}, np.arange(12301, 12310), 600.0), + ((dms2float(20.5, seconds=11.2), dms2float(20.5, seconds=53.3), 5), + {'threshold_factor': 1}, np.arange(20502, 20517, 2), 1000.0), +]) +def test_select_step360(args, kwargs, expected_levels, expected_factor): + levels, n, factor = select_step360(*args, **kwargs) + + assert n == len(levels) + np.testing.assert_array_equal(levels, expected_levels) + assert factor == expected_factor + + +@pytest.mark.parametrize('Formatter, regex', + [(FormatterDMS, DMS_RE), + (FormatterHMS, HMS_RE)], + ids=['Degree/Minute/Second', 'Hour/Minute/Second']) +@pytest.mark.parametrize('direction, factor, values', [ + ("left", 60, [0, -30, -60]), + ("left", 600, [12301, 12302, 12303]), + ("left", 3600, [0, -30, -60]), + ("left", 36000, [738210, 738215, 738220]), + ("left", 360000, [7382120, 7382125, 7382130]), + ("left", 1., [45, 46, 47]), + ("left", 10., [452, 453, 454]), +]) +def test_formatters(Formatter, regex, direction, factor, values): + fmt = Formatter() + result = fmt(direction, factor, values) + + prev_degree = prev_minute = prev_second = None + for tick, value in zip(result, values): + m = regex.match(tick) + assert m is not None, f'{tick!r} is not an expected tick format.' + + sign = sum(m.group(sign + '_sign') is not None + for sign in ('degree', 'minute', 'second')) + assert sign <= 1, f'Only one element of tick {tick!r} may have a sign.' + sign = 1 if sign == 0 else -1 + + degree = float(m.group('degree') or prev_degree or 0) + minute = float(m.group('minute') or prev_minute or 0) + second = float(m.group('second') or prev_second or 0) + if Formatter == FormatterHMS: + # 360 degrees as plot range -> 24 hours as labelled range + expected_value = pytest.approx((value // 15) / factor) + else: + expected_value = pytest.approx(value / factor) + assert sign * dms2float(degree, minute, second) == expected_value, \ + f'{tick!r} does not match expected tick value.' + + prev_degree = degree + prev_minute = minute + prev_second = second diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axis_artist.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axis_artist.py new file mode 100644 index 0000000000000000000000000000000000000000..d44a61b6dd4aebebd14f0e240c130e390b0bc70b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axis_artist.py @@ -0,0 +1,99 @@ +import matplotlib.pyplot as plt +from matplotlib.testing.decorators import image_comparison + +from mpl_toolkits.axisartist import AxisArtistHelperRectlinear +from mpl_toolkits.axisartist.axis_artist import (AxisArtist, AxisLabel, + LabelBase, Ticks, TickLabels) + + +@image_comparison(['axis_artist_ticks.png'], style='default') +def test_ticks(): + fig, ax = plt.subplots() + + ax.xaxis.set_visible(False) + ax.yaxis.set_visible(False) + + locs_angles = [((i / 10, 0.0), i * 30) for i in range(-1, 12)] + + ticks_in = Ticks(ticksize=10, axis=ax.xaxis) + ticks_in.set_locs_angles(locs_angles) + ax.add_artist(ticks_in) + + ticks_out = Ticks(ticksize=10, tick_out=True, color='C3', axis=ax.xaxis) + ticks_out.set_locs_angles(locs_angles) + ax.add_artist(ticks_out) + + +@image_comparison(['axis_artist_labelbase.png'], style='default') +def test_labelbase(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig, ax = plt.subplots() + + ax.plot([0.5], [0.5], "o") + + label = LabelBase(0.5, 0.5, "Test") + label._ref_angle = -90 + label._offset_radius = 50 + label.set_rotation(-90) + label.set(ha="center", va="top") + ax.add_artist(label) + + +@image_comparison(['axis_artist_ticklabels.png'], style='default') +def test_ticklabels(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig, ax = plt.subplots() + + ax.xaxis.set_visible(False) + ax.yaxis.set_visible(False) + + ax.plot([0.2, 0.4], [0.5, 0.5], "o") + + ticks = Ticks(ticksize=10, axis=ax.xaxis) + ax.add_artist(ticks) + locs_angles_labels = [((0.2, 0.5), -90, "0.2"), + ((0.4, 0.5), -120, "0.4")] + tick_locs_angles = [(xy, a + 180) for xy, a, l in locs_angles_labels] + ticks.set_locs_angles(tick_locs_angles) + + ticklabels = TickLabels(axis_direction="left") + ticklabels._locs_angles_labels = locs_angles_labels + ticklabels.set_pad(10) + ax.add_artist(ticklabels) + + ax.plot([0.5], [0.5], "s") + axislabel = AxisLabel(0.5, 0.5, "Test") + axislabel._offset_radius = 20 + axislabel._ref_angle = 0 + axislabel.set_axis_direction("bottom") + ax.add_artist(axislabel) + + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + + +@image_comparison(['axis_artist.png'], style='default') +def test_axis_artist(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig, ax = plt.subplots() + + ax.xaxis.set_visible(False) + ax.yaxis.set_visible(False) + + for loc in ('left', 'right', 'bottom'): + helper = AxisArtistHelperRectlinear.Fixed(ax, loc=loc) + axisline = AxisArtist(ax, helper, offset=None, axis_direction=loc) + ax.add_artist(axisline) + + # Settings for bottom AxisArtist. + axisline.set_label("TTT") + axisline.major_ticks.set_tick_out(False) + axisline.label.set_pad(5) + + ax.set_ylabel("Test") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axislines.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axislines.py new file mode 100644 index 0000000000000000000000000000000000000000..8bc3707421b61fed84a5ba7fc3958cac7de8dc85 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_axislines.py @@ -0,0 +1,145 @@ +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.testing.decorators import image_comparison +from matplotlib.transforms import IdentityTransform + +from mpl_toolkits.axisartist.axislines import AxesZero, SubplotZero, Subplot +from mpl_toolkits.axisartist import Axes, SubplotHost + + +@image_comparison(['SubplotZero.png'], style='default') +def test_SubplotZero(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig = plt.figure() + + ax = SubplotZero(fig, 1, 1, 1) + fig.add_subplot(ax) + + ax.axis["xzero"].set_visible(True) + ax.axis["xzero"].label.set_text("Axis Zero") + + for n in ["top", "right"]: + ax.axis[n].set_visible(False) + + xx = np.arange(0, 2 * np.pi, 0.01) + ax.plot(xx, np.sin(xx)) + ax.set_ylabel("Test") + + +@image_comparison(['Subplot.png'], style='default') +def test_Subplot(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig = plt.figure() + + ax = Subplot(fig, 1, 1, 1) + fig.add_subplot(ax) + + xx = np.arange(0, 2 * np.pi, 0.01) + ax.plot(xx, np.sin(xx)) + ax.set_ylabel("Test") + + ax.axis["top"].major_ticks.set_tick_out(True) + ax.axis["bottom"].major_ticks.set_tick_out(True) + + ax.axis["bottom"].set_label("Tk0") + + +def test_Axes(): + fig = plt.figure() + ax = Axes(fig, [0.15, 0.1, 0.65, 0.8]) + fig.add_axes(ax) + ax.plot([1, 2, 3], [0, 1, 2]) + ax.set_xscale('log') + fig.canvas.draw() + + +@image_comparison(['ParasiteAxesAuxTrans_meshplot.png'], + remove_text=True, style='default', tol=0.075) +def test_ParasiteAxesAuxTrans(): + data = np.ones((6, 6)) + data[2, 2] = 2 + data[0, :] = 0 + data[-2, :] = 0 + data[:, 0] = 0 + data[:, -2] = 0 + x = np.arange(6) + y = np.arange(6) + xx, yy = np.meshgrid(x, y) + + funcnames = ['pcolor', 'pcolormesh', 'contourf'] + + fig = plt.figure() + for i, name in enumerate(funcnames): + + ax1 = SubplotHost(fig, 1, 3, i+1) + fig.add_subplot(ax1) + + ax2 = ax1.get_aux_axes(IdentityTransform(), viewlim_mode=None) + if name.startswith('pcolor'): + getattr(ax2, name)(xx, yy, data[:-1, :-1]) + else: + getattr(ax2, name)(xx, yy, data) + ax1.set_xlim((0, 5)) + ax1.set_ylim((0, 5)) + + ax2.contour(xx, yy, data, colors='k') + + +@image_comparison(['axisline_style.png'], remove_text=True, style='mpl20') +def test_axisline_style(): + fig = plt.figure(figsize=(2, 2)) + ax = fig.add_subplot(axes_class=AxesZero) + ax.axis["xzero"].set_axisline_style("-|>") + ax.axis["xzero"].set_visible(True) + ax.axis["yzero"].set_axisline_style("->") + ax.axis["yzero"].set_visible(True) + + for direction in ("left", "right", "bottom", "top"): + ax.axis[direction].set_visible(False) + + +@image_comparison(['axisline_style_size_color.png'], remove_text=True, + style='mpl20') +def test_axisline_style_size_color(): + fig = plt.figure(figsize=(2, 2)) + ax = fig.add_subplot(axes_class=AxesZero) + ax.axis["xzero"].set_axisline_style("-|>", size=2.0, facecolor='r') + ax.axis["xzero"].set_visible(True) + ax.axis["yzero"].set_axisline_style("->, size=1.5") + ax.axis["yzero"].set_visible(True) + + for direction in ("left", "right", "bottom", "top"): + ax.axis[direction].set_visible(False) + + +@image_comparison(['axisline_style_tight.png'], remove_text=True, + style='mpl20') +def test_axisline_style_tight(): + fig = plt.figure(figsize=(2, 2), layout='tight') + ax = fig.add_subplot(axes_class=AxesZero) + ax.axis["xzero"].set_axisline_style("-|>", size=5, facecolor='g') + ax.axis["xzero"].set_visible(True) + ax.axis["yzero"].set_axisline_style("->, size=8") + ax.axis["yzero"].set_visible(True) + + for direction in ("left", "right", "bottom", "top"): + ax.axis[direction].set_visible(False) + + +@image_comparison(['subplotzero_ylabel.png'], style='mpl20') +def test_subplotzero_ylabel(): + fig = plt.figure() + ax = fig.add_subplot(111, axes_class=SubplotZero) + + ax.set(xlim=(-3, 7), ylim=(-3, 7), xlabel="x", ylabel="y") + + zero_axis = ax.axis["xzero", "yzero"] + zero_axis.set_visible(True) # they are hidden by default + + ax.axis["left", "right", "bottom", "top"].set_visible(False) + + zero_axis.set_axisline_style("->") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_floating_axes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_floating_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..7644fea1696599273f6214d7735bb919b500108c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_floating_axes.py @@ -0,0 +1,115 @@ +import numpy as np + +import matplotlib.pyplot as plt +import matplotlib.projections as mprojections +import matplotlib.transforms as mtransforms +from matplotlib.testing.decorators import image_comparison +from mpl_toolkits.axisartist.axislines import Subplot +from mpl_toolkits.axisartist.floating_axes import ( + FloatingAxes, GridHelperCurveLinear) +from mpl_toolkits.axisartist.grid_finder import FixedLocator +from mpl_toolkits.axisartist import angle_helper + + +def test_subplot(): + fig = plt.figure(figsize=(5, 5)) + ax = Subplot(fig, 111) + fig.add_subplot(ax) + + +# Rather high tolerance to allow ongoing work with floating axes internals; +# remove when image is regenerated. +@image_comparison(['curvelinear3.png'], style='default', tol=5) +def test_curvelinear3(): + fig = plt.figure(figsize=(5, 5)) + + tr = (mtransforms.Affine2D().scale(np.pi / 180, 1) + + mprojections.PolarAxes.PolarTransform(apply_theta_transforms=False)) + grid_helper = GridHelperCurveLinear( + tr, + extremes=(0, 360, 10, 3), + grid_locator1=angle_helper.LocatorDMS(15), + grid_locator2=FixedLocator([2, 4, 6, 8, 10]), + tick_formatter1=angle_helper.FormatterDMS(), + tick_formatter2=None) + ax1 = fig.add_subplot(axes_class=FloatingAxes, grid_helper=grid_helper) + + r_scale = 10 + tr2 = mtransforms.Affine2D().scale(1, 1 / r_scale) + tr + grid_helper2 = GridHelperCurveLinear( + tr2, + extremes=(0, 360, 10 * r_scale, 3 * r_scale), + grid_locator2=FixedLocator([30, 60, 90])) + + ax1.axis["right"] = axis = grid_helper2.new_fixed_axis("right", axes=ax1) + + ax1.axis["left"].label.set_text("Test 1") + ax1.axis["right"].label.set_text("Test 2") + ax1.axis["left", "right"].set_visible(False) + + axis = grid_helper.new_floating_axis(1, 7, axes=ax1, + axis_direction="bottom") + ax1.axis["z"] = axis + axis.toggle(all=True, label=True) + axis.label.set_text("z = ?") + axis.label.set_visible(True) + axis.line.set_color("0.5") + + ax2 = ax1.get_aux_axes(tr) + + xx, yy = [67, 90, 75, 30], [2, 5, 8, 4] + ax2.scatter(xx, yy) + l, = ax2.plot(xx, yy, "k-") + l.set_clip_path(ax1.patch) + + +# Rather high tolerance to allow ongoing work with floating axes internals; +# remove when image is regenerated. +@image_comparison(['curvelinear4.png'], style='default', tol=0.9) +def test_curvelinear4(): + # Remove this line when this test image is regenerated. + plt.rcParams['text.kerning_factor'] = 6 + + fig = plt.figure(figsize=(5, 5)) + + tr = (mtransforms.Affine2D().scale(np.pi / 180, 1) + + mprojections.PolarAxes.PolarTransform(apply_theta_transforms=False)) + grid_helper = GridHelperCurveLinear( + tr, + extremes=(120, 30, 10, 0), + grid_locator1=angle_helper.LocatorDMS(5), + grid_locator2=FixedLocator([2, 4, 6, 8, 10]), + tick_formatter1=angle_helper.FormatterDMS(), + tick_formatter2=None) + ax1 = fig.add_subplot(axes_class=FloatingAxes, grid_helper=grid_helper) + ax1.clear() # Check that clear() also restores the correct limits on ax1. + + ax1.axis["left"].label.set_text("Test 1") + ax1.axis["right"].label.set_text("Test 2") + ax1.axis["top"].set_visible(False) + + axis = grid_helper.new_floating_axis(1, 70, axes=ax1, + axis_direction="bottom") + ax1.axis["z"] = axis + axis.toggle(all=True, label=True) + axis.label.set_axis_direction("top") + axis.label.set_text("z = ?") + axis.label.set_visible(True) + axis.line.set_color("0.5") + + ax2 = ax1.get_aux_axes(tr) + + xx, yy = [67, 90, 75, 30], [2, 5, 8, 4] + ax2.scatter(xx, yy) + l, = ax2.plot(xx, yy, "k-") + l.set_clip_path(ax1.patch) + + +def test_axis_direction(): + # Check that axis direction is propagated on a floating axis + fig = plt.figure() + ax = Subplot(fig, 111) + fig.add_subplot(ax) + ax.axis['y'] = ax.new_floating_axis(nth_coord=1, value=0, + axis_direction='left') + assert ax.axis['y']._axis_direction == 'left' diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_finder.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_finder.py new file mode 100644 index 0000000000000000000000000000000000000000..6b397675ee10098a93545cc712a4b3c07384cbc2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_finder.py @@ -0,0 +1,34 @@ +import numpy as np +import pytest + +from matplotlib.transforms import Bbox +from mpl_toolkits.axisartist.grid_finder import ( + _find_line_box_crossings, FormatterPrettyPrint, MaxNLocator) + + +def test_find_line_box_crossings(): + x = np.array([-3, -2, -1, 0., 1, 2, 3, 2, 1, 0, -1, -2, -3, 5]) + y = np.arange(len(x)) + bbox = Bbox.from_extents(-2, 3, 2, 12.5) + left, right, bottom, top = _find_line_box_crossings( + np.column_stack([x, y]), bbox) + ((lx0, ly0), la0), ((lx1, ly1), la1), = left + ((rx0, ry0), ra0), ((rx1, ry1), ra1), = right + ((bx0, by0), ba0), = bottom + ((tx0, ty0), ta0), = top + assert (lx0, ly0, la0) == (-2, 11, 135) + assert (lx1, ly1, la1) == pytest.approx((-2., 12.125, 7.125016)) + assert (rx0, ry0, ra0) == (2, 5, 45) + assert (rx1, ry1, ra1) == (2, 7, 135) + assert (bx0, by0, ba0) == (0, 3, 45) + assert (tx0, ty0, ta0) == pytest.approx((1., 12.5, 7.125016)) + + +def test_pretty_print_format(): + locator = MaxNLocator() + locs, nloc, factor = locator(0, 100) + + fmt = FormatterPrettyPrint() + + assert fmt("left", None, locs) == \ + [r'$\mathdefault{%d}$' % (l, ) for l in locs] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_helper_curvelinear.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_helper_curvelinear.py new file mode 100644 index 0000000000000000000000000000000000000000..1b266044bdd06d995a1df86779dac97dae8cb13e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/axisartist/tests/test_grid_helper_curvelinear.py @@ -0,0 +1,207 @@ +import numpy as np + +import matplotlib.pyplot as plt +from matplotlib.path import Path +from matplotlib.projections import PolarAxes +from matplotlib.ticker import FuncFormatter +from matplotlib.transforms import Affine2D, Transform +from matplotlib.testing.decorators import image_comparison + +from mpl_toolkits.axisartist import SubplotHost +from mpl_toolkits.axes_grid1.parasite_axes import host_axes_class_factory +from mpl_toolkits.axisartist import angle_helper +from mpl_toolkits.axisartist.axislines import Axes +from mpl_toolkits.axisartist.grid_helper_curvelinear import \ + GridHelperCurveLinear + + +@image_comparison(['custom_transform.png'], style='default', tol=0.2) +def test_custom_transform(): + class MyTransform(Transform): + input_dims = output_dims = 2 + + def __init__(self, resolution): + """ + Resolution is the number of steps to interpolate between each input + line segment to approximate its path in transformed space. + """ + Transform.__init__(self) + self._resolution = resolution + + def transform(self, ll): + x, y = ll.T + return np.column_stack([x, y - x]) + + transform_non_affine = transform + + def transform_path(self, path): + ipath = path.interpolated(self._resolution) + return Path(self.transform(ipath.vertices), ipath.codes) + + transform_path_non_affine = transform_path + + def inverted(self): + return MyTransformInv(self._resolution) + + class MyTransformInv(Transform): + input_dims = output_dims = 2 + + def __init__(self, resolution): + Transform.__init__(self) + self._resolution = resolution + + def transform(self, ll): + x, y = ll.T + return np.column_stack([x, y + x]) + + def inverted(self): + return MyTransform(self._resolution) + + fig = plt.figure() + + SubplotHost = host_axes_class_factory(Axes) + + tr = MyTransform(1) + grid_helper = GridHelperCurveLinear(tr) + ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper) + fig.add_subplot(ax1) + + ax2 = ax1.get_aux_axes(tr, viewlim_mode="equal") + ax2.plot([3, 6], [5.0, 10.]) + + ax1.set_aspect(1.) + ax1.set_xlim(0, 10) + ax1.set_ylim(0, 10) + + ax1.grid(True) + + +@image_comparison(['polar_box.png'], style='default', tol=0.04) +def test_polar_box(): + fig = plt.figure(figsize=(5, 5)) + + # PolarAxes.PolarTransform takes radian. However, we want our coordinate + # system in degree + tr = (Affine2D().scale(np.pi / 180., 1.) + + PolarAxes.PolarTransform(apply_theta_transforms=False)) + + # polar projection, which involves cycle, and also has limits in + # its coordinates, needs a special method to find the extremes + # (min, max of the coordinate within the view). + extreme_finder = angle_helper.ExtremeFinderCycle(20, 20, + lon_cycle=360, + lat_cycle=None, + lon_minmax=None, + lat_minmax=(0, np.inf)) + + grid_helper = GridHelperCurveLinear( + tr, + extreme_finder=extreme_finder, + grid_locator1=angle_helper.LocatorDMS(12), + tick_formatter1=angle_helper.FormatterDMS(), + tick_formatter2=FuncFormatter(lambda x, p: "eight" if x == 8 else f"{int(x)}"), + ) + + ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper) + + ax1.axis["right"].major_ticklabels.set_visible(True) + ax1.axis["top"].major_ticklabels.set_visible(True) + + # let right axis shows ticklabels for 1st coordinate (angle) + ax1.axis["right"].get_helper().nth_coord_ticks = 0 + # let bottom axis shows ticklabels for 2nd coordinate (radius) + ax1.axis["bottom"].get_helper().nth_coord_ticks = 1 + + fig.add_subplot(ax1) + + ax1.axis["lat"] = axis = grid_helper.new_floating_axis(0, 45, axes=ax1) + axis.label.set_text("Test") + axis.label.set_visible(True) + axis.get_helper().set_extremes(2, 12) + + ax1.axis["lon"] = axis = grid_helper.new_floating_axis(1, 6, axes=ax1) + axis.label.set_text("Test 2") + axis.get_helper().set_extremes(-180, 90) + + # A parasite axes with given transform + ax2 = ax1.get_aux_axes(tr, viewlim_mode="equal") + assert ax2.transData == tr + ax1.transData + # Anything you draw in ax2 will match the ticks and grids of ax1. + ax2.plot(np.linspace(0, 30, 50), np.linspace(10, 10, 50)) + + ax1.set_aspect(1.) + ax1.set_xlim(-5, 12) + ax1.set_ylim(-5, 10) + + ax1.grid(True) + + +# Remove tol & kerning_factor when this test image is regenerated. +@image_comparison(['axis_direction.png'], style='default', tol=0.13) +def test_axis_direction(): + plt.rcParams['text.kerning_factor'] = 6 + + fig = plt.figure(figsize=(5, 5)) + + # PolarAxes.PolarTransform takes radian. However, we want our coordinate + # system in degree + tr = (Affine2D().scale(np.pi / 180., 1.) + + PolarAxes.PolarTransform(apply_theta_transforms=False)) + + # polar projection, which involves cycle, and also has limits in + # its coordinates, needs a special method to find the extremes + # (min, max of the coordinate within the view). + + # 20, 20 : number of sampling points along x, y direction + extreme_finder = angle_helper.ExtremeFinderCycle(20, 20, + lon_cycle=360, + lat_cycle=None, + lon_minmax=None, + lat_minmax=(0, np.inf), + ) + + grid_locator1 = angle_helper.LocatorDMS(12) + tick_formatter1 = angle_helper.FormatterDMS() + + grid_helper = GridHelperCurveLinear(tr, + extreme_finder=extreme_finder, + grid_locator1=grid_locator1, + tick_formatter1=tick_formatter1) + + ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper) + + for axis in ax1.axis.values(): + axis.set_visible(False) + + fig.add_subplot(ax1) + + ax1.axis["lat1"] = axis = grid_helper.new_floating_axis( + 0, 130, + axes=ax1, axis_direction="left") + axis.label.set_text("Test") + axis.label.set_visible(True) + axis.get_helper().set_extremes(0.001, 10) + + ax1.axis["lat2"] = axis = grid_helper.new_floating_axis( + 0, 50, + axes=ax1, axis_direction="right") + axis.label.set_text("Test") + axis.label.set_visible(True) + axis.get_helper().set_extremes(0.001, 10) + + ax1.axis["lon"] = axis = grid_helper.new_floating_axis( + 1, 10, + axes=ax1, axis_direction="bottom") + axis.label.set_text("Test 2") + axis.get_helper().set_extremes(50, 130) + axis.major_ticklabels.set_axis_direction("top") + axis.label.set_axis_direction("top") + + grid_helper.grid_finder.grid_locator1.set_params(nbins=5) + grid_helper.grid_finder.grid_locator2.set_params(nbins=5) + + ax1.set_aspect(1.) + ax1.set_xlim(-8, 8) + ax1.set_ylim(-4, 12) + + ax1.grid(True) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/__init__.py 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--git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/__pycache__/proj3d.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/__pycache__/proj3d.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5bdffc87d8597e82ddb3b26cf15ae7244a124fd4 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/__pycache__/proj3d.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/art3d.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/art3d.py new file mode 100644 index 0000000000000000000000000000000000000000..deb0ca34302c03cda08e57b28a82cb3654e45aaa --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/art3d.py @@ -0,0 +1,1427 @@ +# art3d.py, original mplot3d version by John Porter +# Parts rewritten by Reinier Heeres +# Minor additions by Ben Axelrod + +""" +Module containing 3D artist code and functions to convert 2D +artists into 3D versions which can be added to an Axes3D. +""" + +import math + +import numpy as np + +from contextlib import contextmanager + +from matplotlib import ( + _api, artist, cbook, colors as mcolors, lines, text as mtext, + path as mpath) +from matplotlib.collections import ( + Collection, LineCollection, PolyCollection, PatchCollection, PathCollection) +from matplotlib.colors import Normalize +from matplotlib.patches import Patch +from . import proj3d + + +def _norm_angle(a): + """Return the given angle normalized to -180 < *a* <= 180 degrees.""" + a = (a + 360) % 360 + if a > 180: + a = a - 360 + return a + + +def _norm_text_angle(a): + """Return the given angle normalized to -90 < *a* <= 90 degrees.""" + a = (a + 180) % 180 + if a > 90: + a = a - 180 + return a + + +def get_dir_vector(zdir): + """ + Return a direction vector. + + Parameters + ---------- + zdir : {'x', 'y', 'z', None, 3-tuple} + The direction. Possible values are: + + - 'x': equivalent to (1, 0, 0) + - 'y': equivalent to (0, 1, 0) + - 'z': equivalent to (0, 0, 1) + - *None*: equivalent to (0, 0, 0) + - an iterable (x, y, z) is converted to an array + + Returns + ------- + x, y, z : array + The direction vector. + """ + if zdir == 'x': + return np.array((1, 0, 0)) + elif zdir == 'y': + return np.array((0, 1, 0)) + elif zdir == 'z': + return np.array((0, 0, 1)) + elif zdir is None: + return np.array((0, 0, 0)) + elif np.iterable(zdir) and len(zdir) == 3: + return np.array(zdir) + else: + raise ValueError("'x', 'y', 'z', None or vector of length 3 expected") + + +def _viewlim_mask(xs, ys, zs, axes): + """ + Return original points with points outside the axes view limits masked. + + Parameters + ---------- + xs, ys, zs : array-like + The points to mask. + axes : Axes3D + The axes to use for the view limits. + + Returns + ------- + xs_masked, ys_masked, zs_masked : np.ma.array + The masked points. + """ + mask = np.logical_or.reduce((xs < axes.xy_viewLim.xmin, + xs > axes.xy_viewLim.xmax, + ys < axes.xy_viewLim.ymin, + ys > axes.xy_viewLim.ymax, + zs < axes.zz_viewLim.xmin, + zs > axes.zz_viewLim.xmax)) + xs_masked = np.ma.array(xs, mask=mask) + ys_masked = np.ma.array(ys, mask=mask) + zs_masked = np.ma.array(zs, mask=mask) + return xs_masked, ys_masked, zs_masked + + +class Text3D(mtext.Text): + """ + Text object with 3D position and direction. + + Parameters + ---------- + x, y, z : float + The position of the text. + text : str + The text string to display. + zdir : {'x', 'y', 'z', None, 3-tuple} + The direction of the text. See `.get_dir_vector` for a description of + the values. + axlim_clip : bool, default: False + Whether to hide text outside the axes view limits. + + Other Parameters + ---------------- + **kwargs + All other parameters are passed on to `~matplotlib.text.Text`. + """ + + def __init__(self, x=0, y=0, z=0, text='', zdir='z', axlim_clip=False, + **kwargs): + mtext.Text.__init__(self, x, y, text, **kwargs) + self.set_3d_properties(z, zdir, axlim_clip) + + def get_position_3d(self): + """Return the (x, y, z) position of the text.""" + return self._x, self._y, self._z + + def set_position_3d(self, xyz, zdir=None): + """ + Set the (*x*, *y*, *z*) position of the text. + + Parameters + ---------- + xyz : (float, float, float) + The position in 3D space. + zdir : {'x', 'y', 'z', None, 3-tuple} + The direction of the text. If unspecified, the *zdir* will not be + changed. See `.get_dir_vector` for a description of the values. + """ + super().set_position(xyz[:2]) + self.set_z(xyz[2]) + if zdir is not None: + self._dir_vec = get_dir_vector(zdir) + + def set_z(self, z): + """ + Set the *z* position of the text. + + Parameters + ---------- + z : float + """ + self._z = z + self.stale = True + + def set_3d_properties(self, z=0, zdir='z', axlim_clip=False): + """ + Set the *z* position and direction of the text. + + Parameters + ---------- + z : float + The z-position in 3D space. + zdir : {'x', 'y', 'z', 3-tuple} + The direction of the text. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide text outside the axes view limits. + """ + self._z = z + self._dir_vec = get_dir_vector(zdir) + self._axlim_clip = axlim_clip + self.stale = True + + @artist.allow_rasterization + def draw(self, renderer): + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(self._x, self._y, self._z, self.axes) + position3d = np.ma.row_stack((xs, ys, zs)).ravel().filled(np.nan) + else: + xs, ys, zs = self._x, self._y, self._z + position3d = np.asanyarray([xs, ys, zs]) + + proj = proj3d._proj_trans_points( + [position3d, position3d + self._dir_vec], self.axes.M) + dx = proj[0][1] - proj[0][0] + dy = proj[1][1] - proj[1][0] + angle = math.degrees(math.atan2(dy, dx)) + with cbook._setattr_cm(self, _x=proj[0][0], _y=proj[1][0], + _rotation=_norm_text_angle(angle)): + mtext.Text.draw(self, renderer) + self.stale = False + + def get_tightbbox(self, renderer=None): + # Overwriting the 2d Text behavior which is not valid for 3d. + # For now, just return None to exclude from layout calculation. + return None + + +def text_2d_to_3d(obj, z=0, zdir='z', axlim_clip=False): + """ + Convert a `.Text` to a `.Text3D` object. + + Parameters + ---------- + z : float + The z-position in 3D space. + zdir : {'x', 'y', 'z', 3-tuple} + The direction of the text. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide text outside the axes view limits. + """ + obj.__class__ = Text3D + obj.set_3d_properties(z, zdir, axlim_clip) + + +class Line3D(lines.Line2D): + """ + 3D line object. + + .. note:: Use `get_data_3d` to obtain the data associated with the line. + `~.Line2D.get_data`, `~.Line2D.get_xdata`, and `~.Line2D.get_ydata` return + the x- and y-coordinates of the projected 2D-line, not the x- and y-data of + the 3D-line. Similarly, use `set_data_3d` to set the data, not + `~.Line2D.set_data`, `~.Line2D.set_xdata`, and `~.Line2D.set_ydata`. + """ + + def __init__(self, xs, ys, zs, *args, axlim_clip=False, **kwargs): + """ + + Parameters + ---------- + xs : array-like + The x-data to be plotted. + ys : array-like + The y-data to be plotted. + zs : array-like + The z-data to be plotted. + *args, **kwargs + Additional arguments are passed to `~matplotlib.lines.Line2D`. + """ + super().__init__([], [], *args, **kwargs) + self.set_data_3d(xs, ys, zs) + self._axlim_clip = axlim_clip + + def set_3d_properties(self, zs=0, zdir='z', axlim_clip=False): + """ + Set the *z* position and direction of the line. + + Parameters + ---------- + zs : float or array of floats + The location along the *zdir* axis in 3D space to position the + line. + zdir : {'x', 'y', 'z'} + Plane to plot line orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide lines with an endpoint outside the axes view limits. + """ + xs = self.get_xdata() + ys = self.get_ydata() + zs = cbook._to_unmasked_float_array(zs).ravel() + zs = np.broadcast_to(zs, len(xs)) + self._verts3d = juggle_axes(xs, ys, zs, zdir) + self._axlim_clip = axlim_clip + self.stale = True + + def set_data_3d(self, *args): + """ + Set the x, y and z data + + Parameters + ---------- + x : array-like + The x-data to be plotted. + y : array-like + The y-data to be plotted. + z : array-like + The z-data to be plotted. + + Notes + ----- + Accepts x, y, z arguments or a single array-like (x, y, z) + """ + if len(args) == 1: + args = args[0] + for name, xyz in zip('xyz', args): + if not np.iterable(xyz): + raise RuntimeError(f'{name} must be a sequence') + self._verts3d = args + self.stale = True + + def get_data_3d(self): + """ + Get the current data + + Returns + ------- + verts3d : length-3 tuple or array-like + The current data as a tuple or array-like. + """ + return self._verts3d + + @artist.allow_rasterization + def draw(self, renderer): + if self._axlim_clip: + xs3d, ys3d, zs3d = _viewlim_mask(*self._verts3d, self.axes) + else: + xs3d, ys3d, zs3d = self._verts3d + xs, ys, zs, tis = proj3d._proj_transform_clip(xs3d, ys3d, zs3d, + self.axes.M, + self.axes._focal_length) + self.set_data(xs, ys) + super().draw(renderer) + self.stale = False + + +def line_2d_to_3d(line, zs=0, zdir='z', axlim_clip=False): + """ + Convert a `.Line2D` to a `.Line3D` object. + + Parameters + ---------- + zs : float + The location along the *zdir* axis in 3D space to position the line. + zdir : {'x', 'y', 'z'} + Plane to plot line orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide lines with an endpoint outside the axes view limits. + """ + + line.__class__ = Line3D + line.set_3d_properties(zs, zdir, axlim_clip) + + +def _path_to_3d_segment(path, zs=0, zdir='z'): + """Convert a path to a 3D segment.""" + + zs = np.broadcast_to(zs, len(path)) + pathsegs = path.iter_segments(simplify=False, curves=False) + seg = [(x, y, z) for (((x, y), code), z) in zip(pathsegs, zs)] + seg3d = [juggle_axes(x, y, z, zdir) for (x, y, z) in seg] + return seg3d + + +def _paths_to_3d_segments(paths, zs=0, zdir='z'): + """Convert paths from a collection object to 3D segments.""" + + if not np.iterable(zs): + zs = np.broadcast_to(zs, len(paths)) + else: + if len(zs) != len(paths): + raise ValueError('Number of z-coordinates does not match paths.') + + segs = [_path_to_3d_segment(path, pathz, zdir) + for path, pathz in zip(paths, zs)] + return segs + + +def _path_to_3d_segment_with_codes(path, zs=0, zdir='z'): + """Convert a path to a 3D segment with path codes.""" + + zs = np.broadcast_to(zs, len(path)) + pathsegs = path.iter_segments(simplify=False, curves=False) + seg_codes = [((x, y, z), code) for ((x, y), code), z in zip(pathsegs, zs)] + if seg_codes: + seg, codes = zip(*seg_codes) + seg3d = [juggle_axes(x, y, z, zdir) for (x, y, z) in seg] + else: + seg3d = [] + codes = [] + return seg3d, list(codes) + + +def _paths_to_3d_segments_with_codes(paths, zs=0, zdir='z'): + """ + Convert paths from a collection object to 3D segments with path codes. + """ + + zs = np.broadcast_to(zs, len(paths)) + segments_codes = [_path_to_3d_segment_with_codes(path, pathz, zdir) + for path, pathz in zip(paths, zs)] + if segments_codes: + segments, codes = zip(*segments_codes) + else: + segments, codes = [], [] + return list(segments), list(codes) + + +class Collection3D(Collection): + """A collection of 3D paths.""" + + def do_3d_projection(self): + """Project the points according to renderer matrix.""" + vs_list = [vs for vs, _ in self._3dverts_codes] + if self._axlim_clip: + vs_list = [np.ma.row_stack(_viewlim_mask(*vs.T, self.axes)).T + for vs in vs_list] + xyzs_list = [proj3d.proj_transform(*vs.T, self.axes.M) for vs in vs_list] + self._paths = [mpath.Path(np.ma.column_stack([xs, ys]), cs) + for (xs, ys, _), (_, cs) in zip(xyzs_list, self._3dverts_codes)] + zs = np.concatenate([zs for _, _, zs in xyzs_list]) + return zs.min() if len(zs) else 1e9 + + +def collection_2d_to_3d(col, zs=0, zdir='z', axlim_clip=False): + """Convert a `.Collection` to a `.Collection3D` object.""" + zs = np.broadcast_to(zs, len(col.get_paths())) + col._3dverts_codes = [ + (np.column_stack(juggle_axes( + *np.column_stack([p.vertices, np.broadcast_to(z, len(p.vertices))]).T, + zdir)), + p.codes) + for p, z in zip(col.get_paths(), zs)] + col.__class__ = cbook._make_class_factory(Collection3D, "{}3D")(type(col)) + col._axlim_clip = axlim_clip + + +class Line3DCollection(LineCollection): + """ + A collection of 3D lines. + """ + def __init__(self, lines, axlim_clip=False, **kwargs): + super().__init__(lines, **kwargs) + self._axlim_clip = axlim_clip + + def set_sort_zpos(self, val): + """Set the position to use for z-sorting.""" + self._sort_zpos = val + self.stale = True + + def set_segments(self, segments): + """ + Set 3D segments. + """ + self._segments3d = segments + super().set_segments([]) + + def do_3d_projection(self): + """ + Project the points according to renderer matrix. + """ + segments = self._segments3d + if self._axlim_clip: + all_points = np.ma.vstack(segments) + masked_points = np.ma.column_stack([*_viewlim_mask(*all_points.T, + self.axes)]) + segment_lengths = [np.shape(segment)[0] for segment in segments] + segments = np.split(masked_points, np.cumsum(segment_lengths[:-1])) + xyslist = [proj3d._proj_trans_points(points, self.axes.M) + for points in segments] + segments_2d = [np.ma.column_stack([xs, ys]) for xs, ys, zs in xyslist] + LineCollection.set_segments(self, segments_2d) + + # FIXME + minz = 1e9 + for xs, ys, zs in xyslist: + minz = min(minz, min(zs)) + return minz + + +def line_collection_2d_to_3d(col, zs=0, zdir='z', axlim_clip=False): + """Convert a `.LineCollection` to a `.Line3DCollection` object.""" + segments3d = _paths_to_3d_segments(col.get_paths(), zs, zdir) + col.__class__ = Line3DCollection + col.set_segments(segments3d) + col._axlim_clip = axlim_clip + + +class Patch3D(Patch): + """ + 3D patch object. + """ + + def __init__(self, *args, zs=(), zdir='z', axlim_clip=False, **kwargs): + """ + Parameters + ---------- + verts : + zs : float + The location along the *zdir* axis in 3D space to position the + patch. + zdir : {'x', 'y', 'z'} + Plane to plot patch orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + """ + super().__init__(*args, **kwargs) + self.set_3d_properties(zs, zdir, axlim_clip) + + def set_3d_properties(self, verts, zs=0, zdir='z', axlim_clip=False): + """ + Set the *z* position and direction of the patch. + + Parameters + ---------- + verts : + zs : float + The location along the *zdir* axis in 3D space to position the + patch. + zdir : {'x', 'y', 'z'} + Plane to plot patch orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + """ + zs = np.broadcast_to(zs, len(verts)) + self._segment3d = [juggle_axes(x, y, z, zdir) + for ((x, y), z) in zip(verts, zs)] + self._axlim_clip = axlim_clip + + def get_path(self): + # docstring inherited + # self._path2d is not initialized until do_3d_projection + if not hasattr(self, '_path2d'): + self.axes.M = self.axes.get_proj() + self.do_3d_projection() + return self._path2d + + def do_3d_projection(self): + s = self._segment3d + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*zip(*s), self.axes) + else: + xs, ys, zs = zip(*s) + vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs, + self.axes.M, + self.axes._focal_length) + self._path2d = mpath.Path(np.ma.column_stack([vxs, vys])) + return min(vzs) + + +class PathPatch3D(Patch3D): + """ + 3D PathPatch object. + """ + + def __init__(self, path, *, zs=(), zdir='z', axlim_clip=False, **kwargs): + """ + Parameters + ---------- + path : + zs : float + The location along the *zdir* axis in 3D space to position the + path patch. + zdir : {'x', 'y', 'z', 3-tuple} + Plane to plot path patch orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide path patches with a point outside the axes view limits. + """ + # Not super().__init__! + Patch.__init__(self, **kwargs) + self.set_3d_properties(path, zs, zdir, axlim_clip) + + def set_3d_properties(self, path, zs=0, zdir='z', axlim_clip=False): + """ + Set the *z* position and direction of the path patch. + + Parameters + ---------- + path : + zs : float + The location along the *zdir* axis in 3D space to position the + path patch. + zdir : {'x', 'y', 'z', 3-tuple} + Plane to plot path patch orthogonal to. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide path patches with a point outside the axes view limits. + """ + Patch3D.set_3d_properties(self, path.vertices, zs=zs, zdir=zdir, + axlim_clip=axlim_clip) + self._code3d = path.codes + + def do_3d_projection(self): + s = self._segment3d + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*zip(*s), self.axes) + else: + xs, ys, zs = zip(*s) + vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs, + self.axes.M, + self.axes._focal_length) + self._path2d = mpath.Path(np.ma.column_stack([vxs, vys]), self._code3d) + return min(vzs) + + +def _get_patch_verts(patch): + """Return a list of vertices for the path of a patch.""" + trans = patch.get_patch_transform() + path = patch.get_path() + polygons = path.to_polygons(trans) + return polygons[0] if len(polygons) else np.array([]) + + +def patch_2d_to_3d(patch, z=0, zdir='z', axlim_clip=False): + """Convert a `.Patch` to a `.Patch3D` object.""" + verts = _get_patch_verts(patch) + patch.__class__ = Patch3D + patch.set_3d_properties(verts, z, zdir, axlim_clip) + + +def pathpatch_2d_to_3d(pathpatch, z=0, zdir='z'): + """Convert a `.PathPatch` to a `.PathPatch3D` object.""" + path = pathpatch.get_path() + trans = pathpatch.get_patch_transform() + + mpath = trans.transform_path(path) + pathpatch.__class__ = PathPatch3D + pathpatch.set_3d_properties(mpath, z, zdir) + + +class Patch3DCollection(PatchCollection): + """ + A collection of 3D patches. + """ + + def __init__(self, *args, + zs=0, zdir='z', depthshade=True, axlim_clip=False, **kwargs): + """ + Create a collection of flat 3D patches with its normal vector + pointed in *zdir* direction, and located at *zs* on the *zdir* + axis. 'zs' can be a scalar or an array-like of the same length as + the number of patches in the collection. + + Constructor arguments are the same as for + :class:`~matplotlib.collections.PatchCollection`. In addition, + keywords *zs=0* and *zdir='z'* are available. + + Also, the keyword argument *depthshade* is available to indicate + whether to shade the patches in order to give the appearance of depth + (default is *True*). This is typically desired in scatter plots. + """ + self._depthshade = depthshade + super().__init__(*args, **kwargs) + self.set_3d_properties(zs, zdir, axlim_clip) + + def get_depthshade(self): + return self._depthshade + + def set_depthshade(self, depthshade): + """ + Set whether depth shading is performed on collection members. + + Parameters + ---------- + depthshade : bool + Whether to shade the patches in order to give the appearance of + depth. + """ + self._depthshade = depthshade + self.stale = True + + def set_sort_zpos(self, val): + """Set the position to use for z-sorting.""" + self._sort_zpos = val + self.stale = True + + def set_3d_properties(self, zs, zdir, axlim_clip=False): + """ + Set the *z* positions and direction of the patches. + + Parameters + ---------- + zs : float or array of floats + The location or locations to place the patches in the collection + along the *zdir* axis. + zdir : {'x', 'y', 'z'} + Plane to plot patches orthogonal to. + All patches must have the same direction. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + """ + # Force the collection to initialize the face and edgecolors + # just in case it is a scalarmappable with a colormap. + self.update_scalarmappable() + offsets = self.get_offsets() + if len(offsets) > 0: + xs, ys = offsets.T + else: + xs = [] + ys = [] + self._offsets3d = juggle_axes(xs, ys, np.atleast_1d(zs), zdir) + self._z_markers_idx = slice(-1) + self._vzs = None + self._axlim_clip = axlim_clip + self.stale = True + + def do_3d_projection(self): + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*self._offsets3d, self.axes) + else: + xs, ys, zs = self._offsets3d + vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs, + self.axes.M, + self.axes._focal_length) + self._vzs = vzs + super().set_offsets(np.ma.column_stack([vxs, vys])) + + if vzs.size > 0: + return min(vzs) + else: + return np.nan + + def _maybe_depth_shade_and_sort_colors(self, color_array): + color_array = ( + _zalpha(color_array, self._vzs) + if self._vzs is not None and self._depthshade + else color_array + ) + if len(color_array) > 1: + color_array = color_array[self._z_markers_idx] + return mcolors.to_rgba_array(color_array, self._alpha) + + def get_facecolor(self): + return self._maybe_depth_shade_and_sort_colors(super().get_facecolor()) + + def get_edgecolor(self): + # We need this check here to make sure we do not double-apply the depth + # based alpha shading when the edge color is "face" which means the + # edge colour should be identical to the face colour. + if cbook._str_equal(self._edgecolors, 'face'): + return self.get_facecolor() + return self._maybe_depth_shade_and_sort_colors(super().get_edgecolor()) + + +class Path3DCollection(PathCollection): + """ + A collection of 3D paths. + """ + + def __init__(self, *args, + zs=0, zdir='z', depthshade=True, axlim_clip=False, **kwargs): + """ + Create a collection of flat 3D paths with its normal vector + pointed in *zdir* direction, and located at *zs* on the *zdir* + axis. 'zs' can be a scalar or an array-like of the same length as + the number of paths in the collection. + + Constructor arguments are the same as for + :class:`~matplotlib.collections.PathCollection`. In addition, + keywords *zs=0* and *zdir='z'* are available. + + Also, the keyword argument *depthshade* is available to indicate + whether to shade the patches in order to give the appearance of depth + (default is *True*). This is typically desired in scatter plots. + """ + self._depthshade = depthshade + self._in_draw = False + super().__init__(*args, **kwargs) + self.set_3d_properties(zs, zdir, axlim_clip) + self._offset_zordered = None + + def draw(self, renderer): + with self._use_zordered_offset(): + with cbook._setattr_cm(self, _in_draw=True): + super().draw(renderer) + + def set_sort_zpos(self, val): + """Set the position to use for z-sorting.""" + self._sort_zpos = val + self.stale = True + + def set_3d_properties(self, zs, zdir, axlim_clip=False): + """ + Set the *z* positions and direction of the paths. + + Parameters + ---------- + zs : float or array of floats + The location or locations to place the paths in the collection + along the *zdir* axis. + zdir : {'x', 'y', 'z'} + Plane to plot paths orthogonal to. + All paths must have the same direction. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide paths with a vertex outside the axes view limits. + """ + # Force the collection to initialize the face and edgecolors + # just in case it is a scalarmappable with a colormap. + self.update_scalarmappable() + offsets = self.get_offsets() + if len(offsets) > 0: + xs, ys = offsets.T + else: + xs = [] + ys = [] + self._zdir = zdir + self._offsets3d = juggle_axes(xs, ys, np.atleast_1d(zs), zdir) + # In the base draw methods we access the attributes directly which + # means we cannot resolve the shuffling in the getter methods like + # we do for the edge and face colors. + # + # This means we need to carry around a cache of the unsorted sizes and + # widths (postfixed with 3d) and in `do_3d_projection` set the + # depth-sorted version of that data into the private state used by the + # base collection class in its draw method. + # + # Grab the current sizes and linewidths to preserve them. + self._sizes3d = self._sizes + self._linewidths3d = np.array(self._linewidths) + xs, ys, zs = self._offsets3d + + # Sort the points based on z coordinates + # Performance optimization: Create a sorted index array and reorder + # points and point properties according to the index array + self._z_markers_idx = slice(-1) + self._vzs = None + + self._axlim_clip = axlim_clip + self.stale = True + + def set_sizes(self, sizes, dpi=72.0): + super().set_sizes(sizes, dpi) + if not self._in_draw: + self._sizes3d = sizes + + def set_linewidth(self, lw): + super().set_linewidth(lw) + if not self._in_draw: + self._linewidths3d = np.array(self._linewidths) + + def get_depthshade(self): + return self._depthshade + + def set_depthshade(self, depthshade): + """ + Set whether depth shading is performed on collection members. + + Parameters + ---------- + depthshade : bool + Whether to shade the patches in order to give the appearance of + depth. + """ + self._depthshade = depthshade + self.stale = True + + def do_3d_projection(self): + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*self._offsets3d, self.axes) + else: + xs, ys, zs = self._offsets3d + vxs, vys, vzs, vis = proj3d._proj_transform_clip(xs, ys, zs, + self.axes.M, + self.axes._focal_length) + # Sort the points based on z coordinates + # Performance optimization: Create a sorted index array and reorder + # points and point properties according to the index array + z_markers_idx = self._z_markers_idx = np.ma.argsort(vzs)[::-1] + self._vzs = vzs + + # we have to special case the sizes because of code in collections.py + # as the draw method does + # self.set_sizes(self._sizes, self.figure.dpi) + # so we cannot rely on doing the sorting on the way out via get_* + + if len(self._sizes3d) > 1: + self._sizes = self._sizes3d[z_markers_idx] + + if len(self._linewidths3d) > 1: + self._linewidths = self._linewidths3d[z_markers_idx] + + PathCollection.set_offsets(self, np.ma.column_stack((vxs, vys))) + + # Re-order items + vzs = vzs[z_markers_idx] + vxs = vxs[z_markers_idx] + vys = vys[z_markers_idx] + + # Store ordered offset for drawing purpose + self._offset_zordered = np.ma.column_stack((vxs, vys)) + + return np.min(vzs) if vzs.size else np.nan + + @contextmanager + def _use_zordered_offset(self): + if self._offset_zordered is None: + # Do nothing + yield + else: + # Swap offset with z-ordered offset + old_offset = self._offsets + super().set_offsets(self._offset_zordered) + try: + yield + finally: + self._offsets = old_offset + + def _maybe_depth_shade_and_sort_colors(self, color_array): + color_array = ( + _zalpha(color_array, self._vzs) + if self._vzs is not None and self._depthshade + else color_array + ) + if len(color_array) > 1: + color_array = color_array[self._z_markers_idx] + return mcolors.to_rgba_array(color_array, self._alpha) + + def get_facecolor(self): + return self._maybe_depth_shade_and_sort_colors(super().get_facecolor()) + + def get_edgecolor(self): + # We need this check here to make sure we do not double-apply the depth + # based alpha shading when the edge color is "face" which means the + # edge colour should be identical to the face colour. + if cbook._str_equal(self._edgecolors, 'face'): + return self.get_facecolor() + return self._maybe_depth_shade_and_sort_colors(super().get_edgecolor()) + + +def patch_collection_2d_to_3d(col, zs=0, zdir='z', depthshade=True, axlim_clip=False): + """ + Convert a `.PatchCollection` into a `.Patch3DCollection` object + (or a `.PathCollection` into a `.Path3DCollection` object). + + Parameters + ---------- + col : `~matplotlib.collections.PatchCollection` or \ +`~matplotlib.collections.PathCollection` + The collection to convert. + zs : float or array of floats + The location or locations to place the patches in the collection along + the *zdir* axis. Default: 0. + zdir : {'x', 'y', 'z'} + The axis in which to place the patches. Default: "z". + See `.get_dir_vector` for a description of the values. + depthshade : bool, default: True + Whether to shade the patches to give a sense of depth. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + """ + if isinstance(col, PathCollection): + col.__class__ = Path3DCollection + col._offset_zordered = None + elif isinstance(col, PatchCollection): + col.__class__ = Patch3DCollection + col._depthshade = depthshade + col._in_draw = False + col.set_3d_properties(zs, zdir, axlim_clip) + + +class Poly3DCollection(PolyCollection): + """ + A collection of 3D polygons. + + .. note:: + **Filling of 3D polygons** + + There is no simple definition of the enclosed surface of a 3D polygon + unless the polygon is planar. + + In practice, Matplotlib fills the 2D projection of the polygon. This + gives a correct filling appearance only for planar polygons. For all + other polygons, you'll find orientations in which the edges of the + polygon intersect in the projection. This will lead to an incorrect + visualization of the 3D area. + + If you need filled areas, it is recommended to create them via + `~mpl_toolkits.mplot3d.axes3d.Axes3D.plot_trisurf`, which creates a + triangulation and thus generates consistent surfaces. + """ + + def __init__(self, verts, *args, zsort='average', shade=False, + lightsource=None, axlim_clip=False, **kwargs): + """ + Parameters + ---------- + verts : list of (N, 3) array-like + The sequence of polygons [*verts0*, *verts1*, ...] where each + element *verts_i* defines the vertices of polygon *i* as a 2D + array-like of shape (N, 3). + zsort : {'average', 'min', 'max'}, default: 'average' + The calculation method for the z-order. + See `~.Poly3DCollection.set_zsort` for details. + shade : bool, default: False + Whether to shade *facecolors* and *edgecolors*. When activating + *shade*, *facecolors* and/or *edgecolors* must be provided. + + .. versionadded:: 3.7 + + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + + .. versionadded:: 3.7 + + axlim_clip : bool, default: False + Whether to hide polygons with a vertex outside the view limits. + + *args, **kwargs + All other parameters are forwarded to `.PolyCollection`. + + Notes + ----- + Note that this class does a bit of magic with the _facecolors + and _edgecolors properties. + """ + if shade: + normals = _generate_normals(verts) + facecolors = kwargs.get('facecolors', None) + if facecolors is not None: + kwargs['facecolors'] = _shade_colors( + facecolors, normals, lightsource + ) + + edgecolors = kwargs.get('edgecolors', None) + if edgecolors is not None: + kwargs['edgecolors'] = _shade_colors( + edgecolors, normals, lightsource + ) + if facecolors is None and edgecolors is None: + raise ValueError( + "You must provide facecolors, edgecolors, or both for " + "shade to work.") + super().__init__(verts, *args, **kwargs) + if isinstance(verts, np.ndarray): + if verts.ndim != 3: + raise ValueError('verts must be a list of (N, 3) array-like') + else: + if any(len(np.shape(vert)) != 2 for vert in verts): + raise ValueError('verts must be a list of (N, 3) array-like') + self.set_zsort(zsort) + self._codes3d = None + self._axlim_clip = axlim_clip + + _zsort_functions = { + 'average': np.average, + 'min': np.min, + 'max': np.max, + } + + def set_zsort(self, zsort): + """ + Set the calculation method for the z-order. + + Parameters + ---------- + zsort : {'average', 'min', 'max'} + The function applied on the z-coordinates of the vertices in the + viewer's coordinate system, to determine the z-order. + """ + self._zsortfunc = self._zsort_functions[zsort] + self._sort_zpos = None + self.stale = True + + @_api.deprecated("3.10") + def get_vector(self, segments3d): + return self._get_vector(segments3d) + + def _get_vector(self, segments3d): + """Optimize points for projection.""" + if len(segments3d): + xs, ys, zs = np.vstack(segments3d).T + else: # vstack can't stack zero arrays. + xs, ys, zs = [], [], [] + ones = np.ones(len(xs)) + self._vec = np.array([xs, ys, zs, ones]) + + indices = [0, *np.cumsum([len(segment) for segment in segments3d])] + self._segslices = [*map(slice, indices[:-1], indices[1:])] + + def set_verts(self, verts, closed=True): + """ + Set 3D vertices. + + Parameters + ---------- + verts : list of (N, 3) array-like + The sequence of polygons [*verts0*, *verts1*, ...] where each + element *verts_i* defines the vertices of polygon *i* as a 2D + array-like of shape (N, 3). + closed : bool, default: True + Whether the polygon should be closed by adding a CLOSEPOLY + connection at the end. + """ + self._get_vector(verts) + # 2D verts will be updated at draw time + super().set_verts([], False) + self._closed = closed + + def set_verts_and_codes(self, verts, codes): + """Set 3D vertices with path codes.""" + # set vertices with closed=False to prevent PolyCollection from + # setting path codes + self.set_verts(verts, closed=False) + # and set our own codes instead. + self._codes3d = codes + + def set_3d_properties(self, axlim_clip=False): + # Force the collection to initialize the face and edgecolors + # just in case it is a scalarmappable with a colormap. + self.update_scalarmappable() + self._sort_zpos = None + self.set_zsort('average') + self._facecolor3d = PolyCollection.get_facecolor(self) + self._edgecolor3d = PolyCollection.get_edgecolor(self) + self._alpha3d = PolyCollection.get_alpha(self) + self.stale = True + + def set_sort_zpos(self, val): + """Set the position to use for z-sorting.""" + self._sort_zpos = val + self.stale = True + + def do_3d_projection(self): + """ + Perform the 3D projection for this object. + """ + if self._A is not None: + # force update of color mapping because we re-order them + # below. If we do not do this here, the 2D draw will call + # this, but we will never port the color mapped values back + # to the 3D versions. + # + # We hold the 3D versions in a fixed order (the order the user + # passed in) and sort the 2D version by view depth. + self.update_scalarmappable() + if self._face_is_mapped: + self._facecolor3d = self._facecolors + if self._edge_is_mapped: + self._edgecolor3d = self._edgecolors + if self._axlim_clip: + xs, ys, zs = _viewlim_mask(*self._vec[0:3], self.axes) + if self._vec.shape[0] == 4: # Will be 3 (xyz) or 4 (xyzw) + w_masked = np.ma.masked_where(zs.mask, self._vec[3]) + vec = np.ma.array([xs, ys, zs, w_masked]) + else: + vec = np.ma.array([xs, ys, zs]) + else: + vec = self._vec + txs, tys, tzs = proj3d._proj_transform_vec(vec, self.axes.M) + xyzlist = [(txs[sl], tys[sl], tzs[sl]) for sl in self._segslices] + + # This extra fuss is to re-order face / edge colors + cface = self._facecolor3d + cedge = self._edgecolor3d + if len(cface) != len(xyzlist): + cface = cface.repeat(len(xyzlist), axis=0) + if len(cedge) != len(xyzlist): + if len(cedge) == 0: + cedge = cface + else: + cedge = cedge.repeat(len(xyzlist), axis=0) + + if xyzlist: + # sort by depth (furthest drawn first) + z_segments_2d = sorted( + ((self._zsortfunc(zs.data), np.ma.column_stack([xs, ys]), fc, ec, idx) + for idx, ((xs, ys, zs), fc, ec) + in enumerate(zip(xyzlist, cface, cedge))), + key=lambda x: x[0], reverse=True) + + _, segments_2d, self._facecolors2d, self._edgecolors2d, idxs = \ + zip(*z_segments_2d) + else: + segments_2d = [] + self._facecolors2d = np.empty((0, 4)) + self._edgecolors2d = np.empty((0, 4)) + idxs = [] + + if self._codes3d is not None: + codes = [self._codes3d[idx] for idx in idxs] + PolyCollection.set_verts_and_codes(self, segments_2d, codes) + else: + PolyCollection.set_verts(self, segments_2d, self._closed) + + if len(self._edgecolor3d) != len(cface): + self._edgecolors2d = self._edgecolor3d + + # Return zorder value + if self._sort_zpos is not None: + zvec = np.array([[0], [0], [self._sort_zpos], [1]]) + ztrans = proj3d._proj_transform_vec(zvec, self.axes.M) + return ztrans[2][0] + elif tzs.size > 0: + # FIXME: Some results still don't look quite right. + # In particular, examine contourf3d_demo2.py + # with az = -54 and elev = -45. + return np.min(tzs) + else: + return np.nan + + def set_facecolor(self, colors): + # docstring inherited + super().set_facecolor(colors) + self._facecolor3d = PolyCollection.get_facecolor(self) + + def set_edgecolor(self, colors): + # docstring inherited + super().set_edgecolor(colors) + self._edgecolor3d = PolyCollection.get_edgecolor(self) + + def set_alpha(self, alpha): + # docstring inherited + artist.Artist.set_alpha(self, alpha) + try: + self._facecolor3d = mcolors.to_rgba_array( + self._facecolor3d, self._alpha) + except (AttributeError, TypeError, IndexError): + pass + try: + self._edgecolors = mcolors.to_rgba_array( + self._edgecolor3d, self._alpha) + except (AttributeError, TypeError, IndexError): + pass + self.stale = True + + def get_facecolor(self): + # docstring inherited + # self._facecolors2d is not initialized until do_3d_projection + if not hasattr(self, '_facecolors2d'): + self.axes.M = self.axes.get_proj() + self.do_3d_projection() + return np.asarray(self._facecolors2d) + + def get_edgecolor(self): + # docstring inherited + # self._edgecolors2d is not initialized until do_3d_projection + if not hasattr(self, '_edgecolors2d'): + self.axes.M = self.axes.get_proj() + self.do_3d_projection() + return np.asarray(self._edgecolors2d) + + +def poly_collection_2d_to_3d(col, zs=0, zdir='z', axlim_clip=False): + """ + Convert a `.PolyCollection` into a `.Poly3DCollection` object. + + Parameters + ---------- + col : `~matplotlib.collections.PolyCollection` + The collection to convert. + zs : float or array of floats + The location or locations to place the polygons in the collection along + the *zdir* axis. Default: 0. + zdir : {'x', 'y', 'z'} + The axis in which to place the patches. Default: 'z'. + See `.get_dir_vector` for a description of the values. + """ + segments_3d, codes = _paths_to_3d_segments_with_codes( + col.get_paths(), zs, zdir) + col.__class__ = Poly3DCollection + col.set_verts_and_codes(segments_3d, codes) + col.set_3d_properties() + col._axlim_clip = axlim_clip + + +def juggle_axes(xs, ys, zs, zdir): + """ + Reorder coordinates so that 2D *xs*, *ys* can be plotted in the plane + orthogonal to *zdir*. *zdir* is normally 'x', 'y' or 'z'. However, if + *zdir* starts with a '-' it is interpreted as a compensation for + `rotate_axes`. + """ + if zdir == 'x': + return zs, xs, ys + elif zdir == 'y': + return xs, zs, ys + elif zdir[0] == '-': + return rotate_axes(xs, ys, zs, zdir) + else: + return xs, ys, zs + + +def rotate_axes(xs, ys, zs, zdir): + """ + Reorder coordinates so that the axes are rotated with *zdir* along + the original z axis. Prepending the axis with a '-' does the + inverse transform, so *zdir* can be 'x', '-x', 'y', '-y', 'z' or '-z'. + """ + if zdir in ('x', '-y'): + return ys, zs, xs + elif zdir in ('-x', 'y'): + return zs, xs, ys + else: + return xs, ys, zs + + +def _zalpha(colors, zs): + """Modify the alphas of the color list according to depth.""" + # FIXME: This only works well if the points for *zs* are well-spaced + # in all three dimensions. Otherwise, at certain orientations, + # the min and max zs are very close together. + # Should really normalize against the viewing depth. + if len(colors) == 0 or len(zs) == 0: + return np.zeros((0, 4)) + norm = Normalize(min(zs), max(zs)) + sats = 1 - norm(zs) * 0.7 + rgba = np.broadcast_to(mcolors.to_rgba_array(colors), (len(zs), 4)) + return np.column_stack([rgba[:, :3], rgba[:, 3] * sats]) + + +def _all_points_on_plane(xs, ys, zs, atol=1e-8): + """ + Check if all points are on the same plane. Note that NaN values are + ignored. + + Parameters + ---------- + xs, ys, zs : array-like + The x, y, and z coordinates of the points. + atol : float, default: 1e-8 + The tolerance for the equality check. + """ + xs, ys, zs = np.asarray(xs), np.asarray(ys), np.asarray(zs) + points = np.column_stack([xs, ys, zs]) + points = points[~np.isnan(points).any(axis=1)] + # Check for the case where we have less than 3 unique points + points = np.unique(points, axis=0) + if len(points) <= 3: + return True + # Calculate the vectors from the first point to all other points + vs = (points - points[0])[1:] + vs = vs / np.linalg.norm(vs, axis=1)[:, np.newaxis] + # Filter out parallel vectors + vs = np.unique(vs, axis=0) + if len(vs) <= 2: + return True + # Filter out parallel and antiparallel vectors to the first vector + cross_norms = np.linalg.norm(np.cross(vs[0], vs[1:]), axis=1) + zero_cross_norms = np.where(np.isclose(cross_norms, 0, atol=atol))[0] + 1 + vs = np.delete(vs, zero_cross_norms, axis=0) + if len(vs) <= 2: + return True + # Calculate the normal vector from the first three points + n = np.cross(vs[0], vs[1]) + n = n / np.linalg.norm(n) + # If the dot product of the normal vector and all other vectors is zero, + # all points are on the same plane + dots = np.dot(n, vs.transpose()) + return np.allclose(dots, 0, atol=atol) + + +def _generate_normals(polygons): + """ + Compute the normals of a list of polygons, one normal per polygon. + + Normals point towards the viewer for a face with its vertices in + counterclockwise order, following the right hand rule. + + Uses three points equally spaced around the polygon. This method assumes + that the points are in a plane. Otherwise, more than one shade is required, + which is not supported. + + Parameters + ---------- + polygons : list of (M_i, 3) array-like, or (..., M, 3) array-like + A sequence of polygons to compute normals for, which can have + varying numbers of vertices. If the polygons all have the same + number of vertices and array is passed, then the operation will + be vectorized. + + Returns + ------- + normals : (..., 3) array + A normal vector estimated for the polygon. + """ + if isinstance(polygons, np.ndarray): + # optimization: polygons all have the same number of points, so can + # vectorize + n = polygons.shape[-2] + i1, i2, i3 = 0, n//3, 2*n//3 + v1 = polygons[..., i1, :] - polygons[..., i2, :] + v2 = polygons[..., i2, :] - polygons[..., i3, :] + else: + # The subtraction doesn't vectorize because polygons is jagged. + v1 = np.empty((len(polygons), 3)) + v2 = np.empty((len(polygons), 3)) + for poly_i, ps in enumerate(polygons): + n = len(ps) + ps = np.asarray(ps) + i1, i2, i3 = 0, n//3, 2*n//3 + v1[poly_i, :] = ps[i1, :] - ps[i2, :] + v2[poly_i, :] = ps[i2, :] - ps[i3, :] + return np.cross(v1, v2) + + +def _shade_colors(color, normals, lightsource=None): + """ + Shade *color* using normal vectors given by *normals*, + assuming a *lightsource* (using default position if not given). + *color* can also be an array of the same length as *normals*. + """ + if lightsource is None: + # chosen for backwards-compatibility + lightsource = mcolors.LightSource(azdeg=225, altdeg=19.4712) + + with np.errstate(invalid="ignore"): + shade = ((normals / np.linalg.norm(normals, axis=1, keepdims=True)) + @ lightsource.direction) + mask = ~np.isnan(shade) + + if mask.any(): + # convert dot product to allowed shading fractions + in_norm = mcolors.Normalize(-1, 1) + out_norm = mcolors.Normalize(0.3, 1).inverse + + def norm(x): + return out_norm(in_norm(x)) + + shade[~mask] = 0 + + color = mcolors.to_rgba_array(color) + # shape of color should be (M, 4) (where M is number of faces) + # shape of shade should be (M,) + # colors should have final shape of (M, 4) + alpha = color[:, 3] + colors = norm(shade)[:, np.newaxis] * color + colors[:, 3] = alpha + else: + colors = np.asanyarray(color).copy() + + return colors diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py new file mode 100644 index 0000000000000000000000000000000000000000..d0ba360c314bbaa39efd5e64936a07e6cd0ec0f6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axes3d.py @@ -0,0 +1,4162 @@ +""" +axes3d.py, original mplot3d version by John Porter +Created: 23 Sep 2005 + +Parts fixed by Reinier Heeres +Minor additions by Ben Axelrod +Significant updates and revisions by Ben Root + +Module containing Axes3D, an object which can plot 3D objects on a +2D matplotlib figure. +""" + +from collections import defaultdict +import itertools +import math +import textwrap +import warnings + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api, cbook, _docstring, _preprocess_data +import matplotlib.artist as martist +import matplotlib.collections as mcoll +import matplotlib.colors as mcolors +import matplotlib.image as mimage +import matplotlib.lines as mlines +import matplotlib.patches as mpatches +import matplotlib.container as mcontainer +import matplotlib.transforms as mtransforms +from matplotlib.axes import Axes +from matplotlib.axes._base import _axis_method_wrapper, _process_plot_format +from matplotlib.transforms import Bbox +from matplotlib.tri._triangulation import Triangulation + +from . import art3d +from . import proj3d +from . import axis3d + + +@_docstring.interpd +@_api.define_aliases({ + "xlim": ["xlim3d"], "ylim": ["ylim3d"], "zlim": ["zlim3d"]}) +class Axes3D(Axes): + """ + 3D Axes object. + + .. note:: + + As a user, you do not instantiate Axes directly, but use Axes creation + methods instead; e.g. from `.pyplot` or `.Figure`: + `~.pyplot.subplots`, `~.pyplot.subplot_mosaic` or `.Figure.add_axes`. + """ + name = '3d' + + _axis_names = ("x", "y", "z") + Axes._shared_axes["z"] = cbook.Grouper() + Axes._shared_axes["view"] = cbook.Grouper() + + def __init__( + self, fig, rect=None, *args, + elev=30, azim=-60, roll=0, shareview=None, sharez=None, + proj_type='persp', focal_length=None, + box_aspect=None, + computed_zorder=True, + **kwargs, + ): + """ + Parameters + ---------- + fig : Figure + The parent figure. + rect : tuple (left, bottom, width, height), default: None. + The ``(left, bottom, width, height)`` Axes position. + elev : float, default: 30 + The elevation angle in degrees rotates the camera above and below + the x-y plane, with a positive angle corresponding to a location + above the plane. + azim : float, default: -60 + The azimuthal angle in degrees rotates the camera about the z axis, + with a positive angle corresponding to a right-handed rotation. In + other words, a positive azimuth rotates the camera about the origin + from its location along the +x axis towards the +y axis. + roll : float, default: 0 + The roll angle in degrees rotates the camera about the viewing + axis. A positive angle spins the camera clockwise, causing the + scene to rotate counter-clockwise. + shareview : Axes3D, optional + Other Axes to share view angles with. Note that it is not possible + to unshare axes. + sharez : Axes3D, optional + Other Axes to share z-limits with. Note that it is not possible to + unshare axes. + proj_type : {'persp', 'ortho'} + The projection type, default 'persp'. + focal_length : float, default: None + For a projection type of 'persp', the focal length of the virtual + camera. Must be > 0. If None, defaults to 1. + For a projection type of 'ortho', must be set to either None + or infinity (numpy.inf). If None, defaults to infinity. + The focal length can be computed from a desired Field Of View via + the equation: focal_length = 1/tan(FOV/2) + box_aspect : 3-tuple of floats, default: None + Changes the physical dimensions of the Axes3D, such that the ratio + of the axis lengths in display units is x:y:z. + If None, defaults to 4:4:3 + computed_zorder : bool, default: True + If True, the draw order is computed based on the average position + of the `.Artist`\\s along the view direction. + Set to False if you want to manually control the order in which + Artists are drawn on top of each other using their *zorder* + attribute. This can be used for fine-tuning if the automatic order + does not produce the desired result. Note however, that a manual + zorder will only be correct for a limited view angle. If the figure + is rotated by the user, it will look wrong from certain angles. + + **kwargs + Other optional keyword arguments: + + %(Axes3D:kwdoc)s + """ + + if rect is None: + rect = [0.0, 0.0, 1.0, 1.0] + + self.initial_azim = azim + self.initial_elev = elev + self.initial_roll = roll + self.set_proj_type(proj_type, focal_length) + self.computed_zorder = computed_zorder + + self.xy_viewLim = Bbox.unit() + self.zz_viewLim = Bbox.unit() + xymargin = 0.05 * 10/11 # match mpl3.8 appearance + self.xy_dataLim = Bbox([[xymargin, xymargin], + [1 - xymargin, 1 - xymargin]]) + # z-limits are encoded in the x-component of the Bbox, y is un-used + self.zz_dataLim = Bbox.unit() + + # inhibit autoscale_view until the axes are defined + # they can't be defined until Axes.__init__ has been called + self.view_init(self.initial_elev, self.initial_azim, self.initial_roll) + + self._sharez = sharez + if sharez is not None: + self._shared_axes["z"].join(self, sharez) + self._adjustable = 'datalim' + + self._shareview = shareview + if shareview is not None: + self._shared_axes["view"].join(self, shareview) + + if kwargs.pop('auto_add_to_figure', False): + raise AttributeError( + 'auto_add_to_figure is no longer supported for Axes3D. ' + 'Use fig.add_axes(ax) instead.' + ) + + super().__init__( + fig, rect, frameon=True, box_aspect=box_aspect, *args, **kwargs + ) + # Disable drawing of axes by base class + super().set_axis_off() + # Enable drawing of axes by Axes3D class + self.set_axis_on() + self.M = None + self.invM = None + + self._view_margin = 1/48 # default value to match mpl3.8 + self.autoscale_view() + + # func used to format z -- fall back on major formatters + self.fmt_zdata = None + + self.mouse_init() + fig = self.get_figure(root=True) + fig.canvas.callbacks._connect_picklable( + 'motion_notify_event', self._on_move) + fig.canvas.callbacks._connect_picklable( + 'button_press_event', self._button_press) + fig.canvas.callbacks._connect_picklable( + 'button_release_event', self._button_release) + self.set_top_view() + + self.patch.set_linewidth(0) + # Calculate the pseudo-data width and height + pseudo_bbox = self.transLimits.inverted().transform([(0, 0), (1, 1)]) + self._pseudo_w, self._pseudo_h = pseudo_bbox[1] - pseudo_bbox[0] + + # mplot3d currently manages its own spines and needs these turned off + # for bounding box calculations + self.spines[:].set_visible(False) + + def set_axis_off(self): + self._axis3don = False + self.stale = True + + def set_axis_on(self): + self._axis3don = True + self.stale = True + + def convert_zunits(self, z): + """ + For artists in an Axes, if the zaxis has units support, + convert *z* using zaxis unit type + """ + return self.zaxis.convert_units(z) + + def set_top_view(self): + # this happens to be the right view for the viewing coordinates + # moved up and to the left slightly to fit labels and axes + xdwl = 0.95 / self._dist + xdw = 0.9 / self._dist + ydwl = 0.95 / self._dist + ydw = 0.9 / self._dist + # Set the viewing pane. + self.viewLim.intervalx = (-xdwl, xdw) + self.viewLim.intervaly = (-ydwl, ydw) + self.stale = True + + def _init_axis(self): + """Init 3D Axes; overrides creation of regular X/Y Axes.""" + self.xaxis = axis3d.XAxis(self) + self.yaxis = axis3d.YAxis(self) + self.zaxis = axis3d.ZAxis(self) + + def get_zaxis(self): + """Return the ``ZAxis`` (`~.axis3d.Axis`) instance.""" + return self.zaxis + + get_zgridlines = _axis_method_wrapper("zaxis", "get_gridlines") + get_zticklines = _axis_method_wrapper("zaxis", "get_ticklines") + + def _transformed_cube(self, vals): + """Return cube with limits from *vals* transformed by self.M.""" + minx, maxx, miny, maxy, minz, maxz = vals + xyzs = [(minx, miny, minz), + (maxx, miny, minz), + (maxx, maxy, minz), + (minx, maxy, minz), + (minx, miny, maxz), + (maxx, miny, maxz), + (maxx, maxy, maxz), + (minx, maxy, maxz)] + return proj3d._proj_points(xyzs, self.M) + + def set_aspect(self, aspect, adjustable=None, anchor=None, share=False): + """ + Set the aspect ratios. + + Parameters + ---------- + aspect : {'auto', 'equal', 'equalxy', 'equalxz', 'equalyz'} + Possible values: + + ========= ================================================== + value description + ========= ================================================== + 'auto' automatic; fill the position rectangle with data. + 'equal' adapt all the axes to have equal aspect ratios. + 'equalxy' adapt the x and y axes to have equal aspect ratios. + 'equalxz' adapt the x and z axes to have equal aspect ratios. + 'equalyz' adapt the y and z axes to have equal aspect ratios. + ========= ================================================== + + adjustable : None or {'box', 'datalim'}, optional + If not *None*, this defines which parameter will be adjusted to + meet the required aspect. See `.set_adjustable` for further + details. + + anchor : None or str or 2-tuple of float, optional + If not *None*, this defines where the Axes will be drawn if there + is extra space due to aspect constraints. The most common way to + specify the anchor are abbreviations of cardinal directions: + + ===== ===================== + value description + ===== ===================== + 'C' centered + 'SW' lower left corner + 'S' middle of bottom edge + 'SE' lower right corner + etc. + ===== ===================== + + See `~.Axes.set_anchor` for further details. + + share : bool, default: False + If ``True``, apply the settings to all shared Axes. + + See Also + -------- + mpl_toolkits.mplot3d.axes3d.Axes3D.set_box_aspect + """ + _api.check_in_list(('auto', 'equal', 'equalxy', 'equalyz', 'equalxz'), + aspect=aspect) + super().set_aspect( + aspect='auto', adjustable=adjustable, anchor=anchor, share=share) + self._aspect = aspect + + if aspect in ('equal', 'equalxy', 'equalxz', 'equalyz'): + ax_indices = self._equal_aspect_axis_indices(aspect) + + view_intervals = np.array([self.xaxis.get_view_interval(), + self.yaxis.get_view_interval(), + self.zaxis.get_view_interval()]) + ptp = np.ptp(view_intervals, axis=1) + if self._adjustable == 'datalim': + mean = np.mean(view_intervals, axis=1) + scale = max(ptp[ax_indices] / self._box_aspect[ax_indices]) + deltas = scale * self._box_aspect + + for i, set_lim in enumerate((self.set_xlim3d, + self.set_ylim3d, + self.set_zlim3d)): + if i in ax_indices: + set_lim(mean[i] - deltas[i]/2., mean[i] + deltas[i]/2., + auto=True, view_margin=None) + else: # 'box' + # Change the box aspect such that the ratio of the length of + # the unmodified axis to the length of the diagonal + # perpendicular to it remains unchanged. + box_aspect = np.array(self._box_aspect) + box_aspect[ax_indices] = ptp[ax_indices] + remaining_ax_indices = {0, 1, 2}.difference(ax_indices) + if remaining_ax_indices: + remaining = remaining_ax_indices.pop() + old_diag = np.linalg.norm(self._box_aspect[ax_indices]) + new_diag = np.linalg.norm(box_aspect[ax_indices]) + box_aspect[remaining] *= new_diag / old_diag + self.set_box_aspect(box_aspect) + + def _equal_aspect_axis_indices(self, aspect): + """ + Get the indices for which of the x, y, z axes are constrained to have + equal aspect ratios. + + Parameters + ---------- + aspect : {'auto', 'equal', 'equalxy', 'equalxz', 'equalyz'} + See descriptions in docstring for `.set_aspect()`. + """ + ax_indices = [] # aspect == 'auto' + if aspect == 'equal': + ax_indices = [0, 1, 2] + elif aspect == 'equalxy': + ax_indices = [0, 1] + elif aspect == 'equalxz': + ax_indices = [0, 2] + elif aspect == 'equalyz': + ax_indices = [1, 2] + return ax_indices + + def set_box_aspect(self, aspect, *, zoom=1): + """ + Set the Axes box aspect. + + The box aspect is the ratio of height to width in display + units for each face of the box when viewed perpendicular to + that face. This is not to be confused with the data aspect (see + `~.Axes3D.set_aspect`). The default ratios are 4:4:3 (x:y:z). + + To simulate having equal aspect in data space, set the box + aspect to match your data range in each dimension. + + *zoom* controls the overall size of the Axes3D in the figure. + + Parameters + ---------- + aspect : 3-tuple of floats or None + Changes the physical dimensions of the Axes3D, such that the ratio + of the axis lengths in display units is x:y:z. + If None, defaults to (4, 4, 3). + + zoom : float, default: 1 + Control overall size of the Axes3D in the figure. Must be > 0. + """ + if zoom <= 0: + raise ValueError(f'Argument zoom = {zoom} must be > 0') + + if aspect is None: + aspect = np.asarray((4, 4, 3), dtype=float) + else: + aspect = np.asarray(aspect, dtype=float) + _api.check_shape((3,), aspect=aspect) + # The scale 1.8294640721620434 is tuned to match the mpl3.2 appearance. + # The 25/24 factor is to compensate for the change in automargin + # behavior in mpl3.9. This comes from the padding of 1/48 on both sides + # of the axes in mpl3.8. + aspect *= 1.8294640721620434 * 25/24 * zoom / np.linalg.norm(aspect) + + self._box_aspect = self._roll_to_vertical(aspect, reverse=True) + self.stale = True + + def apply_aspect(self, position=None): + if position is None: + position = self.get_position(original=True) + + # in the superclass, we would go through and actually deal with axis + # scales and box/datalim. Those are all irrelevant - all we need to do + # is make sure our coordinate system is square. + trans = self.get_figure().transSubfigure + bb = mtransforms.Bbox.unit().transformed(trans) + # this is the physical aspect of the panel (or figure): + fig_aspect = bb.height / bb.width + + box_aspect = 1 + pb = position.frozen() + pb1 = pb.shrunk_to_aspect(box_aspect, pb, fig_aspect) + self._set_position(pb1.anchored(self.get_anchor(), pb), 'active') + + @martist.allow_rasterization + def draw(self, renderer): + if not self.get_visible(): + return + self._unstale_viewLim() + + # draw the background patch + self.patch.draw(renderer) + self._frameon = False + + # first, set the aspect + # this is duplicated from `axes._base._AxesBase.draw` + # but must be called before any of the artist are drawn as + # it adjusts the view limits and the size of the bounding box + # of the Axes + locator = self.get_axes_locator() + self.apply_aspect(locator(self, renderer) if locator else None) + + # add the projection matrix to the renderer + self.M = self.get_proj() + self.invM = np.linalg.inv(self.M) + + collections_and_patches = ( + artist for artist in self._children + if isinstance(artist, (mcoll.Collection, mpatches.Patch)) + and artist.get_visible()) + if self.computed_zorder: + # Calculate projection of collections and patches and zorder + # them. Make sure they are drawn above the grids. + zorder_offset = max(axis.get_zorder() + for axis in self._axis_map.values()) + 1 + collection_zorder = patch_zorder = zorder_offset + + for artist in sorted(collections_and_patches, + key=lambda artist: artist.do_3d_projection(), + reverse=True): + if isinstance(artist, mcoll.Collection): + artist.zorder = collection_zorder + collection_zorder += 1 + elif isinstance(artist, mpatches.Patch): + artist.zorder = patch_zorder + patch_zorder += 1 + else: + for artist in collections_and_patches: + artist.do_3d_projection() + + if self._axis3don: + # Draw panes first + for axis in self._axis_map.values(): + axis.draw_pane(renderer) + # Then gridlines + for axis in self._axis_map.values(): + axis.draw_grid(renderer) + # Then axes, labels, text, and ticks + for axis in self._axis_map.values(): + axis.draw(renderer) + + # Then rest + super().draw(renderer) + + def get_axis_position(self): + tc = self._transformed_cube(self.get_w_lims()) + xhigh = tc[1][2] > tc[2][2] + yhigh = tc[3][2] > tc[2][2] + zhigh = tc[0][2] > tc[2][2] + return xhigh, yhigh, zhigh + + def update_datalim(self, xys, **kwargs): + """ + Not implemented in `~mpl_toolkits.mplot3d.axes3d.Axes3D`. + """ + pass + + get_autoscalez_on = _axis_method_wrapper("zaxis", "_get_autoscale_on") + set_autoscalez_on = _axis_method_wrapper("zaxis", "_set_autoscale_on") + + def get_zmargin(self): + """ + Retrieve autoscaling margin of the z-axis. + + .. versionadded:: 3.9 + + Returns + ------- + zmargin : float + + See Also + -------- + mpl_toolkits.mplot3d.axes3d.Axes3D.set_zmargin + """ + return self._zmargin + + def set_zmargin(self, m): + """ + Set padding of Z data limits prior to autoscaling. + + *m* times the data interval will be added to each end of that interval + before it is used in autoscaling. If *m* is negative, this will clip + the data range instead of expanding it. + + For example, if your data is in the range [0, 2], a margin of 0.1 will + result in a range [-0.2, 2.2]; a margin of -0.1 will result in a range + of [0.2, 1.8]. + + Parameters + ---------- + m : float greater than -0.5 + """ + if m <= -0.5: + raise ValueError("margin must be greater than -0.5") + self._zmargin = m + self._request_autoscale_view("z") + self.stale = True + + def margins(self, *margins, x=None, y=None, z=None, tight=True): + """ + Set or retrieve autoscaling margins. + + See `.Axes.margins` for full documentation. Because this function + applies to 3D Axes, it also takes a *z* argument, and returns + ``(xmargin, ymargin, zmargin)``. + """ + if margins and (x is not None or y is not None or z is not None): + raise TypeError('Cannot pass both positional and keyword ' + 'arguments for x, y, and/or z.') + elif len(margins) == 1: + x = y = z = margins[0] + elif len(margins) == 3: + x, y, z = margins + elif margins: + raise TypeError('Must pass a single positional argument for all ' + 'margins, or one for each margin (x, y, z).') + + if x is None and y is None and z is None: + if tight is not True: + _api.warn_external(f'ignoring tight={tight!r} in get mode') + return self._xmargin, self._ymargin, self._zmargin + + if x is not None: + self.set_xmargin(x) + if y is not None: + self.set_ymargin(y) + if z is not None: + self.set_zmargin(z) + + self.autoscale_view( + tight=tight, scalex=(x is not None), scaley=(y is not None), + scalez=(z is not None) + ) + + def autoscale(self, enable=True, axis='both', tight=None): + """ + Convenience method for simple axis view autoscaling. + + See `.Axes.autoscale` for full documentation. Because this function + applies to 3D Axes, *axis* can also be set to 'z', and setting *axis* + to 'both' autoscales all three axes. + """ + if enable is None: + scalex = True + scaley = True + scalez = True + else: + if axis in ['x', 'both']: + self.set_autoscalex_on(enable) + scalex = self.get_autoscalex_on() + else: + scalex = False + if axis in ['y', 'both']: + self.set_autoscaley_on(enable) + scaley = self.get_autoscaley_on() + else: + scaley = False + if axis in ['z', 'both']: + self.set_autoscalez_on(enable) + scalez = self.get_autoscalez_on() + else: + scalez = False + if scalex: + self._request_autoscale_view("x", tight=tight) + if scaley: + self._request_autoscale_view("y", tight=tight) + if scalez: + self._request_autoscale_view("z", tight=tight) + + def auto_scale_xyz(self, X, Y, Z=None, had_data=None): + # This updates the bounding boxes as to keep a record as to what the + # minimum sized rectangular volume holds the data. + if np.shape(X) == np.shape(Y): + self.xy_dataLim.update_from_data_xy( + np.column_stack([np.ravel(X), np.ravel(Y)]), not had_data) + else: + self.xy_dataLim.update_from_data_x(X, not had_data) + self.xy_dataLim.update_from_data_y(Y, not had_data) + if Z is not None: + self.zz_dataLim.update_from_data_x(Z, not had_data) + # Let autoscale_view figure out how to use this data. + self.autoscale_view() + + def autoscale_view(self, tight=None, + scalex=True, scaley=True, scalez=True): + """ + Autoscale the view limits using the data limits. + + See `.Axes.autoscale_view` for full documentation. Because this + function applies to 3D Axes, it also takes a *scalez* argument. + """ + # This method looks at the rectangular volume (see above) + # of data and decides how to scale the view portal to fit it. + if tight is None: + _tight = self._tight + if not _tight: + # if image data only just use the datalim + for artist in self._children: + if isinstance(artist, mimage.AxesImage): + _tight = True + elif isinstance(artist, (mlines.Line2D, mpatches.Patch)): + _tight = False + break + else: + _tight = self._tight = bool(tight) + + if scalex and self.get_autoscalex_on(): + x0, x1 = self.xy_dataLim.intervalx + xlocator = self.xaxis.get_major_locator() + x0, x1 = xlocator.nonsingular(x0, x1) + if self._xmargin > 0: + delta = (x1 - x0) * self._xmargin + x0 -= delta + x1 += delta + if not _tight: + x0, x1 = xlocator.view_limits(x0, x1) + self.set_xbound(x0, x1, self._view_margin) + + if scaley and self.get_autoscaley_on(): + y0, y1 = self.xy_dataLim.intervaly + ylocator = self.yaxis.get_major_locator() + y0, y1 = ylocator.nonsingular(y0, y1) + if self._ymargin > 0: + delta = (y1 - y0) * self._ymargin + y0 -= delta + y1 += delta + if not _tight: + y0, y1 = ylocator.view_limits(y0, y1) + self.set_ybound(y0, y1, self._view_margin) + + if scalez and self.get_autoscalez_on(): + z0, z1 = self.zz_dataLim.intervalx + zlocator = self.zaxis.get_major_locator() + z0, z1 = zlocator.nonsingular(z0, z1) + if self._zmargin > 0: + delta = (z1 - z0) * self._zmargin + z0 -= delta + z1 += delta + if not _tight: + z0, z1 = zlocator.view_limits(z0, z1) + self.set_zbound(z0, z1, self._view_margin) + + def get_w_lims(self): + """Get 3D world limits.""" + minx, maxx = self.get_xlim3d() + miny, maxy = self.get_ylim3d() + minz, maxz = self.get_zlim3d() + return minx, maxx, miny, maxy, minz, maxz + + def _set_bound3d(self, get_bound, set_lim, axis_inverted, + lower=None, upper=None, view_margin=None): + """ + Set 3D axis bounds. + """ + if upper is None and np.iterable(lower): + lower, upper = lower + + old_lower, old_upper = get_bound() + if lower is None: + lower = old_lower + if upper is None: + upper = old_upper + + set_lim(sorted((lower, upper), reverse=bool(axis_inverted())), + auto=None, view_margin=view_margin) + + def set_xbound(self, lower=None, upper=None, view_margin=None): + """ + Set the lower and upper numerical bounds of the x-axis. + + This method will honor axis inversion regardless of parameter order. + It will not change the autoscaling setting (`.get_autoscalex_on()`). + + Parameters + ---------- + lower, upper : float or None + The lower and upper bounds. If *None*, the respective axis bound + is not modified. + view_margin : float or None + The margin to apply to the bounds. If *None*, the margin is handled + by `.set_xlim`. + + See Also + -------- + get_xbound + get_xlim, set_xlim + invert_xaxis, xaxis_inverted + """ + self._set_bound3d(self.get_xbound, self.set_xlim, self.xaxis_inverted, + lower, upper, view_margin) + + def set_ybound(self, lower=None, upper=None, view_margin=None): + """ + Set the lower and upper numerical bounds of the y-axis. + + This method will honor axis inversion regardless of parameter order. + It will not change the autoscaling setting (`.get_autoscaley_on()`). + + Parameters + ---------- + lower, upper : float or None + The lower and upper bounds. If *None*, the respective axis bound + is not modified. + view_margin : float or None + The margin to apply to the bounds. If *None*, the margin is handled + by `.set_ylim`. + + See Also + -------- + get_ybound + get_ylim, set_ylim + invert_yaxis, yaxis_inverted + """ + self._set_bound3d(self.get_ybound, self.set_ylim, self.yaxis_inverted, + lower, upper, view_margin) + + def set_zbound(self, lower=None, upper=None, view_margin=None): + """ + Set the lower and upper numerical bounds of the z-axis. + This method will honor axis inversion regardless of parameter order. + It will not change the autoscaling setting (`.get_autoscaley_on()`). + + Parameters + ---------- + lower, upper : float or None + The lower and upper bounds. If *None*, the respective axis bound + is not modified. + view_margin : float or None + The margin to apply to the bounds. If *None*, the margin is handled + by `.set_zlim`. + + See Also + -------- + get_zbound + get_zlim, set_zlim + invert_zaxis, zaxis_inverted + """ + self._set_bound3d(self.get_zbound, self.set_zlim, self.zaxis_inverted, + lower, upper, view_margin) + + def _set_lim3d(self, axis, lower=None, upper=None, *, emit=True, + auto=False, view_margin=None, axmin=None, axmax=None): + """ + Set 3D axis limits. + """ + if upper is None: + if np.iterable(lower): + lower, upper = lower + elif axmax is None: + upper = axis.get_view_interval()[1] + if lower is None and axmin is None: + lower = axis.get_view_interval()[0] + if axmin is not None: + if lower is not None: + raise TypeError("Cannot pass both 'lower' and 'min'") + lower = axmin + if axmax is not None: + if upper is not None: + raise TypeError("Cannot pass both 'upper' and 'max'") + upper = axmax + if np.isinf(lower) or np.isinf(upper): + raise ValueError(f"Axis limits {lower}, {upper} cannot be infinite") + if view_margin is None: + if mpl.rcParams['axes3d.automargin']: + view_margin = self._view_margin + else: + view_margin = 0 + delta = (upper - lower) * view_margin + lower -= delta + upper += delta + return axis._set_lim(lower, upper, emit=emit, auto=auto) + + def set_xlim(self, left=None, right=None, *, emit=True, auto=False, + view_margin=None, xmin=None, xmax=None): + """ + Set the 3D x-axis view limits. + + Parameters + ---------- + left : float, optional + The left xlim in data coordinates. Passing *None* leaves the + limit unchanged. + + The left and right xlims may also be passed as the tuple + (*left*, *right*) as the first positional argument (or as + the *left* keyword argument). + + .. ACCEPTS: (left: float, right: float) + + right : float, optional + The right xlim in data coordinates. Passing *None* leaves the + limit unchanged. + + emit : bool, default: True + Whether to notify observers of limit change. + + auto : bool or None, default: False + Whether to turn on autoscaling of the x-axis. *True* turns on, + *False* turns off, *None* leaves unchanged. + + view_margin : float, optional + The additional margin to apply to the limits. + + xmin, xmax : float, optional + They are equivalent to left and right respectively, and it is an + error to pass both *xmin* and *left* or *xmax* and *right*. + + Returns + ------- + left, right : (float, float) + The new x-axis limits in data coordinates. + + See Also + -------- + get_xlim + set_xbound, get_xbound + invert_xaxis, xaxis_inverted + + Notes + ----- + The *left* value may be greater than the *right* value, in which + case the x-axis values will decrease from *left* to *right*. + + Examples + -------- + >>> set_xlim(left, right) + >>> set_xlim((left, right)) + >>> left, right = set_xlim(left, right) + + One limit may be left unchanged. + + >>> set_xlim(right=right_lim) + + Limits may be passed in reverse order to flip the direction of + the x-axis. For example, suppose ``x`` represents depth of the + ocean in m. The x-axis limits might be set like the following + so 5000 m depth is at the left of the plot and the surface, + 0 m, is at the right. + + >>> set_xlim(5000, 0) + """ + return self._set_lim3d(self.xaxis, left, right, emit=emit, auto=auto, + view_margin=view_margin, axmin=xmin, axmax=xmax) + + def set_ylim(self, bottom=None, top=None, *, emit=True, auto=False, + view_margin=None, ymin=None, ymax=None): + """ + Set the 3D y-axis view limits. + + Parameters + ---------- + bottom : float, optional + The bottom ylim in data coordinates. Passing *None* leaves the + limit unchanged. + + The bottom and top ylims may also be passed as the tuple + (*bottom*, *top*) as the first positional argument (or as + the *bottom* keyword argument). + + .. ACCEPTS: (bottom: float, top: float) + + top : float, optional + The top ylim in data coordinates. Passing *None* leaves the + limit unchanged. + + emit : bool, default: True + Whether to notify observers of limit change. + + auto : bool or None, default: False + Whether to turn on autoscaling of the y-axis. *True* turns on, + *False* turns off, *None* leaves unchanged. + + view_margin : float, optional + The additional margin to apply to the limits. + + ymin, ymax : float, optional + They are equivalent to bottom and top respectively, and it is an + error to pass both *ymin* and *bottom* or *ymax* and *top*. + + Returns + ------- + bottom, top : (float, float) + The new y-axis limits in data coordinates. + + See Also + -------- + get_ylim + set_ybound, get_ybound + invert_yaxis, yaxis_inverted + + Notes + ----- + The *bottom* value may be greater than the *top* value, in which + case the y-axis values will decrease from *bottom* to *top*. + + Examples + -------- + >>> set_ylim(bottom, top) + >>> set_ylim((bottom, top)) + >>> bottom, top = set_ylim(bottom, top) + + One limit may be left unchanged. + + >>> set_ylim(top=top_lim) + + Limits may be passed in reverse order to flip the direction of + the y-axis. For example, suppose ``y`` represents depth of the + ocean in m. The y-axis limits might be set like the following + so 5000 m depth is at the bottom of the plot and the surface, + 0 m, is at the top. + + >>> set_ylim(5000, 0) + """ + return self._set_lim3d(self.yaxis, bottom, top, emit=emit, auto=auto, + view_margin=view_margin, axmin=ymin, axmax=ymax) + + def set_zlim(self, bottom=None, top=None, *, emit=True, auto=False, + view_margin=None, zmin=None, zmax=None): + """ + Set the 3D z-axis view limits. + + Parameters + ---------- + bottom : float, optional + The bottom zlim in data coordinates. Passing *None* leaves the + limit unchanged. + + The bottom and top zlims may also be passed as the tuple + (*bottom*, *top*) as the first positional argument (or as + the *bottom* keyword argument). + + .. ACCEPTS: (bottom: float, top: float) + + top : float, optional + The top zlim in data coordinates. Passing *None* leaves the + limit unchanged. + + emit : bool, default: True + Whether to notify observers of limit change. + + auto : bool or None, default: False + Whether to turn on autoscaling of the z-axis. *True* turns on, + *False* turns off, *None* leaves unchanged. + + view_margin : float, optional + The additional margin to apply to the limits. + + zmin, zmax : float, optional + They are equivalent to bottom and top respectively, and it is an + error to pass both *zmin* and *bottom* or *zmax* and *top*. + + Returns + ------- + bottom, top : (float, float) + The new z-axis limits in data coordinates. + + See Also + -------- + get_zlim + set_zbound, get_zbound + invert_zaxis, zaxis_inverted + + Notes + ----- + The *bottom* value may be greater than the *top* value, in which + case the z-axis values will decrease from *bottom* to *top*. + + Examples + -------- + >>> set_zlim(bottom, top) + >>> set_zlim((bottom, top)) + >>> bottom, top = set_zlim(bottom, top) + + One limit may be left unchanged. + + >>> set_zlim(top=top_lim) + + Limits may be passed in reverse order to flip the direction of + the z-axis. For example, suppose ``z`` represents depth of the + ocean in m. The z-axis limits might be set like the following + so 5000 m depth is at the bottom of the plot and the surface, + 0 m, is at the top. + + >>> set_zlim(5000, 0) + """ + return self._set_lim3d(self.zaxis, bottom, top, emit=emit, auto=auto, + view_margin=view_margin, axmin=zmin, axmax=zmax) + + set_xlim3d = set_xlim + set_ylim3d = set_ylim + set_zlim3d = set_zlim + + def get_xlim(self): + # docstring inherited + return tuple(self.xy_viewLim.intervalx) + + def get_ylim(self): + # docstring inherited + return tuple(self.xy_viewLim.intervaly) + + def get_zlim(self): + """ + Return the 3D z-axis view limits. + + Returns + ------- + left, right : (float, float) + The current z-axis limits in data coordinates. + + See Also + -------- + set_zlim + set_zbound, get_zbound + invert_zaxis, zaxis_inverted + + Notes + ----- + The z-axis may be inverted, in which case the *left* value will + be greater than the *right* value. + """ + return tuple(self.zz_viewLim.intervalx) + + get_zscale = _axis_method_wrapper("zaxis", "get_scale") + + # Redefine all three methods to overwrite their docstrings. + set_xscale = _axis_method_wrapper("xaxis", "_set_axes_scale") + set_yscale = _axis_method_wrapper("yaxis", "_set_axes_scale") + set_zscale = _axis_method_wrapper("zaxis", "_set_axes_scale") + set_xscale.__doc__, set_yscale.__doc__, set_zscale.__doc__ = map( + """ + Set the {}-axis scale. + + Parameters + ---------- + value : {{"linear"}} + The axis scale type to apply. 3D Axes currently only support + linear scales; other scales yield nonsensical results. + + **kwargs + Keyword arguments are nominally forwarded to the scale class, but + none of them is applicable for linear scales. + """.format, + ["x", "y", "z"]) + + get_zticks = _axis_method_wrapper("zaxis", "get_ticklocs") + set_zticks = _axis_method_wrapper("zaxis", "set_ticks") + get_zmajorticklabels = _axis_method_wrapper("zaxis", "get_majorticklabels") + get_zminorticklabels = _axis_method_wrapper("zaxis", "get_minorticklabels") + get_zticklabels = _axis_method_wrapper("zaxis", "get_ticklabels") + set_zticklabels = _axis_method_wrapper( + "zaxis", "set_ticklabels", + doc_sub={"Axis.set_ticks": "Axes3D.set_zticks"}) + + zaxis_date = _axis_method_wrapper("zaxis", "axis_date") + if zaxis_date.__doc__: + zaxis_date.__doc__ += textwrap.dedent(""" + + Notes + ----- + This function is merely provided for completeness, but 3D Axes do not + support dates for ticks, and so this may not work as expected. + """) + + def clabel(self, *args, **kwargs): + """Currently not implemented for 3D Axes, and returns *None*.""" + return None + + def view_init(self, elev=None, azim=None, roll=None, vertical_axis="z", + share=False): + """ + Set the elevation and azimuth of the Axes in degrees (not radians). + + This can be used to rotate the Axes programmatically. + + To look normal to the primary planes, the following elevation and + azimuth angles can be used. A roll angle of 0, 90, 180, or 270 deg + will rotate these views while keeping the axes at right angles. + + ========== ==== ==== + view plane elev azim + ========== ==== ==== + XY 90 -90 + XZ 0 -90 + YZ 0 0 + -XY -90 90 + -XZ 0 90 + -YZ 0 180 + ========== ==== ==== + + Parameters + ---------- + elev : float, default: None + The elevation angle in degrees rotates the camera above the plane + pierced by the vertical axis, with a positive angle corresponding + to a location above that plane. For example, with the default + vertical axis of 'z', the elevation defines the angle of the camera + location above the x-y plane. + If None, then the initial value as specified in the `Axes3D` + constructor is used. + azim : float, default: None + The azimuthal angle in degrees rotates the camera about the + vertical axis, with a positive angle corresponding to a + right-handed rotation. For example, with the default vertical axis + of 'z', a positive azimuth rotates the camera about the origin from + its location along the +x axis towards the +y axis. + If None, then the initial value as specified in the `Axes3D` + constructor is used. + roll : float, default: None + The roll angle in degrees rotates the camera about the viewing + axis. A positive angle spins the camera clockwise, causing the + scene to rotate counter-clockwise. + If None, then the initial value as specified in the `Axes3D` + constructor is used. + vertical_axis : {"z", "x", "y"}, default: "z" + The axis to align vertically. *azim* rotates about this axis. + share : bool, default: False + If ``True``, apply the settings to all Axes with shared views. + """ + + self._dist = 10 # The camera distance from origin. Behaves like zoom + + if elev is None: + elev = self.initial_elev + if azim is None: + azim = self.initial_azim + if roll is None: + roll = self.initial_roll + vertical_axis = _api.check_getitem( + {name: idx for idx, name in enumerate(self._axis_names)}, + vertical_axis=vertical_axis, + ) + + if share: + axes = {sibling for sibling + in self._shared_axes['view'].get_siblings(self)} + else: + axes = [self] + + for ax in axes: + ax.elev = elev + ax.azim = azim + ax.roll = roll + ax._vertical_axis = vertical_axis + + def set_proj_type(self, proj_type, focal_length=None): + """ + Set the projection type. + + Parameters + ---------- + proj_type : {'persp', 'ortho'} + The projection type. + focal_length : float, default: None + For a projection type of 'persp', the focal length of the virtual + camera. Must be > 0. If None, defaults to 1. + The focal length can be computed from a desired Field Of View via + the equation: focal_length = 1/tan(FOV/2) + """ + _api.check_in_list(['persp', 'ortho'], proj_type=proj_type) + if proj_type == 'persp': + if focal_length is None: + focal_length = 1 + elif focal_length <= 0: + raise ValueError(f"focal_length = {focal_length} must be " + "greater than 0") + self._focal_length = focal_length + else: # 'ortho': + if focal_length not in (None, np.inf): + raise ValueError(f"focal_length = {focal_length} must be " + f"None for proj_type = {proj_type}") + self._focal_length = np.inf + + def _roll_to_vertical( + self, arr: "np.typing.ArrayLike", reverse: bool = False + ) -> np.ndarray: + """ + Roll arrays to match the different vertical axis. + + Parameters + ---------- + arr : ArrayLike + Array to roll. + reverse : bool, default: False + Reverse the direction of the roll. + """ + if reverse: + return np.roll(arr, (self._vertical_axis - 2) * -1) + else: + return np.roll(arr, (self._vertical_axis - 2)) + + def get_proj(self): + """Create the projection matrix from the current viewing position.""" + + # Transform to uniform world coordinates 0-1, 0-1, 0-1 + box_aspect = self._roll_to_vertical(self._box_aspect) + worldM = proj3d.world_transformation( + *self.get_xlim3d(), + *self.get_ylim3d(), + *self.get_zlim3d(), + pb_aspect=box_aspect, + ) + + # Look into the middle of the world coordinates: + R = 0.5 * box_aspect + + # elev: elevation angle in the z plane. + # azim: azimuth angle in the xy plane. + # Coordinates for a point that rotates around the box of data. + # p0, p1 corresponds to rotating the box only around the vertical axis. + # p2 corresponds to rotating the box only around the horizontal axis. + elev_rad = np.deg2rad(self.elev) + azim_rad = np.deg2rad(self.azim) + p0 = np.cos(elev_rad) * np.cos(azim_rad) + p1 = np.cos(elev_rad) * np.sin(azim_rad) + p2 = np.sin(elev_rad) + + # When changing vertical axis the coordinates changes as well. + # Roll the values to get the same behaviour as the default: + ps = self._roll_to_vertical([p0, p1, p2]) + + # The coordinates for the eye viewing point. The eye is looking + # towards the middle of the box of data from a distance: + eye = R + self._dist * ps + + # Calculate the viewing axes for the eye position + u, v, w = self._calc_view_axes(eye) + self._view_u = u # _view_u is towards the right of the screen + self._view_v = v # _view_v is towards the top of the screen + self._view_w = w # _view_w is out of the screen + + # Generate the view and projection transformation matrices + if self._focal_length == np.inf: + # Orthographic projection + viewM = proj3d._view_transformation_uvw(u, v, w, eye) + projM = proj3d._ortho_transformation(-self._dist, self._dist) + else: + # Perspective projection + # Scale the eye dist to compensate for the focal length zoom effect + eye_focal = R + self._dist * ps * self._focal_length + viewM = proj3d._view_transformation_uvw(u, v, w, eye_focal) + projM = proj3d._persp_transformation(-self._dist, + self._dist, + self._focal_length) + + # Combine all the transformation matrices to get the final projection + M0 = np.dot(viewM, worldM) + M = np.dot(projM, M0) + return M + + def mouse_init(self, rotate_btn=1, pan_btn=2, zoom_btn=3): + """ + Set the mouse buttons for 3D rotation and zooming. + + Parameters + ---------- + rotate_btn : int or list of int, default: 1 + The mouse button or buttons to use for 3D rotation of the Axes. + pan_btn : int or list of int, default: 2 + The mouse button or buttons to use to pan the 3D Axes. + zoom_btn : int or list of int, default: 3 + The mouse button or buttons to use to zoom the 3D Axes. + """ + self.button_pressed = None + # coerce scalars into array-like, then convert into + # a regular list to avoid comparisons against None + # which breaks in recent versions of numpy. + self._rotate_btn = np.atleast_1d(rotate_btn).tolist() + self._pan_btn = np.atleast_1d(pan_btn).tolist() + self._zoom_btn = np.atleast_1d(zoom_btn).tolist() + + def disable_mouse_rotation(self): + """Disable mouse buttons for 3D rotation, panning, and zooming.""" + self.mouse_init(rotate_btn=[], pan_btn=[], zoom_btn=[]) + + def can_zoom(self): + # doc-string inherited + return True + + def can_pan(self): + # doc-string inherited + return True + + def sharez(self, other): + """ + Share the z-axis with *other*. + + This is equivalent to passing ``sharez=other`` when constructing the + Axes, and cannot be used if the z-axis is already being shared with + another Axes. Note that it is not possible to unshare axes. + """ + _api.check_isinstance(Axes3D, other=other) + if self._sharez is not None and other is not self._sharez: + raise ValueError("z-axis is already shared") + self._shared_axes["z"].join(self, other) + self._sharez = other + self.zaxis.major = other.zaxis.major # Ticker instances holding + self.zaxis.minor = other.zaxis.minor # locator and formatter. + z0, z1 = other.get_zlim() + self.set_zlim(z0, z1, emit=False, auto=other.get_autoscalez_on()) + self.zaxis._scale = other.zaxis._scale + + def shareview(self, other): + """ + Share the view angles with *other*. + + This is equivalent to passing ``shareview=other`` when constructing the + Axes, and cannot be used if the view angles are already being shared + with another Axes. Note that it is not possible to unshare axes. + """ + _api.check_isinstance(Axes3D, other=other) + if self._shareview is not None and other is not self._shareview: + raise ValueError("view angles are already shared") + self._shared_axes["view"].join(self, other) + self._shareview = other + vertical_axis = self._axis_names[other._vertical_axis] + self.view_init(elev=other.elev, azim=other.azim, roll=other.roll, + vertical_axis=vertical_axis, share=True) + + def clear(self): + # docstring inherited. + super().clear() + if self._focal_length == np.inf: + self._zmargin = mpl.rcParams['axes.zmargin'] + else: + self._zmargin = 0. + + xymargin = 0.05 * 10/11 # match mpl3.8 appearance + self.xy_dataLim = Bbox([[xymargin, xymargin], + [1 - xymargin, 1 - xymargin]]) + # z-limits are encoded in the x-component of the Bbox, y is un-used + self.zz_dataLim = Bbox.unit() + self._view_margin = 1/48 # default value to match mpl3.8 + self.autoscale_view() + + self.grid(mpl.rcParams['axes3d.grid']) + + def _button_press(self, event): + if event.inaxes == self: + self.button_pressed = event.button + self._sx, self._sy = event.xdata, event.ydata + toolbar = self.get_figure(root=True).canvas.toolbar + if toolbar and toolbar._nav_stack() is None: + toolbar.push_current() + if toolbar: + toolbar.set_message(toolbar._mouse_event_to_message(event)) + + def _button_release(self, event): + self.button_pressed = None + toolbar = self.get_figure(root=True).canvas.toolbar + # backend_bases.release_zoom and backend_bases.release_pan call + # push_current, so check the navigation mode so we don't call it twice + if toolbar and self.get_navigate_mode() is None: + toolbar.push_current() + if toolbar: + toolbar.set_message(toolbar._mouse_event_to_message(event)) + + def _get_view(self): + # docstring inherited + return { + "xlim": self.get_xlim(), "autoscalex_on": self.get_autoscalex_on(), + "ylim": self.get_ylim(), "autoscaley_on": self.get_autoscaley_on(), + "zlim": self.get_zlim(), "autoscalez_on": self.get_autoscalez_on(), + }, (self.elev, self.azim, self.roll) + + def _set_view(self, view): + # docstring inherited + props, (elev, azim, roll) = view + self.set(**props) + self.elev = elev + self.azim = azim + self.roll = roll + + def format_zdata(self, z): + """ + Return *z* string formatted. This function will use the + :attr:`fmt_zdata` attribute if it is callable, else will fall + back on the zaxis major formatter + """ + try: + return self.fmt_zdata(z) + except (AttributeError, TypeError): + func = self.zaxis.get_major_formatter().format_data_short + val = func(z) + return val + + def format_coord(self, xv, yv, renderer=None): + """ + Return a string giving the current view rotation angles, or the x, y, z + coordinates of the point on the nearest axis pane underneath the mouse + cursor, depending on the mouse button pressed. + """ + coords = '' + + if self.button_pressed in self._rotate_btn: + # ignore xv and yv and display angles instead + coords = self._rotation_coords() + + elif self.M is not None: + coords = self._location_coords(xv, yv, renderer) + + return coords + + def _rotation_coords(self): + """ + Return the rotation angles as a string. + """ + norm_elev = art3d._norm_angle(self.elev) + norm_azim = art3d._norm_angle(self.azim) + norm_roll = art3d._norm_angle(self.roll) + coords = (f"elevation={norm_elev:.0f}\N{DEGREE SIGN}, " + f"azimuth={norm_azim:.0f}\N{DEGREE SIGN}, " + f"roll={norm_roll:.0f}\N{DEGREE SIGN}" + ).replace("-", "\N{MINUS SIGN}") + return coords + + def _location_coords(self, xv, yv, renderer): + """ + Return the location on the axis pane underneath the cursor as a string. + """ + p1, pane_idx = self._calc_coord(xv, yv, renderer) + xs = self.format_xdata(p1[0]) + ys = self.format_ydata(p1[1]) + zs = self.format_zdata(p1[2]) + if pane_idx == 0: + coords = f'x pane={xs}, y={ys}, z={zs}' + elif pane_idx == 1: + coords = f'x={xs}, y pane={ys}, z={zs}' + elif pane_idx == 2: + coords = f'x={xs}, y={ys}, z pane={zs}' + return coords + + def _get_camera_loc(self): + """ + Returns the current camera location in data coordinates. + """ + cx, cy, cz, dx, dy, dz = self._get_w_centers_ranges() + c = np.array([cx, cy, cz]) + r = np.array([dx, dy, dz]) + + if self._focal_length == np.inf: # orthographic projection + focal_length = 1e9 # large enough to be effectively infinite + else: # perspective projection + focal_length = self._focal_length + eye = c + self._view_w * self._dist * r / self._box_aspect * focal_length + return eye + + def _calc_coord(self, xv, yv, renderer=None): + """ + Given the 2D view coordinates, find the point on the nearest axis pane + that lies directly below those coordinates. Returns a 3D point in data + coordinates. + """ + if self._focal_length == np.inf: # orthographic projection + zv = 1 + else: # perspective projection + zv = -1 / self._focal_length + + # Convert point on view plane to data coordinates + p1 = np.array(proj3d.inv_transform(xv, yv, zv, self.invM)).ravel() + + # Get the vector from the camera to the point on the view plane + vec = self._get_camera_loc() - p1 + + # Get the pane locations for each of the axes + pane_locs = [] + for axis in self._axis_map.values(): + xys, loc = axis.active_pane() + pane_locs.append(loc) + + # Find the distance to the nearest pane by projecting the view vector + scales = np.zeros(3) + for i in range(3): + if vec[i] == 0: + scales[i] = np.inf + else: + scales[i] = (p1[i] - pane_locs[i]) / vec[i] + pane_idx = np.argmin(abs(scales)) + scale = scales[pane_idx] + + # Calculate the point on the closest pane + p2 = p1 - scale*vec + return p2, pane_idx + + def _arcball(self, x: float, y: float) -> np.ndarray: + """ + Convert a point (x, y) to a point on a virtual trackball. + + This is Ken Shoemake's arcball (a sphere), modified + to soften the abrupt edge (optionally). + See: Ken Shoemake, "ARCBALL: A user interface for specifying + three-dimensional rotation using a mouse." in + Proceedings of Graphics Interface '92, 1992, pp. 151-156, + https://doi.org/10.20380/GI1992.18 + The smoothing of the edge is inspired by Gavin Bell's arcball + (a sphere combined with a hyperbola), but here, the sphere + is combined with a section of a cylinder, so it has finite support. + """ + s = mpl.rcParams['axes3d.trackballsize'] / 2 + b = mpl.rcParams['axes3d.trackballborder'] / s + x /= s + y /= s + r2 = x*x + y*y + r = np.sqrt(r2) + ra = 1 + b + a = b * (1 + b/2) + ri = 2/(ra + 1/ra) + if r < ri: + p = np.array([np.sqrt(1 - r2), x, y]) + elif r < ra: + dr = ra - r + p = np.array([a - np.sqrt((a + dr) * (a - dr)), x, y]) + p /= np.linalg.norm(p) + else: + p = np.array([0, x/r, y/r]) + return p + + def _on_move(self, event): + """ + Mouse moving. + + By default, button-1 rotates, button-2 pans, and button-3 zooms; + these buttons can be modified via `mouse_init`. + """ + + if not self.button_pressed: + return + + if self.get_navigate_mode() is not None: + # we don't want to rotate if we are zooming/panning + # from the toolbar + return + + if self.M is None: + return + + x, y = event.xdata, event.ydata + # In case the mouse is out of bounds. + if x is None or event.inaxes != self: + return + + dx, dy = x - self._sx, y - self._sy + w = self._pseudo_w + h = self._pseudo_h + + # Rotation + if self.button_pressed in self._rotate_btn: + # rotate viewing point + # get the x and y pixel coords + if dx == 0 and dy == 0: + return + + style = mpl.rcParams['axes3d.mouserotationstyle'] + if style == 'azel': + roll = np.deg2rad(self.roll) + delev = -(dy/h)*180*np.cos(roll) + (dx/w)*180*np.sin(roll) + dazim = -(dy/h)*180*np.sin(roll) - (dx/w)*180*np.cos(roll) + elev = self.elev + delev + azim = self.azim + dazim + roll = self.roll + else: + q = _Quaternion.from_cardan_angles( + *np.deg2rad((self.elev, self.azim, self.roll))) + + if style == 'trackball': + k = np.array([0, -dy/h, dx/w]) + nk = np.linalg.norm(k) + th = nk / mpl.rcParams['axes3d.trackballsize'] + dq = _Quaternion(np.cos(th), k*np.sin(th)/nk) + else: # 'sphere', 'arcball' + current_vec = self._arcball(self._sx/w, self._sy/h) + new_vec = self._arcball(x/w, y/h) + if style == 'sphere': + dq = _Quaternion.rotate_from_to(current_vec, new_vec) + else: # 'arcball' + dq = _Quaternion(0, new_vec) * _Quaternion(0, -current_vec) + + q = dq * q + elev, azim, roll = np.rad2deg(q.as_cardan_angles()) + + # update view + vertical_axis = self._axis_names[self._vertical_axis] + self.view_init( + elev=elev, + azim=azim, + roll=roll, + vertical_axis=vertical_axis, + share=True, + ) + self.stale = True + + # Pan + elif self.button_pressed in self._pan_btn: + # Start the pan event with pixel coordinates + px, py = self.transData.transform([self._sx, self._sy]) + self.start_pan(px, py, 2) + # pan view (takes pixel coordinate input) + self.drag_pan(2, None, event.x, event.y) + self.end_pan() + + # Zoom + elif self.button_pressed in self._zoom_btn: + # zoom view (dragging down zooms in) + scale = h/(h - dy) + self._scale_axis_limits(scale, scale, scale) + + # Store the event coordinates for the next time through. + self._sx, self._sy = x, y + # Always request a draw update at the end of interaction + self.get_figure(root=True).canvas.draw_idle() + + def drag_pan(self, button, key, x, y): + # docstring inherited + + # Get the coordinates from the move event + p = self._pan_start + (xdata, ydata), (xdata_start, ydata_start) = p.trans_inverse.transform( + [(x, y), (p.x, p.y)]) + self._sx, self._sy = xdata, ydata + # Calling start_pan() to set the x/y of this event as the starting + # move location for the next event + self.start_pan(x, y, button) + du, dv = xdata - xdata_start, ydata - ydata_start + dw = 0 + if key == 'x': + dv = 0 + elif key == 'y': + du = 0 + if du == 0 and dv == 0: + return + + # Transform the pan from the view axes to the data axes + R = np.array([self._view_u, self._view_v, self._view_w]) + R = -R / self._box_aspect * self._dist + duvw_projected = R.T @ np.array([du, dv, dw]) + + # Calculate pan distance + minx, maxx, miny, maxy, minz, maxz = self.get_w_lims() + dx = (maxx - minx) * duvw_projected[0] + dy = (maxy - miny) * duvw_projected[1] + dz = (maxz - minz) * duvw_projected[2] + + # Set the new axis limits + self.set_xlim3d(minx + dx, maxx + dx, auto=None) + self.set_ylim3d(miny + dy, maxy + dy, auto=None) + self.set_zlim3d(minz + dz, maxz + dz, auto=None) + + def _calc_view_axes(self, eye): + """ + Get the unit vectors for the viewing axes in data coordinates. + `u` is towards the right of the screen + `v` is towards the top of the screen + `w` is out of the screen + """ + elev_rad = np.deg2rad(art3d._norm_angle(self.elev)) + roll_rad = np.deg2rad(art3d._norm_angle(self.roll)) + + # Look into the middle of the world coordinates + R = 0.5 * self._roll_to_vertical(self._box_aspect) + + # Define which axis should be vertical. A negative value + # indicates the plot is upside down and therefore the values + # have been reversed: + V = np.zeros(3) + V[self._vertical_axis] = -1 if abs(elev_rad) > np.pi/2 else 1 + + u, v, w = proj3d._view_axes(eye, R, V, roll_rad) + return u, v, w + + def _set_view_from_bbox(self, bbox, direction='in', + mode=None, twinx=False, twiny=False): + """ + Zoom in or out of the bounding box. + + Will center the view in the center of the bounding box, and zoom by + the ratio of the size of the bounding box to the size of the Axes3D. + """ + (start_x, start_y, stop_x, stop_y) = bbox + if mode == 'x': + start_y = self.bbox.min[1] + stop_y = self.bbox.max[1] + elif mode == 'y': + start_x = self.bbox.min[0] + stop_x = self.bbox.max[0] + + # Clip to bounding box limits + start_x, stop_x = np.clip(sorted([start_x, stop_x]), + self.bbox.min[0], self.bbox.max[0]) + start_y, stop_y = np.clip(sorted([start_y, stop_y]), + self.bbox.min[1], self.bbox.max[1]) + + # Move the center of the view to the center of the bbox + zoom_center_x = (start_x + stop_x)/2 + zoom_center_y = (start_y + stop_y)/2 + + ax_center_x = (self.bbox.max[0] + self.bbox.min[0])/2 + ax_center_y = (self.bbox.max[1] + self.bbox.min[1])/2 + + self.start_pan(zoom_center_x, zoom_center_y, 2) + self.drag_pan(2, None, ax_center_x, ax_center_y) + self.end_pan() + + # Calculate zoom level + dx = abs(start_x - stop_x) + dy = abs(start_y - stop_y) + scale_u = dx / (self.bbox.max[0] - self.bbox.min[0]) + scale_v = dy / (self.bbox.max[1] - self.bbox.min[1]) + + # Keep aspect ratios equal + scale = max(scale_u, scale_v) + + # Zoom out + if direction == 'out': + scale = 1 / scale + + self._zoom_data_limits(scale, scale, scale) + + def _zoom_data_limits(self, scale_u, scale_v, scale_w): + """ + Zoom in or out of a 3D plot. + + Will scale the data limits by the scale factors. These will be + transformed to the x, y, z data axes based on the current view angles. + A scale factor > 1 zooms out and a scale factor < 1 zooms in. + + For an Axes that has had its aspect ratio set to 'equal', 'equalxy', + 'equalyz', or 'equalxz', the relevant axes are constrained to zoom + equally. + + Parameters + ---------- + scale_u : float + Scale factor for the u view axis (view screen horizontal). + scale_v : float + Scale factor for the v view axis (view screen vertical). + scale_w : float + Scale factor for the w view axis (view screen depth). + """ + scale = np.array([scale_u, scale_v, scale_w]) + + # Only perform frame conversion if unequal scale factors + if not np.allclose(scale, scale_u): + # Convert the scale factors from the view frame to the data frame + R = np.array([self._view_u, self._view_v, self._view_w]) + S = scale * np.eye(3) + scale = np.linalg.norm(R.T @ S, axis=1) + + # Set the constrained scale factors to the factor closest to 1 + if self._aspect in ('equal', 'equalxy', 'equalxz', 'equalyz'): + ax_idxs = self._equal_aspect_axis_indices(self._aspect) + min_ax_idxs = np.argmin(np.abs(scale[ax_idxs] - 1)) + scale[ax_idxs] = scale[ax_idxs][min_ax_idxs] + + self._scale_axis_limits(scale[0], scale[1], scale[2]) + + def _scale_axis_limits(self, scale_x, scale_y, scale_z): + """ + Keeping the center of the x, y, and z data axes fixed, scale their + limits by scale factors. A scale factor > 1 zooms out and a scale + factor < 1 zooms in. + + Parameters + ---------- + scale_x : float + Scale factor for the x data axis. + scale_y : float + Scale factor for the y data axis. + scale_z : float + Scale factor for the z data axis. + """ + # Get the axis centers and ranges + cx, cy, cz, dx, dy, dz = self._get_w_centers_ranges() + + # Set the scaled axis limits + self.set_xlim3d(cx - dx*scale_x/2, cx + dx*scale_x/2, auto=None) + self.set_ylim3d(cy - dy*scale_y/2, cy + dy*scale_y/2, auto=None) + self.set_zlim3d(cz - dz*scale_z/2, cz + dz*scale_z/2, auto=None) + + def _get_w_centers_ranges(self): + """Get 3D world centers and axis ranges.""" + # Calculate center of axis limits + minx, maxx, miny, maxy, minz, maxz = self.get_w_lims() + cx = (maxx + minx)/2 + cy = (maxy + miny)/2 + cz = (maxz + minz)/2 + + # Calculate range of axis limits + dx = (maxx - minx) + dy = (maxy - miny) + dz = (maxz - minz) + return cx, cy, cz, dx, dy, dz + + def set_zlabel(self, zlabel, fontdict=None, labelpad=None, **kwargs): + """ + Set zlabel. See doc for `.set_ylabel` for description. + """ + if labelpad is not None: + self.zaxis.labelpad = labelpad + return self.zaxis.set_label_text(zlabel, fontdict, **kwargs) + + def get_zlabel(self): + """ + Get the z-label text string. + """ + label = self.zaxis.label + return label.get_text() + + # Axes rectangle characteristics + + # The frame_on methods are not available for 3D axes. + # Python will raise a TypeError if they are called. + get_frame_on = None + set_frame_on = None + + def grid(self, visible=True, **kwargs): + """ + Set / unset 3D grid. + + .. note:: + + Currently, this function does not behave the same as + `.axes.Axes.grid`, but it is intended to eventually support that + behavior. + """ + # TODO: Operate on each axes separately + if len(kwargs): + visible = True + self._draw_grid = visible + self.stale = True + + def tick_params(self, axis='both', **kwargs): + """ + Convenience method for changing the appearance of ticks and + tick labels. + + See `.Axes.tick_params` for full documentation. Because this function + applies to 3D Axes, *axis* can also be set to 'z', and setting *axis* + to 'both' autoscales all three axes. + + Also, because of how Axes3D objects are drawn very differently + from regular 2D Axes, some of these settings may have + ambiguous meaning. For simplicity, the 'z' axis will + accept settings as if it was like the 'y' axis. + + .. note:: + Axes3D currently ignores some of these settings. + """ + _api.check_in_list(['x', 'y', 'z', 'both'], axis=axis) + if axis in ['x', 'y', 'both']: + super().tick_params(axis, **kwargs) + if axis in ['z', 'both']: + zkw = dict(kwargs) + zkw.pop('top', None) + zkw.pop('bottom', None) + zkw.pop('labeltop', None) + zkw.pop('labelbottom', None) + self.zaxis.set_tick_params(**zkw) + + # data limits, ticks, tick labels, and formatting + + def invert_zaxis(self): + """ + Invert the z-axis. + + See Also + -------- + zaxis_inverted + get_zlim, set_zlim + get_zbound, set_zbound + """ + bottom, top = self.get_zlim() + self.set_zlim(top, bottom, auto=None) + + zaxis_inverted = _axis_method_wrapper("zaxis", "get_inverted") + + def get_zbound(self): + """ + Return the lower and upper z-axis bounds, in increasing order. + + See Also + -------- + set_zbound + get_zlim, set_zlim + invert_zaxis, zaxis_inverted + """ + lower, upper = self.get_zlim() + if lower < upper: + return lower, upper + else: + return upper, lower + + def text(self, x, y, z, s, zdir=None, *, axlim_clip=False, **kwargs): + """ + Add the text *s* to the 3D Axes at location *x*, *y*, *z* in data coordinates. + + Parameters + ---------- + x, y, z : float + The position to place the text. + s : str + The text. + zdir : {'x', 'y', 'z', 3-tuple}, optional + The direction to be used as the z-direction. Default: 'z'. + See `.get_dir_vector` for a description of the values. + axlim_clip : bool, default: False + Whether to hide text that is outside the axes view limits. + + .. versionadded:: 3.10 + **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.text`. + + Returns + ------- + `.Text3D` + The created `.Text3D` instance. + """ + text = super().text(x, y, s, **kwargs) + art3d.text_2d_to_3d(text, z, zdir, axlim_clip) + return text + + text3D = text + text2D = Axes.text + + def plot(self, xs, ys, *args, zdir='z', axlim_clip=False, **kwargs): + """ + Plot 2D or 3D data. + + Parameters + ---------- + xs : 1D array-like + x coordinates of vertices. + ys : 1D array-like + y coordinates of vertices. + zs : float or 1D array-like + z coordinates of vertices; either one for all points or one for + each point. + zdir : {'x', 'y', 'z'}, default: 'z' + When plotting 2D data, the direction to use as z. + axlim_clip : bool, default: False + Whether to hide data that is outside the axes view limits. + + .. versionadded:: 3.10 + **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.plot`. + """ + had_data = self.has_data() + + # `zs` can be passed positionally or as keyword; checking whether + # args[0] is a string matches the behavior of 2D `plot` (via + # `_process_plot_var_args`). + if args and not isinstance(args[0], str): + zs, *args = args + if 'zs' in kwargs: + raise TypeError("plot() for multiple values for argument 'zs'") + else: + zs = kwargs.pop('zs', 0) + + xs, ys, zs = cbook._broadcast_with_masks(xs, ys, zs) + + lines = super().plot(xs, ys, *args, **kwargs) + for line in lines: + art3d.line_2d_to_3d(line, zs=zs, zdir=zdir, axlim_clip=axlim_clip) + + xs, ys, zs = art3d.juggle_axes(xs, ys, zs, zdir) + self.auto_scale_xyz(xs, ys, zs, had_data) + return lines + + plot3D = plot + + def fill_between(self, x1, y1, z1, x2, y2, z2, *, + where=None, mode='auto', facecolors=None, shade=None, + axlim_clip=False, **kwargs): + """ + Fill the area between two 3D curves. + + The curves are defined by the points (*x1*, *y1*, *z1*) and + (*x2*, *y2*, *z2*). This creates one or multiple quadrangle + polygons that are filled. All points must be the same length N, or a + single value to be used for all points. + + Parameters + ---------- + x1, y1, z1 : float or 1D array-like + x, y, and z coordinates of vertices for 1st line. + + x2, y2, z2 : float or 1D array-like + x, y, and z coordinates of vertices for 2nd line. + + where : array of bool (length N), optional + Define *where* to exclude some regions from being filled. The + filled regions are defined by the coordinates ``pts[where]``, + for all x, y, and z pts. More precisely, fill between ``pts[i]`` + and ``pts[i+1]`` if ``where[i] and where[i+1]``. Note that this + definition implies that an isolated *True* value between two + *False* values in *where* will not result in filling. Both sides of + the *True* position remain unfilled due to the adjacent *False* + values. + + mode : {'quad', 'polygon', 'auto'}, default: 'auto' + The fill mode. One of: + + - 'quad': A separate quadrilateral polygon is created for each + pair of subsequent points in the two lines. + - 'polygon': The two lines are connected to form a single polygon. + This is faster and can render more cleanly for simple shapes + (e.g. for filling between two lines that lie within a plane). + - 'auto': If the points all lie on the same 3D plane, 'polygon' is + used. Otherwise, 'quad' is used. + + facecolors : list of :mpltype:`color`, default: None + Colors of each individual patch, or a single color to be used for + all patches. + + shade : bool, default: None + Whether to shade the facecolors. If *None*, then defaults to *True* + for 'quad' mode and *False* for 'polygon' mode. + + axlim_clip : bool, default: False + Whether to hide data that is outside the axes view limits. + + .. versionadded:: 3.10 + + **kwargs + All other keyword arguments are passed on to `.Poly3DCollection`. + + Returns + ------- + `.Poly3DCollection` + A `.Poly3DCollection` containing the plotted polygons. + + """ + _api.check_in_list(['auto', 'quad', 'polygon'], mode=mode) + + had_data = self.has_data() + x1, y1, z1, x2, y2, z2 = cbook._broadcast_with_masks(x1, y1, z1, x2, y2, z2) + + if facecolors is None: + facecolors = [self._get_patches_for_fill.get_next_color()] + facecolors = list(mcolors.to_rgba_array(facecolors)) + + if where is None: + where = True + else: + where = np.asarray(where, dtype=bool) + if where.size != x1.size: + raise ValueError(f"where size ({where.size}) does not match " + f"size ({x1.size})") + where = where & ~np.isnan(x1) # NaNs were broadcast in _broadcast_with_masks + + if mode == 'auto': + if art3d._all_points_on_plane(np.concatenate((x1[where], x2[where])), + np.concatenate((y1[where], y2[where])), + np.concatenate((z1[where], z2[where])), + atol=1e-12): + mode = 'polygon' + else: + mode = 'quad' + + if shade is None: + if mode == 'quad': + shade = True + else: + shade = False + + polys = [] + for idx0, idx1 in cbook.contiguous_regions(where): + x1i = x1[idx0:idx1] + y1i = y1[idx0:idx1] + z1i = z1[idx0:idx1] + x2i = x2[idx0:idx1] + y2i = y2[idx0:idx1] + z2i = z2[idx0:idx1] + + if not len(x1i): + continue + + if mode == 'quad': + # Preallocate the array for the region's vertices, and fill it in + n_polys_i = len(x1i) - 1 + polys_i = np.empty((n_polys_i, 4, 3)) + polys_i[:, 0, :] = np.column_stack((x1i[:-1], y1i[:-1], z1i[:-1])) + polys_i[:, 1, :] = np.column_stack((x1i[1:], y1i[1:], z1i[1:])) + polys_i[:, 2, :] = np.column_stack((x2i[1:], y2i[1:], z2i[1:])) + polys_i[:, 3, :] = np.column_stack((x2i[:-1], y2i[:-1], z2i[:-1])) + polys = polys + [*polys_i] + elif mode == 'polygon': + line1 = np.column_stack((x1i, y1i, z1i)) + line2 = np.column_stack((x2i[::-1], y2i[::-1], z2i[::-1])) + poly = np.concatenate((line1, line2), axis=0) + polys.append(poly) + + polyc = art3d.Poly3DCollection(polys, facecolors=facecolors, shade=shade, + axlim_clip=axlim_clip, **kwargs) + self.add_collection(polyc) + + self.auto_scale_xyz([x1, x2], [y1, y2], [z1, z2], had_data) + return polyc + + def plot_surface(self, X, Y, Z, *, norm=None, vmin=None, + vmax=None, lightsource=None, axlim_clip=False, **kwargs): + """ + Create a surface plot. + + By default, it will be colored in shades of a solid color, but it also + supports colormapping by supplying the *cmap* argument. + + .. note:: + + The *rcount* and *ccount* kwargs, which both default to 50, + determine the maximum number of samples used in each direction. If + the input data is larger, it will be downsampled (by slicing) to + these numbers of points. + + .. note:: + + To maximize rendering speed consider setting *rstride* and *cstride* + to divisors of the number of rows minus 1 and columns minus 1 + respectively. For example, given 51 rows rstride can be any of the + divisors of 50. + + Similarly, a setting of *rstride* and *cstride* equal to 1 (or + *rcount* and *ccount* equal the number of rows and columns) can use + the optimized path. + + Parameters + ---------- + X, Y, Z : 2D arrays + Data values. + + rcount, ccount : int + Maximum number of samples used in each direction. If the input + data is larger, it will be downsampled (by slicing) to these + numbers of points. Defaults to 50. + + rstride, cstride : int + Downsampling stride in each direction. These arguments are + mutually exclusive with *rcount* and *ccount*. If only one of + *rstride* or *cstride* is set, the other defaults to 10. + + 'classic' mode uses a default of ``rstride = cstride = 10`` instead + of the new default of ``rcount = ccount = 50``. + + color : :mpltype:`color` + Color of the surface patches. + + cmap : Colormap, optional + Colormap of the surface patches. + + facecolors : list of :mpltype:`color` + Colors of each individual patch. + + norm : `~matplotlib.colors.Normalize`, optional + Normalization for the colormap. + + vmin, vmax : float, optional + Bounds for the normalization. + + shade : bool, default: True + Whether to shade the facecolors. Shading is always disabled when + *cmap* is specified. + + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + + **kwargs + Other keyword arguments are forwarded to `.Poly3DCollection`. + """ + + had_data = self.has_data() + + if Z.ndim != 2: + raise ValueError("Argument Z must be 2-dimensional.") + + Z = cbook._to_unmasked_float_array(Z) + X, Y, Z = np.broadcast_arrays(X, Y, Z) + rows, cols = Z.shape + + has_stride = 'rstride' in kwargs or 'cstride' in kwargs + has_count = 'rcount' in kwargs or 'ccount' in kwargs + + if has_stride and has_count: + raise ValueError("Cannot specify both stride and count arguments") + + rstride = kwargs.pop('rstride', 10) + cstride = kwargs.pop('cstride', 10) + rcount = kwargs.pop('rcount', 50) + ccount = kwargs.pop('ccount', 50) + + if mpl.rcParams['_internal.classic_mode']: + # Strides have priority over counts in classic mode. + # So, only compute strides from counts + # if counts were explicitly given + compute_strides = has_count + else: + # If the strides are provided then it has priority. + # Otherwise, compute the strides from the counts. + compute_strides = not has_stride + + if compute_strides: + rstride = int(max(np.ceil(rows / rcount), 1)) + cstride = int(max(np.ceil(cols / ccount), 1)) + + fcolors = kwargs.pop('facecolors', None) + + cmap = kwargs.get('cmap', None) + shade = kwargs.pop('shade', cmap is None) + if shade is None: + raise ValueError("shade cannot be None.") + + colset = [] # the sampled facecolor + if (rows - 1) % rstride == 0 and \ + (cols - 1) % cstride == 0 and \ + fcolors is None: + polys = np.stack( + [cbook._array_patch_perimeters(a, rstride, cstride) + for a in (X, Y, Z)], + axis=-1) + else: + # evenly spaced, and including both endpoints + row_inds = list(range(0, rows-1, rstride)) + [rows-1] + col_inds = list(range(0, cols-1, cstride)) + [cols-1] + + polys = [] + for rs, rs_next in itertools.pairwise(row_inds): + for cs, cs_next in itertools.pairwise(col_inds): + ps = [ + # +1 ensures we share edges between polygons + cbook._array_perimeter(a[rs:rs_next+1, cs:cs_next+1]) + for a in (X, Y, Z) + ] + # ps = np.stack(ps, axis=-1) + ps = np.array(ps).T + polys.append(ps) + + if fcolors is not None: + colset.append(fcolors[rs][cs]) + + # In cases where there are non-finite values in the data (possibly NaNs from + # masked arrays), artifacts can be introduced. Here check whether such values + # are present and remove them. + if not isinstance(polys, np.ndarray) or not np.isfinite(polys).all(): + new_polys = [] + new_colset = [] + + # Depending on fcolors, colset is either an empty list or has as + # many elements as polys. In the former case new_colset results in + # a list with None entries, that is discarded later. + for p, col in itertools.zip_longest(polys, colset): + new_poly = np.array(p)[np.isfinite(p).all(axis=1)] + if len(new_poly): + new_polys.append(new_poly) + new_colset.append(col) + + # Replace previous polys and, if fcolors is not None, colset + polys = new_polys + if fcolors is not None: + colset = new_colset + + # note that the striding causes some polygons to have more coordinates + # than others + + if fcolors is not None: + polyc = art3d.Poly3DCollection( + polys, edgecolors=colset, facecolors=colset, shade=shade, + lightsource=lightsource, axlim_clip=axlim_clip, **kwargs) + elif cmap: + polyc = art3d.Poly3DCollection(polys, axlim_clip=axlim_clip, **kwargs) + # can't always vectorize, because polys might be jagged + if isinstance(polys, np.ndarray): + avg_z = polys[..., 2].mean(axis=-1) + else: + avg_z = np.array([ps[:, 2].mean() for ps in polys]) + polyc.set_array(avg_z) + if vmin is not None or vmax is not None: + polyc.set_clim(vmin, vmax) + if norm is not None: + polyc.set_norm(norm) + else: + color = kwargs.pop('color', None) + if color is None: + color = self._get_lines.get_next_color() + color = np.array(mcolors.to_rgba(color)) + + polyc = art3d.Poly3DCollection( + polys, facecolors=color, shade=shade, lightsource=lightsource, + axlim_clip=axlim_clip, **kwargs) + + self.add_collection(polyc) + self.auto_scale_xyz(X, Y, Z, had_data) + + return polyc + + def plot_wireframe(self, X, Y, Z, *, axlim_clip=False, **kwargs): + """ + Plot a 3D wireframe. + + .. note:: + + The *rcount* and *ccount* kwargs, which both default to 50, + determine the maximum number of samples used in each direction. If + the input data is larger, it will be downsampled (by slicing) to + these numbers of points. + + Parameters + ---------- + X, Y, Z : 2D arrays + Data values. + + axlim_clip : bool, default: False + Whether to hide lines and patches with vertices outside the axes + view limits. + + .. versionadded:: 3.10 + + rcount, ccount : int + Maximum number of samples used in each direction. If the input + data is larger, it will be downsampled (by slicing) to these + numbers of points. Setting a count to zero causes the data to be + not sampled in the corresponding direction, producing a 3D line + plot rather than a wireframe plot. Defaults to 50. + + rstride, cstride : int + Downsampling stride in each direction. These arguments are + mutually exclusive with *rcount* and *ccount*. If only one of + *rstride* or *cstride* is set, the other defaults to 1. Setting a + stride to zero causes the data to be not sampled in the + corresponding direction, producing a 3D line plot rather than a + wireframe plot. + + 'classic' mode uses a default of ``rstride = cstride = 1`` instead + of the new default of ``rcount = ccount = 50``. + + **kwargs + Other keyword arguments are forwarded to `.Line3DCollection`. + """ + + had_data = self.has_data() + if Z.ndim != 2: + raise ValueError("Argument Z must be 2-dimensional.") + # FIXME: Support masked arrays + X, Y, Z = np.broadcast_arrays(X, Y, Z) + rows, cols = Z.shape + + has_stride = 'rstride' in kwargs or 'cstride' in kwargs + has_count = 'rcount' in kwargs or 'ccount' in kwargs + + if has_stride and has_count: + raise ValueError("Cannot specify both stride and count arguments") + + rstride = kwargs.pop('rstride', 1) + cstride = kwargs.pop('cstride', 1) + rcount = kwargs.pop('rcount', 50) + ccount = kwargs.pop('ccount', 50) + + if mpl.rcParams['_internal.classic_mode']: + # Strides have priority over counts in classic mode. + # So, only compute strides from counts + # if counts were explicitly given + if has_count: + rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0 + cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0 + else: + # If the strides are provided then it has priority. + # Otherwise, compute the strides from the counts. + if not has_stride: + rstride = int(max(np.ceil(rows / rcount), 1)) if rcount else 0 + cstride = int(max(np.ceil(cols / ccount), 1)) if ccount else 0 + + # We want two sets of lines, one running along the "rows" of + # Z and another set of lines running along the "columns" of Z. + # This transpose will make it easy to obtain the columns. + tX, tY, tZ = np.transpose(X), np.transpose(Y), np.transpose(Z) + + if rstride: + rii = list(range(0, rows, rstride)) + # Add the last index only if needed + if rows > 0 and rii[-1] != (rows - 1): + rii += [rows-1] + else: + rii = [] + if cstride: + cii = list(range(0, cols, cstride)) + # Add the last index only if needed + if cols > 0 and cii[-1] != (cols - 1): + cii += [cols-1] + else: + cii = [] + + if rstride == 0 and cstride == 0: + raise ValueError("Either rstride or cstride must be non zero") + + # If the inputs were empty, then just + # reset everything. + if Z.size == 0: + rii = [] + cii = [] + + xlines = [X[i] for i in rii] + ylines = [Y[i] for i in rii] + zlines = [Z[i] for i in rii] + + txlines = [tX[i] for i in cii] + tylines = [tY[i] for i in cii] + tzlines = [tZ[i] for i in cii] + + lines = ([list(zip(xl, yl, zl)) + for xl, yl, zl in zip(xlines, ylines, zlines)] + + [list(zip(xl, yl, zl)) + for xl, yl, zl in zip(txlines, tylines, tzlines)]) + + linec = art3d.Line3DCollection(lines, axlim_clip=axlim_clip, **kwargs) + self.add_collection(linec) + self.auto_scale_xyz(X, Y, Z, had_data) + + return linec + + def plot_trisurf(self, *args, color=None, norm=None, vmin=None, vmax=None, + lightsource=None, axlim_clip=False, **kwargs): + """ + Plot a triangulated surface. + + The (optional) triangulation can be specified in one of two ways; + either:: + + plot_trisurf(triangulation, ...) + + where triangulation is a `~matplotlib.tri.Triangulation` object, or:: + + plot_trisurf(X, Y, ...) + plot_trisurf(X, Y, triangles, ...) + plot_trisurf(X, Y, triangles=triangles, ...) + + in which case a Triangulation object will be created. See + `.Triangulation` for an explanation of these possibilities. + + The remaining arguments are:: + + plot_trisurf(..., Z) + + where *Z* is the array of values to contour, one per point + in the triangulation. + + Parameters + ---------- + X, Y, Z : array-like + Data values as 1D arrays. + color + Color of the surface patches. + cmap + A colormap for the surface patches. + norm : `~matplotlib.colors.Normalize`, optional + An instance of Normalize to map values to colors. + vmin, vmax : float, optional + Minimum and maximum value to map. + shade : bool, default: True + Whether to shade the facecolors. Shading is always disabled when + *cmap* is specified. + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + axlim_clip : bool, default: False + Whether to hide patches with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + **kwargs + All other keyword arguments are passed on to + :class:`~mpl_toolkits.mplot3d.art3d.Poly3DCollection` + + Examples + -------- + .. plot:: gallery/mplot3d/trisurf3d.py + .. plot:: gallery/mplot3d/trisurf3d_2.py + """ + + had_data = self.has_data() + + # TODO: Support custom face colours + if color is None: + color = self._get_lines.get_next_color() + color = np.array(mcolors.to_rgba(color)) + + cmap = kwargs.get('cmap', None) + shade = kwargs.pop('shade', cmap is None) + + tri, args, kwargs = \ + Triangulation.get_from_args_and_kwargs(*args, **kwargs) + try: + z = kwargs.pop('Z') + except KeyError: + # We do this so Z doesn't get passed as an arg to PolyCollection + z, *args = args + z = np.asarray(z) + + triangles = tri.get_masked_triangles() + xt = tri.x[triangles] + yt = tri.y[triangles] + zt = z[triangles] + verts = np.stack((xt, yt, zt), axis=-1) + + if cmap: + polyc = art3d.Poly3DCollection(verts, *args, + axlim_clip=axlim_clip, **kwargs) + # average over the three points of each triangle + avg_z = verts[:, :, 2].mean(axis=1) + polyc.set_array(avg_z) + if vmin is not None or vmax is not None: + polyc.set_clim(vmin, vmax) + if norm is not None: + polyc.set_norm(norm) + else: + polyc = art3d.Poly3DCollection( + verts, *args, shade=shade, lightsource=lightsource, + facecolors=color, axlim_clip=axlim_clip, **kwargs) + + self.add_collection(polyc) + self.auto_scale_xyz(tri.x, tri.y, z, had_data) + + return polyc + + def _3d_extend_contour(self, cset, stride=5): + """ + Extend a contour in 3D by creating + """ + + dz = (cset.levels[1] - cset.levels[0]) / 2 + polyverts = [] + colors = [] + for idx, level in enumerate(cset.levels): + path = cset.get_paths()[idx] + subpaths = [*path._iter_connected_components()] + color = cset.get_edgecolor()[idx] + top = art3d._paths_to_3d_segments(subpaths, level - dz) + bot = art3d._paths_to_3d_segments(subpaths, level + dz) + if not len(top[0]): + continue + nsteps = max(round(len(top[0]) / stride), 2) + stepsize = (len(top[0]) - 1) / (nsteps - 1) + polyverts.extend([ + (top[0][round(i * stepsize)], top[0][round((i + 1) * stepsize)], + bot[0][round((i + 1) * stepsize)], bot[0][round(i * stepsize)]) + for i in range(round(nsteps) - 1)]) + colors.extend([color] * (round(nsteps) - 1)) + self.add_collection3d(art3d.Poly3DCollection( + np.array(polyverts), # All polygons have 4 vertices, so vectorize. + facecolors=colors, edgecolors=colors, shade=True)) + cset.remove() + + def add_contour_set( + self, cset, extend3d=False, stride=5, zdir='z', offset=None, + axlim_clip=False): + zdir = '-' + zdir + if extend3d: + self._3d_extend_contour(cset, stride) + else: + art3d.collection_2d_to_3d( + cset, zs=offset if offset is not None else cset.levels, zdir=zdir, + axlim_clip=axlim_clip) + + def add_contourf_set(self, cset, zdir='z', offset=None, *, axlim_clip=False): + self._add_contourf_set(cset, zdir=zdir, offset=offset, + axlim_clip=axlim_clip) + + def _add_contourf_set(self, cset, zdir='z', offset=None, axlim_clip=False): + """ + Returns + ------- + levels : `numpy.ndarray` + Levels at which the filled contours are added. + """ + zdir = '-' + zdir + + midpoints = cset.levels[:-1] + np.diff(cset.levels) / 2 + # Linearly interpolate to get levels for any extensions + if cset._extend_min: + min_level = cset.levels[0] - np.diff(cset.levels[:2]) / 2 + midpoints = np.insert(midpoints, 0, min_level) + if cset._extend_max: + max_level = cset.levels[-1] + np.diff(cset.levels[-2:]) / 2 + midpoints = np.append(midpoints, max_level) + + art3d.collection_2d_to_3d( + cset, zs=offset if offset is not None else midpoints, zdir=zdir, + axlim_clip=axlim_clip) + return midpoints + + @_preprocess_data() + def contour(self, X, Y, Z, *args, + extend3d=False, stride=5, zdir='z', offset=None, axlim_clip=False, + **kwargs): + """ + Create a 3D contour plot. + + Parameters + ---------- + X, Y, Z : array-like, + Input data. See `.Axes.contour` for supported data shapes. + extend3d : bool, default: False + Whether to extend contour in 3D. + stride : int, default: 5 + Step size for extending contour. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use. + offset : float, optional + If specified, plot a projection of the contour lines at this + position in a plane normal to *zdir*. + axlim_clip : bool, default: False + Whether to hide lines with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + *args, **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.contour`. + + Returns + ------- + matplotlib.contour.QuadContourSet + """ + had_data = self.has_data() + + jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) + cset = super().contour(jX, jY, jZ, *args, **kwargs) + self.add_contour_set(cset, extend3d, stride, zdir, offset, axlim_clip) + + self.auto_scale_xyz(X, Y, Z, had_data) + return cset + + contour3D = contour + + @_preprocess_data() + def tricontour(self, *args, + extend3d=False, stride=5, zdir='z', offset=None, axlim_clip=False, + **kwargs): + """ + Create a 3D contour plot. + + .. note:: + This method currently produces incorrect output due to a + longstanding bug in 3D PolyCollection rendering. + + Parameters + ---------- + X, Y, Z : array-like + Input data. See `.Axes.tricontour` for supported data shapes. + extend3d : bool, default: False + Whether to extend contour in 3D. + stride : int, default: 5 + Step size for extending contour. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use. + offset : float, optional + If specified, plot a projection of the contour lines at this + position in a plane normal to *zdir*. + axlim_clip : bool, default: False + Whether to hide lines with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + *args, **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.tricontour`. + + Returns + ------- + matplotlib.tri._tricontour.TriContourSet + """ + had_data = self.has_data() + + tri, args, kwargs = Triangulation.get_from_args_and_kwargs( + *args, **kwargs) + X = tri.x + Y = tri.y + if 'Z' in kwargs: + Z = kwargs.pop('Z') + else: + # We do this so Z doesn't get passed as an arg to Axes.tricontour + Z, *args = args + + jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) + tri = Triangulation(jX, jY, tri.triangles, tri.mask) + + cset = super().tricontour(tri, jZ, *args, **kwargs) + self.add_contour_set(cset, extend3d, stride, zdir, offset, axlim_clip) + + self.auto_scale_xyz(X, Y, Z, had_data) + return cset + + def _auto_scale_contourf(self, X, Y, Z, zdir, levels, had_data): + # Autoscale in the zdir based on the levels added, which are + # different from data range if any contour extensions are present + dim_vals = {'x': X, 'y': Y, 'z': Z, zdir: levels} + # Input data and levels have different sizes, but auto_scale_xyz + # expected same-size input, so manually take min/max limits + limits = [(np.nanmin(dim_vals[dim]), np.nanmax(dim_vals[dim])) + for dim in ['x', 'y', 'z']] + self.auto_scale_xyz(*limits, had_data) + + @_preprocess_data() + def contourf(self, X, Y, Z, *args, + zdir='z', offset=None, axlim_clip=False, **kwargs): + """ + Create a 3D filled contour plot. + + Parameters + ---------- + X, Y, Z : array-like + Input data. See `.Axes.contourf` for supported data shapes. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use. + offset : float, optional + If specified, plot a projection of the contour lines at this + position in a plane normal to *zdir*. + axlim_clip : bool, default: False + Whether to hide lines with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + *args, **kwargs + Other arguments are forwarded to `matplotlib.axes.Axes.contourf`. + + Returns + ------- + matplotlib.contour.QuadContourSet + """ + had_data = self.has_data() + + jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) + cset = super().contourf(jX, jY, jZ, *args, **kwargs) + levels = self._add_contourf_set(cset, zdir, offset, axlim_clip) + + self._auto_scale_contourf(X, Y, Z, zdir, levels, had_data) + return cset + + contourf3D = contourf + + @_preprocess_data() + def tricontourf(self, *args, zdir='z', offset=None, axlim_clip=False, **kwargs): + """ + Create a 3D filled contour plot. + + .. note:: + This method currently produces incorrect output due to a + longstanding bug in 3D PolyCollection rendering. + + Parameters + ---------- + X, Y, Z : array-like + Input data. See `.Axes.tricontourf` for supported data shapes. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use. + offset : float, optional + If specified, plot a projection of the contour lines at this + position in a plane normal to zdir. + axlim_clip : bool, default: False + Whether to hide lines with a vertex outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + *args, **kwargs + Other arguments are forwarded to + `matplotlib.axes.Axes.tricontourf`. + + Returns + ------- + matplotlib.tri._tricontour.TriContourSet + """ + had_data = self.has_data() + + tri, args, kwargs = Triangulation.get_from_args_and_kwargs( + *args, **kwargs) + X = tri.x + Y = tri.y + if 'Z' in kwargs: + Z = kwargs.pop('Z') + else: + # We do this so Z doesn't get passed as an arg to Axes.tricontourf + Z, *args = args + + jX, jY, jZ = art3d.rotate_axes(X, Y, Z, zdir) + tri = Triangulation(jX, jY, tri.triangles, tri.mask) + + cset = super().tricontourf(tri, jZ, *args, **kwargs) + levels = self._add_contourf_set(cset, zdir, offset, axlim_clip) + + self._auto_scale_contourf(X, Y, Z, zdir, levels, had_data) + return cset + + def add_collection3d(self, col, zs=0, zdir='z', autolim=True, *, + axlim_clip=False): + """ + Add a 3D collection object to the plot. + + 2D collection types are converted to a 3D version by + modifying the object and adding z coordinate information, + *zs* and *zdir*. + + Supported 2D collection types are: + + - `.PolyCollection` + - `.LineCollection` + - `.PatchCollection` (currently not supporting *autolim*) + + Parameters + ---------- + col : `.Collection` + A 2D collection object. + zs : float or array-like, default: 0 + The z-positions to be used for the 2D objects. + zdir : {'x', 'y', 'z'}, default: 'z' + The direction to use for the z-positions. + autolim : bool, default: True + Whether to update the data limits. + axlim_clip : bool, default: False + Whether to hide the scatter points outside the axes view limits. + + .. versionadded:: 3.10 + """ + had_data = self.has_data() + + zvals = np.atleast_1d(zs) + zsortval = (np.min(zvals) if zvals.size + else 0) # FIXME: arbitrary default + + # FIXME: use issubclass() (although, then a 3D collection + # object would also pass.) Maybe have a collection3d + # abstract class to test for and exclude? + if type(col) is mcoll.PolyCollection: + art3d.poly_collection_2d_to_3d(col, zs=zs, zdir=zdir, + axlim_clip=axlim_clip) + col.set_sort_zpos(zsortval) + elif type(col) is mcoll.LineCollection: + art3d.line_collection_2d_to_3d(col, zs=zs, zdir=zdir, + axlim_clip=axlim_clip) + col.set_sort_zpos(zsortval) + elif type(col) is mcoll.PatchCollection: + art3d.patch_collection_2d_to_3d(col, zs=zs, zdir=zdir, + axlim_clip=axlim_clip) + col.set_sort_zpos(zsortval) + + if autolim: + if isinstance(col, art3d.Line3DCollection): + self.auto_scale_xyz(*np.array(col._segments3d).transpose(), + had_data=had_data) + elif isinstance(col, art3d.Poly3DCollection): + self.auto_scale_xyz(*col._vec[:-1], had_data=had_data) + elif isinstance(col, art3d.Patch3DCollection): + pass + # FIXME: Implement auto-scaling function for Patch3DCollection + # Currently unable to do so due to issues with Patch3DCollection + # See https://github.com/matplotlib/matplotlib/issues/14298 for details + + collection = super().add_collection(col) + return collection + + @_preprocess_data(replace_names=["xs", "ys", "zs", "s", + "edgecolors", "c", "facecolor", + "facecolors", "color"]) + def scatter(self, xs, ys, + zs=0, zdir='z', s=20, c=None, depthshade=True, *args, + axlim_clip=False, **kwargs): + """ + Create a scatter plot. + + Parameters + ---------- + xs, ys : array-like + The data positions. + zs : float or array-like, default: 0 + The z-positions. Either an array of the same length as *xs* and + *ys* or a single value to place all points in the same plane. + zdir : {'x', 'y', 'z', '-x', '-y', '-z'}, default: 'z' + The axis direction for the *zs*. This is useful when plotting 2D + data on a 3D Axes. The data must be passed as *xs*, *ys*. Setting + *zdir* to 'y' then plots the data to the x-z-plane. + + See also :doc:`/gallery/mplot3d/2dcollections3d`. + + s : float or array-like, default: 20 + The marker size in points**2. Either an array of the same length + as *xs* and *ys* or a single value to make all markers the same + size. + c : :mpltype:`color`, sequence, or sequence of colors, optional + The marker color. Possible values: + + - A single color format string. + - A sequence of colors of length n. + - A sequence of n numbers to be mapped to colors using *cmap* and + *norm*. + - A 2D array in which the rows are RGB or RGBA. + + For more details see the *c* argument of `~.axes.Axes.scatter`. + depthshade : bool, default: True + Whether to shade the scatter markers to give the appearance of + depth. Each call to ``scatter()`` will perform its depthshading + independently. + axlim_clip : bool, default: False + Whether to hide the scatter points outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + **kwargs + All other keyword arguments are passed on to `~.axes.Axes.scatter`. + + Returns + ------- + paths : `~matplotlib.collections.PathCollection` + """ + + had_data = self.has_data() + zs_orig = zs + + xs, ys, zs = cbook._broadcast_with_masks(xs, ys, zs) + s = np.ma.ravel(s) # This doesn't have to match x, y in size. + + xs, ys, zs, s, c, color = cbook.delete_masked_points( + xs, ys, zs, s, c, kwargs.get('color', None) + ) + if kwargs.get("color") is not None: + kwargs['color'] = color + + # For xs and ys, 2D scatter() will do the copying. + if np.may_share_memory(zs_orig, zs): # Avoid unnecessary copies. + zs = zs.copy() + + patches = super().scatter(xs, ys, s=s, c=c, *args, **kwargs) + art3d.patch_collection_2d_to_3d(patches, zs=zs, zdir=zdir, + depthshade=depthshade, + axlim_clip=axlim_clip) + + if self._zmargin < 0.05 and xs.size > 0: + self.set_zmargin(0.05) + + self.auto_scale_xyz(xs, ys, zs, had_data) + + return patches + + scatter3D = scatter + + @_preprocess_data() + def bar(self, left, height, zs=0, zdir='z', *args, + axlim_clip=False, **kwargs): + """ + Add 2D bar(s). + + Parameters + ---------- + left : 1D array-like + The x coordinates of the left sides of the bars. + height : 1D array-like + The height of the bars. + zs : float or 1D array-like, default: 0 + Z coordinate of bars; if a single value is specified, it will be + used for all bars. + zdir : {'x', 'y', 'z'}, default: 'z' + When plotting 2D data, the direction to use as z ('x', 'y' or 'z'). + axlim_clip : bool, default: False + Whether to hide bars with points outside the axes view limits. + + .. versionadded:: 3.10 + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + **kwargs + Other keyword arguments are forwarded to + `matplotlib.axes.Axes.bar`. + + Returns + ------- + mpl_toolkits.mplot3d.art3d.Patch3DCollection + """ + had_data = self.has_data() + + patches = super().bar(left, height, *args, **kwargs) + + zs = np.broadcast_to(zs, len(left), subok=True) + + verts = [] + verts_zs = [] + for p, z in zip(patches, zs): + vs = art3d._get_patch_verts(p) + verts += vs.tolist() + verts_zs += [z] * len(vs) + art3d.patch_2d_to_3d(p, z, zdir, axlim_clip) + if 'alpha' in kwargs: + p.set_alpha(kwargs['alpha']) + + if len(verts) > 0: + # the following has to be skipped if verts is empty + # NOTE: Bugs could still occur if len(verts) > 0, + # but the "2nd dimension" is empty. + xs, ys = zip(*verts) + else: + xs, ys = [], [] + + xs, ys, verts_zs = art3d.juggle_axes(xs, ys, verts_zs, zdir) + self.auto_scale_xyz(xs, ys, verts_zs, had_data) + + return patches + + @_preprocess_data() + def bar3d(self, x, y, z, dx, dy, dz, color=None, + zsort='average', shade=True, lightsource=None, *args, + axlim_clip=False, **kwargs): + """ + Generate a 3D barplot. + + This method creates three-dimensional barplot where the width, + depth, height, and color of the bars can all be uniquely set. + + Parameters + ---------- + x, y, z : array-like + The coordinates of the anchor point of the bars. + + dx, dy, dz : float or array-like + The width, depth, and height of the bars, respectively. + + color : sequence of colors, optional + The color of the bars can be specified globally or + individually. This parameter can be: + + - A single color, to color all bars the same color. + - An array of colors of length N bars, to color each bar + independently. + - An array of colors of length 6, to color the faces of the + bars similarly. + - An array of colors of length 6 * N bars, to color each face + independently. + + When coloring the faces of the boxes specifically, this is + the order of the coloring: + + 1. -Z (bottom of box) + 2. +Z (top of box) + 3. -Y + 4. +Y + 5. -X + 6. +X + + zsort : {'average', 'min', 'max'}, default: 'average' + The z-axis sorting scheme passed onto `~.art3d.Poly3DCollection` + + shade : bool, default: True + When true, this shades the dark sides of the bars (relative + to the plot's source of light). + + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + + axlim_clip : bool, default: False + Whether to hide the bars with points outside the axes view limits. + + .. versionadded:: 3.10 + + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + **kwargs + Any additional keyword arguments are passed onto + `~.art3d.Poly3DCollection`. + + Returns + ------- + collection : `~.art3d.Poly3DCollection` + A collection of three-dimensional polygons representing the bars. + """ + + had_data = self.has_data() + + x, y, z, dx, dy, dz = np.broadcast_arrays( + np.atleast_1d(x), y, z, dx, dy, dz) + minx = np.min(x) + maxx = np.max(x + dx) + miny = np.min(y) + maxy = np.max(y + dy) + minz = np.min(z) + maxz = np.max(z + dz) + + # shape (6, 4, 3) + # All faces are oriented facing outwards - when viewed from the + # outside, their vertices are in a counterclockwise ordering. + cuboid = np.array([ + # -z + ( + (0, 0, 0), + (0, 1, 0), + (1, 1, 0), + (1, 0, 0), + ), + # +z + ( + (0, 0, 1), + (1, 0, 1), + (1, 1, 1), + (0, 1, 1), + ), + # -y + ( + (0, 0, 0), + (1, 0, 0), + (1, 0, 1), + (0, 0, 1), + ), + # +y + ( + (0, 1, 0), + (0, 1, 1), + (1, 1, 1), + (1, 1, 0), + ), + # -x + ( + (0, 0, 0), + (0, 0, 1), + (0, 1, 1), + (0, 1, 0), + ), + # +x + ( + (1, 0, 0), + (1, 1, 0), + (1, 1, 1), + (1, 0, 1), + ), + ]) + + # indexed by [bar, face, vertex, coord] + polys = np.empty(x.shape + cuboid.shape) + + # handle each coordinate separately + for i, p, dp in [(0, x, dx), (1, y, dy), (2, z, dz)]: + p = p[..., np.newaxis, np.newaxis] + dp = dp[..., np.newaxis, np.newaxis] + polys[..., i] = p + dp * cuboid[..., i] + + # collapse the first two axes + polys = polys.reshape((-1,) + polys.shape[2:]) + + facecolors = [] + if color is None: + color = [self._get_patches_for_fill.get_next_color()] + + color = list(mcolors.to_rgba_array(color)) + + if len(color) == len(x): + # bar colors specified, need to expand to number of faces + for c in color: + facecolors.extend([c] * 6) + else: + # a single color specified, or face colors specified explicitly + facecolors = color + if len(facecolors) < len(x): + facecolors *= (6 * len(x)) + + col = art3d.Poly3DCollection(polys, + zsort=zsort, + facecolors=facecolors, + shade=shade, + lightsource=lightsource, + axlim_clip=axlim_clip, + *args, **kwargs) + self.add_collection(col) + + self.auto_scale_xyz((minx, maxx), (miny, maxy), (minz, maxz), had_data) + + return col + + def set_title(self, label, fontdict=None, loc='center', **kwargs): + # docstring inherited + ret = super().set_title(label, fontdict=fontdict, loc=loc, **kwargs) + (x, y) = self.title.get_position() + self.title.set_y(0.92 * y) + return ret + + @_preprocess_data() + def quiver(self, X, Y, Z, U, V, W, *, + length=1, arrow_length_ratio=.3, pivot='tail', normalize=False, + axlim_clip=False, **kwargs): + """ + Plot a 3D field of arrows. + + The arguments can be array-like or scalars, so long as they can be + broadcast together. The arguments can also be masked arrays. If an + element in any of argument is masked, then that corresponding quiver + element will not be plotted. + + Parameters + ---------- + X, Y, Z : array-like + The x, y and z coordinates of the arrow locations (default is + tail of arrow; see *pivot* kwarg). + + U, V, W : array-like + The x, y and z components of the arrow vectors. + + length : float, default: 1 + The length of each quiver. + + arrow_length_ratio : float, default: 0.3 + The ratio of the arrow head with respect to the quiver. + + pivot : {'tail', 'middle', 'tip'}, default: 'tail' + The part of the arrow that is at the grid point; the arrow + rotates about this point, hence the name *pivot*. + + normalize : bool, default: False + Whether all arrows are normalized to have the same length, or keep + the lengths defined by *u*, *v*, and *w*. + + axlim_clip : bool, default: False + Whether to hide arrows with points outside the axes view limits. + + .. versionadded:: 3.10 + + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + **kwargs + Any additional keyword arguments are delegated to + :class:`.Line3DCollection` + """ + + def calc_arrows(UVW): + # get unit direction vector perpendicular to (u, v, w) + x = UVW[:, 0] + y = UVW[:, 1] + norm = np.linalg.norm(UVW[:, :2], axis=1) + x_p = np.divide(y, norm, where=norm != 0, out=np.zeros_like(x)) + y_p = np.divide(-x, norm, where=norm != 0, out=np.ones_like(x)) + # compute the two arrowhead direction unit vectors + rangle = math.radians(15) + c = math.cos(rangle) + s = math.sin(rangle) + # construct the rotation matrices of shape (3, 3, n) + r13 = y_p * s + r32 = x_p * s + r12 = x_p * y_p * (1 - c) + Rpos = np.array( + [[c + (x_p ** 2) * (1 - c), r12, r13], + [r12, c + (y_p ** 2) * (1 - c), -r32], + [-r13, r32, np.full_like(x_p, c)]]) + # opposite rotation negates all the sin terms + Rneg = Rpos.copy() + Rneg[[0, 1, 2, 2], [2, 2, 0, 1]] *= -1 + # Batch n (3, 3) x (3) matrix multiplications ((3, 3, n) x (n, 3)). + Rpos_vecs = np.einsum("ij...,...j->...i", Rpos, UVW) + Rneg_vecs = np.einsum("ij...,...j->...i", Rneg, UVW) + # Stack into (n, 2, 3) result. + return np.stack([Rpos_vecs, Rneg_vecs], axis=1) + + had_data = self.has_data() + + input_args = cbook._broadcast_with_masks(X, Y, Z, U, V, W, + compress=True) + + if any(len(v) == 0 for v in input_args): + # No quivers, so just make an empty collection and return early + linec = art3d.Line3DCollection([], **kwargs) + self.add_collection(linec) + return linec + + shaft_dt = np.array([0., length], dtype=float) + arrow_dt = shaft_dt * arrow_length_ratio + + _api.check_in_list(['tail', 'middle', 'tip'], pivot=pivot) + if pivot == 'tail': + shaft_dt -= length + elif pivot == 'middle': + shaft_dt -= length / 2 + + XYZ = np.column_stack(input_args[:3]) + UVW = np.column_stack(input_args[3:]).astype(float) + + # Normalize rows of UVW + if normalize: + norm = np.linalg.norm(UVW, axis=1) + norm[norm == 0] = 1 + UVW = UVW / norm.reshape((-1, 1)) + + if len(XYZ) > 0: + # compute the shaft lines all at once with an outer product + shafts = (XYZ - np.multiply.outer(shaft_dt, UVW)).swapaxes(0, 1) + # compute head direction vectors, n heads x 2 sides x 3 dimensions + head_dirs = calc_arrows(UVW) + # compute all head lines at once, starting from the shaft ends + heads = shafts[:, :1] - np.multiply.outer(arrow_dt, head_dirs) + # stack left and right head lines together + heads = heads.reshape((len(arrow_dt), -1, 3)) + # transpose to get a list of lines + heads = heads.swapaxes(0, 1) + + lines = [*shafts, *heads[::2], *heads[1::2]] + else: + lines = [] + + linec = art3d.Line3DCollection(lines, axlim_clip=axlim_clip, **kwargs) + self.add_collection(linec) + + self.auto_scale_xyz(XYZ[:, 0], XYZ[:, 1], XYZ[:, 2], had_data) + + return linec + + quiver3D = quiver + + def voxels(self, *args, facecolors=None, edgecolors=None, shade=True, + lightsource=None, axlim_clip=False, **kwargs): + """ + ax.voxels([x, y, z,] /, filled, facecolors=None, edgecolors=None, \ +**kwargs) + + Plot a set of filled voxels + + All voxels are plotted as 1x1x1 cubes on the axis, with + ``filled[0, 0, 0]`` placed with its lower corner at the origin. + Occluded faces are not plotted. + + Parameters + ---------- + filled : 3D np.array of bool + A 3D array of values, with truthy values indicating which voxels + to fill + + x, y, z : 3D np.array, optional + The coordinates of the corners of the voxels. This should broadcast + to a shape one larger in every dimension than the shape of + *filled*. These can be used to plot non-cubic voxels. + + If not specified, defaults to increasing integers along each axis, + like those returned by :func:`~numpy.indices`. + As indicated by the ``/`` in the function signature, these + arguments can only be passed positionally. + + facecolors, edgecolors : array-like, optional + The color to draw the faces and edges of the voxels. Can only be + passed as keyword arguments. + These parameters can be: + + - A single color value, to color all voxels the same color. This + can be either a string, or a 1D RGB/RGBA array + - ``None``, the default, to use a single color for the faces, and + the style default for the edges. + - A 3D `~numpy.ndarray` of color names, with each item the color + for the corresponding voxel. The size must match the voxels. + - A 4D `~numpy.ndarray` of RGB/RGBA data, with the components + along the last axis. + + shade : bool, default: True + Whether to shade the facecolors. + + lightsource : `~matplotlib.colors.LightSource`, optional + The lightsource to use when *shade* is True. + + axlim_clip : bool, default: False + Whether to hide voxels with points outside the axes view limits. + + .. versionadded:: 3.10 + + **kwargs + Additional keyword arguments to pass onto + `~mpl_toolkits.mplot3d.art3d.Poly3DCollection`. + + Returns + ------- + faces : dict + A dictionary indexed by coordinate, where ``faces[i, j, k]`` is a + `.Poly3DCollection` of the faces drawn for the voxel + ``filled[i, j, k]``. If no faces were drawn for a given voxel, + either because it was not asked to be drawn, or it is fully + occluded, then ``(i, j, k) not in faces``. + + Examples + -------- + .. plot:: gallery/mplot3d/voxels.py + .. plot:: gallery/mplot3d/voxels_rgb.py + .. plot:: gallery/mplot3d/voxels_torus.py + .. plot:: gallery/mplot3d/voxels_numpy_logo.py + """ + + # work out which signature we should be using, and use it to parse + # the arguments. Name must be voxels for the correct error message + if len(args) >= 3: + # underscores indicate position only + def voxels(__x, __y, __z, filled, **kwargs): + return (__x, __y, __z), filled, kwargs + else: + def voxels(filled, **kwargs): + return None, filled, kwargs + + xyz, filled, kwargs = voxels(*args, **kwargs) + + # check dimensions + if filled.ndim != 3: + raise ValueError("Argument filled must be 3-dimensional") + size = np.array(filled.shape, dtype=np.intp) + + # check xyz coordinates, which are one larger than the filled shape + coord_shape = tuple(size + 1) + if xyz is None: + x, y, z = np.indices(coord_shape) + else: + x, y, z = (np.broadcast_to(c, coord_shape) for c in xyz) + + def _broadcast_color_arg(color, name): + if np.ndim(color) in (0, 1): + # single color, like "red" or [1, 0, 0] + return np.broadcast_to(color, filled.shape + np.shape(color)) + elif np.ndim(color) in (3, 4): + # 3D array of strings, or 4D array with last axis rgb + if np.shape(color)[:3] != filled.shape: + raise ValueError( + f"When multidimensional, {name} must match the shape " + "of filled") + return color + else: + raise ValueError(f"Invalid {name} argument") + + # broadcast and default on facecolors + if facecolors is None: + facecolors = self._get_patches_for_fill.get_next_color() + facecolors = _broadcast_color_arg(facecolors, 'facecolors') + + # broadcast but no default on edgecolors + edgecolors = _broadcast_color_arg(edgecolors, 'edgecolors') + + # scale to the full array, even if the data is only in the center + self.auto_scale_xyz(x, y, z) + + # points lying on corners of a square + square = np.array([ + [0, 0, 0], + [1, 0, 0], + [1, 1, 0], + [0, 1, 0], + ], dtype=np.intp) + + voxel_faces = defaultdict(list) + + def permutation_matrices(n): + """Generate cyclic permutation matrices.""" + mat = np.eye(n, dtype=np.intp) + for i in range(n): + yield mat + mat = np.roll(mat, 1, axis=0) + + # iterate over each of the YZ, ZX, and XY orientations, finding faces + # to render + for permute in permutation_matrices(3): + # find the set of ranges to iterate over + pc, qc, rc = permute.T.dot(size) + pinds = np.arange(pc) + qinds = np.arange(qc) + rinds = np.arange(rc) + + square_rot_pos = square.dot(permute.T) + square_rot_neg = square_rot_pos[::-1] + + # iterate within the current plane + for p in pinds: + for q in qinds: + # iterate perpendicularly to the current plane, handling + # boundaries. We only draw faces between a voxel and an + # empty space, to avoid drawing internal faces. + + # draw lower faces + p0 = permute.dot([p, q, 0]) + i0 = tuple(p0) + if filled[i0]: + voxel_faces[i0].append(p0 + square_rot_neg) + + # draw middle faces + for r1, r2 in itertools.pairwise(rinds): + p1 = permute.dot([p, q, r1]) + p2 = permute.dot([p, q, r2]) + + i1 = tuple(p1) + i2 = tuple(p2) + + if filled[i1] and not filled[i2]: + voxel_faces[i1].append(p2 + square_rot_pos) + elif not filled[i1] and filled[i2]: + voxel_faces[i2].append(p2 + square_rot_neg) + + # draw upper faces + pk = permute.dot([p, q, rc-1]) + pk2 = permute.dot([p, q, rc]) + ik = tuple(pk) + if filled[ik]: + voxel_faces[ik].append(pk2 + square_rot_pos) + + # iterate over the faces, and generate a Poly3DCollection for each + # voxel + polygons = {} + for coord, faces_inds in voxel_faces.items(): + # convert indices into 3D positions + if xyz is None: + faces = faces_inds + else: + faces = [] + for face_inds in faces_inds: + ind = face_inds[:, 0], face_inds[:, 1], face_inds[:, 2] + face = np.empty(face_inds.shape) + face[:, 0] = x[ind] + face[:, 1] = y[ind] + face[:, 2] = z[ind] + faces.append(face) + + # shade the faces + facecolor = facecolors[coord] + edgecolor = edgecolors[coord] + + poly = art3d.Poly3DCollection( + faces, facecolors=facecolor, edgecolors=edgecolor, + shade=shade, lightsource=lightsource, axlim_clip=axlim_clip, + **kwargs) + self.add_collection3d(poly) + polygons[coord] = poly + + return polygons + + @_preprocess_data(replace_names=["x", "y", "z", "xerr", "yerr", "zerr"]) + def errorbar(self, x, y, z, zerr=None, yerr=None, xerr=None, fmt='', + barsabove=False, errorevery=1, ecolor=None, elinewidth=None, + capsize=None, capthick=None, xlolims=False, xuplims=False, + ylolims=False, yuplims=False, zlolims=False, zuplims=False, + axlim_clip=False, + **kwargs): + """ + Plot lines and/or markers with errorbars around them. + + *x*/*y*/*z* define the data locations, and *xerr*/*yerr*/*zerr* define + the errorbar sizes. By default, this draws the data markers/lines as + well the errorbars. Use fmt='none' to draw errorbars only. + + Parameters + ---------- + x, y, z : float or array-like + The data positions. + + xerr, yerr, zerr : float or array-like, shape (N,) or (2, N), optional + The errorbar sizes: + + - scalar: Symmetric +/- values for all data points. + - shape(N,): Symmetric +/-values for each data point. + - shape(2, N): Separate - and + values for each bar. First row + contains the lower errors, the second row contains the upper + errors. + - *None*: No errorbar. + + Note that all error arrays should have *positive* values. + + fmt : str, default: '' + The format for the data points / data lines. See `.plot` for + details. + + Use 'none' (case-insensitive) to plot errorbars without any data + markers. + + ecolor : :mpltype:`color`, default: None + The color of the errorbar lines. If None, use the color of the + line connecting the markers. + + elinewidth : float, default: None + The linewidth of the errorbar lines. If None, the linewidth of + the current style is used. + + capsize : float, default: :rc:`errorbar.capsize` + The length of the error bar caps in points. + + capthick : float, default: None + An alias to the keyword argument *markeredgewidth* (a.k.a. *mew*). + This setting is a more sensible name for the property that + controls the thickness of the error bar cap in points. For + backwards compatibility, if *mew* or *markeredgewidth* are given, + then they will over-ride *capthick*. This may change in future + releases. + + barsabove : bool, default: False + If True, will plot the errorbars above the plot + symbols. Default is below. + + xlolims, ylolims, zlolims : bool, default: False + These arguments can be used to indicate that a value gives only + lower limits. In that case a caret symbol is used to indicate + this. *lims*-arguments may be scalars, or array-likes of the same + length as the errors. To use limits with inverted axes, + `~.set_xlim`, `~.set_ylim`, or `~.set_zlim` must be + called before `errorbar`. Note the tricky parameter names: setting + e.g. *ylolims* to True means that the y-value is a *lower* limit of + the True value, so, only an *upward*-pointing arrow will be drawn! + + xuplims, yuplims, zuplims : bool, default: False + Same as above, but for controlling the upper limits. + + errorevery : int or (int, int), default: 1 + draws error bars on a subset of the data. *errorevery* =N draws + error bars on the points (x[::N], y[::N], z[::N]). + *errorevery* =(start, N) draws error bars on the points + (x[start::N], y[start::N], z[start::N]). e.g. *errorevery* =(6, 3) + adds error bars to the data at (x[6], x[9], x[12], x[15], ...). + Used to avoid overlapping error bars when two series share x-axis + values. + + axlim_clip : bool, default: False + Whether to hide error bars that are outside the axes limits. + + .. versionadded:: 3.10 + + Returns + ------- + errlines : list + List of `~mpl_toolkits.mplot3d.art3d.Line3DCollection` instances + each containing an errorbar line. + caplines : list + List of `~mpl_toolkits.mplot3d.art3d.Line3D` instances each + containing a capline object. + limmarks : list + List of `~mpl_toolkits.mplot3d.art3d.Line3D` instances each + containing a marker with an upper or lower limit. + + Other Parameters + ---------------- + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + **kwargs + All other keyword arguments for styling errorbar lines are passed + `~mpl_toolkits.mplot3d.art3d.Line3DCollection`. + + Examples + -------- + .. plot:: gallery/mplot3d/errorbar3d.py + """ + had_data = self.has_data() + + kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D) + # Drop anything that comes in as None to use the default instead. + kwargs = {k: v for k, v in kwargs.items() if v is not None} + kwargs.setdefault('zorder', 2) + + self._process_unit_info([("x", x), ("y", y), ("z", z)], kwargs, + convert=False) + + # make sure all the args are iterable; use lists not arrays to + # preserve units + x = x if np.iterable(x) else [x] + y = y if np.iterable(y) else [y] + z = z if np.iterable(z) else [z] + + if not len(x) == len(y) == len(z): + raise ValueError("'x', 'y', and 'z' must have the same size") + + everymask = self._errorevery_to_mask(x, errorevery) + + label = kwargs.pop("label", None) + kwargs['label'] = '_nolegend_' + + # Create the main line and determine overall kwargs for child artists. + # We avoid calling self.plot() directly, or self._get_lines(), because + # that would call self._process_unit_info again, and do other indirect + # data processing. + (data_line, base_style), = self._get_lines._plot_args( + self, (x, y) if fmt == '' else (x, y, fmt), kwargs, return_kwargs=True) + art3d.line_2d_to_3d(data_line, zs=z, axlim_clip=axlim_clip) + + # Do this after creating `data_line` to avoid modifying `base_style`. + if barsabove: + data_line.set_zorder(kwargs['zorder'] - .1) + else: + data_line.set_zorder(kwargs['zorder'] + .1) + + # Add line to plot, or throw it away and use it to determine kwargs. + if fmt.lower() != 'none': + self.add_line(data_line) + else: + data_line = None + # Remove alpha=0 color that _process_plot_format returns. + base_style.pop('color') + + if 'color' not in base_style: + base_style['color'] = 'C0' + if ecolor is None: + ecolor = base_style['color'] + + # Eject any line-specific information from format string, as it's not + # needed for bars or caps. + for key in ['marker', 'markersize', 'markerfacecolor', + 'markeredgewidth', 'markeredgecolor', 'markevery', + 'linestyle', 'fillstyle', 'drawstyle', 'dash_capstyle', + 'dash_joinstyle', 'solid_capstyle', 'solid_joinstyle']: + base_style.pop(key, None) + + # Make the style dict for the line collections (the bars). + eb_lines_style = {**base_style, 'color': ecolor} + + if elinewidth: + eb_lines_style['linewidth'] = elinewidth + elif 'linewidth' in kwargs: + eb_lines_style['linewidth'] = kwargs['linewidth'] + + for key in ('transform', 'alpha', 'zorder', 'rasterized'): + if key in kwargs: + eb_lines_style[key] = kwargs[key] + + # Make the style dict for caps (the "hats"). + eb_cap_style = {**base_style, 'linestyle': 'None'} + if capsize is None: + capsize = mpl.rcParams["errorbar.capsize"] + if capsize > 0: + eb_cap_style['markersize'] = 2. * capsize + if capthick is not None: + eb_cap_style['markeredgewidth'] = capthick + eb_cap_style['color'] = ecolor + + def _apply_mask(arrays, mask): + # Return, for each array in *arrays*, the elements for which *mask* + # is True, without using fancy indexing. + return [[*itertools.compress(array, mask)] for array in arrays] + + def _extract_errs(err, data, lomask, himask): + # For separate +/- error values we need to unpack err + if len(err.shape) == 2: + low_err, high_err = err + else: + low_err, high_err = err, err + + lows = np.where(lomask | ~everymask, data, data - low_err) + highs = np.where(himask | ~everymask, data, data + high_err) + + return lows, highs + + # collect drawn items while looping over the three coordinates + errlines, caplines, limmarks = [], [], [] + + # list of endpoint coordinates, used for auto-scaling + coorderrs = [] + + # define the markers used for errorbar caps and limits below + # the dictionary key is mapped by the `i_xyz` helper dictionary + capmarker = {0: '|', 1: '|', 2: '_'} + i_xyz = {'x': 0, 'y': 1, 'z': 2} + + # Calculate marker size from points to quiver length. Because these are + # not markers, and 3D Axes do not use the normal transform stack, this + # is a bit involved. Since the quiver arrows will change size as the + # scene is rotated, they are given a standard size based on viewing + # them directly in planar form. + quiversize = eb_cap_style.get('markersize', + mpl.rcParams['lines.markersize']) ** 2 + quiversize *= self.get_figure(root=True).dpi / 72 + quiversize = self.transAxes.inverted().transform([ + (0, 0), (quiversize, quiversize)]) + quiversize = np.mean(np.diff(quiversize, axis=0)) + # quiversize is now in Axes coordinates, and to convert back to data + # coordinates, we need to run it through the inverse 3D transform. For + # consistency, this uses a fixed elevation, azimuth, and roll. + with cbook._setattr_cm(self, elev=0, azim=0, roll=0): + invM = np.linalg.inv(self.get_proj()) + # elev=azim=roll=0 produces the Y-Z plane, so quiversize in 2D 'x' is + # 'y' in 3D, hence the 1 index. + quiversize = np.dot(invM, [quiversize, 0, 0, 0])[1] + # Quivers use a fixed 15-degree arrow head, so scale up the length so + # that the size corresponds to the base. In other words, this constant + # corresponds to the equation tan(15) = (base / 2) / (arrow length). + quiversize *= 1.8660254037844388 + eb_quiver_style = {**eb_cap_style, + 'length': quiversize, 'arrow_length_ratio': 1} + eb_quiver_style.pop('markersize', None) + + # loop over x-, y-, and z-direction and draw relevant elements + for zdir, data, err, lolims, uplims in zip( + ['x', 'y', 'z'], [x, y, z], [xerr, yerr, zerr], + [xlolims, ylolims, zlolims], [xuplims, yuplims, zuplims]): + + dir_vector = art3d.get_dir_vector(zdir) + i_zdir = i_xyz[zdir] + + if err is None: + continue + + if not np.iterable(err): + err = [err] * len(data) + + err = np.atleast_1d(err) + + # arrays fine here, they are booleans and hence not units + lolims = np.broadcast_to(lolims, len(data)).astype(bool) + uplims = np.broadcast_to(uplims, len(data)).astype(bool) + + # a nested list structure that expands to (xl,xh),(yl,yh),(zl,zh), + # where x/y/z and l/h correspond to dimensions and low/high + # positions of errorbars in a dimension we're looping over + coorderr = [ + _extract_errs(err * dir_vector[i], coord, lolims, uplims) + for i, coord in enumerate([x, y, z])] + (xl, xh), (yl, yh), (zl, zh) = coorderr + + # draws capmarkers - flat caps orthogonal to the error bars + nolims = ~(lolims | uplims) + if nolims.any() and capsize > 0: + lo_caps_xyz = _apply_mask([xl, yl, zl], nolims & everymask) + hi_caps_xyz = _apply_mask([xh, yh, zh], nolims & everymask) + + # setting '_' for z-caps and '|' for x- and y-caps; + # these markers will rotate as the viewing angle changes + cap_lo = art3d.Line3D(*lo_caps_xyz, ls='', + marker=capmarker[i_zdir], + axlim_clip=axlim_clip, + **eb_cap_style) + cap_hi = art3d.Line3D(*hi_caps_xyz, ls='', + marker=capmarker[i_zdir], + axlim_clip=axlim_clip, + **eb_cap_style) + self.add_line(cap_lo) + self.add_line(cap_hi) + caplines.append(cap_lo) + caplines.append(cap_hi) + + if lolims.any(): + xh0, yh0, zh0 = _apply_mask([xh, yh, zh], lolims & everymask) + self.quiver(xh0, yh0, zh0, *dir_vector, **eb_quiver_style) + if uplims.any(): + xl0, yl0, zl0 = _apply_mask([xl, yl, zl], uplims & everymask) + self.quiver(xl0, yl0, zl0, *-dir_vector, **eb_quiver_style) + + errline = art3d.Line3DCollection(np.array(coorderr).T, + axlim_clip=axlim_clip, + **eb_lines_style) + self.add_collection(errline) + errlines.append(errline) + coorderrs.append(coorderr) + + coorderrs = np.array(coorderrs) + + def _digout_minmax(err_arr, coord_label): + return (np.nanmin(err_arr[:, i_xyz[coord_label], :, :]), + np.nanmax(err_arr[:, i_xyz[coord_label], :, :])) + + minx, maxx = _digout_minmax(coorderrs, 'x') + miny, maxy = _digout_minmax(coorderrs, 'y') + minz, maxz = _digout_minmax(coorderrs, 'z') + self.auto_scale_xyz((minx, maxx), (miny, maxy), (minz, maxz), had_data) + + # Adapting errorbar containers for 3d case, assuming z-axis points "up" + errorbar_container = mcontainer.ErrorbarContainer( + (data_line, tuple(caplines), tuple(errlines)), + has_xerr=(xerr is not None or yerr is not None), + has_yerr=(zerr is not None), + label=label) + self.containers.append(errorbar_container) + + return errlines, caplines, limmarks + + def get_tightbbox(self, renderer=None, *, call_axes_locator=True, + bbox_extra_artists=None, for_layout_only=False): + ret = super().get_tightbbox(renderer, + call_axes_locator=call_axes_locator, + bbox_extra_artists=bbox_extra_artists, + for_layout_only=for_layout_only) + batch = [ret] + if self._axis3don: + for axis in self._axis_map.values(): + if axis.get_visible(): + axis_bb = martist._get_tightbbox_for_layout_only( + axis, renderer) + if axis_bb: + batch.append(axis_bb) + return mtransforms.Bbox.union(batch) + + @_preprocess_data() + def stem(self, x, y, z, *, linefmt='C0-', markerfmt='C0o', basefmt='C3-', + bottom=0, label=None, orientation='z', axlim_clip=False): + """ + Create a 3D stem plot. + + A stem plot draws lines perpendicular to a baseline, and places markers + at the heads. By default, the baseline is defined by *x* and *y*, and + stems are drawn vertically from *bottom* to *z*. + + Parameters + ---------- + x, y, z : array-like + The positions of the heads of the stems. The stems are drawn along + the *orientation*-direction from the baseline at *bottom* (in the + *orientation*-coordinate) to the heads. By default, the *x* and *y* + positions are used for the baseline and *z* for the head position, + but this can be changed by *orientation*. + + linefmt : str, default: 'C0-' + A string defining the properties of the vertical lines. Usually, + this will be a color or a color and a linestyle: + + ========= ============= + Character Line Style + ========= ============= + ``'-'`` solid line + ``'--'`` dashed line + ``'-.'`` dash-dot line + ``':'`` dotted line + ========= ============= + + Note: While it is technically possible to specify valid formats + other than color or color and linestyle (e.g. 'rx' or '-.'), this + is beyond the intention of the method and will most likely not + result in a reasonable plot. + + markerfmt : str, default: 'C0o' + A string defining the properties of the markers at the stem heads. + + basefmt : str, default: 'C3-' + A format string defining the properties of the baseline. + + bottom : float, default: 0 + The position of the baseline, in *orientation*-coordinates. + + label : str, optional + The label to use for the stems in legends. + + orientation : {'x', 'y', 'z'}, default: 'z' + The direction along which stems are drawn. + + axlim_clip : bool, default: False + Whether to hide stems that are outside the axes limits. + + .. versionadded:: 3.10 + + data : indexable object, optional + DATA_PARAMETER_PLACEHOLDER + + Returns + ------- + `.StemContainer` + The container may be treated like a tuple + (*markerline*, *stemlines*, *baseline*) + + Examples + -------- + .. plot:: gallery/mplot3d/stem3d_demo.py + """ + + from matplotlib.container import StemContainer + + had_data = self.has_data() + + _api.check_in_list(['x', 'y', 'z'], orientation=orientation) + + xlim = (np.min(x), np.max(x)) + ylim = (np.min(y), np.max(y)) + zlim = (np.min(z), np.max(z)) + + # Determine the appropriate plane for the baseline and the direction of + # stemlines based on the value of orientation. + if orientation == 'x': + basex, basexlim = y, ylim + basey, baseylim = z, zlim + lines = [[(bottom, thisy, thisz), (thisx, thisy, thisz)] + for thisx, thisy, thisz in zip(x, y, z)] + elif orientation == 'y': + basex, basexlim = x, xlim + basey, baseylim = z, zlim + lines = [[(thisx, bottom, thisz), (thisx, thisy, thisz)] + for thisx, thisy, thisz in zip(x, y, z)] + else: + basex, basexlim = x, xlim + basey, baseylim = y, ylim + lines = [[(thisx, thisy, bottom), (thisx, thisy, thisz)] + for thisx, thisy, thisz in zip(x, y, z)] + + # Determine style for stem lines. + linestyle, linemarker, linecolor = _process_plot_format(linefmt) + if linestyle is None: + linestyle = mpl.rcParams['lines.linestyle'] + + # Plot everything in required order. + baseline, = self.plot(basex, basey, basefmt, zs=bottom, + zdir=orientation, label='_nolegend_') + stemlines = art3d.Line3DCollection( + lines, linestyles=linestyle, colors=linecolor, label='_nolegend_', + axlim_clip=axlim_clip) + self.add_collection(stemlines) + markerline, = self.plot(x, y, z, markerfmt, label='_nolegend_') + + stem_container = StemContainer((markerline, stemlines, baseline), + label=label) + self.add_container(stem_container) + + jx, jy, jz = art3d.juggle_axes(basexlim, baseylim, [bottom, bottom], + orientation) + self.auto_scale_xyz([*jx, *xlim], [*jy, *ylim], [*jz, *zlim], had_data) + + return stem_container + + stem3D = stem + + +def get_test_data(delta=0.05): + """Return a tuple X, Y, Z with a test data set.""" + x = y = np.arange(-3.0, 3.0, delta) + X, Y = np.meshgrid(x, y) + + Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi) + Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) / + (2 * np.pi * 0.5 * 1.5)) + Z = Z2 - Z1 + + X = X * 10 + Y = Y * 10 + Z = Z * 500 + return X, Y, Z + + +class _Quaternion: + """ + Quaternions + consisting of scalar, along 1, and vector, with components along i, j, k + """ + + def __init__(self, scalar, vector): + self.scalar = scalar + self.vector = np.array(vector) + + def __neg__(self): + return self.__class__(-self.scalar, -self.vector) + + def __mul__(self, other): + """ + Product of two quaternions + i*i = j*j = k*k = i*j*k = -1 + Quaternion multiplication can be expressed concisely + using scalar and vector parts, + see + """ + return self.__class__( + self.scalar*other.scalar - np.dot(self.vector, other.vector), + self.scalar*other.vector + self.vector*other.scalar + + np.cross(self.vector, other.vector)) + + def conjugate(self): + """The conjugate quaternion -(1/2)*(q+i*q*i+j*q*j+k*q*k)""" + return self.__class__(self.scalar, -self.vector) + + @property + def norm(self): + """The 2-norm, q*q', a scalar""" + return self.scalar*self.scalar + np.dot(self.vector, self.vector) + + def normalize(self): + """Scaling such that norm equals 1""" + n = np.sqrt(self.norm) + return self.__class__(self.scalar/n, self.vector/n) + + def reciprocal(self): + """The reciprocal, 1/q = q'/(q*q') = q' / norm(q)""" + n = self.norm + return self.__class__(self.scalar/n, -self.vector/n) + + def __div__(self, other): + return self*other.reciprocal() + + __truediv__ = __div__ + + def rotate(self, v): + # Rotate the vector v by the quaternion q, i.e., + # calculate (the vector part of) q*v/q + v = self.__class__(0, v) + v = self*v/self + return v.vector + + def __eq__(self, other): + return (self.scalar == other.scalar) and (self.vector == other.vector).all + + def __repr__(self): + return "_Quaternion({}, {})".format(repr(self.scalar), repr(self.vector)) + + @classmethod + def rotate_from_to(cls, r1, r2): + """ + The quaternion for the shortest rotation from vector r1 to vector r2 + i.e., q = sqrt(r2*r1'), normalized. + If r1 and r2 are antiparallel, then the result is ambiguous; + a normal vector will be returned, and a warning will be issued. + """ + k = np.cross(r1, r2) + nk = np.linalg.norm(k) + th = np.arctan2(nk, np.dot(r1, r2)) + th /= 2 + if nk == 0: # r1 and r2 are parallel or anti-parallel + if np.dot(r1, r2) < 0: + warnings.warn("Rotation defined by anti-parallel vectors is ambiguous") + k = np.zeros(3) + k[np.argmin(r1*r1)] = 1 # basis vector most perpendicular to r1-r2 + k = np.cross(r1, k) + k = k / np.linalg.norm(k) # unit vector normal to r1-r2 + q = cls(0, k) + else: + q = cls(1, [0, 0, 0]) # = 1, no rotation + else: + q = cls(np.cos(th), k*np.sin(th)/nk) + return q + + @classmethod + def from_cardan_angles(cls, elev, azim, roll): + """ + Converts the angles to a quaternion + q = exp((roll/2)*e_x)*exp((elev/2)*e_y)*exp((-azim/2)*e_z) + i.e., the angles are a kind of Tait-Bryan angles, -z,y',x". + The angles should be given in radians, not degrees. + """ + ca, sa = np.cos(azim/2), np.sin(azim/2) + ce, se = np.cos(elev/2), np.sin(elev/2) + cr, sr = np.cos(roll/2), np.sin(roll/2) + + qw = ca*ce*cr + sa*se*sr + qx = ca*ce*sr - sa*se*cr + qy = ca*se*cr + sa*ce*sr + qz = ca*se*sr - sa*ce*cr + return cls(qw, [qx, qy, qz]) + + def as_cardan_angles(self): + """ + The inverse of `from_cardan_angles()`. + Note that the angles returned are in radians, not degrees. + The angles are not sensitive to the quaternion's norm(). + """ + qw = self.scalar + qx, qy, qz = self.vector[..., :] + azim = np.arctan2(2*(-qw*qz+qx*qy), qw*qw+qx*qx-qy*qy-qz*qz) + elev = np.arcsin(np.clip(2*(qw*qy+qz*qx)/(qw*qw+qx*qx+qy*qy+qz*qz), -1, 1)) + roll = np.arctan2(2*(qw*qx-qy*qz), qw*qw-qx*qx-qy*qy+qz*qz) + return elev, azim, roll diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axis3d.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axis3d.py new file mode 100644 index 0000000000000000000000000000000000000000..4da5031b990ca742eb0d303003af5c6a821c4db9 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/axis3d.py @@ -0,0 +1,750 @@ +# axis3d.py, original mplot3d version by John Porter +# Created: 23 Sep 2005 +# Parts rewritten by Reinier Heeres + +import inspect + +import numpy as np + +import matplotlib as mpl +from matplotlib import ( + _api, artist, lines as mlines, axis as maxis, patches as mpatches, + transforms as mtransforms, colors as mcolors) +from . import art3d, proj3d + + +def _move_from_center(coord, centers, deltas, axmask=(True, True, True)): + """ + For each coordinate where *axmask* is True, move *coord* away from + *centers* by *deltas*. + """ + coord = np.asarray(coord) + return coord + axmask * np.copysign(1, coord - centers) * deltas + + +def _tick_update_position(tick, tickxs, tickys, labelpos): + """Update tick line and label position and style.""" + + tick.label1.set_position(labelpos) + tick.label2.set_position(labelpos) + tick.tick1line.set_visible(True) + tick.tick2line.set_visible(False) + tick.tick1line.set_linestyle('-') + tick.tick1line.set_marker('') + tick.tick1line.set_data(tickxs, tickys) + tick.gridline.set_data([0], [0]) + + +class Axis(maxis.XAxis): + """An Axis class for the 3D plots.""" + # These points from the unit cube make up the x, y and z-planes + _PLANES = ( + (0, 3, 7, 4), (1, 2, 6, 5), # yz planes + (0, 1, 5, 4), (3, 2, 6, 7), # xz planes + (0, 1, 2, 3), (4, 5, 6, 7), # xy planes + ) + + # Some properties for the axes + _AXINFO = { + 'x': {'i': 0, 'tickdir': 1, 'juggled': (1, 0, 2)}, + 'y': {'i': 1, 'tickdir': 0, 'juggled': (0, 1, 2)}, + 'z': {'i': 2, 'tickdir': 0, 'juggled': (0, 2, 1)}, + } + + def _old_init(self, adir, v_intervalx, d_intervalx, axes, *args, + rotate_label=None, **kwargs): + return locals() + + def _new_init(self, axes, *, rotate_label=None, **kwargs): + return locals() + + def __init__(self, *args, **kwargs): + params = _api.select_matching_signature( + [self._old_init, self._new_init], *args, **kwargs) + if "adir" in params: + _api.warn_deprecated( + "3.6", message=f"The signature of 3D Axis constructors has " + f"changed in %(since)s; the new signature is " + f"{inspect.signature(type(self).__init__)}", pending=True) + if params["adir"] != self.axis_name: + raise ValueError(f"Cannot instantiate {type(self).__name__} " + f"with adir={params['adir']!r}") + axes = params["axes"] + rotate_label = params["rotate_label"] + args = params.get("args", ()) + kwargs = params["kwargs"] + + name = self.axis_name + + self._label_position = 'default' + self._tick_position = 'default' + + # This is a temporary member variable. + # Do not depend on this existing in future releases! + self._axinfo = self._AXINFO[name].copy() + # Common parts + self._axinfo.update({ + 'label': {'va': 'center', 'ha': 'center', + 'rotation_mode': 'anchor'}, + 'color': mpl.rcParams[f'axes3d.{name}axis.panecolor'], + 'tick': { + 'inward_factor': 0.2, + 'outward_factor': 0.1, + }, + }) + + if mpl.rcParams['_internal.classic_mode']: + self._axinfo.update({ + 'axisline': {'linewidth': 0.75, 'color': (0, 0, 0, 1)}, + 'grid': { + 'color': (0.9, 0.9, 0.9, 1), + 'linewidth': 1.0, + 'linestyle': '-', + }, + }) + self._axinfo['tick'].update({ + 'linewidth': { + True: mpl.rcParams['lines.linewidth'], # major + False: mpl.rcParams['lines.linewidth'], # minor + } + }) + else: + self._axinfo.update({ + 'axisline': { + 'linewidth': mpl.rcParams['axes.linewidth'], + 'color': mpl.rcParams['axes.edgecolor'], + }, + 'grid': { + 'color': mpl.rcParams['grid.color'], + 'linewidth': mpl.rcParams['grid.linewidth'], + 'linestyle': mpl.rcParams['grid.linestyle'], + }, + }) + self._axinfo['tick'].update({ + 'linewidth': { + True: ( # major + mpl.rcParams['xtick.major.width'] if name in 'xz' + else mpl.rcParams['ytick.major.width']), + False: ( # minor + mpl.rcParams['xtick.minor.width'] if name in 'xz' + else mpl.rcParams['ytick.minor.width']), + } + }) + + super().__init__(axes, *args, **kwargs) + + # data and viewing intervals for this direction + if "d_intervalx" in params: + self.set_data_interval(*params["d_intervalx"]) + if "v_intervalx" in params: + self.set_view_interval(*params["v_intervalx"]) + self.set_rotate_label(rotate_label) + self._init3d() # Inline after init3d deprecation elapses. + + __init__.__signature__ = inspect.signature(_new_init) + adir = _api.deprecated("3.6", pending=True)( + property(lambda self: self.axis_name)) + + def _init3d(self): + self.line = mlines.Line2D( + xdata=(0, 0), ydata=(0, 0), + linewidth=self._axinfo['axisline']['linewidth'], + color=self._axinfo['axisline']['color'], + antialiased=True) + + # Store dummy data in Polygon object + self.pane = mpatches.Polygon([[0, 0], [0, 1]], closed=False) + self.set_pane_color(self._axinfo['color']) + + self.axes._set_artist_props(self.line) + self.axes._set_artist_props(self.pane) + self.gridlines = art3d.Line3DCollection([]) + self.axes._set_artist_props(self.gridlines) + self.axes._set_artist_props(self.label) + self.axes._set_artist_props(self.offsetText) + # Need to be able to place the label at the correct location + self.label._transform = self.axes.transData + self.offsetText._transform = self.axes.transData + + @_api.deprecated("3.6", pending=True) + def init3d(self): # After deprecation elapses, inline _init3d to __init__. + self._init3d() + + def get_major_ticks(self, numticks=None): + ticks = super().get_major_ticks(numticks) + for t in ticks: + for obj in [ + t.tick1line, t.tick2line, t.gridline, t.label1, t.label2]: + obj.set_transform(self.axes.transData) + return ticks + + def get_minor_ticks(self, numticks=None): + ticks = super().get_minor_ticks(numticks) + for t in ticks: + for obj in [ + t.tick1line, t.tick2line, t.gridline, t.label1, t.label2]: + obj.set_transform(self.axes.transData) + return ticks + + def set_ticks_position(self, position): + """ + Set the ticks position. + + Parameters + ---------- + position : {'lower', 'upper', 'both', 'default', 'none'} + The position of the bolded axis lines, ticks, and tick labels. + """ + _api.check_in_list(['lower', 'upper', 'both', 'default', 'none'], + position=position) + self._tick_position = position + + def get_ticks_position(self): + """ + Get the ticks position. + + Returns + ------- + str : {'lower', 'upper', 'both', 'default', 'none'} + The position of the bolded axis lines, ticks, and tick labels. + """ + return self._tick_position + + def set_label_position(self, position): + """ + Set the label position. + + Parameters + ---------- + position : {'lower', 'upper', 'both', 'default', 'none'} + The position of the axis label. + """ + _api.check_in_list(['lower', 'upper', 'both', 'default', 'none'], + position=position) + self._label_position = position + + def get_label_position(self): + """ + Get the label position. + + Returns + ------- + str : {'lower', 'upper', 'both', 'default', 'none'} + The position of the axis label. + """ + return self._label_position + + def set_pane_color(self, color, alpha=None): + """ + Set pane color. + + Parameters + ---------- + color : :mpltype:`color` + Color for axis pane. + alpha : float, optional + Alpha value for axis pane. If None, base it on *color*. + """ + color = mcolors.to_rgba(color, alpha) + self._axinfo['color'] = color + self.pane.set_edgecolor(color) + self.pane.set_facecolor(color) + self.pane.set_alpha(color[-1]) + self.stale = True + + def set_rotate_label(self, val): + """ + Whether to rotate the axis label: True, False or None. + If set to None the label will be rotated if longer than 4 chars. + """ + self._rotate_label = val + self.stale = True + + def get_rotate_label(self, text): + if self._rotate_label is not None: + return self._rotate_label + else: + return len(text) > 4 + + def _get_coord_info(self): + mins, maxs = np.array([ + self.axes.get_xbound(), + self.axes.get_ybound(), + self.axes.get_zbound(), + ]).T + + # Project the bounds along the current position of the cube: + bounds = mins[0], maxs[0], mins[1], maxs[1], mins[2], maxs[2] + bounds_proj = self.axes._transformed_cube(bounds) + + # Determine which one of the parallel planes are higher up: + means_z0 = np.zeros(3) + means_z1 = np.zeros(3) + for i in range(3): + means_z0[i] = np.mean(bounds_proj[self._PLANES[2 * i], 2]) + means_z1[i] = np.mean(bounds_proj[self._PLANES[2 * i + 1], 2]) + highs = means_z0 < means_z1 + + # Special handling for edge-on views + equals = np.abs(means_z0 - means_z1) <= np.finfo(float).eps + if np.sum(equals) == 2: + vertical = np.where(~equals)[0][0] + if vertical == 2: # looking at XY plane + highs = np.array([True, True, highs[2]]) + elif vertical == 1: # looking at XZ plane + highs = np.array([True, highs[1], False]) + elif vertical == 0: # looking at YZ plane + highs = np.array([highs[0], False, False]) + + return mins, maxs, bounds_proj, highs + + def _calc_centers_deltas(self, maxs, mins): + centers = 0.5 * (maxs + mins) + # In mpl3.8, the scale factor was 1/12. mpl3.9 changes this to + # 1/12 * 24/25 = 0.08 to compensate for the change in automargin + # behavior and keep appearance the same. The 24/25 factor is from the + # 1/48 padding added to each side of the axis in mpl3.8. + scale = 0.08 + deltas = (maxs - mins) * scale + return centers, deltas + + def _get_axis_line_edge_points(self, minmax, maxmin, position=None): + """Get the edge points for the black bolded axis line.""" + # When changing vertical axis some of the axes has to be + # moved to the other plane so it looks the same as if the z-axis + # was the vertical axis. + mb = [minmax, maxmin] # line from origin to nearest corner to camera + mb_rev = mb[::-1] + mm = [[mb, mb_rev, mb_rev], [mb_rev, mb_rev, mb], [mb, mb, mb]] + mm = mm[self.axes._vertical_axis][self._axinfo["i"]] + + juggled = self._axinfo["juggled"] + edge_point_0 = mm[0].copy() # origin point + + if ((position == 'lower' and mm[1][juggled[-1]] < mm[0][juggled[-1]]) or + (position == 'upper' and mm[1][juggled[-1]] > mm[0][juggled[-1]])): + edge_point_0[juggled[-1]] = mm[1][juggled[-1]] + else: + edge_point_0[juggled[0]] = mm[1][juggled[0]] + + edge_point_1 = edge_point_0.copy() + edge_point_1[juggled[1]] = mm[1][juggled[1]] + + return edge_point_0, edge_point_1 + + def _get_all_axis_line_edge_points(self, minmax, maxmin, axis_position=None): + # Determine edge points for the axis lines + edgep1s = [] + edgep2s = [] + position = [] + if axis_position in (None, 'default'): + edgep1, edgep2 = self._get_axis_line_edge_points(minmax, maxmin) + edgep1s = [edgep1] + edgep2s = [edgep2] + position = ['default'] + else: + edgep1_l, edgep2_l = self._get_axis_line_edge_points(minmax, maxmin, + position='lower') + edgep1_u, edgep2_u = self._get_axis_line_edge_points(minmax, maxmin, + position='upper') + if axis_position in ('lower', 'both'): + edgep1s.append(edgep1_l) + edgep2s.append(edgep2_l) + position.append('lower') + if axis_position in ('upper', 'both'): + edgep1s.append(edgep1_u) + edgep2s.append(edgep2_u) + position.append('upper') + return edgep1s, edgep2s, position + + def _get_tickdir(self, position): + """ + Get the direction of the tick. + + Parameters + ---------- + position : str, optional : {'upper', 'lower', 'default'} + The position of the axis. + + Returns + ------- + tickdir : int + Index which indicates which coordinate the tick line will + align with. + """ + _api.check_in_list(('upper', 'lower', 'default'), position=position) + + # TODO: Move somewhere else where it's triggered less: + tickdirs_base = [v["tickdir"] for v in self._AXINFO.values()] # default + elev_mod = np.mod(self.axes.elev + 180, 360) - 180 + azim_mod = np.mod(self.axes.azim, 360) + if position == 'upper': + if elev_mod >= 0: + tickdirs_base = [2, 2, 0] + else: + tickdirs_base = [1, 0, 0] + if 0 <= azim_mod < 180: + tickdirs_base[2] = 1 + elif position == 'lower': + if elev_mod >= 0: + tickdirs_base = [1, 0, 1] + else: + tickdirs_base = [2, 2, 1] + if 0 <= azim_mod < 180: + tickdirs_base[2] = 0 + info_i = [v["i"] for v in self._AXINFO.values()] + + i = self._axinfo["i"] + vert_ax = self.axes._vertical_axis + j = vert_ax - 2 + # default: tickdir = [[1, 2, 1], [2, 2, 0], [1, 0, 0]][vert_ax][i] + tickdir = np.roll(info_i, -j)[np.roll(tickdirs_base, j)][i] + return tickdir + + def active_pane(self): + mins, maxs, tc, highs = self._get_coord_info() + info = self._axinfo + index = info['i'] + if not highs[index]: + loc = mins[index] + plane = self._PLANES[2 * index] + else: + loc = maxs[index] + plane = self._PLANES[2 * index + 1] + xys = np.array([tc[p] for p in plane]) + return xys, loc + + def draw_pane(self, renderer): + """ + Draw pane. + + Parameters + ---------- + renderer : `~matplotlib.backend_bases.RendererBase` subclass + """ + renderer.open_group('pane3d', gid=self.get_gid()) + xys, loc = self.active_pane() + self.pane.xy = xys[:, :2] + self.pane.draw(renderer) + renderer.close_group('pane3d') + + def _axmask(self): + axmask = [True, True, True] + axmask[self._axinfo["i"]] = False + return axmask + + def _draw_ticks(self, renderer, edgep1, centers, deltas, highs, + deltas_per_point, pos): + ticks = self._update_ticks() + info = self._axinfo + index = info["i"] + juggled = info["juggled"] + + mins, maxs, tc, highs = self._get_coord_info() + centers, deltas = self._calc_centers_deltas(maxs, mins) + + # Draw ticks: + tickdir = self._get_tickdir(pos) + tickdelta = deltas[tickdir] if highs[tickdir] else -deltas[tickdir] + + tick_info = info['tick'] + tick_out = tick_info['outward_factor'] * tickdelta + tick_in = tick_info['inward_factor'] * tickdelta + tick_lw = tick_info['linewidth'] + edgep1_tickdir = edgep1[tickdir] + out_tickdir = edgep1_tickdir + tick_out + in_tickdir = edgep1_tickdir - tick_in + + default_label_offset = 8. # A rough estimate + points = deltas_per_point * deltas + for tick in ticks: + # Get tick line positions + pos = edgep1.copy() + pos[index] = tick.get_loc() + pos[tickdir] = out_tickdir + x1, y1, z1 = proj3d.proj_transform(*pos, self.axes.M) + pos[tickdir] = in_tickdir + x2, y2, z2 = proj3d.proj_transform(*pos, self.axes.M) + + # Get position of label + labeldeltas = (tick.get_pad() + default_label_offset) * points + + pos[tickdir] = edgep1_tickdir + pos = _move_from_center(pos, centers, labeldeltas, self._axmask()) + lx, ly, lz = proj3d.proj_transform(*pos, self.axes.M) + + _tick_update_position(tick, (x1, x2), (y1, y2), (lx, ly)) + tick.tick1line.set_linewidth(tick_lw[tick._major]) + tick.draw(renderer) + + def _draw_offset_text(self, renderer, edgep1, edgep2, labeldeltas, centers, + highs, pep, dx, dy): + # Get general axis information: + info = self._axinfo + index = info["i"] + juggled = info["juggled"] + tickdir = info["tickdir"] + + # Which of the two edge points do we want to + # use for locating the offset text? + if juggled[2] == 2: + outeredgep = edgep1 + outerindex = 0 + else: + outeredgep = edgep2 + outerindex = 1 + + pos = _move_from_center(outeredgep, centers, labeldeltas, + self._axmask()) + olx, oly, olz = proj3d.proj_transform(*pos, self.axes.M) + self.offsetText.set_text(self.major.formatter.get_offset()) + self.offsetText.set_position((olx, oly)) + angle = art3d._norm_text_angle(np.rad2deg(np.arctan2(dy, dx))) + self.offsetText.set_rotation(angle) + # Must set rotation mode to "anchor" so that + # the alignment point is used as the "fulcrum" for rotation. + self.offsetText.set_rotation_mode('anchor') + + # ---------------------------------------------------------------------- + # Note: the following statement for determining the proper alignment of + # the offset text. This was determined entirely by trial-and-error + # and should not be in any way considered as "the way". There are + # still some edge cases where alignment is not quite right, but this + # seems to be more of a geometry issue (in other words, I might be + # using the wrong reference points). + # + # (TT, FF, TF, FT) are the shorthand for the tuple of + # (centpt[tickdir] <= pep[tickdir, outerindex], + # centpt[index] <= pep[index, outerindex]) + # + # Three-letters (e.g., TFT, FTT) are short-hand for the array of bools + # from the variable 'highs'. + # --------------------------------------------------------------------- + centpt = proj3d.proj_transform(*centers, self.axes.M) + if centpt[tickdir] > pep[tickdir, outerindex]: + # if FT and if highs has an even number of Trues + if (centpt[index] <= pep[index, outerindex] + and np.count_nonzero(highs) % 2 == 0): + # Usually, this means align right, except for the FTT case, + # in which offset for axis 1 and 2 are aligned left. + if highs.tolist() == [False, True, True] and index in (1, 2): + align = 'left' + else: + align = 'right' + else: + # The FF case + align = 'left' + else: + # if TF and if highs has an even number of Trues + if (centpt[index] > pep[index, outerindex] + and np.count_nonzero(highs) % 2 == 0): + # Usually mean align left, except if it is axis 2 + align = 'right' if index == 2 else 'left' + else: + # The TT case + align = 'right' + + self.offsetText.set_va('center') + self.offsetText.set_ha(align) + self.offsetText.draw(renderer) + + def _draw_labels(self, renderer, edgep1, edgep2, labeldeltas, centers, dx, dy): + label = self._axinfo["label"] + + # Draw labels + lxyz = 0.5 * (edgep1 + edgep2) + lxyz = _move_from_center(lxyz, centers, labeldeltas, self._axmask()) + tlx, tly, tlz = proj3d.proj_transform(*lxyz, self.axes.M) + self.label.set_position((tlx, tly)) + if self.get_rotate_label(self.label.get_text()): + angle = art3d._norm_text_angle(np.rad2deg(np.arctan2(dy, dx))) + self.label.set_rotation(angle) + self.label.set_va(label['va']) + self.label.set_ha(label['ha']) + self.label.set_rotation_mode(label['rotation_mode']) + self.label.draw(renderer) + + @artist.allow_rasterization + def draw(self, renderer): + self.label._transform = self.axes.transData + self.offsetText._transform = self.axes.transData + renderer.open_group("axis3d", gid=self.get_gid()) + + # Get general axis information: + mins, maxs, tc, highs = self._get_coord_info() + centers, deltas = self._calc_centers_deltas(maxs, mins) + + # Calculate offset distances + # A rough estimate; points are ambiguous since 3D plots rotate + reltoinches = self.get_figure(root=False).dpi_scale_trans.inverted() + ax_inches = reltoinches.transform(self.axes.bbox.size) + ax_points_estimate = sum(72. * ax_inches) + deltas_per_point = 48 / ax_points_estimate + default_offset = 21. + labeldeltas = (self.labelpad + default_offset) * deltas_per_point * deltas + + # Determine edge points for the axis lines + minmax = np.where(highs, maxs, mins) # "origin" point + maxmin = np.where(~highs, maxs, mins) # "opposite" corner near camera + + for edgep1, edgep2, pos in zip(*self._get_all_axis_line_edge_points( + minmax, maxmin, self._tick_position)): + # Project the edge points along the current position + pep = proj3d._proj_trans_points([edgep1, edgep2], self.axes.M) + pep = np.asarray(pep) + + # The transAxes transform is used because the Text object + # rotates the text relative to the display coordinate system. + # Therefore, if we want the labels to remain parallel to the + # axis regardless of the aspect ratio, we need to convert the + # edge points of the plane to display coordinates and calculate + # an angle from that. + # TODO: Maybe Text objects should handle this themselves? + dx, dy = (self.axes.transAxes.transform([pep[0:2, 1]]) - + self.axes.transAxes.transform([pep[0:2, 0]]))[0] + + # Draw the lines + self.line.set_data(pep[0], pep[1]) + self.line.draw(renderer) + + # Draw ticks + self._draw_ticks(renderer, edgep1, centers, deltas, highs, + deltas_per_point, pos) + + # Draw Offset text + self._draw_offset_text(renderer, edgep1, edgep2, labeldeltas, + centers, highs, pep, dx, dy) + + for edgep1, edgep2, pos in zip(*self._get_all_axis_line_edge_points( + minmax, maxmin, self._label_position)): + # See comments above + pep = proj3d._proj_trans_points([edgep1, edgep2], self.axes.M) + pep = np.asarray(pep) + dx, dy = (self.axes.transAxes.transform([pep[0:2, 1]]) - + self.axes.transAxes.transform([pep[0:2, 0]]))[0] + + # Draw labels + self._draw_labels(renderer, edgep1, edgep2, labeldeltas, centers, dx, dy) + + renderer.close_group('axis3d') + self.stale = False + + @artist.allow_rasterization + def draw_grid(self, renderer): + if not self.axes._draw_grid: + return + + renderer.open_group("grid3d", gid=self.get_gid()) + + ticks = self._update_ticks() + if len(ticks): + # Get general axis information: + info = self._axinfo + index = info["i"] + + mins, maxs, tc, highs = self._get_coord_info() + + minmax = np.where(highs, maxs, mins) + maxmin = np.where(~highs, maxs, mins) + + # Grid points where the planes meet + xyz0 = np.tile(minmax, (len(ticks), 1)) + xyz0[:, index] = [tick.get_loc() for tick in ticks] + + # Grid lines go from the end of one plane through the plane + # intersection (at xyz0) to the end of the other plane. The first + # point (0) differs along dimension index-2 and the last (2) along + # dimension index-1. + lines = np.stack([xyz0, xyz0, xyz0], axis=1) + lines[:, 0, index - 2] = maxmin[index - 2] + lines[:, 2, index - 1] = maxmin[index - 1] + self.gridlines.set_segments(lines) + gridinfo = info['grid'] + self.gridlines.set_color(gridinfo['color']) + self.gridlines.set_linewidth(gridinfo['linewidth']) + self.gridlines.set_linestyle(gridinfo['linestyle']) + self.gridlines.do_3d_projection() + self.gridlines.draw(renderer) + + renderer.close_group('grid3d') + + # TODO: Get this to work (more) properly when mplot3d supports the + # transforms framework. + def get_tightbbox(self, renderer=None, *, for_layout_only=False): + # docstring inherited + if not self.get_visible(): + return + # We have to directly access the internal data structures + # (and hope they are up to date) because at draw time we + # shift the ticks and their labels around in (x, y) space + # based on the projection, the current view port, and their + # position in 3D space. If we extend the transforms framework + # into 3D we would not need to do this different book keeping + # than we do in the normal axis + major_locs = self.get_majorticklocs() + minor_locs = self.get_minorticklocs() + + ticks = [*self.get_minor_ticks(len(minor_locs)), + *self.get_major_ticks(len(major_locs))] + view_low, view_high = self.get_view_interval() + if view_low > view_high: + view_low, view_high = view_high, view_low + interval_t = self.get_transform().transform([view_low, view_high]) + + ticks_to_draw = [] + for tick in ticks: + try: + loc_t = self.get_transform().transform(tick.get_loc()) + except AssertionError: + # Transform.transform doesn't allow masked values but + # some scales might make them, so we need this try/except. + pass + else: + if mtransforms._interval_contains_close(interval_t, loc_t): + ticks_to_draw.append(tick) + + ticks = ticks_to_draw + + bb_1, bb_2 = self._get_ticklabel_bboxes(ticks, renderer) + other = [] + + if self.line.get_visible(): + other.append(self.line.get_window_extent(renderer)) + if (self.label.get_visible() and not for_layout_only and + self.label.get_text()): + other.append(self.label.get_window_extent(renderer)) + + return mtransforms.Bbox.union([*bb_1, *bb_2, *other]) + + d_interval = _api.deprecated( + "3.6", alternative="get_data_interval", pending=True)( + property(lambda self: self.get_data_interval(), + lambda self, minmax: self.set_data_interval(*minmax))) + v_interval = _api.deprecated( + "3.6", alternative="get_view_interval", pending=True)( + property(lambda self: self.get_view_interval(), + lambda self, minmax: self.set_view_interval(*minmax))) + + +class XAxis(Axis): + axis_name = "x" + get_view_interval, set_view_interval = maxis._make_getset_interval( + "view", "xy_viewLim", "intervalx") + get_data_interval, set_data_interval = maxis._make_getset_interval( + "data", "xy_dataLim", "intervalx") + + +class YAxis(Axis): + axis_name = "y" + get_view_interval, set_view_interval = maxis._make_getset_interval( + "view", "xy_viewLim", "intervaly") + get_data_interval, set_data_interval = maxis._make_getset_interval( + "data", "xy_dataLim", "intervaly") + + +class ZAxis(Axis): + axis_name = "z" + get_view_interval, set_view_interval = maxis._make_getset_interval( + "view", "zz_viewLim", "intervalx") + get_data_interval, set_data_interval = maxis._make_getset_interval( + "data", "zz_dataLim", "intervalx") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/proj3d.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/proj3d.py new file mode 100644 index 0000000000000000000000000000000000000000..923bd32c9ce032ad27379f8d8c6436564ec16d75 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/proj3d.py @@ -0,0 +1,219 @@ +""" +Various transforms used for by the 3D code +""" + +import numpy as np + +from matplotlib import _api + + +def world_transformation(xmin, xmax, + ymin, ymax, + zmin, zmax, pb_aspect=None): + """ + Produce a matrix that scales homogeneous coords in the specified ranges + to [0, 1], or [0, pb_aspect[i]] if the plotbox aspect ratio is specified. + """ + dx = xmax - xmin + dy = ymax - ymin + dz = zmax - zmin + if pb_aspect is not None: + ax, ay, az = pb_aspect + dx /= ax + dy /= ay + dz /= az + + return np.array([[1/dx, 0, 0, -xmin/dx], + [ 0, 1/dy, 0, -ymin/dy], + [ 0, 0, 1/dz, -zmin/dz], + [ 0, 0, 0, 1]]) + + +def _rotation_about_vector(v, angle): + """ + Produce a rotation matrix for an angle in radians about a vector. + """ + vx, vy, vz = v / np.linalg.norm(v) + s = np.sin(angle) + c = np.cos(angle) + t = 2*np.sin(angle/2)**2 # more numerically stable than t = 1-c + + R = np.array([ + [t*vx*vx + c, t*vx*vy - vz*s, t*vx*vz + vy*s], + [t*vy*vx + vz*s, t*vy*vy + c, t*vy*vz - vx*s], + [t*vz*vx - vy*s, t*vz*vy + vx*s, t*vz*vz + c]]) + + return R + + +def _view_axes(E, R, V, roll): + """ + Get the unit viewing axes in data coordinates. + + Parameters + ---------- + E : 3-element numpy array + The coordinates of the eye/camera. + R : 3-element numpy array + The coordinates of the center of the view box. + V : 3-element numpy array + Unit vector in the direction of the vertical axis. + roll : float + The roll angle in radians. + + Returns + ------- + u : 3-element numpy array + Unit vector pointing towards the right of the screen. + v : 3-element numpy array + Unit vector pointing towards the top of the screen. + w : 3-element numpy array + Unit vector pointing out of the screen. + """ + w = (E - R) + w = w/np.linalg.norm(w) + u = np.cross(V, w) + u = u/np.linalg.norm(u) + v = np.cross(w, u) # Will be a unit vector + + # Save some computation for the default roll=0 + if roll != 0: + # A positive rotation of the camera is a negative rotation of the world + Rroll = _rotation_about_vector(w, -roll) + u = np.dot(Rroll, u) + v = np.dot(Rroll, v) + return u, v, w + + +def _view_transformation_uvw(u, v, w, E): + """ + Return the view transformation matrix. + + Parameters + ---------- + u : 3-element numpy array + Unit vector pointing towards the right of the screen. + v : 3-element numpy array + Unit vector pointing towards the top of the screen. + w : 3-element numpy array + Unit vector pointing out of the screen. + E : 3-element numpy array + The coordinates of the eye/camera. + """ + Mr = np.eye(4) + Mt = np.eye(4) + Mr[:3, :3] = [u, v, w] + Mt[:3, -1] = -E + M = np.dot(Mr, Mt) + return M + + +def _persp_transformation(zfront, zback, focal_length): + e = focal_length + a = 1 # aspect ratio + b = (zfront+zback)/(zfront-zback) + c = -2*(zfront*zback)/(zfront-zback) + proj_matrix = np.array([[e, 0, 0, 0], + [0, e/a, 0, 0], + [0, 0, b, c], + [0, 0, -1, 0]]) + return proj_matrix + + +def _ortho_transformation(zfront, zback): + # note: w component in the resulting vector will be (zback-zfront), not 1 + a = -(zfront + zback) + b = -(zfront - zback) + proj_matrix = np.array([[2, 0, 0, 0], + [0, 2, 0, 0], + [0, 0, -2, 0], + [0, 0, a, b]]) + return proj_matrix + + +def _proj_transform_vec(vec, M): + vecw = np.dot(M, vec.data) + w = vecw[3] + txs, tys, tzs = vecw[0]/w, vecw[1]/w, vecw[2]/w + if np.ma.isMA(vec[0]): # we check each to protect for scalars + txs = np.ma.array(txs, mask=vec[0].mask) + if np.ma.isMA(vec[1]): + tys = np.ma.array(tys, mask=vec[1].mask) + if np.ma.isMA(vec[2]): + tzs = np.ma.array(tzs, mask=vec[2].mask) + return txs, tys, tzs + + +def _proj_transform_vec_clip(vec, M, focal_length): + vecw = np.dot(M, vec.data) + w = vecw[3] + txs, tys, tzs = vecw[0] / w, vecw[1] / w, vecw[2] / w + if np.isinf(focal_length): # don't clip orthographic projection + tis = np.ones(txs.shape, dtype=bool) + else: + tis = (-1 <= txs) & (txs <= 1) & (-1 <= tys) & (tys <= 1) & (tzs <= 0) + if np.ma.isMA(vec[0]): + tis = tis & ~vec[0].mask + if np.ma.isMA(vec[1]): + tis = tis & ~vec[1].mask + if np.ma.isMA(vec[2]): + tis = tis & ~vec[2].mask + + txs = np.ma.masked_array(txs, ~tis) + tys = np.ma.masked_array(tys, ~tis) + tzs = np.ma.masked_array(tzs, ~tis) + return txs, tys, tzs, tis + + +def inv_transform(xs, ys, zs, invM): + """ + Transform the points by the inverse of the projection matrix, *invM*. + """ + vec = _vec_pad_ones(xs, ys, zs) + vecr = np.dot(invM, vec) + if vecr.shape == (4,): + vecr = vecr.reshape((4, 1)) + for i in range(vecr.shape[1]): + if vecr[3][i] != 0: + vecr[:, i] = vecr[:, i] / vecr[3][i] + return vecr[0], vecr[1], vecr[2] + + +def _vec_pad_ones(xs, ys, zs): + if np.ma.isMA(xs) or np.ma.isMA(ys) or np.ma.isMA(zs): + return np.ma.array([xs, ys, zs, np.ones_like(xs)]) + else: + return np.array([xs, ys, zs, np.ones_like(xs)]) + + +def proj_transform(xs, ys, zs, M): + """ + Transform the points by the projection matrix *M*. + """ + vec = _vec_pad_ones(xs, ys, zs) + return _proj_transform_vec(vec, M) + + +@_api.deprecated("3.10") +def proj_transform_clip(xs, ys, zs, M): + return _proj_transform_clip(xs, ys, zs, M, focal_length=np.inf) + + +def _proj_transform_clip(xs, ys, zs, M, focal_length): + """ + Transform the points by the projection matrix + and return the clipping result + returns txs, tys, tzs, tis + """ + vec = _vec_pad_ones(xs, ys, zs) + return _proj_transform_vec_clip(vec, M, focal_length) + + +def _proj_points(points, M): + return np.column_stack(_proj_trans_points(points, M)) + + +def _proj_trans_points(points, M): + points = np.asanyarray(points) + xs, ys, zs = points[:, 0], points[:, 1], points[:, 2] + return proj_transform(xs, ys, zs, M) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ea4d8ed16a6a24a8c15ab2956ef678a7f256cd80 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/__init__.py @@ -0,0 +1,10 @@ +from pathlib import Path + + +# Check that the test directories exist +if not (Path(__file__).parent / "baseline_images").exists(): + raise OSError( + 'The baseline image directory does not exist. ' + 'This is most likely because the test data is not installed. ' + 'You may need to install matplotlib from source to get the ' + 'test data.') diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/conftest.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..61c2de3e07bac4db323f8704961264d123e01544 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/conftest.py @@ -0,0 +1,2 @@ +from matplotlib.testing.conftest import (mpl_test_settings, # noqa + pytest_configure, pytest_unconfigure) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_art3d.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_art3d.py new file mode 100644 index 0000000000000000000000000000000000000000..174c12608ae933c1b918f45e57abfe6a2fb552bd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_art3d.py @@ -0,0 +1,102 @@ +import numpy as np + +import matplotlib.pyplot as plt + +from matplotlib.backend_bases import MouseEvent +from mpl_toolkits.mplot3d.art3d import ( + Line3DCollection, + Poly3DCollection, + _all_points_on_plane, +) + + +def test_scatter_3d_projection_conservation(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + # fix axes3d projection + ax.roll = 0 + ax.elev = 0 + ax.azim = -45 + ax.stale = True + + x = [0, 1, 2, 3, 4] + scatter_collection = ax.scatter(x, x, x) + fig.canvas.draw_idle() + + # Get scatter location on canvas and freeze the data + scatter_offset = scatter_collection.get_offsets() + scatter_location = ax.transData.transform(scatter_offset) + + # Yaw -44 and -46 are enough to produce two set of scatter + # with opposite z-order without moving points too far + for azim in (-44, -46): + ax.azim = azim + ax.stale = True + fig.canvas.draw_idle() + + for i in range(5): + # Create a mouse event used to locate and to get index + # from each dots + event = MouseEvent("button_press_event", fig.canvas, + *scatter_location[i, :]) + contains, ind = scatter_collection.contains(event) + assert contains is True + assert len(ind["ind"]) == 1 + assert ind["ind"][0] == i + + +def test_zordered_error(): + # Smoke test for https://github.com/matplotlib/matplotlib/issues/26497 + lc = [(np.fromiter([0.0, 0.0, 0.0], dtype="float"), + np.fromiter([1.0, 1.0, 1.0], dtype="float"))] + pc = [np.fromiter([0.0, 0.0], dtype="float"), + np.fromiter([0.0, 1.0], dtype="float"), + np.fromiter([1.0, 1.0], dtype="float")] + + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + ax.add_collection(Line3DCollection(lc)) + ax.scatter(*pc, visible=False) + plt.draw() + + +def test_all_points_on_plane(): + # Non-coplanar points + points = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1]]) + assert not _all_points_on_plane(*points.T) + + # Duplicate points + points = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 0]]) + assert _all_points_on_plane(*points.T) + + # NaN values + points = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, np.nan]]) + assert _all_points_on_plane(*points.T) + + # Less than 3 unique points + points = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0]]) + assert _all_points_on_plane(*points.T) + + # All points lie on a line + points = np.array([[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0]]) + assert _all_points_on_plane(*points.T) + + # All points lie on two lines, with antiparallel vectors + points = np.array([[-2, 2, 0], [-1, 1, 0], [1, -1, 0], + [0, 0, 0], [2, 0, 0], [1, 0, 0]]) + assert _all_points_on_plane(*points.T) + + # All points lie on a plane + points = np.array([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0], [1, 2, 0]]) + assert _all_points_on_plane(*points.T) + + +def test_generate_normals(): + # Smoke test for https://github.com/matplotlib/matplotlib/issues/29156 + vertices = ((0, 0, 0), (0, 5, 0), (5, 5, 0), (5, 0, 0)) + shape = Poly3DCollection([vertices], edgecolors='r', shade=True) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.add_collection3d(shape) + plt.draw() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_axes3d.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_axes3d.py new file mode 100644 index 0000000000000000000000000000000000000000..b30c2556b105e70e3e08fa3877c712f78f09d95c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_axes3d.py @@ -0,0 +1,2688 @@ +import functools +import itertools +import platform +import sys + +import pytest + +from mpl_toolkits.mplot3d import Axes3D, axes3d, proj3d, art3d +from mpl_toolkits.mplot3d.axes3d import _Quaternion as Quaternion +import matplotlib as mpl +from matplotlib.backend_bases import (MouseButton, MouseEvent, + NavigationToolbar2) +from matplotlib import cm +from matplotlib import colors as mcolors, patches as mpatch +from matplotlib.testing.decorators import image_comparison, check_figures_equal +from matplotlib.testing.widgets import mock_event +from matplotlib.collections import LineCollection, PolyCollection +from matplotlib.patches import Circle, PathPatch +from matplotlib.path import Path +from matplotlib.text import Text + +import matplotlib.pyplot as plt +import numpy as np + + +mpl3d_image_comparison = functools.partial( + image_comparison, remove_text=True, style='default') + + +def plot_cuboid(ax, scale): + # plot a rectangular cuboid with side lengths given by scale (x, y, z) + r = [0, 1] + pts = itertools.combinations(np.array(list(itertools.product(r, r, r))), 2) + for start, end in pts: + if np.sum(np.abs(start - end)) == r[1] - r[0]: + ax.plot3D(*zip(start*np.array(scale), end*np.array(scale))) + + +@check_figures_equal(extensions=["png"]) +def test_invisible_axes(fig_test, fig_ref): + ax = fig_test.subplots(subplot_kw=dict(projection='3d')) + ax.set_visible(False) + + +@mpl3d_image_comparison(['grid_off.png'], style='mpl20') +def test_grid_off(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.grid(False) + + +@mpl3d_image_comparison(['invisible_ticks_axis.png'], style='mpl20') +def test_invisible_ticks_axis(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_xticks([]) + ax.set_yticks([]) + ax.set_zticks([]) + for axis in [ax.xaxis, ax.yaxis, ax.zaxis]: + axis.line.set_visible(False) + + +@mpl3d_image_comparison(['axis_positions.png'], remove_text=False, style='mpl20') +def test_axis_positions(): + positions = ['upper', 'lower', 'both', 'none'] + fig, axs = plt.subplots(2, 2, subplot_kw={'projection': '3d'}) + for ax, pos in zip(axs.flatten(), positions): + for axis in ax.xaxis, ax.yaxis, ax.zaxis: + axis.set_label_position(pos) + axis.set_ticks_position(pos) + title = f'{pos}' + ax.set(xlabel='x', ylabel='y', zlabel='z', title=title) + + +@mpl3d_image_comparison(['aspects.png'], remove_text=False, style='mpl20') +def test_aspects(): + aspects = ('auto', 'equal', 'equalxy', 'equalyz', 'equalxz', 'equal') + _, axs = plt.subplots(2, 3, subplot_kw={'projection': '3d'}) + + for ax in axs.flatten()[0:-1]: + plot_cuboid(ax, scale=[1, 1, 5]) + # plot a cube as well to cover github #25443 + plot_cuboid(axs[1][2], scale=[1, 1, 1]) + + for i, ax in enumerate(axs.flatten()): + ax.set_title(aspects[i]) + ax.set_box_aspect((3, 4, 5)) + ax.set_aspect(aspects[i], adjustable='datalim') + axs[1][2].set_title('equal (cube)') + + +@mpl3d_image_comparison(['aspects_adjust_box.png'], + remove_text=False, style='mpl20') +def test_aspects_adjust_box(): + aspects = ('auto', 'equal', 'equalxy', 'equalyz', 'equalxz') + fig, axs = plt.subplots(1, len(aspects), subplot_kw={'projection': '3d'}, + figsize=(11, 3)) + + for i, ax in enumerate(axs): + plot_cuboid(ax, scale=[4, 3, 5]) + ax.set_title(aspects[i]) + ax.set_aspect(aspects[i], adjustable='box') + + +def test_axes3d_repr(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_label('label') + ax.set_title('title') + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_zlabel('z') + assert repr(ax) == ( + "") + + +@mpl3d_image_comparison(['axes3d_primary_views.png'], style='mpl20', + tol=0.05 if sys.platform == "darwin" else 0) +def test_axes3d_primary_views(): + # (elev, azim, roll) + views = [(90, -90, 0), # XY + (0, -90, 0), # XZ + (0, 0, 0), # YZ + (-90, 90, 0), # -XY + (0, 90, 0), # -XZ + (0, 180, 0)] # -YZ + # When viewing primary planes, draw the two visible axes so they intersect + # at their low values + fig, axs = plt.subplots(2, 3, subplot_kw={'projection': '3d'}) + for i, ax in enumerate(axs.flat): + ax.set_xlabel('x') + ax.set_ylabel('y') + ax.set_zlabel('z') + ax.set_proj_type('ortho') + ax.view_init(elev=views[i][0], azim=views[i][1], roll=views[i][2]) + plt.tight_layout() + + +@mpl3d_image_comparison(['bar3d.png'], style='mpl20') +def test_bar3d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + for c, z in zip(['r', 'g', 'b', 'y'], [30, 20, 10, 0]): + xs = np.arange(20) + ys = np.arange(20) + cs = [c] * len(xs) + cs[0] = 'c' + ax.bar(xs, ys, zs=z, zdir='y', align='edge', color=cs, alpha=0.8) + + +def test_bar3d_colors(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + for c in ['red', 'green', 'blue', 'yellow']: + xs = np.arange(len(c)) + ys = np.zeros_like(xs) + zs = np.zeros_like(ys) + # Color names with same length as xs/ys/zs should not be split into + # individual letters. + ax.bar3d(xs, ys, zs, 1, 1, 1, color=c) + + +@mpl3d_image_comparison(['bar3d_shaded.png'], style='mpl20') +def test_bar3d_shaded(): + x = np.arange(4) + y = np.arange(5) + x2d, y2d = np.meshgrid(x, y) + x2d, y2d = x2d.ravel(), y2d.ravel() + z = x2d + y2d + 1 # Avoid triggering bug with zero-depth boxes. + + views = [(30, -60, 0), (30, 30, 30), (-30, 30, -90), (300, -30, 0)] + fig = plt.figure(figsize=plt.figaspect(1 / len(views))) + axs = fig.subplots( + 1, len(views), + subplot_kw=dict(projection='3d') + ) + for ax, (elev, azim, roll) in zip(axs, views): + ax.bar3d(x2d, y2d, x2d * 0, 1, 1, z, shade=True) + ax.view_init(elev=elev, azim=azim, roll=roll) + fig.canvas.draw() + + +@mpl3d_image_comparison(['bar3d_notshaded.png'], style='mpl20') +def test_bar3d_notshaded(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + x = np.arange(4) + y = np.arange(5) + x2d, y2d = np.meshgrid(x, y) + x2d, y2d = x2d.ravel(), y2d.ravel() + z = x2d + y2d + ax.bar3d(x2d, y2d, x2d * 0, 1, 1, z, shade=False) + fig.canvas.draw() + + +def test_bar3d_lightsource(): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection="3d") + + ls = mcolors.LightSource(azdeg=0, altdeg=90) + + length, width = 3, 4 + area = length * width + + x, y = np.meshgrid(np.arange(length), np.arange(width)) + x = x.ravel() + y = y.ravel() + dz = x + y + + color = [cm.coolwarm(i/area) for i in range(area)] + + collection = ax.bar3d(x=x, y=y, z=0, + dx=1, dy=1, dz=dz, + color=color, shade=True, lightsource=ls) + + # Testing that the custom 90° lightsource produces different shading on + # the top facecolors compared to the default, and that those colors are + # precisely (within floating point rounding errors of 4 ULP) the colors + # from the colormap, due to the illumination parallel to the z-axis. + np.testing.assert_array_max_ulp(color, collection._facecolor3d[1::6], 4) + + +@mpl3d_image_comparison(['contour3d.png'], style='mpl20', + tol=0 if platform.machine() == 'x86_64' else 0.002) +def test_contour3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm) + ax.contour(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm) + ax.contour(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm) + ax.axis(xmin=-40, xmax=40, ymin=-40, ymax=40, zmin=-100, zmax=100) + + +@mpl3d_image_comparison(['contour3d_extend3d.png'], style='mpl20') +def test_contour3d_extend3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm, extend3d=True) + ax.set_xlim(-30, 30) + ax.set_ylim(-20, 40) + ax.set_zlim(-80, 80) + + +@mpl3d_image_comparison(['contourf3d.png'], style='mpl20') +def test_contourf3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm) + ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm) + ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm) + ax.set_xlim(-40, 40) + ax.set_ylim(-40, 40) + ax.set_zlim(-100, 100) + + +@mpl3d_image_comparison(['contourf3d_fill.png'], style='mpl20') +def test_contourf3d_fill(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y = np.meshgrid(np.arange(-2, 2, 0.25), np.arange(-2, 2, 0.25)) + Z = X.clip(0, 0) + # This produces holes in the z=0 surface that causes rendering errors if + # the Poly3DCollection is not aware of path code information (issue #4784) + Z[::5, ::5] = 0.1 + ax.contourf(X, Y, Z, offset=0, levels=[-0.1, 0], cmap=cm.coolwarm) + ax.set_xlim(-2, 2) + ax.set_ylim(-2, 2) + ax.set_zlim(-1, 1) + + +@pytest.mark.parametrize('extend, levels', [['both', [2, 4, 6]], + ['min', [2, 4, 6, 8]], + ['max', [0, 2, 4, 6]]]) +@check_figures_equal(extensions=["png"]) +def test_contourf3d_extend(fig_test, fig_ref, extend, levels): + X, Y = np.meshgrid(np.arange(-2, 2, 0.25), np.arange(-2, 2, 0.25)) + # Z is in the range [0, 8] + Z = X**2 + Y**2 + + # Manually set the over/under colors to be the end of the colormap + cmap = mpl.colormaps['viridis'].copy() + cmap.set_under(cmap(0)) + cmap.set_over(cmap(255)) + # Set vmin/max to be the min/max values plotted on the reference image + kwargs = {'vmin': 1, 'vmax': 7, 'cmap': cmap} + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.contourf(X, Y, Z, levels=[0, 2, 4, 6, 8], **kwargs) + + ax_test = fig_test.add_subplot(projection='3d') + ax_test.contourf(X, Y, Z, levels, extend=extend, **kwargs) + + for ax in [ax_ref, ax_test]: + ax.set_xlim(-2, 2) + ax.set_ylim(-2, 2) + ax.set_zlim(-10, 10) + + +@mpl3d_image_comparison(['tricontour.png'], tol=0.02, style='mpl20') +def test_tricontour(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + + np.random.seed(19680801) + x = np.random.rand(1000) - 0.5 + y = np.random.rand(1000) - 0.5 + z = -(x**2 + y**2) + + ax = fig.add_subplot(1, 2, 1, projection='3d') + ax.tricontour(x, y, z) + ax = fig.add_subplot(1, 2, 2, projection='3d') + ax.tricontourf(x, y, z) + + +def test_contour3d_1d_input(): + # Check that 1D sequences of different length for {x, y} doesn't error + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + nx, ny = 30, 20 + x = np.linspace(-10, 10, nx) + y = np.linspace(-10, 10, ny) + z = np.random.randint(0, 2, [ny, nx]) + ax.contour(x, y, z, [0.5]) + + +@mpl3d_image_comparison(['lines3d.png'], style='mpl20') +def test_lines3d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + theta = np.linspace(-4 * np.pi, 4 * np.pi, 100) + z = np.linspace(-2, 2, 100) + r = z ** 2 + 1 + x = r * np.sin(theta) + y = r * np.cos(theta) + ax.plot(x, y, z) + + +@check_figures_equal(extensions=["png"]) +def test_plot_scalar(fig_test, fig_ref): + ax1 = fig_test.add_subplot(projection='3d') + ax1.plot([1], [1], "o") + ax2 = fig_ref.add_subplot(projection='3d') + ax2.plot(1, 1, "o") + + +def test_invalid_line_data(): + with pytest.raises(RuntimeError, match='x must be'): + art3d.Line3D(0, [], []) + with pytest.raises(RuntimeError, match='y must be'): + art3d.Line3D([], 0, []) + with pytest.raises(RuntimeError, match='z must be'): + art3d.Line3D([], [], 0) + + line = art3d.Line3D([], [], []) + with pytest.raises(RuntimeError, match='x must be'): + line.set_data_3d(0, [], []) + with pytest.raises(RuntimeError, match='y must be'): + line.set_data_3d([], 0, []) + with pytest.raises(RuntimeError, match='z must be'): + line.set_data_3d([], [], 0) + + +@mpl3d_image_comparison(['mixedsubplot.png'], style='mpl20') +def test_mixedsubplots(): + def f(t): + return np.cos(2*np.pi*t) * np.exp(-t) + + t1 = np.arange(0.0, 5.0, 0.1) + t2 = np.arange(0.0, 5.0, 0.02) + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure(figsize=plt.figaspect(2.)) + ax = fig.add_subplot(2, 1, 1) + ax.plot(t1, f(t1), 'bo', t2, f(t2), 'k--', markerfacecolor='green') + ax.grid(True) + + ax = fig.add_subplot(2, 1, 2, projection='3d') + X, Y = np.meshgrid(np.arange(-5, 5, 0.25), np.arange(-5, 5, 0.25)) + R = np.hypot(X, Y) + Z = np.sin(R) + + ax.plot_surface(X, Y, Z, rcount=40, ccount=40, + linewidth=0, antialiased=False) + + ax.set_zlim3d(-1, 1) + + +@check_figures_equal(extensions=['png']) +def test_tight_layout_text(fig_test, fig_ref): + # text is currently ignored in tight layout. So the order of text() and + # tight_layout() calls should not influence the result. + ax1 = fig_test.add_subplot(projection='3d') + ax1.text(.5, .5, .5, s='some string') + fig_test.tight_layout() + + ax2 = fig_ref.add_subplot(projection='3d') + fig_ref.tight_layout() + ax2.text(.5, .5, .5, s='some string') + + +@mpl3d_image_comparison(['scatter3d.png'], style='mpl20') +def test_scatter3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.scatter(np.arange(10), np.arange(10), np.arange(10), + c='r', marker='o') + x = y = z = np.arange(10, 20) + ax.scatter(x, y, z, c='b', marker='^') + z[-1] = 0 # Check that scatter() copies the data. + # Ensure empty scatters do not break. + ax.scatter([], [], [], c='r', marker='X') + + +@mpl3d_image_comparison(['scatter3d_color.png'], style='mpl20') +def test_scatter3d_color(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + # Check that 'none' color works; these two should overlay to produce the + # same as setting just `color`. + ax.scatter(np.arange(10), np.arange(10), np.arange(10), + facecolor='r', edgecolor='none', marker='o') + ax.scatter(np.arange(10), np.arange(10), np.arange(10), + facecolor='none', edgecolor='r', marker='o') + + ax.scatter(np.arange(10, 20), np.arange(10, 20), np.arange(10, 20), + color='b', marker='s') + + +@mpl3d_image_comparison(['scatter3d_linewidth.png'], style='mpl20') +def test_scatter3d_linewidth(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + # Check that array-like linewidth can be set + ax.scatter(np.arange(10), np.arange(10), np.arange(10), + marker='o', linewidth=np.arange(10)) + + +@check_figures_equal(extensions=['png']) +def test_scatter3d_linewidth_modification(fig_ref, fig_test): + # Changing Path3DCollection linewidths with array-like post-creation + # should work correctly. + ax_test = fig_test.add_subplot(projection='3d') + c = ax_test.scatter(np.arange(10), np.arange(10), np.arange(10), + marker='o') + c.set_linewidths(np.arange(10)) + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.scatter(np.arange(10), np.arange(10), np.arange(10), marker='o', + linewidths=np.arange(10)) + + +@check_figures_equal(extensions=['png']) +def test_scatter3d_modification(fig_ref, fig_test): + # Changing Path3DCollection properties post-creation should work correctly. + ax_test = fig_test.add_subplot(projection='3d') + c = ax_test.scatter(np.arange(10), np.arange(10), np.arange(10), + marker='o') + c.set_facecolor('C1') + c.set_edgecolor('C2') + c.set_alpha([0.3, 0.7] * 5) + assert c.get_depthshade() + c.set_depthshade(False) + assert not c.get_depthshade() + c.set_sizes(np.full(10, 75)) + c.set_linewidths(3) + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.scatter(np.arange(10), np.arange(10), np.arange(10), marker='o', + facecolor='C1', edgecolor='C2', alpha=[0.3, 0.7] * 5, + depthshade=False, s=75, linewidths=3) + + +@pytest.mark.parametrize('depthshade', [True, False]) +@check_figures_equal(extensions=['png']) +def test_scatter3d_sorting(fig_ref, fig_test, depthshade): + """Test that marker properties are correctly sorted.""" + + y, x = np.mgrid[:10, :10] + z = np.arange(x.size).reshape(x.shape) + + sizes = np.full(z.shape, 25) + sizes[0::2, 0::2] = 100 + sizes[1::2, 1::2] = 100 + + facecolors = np.full(z.shape, 'C0') + facecolors[:5, :5] = 'C1' + facecolors[6:, :4] = 'C2' + facecolors[6:, 6:] = 'C3' + + edgecolors = np.full(z.shape, 'C4') + edgecolors[1:5, 1:5] = 'C5' + edgecolors[5:9, 1:5] = 'C6' + edgecolors[5:9, 5:9] = 'C7' + + linewidths = np.full(z.shape, 2) + linewidths[0::2, 0::2] = 5 + linewidths[1::2, 1::2] = 5 + + x, y, z, sizes, facecolors, edgecolors, linewidths = ( + a.flatten() + for a in [x, y, z, sizes, facecolors, edgecolors, linewidths] + ) + + ax_ref = fig_ref.add_subplot(projection='3d') + sets = (np.unique(a) for a in [sizes, facecolors, edgecolors, linewidths]) + for s, fc, ec, lw in itertools.product(*sets): + subset = ( + (sizes != s) | + (facecolors != fc) | + (edgecolors != ec) | + (linewidths != lw) + ) + subset = np.ma.masked_array(z, subset, dtype=float) + + # When depth shading is disabled, the colors are passed through as + # single-item lists; this triggers single path optimization. The + # following reshaping is a hack to disable that, since the optimization + # would not occur for the full scatter which has multiple colors. + fc = np.repeat(fc, sum(~subset.mask)) + + ax_ref.scatter(x, y, subset, s=s, fc=fc, ec=ec, lw=lw, alpha=1, + depthshade=depthshade) + + ax_test = fig_test.add_subplot(projection='3d') + ax_test.scatter(x, y, z, s=sizes, fc=facecolors, ec=edgecolors, + lw=linewidths, alpha=1, depthshade=depthshade) + + +@pytest.mark.parametrize('azim', [-50, 130]) # yellow first, blue first +@check_figures_equal(extensions=['png']) +def test_marker_draw_order_data_reversed(fig_test, fig_ref, azim): + """ + Test that the draw order does not depend on the data point order. + + For the given viewing angle at azim=-50, the yellow marker should be in + front. For azim=130, the blue marker should be in front. + """ + x = [-1, 1] + y = [1, -1] + z = [0, 0] + color = ['b', 'y'] + ax = fig_test.add_subplot(projection='3d') + ax.scatter(x, y, z, s=3500, c=color) + ax.view_init(elev=0, azim=azim, roll=0) + ax = fig_ref.add_subplot(projection='3d') + ax.scatter(x[::-1], y[::-1], z[::-1], s=3500, c=color[::-1]) + ax.view_init(elev=0, azim=azim, roll=0) + + +@check_figures_equal(extensions=['png']) +def test_marker_draw_order_view_rotated(fig_test, fig_ref): + """ + Test that the draw order changes with the direction. + + If we rotate *azim* by 180 degrees and exchange the colors, the plot + plot should look the same again. + """ + azim = 130 + x = [-1, 1] + y = [1, -1] + z = [0, 0] + color = ['b', 'y'] + ax = fig_test.add_subplot(projection='3d') + # axis are not exactly invariant under 180 degree rotation -> deactivate + ax.set_axis_off() + ax.scatter(x, y, z, s=3500, c=color) + ax.view_init(elev=0, azim=azim, roll=0) + ax = fig_ref.add_subplot(projection='3d') + ax.set_axis_off() + ax.scatter(x, y, z, s=3500, c=color[::-1]) # color reversed + ax.view_init(elev=0, azim=azim - 180, roll=0) # view rotated by 180 deg + + +@mpl3d_image_comparison(['plot_3d_from_2d.png'], tol=0.019, style='mpl20') +def test_plot_3d_from_2d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + xs = np.arange(0, 5) + ys = np.arange(5, 10) + ax.plot(xs, ys, zs=0, zdir='x') + ax.plot(xs, ys, zs=0, zdir='y') + + +@mpl3d_image_comparison(['fill_between_quad.png'], style='mpl20') +def test_fill_between_quad(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + theta = np.linspace(0, 2*np.pi, 50) + + x1 = np.cos(theta) + y1 = np.sin(theta) + z1 = 0.1 * np.sin(6 * theta) + + x2 = 0.6 * np.cos(theta) + y2 = 0.6 * np.sin(theta) + z2 = 2 + + where = (theta < np.pi/2) | (theta > 3*np.pi/2) + + # Since none of x1 == x2, y1 == y2, or z1 == z2 is True, the fill_between + # mode will map to 'quad' + ax.fill_between(x1, y1, z1, x2, y2, z2, + where=where, mode='auto', alpha=0.5, edgecolor='k') + + +@mpl3d_image_comparison(['fill_between_polygon.png'], style='mpl20') +def test_fill_between_polygon(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + theta = np.linspace(0, 2*np.pi, 50) + + x1 = x2 = theta + y1 = y2 = 0 + z1 = np.cos(theta) + z2 = z1 + 1 + + where = (theta < np.pi/2) | (theta > 3*np.pi/2) + + # Since x1 == x2 and y1 == y2, the fill_between mode will be 'polygon' + ax.fill_between(x1, y1, z1, x2, y2, z2, + where=where, mode='auto', edgecolor='k') + + +@mpl3d_image_comparison(['surface3d.png'], style='mpl20') +def test_surface3d(): + # Remove this line when this test image is regenerated. + plt.rcParams['pcolormesh.snap'] = False + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X = np.arange(-5, 5, 0.25) + Y = np.arange(-5, 5, 0.25) + X, Y = np.meshgrid(X, Y) + R = np.hypot(X, Y) + Z = np.sin(R) + surf = ax.plot_surface(X, Y, Z, rcount=40, ccount=40, cmap=cm.coolwarm, + lw=0, antialiased=False) + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_zlim(-1.01, 1.01) + fig.colorbar(surf, shrink=0.5, aspect=5) + + +@image_comparison(['surface3d_label_offset_tick_position.png'], style='mpl20') +def test_surface3d_label_offset_tick_position(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax = plt.figure().add_subplot(projection="3d") + + x, y = np.mgrid[0:6 * np.pi:0.25, 0:4 * np.pi:0.25] + z = np.sqrt(np.abs(np.cos(x) + np.cos(y))) + + ax.plot_surface(x * 1e5, y * 1e6, z * 1e8, cmap='autumn', cstride=2, rstride=2) + ax.set_xlabel("X label") + ax.set_ylabel("Y label") + ax.set_zlabel("Z label") + + +@mpl3d_image_comparison(['surface3d_shaded.png'], style='mpl20') +def test_surface3d_shaded(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X = np.arange(-5, 5, 0.25) + Y = np.arange(-5, 5, 0.25) + X, Y = np.meshgrid(X, Y) + R = np.sqrt(X ** 2 + Y ** 2) + Z = np.sin(R) + ax.plot_surface(X, Y, Z, rstride=5, cstride=5, + color=[0.25, 1, 0.25], lw=1, antialiased=False) + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_zlim(-1.01, 1.01) + + +@mpl3d_image_comparison(['surface3d_masked.png'], style='mpl20') +def test_surface3d_masked(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11] + y = [1, 2, 3, 4, 5, 6, 7, 8] + + x, y = np.meshgrid(x, y) + matrix = np.array( + [ + [-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [-1, 1, 2, 3, 4, 4, 4, 3, 2, 1, 1], + [-1, -1., 4, 5, 6, 8, 6, 5, 4, 3, -1.], + [-1, -1., 7, 8, 11, 12, 11, 8, 7, -1., -1.], + [-1, -1., 8, 9, 10, 16, 10, 9, 10, 7, -1.], + [-1, -1., -1., 12, 16, 20, 16, 12, 11, -1., -1.], + [-1, -1., -1., -1., 22, 24, 22, 20, 18, -1., -1.], + [-1, -1., -1., -1., -1., 28, 26, 25, -1., -1., -1.], + ] + ) + z = np.ma.masked_less(matrix, 0) + norm = mcolors.Normalize(vmax=z.max(), vmin=z.min()) + colors = mpl.colormaps["plasma"](norm(z)) + ax.plot_surface(x, y, z, facecolors=colors) + ax.view_init(30, -80, 0) + + +@check_figures_equal(extensions=["png"]) +def test_plot_scatter_masks(fig_test, fig_ref): + x = np.linspace(0, 10, 100) + y = np.linspace(0, 10, 100) + z = np.sin(x) * np.cos(y) + mask = z > 0 + + z_masked = np.ma.array(z, mask=mask) + ax_test = fig_test.add_subplot(projection='3d') + ax_test.scatter(x, y, z_masked) + ax_test.plot(x, y, z_masked) + + x[mask] = y[mask] = z[mask] = np.nan + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.scatter(x, y, z) + ax_ref.plot(x, y, z) + + +@check_figures_equal(extensions=["png"]) +def test_plot_surface_None_arg(fig_test, fig_ref): + x, y = np.meshgrid(np.arange(5), np.arange(5)) + z = x + y + ax_test = fig_test.add_subplot(projection='3d') + ax_test.plot_surface(x, y, z, facecolors=None) + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.plot_surface(x, y, z) + + +@mpl3d_image_comparison(['surface3d_masked_strides.png'], style='mpl20') +def test_surface3d_masked_strides(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + x, y = np.mgrid[-6:6.1:1, -6:6.1:1] + z = np.ma.masked_less(x * y, 2) + + ax.plot_surface(x, y, z, rstride=4, cstride=4) + ax.view_init(60, -45, 0) + + +@mpl3d_image_comparison(['text3d.png'], remove_text=False, style='mpl20') +def test_text3d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + zdirs = (None, 'x', 'y', 'z', (1, 1, 0), (1, 1, 1)) + xs = (2, 6, 4, 9, 7, 2) + ys = (6, 4, 8, 7, 2, 2) + zs = (4, 2, 5, 6, 1, 7) + + for zdir, x, y, z in zip(zdirs, xs, ys, zs): + label = '(%d, %d, %d), dir=%s' % (x, y, z, zdir) + ax.text(x, y, z, label, zdir) + + ax.text(1, 1, 1, "red", color='red') + ax.text2D(0.05, 0.95, "2D Text", transform=ax.transAxes) + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim3d(0, 10) + ax.set_ylim3d(0, 10) + ax.set_zlim3d(0, 10) + ax.set_xlabel('X axis') + ax.set_ylabel('Y axis') + ax.set_zlabel('Z axis') + + +@check_figures_equal(extensions=['png']) +def test_text3d_modification(fig_ref, fig_test): + # Modifying the Text position after the fact should work the same as + # setting it directly. + zdirs = (None, 'x', 'y', 'z', (1, 1, 0), (1, 1, 1)) + xs = (2, 6, 4, 9, 7, 2) + ys = (6, 4, 8, 7, 2, 2) + zs = (4, 2, 5, 6, 1, 7) + + ax_test = fig_test.add_subplot(projection='3d') + ax_test.set_xlim3d(0, 10) + ax_test.set_ylim3d(0, 10) + ax_test.set_zlim3d(0, 10) + for zdir, x, y, z in zip(zdirs, xs, ys, zs): + t = ax_test.text(0, 0, 0, f'({x}, {y}, {z}), dir={zdir}') + t.set_position_3d((x, y, z), zdir=zdir) + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.set_xlim3d(0, 10) + ax_ref.set_ylim3d(0, 10) + ax_ref.set_zlim3d(0, 10) + for zdir, x, y, z in zip(zdirs, xs, ys, zs): + ax_ref.text(x, y, z, f'({x}, {y}, {z}), dir={zdir}', zdir=zdir) + + +@mpl3d_image_comparison(['trisurf3d.png'], tol=0.061, style='mpl20') +def test_trisurf3d(): + n_angles = 36 + n_radii = 8 + radii = np.linspace(0.125, 1.0, n_radii) + angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False) + angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1) + angles[:, 1::2] += np.pi/n_angles + + x = np.append(0, (radii*np.cos(angles)).flatten()) + y = np.append(0, (radii*np.sin(angles)).flatten()) + z = np.sin(-x*y) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.plot_trisurf(x, y, z, cmap=cm.jet, linewidth=0.2) + + +@mpl3d_image_comparison(['trisurf3d_shaded.png'], tol=0.03, style='mpl20') +def test_trisurf3d_shaded(): + n_angles = 36 + n_radii = 8 + radii = np.linspace(0.125, 1.0, n_radii) + angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False) + angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1) + angles[:, 1::2] += np.pi/n_angles + + x = np.append(0, (radii*np.cos(angles)).flatten()) + y = np.append(0, (radii*np.sin(angles)).flatten()) + z = np.sin(-x*y) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.plot_trisurf(x, y, z, color=[1, 0.5, 0], linewidth=0.2) + + +@mpl3d_image_comparison(['wireframe3d.png'], style='mpl20') +def test_wireframe3d(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.plot_wireframe(X, Y, Z, rcount=13, ccount=13) + + +@mpl3d_image_comparison(['wireframe3dzerocstride.png'], style='mpl20') +def test_wireframe3dzerocstride(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.plot_wireframe(X, Y, Z, rcount=13, ccount=0) + + +@mpl3d_image_comparison(['wireframe3dzerorstride.png'], style='mpl20') +def test_wireframe3dzerorstride(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + ax.plot_wireframe(X, Y, Z, rstride=0, cstride=10) + + +def test_wireframe3dzerostrideraises(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + with pytest.raises(ValueError): + ax.plot_wireframe(X, Y, Z, rstride=0, cstride=0) + + +def test_mixedsamplesraises(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + X, Y, Z = axes3d.get_test_data(0.05) + with pytest.raises(ValueError): + ax.plot_wireframe(X, Y, Z, rstride=10, ccount=50) + with pytest.raises(ValueError): + ax.plot_surface(X, Y, Z, cstride=50, rcount=10) + + +# remove tolerance when regenerating the test image +@mpl3d_image_comparison(['quiver3d.png'], style='mpl20', tol=0.003) +def test_quiver3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + pivots = ['tip', 'middle', 'tail'] + colors = ['tab:blue', 'tab:orange', 'tab:green'] + for i, (pivot, color) in enumerate(zip(pivots, colors)): + x, y, z = np.meshgrid([-0.5, 0.5], [-0.5, 0.5], [-0.5, 0.5]) + u = -x + v = -y + w = -z + # Offset each set in z direction + z += 2 * i + ax.quiver(x, y, z, u, v, w, length=1, pivot=pivot, color=color) + ax.scatter(x, y, z, color=color) + + ax.set_xlim(-3, 3) + ax.set_ylim(-3, 3) + ax.set_zlim(-1, 5) + + +@check_figures_equal(extensions=["png"]) +def test_quiver3d_empty(fig_test, fig_ref): + fig_ref.add_subplot(projection='3d') + x = y = z = u = v = w = [] + ax = fig_test.add_subplot(projection='3d') + ax.quiver(x, y, z, u, v, w, length=0.1, pivot='tip', normalize=True) + + +@mpl3d_image_comparison(['quiver3d_masked.png'], style='mpl20') +def test_quiver3d_masked(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + # Using mgrid here instead of ogrid because masked_where doesn't + # seem to like broadcasting very much... + x, y, z = np.mgrid[-1:0.8:10j, -1:0.8:10j, -1:0.6:3j] + + u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z) + v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z) + w = (2/3)**0.5 * np.cos(np.pi * x) * np.cos(np.pi * y) * np.sin(np.pi * z) + u = np.ma.masked_where((-0.4 < x) & (x < 0.1), u, copy=False) + v = np.ma.masked_where((0.1 < y) & (y < 0.7), v, copy=False) + + ax.quiver(x, y, z, u, v, w, length=0.1, pivot='tip', normalize=True) + + +@mpl3d_image_comparison(['quiver3d_colorcoded.png'], style='mpl20') +def test_quiver3d_colorcoded(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + x = y = dx = dz = np.zeros(10) + z = dy = np.arange(10.) + + color = plt.cm.Reds(dy/dy.max()) + ax.quiver(x, y, z, dx, dy, dz, colors=color) + ax.set_ylim(0, 10) + + +def test_patch_modification(): + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + circle = Circle((0, 0)) + ax.add_patch(circle) + art3d.patch_2d_to_3d(circle) + circle.set_facecolor((1.0, 0.0, 0.0, 1)) + + assert mcolors.same_color(circle.get_facecolor(), (1, 0, 0, 1)) + fig.canvas.draw() + assert mcolors.same_color(circle.get_facecolor(), (1, 0, 0, 1)) + + +@check_figures_equal(extensions=['png']) +def test_patch_collection_modification(fig_test, fig_ref): + # Test that modifying Patch3DCollection properties after creation works. + patch1 = Circle((0, 0), 0.05) + patch2 = Circle((0.1, 0.1), 0.03) + facecolors = np.array([[0., 0.5, 0., 1.], [0.5, 0., 0., 0.5]]) + c = art3d.Patch3DCollection([patch1, patch2], linewidths=3) + + ax_test = fig_test.add_subplot(projection='3d') + ax_test.add_collection3d(c) + c.set_edgecolor('C2') + c.set_facecolor(facecolors) + c.set_alpha(0.7) + assert c.get_depthshade() + c.set_depthshade(False) + assert not c.get_depthshade() + + patch1 = Circle((0, 0), 0.05) + patch2 = Circle((0.1, 0.1), 0.03) + facecolors = np.array([[0., 0.5, 0., 1.], [0.5, 0., 0., 0.5]]) + c = art3d.Patch3DCollection([patch1, patch2], linewidths=3, + edgecolor='C2', facecolor=facecolors, + alpha=0.7, depthshade=False) + + ax_ref = fig_ref.add_subplot(projection='3d') + ax_ref.add_collection3d(c) + + +def test_poly3dcollection_verts_validation(): + poly = [[0, 0, 1], [0, 1, 1], [0, 1, 0], [0, 0, 0]] + with pytest.raises(ValueError, match=r'list of \(N, 3\) array-like'): + art3d.Poly3DCollection(poly) # should be Poly3DCollection([poly]) + + poly = np.array(poly, dtype=float) + with pytest.raises(ValueError, match=r'list of \(N, 3\) array-like'): + art3d.Poly3DCollection(poly) # should be Poly3DCollection([poly]) + + +@mpl3d_image_comparison(['poly3dcollection_closed.png'], style='mpl20') +def test_poly3dcollection_closed(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + poly1 = np.array([[0, 0, 1], [0, 1, 1], [0, 0, 0]], float) + poly2 = np.array([[0, 1, 1], [1, 1, 1], [1, 1, 0]], float) + c1 = art3d.Poly3DCollection([poly1], linewidths=3, edgecolor='k', + facecolor=(0.5, 0.5, 1, 0.5), closed=True) + c2 = art3d.Poly3DCollection([poly2], linewidths=3, edgecolor='k', + facecolor=(1, 0.5, 0.5, 0.5), closed=False) + ax.add_collection3d(c1, autolim=False) + ax.add_collection3d(c2, autolim=False) + + +def test_poly_collection_2d_to_3d_empty(): + poly = PolyCollection([]) + art3d.poly_collection_2d_to_3d(poly) + assert isinstance(poly, art3d.Poly3DCollection) + assert poly.get_paths() == [] + + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + ax.add_artist(poly) + minz = poly.do_3d_projection() + assert np.isnan(minz) + + # Ensure drawing actually works. + fig.canvas.draw() + + +@mpl3d_image_comparison(['poly3dcollection_alpha.png'], style='mpl20') +def test_poly3dcollection_alpha(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + poly1 = np.array([[0, 0, 1], [0, 1, 1], [0, 0, 0]], float) + poly2 = np.array([[0, 1, 1], [1, 1, 1], [1, 1, 0]], float) + c1 = art3d.Poly3DCollection([poly1], linewidths=3, edgecolor='k', + facecolor=(0.5, 0.5, 1), closed=True) + c1.set_alpha(0.5) + c2 = art3d.Poly3DCollection([poly2], linewidths=3, closed=False) + # Post-creation modification should work. + c2.set_facecolor((1, 0.5, 0.5)) + c2.set_edgecolor('k') + c2.set_alpha(0.5) + ax.add_collection3d(c1, autolim=False) + ax.add_collection3d(c2, autolim=False) + + +@mpl3d_image_comparison(['add_collection3d_zs_array.png'], style='mpl20') +def test_add_collection3d_zs_array(): + theta = np.linspace(-4 * np.pi, 4 * np.pi, 100) + z = np.linspace(-2, 2, 100) + r = z**2 + 1 + x = r * np.sin(theta) + y = r * np.cos(theta) + + points = np.column_stack([x, y, z]).reshape(-1, 1, 3) + segments = np.concatenate([points[:-1], points[1:]], axis=1) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + norm = plt.Normalize(0, 2*np.pi) + # 2D LineCollection from x & y values + lc = LineCollection(segments[:, :, :2], cmap='twilight', norm=norm) + lc.set_array(np.mod(theta, 2*np.pi)) + # Add 2D collection at z values to ax + line = ax.add_collection3d(lc, zs=segments[:, :, 2]) + + assert line is not None + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim(-5, 5) + ax.set_ylim(-4, 6) + ax.set_zlim(-2, 2) + + +@mpl3d_image_comparison(['add_collection3d_zs_scalar.png'], style='mpl20') +def test_add_collection3d_zs_scalar(): + theta = np.linspace(0, 2 * np.pi, 100) + z = 1 + r = z**2 + 1 + x = r * np.sin(theta) + y = r * np.cos(theta) + + points = np.column_stack([x, y]).reshape(-1, 1, 2) + segments = np.concatenate([points[:-1], points[1:]], axis=1) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + norm = plt.Normalize(0, 2*np.pi) + lc = LineCollection(segments, cmap='twilight', norm=norm) + lc.set_array(theta) + line = ax.add_collection3d(lc, zs=z) + + assert line is not None + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim(-5, 5) + ax.set_ylim(-4, 6) + ax.set_zlim(0, 2) + + +def test_line3dCollection_autoscaling(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + lines = [[(0, 0, 0), (1, 4, 2)], + [(1, 1, 3), (2, 0, 2)], + [(1, 0, 4), (1, 4, 5)]] + + lc = art3d.Line3DCollection(lines) + ax.add_collection3d(lc) + assert np.allclose(ax.get_xlim3d(), (-0.041666666666666664, 2.0416666666666665)) + assert np.allclose(ax.get_ylim3d(), (-0.08333333333333333, 4.083333333333333)) + assert np.allclose(ax.get_zlim3d(), (-0.10416666666666666, 5.104166666666667)) + + +def test_poly3dCollection_autoscaling(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + poly = np.array([[0, 0, 0], [1, 1, 3], [1, 0, 4]]) + col = art3d.Poly3DCollection([poly]) + ax.add_collection3d(col) + assert np.allclose(ax.get_xlim3d(), (-0.020833333333333332, 1.0208333333333333)) + assert np.allclose(ax.get_ylim3d(), (-0.020833333333333332, 1.0208333333333333)) + assert np.allclose(ax.get_zlim3d(), (-0.0833333333333333, 4.083333333333333)) + + +@mpl3d_image_comparison(['axes3d_labelpad.png'], + remove_text=False, style='mpl20') +def test_axes3d_labelpad(): + fig = plt.figure() + ax = fig.add_axes(Axes3D(fig)) + # labelpad respects rcParams + assert ax.xaxis.labelpad == mpl.rcParams['axes.labelpad'] + # labelpad can be set in set_label + ax.set_xlabel('X LABEL', labelpad=10) + assert ax.xaxis.labelpad == 10 + ax.set_ylabel('Y LABEL') + ax.set_zlabel('Z LABEL', labelpad=20) + assert ax.zaxis.labelpad == 20 + assert ax.get_zlabel() == 'Z LABEL' + # or manually + ax.yaxis.labelpad = 20 + ax.zaxis.labelpad = -40 + + # Tick labels also respect tick.pad (also from rcParams) + for i, tick in enumerate(ax.yaxis.get_major_ticks()): + tick.set_pad(tick.get_pad() + 5 - i * 5) + + +@mpl3d_image_comparison(['axes3d_cla.png'], remove_text=False, style='mpl20') +def test_axes3d_cla(): + # fixed in pull request 4553 + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection='3d') + ax.set_axis_off() + ax.cla() # make sure the axis displayed is 3D (not 2D) + + +@mpl3d_image_comparison(['axes3d_rotated.png'], + remove_text=False, style='mpl20') +def test_axes3d_rotated(): + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection='3d') + ax.view_init(90, 45, 0) # look down, rotated. Should be square + + +def test_plotsurface_1d_raises(): + x = np.linspace(0.5, 10, num=100) + y = np.linspace(0.5, 10, num=100) + X, Y = np.meshgrid(x, y) + z = np.random.randn(100) + + fig = plt.figure(figsize=(14, 6)) + ax = fig.add_subplot(1, 2, 1, projection='3d') + with pytest.raises(ValueError): + ax.plot_surface(X, Y, z) + + +def _test_proj_make_M(): + # eye point + E = np.array([1000, -1000, 2000]) + R = np.array([100, 100, 100]) + V = np.array([0, 0, 1]) + roll = 0 + u, v, w = proj3d._view_axes(E, R, V, roll) + viewM = proj3d._view_transformation_uvw(u, v, w, E) + perspM = proj3d._persp_transformation(100, -100, 1) + M = np.dot(perspM, viewM) + return M + + +def test_proj_transform(): + M = _test_proj_make_M() + invM = np.linalg.inv(M) + + xs = np.array([0, 1, 1, 0, 0, 0, 1, 1, 0, 0]) * 300.0 + ys = np.array([0, 0, 1, 1, 0, 0, 0, 1, 1, 0]) * 300.0 + zs = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) * 300.0 + + txs, tys, tzs = proj3d.proj_transform(xs, ys, zs, M) + ixs, iys, izs = proj3d.inv_transform(txs, tys, tzs, invM) + + np.testing.assert_almost_equal(ixs, xs) + np.testing.assert_almost_equal(iys, ys) + np.testing.assert_almost_equal(izs, zs) + + +def _test_proj_draw_axes(M, s=1, *args, **kwargs): + xs = [0, s, 0, 0] + ys = [0, 0, s, 0] + zs = [0, 0, 0, s] + txs, tys, tzs = proj3d.proj_transform(xs, ys, zs, M) + o, ax, ay, az = zip(txs, tys) + lines = [(o, ax), (o, ay), (o, az)] + + fig, ax = plt.subplots(*args, **kwargs) + linec = LineCollection(lines) + ax.add_collection(linec) + for x, y, t in zip(txs, tys, ['o', 'x', 'y', 'z']): + ax.text(x, y, t) + + return fig, ax + + +@mpl3d_image_comparison(['proj3d_axes_cube.png'], style='mpl20') +def test_proj_axes_cube(): + M = _test_proj_make_M() + + ts = '0 1 2 3 0 4 5 6 7 4'.split() + xs = np.array([0, 1, 1, 0, 0, 0, 1, 1, 0, 0]) * 300.0 + ys = np.array([0, 0, 1, 1, 0, 0, 0, 1, 1, 0]) * 300.0 + zs = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) * 300.0 + + txs, tys, tzs = proj3d.proj_transform(xs, ys, zs, M) + + fig, ax = _test_proj_draw_axes(M, s=400) + + ax.scatter(txs, tys, c=tzs) + ax.plot(txs, tys, c='r') + for x, y, t in zip(txs, tys, ts): + ax.text(x, y, t) + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim(-0.2, 0.2) + ax.set_ylim(-0.2, 0.2) + + +@mpl3d_image_comparison(['proj3d_axes_cube_ortho.png'], style='mpl20') +def test_proj_axes_cube_ortho(): + E = np.array([200, 100, 100]) + R = np.array([0, 0, 0]) + V = np.array([0, 0, 1]) + roll = 0 + u, v, w = proj3d._view_axes(E, R, V, roll) + viewM = proj3d._view_transformation_uvw(u, v, w, E) + orthoM = proj3d._ortho_transformation(-1, 1) + M = np.dot(orthoM, viewM) + + ts = '0 1 2 3 0 4 5 6 7 4'.split() + xs = np.array([0, 1, 1, 0, 0, 0, 1, 1, 0, 0]) * 100 + ys = np.array([0, 0, 1, 1, 0, 0, 0, 1, 1, 0]) * 100 + zs = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1]) * 100 + + txs, tys, tzs = proj3d.proj_transform(xs, ys, zs, M) + + fig, ax = _test_proj_draw_axes(M, s=150) + + ax.scatter(txs, tys, s=300-tzs) + ax.plot(txs, tys, c='r') + for x, y, t in zip(txs, tys, ts): + ax.text(x, y, t) + + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + ax.set_xlim(-200, 200) + ax.set_ylim(-200, 200) + + +def test_world(): + xmin, xmax = 100, 120 + ymin, ymax = -100, 100 + zmin, zmax = 0.1, 0.2 + M = proj3d.world_transformation(xmin, xmax, ymin, ymax, zmin, zmax) + np.testing.assert_allclose(M, + [[5e-2, 0, 0, -5], + [0, 5e-3, 0, 5e-1], + [0, 0, 1e1, -1], + [0, 0, 0, 1]]) + + +def test_autoscale(): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + assert ax.get_zscale() == 'linear' + ax._view_margin = 0 + ax.margins(x=0, y=.1, z=.2) + ax.plot([0, 1], [0, 1], [0, 1]) + assert ax.get_w_lims() == (0, 1, -.1, 1.1, -.2, 1.2) + ax.autoscale(False) + ax.set_autoscalez_on(True) + ax.plot([0, 2], [0, 2], [0, 2]) + assert ax.get_w_lims() == (0, 1, -.1, 1.1, -.4, 2.4) + ax.autoscale(axis='x') + ax.plot([0, 2], [0, 2], [0, 2]) + assert ax.get_w_lims() == (0, 2, -.1, 1.1, -.4, 2.4) + + +@pytest.mark.parametrize('axis', ('x', 'y', 'z')) +@pytest.mark.parametrize('auto', (True, False, None)) +def test_unautoscale(axis, auto): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + x = np.arange(100) + y = np.linspace(-0.1, 0.1, 100) + ax.scatter(x, y) + + get_autoscale_on = getattr(ax, f'get_autoscale{axis}_on') + set_lim = getattr(ax, f'set_{axis}lim') + get_lim = getattr(ax, f'get_{axis}lim') + + post_auto = get_autoscale_on() if auto is None else auto + + set_lim((-0.5, 0.5), auto=auto) + assert post_auto == get_autoscale_on() + fig.canvas.draw() + np.testing.assert_array_equal(get_lim(), (-0.5, 0.5)) + + +@check_figures_equal(extensions=["png"]) +def test_culling(fig_test, fig_ref): + xmins = (-100, -50) + for fig, xmin in zip((fig_test, fig_ref), xmins): + ax = fig.add_subplot(projection='3d') + n = abs(xmin) + 1 + xs = np.linspace(0, xmin, n) + ys = np.ones(n) + zs = np.zeros(n) + ax.plot(xs, ys, zs, 'k') + + ax.set(xlim=(-5, 5), ylim=(-5, 5), zlim=(-5, 5)) + ax.view_init(5, 180, 0) + + +def test_axes3d_focal_length_checks(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + with pytest.raises(ValueError): + ax.set_proj_type('persp', focal_length=0) + with pytest.raises(ValueError): + ax.set_proj_type('ortho', focal_length=1) + + +@mpl3d_image_comparison(['axes3d_focal_length.png'], + remove_text=False, style='mpl20') +def test_axes3d_focal_length(): + fig, axs = plt.subplots(1, 2, subplot_kw={'projection': '3d'}) + axs[0].set_proj_type('persp', focal_length=np.inf) + axs[1].set_proj_type('persp', focal_length=0.15) + + +@mpl3d_image_comparison(['axes3d_ortho.png'], remove_text=False, style='mpl20') +def test_axes3d_ortho(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_proj_type('ortho') + + +@mpl3d_image_comparison(['axes3d_isometric.png'], style='mpl20') +def test_axes3d_isometric(): + from itertools import combinations, product + fig, ax = plt.subplots(subplot_kw=dict( + projection='3d', + proj_type='ortho', + box_aspect=(4, 4, 4) + )) + r = (-1, 1) # stackoverflow.com/a/11156353 + for s, e in combinations(np.array(list(product(r, r, r))), 2): + if abs(s - e).sum() == r[1] - r[0]: + ax.plot3D(*zip(s, e), c='k') + ax.view_init(elev=np.degrees(np.arctan(1. / np.sqrt(2))), azim=-45, roll=0) + ax.grid(True) + + +@check_figures_equal(extensions=["png"]) +def test_axlim_clip(fig_test, fig_ref): + # With axlim clipping + ax = fig_test.add_subplot(projection="3d") + x = np.linspace(0, 1, 11) + y = np.linspace(0, 1, 11) + X, Y = np.meshgrid(x, y) + Z = X + Y + ax.plot_surface(X, Y, Z, facecolor='C1', edgecolors=None, + rcount=50, ccount=50, axlim_clip=True) + # This ax.plot is to cover the extra surface edge which is not clipped out + ax.plot([0.5, 0.5], [0, 1], [0.5, 1.5], + color='k', linewidth=3, zorder=5, axlim_clip=True) + ax.scatter(X.ravel(), Y.ravel(), Z.ravel() + 1, axlim_clip=True) + ax.quiver(X.ravel(), Y.ravel(), Z.ravel() + 2, + 0*X.ravel(), 0*Y.ravel(), 0*Z.ravel() + 1, + arrow_length_ratio=0, axlim_clip=True) + ax.plot(X[0], Y[0], Z[0] + 3, color='C2', axlim_clip=True) + ax.text(1.1, 0.5, 4, 'test', axlim_clip=True) # won't be visible + ax.set(xlim=(0, 0.5), ylim=(0, 1), zlim=(0, 5)) + + # With manual clipping + ax = fig_ref.add_subplot(projection="3d") + idx = (X <= 0.5) + X = X[idx].reshape(11, 6) + Y = Y[idx].reshape(11, 6) + Z = Z[idx].reshape(11, 6) + ax.plot_surface(X, Y, Z, facecolor='C1', edgecolors=None, + rcount=50, ccount=50, axlim_clip=False) + ax.plot([0.5, 0.5], [0, 1], [0.5, 1.5], + color='k', linewidth=3, zorder=5, axlim_clip=False) + ax.scatter(X.ravel(), Y.ravel(), Z.ravel() + 1, axlim_clip=False) + ax.quiver(X.ravel(), Y.ravel(), Z.ravel() + 2, + 0*X.ravel(), 0*Y.ravel(), 0*Z.ravel() + 1, + arrow_length_ratio=0, axlim_clip=False) + ax.plot(X[0], Y[0], Z[0] + 3, color='C2', axlim_clip=False) + ax.set(xlim=(0, 0.5), ylim=(0, 1), zlim=(0, 5)) + + +@pytest.mark.parametrize('value', [np.inf, np.nan]) +@pytest.mark.parametrize(('setter', 'side'), [ + ('set_xlim3d', 'left'), + ('set_xlim3d', 'right'), + ('set_ylim3d', 'bottom'), + ('set_ylim3d', 'top'), + ('set_zlim3d', 'bottom'), + ('set_zlim3d', 'top'), +]) +def test_invalid_axes_limits(setter, side, value): + limit = {side: value} + fig = plt.figure() + obj = fig.add_subplot(projection='3d') + with pytest.raises(ValueError): + getattr(obj, setter)(**limit) + + +class TestVoxels: + @mpl3d_image_comparison(['voxels-simple.png'], style='mpl20') + def test_simple(self): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((5, 4, 3)) + voxels = (x == y) | (y == z) + ax.voxels(voxels) + + @mpl3d_image_comparison(['voxels-edge-style.png'], style='mpl20') + def test_edge_style(self): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((5, 5, 4)) + voxels = ((x - 2)**2 + (y - 2)**2 + (z-1.5)**2) < 2.2**2 + v = ax.voxels(voxels, linewidths=3, edgecolor='C1') + + # change the edge color of one voxel + v[max(v.keys())].set_edgecolor('C2') + + @mpl3d_image_comparison(['voxels-named-colors.png'], style='mpl20') + def test_named_colors(self): + """Test with colors set to a 3D object array of strings.""" + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((10, 10, 10)) + voxels = (x == y) | (y == z) + voxels = voxels & ~(x * y * z < 1) + colors = np.full((10, 10, 10), 'C0', dtype=np.object_) + colors[(x < 5) & (y < 5)] = '0.25' + colors[(x + z) < 10] = 'cyan' + ax.voxels(voxels, facecolors=colors) + + @mpl3d_image_comparison(['voxels-rgb-data.png'], style='mpl20') + def test_rgb_data(self): + """Test with colors set to a 4d float array of rgb data.""" + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((10, 10, 10)) + voxels = (x == y) | (y == z) + colors = np.zeros((10, 10, 10, 3)) + colors[..., 0] = x / 9 + colors[..., 1] = y / 9 + colors[..., 2] = z / 9 + ax.voxels(voxels, facecolors=colors) + + @mpl3d_image_comparison(['voxels-alpha.png'], style='mpl20') + def test_alpha(self): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + x, y, z = np.indices((10, 10, 10)) + v1 = x == y + v2 = np.abs(x - y) < 2 + voxels = v1 | v2 + colors = np.zeros((10, 10, 10, 4)) + colors[v2] = [1, 0, 0, 0.5] + colors[v1] = [0, 1, 0, 0.5] + v = ax.voxels(voxels, facecolors=colors) + + assert type(v) is dict + for coord, poly in v.items(): + assert voxels[coord], "faces returned for absent voxel" + assert isinstance(poly, art3d.Poly3DCollection) + + @mpl3d_image_comparison(['voxels-xyz.png'], + tol=0.01, remove_text=False, style='mpl20') + def test_xyz(self): + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + def midpoints(x): + sl = () + for i in range(x.ndim): + x = (x[sl + np.index_exp[:-1]] + + x[sl + np.index_exp[1:]]) / 2.0 + sl += np.index_exp[:] + return x + + # prepare some coordinates, and attach rgb values to each + r, g, b = np.indices((17, 17, 17)) / 16.0 + rc = midpoints(r) + gc = midpoints(g) + bc = midpoints(b) + + # define a sphere about [0.5, 0.5, 0.5] + sphere = (rc - 0.5)**2 + (gc - 0.5)**2 + (bc - 0.5)**2 < 0.5**2 + + # combine the color components + colors = np.zeros(sphere.shape + (3,)) + colors[..., 0] = rc + colors[..., 1] = gc + colors[..., 2] = bc + + # and plot everything + ax.voxels(r, g, b, sphere, + facecolors=colors, + edgecolors=np.clip(2*colors - 0.5, 0, 1), # brighter + linewidth=0.5) + + def test_calling_conventions(self): + x, y, z = np.indices((3, 4, 5)) + filled = np.ones((2, 3, 4)) + + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + + # all the valid calling conventions + for kw in (dict(), dict(edgecolor='k')): + ax.voxels(filled, **kw) + ax.voxels(filled=filled, **kw) + ax.voxels(x, y, z, filled, **kw) + ax.voxels(x, y, z, filled=filled, **kw) + + # duplicate argument + with pytest.raises(TypeError, match='voxels'): + ax.voxels(x, y, z, filled, filled=filled) + # missing arguments + with pytest.raises(TypeError, match='voxels'): + ax.voxels(x, y) + # x, y, z are positional only - this passes them on as attributes of + # Poly3DCollection + with pytest.raises(AttributeError, match="keyword argument 'x'") as exec_info: + ax.voxels(filled=filled, x=x, y=y, z=z) + assert exec_info.value.name == 'x' + + +def test_line3d_set_get_data_3d(): + x, y, z = [0, 1], [2, 3], [4, 5] + x2, y2, z2 = [6, 7], [8, 9], [10, 11] + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + lines = ax.plot(x, y, z) + line = lines[0] + np.testing.assert_array_equal((x, y, z), line.get_data_3d()) + line.set_data_3d(x2, y2, z2) + np.testing.assert_array_equal((x2, y2, z2), line.get_data_3d()) + line.set_xdata(x) + line.set_ydata(y) + line.set_3d_properties(zs=z, zdir='z') + np.testing.assert_array_equal((x, y, z), line.get_data_3d()) + line.set_3d_properties(zs=0, zdir='z') + np.testing.assert_array_equal((x, y, np.zeros_like(z)), line.get_data_3d()) + + +@check_figures_equal(extensions=["png"]) +def test_inverted(fig_test, fig_ref): + # Plot then invert. + ax = fig_test.add_subplot(projection="3d") + ax.plot([1, 1, 10, 10], [1, 10, 10, 10], [1, 1, 1, 10]) + ax.invert_yaxis() + # Invert then plot. + ax = fig_ref.add_subplot(projection="3d") + ax.invert_yaxis() + ax.plot([1, 1, 10, 10], [1, 10, 10, 10], [1, 1, 1, 10]) + + +def test_inverted_cla(): + # GitHub PR #5450. Setting autoscale should reset + # axes to be non-inverted. + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + # 1. test that a new axis is not inverted per default + assert not ax.xaxis_inverted() + assert not ax.yaxis_inverted() + assert not ax.zaxis_inverted() + ax.set_xlim(1, 0) + ax.set_ylim(1, 0) + ax.set_zlim(1, 0) + assert ax.xaxis_inverted() + assert ax.yaxis_inverted() + assert ax.zaxis_inverted() + ax.cla() + assert not ax.xaxis_inverted() + assert not ax.yaxis_inverted() + assert not ax.zaxis_inverted() + + +def test_ax3d_tickcolour(): + fig = plt.figure() + ax = Axes3D(fig) + + ax.tick_params(axis='x', colors='red') + ax.tick_params(axis='y', colors='red') + ax.tick_params(axis='z', colors='red') + fig.canvas.draw() + + for tick in ax.xaxis.get_major_ticks(): + assert tick.tick1line._color == 'red' + for tick in ax.yaxis.get_major_ticks(): + assert tick.tick1line._color == 'red' + for tick in ax.zaxis.get_major_ticks(): + assert tick.tick1line._color == 'red' + + +@check_figures_equal(extensions=["png"]) +def test_ticklabel_format(fig_test, fig_ref): + axs = fig_test.subplots(4, 5, subplot_kw={"projection": "3d"}) + for ax in axs.flat: + ax.set_xlim(1e7, 1e7 + 10) + for row, name in zip(axs, ["x", "y", "z", "both"]): + row[0].ticklabel_format( + axis=name, style="plain") + row[1].ticklabel_format( + axis=name, scilimits=(-2, 2)) + row[2].ticklabel_format( + axis=name, useOffset=not mpl.rcParams["axes.formatter.useoffset"]) + row[3].ticklabel_format( + axis=name, useLocale=not mpl.rcParams["axes.formatter.use_locale"]) + row[4].ticklabel_format( + axis=name, + useMathText=not mpl.rcParams["axes.formatter.use_mathtext"]) + + def get_formatters(ax, names): + return [getattr(ax, name).get_major_formatter() for name in names] + + axs = fig_ref.subplots(4, 5, subplot_kw={"projection": "3d"}) + for ax in axs.flat: + ax.set_xlim(1e7, 1e7 + 10) + for row, names in zip( + axs, [["xaxis"], ["yaxis"], ["zaxis"], ["xaxis", "yaxis", "zaxis"]] + ): + for fmt in get_formatters(row[0], names): + fmt.set_scientific(False) + for fmt in get_formatters(row[1], names): + fmt.set_powerlimits((-2, 2)) + for fmt in get_formatters(row[2], names): + fmt.set_useOffset(not mpl.rcParams["axes.formatter.useoffset"]) + for fmt in get_formatters(row[3], names): + fmt.set_useLocale(not mpl.rcParams["axes.formatter.use_locale"]) + for fmt in get_formatters(row[4], names): + fmt.set_useMathText( + not mpl.rcParams["axes.formatter.use_mathtext"]) + + +@check_figures_equal(extensions=["png"]) +def test_quiver3D_smoke(fig_test, fig_ref): + pivot = "middle" + # Make the grid + x, y, z = np.meshgrid( + np.arange(-0.8, 1, 0.2), + np.arange(-0.8, 1, 0.2), + np.arange(-0.8, 1, 0.8) + ) + u = v = w = np.ones_like(x) + + for fig, length in zip((fig_ref, fig_test), (1, 1.0)): + ax = fig.add_subplot(projection="3d") + ax.quiver(x, y, z, u, v, w, length=length, pivot=pivot) + + +@image_comparison(["minor_ticks.png"], style="mpl20") +def test_minor_ticks(): + ax = plt.figure().add_subplot(projection="3d") + ax.set_xticks([0.25], minor=True) + ax.set_xticklabels(["quarter"], minor=True) + ax.set_yticks([0.33], minor=True) + ax.set_yticklabels(["third"], minor=True) + ax.set_zticks([0.50], minor=True) + ax.set_zticklabels(["half"], minor=True) + + +# remove tolerance when regenerating the test image +@mpl3d_image_comparison(['errorbar3d_errorevery.png'], style='mpl20', tol=0.003) +def test_errorbar3d_errorevery(): + """Tests errorevery functionality for 3D errorbars.""" + t = np.arange(0, 2*np.pi+.1, 0.01) + x, y, z = np.sin(t), np.cos(3*t), np.sin(5*t) + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + estep = 15 + i = np.arange(t.size) + zuplims = (i % estep == 0) & (i // estep % 3 == 0) + zlolims = (i % estep == 0) & (i // estep % 3 == 2) + + ax.errorbar(x, y, z, 0.2, zuplims=zuplims, zlolims=zlolims, + errorevery=estep) + + +@mpl3d_image_comparison(['errorbar3d.png'], style='mpl20', + tol=0 if platform.machine() == 'x86_64' else 0.02) +def test_errorbar3d(): + """Tests limits, color styling, and legend for 3D errorbars.""" + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + d = [1, 2, 3, 4, 5] + e = [.5, .5, .5, .5, .5] + ax.errorbar(x=d, y=d, z=d, xerr=e, yerr=e, zerr=e, capsize=3, + zuplims=[False, True, False, True, True], + zlolims=[True, False, False, True, False], + yuplims=True, + ecolor='purple', label='Error lines') + ax.legend() + + +@image_comparison(['stem3d.png'], style='mpl20', tol=0.009) +def test_stem3d(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig, axs = plt.subplots(2, 3, figsize=(8, 6), + constrained_layout=True, + subplot_kw={'projection': '3d'}) + + theta = np.linspace(0, 2*np.pi) + x = np.cos(theta - np.pi/2) + y = np.sin(theta - np.pi/2) + z = theta + + for ax, zdir in zip(axs[0], ['x', 'y', 'z']): + ax.stem(x, y, z, orientation=zdir) + ax.set_title(f'orientation={zdir}') + + x = np.linspace(-np.pi/2, np.pi/2, 20) + y = np.ones_like(x) + z = np.cos(x) + + for ax, zdir in zip(axs[1], ['x', 'y', 'z']): + markerline, stemlines, baseline = ax.stem( + x, y, z, + linefmt='C4-.', markerfmt='C1D', basefmt='C2', + orientation=zdir) + ax.set_title(f'orientation={zdir}') + markerline.set(markerfacecolor='none', markeredgewidth=2) + baseline.set_linewidth(3) + + +@image_comparison(["equal_box_aspect.png"], style="mpl20") +def test_equal_box_aspect(): + from itertools import product, combinations + + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + + # Make data + u = np.linspace(0, 2 * np.pi, 100) + v = np.linspace(0, np.pi, 100) + x = np.outer(np.cos(u), np.sin(v)) + y = np.outer(np.sin(u), np.sin(v)) + z = np.outer(np.ones_like(u), np.cos(v)) + + # Plot the surface + ax.plot_surface(x, y, z) + + # draw cube + r = [-1, 1] + for s, e in combinations(np.array(list(product(r, r, r))), 2): + if np.sum(np.abs(s - e)) == r[1] - r[0]: + ax.plot3D(*zip(s, e), color="b") + + # Make axes limits + xyzlim = np.column_stack( + [ax.get_xlim3d(), ax.get_ylim3d(), ax.get_zlim3d()] + ) + XYZlim = [min(xyzlim[0]), max(xyzlim[1])] + ax.set_xlim3d(XYZlim) + ax.set_ylim3d(XYZlim) + ax.set_zlim3d(XYZlim) + ax.axis('off') + ax.set_box_aspect((1, 1, 1)) + + with pytest.raises(ValueError, match="Argument zoom ="): + ax.set_box_aspect((1, 1, 1), zoom=-1) + + +def test_colorbar_pos(): + num_plots = 2 + fig, axs = plt.subplots(1, num_plots, figsize=(4, 5), + constrained_layout=True, + subplot_kw={'projection': '3d'}) + for ax in axs: + p_tri = ax.plot_trisurf(np.random.randn(5), np.random.randn(5), + np.random.randn(5)) + + cbar = plt.colorbar(p_tri, ax=axs, orientation='horizontal') + + fig.canvas.draw() + # check that actually on the bottom + assert cbar.ax.get_position().extents[1] < 0.2 + + +def test_inverted_zaxis(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.set_zlim(0, 1) + assert not ax.zaxis_inverted() + assert ax.get_zlim() == (0, 1) + assert ax.get_zbound() == (0, 1) + + # Change bound + ax.set_zbound((0, 2)) + assert not ax.zaxis_inverted() + assert ax.get_zlim() == (0, 2) + assert ax.get_zbound() == (0, 2) + + # Change invert + ax.invert_zaxis() + assert ax.zaxis_inverted() + assert ax.get_zlim() == (2, 0) + assert ax.get_zbound() == (0, 2) + + # Set upper bound + ax.set_zbound(upper=1) + assert ax.zaxis_inverted() + assert ax.get_zlim() == (1, 0) + assert ax.get_zbound() == (0, 1) + + # Set lower bound + ax.set_zbound(lower=2) + assert ax.zaxis_inverted() + assert ax.get_zlim() == (2, 1) + assert ax.get_zbound() == (1, 2) + + +def test_set_zlim(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + assert np.allclose(ax.get_zlim(), (-1/48, 49/48)) + ax.set_zlim(zmax=2) + assert np.allclose(ax.get_zlim(), (-1/48, 2)) + ax.set_zlim(zmin=1) + assert ax.get_zlim() == (1, 2) + + with pytest.raises( + TypeError, match="Cannot pass both 'lower' and 'min'"): + ax.set_zlim(bottom=0, zmin=1) + with pytest.raises( + TypeError, match="Cannot pass both 'upper' and 'max'"): + ax.set_zlim(top=0, zmax=1) + + +@check_figures_equal(extensions=["png"]) +def test_shared_view(fig_test, fig_ref): + elev, azim, roll = 5, 20, 30 + ax1 = fig_test.add_subplot(131, projection="3d") + ax2 = fig_test.add_subplot(132, projection="3d", shareview=ax1) + ax3 = fig_test.add_subplot(133, projection="3d") + ax3.shareview(ax1) + ax2.view_init(elev=elev, azim=azim, roll=roll, share=True) + + for subplot_num in (131, 132, 133): + ax = fig_ref.add_subplot(subplot_num, projection="3d") + ax.view_init(elev=elev, azim=azim, roll=roll) + + +def test_shared_axes_retick(): + fig = plt.figure() + ax1 = fig.add_subplot(211, projection="3d") + ax2 = fig.add_subplot(212, projection="3d", sharez=ax1) + ax1.plot([0, 1], [0, 1], [0, 2]) + ax2.plot([0, 1], [0, 1], [0, 2]) + ax1.set_zticks([-0.5, 0, 2, 2.5]) + # check that setting ticks on a shared axis is synchronized + assert ax1.get_zlim() == (-0.5, 2.5) + assert ax2.get_zlim() == (-0.5, 2.5) + + +def test_quaternion(): + # 1: + q1 = Quaternion(1, [0, 0, 0]) + assert q1.scalar == 1 + assert (q1.vector == [0, 0, 0]).all + # __neg__: + assert (-q1).scalar == -1 + assert ((-q1).vector == [0, 0, 0]).all + # i, j, k: + qi = Quaternion(0, [1, 0, 0]) + assert qi.scalar == 0 + assert (qi.vector == [1, 0, 0]).all + qj = Quaternion(0, [0, 1, 0]) + assert qj.scalar == 0 + assert (qj.vector == [0, 1, 0]).all + qk = Quaternion(0, [0, 0, 1]) + assert qk.scalar == 0 + assert (qk.vector == [0, 0, 1]).all + # i^2 = j^2 = k^2 = -1: + assert qi*qi == -q1 + assert qj*qj == -q1 + assert qk*qk == -q1 + # identity: + assert q1*qi == qi + assert q1*qj == qj + assert q1*qk == qk + # i*j=k, j*k=i, k*i=j: + assert qi*qj == qk + assert qj*qk == qi + assert qk*qi == qj + assert qj*qi == -qk + assert qk*qj == -qi + assert qi*qk == -qj + # __mul__: + assert (Quaternion(2, [3, 4, 5]) * Quaternion(6, [7, 8, 9]) + == Quaternion(-86, [28, 48, 44])) + # conjugate(): + for q in [q1, qi, qj, qk]: + assert q.conjugate().scalar == q.scalar + assert (q.conjugate().vector == -q.vector).all + assert q.conjugate().conjugate() == q + assert ((q*q.conjugate()).vector == 0).all + # norm: + q0 = Quaternion(0, [0, 0, 0]) + assert q0.norm == 0 + assert q1.norm == 1 + assert qi.norm == 1 + assert qj.norm == 1 + assert qk.norm == 1 + for q in [q0, q1, qi, qj, qk]: + assert q.norm == (q*q.conjugate()).scalar + # normalize(): + for q in [ + Quaternion(2, [0, 0, 0]), + Quaternion(0, [3, 0, 0]), + Quaternion(0, [0, 4, 0]), + Quaternion(0, [0, 0, 5]), + Quaternion(6, [7, 8, 9]) + ]: + assert q.normalize().norm == 1 + # reciprocal(): + for q in [q1, qi, qj, qk]: + assert q*q.reciprocal() == q1 + assert q.reciprocal()*q == q1 + # rotate(): + assert (qi.rotate([1, 2, 3]) == np.array([1, -2, -3])).all + # rotate_from_to(): + for r1, r2, q in [ + ([1, 0, 0], [0, 1, 0], Quaternion(np.sqrt(1/2), [0, 0, np.sqrt(1/2)])), + ([1, 0, 0], [0, 0, 1], Quaternion(np.sqrt(1/2), [0, -np.sqrt(1/2), 0])), + ([1, 0, 0], [1, 0, 0], Quaternion(1, [0, 0, 0])) + ]: + assert Quaternion.rotate_from_to(r1, r2) == q + # rotate_from_to(), special case: + for r1 in [[1, 0, 0], [0, 1, 0], [0, 0, 1], [1, 1, 1]]: + r1 = np.array(r1) + with pytest.warns(UserWarning): + q = Quaternion.rotate_from_to(r1, -r1) + assert np.isclose(q.norm, 1) + assert np.dot(q.vector, r1) == 0 + # from_cardan_angles(), as_cardan_angles(): + for elev, azim, roll in [(0, 0, 0), + (90, 0, 0), (0, 90, 0), (0, 0, 90), + (0, 30, 30), (30, 0, 30), (30, 30, 0), + (47, 11, -24)]: + for mag in [1, 2]: + q = Quaternion.from_cardan_angles( + np.deg2rad(elev), np.deg2rad(azim), np.deg2rad(roll)) + assert np.isclose(q.norm, 1) + q = Quaternion(mag * q.scalar, mag * q.vector) + np.testing.assert_allclose(np.rad2deg(Quaternion.as_cardan_angles(q)), + (elev, azim, roll), atol=1e-6) + + +@pytest.mark.parametrize('style', + ('azel', 'trackball', 'sphere', 'arcball')) +def test_rotate(style): + """Test rotating using the left mouse button.""" + if style == 'azel': + s = 0.5 + else: + s = mpl.rcParams['axes3d.trackballsize'] / 2 + s *= 0.5 + mpl.rcParams['axes3d.trackballborder'] = 0 + with mpl.rc_context({'axes3d.mouserotationstyle': style}): + for roll, dx, dy in [ + [0, 1, 0], + [30, 1, 0], + [0, 0, 1], + [30, 0, 1], + [0, 0.5, np.sqrt(3)/2], + [30, 0.5, np.sqrt(3)/2], + [0, 2, 0]]: + fig = plt.figure() + ax = fig.add_subplot(1, 1, 1, projection='3d') + ax.view_init(0, 0, roll) + ax.figure.canvas.draw() + + # drag mouse to change orientation + ax._button_press( + mock_event(ax, button=MouseButton.LEFT, xdata=0, ydata=0)) + ax._on_move( + mock_event(ax, button=MouseButton.LEFT, + xdata=s*dx*ax._pseudo_w, ydata=s*dy*ax._pseudo_h)) + ax.figure.canvas.draw() + + c = np.sqrt(3)/2 + expectations = { + ('azel', 0, 1, 0): (0, -45, 0), + ('azel', 0, 0, 1): (-45, 0, 0), + ('azel', 0, 0.5, c): (-38.971143, -22.5, 0), + ('azel', 0, 2, 0): (0, -90, 0), + ('azel', 30, 1, 0): (22.5, -38.971143, 30), + ('azel', 30, 0, 1): (-38.971143, -22.5, 30), + ('azel', 30, 0.5, c): (-22.5, -38.971143, 30), + + ('trackball', 0, 1, 0): (0, -28.64789, 0), + ('trackball', 0, 0, 1): (-28.64789, 0, 0), + ('trackball', 0, 0.5, c): (-24.531578, -15.277726, 3.340403), + ('trackball', 0, 2, 0): (0, -180/np.pi, 0), + ('trackball', 30, 1, 0): (13.869588, -25.319385, 26.87008), + ('trackball', 30, 0, 1): (-24.531578, -15.277726, 33.340403), + ('trackball', 30, 0.5, c): (-13.869588, -25.319385, 33.129920), + + ('sphere', 0, 1, 0): (0, -30, 0), + ('sphere', 0, 0, 1): (-30, 0, 0), + ('sphere', 0, 0.5, c): (-25.658906, -16.102114, 3.690068), + ('sphere', 0, 2, 0): (0, -90, 0), + ('sphere', 30, 1, 0): (14.477512, -26.565051, 26.565051), + ('sphere', 30, 0, 1): (-25.658906, -16.102114, 33.690068), + ('sphere', 30, 0.5, c): (-14.477512, -26.565051, 33.434949), + + ('arcball', 0, 1, 0): (0, -60, 0), + ('arcball', 0, 0, 1): (-60, 0, 0), + ('arcball', 0, 0.5, c): (-48.590378, -40.893395, 19.106605), + ('arcball', 0, 2, 0): (0, 180, 0), + ('arcball', 30, 1, 0): (25.658906, -56.309932, 16.102114), + ('arcball', 30, 0, 1): (-48.590378, -40.893395, 49.106605), + ('arcball', 30, 0.5, c): (-25.658906, -56.309932, 43.897886)} + new_elev, new_azim, new_roll = expectations[(style, roll, dx, dy)] + np.testing.assert_allclose((ax.elev, ax.azim, ax.roll), + (new_elev, new_azim, new_roll), atol=1e-6) + + +def test_pan(): + """Test mouse panning using the middle mouse button.""" + + def convert_lim(dmin, dmax): + """Convert min/max limits to center and range.""" + center = (dmin + dmax) / 2 + range_ = dmax - dmin + return center, range_ + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.scatter(0, 0, 0) + fig.canvas.draw() + + x_center0, x_range0 = convert_lim(*ax.get_xlim3d()) + y_center0, y_range0 = convert_lim(*ax.get_ylim3d()) + z_center0, z_range0 = convert_lim(*ax.get_zlim3d()) + + # move mouse diagonally to pan along all axis. + ax._button_press( + mock_event(ax, button=MouseButton.MIDDLE, xdata=0, ydata=0)) + ax._on_move( + mock_event(ax, button=MouseButton.MIDDLE, xdata=1, ydata=1)) + + x_center, x_range = convert_lim(*ax.get_xlim3d()) + y_center, y_range = convert_lim(*ax.get_ylim3d()) + z_center, z_range = convert_lim(*ax.get_zlim3d()) + + # Ranges have not changed + assert x_range == pytest.approx(x_range0) + assert y_range == pytest.approx(y_range0) + assert z_range == pytest.approx(z_range0) + + # But center positions have + assert x_center != pytest.approx(x_center0) + assert y_center != pytest.approx(y_center0) + assert z_center != pytest.approx(z_center0) + + +@pytest.mark.parametrize("tool,button,key,expected", + [("zoom", MouseButton.LEFT, None, # zoom in + ((0.00, 0.06), (0.01, 0.07), (0.02, 0.08))), + ("zoom", MouseButton.LEFT, 'x', # zoom in + ((-0.01, 0.10), (-0.03, 0.08), (-0.06, 0.06))), + ("zoom", MouseButton.LEFT, 'y', # zoom in + ((-0.07, 0.05), (-0.04, 0.08), (0.00, 0.12))), + ("zoom", MouseButton.RIGHT, None, # zoom out + ((-0.09, 0.15), (-0.08, 0.17), (-0.07, 0.18))), + ("pan", MouseButton.LEFT, None, + ((-0.70, -0.58), (-1.04, -0.91), (-1.27, -1.15))), + ("pan", MouseButton.LEFT, 'x', + ((-0.97, -0.84), (-0.58, -0.46), (-0.06, 0.06))), + ("pan", MouseButton.LEFT, 'y', + ((0.20, 0.32), (-0.51, -0.39), (-1.27, -1.15)))]) +def test_toolbar_zoom_pan(tool, button, key, expected): + # NOTE: The expected zoom values are rough ballparks of moving in the view + # to make sure we are getting the right direction of motion. + # The specific values can and should change if the zoom movement + # scaling factor gets updated. + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.scatter(0, 0, 0) + fig.canvas.draw() + xlim0, ylim0, zlim0 = ax.get_xlim3d(), ax.get_ylim3d(), ax.get_zlim3d() + + # Mouse from (0, 0) to (1, 1) + d0 = (0, 0) + d1 = (1, 1) + # Convert to screen coordinates ("s"). Events are defined only with pixel + # precision, so round the pixel values, and below, check against the + # corresponding xdata/ydata, which are close but not equal to d0/d1. + s0 = ax.transData.transform(d0).astype(int) + s1 = ax.transData.transform(d1).astype(int) + + # Set up the mouse movements + start_event = MouseEvent( + "button_press_event", fig.canvas, *s0, button, key=key) + stop_event = MouseEvent( + "button_release_event", fig.canvas, *s1, button, key=key) + + tb = NavigationToolbar2(fig.canvas) + if tool == "zoom": + tb.zoom() + tb.press_zoom(start_event) + tb.drag_zoom(stop_event) + tb.release_zoom(stop_event) + else: + tb.pan() + tb.press_pan(start_event) + tb.drag_pan(stop_event) + tb.release_pan(stop_event) + + # Should be close, but won't be exact due to screen integer resolution + xlim, ylim, zlim = expected + assert ax.get_xlim3d() == pytest.approx(xlim, abs=0.01) + assert ax.get_ylim3d() == pytest.approx(ylim, abs=0.01) + assert ax.get_zlim3d() == pytest.approx(zlim, abs=0.01) + + # Ensure that back, forward, and home buttons work + tb.back() + assert ax.get_xlim3d() == pytest.approx(xlim0) + assert ax.get_ylim3d() == pytest.approx(ylim0) + assert ax.get_zlim3d() == pytest.approx(zlim0) + + tb.forward() + assert ax.get_xlim3d() == pytest.approx(xlim, abs=0.01) + assert ax.get_ylim3d() == pytest.approx(ylim, abs=0.01) + assert ax.get_zlim3d() == pytest.approx(zlim, abs=0.01) + + tb.home() + assert ax.get_xlim3d() == pytest.approx(xlim0) + assert ax.get_ylim3d() == pytest.approx(ylim0) + assert ax.get_zlim3d() == pytest.approx(zlim0) + + +@mpl.style.context('default') +@check_figures_equal(extensions=["png"]) +def test_scalarmap_update(fig_test, fig_ref): + + x, y, z = np.array(list(itertools.product(*[np.arange(0, 5, 1), + np.arange(0, 5, 1), + np.arange(0, 5, 1)]))).T + c = x + y + + # test + ax_test = fig_test.add_subplot(111, projection='3d') + sc_test = ax_test.scatter(x, y, z, c=c, s=40, cmap='viridis') + # force a draw + fig_test.canvas.draw() + # mark it as "stale" + sc_test.changed() + + # ref + ax_ref = fig_ref.add_subplot(111, projection='3d') + sc_ref = ax_ref.scatter(x, y, z, c=c, s=40, cmap='viridis') + + +def test_subfigure_simple(): + # smoketest that subfigures can work... + fig = plt.figure() + sf = fig.subfigures(1, 2) + ax = sf[0].add_subplot(1, 1, 1, projection='3d') + ax = sf[1].add_subplot(1, 1, 1, projection='3d', label='other') + + +# Update style when regenerating the test image +@image_comparison(baseline_images=['computed_zorder'], remove_text=True, + extensions=['png'], style=('mpl20')) +def test_computed_zorder(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax1 = fig.add_subplot(221, projection='3d') + ax2 = fig.add_subplot(222, projection='3d') + ax2.computed_zorder = False + + # create a horizontal plane + corners = ((0, 0, 0), (0, 5, 0), (5, 5, 0), (5, 0, 0)) + for ax in (ax1, ax2): + tri = art3d.Poly3DCollection([corners], + facecolors='white', + edgecolors='black', + zorder=1) + ax.add_collection3d(tri) + + # plot a vector + ax.plot((2, 2), (2, 2), (0, 4), c='red', zorder=2) + + # plot some points + ax.scatter((3, 3), (1, 3), (1, 3), c='red', zorder=10) + + ax.set_xlim((0, 5.0)) + ax.set_ylim((0, 5.0)) + ax.set_zlim((0, 2.5)) + + ax3 = fig.add_subplot(223, projection='3d') + ax4 = fig.add_subplot(224, projection='3d') + ax4.computed_zorder = False + + dim = 10 + X, Y = np.meshgrid((-dim, dim), (-dim, dim)) + Z = np.zeros((2, 2)) + + angle = 0.5 + X2, Y2 = np.meshgrid((-dim, dim), (0, dim)) + Z2 = Y2 * angle + X3, Y3 = np.meshgrid((-dim, dim), (-dim, 0)) + Z3 = Y3 * angle + + r = 7 + M = 1000 + th = np.linspace(0, 2 * np.pi, M) + x, y, z = r * np.cos(th), r * np.sin(th), angle * r * np.sin(th) + for ax in (ax3, ax4): + ax.plot_surface(X2, Y3, Z3, + color='blue', + alpha=0.5, + linewidth=0, + zorder=-1) + ax.plot(x[y < 0], y[y < 0], z[y < 0], + lw=5, + linestyle='--', + color='green', + zorder=0) + + ax.plot_surface(X, Y, Z, + color='red', + alpha=0.5, + linewidth=0, + zorder=1) + + ax.plot(r * np.sin(th), r * np.cos(th), np.zeros(M), + lw=5, + linestyle='--', + color='black', + zorder=2) + + ax.plot_surface(X2, Y2, Z2, + color='blue', + alpha=0.5, + linewidth=0, + zorder=3) + + ax.plot(x[y > 0], y[y > 0], z[y > 0], lw=5, + linestyle='--', + color='green', + zorder=4) + ax.view_init(elev=20, azim=-20, roll=0) + ax.axis('off') + + +def test_format_coord(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + x = np.arange(10) + ax.plot(x, np.sin(x)) + xv = 0.1 + yv = 0.1 + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x=10.5227, y pane=1.0417, z=0.1444' + + # Modify parameters + ax.view_init(roll=30, vertical_axis="y") + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x pane=9.1875, y=0.9761, z=0.1291' + + # Reset parameters + ax.view_init() + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x=10.5227, y pane=1.0417, z=0.1444' + + # Check orthographic projection + ax.set_proj_type('ortho') + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x=10.8869, y pane=1.0417, z=0.1528' + + # Check non-default perspective projection + ax.set_proj_type('persp', focal_length=0.1) + fig.canvas.draw() + assert ax.format_coord(xv, yv) == 'x=9.0620, y pane=1.0417, z=0.1110' + + +def test_get_axis_position(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + x = np.arange(10) + ax.plot(x, np.sin(x)) + fig.canvas.draw() + assert ax.get_axis_position() == (False, True, False) + + +def test_margins(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.margins(0.2) + assert ax.margins() == (0.2, 0.2, 0.2) + ax.margins(0.1, 0.2, 0.3) + assert ax.margins() == (0.1, 0.2, 0.3) + ax.margins(x=0) + assert ax.margins() == (0, 0.2, 0.3) + ax.margins(y=0.1) + assert ax.margins() == (0, 0.1, 0.3) + ax.margins(z=0) + assert ax.margins() == (0, 0.1, 0) + + +def test_margin_getters(): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.margins(0.1, 0.2, 0.3) + assert ax.get_xmargin() == 0.1 + assert ax.get_ymargin() == 0.2 + assert ax.get_zmargin() == 0.3 + + +@pytest.mark.parametrize('err, args, kwargs, match', ( + (ValueError, (-1,), {}, r'margin must be greater than -0\.5'), + (ValueError, (1, -1, 1), {}, r'margin must be greater than -0\.5'), + (ValueError, (1, 1, -1), {}, r'margin must be greater than -0\.5'), + (ValueError, tuple(), {'x': -1}, r'margin must be greater than -0\.5'), + (ValueError, tuple(), {'y': -1}, r'margin must be greater than -0\.5'), + (ValueError, tuple(), {'z': -1}, r'margin must be greater than -0\.5'), + (TypeError, (1, ), {'x': 1}, + 'Cannot pass both positional and keyword'), + (TypeError, (1, ), {'x': 1, 'y': 1, 'z': 1}, + 'Cannot pass both positional and keyword'), + (TypeError, (1, ), {'x': 1, 'y': 1}, + 'Cannot pass both positional and keyword'), + (TypeError, (1, 1), {}, 'Must pass a single positional argument for'), +)) +def test_margins_errors(err, args, kwargs, match): + with pytest.raises(err, match=match): + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.margins(*args, **kwargs) + + +@check_figures_equal(extensions=["png"]) +def test_text_3d(fig_test, fig_ref): + ax = fig_ref.add_subplot(projection="3d") + txt = Text(0.5, 0.5, r'Foo bar $\int$') + art3d.text_2d_to_3d(txt, z=1) + ax.add_artist(txt) + assert txt.get_position_3d() == (0.5, 0.5, 1) + + ax = fig_test.add_subplot(projection="3d") + t3d = art3d.Text3D(0.5, 0.5, 1, r'Foo bar $\int$') + ax.add_artist(t3d) + assert t3d.get_position_3d() == (0.5, 0.5, 1) + + +def test_draw_single_lines_from_Nx1(): + # Smoke test for GH#23459 + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + ax.plot([[0], [1]], [[0], [1]], [[0], [1]]) + + +@check_figures_equal(extensions=["png"]) +def test_pathpatch_3d(fig_test, fig_ref): + ax = fig_ref.add_subplot(projection="3d") + path = Path.unit_rectangle() + patch = PathPatch(path) + art3d.pathpatch_2d_to_3d(patch, z=(0, 0.5, 0.7, 1, 0), zdir='y') + ax.add_artist(patch) + + ax = fig_test.add_subplot(projection="3d") + pp3d = art3d.PathPatch3D(path, zs=(0, 0.5, 0.7, 1, 0), zdir='y') + ax.add_artist(pp3d) + + +@image_comparison(baseline_images=['scatter_spiral.png'], + remove_text=True, + style='mpl20') +def test_scatter_spiral(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + th = np.linspace(0, 2 * np.pi * 6, 256) + sc = ax.scatter(np.sin(th), np.cos(th), th, s=(1 + th * 5), c=th ** 2) + + # force at least 1 draw! + fig.canvas.draw() + + +def test_Poly3DCollection_get_path(): + # Smoke test to see that get_path does not raise + # See GH#27361 + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + p = Circle((0, 0), 1.0) + ax.add_patch(p) + art3d.pathpatch_2d_to_3d(p) + p.get_path() + + +def test_Poly3DCollection_get_facecolor(): + # Smoke test to see that get_facecolor does not raise + # See GH#4067 + y, x = np.ogrid[1:10:100j, 1:10:100j] + z2 = np.cos(x) ** 3 - np.sin(y) ** 2 + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + r = ax.plot_surface(x, y, z2, cmap='hot') + r.get_facecolor() + + +def test_Poly3DCollection_get_edgecolor(): + # Smoke test to see that get_edgecolor does not raise + # See GH#4067 + y, x = np.ogrid[1:10:100j, 1:10:100j] + z2 = np.cos(x) ** 3 - np.sin(y) ** 2 + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + r = ax.plot_surface(x, y, z2, cmap='hot') + r.get_edgecolor() + + +@pytest.mark.parametrize( + "vertical_axis, proj_expected, axis_lines_expected, tickdirs_expected", + [ + ( + "z", + [ + [0.0, 1.142857, 0.0, -0.571429], + [0.0, 0.0, 0.857143, -0.428571], + [0.0, 0.0, 0.0, -10.0], + [-1.142857, 0.0, 0.0, 10.571429], + ], + [ + ([0.05617978, 0.06329114], [-0.04213483, -0.04746835]), + ([-0.06329114, 0.06329114], [-0.04746835, -0.04746835]), + ([-0.06329114, -0.06329114], [-0.04746835, 0.04746835]), + ], + [1, 0, 0], + ), + ( + "y", + [ + [1.142857, 0.0, 0.0, -0.571429], + [0.0, 0.857143, 0.0, -0.428571], + [0.0, 0.0, 0.0, -10.0], + [0.0, 0.0, -1.142857, 10.571429], + ], + [ + ([-0.06329114, 0.06329114], [0.04746835, 0.04746835]), + ([0.06329114, 0.06329114], [-0.04746835, 0.04746835]), + ([-0.05617978, -0.06329114], [0.04213483, 0.04746835]), + ], + [2, 2, 0], + ), + ( + "x", + [ + [0.0, 0.0, 1.142857, -0.571429], + [0.857143, 0.0, 0.0, -0.428571], + [0.0, 0.0, 0.0, -10.0], + [0.0, -1.142857, 0.0, 10.571429], + ], + [ + ([-0.06329114, -0.06329114], [0.04746835, -0.04746835]), + ([0.06329114, 0.05617978], [0.04746835, 0.04213483]), + ([0.06329114, -0.06329114], [0.04746835, 0.04746835]), + ], + [1, 2, 1], + ), + ], +) +def test_view_init_vertical_axis( + vertical_axis, proj_expected, axis_lines_expected, tickdirs_expected +): + """ + Test the actual projection, axis lines and ticks matches expected values. + + Parameters + ---------- + vertical_axis : str + Axis to align vertically. + proj_expected : ndarray + Expected values from ax.get_proj(). + axis_lines_expected : tuple of arrays + Edgepoints of the axis line. Expected values retrieved according + to ``ax.get_[xyz]axis().line.get_data()``. + tickdirs_expected : list of int + indexes indicating which axis to create a tick line along. + """ + rtol = 2e-06 + ax = plt.subplot(1, 1, 1, projection="3d") + ax.view_init(elev=0, azim=0, roll=0, vertical_axis=vertical_axis) + ax.get_figure().canvas.draw() + + # Assert the projection matrix: + proj_actual = ax.get_proj() + np.testing.assert_allclose(proj_expected, proj_actual, rtol=rtol) + + for i, axis in enumerate([ax.get_xaxis(), ax.get_yaxis(), ax.get_zaxis()]): + # Assert black lines are correctly aligned: + axis_line_expected = axis_lines_expected[i] + axis_line_actual = axis.line.get_data() + np.testing.assert_allclose(axis_line_expected, axis_line_actual, + rtol=rtol) + + # Assert ticks are correctly aligned: + tickdir_expected = tickdirs_expected[i] + tickdir_actual = axis._get_tickdir('default') + np.testing.assert_array_equal(tickdir_expected, tickdir_actual) + + +@pytest.mark.parametrize("vertical_axis", ["x", "y", "z"]) +def test_on_move_vertical_axis(vertical_axis: str) -> None: + """ + Test vertical axis is respected when rotating the plot interactively. + """ + ax = plt.subplot(1, 1, 1, projection="3d") + ax.view_init(elev=0, azim=0, roll=0, vertical_axis=vertical_axis) + ax.get_figure().canvas.draw() + + proj_before = ax.get_proj() + event_click = mock_event(ax, button=MouseButton.LEFT, xdata=0, ydata=1) + ax._button_press(event_click) + + event_move = mock_event(ax, button=MouseButton.LEFT, xdata=0.5, ydata=0.8) + ax._on_move(event_move) + + assert ax._axis_names.index(vertical_axis) == ax._vertical_axis + + # Make sure plot has actually moved: + proj_after = ax.get_proj() + np.testing.assert_raises( + AssertionError, np.testing.assert_allclose, proj_before, proj_after + ) + + +@pytest.mark.parametrize( + "vertical_axis, aspect_expected", + [ + ("x", [1.190476, 0.892857, 1.190476]), + ("y", [0.892857, 1.190476, 1.190476]), + ("z", [1.190476, 1.190476, 0.892857]), + ], +) +def test_set_box_aspect_vertical_axis(vertical_axis, aspect_expected): + ax = plt.subplot(1, 1, 1, projection="3d") + ax.view_init(elev=0, azim=0, roll=0, vertical_axis=vertical_axis) + ax.get_figure().canvas.draw() + + ax.set_box_aspect(None) + + np.testing.assert_allclose(aspect_expected, ax._box_aspect, rtol=1e-6) + + +@image_comparison(baseline_images=['arc_pathpatch.png'], + remove_text=True, + style='mpl20') +def test_arc_pathpatch(): + ax = plt.subplot(1, 1, 1, projection="3d") + a = mpatch.Arc((0.5, 0.5), width=0.5, height=0.9, + angle=20, theta1=10, theta2=130) + ax.add_patch(a) + art3d.pathpatch_2d_to_3d(a, z=0, zdir='z') + + +@image_comparison(baseline_images=['panecolor_rcparams.png'], + remove_text=True, + style='mpl20') +def test_panecolor_rcparams(): + with plt.rc_context({'axes3d.xaxis.panecolor': 'r', + 'axes3d.yaxis.panecolor': 'g', + 'axes3d.zaxis.panecolor': 'b'}): + fig = plt.figure(figsize=(1, 1)) + fig.add_subplot(projection='3d') + + +@check_figures_equal(extensions=["png"]) +def test_mutating_input_arrays_y_and_z(fig_test, fig_ref): + """ + Test to see if the `z` axis does not get mutated + after a call to `Axes3D.plot` + + test cases came from GH#8990 + """ + ax1 = fig_test.add_subplot(111, projection='3d') + x = [1, 2, 3] + y = [0.0, 0.0, 0.0] + z = [0.0, 0.0, 0.0] + ax1.plot(x, y, z, 'o-') + + # mutate y,z to get a nontrivial line + y[:] = [1, 2, 3] + z[:] = [1, 2, 3] + + # draw the same plot without mutating x and y + ax2 = fig_ref.add_subplot(111, projection='3d') + x = [1, 2, 3] + y = [0.0, 0.0, 0.0] + z = [0.0, 0.0, 0.0] + ax2.plot(x, y, z, 'o-') + + +def test_scatter_masked_color(): + """ + Test color parameter usage with non-finite coordinate arrays. + + GH#26236 + """ + + x = [np.nan, 1, 2, 1] + y = [0, np.inf, 2, 1] + z = [0, 1, -np.inf, 1] + colors = [ + [0.0, 0.0, 0.0, 1], + [0.0, 0.0, 0.0, 1], + [0.0, 0.0, 0.0, 1], + [0.0, 0.0, 0.0, 1] + ] + + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + path3d = ax.scatter(x, y, z, color=colors) + + # Assert sizes' equality + assert len(path3d.get_offsets()) ==\ + len(super(type(path3d), path3d).get_facecolors()) + + +@mpl3d_image_comparison(['surface3d_zsort_inf.png'], style='mpl20') +def test_surface3d_zsort_inf(): + plt.rcParams['axes3d.automargin'] = True # Remove when image is regenerated + fig = plt.figure() + ax = fig.add_subplot(projection='3d') + + x, y = np.mgrid[-2:2:0.1, -2:2:0.1] + z = np.sin(x)**2 + np.cos(y)**2 + z[x.shape[0] // 2:, x.shape[1] // 2:] = np.inf + + ax.plot_surface(x, y, z, cmap='jet') + ax.view_init(elev=45, azim=145) + + +def test_Poly3DCollection_init_value_error(): + # smoke test to ensure the input check works + # GH#26420 + with pytest.raises(ValueError, + match='You must provide facecolors, edgecolors, ' + 'or both for shade to work.'): + poly = np.array([[0, 0, 1], [0, 1, 1], [0, 0, 0]], float) + c = art3d.Poly3DCollection([poly], shade=True) + + +def test_ndarray_color_kwargs_value_error(): + # smoke test + # ensures ndarray can be passed to color in kwargs for 3d projection plot + fig = plt.figure() + ax = fig.add_subplot(111, projection='3d') + ax.scatter(1, 0, 0, color=np.array([0, 0, 0, 1])) + fig.canvas.draw() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_legend3d.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_legend3d.py new file mode 100644 index 0000000000000000000000000000000000000000..7fd676df1e31591f343a4a1bd27c27b98d564949 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpl_toolkits/mplot3d/tests/test_legend3d.py @@ -0,0 +1,117 @@ +import platform + +import numpy as np + +import matplotlib as mpl +from matplotlib.colors import same_color +from matplotlib.testing.decorators import image_comparison +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import art3d + + +@image_comparison(['legend_plot.png'], remove_text=True, style='mpl20') +def test_legend_plot(): + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + x = np.arange(10) + ax.plot(x, 5 - x, 'o', zdir='y', label='z=1') + ax.plot(x, x - 5, 'o', zdir='y', label='z=-1') + ax.legend() + + +@image_comparison(['legend_bar.png'], remove_text=True, style='mpl20') +def test_legend_bar(): + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + x = np.arange(10) + b1 = ax.bar(x, x, zdir='y', align='edge', color='m') + b2 = ax.bar(x, x[::-1], zdir='x', align='edge', color='g') + ax.legend([b1[0], b2[0]], ['up', 'down']) + + +@image_comparison(['fancy.png'], remove_text=True, style='mpl20', + tol=0 if platform.machine() == 'x86_64' else 0.011) +def test_fancy(): + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + ax.plot(np.arange(10), np.full(10, 5), np.full(10, 5), 'o--', label='line') + ax.scatter(np.arange(10), np.arange(10, 0, -1), label='scatter') + ax.errorbar(np.full(10, 5), np.arange(10), np.full(10, 10), + xerr=0.5, zerr=0.5, label='errorbar') + ax.legend(loc='lower left', ncols=2, title='My legend', numpoints=1) + + +def test_linecollection_scaled_dashes(): + lines1 = [[(0, .5), (.5, 1)], [(.3, .6), (.2, .2)]] + lines2 = [[[0.7, .2], [.8, .4]], [[.5, .7], [.6, .1]]] + lines3 = [[[0.6, .2], [.8, .4]], [[.5, .7], [.1, .1]]] + lc1 = art3d.Line3DCollection(lines1, linestyles="--", lw=3) + lc2 = art3d.Line3DCollection(lines2, linestyles="-.") + lc3 = art3d.Line3DCollection(lines3, linestyles=":", lw=.5) + + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + ax.add_collection(lc1) + ax.add_collection(lc2) + ax.add_collection(lc3) + + leg = ax.legend([lc1, lc2, lc3], ['line1', 'line2', 'line 3']) + h1, h2, h3 = leg.legend_handles + + for oh, lh in zip((lc1, lc2, lc3), (h1, h2, h3)): + assert oh.get_linestyles()[0] == lh._dash_pattern + + +def test_handlerline3d(): + # Test marker consistency for monolithic Line3D legend handler. + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + ax.scatter([0, 1], [0, 1], marker="v") + handles = [art3d.Line3D([0], [0], [0], marker="v")] + leg = ax.legend(handles, ["Aardvark"], numpoints=1) + assert handles[0].get_marker() == leg.legend_handles[0].get_marker() + + +def test_contour_legend_elements(): + x, y = np.mgrid[1:10, 1:10] + h = x * y + colors = ['blue', '#00FF00', 'red'] + + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + cs = ax.contour(x, y, h, levels=[10, 30, 50], colors=colors, extend='both') + + artists, labels = cs.legend_elements() + assert labels == ['$x = 10.0$', '$x = 30.0$', '$x = 50.0$'] + assert all(isinstance(a, mpl.lines.Line2D) for a in artists) + assert all(same_color(a.get_color(), c) + for a, c in zip(artists, colors)) + + +def test_contourf_legend_elements(): + x, y = np.mgrid[1:10, 1:10] + h = x * y + + fig, ax = plt.subplots(subplot_kw=dict(projection='3d')) + cs = ax.contourf(x, y, h, levels=[10, 30, 50], + colors=['#FFFF00', '#FF00FF', '#00FFFF'], + extend='both') + cs.cmap.set_over('red') + cs.cmap.set_under('blue') + cs.changed() + artists, labels = cs.legend_elements() + assert labels == ['$x \\leq -1e+250s$', + '$10.0 < x \\leq 30.0$', + '$30.0 < x \\leq 50.0$', + '$x > 1e+250s$'] + expected_colors = ('blue', '#FFFF00', '#FF00FF', 'red') + assert all(isinstance(a, mpl.patches.Rectangle) for a in artists) + assert all(same_color(a.get_facecolor(), c) + for a, c in zip(artists, expected_colors)) + + +def test_legend_Poly3dCollection(): + + verts = np.asarray([[0, 0, 0], [0, 1, 1], [1, 0, 1]]) + mesh = art3d.Poly3DCollection([verts], label="surface") + + fig, ax = plt.subplots(subplot_kw={"projection": "3d"}) + mesh.set_edgecolor('k') + handle = ax.add_collection3d(mesh) + leg = ax.legend() + assert (leg.legend_handles[0].get_facecolor() + == handle.get_facecolor()).all() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/INSTALLER b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..5c69047b2eb8235994febeeae1da4a82365a240a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/INSTALLER @@ -0,0 +1 @@ +uv \ No newline at end of file diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/LICENSE b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..9ecdc7586d08805bc984539f6672476e86e538b6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/LICENSE @@ -0,0 +1,27 @@ +Copyright (c) 2005-2021 Fredrik Johansson and mpmath contributors + +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + + a. Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + b. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + c. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT +LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY +OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH +DAMAGE. diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/METADATA b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..994b48acdba5cd0fdfb28cd1fbb0a84ebf81cba5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/METADATA @@ -0,0 +1,233 @@ +Metadata-Version: 2.1 +Name: mpmath +Version: 1.3.0 +Summary: Python library for arbitrary-precision floating-point arithmetic +Home-page: http://mpmath.org/ +Author: Fredrik Johansson +Author-email: fredrik.johansson@gmail.com +License: BSD +Project-URL: Source, https://github.com/fredrik-johansson/mpmath +Project-URL: Tracker, https://github.com/fredrik-johansson/mpmath/issues +Project-URL: Documentation, http://mpmath.org/doc/current/ +Classifier: License :: OSI Approved :: BSD License +Classifier: Topic :: Scientific/Engineering :: Mathematics +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 2 +Classifier: Programming Language :: Python :: 2.7 +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.5 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +License-File: LICENSE +Provides-Extra: develop +Requires-Dist: pytest (>=4.6) ; extra == 'develop' +Requires-Dist: pycodestyle ; extra == 'develop' +Requires-Dist: pytest-cov ; extra == 'develop' +Requires-Dist: codecov ; extra == 'develop' +Requires-Dist: wheel ; extra == 'develop' +Provides-Extra: docs +Requires-Dist: sphinx ; extra == 'docs' +Provides-Extra: gmpy +Requires-Dist: gmpy2 (>=2.1.0a4) ; (platform_python_implementation != "PyPy") and extra == 'gmpy' +Provides-Extra: tests +Requires-Dist: pytest (>=4.6) ; extra == 'tests' + +mpmath +====== + +|pypi version| |Build status| |Code coverage status| |Zenodo Badge| + +.. |pypi version| image:: https://img.shields.io/pypi/v/mpmath.svg + :target: https://pypi.python.org/pypi/mpmath +.. |Build status| image:: https://github.com/fredrik-johansson/mpmath/workflows/test/badge.svg + :target: https://github.com/fredrik-johansson/mpmath/actions?workflow=test +.. |Code coverage status| image:: https://codecov.io/gh/fredrik-johansson/mpmath/branch/master/graph/badge.svg + :target: https://codecov.io/gh/fredrik-johansson/mpmath +.. |Zenodo Badge| image:: https://zenodo.org/badge/2934512.svg + :target: https://zenodo.org/badge/latestdoi/2934512 + +A Python library for arbitrary-precision floating-point arithmetic. + +Website: http://mpmath.org/ +Main author: Fredrik Johansson + +Mpmath is free software released under the New BSD License (see the +LICENSE file for details) + +0. History and credits +---------------------- + +The following people (among others) have contributed major patches +or new features to mpmath: + +* Pearu Peterson +* Mario Pernici +* Ondrej Certik +* Vinzent Steinberg +* Nimish Telang +* Mike Taschuk +* Case Van Horsen +* Jorn Baayen +* Chris Smith +* Juan Arias de Reyna +* Ioannis Tziakos +* Aaron Meurer +* Stefan Krastanov +* Ken Allen +* Timo Hartmann +* Sergey B Kirpichev +* Kris Kuhlman +* Paul Masson +* Michael Kagalenko +* Jonathan Warner +* Max Gaukler +* Guillermo Navas-Palencia +* Nike Dattani + +Numerous other people have contributed by reporting bugs, +requesting new features, or suggesting improvements to the +documentation. + +For a detailed changelog, including individual contributions, +see the CHANGES file. + +Fredrik's work on mpmath during summer 2008 was sponsored by Google +as part of the Google Summer of Code program. + +Fredrik's work on mpmath during summer 2009 was sponsored by the +American Institute of Mathematics under the support of the National Science +Foundation Grant No. 0757627 (FRG: L-functions and Modular Forms). + +Any opinions, findings, and conclusions or recommendations expressed in this +material are those of the author(s) and do not necessarily reflect the +views of the sponsors. + +Credit also goes to: + +* The authors of the GMP library and the Python wrapper + gmpy, enabling mpmath to become much faster at + high precision +* The authors of MPFR, pari/gp, MPFUN, and other arbitrary- + precision libraries, whose documentation has been helpful + for implementing many of the algorithms in mpmath +* Wikipedia contributors; Abramowitz & Stegun; Gradshteyn & Ryzhik; + Wolfram Research for MathWorld and the Wolfram Functions site. + These are the main references used for special functions + implementations. +* George Brandl for developing the Sphinx documentation tool + used to build mpmath's documentation + +Release history: + +* Version 1.3.0 released on March 7, 2023 +* Version 1.2.0 released on February 1, 2021 +* Version 1.1.0 released on December 11, 2018 +* Version 1.0.0 released on September 27, 2017 +* Version 0.19 released on June 10, 2014 +* Version 0.18 released on December 31, 2013 +* Version 0.17 released on February 1, 2011 +* Version 0.16 released on September 24, 2010 +* Version 0.15 released on June 6, 2010 +* Version 0.14 released on February 5, 2010 +* Version 0.13 released on August 13, 2009 +* Version 0.12 released on June 9, 2009 +* Version 0.11 released on January 26, 2009 +* Version 0.10 released on October 15, 2008 +* Version 0.9 released on August 23, 2008 +* Version 0.8 released on April 20, 2008 +* Version 0.7 released on March 12, 2008 +* Version 0.6 released on January 13, 2008 +* Version 0.5 released on November 24, 2007 +* Version 0.4 released on November 3, 2007 +* Version 0.3 released on October 5, 2007 +* Version 0.2 released on October 2, 2007 +* Version 0.1 released on September 27, 2007 + +1. Download & installation +-------------------------- + +Mpmath requires Python 2.7 or 3.5 (or later versions). It has been tested +with CPython 2.7, 3.5 through 3.7 and for PyPy. + +The latest release of mpmath can be downloaded from the mpmath +website and from https://github.com/fredrik-johansson/mpmath/releases + +It should also be available in the Python Package Index at +https://pypi.python.org/pypi/mpmath + +To install latest release of Mpmath with pip, simply run + +``pip install mpmath`` + +Or unpack the mpmath archive and run + +``python setup.py install`` + +Mpmath can also be installed using + +``python -m easy_install mpmath`` + +The latest development code is available from +https://github.com/fredrik-johansson/mpmath + +See the main documentation for more detailed instructions. + +2. Running tests +---------------- + +The unit tests in mpmath/tests/ can be run via the script +runtests.py, but it is recommended to run them with py.test +(https://pytest.org/), especially +to generate more useful reports in case there are failures. + +You may also want to check out the demo scripts in the demo +directory. + +The master branch is automatically tested by Travis CI. + +3. Documentation +---------------- + +Documentation in reStructuredText format is available in the +doc directory included with the source package. These files +are human-readable, but can be compiled to prettier HTML using +the build.py script (requires Sphinx, http://sphinx.pocoo.org/). + +See setup.txt in the documentation for more information. + +The most recent documentation is also available in HTML format: + +http://mpmath.org/doc/current/ + +4. Known problems +----------------- + +Mpmath is a work in progress. Major issues include: + +* Some functions may return incorrect values when given extremely + large arguments or arguments very close to singularities. + +* Directed rounding works for arithmetic operations. It is implemented + heuristically for other operations, and their results may be off by one + or two units in the last place (even if otherwise accurate). + +* Some IEEE 754 features are not available. Inifinities and NaN are + partially supported; denormal rounding is currently not available + at all. + +* The interface for switching precision and rounding is not finalized. + The current method is not threadsafe. + +5. Help and bug reports +----------------------- + +General questions and comments can be sent to the mpmath mailinglist, +mpmath@googlegroups.com + +You can also report bugs and send patches to the mpmath issue tracker, +https://github.com/fredrik-johansson/mpmath/issues diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/RECORD b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..45e6fb5270fece6b354c40b8f57c1cfa617e890a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/RECORD @@ -0,0 +1,94 @@ +mpmath-1.3.0.dist-info/INSTALLER,sha256=5hhM4Q4mYTT9z6QB6PGpUAW81PGNFrYrdXMj4oM_6ak,2 +mpmath-1.3.0.dist-info/LICENSE,sha256=wmyugdpFCOXiSZhXd6M4IfGDIj67dNf4z7-Q_n7vL7c,1537 +mpmath-1.3.0.dist-info/METADATA,sha256=RLZupES5wNGa6UgV01a_BHrmtoDBkmi1wmVofNaoFAY,8630 +mpmath-1.3.0.dist-info/RECORD,, 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a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/REQUESTED b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/WHEEL b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..57e3d840d59a650ac5bccbad5baeec47d155f0ad --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: bdist_wheel (0.38.4) +Root-Is-Purelib: true +Tag: py3-none-any + diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/top_level.txt b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..dda7c273a8dd1c6adffa9d2d9901e0ce6876f4ac --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/mpmath-1.3.0.dist-info/top_level.txt @@ -0,0 +1 @@ +mpmath diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b057eb43802b02d6a10d2c5a62046db1d271670c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/__init__.py @@ -0,0 +1,57 @@ +from optuna import distributions +from optuna import exceptions +from optuna import integration +from optuna import logging +from optuna import pruners +from optuna import samplers +from optuna import search_space +from optuna import storages +from optuna import study +from optuna import trial +from optuna import version +from optuna._imports import _LazyImport +from optuna.exceptions import TrialPruned +from optuna.study import copy_study +from optuna.study import create_study +from optuna.study import delete_study +from optuna.study import get_all_study_names +from optuna.study import get_all_study_summaries +from optuna.study import load_study +from optuna.study import Study +from optuna.trial import create_trial +from optuna.trial import Trial +from optuna.version import __version__ + + +__all__ = [ + "Study", + "Trial", + "TrialPruned", + "__version__", + "artifacts", + "copy_study", + "create_study", + "create_trial", + "delete_study", + "distributions", + "exceptions", + "get_all_study_names", + "get_all_study_summaries", + "importance", + "integration", + "load_study", + "logging", + "pruners", + "samplers", + "search_space", + "storages", + "study", + "trial", + "version", + "visualization", +] + + +artifacts = 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import annotations + +from typing import TYPE_CHECKING + +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from collections.abc import Container + + from optuna.study import Study + from optuna.trial import FrozenTrial + + +class MaxTrialsCallback: + """Set a maximum number of trials before ending the study. + + While the ``n_trials`` argument of :meth:`optuna.study.Study.optimize` sets the number of + trials that will be run, you may want to continue running until you have a certain number of + successfully completed trials or stop the study when you have a certain number of trials that + fail. This ``MaxTrialsCallback`` class allows you to set a maximum number of trials for a + particular :class:`~optuna.trial.TrialState` before stopping the study. + + Example: + + .. testcode:: + + import optuna + from optuna.study import MaxTrialsCallback + from optuna.trial import TrialState + + + def objective(trial): + x = trial.suggest_float("x", -1, 1) + return x**2 + + + study = optuna.create_study() + study.optimize( + objective, + callbacks=[MaxTrialsCallback(10, states=(TrialState.COMPLETE,))], + ) + + Args: + n_trials: + The max number of trials. Must be set to an integer. + states: + Tuple of the :class:`~optuna.trial.TrialState` to be counted + towards the max trials limit. Default value is ``(TrialState.COMPLETE,)``. + If :obj:`None`, count all states. + """ + + def __init__( + self, n_trials: int, states: Container[TrialState] | None = (TrialState.COMPLETE,) + ) -> None: + self._n_trials = n_trials + self._states = states + + def __call__(self, study: Study, trial: FrozenTrial) -> None: + trials = study.get_trials(deepcopy=False, states=self._states) + n_complete = len(trials) + if n_complete >= self._n_trials: + study.stop() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_convert_positional_args.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_convert_positional_args.py new file mode 100644 index 0000000000000000000000000000000000000000..4865d9e6297cee46073e167d12ac04c0fca64ed7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_convert_positional_args.py @@ -0,0 +1,139 @@ +from __future__ import annotations + +from functools import wraps +from inspect import Parameter +from inspect import signature +from typing import Any +from typing import TYPE_CHECKING +from typing import TypeVar +import warnings + +from optuna._deprecated import _validate_two_version +from optuna._experimental import _validate_version + + +if TYPE_CHECKING: + from collections.abc import Callable + from collections.abc import Sequence + + from typing_extensions import ParamSpec + + _P = ParamSpec("_P") + _T = TypeVar("_T") + + +_DEPRECATION_WARNING_TEMPLATE = ( + "Positional arguments {deprecated_positional_arg_names} in {func_name}() " + "have been deprecated since v{d_ver}. " + "They will be replaced with the corresponding keyword arguments in v{r_ver}, " + "so please use the keyword specification instead. " + "See https://github.com/optuna/optuna/releases/tag/v{d_ver} for details." +) + + +def _get_positional_arg_names(func: "Callable[_P, _T]") -> list[str]: + params = signature(func).parameters + positional_arg_names = [ + name + for name, p in params.items() + if p.default == Parameter.empty and p.kind == p.POSITIONAL_OR_KEYWORD + ] + return positional_arg_names + + +def _infer_kwargs(previous_positional_arg_names: Sequence[str], *args: Any) -> dict[str, Any]: + inferred_kwargs = {arg_name: val for val, arg_name in zip(args, previous_positional_arg_names)} + return inferred_kwargs + + +def convert_positional_args( + *, + previous_positional_arg_names: Sequence[str], + deprecated_version: str, + removed_version: str, + warning_stacklevel: int = 2, +) -> "Callable[[Callable[_P, _T]], Callable[_P, _T]]": + """Convert positional arguments to keyword arguments. + + Args: + previous_positional_arg_names: + List of names previously given as positional arguments. + warning_stacklevel: + Level of the stack trace where decorated function locates. + deprecated_version: + The version in which the use of positional arguments is deprecated. + removed_version: + The version in which the use of positional arguments will be removed. + """ + + if deprecated_version is not None or removed_version is not None: + if deprecated_version is None: + raise ValueError( + "deprecated_version must not be None when removed_version is specified." + ) + if removed_version is None: + raise ValueError( + "removed_version must not be None when deprecated_version is specified." + ) + + _validate_version(deprecated_version) + _validate_version(removed_version) + _validate_two_version(deprecated_version, removed_version) + + def converter_decorator(func: "Callable[_P, _T]") -> "Callable[_P, _T]": + + assert set(previous_positional_arg_names).issubset(set(signature(func).parameters)), ( + f"{set(previous_positional_arg_names)} is not a subset of" + f" {set(signature(func).parameters)}" + ) + + @wraps(func) + def converter_wrapper(*args: Any, **kwargs: Any) -> "_T": + warning_messages = [] + positional_arg_names = _get_positional_arg_names(func) + inferred_kwargs = _infer_kwargs(previous_positional_arg_names, *args) + + if len(inferred_kwargs) > len(positional_arg_names): + expected_kwds = set(inferred_kwargs) - set(positional_arg_names) + warning_messages.append( + f"{func.__name__}() got {expected_kwds} as positional arguments " + "but they were expected to be given as keyword arguments." + ) + + if deprecated_version or removed_version: + warning_messages.append( + _DEPRECATION_WARNING_TEMPLATE.format( + deprecated_positional_arg_names=previous_positional_arg_names, + func_name=func.__name__, + d_ver=deprecated_version, + r_ver=removed_version, + ) + ) + + if warning_messages: + warnings.warn( + "\n".join(warning_messages), FutureWarning, stacklevel=warning_stacklevel + ) + + if len(args) > len(previous_positional_arg_names): + raise TypeError( + f"{func.__name__}() takes {len(previous_positional_arg_names)} positional" + f" arguments but {len(args)} were given." + ) + + duplicated_kwds = set(kwargs).intersection(inferred_kwargs) + if len(duplicated_kwds): + # When specifying positional arguments that are not located at the end of args as + # keyword arguments, raise TypeError as follows by imitating the Python standard + # behavior + raise TypeError( + f"{func.__name__}() got multiple values for arguments {duplicated_kwds}." + ) + + kwargs.update(inferred_kwargs) + + return func(**kwargs) # type: ignore[call-arg] + + return converter_wrapper + + return converter_decorator diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_deprecated.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_deprecated.py new file mode 100644 index 0000000000000000000000000000000000000000..b5c621352933058afe207f205c56760f89a69d22 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_deprecated.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import functools +import textwrap +from typing import Any +from typing import TYPE_CHECKING +from typing import TypeVar +import warnings + +from packaging import version + +from optuna._experimental import _get_docstring_indent +from optuna._experimental import _validate_version + + +if TYPE_CHECKING: + from collections.abc import Callable + + from typing_extensions import ParamSpec + + FT = TypeVar("FT") + FP = ParamSpec("FP") + CT = TypeVar("CT") + + +_DEPRECATION_NOTE_TEMPLATE = """ + +.. warning:: + Deprecated in v{d_ver}. This feature will be removed in the future. The removal of this + feature is currently scheduled for v{r_ver}, but this schedule is subject to change. + See https://github.com/optuna/optuna/releases/tag/v{d_ver}. +""" + + +_DEPRECATION_WARNING_TEMPLATE = ( + "{name} has been deprecated in v{d_ver}. " + "This feature will be removed in v{r_ver}. " + "See https://github.com/optuna/optuna/releases/tag/v{d_ver}." +) + + +def _validate_two_version(old_version: str, new_version: str) -> None: + if version.parse(old_version) > version.parse(new_version): + raise ValueError( + "Invalid version relationship. The deprecated version must be smaller than " + "the removed version, but (deprecated version, removed version) = ({}, {}) are " + "specified.".format(old_version, new_version) + ) + + +def _format_text(text: str) -> str: + return "\n\n" + textwrap.indent(text.strip(), " ") + "\n" + + +def deprecated_func( + deprecated_version: str, + removed_version: str, + name: str | None = None, + text: str | None = None, +) -> "Callable[[Callable[FP, FT]], Callable[FP, FT]]": + """Decorate function as deprecated. + + Args: + deprecated_version: + The version in which the target feature is deprecated. + removed_version: + The version in which the target feature will be removed. + name: + The name of the feature. Defaults to the function name. Optional. + text: + The additional text for the deprecation note. The default note is build using specified + ``deprecated_version`` and ``removed_version``. If you want to provide additional + information, please specify this argument yourself. + + .. note:: + The default deprecation note is as follows: "Deprecated in v{d_ver}. This feature + will be removed in the future. The removal of this feature is currently scheduled + for v{r_ver}, but this schedule is subject to change. See + https://github.com/optuna/optuna/releases/tag/v{d_ver}." + + .. note:: + The specified text is concatenated after the default deprecation note. + """ + + _validate_version(deprecated_version) + _validate_version(removed_version) + _validate_two_version(deprecated_version, removed_version) + + def decorator(func: "Callable[FP, FT]") -> "Callable[FP, FT]": + if func.__doc__ is None: + func.__doc__ = "" + + note = _DEPRECATION_NOTE_TEMPLATE.format(d_ver=deprecated_version, r_ver=removed_version) + if text is not None: + note += _format_text(text) + indent = _get_docstring_indent(func.__doc__) + func.__doc__ = func.__doc__.strip() + textwrap.indent(note, indent) + indent + + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> "FT": + """Decorates a function as deprecated. + + This decorator is supposed to be applied to the deprecated function. + """ + + message = _DEPRECATION_WARNING_TEMPLATE.format( + name=(name if name is not None else func.__name__), + d_ver=deprecated_version, + r_ver=removed_version, + ) + if text is not None: + message += " " + text + warnings.warn(message, FutureWarning, stacklevel=2) + + return func(*args, **kwargs) + + return wrapper + + return decorator + + +def deprecated_class( + deprecated_version: str, + removed_version: str, + name: str | None = None, + text: str | None = None, +) -> "Callable[[CT], CT]": + """Decorate class as deprecated. + + Args: + deprecated_version: + The version in which the target feature is deprecated. + removed_version: + The version in which the target feature will be removed. + name: + The name of the feature. Defaults to the class name. Optional. + text: + The additional text for the deprecation note. The default note is build using specified + ``deprecated_version`` and ``removed_version``. If you want to provide additional + information, please specify this argument yourself. + + .. note:: + The default deprecation note is as follows: "Deprecated in v{d_ver}. This feature + will be removed in the future. The removal of this feature is currently scheduled + for v{r_ver}, but this schedule is subject to change. See + https://github.com/optuna/optuna/releases/tag/v{d_ver}." + + .. note:: + The specified text is concatenated after the default deprecation note. + """ + + _validate_version(deprecated_version) + _validate_version(removed_version) + _validate_two_version(deprecated_version, removed_version) + + def decorator(cls: "CT") -> "CT": + def wrapper(cls: "CT") -> "CT": + """Decorates a class as deprecated. + + This decorator is supposed to be applied to the deprecated class. + """ + _original_init = getattr(cls, "__init__") + _original_name = getattr(cls, "__name__") + + @functools.wraps(_original_init) + def wrapped_init(self: Any, *args: Any, **kwargs: Any) -> None: + message = _DEPRECATION_WARNING_TEMPLATE.format( + name=(name if name is not None else _original_name), + d_ver=deprecated_version, + r_ver=removed_version, + ) + if text is not None: + message += " " + text + warnings.warn( + message, + FutureWarning, + stacklevel=2, + ) + + _original_init(self, *args, **kwargs) + + setattr(cls, "__init__", wrapped_init) + + if cls.__doc__ is None: + cls.__doc__ = "" + + note = _DEPRECATION_NOTE_TEMPLATE.format( + d_ver=deprecated_version, r_ver=removed_version + ) + if text is not None: + note += _format_text(text) + indent = _get_docstring_indent(cls.__doc__) + cls.__doc__ = cls.__doc__.strip() + textwrap.indent(note, indent) + indent + + return cls + + return wrapper(cls) + + return decorator diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_experimental.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_experimental.py new file mode 100644 index 0000000000000000000000000000000000000000..8cb0f87554299e8a5a4db372186e9882e9e6df2f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_experimental.py @@ -0,0 +1,140 @@ +from __future__ import annotations + +import functools +import textwrap +from typing import Any +from typing import TYPE_CHECKING +from typing import TypeVar +import warnings + +from optuna.exceptions import ExperimentalWarning + + +if TYPE_CHECKING: + from collections.abc import Callable + + from typing_extensions import ParamSpec + + FT = TypeVar("FT") + FP = ParamSpec("FP") + CT = TypeVar("CT") + + +_EXPERIMENTAL_NOTE_TEMPLATE = """ + +.. note:: + Added in v{ver} as an experimental feature. The interface may change in newer versions + without prior notice. See https://github.com/optuna/optuna/releases/tag/v{ver}. +""" + + +def warn_experimental_argument(option_name: str) -> None: + warnings.warn( + f"Argument ``{option_name}`` is an experimental feature." + " The interface can change in the future.", + ExperimentalWarning, + ) + + +def _validate_version(version: str) -> None: + if not isinstance(version, str) or len(version.split(".")) != 3: + raise ValueError( + "Invalid version specification. Must follow `x.y.z` format but `{}` is given".format( + version + ) + ) + + +def _get_docstring_indent(docstring: str) -> str: + return docstring.split("\n")[-1] if "\n" in docstring else "" + + +def experimental_func( + version: str, + name: str | None = None, +) -> Callable[[Callable[FP, FT]], Callable[FP, FT]]: + """Decorate function as experimental. + + Args: + version: The first version that supports the target feature. + name: The name of the feature. Defaults to fully qualified name of + the function, i.e. `f"{func.__module__}.{func.__qualname__}"`. Optional. + """ + + _validate_version(version) + + def decorator(func: Callable[FP, FT]) -> Callable[FP, FT]: + if func.__doc__ is None: + func.__doc__ = "" + + note = _EXPERIMENTAL_NOTE_TEMPLATE.format(ver=version) + indent = _get_docstring_indent(func.__doc__) + func.__doc__ = func.__doc__.strip() + textwrap.indent(note, indent) + indent + + _name = name or f"{func.__module__}.{func.__qualname__}" + + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> FT: + warnings.warn( + "{} is experimental (supported from v{}). " + "The interface can change in the future.".format(_name, version), + ExperimentalWarning, + stacklevel=2, + ) + + return func(*args, **kwargs) + + return wrapper + + return decorator + + +def experimental_class( + version: str, + name: str | None = None, +) -> Callable[[CT], CT]: + """Decorate class as experimental. + + Args: + version: The first version that supports the target feature. + name: The name of the feature. Defaults to the class name. Optional. + """ + + _validate_version(version) + + def decorator(cls: CT) -> CT: + def wrapper(cls: CT) -> CT: + """Decorates a class as experimental. + + This decorator is supposed to be applied to the experimental class. + """ + _original_init = getattr(cls, "__init__") + _original_name = getattr(cls, "__name__") + + @functools.wraps(_original_init) + def wrapped_init(self: Any, *args: Any, **kwargs: Any) -> None: + warnings.warn( + "{} is experimental (supported from v{}). " + "The interface can change in the future.".format( + name if name is not None else _original_name, version + ), + ExperimentalWarning, + stacklevel=2, + ) + + _original_init(self, *args, **kwargs) + + setattr(cls, "__init__", wrapped_init) + + if cls.__doc__ is None: + cls.__doc__ = "" + + note = _EXPERIMENTAL_NOTE_TEMPLATE.format(ver=version) + indent = _get_docstring_indent(cls.__doc__) + cls.__doc__ = cls.__doc__.strip() + textwrap.indent(note, indent) + indent + + return cls + + return wrapper(cls) + + return decorator diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/acqf.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/acqf.py new file mode 100644 index 0000000000000000000000000000000000000000..dff9f7799d3c75cc8723b1df1a5a4a47c2304205 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/acqf.py @@ -0,0 +1,310 @@ +from __future__ import annotations + +from abc import ABC +from abc import abstractmethod +import math +from typing import cast +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._hypervolume import get_non_dominated_box_bounds +from optuna.study._multi_objective import _is_pareto_front + + +if TYPE_CHECKING: + import torch + + from optuna._gp.gp import GPRegressor + from optuna._gp.search_space import SearchSpace +else: + from optuna._imports import _LazyImport + + torch = _LazyImport("torch") + + +def _sample_from_normal_sobol(dim: int, n_samples: int, seed: int | None) -> torch.Tensor: + # NOTE(nabenabe): Normal Sobol sampling based on BoTorch. + # https://github.com/pytorch/botorch/blob/v0.13.0/botorch/sampling/qmc.py#L26-L97 + # https://github.com/pytorch/botorch/blob/v0.13.0/botorch/utils/sampling.py#L109-L138 + sobol_samples = torch.quasirandom.SobolEngine( # type: ignore[no-untyped-call] + dimension=dim, scramble=True, seed=seed + ).draw(n_samples, dtype=torch.float64) + samples = 2.0 * (sobol_samples - 0.5) # The Sobol sequence in [-1, 1]. + # Inverse transform to standard normal (values to close to -1 or 1 result in infinity). + return torch.erfinv(samples) * float(np.sqrt(2)) + + +def logehvi( + Y_post: torch.Tensor, # (..., n_qmc_samples, n_objectives) + non_dominated_box_lower_bounds: torch.Tensor, # (n_boxes, n_objectives) + non_dominated_box_upper_bounds: torch.Tensor, # (n_boxes, n_objectives) +) -> torch.Tensor: # (..., ) + log_n_qmc_samples = float(np.log(Y_post.shape[-2])) + # This function calculates Eq. (1) of https://arxiv.org/abs/2006.05078. + # TODO(nabenabe): Adapt to Eq. (3) when we support batch optimization. + # TODO(nabenabe): Make the calculation here more numerically stable. + # cf. https://arxiv.org/abs/2310.20708 + # Check the implementations here: + # https://github.com/pytorch/botorch/blob/v0.13.0/botorch/utils/safe_math.py + # https://github.com/pytorch/botorch/blob/v0.13.0/botorch/acquisition/multi_objective/logei.py#L146-L266 + _EPS = torch.tensor(1e-12, dtype=torch.float64) # NOTE(nabenabe): grad becomes nan when EPS=0. + diff = torch.maximum( + _EPS, + torch.minimum(Y_post[..., None, :], non_dominated_box_upper_bounds) + - non_dominated_box_lower_bounds, + ) + # NOTE(nabenabe): logsumexp with dim=-1 is for the HVI calculation and that with dim=-2 is for + # expectation of the HVIs over the fixed_samples. + return torch.special.logsumexp(diff.log().sum(dim=-1), dim=(-2, -1)) - log_n_qmc_samples + + +def standard_logei(z: torch.Tensor) -> torch.Tensor: + # Return E_{x ~ N(0, 1)}[max(0, x+z)] + + # We switch the implementation depending on the value of z to + # avoid numerical instability. + small = z < -25 + + vals = torch.empty_like(z) + # Eq. (9) in ref: https://arxiv.org/pdf/2310.20708.pdf + # NOTE: We do not use the third condition because ours is good enough. + z_small = z[small] + z_normal = z[~small] + sqrt_2pi = math.sqrt(2 * math.pi) + # First condition + cdf = 0.5 * torch.special.erfc(-z_normal * math.sqrt(0.5)) + pdf = torch.exp(-0.5 * z_normal**2) * (1 / sqrt_2pi) + vals[~small] = torch.log(z_normal * cdf + pdf) + # Second condition + r = math.sqrt(0.5 * math.pi) * torch.special.erfcx(-z_small * math.sqrt(0.5)) + vals[small] = -0.5 * z_small**2 + torch.log((z_small * r + 1) * (1 / sqrt_2pi)) + return vals + + +def logei(mean: torch.Tensor, var: torch.Tensor, f0: float) -> torch.Tensor: + # Return E_{y ~ N(mean, var)}[max(0, y-f0)] + sigma = torch.sqrt(var) + st_val = standard_logei((mean - f0) / sigma) + val = torch.log(sigma) + st_val + return val + + +class BaseAcquisitionFunc(ABC): + def __init__(self, length_scales: np.ndarray, search_space: SearchSpace) -> None: + self.length_scales = length_scales + self.search_space = search_space + + @abstractmethod + def eval_acqf(self, x: torch.Tensor) -> torch.Tensor: + raise NotImplementedError + + def eval_acqf_no_grad(self, x: np.ndarray) -> np.ndarray: + with torch.no_grad(): + return self.eval_acqf(torch.from_numpy(x)).detach().numpy() + + def eval_acqf_with_grad(self, x: np.ndarray) -> tuple[float, np.ndarray]: + assert x.ndim == 1 + x_tensor = torch.from_numpy(x).requires_grad_(True) + val = self.eval_acqf(x_tensor) + val.backward() # type: ignore + return val.item(), x_tensor.grad.detach().numpy() # type: ignore + + +class LogEI(BaseAcquisitionFunc): + def __init__( + self, + gpr: GPRegressor, + search_space: SearchSpace, + threshold: float, + stabilizing_noise: float = 1e-12, + ) -> None: + self._gpr = gpr + self._stabilizing_noise = stabilizing_noise + self._threshold = threshold + super().__init__(gpr.length_scales, search_space) + + def eval_acqf(self, x: torch.Tensor) -> torch.Tensor: + mean, var = self._gpr.posterior(x) + # If there are no feasible trials, max_Y is set to -np.inf. + # If max_Y is set to -np.inf, we set logEI to zero to ignore it. + return ( + logei(mean=mean, var=var + self._stabilizing_noise, f0=self._threshold) + if not np.isneginf(self._threshold) + else torch.zeros(x.shape[:-1], dtype=torch.float64) + ) + + +class LogPI(BaseAcquisitionFunc): + def __init__( + self, + gpr: GPRegressor, + search_space: SearchSpace, + threshold: float, + stabilizing_noise: float = 1e-12, + ) -> None: + self._gpr = gpr + self._stabilizing_noise = stabilizing_noise + self._threshold = threshold + super().__init__(gpr.length_scales, search_space) + + def eval_acqf(self, x: torch.Tensor) -> torch.Tensor: + # Return the integral of N(mean, var) from f0 to inf. + # This is identical to the integral of N(0, 1) from (f0-mean)/sigma to inf. + # Return E_{y ~ N(mean, var)}[bool(y >= f0)] + mean, var = self._gpr.posterior(x) + sigma = torch.sqrt(var + self._stabilizing_noise) + # NOTE(nabenabe): integral from a to b of f(x) is integral from -b to -a of f(-x). + return torch.special.log_ndtr((mean - self._threshold) / sigma) + + +class UCB(BaseAcquisitionFunc): + def __init__( + self, + gpr: GPRegressor, + search_space: SearchSpace, + beta: float, + ) -> None: + self._gpr = gpr + self._beta = beta + super().__init__(gpr.length_scales, search_space) + + def eval_acqf(self, x: torch.Tensor) -> torch.Tensor: + mean, var = self._gpr.posterior(x) + return mean + torch.sqrt(self._beta * var) + + +class LCB(BaseAcquisitionFunc): + def __init__( + self, + gpr: GPRegressor, + search_space: SearchSpace, + beta: float, + ) -> None: + self._gpr = gpr + self._beta = beta + super().__init__(gpr.length_scales, search_space) + + def eval_acqf(self, x: torch.Tensor) -> torch.Tensor: + mean, var = self._gpr.posterior(x) + return mean - torch.sqrt(self._beta * var) + + +class ConstrainedLogEI(BaseAcquisitionFunc): + def __init__( + self, + gpr: GPRegressor, + search_space: SearchSpace, + threshold: float, + constraints_gpr_list: list[GPRegressor], + constraints_threshold_list: list[float], + stabilizing_noise: float = 1e-12, + ) -> None: + assert ( + len(constraints_gpr_list) == len(constraints_threshold_list) and constraints_gpr_list + ) + self._acqf = LogEI(gpr, search_space, threshold, stabilizing_noise) + self._constraints_acqf_list = [ + LogPI(_gpr, search_space, _threshold, stabilizing_noise) + for _gpr, _threshold in zip(constraints_gpr_list, constraints_threshold_list) + ] + super().__init__(gpr.length_scales, search_space) + + def eval_acqf(self, x: torch.Tensor) -> torch.Tensor: + # TODO(kAIto47802): Handle the infeasible case inside `ConstrainedLogEI` + # instead of `LogEI`. + return self._acqf.eval_acqf(x) + sum( + acqf.eval_acqf(x) for acqf in self._constraints_acqf_list + ) + + +class LogEHVI(BaseAcquisitionFunc): + def __init__( + self, + gpr_list: list[GPRegressor], + search_space: SearchSpace, + Y_train: torch.Tensor, + n_qmc_samples: int, + qmc_seed: int | None, + stabilizing_noise: float = 1e-12, + ) -> None: + def _get_non_dominated_box_bounds() -> tuple[torch.Tensor, torch.Tensor]: + # NOTE(nabenabe): Y is to be maximized, loss_vals is to be minimized. + loss_vals = -Y_train.numpy() + pareto_sols = loss_vals[_is_pareto_front(loss_vals, assume_unique_lexsorted=False)] + ref_point = np.max(loss_vals, axis=0) + ref_point = np.nextafter(np.maximum(1.1 * ref_point, 0.9 * ref_point), np.inf) + lbs, ubs = get_non_dominated_box_bounds(pareto_sols, ref_point) + # NOTE(nabenabe): Flip back the sign to make them compatible with maximization. + return torch.from_numpy(-ubs), torch.from_numpy(-lbs) + + self._stabilizing_noise = stabilizing_noise + self._gpr_list = gpr_list + self._fixed_samples = _sample_from_normal_sobol( + dim=Y_train.shape[-1], n_samples=n_qmc_samples, seed=qmc_seed + ) + self._non_dominated_box_lower_bounds, self._non_dominated_box_upper_bounds = ( + _get_non_dominated_box_bounds() + ) + # Since all the objectives are equally important, we simply use the mean of + # inverse of squared mean lengthscales over all the objectives. + # inverse_squared_lengthscales is used in optim_mixed.py. + # cf. https://github.com/optuna/optuna/blob/v4.3.0/optuna/_gp/optim_mixed.py#L200-L209 + super().__init__(np.mean([gpr.length_scales for gpr in gpr_list], axis=0), search_space) + + def eval_acqf(self, x: torch.Tensor) -> torch.Tensor: + Y_post = [] + for i, gpr in enumerate(self._gpr_list): + mean, var = gpr.posterior(x) + stdev = torch.sqrt(var + self._stabilizing_noise) + # NOTE(nabenabe): By using fixed samples from the Sobol sequence, EHVI becomes + # deterministic, making it possible to optimize the acqf by l-BFGS. + # Sobol is better than the standard Monte-Carlo w.r.t. the approximation stability. + # cf. Appendix D of https://arxiv.org/pdf/2006.05078 + Y_post.append(mean[..., None] + stdev[..., None] * self._fixed_samples[..., i]) + + # NOTE(nabenabe): Use the following once multi-task GP is supported. + # L = torch.linalg.cholesky(cov) + # Y_post = means[..., None, :] + torch.einsum("...MM,SM->...SM", L, fixed_samples) + return logehvi( + Y_post=torch.stack(Y_post, dim=-1), + non_dominated_box_lower_bounds=self._non_dominated_box_lower_bounds, + non_dominated_box_upper_bounds=self._non_dominated_box_upper_bounds, + ) + + +class ConstrainedLogEHVI(BaseAcquisitionFunc): + def __init__( + self, + gpr_list: list[GPRegressor], + search_space: SearchSpace, + Y_feasible: torch.Tensor | None, + n_qmc_samples: int, + qmc_seed: int | None, + constraints_gpr_list: list[GPRegressor], + constraints_threshold_list: list[float], + stabilizing_noise: float = 1e-12, + ) -> None: + assert ( + len(constraints_gpr_list) == len(constraints_threshold_list) and constraints_gpr_list + ) + self._acqf = ( + LogEHVI(gpr_list, search_space, Y_feasible, n_qmc_samples, qmc_seed, stabilizing_noise) + if Y_feasible is not None + else None + ) + self._constraints_acqf_list = [ + LogPI(_gpr, search_space, _threshold, stabilizing_noise) + for _gpr, _threshold in zip(constraints_gpr_list, constraints_threshold_list) + ] + # Since all the objectives are equally important, we simply use the mean of + # inverse of squared mean lengthscales over all the objectives. + # inverse_squared_lengthscales is used in optim_mixed.py. + # cf. https://github.com/optuna/optuna/blob/v4.3.0/optuna/_gp/optim_mixed.py#L200-L209 + super().__init__(np.mean([gpr.length_scales for gpr in gpr_list], axis=0), search_space) + + def eval_acqf(self, x: torch.Tensor) -> torch.Tensor: + constraints_acqf_values = sum(acqf.eval_acqf(x) for acqf in self._constraints_acqf_list) + if self._acqf is None: + return cast(torch.Tensor, constraints_acqf_values) + return constraints_acqf_values + self._acqf.eval_acqf(x) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/gp.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/gp.py new file mode 100644 index 0000000000000000000000000000000000000000..f0eccc1d9427895188daa43a9f91a94a23a1c820 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/gp.py @@ -0,0 +1,340 @@ +"""Notations in this Gaussian process implementation + +X_train: Observed parameter values with the shape of (len(trials), len(params)). +y_train: Observed objective values with the shape of (len(trials), ). +x: (Possibly batched) parameter value(s) to evaluate with the shape of (..., len(params)). +cov_fX_fX: Kernel matrix X = V[f(X)] with the shape of (len(trials), len(trials)). +cov_fx_fX: Kernel matrix Cov[f(x), f(X)] with the shape of (..., len(trials)). +cov_fx_fx: Kernel scalar value x = V[f(x)]. This value is constant for the Matern 5/2 kernel. +cov_Y_Y_inv: + The inverse of the covariance matrix (V[f(X) + noise_var])^-1 with the shape of + (len(trials), len(trials)). +cov_Y_Y_inv_Y: `cov_Y_Y_inv @ y` with the shape of (len(trials), ). +max_Y: The maximum of Y (Note that we transform the objective values such that it is maximized.) +d2: The squared distance between two points. +is_categorical: + A boolean array with the shape of (len(params), ). If is_categorical[i] is True, the i-th + parameter is categorical. +""" + +from __future__ import annotations + +import math +from typing import Any +from typing import TYPE_CHECKING +import warnings + +import numpy as np + +from optuna._gp.scipy_blas_thread_patch import single_blas_thread_if_scipy_v1_15_or_newer +from optuna.logging import get_logger + + +if TYPE_CHECKING: + from collections.abc import Callable + + import scipy.optimize as so + import torch +else: + from optuna._imports import _LazyImport + + so = _LazyImport("scipy.optimize") + torch = _LazyImport("torch") + +logger = get_logger(__name__) + + +def warn_and_convert_inf(values: np.ndarray) -> np.ndarray: + is_values_finite = np.isfinite(values) + if np.all(is_values_finite): + return values + + warnings.warn("Clip non-finite values to the min/max finite values for GP fittings.") + is_any_finite = np.any(is_values_finite, axis=0) + # NOTE(nabenabe): values cannot include nan to apply np.clip properly, but Optuna anyways won't + # pass nan in values by design. + return np.clip( + values, + np.where(is_any_finite, np.min(np.where(is_values_finite, values, np.inf), axis=0), 0.0), + np.where(is_any_finite, np.max(np.where(is_values_finite, values, -np.inf), axis=0), 0.0), + ) + + +class Matern52Kernel(torch.autograd.Function): + @staticmethod + def forward(ctx: Any, squared_distance: torch.Tensor) -> torch.Tensor: + """ + This method calculates `exp(-sqrt5d) * (1/3 * sqrt5d ** 2 + sqrt5d + 1)` where + `sqrt5d = sqrt(5 * squared_distance)`. + + Please note that automatic differentiation by PyTorch does not work well at + `squared_distance = 0` due to zero division, so we manually save the derivative, i.e., + `-5/6 * (1 + sqrt5d) * exp(-sqrt5d)`, for the exact derivative calculation. + + Notice that the derivative of this function is taken w.r.t. d**2, but not w.r.t. d. + """ + sqrt5d = torch.sqrt(5 * squared_distance) + exp_part = torch.exp(-sqrt5d) + val = exp_part * ((5 / 3) * squared_distance + sqrt5d + 1) + deriv = (-5 / 6) * (sqrt5d + 1) * exp_part + ctx.save_for_backward(deriv) + return val + + @staticmethod + def backward(ctx: Any, grad: torch.Tensor) -> torch.Tensor: + """ + Let x be squared_distance, f(x) be forward(ctx, x), and g(f) be a provided function, then + deriv := df/dx, grad := dg/df, and deriv * grad = df/dx * dg/df = dg/dx. + """ + (deriv,) = ctx.saved_tensors + return deriv * grad + + +class GPRegressor: + def __init__( + self, + is_categorical: torch.Tensor, + X_train: torch.Tensor, + y_train: torch.Tensor, + inverse_squared_lengthscales: torch.Tensor, # (len(params), ) + kernel_scale: torch.Tensor, # Scalar + noise_var: torch.Tensor, # Scalar + ) -> None: + self._is_categorical = is_categorical + self._X_train = X_train + self._y_train = y_train + self._cov_Y_Y_inv: torch.Tensor | None = None + self._cov_Y_Y_inv_Y: torch.Tensor | None = None + # TODO(nabenabe): Rename the attributes to private with `_`. + self.inverse_squared_lengthscales = inverse_squared_lengthscales + self.kernel_scale = kernel_scale + self.noise_var = noise_var + + @property + def length_scales(self) -> np.ndarray: + return 1.0 / np.sqrt(self.inverse_squared_lengthscales.detach().numpy()) + + def _cache_matrix(self) -> None: + with torch.no_grad(): + cov_Y_Y = self.kernel(self._X_train, self._X_train).detach().numpy() + + cov_Y_Y[np.diag_indices(self._X_train.shape[0])] += self.noise_var.item() + cov_Y_Y_inv = np.linalg.inv(cov_Y_Y) + cov_Y_Y_inv_Y = cov_Y_Y_inv @ self._y_train.numpy() + # NOTE(nabenabe): Here we use NumPy to guarantee the reproducibility from the past. + self._cov_Y_Y_inv = torch.from_numpy(cov_Y_Y_inv) + self._cov_Y_Y_inv_Y = torch.from_numpy(cov_Y_Y_inv_Y) + + def kernel(self, X1: torch.Tensor, X2: torch.Tensor) -> torch.Tensor: + """ + Return the kernel matrix with the shape of (..., n_A, n_B) given X1 and X2 each with the + shapes of (..., n_A, len(params)) and (..., n_B, len(params)). + + If x1 and x2 have the shape of (len(params), ), kernel(x1, x2) is computed as: + kernel_scale * Matern52Kernel.apply( + d2(x1, x2) @ inverse_squared_lengthscales + ) + where if x1[i] is continuous, d2(x1, x2)[i] = (x1[i] - x2[i]) ** 2 and if x1[i] is + categorical, d2(x1, x2)[i] = int(x1[i] != x2[i]). + Note that the distance for categorical parameters is the Hamming distance. + """ + d2 = (X1[..., :, None, :] - X2[..., None, :, :]) ** 2 + d2[..., self._is_categorical] = (d2[..., self._is_categorical] > 0.0).type(torch.float64) + d2 = (d2 * self.inverse_squared_lengthscales).sum(dim=-1) + return Matern52Kernel.apply(d2) * self.kernel_scale # type: ignore + + def posterior(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """ + This method computes the posterior mean and variance given the points `x` where both mean + and variance tensors will have the shape of x.shape[:-1]. + + The posterior mean and variance are computed as: + mean = cov_fx_fX @ inv(cov_fX_fX + noise_var * I) @ y, and + var = cov_fx_fx - cov_fx_fX @ inv(cov_fX_fX + noise_var * I) @ cov_fx_fX.T. + + Please note that we clamp the variance to avoid negative values due to numerical errors. + """ + assert ( + self._cov_Y_Y_inv is not None and self._cov_Y_Y_inv_Y is not None + ), "Call cache_matrix before calling posterior." + cov_fx_fX = self.kernel(x[..., None, :], self._X_train)[..., 0, :] + cov_fx_fx = self.kernel_scale # kernel(x, x) = kernel_scale + mean = cov_fx_fX @ self._cov_Y_Y_inv_Y + var = cov_fx_fx - (cov_fx_fX * (cov_fx_fX @ self._cov_Y_Y_inv)).sum(dim=-1) + return mean, torch.clamp(var, min=0.0) + + def marginal_log_likelihood(self) -> torch.Tensor: # Scalar + """ + This method computes the marginal log-likelihood of the kernel hyperparameters given the + training dataset (X, y). + Assume that N = len(X) in this method. + + Mathematically, the closed form is given as: + -0.5 * log((2*pi)**N * det(C)) - 0.5 * y.T @ inv(C) @ y + = -0.5 * log(det(C)) - 0.5 * y.T @ inv(C) @ y + const, + where C = cov_Y_Y = cov_fX_fX + noise_var * I and inv(...) is the inverse operator. + + We exploit the full advantages of the Cholesky decomposition (C = L @ L.T) in this method: + 1. The determinant of a lower triangular matrix is the diagonal product, which can be + computed with N flops where log(det(C)) = log(det(L.T @ L)) = 2 * log(det(L)). + 2. Solving linear system L @ u = y, which yields u = inv(L) @ y, costs N**2 flops. + Note that given `u = inv(L) @ y` and `inv(C) = inv(L @ L.T) = inv(L).T @ inv(L)`, + y.T @ inv(C) @ y is calculated as (inv(L) @ y) @ (inv(L) @ y). + + In principle, we could invert the matrix C first, but in this case, it costs: + 1. 1/3*N**3 flops for the determinant of inv(C). + 2. 2*N**2-N flops to solve C @ alpha = y, which is alpha = inv(C) @ y. + + Since the Cholesky decomposition costs 1/3*N**3 flops and the matrix inversion costs + 2/3*N**3 flops, the overall cost for the former is 1/3*N**3+N**2+N flops and that for the + latter is N**3+2*N**2-N flops. + """ + n_points = self._X_train.shape[0] + const = -0.5 * n_points * math.log(2 * math.pi) + cov_Y_Y = self.kernel(self._X_train, self._X_train) + self.noise_var * torch.eye( + n_points, dtype=torch.float64 + ) + L = torch.linalg.cholesky(cov_Y_Y) + logdet_part = -L.diagonal().log().sum() + inv_L_y = torch.linalg.solve_triangular(L, self._y_train[:, None], upper=False)[:, 0] + quad_part = -0.5 * (inv_L_y @ inv_L_y) + return logdet_part + const + quad_part + + +def _fit_kernel_params( + X: np.ndarray, + Y: np.ndarray, + is_categorical: np.ndarray, + log_prior: Callable[[GPRegressor], torch.Tensor], + minimum_noise: float, + deterministic_objective: bool, + gpr_cache: GPRegressor, + gtol: float, +) -> GPRegressor: + n_params = X.shape[1] + + # We apply log transform to enforce the positivity of the kernel parameters. + # Note that we cannot just use the constraint because of the numerical unstability + # of the marginal log likelihood. + # We also enforce the noise parameter to be greater than `minimum_noise` to avoid + # pathological behavior of maximum likelihood estimation. + initial_raw_params = np.concatenate( + [ + np.log(gpr_cache.inverse_squared_lengthscales.detach().numpy()), + [ + np.log(gpr_cache.kernel_scale.item()), + # We add 0.01 * minimum_noise to initial noise_var to avoid instability. + np.log(gpr_cache.noise_var.item() - 0.99 * minimum_noise), + ], + ] + ) + + def loss_func(raw_params: np.ndarray) -> tuple[float, np.ndarray]: + raw_params_tensor = torch.from_numpy(raw_params) + raw_params_tensor.requires_grad_(True) + with torch.enable_grad(): # type: ignore[no-untyped-call] + gpr = GPRegressor( + is_categorical=torch.from_numpy(is_categorical), + X_train=torch.from_numpy(X), + y_train=torch.from_numpy(Y), + inverse_squared_lengthscales=torch.exp(raw_params_tensor[:n_params]), + kernel_scale=torch.exp(raw_params_tensor[n_params]), + noise_var=( + torch.tensor(minimum_noise, dtype=torch.float64) + if deterministic_objective + else torch.exp(raw_params_tensor[n_params + 1]) + minimum_noise + ), + ) + loss = -gpr.marginal_log_likelihood() - log_prior(gpr) + loss.backward() # type: ignore + # scipy.minimize requires all the gradients to be zero for termination. + raw_noise_var_grad = raw_params_tensor.grad[n_params + 1] # type: ignore + assert not deterministic_objective or raw_noise_var_grad == 0 + return loss.item(), raw_params_tensor.grad.detach().numpy() # type: ignore + + with single_blas_thread_if_scipy_v1_15_or_newer(): + # jac=True means loss_func returns the gradient for gradient descent. + res = so.minimize( + # Too small `gtol` causes instability in loss_func optimization. + loss_func, + initial_raw_params, + jac=True, + method="l-bfgs-b", + options={"gtol": gtol}, + ) + if not res.success: + raise RuntimeError(f"Optimization failed: {res.message}") + + raw_params_opt_tensor = torch.from_numpy(res.x) + + gpr = GPRegressor( + is_categorical=torch.from_numpy(is_categorical), + X_train=torch.from_numpy(X), + y_train=torch.from_numpy(Y), + inverse_squared_lengthscales=torch.exp(raw_params_opt_tensor[:n_params]), + kernel_scale=torch.exp(raw_params_opt_tensor[n_params]), + noise_var=( + torch.tensor(minimum_noise, dtype=torch.float64) + if deterministic_objective + else minimum_noise + torch.exp(raw_params_opt_tensor[n_params + 1]) + ), + ) + gpr._cache_matrix() + return gpr + + +def fit_kernel_params( + X: np.ndarray, + Y: np.ndarray, + is_categorical: np.ndarray, + log_prior: Callable[[GPRegressor], torch.Tensor], + minimum_noise: float, + deterministic_objective: bool, + gpr_cache: GPRegressor | None = None, + gtol: float = 1e-2, +) -> GPRegressor: + default_kernel_params = torch.ones(X.shape[1] + 2, dtype=torch.float64) + default_gpr_cache = GPRegressor( + is_categorical=torch.from_numpy(is_categorical), + X_train=torch.from_numpy(X), + y_train=torch.from_numpy(Y), + inverse_squared_lengthscales=default_kernel_params[:-2].clone(), + kernel_scale=default_kernel_params[-2].clone(), + noise_var=default_kernel_params[-1].clone(), + ) + if gpr_cache is None: + gpr_cache = default_gpr_cache + + error = None + # First try optimizing the kernel params with the provided kernel parameters in gpr_cache, + # but if it fails, rerun the optimization with the default kernel parameters above. + # This increases the robustness of the optimization. + for gpr_cache_to_use in [gpr_cache, default_gpr_cache]: + try: + return _fit_kernel_params( + X=X, + Y=Y, + is_categorical=is_categorical, + log_prior=log_prior, + minimum_noise=minimum_noise, + gpr_cache=gpr_cache_to_use, + deterministic_objective=deterministic_objective, + gtol=gtol, + ) + except RuntimeError as e: + error = e + + logger.warning( + f"The optimization of kernel parameters failed: \n{error}\n" + "The default initial kernel parameters will be used instead." + ) + default_gpr = GPRegressor( + is_categorical=torch.from_numpy(is_categorical), + X_train=torch.from_numpy(X), + y_train=torch.from_numpy(Y), + inverse_squared_lengthscales=default_kernel_params[:-2].clone(), + kernel_scale=default_kernel_params[-2].clone(), + noise_var=default_kernel_params[-1].clone(), + ) + default_gpr._cache_matrix() + return default_gpr diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/optim_mixed.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/optim_mixed.py new file mode 100644 index 0000000000000000000000000000000000000000..156f49ebcf0a8792b302ed302f0c67ae1f3832e7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/optim_mixed.py @@ -0,0 +1,306 @@ +from __future__ import annotations + +import math +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._gp.scipy_blas_thread_patch import single_blas_thread_if_scipy_v1_15_or_newer +from optuna.logging import get_logger + + +if TYPE_CHECKING: + import scipy.optimize as so + + from optuna._gp.acqf import BaseAcquisitionFunc +else: + from optuna import _LazyImport + + so = _LazyImport("scipy.optimize") + +_logger = get_logger(__name__) + + +def _gradient_ascent( + acqf: BaseAcquisitionFunc, + initial_params: np.ndarray, + initial_fval: float, + continuous_indices: np.ndarray, + lengthscales: np.ndarray, + tol: float, +) -> tuple[np.ndarray, float, bool]: + """ + This function optimizes the acquisition function using preconditioning. + Preconditioning equalizes the variances caused by each parameter and + speeds up the convergence. + + In Optuna, acquisition functions use Matern 5/2 kernel, which is a function of `x / l` + where `x` is `normalized_params` and `l` is the corresponding lengthscales. + Then acquisition functions are a function of `x / l`, i.e. `f(x / l)`. + As `l` has different values for each param, it makes the function ill-conditioned. + By transforming `x / l` to `zl / l = z`, the function becomes `f(z)` and has + equal variances w.r.t. `z`. + So optimization w.r.t. `z` instead of `x` is the preconditioning here and + speeds up the convergence. + As the domain of `x` is [0, 1], that of `z` becomes [0, 1/l]. + """ + if len(continuous_indices) == 0: + return initial_params, initial_fval, False + normalized_params = initial_params.copy() + + def negative_acqf_with_grad(scaled_x: np.ndarray) -> tuple[float, np.ndarray]: + # Scale back to the original domain, i.e. [0, 1], from [0, 1/s]. + normalized_params[continuous_indices] = scaled_x * lengthscales + (fval, grad) = acqf.eval_acqf_with_grad(normalized_params) + # Flip sign because scipy minimizes functions. + # Let the scaled acqf be g(x) and the acqf be f(sx), then dg/dx = df/dx * s. + return -fval, -grad[continuous_indices] * lengthscales + + with single_blas_thread_if_scipy_v1_15_or_newer(): + scaled_cont_x_opt, neg_fval_opt, info = so.fmin_l_bfgs_b( + func=negative_acqf_with_grad, + x0=normalized_params[continuous_indices] / lengthscales, + bounds=[(0, 1 / s) for s in lengthscales], + pgtol=math.sqrt(tol), + maxiter=200, + ) + + if -neg_fval_opt > initial_fval and info["nit"] > 0: # Improved. + # `nit` is the number of iterations. + normalized_params[continuous_indices] = scaled_cont_x_opt * lengthscales + return normalized_params, -neg_fval_opt, True + + return initial_params, initial_fval, False # No improvement. + + +def _exhaustive_search( + acqf: BaseAcquisitionFunc, + initial_params: np.ndarray, + initial_fval: float, + param_idx: int, + choices: np.ndarray, +) -> tuple[np.ndarray, float, bool]: + choices_except_current = choices[choices != initial_params[param_idx]] + + all_params = np.repeat(initial_params[None, :], len(choices_except_current), axis=0) + all_params[:, param_idx] = choices_except_current + fvals = acqf.eval_acqf_no_grad(all_params) + best_idx = np.argmax(fvals) + + if fvals[best_idx] > initial_fval: # Improved. + return all_params[best_idx, :], fvals[best_idx], True + + return initial_params, initial_fval, False # No improvement. + + +def _discrete_line_search( + acqf: BaseAcquisitionFunc, + initial_params: np.ndarray, + initial_fval: float, + param_idx: int, + grids: np.ndarray, + xtol: float, +) -> tuple[np.ndarray, float, bool]: + if len(grids) == 1: + # Do not optimize anything when there's only one choice. + return initial_params, initial_fval, False + + def find_nearest_index(x: float) -> int: + i = int(np.clip(np.searchsorted(grids, x), 1, len(grids) - 1)) + return i - 1 if abs(x - grids[i - 1]) < abs(x - grids[i]) else i + + current_choice_i = find_nearest_index(initial_params[param_idx]) + assert np.isclose(initial_params[param_idx], grids[current_choice_i]) + + negative_fval_cache = {current_choice_i: -initial_fval} + + normalized_params = initial_params.copy() + + def negative_acqf_with_cache(i: int) -> float: + # Function value at choices[i]. + cache_val = negative_fval_cache.get(i) + if cache_val is not None: + return cache_val + normalized_params[param_idx] = grids[i] + + # Flip sign because scipy minimizes functions. + negval = -float(acqf.eval_acqf_no_grad(normalized_params)) + negative_fval_cache[i] = negval + return negval + + def interpolated_negative_acqf(x: float) -> float: + if x < grids[0] or x > grids[-1]: + return np.inf + right = int(np.clip(np.searchsorted(grids, x), 1, len(grids) - 1)) + left = right - 1 + neg_acqf_left, neg_acqf_right = negative_acqf_with_cache(left), negative_acqf_with_cache( + right + ) + w_left = (grids[right] - x) / (grids[right] - grids[left]) + w_right = 1.0 - w_left + return w_left * neg_acqf_left + w_right * neg_acqf_right + + EPS = 1e-12 + res = so.minimize_scalar( + interpolated_negative_acqf, + # The values of this bracket are (inf, -fval, inf). + # This trivially satisfies the bracket condition if fval is finite. + bracket=(grids[0] - EPS, grids[current_choice_i], grids[-1] + EPS), + method="brent", + tol=xtol, + ) + opt_idx = find_nearest_index(res.x) + fval_opt = -negative_acqf_with_cache(opt_idx) + + # We check both conditions because of numerical errors. + if opt_idx != current_choice_i and fval_opt > initial_fval: + normalized_params[param_idx] = grids[opt_idx] + return normalized_params, fval_opt, True + + return initial_params, initial_fval, False # No improvement. + + +def _local_search_discrete( + acqf: BaseAcquisitionFunc, + initial_params: np.ndarray, + initial_fval: float, + param_idx: int, + choices: np.ndarray, + xtol: float, +) -> tuple[np.ndarray, float, bool]: + + # If the number of possible parameter values is small, we just perform an exhaustive search. + # This is faster and better than the line search. + MAX_INT_EXHAUSTIVE_SEARCH_PARAMS = 16 + + is_categorical = acqf.search_space.is_categorical[param_idx] + if is_categorical or len(choices) <= MAX_INT_EXHAUSTIVE_SEARCH_PARAMS: + return _exhaustive_search(acqf, initial_params, initial_fval, param_idx, choices) + else: + return _discrete_line_search(acqf, initial_params, initial_fval, param_idx, choices, xtol) + + +def local_search_mixed( + acqf: BaseAcquisitionFunc, + initial_normalized_params: np.ndarray, + *, + tol: float = 1e-4, + max_iter: int = 100, +) -> tuple[np.ndarray, float]: + continuous_indices = acqf.search_space.continuous_indices + + # This is a technique for speeding up optimization. + # We use an isotropic kernel, so scaling the gradient will make + # the hessian better-conditioned. + # NOTE: Ideally, separating lengthscales should be used for the constraint functions, + # but for simplicity, the ones from the objective function are being reused. + # TODO(kAIto47802): Think of a better way to handle this. + lengthscales = acqf.length_scales[continuous_indices] + + choices_of_discrete_params = acqf.search_space.get_choices_of_discrete_params() + + discrete_xtols = [ + # Terminate discrete optimizations once the change in x becomes smaller than this. + # Basically, if the change is smaller than min(dx) / 4, it is useless to see more details. + np.min(np.diff(choices), initial=np.inf) / 4 + for choices in choices_of_discrete_params + ] + + best_normalized_params = initial_normalized_params.copy() + best_fval = float(acqf.eval_acqf_no_grad(best_normalized_params)) + + CONTINUOUS = -1 + last_changed_param: int | None = None + + for _ in range(max_iter): + if last_changed_param == CONTINUOUS: + # Parameters not changed since last time. + return best_normalized_params, best_fval + (best_normalized_params, best_fval, updated) = _gradient_ascent( + acqf, + best_normalized_params, + best_fval, + continuous_indices, + lengthscales, + tol, + ) + if updated: + last_changed_param = CONTINUOUS + + for i, choices, xtol in zip( + acqf.search_space.discrete_indices, choices_of_discrete_params, discrete_xtols + ): + if last_changed_param == i: + # Parameters not changed since last time. + return best_normalized_params, best_fval + (best_normalized_params, best_fval, updated) = _local_search_discrete( + acqf, best_normalized_params, best_fval, i, choices, xtol + ) + if updated: + last_changed_param = i + + if last_changed_param is None: + # Parameters not changed from the beginning. + return best_normalized_params, best_fval + + _logger.warning("local_search_mixed: Local search did not converge.") + return best_normalized_params, best_fval + + +def optimize_acqf_mixed( + acqf: BaseAcquisitionFunc, + *, + warmstart_normalized_params_array: np.ndarray | None = None, + n_preliminary_samples: int = 2048, + n_local_search: int = 10, + tol: float = 1e-4, + rng: np.random.RandomState | None = None, +) -> tuple[np.ndarray, float]: + + rng = rng or np.random.RandomState() + + if warmstart_normalized_params_array is None: + warmstart_normalized_params_array = np.empty((0, acqf.search_space.dim)) + + assert ( + len(warmstart_normalized_params_array) <= n_local_search - 1 + ), "We must choose at least 1 best sampled point + given_initial_xs as start points." + + sampled_xs = acqf.search_space.sample_normalized_params(n_preliminary_samples, rng=rng) + + # Evaluate all values at initial samples + f_vals = acqf.eval_acqf_no_grad(sampled_xs) + assert isinstance(f_vals, np.ndarray) + + max_i = np.argmax(f_vals) + + # TODO(nabenabe): Benchmark the BoTorch roulette selection as well. + # https://github.com/pytorch/botorch/blob/v0.14.0/botorch/optim/initializers.py#L942 + # We use a modified roulette wheel selection to pick the initial param for each local search. + probs = np.exp(f_vals - f_vals[max_i]) + probs[max_i] = 0.0 # We already picked the best param, so remove it from roulette. + probs /= probs.sum() + n_non_zero_probs_improvement = int(np.count_nonzero(probs > 0.0)) + # n_additional_warmstart becomes smaller when study starts to converge. + n_additional_warmstart = min( + n_local_search - len(warmstart_normalized_params_array) - 1, n_non_zero_probs_improvement + ) + if n_additional_warmstart == n_non_zero_probs_improvement: + _logger.warning("Study already converged, so the number of local search is reduced.") + chosen_idxs = np.array([max_i]) + if n_additional_warmstart > 0: + additional_idxs = rng.choice( + len(sampled_xs), size=n_additional_warmstart, replace=False, p=probs + ) + chosen_idxs = np.append(chosen_idxs, additional_idxs) + + best_x = sampled_xs[max_i, :] + best_f = float(f_vals[max_i]) + + for x_warmstart in np.vstack([sampled_xs[chosen_idxs, :], warmstart_normalized_params_array]): + x, f = local_search_mixed(acqf, x_warmstart, tol=tol) + if f > best_f: + best_x = x + best_f = f + + return best_x, best_f diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/optim_sample.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/optim_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..8e4c5e29c8ff37f9577920b10d79f6fa4d06910a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/optim_sample.py @@ -0,0 +1,19 @@ +from __future__ import annotations + +import numpy as np + +from optuna._gp import acqf as acqf_module + + +def optimize_acqf_sample( + acqf: acqf_module.BaseAcquisitionFunc, + *, + n_samples: int = 2048, + rng: np.random.RandomState | None = None, +) -> tuple[np.ndarray, float]: + # Normalized parameter values are sampled. + xs = acqf.search_space.sample_normalized_params(n_samples, rng=rng) + res = acqf.eval_acqf_no_grad(xs) + + best_i = np.argmax(res) + return xs[best_i, :], res[best_i] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/prior.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/prior.py new file mode 100644 index 0000000000000000000000000000000000000000..e5bb7c58c7aefae57dc7f939efa08b566135d4e9 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/prior.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + + +if TYPE_CHECKING: + import torch + + from optuna._gp import gp +else: + from optuna._imports import _LazyImport + + torch = _LazyImport("torch") + + +DEFAULT_MINIMUM_NOISE_VAR = 1e-6 + + +def default_log_prior(gpr: gp.GPRegressor) -> torch.Tensor: + # Log of prior distribution of kernel parameters. + + def gamma_log_prior(x: torch.Tensor, concentration: float, rate: float) -> torch.Tensor: + # We omit the constant factor `rate ** concentration / Gamma(concentration)`. + return (concentration - 1) * torch.log(x) - rate * x + + # NOTE(contramundum53): The priors below (params and function + # shape for inverse_squared_lengthscales) were picked by heuristics. + # TODO(contramundum53): Check whether these priors are appropriate. + return ( + -(0.1 / gpr.inverse_squared_lengthscales + 0.1 * gpr.inverse_squared_lengthscales).sum() + + gamma_log_prior(gpr.kernel_scale, 2, 1) + + gamma_log_prior(gpr.noise_var, 1.1, 30) + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/scipy_blas_thread_patch.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/scipy_blas_thread_patch.py new file mode 100644 index 0000000000000000000000000000000000000000..921fdaa2ccc2a4a8f43de44a72f6884e471c5c39 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/scipy_blas_thread_patch.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +from contextlib import contextmanager +import os +from typing import TYPE_CHECKING + +from packaging.version import Version +import scipy + + +if TYPE_CHECKING: + from typing import Generator + + +@contextmanager +def single_blas_thread_if_scipy_v1_15_or_newer() -> Generator[None, None, None]: + """ + This function limits the thread count in the context to 1. + We need to do so because the L-BFGS-B in SciPy v1.15 or newer uses OpenBLAS and it apparently + causes slowdown due to the unmatched thread setup. This context manager aims to solve this + issue. If the SciPy version is 1.14.1 or older, this issue does not happen because it uses + the Fortran implementation. + + Reference: + https://github.com/scipy/scipy/issues/22438 + + TODO(nabe): Watch the SciPy update and remove this context manager once it becomes unnecessary. + TODO(nabe): Benchmark the speed without this context manager for any SciPy updates. + NOTE(nabe): I don't know why, but `fmin_l_bfgs_b` in optim_mixed.py seems unaffected. + """ + if Version(scipy.__version__) < Version("1.15.0"): + # If SciPy is older than 1.15.0, the context manager is unnecessary. + yield + else: + old_val = os.environ.get("OPENBLAS_NUM_THREADS") + os.environ["OPENBLAS_NUM_THREADS"] = "1" + try: + yield + finally: + if old_val is None: + os.environ.pop("OPENBLAS_NUM_THREADS", None) + else: + os.environ["OPENBLAS_NUM_THREADS"] = old_val diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/search_space.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/search_space.py new file mode 100644 index 0000000000000000000000000000000000000000..32132ab16ffe2782d33da8e970a7e2f2ded165e8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_gp/search_space.py @@ -0,0 +1,215 @@ +from __future__ import annotations + +from enum import IntEnum +import math +import threading +from typing import Any +from typing import TYPE_CHECKING + +import numpy as np + +from optuna.distributions import BaseDistribution +from optuna.distributions import CategoricalDistribution +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution + + +if TYPE_CHECKING: + import scipy.stats.qmc as qmc + + from optuna.trial import FrozenTrial +else: + from optuna._imports import _LazyImport + + qmc = _LazyImport("scipy.stats.qmc") + + +_threading_lock = threading.Lock() + + +class _ScaleType(IntEnum): + LINEAR = 0 + LOG = 1 + CATEGORICAL = 2 + + +class SearchSpace: + def __init__( + self, + optuna_search_space: dict[str, BaseDistribution], + ) -> None: + self._optuna_search_space = optuna_search_space + self._scale_types = np.empty(len(optuna_search_space), dtype=np.int64) + self._bounds = np.empty((len(optuna_search_space), 2), dtype=float) + self._steps = np.empty(len(optuna_search_space), dtype=float) + for i, distribution in enumerate(optuna_search_space.values()): + if isinstance(distribution, CategoricalDistribution): + self._scale_types[i] = _ScaleType.CATEGORICAL + self._bounds[i, :] = (0.0, len(distribution.choices)) + self._steps[i] = 1.0 + else: + assert isinstance(distribution, (FloatDistribution, IntDistribution)) + self._scale_types[i] = _ScaleType.LOG if distribution.log else _ScaleType.LINEAR + self._bounds[i, :] = (distribution.low, distribution.high) + self._steps[i] = distribution.step or 0.0 + self.dim = len(optuna_search_space) + # TODO: Make it an index array. + self.is_categorical = self._scale_types == _ScaleType.CATEGORICAL + # NOTE(nabenabe): MyPy Redefinition for NumPy v2.2.0. (Cast signed int to int) + self.discrete_indices = np.flatnonzero(self._steps > 0).astype(int) + self.continuous_indices = np.flatnonzero(self._steps == 0.0).astype(int) + + def get_normalized_params( + self, + trials: list[FrozenTrial], + ) -> np.ndarray: + values = np.empty((len(trials), len(self._optuna_search_space)), dtype=float) + for i, (param, distribution) in enumerate(self._optuna_search_space.items()): + if isinstance(distribution, CategoricalDistribution): + values[:, i] = [distribution.to_internal_repr(t.params[param]) for t in trials] + else: + values[:, i] = _normalize_one_param( + np.array([trial.params[param] for trial in trials]), + self._scale_types[i], + (self._bounds[i, 0], self._bounds[i, 1]), + self._steps[i], + ) + return values + + def get_unnormalized_param( + self, + normalized_param: np.ndarray, + ) -> dict[str, Any]: + # TODO(kAIto47802): Move the implementation of `_get_unnormalized_param` here + # instead of wrapping it. + return _get_unnormalized_param(self._optuna_search_space, normalized_param) + + def sample_normalized_params(self, n: int, rng: np.random.RandomState | None) -> np.ndarray: + # TODO(kAIto47802): Move the implementation of `_sample_normalized_params` here + # instead of wrapping it. + return _sample_normalized_params(n, self, rng) + + def get_choices_of_discrete_params(self) -> list[np.ndarray]: + return [ + ( + np.arange(self._bounds[i, 1]) + if self.is_categorical[i] + else _normalize_one_param( + param_value=np.arange( + self._bounds[i, 0], + self._bounds[i, 1] + 0.5 * self._steps[i], + self._steps[i], + ), + scale_type=_ScaleType(self._scale_types[i]), + bounds=(self._bounds[i, 0], self._bounds[i, 1]), + step=self._steps[i], + ) + ) + for i in self.discrete_indices + ] + + +def _unnormalize_one_param( + param_value: np.ndarray, scale_type: _ScaleType, bounds: tuple[float, float], step: float +) -> np.ndarray: + # param_value can be batched, or not. + if scale_type == _ScaleType.CATEGORICAL: + return param_value + low, high = (bounds[0] - 0.5 * step, bounds[1] + 0.5 * step) + if scale_type == _ScaleType.LOG: + low, high = (math.log(low), math.log(high)) + param_value = param_value * (high - low) + low + if scale_type == _ScaleType.LOG: + param_value = np.exp(param_value) + return param_value + + +def _normalize_one_param( + param_value: np.ndarray, scale_type: _ScaleType, bounds: tuple[float, float], step: float +) -> np.ndarray: + # param_value can be batched, or not. + if scale_type == _ScaleType.CATEGORICAL: + return param_value + low, high = (bounds[0] - 0.5 * step, bounds[1] + 0.5 * step) + if scale_type == _ScaleType.LOG: + low, high = (math.log(low), math.log(high)) + param_value = np.log(param_value) + if high == low: + return np.full_like(param_value, 0.5) + param_value = (param_value - low) / (high - low) + return param_value + + +def _round_one_normalized_param( + param_value: np.ndarray, scale_type: _ScaleType, bounds: tuple[float, float], step: float +) -> np.ndarray: + assert scale_type != _ScaleType.CATEGORICAL + if step == 0.0: + return param_value + + param_value = _unnormalize_one_param(param_value, scale_type, bounds, step) + param_value = np.clip( + (param_value - bounds[0] + 0.5 * step) // step * step + bounds[0], + bounds[0], + bounds[1], + ) + param_value = _normalize_one_param(param_value, scale_type, bounds, step) + return param_value + + +def _sample_normalized_params( + n: int, search_space: SearchSpace, rng: np.random.RandomState | None +) -> np.ndarray: + rng = rng or np.random.RandomState() + dim = search_space._scale_types.shape[0] + scale_types = search_space._scale_types + bounds = search_space._bounds + steps = search_space._steps + + # Sobol engine likely shares its internal state among threads. + # Without threading.Lock, ValueError exceptions are raised in Sobol engine as discussed in + # https://github.com/optuna/optunahub-registry/pull/168#pullrequestreview-2404054969 + with _threading_lock: + qmc_engine = qmc.Sobol(dim, scramble=True, seed=rng.randint(np.iinfo(np.int32).max)) + param_values = qmc_engine.random(n) + + for i in range(dim): + if scale_types[i] == _ScaleType.CATEGORICAL: + param_values[:, i] = np.floor(param_values[:, i] * bounds[i, 1]) + elif steps[i] != 0.0: + param_values[:, i] = _round_one_normalized_param( + param_values[:, i], scale_types[i], (bounds[i, 0], bounds[i, 1]), steps[i] + ) + return param_values + + +def _get_unnormalized_param( + optuna_search_space: dict[str, BaseDistribution], + normalized_param: np.ndarray, +) -> dict[str, Any]: + ret = {} + for i, (param, distribution) in enumerate(optuna_search_space.items()): + if isinstance(distribution, CategoricalDistribution): + ret[param] = distribution.to_external_repr(normalized_param[i]) + else: + assert isinstance( + distribution, + ( + FloatDistribution, + IntDistribution, + ), + ) + scale_type = _ScaleType.LOG if distribution.log else _ScaleType.LINEAR + step = 0.0 if distribution.step is None else distribution.step + bounds = (distribution.low, distribution.high) + param_value = float( + np.clip( + _unnormalize_one_param(normalized_param[i], scale_type, bounds, step), + distribution.low, + distribution.high, + ) + ) + if isinstance(distribution, IntDistribution): + param_value = round(param_value) + ret[param] = param_value + return ret diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bdc68fbd8095d3b8d664cad352a94f6e56cc22e1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/__init__.py @@ -0,0 +1,6 @@ +from optuna._hypervolume.box_decomposition import get_non_dominated_box_bounds +from optuna._hypervolume.hssp import _solve_hssp +from optuna._hypervolume.wfg import compute_hypervolume + + +__all__ = ["_solve_hssp", "compute_hypervolume", "get_non_dominated_box_bounds"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4e83516af13b9a3b2a8f895c72269a918329430a Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/__pycache__/box_decomposition.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/__pycache__/box_decomposition.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..88ddb1f2b99903818f92b643faf8f876838476b4 Binary files /dev/null and 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https://github.com/pytorch/botorch/blob/v0.13.0/botorch/utils/multi_objective/box_decompositions/utils.py#L163-L193 + +For ``_get_non_dominated_box_bounds``, look at: + * https://github.com/pytorch/botorch/blob/v0.13.0/botorch/utils/multi_objective/box_decompositions/non_dominated.py#L395-L430 + +The preprocessing for four or fewer objectives, we use the algorithm proposed by: + Title: A Box Decomposition Algorithm to Compute the Hypervolume Indicator + Authors: Renaud Lacour, Kathrin Klamroth, and Carlos M. Fonseca + URL: https://arxiv.org/abs/1510.01963 +We refer this paper as Lacour17 in this file. + +""" # NOQA: E501 + +from __future__ import annotations + +import warnings + +import numpy as np + +from optuna.study._multi_objective import _is_pareto_front + + +def _get_upper_bound_set( + sorted_pareto_sols: np.ndarray, ref_point: np.ndarray +) -> tuple[np.ndarray, np.ndarray]: + """ + This function follows Algorithm 2 of Lacour17. + + Args: + sorted_pareto_sols: Pareto solutions sorted with respect to the first objective. + ref_point: The reference point. + + Returns: + upper_bound_set: The upper bound set, which is ``U(N)`` in the paper. The shape is + ``(n_bounds, n_objectives)``. + def_points: The defining points of each vector in ``U(N)``. The shape is + ``(n_bounds, n_objectives, n_objectives)``. + + NOTE: + ``pareto_sols`` corresponds to ``N`` and ``upper_bound_set`` corresponds to ``U(N)`` in the + paper. + ``def_points`` (the shape is ``(n_bounds, n_objectives, n_objectives)``) is not well + explained in the paper, but basically, ``def_points[i, j] = z[j]`` of + ``upper_bound_set[i]``. + """ + (_, n_objectives) = sorted_pareto_sols.shape + objective_indices = np.arange(n_objectives) + skip_ineq_judge = np.eye(n_objectives, dtype=bool) + # NOTE(nabenabe): True at 0 comes from Line 2 of Alg. 2. (loss_vals is sorted w.r.t. 0-th obj) + skip_ineq_judge[:, 0] = True + + def update(sol: np.ndarray, ubs: np.ndarray, dps: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + # The update rule is written in Section 2.2 of Lacour17. + is_dominated = np.all(sol < ubs, axis=-1) + if not any(is_dominated): + return ubs, dps + + # The defining points `z(u)` for each `u in A` in Line 5 of Alg. 2. + dominated_dps = dps[is_dominated] + n_bounds = dominated_dps.shape[0] + # NOTE(nabenabe): `-inf` comes from Line 2 and `k!=j` in Line 3 of Alg. 2. + # NOTE(nabenabe): If `update[i,j]=True`, update `ubs[i,j]` to `(z_j, u_{-j})`, i.e., + # `np.where(objective_indices != j, ubs[i], sol[j])` in `new_ubs`. cf. Lines 2,3 of Alg. 2. + update = sol >= np.max(np.where(skip_ineq_judge, -np.inf, dominated_dps), axis=-2) + # NOTE(nabenabe): The indices of `u` with `True` in update. Each `u` may yield `True` + # multiple times for different indices `j`. + ubs_indices_to_update = np.tile(np.arange(n_bounds)[:, np.newaxis], n_objectives)[update] + # The dimension `j` for each `u` s.t. `\hat{z}_j \geq \max_{k \neq j}{z_j^k(u)}`. + dimensions_to_update = np.tile(objective_indices, (n_bounds, 1))[update] + assert ubs_indices_to_update.size == dimensions_to_update.size + indices_for_sweeping = np.arange(dimensions_to_update.size) + # The last Eq in Page 5. + new_dps = dominated_dps[ubs_indices_to_update] + new_dps[indices_for_sweeping, dimensions_to_update] = sol + # Line 3 of Alg. 2. `sol[dimensions_to_update]` is equivalent to `\bar{z}_j`. + new_ubs = ubs[is_dominated][ubs_indices_to_update] + new_ubs[indices_for_sweeping, dimensions_to_update] = sol[dimensions_to_update] + return np.vstack([ubs[~is_dominated], new_ubs]), np.vstack([dps[~is_dominated], new_dps]) + + upper_bound_set = np.asarray([ref_point]) # Line 1 of Alg. 2. + def_points = np.full((1, n_objectives, n_objectives), -np.inf) # z^k(z^r) = \hat{z}^k + def_points[0, objective_indices, objective_indices] = ref_point # \hat{z}^k is a dummy point. + for solution in sorted_pareto_sols: # NOTE(nabenabe): Sorted must be fulfilled. + upper_bound_set, def_points = update(solution, upper_bound_set, def_points) + + return upper_bound_set, def_points + + +def _get_box_bounds( + upper_bound_set: np.ndarray, def_points: np.ndarray, ref_point: np.ndarray +) -> np.ndarray: + # Eq. (2) of Lacour17. + n_objectives = upper_bound_set.shape[-1] + assert n_objectives > 1, "This function is used only for multi-objective problems." + bounds = np.empty((2, *upper_bound_set.shape)) + bounds[0, :, 0] = def_points[:, 0, 0] + bounds[1, :, 0] = ref_point[0] + row, col = np.diag_indices(n_objectives - 1) + bounds[0, :, 1:] = np.maximum.accumulate(def_points, axis=-2)[:, row, col + 1] + bounds[1, :, 1:] = upper_bound_set[:, 1:] + not_empty = ~np.any(bounds[1] <= bounds[0], axis=-1) # Remove [inf, inf] or [-inf, -inf]. + return bounds[:, not_empty] + + +def _get_non_dominated_box_bounds( + sorted_pareto_sols: np.ndarray, ref_point: np.ndarray +) -> tuple[np.ndarray, np.ndarray]: # (n_bounds, n_objectives) and (n_bounds, n_objectives) + # The calculation of u[k] and l[k] in the paper: https://arxiv.org/abs/2006.05078 + # See below for the proof of this function's validity: + # cf. https://github.com/optuna/optuna/pull/6039#issuecomment-2831926573 + # NOTE(nabenabe): The paper handles maximization problems, but we consider minimization here. + neg_upper_bound_set = -_get_upper_bound_set(sorted_pareto_sols, ref_point)[0] + sorted_neg_upper_bound_set = np.unique(neg_upper_bound_set, axis=0) # lexsort by np.unique. + # Use the sign-flipped upper_bound_set as the Pareto solutions. Then we can calculate the + # lower bound set as well. + point_at_infinity = np.full_like(ref_point, np.inf) + # NOTE(nabenabe): Since our goal is to partition the non-dominated space, we only need + # the Pareto solutions in the `neg_upper_bound_set`. + neg_lower_bound_set, neg_def_points = _get_upper_bound_set( + sorted_pareto_sols=sorted_neg_upper_bound_set[ + _is_pareto_front(sorted_neg_upper_bound_set, assume_unique_lexsorted=True) + ], + ref_point=point_at_infinity, + ) + box_upper_bounds, box_lower_bounds = -_get_box_bounds( + neg_lower_bound_set, neg_def_points, point_at_infinity + ) + return box_lower_bounds, box_upper_bounds + + +def get_non_dominated_box_bounds( + loss_vals: np.ndarray, ref_point: np.ndarray +) -> tuple[np.ndarray, np.ndarray]: # (n_bounds, n_objectives) and (n_bounds, n_objectives) + assert np.all(np.isfinite(loss_vals)), "loss_vals must be clipped before box decomposition." + # Remove duplications and lexsort the solutions by ``np.unique``. + unique_lexsorted_loss_vals = np.unique(loss_vals, axis=0) + sorted_pareto_sols = unique_lexsorted_loss_vals[ + _is_pareto_front(unique_lexsorted_loss_vals, assume_unique_lexsorted=True) + ] + n_objectives = loss_vals.shape[-1] + # The condition here follows BoTorch. + # https://github.com/pytorch/botorch/blob/v0.13.0/botorch/acquisition/multi_objective/utils.py#L55-L63 + assert n_objectives > 1, "This function is used only for multi-objective problems." + if n_objectives > 4: + warnings.warn( + "Box decomposition (typically used by `GPSampler`) might be significantly slow for " + "n_objectives > 4. Please consider using another sampler instead." + ) + + return _get_non_dominated_box_bounds(sorted_pareto_sols, ref_point) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/hssp.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/hssp.py new file mode 100644 index 0000000000000000000000000000000000000000..c5beee798a0edf9569d3861fa4638324938dcb94 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/hssp.py @@ -0,0 +1,176 @@ +from __future__ import annotations + +import math + +import numpy as np + +from optuna._hypervolume.wfg import compute_hypervolume + + +def _solve_hssp_2d( + rank_i_loss_vals: np.ndarray, + rank_i_indices: np.ndarray, + subset_size: int, + reference_point: np.ndarray, +) -> np.ndarray: + # This function can be used for non-unique rank_i_loss_vals as well. + # The time complexity is O(subset_size * rank_i_loss_vals.shape[0]). + assert rank_i_loss_vals.shape[-1] == 2 and subset_size <= rank_i_loss_vals.shape[0] + n_trials = rank_i_loss_vals.shape[0] + # rank_i_loss_vals is unique-lexsorted in solve_hssp. + sorted_indices = np.arange(rank_i_loss_vals.shape[0]) + sorted_loss_vals = rank_i_loss_vals.copy() + # The diagonal points for each rectangular to calculate the hypervolume contributions. + rect_diags = np.repeat(reference_point[np.newaxis, :], n_trials, axis=0) + selected_indices = np.zeros(subset_size, dtype=int) + for i in range(subset_size): + contribs = np.prod(rect_diags - sorted_loss_vals, axis=-1) + max_index = np.argmax(contribs) + selected_indices[i] = rank_i_indices[sorted_indices[max_index]] + loss_vals = sorted_loss_vals[max_index].copy() + + keep = np.ones(n_trials - i, dtype=bool) + keep[max_index] = False + # Remove the chosen point. + sorted_indices = sorted_indices[keep] + rect_diags = rect_diags[keep] + sorted_loss_vals = sorted_loss_vals[keep] + # Update the diagonal points for each hypervolume contribution calculation. + rect_diags[:max_index, 0] = np.minimum(loss_vals[0], rect_diags[:max_index, 0]) + rect_diags[max_index:, 1] = np.minimum(loss_vals[1], rect_diags[max_index:, 1]) + + return selected_indices + + +def _lazy_contribs_update( + contribs: np.ndarray, + pareto_loss_values: np.ndarray, + selected_vecs: np.ndarray, + reference_point: np.ndarray, + hv_selected: float, +) -> np.ndarray: + """Lazy update the hypervolume contributions. + + (1) Lazy update of the hypervolume contributions + S=selected_indices - {indices[max_index]}, T=selected_indices, and S' is a subset of S. + As we would like to know argmax H(T v {i}) in the next iteration, we can skip HV + calculations for j if H(T v {i}) - H(T) > H(S' v {j}) - H(S') >= H(T v {j}) - H(T). + We used the submodularity for the inequality above. As the upper bound of contribs[i] is + H(S' v {j}) - H(S'), we start to update from i with a higher upper bound so that we can + skip more HV calculations. + + (2) A simple cheap-to-evaluate contribution upper bound + The HV difference only using the latest selected point and a candidate is a simple, yet + obvious, contribution upper bound. Denote t as the latest selected index and j as an unselected + index. Then, H(T v {j}) - H(T) <= H({t} v {j}) - H({t}) holds where the inequality comes from + submodularity. We use the inclusion-exclusion principle to calculate the RHS. + """ + if math.isinf(hv_selected): + # NOTE(nabenabe): This part eliminates the possibility of inf - inf in this function. + return np.full_like(contribs, np.inf) + + intersec = np.maximum(pareto_loss_values[:, np.newaxis], selected_vecs[:-1]) + inclusive_hvs = np.prod(reference_point - pareto_loss_values, axis=1) + is_contrib_inf = np.isinf(inclusive_hvs) # NOTE(nabe): inclusive_hvs[i] >= contribs[i]. + contribs = np.minimum( # Please see (2) in the docstring for more details. + contribs, inclusive_hvs - np.prod(reference_point - intersec[:, -1], axis=1) + ) + max_contrib = 0.0 + is_hv_calc_fast = pareto_loss_values.shape[1] <= 3 + for i in np.argsort(-contribs): # Check from larger upper bound contribs to skip more. + if is_contrib_inf[i]: + max_contrib = contribs[i] = np.inf + continue + if contribs[i] < max_contrib: # Please see (1) in the docstring for more details. + continue + + # NOTE(nabenabe): contribs[i] = H(S v {i)) - H(S) = H({i}) - H(S ^ {i}). + # If HV calc is fast, the decremental approach, which involves Pareto checks, is slower. + if is_hv_calc_fast: # Use contribs[i] = H(S v {i)) - H(S) (incremental approach). + selected_vecs[-1] = pareto_loss_values[i].copy() + hv_plus = compute_hypervolume(selected_vecs, reference_point, assume_pareto=True) + contribs[i] = hv_plus - hv_selected + else: # Use contribs[i] = H({i}) - H(S ^ {i}) (decremental approach). + contribs[i] = inclusive_hvs[i] - compute_hypervolume(intersec[i], reference_point) + max_contrib = max(contribs[i], max_contrib) + + return contribs + + +def _solve_hssp_on_unique_loss_vals( + rank_i_loss_vals: np.ndarray, + rank_i_indices: np.ndarray, + subset_size: int, + reference_point: np.ndarray, +) -> np.ndarray: + if not np.isfinite(reference_point).all(): + return rank_i_indices[:subset_size] + if rank_i_indices.size == subset_size: + return rank_i_indices + if rank_i_loss_vals.shape[-1] == 2: + return _solve_hssp_2d(rank_i_loss_vals, rank_i_indices, subset_size, reference_point) + + assert subset_size < rank_i_indices.size + # The following logic can be used for non-unique rank_i_loss_vals as well. + diff_of_loss_vals_and_ref_point = reference_point - rank_i_loss_vals + (n_solutions, n_objectives) = rank_i_loss_vals.shape + contribs = np.prod(diff_of_loss_vals_and_ref_point, axis=-1) + selected_indices = np.zeros(subset_size, dtype=int) + selected_vecs = np.empty((subset_size, n_objectives)) + indices = np.arange(n_solutions) + hv = 0 + for k in range(subset_size): + max_index = int(np.argmax(contribs)) + hv += contribs[max_index] + selected_indices[k] = indices[max_index] + selected_vecs[k] = rank_i_loss_vals[max_index].copy() + keep = np.ones(contribs.size, dtype=bool) + keep[max_index] = False + contribs = contribs[keep] + indices = indices[keep] + rank_i_loss_vals = rank_i_loss_vals[keep] + if k == subset_size - 1: + # We do not need to update contribs at the last iteration. + break + + contribs = _lazy_contribs_update( + contribs, rank_i_loss_vals, selected_vecs[: k + 2], reference_point, hv + ) + + return rank_i_indices[selected_indices] + + +def _solve_hssp( + rank_i_loss_vals: np.ndarray, + rank_i_indices: np.ndarray, + subset_size: int, + reference_point: np.ndarray, +) -> np.ndarray: + """Solve a hypervolume subset selection problem (HSSP) via a greedy algorithm. + + This method is a 1-1/e approximation algorithm to solve HSSP. + + For further information about algorithms to solve HSSP, please refer to the following + paper: + + - `Greedy Hypervolume Subset Selection in Low Dimensions + `__ + """ + if subset_size == rank_i_indices.size: + return rank_i_indices + + rank_i_unique_loss_vals, indices_of_unique_loss_vals = np.unique( + rank_i_loss_vals, return_index=True, axis=0 + ) + n_unique = indices_of_unique_loss_vals.size + if n_unique < subset_size: + chosen = np.zeros(rank_i_indices.size, dtype=bool) + chosen[indices_of_unique_loss_vals] = True + duplicated_indices = np.arange(rank_i_indices.size)[~chosen] + chosen[duplicated_indices[: subset_size - n_unique]] = True + return rank_i_indices[chosen] + + selected_indices_of_unique_loss_vals = _solve_hssp_on_unique_loss_vals( + rank_i_unique_loss_vals, indices_of_unique_loss_vals, subset_size, reference_point + ) + return rank_i_indices[selected_indices_of_unique_loss_vals] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/wfg.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/wfg.py new file mode 100644 index 0000000000000000000000000000000000000000..356ec99ef967fe687dc44263663febe8857fce56 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_hypervolume/wfg.py @@ -0,0 +1,178 @@ +from __future__ import annotations + +import numpy as np + +from optuna.study._multi_objective import _is_pareto_front + + +def _compute_2d(sorted_pareto_sols: np.ndarray, reference_point: np.ndarray) -> float: + assert sorted_pareto_sols.shape[1] == reference_point.shape[0] == 2 + rect_diag_y = np.concatenate([reference_point[1:], sorted_pareto_sols[:-1, 1]]) + edge_length_x = reference_point[0] - sorted_pareto_sols[:, 0] + edge_length_y = rect_diag_y - sorted_pareto_sols[:, 1] + return edge_length_x @ edge_length_y + + +def _compute_3d(sorted_pareto_sols: np.ndarray, reference_point: np.ndarray) -> float: + """ + Compute hypervolume in 3D. Time complexity is O(N^2) where N is sorted_pareto_sols.shape[0]. + If X, Y, Z coordinates are permutations of 0, 1, ..., N-1 and reference_point is (N, N, N), the + hypervolume is calculated as the number of voxels (x, y, z) dominated by at least one point. + If we fix x and y, this number is equal to the minimum of z' over all points (x', y', z') + satisfying x' <= x and y' <= y. This can be efficiently computed using cumulative minimum + (`np.minimum.accumulate`). Non-permutation coordinates can be transformed into permutation + coordinates by using coordinate compression. + """ + assert sorted_pareto_sols.shape[1] == reference_point.shape[0] == 3 + n = sorted_pareto_sols.shape[0] + y_order = np.argsort(sorted_pareto_sols[:, 1]) + z_delta = np.zeros((n, n), dtype=float) + z_delta[y_order, np.arange(n)] = reference_point[2] - sorted_pareto_sols[y_order, 2] + z_delta = np.maximum.accumulate(np.maximum.accumulate(z_delta, axis=0), axis=1) + # The x axis is already sorted, so no need to compress this coordinate. + x_vals = sorted_pareto_sols[:, 0] + y_vals = sorted_pareto_sols[y_order, 1] + x_delta = np.concatenate([x_vals[1:], reference_point[:1]]) - x_vals + y_delta = np.concatenate([y_vals[1:], reference_point[1:2]]) - y_vals + # NOTE(nabenabe): Below is the faster alternative of `np.sum(dx[:, None] * dy * dz)`. + return np.dot(np.dot(z_delta, y_delta), x_delta) + + +def _compute_hv(sorted_loss_vals: np.ndarray, reference_point: np.ndarray) -> float: + if sorted_loss_vals.shape[0] == 1: + # NOTE(nabenabe): NumPy overhead is slower than the simple for-loop here. + inclusive_hv = 1.0 + for r, v in zip(reference_point, sorted_loss_vals[0]): + inclusive_hv *= r - v + return float(inclusive_hv) + elif sorted_loss_vals.shape[0] == 2: + # NOTE(nabenabe): NumPy overhead is slower than the simple for-loop here. + # S(A v B) = S(A) + S(B) - S(A ^ B). + hv1, hv2, intersec = 1.0, 1.0, 1.0 + for r, v1, v2 in zip(reference_point, sorted_loss_vals[0], sorted_loss_vals[1]): + hv1 *= r - v1 + hv2 *= r - v2 + intersec *= r - max(v1, v2) + return hv1 + hv2 - intersec + + inclusive_hvs = (reference_point - sorted_loss_vals).prod(axis=-1) + # c.f. Eqs. (6) and (7) of ``A Fast Way of Calculating Exact Hypervolumes``. + limited_sols_array = np.maximum(sorted_loss_vals[:, np.newaxis], sorted_loss_vals) + return inclusive_hvs[-1] + sum( + _compute_exclusive_hv(limited_sols_array[i, i + 1 :], inclusive_hvs[i], reference_point) + for i in range(inclusive_hvs.size - 1) + ) + + +def _compute_exclusive_hv( + limited_sols: np.ndarray, inclusive_hv: float, reference_point: np.ndarray +) -> float: + assert limited_sols.shape[0] >= 1 + if limited_sols.shape[0] <= 3: + # NOTE(nabenabe): Don't use _is_pareto_front for 3 or fewer points to avoid its overhead. + return inclusive_hv - _compute_hv(limited_sols, reference_point) + + # NOTE(nabenabe): As the following line is a hack for speedup, I will describe several + # important points to note. Even if we do not run _is_pareto_front below or use + # assume_unique_lexsorted=False instead, the result of this function does not change, but this + # function simply becomes slower. + # + # For simplicity, I call an array ``quasi-lexsorted`` if it is sorted by the first objective. + # + # Reason why it will be faster with _is_pareto_front + # Hypervolume of a given solution set and a reference point does not change even when we + # remove non Pareto solutions from the solution set. However, the calculation becomes slower + # if the solution set contains many non Pareto solutions. By removing some obvious non Pareto + # solutions, the calculation becomes faster. + # + # Reason why assume_unique_lexsorted must be True for _is_pareto_front + # assume_unique_lexsorted=True actually checks weak dominance and solutions will be weakly + # dominated if there are duplications, so we can remove duplicated solutions by this option. + # In other words, assume_unique_lexsorted=False may significantly slow down when limited_sols + # has many duplicated Pareto solutions because this function becomes an exponential algorithm + # without duplication removal. + # + # NOTE(nabenabe): limited_sols can be non-unique and/or non-lexsorted, so I will describe why + # it is fine. + # + # Reason why we can specify assume_unique_lexsorted=True even when limited_sols is not + # All ``False`` in on_front will be correct (, but it may not be the case for ``True``) even + # if limited_sols is not unique or not lexsorted as long as limited_sols is quasi-lexsorted, + # which is guaranteed. As mentioned earlier, if all ``False`` in on_front is correct, the + # result of this function does not change. + on_front = _is_pareto_front(limited_sols, assume_unique_lexsorted=True) + return inclusive_hv - _compute_hv(limited_sols[on_front], reference_point) + + +def compute_hypervolume( + loss_vals: np.ndarray, reference_point: np.ndarray, assume_pareto: bool = False +) -> float: + """Hypervolume calculator for any dimension. + + This class exactly calculates the hypervolume for any dimension. + For 3 dimensions or higher, the WFG algorithm will be used. + Please refer to ``A Fast Way of Calculating Exact Hypervolumes`` for the WFG algorithm. + + .. note:: + This class is used for computing the hypervolumes of points in multi-objective space. + Each coordinate of each point represents a ``values`` of the multi-objective function. + + .. note:: + We check that each objective is to be minimized. Transform objective values that are + to be maximized before calling this class's ``compute`` method. + + Args: + loss_vals: + An array of loss value vectors to calculate the hypervolume. + reference_point: + The reference point used to calculate the hypervolume. + assume_pareto: + Whether to assume the Pareto optimality to ``loss_vals``. + In other words, if ``True``, none of loss vectors are dominated by another. + ``assume_pareto`` is used only for speedup and it does not change the result even if + this argument is wrongly given. If there are many non-Pareto solutions in + ``loss_vals``, ``assume_pareto=True`` will speed up the calculation. + + Returns: + The hypervolume of the given arguments. + + """ + + if not np.all(loss_vals <= reference_point): + raise ValueError( + "All points must dominate or equal the reference point. " + "That is, for all points in the loss_vals and the coordinate `i`, " + "`loss_vals[i] <= reference_point[i]`." + ) + if not np.all(np.isfinite(reference_point)): + # reference_point does not have nan, thanks to the verification above. + return float("inf") + if loss_vals.size == 0: + return 0.0 + + if not assume_pareto: + unique_lexsorted_loss_vals = np.unique(loss_vals, axis=0) + on_front = _is_pareto_front(unique_lexsorted_loss_vals, assume_unique_lexsorted=True) + sorted_pareto_sols = unique_lexsorted_loss_vals[on_front] + else: + # NOTE(nabenabe): The result of this function does not change both by + # np.argsort(loss_vals[:, 0]) and np.unique(loss_vals, axis=0). + # But many duplications in loss_vals significantly slows down the function. + # TODO(nabenabe): Make an option to use np.unique. + sorted_pareto_sols = loss_vals[loss_vals[:, 0].argsort()] + + if reference_point.shape[0] == 2: + hv = _compute_2d(sorted_pareto_sols, reference_point) + elif reference_point.shape[0] == 3: + # NOTE: For 3D points, we always prefer _compute_3d to _compute_hv because the time + # complexity of _compute_3d is O(N^2), while that of _compute_nd is \\Omega(N^3) + # - It calls _compute_exclusive_hv with i points for i = 0, 1, ..., N-1 + # - _compute_exclusive_hv calls _is_pareto_front, which is quadratic + # with the number of points + hv = _compute_3d(sorted_pareto_sols, reference_point) + else: + hv = _compute_hv(sorted_pareto_sols, reference_point) + + # NOTE(nabenabe): `nan` happens when inf - inf happens, but this is inf in hypervolume due to + # the submodularity. + return hv if np.isfinite(hv) else float("inf") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_imports.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_imports.py new file mode 100644 index 0000000000000000000000000000000000000000..572caf535cb5023427cc0d651ec9064386946225 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_imports.py @@ -0,0 +1,133 @@ +from __future__ import annotations + +import importlib +import types +from typing import Any +from typing import TYPE_CHECKING + + +if TYPE_CHECKING: + from types import TracebackType + +_INTEGRATION_IMPORT_ERROR_TEMPLATE = ( + "\nCould not find `optuna-integration` for `{0}`.\n" + "Please run `pip install optuna-integration[{0}]`." +) + + +class _DeferredImportExceptionContextManager: + """Context manager to defer exceptions from imports. + + Catches :exc:`ImportError` and :exc:`SyntaxError`. + If any exception is caught, this class raises an :exc:`ImportError` when being checked. + + """ + + def __init__(self) -> None: + self._deferred: tuple[Exception, str] | None = None + + def __enter__(self) -> "_DeferredImportExceptionContextManager": + """Enter the context manager. + + Returns: + Itself. + + """ + return self + + def __exit__( + self, + exc_type: type[Exception] | None, + exc_value: Exception | None, + traceback: TracebackType | None, + ) -> bool | None: + """Exit the context manager. + + Args: + exc_type: + Raised exception type. :obj:`None` if nothing is raised. + exc_value: + Raised exception object. :obj:`None` if nothing is raised. + traceback: + Associated traceback. :obj:`None` if nothing is raised. + + Returns: + :obj:`None` if nothing is deferred, otherwise :obj:`True`. + :obj:`True` will suppress any exceptions avoiding them from propagating. + + """ + if isinstance(exc_value, (ImportError, SyntaxError)): + if isinstance(exc_value, ImportError): + message = ( + "Tried to import '{}' but failed. Please make sure that the package is " + "installed correctly to use this feature. Actual error: {}." + ).format(exc_value.name, exc_value) + elif isinstance(exc_value, SyntaxError): + message = ( + "Tried to import a package but failed due to a syntax error in {}. Please " + "make sure that the Python version is correct to use this feature. Actual " + "error: {}." + ).format(exc_value.filename, exc_value) + else: + assert False + + self._deferred = (exc_value, message) + return True + return None + + def is_successful(self) -> bool: + """Return whether the context manager has caught any exceptions. + + Returns: + :obj:`True` if no exceptions are caught, :obj:`False` otherwise. + + """ + return self._deferred is None + + def check(self) -> None: + """Check whether the context manager has caught any exceptions. + + Raises: + :exc:`ImportError`: + If any exception was caught from the caught exception. + + """ + if self._deferred is not None: + exc_value, message = self._deferred + raise ImportError(message) from exc_value + + +def try_import() -> _DeferredImportExceptionContextManager: + """Create a context manager that can wrap imports of optional packages to defer exceptions. + + Returns: + Deferred import context manager. + + """ + return _DeferredImportExceptionContextManager() + + +class _LazyImport(types.ModuleType): + """Module wrapper for lazy import. + + This class wraps the specified modules and lazily imports them only when accessed. + Otherwise, `import optuna` is slowed down by importing all submodules and + dependencies even if not required. + Within this project's usage, importlib override this module's attribute on the first + access and the imported submodule is directly accessed from the second access. + + Args: + name: Name of module to apply lazy import. + """ + + def __init__(self, name: str) -> None: + super().__init__(name) + self._name = name + + def _load(self) -> types.ModuleType: + module = importlib.import_module(self._name) + self.__dict__.update(module.__dict__) + return module + + def __getattr__(self, item: str) -> Any: + return getattr(self._load(), item) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_transform.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..cb9ce7047419c203118dbbe1fd1fc833521f300a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_transform.py @@ -0,0 +1,301 @@ +from __future__ import annotations + +import math +from typing import Any + +import numpy as np + +from optuna.distributions import BaseDistribution +from optuna.distributions import CategoricalDistribution +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution + + +class _SearchSpaceTransform: + """Transform a search space and parameter configurations to continuous space. + + The search space bounds and parameter configurations are represented as ``numpy.ndarray``s and + transformed into continuous space. Bounds and parameters associated with categorical + distributions are one-hot encoded. Parameter configurations in this space can additionally be + untransformed, or mapped back to the original space. This type of + transformation/untransformation is useful for e.g. implementing samplers without having to + condition on distribution types before sampling parameter values. + + Args: + search_space: + The search space. If any transformations are to be applied, parameter configurations + are assumed to hold parameter values for all of the distributions defined in this + search space. Otherwise, assertion failures will be raised. + transform_log: + If :obj:`True`, apply log/exp operations to the bounds and parameters with + corresponding distributions in log space during transformation/untransformation. + Should always be :obj:`True` if any parameters are going to be sampled from the + transformed space. + transform_step: + If :obj:`True`, offset the lower and higher bounds by a half step each, increasing the + space by one step. This allows fair sampling for values close to the bounds. + Should always be :obj:`True` if any parameters are going to be sampled from the + transformed space. + transform_0_1: + If :obj:`True`, apply a linear transformation to the bounds and parameters so that + they are in the unit cube. + + Attributes: + bounds: + Constructed bounds from the given search space. + column_to_encoded_columns: + Constructed mapping from original parameter column index to encoded column indices. + encoded_column_to_column: + Constructed mapping from encoded column index to original parameter column index. + + Note: + Parameter values are not scaled to the unit cube. + + Note: + ``transform_log`` and ``transform_step`` are useful for constructing bounds and parameters + without any actual transformations by setting those arguments to :obj:`False`. This is + needed for e.g. the hyperparameter importance assessments. + + """ + + def __init__( + self, + search_space: dict[str, BaseDistribution], + transform_log: bool = True, + transform_step: bool = True, + transform_0_1: bool = False, + ) -> None: + bounds, column_to_encoded_columns, encoded_column_to_column = _transform_search_space( + search_space, transform_log, transform_step + ) + self._raw_bounds = bounds + self._column_to_encoded_columns = column_to_encoded_columns + self._encoded_column_to_column = encoded_column_to_column + self._search_space = search_space + self._transform_log = transform_log + self._transform_0_1 = transform_0_1 + + @property + def bounds(self) -> np.ndarray: + if self._transform_0_1: + return np.array([[0.0, 1.0]] * self._raw_bounds.shape[0]) + else: + return self._raw_bounds + + @property + def column_to_encoded_columns(self) -> list[np.ndarray]: + return self._column_to_encoded_columns + + @property + def encoded_column_to_column(self) -> np.ndarray: + return self._encoded_column_to_column + + def transform(self, params: dict[str, Any]) -> np.ndarray: + """Transform a parameter configuration from actual values to continuous space. + + Args: + params: + A parameter configuration to transform. + + Returns: + A 1-dimensional ``numpy.ndarray`` holding the transformed parameters in the + configuration. + + """ + trans_params = np.zeros(self._raw_bounds.shape[0], dtype=np.float64) + + bound_idx = 0 + for name, distribution in self._search_space.items(): + assert name in params, "Parameter configuration must contain all distributions." + param = params[name] + + if isinstance(distribution, CategoricalDistribution): + choice_idx = int(distribution.to_internal_repr(param)) + trans_params[bound_idx + choice_idx] = 1 + bound_idx += len(distribution.choices) + else: + trans_params[bound_idx] = _transform_numerical_param( + param, distribution, self._transform_log + ) + bound_idx += 1 + + if self._transform_0_1: + single_mask = self._raw_bounds[:, 0] == self._raw_bounds[:, 1] + trans_params[single_mask] = 0.5 + trans_params[~single_mask] = ( + trans_params[~single_mask] - self._raw_bounds[~single_mask, 0] + ) / (self._raw_bounds[~single_mask, 1] - self._raw_bounds[~single_mask, 0]) + + return trans_params + + def untransform(self, trans_params: np.ndarray) -> dict[str, Any]: + """Untransform a parameter configuration from continuous space to actual values. + + Args: + trans_params: + A 1-dimensional ``numpy.ndarray`` in the transformed space corresponding to a + parameter configuration. + + Returns: + A dictionary of an untransformed parameter configuration. Keys are parameter names. + Values are untransformed parameter values. + + """ + assert trans_params.shape == (self._raw_bounds.shape[0],) + + if self._transform_0_1: + trans_params = self._raw_bounds[:, 0] + trans_params * ( + self._raw_bounds[:, 1] - self._raw_bounds[:, 0] + ) + + params = {} + + for (name, distribution), encoded_columns in zip( + self._search_space.items(), self.column_to_encoded_columns + ): + trans_param = trans_params[encoded_columns] + + if isinstance(distribution, CategoricalDistribution): + # Select the highest rated one-hot encoding. + param = distribution.to_external_repr(trans_param.argmax()) + else: + param = _untransform_numerical_param( + trans_param.item(), distribution, self._transform_log + ) + + params[name] = param + + return params + + +def _transform_search_space( + search_space: dict[str, BaseDistribution], transform_log: bool, transform_step: bool +) -> tuple[np.ndarray, list[np.ndarray], np.ndarray]: + assert len(search_space) > 0, "Cannot transform if no distributions are given." + + n_bounds = sum( + len(d.choices) if isinstance(d, CategoricalDistribution) else 1 + for d in search_space.values() + ) + + bounds = np.empty((n_bounds, 2), dtype=np.float64) + column_to_encoded_columns: list[np.ndarray] = [] + encoded_column_to_column = np.empty(n_bounds, dtype=np.int64) + + bound_idx = 0 + for distribution in search_space.values(): + d = distribution + if isinstance(d, CategoricalDistribution): + n_choices = len(d.choices) + bounds[bound_idx : bound_idx + n_choices] = (0, 1) # Broadcast across all choices. + encoded_columns = np.arange(bound_idx, bound_idx + n_choices) + encoded_column_to_column[encoded_columns] = len(column_to_encoded_columns) + column_to_encoded_columns.append(encoded_columns) + bound_idx += n_choices + elif isinstance( + d, + ( + FloatDistribution, + IntDistribution, + ), + ): + if isinstance(d, FloatDistribution): + if d.step is not None: + half_step = 0.5 * d.step if transform_step else 0.0 + bds = ( + _transform_numerical_param(d.low, d, transform_log) - half_step, + _transform_numerical_param(d.high, d, transform_log) + half_step, + ) + else: + bds = ( + _transform_numerical_param(d.low, d, transform_log), + _transform_numerical_param(d.high, d, transform_log), + ) + elif isinstance(d, IntDistribution): + half_step = 0.5 * d.step if transform_step else 0.0 + if d.log: + bds = ( + _transform_numerical_param(d.low - half_step, d, transform_log), + _transform_numerical_param(d.high + half_step, d, transform_log), + ) + else: + bds = ( + _transform_numerical_param(d.low, d, transform_log) - half_step, + _transform_numerical_param(d.high, d, transform_log) + half_step, + ) + else: + assert False, "Should not reach. Unexpected distribution." + + bounds[bound_idx] = bds + encoded_column = np.atleast_1d(bound_idx) + encoded_column_to_column[encoded_column] = len(column_to_encoded_columns) + column_to_encoded_columns.append(encoded_column) + bound_idx += 1 + else: + assert False, "Should not reach. Unexpected distribution." + + assert bound_idx == n_bounds + + return bounds, column_to_encoded_columns, encoded_column_to_column + + +def _transform_numerical_param( + param: int | float, distribution: BaseDistribution, transform_log: bool +) -> float: + d = distribution + + if isinstance(d, CategoricalDistribution): + assert False, "Should not reach. Should be one-hot encoded." + elif isinstance(d, FloatDistribution): + if d.log: + trans_param = math.log(param) if transform_log else float(param) + else: + trans_param = float(param) + elif isinstance(d, IntDistribution): + if d.log: + trans_param = math.log(param) if transform_log else float(param) + else: + trans_param = float(param) + else: + assert False, "Should not reach. Unexpected distribution." + + return trans_param + + +def _untransform_numerical_param( + trans_param: float, distribution: BaseDistribution, transform_log: bool +) -> int | float: + d = distribution + + if isinstance(d, CategoricalDistribution): + assert False, "Should not reach. Should be one-hot encoded." + elif isinstance(d, FloatDistribution): + if d.log: + param = math.exp(trans_param) if transform_log else trans_param + if d.single(): + pass + else: + param = min(param, np.nextafter(d.high, d.high - 1)) + elif d.step is not None: + param = float( + np.clip(np.round((trans_param - d.low) / d.step) * d.step + d.low, d.low, d.high) + ) + else: + if d.single(): + param = trans_param + else: + param = min(trans_param, np.nextafter(d.high, d.high - 1)) + elif isinstance(d, IntDistribution): + if d.log: + if transform_log: + param = int(np.clip(np.round(math.exp(trans_param)), d.low, d.high)) + else: + param = int(trans_param) + else: + param = int( + np.clip(np.round((trans_param - d.low) / d.step) * d.step + d.low, d.low, d.high) + ) + else: + assert False, "Should not reach. Unexpected distribution." + + return param diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_typing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_typing.py new file mode 100644 index 0000000000000000000000000000000000000000..fe8ebbb3642d685d88f4cd1d5a4490d8c7ea22ec --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/_typing.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +from typing import Mapping +from typing import Sequence +from typing import Union + + +JSONSerializable = Union[ + Mapping[str, "JSONSerializable"], + Sequence["JSONSerializable"], + str, + int, + float, + bool, + None, +] + +__all__ = ["JSONSerializable"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ae52b244a0055f4ab2aabdf7d823787472e27940 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/__init__.py @@ -0,0 +1,20 @@ +from optuna.artifacts._backoff import Backoff +from optuna.artifacts._boto3 import Boto3ArtifactStore +from optuna.artifacts._download import download_artifact +from optuna.artifacts._filesystem import FileSystemArtifactStore +from optuna.artifacts._gcs import GCSArtifactStore +from optuna.artifacts._list_artifact_meta import get_all_artifact_meta +from optuna.artifacts._upload import ArtifactMeta +from optuna.artifacts._upload import upload_artifact + + +__all__ = [ + "ArtifactMeta", + "FileSystemArtifactStore", + "Boto3ArtifactStore", + "GCSArtifactStore", + "Backoff", + "get_all_artifact_meta", + "upload_artifact", + "download_artifact", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_backoff.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_backoff.py new file mode 100644 index 0000000000000000000000000000000000000000..823a9c62b22465f651d1b875c388876a7fc1c10f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_backoff.py @@ -0,0 +1,117 @@ +from __future__ import annotations + +import logging +import time +from typing import TYPE_CHECKING + +from optuna.artifacts.exceptions import ArtifactNotFound + + +_logger = logging.getLogger(__name__) + + +if TYPE_CHECKING: + from typing import BinaryIO + + from optuna.artifacts._protocol import ArtifactStore + + +class Backoff: + """An artifact store's middleware for exponential backoff. + + Example: + .. code-block:: python + + import optuna + from optuna.artifacts import upload_artifact + from optuna.artifacts import Boto3ArtifactStore + from optuna.artifacts import Backoff + + + artifact_store = Backoff(Boto3ArtifactStore("my-bucket")) + + + def objective(trial: optuna.Trial) -> float: + ... = trial.suggest_float("x", -10, 10) + file_path = generate_example(...) + upload_artifact( + artifact_store=artifact_store, + file_path=file_path, + study_or_trial=trial, + ) + return ... + """ + + def __init__( + self, + backend: ArtifactStore, + *, + max_retries: int = 10, + multiplier: float = 2, + min_delay: float = 0.1, + max_delay: float = 30, + ) -> None: + # Default sleep seconds: + # 0.1, 0.2, 0.4, 0.8, 1.6, 3.2, 6.4, 12.8, 25.6, 30 + self._backend = backend + assert max_retries > 0 + assert multiplier > 0 + assert min_delay > 0 + assert max_delay > min_delay + self._max_retries = max_retries + self._multiplier = multiplier + self._min_delay = min_delay + self._max_delay = max_delay + + def _get_sleep_secs(self, n_retry: int) -> float: + return min(self._min_delay * self._multiplier**n_retry, self._max_delay) + + def open_reader(self, artifact_id: str) -> BinaryIO: + for i in range(self._max_retries): + try: + return self._backend.open_reader(artifact_id) + except ArtifactNotFound: + raise + except Exception as e: + if i == self._max_retries - 1: + raise + else: + _logger.error(f"Failed to open artifact={artifact_id} n_retry={i}", exc_info=e) + time.sleep(self._get_sleep_secs(i)) + assert False, "must not reach here" + + def write(self, artifact_id: str, content_body: BinaryIO) -> None: + for i in range(self._max_retries): + try: + self._backend.write(artifact_id, content_body) + break + except ArtifactNotFound: + raise + except Exception as e: + if i == self._max_retries - 1: + raise + else: + _logger.error(f"Failed to open artifact={artifact_id} n_retry={i}", exc_info=e) + content_body.seek(0) + time.sleep(self._get_sleep_secs(i)) + + def remove(self, artifact_id: str) -> None: + for i in range(self._max_retries): + try: + self._backend.remove(artifact_id) + except ArtifactNotFound: + raise + except Exception as e: + if i == self._max_retries - 1: + raise + else: + _logger.error(f"Failed to delete artifact={artifact_id}", exc_info=e) + time.sleep(self._get_sleep_secs(i)) + + +if TYPE_CHECKING: + # A mypy-runtime assertion to ensure that the Backoff middleware implements + # all abstract methods in ArtifactStore. + from optuna.artifacts import FileSystemArtifactStore + + _: ArtifactStore = Backoff(FileSystemArtifactStore(".")) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_boto3.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_boto3.py new file mode 100644 index 0000000000000000000000000000000000000000..d896cc951e14a6e3465d7691386a0a87f8f148a3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_boto3.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import io +import shutil +from typing import TYPE_CHECKING + +from optuna._imports import try_import +from optuna.artifacts.exceptions import ArtifactNotFound + + +if TYPE_CHECKING: + from typing import BinaryIO + + from mypy_boto3_s3 import S3Client + +with try_import() as _imports: + import boto3 + from botocore.exceptions import ClientError + + +class Boto3ArtifactStore: + """An artifact backend for Boto3. + + Args: + bucket_name: + The name of the bucket to store artifacts. + + client: + A Boto3 client to use for storage operations. If not specified, a new client will + be created. + + avoid_buf_copy: + If True, skip procedure to copy the content of the source file object to a buffer + before uploading it to S3 ins. This is default to False because using + ``upload_fileobj()`` method of Boto3 client might close the source file object. + + Example: + .. code-block:: python + + import optuna + from optuna.artifacts import upload_artifact + from optuna.artifacts import Boto3ArtifactStore + + + artifact_store = Boto3ArtifactStore("my-bucket") + + + def objective(trial: optuna.Trial) -> float: + ... = trial.suggest_float("x", -10, 10) + file_path = generate_example(...) + upload_artifact( + artifact_store=artifact_store, + file_path=file_path, + study_or_trial=trial, + ) + return ... + """ + + def __init__( + self, bucket_name: str, client: S3Client | None = None, *, avoid_buf_copy: bool = False + ) -> None: + _imports.check() + self.bucket = bucket_name + self.client = client or boto3.client("s3") + # This flag is added to avoid that upload_fileobj() method of Boto3 client may close the + # source file object. See https://github.com/boto/boto3/issues/929. + self._avoid_buf_copy = avoid_buf_copy + + def open_reader(self, artifact_id: str) -> BinaryIO: + try: + obj = self.client.get_object(Bucket=self.bucket, Key=artifact_id) + except ClientError as e: + if _is_not_found_error(e): + raise ArtifactNotFound( + f"Artifact storage with bucket: {self.bucket}, artifact_id: {artifact_id} was" + " not found" + ) from e + raise + body = obj.get("Body") + assert body is not None + return body + + def write(self, artifact_id: str, content_body: BinaryIO) -> None: + fsrc: BinaryIO = content_body + if not self._avoid_buf_copy: + buf = io.BytesIO() + shutil.copyfileobj(content_body, buf) + buf.seek(0) + fsrc = buf + self.client.upload_fileobj(fsrc, self.bucket, artifact_id) + + def remove(self, artifact_id: str) -> None: + self.client.delete_object(Bucket=self.bucket, Key=artifact_id) + + +def _is_not_found_error(e: ClientError) -> bool: + error_code = e.response.get("Error", {}).get("Code") + http_status_code = e.response.get("ResponseMetadata", {}).get("HTTPStatusCode") + return error_code == "NoSuchKey" or http_status_code == 404 + + +if TYPE_CHECKING: + # A mypy-runtime assertion to ensure that Boto3ArtifactStore implements all abstract methods + # in ArtifactStore. + from optuna.artifacts._protocol import ArtifactStore + + _: ArtifactStore = Boto3ArtifactStore("") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_download.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_download.py new file mode 100644 index 0000000000000000000000000000000000000000..c754dec66a37e7c14c9772d0b3f72104213a6c63 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_download.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +import os +import shutil +from typing import TYPE_CHECKING + + +if TYPE_CHECKING: + from optuna.artifacts._protocol import ArtifactStore + + +def download_artifact(*, artifact_store: ArtifactStore, file_path: str, artifact_id: str) -> None: + """Download an artifact from the artifact store. + + Args: + artifact_store: + An artifact store. + file_path: + A path to save the downloaded artifact. + artifact_id: + The identifier of the artifact to download. + """ + if os.path.exists(file_path): + raise FileExistsError(f"File already exists: {file_path}") + + with artifact_store.open_reader(artifact_id) as reader, open(file_path, "wb") as writer: + shutil.copyfileobj(reader, writer) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_filesystem.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_filesystem.py new file mode 100644 index 0000000000000000000000000000000000000000..32318996bd9509375cca88ad6bd02740575fd17f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_filesystem.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +import os +from pathlib import Path +import shutil +from typing import TYPE_CHECKING + +from optuna.artifacts.exceptions import ArtifactNotFound + + +if TYPE_CHECKING: + from typing import BinaryIO + + +class FileSystemArtifactStore: + """An artifact store for file systems. + + Args: + base_path: + The base path to a directory to store artifacts. + + Example: + .. code-block:: python + + import os + + import optuna + from optuna.artifacts import FileSystemArtifactStore + from optuna.artifacts import upload_artifact + + + base_path = "./artifacts" + os.makedirs(base_path, exist_ok=True) + artifact_store = FileSystemArtifactStore(base_path=base_path) + + + def objective(trial: optuna.Trial) -> float: + ... = trial.suggest_float("x", -10, 10) + file_path = generate_example(...) + upload_artifact( + artifact_store=artifact_store, + file_path=file_path, + study_or_trial=trial, + ) + return ... + """ + + def __init__(self, base_path: str | Path) -> None: + if isinstance(base_path, str): + base_path = Path(base_path) + # TODO(Shinichi): Check if the base_path is valid directory. + self._base_path = base_path + + def open_reader(self, artifact_id: str) -> BinaryIO: + filepath = os.path.join(self._base_path, artifact_id) + try: + f = open(filepath, "rb") + except FileNotFoundError as e: + raise ArtifactNotFound("not found") from e + return f + + def write(self, artifact_id: str, content_body: BinaryIO) -> None: + filepath = os.path.join(self._base_path, artifact_id) + with open(filepath, "wb") as f: + shutil.copyfileobj(content_body, f) + + def remove(self, artifact_id: str) -> None: + filepath = os.path.join(self._base_path, artifact_id) + try: + os.remove(filepath) + except FileNotFoundError as e: + raise ArtifactNotFound("not found") from e + + +if TYPE_CHECKING: + # A mypy-runtime assertion to ensure that LocalArtifactBackend + # implements all abstract methods in ArtifactBackendProtocol. + from optuna.artifacts._protocol import ArtifactStore + + _: ArtifactStore = FileSystemArtifactStore("") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_gcs.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_gcs.py new file mode 100644 index 0000000000000000000000000000000000000000..63e469ce7f3ee20dc1fa9cdf5a0a3884feb322ef --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_gcs.py @@ -0,0 +1,95 @@ +from __future__ import annotations + +from io import BytesIO +from typing import TYPE_CHECKING + +from optuna._experimental import experimental_class +from optuna._imports import try_import +from optuna.artifacts.exceptions import ArtifactNotFound + + +if TYPE_CHECKING: + from typing import BinaryIO + +with try_import() as _imports: + import google.cloud.storage + + +@experimental_class("3.4.0") +class GCSArtifactStore: + """An artifact backend for Google Cloud Storage (GCS). + + Args: + bucket_name: + The name of the bucket to store artifacts. + + client: + A google-cloud-storage ``Client`` to use for storage operations. If not specified, a + new client will be created with default settings. + + Example: + .. code-block:: python + + import optuna + from optuna.artifacts import GCSArtifactStore, upload_artifact + + + artifact_backend = GCSArtifactStore("my-bucket") + + + def objective(trial: optuna.Trial) -> float: + ... = trial.suggest_float("x", -10, 10) + file_path = generate_example(...) + upload_artifact( + artifact_store=artifact_store, + file_path=file_path, + study_or_trial=trial, + ) + return ... + + Before running this code, you will have to install ``gcloud`` and run + + .. code-block:: bash + + gcloud auth application-default login + + so that the Cloud Storage library can automatically find the credential. + """ + + def __init__( + self, + bucket_name: str, + client: google.cloud.storage.Client | None = None, + ) -> None: + _imports.check() + self.bucket_name = bucket_name + self.client = client or google.cloud.storage.Client() + self.bucket_obj = self.client.bucket(bucket_name) + + def open_reader(self, artifact_id: str) -> "BinaryIO": + blob = self.bucket_obj.get_blob(artifact_id) + + if blob is None: + raise ArtifactNotFound( + f"Artifact storage with bucket: {self.bucket_name}, artifact_id: {artifact_id} was" + " not found" + ) + + body = blob.download_as_bytes() + return BytesIO(body) + + def write(self, artifact_id: str, content_body: "BinaryIO") -> None: + blob = self.bucket_obj.blob(artifact_id) + data = content_body.read() + blob.upload_from_string(data) + + def remove(self, artifact_id: str) -> None: + self.bucket_obj.delete_blob(artifact_id) + + +if TYPE_CHECKING: + # A mypy-runtime assertion to ensure that GCS3ArtifactStore implements all abstract methods + # in ArtifactStore. + from optuna.artifacts._protocol import ArtifactStore + + _: ArtifactStore = GCSArtifactStore("") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_protocol.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_protocol.py new file mode 100644 index 0000000000000000000000000000000000000000..02cc08937ad2e132fd14199b64099afc42c59ea8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_protocol.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + + +try: + from typing import Protocol +except ImportError: + from typing_extensions import Protocol # type: ignore + + +if TYPE_CHECKING: + from typing import BinaryIO + + +class ArtifactStore(Protocol): + """A protocol defining the interface for an artifact backend. + + The methods defined in this protocol are not supposed to be directly called by library users. + + An artifact backend is responsible for managing the storage and retrieval + of artifact data. The backend should provide methods for opening, writing + and removing artifacts. + """ + + def open_reader(self, artifact_id: str) -> BinaryIO: + """Open the artifact identified by the artifact_id. + + This method should return a binary file-like object in read mode, similar to + ``open(..., mode="rb")``. If the artifact does not exist, an + :exc:`~optuna.artifacts.exceptions.ArtifactNotFound` exception + should be raised. + + Args: + artifact_id: The identifier of the artifact to open. + + Returns: + BinaryIO: A binary file-like object that can be read from. + """ + ... + + def write(self, artifact_id: str, content_body: BinaryIO) -> None: + """Save the content to the backend. + + Args: + artifact_id: The identifier of the artifact to write to. + content_body: The content to write to the artifact. + """ + ... + + def remove(self, artifact_id: str) -> None: + """Remove the artifact identified by the artifact_id. + + This method should delete the artifact from the backend. If the artifact does not + exist, an :exc:`~optuna.artifacts.exceptions.ArtifactNotFound` exception + may be raised. + + Args: + artifact_id: The identifier of the artifact to remove. + """ + ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_upload.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_upload.py new file mode 100644 index 0000000000000000000000000000000000000000..04725ac082cde5e10f696c539786402d1aec4a67 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/_upload.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +from dataclasses import asdict +from dataclasses import dataclass +import json +import mimetypes +import os +import uuid + +from optuna._convert_positional_args import convert_positional_args +from optuna.artifacts._protocol import ArtifactStore +from optuna.storages import BaseStorage +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.trial import Trial + + +ARTIFACTS_ATTR_PREFIX = "artifacts:" +DEFAULT_MIME_TYPE = "application/octet-stream" + + +@dataclass +class ArtifactMeta: + """Meta information for an artifact. + + .. note:: + All the artifact meta linked to a study or trial can be listed by + :func:`~optuna.artifacts.get_all_artifact_meta`. + The artifact meta can be used for :func:`~optuna.artifacts.download_artifact`. + + Args: + artifact_id: + The identifier of the artifact. + filename: + The artifact file name used for the upload. + mimetype: + A MIME type of the artifact. + If not specified, the MIME type is guessed from the file extension. + encoding: + An encoding of the artifact, which is suitable for use as a Content-Encoding header, + e.g., gzip. If not specified, the encoding is guessed from the file extension. + """ + + artifact_id: str + filename: str + mimetype: str + encoding: str | None + + +@convert_positional_args( + previous_positional_arg_names=["study_or_trial", "file_path", "artifact_store"], + deprecated_version="4.0.0", + removed_version="6.0.0", +) +def upload_artifact( + *, + artifact_store: ArtifactStore, + file_path: str, + study_or_trial: Trial | FrozenTrial | Study, + storage: BaseStorage | None = None, + mimetype: str | None = None, + encoding: str | None = None, +) -> str: + """Upload an artifact to the artifact store. + + Args: + artifact_store: + An artifact store. + file_path: + A path to the file to be uploaded. + study_or_trial: + A :class:`~optuna.trial.Trial` object, a :class:`~optuna.trial.FrozenTrial`, or + a :class:`~optuna.study.Study` object. + storage: + A storage object. This argument is required only if ``study_or_trial`` is + :class:`~optuna.trial.FrozenTrial`. + mimetype: + A MIME type of the artifact. If not specified, the MIME type is guessed from the file + extension. + encoding: + An encoding of the artifact, which is suitable for use as a ``Content-Encoding`` + header (e.g. gzip). If not specified, the encoding is guessed from the file extension. + + Returns: + An artifact ID. + """ + + filename = os.path.basename(file_path) + + if isinstance(study_or_trial, Trial) and storage is None: + storage = study_or_trial.storage + elif isinstance(study_or_trial, Study) and storage is None: + storage = study_or_trial._storage + + if storage is None: + raise ValueError("storage is required for FrozenTrial.") + + artifact_id = str(uuid.uuid4()) + guess_mimetype, guess_encoding = mimetypes.guess_type(filename) + artifact = ArtifactMeta( + artifact_id=artifact_id, + filename=filename, + mimetype=mimetype or guess_mimetype or DEFAULT_MIME_TYPE, + encoding=encoding or guess_encoding, + ) + attr_key = ARTIFACTS_ATTR_PREFIX + artifact_id + if isinstance(study_or_trial, (Trial, FrozenTrial)): + trial_id = study_or_trial._trial_id + storage.set_trial_system_attr(trial_id, attr_key, json.dumps(asdict(artifact))) + else: + study_id = study_or_trial._study_id + storage.set_study_system_attr(study_id, attr_key, json.dumps(asdict(artifact))) + + with open(file_path, "rb") as f: + artifact_store.write(artifact_id, f) + return artifact_id diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/exceptions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..724afe74022414e985776c9eabb28e718c34e279 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/artifacts/exceptions.py @@ -0,0 +1,12 @@ +from optuna.exceptions import OptunaError + + +class ArtifactNotFound(OptunaError): + """Exception raised when an artifact is not found. + + It is typically raised while calling + :meth:`~optuna.artifacts._protocol.ArtifactStore.open_reader` or + :meth:`~optuna.artifacts._protocol.ArtifactStore.remove` methods. + """ + + ... diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/cli.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..3b87f210c22a6648a47ab5760cea915cf869b51d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/cli.py @@ -0,0 +1,1007 @@ +"""Optuna CLI module. +If you want to add a new command, you also need to update the constant `_COMMANDS` +""" + +from __future__ import annotations + +import argparse +from argparse import ArgumentParser +from argparse import Namespace +import datetime +from enum import Enum +import inspect +import json +import logging +import os +import sys +from typing import Any +import warnings + +import sqlalchemy.exc +import yaml + +import optuna +from optuna._imports import _LazyImport +from optuna.exceptions import CLIUsageError +from optuna.exceptions import ExperimentalWarning +from optuna.storages import BaseStorage +from optuna.storages import JournalFileStorage +from optuna.storages import JournalRedisStorage +from optuna.storages import JournalStorage +from optuna.storages import RDBStorage +from optuna.storages.journal import JournalFileBackend +from optuna.storages.journal import JournalRedisBackend +from optuna.trial import TrialState + + +_dataframe = _LazyImport("optuna.study._dataframe") + +_DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S" + + +def _check_storage_url(storage_url: str | None) -> str: + if storage_url is not None: + return storage_url + + env_storage = os.environ.get("OPTUNA_STORAGE") + if env_storage is not None: + warnings.warn( + "Specifying the storage url via 'OPTUNA_STORAGE' environment variable" + " is an experimental feature. The interface can change in the future.", + ExperimentalWarning, + ) + return env_storage + raise CLIUsageError("Storage URL is not specified.") + + +def _get_storage(storage_url: str | None, storage_class: str | None) -> BaseStorage: + storage_url = _check_storage_url(storage_url) + if storage_class: + if storage_class == JournalRedisBackend.__name__: + return JournalStorage(JournalRedisBackend(storage_url)) + if storage_class == JournalRedisStorage.__name__: + return JournalStorage(JournalRedisStorage(storage_url)) + if storage_class == JournalFileBackend.__name__: + return JournalStorage(JournalFileBackend(storage_url)) + if storage_class == JournalFileStorage.__name__: + return JournalStorage(JournalFileStorage(storage_url)) + if storage_class == RDBStorage.__name__: + return RDBStorage(storage_url) + raise CLIUsageError("Unsupported storage class") + + if storage_url.startswith("redis"): + return JournalStorage(JournalRedisBackend(storage_url)) + if os.path.isfile(storage_url): + return JournalStorage(JournalFileBackend(storage_url)) + try: + return RDBStorage(storage_url) + except sqlalchemy.exc.ArgumentError: + raise CLIUsageError("Failed to guess storage class from storage_url") + + +def _format_value(value: Any) -> Any: + # Format value that can be serialized to JSON or YAML. + if value is None or isinstance(value, (int, float)): + return value + elif isinstance(value, datetime.datetime): + return value.strftime(_DATETIME_FORMAT) + elif isinstance(value, list): + return list(_format_value(v) for v in value) + elif isinstance(value, tuple): + return tuple(_format_value(v) for v in value) + elif isinstance(value, dict): + return {_format_value(k): _format_value(v) for k, v in value.items()} + else: + return str(value) + + +def _convert_to_dict( + records: list[dict[tuple[str, str], Any]], columns: list[tuple[str, str]], flatten: bool +) -> tuple[list[dict[str, Any]], list[str]]: + header = [] + ret = [] + if flatten: + for column in columns: + if column[1] != "": + header.append(f"{column[0]}_{column[1]}") + elif any(isinstance(record.get(column), (list, tuple)) for record in records): + max_length = 0 + for record in records: + if column in record: + max_length = max(max_length, len(record[column])) + for i in range(max_length): + header.append(f"{column[0]}_{i}") + else: + header.append(column[0]) + for record in records: + row = {} + for column in columns: + if column not in record: + continue + value = _format_value(record[column]) + if column[1] != "": + row[f"{column[0]}_{column[1]}"] = value + elif any(isinstance(record.get(column), (list, tuple)) for record in records): + for i, v in enumerate(value): + row[f"{column[0]}_{i}"] = v + else: + row[f"{column[0]}"] = value + ret.append(row) + else: + for column in columns: + if column[0] not in header: + header.append(column[0]) + for record in records: + attrs: dict[str, Any] = {column_name: {} for column_name in header} + for column in columns: + if column not in record: + continue + value = _format_value(record[column]) + if isinstance(column[1], int): + # Reconstruct list of values. `_dataframe._create_records_and_aggregate_column` + # returns indices of list as the second key of column. + if attrs[column[0]] == {}: + attrs[column[0]] = [] + attrs[column[0]] += [None] * max(column[1] + 1 - len(attrs[column[0]]), 0) + attrs[column[0]][column[1]] = value + elif column[1] != "": + attrs[column[0]][column[1]] = value + else: + attrs[column[0]] = value + ret.append(attrs) + + return ret, header + + +class ValueType(Enum): + NONE = 0 + NUMERIC = 1 + STRING = 2 + + +class CellValue: + def __init__(self, value: Any) -> None: + self.value = value + if value is None: + self.value_type = ValueType.NONE + elif isinstance(value, (int, float)): + self.value_type = ValueType.NUMERIC + else: + self.value_type = ValueType.STRING + + def __str__(self) -> str: + if isinstance(self.value, datetime.datetime): + return self.value.strftime(_DATETIME_FORMAT) + else: + return str(self.value) + + def width(self) -> int: + return len(str(self.value)) + + def get_string(self, value_type: ValueType, width: int) -> str: + value = str(self.value) + if self.value is None: + return " " * width + elif value_type == ValueType.NUMERIC: + return f"{value:>{width}}" + else: + return f"{value:<{width}}" + + +def _dump_value(records: list[dict[str, Any]], header: list[str]) -> str: + values = [] + for record in records: + row = [] + for column_name in header: + # Below follows the table formatting convention where record[column_name] is treated as + # an empty string if record[column_name] is None. e.g., {"a": None} is replaced with + # {"a": ""} + row.append(str(record[column_name]) if record.get(column_name) is not None else "") + values.append(" ".join(row)) + return "\n".join(values) + + +def _dump_table(records: list[dict[str, Any]], header: list[str]) -> str: + rows = [] + for record in records: + row = [] + for column_name in header: + row.append(CellValue(record.get(column_name))) + rows.append(row) + + separator = "+" + header_string = "|" + rows_string = ["|" for _ in rows] + for column in range(len(header)): + value_types = [row[column].value_type for row in rows] + value_type = ValueType.NUMERIC + for t in value_types: + if t == ValueType.STRING: + value_type = ValueType.STRING + if len(rows) == 0: + max_width = len(header[column]) + else: + max_width = max(len(header[column]), max(row[column].width() for row in rows)) + separator += "-" * (max_width + 2) + "+" + if value_type == ValueType.NUMERIC: + header_string += f" {header[column]:>{max_width}} |" + else: + header_string += f" {header[column]:<{max_width}} |" + for i, row in enumerate(rows): + rows_string[i] += " " + row[column].get_string(value_type, max_width) + " |" + + ret = "" + ret += separator + "\n" + ret += header_string + "\n" + ret += separator + "\n" + for row_string in rows_string: + ret += row_string + "\n" + ret += separator + "\n" + + return ret + + +def _format_output( + records: list[dict[tuple[str, str], Any]] | dict[tuple[str, str], Any], + columns: list[tuple[str, str]], + output_format: str, + flatten: bool, +) -> str: + if isinstance(records, list): + values, header = _convert_to_dict(records, columns, flatten) + else: + values, header = _convert_to_dict([records], columns, flatten) + + if output_format == "value": + return _dump_value(values, header).strip() + elif output_format == "table": + return _dump_table(values, header).strip() + elif output_format == "json": + if isinstance(records, list): + return json.dumps(values).strip() + else: + return json.dumps(values[0]).strip() + elif output_format == "yaml": + if isinstance(records, list): + return yaml.safe_dump(values).strip() + else: + return yaml.safe_dump(values[0]).strip() + else: + raise CLIUsageError(f"Optuna CLI does not supported the {output_format} format.") + + +class _BaseCommand: + """Base class for commands. + + Note that command classes are not intended to be called by library users. + They are exclusively used within this file to manage Optuna CLI commands. + """ + + def __init__(self) -> None: + self.logger = optuna.logging.get_logger(__name__) + + def add_arguments(self, parser: ArgumentParser) -> None: + """Add arguments required for each command. + + Args: + parser: + `ArgumentParser` object to add arguments + """ + pass + + def take_action(self, parsed_args: Namespace) -> int: + """Define action if the command is called. + + Args: + parsed_args: + `Namespace` object including arguments specified by user. + + Returns: + Running status of the action. + 0 if this method finishes normally, otherwise 1. + """ + + raise NotImplementedError + + +class _CreateStudy(_BaseCommand): + """Create a new study.""" + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument( + "--study-name", + default=None, + help="A human-readable name of a study to distinguish it from others.", + ) + parser.add_argument( + "--direction", + default=None, + type=str, + choices=("minimize", "maximize"), + help="Set direction of optimization to a new study. Set 'minimize' " + "for minimization and 'maximize' for maximization.", + ) + parser.add_argument( + "--skip-if-exists", + default=False, + action="store_true", + help="If specified, the creation of the study is skipped " + "without any error when the study name is duplicated.", + ) + parser.add_argument( + "--directions", + type=str, + default=None, + choices=("minimize", "maximize"), + help="Set directions of optimization to a new study." + " Put whitespace between directions. Each direction should be" + ' either "minimize" or "maximize".', + nargs="+", + ) + + def take_action(self, parsed_args: Namespace) -> int: + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + study_name = optuna.create_study( + storage=storage, + study_name=parsed_args.study_name, + direction=parsed_args.direction, + directions=parsed_args.directions, + load_if_exists=parsed_args.skip_if_exists, + ).study_name + print(study_name) + return 0 + + +class _DeleteStudy(_BaseCommand): + """Delete a specified study.""" + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument("--study-name", default=None, help="The name of the study to delete.") + + def take_action(self, parsed_args: Namespace) -> int: + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + study_id = storage.get_study_id_from_name(parsed_args.study_name) + storage.delete_study(study_id) + return 0 + + +class _StudySetUserAttribute(_BaseCommand): + """Set a user attribute to a study.""" + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument( + "--study-name", + required=True, + help="The name of the study to set the user attribute to.", + ) + parser.add_argument("--key", "-k", required=True, help="Key of the user attribute.") + parser.add_argument("--value", required=True, help="Value to be set.") + + def take_action(self, parsed_args: Namespace) -> int: + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + + study = optuna.load_study(storage=storage, study_name=parsed_args.study_name) + + study.set_user_attr(parsed_args.key, parsed_args.value) + + self.logger.info("Attribute successfully written.") + return 0 + + +class _StudyNames(_BaseCommand): + """Get all study names stored in a specified storage""" + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument( + "-f", + "--format", + type=str, + choices=("value", "json", "table", "yaml"), + default="value", + help="Output format.", + ) + + def take_action(self, parsed_args: Namespace) -> int: + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + all_study_names = optuna.get_all_study_names(storage) + records = [] + record_key = ("name", "") + for study_name in all_study_names: + records.append({record_key: study_name}) + print(_format_output(records, [record_key], parsed_args.format, flatten=False)) + return 0 + + +class _Studies(_BaseCommand): + """Show a list of studies.""" + + _study_list_header = [ + ("name", ""), + ("direction", ""), + ("n_trials", ""), + ("datetime_start", ""), + ] + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument( + "-f", + "--format", + type=str, + choices=("value", "json", "table", "yaml"), + default="table", + help="Output format.", + ) + parser.add_argument( + "--flatten", + default=False, + action="store_true", + help="Flatten nested columns such as directions.", + ) + + def take_action(self, parsed_args: Namespace) -> int: + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + summaries = optuna.get_all_study_summaries(storage, include_best_trial=False) + + records = [] + for s in summaries: + start = ( + s.datetime_start.strftime(_DATETIME_FORMAT) + if s.datetime_start is not None + else None + ) + record: dict[tuple[str, str], Any] = {} + record[("name", "")] = s.study_name + record[("direction", "")] = tuple(d.name for d in s.directions) + record[("n_trials", "")] = s.n_trials + record[("datetime_start", "")] = start + record[("user_attrs", "")] = s.user_attrs + records.append(record) + + if any(r[("user_attrs", "")] != {} for r in records): + self._study_list_header.append(("user_attrs", "")) + print( + _format_output( + records, self._study_list_header, parsed_args.format, parsed_args.flatten + ) + ) + return 0 + + +class _Trials(_BaseCommand): + """Show a list of trials.""" + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument( + "--study-name", + type=str, + required=True, + help="The name of the study which includes trials.", + ) + parser.add_argument( + "-f", + "--format", + type=str, + choices=("value", "json", "table", "yaml"), + default="table", + help="Output format.", + ) + parser.add_argument( + "--flatten", + default=False, + action="store_true", + help="Flatten nested columns such as params and user_attrs.", + ) + + def take_action(self, parsed_args: Namespace) -> int: + warnings.warn( + "'trials' is an experimental CLI command. The interface can change in the future.", + ExperimentalWarning, + ) + + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + study = optuna.load_study(storage=storage, study_name=parsed_args.study_name) + attrs = ( + "number", + "value" if not study._is_multi_objective() else "values", + "datetime_start", + "datetime_complete", + "duration", + "params", + "user_attrs", + "state", + ) + + records, columns = _dataframe._create_records_and_aggregate_column(study, attrs) + print(_format_output(records, columns, parsed_args.format, parsed_args.flatten)) + + return 0 + + +class _BestTrial(_BaseCommand): + """Show the best trial.""" + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument( + "--study-name", + type=str, + required=True, + help="The name of the study to get the best trial.", + ) + parser.add_argument( + "-f", + "--format", + type=str, + choices=("value", "json", "table", "yaml"), + default="table", + help="Output format.", + ) + parser.add_argument( + "--flatten", + default=False, + action="store_true", + help="Flatten nested columns such as params and user_attrs.", + ) + + def take_action(self, parsed_args: Namespace) -> int: + warnings.warn( + "'best-trial' is an experimental CLI command. The interface can change in the future.", + ExperimentalWarning, + ) + + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + study = optuna.load_study(storage=storage, study_name=parsed_args.study_name) + attrs = ( + "number", + "value" if not study._is_multi_objective() else "values", + "datetime_start", + "datetime_complete", + "duration", + "params", + "user_attrs", + "state", + ) + + records, columns = _dataframe._create_records_and_aggregate_column(study, attrs) + print( + _format_output( + records[study.best_trial.number], columns, parsed_args.format, parsed_args.flatten + ) + ) + return 0 + + +class _BestTrials(_BaseCommand): + """Show a list of trials located at the Pareto front.""" + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument( + "--study-name", + type=str, + required=True, + help="The name of the study to get the best trials (trials at the Pareto front).", + ) + parser.add_argument( + "-f", + "--format", + type=str, + choices=("value", "json", "table", "yaml"), + default="table", + help="Output format.", + ) + parser.add_argument( + "--flatten", + default=False, + action="store_true", + help="Flatten nested columns such as params and user_attrs.", + ) + + def take_action(self, parsed_args: Namespace) -> int: + warnings.warn( + "'best-trials' is an experimental CLI command. The interface can change in the " + "future.", + ExperimentalWarning, + ) + + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + study = optuna.load_study(storage=storage, study_name=parsed_args.study_name) + best_trials = [trial.number for trial in study.best_trials] + attrs = ( + "number", + "value" if not study._is_multi_objective() else "values", + "datetime_start", + "datetime_complete", + "duration", + "params", + "user_attrs", + "state", + ) + + records, columns = _dataframe._create_records_and_aggregate_column(study, attrs) + best_records = list(filter(lambda record: record[("number", "")] in best_trials, records)) + print(_format_output(best_records, columns, parsed_args.format, parsed_args.flatten)) + return 0 + + +class _StorageUpgrade(_BaseCommand): + """Upgrade the schema of an RDB storage.""" + + def take_action(self, parsed_args: Namespace) -> int: + storage_url = _check_storage_url(parsed_args.storage) + try: + storage = RDBStorage( + storage_url, skip_compatibility_check=True, skip_table_creation=True + ) + except sqlalchemy.exc.ArgumentError: + self.logger.error("Invalid RDBStorage URL.") + return 1 + current_version = storage.get_current_version() + head_version = storage.get_head_version() + known_versions = storage.get_all_versions() + if current_version == head_version: + self.logger.info("This storage is up-to-date.") + elif current_version in known_versions: + self.logger.info("Upgrading the storage schema to the latest version.") + storage.upgrade() + self.logger.info("Completed to upgrade the storage.") + else: + warnings.warn( + "Your optuna version seems outdated against the storage version. " + "Please try updating optuna to the latest version by " + "`$ pip install -U optuna`." + ) + return 0 + + +class _Ask(_BaseCommand): + """Create a new trial and suggest parameters.""" + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument("--study-name", type=str, help="Name of study.") + parser.add_argument("--sampler", type=str, help="Class name of sampler object to create.") + parser.add_argument( + "--sampler-kwargs", + type=str, + help="Sampler object initialization keyword arguments as JSON.", + ) + parser.add_argument( + "--search-space", + type=str, + help=( + "Search space as JSON. Keys are names and values are outputs from " + ":func:`~optuna.distributions.distribution_to_json`." + ), + ) + parser.add_argument( + "-f", + "--format", + type=str, + choices=("value", "json", "table", "yaml"), + default="json", + help="Output format.", + ) + parser.add_argument( + "--flatten", + default=False, + action="store_true", + help="Flatten nested columns such as params.", + ) + + def take_action(self, parsed_args: Namespace) -> int: + warnings.warn( + "'ask' is an experimental CLI command. The interface can change in the future.", + ExperimentalWarning, + ) + + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + + create_study_kwargs = { + "storage": storage, + "study_name": parsed_args.study_name, + "load_if_exists": True, + } + + if parsed_args.sampler is not None: + if parsed_args.sampler_kwargs is not None: + sampler_kwargs = json.loads(parsed_args.sampler_kwargs) + else: + sampler_kwargs = {} + sampler_cls = getattr(optuna.samplers, parsed_args.sampler) + sampler = sampler_cls(**sampler_kwargs) + create_study_kwargs["sampler"] = sampler + else: + if parsed_args.sampler_kwargs is not None: + raise ValueError( + "`--sampler_kwargs` is set without `--sampler`. Please specify `--sampler` as" + " well or omit `--sampler-kwargs`." + ) + + if parsed_args.search_space is not None: + # The search space is expected to be a JSON serialized string, e.g. + # '{"x": {"name": "FloatDistribution", "attributes": {"low": 0.0, "high": 1.0}}, + # "y": ...}'. + search_space = { + name: optuna.distributions.json_to_distribution(json.dumps(dist)) + for name, dist in json.loads(parsed_args.search_space).items() + } + else: + search_space = {} + + try: + study = optuna.load_study( + study_name=create_study_kwargs["study_name"], + storage=create_study_kwargs["storage"], + sampler=create_study_kwargs.get("sampler"), + ) + + except KeyError: + raise KeyError( + "Implicit study creation within the 'ask' command was dropped in Optuna v4.0.0. " + "Please use the 'create-study' command beforehand." + ) + trial = study.ask(fixed_distributions=search_space) + + self.logger.info(f"Asked trial {trial.number} with parameters {trial.params}.") + + record: dict[tuple[str, str], Any] = {("number", ""): trial.number} + columns = [("number", "")] + + if len(trial.params) == 0 and not parsed_args.flatten: + record[("params", "")] = {} + columns.append(("params", "")) + else: + for param_name, param_value in trial.params.items(): + record[("params", param_name)] = param_value + columns.append(("params", param_name)) + + print(_format_output(record, columns, parsed_args.format, parsed_args.flatten)) + return 0 + + +class _Tell(_BaseCommand): + """Finish a trial, which was created by the ask command.""" + + def add_arguments(self, parser: ArgumentParser) -> None: + parser.add_argument("--study-name", type=str, help="Name of study.") + parser.add_argument("--trial-number", type=int, help="Trial number.") + parser.add_argument("--values", type=float, nargs="+", help="Objective values.") + parser.add_argument( + "--state", + type=str, + help="Trial state.", + choices=("complete", "pruned", "fail"), + ) + parser.add_argument( + "--skip-if-finished", + default=False, + action="store_true", + help="If specified, tell is skipped without any error when the trial is already " + "finished.", + ) + + def take_action(self, parsed_args: Namespace) -> int: + warnings.warn( + "'tell' is an experimental CLI command. The interface can change in the future.", + ExperimentalWarning, + ) + + storage = _get_storage(parsed_args.storage, parsed_args.storage_class) + + study = optuna.load_study( + storage=storage, + study_name=parsed_args.study_name, + ) + + if parsed_args.state is not None: + state: TrialState | None = TrialState[parsed_args.state.upper()] + else: + state = None + + trial_number = parsed_args.trial_number + values = parsed_args.values + + study.tell( + trial=trial_number, + values=values, + state=state, + skip_if_finished=parsed_args.skip_if_finished, + ) + + self.logger.info(f"Told trial {trial_number} with values {values} and state {state}.") + + return 0 + + +_COMMANDS: dict[str, type[_BaseCommand]] = { + "create-study": _CreateStudy, + "delete-study": _DeleteStudy, + "study set-user-attr": _StudySetUserAttribute, + "study-names": _StudyNames, + "studies": _Studies, + "trials": _Trials, + "best-trial": _BestTrial, + "best-trials": _BestTrials, + "storage upgrade": _StorageUpgrade, + "ask": _Ask, + "tell": _Tell, +} + + +def _parse_storage_class_without_suggesting_deprecated_choices(value: str) -> str: + choices = [ + RDBStorage.__name__, + JournalFileBackend.__name__, + JournalRedisBackend.__name__, + ] + deprecated_choices = [ + JournalFileStorage.__name__, + JournalRedisStorage.__name__, + ] + if value in choices + deprecated_choices: + return value + raise argparse.ArgumentTypeError( + f"Invalid choice: {value} (choose from {str(choices)[1:-1]})" + ) + + +def _add_common_arguments(parser: ArgumentParser) -> ArgumentParser: + parser.add_argument( + "--storage", + default=None, + help=( + "DB URL. (e.g. sqlite:///example.db) " + "Also can be specified via OPTUNA_STORAGE environment variable." + ), + ) + parser.add_argument( + "--storage-class", + help="Storage class hint (e.g. JournalFileBackend)", + default=None, + type=_parse_storage_class_without_suggesting_deprecated_choices, + ) + verbose_group = parser.add_mutually_exclusive_group() + verbose_group.add_argument( + "-v", + "--verbose", + action="count", + dest="verbose_level", + default=1, + help="Increase verbosity of output. Can be repeated.", + ) + verbose_group.add_argument( + "-q", + "--quiet", + action="store_const", + dest="verbose_level", + const=0, + help="Suppress output except warnings and errors.", + ) + parser.add_argument( + "--log-file", + action="store", + default=None, + help="Specify a file to log output. Disabled by default.", + ) + parser.add_argument( + "--debug", + default=False, + action="store_true", + help="Show tracebacks on errors.", + ) + return parser + + +def _add_commands( + main_parser: ArgumentParser, parent_parser: ArgumentParser +) -> dict[str, ArgumentParser]: + subparsers = main_parser.add_subparsers() + command_name_to_subparser = {} + + for command_name, command_type in _COMMANDS.items(): + command = command_type() + subparser = subparsers.add_parser( + command_name, parents=[parent_parser], help=inspect.getdoc(command_type) + ) + command.add_arguments(subparser) + subparser.set_defaults(handler=command.take_action) + command_name_to_subparser[command_name] = subparser + + def _print_help(args: Namespace) -> None: + main_parser.print_help() + + subparsers.add_parser("help", help="Show help message and exit.").set_defaults( + handler=_print_help + ) + return command_name_to_subparser + + +def _get_parser(description: str = "") -> tuple[ArgumentParser, dict[str, ArgumentParser]]: + # Use `parent_parser` is necessary to avoid namespace conflict for -h/--help + # between `main_parser` and `subparser`. + parent_parser = ArgumentParser(add_help=False) + parent_parser = _add_common_arguments(parent_parser) + + main_parser = ArgumentParser(description=description, parents=[parent_parser]) + main_parser.add_argument( + "--version", action="version", version="{0} {1}".format("optuna", optuna.__version__) + ) + command_name_to_subparser = _add_commands(main_parser, parent_parser) + return main_parser, command_name_to_subparser + + +def _preprocess_argv(argv: list[str]) -> list[str]: + # Some preprocess is necessary for argv because some subcommand includes space + # (e.g. optuna storage upgrade). + argv = argv[1:] if len(argv) > 1 else ["help"] + + for i in range(len(argv)): + for j in range(i, i + 2): # Commands consist of one or two words. + command_candidate = " ".join(argv[i : j + 1]) + if command_candidate in _COMMANDS: + options = argv[:i] + argv[j + 1 :] + return [command_candidate] + options + + # No subcommand is found. + return argv + + +def _set_verbosity(args: Namespace) -> None: + root_logger = logging.getLogger() + root_logger.setLevel(logging.DEBUG) + stream_handler = logging.StreamHandler(sys.stderr) + + logging_level = { + 0: logging.WARNING, + 1: logging.INFO, + 2: logging.DEBUG, + }.get(args.verbose_level, logging.DEBUG) + + stream_handler.setLevel(logging_level) + stream_handler.setFormatter(optuna.logging.create_default_formatter()) + root_logger.addHandler(stream_handler) + + optuna.logging.set_verbosity(logging_level) + + +def _set_log_file(args: Namespace) -> None: + if args.log_file is None: + return + + root_logger = logging.getLogger() + root_logger.setLevel(logging.DEBUG) + + file_handler = logging.FileHandler( + filename=args.log_file, + ) + file_handler.setFormatter(optuna.logging.create_default_formatter()) + root_logger.addHandler(file_handler) + + +def main() -> int: + main_parser, command_name_to_subparser = _get_parser() + + argv = sys.argv + preprocessed_argv = _preprocess_argv(argv) + args = main_parser.parse_args(preprocessed_argv) + + _set_verbosity(args) + _set_log_file(args) + + logger = logging.getLogger("optuna") + try: + return args.handler(args) + except CLIUsageError as e: + if args.debug: + logger.exception(e) + else: + logger.error(e) + # This code is required to show help for each subcommand. + # NOTE: the first element of `preprocessed_argv` is command name. + command_name_to_subparser[preprocessed_argv[0]].print_help() + return 1 + except AttributeError: + # Exception for the case -v/--verbose/-q/--quiet/--log-file/--debug + # without any subcommand. + argv_str = " ".join(argv[1:]) + logger.error(f"'{argv_str}' is not an optuna command. see 'optuna --help'") + main_parser.print_help() + return 1 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/distributions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..bf9984f20e61eec0fe63654a42745f71e535b118 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/distributions.py @@ -0,0 +1,792 @@ +from __future__ import annotations + +import abc +import copy +import decimal +import json +import math +from numbers import Real +from typing import Any +from typing import cast +from typing import TYPE_CHECKING +from typing import Union +import warnings + +from optuna._deprecated import deprecated_class + + +if TYPE_CHECKING: + from collections.abc import Sequence + + +CategoricalChoiceType = Union[None, bool, int, float, str] + + +_float_distribution_deprecated_msg = ( + "Use :class:`~optuna.distributions.FloatDistribution` instead." +) +_int_distribution_deprecated_msg = "Use :class:`~optuna.distributions.IntDistribution` instead." + + +class BaseDistribution(abc.ABC): + """Base class for distributions. + + Note that distribution classes are not supposed to be called by library users. + They are used by :class:`~optuna.trial.Trial` and :class:`~optuna.samplers` internally. + """ + + def to_external_repr(self, param_value_in_internal_repr: float) -> Any: + """Convert internal representation of a parameter value into external representation. + + Args: + param_value_in_internal_repr: + Optuna's internal representation of a parameter value. + + Returns: + Optuna's external representation of a parameter value. + """ + + return param_value_in_internal_repr + + @abc.abstractmethod + def to_internal_repr(self, param_value_in_external_repr: Any) -> float: + """Convert external representation of a parameter value into internal representation. + + Args: + param_value_in_external_repr: + Optuna's external representation of a parameter value. + + Returns: + Optuna's internal representation of a parameter value. + """ + + raise NotImplementedError + + @abc.abstractmethod + def single(self) -> bool: + """Test whether the range of this distribution contains just a single value. + + Returns: + :obj:`True` if the range of this distribution contains just a single value, + otherwise :obj:`False`. + """ + + raise NotImplementedError + + @abc.abstractmethod + def _contains(self, param_value_in_internal_repr: float) -> bool: + """Test if a parameter value is contained in the range of this distribution. + + Args: + param_value_in_internal_repr: + Optuna's internal representation of a parameter value. + + Returns: + :obj:`True` if the parameter value is contained in the range of this distribution, + otherwise :obj:`False`. + """ + + raise NotImplementedError + + def _asdict(self) -> dict: + return self.__dict__ + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, BaseDistribution): + return NotImplemented + if type(self) is not type(other): + return False + return self.__dict__ == other.__dict__ + + def __hash__(self) -> int: + return hash((self.__class__,) + tuple(sorted(self.__dict__.items()))) + + def __repr__(self) -> str: + kwargs = ", ".join("{}={}".format(k, v) for k, v in sorted(self._asdict().items())) + return "{}({})".format(self.__class__.__name__, kwargs) + + +class FloatDistribution(BaseDistribution): + """A distribution on floats. + + This object is instantiated by :func:`~optuna.trial.Trial.suggest_float`, and passed to + :mod:`~optuna.samplers` in general. + + .. note:: + When ``step`` is not :obj:`None`, if the range :math:`[\\mathsf{low}, \\mathsf{high}]` + is not divisible by :math:`\\mathsf{step}`, :math:`\\mathsf{high}` will be replaced + with the maximum of :math:`k \\times \\mathsf{step} + \\mathsf{low} < \\mathsf{high}`, + where :math:`k` is an integer. + + Attributes: + low: + Lower endpoint of the range of the distribution. ``low`` is included in the range. + ``low`` must be less than or equal to ``high``. If ``log`` is :obj:`True`, + ``low`` must be larger than 0. + high: + Upper endpoint of the range of the distribution. ``high`` is included in the range. + ``high`` must be greater than or equal to ``low``. + log: + If ``log`` is :obj:`True`, this distribution is in log-scaled domain. + In this case, all parameters enqueued to the distribution must be positive values. + This parameter must be :obj:`False` when the parameter ``step`` is not :obj:`None`. + step: + A discretization step. ``step`` must be larger than 0. + This parameter must be :obj:`None` when the parameter ``log`` is :obj:`True`. + + """ + + def __init__( + self, low: float, high: float, log: bool = False, step: None | float = None + ) -> None: + if log and step is not None: + raise ValueError("The parameter `step` is not supported when `log` is true.") + + if low > high: + raise ValueError( + "The `low` value must be smaller than or equal to the `high` value " + "(low={}, high={}).".format(low, high) + ) + + if log and low <= 0.0: + raise ValueError( + "The `low` value must be larger than 0 for a log distribution " + "(low={}, high={}).".format(low, high) + ) + + if step is not None and step <= 0: + raise ValueError( + "The `step` value must be non-zero positive value, " "but step={}.".format(step) + ) + + self.step = None + if step is not None: + high = _adjust_discrete_uniform_high(low, high, step) + self.step = float(step) + + self.low = float(low) + self.high = float(high) + self.log = log + + def single(self) -> bool: + if self.step is None: + return self.low == self.high + else: + if self.low == self.high: + return True + high = decimal.Decimal(str(self.high)) + low = decimal.Decimal(str(self.low)) + step = decimal.Decimal(str(self.step)) + return (high - low) < step + + def _contains(self, param_value_in_internal_repr: float) -> bool: + value = param_value_in_internal_repr + if self.step is None: + return self.low <= value <= self.high + else: + k = (value - self.low) / self.step + return self.low <= value <= self.high and abs(k - round(k)) < 1.0e-8 + + def to_internal_repr(self, param_value_in_external_repr: float) -> float: + try: + internal_repr = float(param_value_in_external_repr) + except (ValueError, TypeError) as e: + raise ValueError( + f"'{param_value_in_external_repr}' is not a valid type. " + "float-castable value is expected." + ) from e + + if math.isnan(internal_repr): + raise ValueError(f"`{param_value_in_external_repr}` is invalid value.") + if self.log and internal_repr <= 0.0: + raise ValueError( + f"`{param_value_in_external_repr}` is invalid value for the case log=True." + ) + return internal_repr + + +@deprecated_class("3.0.0", "6.0.0", text=_float_distribution_deprecated_msg) +class UniformDistribution(FloatDistribution): + """A uniform distribution in the linear domain. + + This object is instantiated by :func:`~optuna.trial.Trial.suggest_float`, and passed to + :mod:`~optuna.samplers` in general. + + Attributes: + low: + Lower endpoint of the range of the distribution. ``low`` is included in the range. + ``low`` must be less than or equal to ``high``. + high: + Upper endpoint of the range of the distribution. ``high`` is included in the range. + ``high`` must be greater than or equal to ``low``. + + """ + + def __init__(self, low: float, high: float) -> None: + super().__init__(low=low, high=high, log=False, step=None) + + def _asdict(self) -> dict: + d = copy.deepcopy(self.__dict__) + d.pop("log") + d.pop("step") + return d + + +@deprecated_class("3.0.0", "6.0.0", text=_float_distribution_deprecated_msg) +class LogUniformDistribution(FloatDistribution): + """A uniform distribution in the log domain. + + This object is instantiated by :func:`~optuna.trial.Trial.suggest_float` with ``log=True``, + and passed to :mod:`~optuna.samplers` in general. + + Attributes: + low: + Lower endpoint of the range of the distribution. ``low`` is included in the range. + ``low`` must be larger than 0. ``low`` must be less than or equal to ``high``. + high: + Upper endpoint of the range of the distribution. ``high`` is included in the range. + ``high`` must be greater than or equal to ``low``. + + """ + + def __init__(self, low: float, high: float) -> None: + super().__init__(low=low, high=high, log=True, step=None) + + def _asdict(self) -> dict: + d = copy.deepcopy(self.__dict__) + d.pop("log") + d.pop("step") + return d + + +@deprecated_class("3.0.0", "6.0.0", text=_float_distribution_deprecated_msg) +class DiscreteUniformDistribution(FloatDistribution): + """A discretized uniform distribution in the linear domain. + + This object is instantiated by :func:`~optuna.trial.Trial.suggest_float` with ``step`` + argument, and passed to :mod:`~optuna.samplers` in general. + + .. note:: + If the range :math:`[\\mathsf{low}, \\mathsf{high}]` is not divisible by :math:`q`, + :math:`\\mathsf{high}` will be replaced with the maximum of :math:`k q + \\mathsf{low} + < \\mathsf{high}`, where :math:`k` is an integer. + + Args: + low: + Lower endpoint of the range of the distribution. ``low`` is included in the range. + ``low`` must be less than or equal to ``high``. + high: + Upper endpoint of the range of the distribution. ``high`` is included in the range. + ``high`` must be greater than or equal to ``low``. + q: + A discretization step. ``q`` must be larger than 0. + + Attributes: + low: + Lower endpoint of the range of the distribution. ``low`` is included in the range. + high: + Upper endpoint of the range of the distribution. ``high`` is included in the range. + + """ + + def __init__(self, low: float, high: float, q: float) -> None: + super().__init__(low=low, high=high, step=q) + + def _asdict(self) -> dict: + d = copy.deepcopy(self.__dict__) + d.pop("log") + + step = d.pop("step") + d["q"] = step + return d + + @property + def q(self) -> float: + """Discretization step. + + :class:`~optuna.distributions.DiscreteUniformDistribution` is a subtype of + :class:`~optuna.distributions.FloatDistribution`. + This property is a proxy for its ``step`` attribute. + """ + return cast("float", self.step) + + @q.setter + def q(self, v: float) -> None: + self.step = v + + +class IntDistribution(BaseDistribution): + """A distribution on integers. + + This object is instantiated by :func:`~optuna.trial.Trial.suggest_int`, and passed to + :mod:`~optuna.samplers` in general. + + .. note:: + When ``step`` is not :obj:`None`, if the range :math:`[\\mathsf{low}, \\mathsf{high}]` + is not divisible by :math:`\\mathsf{step}`, :math:`\\mathsf{high}` will be replaced + with the maximum of :math:`k \\times \\mathsf{step} + \\mathsf{low} < \\mathsf{high}`, + where :math:`k` is an integer. + + Attributes: + low: + Lower endpoint of the range of the distribution. ``low`` is included in the range. + ``low`` must be less than or equal to ``high``. If ``log`` is :obj:`True`, + ``low`` must be larger than or equal to 1. + high: + Upper endpoint of the range of the distribution. ``high`` is included in the range. + ``high`` must be greater than or equal to ``low``. + log: + If ``log`` is :obj:`True`, this distribution is in log-scaled domain. + In this case, all parameters enqueued to the distribution must be positive values. + This parameter must be :obj:`False` when the parameter ``step`` is not 1. + step: + A discretization step. ``step`` must be a positive integer. This parameter must be 1 + when the parameter ``log`` is :obj:`True`. + + """ + + def __init__(self, low: int, high: int, log: bool = False, step: int = 1) -> None: + if log and step != 1: + raise ValueError( + "Samplers and other components in Optuna only accept step is 1 " + "when `log` argument is True." + ) + + if low > high: + raise ValueError( + "The `low` value must be smaller than or equal to the `high` value " + "(low={}, high={}).".format(low, high) + ) + + if log and low < 1: + raise ValueError( + "The `low` value must be equal to or greater than 1 for a log distribution " + "(low={}, high={}).".format(low, high) + ) + + if step <= 0: + raise ValueError( + "The `step` value must be non-zero positive value, but step={}.".format(step) + ) + + self.log = log + self.step = int(step) + self.low = int(low) + high = int(high) + self.high = _adjust_int_uniform_high(self.low, high, self.step) + + def to_external_repr(self, param_value_in_internal_repr: float) -> int: + return int(param_value_in_internal_repr) + + def to_internal_repr(self, param_value_in_external_repr: int) -> float: + try: + internal_repr = float(param_value_in_external_repr) + except (ValueError, TypeError) as e: + raise ValueError( + f"'{param_value_in_external_repr}' is not a valid type. " + "float-castable value is expected." + ) from e + + if math.isnan(internal_repr): + raise ValueError(f"`{param_value_in_external_repr}` is invalid value.") + if self.log and internal_repr <= 0.0: + raise ValueError( + f"`{param_value_in_external_repr}` is invalid value for the case log=True." + ) + return internal_repr + + def single(self) -> bool: + if self.log: + return self.low == self.high + + if self.low == self.high: + return True + return (self.high - self.low) < self.step + + def _contains(self, param_value_in_internal_repr: float) -> bool: + value = param_value_in_internal_repr + return self.low <= value <= self.high and (value - self.low) % self.step == 0 + + +@deprecated_class("3.0.0", "6.0.0", text=_int_distribution_deprecated_msg) +class IntUniformDistribution(IntDistribution): + """A uniform distribution on integers. + + This object is instantiated by :func:`~optuna.trial.Trial.suggest_int`, and passed to + :mod:`~optuna.samplers` in general. + + .. note:: + If the range :math:`[\\mathsf{low}, \\mathsf{high}]` is not divisible by + :math:`\\mathsf{step}`, :math:`\\mathsf{high}` will be replaced with the maximum of + :math:`k \\times \\mathsf{step} + \\mathsf{low} < \\mathsf{high}`, where :math:`k` is + an integer. + + Attributes: + low: + Lower endpoint of the range of the distribution. ``low`` is included in the range. + ``low`` must be less than or equal to ``high``. + high: + Upper endpoint of the range of the distribution. ``high`` is included in the range. + ``high`` must be greater than or equal to ``low``. + step: + A discretization step. ``step`` must be a positive integer. + + """ + + def __init__(self, low: int, high: int, step: int = 1) -> None: + super().__init__(low=low, high=high, log=False, step=step) + + def _asdict(self) -> dict: + d = copy.deepcopy(self.__dict__) + d.pop("log") + return d + + +@deprecated_class("3.0.0", "6.0.0", text=_int_distribution_deprecated_msg) +class IntLogUniformDistribution(IntDistribution): + """A uniform distribution on integers in the log domain. + + This object is instantiated by :func:`~optuna.trial.Trial.suggest_int`, and passed to + :mod:`~optuna.samplers` in general. + + Attributes: + low: + Lower endpoint of the range of the distribution. ``low`` is included in the range + and must be larger than or equal to 1. ``low`` must be less than or equal to ``high``. + high: + Upper endpoint of the range of the distribution. ``high`` is included in the range. + ``high`` must be greater than or equal to ``low``. + step: + A discretization step. ``step`` must be a positive integer. + + """ + + def __init__(self, low: int, high: int, step: int = 1) -> None: + super().__init__(low=low, high=high, log=True, step=step) + + def _asdict(self) -> dict: + d = copy.deepcopy(self.__dict__) + d.pop("log") + return d + + +def _categorical_choice_equal( + value1: CategoricalChoiceType, value2: CategoricalChoiceType +) -> bool: + """A function to check two choices equal considering NaN. + + This function can handle NaNs like np.float32("nan") other than float. + """ + + value1_is_nan = isinstance(value1, Real) and math.isnan(float(value1)) + value2_is_nan = isinstance(value2, Real) and math.isnan(float(value2)) + return (value1 == value2) or (value1_is_nan and value2_is_nan) + + +class CategoricalDistribution(BaseDistribution): + """A categorical distribution. + + This object is instantiated by :func:`~optuna.trial.Trial.suggest_categorical`, and + passed to :mod:`~optuna.samplers` in general. + + Args: + choices: + Parameter value candidates. ``choices`` must have one element at least. + + .. note:: + + Not all types are guaranteed to be compatible with all storages. It is recommended to + restrict the types of the choices to :obj:`None`, :class:`bool`, :class:`int`, + :class:`float` and :class:`str`. + + Attributes: + choices: + Parameter value candidates. + + """ + + def __init__(self, choices: Sequence[CategoricalChoiceType]) -> None: + if len(choices) == 0: + raise ValueError("The `choices` must contain one or more elements.") + for choice in choices: + if choice is not None and not isinstance(choice, (bool, int, float, str)): + message = ( + "Choices for a categorical distribution should be a tuple of None, bool, " + "int, float and str for persistent storage but contains {} which is of type " + "{}.".format(choice, type(choice).__name__) + ) + warnings.warn(message) + + self.choices = tuple(choices) + + def to_external_repr(self, param_value_in_internal_repr: float) -> CategoricalChoiceType: + return self.choices[int(param_value_in_internal_repr)] + + def to_internal_repr(self, param_value_in_external_repr: CategoricalChoiceType) -> float: + try: + # NOTE(nabenabe): With this implementation, we cannot distinguish some values + # such as True and 1, or 1.0 and 1. For example, if choices=[True, 1] and external_repr + # is 1, this method wrongly returns 0 instead of 1. However, we decided to accept this + # bug for such exceptional choices for less complexity and faster processing. + return self.choices.index(param_value_in_external_repr) + except ValueError: # ValueError: param_value_in_external_repr is not in choices. + # ValueError also happens if external_repr is nan or includes precision error in float. + for index, choice in enumerate(self.choices): + if _categorical_choice_equal(param_value_in_external_repr, choice): + return index + + raise ValueError(f"'{param_value_in_external_repr}' not in {self.choices}.") + + def single(self) -> bool: + return len(self.choices) == 1 + + def _contains(self, param_value_in_internal_repr: float) -> bool: + index = int(param_value_in_internal_repr) + return 0 <= index < len(self.choices) + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, BaseDistribution): + return NotImplemented + if not isinstance(other, self.__class__): + return False + if self.__dict__.keys() != other.__dict__.keys(): + return False + for key, value in self.__dict__.items(): + if key == "choices": + if len(value) != len(getattr(other, key)): + return False + for choice, other_choice in zip(value, getattr(other, key)): + if not _categorical_choice_equal(choice, other_choice): + return False + else: + if value != getattr(other, key): + return False + return True + + __hash__ = BaseDistribution.__hash__ + + +DISTRIBUTION_CLASSES = ( + IntDistribution, + IntLogUniformDistribution, + IntUniformDistribution, + FloatDistribution, + UniformDistribution, + LogUniformDistribution, + DiscreteUniformDistribution, + CategoricalDistribution, +) + + +def json_to_distribution(json_str: str) -> BaseDistribution: + """Deserialize a distribution in JSON format. + + Args: + json_str: A JSON-serialized distribution. + + Returns: + A deserialized distribution. + + """ + + json_dict = json.loads(json_str) + + if "name" in json_dict: + if json_dict["name"] == CategoricalDistribution.__name__: + json_dict["attributes"]["choices"] = tuple(json_dict["attributes"]["choices"]) + + for cls in DISTRIBUTION_CLASSES: + if json_dict["name"] == cls.__name__: + return cls(**json_dict["attributes"]) + + raise ValueError("Unknown distribution class: {}".format(json_dict["name"])) + + else: + # Deserialize a distribution from an abbreviated format. + if json_dict["type"] == "categorical": + return CategoricalDistribution(json_dict["choices"]) + elif json_dict["type"] in ("float", "int"): + low = json_dict["low"] + high = json_dict["high"] + step = json_dict.get("step") + log = json_dict.get("log", False) + + if json_dict["type"] == "float": + return FloatDistribution(low, high, log=log, step=step) + + else: + if step is None: + step = 1 + return IntDistribution(low=low, high=high, log=log, step=step) + + raise ValueError("Unknown distribution type: {}".format(json_dict["type"])) + + +def distribution_to_json(dist: BaseDistribution) -> str: + """Serialize a distribution to JSON format. + + Args: + dist: A distribution to be serialized. + + Returns: + A JSON string of a given distribution. + + """ + + return json.dumps({"name": dist.__class__.__name__, "attributes": dist._asdict()}) + + +def check_distribution_compatibility( + dist_old: BaseDistribution, dist_new: BaseDistribution +) -> None: + """A function to check compatibility of two distributions. + + It checks whether ``dist_old`` and ``dist_new`` are the same kind of distributions. + If ``dist_old`` is :class:`~optuna.distributions.CategoricalDistribution`, + it further checks ``choices`` are the same between ``dist_old`` and ``dist_new``. + Note that this method is not supposed to be called by library users. + + Args: + dist_old: + A distribution previously recorded in storage. + dist_new: + A distribution newly added to storage. + + """ + + if dist_old.__class__ != dist_new.__class__: + raise ValueError("Cannot set different distribution kind to the same parameter name.") + + if isinstance(dist_old, (FloatDistribution, IntDistribution)): + # For mypy. + assert isinstance(dist_new, (FloatDistribution, IntDistribution)) + + if dist_old.log != dist_new.log: + raise ValueError("Cannot set different log configuration to the same parameter name.") + + if not isinstance(dist_old, CategoricalDistribution): + return + if not isinstance(dist_new, CategoricalDistribution): + return + if dist_old != dist_new: + raise ValueError( + CategoricalDistribution.__name__ + " does not support dynamic value space." + ) + + +def _adjust_discrete_uniform_high(low: float, high: float, step: float) -> float: + d_high = decimal.Decimal(str(high)) + d_low = decimal.Decimal(str(low)) + d_step = decimal.Decimal(str(step)) + + d_r = d_high - d_low + + if d_r % d_step != decimal.Decimal("0"): + old_high = high + high = float((d_r // d_step) * d_step + d_low) + warnings.warn( + "The distribution is specified by [{low}, {old_high}] and step={step}, but the range " + "is not divisible by `step`. It will be replaced by [{low}, {high}].".format( + low=low, old_high=old_high, high=high, step=step + ) + ) + + return high + + +def _adjust_int_uniform_high(low: int, high: int, step: int) -> int: + r = high - low + if r % step != 0: + old_high = high + high = r // step * step + low + warnings.warn( + "The distribution is specified by [{low}, {old_high}] and step={step}, but the range " + "is not divisible by `step`. It will be replaced by [{low}, {high}].".format( + low=low, old_high=old_high, high=high, step=step + ) + ) + return high + + +def _get_single_value(distribution: BaseDistribution) -> int | float | CategoricalChoiceType: + assert distribution.single() + + if isinstance( + distribution, + ( + FloatDistribution, + IntDistribution, + ), + ): + return distribution.low + elif isinstance(distribution, CategoricalDistribution): + return distribution.choices[0] + assert False + + +# TODO(himkt): Remove this method with the deletion of deprecated distributions. +# https://github.com/optuna/optuna/issues/2941 +def _convert_old_distribution_to_new_distribution( + distribution: BaseDistribution, + suppress_warning: bool = False, +) -> BaseDistribution: + new_distribution: BaseDistribution + + # Float distributions. + if isinstance(distribution, UniformDistribution): + new_distribution = FloatDistribution( + low=distribution.low, + high=distribution.high, + log=False, + step=None, + ) + elif isinstance(distribution, LogUniformDistribution): + new_distribution = FloatDistribution( + low=distribution.low, + high=distribution.high, + log=True, + step=None, + ) + elif isinstance(distribution, DiscreteUniformDistribution): + new_distribution = FloatDistribution( + low=distribution.low, + high=distribution.high, + log=False, + step=distribution.q, + ) + + # Integer distributions. + elif isinstance(distribution, IntUniformDistribution): + new_distribution = IntDistribution( + low=distribution.low, + high=distribution.high, + log=False, + step=distribution.step, + ) + elif isinstance(distribution, IntLogUniformDistribution): + new_distribution = IntDistribution( + low=distribution.low, + high=distribution.high, + log=True, + step=distribution.step, + ) + + # Categorical distribution. + else: + new_distribution = distribution + + if new_distribution != distribution and not suppress_warning: + message = ( + f"{distribution} is deprecated and internally converted to" + f" {new_distribution}. See https://github.com/optuna/optuna/issues/2941." + ) + warnings.warn(message, FutureWarning) + + return new_distribution + + +def _is_distribution_log(distribution: BaseDistribution) -> bool: + if isinstance(distribution, (FloatDistribution, IntDistribution)): + return distribution.log + + return False diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/exceptions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..d21204157ae66f6a04987d480a72958ff861034a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/exceptions.py @@ -0,0 +1,101 @@ +class OptunaError(Exception): + """Base class for Optuna specific errors.""" + + pass + + +class TrialPruned(OptunaError): + """Exception for pruned trials. + + This error tells a trainer that the current :class:`~optuna.trial.Trial` was pruned. It is + supposed to be raised after :func:`optuna.trial.Trial.should_prune` as shown in the following + example. + + See also: + :class:`optuna.TrialPruned` is an alias of :class:`optuna.exceptions.TrialPruned`. + + Example: + + .. testcode:: + + import numpy as np + from sklearn.datasets import load_iris + from sklearn.linear_model import SGDClassifier + from sklearn.model_selection import train_test_split + + import optuna + + X, y = load_iris(return_X_y=True) + X_train, X_valid, y_train, y_valid = train_test_split(X, y) + classes = np.unique(y) + + + def objective(trial): + alpha = trial.suggest_float("alpha", 0.0, 1.0) + clf = SGDClassifier(alpha=alpha) + n_train_iter = 100 + + for step in range(n_train_iter): + clf.partial_fit(X_train, y_train, classes=classes) + + intermediate_value = clf.score(X_valid, y_valid) + trial.report(intermediate_value, step) + + if trial.should_prune(): + raise optuna.TrialPruned() + + return clf.score(X_valid, y_valid) + + + study = optuna.create_study(direction="maximize") + study.optimize(objective, n_trials=20) + """ + + pass + + +class CLIUsageError(OptunaError): + """Exception for CLI. + + CLI raises this exception when it receives invalid configuration. + """ + + pass + + +class StorageInternalError(OptunaError): + """Exception for storage operation. + + This error is raised when an operation failed in backend DB of storage. + """ + + pass + + +class DuplicatedStudyError(OptunaError): + """Exception for a duplicated study name. + + This error is raised when a specified study name already exists in the storage. + """ + + pass + + +class UpdateFinishedTrialError(OptunaError, RuntimeError): + """Exception for updating a finished trial. + + This error is raised when attempting to update a finished trial. + """ + + pass + + +class ExperimentalWarning(Warning): + """Experimental Warning class. + + This implementation exists here because the policy of `FutureWarning` has been changed + since Python 3.7 was released. See the details in + https://docs.python.org/3/library/warnings.html#warning-categories. + """ + + pass diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_base.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_base.py new file mode 100644 index 0000000000000000000000000000000000000000..ab2a770553472a96312e6d4024871ad88f35b287 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_base.py @@ -0,0 +1,185 @@ +from __future__ import annotations + +import abc +from typing import cast +from typing import TYPE_CHECKING + +import numpy as np + +from optuna.search_space import intersection_search_space +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from collections.abc import Callable + from collections.abc import Collection + + from optuna._transform import _SearchSpaceTransform + from optuna.distributions import BaseDistribution + from optuna.study import Study + from optuna.trial import FrozenTrial + + +class BaseImportanceEvaluator(abc.ABC): + """Abstract parameter importance evaluator.""" + + @abc.abstractmethod + def evaluate( + self, + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + ) -> dict[str, float]: + """Evaluate parameter importances based on completed trials in the given study. + + .. note:: + + This method is not meant to be called by library users. + + .. seealso:: + + Please refer to :func:`~optuna.importance.get_param_importances` for how a concrete + evaluator should implement this method. + + Args: + study: + An optimized study. + params: + A list of names of parameters to assess. + If :obj:`None`, all parameters that are present in all of the completed trials are + assessed. + target: + A function to specify the value to evaluate importances. + If it is :obj:`None` and ``study`` is being used for single-objective optimization, + the objective values are used. Can also be used for other trial attributes, such as + the duration, like ``target=lambda t: t.duration.total_seconds()``. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective + optimization. For example, to get the hyperparameter importance of the first + objective, use ``target=lambda t: t.values[0]`` for the target parameter. + + Returns: + A :obj:`dict` where the keys are parameter names and the values are assessed + importances. + + """ + # TODO(hvy): Reconsider the interface as logic might violate DRY among multiple evaluators. + raise NotImplementedError + + +def _get_distributions(study: Study, params: list[str] | None) -> dict[str, BaseDistribution]: + completed_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)) + _check_evaluate_args(completed_trials, params) + + if params is None: + return intersection_search_space(study.get_trials(deepcopy=False)) + + # New temporary required to pass mypy. Seems like a bug. + params_not_none = params + assert params_not_none is not None + + # Compute the search space based on the subset of trials containing all parameters. + distributions = None + for trial in completed_trials: + trial_distributions = trial.distributions + if not all(name in trial_distributions for name in params_not_none): + continue + + if distributions is None: + distributions = dict( + filter( + lambda name_and_distribution: name_and_distribution[0] in params_not_none, + trial_distributions.items(), + ) + ) + continue + + if any( + trial_distributions[name] != distribution + for name, distribution in distributions.items() + ): + raise ValueError( + "Parameters importances cannot be assessed with dynamic search spaces if " + "parameters are specified. Specified parameters: {}.".format(params) + ) + + assert distributions is not None # Required to pass mypy. + distributions = dict( + sorted(distributions.items(), key=lambda name_and_distribution: name_and_distribution[0]) + ) + return distributions + + +def _check_evaluate_args(completed_trials: list[FrozenTrial], params: list[str] | None) -> None: + if len(completed_trials) == 0: + raise ValueError("Cannot evaluate parameter importances without completed trials.") + if len(completed_trials) == 1: + raise ValueError("Cannot evaluate parameter importances with only a single trial.") + + if params is not None: + if not isinstance(params, (list, tuple)): + raise TypeError( + "Parameters must be specified as a list. Actual parameters: {}.".format(params) + ) + if any(not isinstance(p, str) for p in params): + raise TypeError( + "Parameters must be specified by their names with strings. Actual parameters: " + "{}.".format(params) + ) + + if len(params) > 0: + at_least_one_trial = False + for trial in completed_trials: + if all(p in trial.distributions for p in params): + at_least_one_trial = True + break + if not at_least_one_trial: + raise ValueError( + "Study must contain completed trials with all specified parameters. " + "Specified parameters: {}.".format(params) + ) + + +def _get_filtered_trials( + study: Study, params: Collection[str], target: Callable[[FrozenTrial], float] | None +) -> list[FrozenTrial]: + trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)) + return [ + trial + for trial in trials + if set(params) <= set(trial.params) + and np.isfinite( + target(trial) if target is not None else cast("float", trial.value) + ) # TC006 + ] + + +def _param_importances_to_dict( + params: Collection[str], param_importances: np.ndarray | float +) -> dict[str, float]: + return { + name: value + for name, value in zip(params, np.broadcast_to(param_importances, (len(params),))) + } + + +def _get_trans_params(trials: list[FrozenTrial], trans: _SearchSpaceTransform) -> np.ndarray: + return np.array([trans.transform(trial.params) for trial in trials]) + + +def _get_target_values( + trials: list[FrozenTrial], target: Callable[[FrozenTrial], float] | None +) -> np.ndarray: + return np.array([target(trial) if target is not None else trial.value for trial in trials]) + + +def _sort_dict_by_importance(param_importances: dict[str, float]) -> dict[str, float]: + return dict( + reversed( + sorted( + param_importances.items(), key=lambda name_and_importance: name_and_importance[1] + ) + ) + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/__pycache__/_evaluator.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/__pycache__/_evaluator.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d3f8aa9d5c87370c8e4afa1ee0b3ed494122d178 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/__pycache__/_evaluator.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/__pycache__/_fanova.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/__pycache__/_fanova.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c652b0d1c4f98477289ef976c8d90af2041f7922 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/__pycache__/_fanova.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/_evaluator.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/_evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..e6198dd29a00413263b40ef48ed60f378f4e66ca --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/_evaluator.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +from collections.abc import Callable + +import numpy as np + +from optuna._transform import _SearchSpaceTransform +from optuna.importance._base import _get_distributions +from optuna.importance._base import _get_filtered_trials +from optuna.importance._base import _get_target_values +from optuna.importance._base import _get_trans_params +from optuna.importance._base import _param_importances_to_dict +from optuna.importance._base import _sort_dict_by_importance +from optuna.importance._base import BaseImportanceEvaluator +from optuna.importance._fanova._fanova import _Fanova +from optuna.study import Study +from optuna.trial import FrozenTrial + + +class FanovaImportanceEvaluator(BaseImportanceEvaluator): + """fANOVA importance evaluator. + + Implements the fANOVA hyperparameter importance evaluation algorithm in + `An Efficient Approach for Assessing Hyperparameter Importance + `__. + + fANOVA fits a random forest regression model that predicts the objective values + of :class:`~optuna.trial.TrialState.COMPLETE` trials given their parameter configurations. + The more accurate this model is, the more reliable the importances assessed + by this class are. + + .. note:: + + Requires the `sklearn `__ Python package. + + .. note:: + + The performance of fANOVA depends on the prediction performance of the underlying + random forest model. In order to obtain high prediction performance, it is necessary to + cover a wide range of the hyperparameter search space. It is recommended to use an + exploration-oriented sampler such as :class:`~optuna.samplers.RandomSampler`. + + .. note:: + + For how to cite the original work, please refer to + https://automl.github.io/fanova/cite.html. + + Args: + n_trees: + The number of trees in the forest. + max_depth: + The maximum depth of the trees in the forest. + seed: + Controls the randomness of the forest. For deterministic behavior, specify a value + other than :obj:`None`. + + """ + + def __init__(self, *, n_trees: int = 64, max_depth: int = 64, seed: int | None = None) -> None: + self._evaluator = _Fanova( + n_trees=n_trees, + max_depth=max_depth, + min_samples_split=2, + min_samples_leaf=1, + seed=seed, + ) + + def evaluate( + self, + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + ) -> dict[str, float]: + if target is None and study._is_multi_objective(): + raise ValueError( + "If the `study` is being used for multi-objective optimization, " + "please specify the `target`. For example, use " + "`target=lambda t: t.values[0]` for the first objective value." + ) + + distributions = _get_distributions(study, params=params) + if params is None: + params = list(distributions.keys()) + assert params is not None + + # fANOVA does not support parameter distributions with a single value. + # However, there is no reason to calculate parameter importance in such case anyway, + # since it will always be 0 as the parameter is constant in the objective function. + non_single_distributions = { + name: dist for name, dist in distributions.items() if not dist.single() + } + single_distributions = { + name: dist for name, dist in distributions.items() if dist.single() + } + + if len(non_single_distributions) == 0: + return {} + + trials: list[FrozenTrial] = _get_filtered_trials(study, params=params, target=target) + + trans = _SearchSpaceTransform( + non_single_distributions, transform_log=False, transform_step=False + ) + + trans_params: np.ndarray = _get_trans_params(trials, trans) + target_values: np.ndarray = _get_target_values(trials, target) + + evaluator = self._evaluator + evaluator.fit( + X=trans_params, + y=target_values, + search_spaces=trans.bounds, + column_to_encoded_columns=trans.column_to_encoded_columns, + ) + param_importances = np.array( + [evaluator.get_importance(i)[0] for i in range(len(non_single_distributions))] + ) + + return _sort_dict_by_importance( + { + **_param_importances_to_dict(non_single_distributions.keys(), param_importances), + **_param_importances_to_dict(single_distributions.keys(), 0.0), + } + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/_tree.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/_tree.py new file mode 100644 index 0000000000000000000000000000000000000000..84fd915c9aaad99f9c780b6d15a93535d7f1940a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_fanova/_tree.py @@ -0,0 +1,319 @@ +from __future__ import annotations + +from functools import lru_cache +import itertools +from typing import TYPE_CHECKING + +import numpy as np + + +if TYPE_CHECKING: + import sklearn.tree + + +class _FanovaTree: + def __init__(self, tree: "sklearn.tree._tree.Tree", search_spaces: np.ndarray) -> None: + assert search_spaces.shape[0] == tree.n_features + assert search_spaces.shape[1] == 2 + + self._tree = tree + self._search_spaces = search_spaces + + statistics = self._precompute_statistics() + split_midpoints, split_sizes = self._precompute_split_midpoints_and_sizes() + subtree_active_features = self._precompute_subtree_active_features() + + self._statistics = statistics + self._split_midpoints = split_midpoints + self._split_sizes = split_sizes + self._subtree_active_features = subtree_active_features + self._variance = None # Computed lazily and requires `self._statistics`. + + @property + def variance(self) -> float: + if self._variance is None: + leaf_node_indices = np.nonzero(np.array(self._tree.feature) < 0)[0] + statistics = self._statistics[leaf_node_indices] + values = statistics[:, 0] + weights = statistics[:, 1] + average_values = np.average(values, weights=weights) + variance = np.average((values - average_values) ** 2, weights=weights) + + self._variance = variance + + assert self._variance is not None + return self._variance + + def get_marginal_variance(self, features: np.ndarray) -> float: + assert features.size > 0 + + # For each midpoint along the given dimensions, traverse this tree to compute the + # marginal predictions. + selected_midpoints = [self._split_midpoints[f] for f in features] + selected_sizes = [self._split_sizes[f] for f in features] + + product_midpoints = itertools.product(*selected_midpoints) + product_sizes = itertools.product(*selected_sizes) + + sample = np.full(self._n_features, fill_value=np.nan, dtype=np.float64) + + values: list[float] | np.ndarray = [] + weights: list[float] | np.ndarray = [] + + for midpoints, sizes in zip(product_midpoints, product_sizes): + sample[features] = np.array(midpoints) + + value, weight = self._get_marginalized_statistics(sample) + weight *= float(np.prod(sizes)) + + values = np.append(values, value) + weights = np.append(weights, weight) + + weights = np.asarray(weights) + values = np.asarray(values) + average_values = np.average(values, weights=weights) + variance = np.average((values - average_values) ** 2, weights=weights) + + assert variance >= 0.0 + return variance + + def _get_marginalized_statistics(self, feature_vector: np.ndarray) -> tuple[float, float]: + assert feature_vector.size == self._n_features + + marginalized_features = np.isnan(feature_vector) + active_features = ~marginalized_features + + # Reduce search space cardinalities to 1 for non-active features. + search_spaces = self._search_spaces.copy() + search_spaces[marginalized_features] = [0.0, 1.0] + + # Start from the root and traverse towards the leafs. + active_nodes = [0] + active_search_spaces = [search_spaces] + + node_indices = [] + active_leaf_search_spaces = [] + + while len(active_nodes) > 0: + node_index = active_nodes.pop() + search_spaces = active_search_spaces.pop() + + feature = self._get_node_split_feature(node_index) + if feature >= 0: # Not leaf. Avoid unnecessary call to `_is_node_leaf`. + # If node splits on an active feature, push the child node that we end up in. + response = feature_vector[feature] + if not np.isnan(response): + if response <= self._get_node_split_threshold(node_index): + next_node_index = self._get_node_left_child(node_index) + next_subspace = self._get_node_left_child_subspaces( + node_index, search_spaces + ) + else: + next_node_index = self._get_node_right_child(node_index) + next_subspace = self._get_node_right_child_subspaces( + node_index, search_spaces + ) + + active_nodes.append(next_node_index) + active_search_spaces.append(next_subspace) + continue + + # If subtree starting from node splits on an active feature, push both child nodes. + # Here, we use `any` for list because `ndarray.any` is slow. + if any(self._subtree_active_features[node_index][active_features].tolist()): + for child_node_index in self._get_node_children(node_index): + active_nodes.append(child_node_index) + active_search_spaces.append(search_spaces) + continue + + # If node is a leaf or the subtree does not split on any of the active features. + node_indices.append(node_index) + active_leaf_search_spaces.append(search_spaces) + + statistics = self._statistics[node_indices] + values = statistics[:, 0] + weights = statistics[:, 1] + active_features_cardinalities = _get_cardinality_batched(active_leaf_search_spaces) + weights = weights / active_features_cardinalities + + value = np.average(values, weights=weights) + weight = weights.sum() + + return value, weight + + def _precompute_statistics(self) -> np.ndarray: + n_nodes = self._n_nodes + + # Holds for each node, its weighted average value and the sum of weights. + statistics = np.empty((n_nodes, 2), dtype=np.float64) + + subspaces = np.array([None for _ in range(n_nodes)]) + subspaces[0] = self._search_spaces + + # Compute marginals for leaf nodes. + for node_index in range(n_nodes): + subspace = subspaces[node_index] + + if self._is_node_leaf(node_index): + value = self._get_node_value(node_index) + weight = _get_cardinality(subspace) + statistics[node_index] = [value, weight] + else: + for child_node_index, child_subspace in zip( + self._get_node_children(node_index), + self._get_node_children_subspaces(node_index, subspace), + ): + assert subspaces[child_node_index] is None + subspaces[child_node_index] = child_subspace + + # Compute marginals for internal nodes. + for node_index in reversed(range(n_nodes)): + if not self._is_node_leaf(node_index): + child_values = [] + child_weights = [] + for child_node_index in self._get_node_children(node_index): + child_values.append(statistics[child_node_index, 0]) + child_weights.append(statistics[child_node_index, 1]) + value = np.average(child_values, weights=child_weights) + weight = float(np.sum(child_weights)) + statistics[node_index] = [value, weight] + + return statistics + + def _precompute_split_midpoints_and_sizes( + self, + ) -> tuple[list[np.ndarray], list[np.ndarray]]: + midpoints = [] + sizes = [] + + search_spaces = self._search_spaces + for feature, feature_split_values in enumerate(self._compute_features_split_values()): + feature_split_values = np.concatenate( + ( + np.atleast_1d(search_spaces[feature, 0]), + feature_split_values, + np.atleast_1d(search_spaces[feature, 1]), + ) + ) + midpoint = 0.5 * (feature_split_values[1:] + feature_split_values[:-1]) + size = feature_split_values[1:] - feature_split_values[:-1] + + midpoints.append(midpoint) + sizes.append(size) + + return midpoints, sizes + + def _compute_features_split_values(self) -> list[np.ndarray]: + all_split_values: list[set[float]] = [set() for _ in range(self._n_features)] + + for node_index in range(self._n_nodes): + feature = self._get_node_split_feature(node_index) + if feature >= 0: # Not leaf. Avoid unnecessary call to `_is_node_leaf`. + threshold = self._get_node_split_threshold(node_index) + all_split_values[feature].add(threshold) + + sorted_all_split_values: list[np.ndarray] = [] + + for split_values in all_split_values: + split_values_array = np.array(list(split_values), dtype=np.float64) + split_values_array.sort() + sorted_all_split_values.append(split_values_array) + + return sorted_all_split_values + + def _precompute_subtree_active_features(self) -> np.ndarray: + subtree_active_features = np.full((self._n_nodes, self._n_features), fill_value=False) + + for node_index in reversed(range(self._n_nodes)): + feature = self._get_node_split_feature(node_index) + if feature >= 0: # Not leaf. Avoid unnecessary call to `_is_node_leaf`. + subtree_active_features[node_index, feature] = True + for child_node_index in self._get_node_children(node_index): + subtree_active_features[node_index] |= subtree_active_features[ + child_node_index + ] + + return subtree_active_features + + @property + def _n_features(self) -> int: + return len(self._search_spaces) + + @property + def _n_nodes(self) -> int: + return self._tree.node_count + + @lru_cache(maxsize=None) + def _is_node_leaf(self, node_index: int) -> bool: + return self._tree.feature[node_index] < 0 + + @lru_cache(maxsize=None) + def _get_node_left_child(self, node_index: int) -> int: + return self._tree.children_left[node_index] + + @lru_cache(maxsize=None) + def _get_node_right_child(self, node_index: int) -> int: + return self._tree.children_right[node_index] + + @lru_cache(maxsize=None) + def _get_node_children(self, node_index: int) -> tuple[int, int]: + return self._get_node_left_child(node_index), self._get_node_right_child(node_index) + + @lru_cache(maxsize=None) + def _get_node_value(self, node_index: int) -> float: + # self._tree.value: sklearn.tree._tree.Tree.value has + # the shape (node_count, n_outputs, max_n_classes) + return float(self._tree.value[node_index].reshape(-1)[0]) + + @lru_cache(maxsize=None) + def _get_node_split_threshold(self, node_index: int) -> float: + return self._tree.threshold[node_index] + + @lru_cache(maxsize=None) + def _get_node_split_feature(self, node_index: int) -> int: + return self._tree.feature[node_index] + + def _get_node_left_child_subspaces( + self, node_index: int, search_spaces: np.ndarray + ) -> np.ndarray: + return _get_subspaces( + search_spaces, + search_spaces_column=1, + feature=self._get_node_split_feature(node_index), + threshold=self._get_node_split_threshold(node_index), + ) + + def _get_node_right_child_subspaces( + self, node_index: int, search_spaces: np.ndarray + ) -> np.ndarray: + return _get_subspaces( + search_spaces, + search_spaces_column=0, + feature=self._get_node_split_feature(node_index), + threshold=self._get_node_split_threshold(node_index), + ) + + def _get_node_children_subspaces( + self, node_index: int, search_spaces: np.ndarray + ) -> tuple[np.ndarray, np.ndarray]: + return ( + self._get_node_left_child_subspaces(node_index, search_spaces), + self._get_node_right_child_subspaces(node_index, search_spaces), + ) + + +def _get_cardinality(search_spaces: np.ndarray) -> float: + return np.prod(search_spaces[:, 1] - search_spaces[:, 0]) + + +def _get_cardinality_batched(search_spaces_list: list[np.ndarray]) -> float: + search_spaces = np.asarray(search_spaces_list) + return np.prod(search_spaces[:, :, 1] - search_spaces[:, :, 0], axis=1) + + +def _get_subspaces( + search_spaces: np.ndarray, *, search_spaces_column: int, feature: int, threshold: float +) -> np.ndarray: + search_spaces_subspace = np.copy(search_spaces) + search_spaces_subspace[feature, search_spaces_column] = threshold + return search_spaces_subspace diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_mean_decrease_impurity.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_mean_decrease_impurity.py new file mode 100644 index 0000000000000000000000000000000000000000..deff21fbe8dc4eedc221515db4209676242ee99e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/importance/_mean_decrease_impurity.py @@ -0,0 +1,95 @@ +from __future__ import annotations + +from collections.abc import Callable + +import numpy as np + +from optuna._imports import try_import +from optuna._transform import _SearchSpaceTransform +from optuna.importance._base import _get_distributions +from optuna.importance._base import _get_filtered_trials +from optuna.importance._base import _get_target_values +from optuna.importance._base import _get_trans_params +from optuna.importance._base import _param_importances_to_dict +from optuna.importance._base import _sort_dict_by_importance +from optuna.importance._base import BaseImportanceEvaluator +from optuna.study import Study +from optuna.trial import FrozenTrial + + +with try_import() as _imports: + from sklearn.ensemble import RandomForestRegressor + + +class MeanDecreaseImpurityImportanceEvaluator(BaseImportanceEvaluator): + """Mean Decrease Impurity (MDI) parameter importance evaluator. + + This evaluator fits fits a random forest regression model that predicts the objective values + of :class:`~optuna.trial.TrialState.COMPLETE` trials given their parameter configurations. + Feature importances are then computed using MDI. + + .. note:: + + This evaluator requires the `sklearn `__ Python package + and is based on `sklearn.ensemble.RandomForestClassifier.feature_importances_ + `__. + + Args: + n_trees: + Number of trees in the random forest. + max_depth: + The maximum depth of each tree in the random forest. + seed: + Seed for the random forest. + """ + + def __init__(self, *, n_trees: int = 64, max_depth: int = 64, seed: int | None = None) -> None: + _imports.check() + + self._forest = RandomForestRegressor( + n_estimators=n_trees, + max_depth=max_depth, + min_samples_split=2, + min_samples_leaf=1, + random_state=seed, + ) + self._trans_params = np.empty(0) + self._trans_values = np.empty(0) + self._param_names: list[str] = list() + + def evaluate( + self, + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + ) -> dict[str, float]: + if target is None and study._is_multi_objective(): + raise ValueError( + "If the `study` is being used for multi-objective optimization, " + "please specify the `target`. For example, use " + "`target=lambda t: t.values[0]` for the first objective value." + ) + + distributions = _get_distributions(study, params=params) + if params is None: + params = list(distributions.keys()) + assert params is not None + if len(params) == 0: + return {} + + trials: list[FrozenTrial] = _get_filtered_trials(study, params=params, target=target) + trans = _SearchSpaceTransform(distributions, transform_log=False, transform_step=False) + trans_params: np.ndarray = _get_trans_params(trials, trans) + target_values: np.ndarray = _get_target_values(trials, target) + + forest = self._forest + forest.fit(X=trans_params, y=target_values) + feature_importances = forest.feature_importances_ + + # Untransform feature importances to param importances + # by adding up relevant feature importances. + param_importances = np.zeros(len(params)) + np.add.at(param_importances, trans.encoded_column_to_column, feature_importances) + + return _sort_dict_by_importance(_param_importances_to_dict(params, param_importances)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..29d74d848a4a731109f2d7ba7ffc2049cb9e952f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/__init__.py @@ -0,0 +1,136 @@ +import os +import sys +from types import ModuleType +from typing import Any +from typing import TYPE_CHECKING + +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +_import_structure = { + "allennlp": ["AllenNLPExecutor", "AllenNLPPruningCallback"], + "botorch": ["BoTorchSampler"], + "catboost": ["CatBoostPruningCallback"], + "chainer": ["ChainerPruningExtension"], + "chainermn": ["ChainerMNStudy"], + "cma": ["PyCmaSampler"], + "dask": ["DaskStorage"], + "mlflow": ["MLflowCallback"], + "wandb": ["WeightsAndBiasesCallback"], + "keras": ["KerasPruningCallback"], + "lightgbm": ["LightGBMPruningCallback", "LightGBMTuner", "LightGBMTunerCV"], + "pytorch_distributed": ["TorchDistributedTrial"], + "pytorch_ignite": ["PyTorchIgnitePruningHandler"], + "pytorch_lightning": ["PyTorchLightningPruningCallback"], + "sklearn": ["OptunaSearchCV"], + "shap": ["ShapleyImportanceEvaluator"], + "skorch": ["SkorchPruningCallback"], + "mxnet": ["MXNetPruningCallback"], + "tensorboard": ["TensorBoardCallback"], + "tensorflow": ["TensorFlowPruningHook"], + "tfkeras": ["TFKerasPruningCallback"], + "xgboost": ["XGBoostPruningCallback"], + "fastaiv2": ["FastAIV2PruningCallback", "FastAIPruningCallback"], +} + + +__all__ = [ + "AllenNLPExecutor", + "AllenNLPPruningCallback", + "BoTorchSampler", + "CatBoostPruningCallback", + "ChainerPruningExtension", + "ChainerMNStudy", + "PyCmaSampler", + "DaskStorage", + "MLflowCallback", + "WeightsAndBiasesCallback", + "KerasPruningCallback", + "LightGBMPruningCallback", + "LightGBMTuner", + "LightGBMTunerCV", + "TorchDistributedTrial", + "PyTorchIgnitePruningHandler", + "PyTorchLightningPruningCallback", + "OptunaSearchCV", + "ShapleyImportanceEvaluator", + "SkorchPruningCallback", + "MXNetPruningCallback", + "TensorBoardCallback", + "TensorFlowPruningHook", + "TFKerasPruningCallback", + "XGBoostPruningCallback", + "FastAIV2PruningCallback", + "FastAIPruningCallback", +] + + +if TYPE_CHECKING: + from optuna.integration.allennlp import AllenNLPExecutor + from optuna.integration.allennlp import AllenNLPPruningCallback + from optuna.integration.botorch import BoTorchSampler + from optuna.integration.catboost import CatBoostPruningCallback + from optuna.integration.chainer import ChainerPruningExtension + from optuna.integration.chainermn import ChainerMNStudy + from optuna.integration.cma import PyCmaSampler + from optuna.integration.dask import DaskStorage + from optuna.integration.fastaiv2 import FastAIPruningCallback + from optuna.integration.fastaiv2 import FastAIV2PruningCallback + from optuna.integration.keras import KerasPruningCallback + from optuna.integration.lightgbm import LightGBMPruningCallback + from optuna.integration.lightgbm import LightGBMTuner + from optuna.integration.lightgbm import LightGBMTunerCV + from optuna.integration.mlflow import MLflowCallback + from optuna.integration.mxnet import MXNetPruningCallback + from optuna.integration.pytorch_distributed import TorchDistributedTrial + from optuna.integration.pytorch_ignite import PyTorchIgnitePruningHandler + from optuna.integration.pytorch_lightning import PyTorchLightningPruningCallback + from optuna.integration.shap import ShapleyImportanceEvaluator + from optuna.integration.sklearn import OptunaSearchCV + from optuna.integration.skorch import SkorchPruningCallback + from optuna.integration.tensorboard import TensorBoardCallback + from optuna.integration.tensorflow import TensorFlowPruningHook + from optuna.integration.tfkeras import TFKerasPruningCallback + from optuna.integration.wandb import WeightsAndBiasesCallback + from optuna.integration.xgboost import XGBoostPruningCallback +else: + + class _IntegrationModule(ModuleType): + """Module class that implements `optuna.integration` package. + + This class applies lazy import under `optuna.integration`, where submodules are imported + when they are actually accessed. Otherwise, `import optuna` becomes much slower because it + imports all submodules and their dependencies (e.g., chainer, keras, lightgbm) all at once. + """ + + __all__ = __all__ + __file__ = globals()["__file__"] + __path__ = [os.path.dirname(__file__)] + + _modules = set(_import_structure.keys()) + _class_to_module = {} + for key, values in _import_structure.items(): + for value in values: + _class_to_module[value] = key + + def __getattr__(self, name: str) -> Any: + if name in self._modules: + value = self._get_module(name) + elif name in self._class_to_module.keys(): + module = self._get_module(self._class_to_module[name]) + value = getattr(module, name) + else: + raise AttributeError("module {} has no attribute {}".format(self.__name__, name)) + + setattr(self, name, value) + return value + + def _get_module(self, module_name: str) -> ModuleType: + import importlib + + try: + return importlib.import_module("." + module_name, self.__name__) + except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format(module_name)) + + sys.modules[__name__] = _IntegrationModule(__name__) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5d3907364902d94b12485cf7d29e77f9b7df474b Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/allennlp/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/allennlp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ff7dee7092ce293e8e6d187663c1e6c1b9eba042 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/allennlp/__init__.py @@ -0,0 +1,12 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.allennlp._dump_best_config import dump_best_config + from optuna_integration.allennlp._executor import AllenNLPExecutor + from optuna_integration.allennlp._pruner import AllenNLPPruningCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("allennlp")) + + +__all__ = ["dump_best_config", "AllenNLPExecutor", "AllenNLPPruningCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/botorch.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/botorch.py new file mode 100644 index 0000000000000000000000000000000000000000..4cd4f035f8d38110bbdf96a325572eba5b5d8377 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/botorch.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration import BoTorchSampler +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("botorch")) + + +__all__ = ["BoTorchSampler"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/catboost.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/catboost.py new file mode 100644 index 0000000000000000000000000000000000000000..3d3e37a7b72a4ac27e425f46f4224f31f81cb453 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/catboost.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.catboost import CatBoostPruningCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("catboost")) + + +__all__ = ["CatBoostPruningCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/chainer.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/chainer.py new file mode 100644 index 0000000000000000000000000000000000000000..523363c7e59a8101630dd412c060dc3e24f9bb28 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/chainer.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.chainer import ChainerPruningExtension +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("chainer")) + + +__all__ = ["ChainerPruningExtension"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/chainermn.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/chainermn.py new file mode 100644 index 0000000000000000000000000000000000000000..42c61345b1ad4f6238b9748bb88f1a65dfbb6481 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/chainermn.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.chainermn import ChainerMNStudy +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("chainermn")) + + +__all__ = ["ChainerMNStudy"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/cma.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/cma.py new file mode 100644 index 0000000000000000000000000000000000000000..b6a09c310e0ca2c110a9bbd0ee29ebd4e2d04509 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/cma.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.cma import PyCmaSampler +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("cma")) + + +__all__ = ["PyCmaSampler"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/dask.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/dask.py new file mode 100644 index 0000000000000000000000000000000000000000..d017547fe431a46aae8b1885ae4c90b04c5c0ff2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/dask.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.dask import DaskStorage +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("dask")) + + +__all__ = ["DaskStorage"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/fastaiv2.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/fastaiv2.py new file mode 100644 index 0000000000000000000000000000000000000000..2142db65242bca77a6be6ae0348ffc9d641dfc88 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/fastaiv2.py @@ -0,0 +1,11 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.fastaiv2 import FastAIPruningCallback + from optuna_integration.fastaiv2 import FastAIV2PruningCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("fastaiv2")) + + +__all__ = ["FastAIV2PruningCallback", "FastAIPruningCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/keras.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/keras.py new file mode 100644 index 0000000000000000000000000000000000000000..e6e93e9dad5284467ca074784693321ec07df0e9 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/keras.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.keras import KerasPruningCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("keras")) + + +__all__ = ["KerasPruningCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/lightgbm.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/lightgbm.py new file mode 100644 index 0000000000000000000000000000000000000000..7b2ed0764b9a078fc9c73940de403b0b66b8d3c2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/lightgbm.py @@ -0,0 +1,43 @@ +import os +import sys +from types import ModuleType +from typing import Any +from typing import TYPE_CHECKING + +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + import optuna_integration.lightgbm as lgb +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("lightgbm")) + + +if TYPE_CHECKING: + # These modules are from optuna-integration. + from optuna.integration.lightgbm_tuner import LightGBMPruningCallback + from optuna.integration.lightgbm_tuner import LightGBMTuner + from optuna.integration.lightgbm_tuner import LightGBMTunerCV + from optuna.integration.lightgbm_tuner import train + + +__all__ = [ + "LightGBMPruningCallback", + "LightGBMTuner", + "LightGBMTunerCV", + "train", +] + + +class _LightGBMModule(ModuleType): + """Module class that implements `optuna.integration.lightgbm` package.""" + + __all__ = __all__ + __file__ = globals()["__file__"] + __path__ = [os.path.dirname(__file__)] + + def __getattr__(self, name: str) -> Any: + return lgb.__dict__[name] + + +sys.modules[__name__] = _LightGBMModule(__name__) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/mlflow.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/mlflow.py new file mode 100644 index 0000000000000000000000000000000000000000..67b9505b6b286dfffbf189359867c30d03d50fcc --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/mlflow.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.mlflow import MLflowCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("mlflow")) + + +__all__ = ["MLflowCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/mxnet.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/mxnet.py new file mode 100644 index 0000000000000000000000000000000000000000..14aa51aec782a4867d9496f320ac78c4ddba4a60 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/mxnet.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.mxnet import MXNetPruningCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("mxnet")) + + +__all__ = ["MXNetPruningCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/pytorch_distributed.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/pytorch_distributed.py new file mode 100644 index 0000000000000000000000000000000000000000..1325a3250b719abd6eb1dc1d24c674d05926d257 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/pytorch_distributed.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.pytorch_distributed import TorchDistributedTrial +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("pytorch_distributed")) + + +__all__ = ["TorchDistributedTrial"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/pytorch_ignite.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/pytorch_ignite.py new file mode 100644 index 0000000000000000000000000000000000000000..cbf38027b532f524fab2a5eab5413ebb54645f16 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/pytorch_ignite.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.pytorch_ignite import PyTorchIgnitePruningHandler +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("pytorch_ignite")) + + +__all__ = ["PyTorchIgnitePruningHandler"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/pytorch_lightning.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/pytorch_lightning.py new file mode 100644 index 0000000000000000000000000000000000000000..470146be9657b6de5da8b27afd1a2e97aad82f3c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/pytorch_lightning.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.pytorch_lightning import PyTorchLightningPruningCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("pytorch_lightning")) + + +__all__ = ["PyTorchLightningPruningCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/shap.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/shap.py new file mode 100644 index 0000000000000000000000000000000000000000..c9882233b50d875d769e81e873f4a1e7a0e7b80b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/shap.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.shap import ShapleyImportanceEvaluator +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("shap")) + + +__all__ = ["ShapleyImportanceEvaluator"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/sklearn.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/sklearn.py new file mode 100644 index 0000000000000000000000000000000000000000..d7f30433b39334b5bb009871db89ced057581cc8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/sklearn.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.sklearn import OptunaSearchCV +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("sklearn")) + + +__all__ = ["OptunaSearchCV"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/skorch.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/skorch.py new file mode 100644 index 0000000000000000000000000000000000000000..f147960401247caf7d891284f9efea7635817f41 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/skorch.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.skorch import SkorchPruningCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("skorch")) + + +__all__ = ["SkorchPruningCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/tensorboard.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/tensorboard.py new file mode 100644 index 0000000000000000000000000000000000000000..ad360d48861660917fd2b04516b631767c9ba822 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/tensorboard.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.tensorboard import TensorBoardCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("tensorboard")) + + +__all__ = ["TensorBoardCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/tensorflow.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/tensorflow.py new file mode 100644 index 0000000000000000000000000000000000000000..4e04699b38b1dbd98b240818213c49f039d98f37 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/tensorflow.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.tensorflow import TensorFlowPruningHook +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("tensorflow")) + + +__all__ = ["TensorFlowPruningHook"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/tfkeras.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/tfkeras.py new file mode 100644 index 0000000000000000000000000000000000000000..622b5a599f3c57428e538dcd379c7f4f00cfadb4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/tfkeras.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.tfkeras import TFKerasPruningCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("tfkeras")) + + +__all__ = ["TFKerasPruningCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/wandb.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/wandb.py new file mode 100644 index 0000000000000000000000000000000000000000..19863e5da8eb3897b6e1c9004aa0571e5068c3a2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/wandb.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.wandb import WeightsAndBiasesCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("wandb")) + + +__all__ = ["WeightsAndBiasesCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/xgboost.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/xgboost.py new file mode 100644 index 0000000000000000000000000000000000000000..acd9b4a2fc0554cbcf760ea21473b9bb2943434c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/integration/xgboost.py @@ -0,0 +1,10 @@ +from optuna._imports import _INTEGRATION_IMPORT_ERROR_TEMPLATE + + +try: + from optuna_integration.xgboost import XGBoostPruningCallback +except ModuleNotFoundError: + raise ModuleNotFoundError(_INTEGRATION_IMPORT_ERROR_TEMPLATE.format("xgboost")) + + +__all__ = ["XGBoostPruningCallback"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/logging.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..b5a07d67e34212414b7c8f87ed8e140fb2524576 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/logging.py @@ -0,0 +1,357 @@ +from __future__ import annotations + +import logging +from logging import CRITICAL +from logging import DEBUG +from logging import ERROR +from logging import FATAL +from logging import INFO +from logging import WARN +from logging import WARNING +import os +import sys +import threading + +import colorlog + + +__all__ = [ + "CRITICAL", + "DEBUG", + "ERROR", + "FATAL", + "INFO", + "WARN", + "WARNING", +] + +_lock: threading.Lock = threading.Lock() +_default_handler: logging.Handler | None = None + + +def create_default_formatter() -> logging.Formatter: + """Create a default formatter of log messages. + + This function is not supposed to be directly accessed by library users. + """ + header = "[%(levelname)1.1s %(asctime)s]" + message = "%(message)s" + if _color_supported(): + return colorlog.ColoredFormatter( + f"%(log_color)s{header}%(reset)s {message}", + ) + return logging.Formatter(f"{header} {message}") + + +def _color_supported() -> bool: + """Detection of color support.""" + # NO_COLOR environment variable: + if os.environ.get("NO_COLOR", None): + return False + + if not hasattr(sys.stderr, "isatty") or not sys.stderr.isatty(): + return False + else: + return True + + +def _get_library_name() -> str: + return __name__.split(".")[0] + + +def _get_library_root_logger() -> logging.Logger: + return logging.getLogger(_get_library_name()) + + +def _configure_library_root_logger() -> None: + global _default_handler + + with _lock: + if _default_handler: + # This library has already configured the library root logger. + return + _default_handler = logging.StreamHandler() # Set sys.stderr as stream. + _default_handler.setFormatter(create_default_formatter()) + + # Apply our default configuration to the library root logger. + library_root_logger: logging.Logger = _get_library_root_logger() + library_root_logger.addHandler(_default_handler) + library_root_logger.setLevel(logging.INFO) + library_root_logger.propagate = False + + +def _reset_library_root_logger() -> None: + global _default_handler + + with _lock: + if not _default_handler: + return + + library_root_logger: logging.Logger = _get_library_root_logger() + library_root_logger.removeHandler(_default_handler) + library_root_logger.setLevel(logging.NOTSET) + _default_handler = None + + +def get_logger(name: str) -> logging.Logger: + """Return a logger with the specified name. + + This function is not supposed to be directly accessed by library users. + """ + + _configure_library_root_logger() + return logging.getLogger(name) + + +def get_verbosity() -> int: + """Return the current level for the Optuna's root logger. + + Example: + + Get the default verbosity level. + + .. testsetup:: + + def objective(trial): + x = trial.suggest_float("x", -100, 100) + y = trial.suggest_categorical("y", [-1, 0, 1]) + return x**2 + y + + .. testcode:: + + import optuna + + # The default verbosity level of Optuna is `optuna.logging.INFO`. + print(optuna.logging.get_verbosity()) + # 20 + print(optuna.logging.INFO) + # 20 + + # There are logs of the INFO level. + study = optuna.create_study() + study.optimize(objective, n_trials=5) + # [I 2021-10-31 05:35:17,232] A new study created ... + # [I 2021-10-31 05:35:17,238] Trial 0 finished with value: ... + # [I 2021-10-31 05:35:17,245] Trial 1 finished with value: ... + # ... + + .. testoutput:: + :hide: + + 20 + 20 + Returns: + Logging level, e.g., ``optuna.logging.DEBUG`` and ``optuna.logging.INFO``. + + .. note:: + Optuna has following logging levels: + + - ``optuna.logging.CRITICAL``, ``optuna.logging.FATAL`` + - ``optuna.logging.ERROR`` + - ``optuna.logging.WARNING``, ``optuna.logging.WARN`` + - ``optuna.logging.INFO`` + - ``optuna.logging.DEBUG`` + """ + + _configure_library_root_logger() + return _get_library_root_logger().getEffectiveLevel() + + +def set_verbosity(verbosity: int) -> None: + """Set the level for the Optuna's root logger. + + Example: + + Set the logging level ``optuna.logging.WARNING``. + + .. testsetup:: + + def objective(trial): + x = trial.suggest_int("x", -10, 10) + return x**2 + + .. testcode:: + + import optuna + + # There are INFO level logs. + study = optuna.create_study() + study.optimize(objective, n_trials=10) + # [I 2021-10-31 02:59:35,088] Trial 0 finished with value: 16.0 ... + # [I 2021-10-31 02:59:35,091] Trial 1 finished with value: 1.0 ... + # [I 2021-10-31 02:59:35,096] Trial 2 finished with value: 1.0 ... + + # Setting the logging level WARNING, the INFO logs are suppressed. + optuna.logging.set_verbosity(optuna.logging.WARNING) + study.optimize(objective, n_trials=10) + + .. testcleanup:: + + optuna.logging.set_verbosity(optuna.logging.INFO) + + + Args: + verbosity: + Logging level, e.g., ``optuna.logging.DEBUG`` and ``optuna.logging.INFO``. + + .. note:: + Optuna has following logging levels: + + - ``optuna.logging.CRITICAL``, ``optuna.logging.FATAL`` + - ``optuna.logging.ERROR`` + - ``optuna.logging.WARNING``, ``optuna.logging.WARN`` + - ``optuna.logging.INFO`` + - ``optuna.logging.DEBUG`` + """ + + _configure_library_root_logger() + _get_library_root_logger().setLevel(verbosity) + + +def disable_default_handler() -> None: + """Disable the default handler of the Optuna's root logger. + + Example: + + Stop and then resume logging to :obj:`sys.stderr`. + + .. testsetup:: + + def objective(trial): + x = trial.suggest_float("x", -100, 100) + y = trial.suggest_categorical("y", [-1, 0, 1]) + return x**2 + y + + .. testcode:: + + import optuna + + study = optuna.create_study() + + # There are no logs in sys.stderr. + optuna.logging.disable_default_handler() + study.optimize(objective, n_trials=10) + + # There are logs in sys.stderr. + optuna.logging.enable_default_handler() + study.optimize(objective, n_trials=10) + # [I 2020-02-23 17:00:54,314] Trial 10 finished with value: ... + # [I 2020-02-23 17:00:54,356] Trial 11 finished with value: ... + # ... + + """ + + _configure_library_root_logger() + + assert _default_handler is not None + _get_library_root_logger().removeHandler(_default_handler) + + +def enable_default_handler() -> None: + """Enable the default handler of the Optuna's root logger. + + Please refer to the example shown in :func:`~optuna.logging.disable_default_handler()`. + """ + + _configure_library_root_logger() + + assert _default_handler is not None + _get_library_root_logger().addHandler(_default_handler) + + +def disable_propagation() -> None: + """Disable propagation of the library log outputs. + + Note that log propagation is disabled by default. You only need to use this function + to stop log propagation when you use :func:`~optuna.logging.enable_propagation()`. + + Example: + + Stop propagating logs to the root logger on the second optimize call. + + .. testsetup:: + + def objective(trial): + x = trial.suggest_float("x", -100, 100) + y = trial.suggest_categorical("y", [-1, 0, 1]) + return x**2 + y + + .. testcode:: + + import optuna + import logging + + optuna.logging.disable_default_handler() # Disable the default handler. + logger = logging.getLogger() + + logger.setLevel(logging.INFO) # Setup the root logger. + logger.addHandler(logging.FileHandler("foo.log", mode="w")) + + optuna.logging.enable_propagation() # Propagate logs to the root logger. + + study = optuna.create_study() + + logger.info("Logs from first optimize call") # The logs are saved in the logs file. + study.optimize(objective, n_trials=10) + + optuna.logging.disable_propagation() # Stop propogating logs to the root logger. + + logger.info("Logs from second optimize call") + # The new logs for second optimize call are not saved. + study.optimize(objective, n_trials=10) + + with open("foo.log") as f: + assert f.readline().startswith("A new study created") + assert f.readline() == "Logs from first optimize call\\n" + # Check for logs after second optimize call. + assert f.read().split("Logs from second optimize call\\n")[-1] == "" + + """ + + _configure_library_root_logger() + _get_library_root_logger().propagate = False + + +def enable_propagation() -> None: + """Enable propagation of the library log outputs. + + Please disable the Optuna's default handler to prevent double logging if the root logger has + been configured. + + Example: + + Propagate all log output to the root logger in order to save them to the file. + + .. testsetup:: + + def objective(trial): + x = trial.suggest_float("x", -100, 100) + y = trial.suggest_categorical("y", [-1, 0, 1]) + return x**2 + y + + .. testcode:: + + import optuna + import logging + + logger = logging.getLogger() + + logger.setLevel(logging.INFO) # Setup the root logger. + logger.addHandler(logging.FileHandler("foo.log", mode="w")) + + optuna.logging.enable_propagation() # Propagate logs to the root logger. + optuna.logging.disable_default_handler() # Stop showing logs in sys.stderr. + + study = optuna.create_study() + + logger.info("Start optimization.") + study.optimize(objective, n_trials=10) + + with open("foo.log") as f: + assert f.readline().startswith("A new study created") + assert f.readline() == "Start optimization.\\n" + + """ + + _configure_library_root_logger() + _get_library_root_logger().propagate = True diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/multi_objective/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/multi_objective/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f9293782aa4abfee78cb469a1df07925945cd83e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/multi_objective/__init__.py @@ -0,0 +1,10 @@ +# TODO(nabenabe0928): Come up with any ways to remove this file. +# NOTE(nabenabe0928): Discuss when to remove this class. +migration_url = "https://github.com/optuna/optuna/discussions/5573" +raise ModuleNotFoundError( + "\nThe features in `optuna.multi_objective` were integrated with the" + "\nsingle objective optimization API and `optuna.multi_objective` were" + "\ndeleted at v4.0.0. Please update your code based on the migration guide" + f"\nat {migration_url}" + "\nor downgrade your Optuna version." +) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/progress_bar.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/progress_bar.py new file mode 100644 index 0000000000000000000000000000000000000000..63e0e5b8147e6e5e24a854bbdd41ae2bf30053a6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/progress_bar.py @@ -0,0 +1,125 @@ +from __future__ import annotations + +import logging +from typing import Any +from typing import TYPE_CHECKING +import warnings + +from tqdm.auto import tqdm + +from optuna import logging as optuna_logging + + +if TYPE_CHECKING: + from optuna.study import Study + +_tqdm_handler: _TqdmLoggingHandler | None = None + + +# Reference: https://gist.github.com/hvy/8b80c2cedf02b15c24f85d1fa17ebe02 +class _TqdmLoggingHandler(logging.StreamHandler): + def emit(self, record: Any) -> None: + try: + msg = self.format(record) + tqdm.write(msg) + self.flush() + except (KeyboardInterrupt, SystemExit): + raise + except Exception: + self.handleError(record) + + +class _ProgressBar: + """Progress Bar implementation for :func:`~optuna.study.Study.optimize` on the top of `tqdm`. + + Args: + is_valid: + Whether to show progress bars in :func:`~optuna.study.Study.optimize`. + n_trials: + The number of trials. + timeout: + Stop study after the given number of second(s). + """ + + def __init__( + self, + is_valid: bool, + n_trials: int | None = None, + timeout: float | None = None, + ) -> None: + if is_valid and n_trials is None and timeout is None: + warnings.warn("Progress bar won't be displayed because n_trials and timeout are None.") + + self._is_valid = is_valid and (n_trials or timeout) is not None + self._n_trials = n_trials + self._timeout = timeout + self._last_elapsed_seconds = 0.0 + + if self._is_valid: + if self._n_trials is not None: + self._progress_bar = tqdm(total=self._n_trials) + elif self._timeout is not None: + total = tqdm.format_interval(self._timeout) + fmt = "{desc} {percentage:3.0f}%|{bar}| {elapsed}/" + total + self._progress_bar = tqdm(total=self._timeout, bar_format=fmt) + else: + assert False + + global _tqdm_handler + + _tqdm_handler = _TqdmLoggingHandler() + _tqdm_handler.setLevel(logging.INFO) + _tqdm_handler.setFormatter(optuna_logging.create_default_formatter()) + optuna_logging.disable_default_handler() + optuna_logging._get_library_root_logger().addHandler(_tqdm_handler) + + def update(self, elapsed_seconds: float, study: Study) -> None: + """Update the progress bars if ``is_valid`` is :obj:`True`. + + Args: + elapsed_seconds: + The time past since :func:`~optuna.study.Study.optimize` started. + study: + The current study object. + """ + + if self._is_valid: + if not study._is_multi_objective(): + # Not updating the progress bar when there are no complete trial. + try: + msg = ( + f"Best trial: {study.best_trial.number}. " + f"Best value: {study.best_value:.6g}" + ) + + self._progress_bar.set_description(msg) + except ValueError: + pass + + if self._n_trials is not None: + self._progress_bar.update(1) + if self._timeout is not None: + self._progress_bar.set_postfix_str( + "{:.02f}/{} seconds".format(elapsed_seconds, self._timeout) + ) + + elif self._timeout is not None: + time_diff = elapsed_seconds - self._last_elapsed_seconds + if elapsed_seconds > self._timeout: + # Clip elapsed time to avoid tqdm warnings. + time_diff -= elapsed_seconds - self._timeout + + self._progress_bar.update(time_diff) + self._last_elapsed_seconds = elapsed_seconds + + else: + assert False + + def close(self) -> None: + """Close progress bars.""" + + if self._is_valid: + self._progress_bar.close() + assert _tqdm_handler is not None + optuna_logging._get_library_root_logger().removeHandler(_tqdm_handler) + optuna_logging.enable_default_handler() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ae189d185dfe4e39abbfd9cd87d0153736b8bfd6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/__init__.py @@ -0,0 +1,38 @@ +from typing import TYPE_CHECKING + +from optuna.pruners._base import BasePruner +from optuna.pruners._hyperband import HyperbandPruner +from optuna.pruners._median import MedianPruner +from optuna.pruners._nop import NopPruner +from optuna.pruners._patient import PatientPruner +from optuna.pruners._percentile import PercentilePruner +from optuna.pruners._successive_halving import SuccessiveHalvingPruner +from optuna.pruners._threshold import ThresholdPruner +from optuna.pruners._wilcoxon import WilcoxonPruner + + +if TYPE_CHECKING: + from optuna.study import Study + from optuna.trial import FrozenTrial + + +__all__ = [ + "BasePruner", + "HyperbandPruner", + "MedianPruner", + "NopPruner", + "PatientPruner", + "PercentilePruner", + "SuccessiveHalvingPruner", + "ThresholdPruner", + "WilcoxonPruner", +] + + +def _filter_study(study: "Study", trial: "FrozenTrial") -> "Study": + if isinstance(study.pruner, HyperbandPruner): + # Create `_BracketStudy` to use trials that have the same bracket id. + pruner: HyperbandPruner = study.pruner + return pruner._create_bracket_study(study, pruner._get_bracket_id(study, trial)) + else: + return study diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3e2b6ea18381109ba7db6d2476dc2d0b57c1a6c8 Binary files /dev/null and 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b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_base.py @@ -0,0 +1,28 @@ +import abc + +import optuna + + +class BasePruner(abc.ABC): + """Base class for pruners.""" + + @abc.abstractmethod + def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: + """Judge whether the trial should be pruned based on the reported values. + + Note that this method is not supposed to be called by library users. Instead, + :func:`optuna.trial.Trial.report` and :func:`optuna.trial.Trial.should_prune` provide + user interfaces to implement pruning mechanism in an objective function. + + Args: + study: + Study object of the target study. + trial: + FrozenTrial object of the target trial. + Take a copy before modifying this object. + + Returns: + A boolean value representing whether the trial should be pruned. + """ + + raise NotImplementedError diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_hyperband.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_hyperband.py new file mode 100644 index 0000000000000000000000000000000000000000..fcd26af285eb712b005be0c3efdb0e30a49337fd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_hyperband.py @@ -0,0 +1,324 @@ +from __future__ import annotations + +import binascii +from collections.abc import Container +import math + +import optuna +from optuna import logging +from optuna.pruners._base import BasePruner +from optuna.pruners._successive_halving import SuccessiveHalvingPruner +from optuna.trial._state import TrialState + + +_logger = logging.get_logger(__name__) + + +class HyperbandPruner(BasePruner): + """Pruner using Hyperband. + + As SuccessiveHalving (SHA) requires the number of configurations + :math:`n` as its hyperparameter. For a given finite budget :math:`B`, + all the configurations have the resources of :math:`B \\over n` on average. + As you can see, there will be a trade-off of :math:`B` and :math:`B \\over n`. + `Hyperband `__ attacks this trade-off + by trying different :math:`n` values for a fixed budget. + + .. note:: + * In the Hyperband paper, the counterpart of :class:`~optuna.samplers.RandomSampler` + is used. + * Optuna uses :class:`~optuna.samplers.TPESampler` by default. + * `The benchmark result + `__ + shows that :class:`optuna.pruners.HyperbandPruner` supports both samplers. + + .. note:: + If you use ``HyperbandPruner`` with :class:`~optuna.samplers.TPESampler`, + it's recommended to consider setting larger ``n_trials`` or ``timeout`` to make full use of + the characteristics of :class:`~optuna.samplers.TPESampler` + because :class:`~optuna.samplers.TPESampler` uses some (by default, :math:`10`) + :class:`~optuna.trial.Trial`\\ s for its startup. + + As Hyperband runs multiple :class:`~optuna.pruners.SuccessiveHalvingPruner` and collects + trials based on the current :class:`~optuna.trial.Trial`\\ 's bracket ID, each bracket + needs to observe more than :math:`10` :class:`~optuna.trial.Trial`\\ s + for :class:`~optuna.samplers.TPESampler` to adapt its search space. + + Thus, for example, if ``HyperbandPruner`` has :math:`4` pruners in it, + at least :math:`4 \\times 10` trials are consumed for startup. + + .. note:: + Hyperband has several :class:`~optuna.pruners.SuccessiveHalvingPruner`\\ s. Each + :class:`~optuna.pruners.SuccessiveHalvingPruner` is referred to as "bracket" in the + original paper. The number of brackets is an important factor to control the early + stopping behavior of Hyperband and is automatically determined by ``min_resource``, + ``max_resource`` and ``reduction_factor`` as + :math:`\\mathrm{The\\ number\\ of\\ brackets} = + \\mathrm{floor}(\\log_{\\texttt{reduction}\\_\\texttt{factor}} + (\\frac{\\texttt{max}\\_\\texttt{resource}}{\\texttt{min}\\_\\texttt{resource}})) + 1`. + Please set ``reduction_factor`` so that the number of brackets is not too large (about 4 – + 6 in most use cases). Please see Section 3.6 of the `original paper + `__ for the detail. + + .. note:: + ``HyperbandPruner`` computes bracket ID for each trial with a + function taking ``study_name`` of :class:`~optuna.study.Study` and + :attr:`~optuna.trial.Trial.number`. Please specify ``study_name`` + to make the pruning algorithm reproducible. + + Example: + + We minimize an objective function with Hyperband pruning algorithm. + + .. testcode:: + + import numpy as np + from sklearn.datasets import load_iris + from sklearn.linear_model import SGDClassifier + from sklearn.model_selection import train_test_split + + import optuna + + X, y = load_iris(return_X_y=True) + X_train, X_valid, y_train, y_valid = train_test_split(X, y) + classes = np.unique(y) + n_train_iter = 100 + + + def objective(trial): + alpha = trial.suggest_float("alpha", 0.0, 1.0) + clf = SGDClassifier(alpha=alpha) + + for step in range(n_train_iter): + clf.partial_fit(X_train, y_train, classes=classes) + + intermediate_value = clf.score(X_valid, y_valid) + trial.report(intermediate_value, step) + + if trial.should_prune(): + raise optuna.TrialPruned() + + return clf.score(X_valid, y_valid) + + + study = optuna.create_study( + direction="maximize", + pruner=optuna.pruners.HyperbandPruner( + min_resource=1, max_resource=n_train_iter, reduction_factor=3 + ), + ) + study.optimize(objective, n_trials=20) + + Args: + min_resource: + A parameter for specifying the minimum resource allocated to a trial noted as :math:`r` + in the paper. A smaller :math:`r` will give a result faster, but a larger + :math:`r` will give a better guarantee of successful judging between configurations. + See the details for :class:`~optuna.pruners.SuccessiveHalvingPruner`. + max_resource: + A parameter for specifying the maximum resource allocated to a trial. :math:`R` in the + paper corresponds to ``max_resource / min_resource``. This value represents and should + match the maximum iteration steps (e.g., the number of epochs for neural networks). + When this argument is "auto", the maximum resource is estimated according to the + completed trials. The default value of this argument is "auto". + + .. note:: + With "auto", the maximum resource will be the largest step reported by + :meth:`~optuna.trial.Trial.report` in the first, or one of the first if trained in + parallel, completed trial. No trials will be pruned until the maximum resource is + determined. + + .. note:: + If the step of the last intermediate value may change with each trial, please + manually specify the maximum possible step to ``max_resource``. + reduction_factor: + A parameter for specifying reduction factor of promotable trials noted as + :math:`\\eta` in the paper. + See the details for :class:`~optuna.pruners.SuccessiveHalvingPruner`. + bootstrap_count: + Parameter specifying the number of trials required in a rung before any trial can be + promoted. Incompatible with ``max_resource`` is ``"auto"``. + See the details for :class:`~optuna.pruners.SuccessiveHalvingPruner`. + """ + + def __init__( + self, + min_resource: int = 1, + max_resource: str | int = "auto", + reduction_factor: int = 3, + bootstrap_count: int = 0, + ) -> None: + self._min_resource = min_resource + self._max_resource = max_resource + self._reduction_factor = reduction_factor + self._pruners: list[SuccessiveHalvingPruner] = [] + self._bootstrap_count = bootstrap_count + self._total_trial_allocation_budget = 0 + self._trial_allocation_budgets: list[int] = [] + self._n_brackets: int | None = None + + if not isinstance(self._max_resource, int) and self._max_resource != "auto": + raise ValueError( + "The 'max_resource' should be integer or 'auto'. " + "But max_resource = {}".format(self._max_resource) + ) + + if self._bootstrap_count > 0 and self._max_resource == "auto": + raise ValueError( + "bootstrap_count > 0 and max_resource == 'auto' " + "are mutually incompatible, bootstrap_count is {}".format(self._bootstrap_count) + ) + + def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: + if len(self._pruners) == 0: + self._try_initialization(study) + if len(self._pruners) == 0: + return False + + bracket_id = self._get_bracket_id(study, trial) + _logger.debug("{}th bracket is selected".format(bracket_id)) + bracket_study = self._create_bracket_study(study, bracket_id) + return self._pruners[bracket_id].prune(bracket_study, trial) + + def _try_initialization(self, study: "optuna.study.Study") -> None: + if self._max_resource == "auto": + trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)) + n_steps = [t.last_step for t in trials if t.last_step is not None] + + if not n_steps: + return + + self._max_resource = max(n_steps) + 1 + + assert isinstance(self._max_resource, int) + + if self._n_brackets is None: + # In the original paper http://www.jmlr.org/papers/volume18/16-558/16-558.pdf, the + # inputs of Hyperband are `R`: max resource and `\eta`: reduction factor. The + # number of brackets (this is referred as `s_{max} + 1` in the paper) is calculated + # by s_{max} + 1 = \floor{\log_{\eta} (R)} + 1 in Algorithm 1 of the original paper. + # In this implementation, we combine this formula and that of ASHA paper + # https://arxiv.org/abs/1502.07943 as + # `n_brackets = floor(log_{reduction_factor}(max_resource / min_resource)) + 1` + self._n_brackets = ( + math.floor( + math.log(self._max_resource / self._min_resource, self._reduction_factor) + ) + + 1 + ) + + _logger.debug("Hyperband has {} brackets".format(self._n_brackets)) + + for bracket_id in range(self._n_brackets): + trial_allocation_budget = self._calculate_trial_allocation_budget(bracket_id) + self._total_trial_allocation_budget += trial_allocation_budget + self._trial_allocation_budgets.append(trial_allocation_budget) + + pruner = SuccessiveHalvingPruner( + min_resource=self._min_resource, + reduction_factor=self._reduction_factor, + min_early_stopping_rate=bracket_id, + bootstrap_count=self._bootstrap_count, + ) + self._pruners.append(pruner) + + def _calculate_trial_allocation_budget(self, bracket_id: int) -> int: + """Compute the trial allocated budget for a bracket of ``bracket_id``. + + In the `original paper `, the + number of trials per one bracket is referred as ``n`` in Algorithm 1. Since we do not know + the total number of trials in the leaning scheme of Optuna, we calculate the ratio of the + number of trials here instead. + """ + + assert self._n_brackets is not None + s = self._n_brackets - 1 - bracket_id + return math.ceil(self._n_brackets * (self._reduction_factor**s) / (s + 1)) + + def _get_bracket_id( + self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial" + ) -> int: + """Compute the index of bracket for a trial of ``trial_number``. + + The index of a bracket is noted as :math:`s` in + `Hyperband paper `__. + """ + + if len(self._pruners) == 0: + return 0 + + assert self._n_brackets is not None + n = ( + binascii.crc32("{}_{}".format(study.study_name, trial.number).encode()) + % self._total_trial_allocation_budget + ) + for bracket_id in range(self._n_brackets): + n -= self._trial_allocation_budgets[bracket_id] + if n < 0: + return bracket_id + + assert False, "This line should be unreachable." + + def _create_bracket_study( + self, study: "optuna.study.Study", bracket_id: int + ) -> "optuna.study.Study": + # This class is assumed to be passed to + # `SuccessiveHalvingPruner.prune` in which `get_trials`, + # `direction`, and `storage` are used. + # But for safety, prohibit the other attributes explicitly. + class _BracketStudy(optuna.study.Study): + _VALID_ATTRS = ( + "get_trials", + "_get_trials", + "directions", + "direction", + "_directions", + "_storage", + "_study_id", + "pruner", + "study_name", + "_bracket_id", + "sampler", + "trials", + "_is_multi_objective", + "stop", + "_study", + "_thread_local", + ) + + def __init__( + self, study: "optuna.study.Study", pruner: HyperbandPruner, bracket_id: int + ) -> None: + super().__init__( + study_name=study.study_name, + storage=study._storage, + sampler=study.sampler, + pruner=pruner, + ) + self._study = study + self._bracket_id = bracket_id + + def get_trials( + self, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + ) -> list["optuna.trial.FrozenTrial"]: + trials = super()._get_trials(deepcopy=deepcopy, states=states) + pruner = self.pruner + assert isinstance(pruner, HyperbandPruner) + return [t for t in trials if pruner._get_bracket_id(self, t) == self._bracket_id] + + def stop(self) -> None: + # `stop` should stop the original study's optimization loop instead of + # `_BracketStudy`. + self._study.stop() + + def __getattribute__(self, attr_name): # type: ignore + if attr_name not in _BracketStudy._VALID_ATTRS: + raise AttributeError( + "_BracketStudy does not have attribute of '{}'".format(attr_name) + ) + else: + return object.__getattribute__(self, attr_name) + + return _BracketStudy(study, self, bracket_id) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_median.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_median.py new file mode 100644 index 0000000000000000000000000000000000000000..8ebcd6e0bfa64f08cfa3f6e31091b73586f54965 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_median.py @@ -0,0 +1,86 @@ +from optuna.pruners._percentile import PercentilePruner + + +class MedianPruner(PercentilePruner): + """Pruner using the median stopping rule. + + Prune if the trial's best intermediate result is worse than median of intermediate results of + previous trials at the same step. It stops unpromising trials early based on the + intermediate results compared against the median of previous completed trials. + + The pruner handles NaN values in the following manner: + 1. If all intermediate values of the current trial are NaN, the trial will be pruned. + 2. During the median calculation across completed trials, NaN values are ignored. + Only valid numeric values are considered. + + Example: + + We minimize an objective function with the median stopping rule. + + .. testcode:: + + import numpy as np + from sklearn.datasets import load_iris + from sklearn.linear_model import SGDClassifier + from sklearn.model_selection import train_test_split + + import optuna + + X, y = load_iris(return_X_y=True) + X_train, X_valid, y_train, y_valid = train_test_split(X, y) + classes = np.unique(y) + + + def objective(trial): + alpha = trial.suggest_float("alpha", 0.0, 1.0) + clf = SGDClassifier(alpha=alpha) + n_train_iter = 100 + + for step in range(n_train_iter): + clf.partial_fit(X_train, y_train, classes=classes) + + intermediate_value = clf.score(X_valid, y_valid) + trial.report(intermediate_value, step) + + if trial.should_prune(): + raise optuna.TrialPruned() + + return clf.score(X_valid, y_valid) + + + study = optuna.create_study( + direction="maximize", + pruner=optuna.pruners.MedianPruner( + n_startup_trials=5, n_warmup_steps=30, interval_steps=10 + ), + ) + study.optimize(objective, n_trials=20) + + Args: + n_startup_trials: + Pruning is disabled until the given number of trials finish in the same study. + n_warmup_steps: + Pruning is disabled until the trial exceeds the given number of step. Note that + this feature assumes that ``step`` starts at zero. + interval_steps: + Interval in number of steps between the pruning checks, offset by the warmup steps. + If no value has been reported at the time of a pruning check, that particular check + will be postponed until a value is reported. + n_min_trials: + Minimum number of reported trial results at a step to judge whether to prune. + If the number of reported intermediate values from all trials at the current step + is less than ``n_min_trials``, the trial will not be pruned. This can be used to ensure + that a minimum number of trials are run to completion without being pruned. + """ + + def __init__( + self, + n_startup_trials: int = 5, + n_warmup_steps: int = 0, + interval_steps: int = 1, + *, + n_min_trials: int = 1, + ) -> None: + super().__init__( + 50.0, n_startup_trials, n_warmup_steps, interval_steps, n_min_trials=n_min_trials + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_nop.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_nop.py new file mode 100644 index 0000000000000000000000000000000000000000..4d1c155887b15eb4779444d3446ddb06929953e3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_nop.py @@ -0,0 +1,47 @@ +import optuna +from optuna.pruners import BasePruner + + +class NopPruner(BasePruner): + """Pruner which never prunes trials. + + Example: + + .. testcode:: + + import numpy as np + from sklearn.datasets import load_iris + from sklearn.linear_model import SGDClassifier + from sklearn.model_selection import train_test_split + + import optuna + + X, y = load_iris(return_X_y=True) + X_train, X_valid, y_train, y_valid = train_test_split(X, y) + classes = np.unique(y) + + + def objective(trial): + alpha = trial.suggest_float("alpha", 0.0, 1.0) + clf = SGDClassifier(alpha=alpha) + n_train_iter = 100 + + for step in range(n_train_iter): + clf.partial_fit(X_train, y_train, classes=classes) + + intermediate_value = clf.score(X_valid, y_valid) + trial.report(intermediate_value, step) + + if trial.should_prune(): + assert False, "should_prune() should always return False with this pruner." + raise optuna.TrialPruned() + + return clf.score(X_valid, y_valid) + + + study = optuna.create_study(direction="maximize", pruner=optuna.pruners.NopPruner()) + study.optimize(objective, n_trials=20) + """ + + def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: + return False diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_patient.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_patient.py new file mode 100644 index 0000000000000000000000000000000000000000..51a160af8d83cc056fb4c22a2e387cff7617f18d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_patient.py @@ -0,0 +1,130 @@ +from __future__ import annotations + +import numpy as np + +import optuna +from optuna._experimental import experimental_class +from optuna.pruners import BasePruner +from optuna.study._study_direction import StudyDirection + + +@experimental_class("2.8.0") +class PatientPruner(BasePruner): + """Pruner which wraps another pruner with tolerance. + + This pruner monitors intermediate values in a trial and prunes the trial if the improvement in + the intermediate values after a patience period is less than a threshold. + + The pruner handles NaN values in the following manner: + 1. If all intermediate values before or during the patient period are NaN, the trial will + not be pruned + 2. During the pruning calculations, NaN values are ignored. Only valid numeric values are + considered. + + Example: + + .. testcode:: + + import numpy as np + from sklearn.datasets import load_iris + from sklearn.linear_model import SGDClassifier + from sklearn.model_selection import train_test_split + + import optuna + + X, y = load_iris(return_X_y=True) + X_train, X_valid, y_train, y_valid = train_test_split(X, y) + classes = np.unique(y) + + + def objective(trial): + alpha = trial.suggest_float("alpha", 0.0, 1.0) + clf = SGDClassifier(alpha=alpha) + n_train_iter = 100 + + for step in range(n_train_iter): + clf.partial_fit(X_train, y_train, classes=classes) + + intermediate_value = clf.score(X_valid, y_valid) + trial.report(intermediate_value, step) + + if trial.should_prune(): + raise optuna.TrialPruned() + + return clf.score(X_valid, y_valid) + + + study = optuna.create_study( + direction="maximize", + pruner=optuna.pruners.PatientPruner(optuna.pruners.MedianPruner(), patience=1), + ) + study.optimize(objective, n_trials=20) + + Args: + wrapped_pruner: + Wrapped pruner to perform pruning when :class:`~optuna.pruners.PatientPruner` allows a + trial to be pruned. If it is :obj:`None`, this pruner is equivalent to + early-stopping taken the intermediate values in the individual trial. + patience: + Pruning is disabled until the objective doesn't improve for + ``patience`` consecutive steps. + min_delta: + Tolerance value to check whether or not the objective improves. + This value should be non-negative. + + """ + + def __init__( + self, wrapped_pruner: BasePruner | None, patience: int, min_delta: float = 0.0 + ) -> None: + if patience < 0: + raise ValueError(f"patience cannot be negative but got {patience}.") + + if min_delta < 0: + raise ValueError(f"min_delta cannot be negative but got {min_delta}.") + + self._wrapped_pruner = wrapped_pruner + self._patience = patience + self._min_delta = min_delta + + def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: + step = trial.last_step + if step is None: + return False + + intermediate_values = trial.intermediate_values + steps = np.asarray(list(intermediate_values.keys())) + + # Do not prune if number of step to determine are insufficient. + if steps.size <= self._patience + 1: + return False + + steps.sort() + # This is the score patience steps ago + steps_before_patience = steps[: -self._patience - 1] + scores_before_patience = np.asarray( + list(intermediate_values[step] for step in steps_before_patience) + ) + # And these are the scores after that + steps_after_patience = steps[-self._patience - 1 :] + scores_after_patience = np.asarray( + list(intermediate_values[step] for step in steps_after_patience) + ) + + direction = study.direction + if direction == StudyDirection.MINIMIZE: + maybe_prune = np.nanmin(scores_before_patience) + self._min_delta < np.nanmin( + scores_after_patience + ) + else: + maybe_prune = np.nanmax(scores_before_patience) - self._min_delta > np.nanmax( + scores_after_patience + ) + + if maybe_prune: + if self._wrapped_pruner is not None: + return self._wrapped_pruner.prune(study, trial) + else: + return True + else: + return False diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_percentile.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_percentile.py new file mode 100644 index 0000000000000000000000000000000000000000..805e65dee01164d8983830f6a7da1f17e29aaf17 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_percentile.py @@ -0,0 +1,208 @@ +from __future__ import annotations + +from collections.abc import KeysView +import functools +import math + +import numpy as np + +import optuna +from optuna.pruners import BasePruner +from optuna.study._study_direction import StudyDirection +from optuna.trial._state import TrialState + + +def _get_best_intermediate_result_over_steps( + trial: "optuna.trial.FrozenTrial", direction: StudyDirection +) -> float: + values = np.asarray(list(trial.intermediate_values.values()), dtype=float) + if direction == StudyDirection.MAXIMIZE: + return np.nanmax(values) + return np.nanmin(values) + + +def _get_percentile_intermediate_result_over_trials( + completed_trials: list["optuna.trial.FrozenTrial"], + direction: StudyDirection, + step: int, + percentile: float, + n_min_trials: int, +) -> float: + if len(completed_trials) == 0: + raise ValueError("No trials have been completed.") + + intermediate_values = [ + t.intermediate_values[step] for t in completed_trials if step in t.intermediate_values + ] + + if len(intermediate_values) < n_min_trials: + return math.nan + + if direction == StudyDirection.MAXIMIZE: + percentile = 100 - percentile + + return float( + np.nanpercentile( + np.array(intermediate_values, dtype=float), + percentile, + ) + ) + + +def _is_first_in_interval_step( + step: int, intermediate_steps: KeysView[int], n_warmup_steps: int, interval_steps: int +) -> bool: + nearest_lower_pruning_step = ( + step - n_warmup_steps + ) // interval_steps * interval_steps + n_warmup_steps + assert nearest_lower_pruning_step >= 0 + + # `intermediate_steps` may not be sorted so we must go through all elements. + second_last_step = functools.reduce( + lambda second_last_step, s: s if s > second_last_step and s != step else second_last_step, + intermediate_steps, + -1, + ) + + return second_last_step < nearest_lower_pruning_step + + +class PercentilePruner(BasePruner): + """Pruner to keep the specified percentile of the trials. + + Prune if the best intermediate value is in the bottom percentile among trials at the same step. + + Example: + + .. testcode:: + + import numpy as np + from sklearn.datasets import load_iris + from sklearn.linear_model import SGDClassifier + from sklearn.model_selection import train_test_split + + import optuna + + X, y = load_iris(return_X_y=True) + X_train, X_valid, y_train, y_valid = train_test_split(X, y) + classes = np.unique(y) + + + def objective(trial): + alpha = trial.suggest_float("alpha", 0.0, 1.0) + clf = SGDClassifier(alpha=alpha) + n_train_iter = 100 + + for step in range(n_train_iter): + clf.partial_fit(X_train, y_train, classes=classes) + + intermediate_value = clf.score(X_valid, y_valid) + trial.report(intermediate_value, step) + + if trial.should_prune(): + raise optuna.TrialPruned() + + return clf.score(X_valid, y_valid) + + + study = optuna.create_study( + direction="maximize", + pruner=optuna.pruners.PercentilePruner( + 25.0, n_startup_trials=5, n_warmup_steps=30, interval_steps=10 + ), + ) + study.optimize(objective, n_trials=20) + + Args: + percentile: + Percentile which must be between 0 and 100 inclusive + (e.g., When given 25.0, top of 25th percentile trials are kept). + n_startup_trials: + Pruning is disabled until the given number of trials finish in the same study. + n_warmup_steps: + Pruning is disabled until the trial exceeds the given number of step. Note that + this feature assumes that ``step`` starts at zero. + interval_steps: + Interval in number of steps between the pruning checks, offset by the warmup steps. + If no value has been reported at the time of a pruning check, that particular check + will be postponed until a value is reported. Value must be at least 1. + n_min_trials: + Minimum number of reported trial results at a step to judge whether to prune. + If the number of reported intermediate values from all trials at the current step + is less than ``n_min_trials``, the trial will not be pruned. This can be used to ensure + that a minimum number of trials are run to completion without being pruned. + """ + + def __init__( + self, + percentile: float, + n_startup_trials: int = 5, + n_warmup_steps: int = 0, + interval_steps: int = 1, + *, + n_min_trials: int = 1, + ) -> None: + if not 0.0 <= percentile <= 100: + raise ValueError( + "Percentile must be between 0 and 100 inclusive but got {}.".format(percentile) + ) + if n_startup_trials < 0: + raise ValueError( + "Number of startup trials cannot be negative but got {}.".format(n_startup_trials) + ) + if n_warmup_steps < 0: + raise ValueError( + "Number of warmup steps cannot be negative but got {}.".format(n_warmup_steps) + ) + if interval_steps < 1: + raise ValueError( + "Pruning interval steps must be at least 1 but got {}.".format(interval_steps) + ) + if n_min_trials < 1: + raise ValueError( + "Number of trials for pruning must be at least 1 but got {}.".format(n_min_trials) + ) + + self._percentile = percentile + self._n_startup_trials = n_startup_trials + self._n_warmup_steps = n_warmup_steps + self._interval_steps = interval_steps + self._n_min_trials = n_min_trials + + def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: + completed_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)) + n_trials = len(completed_trials) + + if n_trials == 0: + return False + + if n_trials < self._n_startup_trials: + return False + + step = trial.last_step + if step is None: + return False + + n_warmup_steps = self._n_warmup_steps + if step < n_warmup_steps: + return False + + if not _is_first_in_interval_step( + step, trial.intermediate_values.keys(), n_warmup_steps, self._interval_steps + ): + return False + + direction = study.direction + best_intermediate_result = _get_best_intermediate_result_over_steps(trial, direction) + if math.isnan(best_intermediate_result): + return True + + p = _get_percentile_intermediate_result_over_trials( + completed_trials, direction, step, self._percentile, self._n_min_trials + ) + if math.isnan(p): + return False + + if direction == StudyDirection.MAXIMIZE: + return best_intermediate_result < p + return best_intermediate_result > p diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_successive_halving.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_successive_halving.py new file mode 100644 index 0000000000000000000000000000000000000000..6a7480cbe359315d6120eccb70df3275580a03ad --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_successive_halving.py @@ -0,0 +1,267 @@ +from __future__ import annotations + +import math + +import optuna +from optuna.pruners._base import BasePruner +from optuna.study._study_direction import StudyDirection +from optuna.trial._state import TrialState + + +class SuccessiveHalvingPruner(BasePruner): + """Pruner using Asynchronous Successive Halving Algorithm. + + `Successive Halving `__ is a bandit-based + algorithm to identify the best one among multiple configurations. This class implements an + asynchronous version of Successive Halving. Please refer to the paper of + `Asynchronous Successive Halving `__ for detailed descriptions. + + Note that, this class does not take care of the parameter for the maximum + resource, referred to as :math:`R` in the paper. The maximum resource allocated to a trial is + typically limited inside the objective function (e.g., ``step`` number in `simple_pruning.py + `__, + ``EPOCH`` number in `chainer_integration.py + `__). + + .. seealso:: + Please refer to :meth:`~optuna.trial.Trial.report`. + + Example: + + We minimize an objective function with ``SuccessiveHalvingPruner``. + + .. testcode:: + + import numpy as np + from sklearn.datasets import load_iris + from sklearn.linear_model import SGDClassifier + from sklearn.model_selection import train_test_split + + import optuna + + X, y = load_iris(return_X_y=True) + X_train, X_valid, y_train, y_valid = train_test_split(X, y) + classes = np.unique(y) + + + def objective(trial): + alpha = trial.suggest_float("alpha", 0.0, 1.0) + clf = SGDClassifier(alpha=alpha) + n_train_iter = 100 + + for step in range(n_train_iter): + clf.partial_fit(X_train, y_train, classes=classes) + + intermediate_value = clf.score(X_valid, y_valid) + trial.report(intermediate_value, step) + + if trial.should_prune(): + raise optuna.TrialPruned() + + return clf.score(X_valid, y_valid) + + + study = optuna.create_study( + direction="maximize", pruner=optuna.pruners.SuccessiveHalvingPruner() + ) + study.optimize(objective, n_trials=20) + + Args: + min_resource: + A parameter for specifying the minimum resource allocated to a trial + (in the `paper `__ this parameter is referred to as + :math:`r`). + This parameter defaults to 'auto' where the value is determined based on a heuristic + that looks at the number of required steps for the first trial to complete. + + A trial is never pruned until it executes + :math:`\\mathsf{min}\\_\\mathsf{resource} \\times + \\mathsf{reduction}\\_\\mathsf{factor}^{ + \\mathsf{min}\\_\\mathsf{early}\\_\\mathsf{stopping}\\_\\mathsf{rate}}` + steps (i.e., the completion point of the first rung). When the trial completes + the first rung, it will be promoted to the next rung only + if the value of the trial is placed in the top + :math:`{1 \\over \\mathsf{reduction}\\_\\mathsf{factor}}` fraction of + the all trials that already have reached the point (otherwise it will be pruned there). + If the trial won the competition, it runs until the next completion point (i.e., + :math:`\\mathsf{min}\\_\\mathsf{resource} \\times + \\mathsf{reduction}\\_\\mathsf{factor}^{ + (\\mathsf{min}\\_\\mathsf{early}\\_\\mathsf{stopping}\\_\\mathsf{rate} + + \\mathsf{rung})}` steps) + and repeats the same procedure. + + .. note:: + If the step of the last intermediate value may change with each trial, please + manually specify the minimum possible step to ``min_resource``. + reduction_factor: + A parameter for specifying reduction factor of promotable trials + (in the `paper `__ this parameter is + referred to as :math:`\\eta`). At the completion point of each rung, + about :math:`{1 \\over \\mathsf{reduction}\\_\\mathsf{factor}}` + trials will be promoted. + min_early_stopping_rate: + A parameter for specifying the minimum early-stopping rate + (in the `paper `__ this parameter is + referred to as :math:`s`). + bootstrap_count: + Minimum number of trials that need to complete a rung before any trial + is considered for promotion into the next rung. + """ + + def __init__( + self, + min_resource: str | int = "auto", + reduction_factor: int = 4, + min_early_stopping_rate: int = 0, + bootstrap_count: int = 0, + ) -> None: + if isinstance(min_resource, str) and min_resource != "auto": + raise ValueError( + "The value of `min_resource` is {}, " + "but must be either `min_resource` >= 1 or 'auto'".format(min_resource) + ) + + if isinstance(min_resource, int) and min_resource < 1: + raise ValueError( + "The value of `min_resource` is {}, " + "but must be either `min_resource >= 1` or 'auto'".format(min_resource) + ) + + if reduction_factor < 2: + raise ValueError( + "The value of `reduction_factor` is {}, " + "but must be `reduction_factor >= 2`".format(reduction_factor) + ) + + if min_early_stopping_rate < 0: + raise ValueError( + "The value of `min_early_stopping_rate` is {}, " + "but must be `min_early_stopping_rate >= 0`".format(min_early_stopping_rate) + ) + + if bootstrap_count < 0: + raise ValueError( + "The value of `bootstrap_count` is {}, " + "but must be `bootstrap_count >= 0`".format(bootstrap_count) + ) + + if bootstrap_count > 0 and min_resource == "auto": + raise ValueError( + "bootstrap_count > 0 and min_resource == 'auto' " + "are mutually incompatible, bootstrap_count is {}".format(bootstrap_count) + ) + + self._min_resource: int | None = None + if isinstance(min_resource, int): + self._min_resource = min_resource + self._reduction_factor = reduction_factor + self._min_early_stopping_rate = min_early_stopping_rate + self._bootstrap_count = bootstrap_count + + def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: + step = trial.last_step + if step is None: + return False + + rung = _get_current_rung(trial) + value = trial.intermediate_values[step] + trials: list["optuna.trial.FrozenTrial"] | None = None + + while True: + if self._min_resource is None: + if trials is None: + trials = study.get_trials(deepcopy=False) + self._min_resource = _estimate_min_resource(trials) + if self._min_resource is None: + return False + + assert self._min_resource is not None + rung_promotion_step = self._min_resource * ( + self._reduction_factor ** (self._min_early_stopping_rate + rung) + ) + if step < rung_promotion_step: + return False + + if math.isnan(value): + return True + + if trials is None: + trials = study.get_trials(deepcopy=False) + + rung_key = _completed_rung_key(rung) + + study._storage.set_trial_system_attr(trial._trial_id, rung_key, value) + + competing = _get_competing_values(trials, value, rung_key) + + # 'competing' already includes the current trial + # Therefore, we need to use the '<=' operator here + if len(competing) <= self._bootstrap_count: + return True + + if not _is_trial_promotable_to_next_rung( + value, + competing, + self._reduction_factor, + study.direction, + ): + return True + + rung += 1 + + +def _estimate_min_resource(trials: list["optuna.trial.FrozenTrial"]) -> int | None: + n_steps = [ + t.last_step for t in trials if t.state == TrialState.COMPLETE and t.last_step is not None + ] + + if not n_steps: + return None + + # Get the maximum number of steps and divide it by 100. + last_step = max(n_steps) + return max(last_step // 100, 1) + + +def _get_current_rung(trial: "optuna.trial.FrozenTrial") -> int: + # The following loop takes `O(log step)` iterations. + rung = 0 + while _completed_rung_key(rung) in trial.system_attrs: + rung += 1 + return rung + + +def _completed_rung_key(rung: int) -> str: + return "completed_rung_{}".format(rung) + + +def _get_competing_values( + trials: list["optuna.trial.FrozenTrial"], value: float, rung_key: str +) -> list[float]: + competing_values = [t.system_attrs[rung_key] for t in trials if rung_key in t.system_attrs] + competing_values.append(value) + return competing_values + + +def _is_trial_promotable_to_next_rung( + value: float, + competing_values: list[float], + reduction_factor: int, + study_direction: StudyDirection, +) -> bool: + promotable_idx = (len(competing_values) // reduction_factor) - 1 + + if promotable_idx == -1: + # Optuna does not support suspending or resuming ongoing trials. Therefore, for the first + # `eta - 1` trials, this implementation instead promotes the trial if its value is the + # smallest one among the competing values. + promotable_idx = 0 + + competing_values.sort() + if study_direction == StudyDirection.MAXIMIZE: + return value >= competing_values[-(promotable_idx + 1)] + return value <= competing_values[promotable_idx] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_threshold.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_threshold.py new file mode 100644 index 0000000000000000000000000000000000000000..4d2c29cccf4435b1d9651edc0d939db72abced91 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_threshold.py @@ -0,0 +1,138 @@ +from __future__ import annotations + +import math +from typing import Any + +import optuna +from optuna.pruners import BasePruner +from optuna.pruners._percentile import _is_first_in_interval_step + + +def _check_value(value: Any) -> float: + try: + # For convenience, we allow users to report a value that can be cast to `float`. + value = float(value) + except (TypeError, ValueError): + message = "The `value` argument is of type '{}' but supposed to be a float.".format( + type(value).__name__ + ) + raise TypeError(message) from None + + return value + + +class ThresholdPruner(BasePruner): + """Pruner to detect outlying metrics of the trials. + + Prune if a metric exceeds upper threshold, + falls behind lower threshold or reaches ``nan``. + + Example: + .. testcode:: + + from optuna import create_study + from optuna.pruners import ThresholdPruner + from optuna import TrialPruned + + + def objective_for_upper(trial): + for step, y in enumerate(ys_for_upper): + trial.report(y, step) + + if trial.should_prune(): + raise TrialPruned() + return ys_for_upper[-1] + + + def objective_for_lower(trial): + for step, y in enumerate(ys_for_lower): + trial.report(y, step) + + if trial.should_prune(): + raise TrialPruned() + return ys_for_lower[-1] + + + ys_for_upper = [0.0, 0.1, 0.2, 0.5, 1.2] + ys_for_lower = [100.0, 90.0, 0.1, 0.0, -1] + + study = create_study(pruner=ThresholdPruner(upper=1.0)) + study.optimize(objective_for_upper, n_trials=10) + + study = create_study(pruner=ThresholdPruner(lower=0.0)) + study.optimize(objective_for_lower, n_trials=10) + + Args: + lower: + A minimum value which determines whether pruner prunes or not. + If an intermediate value is smaller than lower, it prunes. + upper: + A maximum value which determines whether pruner prunes or not. + If an intermediate value is larger than upper, it prunes. + n_warmup_steps: + Pruning is disabled if the step is less than the given number of warmup steps. + interval_steps: + Interval in number of steps between the pruning checks, offset by the warmup steps. + If no value has been reported at the time of a pruning check, that particular check + will be postponed until a value is reported. Value must be at least 1. + + """ + + def __init__( + self, + lower: float | None = None, + upper: float | None = None, + n_warmup_steps: int = 0, + interval_steps: int = 1, + ) -> None: + if lower is None and upper is None: + raise TypeError("Either lower or upper must be specified.") + if lower is not None: + lower = _check_value(lower) + if upper is not None: + upper = _check_value(upper) + + lower = lower if lower is not None else -float("inf") + upper = upper if upper is not None else float("inf") + + if lower > upper: + raise ValueError("lower should be smaller than upper.") + if n_warmup_steps < 0: + raise ValueError( + "Number of warmup steps cannot be negative but got {}.".format(n_warmup_steps) + ) + if interval_steps < 1: + raise ValueError( + "Pruning interval steps must be at least 1 but got {}.".format(interval_steps) + ) + + self._lower = lower + self._upper = upper + self._n_warmup_steps = n_warmup_steps + self._interval_steps = interval_steps + + def prune(self, study: "optuna.study.Study", trial: "optuna.trial.FrozenTrial") -> bool: + step = trial.last_step + if step is None: + return False + + n_warmup_steps = self._n_warmup_steps + if step < n_warmup_steps: + return False + + if not _is_first_in_interval_step( + step, trial.intermediate_values.keys(), n_warmup_steps, self._interval_steps + ): + return False + + latest_value = trial.intermediate_values[step] + if math.isnan(latest_value): + return True + + if latest_value < self._lower: + return True + + if latest_value > self._upper: + return True + + return False diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_wilcoxon.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_wilcoxon.py new file mode 100644 index 0000000000000000000000000000000000000000..2b88ee361542d135e6394f7316a36ac29387a6b1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/pruners/_wilcoxon.py @@ -0,0 +1,225 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING +import warnings + +import numpy as np + +import optuna +from optuna._experimental import experimental_class +from optuna.pruners import BasePruner +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial + + +if TYPE_CHECKING: + from typing import Literal + + import scipy.stats as ss +else: + from optuna._imports import _LazyImport + + ss = _LazyImport("scipy.stats") + + +@experimental_class("3.6.0") +class WilcoxonPruner(BasePruner): + """Pruner based on the `Wilcoxon signed-rank test `__. + + This pruner performs the Wilcoxon signed-rank test between the current trial and the current best trial, + and stops whenever the pruner is sure up to a given p-value that the current trial is worse than the best one. + + This pruner is effective for optimizing the mean/median of some (costly-to-evaluate) performance scores over a set of problem instances. + Example applications include the optimization of: + + * the mean performance of a heuristic method (simulated annealing, genetic algorithm, SAT solver, etc.) on a set of problem instances, + * the k-fold cross-validation score of a machine learning model, and + * the accuracy of outputs of a large language model (LLM) on a set of questions. + + There can be "easy" or "hard" instances (the pruner handles correspondence of the instances between different trials). + In each trial, it is recommended to shuffle the evaluation order, so that the optimization doesn't overfit to the instances in the beginning. + + When you use this pruner, you must call ``Trial.report(value, step)`` method for each step (instance id) with + the evaluated value. The instance id may not be in ascending order. + This is different from other pruners in that the reported value need not converge + to the real value. To use pruners such as :class:`~optuna.pruners.SuccessiveHalvingPruner` + in the same setting, you must provide e.g., the historical average of the evaluated values. + + .. seealso:: + Please refer to :meth:`~optuna.trial.Trial.report`. + + Example: + + .. testcode:: + + import optuna + import numpy as np + + + # We minimize the mean evaluation loss over all the problem instances. + def evaluate(param, instance): + # A toy loss function for demonstrative purpose. + return (param - instance) ** 2 + + + problem_instances = np.linspace(-1, 1, 100) + + + def objective(trial): + # Sample a parameter. + param = trial.suggest_float("param", -1, 1) + + # Evaluate performance of the parameter. + results = [] + + # For best results, shuffle the evaluation order in each trial. + instance_ids = np.random.permutation(len(problem_instances)) + for instance_id in instance_ids: + loss = evaluate(param, problem_instances[instance_id]) + results.append(loss) + + # Report loss together with the instance id. + # CAVEAT: You need to pass the same id for the same instance, + # otherwise WilcoxonPruner cannot correctly pair the losses across trials and + # the pruning performance will degrade. + trial.report(loss, instance_id) + + if trial.should_prune(): + # Return the current predicted value instead of raising `TrialPruned`. + # This is a workaround to tell the Optuna about the evaluation + # results in pruned trials. (See the note below.) + return sum(results) / len(results) + + return sum(results) / len(results) + + + study = optuna.create_study(pruner=optuna.pruners.WilcoxonPruner(p_threshold=0.1)) + study.optimize(objective, n_trials=100) + + + + .. note:: + This pruner cannot handle ``infinity`` or ``nan`` values. + Trials containing those values are never pruned. + + .. note:: + If :func:`~optuna.trial.FrozenTrial.should_prune` returns :obj:`True`, you can return an + estimation of the final value (e.g., the average of all evaluated + values) instead of ``raise optuna.TrialPruned()``. + This is a workaround for the problem that currently there is no way + to tell Optuna the predicted objective value for trials raising + :class:`optuna.TrialPruned`. + + Args: + p_threshold: + The p-value threshold for pruning. This value should be between 0 and 1. + A trial will be pruned whenever the pruner is sure up to the given p-value + that the current trial is worse than the best trial. + The larger this value is, the more aggressive pruning will be performed. + Defaults to 0.1. + + .. note:: + This pruner repeatedly performs statistical tests between the + current trial and the current best trial with increasing samples. + The false-positive rate of such a sequential test is different from + performing the test only once. To get the nominal false-positive rate, + please specify the Pocock-corrected p-value. + + n_startup_steps: + The number of steps before which no trials are pruned. + Pruning starts only after you have ``n_startup_steps`` steps of + available observations for comparison between the current trial + and the best trial. + Defaults to 2. Note that the trial is not pruned at the first and second steps even if + the `n_startup_steps` is set to 0 or 1 due to the lack of enough data for comparison. + """ # NOQA: E501 + + def __init__( + self, + *, + p_threshold: float = 0.1, + n_startup_steps: int = 2, + ) -> None: + if n_startup_steps < 0: # TODO: Consider changing the RHS to 2. + raise ValueError(f"n_startup_steps must be nonnegative but got {n_startup_steps}.") + if not 0.0 <= p_threshold <= 1.0: + raise ValueError(f"p_threshold must be between 0 and 1 but got {p_threshold}.") + + self._n_startup_steps = n_startup_steps + self._p_threshold = p_threshold + + def prune(self, study: "optuna.study.Study", trial: FrozenTrial) -> bool: + if len(trial.intermediate_values) == 0: + return False + + steps, step_values = np.array(list(trial.intermediate_values.items())).T + + if np.any(~np.isfinite(step_values)): + warnings.warn( + f"The intermediate values of the current trial (trial {trial.number}) " + f"contain infinity/NaNs. WilcoxonPruner will not prune this trial." + ) + return False + + try: + best_trial = study.best_trial + except ValueError: + return False + + if len(best_trial.intermediate_values) == 0: + warnings.warn( + "The best trial has no intermediate values so WilcoxonPruner cannot prune trials. " + "If you have added the best trial with Study.add_trial, please consider setting " + "intermediate_values argument." + ) + return False + + best_steps, best_step_values = np.array(list(best_trial.intermediate_values.items())).T + + if np.any(~np.isfinite(best_step_values)): + warnings.warn( + f"The intermediate values of the best trial (trial {best_trial.number}) " + f"contain infinity/NaNs. WilcoxonPruner will not prune the current trial." + ) + return False + + _, idx1, idx2 = np.intersect1d(steps, best_steps, return_indices=True) + + if len(idx1) < len(step_values): + # This if-statement is never satisfied if following "average_is_best" safety works, + # because the safety ensures that the best trial always has the all steps. + warnings.warn( + "WilcoxonPruner finds steps existing in the current trial " + "but does not exist in the best trial. " + "Those values are ignored." + ) + + diff_values = step_values[idx1] - best_step_values[idx2] + + if len(diff_values) < max(2, self._n_startup_steps): + return False + + alt: Literal["less", "greater"] + if study.direction == StudyDirection.MAXIMIZE: + alt = "less" + average_is_best = sum(best_step_values) / len(best_step_values) <= sum( + step_values + ) / len(step_values) + else: + alt = "greater" + average_is_best = sum(best_step_values) / len(best_step_values) >= sum( + step_values + ) / len(step_values) + + # We use zsplit to avoid the problem when all values are zero. + p = ss.wilcoxon(diff_values, alternative=alt, zero_method="zsplit").pvalue + + if p < self._p_threshold and average_is_best: + # ss.wilcoxon found the current trial is probably worse than the best trial, + # but the value of the best trial was not better than + # the average of the current trial's intermediate values. + # For safety, WilcoxonPruner concludes not to prune it for now. + return False + + # convert the `np.bool_` to a `builtins.bool` + return bool(p < self._p_threshold) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/py.typed b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c2713f03137dae008c8e49082f1270563642a5b3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/__init__.py @@ -0,0 +1,30 @@ +from optuna.samplers import nsgaii +from optuna.samplers._base import BaseSampler +from optuna.samplers._brute_force import BruteForceSampler +from optuna.samplers._cmaes import CmaEsSampler +from optuna.samplers._ga import BaseGASampler +from optuna.samplers._gp.sampler import GPSampler +from optuna.samplers._grid import GridSampler +from optuna.samplers._nsgaiii._sampler import NSGAIIISampler +from optuna.samplers._partial_fixed import PartialFixedSampler +from optuna.samplers._qmc import QMCSampler +from optuna.samplers._random import RandomSampler +from optuna.samplers._tpe.sampler import TPESampler +from optuna.samplers.nsgaii._sampler import NSGAIISampler + + +__all__ = [ + "BaseSampler", + "BaseGASampler", + "BruteForceSampler", + "CmaEsSampler", + "GridSampler", + "NSGAIISampler", + "NSGAIIISampler", + "PartialFixedSampler", + "QMCSampler", + "RandomSampler", + "TPESampler", + "GPSampler", + "nsgaii", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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+ +if TYPE_CHECKING: + from optuna.study import Study + + +_INDEPENDENT_SAMPLING_WARNING_TEMPLATE = ( + "The parameter `{param_name}` in Trial#{trial_number} is sampled independently " + "using `{independent_sampler_name}` instead of `{sampler_name}`, potentially " + "degrading the optimization performance. This fallback happend because " + "{fallback_reason}. You can suppress this warning by setting " + "`warn_independent_sampling` to `False` in the constructor of `{sampler_name}` if " + "this independent sampling is intended behavior." +) + + +class BaseSampler(abc.ABC): + """Base class for samplers. + + Optuna combines two types of sampling strategies, which are called *relative sampling* and + *independent sampling*. + + *The relative sampling* determines values of multiple parameters simultaneously so that + sampling algorithms can use relationship between parameters (e.g., correlation). + Target parameters of the relative sampling are described in a relative search space, which + is determined by :func:`~optuna.samplers.BaseSampler.infer_relative_search_space`. + + *The independent sampling* determines a value of a single parameter without considering any + relationship between parameters. Target parameters of the independent sampling are the + parameters not described in the relative search space. + + More specifically, parameters are sampled by the following procedure. + At the beginning of a trial, :meth:`~optuna.samplers.BaseSampler.infer_relative_search_space` + is called to determine the relative search space for the trial. + During the execution of the objective function, + :meth:`~optuna.samplers.BaseSampler.sample_relative` is called only once + when sampling the parameters belonging to the relative search space for the first time. + :meth:`~optuna.samplers.BaseSampler.sample_independent` is used to sample + parameters that don't belong to the relative search space. + + The following figure depicts the lifetime of a trial and how the above three methods are + called in the trial. + + .. image:: ../../../../image/sampling-sequence.png + + | + + """ + + def __str__(self) -> str: + return self.__class__.__name__ + + @abc.abstractmethod + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + """Infer the search space that will be used by relative sampling in the target trial. + + This method is called right before :func:`~optuna.samplers.BaseSampler.sample_relative` + method, and the search space returned by this method is passed to it. The parameters not + contained in the search space will be sampled by using + :func:`~optuna.samplers.BaseSampler.sample_independent` method. + + Args: + study: + Target study object. + trial: + Target trial object. + Take a copy before modifying this object. + + Returns: + A dictionary containing the parameter names and parameter's distributions. + + .. seealso:: + Please refer to :func:`~optuna.search_space.intersection_search_space` as an + implementation of :func:`~optuna.samplers.BaseSampler.infer_relative_search_space`. + """ + + raise NotImplementedError + + @abc.abstractmethod + def sample_relative( + self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution] + ) -> dict[str, Any]: + """Sample parameters in a given search space. + + This method is called once at the beginning of each trial, i.e., right before the + evaluation of the objective function. This method is suitable for sampling algorithms + that use relationship between parameters such as Gaussian Process and CMA-ES. + + .. note:: + The failed trials are ignored by any build-in samplers when they sample new + parameters. Thus, failed trials are regarded as deleted in the samplers' + perspective. + + Args: + study: + Target study object. + trial: + Target trial object. + Take a copy before modifying this object. + search_space: + The search space returned by + :func:`~optuna.samplers.BaseSampler.infer_relative_search_space`. + + Returns: + A dictionary containing the parameter names and the values. + + """ + + raise NotImplementedError + + @abc.abstractmethod + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + """Sample a parameter for a given distribution. + + This method is called only for the parameters not contained in the search space returned + by :func:`~optuna.samplers.BaseSampler.sample_relative` method. This method is suitable + for sampling algorithms that do not use relationship between parameters such as random + sampling and TPE. + + .. note:: + The failed trials are ignored by any build-in samplers when they sample new + parameters. Thus, failed trials are regarded as deleted in the samplers' + perspective. + + Args: + study: + Target study object. + trial: + Target trial object. + Take a copy before modifying this object. + param_name: + Name of the sampled parameter. + param_distribution: + Distribution object that specifies a prior and/or scale of the sampling algorithm. + + Returns: + A parameter value. + + """ + + raise NotImplementedError + + def before_trial(self, study: Study, trial: FrozenTrial) -> None: + """Trial pre-processing. + + This method is called before the objective function is called and right after the trial is + instantiated. More precisely, this method is called during trial initialization, just + before the :func:`~optuna.samplers.BaseSampler.infer_relative_search_space` call. In other + words, it is responsible for pre-processing that should be done before inferring the search + space. + + .. note:: + Added in v3.3.0 as an experimental feature. The interface may change in newer versions + without prior notice. See https://github.com/optuna/optuna/releases/tag/v3.3.0. + + Args: + study: + Target study object. + trial: + Target trial object. + """ + + pass + + def after_trial( + self, + study: Study, + trial: FrozenTrial, + state: TrialState, + values: Sequence[float] | None, + ) -> None: + """Trial post-processing. + + This method is called after the objective function returns and right before the trial is + finished and its state is stored. + + .. note:: + Added in v2.4.0 as an experimental feature. The interface may change in newer versions + without prior notice. See https://github.com/optuna/optuna/releases/tag/v2.4.0. + + Args: + study: + Target study object. + trial: + Target trial object. + Take a copy before modifying this object. + state: + Resulting trial state. + values: + Resulting trial values. Guaranteed to not be :obj:`None` if trial succeeded. + + """ + + pass + + def reseed_rng(self) -> None: + """Reseed sampler's random number generator. + + This method is called by the :class:`~optuna.study.Study` instance if trials are executed + in parallel with the option ``n_jobs>1``. In that case, the sampler instance will be + replicated including the state of the random number generator, and they may suggest the + same values. To prevent this issue, this method assigns a different seed to each random + number generator. + """ + + pass + + def _raise_error_if_multi_objective(self, study: Study) -> None: + if study._is_multi_objective(): + raise ValueError( + "If the study is being used for multi-objective optimization, " + f"{self.__class__.__name__} cannot be used." + ) + + +_CONSTRAINTS_KEY = "constraints" + + +def _process_constraints_after_trial( + constraints_func: Callable[[FrozenTrial], Sequence[float]], + study: Study, + trial: FrozenTrial, + state: TrialState, +) -> None: + if state not in [TrialState.COMPLETE, TrialState.PRUNED]: + return + + constraints = None + try: + con = constraints_func(trial) + if np.any(np.isnan(con)): + raise ValueError("Constraint values cannot be NaN.") + if not isinstance(con, (tuple, list)): + warnings.warn( + f"Constraints should be a sequence of floats but got {type(con).__name__}." + ) + constraints = tuple(con) + finally: + assert constraints is None or isinstance(constraints, tuple) + + study._storage.set_trial_system_attr( + trial._trial_id, + _CONSTRAINTS_KEY, + constraints, + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_brute_force.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_brute_force.py new file mode 100644 index 0000000000000000000000000000000000000000..0e4b273f3dfc77cb77a9c60388fe372c9c1597e3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_brute_force.py @@ -0,0 +1,299 @@ +from __future__ import annotations + +from collections.abc import Iterable +from collections.abc import Sequence +from dataclasses import dataclass +import decimal +from typing import Any +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._experimental import experimental_class +from optuna.distributions import BaseDistribution +from optuna.distributions import CategoricalDistribution +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution +from optuna.samplers import BaseSampler +from optuna.samplers._lazy_random_state import LazyRandomState +from optuna.trial import create_trial +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna.study import Study + + +@dataclass +class _TreeNode: + # This is a class to represent the tree of search space. + + # A tree node has three states: + # 1. Unexpanded. This is represented by children=None. + # 2. Leaf. This is represented by children={} and param_name=None. + # 3. Normal node. It has a param_name and non-empty children. + + param_name: str | None = None + children: dict[float, "_TreeNode"] | None = None + is_running: bool = False + + def expand(self, param_name: str | None, search_space: Iterable[float]) -> None: + # If the node is unexpanded, expand it. + # Otherwise, check if the node is compatible with the given search space. + if self.children is None: + # Expand the node + self.param_name = param_name + self.children = {value: _TreeNode() for value in search_space} + else: + if self.param_name != param_name: + raise ValueError(f"param_name mismatch: {self.param_name} != {param_name}") + if self.children.keys() != set(search_space): + raise ValueError( + f"search_space mismatch: {set(self.children.keys())} != {set(search_space)}" + ) + + def set_running(self) -> None: + self.is_running = True + + def set_leaf(self) -> None: + self.expand(None, []) + + def add_path( + self, params_and_search_spaces: Iterable[tuple[str, Iterable[float], float]] + ) -> "_TreeNode" | None: + # Add a path (i.e. a list of suggested parameters in one trial) to the tree. + current_node = self + for param_name, search_space, value in params_and_search_spaces: + current_node.expand(param_name, search_space) + assert current_node.children is not None + if value not in current_node.children: + return None + current_node = current_node.children[value] + return current_node + + def count_unexpanded(self, exclude_running: bool) -> int: + # Count the number of unexpanded nodes in the subtree. + if self.children is None: + return 0 if exclude_running and self.is_running else 1 + else: + return sum(child.count_unexpanded(exclude_running) for child in self.children.values()) + + def sample_child(self, rng: np.random.RandomState, exclude_running: bool) -> float: + assert self.children is not None + # Sample an unexpanded node in the subtree uniformly, and return the first + # parameter value in the path to the node. + # Equivalently, we sample the child node with weights proportional to the number + # of unexpanded nodes in the subtree. + weights = np.array( + [child.count_unexpanded(exclude_running) for child in self.children.values()], + dtype=np.float64, + ) + if any( + not value.is_running and weights[i] > 0 + for i, value in enumerate(self.children.values()) + ): + # Prioritize picking non-running and unexpanded nodes. + for i, child in enumerate(self.children.values()): + if child.is_running: + weights[i] = 0.0 + weights /= weights.sum() + return rng.choice(list(self.children.keys()), p=weights) + + +@experimental_class("3.1.0") +class BruteForceSampler(BaseSampler): + """Sampler using brute force. + + This sampler performs exhaustive search on the defined search space. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + c = trial.suggest_categorical("c", ["float", "int"]) + if c == "float": + return trial.suggest_float("x", 1, 3, step=0.5) + elif c == "int": + a = trial.suggest_int("a", 1, 3) + b = trial.suggest_int("b", a, 3) + return a + b + + + study = optuna.create_study(sampler=optuna.samplers.BruteForceSampler()) + study.optimize(objective) + + Note: + The defined search space must be finite. Therefore, when using + :class:`~optuna.distributions.FloatDistribution` or + :func:`~optuna.trial.Trial.suggest_float`, ``step=None`` is not allowed. + + Note: + The sampler may fail to try the entire search space in when the suggestion ranges or + parameters are changed in the same :class:`~optuna.study.Study`. + + Args: + seed: + A seed to fix the order of trials as the search order randomly shuffled. Please note + that it is not recommended using this option in distributed optimization settings since + this option cannot ensure the order of trials and may increase the number of duplicate + suggestions during distributed optimization. + avoid_premature_stop: + If :obj:`True`, the sampler performs a strict exhaustive search. Please note + that enabling this option may increase the likelihood of duplicate sampling. + When this option is not enabled (default), the sampler applies a looser criterion for + determining when to stop the search, which may result in incomplete coverage of the + search space. For more information, see https://github.com/optuna/optuna/issues/5780. + """ + + def __init__(self, seed: int | None = None, avoid_premature_stop: bool = False) -> None: + self._rng = LazyRandomState(seed) + self._avoid_premature_stop = avoid_premature_stop + + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + return {} + + def sample_relative( + self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution] + ) -> dict[str, Any]: + return {} + + @staticmethod + def _populate_tree( + tree: _TreeNode, trials: Iterable[FrozenTrial], params: dict[str, Any] + ) -> None: + # Populate tree under given params from the given trials. + for trial in trials: + if not all(p in trial.params and trial.params[p] == v for p, v in params.items()): + continue + leaf = tree.add_path( + ( + ( + param_name, + _enumerate_candidates(param_distribution), + param_distribution.to_internal_repr(trial.params[param_name]), + ) + for param_name, param_distribution in trial.distributions.items() + if param_name not in params + ) + ) + if leaf is not None: + # The parameters are on the defined grid. + if trial.state.is_finished(): + leaf.set_leaf() + else: + leaf.set_running() + + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + exclude_running = not self._avoid_premature_stop + + # We directly query the storage to get trials here instead of `study.get_trials`, + # since some pruners such as `HyperbandPruner` use the study transformed + # to filter trials. See https://github.com/optuna/optuna/issues/2327 for details. + trials = study._storage.get_all_trials( + study._study_id, + deepcopy=False, + states=( + TrialState.COMPLETE, + TrialState.PRUNED, + TrialState.RUNNING, + TrialState.FAIL, + ), + ) + tree = _TreeNode() + candidates = _enumerate_candidates(param_distribution) + tree.expand(param_name, candidates) + # Populating must happen after the initialization above to prevent `tree` from + # being initialized as an empty graph, which is created with n_jobs > 1 + # where we get trials[i].params = {} for some i. + self._populate_tree(tree, (t for t in trials if t.number != trial.number), trial.params) + if tree.count_unexpanded(exclude_running) == 0: + return param_distribution.to_external_repr(self._rng.rng.choice(candidates)) + else: + return param_distribution.to_external_repr( + tree.sample_child(self._rng.rng, exclude_running) + ) + + def after_trial( + self, + study: Study, + trial: FrozenTrial, + state: TrialState, + values: Sequence[float] | None, + ) -> None: + exclude_running = not self._avoid_premature_stop + + # We directly query the storage to get trials here instead of `study.get_trials`, + # since some pruners such as `HyperbandPruner` use the study transformed + # to filter trials. See https://github.com/optuna/optuna/issues/2327 for details. + trials = study._storage.get_all_trials( + study._study_id, + deepcopy=False, + states=( + TrialState.COMPLETE, + TrialState.PRUNED, + TrialState.RUNNING, + TrialState.FAIL, + ), + ) + tree = _TreeNode() + self._populate_tree( + tree, + ( + ( + t + if t.number != trial.number + else create_trial( + state=state, # Set current trial as complete. + values=values, + params=trial.params, + distributions=trial.distributions, + ) + ) + for t in trials + ), + {}, + ) + + if tree.count_unexpanded(exclude_running) == 0: + study.stop() + + +def _enumerate_candidates(param_distribution: BaseDistribution) -> Sequence[float]: + if isinstance(param_distribution, FloatDistribution): + if param_distribution.step is None: + raise ValueError( + "FloatDistribution.step must be given for BruteForceSampler" + " (otherwise, the search space will be infinite)." + ) + low = decimal.Decimal(str(param_distribution.low)) + high = decimal.Decimal(str(param_distribution.high)) + step = decimal.Decimal(str(param_distribution.step)) + + ret = [] + value = low + while value <= high: + ret.append(float(value)) + value += step + + return ret + elif isinstance(param_distribution, IntDistribution): + return list( + range(param_distribution.low, param_distribution.high + 1, param_distribution.step) + ) + elif isinstance(param_distribution, CategoricalDistribution): + return list(range(len(param_distribution.choices))) # Internal representations. + else: + raise ValueError(f"Unknown distribution {param_distribution}.") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_cmaes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_cmaes.py new file mode 100644 index 0000000000000000000000000000000000000000..ffa6a9d5db4f14f077b06ac93d384281cb85bef5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_cmaes.py @@ -0,0 +1,646 @@ +from __future__ import annotations + +from collections.abc import Sequence +import copy +import math +import pickle +from typing import Any +from typing import cast +from typing import TYPE_CHECKING +from typing import Union +import warnings + +import numpy as np + +import optuna +from optuna import _deprecated +from optuna import logging +from optuna._experimental import warn_experimental_argument +from optuna._imports import _LazyImport +from optuna._transform import _SearchSpaceTransform +from optuna.distributions import BaseDistribution +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution +from optuna.samplers import BaseSampler +from optuna.samplers._base import _INDEPENDENT_SAMPLING_WARNING_TEMPLATE +from optuna.samplers._lazy_random_state import LazyRandomState +from optuna.search_space import IntersectionSearchSpace +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + import cmaes + + CmaClass = Union[cmaes.CMA, cmaes.SepCMA, cmaes.CMAwM] +else: + cmaes = _LazyImport("cmaes") + +_logger = logging.get_logger(__name__) + +_EPS = 1e-10 +# The value of system_attrs must be less than 2046 characters on RDBStorage. +_SYSTEM_ATTR_MAX_LENGTH = 2045 + + +class CmaEsSampler(BaseSampler): + """A sampler using `cmaes `__ as the backend. + + Example: + + Optimize a simple quadratic function by using :class:`~optuna.samplers.CmaEsSampler`. + + .. code-block:: console + + $ pip install cmaes + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -1, 1) + y = trial.suggest_int("y", -1, 1) + return x**2 + y + + + sampler = optuna.samplers.CmaEsSampler() + study = optuna.create_study(sampler=sampler) + study.optimize(objective, n_trials=20) + + Please note that this sampler does not support CategoricalDistribution. + However, :class:`~optuna.distributions.FloatDistribution` with ``step``, + (:func:`~optuna.trial.Trial.suggest_float`) and + :class:`~optuna.distributions.IntDistribution` (:func:`~optuna.trial.Trial.suggest_int`) + are supported. + + If your search space contains categorical parameters, I recommend you + to use :class:`~optuna.samplers.TPESampler` instead. + Furthermore, there is room for performance improvements in parallel + optimization settings. This sampler cannot use some trials for updating + the parameters of multivariate normal distribution. + + For further information about CMA-ES algorithm, please refer to the following papers: + + - `N. Hansen, The CMA Evolution Strategy: A Tutorial. arXiv:1604.00772, 2016. + `__ + - `A. Auger and N. Hansen. A restart CMA evolution strategy with increasing population + size. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), + pages 1769–1776. IEEE Press, 2005. `__ + - `N. Hansen. Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed. + GECCO Workshop, 2009. `__ + - `Raymond Ros, Nikolaus Hansen. A Simple Modification in CMA-ES Achieving Linear Time and + Space Complexity. 10th International Conference on Parallel Problem Solving From Nature, + Sep 2008, Dortmund, Germany. inria-00287367. `__ + - `Masahiro Nomura, Shuhei Watanabe, Youhei Akimoto, Yoshihiko Ozaki, Masaki Onishi. + Warm Starting CMA-ES for Hyperparameter Optimization, AAAI. 2021. + `__ + - `R. Hamano, S. Saito, M. Nomura, S. Shirakawa. CMA-ES with Margin: Lower-Bounding Marginal + Probability for Mixed-Integer Black-Box Optimization, GECCO. 2022. + `__ + - `M. Nomura, Y. Akimoto, I. Ono. CMA-ES with Learning Rate Adaptation: Can CMA-ES with + Default Population Size Solve Multimodal and Noisy Problems?, GECCO. 2023. + `__ + + .. seealso:: + You can also use `optuna_integration.PyCmaSampler `__ which is a sampler using cma + library as the backend. + + Args: + + x0: + A dictionary of an initial parameter values for CMA-ES. By default, the mean of ``low`` + and ``high`` for each distribution is used. Note that ``x0`` is sampled uniformly + within the search space domain for each restart if you specify ``restart_strategy`` + argument. + + sigma0: + Initial standard deviation of CMA-ES. By default, ``sigma0`` is set to + ``min_range / 6``, where ``min_range`` denotes the minimum range of the distributions + in the search space. + + seed: + A random seed for CMA-ES. + + n_startup_trials: + The independent sampling is used instead of the CMA-ES algorithm until the given number + of trials finish in the same study. + + independent_sampler: + A :class:`~optuna.samplers.BaseSampler` instance that is used for independent + sampling. The parameters not contained in the relative search space are sampled + by this sampler. + The search space for :class:`~optuna.samplers.CmaEsSampler` is determined by + :func:`~optuna.search_space.intersection_search_space()`. + + If :obj:`None` is specified, :class:`~optuna.samplers.RandomSampler` is used + as the default. + + .. seealso:: + :class:`optuna.samplers` module provides built-in independent samplers + such as :class:`~optuna.samplers.RandomSampler` and + :class:`~optuna.samplers.TPESampler`. + + warn_independent_sampling: + If this is :obj:`True`, a warning message is emitted when + the value of a parameter is sampled by using an independent sampler. + + Note that the parameters of the first trial in a study are always sampled + via an independent sampler, so no warning messages are emitted in this case. + + restart_strategy: + Strategy for restarting CMA-ES optimization when converges to a local minimum. + If :obj:`None` is given, CMA-ES will not restart (default). + If 'ipop' is given, CMA-ES will restart with increasing population size. + if 'bipop' is given, CMA-ES will restart with the population size + increased or decreased. + Please see also ``inc_popsize`` parameter. + + .. warning:: + Deprecated in v4.4.0. ``restart_strategy`` argument will be removed in the future. + The removal of this feature is currently scheduled for v6.0.0, + but this schedule is subject to change. + From v4.4.0 onward, ``restart_strategy`` automatically falls back to ``None``, and + ``restart_strategy`` will be supported in OptunaHub. + See https://github.com/optuna/optuna/releases/tag/v4.4.0. + + popsize: + A population size of CMA-ES. + + inc_popsize: + Multiplier for increasing population size before each restart. + This argument will be used when ``restart_strategy = 'ipop'`` + or ``restart_strategy = 'bipop'`` is specified. + + .. warning:: + Deprecated in v4.4.0. ``inc_popsize`` argument will be removed in the future. + The removal of this feature is currently scheduled for v6.0.0, + but this schedule is subject to change. + From v4.4.0 onward, ``inc_popsize`` is no longer utilized within Optuna, and + ``inc_popsize`` will be supported in OptunaHub. + See https://github.com/optuna/optuna/releases/tag/v4.4.0. + + consider_pruned_trials: + If this is :obj:`True`, the PRUNED trials are considered for sampling. + + .. note:: + Added in v2.0.0 as an experimental feature. The interface may change in newer + versions without prior notice. See + https://github.com/optuna/optuna/releases/tag/v2.0.0. + + .. note:: + It is suggested to set this flag :obj:`False` when the + :class:`~optuna.pruners.MedianPruner` is used. On the other hand, it is suggested + to set this flag :obj:`True` when the :class:`~optuna.pruners.HyperbandPruner` is + used. Please see `the benchmark result + `__ for the details. + + use_separable_cma: + If this is :obj:`True`, the covariance matrix is constrained to be diagonal. + Due to reduce the model complexity, the learning rate for the covariance matrix + is increased. Consequently, this algorithm outperforms CMA-ES on separable functions. + + .. note:: + Added in v2.6.0 as an experimental feature. The interface may change in newer + versions without prior notice. See + https://github.com/optuna/optuna/releases/tag/v2.6.0. + + with_margin: + If this is :obj:`True`, CMA-ES with margin is used. This algorithm prevents samples in + each discrete distribution (:class:`~optuna.distributions.FloatDistribution` with + ``step`` and :class:`~optuna.distributions.IntDistribution`) from being fixed to a single + point. + Currently, this option cannot be used with ``use_separable_cma=True``. + + .. note:: + Added in v3.1.0 as an experimental feature. The interface may change in newer + versions without prior notice. See + https://github.com/optuna/optuna/releases/tag/v3.1.0. + + lr_adapt: + If this is :obj:`True`, CMA-ES with learning rate adaptation is used. + This algorithm focuses on working well on multimodal and/or noisy problems + with default settings. + Currently, this option cannot be used with ``use_separable_cma=True`` or + ``with_margin=True``. + + .. note:: + Added in v3.3.0 or later, as an experimental feature. + The interface may change in newer versions without prior notice. See + https://github.com/optuna/optuna/releases/tag/v3.3.0. + + source_trials: + This option is for Warm Starting CMA-ES, a method to transfer prior knowledge on + similar HPO tasks through the initialization of CMA-ES. This method estimates a + promising distribution from ``source_trials`` and generates the parameter of + multivariate gaussian distribution. Please note that it is prohibited to use + ``x0``, ``sigma0``, or ``use_separable_cma`` argument together. + + .. note:: + Added in v2.6.0 as an experimental feature. The interface may change in newer + versions without prior notice. See + https://github.com/optuna/optuna/releases/tag/v2.6.0. + + """ # NOQA: E501 + + def __init__( + self, + x0: dict[str, Any] | None = None, + sigma0: float | None = None, + n_startup_trials: int = 1, + independent_sampler: BaseSampler | None = None, + warn_independent_sampling: bool = True, + seed: int | None = None, + *, + consider_pruned_trials: bool = False, + restart_strategy: str | None = None, + popsize: int | None = None, + inc_popsize: int = -1, + use_separable_cma: bool = False, + with_margin: bool = False, + lr_adapt: bool = False, + source_trials: list[FrozenTrial] | None = None, + ) -> None: + if restart_strategy is not None or inc_popsize != -1: + msg = _deprecated._DEPRECATION_WARNING_TEMPLATE.format( + name="`restart_strategy`", d_ver="4.4.0", r_ver="6.0.0" + ) + warnings.warn( + f"{msg} From v4.4.0 onward, `restart_strategy` automatically falls back to " + "`None`. `restart_strategy` will be supported in OptunaHub.", + FutureWarning, + ) + + self._x0 = x0 + self._sigma0 = sigma0 + self._independent_sampler = independent_sampler or optuna.samplers.RandomSampler(seed=seed) + self._n_startup_trials = n_startup_trials + self._warn_independent_sampling = warn_independent_sampling + self._cma_rng = LazyRandomState(seed) + self._search_space = IntersectionSearchSpace() + self._consider_pruned_trials = consider_pruned_trials + self._popsize = popsize + self._use_separable_cma = use_separable_cma + self._with_margin = with_margin + self._lr_adapt = lr_adapt + self._source_trials = source_trials + + if self._use_separable_cma: + self._attr_prefix = "sepcma:" + elif self._with_margin: + self._attr_prefix = "cmawm:" + else: + self._attr_prefix = "cma:" + + if self._consider_pruned_trials: + warn_experimental_argument("consider_pruned_trials") + + if self._use_separable_cma: + warn_experimental_argument("use_separable_cma") + + if self._source_trials is not None: + warn_experimental_argument("source_trials") + + if self._with_margin: + warn_experimental_argument("with_margin") + + if self._lr_adapt: + warn_experimental_argument("lr_adapt") + + if source_trials is not None and (x0 is not None or sigma0 is not None): + raise ValueError( + "It is prohibited to pass `source_trials` argument when " + "x0 or sigma0 is specified." + ) + + # TODO(c-bata): Support WS-sep-CMA-ES. + if source_trials is not None and use_separable_cma: + raise ValueError( + "It is prohibited to pass `source_trials` argument when using separable CMA-ES." + ) + + if lr_adapt and (use_separable_cma or with_margin): + raise ValueError( + "It is prohibited to pass `use_separable_cma` or `with_margin` argument when " + "using `lr_adapt`." + ) + + # TODO(knshnb): Support sep-CMA-ES with margin. + if self._use_separable_cma and self._with_margin: + raise ValueError( + "Currently, we do not support `use_separable_cma=True` and `with_margin=True`." + ) + + def reseed_rng(self) -> None: + # _cma_rng doesn't require reseeding because the relative sampling reseeds in each trial. + self._independent_sampler.reseed_rng() + + def infer_relative_search_space( + self, study: "optuna.Study", trial: "optuna.trial.FrozenTrial" + ) -> dict[str, BaseDistribution]: + search_space: dict[str, BaseDistribution] = {} + for name, distribution in self._search_space.calculate(study).items(): + if distribution.single(): + # `cma` cannot handle distributions that contain just a single value, so we skip + # them. Note that the parameter values for such distributions are sampled in + # `Trial`. + continue + + if not isinstance(distribution, (FloatDistribution, IntDistribution)): + # Categorical distribution is unsupported. + continue + search_space[name] = distribution + + return search_space + + def sample_relative( + self, + study: "optuna.Study", + trial: "optuna.trial.FrozenTrial", + search_space: dict[str, BaseDistribution], + ) -> dict[str, Any]: + self._raise_error_if_multi_objective(study) + + if len(search_space) == 0: + return {} + + completed_trials = self._get_trials(study) + if len(completed_trials) < self._n_startup_trials: + return {} + + # When `with_margin=True`, bounds in discrete dimensions are handled inside `CMAwM`. + trans = _SearchSpaceTransform( + search_space, transform_step=not self._with_margin, transform_0_1=True + ) + + optimizer = self._restore_optimizer(completed_trials) + if optimizer is None: + optimizer = self._init_optimizer(trans, study.direction) + + if optimizer.dim != len(trans.bounds): + if self._warn_independent_sampling: + _logger.warning( + "`CmaEsSampler` does not support dynamic search space. " + "`{}` is used instead of `CmaEsSampler`.".format( + self._independent_sampler.__class__.__name__ + ) + ) + self._warn_independent_sampling = False + return {} + + # TODO(c-bata): Reduce the number of wasted trials during parallel optimization. + # See https://github.com/optuna/optuna/pull/920#discussion_r385114002 for details. + solution_trials = self._get_solution_trials(completed_trials, optimizer.generation) + + if len(solution_trials) >= optimizer.population_size: + solutions: list[tuple[np.ndarray, float]] = [] + for t in solution_trials[: optimizer.population_size]: + assert t.value is not None, "completed trials must have a value" + if isinstance(optimizer, cmaes.CMAwM): + x = np.array(t.system_attrs["x_for_tell"]) + else: + x = trans.transform(t.params) + y = t.value if study.direction == StudyDirection.MINIMIZE else -t.value + solutions.append((x, y)) + + optimizer.tell(solutions) + + # Store optimizer. + optimizer_str = pickle.dumps(optimizer).hex() + optimizer_attrs = self._split_optimizer_str(optimizer_str) + for key in optimizer_attrs: + study._storage.set_trial_system_attr(trial._trial_id, key, optimizer_attrs[key]) + + # Caution: optimizer should update its seed value. + seed = self._cma_rng.rng.randint(1, 2**16) + trial.number + optimizer._rng.seed(seed) + if isinstance(optimizer, cmaes.CMAwM): + params, x_for_tell = optimizer.ask() + study._storage.set_trial_system_attr( + trial._trial_id, "x_for_tell", x_for_tell.tolist() + ) + else: + params = optimizer.ask() + + generation_attr_key = self._attr_key_generation + study._storage.set_trial_system_attr( + trial._trial_id, generation_attr_key, optimizer.generation + ) + + external_values = trans.untransform(params) + + return external_values + + @property + def _attr_key_generation(self) -> str: + return self._attr_prefix + "generation" + + @property + def _attr_key_optimizer(self) -> str: + return self._attr_prefix + "optimizer" + + def _concat_optimizer_attrs(self, optimizer_attrs: dict[str, str]) -> str: + return "".join( + optimizer_attrs["{}:{}".format(self._attr_key_optimizer, i)] + for i in range(len(optimizer_attrs)) + ) + + def _split_optimizer_str(self, optimizer_str: str) -> dict[str, str]: + optimizer_len = len(optimizer_str) + attrs = {} + for i in range(math.ceil(optimizer_len / _SYSTEM_ATTR_MAX_LENGTH)): + start = i * _SYSTEM_ATTR_MAX_LENGTH + end = min((i + 1) * _SYSTEM_ATTR_MAX_LENGTH, optimizer_len) + attrs["{}:{}".format(self._attr_key_optimizer, i)] = optimizer_str[start:end] + return attrs + + def _restore_optimizer( + self, + completed_trials: "list[optuna.trial.FrozenTrial]", + ) -> "CmaClass" | None: + # Restore a previous CMA object. + for trial in reversed(completed_trials): + optimizer_attrs = { + key: value + for key, value in trial.system_attrs.items() + if key.startswith(self._attr_key_optimizer) + } + if len(optimizer_attrs) == 0: + continue + + optimizer_str = self._concat_optimizer_attrs(optimizer_attrs) + return pickle.loads(bytes.fromhex(optimizer_str)) + return None + + def _init_optimizer( + self, + trans: _SearchSpaceTransform, + direction: StudyDirection, + ) -> "CmaClass": + lower_bounds = trans.bounds[:, 0] + upper_bounds = trans.bounds[:, 1] + n_dimension = len(trans.bounds) + + if self._source_trials is None: + if self._x0 is None: + mean = lower_bounds + (upper_bounds - lower_bounds) / 2 + else: + # `self._x0` is external representations. + mean = trans.transform(self._x0) + + if self._sigma0 is None: + sigma0 = np.min((upper_bounds - lower_bounds) / 6) + else: + sigma0 = self._sigma0 + + cov = None + else: + expected_states = [TrialState.COMPLETE] + if self._consider_pruned_trials: + expected_states.append(TrialState.PRUNED) + + # TODO(c-bata): Filter parameters by their values instead of checking search space. + sign = 1 if direction == StudyDirection.MINIMIZE else -1 + source_solutions = [ + (trans.transform(t.params), sign * cast(float, t.value)) + for t in self._source_trials + if t.state in expected_states + and _is_compatible_search_space(trans, t.distributions) + ] + if len(source_solutions) == 0: + raise ValueError("No compatible source_trials") + + # TODO(c-bata): Add options to change prior parameters (alpha and gamma). + mean, sigma0, cov = cmaes.get_warm_start_mgd(source_solutions) + + # Avoid ZeroDivisionError in cmaes. + sigma0 = max(sigma0, _EPS) + + if self._use_separable_cma: + if len(trans.bounds) == 1: + warnings.warn( + "Separable CMA-ES does not operate meaningfully on single-dimensional " + "search spaces. The setting `use_separable_cma=True` will be ignored.", + UserWarning, + ) + else: + return cmaes.SepCMA( + mean=mean, + sigma=sigma0, + bounds=trans.bounds, + seed=self._cma_rng.rng.randint(1, 2**31 - 2), + n_max_resampling=10 * n_dimension, + population_size=self._popsize, + ) + + if self._with_margin: + steps = np.empty(len(trans._search_space), dtype=float) + for i, dist in enumerate(trans._search_space.values()): + assert isinstance(dist, (IntDistribution, FloatDistribution)) + # Set step 0.0 for continuous search space. + if dist.step is None or dist.log: + steps[i] = 0.0 + elif dist.low == dist.high: + steps[i] = 1.0 + else: + steps[i] = dist.step / (dist.high - dist.low) + + return cmaes.CMAwM( + mean=mean, + sigma=sigma0, + bounds=trans.bounds, + steps=steps, + cov=cov, + seed=self._cma_rng.rng.randint(1, 2**31 - 2), + n_max_resampling=10 * n_dimension, + population_size=self._popsize, + ) + + return cmaes.CMA( + mean=mean, + sigma=sigma0, + cov=cov, + bounds=trans.bounds, + seed=self._cma_rng.rng.randint(1, 2**31 - 2), + n_max_resampling=10 * n_dimension, + population_size=self._popsize, + lr_adapt=self._lr_adapt, + ) + + def sample_independent( + self, + study: "optuna.Study", + trial: "optuna.trial.FrozenTrial", + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + self._raise_error_if_multi_objective(study) + + if self._warn_independent_sampling: + complete_trials = self._get_trials(study) + if len(complete_trials) >= self._n_startup_trials: + self._log_independent_sampling(trial, param_name) + + return self._independent_sampler.sample_independent( + study, trial, param_name, param_distribution + ) + + def _log_independent_sampling(self, trial: FrozenTrial, param_name: str) -> None: + _logger.warning( + _INDEPENDENT_SAMPLING_WARNING_TEMPLATE.format( + param_name=param_name, + trial_number=trial.number, + independent_sampler_name=self._independent_sampler.__class__.__name__, + sampler_name=self.__class__.__name__, + fallback_reason=( + "dynamic search space and `CategoricalDistribution` are not supported " + "by `CmaEsSampler`" + ), + ) + ) + + def _get_trials(self, study: "optuna.Study") -> list[FrozenTrial]: + complete_trials = [] + for t in study._get_trials(deepcopy=False, use_cache=True): + if t.state == TrialState.COMPLETE: + complete_trials.append(t) + elif ( + t.state == TrialState.PRUNED + and len(t.intermediate_values) > 0 + and self._consider_pruned_trials + ): + _, value = max(t.intermediate_values.items()) + if value is None: + continue + # We rewrite the value of the trial `t` for sampling, so we need a deepcopy. + copied_t = copy.deepcopy(t) + copied_t.value = value + complete_trials.append(copied_t) + return complete_trials + + def _get_solution_trials( + self, trials: list[FrozenTrial], generation: int + ) -> list[FrozenTrial]: + generation_attr_key = self._attr_key_generation + return [t for t in trials if generation == t.system_attrs.get(generation_attr_key, -1)] + + def before_trial(self, study: optuna.Study, trial: FrozenTrial) -> None: + self._independent_sampler.before_trial(study, trial) + + def after_trial( + self, + study: "optuna.Study", + trial: "optuna.trial.FrozenTrial", + state: TrialState, + values: Sequence[float] | None, + ) -> None: + self._independent_sampler.after_trial(study, trial, state, values) + + +def _is_compatible_search_space( + trans: _SearchSpaceTransform, search_space: dict[str, BaseDistribution] +) -> bool: + intersection_size = len(set(trans._search_space.keys()).intersection(search_space.keys())) + return intersection_size == len(trans._search_space) == len(search_space) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c76b3b0eaae4aab4ca0e9f40c94860acd3674cf5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/__init__.py @@ -0,0 +1,4 @@ +from optuna.samplers._ga._base import BaseGASampler + + +__all__ = ["BaseGASampler"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..131a70558a550c1a32d6b0209dfd882c0e2c4eed Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/__pycache__/_base.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/__pycache__/_base.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d30b4d2bc7a8cae036c284cb5cbaa5644da1e34a Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/__pycache__/_base.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/_base.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/_base.py new file mode 100644 index 0000000000000000000000000000000000000000..323d4735ba208471137f58cd6877336d673f5707 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_ga/_base.py @@ -0,0 +1,183 @@ +from __future__ import annotations + +import abc +from typing import Any + +import optuna +from optuna.samplers._base import BaseSampler +from optuna.trial._frozen import FrozenTrial +from optuna.trial._state import TrialState + + +# TODO(gen740): Add the experimental decorator? +class BaseGASampler(BaseSampler, abc.ABC): + """Base class for Genetic Algorithm (GA) samplers. + + Genetic Algorithm samplers generate new trials by mimicking natural selection, using + generations and populations to iteratively improve solutions. This base class defines the + interface for GA samplers in Optuna and provides utility methods for managing generations and + populations. + + The selection process is handled by :meth:`~BaseGASampler.select_parent`, which must be + implemented by subclasses to define the parent selection strategy. + + Generation and population management is facilitated by methods like + :meth:`~BaseGASampler.get_generation` and :meth:`~BaseGASampler.get_population`, ensuring + consistent tracking and selection. + + Note: + This class should be extended by subclasses that define specific GA sampling strategies, + including parent selection and crossover operations. + """ + + _GENERATION_KEY = "BaseGASampler:generation" + _PARENT_CACHE_KEY_PREFIX = "BaseGASampler:parent:" + + def __init_subclass__(cls, **kwargs: Any): + super().__init_subclass__(**kwargs) + cls._GENERATION_KEY = f"{cls.__name__}:generation" + cls._PARENT_CACHE_KEY_PREFIX = f"{cls.__name__}:parent:" + + @classmethod + def _get_generation_key(cls) -> str: + return cls._GENERATION_KEY + + @classmethod + def _get_parent_cache_key_prefix(cls) -> str: + return cls._PARENT_CACHE_KEY_PREFIX + + def __init__(self, population_size: int | None): + self._population_size = population_size + + @property + def population_size(self) -> int | None: + return self._population_size + + @population_size.setter + def population_size(self, value: int) -> None: + self._population_size = value + + @abc.abstractmethod + def select_parent(self, study: optuna.Study, generation: int) -> list[FrozenTrial]: + """Select parent trials from the population for the given generation. + + This method is called once per generation to select parents from + the population of the current generation. + + Output of this function is cached in the study system attributes. + + This method must be implemented in a subclass to define the specific selection strategy. + + Args: + study: + Target study object. + generation: + Target generation number. + + Returns: + List of parent frozen trials. + """ + raise NotImplementedError + + def get_trial_generation(self, study: optuna.Study, trial: FrozenTrial) -> int: + """Get the generation number of the given trial. + + This method returns the generation number of the specified trial. If the generation number + is not set in the trial's system attributes, it will calculate and set the generation + number. + + The current generation number depends on the maximum generation number of all completed + trials. + + Args: + study: + Study object which trial belongs to. + trial: + Trial object to get the generation number. + + Returns: + Generation number of the given trial. + """ + generation = trial.system_attrs.get(self._get_generation_key(), None) + if generation is not None: + return generation + + trials = study._get_trials(deepcopy=False, states=[TrialState.COMPLETE], use_cache=True) + + max_generation, max_generation_count = 0, 0 + + for t in reversed(trials): + generation = t.system_attrs.get(self._get_generation_key(), -1) + + if generation < max_generation: + continue + elif generation > max_generation: + max_generation = generation + max_generation_count = 1 + else: + max_generation_count += 1 + + assert self._population_size is not None, "Population size must be set." + if max_generation_count < self._population_size: + generation = max_generation + else: + generation = max_generation + 1 + study._storage.set_trial_system_attr( + trial._trial_id, self._get_generation_key(), generation + ) + return generation + + def get_population(self, study: optuna.Study, generation: int) -> list[FrozenTrial]: + """Get the population of the given generation. + + Args: + study: + Target study object. + generation: + Target generation number. + + Returns: + List of frozen trials in the given generation. + """ + return [ + trial + for trial in study._get_trials( + deepcopy=False, states=[TrialState.COMPLETE], use_cache=True + ) + if trial.system_attrs.get(self._get_generation_key(), None) == generation + ] + + def get_parent_population(self, study: optuna.Study, generation: int) -> list[FrozenTrial]: + """Get the parent population of the given generation. + + This method caches the parent population in the study's system attributes. + + Args: + study: + Target study object. + generation: + Target generation number. + + Returns: + List of parent frozen trials. If `generation == 0`, returns an empty list. + """ + if generation == 0: + return [] + + study_system_attrs = study._storage.get_study_system_attrs(study._study_id) + cached_parent_population_ids = study_system_attrs.get( + self._get_parent_cache_key_prefix() + str(generation), None + ) + + if cached_parent_population_ids is not None: + trials = study._get_trials(deepcopy=False) + parent_population_ids = set(cached_parent_population_ids) + return [trial for trial in trials if trial._trial_id in parent_population_ids] + else: + parent_population = self.select_parent(study, generation) + study._storage.set_study_system_attr( + study._study_id, + self._get_parent_cache_key_prefix() + str(generation), + [trial._trial_id for trial in parent_population], + ) + return parent_population diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ff09f1235a93da0998f5eee1df2a7aab2e291140 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/__init__.py @@ -0,0 +1,4 @@ +from optuna.samplers._gp.sampler import GPSampler + + +__all__ = ["GPSampler"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9fc226f0c34755381b8e9cdfe2a0f83f65fea661 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/__pycache__/sampler.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/__pycache__/sampler.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2e3826d6999f10768ea54fd60b77e01392700f1e Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/__pycache__/sampler.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/sampler.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..12f05b3bb1708517706cc99682fcad66ead8d77e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_gp/sampler.py @@ -0,0 +1,457 @@ +from __future__ import annotations + +from typing import Any +from typing import TYPE_CHECKING + +import numpy as np + +import optuna +from optuna._experimental import experimental_class +from optuna._experimental import warn_experimental_argument +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.samplers._base import _INDEPENDENT_SAMPLING_WARNING_TEMPLATE +from optuna.samplers._base import _process_constraints_after_trial +from optuna.samplers._base import BaseSampler +from optuna.samplers._lazy_random_state import LazyRandomState +from optuna.study import StudyDirection +from optuna.study._multi_objective import _is_pareto_front +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from collections.abc import Callable + from collections.abc import Sequence + + import torch + + import optuna._gp.acqf as acqf_module + import optuna._gp.gp as gp + import optuna._gp.optim_mixed as optim_mixed + import optuna._gp.prior as prior + import optuna._gp.search_space as gp_search_space + from optuna.distributions import BaseDistribution + from optuna.study import Study +else: + from optuna._imports import _LazyImport + + torch = _LazyImport("torch") + gp_search_space = _LazyImport("optuna._gp.search_space") + gp = _LazyImport("optuna._gp.gp") + optim_mixed = _LazyImport("optuna._gp.optim_mixed") + acqf_module = _LazyImport("optuna._gp.acqf") + prior = _LazyImport("optuna._gp.prior") + +import logging + + +_logger = logging.getLogger(__name__) + +EPS = 1e-10 + + +def _standardize_values(values: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + clipped_values = gp.warn_and_convert_inf(values) + means = np.mean(clipped_values, axis=0) + stds = np.std(clipped_values, axis=0) + standardized_values = (clipped_values - means) / np.maximum(EPS, stds) + return standardized_values, means, stds + + +@experimental_class("3.6.0") +class GPSampler(BaseSampler): + """Sampler using Gaussian process-based Bayesian optimization. + + This sampler fits a Gaussian process (GP) to the objective function and optimizes + the acquisition function to suggest the next parameters. + + The current implementation uses Matern kernel with nu=2.5 (twice differentiable) with automatic + relevance determination (ARD) for the length scale of each parameter. + The hyperparameters of the kernel are obtained by maximizing the marginal log-likelihood of the + hyperparameters given the past trials. + To prevent overfitting, Gamma prior is introduced for kernel scale and noise variance and + a hand-crafted prior is introduced for inverse squared lengthscales. + + As an acquisition function, we use: + + - log expected improvement (logEI) for single-objective optimization, + - log expected hypervolume improvement (logEHVI) for Multi-objective optimization, and + - the summation of logEI and the logarithm of the feasible probability with the independent + assumption of each constraint for (black-box inequality) constrained optimization. + + For further information about these acquisition functions, please refer to the following + papers: + + - `Unexpected Improvements to Expected Improvement for Bayesian Optimization + `__ + - `Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian + Optimization `__ + - `Bayesian Optimization with Inequality Constraints + `__ + + The optimization of the acquisition function is performed via: + + 1. Collect the best param from the past trials, + 2. Collect ``n_preliminary_samples`` points using Quasi-Monte Carlo (QMC) sampling, + 3. Choose the best point from the collected points, + 4. Choose ``n_local_search - 2`` points from the collected points using the roulette + selection, + 5. Perform a local search for each chosen point as an initial point, and + 6. Return the point with the best acquisition function value as the next parameter. + + Note that the procedures for non single-objective optimization setups are slightly different + from the single-objective version described above, but we omit the descriptions for the others + for brevity. + + The local search iteratively optimizes the acquisition function by repeating: + + 1. Gradient ascent using l-BFGS-B for continuous parameters, and + 2. Line search or exhaustive search for each discrete parameter independently. + + The local search is terminated if the routine stops updating the best parameter set or the + maximum number of iterations is reached. + + We use line search instead of rounding the results from the continuous optimization since EI + typically yields a high value between one grid and its adjacent grid. + + .. note:: + This sampler requires ``scipy`` and ``torch``. + You can install these dependencies with ``pip install scipy torch``. + + Args: + seed: + Random seed to initialize internal random number generator. + Defaults to :obj:`None` (a seed is picked randomly). + independent_sampler: + Sampler used for initial sampling (for the first ``n_startup_trials`` trials) + and for conditional parameters. Defaults to :obj:`None` + (a random sampler with the same ``seed`` is used). + n_startup_trials: + Number of initial trials. Defaults to 10. + deterministic_objective: + Whether the objective function is deterministic or not. + If :obj:`True`, the sampler will fix the noise variance of the surrogate model to + the minimum value (slightly above 0 to ensure numerical stability). + Defaults to :obj:`False`. Currently, all the objectives will be assume to be + deterministic if :obj:`True`. + constraints_func: + An optional function that computes the objective constraints. It must take a + :class:`~optuna.trial.FrozenTrial` and return the constraints. The return value must + be a sequence of :obj:`float` s. A value strictly larger than 0 means that a + constraints is violated. A value equal to or smaller than 0 is considered feasible. + If ``constraints_func`` returns more than one value for a trial, that trial is + considered feasible if and only if all values are equal to 0 or smaller. + + The ``constraints_func`` will be evaluated after each successful trial. + The function won't be called when trials fail or are pruned, but this behavior is + subject to change in future releases. + Currently, the ``constraints_func`` option is not supported for multi-objective + optimization. + warn_independent_sampling: + If this is :obj:`True`, a warning message is emitted when + the value of a parameter is sampled by using an independent sampler, + meaning that no GP model is used in the sampling. + Note that the parameters of the first trial in a study are always sampled + via an independent sampler, so no warning messages are emitted in this case. + """ + + def __init__( + self, + *, + seed: int | None = None, + independent_sampler: BaseSampler | None = None, + n_startup_trials: int = 10, + deterministic_objective: bool = False, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + warn_independent_sampling: bool = True, + ) -> None: + self._rng = LazyRandomState(seed) + self._independent_sampler = independent_sampler or optuna.samplers.RandomSampler(seed=seed) + self._intersection_search_space = optuna.search_space.IntersectionSearchSpace() + self._n_startup_trials = n_startup_trials + self._log_prior: Callable[[gp.GPRegressor], torch.Tensor] = prior.default_log_prior + self._minimum_noise: float = prior.DEFAULT_MINIMUM_NOISE_VAR + # We cache the kernel parameters for initial values of fitting the next time. + # TODO(nabenabe): Make the cache lists system_attrs to make GPSampler stateless. + self._gprs_cache_list: list[gp.GPRegressor] | None = None + self._constraints_gprs_cache_list: list[gp.GPRegressor] | None = None + self._deterministic = deterministic_objective + self._constraints_func = constraints_func + self._warn_independent_sampling = warn_independent_sampling + + if constraints_func is not None: + warn_experimental_argument("constraints_func") + + # Control parameters of the acquisition function optimization. + self._n_preliminary_samples: int = 2048 + # NOTE(nabenabe): ehvi in BoTorchSampler uses 20. + self._n_local_search = 10 + self._tol = 1e-4 + + def _log_independent_sampling(self, trial: FrozenTrial, param_name: str) -> None: + msg = _INDEPENDENT_SAMPLING_WARNING_TEMPLATE.format( + param_name=param_name, + trial_number=trial.number, + independent_sampler_name=self._independent_sampler.__class__.__name__, + sampler_name=self.__class__.__name__, + fallback_reason="dynamic search space is not supported by GPSampler", + ) + _logger.warning(msg) + + def reseed_rng(self) -> None: + self._rng.rng.seed() + self._independent_sampler.reseed_rng() + + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + search_space = {} + for name, distribution in self._intersection_search_space.calculate(study).items(): + if distribution.single(): + continue + search_space[name] = distribution + + return search_space + + def _optimize_acqf( + self, acqf: acqf_module.BaseAcquisitionFunc, best_params: np.ndarray | None + ) -> np.ndarray: + # Advanced users can override this method to change the optimization algorithm. + # However, we do not make any effort to keep backward compatibility between versions. + # Particularly, we may remove this function in future refactoring. + assert best_params is None or len(best_params.shape) == 2 + normalized_params, _acqf_val = optim_mixed.optimize_acqf_mixed( + acqf, + warmstart_normalized_params_array=best_params, + n_preliminary_samples=self._n_preliminary_samples, + n_local_search=self._n_local_search, + tol=self._tol, + rng=self._rng.rng, + ) + return normalized_params + + def _get_constraints_acqf_args( + self, + constraint_vals: np.ndarray, + internal_search_space: gp_search_space.SearchSpace, + normalized_params: np.ndarray, + ) -> tuple[list[gp.GPRegressor], list[float]]: + # NOTE(nabenabe): Flip the sign of constraints since they are always to be minimized. + standardized_constraint_vals, means, stds = _standardize_values(-constraint_vals) + if ( + self._gprs_cache_list is not None + and len(self._gprs_cache_list[0].inverse_squared_lengthscales) + != internal_search_space.dim + ): + # Clear cache if the search space changes. + self._constraints_gprs_cache_list = None + + is_categorical = internal_search_space.is_categorical + constraints_gprs = [] + constraints_threshold_list = [] + constraints_threshold_list = (-means / np.maximum(EPS, stds)).tolist() + for i, vals in enumerate(standardized_constraint_vals.T): + cache = ( + self._constraints_gprs_cache_list[i] + if self._constraints_gprs_cache_list is not None + else None + ) + gpr = gp.fit_kernel_params( + X=normalized_params, + Y=vals, + is_categorical=is_categorical, + log_prior=self._log_prior, + minimum_noise=self._minimum_noise, + gpr_cache=cache, + deterministic_objective=self._deterministic, + ) + constraints_gprs.append(gpr) + + self._constraints_gprs_cache_list = constraints_gprs + return constraints_gprs, constraints_threshold_list + + def _get_best_params_for_multi_objective( + self, + normalized_params: np.ndarray, + standardized_score_vals: np.ndarray, + ) -> np.ndarray: + pareto_params = normalized_params[ + _is_pareto_front(-standardized_score_vals, assume_unique_lexsorted=False) + ] + n_pareto_sols = len(pareto_params) + # TODO(nabenabe): Verify the validity of this choice. + size = min(self._n_local_search // 2, n_pareto_sols) + chosen_indices = self._rng.rng.choice(n_pareto_sols, size=size, replace=False) + return pareto_params[chosen_indices] + + def sample_relative( + self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution] + ) -> dict[str, Any]: + if search_space == {}: + return {} + + states = (TrialState.COMPLETE,) + trials = study._get_trials(deepcopy=False, states=states, use_cache=True) + + if len(trials) < self._n_startup_trials: + return {} + + internal_search_space = gp_search_space.SearchSpace(search_space) + normalized_params = internal_search_space.get_normalized_params(trials) + + _sign = np.array([-1.0 if d == StudyDirection.MINIMIZE else 1.0 for d in study.directions]) + standardized_score_vals, _, _ = _standardize_values( + _sign * np.array([trial.values for trial in trials]) + ) + + if ( + self._gprs_cache_list is not None + and len(self._gprs_cache_list[0].inverse_squared_lengthscales) + != internal_search_space.dim + ): + # Clear cache if the search space changes. + self._gprs_cache_list = None + + gprs_list = [] + n_objectives = standardized_score_vals.shape[-1] + is_categorical = internal_search_space.is_categorical + for i in range(n_objectives): + cache = self._gprs_cache_list[i] if self._gprs_cache_list is not None else None + gprs_list.append( + gp.fit_kernel_params( + X=normalized_params, + Y=standardized_score_vals[:, i], + is_categorical=is_categorical, + log_prior=self._log_prior, + minimum_noise=self._minimum_noise, + gpr_cache=cache, + deterministic_objective=self._deterministic, + ) + ) + self._gprs_cache_list = gprs_list + + best_params: np.ndarray | None + acqf: acqf_module.BaseAcquisitionFunc + if self._constraints_func is None: + if n_objectives == 1: + assert len(gprs_list) == 1 + acqf = acqf_module.LogEI( + gpr=gprs_list[0], + search_space=internal_search_space, + threshold=standardized_score_vals[:, 0].max(), + ) + best_params = normalized_params[np.argmax(standardized_score_vals), np.newaxis] + else: + acqf = acqf_module.LogEHVI( + gpr_list=gprs_list, + search_space=internal_search_space, + Y_train=torch.from_numpy(standardized_score_vals), + n_qmc_samples=128, # NOTE(nabenabe): The BoTorch default value. + qmc_seed=self._rng.rng.randint(1 << 30), + ) + best_params = self._get_best_params_for_multi_objective( + normalized_params, standardized_score_vals + ) + else: + if n_objectives == 1: + assert len(gprs_list) == 1 + constraint_vals, is_feasible = _get_constraint_vals_and_feasibility(study, trials) + y_with_neginf = np.where(is_feasible, standardized_score_vals[:, 0], -np.inf) + # TODO(kAIto47802): If all trials are infeasible, the acquisition function + # for the objective function can be ignored, so skipping the computation + # of gpr can speed up. + # TODO(kAIto47802): Consider the case where all trials are feasible. + # We can ignore constraints in this case. + constr_gpr_list, constr_threshold_list = self._get_constraints_acqf_args( + constraint_vals, internal_search_space, normalized_params + ) + i_opt = np.argmax(y_with_neginf) + best_feasible_y = y_with_neginf[i_opt] + acqf = acqf_module.ConstrainedLogEI( + gpr=gprs_list[0], + search_space=internal_search_space, + threshold=best_feasible_y, + constraints_gpr_list=constr_gpr_list, + constraints_threshold_list=constr_threshold_list, + ) + assert normalized_params.shape[:-1] == y_with_neginf.shape + best_params = ( + None if np.isneginf(best_feasible_y) else normalized_params[i_opt, np.newaxis] + ) + else: + constraint_vals, is_feasible = _get_constraint_vals_and_feasibility(study, trials) + constr_gpr_list, constr_threshold_list = self._get_constraints_acqf_args( + constraint_vals, internal_search_space, normalized_params + ) + is_all_infeasible = not any(is_feasible) + acqf = acqf_module.ConstrainedLogEHVI( + gpr_list=gprs_list, + search_space=internal_search_space, + Y_feasible=( + torch.from_numpy(standardized_score_vals[is_feasible]) + if not is_all_infeasible + else None + ), + n_qmc_samples=128, # NOTE(nabenabe): The BoTorch default value. + qmc_seed=self._rng.rng.randint(1 << 30), + constraints_gpr_list=constr_gpr_list, + constraints_threshold_list=constr_threshold_list, + ) + best_params = ( + self._get_best_params_for_multi_objective( + normalized_params[is_feasible], + standardized_score_vals[is_feasible], + ) + if not is_all_infeasible + else None + ) + + normalized_param = self._optimize_acqf(acqf, best_params) + return internal_search_space.get_unnormalized_param(normalized_param) + + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + if self._warn_independent_sampling: + states = (TrialState.COMPLETE,) + complete_trials = study._get_trials(deepcopy=False, states=states, use_cache=True) + if len(complete_trials) >= self._n_startup_trials: + self._log_independent_sampling(trial, param_name) + return self._independent_sampler.sample_independent( + study, trial, param_name, param_distribution + ) + + def before_trial(self, study: Study, trial: FrozenTrial) -> None: + self._independent_sampler.before_trial(study, trial) + + def after_trial( + self, + study: Study, + trial: FrozenTrial, + state: TrialState, + values: Sequence[float] | None, + ) -> None: + if self._constraints_func is not None: + _process_constraints_after_trial(self._constraints_func, study, trial, state) + self._independent_sampler.after_trial(study, trial, state, values) + + +def _get_constraint_vals_and_feasibility( + study: Study, trials: list[FrozenTrial] +) -> tuple[np.ndarray, np.ndarray]: + _constraint_vals = [ + study._storage.get_trial_system_attrs(trial._trial_id).get(_CONSTRAINTS_KEY, ()) + for trial in trials + ] + if any(len(_constraint_vals[0]) != len(c) for c in _constraint_vals): + raise ValueError("The number of constraints must be the same for all trials.") + + constraint_vals = np.array(_constraint_vals) + assert len(constraint_vals.shape) == 2, "constraint_vals must be a 2d array." + is_feasible = np.all(constraint_vals <= 0, axis=1) + assert not isinstance(is_feasible, np.bool_), "MyPy Redefinition for NumPy v2.2.0." + return constraint_vals, is_feasible diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_grid.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_grid.py new file mode 100644 index 0000000000000000000000000000000000000000..7a87576ed8e237080c9aa2ddd03ebdb4486cd1d6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_grid.py @@ -0,0 +1,286 @@ +from __future__ import annotations + +from collections.abc import Mapping +from collections.abc import Sequence +import itertools +from numbers import Real +from typing import Any +from typing import TYPE_CHECKING +from typing import Union +import warnings + +import numpy as np + +from optuna.distributions import BaseDistribution +from optuna.logging import get_logger +from optuna.samplers import BaseSampler +from optuna.samplers._lazy_random_state import LazyRandomState +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna.study import Study + + +GridValueType = Union[str, float, int, bool, None] + + +_logger = get_logger(__name__) + + +class GridSampler(BaseSampler): + """Sampler using grid search. + + With :class:`~optuna.samplers.GridSampler`, the trials suggest all combinations of parameters + in the given search space during the study. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -100, 100) + y = trial.suggest_int("y", -100, 100) + return x**2 + y**2 + + + search_space = {"x": [-50, 0, 50], "y": [-99, 0, 99]} + study = optuna.create_study(sampler=optuna.samplers.GridSampler(search_space)) + study.optimize(objective) + + Note: + + This sampler with :ref:`ask_and_tell` raises :exc:`RuntimeError` just after evaluating + the final grid. This is because :class:`~optuna.samplers.GridSampler` automatically + stops the optimization if all combinations in the passed ``search_space`` have already + been evaluated, internally invoking the :func:`~optuna.study.Study.stop` method. + As a workaround, we need to handle the error manually as in + https://github.com/optuna/optuna/issues/4121#issuecomment-1305289910. + + Note: + + :class:`~optuna.samplers.GridSampler` does not take care of a parameter's quantization + specified by discrete suggest methods but just samples one of values specified in the + search space. E.g., in the following code snippet, either of ``-0.5`` or ``0.5`` is + sampled as ``x`` instead of an integer point. + + .. testcode:: + + import optuna + + + def objective(trial): + # The following suggest method specifies integer points between -5 and 5. + x = trial.suggest_float("x", -5, 5, step=1) + return x**2 + + + # Non-int points are specified in the grid. + search_space = {"x": [-0.5, 0.5]} + study = optuna.create_study(sampler=optuna.samplers.GridSampler(search_space)) + study.optimize(objective, n_trials=2) + + Note: + A parameter configuration in the grid is not considered finished until its trial is + finished. Therefore, during distributed optimization where trials run concurrently, + different workers will occasionally suggest the same parameter configuration. + The total number of actual trials may therefore exceed the size of the grid. + + Note: + All parameters must be specified when using :class:`~optuna.samplers.GridSampler` with + :meth:`~optuna.study.Study.enqueue_trial`. + + Args: + search_space: + A dictionary whose key and value are a parameter name and the corresponding candidates + of values, respectively. + seed: + A seed to fix the order of trials as the grid is randomly shuffled. This shuffle is + beneficial when the number of grids is larger than ``n_trials`` in + :meth:`~optuna.Study.optimize` to suppress suggesting similar grids. Please note + that fixing ``seed`` for each process is strongly recommended in distributed + optimization to avoid duplicated suggestions. + """ + + def __init__( + self, search_space: Mapping[str, Sequence[GridValueType]], seed: int | None = None + ) -> None: + for param_name, param_values in search_space.items(): + for value in param_values: + self._check_value(param_name, value) + + self._search_space = {} + for param_name, param_values in sorted(search_space.items()): + self._search_space[param_name] = list(param_values) + + self._all_grids = list(itertools.product(*self._search_space.values())) + self._param_names = sorted(search_space.keys()) + self._n_min_trials = len(self._all_grids) + self._rng = LazyRandomState(seed or 0) + self._rng.rng.shuffle(self._all_grids) # type: ignore[arg-type] + + def reseed_rng(self) -> None: + self._rng.rng.seed() + + def before_trial(self, study: Study, trial: FrozenTrial) -> None: + # Instead of returning param values, GridSampler puts the target grid id as a system attr, + # and the values are returned from `sample_independent`. This is because the distribution + # object is hard to get at the beginning of trial, while we need the access to the object + # to validate the sampled value. + + # When the trial is created by RetryFailedTrialCallback or enqueue_trial, we should not + # assign a new grid_id. + if "grid_id" in trial.system_attrs or "fixed_params" in trial.system_attrs: + return + + if 0 <= trial.number and trial.number < self._n_min_trials: + study._storage.set_trial_system_attr( + trial._trial_id, "search_space", self._search_space + ) + study._storage.set_trial_system_attr(trial._trial_id, "grid_id", trial.number) + return + + target_grids = self._get_unvisited_grid_ids(study) + + if len(target_grids) == 0: + # This case may occur with distributed optimization or trial queue. If there is no + # target grid, `GridSampler` evaluates a visited, duplicated point with the current + # trial. After that, the optimization stops. + + _logger.warning( + "`GridSampler` is re-evaluating a configuration because the grid has been " + "exhausted. This may happen due to a timing issue during distributed optimization " + "or when re-running optimizations on already finished studies." + ) + + # One of all grids is randomly picked up in this case. + target_grids = list(range(len(self._all_grids))) + + # In distributed optimization, multiple workers may simultaneously pick up the same grid. + # To make the conflict less frequent, the grid is chosen randomly. + grid_id = int(self._rng.rng.choice(target_grids)) + + study._storage.set_trial_system_attr(trial._trial_id, "search_space", self._search_space) + study._storage.set_trial_system_attr(trial._trial_id, "grid_id", grid_id) + + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + return {} + + def sample_relative( + self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution] + ) -> dict[str, Any]: + return {} + + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + if "grid_id" not in trial.system_attrs: + message = "All parameters must be specified when using GridSampler with enqueue_trial." + raise ValueError(message) + + if param_name not in self._search_space: + message = "The parameter name, {}, is not found in the given grid.".format(param_name) + raise ValueError(message) + + grid_id = trial.system_attrs["grid_id"] + param_value = self._all_grids[grid_id][self._param_names.index(param_name)] + contains = param_distribution._contains(param_distribution.to_internal_repr(param_value)) + if not contains: + warnings.warn( + f"The value `{param_value}` is out of range of the parameter `{param_name}`. " + f"The value will be used but the actual distribution is: `{param_distribution}`." + ) + + return param_value + + def after_trial( + self, + study: Study, + trial: FrozenTrial, + state: TrialState, + values: Sequence[float] | None, + ) -> None: + target_grids = self._get_unvisited_grid_ids(study) + + if len(target_grids) == 0: + study.stop() + elif len(target_grids) == 1: + grid_id = study._storage.get_trial_system_attrs(trial._trial_id)["grid_id"] + if grid_id == target_grids[0]: + study.stop() + + @staticmethod + def _check_value(param_name: str, param_value: Any) -> None: + if param_value is None or isinstance(param_value, (str, int, float, bool)): + return + + message = ( + "{} contains a value with the type of {}, which is not supported by " + "`GridSampler`. Please make sure a value is `str`, `int`, `float`, `bool`" + " or `None` for persistent storage.".format(param_name, type(param_value)) + ) + warnings.warn(message) + + def _get_unvisited_grid_ids(self, study: Study) -> list[int]: + # List up unvisited grids based on already finished ones. + visited_grids = [] + running_grids = [] + + # We directly query the storage to get trials here instead of `study.get_trials`, + # since some pruners such as `HyperbandPruner` use the study transformed + # to filter trials. See https://github.com/optuna/optuna/issues/2327 for details. + trials = study._storage.get_all_trials(study._study_id, deepcopy=False) + + for t in trials: + if "grid_id" in t.system_attrs and self._same_search_space( + t.system_attrs["search_space"] + ): + if t.state.is_finished(): + visited_grids.append(t.system_attrs["grid_id"]) + elif t.state == TrialState.RUNNING: + running_grids.append(t.system_attrs["grid_id"]) + + unvisited_grids = set(range(self._n_min_trials)) - set(visited_grids) - set(running_grids) + + # If evaluations for all grids have been started, return grids that have not yet finished + # because all grids should be evaluated before stopping the optimization. + if len(unvisited_grids) == 0: + unvisited_grids = set(range(self._n_min_trials)) - set(visited_grids) + + return list(unvisited_grids) + + @staticmethod + def _grid_value_equal(value1: GridValueType, value2: GridValueType) -> bool: + value1_is_nan = isinstance(value1, Real) and np.isnan(float(value1)) + value2_is_nan = isinstance(value2, Real) and np.isnan(float(value2)) + return (value1 == value2) or (value1_is_nan and value2_is_nan) + + def _same_search_space(self, search_space: Mapping[str, Sequence[GridValueType]]) -> bool: + if set(search_space.keys()) != set(self._search_space.keys()): + return False + + for param_name in search_space.keys(): + if len(search_space[param_name]) != len(self._search_space[param_name]): + return False + + for i, param_value in enumerate(search_space[param_name]): + if not self._grid_value_equal(param_value, self._search_space[param_name][i]): + return False + + return True + + def is_exhausted(self, study: Study) -> bool: + """ + Return True if all the possible params are evaluated, otherwise return False. + """ + return len(self._get_unvisited_grid_ids(study)) == 0 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_lazy_random_state.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_lazy_random_state.py new file mode 100644 index 0000000000000000000000000000000000000000..3c2e23d0269adaad0adc03c88f5129927fbeff16 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_lazy_random_state.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +import numpy as np + + +class LazyRandomState: + """Lazy Random State class. + + + This is a class to initialize the random state just before use to prevent + duplication of the same random state when deepcopy is applied to the instance of sampler. + """ + + def __init__(self, seed: int | None = None) -> None: + self._rng: np.random.RandomState | None = None + if seed is not None: + self.rng.seed(seed=seed) + + def _set_rng(self) -> None: + self._rng = np.random.RandomState() + + @property + def rng(self) -> np.random.RandomState: + if self._rng is None: + self._set_rng() + assert self._rng is not None + return 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optuna.samplers.nsgaii._constraints_evaluation import _validate_constraints +from optuna.samplers.nsgaii._elite_population_selection_strategy import _rank_population +from optuna.trial import FrozenTrial + + +if TYPE_CHECKING: + from optuna.study import Study + + +# Define a coefficient for scaling intervals, used in _filter_inf() to replace +-inf. +_COEF = 3 + + +class NSGAIIIElitePopulationSelectionStrategy: + def __init__( + self, + *, + population_size: int, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + reference_points: np.ndarray | None = None, + dividing_parameter: int = 3, + rng: LazyRandomState, + ) -> None: + if population_size < 2: + raise ValueError("`population_size` must be greater than or equal to 2.") + + self._population_size = population_size + self._constraints_func = constraints_func + self._reference_points = reference_points + self._dividing_parameter = dividing_parameter + self._rng = rng + + def __call__(self, study: Study, population: list[FrozenTrial]) -> list[FrozenTrial]: + """Select elite population from the given trials by NSGA-III algorithm. + + Args: + study: + Target study object. + population: + Trials in the study. + + Returns: + A list of trials that are selected as elite population. + """ + _validate_constraints(population, is_constrained=self._constraints_func is not None) + population_per_rank = _rank_population( + population, study.directions, is_constrained=self._constraints_func is not None + ) + + elite_population: list[FrozenTrial] = [] + for population in population_per_rank: + if len(elite_population) + len(population) < self._population_size: + elite_population.extend(population) + else: + n_objectives = len(study.directions) + # Construct reference points in the first run. + if self._reference_points is None: + self._reference_points = _generate_default_reference_point( + n_objectives, self._dividing_parameter + ) + elif np.shape(self._reference_points)[1] != n_objectives: + raise ValueError( + "The dimension of reference points vectors must be the same as the number " + "of objectives of the study." + ) + + # Normalize objective values after filtering +-inf. + objective_matrix = _normalize_objective_values( + _filter_inf(elite_population + population) + ) + ( + closest_reference_points, + distance_reference_points, + ) = _associate_individuals_with_reference_points( + objective_matrix, self._reference_points + ) + + elite_population_num = len(elite_population) + target_population_size = self._population_size - elite_population_num + additional_elite_population = _preserve_niche_individuals( + target_population_size, + elite_population_num, + population, + closest_reference_points, + distance_reference_points, + self._rng.rng, + ) + elite_population.extend(additional_elite_population) + break + return elite_population + + +def _generate_default_reference_point( + n_objectives: int, dividing_parameter: int = 3 +) -> np.ndarray: + """Generates default reference points which are `uniformly` spread on a hyperplane.""" + indices = np.array( + list(combinations_with_replacement(range(n_objectives), dividing_parameter)) + ) + row_indices = np.repeat(np.arange(len(indices)), dividing_parameter) + col_indices = indices.flatten() + reference_points = np.zeros((len(indices), n_objectives), dtype=float) + np.add.at(reference_points, (row_indices, col_indices), 1.0) + return reference_points + + +def _filter_inf(population: list[FrozenTrial]) -> np.ndarray: + objective_matrix = np.asarray([t.values for t in population]) + objective_matrix_with_nan = np.where(np.isfinite(objective_matrix), objective_matrix, np.nan) + max_objectives = np.nanmax(objective_matrix_with_nan, axis=0) + min_objectives = np.nanmin(objective_matrix_with_nan, axis=0) + margins = _COEF * (max_objectives - min_objectives) + return np.clip(objective_matrix, min_objectives - margins, max_objectives + margins) + + +def _normalize_objective_values(objective_matrix: np.ndarray) -> np.ndarray: + """Normalizes objective values of population. + + An ideal point z* consists of minimums in each axis. Each objective value of population is + then subtracted by the ideal point. + An extreme point of each axis is (originally) defined as a minimum solution of achievement + scalarizing function from the population. After that, intercepts are calculate as intercepts + of hyperplane which has all the extreme points on it and used to rescale objective values. + + We adopt weights and achievement scalarizing function(ASF) used in pre-print of the NSGA-III + paper (See https://www.egr.msu.edu/~kdeb/papers/k2012009.pdf). + """ + n_objectives = np.shape(objective_matrix)[1] + # Subtract ideal point from objective values. + objective_matrix -= np.min(objective_matrix, axis=0) + # Initialize weights. + weights = np.eye(n_objectives) + weights[weights == 0] = 1e6 + + # Calculate extreme points to normalize objective values. + # TODO(Shinichi) Reimplement to reduce time complexity. + asf_value = np.max( + np.einsum("nm,dm->dnm", objective_matrix, weights), + axis=2, + ) + extreme_points = objective_matrix[np.argmin(asf_value, axis=1), :] + + # Normalize objective_matrix with extreme points. + # Note that extreme_points can be degenerate, but no proper operation is remarked in the + # paper. Therefore, the maximum value of population in each axis is used in such cases. + if np.all(np.isfinite(extreme_points)) and np.linalg.matrix_rank(extreme_points) == len( + extreme_points + ): + intercepts_inv = np.linalg.solve(extreme_points, np.ones(n_objectives)) + else: + intercepts = np.max(objective_matrix, axis=0) + intercepts_inv = 1 / np.where(intercepts == 0, 1, intercepts) + objective_matrix *= np.where(np.isfinite(intercepts_inv), intercepts_inv, 1) + + return objective_matrix + + +def _associate_individuals_with_reference_points( + objective_matrix: np.ndarray, reference_points: np.ndarray +) -> tuple[np.ndarray, np.ndarray]: + """Associates each objective value to the closest reference point. + + Associate each normalized objective value to the closest reference point. The distance is + calculated by Euclidean norm. + + Args: + objective_matrix: + A 2 dimension ``numpy.ndarray`` with columns of objective dimension and rows of + generation size. Each row is the normalized objective value of the corresponding + individual. + + Returns: + closest_reference_points: + A ``numpy.ndarray`` with rows of generation size. Each row is the index of + the closest reference point to the corresponding individual. + distance_reference_points: + A ``numpy.ndarray`` with rows of generation size. Each row is the distance from + the corresponding individual to the closest reference point. + """ + # TODO(Shinichi) Implement faster assignment for the default reference points because it does + # not seem necessary to calculate distance from all reference points. + + # TODO(Shinichi) Normalize reference_points in constructor to remove reference_point_norms. + # In addition, the minimum distance from each reference point can be replaced with maximum + # inner product between the given individual and each normalized reference points. + + # distance_from_reference_lines is a ndarray of shape (n, p), where n is the size of the + # population and p is the number of reference points. Its (i,j) entry keeps distance between + # the i-th individual values and the j-th reference line. + reference_point_norm_squared = np.linalg.norm(reference_points, axis=1) ** 2 + perpendicular_vectors_to_reference_lines = np.einsum( + "ni,pi,p,pm->npm", + objective_matrix, + reference_points, + 1 / reference_point_norm_squared, + reference_points, + ) + distance_from_reference_lines = np.linalg.norm( + objective_matrix[:, np.newaxis, :] - perpendicular_vectors_to_reference_lines, + axis=2, + ) + closest_reference_points: np.ndarray = np.argmin(distance_from_reference_lines, axis=1) + distance_reference_points: np.ndarray = np.min(distance_from_reference_lines, axis=1) + + return closest_reference_points, distance_reference_points + + +def _preserve_niche_individuals( + target_population_size: int, + elite_population_num: int, + population: list[FrozenTrial], + closest_reference_points: np.ndarray, + distance_reference_points: np.ndarray, + rng: np.random.RandomState, +) -> list[FrozenTrial]: + """Determine who survives form the borderline front. + + Who survive form the borderline front is determined according to the sparsity of each closest + reference point. The algorithm picks a reference point from those who have the least neighbors + in elite population and adds one of borderline front member who has the same closest reference + point. + + Args: + target_population_size: + The number of individuals to select. + elite_population_num: + The number of individuals which are already selected as the elite population. + population: + List of all the trials in the current surviving generation. + distance_reference_points: + A ``numpy.ndarray`` with rows of generation size. Each row is the distance from the + corresponding individual to the closest reference point. + closest_reference_points: + A ``numpy.ndarray`` with rows of generation size. Each row is the index of the closest + reference point to the corresponding individual. + rng: + Random number generator. + + Returns: + A list of trials which are selected as the next generation. + """ + if len(population) < target_population_size: + raise ValueError( + "The population size must be greater than or equal to the target population size." + ) + + # reference_point_to_borderline_population keeps pairs of a neighbor and the distance of + # each reference point from borderline front population. + reference_point_to_borderline_population = defaultdict(list) + for i, reference_point_idx in enumerate(closest_reference_points[elite_population_num:]): + population_idx = i + elite_population_num + reference_point_to_borderline_population[reference_point_idx].append( + (distance_reference_points[population_idx], i) + ) + + # reference_points_to_elite_population_count keeps how many elite neighbors each reference + # point has. + reference_point_to_elite_population_count: dict[int, int] = defaultdict(int) + for i, reference_point_idx in enumerate(closest_reference_points[:elite_population_num]): + reference_point_to_elite_population_count[reference_point_idx] += 1 + # nearest_points_count_to_reference_points classifies reference points which have at least one + # closest borderline population member by the number of elite neighbors they have. Each key + # corresponds to the number of elite neighbors and the value to the reference point indices. + nearest_points_count_to_reference_points = defaultdict(list) + for reference_point_idx in reference_point_to_borderline_population: + elite_population_count = reference_point_to_elite_population_count[reference_point_idx] + nearest_points_count_to_reference_points[elite_population_count].append( + reference_point_idx + ) + + count = -1 + additional_elite_population: list[FrozenTrial] = [] + is_shuffled: defaultdict[int, bool] = defaultdict(bool) + while len(additional_elite_population) < target_population_size: + if len(nearest_points_count_to_reference_points[count]) == 0: + count += 1 + rng.shuffle(nearest_points_count_to_reference_points[count]) + continue + + reference_point_idx = nearest_points_count_to_reference_points[count].pop() + if count > 0 and not is_shuffled[reference_point_idx]: + rng.shuffle(reference_point_to_borderline_population[reference_point_idx]) + is_shuffled[reference_point_idx] = True + elif count == 0: + reference_point_to_borderline_population[reference_point_idx].sort(reverse=True) + + _, selected_individual_id = reference_point_to_borderline_population[ + reference_point_idx + ].pop() + additional_elite_population.append(population[selected_individual_id]) + if reference_point_to_borderline_population[reference_point_idx]: + nearest_points_count_to_reference_points[count + 1].append(reference_point_idx) + + return additional_elite_population diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_nsgaiii/_sampler.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_nsgaiii/_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..5ccee835b8196d3a6ef40972b02443dbfe59da02 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_nsgaiii/_sampler.py @@ -0,0 +1,211 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +from typing import Any +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._experimental import experimental_class +from optuna.distributions import BaseDistribution +from optuna.samplers._ga import BaseGASampler +from optuna.samplers._lazy_random_state import LazyRandomState +from optuna.samplers._nsgaiii._elite_population_selection_strategy import ( + NSGAIIIElitePopulationSelectionStrategy, +) +from optuna.samplers._random import RandomSampler +from optuna.samplers.nsgaii._after_trial_strategy import NSGAIIAfterTrialStrategy +from optuna.samplers.nsgaii._child_generation_strategy import NSGAIIChildGenerationStrategy +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover +from optuna.samplers.nsgaii._crossovers._uniform import UniformCrossover +from optuna.search_space import IntersectionSearchSpace +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna.study import Study + + +@experimental_class("3.2.0") +class NSGAIIISampler(BaseGASampler): + """Multi-objective sampler using the NSGA-III algorithm. + + NSGA-III stands for "Nondominated Sorting Genetic Algorithm III", + which is a modified version of NSGA-II for many objective optimization problem. + + For further information about NSGA-III, please refer to the following papers: + + - `An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based + Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints + `__ + - `An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based + Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive + Approach + `__ + + Args: + reference_points: + A 2 dimension ``numpy.ndarray`` with objective dimension columns. Represents + a list of reference points which is used to determine who to survive. + After non-dominated sort, who out of borderline front are going to survived is + determined according to how sparse the closest reference point of each individual is. + In the default setting the algorithm uses `uniformly` spread points to diversify the + result. It is also possible to reflect your `preferences` by giving an arbitrary set of + `target` points since the algorithm prioritizes individuals around reference points. + + dividing_parameter: + A parameter to determine the density of default reference points. This parameter + determines how many divisions are made between reference points on each axis. The + smaller this value is, the less reference points you have. The default value is 3. + Note that this parameter is not used when ``reference_points`` is not :obj:`None`. + + .. note:: + Other parameters than ``reference_points`` and ``dividing_parameter`` are the same as + :class:`~optuna.samplers.NSGAIISampler`. + + """ + + def __init__( + self, + *, + population_size: int = 50, + mutation_prob: float | None = None, + crossover: BaseCrossover | None = None, + crossover_prob: float = 0.9, + swapping_prob: float = 0.5, + seed: int | None = None, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + reference_points: np.ndarray | None = None, + dividing_parameter: int = 3, + elite_population_selection_strategy: ( + Callable[[Study, list[FrozenTrial]], list[FrozenTrial]] | None + ) = None, + child_generation_strategy: ( + Callable[[Study, dict[str, BaseDistribution], list[FrozenTrial]], dict[str, Any]] + | None + ) = None, + after_trial_strategy: ( + Callable[[Study, FrozenTrial, TrialState, Sequence[float] | None], None] | None + ) = None, + ) -> None: + # TODO(ohta): Reconsider the default value of each parameter. + + if population_size < 2: + raise ValueError("`population_size` must be greater than or equal to 2.") + + if crossover is None: + crossover = UniformCrossover(swapping_prob) + + if not isinstance(crossover, BaseCrossover): + raise ValueError( + f"'{crossover}' is not a valid crossover." + " For valid crossovers see" + " https://optuna.readthedocs.io/en/stable/reference/samplers.html." + ) + + if population_size < crossover.n_parents: + raise ValueError( + f"Using {crossover}," + f" the population size should be greater than or equal to {crossover.n_parents}." + f" The specified `population_size` is {population_size}." + ) + + super().__init__(population_size=population_size) + self._random_sampler = RandomSampler(seed=seed) + self._rng = LazyRandomState(seed) + self._constraints_func = constraints_func + self._search_space = IntersectionSearchSpace() + + self._elite_population_selection_strategy = ( + elite_population_selection_strategy + or NSGAIIIElitePopulationSelectionStrategy( + population_size=population_size, + constraints_func=constraints_func, + reference_points=reference_points, + dividing_parameter=dividing_parameter, + rng=self._rng, + ) + ) + self._child_generation_strategy = ( + child_generation_strategy + or NSGAIIChildGenerationStrategy( + crossover_prob=crossover_prob, + mutation_prob=mutation_prob, + swapping_prob=swapping_prob, + crossover=crossover, + constraints_func=constraints_func, + rng=self._rng, + ) + ) + self._after_trial_strategy = after_trial_strategy or NSGAIIAfterTrialStrategy( + constraints_func=constraints_func + ) + + def reseed_rng(self) -> None: + self._random_sampler.reseed_rng() + self._rng.rng.seed() + + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + search_space: dict[str, BaseDistribution] = {} + for name, distribution in self._search_space.calculate(study).items(): + if distribution.single(): + # The `untransform` method of `optuna._transform._SearchSpaceTransform` + # does not assume a single value, + # so single value objects are not sampled with the `sample_relative` method, + # but with the `sample_independent` method. + continue + search_space[name] = distribution + return search_space + + def select_parent(self, study: Study, generation: int) -> list[FrozenTrial]: + return self._elite_population_selection_strategy( + study, + self.get_population(study, generation - 1) + + self.get_parent_population(study, generation - 1), + ) + + def sample_relative( + self, + study: Study, + trial: FrozenTrial, + search_space: dict[str, BaseDistribution], + ) -> dict[str, Any]: + generation = self.get_trial_generation(study, trial) + parent_population = self.get_parent_population(study, generation) + if len(parent_population) == 0: + return {} + return self._child_generation_strategy(study, search_space, parent_population) + + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + # Following parameters are randomly sampled here. + # 1. A parameter in the initial population/first generation. + # 2. A parameter to mutate. + # 3. A parameter excluded from the intersection search space. + + return self._random_sampler.sample_independent( + study, trial, param_name, param_distribution + ) + + def before_trial(self, study: Study, trial: FrozenTrial) -> None: + self._random_sampler.before_trial(study, trial) + + def after_trial( + self, + study: Study, + trial: FrozenTrial, + state: TrialState, + values: Sequence[float] | None, + ) -> None: + assert state in [TrialState.COMPLETE, TrialState.FAIL, TrialState.PRUNED] + self._after_trial_strategy(study, trial, state, values) + self._random_sampler.after_trial(study, trial, state, values) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_partial_fixed.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_partial_fixed.py new file mode 100644 index 0000000000000000000000000000000000000000..419860e89b86a1dd4e3835e4ba8423bbb298f760 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_partial_fixed.py @@ -0,0 +1,123 @@ +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any +from typing import TYPE_CHECKING +import warnings + +from optuna._experimental import experimental_class +from optuna.distributions import BaseDistribution +from optuna.samplers import BaseSampler +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna.study import Study + + +@experimental_class("2.4.0") +class PartialFixedSampler(BaseSampler): + """Sampler with partially fixed parameters. + + Example: + + After several steps of optimization, you can fix the value of ``y`` and re-optimize it. + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -1, 1) + y = trial.suggest_int("y", -1, 1) + return x**2 + y + + + study = optuna.create_study() + study.optimize(objective, n_trials=10) + + best_params = study.best_params + fixed_params = {"y": best_params["y"]} + partial_sampler = optuna.samplers.PartialFixedSampler(fixed_params, study.sampler) + + study.sampler = partial_sampler + study.optimize(objective, n_trials=10) + + Args: + + fixed_params: + A dictionary of parameters to be fixed. + + base_sampler: + A sampler which samples unfixed parameters. + + """ + + def __init__(self, fixed_params: dict[str, Any], base_sampler: BaseSampler) -> None: + self._fixed_params = fixed_params + self._base_sampler = base_sampler + + def reseed_rng(self) -> None: + self._base_sampler.reseed_rng() + + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + search_space = self._base_sampler.infer_relative_search_space(study, trial) + + # Remove fixed params from relative search space to return fixed values. + for param_name in self._fixed_params.keys(): + if param_name in search_space: + del search_space[param_name] + + return search_space + + def sample_relative( + self, + study: Study, + trial: FrozenTrial, + search_space: dict[str, BaseDistribution], + ) -> dict[str, Any]: + # Fixed params are never sampled here. + return self._base_sampler.sample_relative(study, trial, search_space) + + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + if param_name not in self._fixed_params: + # Unfixed params are sampled here. + return self._base_sampler.sample_independent( + study, trial, param_name, param_distribution + ) + else: + # Fixed params are sampled here. + # Check if a parameter value is contained in the range of this distribution. + param_value = self._fixed_params[param_name] + + param_value_in_internal_repr = param_distribution.to_internal_repr(param_value) + contained = param_distribution._contains(param_value_in_internal_repr) + + if not contained: + warnings.warn( + f"Fixed parameter '{param_name}' with value {param_value} is out of range " + f"for distribution {param_distribution}." + ) + return param_value + + def before_trial(self, study: Study, trial: FrozenTrial) -> None: + self._base_sampler.before_trial(study, trial) + + def after_trial( + self, + study: Study, + trial: FrozenTrial, + state: TrialState, + values: Sequence[float] | None, + ) -> None: + self._base_sampler.after_trial(study, trial, state, values) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_qmc.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_qmc.py new file mode 100644 index 0000000000000000000000000000000000000000..511cdb7b9e55eb89f03ba5c26ef82cdb07d19ea3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_qmc.py @@ -0,0 +1,322 @@ +from __future__ import annotations + +from collections.abc import Sequence +import threading +from typing import Any +from typing import TYPE_CHECKING + +import numpy as np + +import optuna +from optuna import logging +from optuna._experimental import experimental_class +from optuna._imports import _LazyImport +from optuna._transform import _SearchSpaceTransform +from optuna.distributions import BaseDistribution +from optuna.distributions import CategoricalDistribution +from optuna.samplers import BaseSampler +from optuna.samplers._base import _INDEPENDENT_SAMPLING_WARNING_TEMPLATE +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna.study import Study + + +_logger = logging.get_logger(__name__) + +_SUGGESTED_STATES = (TrialState.COMPLETE, TrialState.PRUNED) +_threading_lock = threading.Lock() + + +@experimental_class("3.0.0") +class QMCSampler(BaseSampler): + """A Quasi Monte Carlo Sampler that generates low-discrepancy sequences. + + Quasi Monte Carlo (QMC) sequences are designed to have lower discrepancies than + standard random sequences. They are known to perform better than the standard + random sequences in hyperparameter optimization. + + For further information about the use of QMC sequences for hyperparameter optimization, + please refer to the following paper: + + - `Bergstra, James, and Yoshua Bengio. Random search for hyper-parameter optimization. + Journal of machine learning research 13.2, 2012. + `__ + + We use the QMC implementations in Scipy. For the details of the QMC algorithm, + see the Scipy API references on `scipy.stats.qmc + `__. + + .. note: + If your search space contains categorical parameters, it samples the categorical + parameters by its `independent_sampler` without using QMC algorithm. + + .. note:: + The search space of the sampler is determined by either previous trials in the study or + the first trial that this sampler samples. + + If there are previous trials in the study, :class:`~optuna.samplers.QMCSampler` infers its + search space using the trial which was created first in the study. + + Otherwise (if the study has no previous trials), :class:`~optuna.samplers.QMCSampler` + samples the first trial using its `independent_sampler` and then infers the search space + in the second trial. + + As mentioned above, the search space of the :class:`~optuna.samplers.QMCSampler` is + determined by the first trial of the study. Once the search space is determined, it cannot + be changed afterwards. + + Args: + qmc_type: + The type of QMC sequence to be sampled. This must be one of + `"halton"` and `"sobol"`. Default is `"sobol"`. + + .. note:: + Sobol' sequence is designed to have low-discrepancy property when the number of + samples is :math:`n=2^m` for each positive integer :math:`m`. When it is possible + to pre-specify the number of trials suggested by `QMCSampler`, it is recommended + that the number of trials should be set as power of two. + + scramble: + If this option is :obj:`True`, scrambling (randomization) is applied to the QMC + sequences. + + seed: + A seed for ``QMCSampler``. This argument is used only when ``scramble`` is :obj:`True`. + If this is :obj:`None`, the seed is initialized randomly. Default is :obj:`None`. + + .. note:: + When using multiple :class:`~optuna.samplers.QMCSampler`'s in parallel and/or + distributed optimization, all the samplers must share the same seed when the + `scrambling` is enabled. Otherwise, the low-discrepancy property of the samples + will be degraded. + + independent_sampler: + A :class:`~optuna.samplers.BaseSampler` instance that is used for independent + sampling. The first trial of the study and the parameters not contained in the + relative search space are sampled by this sampler. + + If :obj:`None` is specified, :class:`~optuna.samplers.RandomSampler` is used + as the default. + + .. seealso:: + :class:`~optuna.samplers` module provides built-in independent samplers + such as :class:`~optuna.samplers.RandomSampler` and + :class:`~optuna.samplers.TPESampler`. + + warn_independent_sampling: + If this is :obj:`True`, a warning message is emitted when + the value of a parameter is sampled by using an independent sampler. + + Note that the parameters of the first trial in a study are sampled via an + independent sampler in most cases, so no warning messages are emitted in such cases. + + warn_asynchronous_seeding: + If this is :obj:`True`, a warning message is emitted when the scrambling + (randomization) is applied to the QMC sequence and the random seed of the sampler is + not set manually. + + .. note:: + When using parallel and/or distributed optimization without manually + setting the seed, the seed is set randomly for each instances of + :class:`~optuna.samplers.QMCSampler` for different workers, which ends up + asynchronous seeding for multiple samplers used in the optimization. + + .. seealso:: + See parameter ``seed`` in :class:`~optuna.samplers.QMCSampler`. + + Example: + + Optimize a simple quadratic function by using :class:`~optuna.samplers.QMCSampler`. + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -1, 1) + y = trial.suggest_int("y", -1, 1) + return x**2 + y + + + sampler = optuna.samplers.QMCSampler() + study = optuna.create_study(sampler=sampler) + study.optimize(objective, n_trials=8) + + """ + + def __init__( + self, + *, + qmc_type: str = "sobol", + scramble: bool = False, # default is False for simplicity in distributed environment. + seed: int | None = None, + independent_sampler: BaseSampler | None = None, + warn_asynchronous_seeding: bool = True, + warn_independent_sampling: bool = True, + ) -> None: + self._scramble = scramble + self._seed = np.random.PCG64().random_raw() if seed is None else seed + self._independent_sampler = independent_sampler or optuna.samplers.RandomSampler(seed=seed) + self._initial_search_space: dict[str, BaseDistribution] | None = None + self._warn_independent_sampling = warn_independent_sampling + + if qmc_type in ("halton", "sobol"): + self._qmc_type = qmc_type + else: + message = ( + f'The `qmc_type`, "{qmc_type}", is not a valid. ' + 'It must be one of "halton" and "sobol".' + ) + raise ValueError(message) + + if seed is None and scramble and warn_asynchronous_seeding: + # Sobol/Halton sequences without scrambling do not use seed. + self._log_asynchronous_seeding() + + def reseed_rng(self) -> None: + # We must not reseed the `self._seed` like below. Otherwise, workers will have different + # seed under parallel execution because `self.reseed_rng()` is called when starting each + # parallel executor. + # >>> self._seed = np.random.MT19937().random_raw() + + self._independent_sampler.reseed_rng() + + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + if self._initial_search_space is not None: + return self._initial_search_space + + past_trials = study._get_trials(deepcopy=False, states=_SUGGESTED_STATES, use_cache=True) + # The initial trial is sampled by the independent sampler. + if len(past_trials) == 0: + return {} + # If an initial trial was already made, + # construct search_space of this sampler from the initial trial. + first_trial = min(past_trials, key=lambda t: t.number) + self._initial_search_space = self._infer_initial_search_space(first_trial) + return self._initial_search_space + + def _infer_initial_search_space(self, trial: FrozenTrial) -> dict[str, BaseDistribution]: + search_space: dict[str, BaseDistribution] = {} + for param_name, distribution in trial.distributions.items(): + if isinstance(distribution, CategoricalDistribution): + continue + search_space[param_name] = distribution + + return search_space + + @staticmethod + def _log_asynchronous_seeding() -> None: + _logger.warning( + "No seed is provided for `QMCSampler` and the seed is set randomly. " + "If you are running multiple `QMCSampler`s in parallel and/or distributed " + " environment, the same seed must be used in all samplers to ensure that resulting " + "samples are taken from the same QMC sequence. " + ) + + def _log_independent_sampling(self, trial: FrozenTrial, param_name: str) -> None: + _logger.warning( + _INDEPENDENT_SAMPLING_WARNING_TEMPLATE.format( + param_name=param_name, + trial_number=trial.number, + independent_sampler_name=self._independent_sampler.__class__.__name__, + sampler_name=self.__class__.__name__, + fallback_reason=( + "dynamic search space and `CategoricalDistribution` are not supported " + "by `QMCSampler`" + ), + ) + ) + + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + if self._initial_search_space is not None: + if self._warn_independent_sampling: + self._log_independent_sampling(trial, param_name) + + return self._independent_sampler.sample_independent( + study, trial, param_name, param_distribution + ) + + def sample_relative( + self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution] + ) -> dict[str, Any]: + if search_space == {}: + return {} + + sample = self._sample_qmc(study, search_space) + trans = _SearchSpaceTransform(search_space) + sample = trans.bounds[:, 0] + sample * (trans.bounds[:, 1] - trans.bounds[:, 0]) + return trans.untransform(sample[0, :]) + + def before_trial(self, study: Study, trial: FrozenTrial) -> None: + self._independent_sampler.before_trial(study, trial) + + def after_trial( + self, + study: Study, + trial: "optuna.trial.FrozenTrial", + state: TrialState, + values: Sequence[float] | None, + ) -> None: + self._independent_sampler.after_trial(study, trial, state, values) + + def _sample_qmc(self, study: Study, search_space: dict[str, BaseDistribution]) -> np.ndarray: + # Lazy import because the `scipy.stats.qmc` is slow to import. + qmc_module = _LazyImport("scipy.stats.qmc") + + sample_id = self._find_sample_id(study) + d = len(search_space) + + if self._qmc_type == "halton": + qmc_engine = qmc_module.Halton(d, seed=self._seed, scramble=self._scramble) + elif self._qmc_type == "sobol": + # Sobol engine likely shares its internal state among threads. + # Without threading.Lock, ValueError exceptions are raised in Sobol engine as discussed + # in https://github.com/optuna/optunahub-registry/pull/168#pullrequestreview-2404054969 + with _threading_lock: + qmc_engine = qmc_module.Sobol(d, seed=self._seed, scramble=self._scramble) + else: + raise ValueError("Invalid `qmc_type`") + + forward_size = sample_id # `sample_id` starts from 0. + # Skip fast_forward with forward_size==0 because Sobol doesn't support the case, + # and fast_forward(0) doesn't affect sampling. + if forward_size > 0: + qmc_engine.fast_forward(forward_size) + sample = qmc_engine.random(1) + + return sample + + def _find_sample_id(self, study: Study) -> int: + qmc_id = "" + qmc_id += self._qmc_type + # Sobol/Halton sequences without scrambling do not use seed. + if self._scramble: + qmc_id += f" (scramble=True, seed={self._seed})" + else: + qmc_id += " (scramble=False)" + key_qmc_id = qmc_id + "'s last sample id" + + # TODO(kstoneriv3): Here, we ideally assume that the following block is + # an atomic transaction. Without such an assumption, the current implementation + # only ensures that each `sample_id` is sampled at least once. + system_attrs = study._storage.get_study_system_attrs(study._study_id) + if key_qmc_id in system_attrs.keys(): + sample_id = system_attrs[key_qmc_id] + sample_id += 1 + else: + sample_id = 0 + study._storage.set_study_system_attr(study._study_id, key_qmc_id, sample_id) + + return sample_id diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_random.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_random.py new file mode 100644 index 0000000000000000000000000000000000000000..f93a4ee2cee1acdd5a9cd9677b45d1d2311d8b49 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_random.py @@ -0,0 +1,71 @@ +from __future__ import annotations + +from typing import Any +from typing import TYPE_CHECKING + +from optuna import distributions +from optuna._transform import _SearchSpaceTransform +from optuna.distributions import BaseDistribution +from optuna.samplers import BaseSampler +from optuna.samplers._lazy_random_state import LazyRandomState +from optuna.trial import FrozenTrial + + +if TYPE_CHECKING: + from optuna.study import Study + + +class RandomSampler(BaseSampler): + """Sampler using random sampling. + + This sampler is based on *independent sampling*. + See also :class:`~optuna.samplers.BaseSampler` for more details of 'independent sampling'. + + Example: + + .. testcode:: + + import optuna + from optuna.samplers import RandomSampler + + + def objective(trial): + x = trial.suggest_float("x", -5, 5) + return x**2 + + + study = optuna.create_study(sampler=RandomSampler()) + study.optimize(objective, n_trials=10) + + Args: + seed: Seed for random number generator. + """ + + def __init__(self, seed: int | None = None) -> None: + self._rng = LazyRandomState(seed) + + def reseed_rng(self) -> None: + self._rng.rng.seed() + + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + return {} + + def sample_relative( + self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution] + ) -> dict[str, Any]: + return {} + + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: distributions.BaseDistribution, + ) -> Any: + search_space = {param_name: param_distribution} + trans = _SearchSpaceTransform(search_space) + trans_params = self._rng.rng.uniform(trans.bounds[:, 0], trans.bounds[:, 1]) + + return trans.untransform(trans_params)[param_name] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git 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Microsystems, Inc. All rights reserved. +# * +# * Developed at SunPro, a Sun Microsystems, Inc. business. +# * Permission to use, copy, modify, and distribute this +# * software is freely granted, provided that this notice +# * is preserved. +# * ==================================================== +# */ + +from __future__ import annotations + +import math +from typing import TYPE_CHECKING + +import numpy as np +from numpy.polynomial import Polynomial + + +if TYPE_CHECKING: + from collections.abc import Callable + + +erx = 8.45062911510467529297e-01 +# /* +# * In the domain [0, 2**-28], only the first term in the power series +# * expansion of erf(x) is used. The magnitude of the first neglected +# * terms is less than 2**-84. +# */ +efx = 1.28379167095512586316e-01 + +# Coefficients for approximation to erf on [0,0.84375] + +pp0 = 1.28379167095512558561e-01 +pp1 = -3.25042107247001499370e-01 +pp2 = -2.84817495755985104766e-02 +pp3 = -5.77027029648944159157e-03 +pp4 = -2.37630166566501626084e-05 +pp = Polynomial([pp0, pp1, pp2, pp3, pp4]) +qq1 = 3.97917223959155352819e-01 +qq2 = 6.50222499887672944485e-02 +qq3 = 5.08130628187576562776e-03 +qq4 = 1.32494738004321644526e-04 +qq5 = -3.96022827877536812320e-06 +qq = Polynomial([1, qq1, qq2, qq3, qq4, qq5]) + +# Coefficients for approximation to erf in [0.84375,1.25] + +pa0 = -2.36211856075265944077e-03 +pa1 = 4.14856118683748331666e-01 +pa2 = -3.72207876035701323847e-01 +pa3 = 3.18346619901161753674e-01 +pa4 = -1.10894694282396677476e-01 +pa5 = 3.54783043256182359371e-02 +pa6 = -2.16637559486879084300e-03 +pa = Polynomial([pa0, pa1, pa2, pa3, pa4, pa5, pa6]) +qa1 = 1.06420880400844228286e-01 +qa2 = 5.40397917702171048937e-01 +qa3 = 7.18286544141962662868e-02 +qa4 = 1.26171219808761642112e-01 +qa5 = 1.36370839120290507362e-02 +qa6 = 1.19844998467991074170e-02 +qa = Polynomial([1, qa1, qa2, qa3, qa4, qa5, qa6]) + +# Coefficients for approximation to erfc in [1.25,1/0.35] + +ra0 = -9.86494403484714822705e-03 +ra1 = -6.93858572707181764372e-01 +ra2 = -1.05586262253232909814e01 +ra3 = -6.23753324503260060396e01 +ra4 = -1.62396669462573470355e02 +ra5 = -1.84605092906711035994e02 +ra6 = -8.12874355063065934246e01 +ra7 = -9.81432934416914548592e00 +ra = Polynomial([ra0, ra1, ra2, ra3, ra4, ra5, ra6, ra7]) +sa1 = 1.96512716674392571292e01 +sa2 = 1.37657754143519042600e02 +sa3 = 4.34565877475229228821e02 +sa4 = 6.45387271733267880336e02 +sa5 = 4.29008140027567833386e02 +sa6 = 1.08635005541779435134e02 +sa7 = 6.57024977031928170135e00 +sa8 = -6.04244152148580987438e-02 +sa = Polynomial([1, sa1, sa2, sa3, sa4, sa5, sa6, sa7, sa8]) + +# Coefficients for approximation to erfc in [1/.35,28] + +rb0 = -9.86494292470009928597e-03 +rb1 = -7.99283237680523006574e-01 +rb2 = -1.77579549177547519889e01 +rb3 = -1.60636384855821916062e02 +rb4 = -6.37566443368389627722e02 +rb5 = -1.02509513161107724954e03 +rb6 = -4.83519191608651397019e02 +rb = Polynomial([rb0, rb1, rb2, rb3, rb4, rb5, rb6]) +sb1 = 3.03380607434824582924e01 +sb2 = 3.25792512996573918826e02 +sb3 = 1.53672958608443695994e03 +sb4 = 3.19985821950859553908e03 +sb5 = 2.55305040643316442583e03 +sb6 = 4.74528541206955367215e02 +sb7 = -2.24409524465858183362e01 +sb = Polynomial([1, sb1, sb2, sb3, sb4, sb5, sb6, sb7]) + + +def _erf_right_non_big(x: np.ndarray) -> np.ndarray: + assert len(x.shape) == 1, "Input must be a 1D array." + # NOTE(nabenabe): Add [6] to the list and use out = np.ones_like(x) to handle the big case. + bin_inds = np.count_nonzero(x >= [[2**-28], [0.84375], [1.25], [1 / 0.35]], axis=0) + out = np.empty_like(x) + erf_approx_in_each_bin: list[Callable[[np.ndarray], np.ndarray]] = [ + lambda x: (1 + efx) * x, # Tiny: x < 2**-28. + lambda x: x * (1 + pp(z := x * x) / qq(z)), # Small1: 2**-28 <= x < 0.84375. + lambda x: erx + pa(s := x - 1) / qa(s), # Small2: 0.84375 <= x < 1.25. + # Med1: 1.25 <= x < 1 / 0.35, Med2: 1 / 0.35 <= x < 6. + # Omit SET_LOW_WORD due to its unavailablility in NumPy and no need for high accuracy. + lambda x: 1 - np.exp(-(z := x * x) - 0.5625 + ra(s := 1 / z) / sa(s)) / x, + lambda x: 1 - np.exp(-(z := x * x) - 0.5625 + rb(s := 1 / z) / sb(s)) / x, + ] + for bin_idx, erf_approx_in_bin in enumerate(erf_approx_in_each_bin): + if (target_inds := np.nonzero(bin_inds == bin_idx)[0]).size: + out[target_inds] = erf_approx_in_bin(x[target_inds]) + + return out + + +def erf(x: np.ndarray) -> np.ndarray: + if x.size < 2000: + return np.asarray([math.erf(v) for v in x.ravel()]).reshape(x.shape) + + a = np.abs(x).ravel() + is_not_nan = ~np.isnan(a) + out = np.where(is_not_nan, 1.0, np.nan) + non_big_inds = np.nonzero(is_not_nan & (a < 6))[0] + out[non_big_inds] = _erf_right_non_big(a[non_big_inds]) + return np.sign(x) * out.reshape(x.shape) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/_truncnorm.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/_truncnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..8a3c00440b7c9c1ad05426a7dd6d68c1d9166fa6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/_truncnorm.py @@ -0,0 +1,293 @@ +# This file contains the codes from SciPy project. +# +# Copyright (c) 2001-2002 Enthought, Inc. 2003-2022, SciPy Developers. +# All rights reserved. + +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions +# are met: + +# 1. Redistributions of source code must retain the above copyright +# notice, this list of conditions and the following disclaimer. + +# 2. Redistributions in binary form must reproduce the above +# copyright notice, this list of conditions and the following +# disclaimer in the documentation and/or other materials provided +# with the distribution. + +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived +# from this software without specific prior written permission. + +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +from __future__ import annotations + +import functools +import math +import sys + +import numpy as np + +from optuna.samplers._tpe._erf import erf + + +_norm_pdf_C = math.sqrt(2 * math.pi) +_norm_pdf_logC = math.log(_norm_pdf_C) +_ndtri_exp_approx_C = math.sqrt(3) / math.pi +_log_2 = math.log(2) + + +def _log_sum(log_p: np.ndarray, log_q: np.ndarray) -> np.ndarray: + return np.logaddexp(log_p, log_q) + + +def _log_diff(log_p: np.ndarray, log_q: np.ndarray) -> np.ndarray: + return log_p + np.log1p(-np.exp(log_q - log_p)) + + +@functools.lru_cache(1000) +def _ndtr_single(a: float) -> float: + x = a / 2**0.5 + + if x < -1 / 2**0.5: + y = 0.5 * math.erfc(-x) + elif x < 1 / 2**0.5: + y = 0.5 + 0.5 * math.erf(x) + else: + y = 1.0 - 0.5 * math.erfc(x) + + return y + + +def _ndtr(a: np.ndarray) -> np.ndarray: + # todo(amylase): implement erfc in _erf.py and use it for big |a| inputs. + return 0.5 + 0.5 * erf(a / 2**0.5) + + +@functools.lru_cache(1000) +def _log_ndtr_single(a: float) -> float: + if a > 6: + return -_ndtr_single(-a) + if a > -20: + return math.log(_ndtr_single(a)) + + log_LHS = -0.5 * a**2 - math.log(-a) - 0.5 * math.log(2 * math.pi) + last_total = 0.0 + right_hand_side = 1.0 + numerator = 1.0 + denom_factor = 1.0 + denom_cons = 1 / a**2 + sign = 1 + i = 0 + + while abs(last_total - right_hand_side) > sys.float_info.epsilon: + i += 1 + last_total = right_hand_side + sign = -sign + denom_factor *= denom_cons + numerator *= 2 * i - 1 + right_hand_side += sign * numerator * denom_factor + + return log_LHS + math.log(right_hand_side) + + +def _log_ndtr(a: np.ndarray) -> np.ndarray: + return np.frompyfunc(_log_ndtr_single, 1, 1)(a).astype(float) + + +def _norm_logpdf(x: np.ndarray) -> np.ndarray: + return -(x**2) / 2.0 - _norm_pdf_logC + + +def _log_gauss_mass(a: np.ndarray, b: np.ndarray) -> np.ndarray: + """Log of Gaussian probability mass within an interval""" + + # Calculations in right tail are inaccurate, so we'll exploit the + # symmetry and work only in the left tail + case_left = b <= 0 + case_right = a > 0 + case_central = ~(case_left | case_right) + + def mass_case_left(a: np.ndarray, b: np.ndarray) -> np.ndarray: + return _log_diff(_log_ndtr(b), _log_ndtr(a)) + + def mass_case_right(a: np.ndarray, b: np.ndarray) -> np.ndarray: + return mass_case_left(-b, -a) + + def mass_case_central(a: np.ndarray, b: np.ndarray) -> np.ndarray: + # Previously, this was implemented as: + # left_mass = mass_case_left(a, 0) + # right_mass = mass_case_right(0, b) + # return _log_sum(left_mass, right_mass) + # Catastrophic cancellation occurs as np.exp(log_mass) approaches 1. + # Correct for this with an alternative formulation. + # We're not concerned with underflow here: if only one term + # underflows, it was insignificant; if both terms underflow, + # the result can't accurately be represented in logspace anyway + # because sc.log1p(x) ~ x for small x. + return np.log1p(-_ndtr(a) - _ndtr(-b)) + + # _lazyselect not working; don't care to debug it + out = np.full_like(a, fill_value=np.nan, dtype=np.complex128) + if (a_left := a[case_left]).size: + out[case_left] = mass_case_left(a_left, b[case_left]) + if (a_right := a[case_right]).size: + out[case_right] = mass_case_right(a_right, b[case_right]) + if (a_central := a[case_central]).size: + out[case_central] = mass_case_central(a_central, b[case_central]) + return np.real(out) # discard ~0j + + +def _ndtri_exp(y: np.ndarray) -> np.ndarray: + """ + Use the Newton method to efficiently find the root. + + `ndtri_exp(y)` returns `x` such that `y = log_ndtr(x)`, meaning that the Newton method is + supposed to find the root of `f(x) := log_ndtr(x) - y = 0`. + + Since `df/dx = d log_ndtr(x)/dx = (ndtr(x))'/ndtr(x) = norm_pdf(x)/ndtr(x)`, the Newton update + is x[n + 1] := x[n] - (log_ndtr(x) - y) * ndtr(x) / norm_pdf(x). + + As an initial guess, we use the Gaussian tail asymptotic approximation: + 1 - ndtr(x) \\simeq norm_pdf(x) / x + --> log(norm_pdf(x) / x) = -1/2 * x**2 - 1/2 * log(2pi) - log(x) + + First recall that y is a non-positive value and y = log_ndtr(inf) = 0 and + y = log_ndtr(-inf) = -inf. + + If abs(y) is very small, we first derive -x such that z = log_ndtr(-x) and then flip the sign. + Please note that the following holds: + z = log_ndtr(-x) --> z = log(1 - ndtr(x)) = log(1 - exp(y)) = log(-expm1(y)). + Recall that as long as ndtr(x) = exp(y) > 0.5 --> y > -log(2) = -0.693..., x becomes positive. + + ndtr(x) = exp(y) \\simeq 1 + y --> -y \\simeq 1 - ndtr(x). From this, we can calculate: + log(1 - ndtr(x)) \\simeq log(-y) \\simeq -1/2 * x**2 - 1/2 * log(2pi) - log(x). + Because x**2 >> log(x), we can ignore the second and third terms, leading to: + log(-y) \\simeq -1/2 * x**2 --> x \\simeq sqrt(-2 log(-y)). + where we take the positive `x` as abs(y) becomes very small only if x >> 0. + The second order approximation version is sqrt(-2 log(-y) - log(-2 log(-y))). + + If abs(y) is very large, we use log_ndtr(x) \\simeq -1/2 * x**2 - 1/2 * log(2pi) - log(x). + To solve this equation analytically, we ignore the log term, yielding: + log_ndtr(x) = y \\simeq -1/2 * x**2 - 1/2 * log(2pi) + --> y + 1/2 * log(2pi) = -1/2 * x**2 --> x**2 = -2 * (y + 1/2 * log(2pi)) + --> x = sqrt(-2 * (y + 1/2 * log(2pi)) + + For the moderate y, we use Eq. (13), i.e., standard logistic CDF, in the following paper: + + - `Approximating the Cumulative Distribution Function of the Normal Distribution + `__ + + The standard logistic CDF approximates ndtr(x) with: + exp(y) = ndtr(x) \\simeq 1 / (1 + exp(-pi * x / sqrt(3))) + --> exp(-y) \\simeq 1 + exp(-pi * x / sqrt(3)) + --> log(exp(-y) - 1) \\simeq -pi * x / sqrt(3) + --> x \\simeq -sqrt(3) / pi * log(exp(-y) - 1). + """ + # Flip the sign of y close to zero for better numerical stability and flip back the sign later. + flipped = y > -1e-2 + z = y.copy() + z[flipped] = np.log(-np.expm1(y[flipped])) + x = np.empty_like(y) + if (small_inds := np.nonzero(z < -5))[0].size: + x[small_inds] = -np.sqrt(-2.0 * (z[small_inds] + _norm_pdf_logC)) + if (moderate_inds := np.nonzero(z >= -5))[0].size: + x[moderate_inds] = -_ndtri_exp_approx_C * np.log(np.expm1(-z[moderate_inds])) + + for _ in range(100): + log_ndtr_x = _log_ndtr(x) + log_norm_pdf_x = -0.5 * x**2 - _norm_pdf_logC + # NOTE(nabenabe): Use exp(log_ndtr_x - log_norm_pdf_x) instead of ndtr_x / norm_pdf_x for + # numerical stability. + dx = (log_ndtr_x - z) * np.exp(log_ndtr_x - log_norm_pdf_x) + x -= dx + if np.all(np.abs(dx) < 1e-8 * np.abs(x)): # NOTE: rtol controls the precision. + # Equivalent to np.isclose with atol=0.0 and rtol=1e-8. + break + x[flipped] *= -1 + # NOTE(nabe): x[y == 0.0] = np.inf, x[np.isneginf(y)] = -np.inf are necessary for the accurate + # computation, but we omit them as the ppf applies clipping, removing the need for them. + return x + + +def ppf(q: np.ndarray, a: np.ndarray | float, b: np.ndarray | float) -> np.ndarray: + """ + Compute the percent point function (inverse of cdf) at q of the given truncated Gaussian. + + Namely, this function returns the value `c` such that: + q = \\int_{a}^{c} f(x) dx + + where `f(x)` is the probability density function of the truncated normal distribution with + the lower limit `a` and the upper limit `b`. + + More precisely, this function returns `c` such that: + ndtr(c) = ndtr(a) + q * (ndtr(b) - ndtr(a)) + for the case where `a < 0`, i.e., `case_left`. For `case_right`, we flip the sign for the + better numerical stability. + """ + q, a, b = np.atleast_1d(q, a, b) + q, a, b = np.broadcast_arrays(q, a, b) + + case_left = a < 0 + case_right = ~case_left + log_mass = _log_gauss_mass(a, b) + + def ppf_left(q: np.ndarray, a: np.ndarray, b: np.ndarray, log_mass: np.ndarray) -> np.ndarray: + log_Phi_x = _log_sum(_log_ndtr(a), np.log(q) + log_mass) + return _ndtri_exp(log_Phi_x) + + def ppf_right(q: np.ndarray, a: np.ndarray, b: np.ndarray, log_mass: np.ndarray) -> np.ndarray: + # NOTE(nabenabe): Since the numerical stability of log_ndtr is better in the left tail, we + # flip the side for a >= 0. + log_Phi_x = _log_sum(_log_ndtr(-b), np.log1p(-q) + log_mass) + return -_ndtri_exp(log_Phi_x) + + out = np.empty_like(q) + if (q_left := q[case_left]).size: + out[case_left] = ppf_left(q_left, a[case_left], b[case_left], log_mass[case_left]) + if (q_right := q[case_right]).size: + out[case_right] = ppf_right(q_right, a[case_right], b[case_right], log_mass[case_right]) + + return np.select([a == b, q == 1, q == 0], [math.nan, b, a], default=out) + + +def rvs( + a: np.ndarray, + b: np.ndarray, + loc: np.ndarray | float = 0, + scale: np.ndarray | float = 1, + random_state: np.random.RandomState | None = None, +) -> np.ndarray: + """ + This function generates random variates from a truncated normal distribution defined between + `a` and `b` with the mean of `loc` and the standard deviation of `scale`. + """ + random_state = random_state or np.random.RandomState() + size = np.broadcast(a, b, loc, scale).shape + quantiles = random_state.uniform(low=0, high=1, size=size) + return ppf(quantiles, a, b) * scale + loc + + +def logpdf( + x: np.ndarray, + a: np.ndarray | float, + b: np.ndarray | float, + loc: np.ndarray | float = 0, + scale: np.ndarray | float = 1, +) -> np.ndarray: + x = (x - loc) / scale + x, a, b = np.atleast_1d(x, a, b) + out = _norm_logpdf(x) - _log_gauss_mass(a, b) - np.log(scale) + x, a, b = np.broadcast_arrays(x, a, b) + return np.select([a == b, (x < a) | (x > b)], [np.nan, -np.inf], default=out) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/parzen_estimator.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/parzen_estimator.py new file mode 100644 index 0000000000000000000000000000000000000000..bf625caab12187067462cd1a7b688de933433bf1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/parzen_estimator.py @@ -0,0 +1,277 @@ +from __future__ import annotations + +from collections.abc import Callable +from typing import NamedTuple + +import numpy as np + +from optuna.distributions import BaseDistribution +from optuna.distributions import CategoricalChoiceType +from optuna.distributions import CategoricalDistribution +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution +from optuna.samplers._tpe.probability_distributions import _BatchedCategoricalDistributions +from optuna.samplers._tpe.probability_distributions import _BatchedDiscreteTruncNormDistributions +from optuna.samplers._tpe.probability_distributions import _BatchedDistributions +from optuna.samplers._tpe.probability_distributions import _BatchedTruncNormDistributions +from optuna.samplers._tpe.probability_distributions import _MixtureOfProductDistribution + + +EPS = 1e-12 + + +class _ParzenEstimatorParameters(NamedTuple): + prior_weight: float + consider_magic_clip: bool + consider_endpoints: bool + weights: Callable[[int], np.ndarray] + multivariate: bool + categorical_distance_func: dict[ + str, Callable[[CategoricalChoiceType, CategoricalChoiceType], float] + ] + + +class _ParzenEstimator: + def __init__( + self, + observations: dict[str, np.ndarray], + search_space: dict[str, BaseDistribution], + parameters: _ParzenEstimatorParameters, + predetermined_weights: np.ndarray | None = None, + ) -> None: + if parameters.prior_weight < 0: + raise ValueError( + "A non-negative value must be specified for prior_weight," + f" but got {parameters.prior_weight}." + ) + + self._search_space = search_space + + transformed_observations = self._transform(observations) + + assert predetermined_weights is None or len(transformed_observations) == len( + predetermined_weights + ) + weights = ( + predetermined_weights + if predetermined_weights is not None + else self._call_weights_func(parameters.weights, len(transformed_observations)) + ) + + if len(transformed_observations) == 0: + weights = np.array([1.0]) + else: + weights = np.append(weights, [parameters.prior_weight]) + weights /= weights.sum() + self._mixture_distribution = _MixtureOfProductDistribution( + weights=weights, + distributions=[ + self._calculate_distributions( + transformed_observations[:, i], param, search_space[param], parameters + ) + for i, param in enumerate(search_space) + ], + ) + + def sample(self, rng: np.random.RandomState, size: int) -> dict[str, np.ndarray]: + sampled = self._mixture_distribution.sample(rng, size) + return self._untransform(sampled) + + def log_pdf(self, samples_dict: dict[str, np.ndarray]) -> np.ndarray: + transformed_samples = self._transform(samples_dict) + return self._mixture_distribution.log_pdf(transformed_samples) + + @staticmethod + def _call_weights_func(weights_func: Callable[[int], np.ndarray], n: int) -> np.ndarray: + w = np.array(weights_func(n))[:n] + if np.any(w < 0): + raise ValueError( + f"The `weights` function is not allowed to return negative values {w}. " + + f"The argument of the `weights` function is {n}." + ) + if len(w) > 0 and np.sum(w) <= 0: + raise ValueError( + f"The `weight` function is not allowed to return all-zero values {w}." + + f" The argument of the `weights` function is {n}." + ) + if not np.all(np.isfinite(w)): + raise ValueError( + "The `weights`function is not allowed to return infinite or NaN values " + + f"{w}. The argument of the `weights` function is {n}." + ) + + # TODO(HideakiImamura) Raise `ValueError` if the weight function returns an ndarray of + # unexpected size. + return w + + @staticmethod + def _is_log(dist: BaseDistribution) -> bool: + return isinstance(dist, (FloatDistribution, IntDistribution)) and dist.log + + def _transform(self, samples_dict: dict[str, np.ndarray]) -> np.ndarray: + return np.array( + [ + ( + np.log(samples_dict[param]) + if self._is_log(self._search_space[param]) + else samples_dict[param] + ) + for param in self._search_space + ] + ).T + + def _untransform(self, samples_array: np.ndarray) -> dict[str, np.ndarray]: + res = { + param: ( + np.exp(samples_array[:, i]) + if self._is_log(self._search_space[param]) + else samples_array[:, i] + ) + for i, param in enumerate(self._search_space) + } + # TODO(contramundum53): Remove this line after fixing log-Int hack. + return { + param: ( + np.clip( + dist.low + np.round((res[param] - dist.low) / dist.step) * dist.step, + dist.low, + dist.high, + ) + if isinstance(dist, IntDistribution) + else res[param] + ) + for (param, dist) in self._search_space.items() + } + + def _calculate_distributions( + self, + transformed_observations: np.ndarray, + param_name: str, + search_space: BaseDistribution, + parameters: _ParzenEstimatorParameters, + ) -> _BatchedDistributions: + if isinstance(search_space, CategoricalDistribution): + return self._calculate_categorical_distributions( + transformed_observations, param_name, search_space, parameters + ) + else: + assert isinstance(search_space, (FloatDistribution, IntDistribution)) + if search_space.log: + low = np.log(search_space.low) + high = np.log(search_space.high) + else: + low = search_space.low + high = search_space.high + step = search_space.step + + # TODO(contramundum53): This is a hack and should be fixed. + if step is not None and search_space.log: + low = np.log(search_space.low - step / 2) + high = np.log(search_space.high + step / 2) + step = None + + return self._calculate_numerical_distributions( + transformed_observations, low, high, step, parameters + ) + + def _calculate_categorical_distributions( + self, + observations: np.ndarray, + param_name: str, + search_space: CategoricalDistribution, + parameters: _ParzenEstimatorParameters, + ) -> _BatchedDistributions: + choices = search_space.choices + n_choices = len(choices) + if len(observations) == 0: + return _BatchedCategoricalDistributions( + weights=np.full((1, n_choices), fill_value=1.0 / n_choices) + ) + + n_kernels = len(observations) + 1 # NOTE(sawa3030): +1 for prior. + weights = np.full( + shape=(n_kernels, n_choices), + fill_value=parameters.prior_weight / n_kernels, + ) + observed_indices = observations.astype(int) + if param_name in parameters.categorical_distance_func: + # TODO(nabenabe0928): Think about how to handle combinatorial explosion. + # The time complexity is O(n_choices * used_indices.size), so n_choices cannot be huge. + used_indices, rev_indices = np.unique(observed_indices, return_inverse=True) + dist_func = parameters.categorical_distance_func[param_name] + dists = np.array([[dist_func(choices[i], c) for c in choices] for i in used_indices]) + coef = np.log(n_kernels / parameters.prior_weight) * np.log(n_choices) / np.log(6) + cat_weights = np.exp(-((dists / np.max(dists, axis=1)[:, np.newaxis]) ** 2) * coef) + weights[: len(observed_indices)] = cat_weights[rev_indices] + else: + weights[np.arange(len(observed_indices)), observed_indices] += 1 + + row_sums = weights.sum(axis=1, keepdims=True) + weights /= np.where(row_sums == 0, 1, row_sums) + return _BatchedCategoricalDistributions(weights) + + def _calculate_numerical_distributions( + self, + observations: np.ndarray, + low: float, + high: float, + step: float | None, + parameters: _ParzenEstimatorParameters, + ) -> _BatchedDistributions: + step_or_0 = step or 0 + + mus = observations + + def compute_sigmas() -> np.ndarray: + if parameters.multivariate: + SIGMA0_MAGNITUDE = 0.2 + sigma = ( + SIGMA0_MAGNITUDE + * max(len(observations), 1) ** (-1.0 / (len(self._search_space) + 4)) + * (high - low + step_or_0) + ) + sigmas = np.full(shape=(len(observations),), fill_value=sigma) + else: + # TODO(contramundum53): Remove dependency on prior_mu + prior_mu = 0.5 * (low + high) + mus_with_prior = np.append(mus, prior_mu) + + sorted_indices = np.argsort(mus_with_prior) + sorted_mus = mus_with_prior[sorted_indices] + sorted_mus_with_endpoints = np.empty(len(mus_with_prior) + 2, dtype=float) + sorted_mus_with_endpoints[0] = low - step_or_0 / 2 + sorted_mus_with_endpoints[1:-1] = sorted_mus + sorted_mus_with_endpoints[-1] = high + step_or_0 / 2 + + sorted_sigmas = np.maximum( + sorted_mus_with_endpoints[1:-1] - sorted_mus_with_endpoints[0:-2], + sorted_mus_with_endpoints[2:] - sorted_mus_with_endpoints[1:-1], + ) + + if not parameters.consider_endpoints and sorted_mus_with_endpoints.shape[0] >= 4: + sorted_sigmas[0] = sorted_mus_with_endpoints[2] - sorted_mus_with_endpoints[1] + sorted_sigmas[-1] = ( + sorted_mus_with_endpoints[-2] - sorted_mus_with_endpoints[-3] + ) + + sigmas = sorted_sigmas[np.argsort(sorted_indices)][: len(observations)] + + # We adjust the range of the 'sigmas' according to the 'consider_magic_clip' flag. + maxsigma = 1.0 * (high - low + step_or_0) + if parameters.consider_magic_clip: + # TODO(contramundum53): Remove dependency of minsigma on consider_prior. + n_kernels = len(observations) + 1 # NOTE(sawa3030): +1 for prior. + minsigma = 1.0 * (high - low + step_or_0) / min(100.0, (1.0 + n_kernels)) + else: + minsigma = EPS + return np.asarray(np.clip(sigmas, minsigma, maxsigma)) + + sigmas = compute_sigmas() + + mus = np.append(mus, [0.5 * (low + high)]) + sigmas = np.append(sigmas, [1.0 * (high - low + step_or_0)]) + + if step is None: + return _BatchedTruncNormDistributions(mus, sigmas, low, high) + else: + return _BatchedDiscreteTruncNormDistributions(mus, sigmas, low, high, step) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/probability_distributions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/probability_distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..f5971cd42dbf4ad309e1918a4ee00f868ce0cee9 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/probability_distributions.py @@ -0,0 +1,158 @@ +from __future__ import annotations + +from typing import NamedTuple +from typing import Union + +import numpy as np + +from optuna.samplers._tpe import _truncnorm + + +class _BatchedCategoricalDistributions(NamedTuple): + weights: np.ndarray + + +class _BatchedTruncNormDistributions(NamedTuple): + mu: np.ndarray + sigma: np.ndarray + low: float # Currently, low and high do not change per trial. + high: float + + +class _BatchedDiscreteTruncNormDistributions(NamedTuple): + mu: np.ndarray + sigma: np.ndarray + low: float # Currently, low, high and step do not change per trial. + high: float + step: float + + +_BatchedDistributions = Union[ + _BatchedCategoricalDistributions, + _BatchedTruncNormDistributions, + _BatchedDiscreteTruncNormDistributions, +] + + +def _unique_inverse_2d(a: np.ndarray, b: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + This function is a quicker version of: + np.unique(np.concatenate([a[:, None], b[:, None]], axis=-1), return_inverse=True). + """ + assert a.shape == b.shape and len(a.shape) == 1 + order = np.argsort(b) + # Stable sorting is required for the tie breaking. + order = order[np.argsort(a[order], kind="stable")] + a_order = a[order] + b_order = b[order] + is_first_occurrence = np.empty_like(a, dtype=bool) + is_first_occurrence[0] = True + is_first_occurrence[1:] = (a_order[1:] != a_order[:-1]) | (b_order[1:] != b_order[:-1]) + inv = np.empty(a_order.size, dtype=int) + inv[order] = np.cumsum(is_first_occurrence) - 1 + return a_order[is_first_occurrence], b_order[is_first_occurrence], inv + + +def _log_gauss_mass_unique(a: np.ndarray, b: np.ndarray) -> np.ndarray: + """ + This function reduces the log Gaussian probability mass computation by avoiding the + duplicated evaluations using the np.unique_inverse(...) equivalent operation. + """ + a_uniq, b_uniq, inv = _unique_inverse_2d(a.ravel(), b.ravel()) + return _truncnorm._log_gauss_mass(a_uniq, b_uniq)[inv].reshape(a.shape) + + +class _MixtureOfProductDistribution(NamedTuple): + weights: np.ndarray + distributions: list[_BatchedDistributions] + + def sample(self, rng: np.random.RandomState, batch_size: int) -> np.ndarray: + active_indices = rng.choice(len(self.weights), p=self.weights, size=batch_size) + ret = np.empty((batch_size, len(self.distributions)), dtype=float) + disc_inds, numerical_inds = [], [] + numerical_dists: list[ + _BatchedTruncNormDistributions | _BatchedDiscreteTruncNormDistributions + ] = [] + for i, d in enumerate(self.distributions): + if isinstance(d, _BatchedCategoricalDistributions): + active_weights = d.weights[active_indices, :] + rnd_quantile = rng.rand(batch_size) + cum_probs = np.cumsum(active_weights, axis=-1) + assert np.isclose(cum_probs[:, -1], 1).all() + cum_probs[:, -1] = 1 # Avoid numerical errors. + ret[:, i] = np.sum(cum_probs < rnd_quantile[:, np.newaxis], axis=-1) + elif isinstance(d, _BatchedTruncNormDistributions): + numerical_dists.append(d) + numerical_inds.append(i) + elif isinstance(d, _BatchedDiscreteTruncNormDistributions): + disc_inds.append(i) + numerical_dists.append(d) + numerical_inds.append(i) + else: + assert False + + if len(numerical_dists): + active_mus = np.asarray([d.mu[active_indices] for d in numerical_dists]) + active_sigmas = np.asarray([d.sigma[active_indices] for d in numerical_dists]) + lows = np.array([d.low for d in numerical_dists]) + highs = np.array([d.high for d in numerical_dists]) + steps = np.array([getattr(d, "step", 0.0) for d in numerical_dists]) + ret[:, numerical_inds] = _truncnorm.rvs( + a=((lows - steps / 2)[:, np.newaxis] - active_mus) / active_sigmas, + b=((highs + steps / 2)[:, np.newaxis] - active_mus) / active_sigmas, + loc=active_mus, + scale=active_sigmas, + random_state=rng, + ).T + steps_not_0 = np.nonzero(steps != 0.0)[0] + low_d, step_d, high_d = lows[steps_not_0], steps[steps_not_0], highs[steps_not_0] + ret[:, disc_inds] = np.clip( + low_d + np.round((ret[:, disc_inds] - low_d) / step_d) * step_d, low_d, high_d + ) + return ret + + def log_pdf(self, x: np.ndarray) -> np.ndarray: + weighted_log_pdf = np.zeros((len(x), len(self.weights)), dtype=np.float64) + cont_dists = [] + cont_inds = [] + for i, d in enumerate(self.distributions): + if isinstance(d, _BatchedCategoricalDistributions): + xi = x[:, i, np.newaxis, np.newaxis].astype(np.int64) + weighted_log_pdf += np.log(np.take_along_axis(d.weights[np.newaxis], xi, axis=-1))[ + ..., 0 + ] + elif isinstance(d, _BatchedTruncNormDistributions): + cont_dists.append(d) + cont_inds.append(i) + elif isinstance(d, _BatchedDiscreteTruncNormDistributions): + xi_uniq, xi_inv = np.unique(x[:, i], return_inverse=True) + mu_uniq, sigma_uniq, mu_sigma_inv = _unique_inverse_2d(d.mu, d.sigma) + weighted_log_pdf += _log_gauss_mass_unique( + ((xi_uniq - d.step / 2)[:, np.newaxis] - mu_uniq) / sigma_uniq, + ((xi_uniq + d.step / 2)[:, np.newaxis] - mu_uniq) / sigma_uniq, + )[np.ix_(xi_inv, mu_sigma_inv)] + # Very unlikely to observe duplications below, so we skip the unique operation. + weighted_log_pdf -= _truncnorm._log_gauss_mass( + (d.low - d.step / 2 - mu_uniq) / sigma_uniq, + (d.high + d.step / 2 - mu_uniq) / sigma_uniq, + )[mu_sigma_inv] + else: + assert False + + if len(cont_inds): + mus_cont = np.asarray([d.mu for d in cont_dists]).T + sigmas_cont = np.asarray([d.sigma for d in cont_dists]).T + weighted_log_pdf += _truncnorm.logpdf( + x[:, np.newaxis, cont_inds], + a=(np.asarray([d.low for d in cont_dists]) - mus_cont) / sigmas_cont, + b=(np.asarray([d.high for d in cont_dists]) - mus_cont) / sigmas_cont, + loc=mus_cont, + scale=sigmas_cont, + ).sum(axis=-1) + + weighted_log_pdf += np.log(self.weights[np.newaxis]) + max_ = weighted_log_pdf.max(axis=1) + # We need to avoid (-inf) - (-inf) when the probability is zero. + max_[np.isneginf(max_)] = 0 + with np.errstate(divide="ignore"): # Suppress warning in log(0). + return np.log(np.exp(weighted_log_pdf - max_[:, None]).sum(axis=1)) + max_ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/sampler.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..a41ca790b5bfe81887cb37ae3e9a0a43615d9978 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/_tpe/sampler.py @@ -0,0 +1,856 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +from functools import lru_cache +import json +import math +from typing import Any +from typing import cast +from typing import TYPE_CHECKING +import warnings + +import numpy as np + +from optuna import _deprecated +from optuna._convert_positional_args import convert_positional_args +from optuna._experimental import warn_experimental_argument +from optuna._hypervolume import compute_hypervolume +from optuna._hypervolume.hssp import _solve_hssp +from optuna.distributions import BaseDistribution +from optuna.distributions import CategoricalChoiceType +from optuna.logging import get_logger +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.samplers._base import _INDEPENDENT_SAMPLING_WARNING_TEMPLATE +from optuna.samplers._base import _process_constraints_after_trial +from optuna.samplers._base import BaseSampler +from optuna.samplers._lazy_random_state import LazyRandomState +from optuna.samplers._random import RandomSampler +from optuna.samplers._tpe.parzen_estimator import _ParzenEstimator +from optuna.samplers._tpe.parzen_estimator import _ParzenEstimatorParameters +from optuna.search_space import IntersectionSearchSpace +from optuna.search_space.group_decomposed import _GroupDecomposedSearchSpace +from optuna.search_space.group_decomposed import _SearchSpaceGroup +from optuna.study._multi_objective import _fast_non_domination_rank +from optuna.study._multi_objective import _is_pareto_front +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna.study import Study + + +EPS = 1e-12 +_logger = get_logger(__name__) + +_RELATIVE_PARAMS_KEY = "tpe:relative_params" +# The value of system_attrs must be less than 2046 characters on RDBStorage. +_SYSTEM_ATTR_MAX_LENGTH = 2045 + + +def default_gamma(x: int) -> int: + return min(math.ceil(0.1 * x), 25) + + +def hyperopt_default_gamma(x: int) -> int: + return min(math.ceil(0.25 * x**0.5), 25) + + +def default_weights(x: int) -> np.ndarray: + if x == 0: + return np.asarray([]) + elif x < 25: + return np.ones(x) + else: + ramp = np.linspace(1.0 / x, 1.0, num=x - 25) + flat = np.ones(25) + return np.concatenate([ramp, flat], axis=0) + + +class TPESampler(BaseSampler): + """Sampler using TPE (Tree-structured Parzen Estimator) algorithm. + + On each trial, for each parameter, TPE fits one Gaussian Mixture Model (GMM) ``l(x)`` to + the set of parameter values associated with the best objective values, and another GMM + ``g(x)`` to the remaining parameter values. It chooses the parameter value ``x`` that + maximizes the ratio ``l(x)/g(x)``. + + For further information about TPE algorithm, please refer to the following papers: + + - `Algorithms for Hyper-Parameter Optimization + `__ + - `Making a Science of Model Search: Hyperparameter Optimization in Hundreds of + Dimensions for Vision Architectures `__ + - `Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for + Better Empirical Performance `__ + + For multi-objective TPE (MOTPE), please refer to the following papers: + + - `Multiobjective Tree-Structured Parzen Estimator for Computationally Expensive Optimization + Problems `__ + - `Multiobjective Tree-Structured Parzen Estimator `__ + + Please also check our articles: + + - `Significant Speed Up of Multi-Objective TPESampler in Optuna v4.0.0 + `__ + - `Multivariate TPE Makes Optuna Even More Powerful + `__ + + Example: + An example of a single-objective optimization is as follows: + + .. testcode:: + + import optuna + from optuna.samplers import TPESampler + + + def objective(trial): + x = trial.suggest_float("x", -10, 10) + return x**2 + + + study = optuna.create_study(sampler=TPESampler()) + study.optimize(objective, n_trials=10) + + .. note:: + :class:`~optuna.samplers.TPESampler`, which became much faster in v4.0.0, c.f. `our article + `__, + can handle multi-objective optimization with many trials as well. + Please note that :class:`~optuna.samplers.NSGAIISampler` will be used by default for + multi-objective optimization, so if users would like to use + :class:`~optuna.samplers.TPESampler` for multi-objective optimization, ``sampler`` must be + explicitly specified when study is created. + + Args: + consider_prior: + Enhance the stability of Parzen estimator by imposing a Gaussian prior when + :obj:`True`. The prior is only effective if the sampling distribution is + either :class:`~optuna.distributions.FloatDistribution`, + or :class:`~optuna.distributions.IntDistribution`. + + .. warning:: + Deprecated in v4.3.0. ``consider_prior`` argument will be removed in the future. + The removal of this feature is currently scheduled for v6.0.0, + but this schedule is subject to change. + From v4.3.0 onward, ``consider_prior`` automatically falls back to ``True``. + See https://github.com/optuna/optuna/releases/tag/v4.3.0. + prior_weight: + The weight of the prior. This argument is used in + :class:`~optuna.distributions.FloatDistribution`, + :class:`~optuna.distributions.IntDistribution`, and + :class:`~optuna.distributions.CategoricalDistribution`. + consider_magic_clip: + Enable a heuristic to limit the smallest variances of Gaussians used in + the Parzen estimator. + consider_endpoints: + Take endpoints of domains into account when calculating variances of Gaussians + in Parzen estimator. See the original paper for details on the heuristics + to calculate the variances. + n_startup_trials: + The random sampling is used instead of the TPE algorithm until the given number + of trials finish in the same study. + n_ei_candidates: + Number of candidate samples used to calculate the expected improvement. + gamma: + A function that takes the number of finished trials and returns the number + of trials to form a density function for samples with low grains. + See the original paper for more details. + weights: + A function that takes the number of finished trials and returns a weight for them. + See `Making a Science of Model Search: Hyperparameter Optimization in Hundreds of + Dimensions for Vision Architectures + `__ for more details. + + .. note:: + In the multi-objective case, this argument is only used to compute the weights of + bad trials, i.e., trials to construct `g(x)` in the `paper + `__ + ). The weights of good trials, i.e., trials to construct `l(x)`, are computed by a + rule based on the hypervolume contribution proposed in the `paper of MOTPE + `__. + seed: + Seed for random number generator. + multivariate: + If this is :obj:`True`, the multivariate TPE is used when suggesting parameters. + The multivariate TPE is reported to outperform the independent TPE. See `BOHB: Robust + and Efficient Hyperparameter Optimization at Scale + `__ and `our article + `__ + for more details. + + .. note:: + Added in v2.2.0 as an experimental feature. The interface may change in newer + versions without prior notice. See + https://github.com/optuna/optuna/releases/tag/v2.2.0. + group: + If this and ``multivariate`` are :obj:`True`, the multivariate TPE with the group + decomposed search space is used when suggesting parameters. + The sampling algorithm decomposes the search space based on past trials and samples + from the joint distribution in each decomposed subspace. + The decomposed subspaces are a partition of the whole search space. Each subspace + is a maximal subset of the whole search space, which satisfies the following: + for a trial in completed trials, the intersection of the subspace and the search space + of the trial becomes subspace itself or an empty set. + Sampling from the joint distribution on the subspace is realized by multivariate TPE. + If ``group`` is :obj:`True`, ``multivariate`` must be :obj:`True` as well. + + .. note:: + Added in v2.8.0 as an experimental feature. The interface may change in newer + versions without prior notice. See + https://github.com/optuna/optuna/releases/tag/v2.8.0. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_categorical("x", ["A", "B"]) + if x == "A": + return trial.suggest_float("y", -10, 10) + else: + return trial.suggest_int("z", -10, 10) + + + sampler = optuna.samplers.TPESampler(multivariate=True, group=True) + study = optuna.create_study(sampler=sampler) + study.optimize(objective, n_trials=10) + warn_independent_sampling: + If this is :obj:`True` and ``multivariate=True``, a warning message is emitted when + the value of a parameter is sampled by using an independent sampler. + If ``multivariate=False``, this flag has no effect. + constant_liar: + If :obj:`True`, penalize running trials to avoid suggesting parameter configurations + nearby. + + .. note:: + Abnormally terminated trials often leave behind a record with a state of + ``RUNNING`` in the storage. + Such "zombie" trial parameters will be avoided by the constant liar algorithm + during subsequent sampling. + When using an :class:`~optuna.storages.RDBStorage`, it is possible to enable the + ``heartbeat_interval`` to change the records for abnormally terminated trials to + ``FAIL``. + + .. note:: + It is recommended to set this value to :obj:`True` during distributed + optimization to avoid having multiple workers evaluating similar parameter + configurations. In particular, if each objective function evaluation is costly + and the durations of the running states are significant, and/or the number of + workers is high. + + .. note:: + Added in v2.8.0 as an experimental feature. The interface may change in newer + versions without prior notice. See + https://github.com/optuna/optuna/releases/tag/v2.8.0. + constraints_func: + An optional function that computes the objective constraints. It must take a + :class:`~optuna.trial.FrozenTrial` and return the constraints. The return value must + be a sequence of :obj:`float` s. A value strictly larger than 0 means that a + constraints is violated. A value equal to or smaller than 0 is considered feasible. + If ``constraints_func`` returns more than one value for a trial, that trial is + considered feasible if and only if all values are equal to 0 or smaller. + + The ``constraints_func`` will be evaluated after each successful trial. + The function won't be called when trials fail or they are pruned, but this behavior is + subject to change in the future releases. + + .. note:: + Added in v3.0.0 as an experimental feature. The interface may change in newer + versions without prior notice. + See https://github.com/optuna/optuna/releases/tag/v3.0.0. + categorical_distance_func: + A dictionary of distance functions for categorical parameters. The key is the name of + the categorical parameter and the value is a distance function that takes two + :class:`~optuna.distributions.CategoricalChoiceType` s and returns a :obj:`float` + value. The distance function must return a non-negative value. + + While categorical choices are handled equally by default, this option allows users to + specify prior knowledge on the structure of categorical parameters. When specified, + categorical choices closer to current best choices are more likely to be sampled. + + .. note:: + Added in v3.4.0 as an experimental feature. The interface may change in newer + versions without prior notice. + See https://github.com/optuna/optuna/releases/tag/v3.4.0. + """ + + @convert_positional_args( + previous_positional_arg_names=[ + "self", + "consider_prior", + "prior_weight", + "consider_magic_clip", + "consider_endpoints", + "n_startup_trials", + "n_ei_candidates", + "gamma", + "weights", + "seed", + ], + deprecated_version="4.4.0", + removed_version="6.0.0", + ) + def __init__( + self, + *, + consider_prior: bool = True, + prior_weight: float = 1.0, + consider_magic_clip: bool = True, + consider_endpoints: bool = False, + n_startup_trials: int = 10, + n_ei_candidates: int = 24, + gamma: Callable[[int], int] = default_gamma, + weights: Callable[[int], np.ndarray] = default_weights, + seed: int | None = None, + multivariate: bool = False, + group: bool = False, + warn_independent_sampling: bool = True, + constant_liar: bool = False, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + categorical_distance_func: ( + dict[str, Callable[[CategoricalChoiceType, CategoricalChoiceType], float]] | None + ) = None, + ) -> None: + if not consider_prior: + msg = _deprecated._DEPRECATION_WARNING_TEMPLATE.format( + name="`consider_prior`", d_ver="4.3.0", r_ver="6.0.0" + ) + warnings.warn( + f"{msg} From v4.3.0 onward, `consider_prior` automatically falls back to `True`.", + FutureWarning, + ) + + self._parzen_estimator_parameters = _ParzenEstimatorParameters( + prior_weight=prior_weight, + consider_magic_clip=consider_magic_clip, + consider_endpoints=consider_endpoints, + weights=weights, + multivariate=multivariate, + categorical_distance_func=categorical_distance_func or {}, + ) + self._n_startup_trials = n_startup_trials + self._n_ei_candidates = n_ei_candidates + self._gamma = gamma + + self._warn_independent_sampling = warn_independent_sampling + self._rng = LazyRandomState(seed) + self._random_sampler = RandomSampler(seed=seed) + + self._multivariate = multivariate + self._group = group + self._group_decomposed_search_space: _GroupDecomposedSearchSpace | None = None + self._search_space_group: _SearchSpaceGroup | None = None + self._search_space = IntersectionSearchSpace(include_pruned=True) + self._constant_liar = constant_liar + self._constraints_func = constraints_func + # NOTE(nabenabe0928): Users can overwrite _ParzenEstimator to customize the TPE behavior. + self._parzen_estimator_cls = _ParzenEstimator + + if multivariate: + warn_experimental_argument("multivariate") + + if group: + if not multivariate: + raise ValueError( + "``group`` option can only be enabled when ``multivariate`` is enabled." + ) + warn_experimental_argument("group") + self._group_decomposed_search_space = _GroupDecomposedSearchSpace(True) + + if constant_liar: + warn_experimental_argument("constant_liar") + + if constraints_func is not None: + warn_experimental_argument("constraints_func") + + if categorical_distance_func is not None: + warn_experimental_argument("categorical_distance_func") + + def reseed_rng(self) -> None: + self._rng.rng.seed() + self._random_sampler.reseed_rng() + + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + if not self._multivariate: + return {} + + search_space: dict[str, BaseDistribution] = {} + + if self._group: + assert self._group_decomposed_search_space is not None + self._search_space_group = self._group_decomposed_search_space.calculate(study) + for sub_space in self._search_space_group.search_spaces: + # Sort keys because Python's string hashing is nondeterministic. + for name, distribution in sorted(sub_space.items()): + if distribution.single(): + continue + search_space[name] = distribution + return search_space + + for name, distribution in self._search_space.calculate(study).items(): + if distribution.single(): + continue + search_space[name] = distribution + + return search_space + + def sample_relative( + self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution] + ) -> dict[str, Any]: + if self._group: + assert self._search_space_group is not None + params = {} + for sub_space in self._search_space_group.search_spaces: + search_space = {} + # Sort keys because Python's string hashing is nondeterministic. + for name, distribution in sorted(sub_space.items()): + if not distribution.single(): + search_space[name] = distribution + params.update(self._sample_relative(study, trial, search_space)) + else: + params = self._sample_relative(study, trial, search_space) + + if params != {} and self._constant_liar: + # Share the params obtained by the relative sampling with the other processes. + params_str = json.dumps(params) + for i in range(0, len(params_str), _SYSTEM_ATTR_MAX_LENGTH): + study._storage.set_trial_system_attr( + trial._trial_id, + f"{_RELATIVE_PARAMS_KEY}:{i // _SYSTEM_ATTR_MAX_LENGTH}", + params_str[i : i + _SYSTEM_ATTR_MAX_LENGTH], + ) + return params + + def _sample_relative( + self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution] + ) -> dict[str, Any]: + if search_space == {}: + return {} + + states = (TrialState.COMPLETE, TrialState.PRUNED) + trials = study._get_trials(deepcopy=False, states=states, use_cache=True) + # If the number of samples is insufficient, we run random trial. + if len(trials) < self._n_startup_trials: + return {} + + return self._sample(study, trial, search_space) + + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + states = (TrialState.COMPLETE, TrialState.PRUNED) + trials = study._get_trials(deepcopy=False, states=states, use_cache=True) + + # If the number of samples is insufficient, we run random trial. + if len(trials) < self._n_startup_trials: + return self._random_sampler.sample_independent( + study, trial, param_name, param_distribution + ) + + if self._warn_independent_sampling and self._multivariate: + # Avoid independent warning at the first sampling of `param_name`. + if any(param_name in trial.params for trial in trials): + _logger.warning( + _INDEPENDENT_SAMPLING_WARNING_TEMPLATE.format( + param_name=param_name, + trial_number=trial.number, + independent_sampler_name=self._random_sampler.__class__.__name__, + sampler_name=self.__class__.__name__, + fallback_reason=( + "dynamic search space is not supported for `multivariate=True`" + ), + ) + ) + + return self._sample(study, trial, {param_name: param_distribution})[param_name] + + def _get_params(self, trial: FrozenTrial) -> dict[str, Any]: + if trial.state.is_finished() or not self._multivariate: + # NOTE(not522): If not multivariate, `relative_params` does not exist and + # `system_attrs` access will be unnecessary, so we skip it. + return trial.params + + params_strs = [] + i = 0 + while params_str_i := trial.system_attrs.get(f"{_RELATIVE_PARAMS_KEY}:{i}"): + params_strs.append(params_str_i) + i += 1 + + if len(params_strs) == 0: + return trial.params + params = json.loads("".join(params_strs)) + params.update(trial.params) + return params + + def _get_internal_repr( + self, trials: list[FrozenTrial], search_space: dict[str, BaseDistribution] + ) -> dict[str, np.ndarray]: + values: dict[str, list[float]] = {param_name: [] for param_name in search_space} + for trial in trials: + params = self._get_params(trial) + if search_space.keys() <= params.keys(): + for param_name, distribution in search_space.items(): + param = params[param_name] + values[param_name].append(distribution.to_internal_repr(param)) + return {k: np.asarray(v) for k, v in values.items()} + + def _sample( + self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution] + ) -> dict[str, Any]: + if self._constant_liar: + states = [TrialState.COMPLETE, TrialState.PRUNED, TrialState.RUNNING] + else: + states = [TrialState.COMPLETE, TrialState.PRUNED] + use_cache = not self._constant_liar + trials = study._get_trials(deepcopy=False, states=states, use_cache=use_cache) + + if self._constant_liar: + # For constant_liar, filter out the current trial. + trials = [t for t in trials if trial.number != t.number] + + # We divide data into below and above. + n = sum(trial.state != TrialState.RUNNING for trial in trials) # Ignore running trials. + below_trials, above_trials = _split_trials( + study, + trials, + self._gamma(n), + self._constraints_func is not None, + ) + + mpe_below = self._build_parzen_estimator( + study, search_space, below_trials, handle_below=True + ) + mpe_above = self._build_parzen_estimator( + study, search_space, above_trials, handle_below=False + ) + + samples_below = mpe_below.sample(self._rng.rng, self._n_ei_candidates) + acq_func_vals = self._compute_acquisition_func(samples_below, mpe_below, mpe_above) + ret = TPESampler._compare(samples_below, acq_func_vals) + + for param_name, dist in search_space.items(): + ret[param_name] = dist.to_external_repr(ret[param_name]) + + return ret + + def _build_parzen_estimator( + self, + study: Study, + search_space: dict[str, BaseDistribution], + trials: list[FrozenTrial], + handle_below: bool, + ) -> _ParzenEstimator: + observations = self._get_internal_repr(trials, search_space) + if handle_below and study._is_multi_objective(): + param_mask_below = [ + search_space.keys() <= self._get_params(trial).keys() for trial in trials + ] + weights_below = _calculate_weights_below_for_multi_objective( + study, trials, self._constraints_func + )[param_mask_below] + assert np.isfinite(weights_below).all() + mpe = self._parzen_estimator_cls( + observations, search_space, self._parzen_estimator_parameters, weights_below + ) + else: + mpe = self._parzen_estimator_cls( + observations, search_space, self._parzen_estimator_parameters + ) + + if not isinstance(mpe, _ParzenEstimator): + raise RuntimeError("_parzen_estimator_cls must override _ParzenEstimator.") + + return mpe + + def _compute_acquisition_func( + self, + samples: dict[str, np.ndarray], + mpe_below: _ParzenEstimator, + mpe_above: _ParzenEstimator, + ) -> np.ndarray: + log_likelihoods_below = mpe_below.log_pdf(samples) + log_likelihoods_above = mpe_above.log_pdf(samples) + acq_func_vals = log_likelihoods_below - log_likelihoods_above + return acq_func_vals + + @classmethod + def _compare( + cls, samples: dict[str, np.ndarray], acquisition_func_vals: np.ndarray + ) -> dict[str, int | float]: + sample_size = next(iter(samples.values())).size + if sample_size == 0: + raise ValueError(f"The size of `samples` must be positive, but got {sample_size}.") + + if sample_size != acquisition_func_vals.size: + raise ValueError( + "The sizes of `samples` and `acquisition_func_vals` must be same, but got " + "(samples.size, acquisition_func_vals.size) = " + f"({sample_size}, {acquisition_func_vals.size})." + ) + + best_idx = np.argmax(acquisition_func_vals) + return {k: v[best_idx].item() for k, v in samples.items()} + + @staticmethod + def hyperopt_parameters() -> dict[str, Any]: + """Return the the default parameters of hyperopt (v0.1.2). + + :class:`~optuna.samplers.TPESampler` can be instantiated with the parameters returned + by this method. + + Example: + + Create a :class:`~optuna.samplers.TPESampler` instance with the default + parameters of `hyperopt `__. + + .. testcode:: + + import optuna + from optuna.samplers import TPESampler + + + def objective(trial): + x = trial.suggest_float("x", -10, 10) + return x**2 + + + sampler = TPESampler(**TPESampler.hyperopt_parameters()) + study = optuna.create_study(sampler=sampler) + study.optimize(objective, n_trials=10) + + Returns: + A dictionary containing the default parameters of hyperopt. + + """ + + return { + "consider_prior": True, + "prior_weight": 1.0, + "consider_magic_clip": True, + "consider_endpoints": False, + "n_startup_trials": 20, + "n_ei_candidates": 24, + "gamma": hyperopt_default_gamma, + "weights": default_weights, + } + + def before_trial(self, study: Study, trial: FrozenTrial) -> None: + self._random_sampler.before_trial(study, trial) + + def after_trial( + self, + study: Study, + trial: FrozenTrial, + state: TrialState, + values: Sequence[float] | None, + ) -> None: + assert state in [TrialState.COMPLETE, TrialState.FAIL, TrialState.PRUNED] + if self._constraints_func is not None: + _process_constraints_after_trial(self._constraints_func, study, trial, state) + self._random_sampler.after_trial(study, trial, state, values) + + +def _get_reference_point(loss_vals: np.ndarray) -> np.ndarray: + worst_point = np.max(loss_vals, axis=0) + reference_point = np.maximum(1.1 * worst_point, 0.9 * worst_point) + reference_point[reference_point == 0] = EPS + return reference_point + + +def _split_trials( + study: Study, trials: list[FrozenTrial], n_below: int, constraints_enabled: bool +) -> tuple[list[FrozenTrial], list[FrozenTrial]]: + complete_trials = [] + pruned_trials = [] + running_trials = [] + infeasible_trials = [] + + for trial in trials: + if trial.state == TrialState.RUNNING: + # We should check if the trial is RUNNING before the feasibility check + # because its constraint values have not yet been set. + running_trials.append(trial) + elif constraints_enabled and _get_infeasible_trial_score(trial) > 0: + infeasible_trials.append(trial) + elif trial.state == TrialState.COMPLETE: + complete_trials.append(trial) + elif trial.state == TrialState.PRUNED: + pruned_trials.append(trial) + else: + assert False + + # We divide data into below and above. + below_complete, above_complete = _split_complete_trials(complete_trials, study, n_below) + # This ensures `n_below` is non-negative to prevent unexpected trial splits. + n_below = max(0, n_below - len(below_complete)) + below_pruned, above_pruned = _split_pruned_trials(pruned_trials, study, n_below) + # This ensures `n_below` is non-negative to prevent unexpected trial splits. + n_below = max(0, n_below - len(below_pruned)) + below_infeasible, above_infeasible = _split_infeasible_trials(infeasible_trials, n_below) + + below_trials = below_complete + below_pruned + below_infeasible + above_trials = above_complete + above_pruned + above_infeasible + running_trials + below_trials.sort(key=lambda trial: trial.number) + above_trials.sort(key=lambda trial: trial.number) + + return below_trials, above_trials + + +def _split_complete_trials( + trials: Sequence[FrozenTrial], study: Study, n_below: int +) -> tuple[list[FrozenTrial], list[FrozenTrial]]: + n_below = min(n_below, len(trials)) + if len(study.directions) <= 1: + return _split_complete_trials_single_objective(trials, study, n_below) + else: + return _split_complete_trials_multi_objective(trials, study, n_below) + + +def _split_complete_trials_single_objective( + trials: Sequence[FrozenTrial], study: Study, n_below: int +) -> tuple[list[FrozenTrial], list[FrozenTrial]]: + if study.direction == StudyDirection.MINIMIZE: + sorted_trials = sorted(trials, key=lambda trial: cast(float, trial.value)) + else: + sorted_trials = sorted(trials, key=lambda trial: cast(float, trial.value), reverse=True) + return sorted_trials[:n_below], sorted_trials[n_below:] + + +def _split_complete_trials_multi_objective( + trials: Sequence[FrozenTrial], study: Study, n_below: int +) -> tuple[list[FrozenTrial], list[FrozenTrial]]: + if n_below == 0: + return [], list(trials) + elif n_below == len(trials): + return list(trials), [] + + assert 0 < n_below < len(trials) + lvals = np.array([trial.values for trial in trials]) + lvals *= [-1.0 if d == StudyDirection.MAXIMIZE else 1.0 for d in study.directions] + nondomination_ranks = _fast_non_domination_rank(lvals, n_below=n_below) + ranks, rank_counts = np.unique(nondomination_ranks, return_counts=True) + last_rank_before_tiebreak = int(np.max(ranks[np.cumsum(rank_counts) <= n_below], initial=-1)) + assert all(ranks[: last_rank_before_tiebreak + 1] == np.arange(last_rank_before_tiebreak + 1)) + indices = np.arange(len(trials)) + indices_below = indices[nondomination_ranks <= last_rank_before_tiebreak] + + if indices_below.size < n_below: # Tie-break with Hypervolume subset selection problem (HSSP). + assert ranks[last_rank_before_tiebreak + 1] == last_rank_before_tiebreak + 1 + need_tiebreak = nondomination_ranks == last_rank_before_tiebreak + 1 + rank_i_lvals = lvals[need_tiebreak] + subset_size = n_below - indices_below.size + selected_indices = _solve_hssp_with_cache( + tuple(rank_i_lvals.ravel()), + tuple(indices[need_tiebreak]), + subset_size, + tuple(_get_reference_point(rank_i_lvals)), + ) + indices_below = np.append(indices_below, selected_indices) + + below_indices_set = set(cast(list, indices_below.tolist())) + below_trials = [trials[i] for i in range(len(trials)) if i in below_indices_set] + above_trials = [trials[i] for i in range(len(trials)) if i not in below_indices_set] + return below_trials, above_trials + + +def _get_pruned_trial_score(trial: FrozenTrial, study: Study) -> tuple[float, float]: + if len(trial.intermediate_values) > 0: + step, intermediate_value = max(trial.intermediate_values.items()) + if math.isnan(intermediate_value): + return -step, float("inf") + elif study.direction == StudyDirection.MINIMIZE: + return -step, intermediate_value + else: + return -step, -intermediate_value + else: + return 1, 0.0 + + +def _split_pruned_trials( + trials: Sequence[FrozenTrial], study: Study, n_below: int +) -> tuple[list[FrozenTrial], list[FrozenTrial]]: + n_below = min(n_below, len(trials)) + sorted_trials = sorted(trials, key=lambda trial: _get_pruned_trial_score(trial, study)) + return sorted_trials[:n_below], sorted_trials[n_below:] + + +def _get_infeasible_trial_score(trial: FrozenTrial) -> float: + constraint = trial.system_attrs.get(_CONSTRAINTS_KEY) + if constraint is None: + warnings.warn( + f"Trial {trial.number} does not have constraint values." + " It will be treated as a lower priority than other trials." + ) + return float("inf") + else: + # Violation values of infeasible dimensions are summed up. + return sum(v for v in constraint if v > 0) + + +def _split_infeasible_trials( + trials: Sequence[FrozenTrial], n_below: int +) -> tuple[list[FrozenTrial], list[FrozenTrial]]: + n_below = min(n_below, len(trials)) + sorted_trials = sorted(trials, key=_get_infeasible_trial_score) + return sorted_trials[:n_below], sorted_trials[n_below:] + + +def _calculate_weights_below_for_multi_objective( + study: Study, + below_trials: list[FrozenTrial], + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None, +) -> np.ndarray: + def _feasible(trial: FrozenTrial) -> bool: + return constraints_func is None or all(c <= 0 for c in constraints_func(trial)) + + is_feasible = np.asarray([_feasible(t) for t in below_trials]) + weights_below = np.where(is_feasible, 1.0, EPS) # Assign EPS to infeasible trials. + n_below_feasible = np.count_nonzero(is_feasible) + if n_below_feasible <= 1: + return weights_below + + lvals = np.asarray([t.values for t in below_trials])[is_feasible] + lvals *= [-1.0 if d == StudyDirection.MAXIMIZE else 1.0 for d in study.directions] + ref_point = _get_reference_point(lvals) + on_front = _is_pareto_front(lvals, assume_unique_lexsorted=False) + pareto_sols = lvals[on_front] + hv = compute_hypervolume(pareto_sols, ref_point, assume_pareto=True) + if math.isinf(hv): + # TODO(nabenabe): Assign EPS to non-Pareto solutions, and + # solutions with finite contrib if hv is inf. Ref: PR#5813. + return weights_below + + loo_mat = ~np.eye(pareto_sols.shape[0], dtype=bool) # Leave-one-out bool matrix. + contribs = np.zeros(n_below_feasible, dtype=float) + contribs[on_front] = hv - np.array( + [compute_hypervolume(pareto_sols[loo], ref_point, assume_pareto=True) for loo in loo_mat] + ) + weights_below[is_feasible] = np.maximum(contribs / max(np.max(contribs), EPS), EPS) + return weights_below + + +@lru_cache(maxsize=1) +def _solve_hssp_with_cache( + rank_i_lvals_tuple: tuple[float, ...], + rank_i_indices_tuple: tuple[int, ...], + subset_size: int, + ref_point_tuple: tuple[float, ...], +) -> np.ndarray: + lvals_shape = (len(rank_i_indices_tuple), len(ref_point_tuple)) + rank_i_lvals = np.reshape(rank_i_lvals_tuple, lvals_shape) + rank_i_indices = np.array(rank_i_indices_tuple) + ref_point = np.array(ref_point_tuple) + return _solve_hssp(rank_i_lvals, rank_i_indices, subset_size, ref_point) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..87247b277b4456c089d30869804ecfeed898e898 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/__init__.py @@ -0,0 +1,18 @@ +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover +from optuna.samplers.nsgaii._crossovers._blxalpha import BLXAlphaCrossover +from optuna.samplers.nsgaii._crossovers._sbx import SBXCrossover +from optuna.samplers.nsgaii._crossovers._spx import SPXCrossover +from optuna.samplers.nsgaii._crossovers._undx import UNDXCrossover +from optuna.samplers.nsgaii._crossovers._uniform import UniformCrossover +from optuna.samplers.nsgaii._crossovers._vsbx import VSBXCrossover + + +__all__ = [ + "BaseCrossover", + "BLXAlphaCrossover", + "SBXCrossover", + "SPXCrossover", + "UNDXCrossover", + "UniformCrossover", + "VSBXCrossover", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..45008ee572f31bf902f05cf430913995a4d75610 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/__pycache__/_after_trial_strategy.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/__pycache__/_after_trial_strategy.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1eb3f72aea6864bb17e970b8d225d5b30e19e846 Binary files 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b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/__pycache__/_sampler.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_after_trial_strategy.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_after_trial_strategy.py new file mode 100644 index 0000000000000000000000000000000000000000..34cb75c487ddcc37f4d4a053f1f565cbaa4c0266 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_after_trial_strategy.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +from typing import TYPE_CHECKING + +from optuna.samplers._base import _process_constraints_after_trial +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna.study import Study + + +class NSGAIIAfterTrialStrategy: + def __init__( + self, *, constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None + ) -> None: + self._constraints_func = constraints_func + + def __call__( + self, + study: Study, + trial: FrozenTrial, + state: TrialState, + values: Sequence[float] | None = None, + ) -> None: + """Carry out the after trial process of default NSGA-II. + + This method is called after each trial of the study, examines whether the trial result is + valid in terms of constraints, and store the results in system_attrs of the study. + """ + if self._constraints_func is not None: + _process_constraints_after_trial(self._constraints_func, study, trial, state) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_child_generation_strategy.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_child_generation_strategy.py new file mode 100644 index 0000000000000000000000000000000000000000..ef633a44a42367fabc5d3dce3d1086ac5ccd92b3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_child_generation_strategy.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +from typing import Any +from typing import TYPE_CHECKING + +from optuna.distributions import BaseDistribution +from optuna.samplers._lazy_random_state import LazyRandomState +from optuna.samplers.nsgaii._constraints_evaluation import _constrained_dominates +from optuna.samplers.nsgaii._crossover import perform_crossover +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover +from optuna.study._multi_objective import _dominates +from optuna.trial import FrozenTrial + + +if TYPE_CHECKING: + from optuna.study import Study + + +class NSGAIIChildGenerationStrategy: + def __init__( + self, + *, + mutation_prob: float | None = None, + crossover: BaseCrossover, + crossover_prob: float, + swapping_prob: float, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + rng: LazyRandomState, + ) -> None: + if not (mutation_prob is None or 0.0 <= mutation_prob <= 1.0): + raise ValueError( + "`mutation_prob` must be None or a float value within the range [0.0, 1.0]." + ) + + if not (0.0 <= crossover_prob <= 1.0): + raise ValueError("`crossover_prob` must be a float value within the range [0.0, 1.0].") + + if not (0.0 <= swapping_prob <= 1.0): + raise ValueError("`swapping_prob` must be a float value within the range [0.0, 1.0].") + + if not isinstance(crossover, BaseCrossover): + raise ValueError( + f"'{crossover}' is not a valid crossover." + " For valid crossovers see" + " https://optuna.readthedocs.io/en/stable/reference/samplers.html." + ) + + self._crossover_prob = crossover_prob + self._mutation_prob = mutation_prob + self._swapping_prob = swapping_prob + self._crossover = crossover + self._constraints_func = constraints_func + self._rng = rng + + def __call__( + self, + study: Study, + search_space: dict[str, BaseDistribution], + parent_population: list[FrozenTrial], + ) -> dict[str, Any]: + """Generate a child parameter from the given parent population by NSGA-II algorithm. + Args: + study: + Target study object. + search_space: + A dictionary containing the parameter names and parameter's distributions. + parent_population: + A list of trials that are selected as parent population. + Returns: + A dictionary containing the parameter names and parameter's values. + """ + dominates = _dominates if self._constraints_func is None else _constrained_dominates + # We choose a child based on the specified crossover method. + if self._rng.rng.rand() < self._crossover_prob: + child_params = perform_crossover( + self._crossover, + study, + parent_population, + search_space, + self._rng.rng, + self._swapping_prob, + dominates, + ) + else: + parent_population_size = len(parent_population) + parent_params = parent_population[self._rng.rng.choice(parent_population_size)].params + child_params = {name: parent_params[name] for name in search_space.keys()} + + n_params = len(child_params) + if self._mutation_prob is None: + mutation_prob = 1.0 / max(1.0, n_params) + else: + mutation_prob = self._mutation_prob + + params = {} + for param_name in child_params.keys(): + if self._rng.rng.rand() >= mutation_prob: + params[param_name] = child_params[param_name] + return params diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_constraints_evaluation.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_constraints_evaluation.py new file mode 100644 index 0000000000000000000000000000000000000000..f7e14638c9a6f8de698af5492d800175bfe9a969 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_constraints_evaluation.py @@ -0,0 +1,127 @@ +from __future__ import annotations + +from collections.abc import Sequence +import warnings + +import numpy as np + +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.study import StudyDirection +from optuna.study._multi_objective import _dominates +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +def _constrained_dominates( + trial0: FrozenTrial, trial1: FrozenTrial, directions: Sequence[StudyDirection] +) -> bool: + """Checks constrained-domination. + + A trial x is said to constrained-dominate a trial y, if any of the following conditions is + true: + 1) Trial x is feasible and trial y is not. + 2) Trial x and y are both infeasible, but solution x has a smaller overall constraint + violation. + 3) Trial x and y are feasible and trial x dominates trial y. + """ + + constraints0 = trial0.system_attrs.get(_CONSTRAINTS_KEY) + constraints1 = trial1.system_attrs.get(_CONSTRAINTS_KEY) + + if constraints0 is None: + warnings.warn( + f"Trial {trial0.number} does not have constraint values." + " It will be dominated by the other trials." + ) + + if constraints1 is None: + warnings.warn( + f"Trial {trial1.number} does not have constraint values." + " It will be dominated by the other trials." + ) + + if constraints0 is None and constraints1 is None: + # Neither Trial x nor y has constraints values + return _dominates(trial0, trial1, directions) + + if constraints0 is not None and constraints1 is None: + # Trial x has constraint values, but y doesn't. + return True + + if constraints0 is None and constraints1 is not None: + # If Trial y has constraint values, but x doesn't. + return False + + assert isinstance(constraints0, (list, tuple)) + assert isinstance(constraints1, (list, tuple)) + + if len(constraints0) != len(constraints1): + raise ValueError("Trials with different numbers of constraints cannot be compared.") + + if trial0.state != TrialState.COMPLETE: + return False + + if trial1.state != TrialState.COMPLETE: + return True + + satisfy_constraints0 = all(v <= 0 for v in constraints0) + satisfy_constraints1 = all(v <= 0 for v in constraints1) + + if satisfy_constraints0 and satisfy_constraints1: + # Both trials satisfy the constraints. + return _dominates(trial0, trial1, directions) + + if satisfy_constraints0: + # trial0 satisfies the constraints, but trial1 violates them. + return True + + if satisfy_constraints1: + # trial1 satisfies the constraints, but trial0 violates them. + return False + + # Both trials violate the constraints. + violation0 = sum(v for v in constraints0 if v > 0) + violation1 = sum(v for v in constraints1 if v > 0) + return violation0 < violation1 + + +def _evaluate_penalty(population: Sequence[FrozenTrial]) -> np.ndarray: + """Evaluate feasibility of trials in population. + Returns: + A list of feasibility status T/F/None of trials in population, where T/F means + feasible/infeasible and None means that the trial does not have constraint values. + """ + + penalty: list[float] = [] + for trial in population: + constraints = trial.system_attrs.get(_CONSTRAINTS_KEY) + if constraints is None: + penalty.append(np.nan) + else: + penalty.append(sum(v for v in constraints if v > 0)) + return np.array(penalty) + + +def _validate_constraints( + population: list[FrozenTrial], + *, + is_constrained: bool = False, +) -> None: + if not is_constrained: + return + + num_constraints = max( + [len(t.system_attrs.get(_CONSTRAINTS_KEY, [])) for t in population], default=0 + ) + for _trial in population: + _constraints = _trial.system_attrs.get(_CONSTRAINTS_KEY) + if _constraints is None: + warnings.warn( + f"Trial {_trial.number} does not have constraint values." + " It will be dominated by the other trials." + ) + continue + if np.any(np.isnan(np.array(_constraints))): + raise ValueError("NaN is not acceptable as constraint value.") + elif len(_constraints) != num_constraints: + raise ValueError("Trials with different numbers of constraints cannot be compared.") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossover.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossover.py new file mode 100644 index 0000000000000000000000000000000000000000..ef27fd97109f1626662da62a143ed3acf7c76462 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossover.py @@ -0,0 +1,178 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +from typing import Any +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._transform import _SearchSpaceTransform +from optuna.distributions import BaseDistribution +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover +from optuna.study import StudyDirection +from optuna.trial import FrozenTrial + + +if TYPE_CHECKING: + from optuna.study import Study + + +_NUMERICAL_DISTRIBUTIONS = ( + FloatDistribution, + IntDistribution, +) + + +def _try_crossover( + parents: list[FrozenTrial], + crossover: BaseCrossover, + study: Study, + rng: np.random.RandomState, + swapping_prob: float, + categorical_search_space: dict[str, BaseDistribution], + numerical_search_space: dict[str, BaseDistribution], + numerical_transform: _SearchSpaceTransform | None, +) -> dict[str, Any]: + child_params: dict[str, Any] = {} + + if len(categorical_search_space) > 0: + parents_categorical_params = np.array( + [ + [parent.params[p] for p in categorical_search_space] + for parent in [parents[0], parents[-1]] + ], + dtype=object, + ) + + child_categorical_array = _inlined_categorical_uniform_crossover( + parents_categorical_params, rng, swapping_prob, categorical_search_space + ) + child_categorical_params = { + param: value for param, value in zip(categorical_search_space, child_categorical_array) + } + child_params.update(child_categorical_params) + + if numerical_transform is None: + return child_params + + # The following is applied only for numerical parameters. + parents_numerical_params = np.stack( + [ + numerical_transform.transform( + { + param_key: parent.params[param_key] + for param_key in numerical_search_space.keys() + } + ) + for parent in parents + ] + ) # Parent individual with NUMERICAL_DISTRIBUTIONS parameter. + + child_numerical_array = crossover.crossover( + parents_numerical_params, rng, study, numerical_transform.bounds + ) + child_numerical_params = numerical_transform.untransform(child_numerical_array) + child_params.update(child_numerical_params) + + return child_params + + +def perform_crossover( + crossover: BaseCrossover, + study: Study, + parent_population: Sequence[FrozenTrial], + search_space: dict[str, BaseDistribution], + rng: np.random.RandomState, + swapping_prob: float, + dominates: Callable[[FrozenTrial, FrozenTrial, Sequence[StudyDirection]], bool], +) -> dict[str, Any]: + numerical_search_space: dict[str, BaseDistribution] = {} + categorical_search_space: dict[str, BaseDistribution] = {} + for key, value in search_space.items(): + if isinstance(value, _NUMERICAL_DISTRIBUTIONS): + numerical_search_space[key] = value + else: + categorical_search_space[key] = value + + numerical_transform: _SearchSpaceTransform | None = None + if len(numerical_search_space) != 0: + numerical_transform = _SearchSpaceTransform(numerical_search_space) + + while True: # Repeat while parameters lie outside search space boundaries. + parents = _select_parents(crossover, study, parent_population, rng, dominates) + child_params = _try_crossover( + parents, + crossover, + study, + rng, + swapping_prob, + categorical_search_space, + numerical_search_space, + numerical_transform, + ) + + if _is_contained(child_params, search_space): + break + + return child_params + + +def _select_parents( + crossover: BaseCrossover, + study: Study, + parent_population: Sequence[FrozenTrial], + rng: np.random.RandomState, + dominates: Callable[[FrozenTrial, FrozenTrial, Sequence[StudyDirection]], bool], +) -> list[FrozenTrial]: + parents: list[FrozenTrial] = [] + for _ in range(crossover.n_parents): + parent = _select_parent( + study, [t for t in parent_population if t not in parents], rng, dominates + ) + parents.append(parent) + + return parents + + +def _select_parent( + study: Study, + parent_population: Sequence[FrozenTrial], + rng: np.random.RandomState, + dominates: Callable[[FrozenTrial, FrozenTrial, Sequence[StudyDirection]], bool], +) -> FrozenTrial: + population_size = len(parent_population) + candidate0 = parent_population[rng.choice(population_size)] + candidate1 = parent_population[rng.choice(population_size)] + + # TODO(ohta): Consider crowding distance. + if dominates(candidate0, candidate1, study.directions): + return candidate0 + else: + return candidate1 + + +def _is_contained(params: dict[str, Any], search_space: dict[str, BaseDistribution]) -> bool: + for param_name in params.keys(): + param, param_distribution = params[param_name], search_space[param_name] + + if not param_distribution._contains(param_distribution.to_internal_repr(param)): + return False + return True + + +def _inlined_categorical_uniform_crossover( + parent_params: np.ndarray, + rng: np.random.RandomState, + swapping_prob: float, + search_space: dict[str, BaseDistribution], +) -> np.ndarray: + # We can't use uniform crossover implementation of `BaseCrossover` for + # parameters from `CategoricalDistribution`, since categorical params are + # passed to crossover untransformed, which is not what `BaseCrossover` + # implementations expect. + n_categorical_params = len(search_space) + masks = (rng.rand(n_categorical_params) >= swapping_prob).astype(int) + return parent_params[masks, range(n_categorical_params)] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/__init__.py 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b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_base.py new file mode 100644 index 0000000000000000000000000000000000000000..6b3af466e565156c184630227c7ce92cf62b20c1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_base.py @@ -0,0 +1,64 @@ +from __future__ import annotations + +import abc +from typing import TYPE_CHECKING + +import numpy as np + + +if TYPE_CHECKING: + from optuna.study import Study + + +class BaseCrossover(abc.ABC): + """Base class for crossovers. + + A crossover operation is used by :class:`~optuna.samplers.NSGAIISampler` + to create new parameter combination from parameters of ``n`` parent individuals. + + .. note:: + Concrete implementations of this class are expected to only accept parameters + from numerical distributions. At the moment, only crossover operation for categorical + parameters (uniform crossover) is built-in into :class:`~optuna.samplers.NSGAIISampler`. + """ + + def __str__(self) -> str: + return self.__class__.__name__ + + @property + @abc.abstractmethod + def n_parents(self) -> int: + """Number of parent individuals required to perform crossover.""" + + raise NotImplementedError + + @abc.abstractmethod + def crossover( + self, + parents_params: np.ndarray, + rng: np.random.RandomState, + study: Study, + search_space_bounds: np.ndarray, + ) -> np.ndarray: + """Perform crossover of selected parent individuals. + + This method is called in :func:`~optuna.samplers.NSGAIISampler.sample_relative`. + + Args: + parents_params: + A ``numpy.ndarray`` with dimensions ``num_parents x num_parameters``. + Represents a parameter space for each parent individual. This space is + continuous for numerical parameters. + rng: + An instance of ``numpy.random.RandomState``. + study: + Target study object. + search_space_bounds: + A ``numpy.ndarray`` with dimensions ``len_search_space x 2`` representing + numerical distribution bounds constructed from transformed search space. + + Returns: + A 1-dimensional ``numpy.ndarray`` containing new parameter combination. + """ + + raise NotImplementedError diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_blxalpha.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_blxalpha.py new file mode 100644 index 0000000000000000000000000000000000000000..f226e0041f696f1568e0aac9abd3387f884397e7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_blxalpha.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._experimental import experimental_class +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover + + +if TYPE_CHECKING: + from optuna.study import Study + + +@experimental_class("3.0.0") +class BLXAlphaCrossover(BaseCrossover): + """Blend Crossover operation used by :class:`~optuna.samplers.NSGAIISampler`. + + Uniformly samples child individuals from the hyper-rectangles created + by the two parent individuals. For further information about BLX-alpha crossover, + please refer to the following paper: + + - `Eshelman, L. and J. D. Schaffer. + Real-Coded Genetic Algorithms and Interval-Schemata. FOGA (1992). + `__ + + Args: + alpha: + Parametrizes blend operation. + """ + + n_parents = 2 + + def __init__(self, alpha: float = 0.5) -> None: + self._alpha = alpha + + def crossover( + self, + parents_params: np.ndarray, + rng: np.random.RandomState, + study: Study, + search_space_bounds: np.ndarray, + ) -> np.ndarray: + # https://doi.org/10.1109/CEC.2001.934452 + # Section 2 Crossover Operators for RCGA 2.1 Blend Crossover + + parents_min = parents_params.min(axis=0) + parents_max = parents_params.max(axis=0) + diff = self._alpha * (parents_max - parents_min) # Equation (1). + low = parents_min - diff # Equation (1). + high = parents_max + diff # Equation (1). + r = rng.rand(len(search_space_bounds)) + child_params = (high - low) * r + low + + return child_params diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_sbx.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_sbx.py new file mode 100644 index 0000000000000000000000000000000000000000..fd4c6ae1520253bb99b52320ad1d2fc7bcebda74 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_sbx.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._experimental import experimental_class +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover + + +if TYPE_CHECKING: + from optuna.study import Study + + +@experimental_class("3.0.0") +class SBXCrossover(BaseCrossover): + """Simulated Binary Crossover operation used by :class:`~optuna.samplers.NSGAIISampler`. + + Generates a child from two parent individuals + according to the polynomial probability distribution. + + In the paper, SBX has only one argument, ``eta``, + and generate two child individuals. + However, Optuna can only return one child individual in one crossover operation, + so it uses the ``uniform_crossover_prob`` and ``use_child_gene_prob`` arguments + to make two individuals into one. + + - `Deb, K. and R. Agrawal. + “Simulated Binary Crossover for Continuous Search Space.” + Complex Syst. 9 (1995): n. pag. + `__ + + Args: + eta: + Distribution index. A small value of ``eta`` allows distant solutions + to be selected as children solutions. If not specified, takes default + value of ``2`` for single objective functions and ``20`` for multi objective. + uniform_crossover_prob: + ``uniform_crossover_prob`` is the probability of uniform crossover + between two individuals selected as candidate child individuals. + This argument is whether or not two individuals are + crossover to make one child individual. + If the ``uniform_crossover_prob`` exceeds 0.5, + the result is equivalent to ``1-uniform_crossover_prob``, + because it returns one of the two individuals of the crossover result. + If not specified, takes default value of ``0.5``. + The range of values is ``[0.0, 1.0]``. + use_child_gene_prob: + ``use_child_gene_prob`` is the probability of using the value of the generated + child variable rather than the value of the parent. + This probability is applied to each variable individually. + where ``1-use_chile_gene_prob`` is the probability of + using the parent's values as it is. + If not specified, takes default value of ``0.5``. + The range of values is ``(0.0, 1.0]``. + """ + + n_parents = 2 + + def __init__( + self, + eta: float | None = None, + uniform_crossover_prob: float = 0.5, + use_child_gene_prob: float = 0.5, + ) -> None: + if (eta is not None) and (eta < 0.0): + raise ValueError("The value of `eta` must be greater than or equal to 0.0.") + self._eta = eta + + if uniform_crossover_prob < 0.0 or uniform_crossover_prob > 1.0: + raise ValueError( + "The value of `uniform_crossover_prob` must be in the range [0.0, 1.0]." + ) + if use_child_gene_prob <= 0.0 or use_child_gene_prob > 1.0: + raise ValueError("The value of `use_child_gene_prob` must be in the range (0.0, 1.0].") + self._uniform_crossover_prob = uniform_crossover_prob + self._use_child_gene_prob = use_child_gene_prob + + def crossover( + self, + parents_params: np.ndarray, + rng: np.random.RandomState, + study: Study, + search_space_bounds: np.ndarray, + ) -> np.ndarray: + # https://www.researchgate.net/profile/M-M-Raghuwanshi/publication/267198495_Simulated_Binary_Crossover_with_Lognormal_Distribution/links/5576c78408ae7536375205d7/Simulated-Binary-Crossover-with-Lognormal-Distribution.pdf + # Section 2 Simulated Binary Crossover (SBX) + + # To avoid generating solutions that violate the box constraints, + # alpha1, alpha2, xls and xus are introduced, unlike the reference. + xls = search_space_bounds[..., 0] + xus = search_space_bounds[..., 1] + + xs_min = np.min(parents_params, axis=0) + xs_max = np.max(parents_params, axis=0) + if self._eta is None: + eta = 20.0 if study._is_multi_objective() else 2.0 + else: + eta = self._eta + + xs_diff = np.clip(xs_max - xs_min, 1e-10, None) + beta1 = 1 + 2 * (xs_min - xls) / xs_diff + beta2 = 1 + 2 * (xus - xs_max) / xs_diff + alpha1 = 2 - np.power(beta1, -(eta + 1)) + alpha2 = 2 - np.power(beta2, -(eta + 1)) + + us = rng.rand(len(search_space_bounds)) + mask1 = us > 1 / alpha1 # Equation (3). + betaq1 = np.power(us * alpha1, 1 / (eta + 1)) # Equation (3). + betaq1[mask1] = np.power((1 / (2 - us * alpha1)), 1 / (eta + 1))[mask1] # Equation (3). + + mask2 = us > 1 / alpha2 # Equation (3). + betaq2 = np.power(us * alpha2, 1 / (eta + 1)) # Equation (3) + betaq2[mask2] = np.power((1 / (2 - us * alpha2)), 1 / (eta + 1))[mask2] # Equation (3). + + c1 = 0.5 * ((xs_min + xs_max) - betaq1 * xs_diff) # Equation (4). + c2 = 0.5 * ((xs_min + xs_max) + betaq2 * xs_diff) # Equation (5). + + # SBX applies crossover with use_child_gene_prob and uniform_crossover_prob. + # the gene of the parent individual is the gene of the child individual. + # The original SBX creates two child individuals, + # but optuna's implementation creates only one child individual. + # Therefore, when there is no crossover, + # the gene is selected with equal probability from the parent individuals x1 and x2. + + child1_params_list = [] + child2_params_list = [] + + for c1_i, c2_i, x1_i, x2_i in zip(c1, c2, parents_params[0], parents_params[1]): + if rng.rand() < self._use_child_gene_prob: + if rng.rand() >= self._uniform_crossover_prob: + child1_params_list.append(c1_i) + child2_params_list.append(c2_i) + else: + child1_params_list.append(c2_i) + child2_params_list.append(c1_i) + else: + if rng.rand() >= self._uniform_crossover_prob: + child1_params_list.append(x1_i) + child2_params_list.append(x2_i) + else: + child1_params_list.append(x2_i) + child2_params_list.append(x1_i) + + child_params_list = child1_params_list if rng.rand() < 0.5 else child2_params_list + child_params = np.array(child_params_list) + + return child_params diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_spx.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_spx.py new file mode 100644 index 0000000000000000000000000000000000000000..7e49ade4c7e6a5bd4e2af90ef5e4cc2bef92948b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_spx.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._experimental import experimental_class +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover + + +if TYPE_CHECKING: + from optuna.study import Study + + +@experimental_class("3.0.0") +class SPXCrossover(BaseCrossover): + """Simplex Crossover operation used by :class:`~optuna.samplers.NSGAIISampler`. + + Uniformly samples child individuals from within a single simplex + that is similar to the simplex produced by the parent individual. + For further information about SPX crossover, please refer to the following paper: + + - `Shigeyoshi Tsutsui and Shigeyoshi Tsutsui and David E. Goldberg and + David E. Goldberg and Kumara Sastry and Kumara Sastry + Progress Toward Linkage Learning in Real-Coded GAs with Simplex Crossover. + IlliGAL Report. 2000. + `__ + + Args: + epsilon: + Expansion rate. If not specified, defaults to ``sqrt(len(search_space) + 2)``. + """ + + n_parents = 3 + + def __init__(self, epsilon: float | None = None) -> None: + self._epsilon = epsilon + + def crossover( + self, + parents_params: np.ndarray, + rng: np.random.RandomState, + study: Study, + search_space_bounds: np.ndarray, + ) -> np.ndarray: + # https://www.researchgate.net/publication/2388486_Progress_Toward_Linkage_Learning_in_Real-Coded_GAs_with_Simplex_Crossover + # Section 2 A Brief Review of SPX + + n = self.n_parents - 1 + G = np.mean(parents_params, axis=0) # Equation (1). + rs = np.power(rng.rand(n), 1 / (np.arange(n) + 1)) # Equation (2). + + epsilon = np.sqrt(len(search_space_bounds) + 2) if self._epsilon is None else self._epsilon + xks = [G + epsilon * (pk - G) for pk in parents_params] # Equation (3). + + ck = 0 # Equation (4). + for k in range(1, self.n_parents): + ck = rs[k - 1] * (xks[k - 1] - xks[k] + ck) + + child_params = xks[-1] + ck # Equation (5). + + return child_params diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_undx.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_undx.py new file mode 100644 index 0000000000000000000000000000000000000000..e46f98691d1db8c7484553faa06684d5ca802bb0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_undx.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._experimental import experimental_class +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover + + +if TYPE_CHECKING: + from optuna.study import Study + + +@experimental_class("3.0.0") +class UNDXCrossover(BaseCrossover): + """Unimodal Normal Distribution Crossover used by :class:`~optuna.samplers.NSGAIISampler`. + + Generates child individuals from the three parents + using a multivariate normal distribution. + + - `H. Kita, I. Ono and S. Kobayashi, + Multi-parental extension of the unimodal normal distribution crossover + for real-coded genetic algorithms, + Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 + (Cat. No. 99TH8406), 1999, pp. 1581-1588 Vol. 2 + `__ + + Args: + sigma_xi: + Parametrizes normal distribution from which ``xi`` is drawn. + sigma_eta: + Parametrizes normal distribution from which ``etas`` are drawn. + If not specified, defaults to ``0.35 / sqrt(len(search_space))``. + """ + + n_parents = 3 + + def __init__(self, sigma_xi: float = 0.5, sigma_eta: float | None = None) -> None: + self._sigma_xi = sigma_xi + self._sigma_eta = sigma_eta + + def _distance_from_x_to_psl(self, parents_params: np.ndarray) -> np.floating: + # The line connecting x1 to x2 is called psl (primary search line). + # Compute the 2-norm of the vector orthogonal to psl from x3. + e_12 = UNDXCrossover._normalized_x1_to_x2( + parents_params + ) # Normalized vector from x1 to x2. + v_13 = parents_params[2] - parents_params[0] # Vector from x1 to x3. + v_12_3 = v_13 - np.dot(v_13, e_12) * e_12 # Vector orthogonal to v_12 through x3. + m_12_3 = np.linalg.norm(v_12_3, ord=2) # 2-norm of v_12_3. + + return m_12_3 + + def _orthonormal_basis_vector_to_psl(self, parents_params: np.ndarray, n: int) -> np.ndarray: + # Compute orthogonal basis vectors for the subspace orthogonal to psl. + e_12 = UNDXCrossover._normalized_x1_to_x2( + parents_params + ) # Normalized vector from x1 to x2. + basis_matrix = np.identity(n) + + if np.count_nonzero(e_12) != 0: + basis_matrix[0] = e_12 + + basis_matrix_t = basis_matrix.T + Q, _ = np.linalg.qr(basis_matrix_t) + + return Q.T[1:] + + def crossover( + self, + parents_params: np.ndarray, + rng: np.random.RandomState, + study: Study, + search_space_bounds: np.ndarray, + ) -> np.ndarray: + # https://doi.org/10.1109/CEC.1999.782672 + # Section 2 Unimodal Normal Distribution Crossover + n = len(search_space_bounds) + xp = (parents_params[0] + parents_params[1]) / 2 # Section 2 (2). + d = parents_params[0] - parents_params[1] # Section 2 (3). + if self._sigma_eta is None: + sigma_eta = 0.35 / np.sqrt(n) + else: + sigma_eta = self._sigma_eta + + etas = rng.normal(0, sigma_eta**2, size=n) + xi = rng.normal(0, self._sigma_xi**2) + es = self._orthonormal_basis_vector_to_psl( + parents_params, n + ) # Orthonormal basis vectors of the subspace orthogonal to the psl. + one = xp # Section 2 (5). + two = xi * d # Section 2 (5). + + if n > 1: # When n=1, there is no subsearch component. + three = np.zeros(n) # Section 2 (5). + D = self._distance_from_x_to_psl(parents_params) # Section 2 (4). + for i in range(n - 1): + three += etas[i] * es[i] + three *= D + child_params = one + two + three + + else: + child_params = one + two + + return child_params + + @staticmethod + def _normalized_x1_to_x2(parents_params: np.ndarray) -> np.ndarray: + # Compute the normalized vector from x1 to x2. + v_12 = parents_params[1] - parents_params[0] + m_12 = np.linalg.norm(v_12, ord=2) + e_12 = v_12 / np.clip(m_12, 1e-10, None) + + return e_12 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_uniform.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_uniform.py new file mode 100644 index 0000000000000000000000000000000000000000..673ea80d031e0a77c9c441a56da4beaf717745a8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_uniform.py @@ -0,0 +1,51 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover + + +if TYPE_CHECKING: + from optuna.study import Study + + +class UniformCrossover(BaseCrossover): + """Uniform Crossover operation used by :class:`~optuna.samplers.NSGAIISampler`. + + Select each parameter with equal probability from the two parent individuals. + For further information about uniform crossover, please refer to the following paper: + + - `Gilbert Syswerda. 1989. Uniform Crossover in Genetic Algorithms. + In Proceedings of the 3rd International Conference on Genetic Algorithms. + Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2-9. + `__ + + Args: + swapping_prob: + Probability of swapping each parameter of the parents during crossover. + """ + + n_parents = 2 + + def __init__(self, swapping_prob: float = 0.5) -> None: + if not (0.0 <= swapping_prob <= 1.0): + raise ValueError("`swapping_prob` must be a float value within the range [0.0, 1.0].") + self._swapping_prob = swapping_prob + + def crossover( + self, + parents_params: np.ndarray, + rng: np.random.RandomState, + study: Study, + search_space_bounds: np.ndarray, + ) -> np.ndarray: + # https://www.researchgate.net/publication/201976488_Uniform_Crossover_in_Genetic_Algorithms + # Section 1 Introduction + + n_params = len(search_space_bounds) + masks = (rng.rand(n_params) >= self._swapping_prob).astype(int) + child_params = parents_params[masks, range(n_params)] + + return child_params diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_vsbx.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_vsbx.py new file mode 100644 index 0000000000000000000000000000000000000000..8e49c68db2b6ff9ff8b05161b014b87d6a77951c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_vsbx.py @@ -0,0 +1,139 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +import numpy as np + +from optuna._experimental import experimental_class +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover + + +if TYPE_CHECKING: + from optuna.study import Study + + +@experimental_class("3.0.0") +class VSBXCrossover(BaseCrossover): + """Modified Simulated Binary Crossover operation used by + :class:`~optuna.samplers.NSGAIISampler`. + + vSBX generates child individuals without excluding any region of the parameter space, + while maintaining the excellent properties of SBX. + + In the paper, vSBX has only one argument, ``eta``, + and generate two child individuals. + However, Optuna can only return one child individual in one crossover operation, + so it uses the ``uniform_crossover_prob`` and ``use_child_gene_prob`` arguments + to make two individuals into one. + + - `Pedro J. Ballester, Jonathan N. Carter. + Real-Parameter Genetic Algorithms for Finding Multiple Optimal Solutions + in Multi-modal Optimization. GECCO 2003: 706-717 + `__ + + Args: + eta: + Distribution index. A small value of ``eta`` allows distant solutions + to be selected as children solutions. If not specified, takes default + value of ``2`` for single objective functions and ``20`` for multi objective. + uniform_crossover_prob: + ``uniform_crossover_prob`` is the probability of uniform crossover + between two individuals selected as candidate child individuals. + This argument is whether or not two individuals are + crossover to make one child individual. + If the ``uniform_crossover_prob`` exceeds 0.5, + the result is equivalent to ``1-uniform_crossover_prob``, + because it returns one of the two individuals of the crossover result. + If not specified, takes default value of ``0.5``. + The range of values is ``[0.0, 1.0]``. + use_child_gene_prob: + ``use_child_gene_prob`` is the probability of using the value of the generated + child variable rather than the value of the parent. + This probability is applied to each variable individually. + where ``1-use_chile_gene_prob`` is the probability of + using the parent's values as it is. + If not specified, takes default value of ``0.5``. + The range of values is ``(0.0, 1.0]``. + """ + + n_parents = 2 + + def __init__( + self, + eta: float | None = None, + uniform_crossover_prob: float = 0.5, + use_child_gene_prob: float = 0.5, + ) -> None: + if (eta is not None) and (eta < 0.0): + raise ValueError("The value of `eta` must be greater than or equal to 0.0.") + self._eta = eta + + if uniform_crossover_prob < 0.0 or uniform_crossover_prob > 1.0: + raise ValueError( + "The value of `uniform_crossover_prob` must be in the range [0.0, 1.0]." + ) + if use_child_gene_prob <= 0.0 or use_child_gene_prob > 1.0: + raise ValueError("The value of `use_child_gene_prob` must be in the range (0.0, 1.0].") + self._uniform_crossover_prob = uniform_crossover_prob + self._use_child_gene_prob = use_child_gene_prob + + def crossover( + self, + parents_params: np.ndarray, + rng: np.random.RandomState, + study: Study, + search_space_bounds: np.ndarray, + ) -> np.ndarray: + # https://doi.org/10.1007/3-540-45105-6_86 + # Section 3.2 Crossover Schemes (vSBX) + if self._eta is None: + eta = 20.0 if study._is_multi_objective() else 2.0 + else: + eta = self._eta + + eps = 1e-10 + us = rng.rand(len(search_space_bounds)) + beta_1 = np.power(1 / np.maximum((2 * us), eps), 1 / (eta + 1)) + beta_2 = np.power(1 / np.maximum((2 * (1 - us)), eps), 1 / (eta + 1)) + + u_1 = rng.rand() + if u_1 <= 0.5: + c1 = 0.5 * ((1 + beta_1) * parents_params[0] + (1 - beta_2) * parents_params[1]) + else: + c1 = 0.5 * ((1 - beta_1) * parents_params[0] + (1 + beta_2) * parents_params[1]) + u_2 = rng.rand() + if u_2 <= 0.5: + c2 = 0.5 * ((3 - beta_1) * parents_params[0] - (1 - beta_2) * parents_params[1]) + else: + c2 = 0.5 * (-(1 - beta_1) * parents_params[0] + (3 - beta_2) * parents_params[1]) + + # vSBX applies crossover with use_child_gene_prob and uniform_crossover_prob. + # the gene of the parent individual is the gene of the child individual. + # The original vSBX creates two child individuals, + # but optuna's implementation creates only one child individual. + # Therefore, when there is no crossover, + # the gene is selected with equal probability from the parent individuals x1 and x2. + + child1_params_list = [] + child2_params_list = [] + + for c1_i, c2_i, x1_i, x2_i in zip(c1, c2, parents_params[0], parents_params[1]): + if rng.rand() < self._use_child_gene_prob: + if rng.rand() >= self._uniform_crossover_prob: + child1_params_list.append(c1_i) + child2_params_list.append(c2_i) + else: + child1_params_list.append(c2_i) + child2_params_list.append(c1_i) + else: + if rng.rand() >= self._uniform_crossover_prob: + child1_params_list.append(x1_i) + child2_params_list.append(x2_i) + else: + child1_params_list.append(x2_i) + child2_params_list.append(x1_i) + + child_params_list = child1_params_list if rng.rand() < 0.5 else child2_params_list + child_params = np.array(child_params_list) + + return child_params diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_elite_population_selection_strategy.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_elite_population_selection_strategy.py new file mode 100644 index 0000000000000000000000000000000000000000..bf416267aee9480768bae72845052c8b2e5c0663 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_elite_population_selection_strategy.py @@ -0,0 +1,139 @@ +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Callable +from collections.abc import Sequence +from typing import TYPE_CHECKING + +import numpy as np + +from optuna.samplers.nsgaii._constraints_evaluation import _evaluate_penalty +from optuna.samplers.nsgaii._constraints_evaluation import _validate_constraints +from optuna.study import StudyDirection +from optuna.study._multi_objective import _fast_non_domination_rank +from optuna.trial import FrozenTrial + + +if TYPE_CHECKING: + from optuna.study import Study + + +class NSGAIIElitePopulationSelectionStrategy: + def __init__( + self, + *, + population_size: int, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + ) -> None: + if population_size < 2: + raise ValueError("`population_size` must be greater than or equal to 2.") + + self._population_size = population_size + self._constraints_func = constraints_func + + def __call__(self, study: Study, population: list[FrozenTrial]) -> list[FrozenTrial]: + """Select elite population from the given trials by NSGA-II algorithm. + + Args: + study: + Target study object. + population: + Trials in the study. + + Returns: + A list of trials that are selected as elite population. + """ + _validate_constraints(population, is_constrained=self._constraints_func is not None) + population_per_rank = _rank_population( + population, study.directions, is_constrained=self._constraints_func is not None + ) + elite_population: list[FrozenTrial] = [] + for individuals in population_per_rank: + if len(elite_population) + len(individuals) < self._population_size: + elite_population.extend(individuals) + else: + n = self._population_size - len(elite_population) + _crowding_distance_sort(individuals) + elite_population.extend(individuals[:n]) + break + + return elite_population + + +def _calc_crowding_distance(population: list[FrozenTrial]) -> defaultdict[int, float]: + """Calculates the crowding distance of population. + + We define the crowding distance as the summation of the crowding distance of each dimension + of value calculated as follows: + + * If all values in that dimension are the same, i.e., [1, 1, 1] or [inf, inf], + the crowding distances of all trials in that dimension are zero. + * Otherwise, the crowding distances of that dimension is the difference between + two nearest values besides that value, one above and one below, divided by the difference + between the maximal and minimal finite value of that dimension. Please note that: + * the nearest value below the minimum is considered to be -inf and the + nearest value above the maximum is considered to be inf, and + * inf - inf and (-inf) - (-inf) is considered to be zero. + """ + + manhattan_distances: defaultdict[int, float] = defaultdict(float) + if len(population) == 0: + return manhattan_distances + + for i in range(len(population[0].values)): + population.sort(key=lambda x: x.values[i]) + + # If all trials in population have the same value in the i-th dimension, ignore the + # objective dimension since it does not make difference. + if population[0].values[i] == population[-1].values[i]: + continue + + vs = [-float("inf")] + [trial.values[i] for trial in population] + [float("inf")] + + # Smallest finite value. + v_min = next(x for x in vs if x != -float("inf")) + + # Largest finite value. + v_max = next(x for x in reversed(vs) if x != float("inf")) + + width = v_max - v_min + if width <= 0: + # width == 0 or width == -inf + width = 1.0 + + for j in range(len(population)): + # inf - inf and (-inf) - (-inf) is considered to be zero. + gap = 0.0 if vs[j] == vs[j + 2] else vs[j + 2] - vs[j] + manhattan_distances[population[j].number] += gap / width + return manhattan_distances + + +def _crowding_distance_sort(population: list[FrozenTrial]) -> None: + manhattan_distances = _calc_crowding_distance(population) + population.sort(key=lambda x: manhattan_distances[x.number]) + population.reverse() + + +def _rank_population( + population: list[FrozenTrial], + directions: Sequence[StudyDirection], + *, + is_constrained: bool = False, +) -> list[list[FrozenTrial]]: + if len(population) == 0: + return [] + + objective_values = np.array([trial.values for trial in population], dtype=np.float64) + objective_values *= np.array( + [-1.0 if d == StudyDirection.MAXIMIZE else 1.0 for d in directions] + ) + penalty = _evaluate_penalty(population) if is_constrained else None + + domination_ranks = _fast_non_domination_rank(objective_values, penalty=penalty) + population_per_rank: list[list[FrozenTrial]] = [[] for _ in range(max(domination_ranks) + 1)] + for trial, rank in zip(population, domination_ranks): + if rank == -1: + continue + population_per_rank[rank].append(trial) + + return population_per_rank diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_sampler.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..66d6f2eba0eb58b09bfe61a5ac592e78082569ba --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/samplers/nsgaii/_sampler.py @@ -0,0 +1,302 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +from typing import Any +from typing import TYPE_CHECKING + +from optuna._experimental import warn_experimental_argument +from optuna.distributions import BaseDistribution +from optuna.samplers._ga import BaseGASampler +from optuna.samplers._lazy_random_state import LazyRandomState +from optuna.samplers._random import RandomSampler +from optuna.samplers.nsgaii._after_trial_strategy import NSGAIIAfterTrialStrategy +from optuna.samplers.nsgaii._child_generation_strategy import NSGAIIChildGenerationStrategy +from optuna.samplers.nsgaii._crossovers._base import BaseCrossover +from optuna.samplers.nsgaii._crossovers._uniform import UniformCrossover +from optuna.samplers.nsgaii._elite_population_selection_strategy import ( + NSGAIIElitePopulationSelectionStrategy, +) +from optuna.search_space import IntersectionSearchSpace +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna.study import Study + + +class NSGAIISampler(BaseGASampler): + """Multi-objective sampler using the NSGA-II algorithm. + + NSGA-II stands for "Nondominated Sorting Genetic Algorithm II", + which is a well known, fast and elitist multi-objective genetic algorithm. + + For further information about NSGA-II, please refer to the following paper: + + - `A fast and elitist multiobjective genetic algorithm: NSGA-II + `__ + + .. note:: + :class:`~optuna.samplers.TPESampler` became much faster in v4.0.0 and supports several + features not supported by ``NSGAIISampler`` such as handling of dynamic search + space and categorical distance. To use :class:`~optuna.samplers.TPESampler`, you need to + explicitly specify the sampler as follows: + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -100, 100) + y = trial.suggest_categorical("y", [-1, 0, 1]) + f1 = x**2 + y + f2 = -((x - 2) ** 2 + y) + return f1, f2 + + + # We minimize the first objective and maximize the second objective. + sampler = optuna.samplers.TPESampler() + study = optuna.create_study(directions=["minimize", "maximize"], sampler=sampler) + study.optimize(objective, n_trials=100) + + Please also check `our article + `__ + for more details of the speedup in v4.0.0. + + Args: + population_size: + Number of individuals (trials) in a generation. + ``population_size`` must be greater than or equal to ``crossover.n_parents``. + For :class:`~optuna.samplers.nsgaii.UNDXCrossover` and + :class:`~optuna.samplers.nsgaii.SPXCrossover`, ``n_parents=3``, and for the other + algorithms, ``n_parents=2``. + + mutation_prob: + Probability of mutating each parameter when creating a new individual. + If :obj:`None` is specified, the value ``1.0 / len(parent_trial.params)`` is used + where ``parent_trial`` is the parent trial of the target individual. + + crossover: + Crossover to be applied when creating child individuals. + The available crossovers are listed here: + https://optuna.readthedocs.io/en/stable/reference/samplers/nsgaii.html. + + :class:`~optuna.samplers.nsgaii.UniformCrossover` is always applied to parameters + sampled from :class:`~optuna.distributions.CategoricalDistribution`, and by + default for parameters sampled from other distributions unless this argument + is specified. + + For more information on each of the crossover method, please refer to + specific crossover documentation. + + crossover_prob: + Probability that a crossover (parameters swapping between parents) will occur + when creating a new individual. + + swapping_prob: + Probability of swapping each parameter of the parents during crossover. + + seed: + Seed for random number generator. + + constraints_func: + An optional function that computes the objective constraints. It must take a + :class:`~optuna.trial.FrozenTrial` and return the constraints. The return value must + be a sequence of :obj:`float` s. A value strictly larger than 0 means that a + constraints is violated. A value equal to or smaller than 0 is considered feasible. + If ``constraints_func`` returns more than one value for a trial, that trial is + considered feasible if and only if all values are equal to 0 or smaller. + + The ``constraints_func`` will be evaluated after each successful trial. + The function won't be called when trials fail or they are pruned, but this behavior is + subject to change in the future releases. + + The constraints are handled by the constrained domination. A trial x is said to + constrained-dominate a trial y, if any of the following conditions is true: + + 1. Trial x is feasible and trial y is not. + 2. Trial x and y are both infeasible, but trial x has a smaller overall violation. + 3. Trial x and y are feasible and trial x dominates trial y. + + .. note:: + Added in v2.5.0 as an experimental feature. The interface may change in newer + versions without prior notice. See + https://github.com/optuna/optuna/releases/tag/v2.5.0. + + elite_population_selection_strategy: + The selection strategy for determining the individuals to survive from the current + population pool. Default to :obj:`None`. + + .. note:: + The arguments ``elite_population_selection_strategy`` was added in v3.3.0 as an + experimental feature. The interface may change in newer versions without prior + notice. + See https://github.com/optuna/optuna/releases/tag/v3.3.0. + + child_generation_strategy: + The strategy for generating child parameters from parent trials. Defaults to + :obj:`None`. + + .. note:: + The arguments ``child_generation_strategy`` was added in v3.3.0 as an experimental + feature. The interface may change in newer versions without prior notice. + See https://github.com/optuna/optuna/releases/tag/v3.3.0. + + after_trial_strategy: + A set of procedure to be conducted after each trial. Defaults to :obj:`None`. + + .. note:: + The arguments ``after_trial_strategy`` was added in v3.3.0 as an experimental + feature. The interface may change in newer versions without prior notice. + See https://github.com/optuna/optuna/releases/tag/v3.3.0. + """ + + def __init__( + self, + *, + population_size: int = 50, + mutation_prob: float | None = None, + crossover: BaseCrossover | None = None, + crossover_prob: float = 0.9, + swapping_prob: float = 0.5, + seed: int | None = None, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + elite_population_selection_strategy: ( + Callable[[Study, list[FrozenTrial]], list[FrozenTrial]] | None + ) = None, + child_generation_strategy: ( + Callable[[Study, dict[str, BaseDistribution], list[FrozenTrial]], dict[str, Any]] + | None + ) = None, + after_trial_strategy: ( + Callable[[Study, FrozenTrial, TrialState, Sequence[float] | None], None] | None + ) = None, + ) -> None: + # TODO(ohta): Reconsider the default value of each parameter. + + if population_size < 2: + raise ValueError("`population_size` must be greater than or equal to 2.") + + if constraints_func is not None: + warn_experimental_argument("constraints_func") + if after_trial_strategy is not None: + warn_experimental_argument("after_trial_strategy") + + if child_generation_strategy is not None: + warn_experimental_argument("child_generation_strategy") + + if elite_population_selection_strategy is not None: + warn_experimental_argument("elite_population_selection_strategy") + + if crossover is None: + crossover = UniformCrossover(swapping_prob) + + if not isinstance(crossover, BaseCrossover): + raise ValueError( + f"'{crossover}' is not a valid crossover." + " For valid crossovers see" + " https://optuna.readthedocs.io/en/stable/reference/samplers.html." + ) + + if population_size < crossover.n_parents: + raise ValueError( + f"Using {crossover}," + f" the population size should be greater than or equal to {crossover.n_parents}." + f" The specified `population_size` is {population_size}." + ) + + super().__init__(population_size=population_size) + self._random_sampler = RandomSampler(seed=seed) + self._rng = LazyRandomState(seed) + self._constraints_func = constraints_func + self._search_space = IntersectionSearchSpace() + + self._elite_population_selection_strategy = ( + elite_population_selection_strategy + or NSGAIIElitePopulationSelectionStrategy( + population_size=population_size, constraints_func=constraints_func + ) + ) + self._child_generation_strategy = ( + child_generation_strategy + or NSGAIIChildGenerationStrategy( + crossover_prob=crossover_prob, + mutation_prob=mutation_prob, + swapping_prob=swapping_prob, + crossover=crossover, + constraints_func=constraints_func, + rng=self._rng, + ) + ) + self._after_trial_strategy = after_trial_strategy or NSGAIIAfterTrialStrategy( + constraints_func=constraints_func + ) + + def reseed_rng(self) -> None: + self._random_sampler.reseed_rng() + self._rng.rng.seed() + + def infer_relative_search_space( + self, study: Study, trial: FrozenTrial + ) -> dict[str, BaseDistribution]: + search_space: dict[str, BaseDistribution] = {} + for name, distribution in self._search_space.calculate(study).items(): + if distribution.single(): + # The `untransform` method of `optuna._transform._SearchSpaceTransform` + # does not assume a single value, + # so single value objects are not sampled with the `sample_relative` method, + # but with the `sample_independent` method. + continue + search_space[name] = distribution + return search_space + + def select_parent(self, study: Study, generation: int) -> list[FrozenTrial]: + return self._elite_population_selection_strategy( + study, + self.get_population(study, generation - 1) + + self.get_parent_population(study, generation - 1), + ) + + def sample_relative( + self, + study: Study, + trial: FrozenTrial, + search_space: dict[str, BaseDistribution], + ) -> dict[str, Any]: + generation = self.get_trial_generation(study, trial) + parent_population = self.get_parent_population(study, generation) + if len(parent_population) == 0: + return {} + return self._child_generation_strategy(study, search_space, parent_population) + + def sample_independent( + self, + study: Study, + trial: FrozenTrial, + param_name: str, + param_distribution: BaseDistribution, + ) -> Any: + # Following parameters are randomly sampled here. + # 1. A parameter in the initial population/first generation. + # 2. A parameter to mutate. + # 3. A parameter excluded from the intersection search space. + + return self._random_sampler.sample_independent( + study, trial, param_name, param_distribution + ) + + def before_trial(self, study: Study, trial: FrozenTrial) -> None: + self._random_sampler.before_trial(study, trial) + + def after_trial( + self, + study: Study, + trial: FrozenTrial, + state: TrialState, + values: Sequence[float] | None, + ) -> None: + assert state in [TrialState.COMPLETE, TrialState.FAIL, TrialState.PRUNED] + self._after_trial_strategy(study, trial, state, values) + self._random_sampler.after_trial(study, trial, state, values) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a898adbd106a10645d29cf0ea05dd72aae50152e Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/__pycache__/intersection.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/__pycache__/intersection.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..522549ad3d8cd48a14a4ceda73c7cb8e8edfcaaf Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/__pycache__/intersection.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/group_decomposed.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/group_decomposed.py new file mode 100644 index 0000000000000000000000000000000000000000..ccddd28a1c2b43299a84144c1c772eace6998c73 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/group_decomposed.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +import copy +from typing import TYPE_CHECKING + +from optuna.distributions import BaseDistribution +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna.study import Study + + +class _SearchSpaceGroup: + def __init__(self) -> None: + self._search_spaces: list[dict[str, BaseDistribution]] = [] + + @property + def search_spaces(self) -> list[dict[str, BaseDistribution]]: + return self._search_spaces + + def add_distributions(self, distributions: dict[str, BaseDistribution]) -> None: + dist_keys = set(distributions.keys()) + next_search_spaces = [] + + for search_space in self._search_spaces: + keys = set(search_space.keys()) + + next_search_spaces.append({name: search_space[name] for name in keys & dist_keys}) + next_search_spaces.append({name: search_space[name] for name in keys - dist_keys}) + + dist_keys -= keys + + next_search_spaces.append({name: distributions[name] for name in dist_keys}) + self._search_spaces = list( + filter(lambda search_space: len(search_space) > 0, next_search_spaces) + ) + + +class _GroupDecomposedSearchSpace: + def __init__(self, include_pruned: bool = False) -> None: + self._search_space = _SearchSpaceGroup() + self._study_id: int | None = None + self._include_pruned = include_pruned + + def calculate(self, study: Study) -> _SearchSpaceGroup: + if self._study_id is None: + self._study_id = study._study_id + else: + # Note that the check below is meaningless when + # :class:`~optuna.storages.InMemoryStorage` is used because + # :func:`~optuna.storages.InMemoryStorage.create_new_study` + # always returns the same study ID. + if self._study_id != study._study_id: + raise ValueError("`_GroupDecomposedSearchSpace` cannot handle multiple studies.") + + states_of_interest: tuple[TrialState, ...] + if self._include_pruned: + states_of_interest = (TrialState.COMPLETE, TrialState.PRUNED) + else: + states_of_interest = (TrialState.COMPLETE,) + + for trial in study._get_trials(deepcopy=False, states=states_of_interest, use_cache=False): + self._search_space.add_distributions(trial.distributions) + + return copy.deepcopy(self._search_space) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/intersection.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/intersection.py new file mode 100644 index 0000000000000000000000000000000000000000..a44d970d6e0f22fe1408c0c8286e90e2eef7b37a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/search_space/intersection.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +import copy +from typing import TYPE_CHECKING + +import optuna +from optuna.distributions import BaseDistribution + + +if TYPE_CHECKING: + from optuna.study import Study + + +def _calculate( + trials: list[optuna.trial.FrozenTrial], + include_pruned: bool = False, + search_space: dict[str, BaseDistribution] | None = None, + cached_trial_number: int = -1, +) -> tuple[dict[str, BaseDistribution] | None, int]: + states_of_interest = [ + optuna.trial.TrialState.COMPLETE, + optuna.trial.TrialState.WAITING, + optuna.trial.TrialState.RUNNING, + ] + + if include_pruned: + states_of_interest.append(optuna.trial.TrialState.PRUNED) + + next_cached_trial_number = -1 + + for trial in reversed(trials): + if trial.state not in states_of_interest: + continue + + if next_cached_trial_number == -1: + next_cached_trial_number = trial.number + 1 + + if cached_trial_number > trial.number: + break + + if not trial.state.is_finished(): + next_cached_trial_number = trial.number + continue + + if search_space is None: + search_space = copy.copy(trial.distributions) + continue + + search_space = { + name: distribution + for name, distribution in search_space.items() + if trial.distributions.get(name) == distribution + } + + return search_space, next_cached_trial_number + + +class IntersectionSearchSpace: + """A class to calculate the intersection search space of a :class:`~optuna.study.Study`. + + Intersection search space contains the intersection of parameter distributions that have been + suggested in the completed trials of the study so far. + If there are multiple parameters that have the same name but different distributions, + neither is included in the resulting search space + (i.e., the parameters with dynamic value ranges are excluded). + + Note that an instance of this class is supposed to be used for only one study. + If different studies are passed to + :func:`~optuna.search_space.IntersectionSearchSpace.calculate`, + a :obj:`ValueError` is raised. + + Args: + include_pruned: + Whether pruned trials should be included in the search space. + """ + + def __init__(self, include_pruned: bool = False) -> None: + self._cached_trial_number: int = -1 + self._search_space: dict[str, BaseDistribution] | None = None + self._study_id: int | None = None + + self._include_pruned = include_pruned + + def calculate(self, study: Study) -> dict[str, BaseDistribution]: + """Returns the intersection search space of the :class:`~optuna.study.Study`. + + Args: + study: + A study with completed trials. The same study must be passed for one instance + of this class through its lifetime. + + Returns: + A dictionary containing the parameter names and parameter's distributions sorted by + parameter names. + """ + + if self._study_id is None: + self._study_id = study._study_id + else: + # Note that the check below is meaningless when + # :class:`~optuna.storages.InMemoryStorage` is used because + # :func:`~optuna.storages.InMemoryStorage.create_new_study` + # always returns the same study ID. + if self._study_id != study._study_id: + raise ValueError("`IntersectionSearchSpace` cannot handle multiple studies.") + + self._search_space, self._cached_trial_number = _calculate( + study.get_trials(deepcopy=False), + self._include_pruned, + self._search_space, + self._cached_trial_number, + ) + search_space = self._search_space or {} + search_space = dict(sorted(search_space.items(), key=lambda x: x[0])) + return copy.deepcopy(search_space) + + +def intersection_search_space( + trials: list[optuna.trial.FrozenTrial], + include_pruned: bool = False, +) -> dict[str, BaseDistribution]: + """Return the intersection search space of the given trials. + + Intersection search space contains the intersection of parameter distributions that have been + suggested in the completed trials of the study so far. + If there are multiple parameters that have the same name but different distributions, + neither is included in the resulting search space + (i.e., the parameters with dynamic value ranges are excluded). + + .. note:: + :class:`~optuna.search_space.IntersectionSearchSpace` provides the same functionality with + a much faster way. Please consider using it if you want to reduce execution time + as much as possible. + + Args: + trials: + A list of trials. + include_pruned: + Whether pruned trials should be included in the search space. + + Returns: + A dictionary containing the parameter names and parameter's distributions sorted by + parameter names. + """ + + search_space, _ = _calculate(trials, include_pruned) + search_space = search_space or {} + search_space = dict(sorted(search_space.items(), key=lambda x: x[0])) + return search_space diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..31d788eb2f5591410531e6d5e23a27d9fadbc7fc --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__init__.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +from optuna.storages._base import BaseStorage +from optuna.storages._cached_storage import _CachedStorage +from optuna.storages._callbacks import RetryFailedTrialCallback +from optuna.storages._grpc import GrpcStorageProxy +from optuna.storages._grpc import run_grpc_proxy_server +from optuna.storages._heartbeat import fail_stale_trials +from optuna.storages._in_memory import InMemoryStorage +from optuna.storages._rdb.storage import RDBStorage +from optuna.storages.journal._base import BaseJournalLogStorage +from optuna.storages.journal._file import ( + DeprecatedJournalFileSymlinkLock as JournalFileSymlinkLock, +) +from optuna.storages.journal._file import DeprecatedJournalFileOpenLock as JournalFileOpenLock +from optuna.storages.journal._file import JournalFileStorage +from optuna.storages.journal._redis import JournalRedisStorage +from optuna.storages.journal._storage import JournalStorage + + +__all__ = [ + "BaseStorage", + "BaseJournalLogStorage", + "InMemoryStorage", + "RDBStorage", + "JournalStorage", + "JournalFileStorage", + "JournalRedisStorage", + "JournalFileSymlinkLock", + "JournalFileOpenLock", + "RetryFailedTrialCallback", + "_CachedStorage", + "fail_stale_trials", + "GrpcStorageProxy", + "run_grpc_proxy_server", +] + + +def get_storage(storage: None | str | BaseStorage) -> BaseStorage: + """Only for internal usage. It might be deprecated in the future.""" + + if storage is None: + return InMemoryStorage() + if isinstance(storage, str): + if storage.startswith("redis"): + raise ValueError( + "RedisStorage is removed at Optuna v3.1.0. Please use JournalRedisBackend instead." + ) + return _CachedStorage(RDBStorage(storage)) + elif isinstance(storage, RDBStorage): + return _CachedStorage(storage) + else: + return storage diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5c26517da56248d1b774d3a2dfdf9444a0d0a836 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_base.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_base.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bd0e9435dfe560e6cd2d051c57eca6e596e59559 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_base.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_cached_storage.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_cached_storage.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5a6a7c71a6a133ea49b7c8cb31ffa458d556feda Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_cached_storage.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_callbacks.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_callbacks.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b86b9857eead3a8759765e3f3ef278a028d1f6e3 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_callbacks.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_heartbeat.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_heartbeat.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a652d7fe78b47d5bcb2c44ee5af5a515d36da357 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_heartbeat.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_in_memory.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_in_memory.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a6ea448f42791ca5fb2ca1fc9a1034ab93223c15 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/__pycache__/_in_memory.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_base.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_base.py new file mode 100644 index 0000000000000000000000000000000000000000..5acce3a24677bba39b5807aad02ac9985cf37e4c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_base.py @@ -0,0 +1,625 @@ +from __future__ import annotations + +import abc +from collections.abc import Container +from collections.abc import Sequence +from typing import Any +from typing import cast + +from optuna._typing import JSONSerializable +from optuna.distributions import BaseDistribution +from optuna.exceptions import UpdateFinishedTrialError +from optuna.study._frozen import FrozenStudy +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +DEFAULT_STUDY_NAME_PREFIX = "no-name-" + + +class BaseStorage(abc.ABC): + """Base class for storages. + + This class is not supposed to be directly accessed by library users. + + This class abstracts a backend database and provides internal interfaces to + read/write histories of studies and trials. + + A storage class implementing this class must meet the following requirements. + + **Thread safety** + + A storage class instance can be shared among multiple threads, and must therefore be + thread-safe. It must guarantee that a data instance read from the storage must not be modified + by subsequent writes. For example, `FrozenTrial` instance returned by `get_trial` + should not be updated by the subsequent `set_trial_xxx`. This is usually achieved by replacing + the old data with a copy on `set_trial_xxx`. + + A storage class can also assume that a data instance returned are never modified by its user. + When a user modifies a return value from a storage class, the internal state of the storage + may become inconsistent. Consequences are undefined. + + **Ownership of RUNNING trials** + + Trials in finished states are not allowed to be modified. + Trials in the WAITING state are not allowed to be modified except for the `state` field. + """ + + # Basic study manipulation + + @abc.abstractmethod + def create_new_study( + self, directions: Sequence[StudyDirection], study_name: str | None = None + ) -> int: + """Create a new study from a name. + + If no name is specified, the storage class generates a name. + The returned study ID is unique among all current and deleted studies. + + Args: + directions: + A sequence of direction whose element is either + :obj:`~optuna.study.StudyDirection.MAXIMIZE` or + :obj:`~optuna.study.StudyDirection.MINIMIZE`. + study_name: + Name of the new study to create. + + Returns: + ID of the created study. + + Raises: + :exc:`optuna.exceptions.DuplicatedStudyError`: + If a study with the same ``study_name`` already exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def delete_study(self, study_id: int) -> None: + """Delete a study. + + Args: + study_id: + ID of the study. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def set_study_user_attr(self, study_id: int, key: str, value: Any) -> None: + """Register a user-defined attribute to a study. + + This method overwrites any existing attribute. + + Args: + study_id: + ID of the study. + key: + Attribute key. + value: + Attribute value. It should be JSON serializable. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def set_study_system_attr(self, study_id: int, key: str, value: JSONSerializable) -> None: + """Register an optuna-internal attribute to a study. + + This method overwrites any existing attribute. + + Args: + study_id: + ID of the study. + key: + Attribute key. + value: + Attribute value. It should be JSON serializable. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + raise NotImplementedError + + # Basic study access + + @abc.abstractmethod + def get_study_id_from_name(self, study_name: str) -> int: + """Read the ID of a study. + + Args: + study_name: + Name of the study. + + Returns: + ID of the study. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_name`` exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def get_study_name_from_id(self, study_id: int) -> str: + """Read the study name of a study. + + Args: + study_id: + ID of the study. + + Returns: + Name of the study. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def get_study_directions(self, study_id: int) -> list[StudyDirection]: + """Read whether a study maximizes or minimizes an objective. + + Args: + study_id: + ID of a study. + + Returns: + Optimization directions list of the study. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def get_study_user_attrs(self, study_id: int) -> dict[str, Any]: + """Read the user-defined attributes of a study. + + Args: + study_id: + ID of the study. + + Returns: + Dictionary with the user attributes of the study. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def get_study_system_attrs(self, study_id: int) -> dict[str, Any]: + """Read the optuna-internal attributes of a study. + + Args: + study_id: + ID of the study. + + Returns: + Dictionary with the optuna-internal attributes of the study. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def get_all_studies(self) -> list[FrozenStudy]: + """Read a list of :class:`~optuna.study.FrozenStudy` objects. + + Returns: + A list of :class:`~optuna.study.FrozenStudy` objects, sorted by ``study_id``. + + """ + raise NotImplementedError + + # Basic trial manipulation + + @abc.abstractmethod + def create_new_trial(self, study_id: int, template_trial: FrozenTrial | None = None) -> int: + """Create and add a new trial to a study. + + The returned trial ID is unique among all current and deleted trials. + + Args: + study_id: + ID of the study. + template_trial: + Template :class:`~optuna.trial.FrozenTrial` with default user-attributes, + system-attributes, intermediate-values, and a state. + + Returns: + ID of the created trial. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def set_trial_param( + self, + trial_id: int, + param_name: str, + param_value_internal: float, + distribution: BaseDistribution, + ) -> None: + """Set a parameter to a trial. + + Args: + trial_id: + ID of the trial. + param_name: + Name of the parameter. + param_value_internal: + Internal representation of the parameter value. + distribution: + Sampled distribution of the parameter. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + :exc:`~optuna.exceptions.UpdateFinishedTrialError`: + If the trial is already finished. + """ + raise NotImplementedError + + def get_trial_id_from_study_id_trial_number(self, study_id: int, trial_number: int) -> int: + """Read the trial ID of a trial. + + Args: + study_id: + ID of the study. + trial_number: + Number of the trial. + + Returns: + ID of the trial. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``study_id`` and ``trial_number`` exists. + """ + trials = self.get_all_trials(study_id, deepcopy=False) + if len(trials) <= trial_number: + raise KeyError( + "No trial with trial number {} exists in study with study_id {}.".format( + trial_number, study_id + ) + ) + return trials[trial_number]._trial_id + + def get_trial_number_from_id(self, trial_id: int) -> int: + """Read the trial number of a trial. + + .. note:: + + The trial number is only unique within a study, and is sequential. + + Args: + trial_id: + ID of the trial. + + Returns: + Number of the trial. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + """ + return self.get_trial(trial_id).number + + def get_trial_param(self, trial_id: int, param_name: str) -> float: + """Read the parameter of a trial. + + Args: + trial_id: + ID of the trial. + param_name: + Name of the parameter. + + Returns: + Internal representation of the parameter. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + If no such parameter exists. + """ + trial = self.get_trial(trial_id) + return trial.distributions[param_name].to_internal_repr(trial.params[param_name]) + + @abc.abstractmethod + def set_trial_state_values( + self, trial_id: int, state: TrialState, values: Sequence[float] | None = None + ) -> bool: + """Update the state and values of a trial. + + Set return values of an objective function to values argument. + If values argument is not :obj:`None`, this method overwrites any existing trial values. + + Args: + trial_id: + ID of the trial. + state: + New state of the trial. + values: + Values of the objective function. + + Returns: + :obj:`True` if the state is successfully updated. + :obj:`False` if the state is kept the same. + The latter happens when this method tries to update the state of + :obj:`~optuna.trial.TrialState.RUNNING` trial to + :obj:`~optuna.trial.TrialState.RUNNING`. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + :exc:`~optuna.exceptions.UpdateFinishedTrialError`: + If the trial is already finished. + """ + raise NotImplementedError + + @abc.abstractmethod + def set_trial_intermediate_value( + self, trial_id: int, step: int, intermediate_value: float + ) -> None: + """Report an intermediate value of an objective function. + + This method overwrites any existing intermediate value associated with the given step. + + Args: + trial_id: + ID of the trial. + step: + Step of the trial (e.g., the epoch when training a neural network). + intermediate_value: + Intermediate value corresponding to the step. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + :exc:`~optuna.exceptions.UpdateFinishedTrialError`: + If the trial is already finished. + """ + raise NotImplementedError + + @abc.abstractmethod + def set_trial_user_attr(self, trial_id: int, key: str, value: Any) -> None: + """Set a user-defined attribute to a trial. + + This method overwrites any existing attribute. + + Args: + trial_id: + ID of the trial. + key: + Attribute key. + value: + Attribute value. It should be JSON serializable. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + :exc:`~optuna.exceptions.UpdateFinishedTrialError`: + If the trial is already finished. + """ + raise NotImplementedError + + @abc.abstractmethod + def set_trial_system_attr(self, trial_id: int, key: str, value: JSONSerializable) -> None: + """Set an optuna-internal attribute to a trial. + + This method overwrites any existing attribute. + + Args: + trial_id: + ID of the trial. + key: + Attribute key. + value: + Attribute value. It should be JSON serializable. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + :exc:`~optuna.exceptions.UpdateFinishedTrialError`: + If the trial is already finished. + """ + raise NotImplementedError + + # Basic trial access + + @abc.abstractmethod + def get_trial(self, trial_id: int) -> FrozenTrial: + """Read a trial. + + Args: + trial_id: + ID of the trial. + + Returns: + Trial with a matching trial ID. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + """ + raise NotImplementedError + + @abc.abstractmethod + def get_all_trials( + self, + study_id: int, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + ) -> list[FrozenTrial]: + """Read all trials in a study. + + Args: + study_id: + ID of the study. + deepcopy: + Whether to copy the list of trials before returning. + Set to :obj:`True` if you intend to update the list or elements of the list. + states: + Trial states to filter on. If :obj:`None`, include all states. + + Returns: + List of trials in the study, sorted by ``trial_id``. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + raise NotImplementedError + + def get_n_trials( + self, study_id: int, state: tuple[TrialState, ...] | TrialState | None = None + ) -> int: + """Count the number of trials in a study. + + Args: + study_id: + ID of the study. + state: + Trial states to filter on. If :obj:`None`, include all states. + + Returns: + Number of trials in the study. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + """ + # TODO(hvy): Align the name and the behavior or the `state` parameter with + # `get_all_trials`'s `states`. + if isinstance(state, TrialState): + state = (state,) + return len(self.get_all_trials(study_id, deepcopy=False, states=state)) + + def get_best_trial(self, study_id: int) -> FrozenTrial: + """Return the trial with the best value in a study. + + This method is valid only during single-objective optimization. + + Args: + study_id: + ID of the study. + + Returns: + The trial with the best objective value among all finished trials in the study. + + Raises: + :exc:`KeyError`: + If no study with the matching ``study_id`` exists. + :exc:`RuntimeError`: + If the study has more than one direction. + :exc:`ValueError`: + If no trials have been completed. + """ + all_trials = self.get_all_trials(study_id, deepcopy=False, states=[TrialState.COMPLETE]) + + if len(all_trials) == 0: + raise ValueError("No trials are completed yet.") + + directions = self.get_study_directions(study_id) + if len(directions) > 1: + raise RuntimeError( + "Best trial can be obtained only for single-objective optimization." + ) + direction = directions[0] + + if direction == StudyDirection.MAXIMIZE: + best_trial = max(all_trials, key=lambda t: cast(float, t.value)) + else: + best_trial = min(all_trials, key=lambda t: cast(float, t.value)) + + return best_trial + + def get_trial_params(self, trial_id: int) -> dict[str, Any]: + """Read the parameter dictionary of a trial. + + Args: + trial_id: + ID of the trial. + + Returns: + Dictionary of a parameters. Keys are parameter names and values are external + representations of the parameter values. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + """ + return self.get_trial(trial_id).params + + def get_trial_user_attrs(self, trial_id: int) -> dict[str, Any]: + """Read the user-defined attributes of a trial. + + Args: + trial_id: + ID of the trial. + + Returns: + Dictionary with the user-defined attributes of the trial. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + """ + return self.get_trial(trial_id).user_attrs + + def get_trial_system_attrs(self, trial_id: int) -> dict[str, Any]: + """Read the optuna-internal attributes of a trial. + + Args: + trial_id: + ID of the trial. + + Returns: + Dictionary with the optuna-internal attributes of the trial. + + Raises: + :exc:`KeyError`: + If no trial with the matching ``trial_id`` exists. + """ + return self.get_trial(trial_id).system_attrs + + def remove_session(self) -> None: + """Clean up all connections to a database.""" + pass + + def check_trial_is_updatable(self, trial_id: int, trial_state: TrialState) -> None: + """Check whether a trial state is updatable. + + Args: + trial_id: + ID of the trial. + Only used for an error message. + trial_state: + Trial state to check. + + Raises: + :exc:`~optuna.exceptions.UpdateFinishedTrialError`: + If the trial is already finished. + """ + if trial_state.is_finished(): + trial = self.get_trial(trial_id) + raise UpdateFinishedTrialError( + "Trial#{} has already finished and can not be updated.".format(trial.number) + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_cached_storage.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_cached_storage.py new file mode 100644 index 0000000000000000000000000000000000000000..c90f2b53f2653609087561b46eeb4457c7a570e3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_cached_storage.py @@ -0,0 +1,279 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Container +from collections.abc import Sequence +import copy +import threading +from typing import Any + +import optuna +from optuna import distributions +from optuna._typing import JSONSerializable +from optuna.storages import BaseStorage +from optuna.storages._heartbeat import BaseHeartbeat +from optuna.storages._rdb.storage import RDBStorage +from optuna.study._frozen import FrozenStudy +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +class _StudyInfo: + def __init__(self) -> None: + # Trial number to corresponding FrozenTrial. + self.trials: dict[int, FrozenTrial] = {} + # A list of trials and the last trial number which require storage access to read latest + # attributes. + self.unfinished_trial_ids: set[int] = set() + self.last_finished_trial_id: int = -1 + self.directions: list[StudyDirection] | None = None + self.name: str | None = None + + +class _CachedStorage(BaseStorage, BaseHeartbeat): + """A wrapper class of storage backends. + + This class is used in :func:`~optuna.get_storage` function and automatically + wraps :class:`~optuna.storages.RDBStorage` class. + + :class:`~optuna.storages._CachedStorage` meets the following **Data persistence** requirements. + + **Data persistence** + + :class:`~optuna.storages._CachedStorage` does not guarantee that write operations are logged + into a persistent storage, even when write methods succeed. + Thus, when process failure occurs, some writes might be lost. + As exceptions, when a persistent storage is available, any writes on any attributes + of `Study` and writes on `state` of `Trial` are guaranteed to be persistent. + Additionally, any preceding writes on any attributes of `Trial` are guaranteed to + be written into a persistent storage before writes on `state` of `Trial` succeed. + The same applies for `param`, `user_attrs', 'system_attrs' and 'intermediate_values` + attributes. + + Args: + backend: + :class:`~optuna.storages.RDBStorage` class instance to wrap. + """ + + def __init__(self, backend: RDBStorage) -> None: + self._backend = backend + self._studies: dict[int, _StudyInfo] = {} + self._trial_id_to_study_id_and_number: dict[int, tuple[int, int]] = {} + self._study_id_and_number_to_trial_id: dict[tuple[int, int], int] = {} + self._lock = threading.Lock() + + def __getstate__(self) -> dict[Any, Any]: + state = self.__dict__.copy() + del state["_lock"] + return state + + def __setstate__(self, state: dict[Any, Any]) -> None: + self.__dict__.update(state) + self._lock = threading.Lock() + + def create_new_study( + self, directions: Sequence[StudyDirection], study_name: str | None = None + ) -> int: + study_id = self._backend.create_new_study(directions=directions, study_name=study_name) + with self._lock: + study = _StudyInfo() + study.name = study_name + study.directions = list(directions) + self._studies[study_id] = study + + return study_id + + def delete_study(self, study_id: int) -> None: + with self._lock: + if study_id in self._studies: + for trial_number in self._studies[study_id].trials: + trial_id = self._study_id_and_number_to_trial_id.get((study_id, trial_number)) + if trial_id in self._trial_id_to_study_id_and_number: + del self._trial_id_to_study_id_and_number[trial_id] + if (study_id, trial_number) in self._study_id_and_number_to_trial_id: + del self._study_id_and_number_to_trial_id[(study_id, trial_number)] + del self._studies[study_id] + + self._backend.delete_study(study_id) + + def set_study_user_attr(self, study_id: int, key: str, value: Any) -> None: + self._backend.set_study_user_attr(study_id, key, value) + + def set_study_system_attr(self, study_id: int, key: str, value: JSONSerializable) -> None: + self._backend.set_study_system_attr(study_id, key, value) + + def get_study_id_from_name(self, study_name: str) -> int: + return self._backend.get_study_id_from_name(study_name) + + def get_study_name_from_id(self, study_id: int) -> str: + with self._lock: + if study_id in self._studies: + name = self._studies[study_id].name + if name is not None: + return name + + name = self._backend.get_study_name_from_id(study_id) + with self._lock: + if study_id not in self._studies: + self._studies[study_id] = _StudyInfo() + self._studies[study_id].name = name + return name + + def get_study_directions(self, study_id: int) -> list[StudyDirection]: + with self._lock: + if study_id in self._studies: + directions = self._studies[study_id].directions + if directions is not None: + return directions + + directions = self._backend.get_study_directions(study_id) + with self._lock: + if study_id not in self._studies: + self._studies[study_id] = _StudyInfo() + self._studies[study_id].directions = directions + return directions + + def get_study_user_attrs(self, study_id: int) -> dict[str, Any]: + return self._backend.get_study_user_attrs(study_id) + + def get_study_system_attrs(self, study_id: int) -> dict[str, Any]: + return self._backend.get_study_system_attrs(study_id) + + def get_all_studies(self) -> list[FrozenStudy]: + return self._backend.get_all_studies() + + def create_new_trial(self, study_id: int, template_trial: FrozenTrial | None = None) -> int: + frozen_trial = self._backend._create_new_trial(study_id, template_trial) + trial_id = frozen_trial._trial_id + with self._lock: + if study_id not in self._studies: + self._studies[study_id] = _StudyInfo() + study = self._studies[study_id] + self._add_trials_to_cache(study_id, [frozen_trial]) + # Since finished trials will not be modified by any worker, we do not + # need storage access for them. + if frozen_trial.state.is_finished(): + study.last_finished_trial_id = max(study.last_finished_trial_id, trial_id) + else: + study.unfinished_trial_ids.add(trial_id) + return trial_id + + def set_trial_param( + self, + trial_id: int, + param_name: str, + param_value_internal: float, + distribution: distributions.BaseDistribution, + ) -> None: + self._backend.set_trial_param(trial_id, param_name, param_value_internal, distribution) + + def get_trial_id_from_study_id_trial_number(self, study_id: int, trial_number: int) -> int: + key = (study_id, trial_number) + with self._lock: + if key in self._study_id_and_number_to_trial_id: + return self._study_id_and_number_to_trial_id[key] + + return self._backend.get_trial_id_from_study_id_trial_number(study_id, trial_number) + + def get_best_trial(self, study_id: int) -> FrozenTrial: + return self._backend.get_best_trial(study_id) + + def set_trial_state_values( + self, trial_id: int, state: TrialState, values: Sequence[float] | None = None + ) -> bool: + return self._backend.set_trial_state_values(trial_id, state=state, values=values) + + def set_trial_intermediate_value( + self, trial_id: int, step: int, intermediate_value: float + ) -> None: + self._backend.set_trial_intermediate_value(trial_id, step, intermediate_value) + + def set_trial_user_attr(self, trial_id: int, key: str, value: Any) -> None: + self._backend.set_trial_user_attr(trial_id, key=key, value=value) + + def set_trial_system_attr(self, trial_id: int, key: str, value: JSONSerializable) -> None: + self._backend.set_trial_system_attr(trial_id, key=key, value=value) + + def _get_cached_trial(self, trial_id: int) -> FrozenTrial | None: + if trial_id not in self._trial_id_to_study_id_and_number: + return None + study_id, number = self._trial_id_to_study_id_and_number[trial_id] + study = self._studies[study_id] + return study.trials[number] if trial_id not in study.unfinished_trial_ids else None + + def get_trial(self, trial_id: int) -> FrozenTrial: + with self._lock: + trial = self._get_cached_trial(trial_id) + if trial is not None: + return trial + + return self._backend.get_trial(trial_id) + + def get_all_trials( + self, + study_id: int, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + ) -> list[FrozenTrial]: + self._read_trials_from_remote_storage(study_id) + + with self._lock: + study = self._studies[study_id] + # We need to sort trials by their number because some samplers assume this behavior. + # The following two lines are latency-sensitive. + + trials: dict[int, FrozenTrial] | list[FrozenTrial] + + if states is not None: + trials = {number: t for number, t in study.trials.items() if t.state in states} + else: + trials = study.trials + trials = list(sorted(trials.values(), key=lambda t: t.number)) + return copy.deepcopy(trials) if deepcopy else trials + + def _read_trials_from_remote_storage(self, study_id: int) -> None: + with self._lock: + if study_id not in self._studies: + self._studies[study_id] = _StudyInfo() + study = self._studies[study_id] + trials = self._backend._get_trials( + study_id, + states=None, + included_trial_ids=study.unfinished_trial_ids, + trial_id_greater_than=study.last_finished_trial_id, + ) + if not trials: + return + + self._add_trials_to_cache(study_id, trials) + for trial in trials: + if not trial.state.is_finished(): + study.unfinished_trial_ids.add(trial._trial_id) + continue + + study.last_finished_trial_id = max(study.last_finished_trial_id, trial._trial_id) + if trial._trial_id in study.unfinished_trial_ids: + study.unfinished_trial_ids.remove(trial._trial_id) + + def _add_trials_to_cache(self, study_id: int, trials: list[FrozenTrial]) -> None: + study = self._studies[study_id] + for trial in trials: + self._trial_id_to_study_id_and_number[trial._trial_id] = ( + study_id, + trial.number, + ) + self._study_id_and_number_to_trial_id[(study_id, trial.number)] = trial._trial_id + study.trials[trial.number] = trial + + def record_heartbeat(self, trial_id: int) -> None: + self._backend.record_heartbeat(trial_id) + + def _get_stale_trial_ids(self, study_id: int) -> list[int]: + return self._backend._get_stale_trial_ids(study_id) + + def get_heartbeat_interval(self) -> int | None: + return self._backend.get_heartbeat_interval() + + def get_failed_trial_callback(self) -> Callable[["optuna.Study", FrozenTrial], None] | None: + return self._backend.get_failed_trial_callback() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_callbacks.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..2f60a10cb5353292077862934a8a6f79629c0690 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_callbacks.py @@ -0,0 +1,121 @@ +from __future__ import annotations + +from typing import Any + +import optuna +from optuna._experimental import experimental_class +from optuna._experimental import experimental_func +from optuna.trial import FrozenTrial + + +@experimental_class("2.8.0") +class RetryFailedTrialCallback: + """Retry a failed trial up to a maximum number of times. + + When a trial fails, this callback can be used with a class in :mod:`optuna.storages` to + recreate the trial in ``TrialState.WAITING`` to queue up the trial to be run again. + + The failed trial can be identified by the + :func:`~optuna.storages.RetryFailedTrialCallback.retried_trial_number` function. + Even if repetitive failure occurs (a retried trial fails again), + this method returns the number of the original trial. + To get a full list including the numbers of the retried trials as well as their original trial, + call the :func:`~optuna.storages.RetryFailedTrialCallback.retry_history` function. + + This callback is helpful in environments where trials may fail due to external conditions, + such as being preempted by other processes. + + Usage: + + .. testcode:: + + import optuna + from optuna.storages import RetryFailedTrialCallback + + storage = optuna.storages.RDBStorage( + url="sqlite:///:memory:", + heartbeat_interval=60, + grace_period=120, + failed_trial_callback=RetryFailedTrialCallback(max_retry=3), + ) + + study = optuna.create_study( + storage=storage, + ) + + .. seealso:: + See :class:`~optuna.storages.RDBStorage`. + + Args: + max_retry: + The max number of times a trial can be retried. Must be set to :obj:`None` or an + integer. If set to the default value of :obj:`None` will retry indefinitely. + If set to an integer, will only retry that many times. + inherit_intermediate_values: + Option to inherit `trial.intermediate_values` reported by + :func:`optuna.trial.Trial.report` from the failed trial. Default is :obj:`False`. + """ + + def __init__( + self, max_retry: int | None = None, inherit_intermediate_values: bool = False + ) -> None: + self._max_retry = max_retry + self._inherit_intermediate_values = inherit_intermediate_values + + def __call__(self, study: "optuna.study.Study", trial: FrozenTrial) -> None: + system_attrs: dict[str, Any] = { + "failed_trial": trial.number, + "retry_history": [], + **trial.system_attrs, + } + system_attrs["retry_history"].append(trial.number) + if self._max_retry is not None: + if self._max_retry < len(system_attrs["retry_history"]): + return + + study.add_trial( + optuna.create_trial( + state=optuna.trial.TrialState.WAITING, + params=trial.params, + distributions=trial.distributions, + user_attrs=trial.user_attrs, + system_attrs=system_attrs, + intermediate_values=( + trial.intermediate_values if self._inherit_intermediate_values else None + ), + ) + ) + + @staticmethod + @experimental_func("2.8.0") + def retried_trial_number(trial: FrozenTrial) -> int | None: + """Return the number of the original trial being retried. + + Args: + trial: + The trial object. + + Returns: + The number of the first failed trial. If not retry of a previous trial, + returns :obj:`None`. + """ + + return trial.system_attrs.get("failed_trial", None) + + @staticmethod + @experimental_func("3.0.0") + def retry_history(trial: FrozenTrial) -> list[int]: + """Return the list of retried trial numbers with respect to the specified trial. + + Args: + trial: + The trial object. + + Returns: + A list of trial numbers in ascending order of the series of retried trials. + The first item of the list indicates the original trial which is identical + to the :func:`~optuna.storages.RetryFailedTrialCallback.retried_trial_number`, + and the last item is the one right before the specified trial in the retry series. + If the specified trial is not a retry of any trial, returns an empty list. + """ + return trial.system_attrs.get("retry_history", []) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f35d9da974755ba56f291985fa13ce3b0954049c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__init__.py @@ -0,0 +1,8 @@ +from optuna.storages._grpc.client import GrpcStorageProxy +from optuna.storages._grpc.server import run_grpc_proxy_server + + +__all__ = [ + "run_grpc_proxy_server", + "GrpcStorageProxy", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..123f668ae14bc5acc5a81790929ea40ea8619132 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__pycache__/client.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__pycache__/client.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c0f1c35a670efc8ca3edf434a011c109c9b6b63d Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__pycache__/client.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__pycache__/server.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__pycache__/server.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4dde1260ba71a849522efb0338bb916485223779 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/__pycache__/server.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/auto_generated/api_pb2.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/auto_generated/api_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..5a03e6861cb992df0237eaa736bde51a7d450134 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/auto_generated/api_pb2.py @@ -0,0 +1,156 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# NO CHECKED-IN PROTOBUF GENCODE +# source: api.proto +# Protobuf Python Version: 5.28.1 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import runtime_version as _runtime_version +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +_runtime_version.ValidateProtobufRuntimeVersion( + _runtime_version.Domain.PUBLIC, + 5, + 28, + 1, + '', + 'api.proto' +) +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\tapi.proto\x12\x06optuna\"W\n\x15\x43reateNewStudyRequest\x12*\n\ndirections\x18\x01 \x03(\x0e\x32\x16.optuna.StudyDirection\x12\x12\n\nstudy_name\x18\x02 \x01(\t\"\'\n\x13\x43reateNewStudyReply\x12\x10\n\x08study_id\x18\x01 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+# @@protoc_insertion_point(module_scope) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/auto_generated/api_pb2.pyi b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/auto_generated/api_pb2.pyi new file mode 100644 index 0000000000000000000000000000000000000000..f0608671c070fbb1067fc2c766ef3a628dd3dbec --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/auto_generated/api_pb2.pyi @@ -0,0 +1,1070 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +* +Optuna GRPC API +The following command generates the Python code from this file: +$ pip install mypy-protobuf==3.6.0 protobuf==5.28.1 grpcio==1.68.1 grpcio-tools==1.68.1 +$ python -m grpc_tools.protoc \\ +--proto_path=optuna/storages/_grpc \\ +--grpc_python_out=optuna/storages/_grpc/auto_generated \\ +--python_out=optuna/storages/_grpc/auto_generated \\ +--mypy_out=optuna/storages/_grpc/auto_generated \\ +optuna/storages/_grpc/api.proto +$ sed -i -e \\ +"s/import api_pb2 as api__pb2/import optuna.storages._grpc.auto_generated.api_pb2 as api__pb2/g" \\ +optuna/storages/_grpc/auto_generated/api_pb2_grpc.py +""" + +import builtins +import collections.abc +import google.protobuf.descriptor +import google.protobuf.internal.containers +import google.protobuf.internal.enum_type_wrapper +import google.protobuf.message +import sys +import typing + +if sys.version_info >= (3, 10): + import typing as typing_extensions +else: + import typing_extensions + +DESCRIPTOR: google.protobuf.descriptor.FileDescriptor + +class _StudyDirection: + ValueType = typing.NewType("ValueType", builtins.int) + V: typing_extensions.TypeAlias = ValueType + +class _StudyDirectionEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_StudyDirection.ValueType], builtins.type): + DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor + MINIMIZE: _StudyDirection.ValueType # 0 + MAXIMIZE: _StudyDirection.ValueType # 1 + +class StudyDirection(_StudyDirection, metaclass=_StudyDirectionEnumTypeWrapper): + """* + Study direction. + """ + +MINIMIZE: StudyDirection.ValueType # 0 +MAXIMIZE: StudyDirection.ValueType # 1 +global___StudyDirection = StudyDirection + +class _TrialState: + ValueType = typing.NewType("ValueType", builtins.int) + V: typing_extensions.TypeAlias = ValueType + +class _TrialStateEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_TrialState.ValueType], builtins.type): + DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor + RUNNING: _TrialState.ValueType # 0 + COMPLETE: _TrialState.ValueType # 1 + PRUNED: _TrialState.ValueType # 2 + FAIL: _TrialState.ValueType # 3 + WAITING: _TrialState.ValueType # 4 + +class TrialState(_TrialState, metaclass=_TrialStateEnumTypeWrapper): + """* + Trial state. + """ + +RUNNING: TrialState.ValueType # 0 +COMPLETE: TrialState.ValueType # 1 +PRUNED: TrialState.ValueType # 2 +FAIL: TrialState.ValueType # 3 +WAITING: TrialState.ValueType # 4 +global___TrialState = TrialState + +@typing.final +class CreateNewStudyRequest(google.protobuf.message.Message): + """* + ======================================== + Messages for Optuna storage service. + ======================================== + + * + Request to create a new study. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + DIRECTIONS_FIELD_NUMBER: builtins.int + STUDY_NAME_FIELD_NUMBER: builtins.int + study_name: builtins.str + @property + def directions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___StudyDirection.ValueType]: ... + def __init__( + self, + *, + directions: collections.abc.Iterable[global___StudyDirection.ValueType] | None = ..., + study_name: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["directions", b"directions", "study_name", b"study_name"]) -> None: ... + +global___CreateNewStudyRequest = CreateNewStudyRequest + +@typing.final +class CreateNewStudyReply(google.protobuf.message.Message): + """* + Reply to create a new study. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + study_id: builtins.int + def __init__( + self, + *, + study_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_id", b"study_id"]) -> None: ... + +global___CreateNewStudyReply = CreateNewStudyReply + +@typing.final +class DeleteStudyRequest(google.protobuf.message.Message): + """* + Request to delete a study. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + study_id: builtins.int + def __init__( + self, + *, + study_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_id", b"study_id"]) -> None: ... + +global___DeleteStudyRequest = DeleteStudyRequest + +@typing.final +class DeleteStudyReply(google.protobuf.message.Message): + """* + Reply to delete a study. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + def __init__( + self, + ) -> None: ... + +global___DeleteStudyReply = DeleteStudyReply + +@typing.final +class SetStudyUserAttributeRequest(google.protobuf.message.Message): + """* + Request to set a study's user attribute. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + study_id: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + study_id: builtins.int = ..., + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "study_id", b"study_id", "value", b"value"]) -> None: ... + +global___SetStudyUserAttributeRequest = SetStudyUserAttributeRequest + +@typing.final +class SetStudyUserAttributeReply(google.protobuf.message.Message): + """* + Reply to set a study's user attribute. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + def __init__( + self, + ) -> None: ... + +global___SetStudyUserAttributeReply = SetStudyUserAttributeReply + +@typing.final +class SetStudySystemAttributeRequest(google.protobuf.message.Message): + """* + Request to set a study's system attribute. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + study_id: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + study_id: builtins.int = ..., + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "study_id", b"study_id", "value", b"value"]) -> None: ... + +global___SetStudySystemAttributeRequest = SetStudySystemAttributeRequest + +@typing.final +class SetStudySystemAttributeReply(google.protobuf.message.Message): + """* + Reply to set a study's system attribute. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + def __init__( + self, + ) -> None: ... + +global___SetStudySystemAttributeReply = SetStudySystemAttributeReply + +@typing.final +class GetStudyIdFromNameRequest(google.protobuf.message.Message): + """* + Request to get a study id by its name. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_NAME_FIELD_NUMBER: builtins.int + study_name: builtins.str + def __init__( + self, + *, + study_name: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_name", b"study_name"]) -> None: ... + +global___GetStudyIdFromNameRequest = GetStudyIdFromNameRequest + +@typing.final +class GetStudyIdFromNameReply(google.protobuf.message.Message): + """* + Reply to get a study id by its name. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + study_id: builtins.int + def __init__( + self, + *, + study_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_id", b"study_id"]) -> None: ... + +global___GetStudyIdFromNameReply = GetStudyIdFromNameReply + +@typing.final +class GetStudyNameFromIdRequest(google.protobuf.message.Message): + """* + Request to get a study name by its id. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + study_id: builtins.int + def __init__( + self, + *, + study_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_id", b"study_id"]) -> None: ... + +global___GetStudyNameFromIdRequest = GetStudyNameFromIdRequest + +@typing.final +class GetStudyNameFromIdReply(google.protobuf.message.Message): + """* + Reply to get a study name by its id. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_NAME_FIELD_NUMBER: builtins.int + study_name: builtins.str + def __init__( + self, + *, + study_name: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_name", b"study_name"]) -> None: ... + +global___GetStudyNameFromIdReply = GetStudyNameFromIdReply + +@typing.final +class GetStudyDirectionsRequest(google.protobuf.message.Message): + """* + Request to get study directions. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + study_id: builtins.int + def __init__( + self, + *, + study_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_id", b"study_id"]) -> None: ... + +global___GetStudyDirectionsRequest = GetStudyDirectionsRequest + +@typing.final +class GetStudyDirectionsReply(google.protobuf.message.Message): + """* + Reply to get study directions. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + DIRECTIONS_FIELD_NUMBER: builtins.int + @property + def directions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___StudyDirection.ValueType]: ... + def __init__( + self, + *, + directions: collections.abc.Iterable[global___StudyDirection.ValueType] | None = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["directions", b"directions"]) -> None: ... + +global___GetStudyDirectionsReply = GetStudyDirectionsReply + +@typing.final +class GetStudyUserAttributesRequest(google.protobuf.message.Message): + """* + Request to get study user attributes. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + study_id: builtins.int + def __init__( + self, + *, + study_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_id", b"study_id"]) -> None: ... + +global___GetStudyUserAttributesRequest = GetStudyUserAttributesRequest + +@typing.final +class GetStudyUserAttributesReply(google.protobuf.message.Message): + """* + Reply to get study user attributes. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + @typing.final + class UserAttributesEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ... + + USER_ATTRIBUTES_FIELD_NUMBER: builtins.int + @property + def user_attributes(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ... + def __init__( + self, + *, + user_attributes: collections.abc.Mapping[builtins.str, builtins.str] | None = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["user_attributes", b"user_attributes"]) -> None: ... + +global___GetStudyUserAttributesReply = GetStudyUserAttributesReply + +@typing.final +class GetStudySystemAttributesRequest(google.protobuf.message.Message): + """* + Request to get study system attributes. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + study_id: builtins.int + def __init__( + self, + *, + study_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_id", b"study_id"]) -> None: ... + +global___GetStudySystemAttributesRequest = GetStudySystemAttributesRequest + +@typing.final +class GetStudySystemAttributesReply(google.protobuf.message.Message): + """* + Reply to get study system attributes. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + @typing.final + class SystemAttributesEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ... + + SYSTEM_ATTRIBUTES_FIELD_NUMBER: builtins.int + @property + def system_attributes(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ... + def __init__( + self, + *, + system_attributes: collections.abc.Mapping[builtins.str, builtins.str] | None = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["system_attributes", b"system_attributes"]) -> None: ... + +global___GetStudySystemAttributesReply = GetStudySystemAttributesReply + +@typing.final +class GetAllStudiesRequest(google.protobuf.message.Message): + """* + Request to get all studies. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + def __init__( + self, + ) -> None: ... + +global___GetAllStudiesRequest = GetAllStudiesRequest + +@typing.final +class GetAllStudiesReply(google.protobuf.message.Message): + """* + Reply to get all studies. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDIES_FIELD_NUMBER: builtins.int + @property + def studies(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___Study]: ... + def __init__( + self, + *, + studies: collections.abc.Iterable[global___Study] | None = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["studies", b"studies"]) -> None: ... + +global___GetAllStudiesReply = GetAllStudiesReply + +@typing.final +class CreateNewTrialRequest(google.protobuf.message.Message): + """* + Request to create a new trial. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + TEMPLATE_TRIAL_FIELD_NUMBER: builtins.int + TEMPLATE_TRIAL_IS_NONE_FIELD_NUMBER: builtins.int + study_id: builtins.int + template_trial_is_none: builtins.bool + @property + def template_trial(self) -> global___Trial: ... + def __init__( + self, + *, + study_id: builtins.int = ..., + template_trial: global___Trial | None = ..., + template_trial_is_none: builtins.bool = ..., + ) -> None: ... + def HasField(self, field_name: typing.Literal["template_trial", b"template_trial"]) -> builtins.bool: ... + def ClearField(self, field_name: typing.Literal["study_id", b"study_id", "template_trial", b"template_trial", "template_trial_is_none", b"template_trial_is_none"]) -> None: ... + +global___CreateNewTrialRequest = CreateNewTrialRequest + +@typing.final +class CreateNewTrialReply(google.protobuf.message.Message): + """* + Reply to create a new trial. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_ID_FIELD_NUMBER: builtins.int + trial_id: builtins.int + def __init__( + self, + *, + trial_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["trial_id", b"trial_id"]) -> None: ... + +global___CreateNewTrialReply = CreateNewTrialReply + +@typing.final +class SetTrialParameterRequest(google.protobuf.message.Message): + """* + Request to set a trial parameter. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_ID_FIELD_NUMBER: builtins.int + PARAM_NAME_FIELD_NUMBER: builtins.int + PARAM_VALUE_INTERNAL_FIELD_NUMBER: builtins.int + DISTRIBUTION_FIELD_NUMBER: builtins.int + trial_id: builtins.int + param_name: builtins.str + param_value_internal: builtins.float + distribution: builtins.str + def __init__( + self, + *, + trial_id: builtins.int = ..., + param_name: builtins.str = ..., + param_value_internal: builtins.float = ..., + distribution: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["distribution", b"distribution", "param_name", b"param_name", "param_value_internal", b"param_value_internal", "trial_id", b"trial_id"]) -> None: ... + +global___SetTrialParameterRequest = SetTrialParameterRequest + +@typing.final +class SetTrialParameterReply(google.protobuf.message.Message): + """* + Reply to set a trial parameter. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + def __init__( + self, + ) -> None: ... + +global___SetTrialParameterReply = SetTrialParameterReply + +@typing.final +class GetTrialIdFromStudyIdTrialNumberRequest(google.protobuf.message.Message): + """* + Request to get a trial id from its study id and trial number. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + TRIAL_NUMBER_FIELD_NUMBER: builtins.int + study_id: builtins.int + trial_number: builtins.int + def __init__( + self, + *, + study_id: builtins.int = ..., + trial_number: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["study_id", b"study_id", "trial_number", b"trial_number"]) -> None: ... + +global___GetTrialIdFromStudyIdTrialNumberRequest = GetTrialIdFromStudyIdTrialNumberRequest + +@typing.final +class GetTrialIdFromStudyIdTrialNumberReply(google.protobuf.message.Message): + """* + Reply to get a trial id from its study id and trial number. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_ID_FIELD_NUMBER: builtins.int + trial_id: builtins.int + def __init__( + self, + *, + trial_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["trial_id", b"trial_id"]) -> None: ... + +global___GetTrialIdFromStudyIdTrialNumberReply = GetTrialIdFromStudyIdTrialNumberReply + +@typing.final +class SetTrialStateValuesRequest(google.protobuf.message.Message): + """* + Request to set trial state and values. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_ID_FIELD_NUMBER: builtins.int + STATE_FIELD_NUMBER: builtins.int + VALUES_FIELD_NUMBER: builtins.int + trial_id: builtins.int + state: global___TrialState.ValueType + @property + def values(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: ... + def __init__( + self, + *, + trial_id: builtins.int = ..., + state: global___TrialState.ValueType = ..., + values: collections.abc.Iterable[builtins.float] | None = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["state", b"state", "trial_id", b"trial_id", "values", b"values"]) -> None: ... + +global___SetTrialStateValuesRequest = SetTrialStateValuesRequest + +@typing.final +class SetTrialStateValuesReply(google.protobuf.message.Message): + """* + Reply to set trial state and values. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_UPDATED_FIELD_NUMBER: builtins.int + trial_updated: builtins.bool + def __init__( + self, + *, + trial_updated: builtins.bool = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["trial_updated", b"trial_updated"]) -> None: ... + +global___SetTrialStateValuesReply = SetTrialStateValuesReply + +@typing.final +class SetTrialIntermediateValueRequest(google.protobuf.message.Message): + """* + Request to set a trial intermediate value. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_ID_FIELD_NUMBER: builtins.int + STEP_FIELD_NUMBER: builtins.int + INTERMEDIATE_VALUE_FIELD_NUMBER: builtins.int + trial_id: builtins.int + step: builtins.int + intermediate_value: builtins.float + def __init__( + self, + *, + trial_id: builtins.int = ..., + step: builtins.int = ..., + intermediate_value: builtins.float = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["intermediate_value", b"intermediate_value", "step", b"step", "trial_id", b"trial_id"]) -> None: ... + +global___SetTrialIntermediateValueRequest = SetTrialIntermediateValueRequest + +@typing.final +class SetTrialIntermediateValueReply(google.protobuf.message.Message): + """* + Reply to set a trial intermediate value. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + def __init__( + self, + ) -> None: ... + +global___SetTrialIntermediateValueReply = SetTrialIntermediateValueReply + +@typing.final +class SetTrialUserAttributeRequest(google.protobuf.message.Message): + """* + Request to set a trial user attribute. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_ID_FIELD_NUMBER: builtins.int + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + trial_id: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + trial_id: builtins.int = ..., + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "trial_id", b"trial_id", "value", b"value"]) -> None: ... + +global___SetTrialUserAttributeRequest = SetTrialUserAttributeRequest + +@typing.final +class SetTrialUserAttributeReply(google.protobuf.message.Message): + """* + Reply to set a trial user attribute. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + def __init__( + self, + ) -> None: ... + +global___SetTrialUserAttributeReply = SetTrialUserAttributeReply + +@typing.final +class SetTrialSystemAttributeRequest(google.protobuf.message.Message): + """* + Request to set a trial system attribute. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_ID_FIELD_NUMBER: builtins.int + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + trial_id: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + trial_id: builtins.int = ..., + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "trial_id", b"trial_id", "value", b"value"]) -> None: ... + +global___SetTrialSystemAttributeRequest = SetTrialSystemAttributeRequest + +@typing.final +class SetTrialSystemAttributeReply(google.protobuf.message.Message): + """* + Reply to set a trial system attribute. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + def __init__( + self, + ) -> None: ... + +global___SetTrialSystemAttributeReply = SetTrialSystemAttributeReply + +@typing.final +class GetTrialRequest(google.protobuf.message.Message): + """* + Request to get a trial by its ID. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_ID_FIELD_NUMBER: builtins.int + trial_id: builtins.int + def __init__( + self, + *, + trial_id: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["trial_id", b"trial_id"]) -> None: ... + +global___GetTrialRequest = GetTrialRequest + +@typing.final +class GetTrialReply(google.protobuf.message.Message): + """* + Reply to get a trial by its ID. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIAL_FIELD_NUMBER: builtins.int + @property + def trial(self) -> global___Trial: ... + def __init__( + self, + *, + trial: global___Trial | None = ..., + ) -> None: ... + def HasField(self, field_name: typing.Literal["trial", b"trial"]) -> builtins.bool: ... + def ClearField(self, field_name: typing.Literal["trial", b"trial"]) -> None: ... + +global___GetTrialReply = GetTrialReply + +@typing.final +class GetTrialsRequest(google.protobuf.message.Message): + """* + Request to get trials in a study. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + STUDY_ID_FIELD_NUMBER: builtins.int + INCLUDED_TRIAL_IDS_FIELD_NUMBER: builtins.int + TRIAL_ID_GREATER_THAN_FIELD_NUMBER: builtins.int + study_id: builtins.int + trial_id_greater_than: builtins.int + @property + def included_trial_ids(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ... + def __init__( + self, + *, + study_id: builtins.int = ..., + included_trial_ids: collections.abc.Iterable[builtins.int] | None = ..., + trial_id_greater_than: builtins.int = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["included_trial_ids", b"included_trial_ids", "study_id", b"study_id", "trial_id_greater_than", b"trial_id_greater_than"]) -> None: ... + +global___GetTrialsRequest = GetTrialsRequest + +@typing.final +class GetTrialsReply(google.protobuf.message.Message): + """* + Reply to get trials in a study. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + TRIALS_FIELD_NUMBER: builtins.int + @property + def trials(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___Trial]: ... + def __init__( + self, + *, + trials: collections.abc.Iterable[global___Trial] | None = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["trials", b"trials"]) -> None: ... + +global___GetTrialsReply = GetTrialsReply + +@typing.final +class Study(google.protobuf.message.Message): + """* + Study. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + @typing.final + class UserAttributesEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ... + + @typing.final + class SystemAttributesEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ... + + STUDY_ID_FIELD_NUMBER: builtins.int + STUDY_NAME_FIELD_NUMBER: builtins.int + DIRECTIONS_FIELD_NUMBER: builtins.int + USER_ATTRIBUTES_FIELD_NUMBER: builtins.int + SYSTEM_ATTRIBUTES_FIELD_NUMBER: builtins.int + study_id: builtins.int + study_name: builtins.str + @property + def directions(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[global___StudyDirection.ValueType]: ... + @property + def user_attributes(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ... + @property + def system_attributes(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ... + def __init__( + self, + *, + study_id: builtins.int = ..., + study_name: builtins.str = ..., + directions: collections.abc.Iterable[global___StudyDirection.ValueType] | None = ..., + user_attributes: collections.abc.Mapping[builtins.str, builtins.str] | None = ..., + system_attributes: collections.abc.Mapping[builtins.str, builtins.str] | None = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["directions", b"directions", "study_id", b"study_id", "study_name", b"study_name", "system_attributes", b"system_attributes", "user_attributes", b"user_attributes"]) -> None: ... + +global___Study = Study + +@typing.final +class Trial(google.protobuf.message.Message): + """* + Trial. + """ + + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + @typing.final + class ParamsEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.str + value: builtins.float + def __init__( + self, + *, + key: builtins.str = ..., + value: builtins.float = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ... + + @typing.final + class DistributionsEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ... + + @typing.final + class UserAttributesEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ... + + @typing.final + class SystemAttributesEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.str + value: builtins.str + def __init__( + self, + *, + key: builtins.str = ..., + value: builtins.str = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ... + + @typing.final + class IntermediateValuesEntry(google.protobuf.message.Message): + DESCRIPTOR: google.protobuf.descriptor.Descriptor + + KEY_FIELD_NUMBER: builtins.int + VALUE_FIELD_NUMBER: builtins.int + key: builtins.int + value: builtins.float + def __init__( + self, + *, + key: builtins.int = ..., + value: builtins.float = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["key", b"key", "value", b"value"]) -> None: ... + + TRIAL_ID_FIELD_NUMBER: builtins.int + NUMBER_FIELD_NUMBER: builtins.int + STATE_FIELD_NUMBER: builtins.int + VALUES_FIELD_NUMBER: builtins.int + DATETIME_START_FIELD_NUMBER: builtins.int + DATETIME_COMPLETE_FIELD_NUMBER: builtins.int + PARAMS_FIELD_NUMBER: builtins.int + DISTRIBUTIONS_FIELD_NUMBER: builtins.int + USER_ATTRIBUTES_FIELD_NUMBER: builtins.int + SYSTEM_ATTRIBUTES_FIELD_NUMBER: builtins.int + INTERMEDIATE_VALUES_FIELD_NUMBER: builtins.int + trial_id: builtins.int + number: builtins.int + state: global___TrialState.ValueType + datetime_start: builtins.str + datetime_complete: builtins.str + @property + def values(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: ... + @property + def params(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.float]: ... + @property + def distributions(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ... + @property + def user_attributes(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ... + @property + def system_attributes(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: ... + @property + def intermediate_values(self) -> google.protobuf.internal.containers.ScalarMap[builtins.int, builtins.float]: ... + def __init__( + self, + *, + trial_id: builtins.int = ..., + number: builtins.int = ..., + state: global___TrialState.ValueType = ..., + values: collections.abc.Iterable[builtins.float] | None = ..., + datetime_start: builtins.str = ..., + datetime_complete: builtins.str = ..., + params: collections.abc.Mapping[builtins.str, builtins.float] | None = ..., + distributions: collections.abc.Mapping[builtins.str, builtins.str] | None = ..., + user_attributes: collections.abc.Mapping[builtins.str, builtins.str] | None = ..., + system_attributes: collections.abc.Mapping[builtins.str, builtins.str] | None = ..., + intermediate_values: collections.abc.Mapping[builtins.int, builtins.float] | None = ..., + ) -> None: ... + def ClearField(self, field_name: typing.Literal["datetime_complete", b"datetime_complete", "datetime_start", b"datetime_start", "distributions", b"distributions", "intermediate_values", b"intermediate_values", "number", b"number", "params", b"params", "state", b"state", "system_attributes", b"system_attributes", "trial_id", b"trial_id", "user_attributes", b"user_attributes", "values", b"values"]) -> None: ... + +global___Trial = Trial diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/auto_generated/api_pb2_grpc.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/auto_generated/api_pb2_grpc.py new file mode 100644 index 0000000000000000000000000000000000000000..15a6bfe355b0c6d822976613b0d3d34b938caec3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/auto_generated/api_pb2_grpc.py @@ -0,0 +1,915 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc +import warnings + +import optuna.storages._grpc.auto_generated.api_pb2 as api__pb2 + +GRPC_GENERATED_VERSION = '1.68.1' +GRPC_VERSION = grpc.__version__ +_version_not_supported = False + +try: + from grpc._utilities import first_version_is_lower + _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) +except ImportError: + _version_not_supported = True + +if _version_not_supported: + raise RuntimeError( + f'The grpc package installed is at version {GRPC_VERSION},' + + f' but the generated code in api_pb2_grpc.py depends on' + + f' grpcio>={GRPC_GENERATED_VERSION}.' + + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' + + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' + ) + + +class StorageServiceStub(object): + """* + Optuna storage service defines APIs to interact with the storage. + """ + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.CreateNewStudy = channel.unary_unary( + '/optuna.StorageService/CreateNewStudy', + request_serializer=api__pb2.CreateNewStudyRequest.SerializeToString, + response_deserializer=api__pb2.CreateNewStudyReply.FromString, + _registered_method=True) + self.DeleteStudy = channel.unary_unary( + '/optuna.StorageService/DeleteStudy', + request_serializer=api__pb2.DeleteStudyRequest.SerializeToString, + response_deserializer=api__pb2.DeleteStudyReply.FromString, + _registered_method=True) + self.SetStudyUserAttribute = channel.unary_unary( + '/optuna.StorageService/SetStudyUserAttribute', + request_serializer=api__pb2.SetStudyUserAttributeRequest.SerializeToString, + response_deserializer=api__pb2.SetStudyUserAttributeReply.FromString, + _registered_method=True) + self.SetStudySystemAttribute = channel.unary_unary( + '/optuna.StorageService/SetStudySystemAttribute', + request_serializer=api__pb2.SetStudySystemAttributeRequest.SerializeToString, + response_deserializer=api__pb2.SetStudySystemAttributeReply.FromString, + _registered_method=True) + self.GetStudyIdFromName = channel.unary_unary( + '/optuna.StorageService/GetStudyIdFromName', + request_serializer=api__pb2.GetStudyIdFromNameRequest.SerializeToString, + response_deserializer=api__pb2.GetStudyIdFromNameReply.FromString, + _registered_method=True) + self.GetStudyNameFromId = channel.unary_unary( + '/optuna.StorageService/GetStudyNameFromId', + request_serializer=api__pb2.GetStudyNameFromIdRequest.SerializeToString, + response_deserializer=api__pb2.GetStudyNameFromIdReply.FromString, + _registered_method=True) + self.GetStudyDirections = channel.unary_unary( + '/optuna.StorageService/GetStudyDirections', + request_serializer=api__pb2.GetStudyDirectionsRequest.SerializeToString, + response_deserializer=api__pb2.GetStudyDirectionsReply.FromString, + _registered_method=True) + self.GetStudyUserAttributes = channel.unary_unary( + '/optuna.StorageService/GetStudyUserAttributes', + request_serializer=api__pb2.GetStudyUserAttributesRequest.SerializeToString, + response_deserializer=api__pb2.GetStudyUserAttributesReply.FromString, + _registered_method=True) + self.GetStudySystemAttributes = channel.unary_unary( + '/optuna.StorageService/GetStudySystemAttributes', + request_serializer=api__pb2.GetStudySystemAttributesRequest.SerializeToString, + response_deserializer=api__pb2.GetStudySystemAttributesReply.FromString, + _registered_method=True) + self.GetAllStudies = channel.unary_unary( + '/optuna.StorageService/GetAllStudies', + request_serializer=api__pb2.GetAllStudiesRequest.SerializeToString, + response_deserializer=api__pb2.GetAllStudiesReply.FromString, + _registered_method=True) + self.CreateNewTrial = channel.unary_unary( + '/optuna.StorageService/CreateNewTrial', + request_serializer=api__pb2.CreateNewTrialRequest.SerializeToString, + response_deserializer=api__pb2.CreateNewTrialReply.FromString, + _registered_method=True) + self.SetTrialParameter = channel.unary_unary( + '/optuna.StorageService/SetTrialParameter', + request_serializer=api__pb2.SetTrialParameterRequest.SerializeToString, + response_deserializer=api__pb2.SetTrialParameterReply.FromString, + _registered_method=True) + self.GetTrialIdFromStudyIdTrialNumber = channel.unary_unary( + '/optuna.StorageService/GetTrialIdFromStudyIdTrialNumber', + request_serializer=api__pb2.GetTrialIdFromStudyIdTrialNumberRequest.SerializeToString, + response_deserializer=api__pb2.GetTrialIdFromStudyIdTrialNumberReply.FromString, + _registered_method=True) + self.SetTrialStateValues = channel.unary_unary( + '/optuna.StorageService/SetTrialStateValues', + request_serializer=api__pb2.SetTrialStateValuesRequest.SerializeToString, + response_deserializer=api__pb2.SetTrialStateValuesReply.FromString, + _registered_method=True) + self.SetTrialIntermediateValue = channel.unary_unary( + '/optuna.StorageService/SetTrialIntermediateValue', + request_serializer=api__pb2.SetTrialIntermediateValueRequest.SerializeToString, + response_deserializer=api__pb2.SetTrialIntermediateValueReply.FromString, + _registered_method=True) + self.SetTrialUserAttribute = channel.unary_unary( + '/optuna.StorageService/SetTrialUserAttribute', + request_serializer=api__pb2.SetTrialUserAttributeRequest.SerializeToString, + response_deserializer=api__pb2.SetTrialUserAttributeReply.FromString, + _registered_method=True) + self.SetTrialSystemAttribute = channel.unary_unary( + '/optuna.StorageService/SetTrialSystemAttribute', + request_serializer=api__pb2.SetTrialSystemAttributeRequest.SerializeToString, + response_deserializer=api__pb2.SetTrialSystemAttributeReply.FromString, + _registered_method=True) + self.GetTrial = channel.unary_unary( + '/optuna.StorageService/GetTrial', + request_serializer=api__pb2.GetTrialRequest.SerializeToString, + response_deserializer=api__pb2.GetTrialReply.FromString, + _registered_method=True) + self.GetTrials = channel.unary_unary( + '/optuna.StorageService/GetTrials', + request_serializer=api__pb2.GetTrialsRequest.SerializeToString, + response_deserializer=api__pb2.GetTrialsReply.FromString, + _registered_method=True) + + +class StorageServiceServicer(object): + """* + Optuna storage service defines APIs to interact with the storage. + """ + + def CreateNewStudy(self, request, context): + """* + Create a new study. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def DeleteStudy(self, request, context): + """* + Delete a study. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetStudyUserAttribute(self, request, context): + """* + Set a study's user attribute. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetStudySystemAttribute(self, request, context): + """* + Set a study's system attribute. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetStudyIdFromName(self, request, context): + """* + Get a study id by its name. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetStudyNameFromId(self, request, context): + """* + Get a study name by its id. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetStudyDirections(self, request, context): + """* + Get study directions. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetStudyUserAttributes(self, request, context): + """* + Get study user attributes. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetStudySystemAttributes(self, request, context): + """* + Get study system attributes. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetAllStudies(self, request, context): + """* + Get all studies. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def CreateNewTrial(self, request, context): + """* + Create a new trial. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetTrialParameter(self, request, context): + """* + Set a trial parameter. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetTrialIdFromStudyIdTrialNumber(self, request, context): + """* + Get a trial id from its study id and trial number. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetTrialStateValues(self, request, context): + """* + Set trial state and values. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetTrialIntermediateValue(self, request, context): + """* + Set a trial intermediate value. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetTrialUserAttribute(self, request, context): + """* + Set a trial user attribute. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def SetTrialSystemAttribute(self, request, context): + """* + Set a trial system attribute. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetTrial(self, request, context): + """* + Get a trial by its ID. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetTrials(self, request, context): + """* + Get trials in a study. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + +def add_StorageServiceServicer_to_server(servicer, server): + rpc_method_handlers = { + 'CreateNewStudy': grpc.unary_unary_rpc_method_handler( + servicer.CreateNewStudy, + request_deserializer=api__pb2.CreateNewStudyRequest.FromString, + response_serializer=api__pb2.CreateNewStudyReply.SerializeToString, + ), + 'DeleteStudy': grpc.unary_unary_rpc_method_handler( + servicer.DeleteStudy, + request_deserializer=api__pb2.DeleteStudyRequest.FromString, + response_serializer=api__pb2.DeleteStudyReply.SerializeToString, + ), + 'SetStudyUserAttribute': grpc.unary_unary_rpc_method_handler( + servicer.SetStudyUserAttribute, + request_deserializer=api__pb2.SetStudyUserAttributeRequest.FromString, + response_serializer=api__pb2.SetStudyUserAttributeReply.SerializeToString, + ), + 'SetStudySystemAttribute': grpc.unary_unary_rpc_method_handler( + servicer.SetStudySystemAttribute, + request_deserializer=api__pb2.SetStudySystemAttributeRequest.FromString, + response_serializer=api__pb2.SetStudySystemAttributeReply.SerializeToString, + ), + 'GetStudyIdFromName': grpc.unary_unary_rpc_method_handler( + servicer.GetStudyIdFromName, + request_deserializer=api__pb2.GetStudyIdFromNameRequest.FromString, + response_serializer=api__pb2.GetStudyIdFromNameReply.SerializeToString, + ), + 'GetStudyNameFromId': grpc.unary_unary_rpc_method_handler( + servicer.GetStudyNameFromId, + request_deserializer=api__pb2.GetStudyNameFromIdRequest.FromString, + response_serializer=api__pb2.GetStudyNameFromIdReply.SerializeToString, + ), + 'GetStudyDirections': grpc.unary_unary_rpc_method_handler( + servicer.GetStudyDirections, + request_deserializer=api__pb2.GetStudyDirectionsRequest.FromString, + response_serializer=api__pb2.GetStudyDirectionsReply.SerializeToString, + ), + 'GetStudyUserAttributes': grpc.unary_unary_rpc_method_handler( + servicer.GetStudyUserAttributes, + request_deserializer=api__pb2.GetStudyUserAttributesRequest.FromString, + response_serializer=api__pb2.GetStudyUserAttributesReply.SerializeToString, + ), + 'GetStudySystemAttributes': grpc.unary_unary_rpc_method_handler( + servicer.GetStudySystemAttributes, + request_deserializer=api__pb2.GetStudySystemAttributesRequest.FromString, + response_serializer=api__pb2.GetStudySystemAttributesReply.SerializeToString, + ), + 'GetAllStudies': grpc.unary_unary_rpc_method_handler( + servicer.GetAllStudies, + request_deserializer=api__pb2.GetAllStudiesRequest.FromString, + response_serializer=api__pb2.GetAllStudiesReply.SerializeToString, + ), + 'CreateNewTrial': grpc.unary_unary_rpc_method_handler( + servicer.CreateNewTrial, + request_deserializer=api__pb2.CreateNewTrialRequest.FromString, + response_serializer=api__pb2.CreateNewTrialReply.SerializeToString, + ), + 'SetTrialParameter': grpc.unary_unary_rpc_method_handler( + servicer.SetTrialParameter, + request_deserializer=api__pb2.SetTrialParameterRequest.FromString, + response_serializer=api__pb2.SetTrialParameterReply.SerializeToString, + ), + 'GetTrialIdFromStudyIdTrialNumber': grpc.unary_unary_rpc_method_handler( + servicer.GetTrialIdFromStudyIdTrialNumber, + request_deserializer=api__pb2.GetTrialIdFromStudyIdTrialNumberRequest.FromString, + response_serializer=api__pb2.GetTrialIdFromStudyIdTrialNumberReply.SerializeToString, + ), + 'SetTrialStateValues': grpc.unary_unary_rpc_method_handler( + servicer.SetTrialStateValues, + request_deserializer=api__pb2.SetTrialStateValuesRequest.FromString, + response_serializer=api__pb2.SetTrialStateValuesReply.SerializeToString, + ), + 'SetTrialIntermediateValue': grpc.unary_unary_rpc_method_handler( + servicer.SetTrialIntermediateValue, + request_deserializer=api__pb2.SetTrialIntermediateValueRequest.FromString, + response_serializer=api__pb2.SetTrialIntermediateValueReply.SerializeToString, + ), + 'SetTrialUserAttribute': grpc.unary_unary_rpc_method_handler( + servicer.SetTrialUserAttribute, + request_deserializer=api__pb2.SetTrialUserAttributeRequest.FromString, + response_serializer=api__pb2.SetTrialUserAttributeReply.SerializeToString, + ), + 'SetTrialSystemAttribute': grpc.unary_unary_rpc_method_handler( + servicer.SetTrialSystemAttribute, + request_deserializer=api__pb2.SetTrialSystemAttributeRequest.FromString, + response_serializer=api__pb2.SetTrialSystemAttributeReply.SerializeToString, + ), + 'GetTrial': grpc.unary_unary_rpc_method_handler( + servicer.GetTrial, + request_deserializer=api__pb2.GetTrialRequest.FromString, + response_serializer=api__pb2.GetTrialReply.SerializeToString, + ), + 'GetTrials': grpc.unary_unary_rpc_method_handler( + servicer.GetTrials, + request_deserializer=api__pb2.GetTrialsRequest.FromString, + response_serializer=api__pb2.GetTrialsReply.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + 'optuna.StorageService', rpc_method_handlers) + server.add_generic_rpc_handlers((generic_handler,)) + server.add_registered_method_handlers('optuna.StorageService', rpc_method_handlers) + + + # This class is part of an EXPERIMENTAL API. +class StorageService(object): + """* + Optuna storage service defines APIs to interact with the storage. + """ + + @staticmethod + def CreateNewStudy(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/CreateNewStudy', + api__pb2.CreateNewStudyRequest.SerializeToString, + api__pb2.CreateNewStudyReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def DeleteStudy(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/DeleteStudy', + api__pb2.DeleteStudyRequest.SerializeToString, + api__pb2.DeleteStudyReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetStudyUserAttribute(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/SetStudyUserAttribute', + api__pb2.SetStudyUserAttributeRequest.SerializeToString, + api__pb2.SetStudyUserAttributeReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetStudySystemAttribute(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/SetStudySystemAttribute', + api__pb2.SetStudySystemAttributeRequest.SerializeToString, + api__pb2.SetStudySystemAttributeReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetStudyIdFromName(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/GetStudyIdFromName', + api__pb2.GetStudyIdFromNameRequest.SerializeToString, + api__pb2.GetStudyIdFromNameReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetStudyNameFromId(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/GetStudyNameFromId', + api__pb2.GetStudyNameFromIdRequest.SerializeToString, + api__pb2.GetStudyNameFromIdReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetStudyDirections(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/GetStudyDirections', + api__pb2.GetStudyDirectionsRequest.SerializeToString, + api__pb2.GetStudyDirectionsReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetStudyUserAttributes(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/GetStudyUserAttributes', + api__pb2.GetStudyUserAttributesRequest.SerializeToString, + api__pb2.GetStudyUserAttributesReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetStudySystemAttributes(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/GetStudySystemAttributes', + api__pb2.GetStudySystemAttributesRequest.SerializeToString, + api__pb2.GetStudySystemAttributesReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetAllStudies(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/GetAllStudies', + api__pb2.GetAllStudiesRequest.SerializeToString, + api__pb2.GetAllStudiesReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def CreateNewTrial(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/CreateNewTrial', + api__pb2.CreateNewTrialRequest.SerializeToString, + api__pb2.CreateNewTrialReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetTrialParameter(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/SetTrialParameter', + api__pb2.SetTrialParameterRequest.SerializeToString, + api__pb2.SetTrialParameterReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetTrialIdFromStudyIdTrialNumber(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/GetTrialIdFromStudyIdTrialNumber', + api__pb2.GetTrialIdFromStudyIdTrialNumberRequest.SerializeToString, + api__pb2.GetTrialIdFromStudyIdTrialNumberReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetTrialStateValues(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/SetTrialStateValues', + api__pb2.SetTrialStateValuesRequest.SerializeToString, + api__pb2.SetTrialStateValuesReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetTrialIntermediateValue(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/SetTrialIntermediateValue', + api__pb2.SetTrialIntermediateValueRequest.SerializeToString, + api__pb2.SetTrialIntermediateValueReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetTrialUserAttribute(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/SetTrialUserAttribute', + api__pb2.SetTrialUserAttributeRequest.SerializeToString, + api__pb2.SetTrialUserAttributeReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def SetTrialSystemAttribute(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/SetTrialSystemAttribute', + api__pb2.SetTrialSystemAttributeRequest.SerializeToString, + api__pb2.SetTrialSystemAttributeReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetTrial(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/GetTrial', + api__pb2.GetTrialRequest.SerializeToString, + api__pb2.GetTrialReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetTrials(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_unary( + request, + target, + '/optuna.StorageService/GetTrials', + api__pb2.GetTrialsRequest.SerializeToString, + api__pb2.GetTrialsReply.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/client.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/client.py new file mode 100644 index 0000000000000000000000000000000000000000..3e53b4e6e92c023fe679b56b3d1c2ae7da6f9ff7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/client.py @@ -0,0 +1,442 @@ +from __future__ import annotations + +from collections.abc import Container +from collections.abc import Sequence +import copy +import json +import threading +from typing import Any +from typing import TYPE_CHECKING +import uuid + +from optuna._experimental import experimental_class +from optuna._imports import _LazyImport +from optuna.distributions import BaseDistribution +from optuna.distributions import distribution_to_json +from optuna.exceptions import DuplicatedStudyError +from optuna.exceptions import UpdateFinishedTrialError +from optuna.storages._base import BaseStorage +from optuna.storages._base import DEFAULT_STUDY_NAME_PREFIX +from optuna.study._frozen import FrozenStudy +from optuna.study._study_direction import StudyDirection +from optuna.trial._frozen import FrozenTrial +from optuna.trial._state import TrialState + + +if TYPE_CHECKING: + import grpc + + from optuna.storages._grpc import servicer as grpc_servicer + from optuna.storages._grpc.auto_generated import api_pb2 + from optuna.storages._grpc.auto_generated import api_pb2_grpc +else: + api_pb2 = _LazyImport("optuna.storages._grpc.auto_generated.api_pb2") + api_pb2_grpc = _LazyImport("optuna.storages._grpc.auto_generated.api_pb2_grpc") + grpc = _LazyImport("grpc") + grpc_servicer = _LazyImport("optuna.storages._grpc.servicer") + + +def create_insecure_channel(host: str, port: int) -> grpc.Channel: + return grpc.insecure_channel( + f"{host}:{port}", options=[("grpc.max_receive_message_length", -1)] + ) + + +@experimental_class("4.2.0") +class GrpcStorageProxy(BaseStorage): + """gRPC client for :func:`~optuna.storages.run_grpc_proxy_server`. + + Example: + + This is a simple example of using :class:`~optuna.storages.GrpcStorageProxy` with + :func:`~optuna.storages.run_grpc_proxy_server`. + + .. code:: + + import optuna + from optuna.storages import GrpcStorageProxy + + storage = GrpcStorageProxy(host="localhost", port=13000) + study = optuna.create_study(storage=storage) + + Please refer to the example in :func:`~optuna.storages.run_grpc_proxy_server` for the + server side code. + + Args: + host: The hostname of the gRPC server. + port: The port of the gRPC server. + + .. warning:: + + Currently, gRPC storage proxy in combination with an SQLite3 database may cause unexpected + behaviors when calling :func:`optuna.delete_study` due to non-invalidated cache. + """ + + def __init__(self, *, host: str = "localhost", port: int = 13000) -> None: + self._host = host + self._port = port + self._setup() + + def _setup(self) -> None: + """Set up the gRPC channel and stub.""" + self._channel = create_insecure_channel(self._host, self._port) + self._stub = api_pb2_grpc.StorageServiceStub(self._channel) + self._cache = GrpcClientCache(self._stub) + + def wait_server_ready(self, timeout: float | None = None) -> None: + """Wait until the gRPC server is ready. + + Args: + timeout: The maximum time to wait in seconds. If :obj:`None`, wait indefinitely. + """ + try: + with create_insecure_channel(self._host, self._port) as channel: + grpc.channel_ready_future(channel).result(timeout=timeout) + except grpc.FutureTimeoutError as e: + raise ConnectionError("GRPC connection timeout") from e + + def close(self) -> None: + """Close the gRPC channel.""" + self._channel.close() + + def __getstate__(self) -> dict[Any, Any]: + state = self.__dict__.copy() + del state["_channel"] + del state["_stub"] + del state["_cache"] + return state + + def __setstate__(self, state: dict[Any, Any]) -> None: + self.__dict__.update(state) + self._setup() + + def create_new_study( + self, directions: Sequence[StudyDirection], study_name: str | None = None + ) -> int: + request = api_pb2.CreateNewStudyRequest( + directions=[ + api_pb2.MINIMIZE if d == StudyDirection.MINIMIZE else api_pb2.MAXIMIZE + for d in directions + ], + study_name=study_name or DEFAULT_STUDY_NAME_PREFIX + str(uuid.uuid4()), + ) + try: + response = self._stub.CreateNewStudy(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.ALREADY_EXISTS: + raise DuplicatedStudyError from e + raise + return response.study_id + + def delete_study(self, study_id: int) -> None: + request = api_pb2.DeleteStudyRequest(study_id=study_id) + try: + self._stub.DeleteStudy(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + # TODO(c-bata): Fix a cache invalidation issue when using SQLite3 + # Please see https://github.com/optuna/optuna/pull/5872/files#r1893708995 for details. + self._cache.delete_study_cache(study_id) + + def set_study_user_attr(self, study_id: int, key: str, value: Any) -> None: + request = api_pb2.SetStudyUserAttributeRequest( + study_id=study_id, key=key, value=json.dumps(value) + ) + try: + self._stub.SetStudyUserAttribute(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + + def set_study_system_attr(self, study_id: int, key: str, value: Any) -> None: + request = api_pb2.SetStudySystemAttributeRequest( + study_id=study_id, key=key, value=json.dumps(value) + ) + try: + self._stub.SetStudySystemAttribute(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + + def get_study_id_from_name(self, study_name: str) -> int: + request = api_pb2.GetStudyIdFromNameRequest(study_name=study_name) + try: + response = self._stub.GetStudyIdFromName(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + return response.study_id + + def get_study_name_from_id(self, study_id: int) -> str: + request = api_pb2.GetStudyNameFromIdRequest(study_id=study_id) + try: + response = self._stub.GetStudyNameFromId(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + return response.study_name + + def get_study_directions(self, study_id: int) -> list[StudyDirection]: + request = api_pb2.GetStudyDirectionsRequest(study_id=study_id) + try: + response = self._stub.GetStudyDirections(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + return [ + StudyDirection.MINIMIZE if d == api_pb2.MINIMIZE else StudyDirection.MAXIMIZE + for d in response.directions + ] + + def get_study_user_attrs(self, study_id: int) -> dict[str, Any]: + request = api_pb2.GetStudyUserAttributesRequest(study_id=study_id) + try: + response = self._stub.GetStudyUserAttributes(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + return {key: json.loads(value) for key, value in response.user_attributes.items()} + + def get_study_system_attrs(self, study_id: int) -> dict[str, Any]: + request = api_pb2.GetStudySystemAttributesRequest(study_id=study_id) + try: + response = self._stub.GetStudySystemAttributes(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + return {key: json.loads(value) for key, value in response.system_attributes.items()} + + def get_all_studies(self) -> list[FrozenStudy]: + request = api_pb2.GetAllStudiesRequest() + response = self._stub.GetAllStudies(request) + return [ + FrozenStudy( + study_id=study.study_id, + study_name=study.study_name, + direction=None, + directions=[ + StudyDirection.MINIMIZE if d == api_pb2.MINIMIZE else StudyDirection.MAXIMIZE + for d in study.directions + ], + user_attrs={ + key: json.loads(value) for key, value in study.user_attributes.items() + }, + system_attrs={ + key: json.loads(value) for key, value in study.system_attributes.items() + }, + ) + for study in response.studies + ] + + def create_new_trial(self, study_id: int, template_trial: FrozenTrial | None = None) -> int: + if template_trial is None: + request = api_pb2.CreateNewTrialRequest(study_id=study_id, template_trial_is_none=True) + else: + request = api_pb2.CreateNewTrialRequest( + study_id=study_id, + template_trial=grpc_servicer._to_proto_trial(template_trial), + template_trial_is_none=False, + ) + try: + response = self._stub.CreateNewTrial(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + return response.trial_id + + def set_trial_param( + self, + trial_id: int, + param_name: str, + param_value_internal: float, + distribution: BaseDistribution, + ) -> None: + request = api_pb2.SetTrialParameterRequest( + trial_id=trial_id, + param_name=param_name, + param_value_internal=param_value_internal, + distribution=distribution_to_json(distribution), + ) + try: + self._stub.SetTrialParameter(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + elif e.code() == grpc.StatusCode.FAILED_PRECONDITION: + raise UpdateFinishedTrialError from e + elif e.code() == grpc.StatusCode.INVALID_ARGUMENT: + raise ValueError from e + else: + raise + + def set_trial_state_values( + self, trial_id: int, state: TrialState, values: Sequence[float] | None = None + ) -> bool: + request = api_pb2.SetTrialStateValuesRequest( + trial_id=trial_id, + state=grpc_servicer._to_proto_trial_state(state), + values=values, + ) + try: + response = self._stub.SetTrialStateValues(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + elif e.code() == grpc.StatusCode.FAILED_PRECONDITION: + raise UpdateFinishedTrialError from e + else: + raise + + return response.trial_updated + + def set_trial_intermediate_value( + self, trial_id: int, step: int, intermediate_value: float + ) -> None: + request = api_pb2.SetTrialIntermediateValueRequest( + trial_id=trial_id, step=step, intermediate_value=intermediate_value + ) + try: + self._stub.SetTrialIntermediateValue(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + elif e.code() == grpc.StatusCode.FAILED_PRECONDITION: + raise UpdateFinishedTrialError from e + else: + raise + + def set_trial_user_attr(self, trial_id: int, key: str, value: Any) -> None: + request = api_pb2.SetTrialUserAttributeRequest( + trial_id=trial_id, key=key, value=json.dumps(value) + ) + try: + self._stub.SetTrialUserAttribute(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + elif e.code() == grpc.StatusCode.FAILED_PRECONDITION: + raise UpdateFinishedTrialError from e + else: + raise + + def set_trial_system_attr(self, trial_id: int, key: str, value: Any) -> None: + request = api_pb2.SetTrialSystemAttributeRequest( + trial_id=trial_id, key=key, value=json.dumps(value) + ) + try: + self._stub.SetTrialSystemAttribute(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + elif e.code() == grpc.StatusCode.FAILED_PRECONDITION: + raise UpdateFinishedTrialError from e + else: + raise + + def get_trial_id_from_study_id_trial_number(self, study_id: int, trial_number: int) -> int: + request = api_pb2.GetTrialIdFromStudyIdTrialNumberRequest( + study_id=study_id, trial_number=trial_number + ) + try: + response = self._stub.GetTrialIdFromStudyIdTrialNumber(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + return response.trial_id + + def get_trial(self, trial_id: int) -> FrozenTrial: + request = api_pb2.GetTrialRequest(trial_id=trial_id) + try: + response = self._stub.GetTrial(request) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + raise KeyError from e + raise + return grpc_servicer._from_proto_trial(response.trial) + + def get_all_trials( + self, + study_id: int, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + ) -> list[FrozenTrial]: + trials = self._cache.get_all_trials(study_id, states) + return copy.deepcopy(trials) if deepcopy else trials + + +class GrpcClientCache: + def __init__(self, grpc_client: api_pb2_grpc.StorageServiceStub) -> None: + self.studies: dict[int, GrpcClientCacheEntry] = {} + self.grpc_client = grpc_client + self.lock = threading.Lock() + + def delete_study_cache(self, study_id: int) -> None: + with self.lock: + self.studies.pop(study_id, None) + + def get_all_trials( + self, study_id: int, states: Container[TrialState] | None + ) -> list[FrozenTrial]: + with self.lock: + self._read_trials_from_remote_storage(study_id) + study = self.studies[study_id] + trials: dict[int, FrozenTrial] | list[FrozenTrial] + if states is not None: + trials = {number: t for number, t in study.trials.items() if t.state in states} + else: + trials = study.trials + trials = list(sorted(trials.values(), key=lambda t: t.number)) + return trials + + def _read_trials_from_remote_storage(self, study_id: int) -> None: + if study_id not in self.studies: + self.studies[study_id] = GrpcClientCacheEntry() + study = self.studies[study_id] + + req = api_pb2.GetTrialsRequest( + study_id=study_id, + included_trial_ids=study.unfinished_trial_ids, + trial_id_greater_than=study.last_finished_trial_id, + ) + try: + res = self.grpc_client.GetTrials(req) + except grpc.RpcError as e: + if e.code() == grpc.StatusCode.NOT_FOUND: + self.studies.pop(study_id, None) + raise KeyError from e + raise + if not res.trials: + return + + for trial_proto in res.trials: + trial = grpc_servicer._from_proto_trial(trial_proto) + self._add_trial_to_cache(study_id, trial) + + def _add_trial_to_cache(self, study_id: int, trial: FrozenTrial) -> None: + study = self.studies[study_id] + study.trials[trial.number] = trial + + if not trial.state.is_finished(): + study.unfinished_trial_ids.add(trial._trial_id) + return + + study.last_finished_trial_id = max(study.last_finished_trial_id, trial._trial_id) + study.unfinished_trial_ids.discard(trial._trial_id) + + +class GrpcClientCacheEntry: + def __init__(self) -> None: + self.trials: dict[int, FrozenTrial] = {} + self.unfinished_trial_ids: set[int] = set() + self.last_finished_trial_id: int = -1 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/server.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/server.py new file mode 100644 index 0000000000000000000000000000000000000000..ae108de053fe20481d3e643e6a8b3eca8ce9261b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/server.py @@ -0,0 +1,84 @@ +from __future__ import annotations + +from concurrent.futures import ThreadPoolExecutor +from typing import TYPE_CHECKING + +from optuna import logging +from optuna._experimental import experimental_func +from optuna._imports import _LazyImport +from optuna.storages import BaseStorage + + +if TYPE_CHECKING: + import grpc + + from optuna.storages._grpc import servicer as grpc_servicer + from optuna.storages._grpc.auto_generated import api_pb2_grpc +else: + grpc = _LazyImport("grpc") + grpc_servicer = _LazyImport("optuna.storages._grpc.servicer") + api_pb2_grpc = _LazyImport("optuna.storages._grpc.auto_generated.api_pb2_grpc") + + +_logger = logging.get_logger(__name__) +DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S.%f" + + +def make_server( + storage: BaseStorage, host: str, port: int, thread_pool: ThreadPoolExecutor | None = None +) -> grpc.Server: + server = grpc.server(thread_pool or ThreadPoolExecutor(max_workers=10)) + api_pb2_grpc.add_StorageServiceServicer_to_server( + grpc_servicer.OptunaStorageProxyService(storage), server + ) + server.add_insecure_port(f"{host}:{port}") + return server + + +@experimental_func("4.2.0") +def run_grpc_proxy_server( + storage: BaseStorage, + *, + host: str = "localhost", + port: int = 13000, + thread_pool: ThreadPoolExecutor | None = None, +) -> None: + """Run a gRPC server for the given storage URL, host, and port. + + Example: + + Run this server with the following way: + + .. code:: + + from optuna.storages import run_grpc_proxy_server + from optuna.storages import get_storage + + storage = get_storage("mysql+pymysql://:@/[?]") + run_grpc_proxy_server(storage, host="localhost", port=13000) + + Please refer to the client class :class:`~optuna.storages.GrpcStorageProxy` for + the client usage. Please use :func:`~optuna.storages.get_storage` instead of + :class:`~optuna.storages.RDBStorage` since ``RDBStorage`` by itself does not use cache in + process and it may cause significant slowdown. + + Args: + storage: A storage object to proxy. + host: Hostname to listen on. + port: Port to listen on. + thread_pool: + Thread pool to use for the server. If :obj:`None`, a default thread pool + with 10 workers will be used. + + .. warning:: + + Currently, gRPC storage proxy does not support the + :class:`~optuna.storages.JournalStorage`. This issue is tracked in + https://github.com/optuna/optuna/issues/6084. Please use + :class:`~optuna.storages.RDBStorage` instead. + """ + server = make_server(storage, host, port, thread_pool) + server.start() + _logger.info(f"Server started at {host}:{port}") + _logger.info("Listening...") + server.wait_for_termination() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/servicer.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/servicer.py new file mode 100644 index 0000000000000000000000000000000000000000..b23d7a136002a9a192fcd1fb37a7cd05df35205d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_grpc/servicer.py @@ -0,0 +1,429 @@ +from __future__ import annotations + +from datetime import datetime +import json +import threading +from typing import TYPE_CHECKING + +from optuna import logging +from optuna._imports import _LazyImport +from optuna.distributions import distribution_to_json +from optuna.distributions import json_to_distribution +from optuna.exceptions import DuplicatedStudyError +from optuna.exceptions import UpdateFinishedTrialError +from optuna.storages import BaseStorage +from optuna.study._study_direction import StudyDirection +from optuna.trial._frozen import FrozenTrial +from optuna.trial._state import TrialState + + +if TYPE_CHECKING: + import grpc + + from optuna.storages._grpc.auto_generated import api_pb2 + from optuna.storages._grpc.auto_generated import api_pb2_grpc +else: + api_pb2 = _LazyImport("optuna.storages._grpc.auto_generated.api_pb2") + api_pb2_grpc = _LazyImport("optuna.storages._grpc.auto_generated.api_pb2_grpc") + grpc = _LazyImport("grpc") + + +_logger = logging.get_logger(__name__) +DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S.%f" + + +class OptunaStorageProxyService(api_pb2_grpc.StorageServiceServicer): + def __init__(self, storage: BaseStorage) -> None: + self._backend = storage + self._lock = threading.Lock() + + def CreateNewStudy( + self, + request: api_pb2.CreateNewStudyRequest, + context: grpc.ServicerContext, + ) -> api_pb2.CreateNewStudyReply: + directions = [ + StudyDirection.MINIMIZE if d == api_pb2.MINIMIZE else StudyDirection.MAXIMIZE + for d in request.directions + ] + study_name = request.study_name + + try: + study_id = self._backend.create_new_study(directions=directions, study_name=study_name) + except DuplicatedStudyError as e: + context.abort(code=grpc.StatusCode.ALREADY_EXISTS, details=str(e)) + return api_pb2.CreateNewStudyReply(study_id=study_id) + + def DeleteStudy( + self, + request: api_pb2.DeleteStudyRequest, + context: grpc.ServicerContext, + ) -> api_pb2.DeleteStudyReply: + study_id = request.study_id + try: + self._backend.delete_study(study_id) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + return api_pb2.DeleteStudyReply() + + def SetStudyUserAttribute( + self, + request: api_pb2.SetStudyUserAttributeRequest, + context: grpc.ServicerContext, + ) -> api_pb2.SetStudyUserAttributeReply: + try: + self._backend.set_study_user_attr( + request.study_id, request.key, json.loads(request.value) + ) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + return api_pb2.SetStudyUserAttributeReply() + + def SetStudySystemAttribute( + self, + request: api_pb2.SetStudySystemAttributeRequest, + context: grpc.ServicerContext, + ) -> api_pb2.SetStudySystemAttributeReply: + try: + self._backend.set_study_system_attr( + request.study_id, request.key, json.loads(request.value) + ) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + return api_pb2.SetStudySystemAttributeReply() + + def GetStudyIdFromName( + self, + request: api_pb2.GetStudyIdFromNameRequest, + context: grpc.ServicerContext, + ) -> api_pb2.GetStudyIdFromNameReply: + try: + study_id = self._backend.get_study_id_from_name(request.study_name) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + return api_pb2.GetStudyIdFromNameReply(study_id=study_id) + + def GetStudyNameFromId( + self, + request: api_pb2.GetStudyNameFromIdRequest, + context: grpc.ServicerContext, + ) -> api_pb2.GetStudyNameFromIdReply: + study_id = request.study_id + + try: + name = self._backend.get_study_name_from_id(study_id) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + assert name is not None + return api_pb2.GetStudyNameFromIdReply(study_name=name) + + def GetStudyDirections( + self, + request: api_pb2.GetStudyDirectionsRequest, + context: grpc.ServicerContext, + ) -> api_pb2.GetStudyDirectionsReply: + study_id = request.study_id + + try: + directions = self._backend.get_study_directions(study_id) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + + assert directions is not None + return api_pb2.GetStudyDirectionsReply( + directions=[ + api_pb2.MINIMIZE if d == StudyDirection.MINIMIZE else api_pb2.MAXIMIZE + for d in directions + ] + ) + + def GetStudyUserAttributes( + self, + request: api_pb2.GetStudyUserAttributesRequest, + context: grpc.ServicerContext, + ) -> api_pb2.GetStudyUserAttributesReply: + try: + attributes = self._backend.get_study_user_attrs(request.study_id) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + return api_pb2.GetStudyUserAttributesReply( + user_attributes={key: json.dumps(value) for key, value in attributes.items()} + ) + + def GetStudySystemAttributes( + self, + request: api_pb2.GetStudySystemAttributesRequest, + context: grpc.ServicerContext, + ) -> api_pb2.GetStudySystemAttributesReply: + try: + attributes = self._backend.get_study_system_attrs(request.study_id) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + return api_pb2.GetStudySystemAttributesReply( + system_attributes={key: json.dumps(value) for key, value in attributes.items()} + ) + + def GetAllStudies( + self, + request: api_pb2.GetAllStudiesRequest, + context: grpc.ServicerContext, + ) -> api_pb2.GetAllStudiesReply: + studies = self._backend.get_all_studies() + return api_pb2.GetAllStudiesReply( + studies=[ + api_pb2.Study( + study_id=study._study_id, + study_name=study.study_name, + directions=[ + api_pb2.MINIMIZE if d == StudyDirection.MINIMIZE else api_pb2.MAXIMIZE + for d in study.directions + ], + user_attributes={ + key: json.dumps(value) for key, value in study.user_attrs.items() + }, + system_attributes={ + key: json.dumps(value) for key, value in study.system_attrs.items() + }, + ) + for study in studies + ] + ) + + def CreateNewTrial( + self, + request: api_pb2.CreateNewTrialRequest, + context: grpc.ServicerContext, + ) -> api_pb2.CreateNewTrialReply: + study_id = request.study_id + + template_trial = None + if not request.template_trial_is_none: + template_trial = _from_proto_trial(request.template_trial) + + try: + trial_id = self._backend.create_new_trial(study_id, template_trial) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + + return api_pb2.CreateNewTrialReply(trial_id=trial_id) + + def SetTrialParameter( + self, + request: api_pb2.SetTrialParameterRequest, + context: grpc.ServicerContext, + ) -> api_pb2.SetTrialParameterReply: + trial_id = request.trial_id + param_name = request.param_name + param_value_internal = request.param_value_internal + distribution = json_to_distribution(request.distribution) + try: + self._backend.set_trial_param(trial_id, param_name, param_value_internal, distribution) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + except UpdateFinishedTrialError as e: + context.abort(code=grpc.StatusCode.FAILED_PRECONDITION, details=str(e)) + except ValueError as e: + context.abort(code=grpc.StatusCode.INVALID_ARGUMENT, details=str(e)) + return api_pb2.SetTrialParameterReply() + + def GetTrialIdFromStudyIdTrialNumber( + self, + request: api_pb2.GetTrialIdFromStudyIdTrialNumberRequest, + context: grpc.ServicerContext, + ) -> api_pb2.GetTrialIdFromStudyIdTrialNumberReply: + study_id = request.study_id + trial_number = request.trial_number + + try: + trial_id = self._backend.get_trial_id_from_study_id_trial_number( + study_id, trial_number + ) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + return api_pb2.GetTrialIdFromStudyIdTrialNumberReply(trial_id=trial_id) + + def SetTrialStateValues( + self, + request: api_pb2.SetTrialStateValuesRequest, + context: grpc.ServicerContext, + ) -> api_pb2.SetTrialStateValuesReply: + trial_id = request.trial_id + state = request.state + values = list(request.values) if request.values else None + try: + trial_updated = self._backend.set_trial_state_values( + trial_id, _from_proto_trial_state(state), values + ) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + except UpdateFinishedTrialError as e: + context.abort(code=grpc.StatusCode.FAILED_PRECONDITION, details=str(e)) + return api_pb2.SetTrialStateValuesReply(trial_updated=trial_updated) + + def SetTrialIntermediateValue( + self, + request: api_pb2.SetTrialIntermediateValueRequest, + context: grpc.ServicerContext, + ) -> api_pb2.SetTrialIntermediateValueReply: + trial_id = request.trial_id + step = request.step + intermediate_value = request.intermediate_value + try: + self._backend.set_trial_intermediate_value(trial_id, step, intermediate_value) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + except UpdateFinishedTrialError as e: + context.abort(code=grpc.StatusCode.FAILED_PRECONDITION, details=str(e)) + return api_pb2.SetTrialIntermediateValueReply() + + def SetTrialUserAttribute( + self, + request: api_pb2.SetTrialUserAttributeRequest, + context: grpc.ServicerContext, + ) -> api_pb2.SetTrialUserAttributeReply: + trial_id = request.trial_id + key = request.key + value = json.loads(request.value) + try: + self._backend.set_trial_user_attr(trial_id, key, value) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + except UpdateFinishedTrialError as e: + context.abort(code=grpc.StatusCode.FAILED_PRECONDITION, details=str(e)) + return api_pb2.SetTrialUserAttributeReply() + + def SetTrialSystemAttribute( + self, + request: api_pb2.SetTrialSystemAttributeRequest, + context: grpc.ServicerContext, + ) -> api_pb2.SetTrialSystemAttributeReply: + trial_id = request.trial_id + key = request.key + value = json.loads(request.value) + try: + self._backend.set_trial_system_attr(trial_id, key, value) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + except UpdateFinishedTrialError as e: + context.abort(code=grpc.StatusCode.FAILED_PRECONDITION, details=str(e)) + return api_pb2.SetTrialSystemAttributeReply() + + def GetTrial( + self, + request: api_pb2.GetTrialRequest, + context: grpc.ServicerContext, + ) -> api_pb2.GetTrialReply: + trial_id = request.trial_id + try: + trial = self._backend.get_trial(trial_id) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + + return api_pb2.GetTrialReply(trial=_to_proto_trial(trial)) + + def GetTrials( + self, + request: api_pb2.GetTrialsRequest, + context: grpc.ServicerContext, + ) -> api_pb2.GetTrialsReply: + study_id = request.study_id + included_trial_ids = set(request.included_trial_ids) + trial_id_greater_than = request.trial_id_greater_than + try: + trials = self._backend.get_all_trials(study_id, deepcopy=False) + except KeyError as e: + context.abort(code=grpc.StatusCode.NOT_FOUND, details=str(e)) + + filtered_trials = [ + _to_proto_trial(t) + for t in trials + if t._trial_id > trial_id_greater_than or t._trial_id in included_trial_ids + ] + return api_pb2.GetTrialsReply(trials=filtered_trials) + + +def _to_proto_trial_state(state: TrialState) -> api_pb2.TrialState.ValueType: + if state == TrialState.RUNNING: + return api_pb2.RUNNING + if state == TrialState.COMPLETE: + return api_pb2.COMPLETE + if state == TrialState.PRUNED: + return api_pb2.PRUNED + if state == TrialState.FAIL: + return api_pb2.FAIL + if state == TrialState.WAITING: + return api_pb2.WAITING + raise ValueError(f"Unknown TrialState: {state}") + + +def _from_proto_trial_state(state: api_pb2.TrialState.ValueType) -> TrialState: + if state == api_pb2.RUNNING: + return TrialState.RUNNING + if state == api_pb2.COMPLETE: + return TrialState.COMPLETE + if state == api_pb2.PRUNED: + return TrialState.PRUNED + if state == api_pb2.FAIL: + return TrialState.FAIL + if state == api_pb2.WAITING: + return TrialState.WAITING + raise ValueError(f"Unknown api_pb2.TrialState: {state}") + + +def _to_proto_trial(trial: FrozenTrial) -> api_pb2.Trial: + params = {} + for key, value in trial.params.items(): + params[key] = trial.distributions[key].to_internal_repr(value) + + return api_pb2.Trial( + trial_id=trial._trial_id, + number=trial.number, + state=_to_proto_trial_state(trial.state), + values=trial.values, + datetime_start=( + trial.datetime_start.strftime(DATETIME_FORMAT) if trial.datetime_start else "" + ), + datetime_complete=( + trial.datetime_complete.strftime(DATETIME_FORMAT) if trial.datetime_complete else "" + ), + distributions={ + key: distribution_to_json(distribution) + for key, distribution in trial.distributions.items() + }, + params=params, + user_attributes={key: json.dumps(value) for key, value in trial.user_attrs.items()}, + system_attributes={key: json.dumps(value) for key, value in trial.system_attrs.items()}, + intermediate_values={step: value for step, value in trial.intermediate_values.items()}, + ) + + +def _from_proto_trial(trial: api_pb2.Trial) -> FrozenTrial: + datetime_start = ( + datetime.strptime(trial.datetime_start, DATETIME_FORMAT) if trial.datetime_start else None + ) + datetime_complete = ( + datetime.strptime(trial.datetime_complete, DATETIME_FORMAT) + if trial.datetime_complete + else None + ) + distributions = { + key: json_to_distribution(value) for key, value in trial.distributions.items() + } + params = {} + for key, value in trial.params.items(): + params[key] = distributions[key].to_external_repr(value) + + return FrozenTrial( + trial_id=trial.trial_id, + number=trial.number, + state=_from_proto_trial_state(trial.state), + value=None, + values=trial.values if trial.values else None, + datetime_start=datetime_start, + datetime_complete=datetime_complete, + params=params, + distributions=distributions, + user_attrs={key: json.loads(value) for key, value in trial.user_attributes.items()}, + system_attrs={key: json.loads(value) for key, value in trial.system_attributes.items()}, + intermediate_values={step: value for step, value in trial.intermediate_values.items()}, + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_heartbeat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_heartbeat.py new file mode 100644 index 0000000000000000000000000000000000000000..a933dbbcd0a1347e9630fa6b8e5cd26ded2ac875 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_heartbeat.py @@ -0,0 +1,191 @@ +from __future__ import annotations + +import abc +from collections.abc import Callable +import copy +from threading import Event +from threading import Thread +from types import TracebackType + +import optuna +from optuna._experimental import experimental_func +from optuna.storages import BaseStorage +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +class BaseHeartbeat(metaclass=abc.ABCMeta): + """Base class for heartbeat. + + This class is not supposed to be directly accessed by library users. + + The heartbeat mechanism periodically checks whether each trial process is alive during an + optimization loop. To support this mechanism, the methods of + :class:`~optuna.storages._heartbeat.BaseHeartbeat` is implemented for the target database + backend, typically with multiple inheritance of :class:`~optuna.storages._base.BaseStorage` + and :class:`~optuna.storages._heartbeat.BaseHeartbeat`. + + .. seealso:: + See :class:`~optuna.storages.RDBStorage`, where the backend supports heartbeat. + """ + + @abc.abstractmethod + def record_heartbeat(self, trial_id: int) -> None: + """Record the heartbeat of the trial. + + Args: + trial_id: + ID of the trial. + """ + raise NotImplementedError() + + @abc.abstractmethod + def _get_stale_trial_ids(self, study_id: int) -> list[int]: + """Get the stale trial ids of the study. + + Args: + study_id: + ID of the study. + Returns: + List of IDs of trials whose heartbeat has not been updated for a long time. + """ + raise NotImplementedError() + + @abc.abstractmethod + def get_heartbeat_interval(self) -> int | None: + """Get the heartbeat interval if it is set. + + Returns: + The heartbeat interval if it is set, otherwise :obj:`None`. + """ + raise NotImplementedError() + + @abc.abstractmethod + def get_failed_trial_callback(self) -> Callable[["optuna.Study", FrozenTrial], None] | None: + """Get the failed trial callback function. + + Returns: + The failed trial callback function if it is set, otherwise :obj:`None`. + """ + raise NotImplementedError() + + +class BaseHeartbeatThread(metaclass=abc.ABCMeta): + def __enter__(self) -> None: + self.start() + + def __exit__( + self, + exc_type: type[Exception] | None, + exc_value: Exception | None, + traceback: TracebackType | None, + ) -> None: + self.join() + + @abc.abstractmethod + def start(self) -> None: + raise NotImplementedError() + + @abc.abstractmethod + def join(self) -> None: + raise NotImplementedError() + + +class NullHeartbeatThread(BaseHeartbeatThread): + def __init__(self) -> None: + pass + + def start(self) -> None: + pass + + def join(self) -> None: + pass + + +class HeartbeatThread(BaseHeartbeatThread): + def __init__(self, trial_id: int, heartbeat: BaseHeartbeat) -> None: + self._trial_id = trial_id + self._heartbeat = heartbeat + self._thread: Thread | None = None + self._stop_event: Event | None = None + + def start(self) -> None: + self._stop_event = Event() + self._thread = Thread( + target=self._record_heartbeat, args=(self._trial_id, self._heartbeat, self._stop_event) + ) + self._thread.start() + + def join(self) -> None: + assert self._stop_event is not None + assert self._thread is not None + self._stop_event.set() + self._thread.join() + + @staticmethod + def _record_heartbeat(trial_id: int, heartbeat: BaseHeartbeat, stop_event: Event) -> None: + heartbeat_interval = heartbeat.get_heartbeat_interval() + assert heartbeat_interval is not None + while True: + heartbeat.record_heartbeat(trial_id) + if stop_event.wait(timeout=heartbeat_interval): + return + + +def get_heartbeat_thread(trial_id: int, storage: BaseStorage) -> BaseHeartbeatThread: + if is_heartbeat_enabled(storage): + assert isinstance(storage, BaseHeartbeat) + return HeartbeatThread(trial_id, storage) + else: + return NullHeartbeatThread() + + +@experimental_func("2.9.0") +def fail_stale_trials(study: "optuna.Study") -> None: + """Fail stale trials and run their failure callbacks. + + The running trials whose heartbeat has not been updated for a long time will be failed, + that is, those states will be changed to :obj:`~optuna.trial.TrialState.FAIL`. + + .. seealso:: + + See :class:`~optuna.storages.RDBStorage`. + + Args: + study: + Study holding the trials to check. + """ + storage = study._storage + + if not isinstance(storage, BaseHeartbeat): + return + + if not is_heartbeat_enabled(storage): + return + + failed_trial_ids = [] + for trial_id in storage._get_stale_trial_ids(study._study_id): + try: + if storage.set_trial_state_values(trial_id, state=TrialState.FAIL): + failed_trial_ids.append(trial_id) + except optuna.exceptions.UpdateFinishedTrialError: + # If another process fails the trial, the storage raises + # optuna.exceptions.UpdateFinishedTrialError. + pass + + failed_trial_callback = storage.get_failed_trial_callback() + if failed_trial_callback is not None: + for trial_id in failed_trial_ids: + failed_trial = copy.deepcopy(storage.get_trial(trial_id)) + failed_trial_callback(study, failed_trial) + + +def is_heartbeat_enabled(storage: BaseStorage) -> bool: + """Check whether the storage enables the heartbeat. + + Returns: + :obj:`True` if the storage also inherits :class:`~optuna.storages._heartbeat.BaseHeartbeat` + and the return value of :meth:`~optuna.storages.BaseStorage.get_heartbeat_interval` is an + integer, otherwise :obj:`False`. + """ + return isinstance(storage, BaseHeartbeat) and storage.get_heartbeat_interval() is not None diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_in_memory.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_in_memory.py new file mode 100644 index 0000000000000000000000000000000000000000..88085f9d9eb62da68dde2b8c2751cec2fe11674d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_in_memory.py @@ -0,0 +1,425 @@ +from __future__ import annotations + +from collections.abc import Container +from collections.abc import Sequence +import copy +from datetime import datetime +import threading +from typing import Any +import uuid + +import optuna +from optuna import distributions # NOQA +from optuna._typing import JSONSerializable +from optuna.exceptions import DuplicatedStudyError +from optuna.storages import BaseStorage +from optuna.storages._base import DEFAULT_STUDY_NAME_PREFIX +from optuna.study._frozen import FrozenStudy +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +_logger = optuna.logging.get_logger(__name__) + + +class InMemoryStorage(BaseStorage): + """Storage class that stores data in memory of the Python process. + + Example: + + Create an :class:`~optuna.storages.InMemoryStorage` instance. + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -100, 100) + return x**2 + + + storage = optuna.storages.InMemoryStorage() + + study = optuna.create_study(storage=storage) + study.optimize(objective, n_trials=10) + """ + + def __init__(self) -> None: + self._trial_id_to_study_id_and_number: dict[int, tuple[int, int]] = {} + self._study_name_to_id: dict[str, int] = {} + self._studies: dict[int, _StudyInfo] = {} + + self._max_study_id = -1 + self._max_trial_id = -1 + + self._lock = threading.RLock() + self._prev_waiting_trial_number: dict[int, int] = {} + + def __getstate__(self) -> dict[Any, Any]: + state = self.__dict__.copy() + del state["_lock"] + return state + + def __setstate__(self, state: dict[Any, Any]) -> None: + self.__dict__.update(state) + self._lock = threading.RLock() + + def create_new_study( + self, directions: Sequence[StudyDirection], study_name: str | None = None + ) -> int: + with self._lock: + study_id = self._max_study_id + 1 + self._max_study_id += 1 + + if study_name is not None: + if study_name in self._study_name_to_id: + raise DuplicatedStudyError + else: + study_uuid = str(uuid.uuid4()) + study_name = DEFAULT_STUDY_NAME_PREFIX + study_uuid + + self._studies[study_id] = _StudyInfo(study_name, list(directions)) + self._study_name_to_id[study_name] = study_id + self._prev_waiting_trial_number[study_id] = 0 + + _logger.info("A new study created in memory with name: {}".format(study_name)) + + return study_id + + def delete_study(self, study_id: int) -> None: + with self._lock: + self._check_study_id(study_id) + + for trial in self._studies[study_id].trials: + del self._trial_id_to_study_id_and_number[trial._trial_id] + study_name = self._studies[study_id].name + del self._study_name_to_id[study_name] + del self._studies[study_id] + del self._prev_waiting_trial_number[study_id] + + def set_study_user_attr(self, study_id: int, key: str, value: Any) -> None: + with self._lock: + self._check_study_id(study_id) + + self._studies[study_id].user_attrs[key] = value + + def set_study_system_attr(self, study_id: int, key: str, value: JSONSerializable) -> None: + with self._lock: + self._check_study_id(study_id) + + self._studies[study_id].system_attrs[key] = value + + def get_study_id_from_name(self, study_name: str) -> int: + with self._lock: + if study_name not in self._study_name_to_id: + raise KeyError("No such study {}.".format(study_name)) + + return self._study_name_to_id[study_name] + + def get_study_name_from_id(self, study_id: int) -> str: + with self._lock: + self._check_study_id(study_id) + return self._studies[study_id].name + + def get_study_directions(self, study_id: int) -> list[StudyDirection]: + with self._lock: + self._check_study_id(study_id) + return self._studies[study_id].directions + + def get_study_user_attrs(self, study_id: int) -> dict[str, Any]: + with self._lock: + self._check_study_id(study_id) + return self._studies[study_id].user_attrs + + def get_study_system_attrs(self, study_id: int) -> dict[str, Any]: + with self._lock: + self._check_study_id(study_id) + return self._studies[study_id].system_attrs + + def get_all_studies(self) -> list[FrozenStudy]: + with self._lock: + return [self._build_frozen_study(study_id) for study_id in self._studies] + + def _build_frozen_study(self, study_id: int) -> FrozenStudy: + study = self._studies[study_id] + return FrozenStudy( + study_name=study.name, + direction=None, + directions=study.directions, + user_attrs=copy.deepcopy(study.user_attrs), + system_attrs=copy.deepcopy(study.system_attrs), + study_id=study_id, + ) + + def create_new_trial(self, study_id: int, template_trial: FrozenTrial | None = None) -> int: + with self._lock: + self._check_study_id(study_id) + + if template_trial is None: + trial = self._create_running_trial() + else: + trial = copy.deepcopy(template_trial) + + trial_id = self._max_trial_id + 1 + self._max_trial_id += 1 + trial.number = len(self._studies[study_id].trials) + trial._trial_id = trial_id + self._trial_id_to_study_id_and_number[trial_id] = (study_id, trial.number) + self._studies[study_id].trials.append(trial) + self._update_cache(trial_id, study_id) + return trial_id + + @staticmethod + def _create_running_trial() -> FrozenTrial: + return FrozenTrial( + trial_id=-1, # dummy value. + number=-1, # dummy value. + state=TrialState.RUNNING, + params={}, + distributions={}, + user_attrs={}, + system_attrs={}, + value=None, + intermediate_values={}, + datetime_start=datetime.now(), + datetime_complete=None, + ) + + def set_trial_param( + self, + trial_id: int, + param_name: str, + param_value_internal: float, + distribution: distributions.BaseDistribution, + ) -> None: + with self._lock: + trial = self._get_trial(trial_id) + + self.check_trial_is_updatable(trial_id, trial.state) + + study_id = self._trial_id_to_study_id_and_number[trial_id][0] + # Check param distribution compatibility with previous trial(s). + if param_name in self._studies[study_id].param_distribution: + distributions.check_distribution_compatibility( + self._studies[study_id].param_distribution[param_name], distribution + ) + + # Set param distribution. + self._studies[study_id].param_distribution[param_name] = distribution + + # Set param. + trial = copy.copy(trial) + trial.params = copy.copy(trial.params) + trial.params[param_name] = distribution.to_external_repr(param_value_internal) + trial.distributions = copy.copy(trial.distributions) + trial.distributions[param_name] = distribution + self._set_trial(trial_id, trial) + + def get_trial_id_from_study_id_trial_number(self, study_id: int, trial_number: int) -> int: + with self._lock: + study = self._studies.get(study_id) + if study is None: + raise KeyError("No study with study_id {} exists.".format(study_id)) + + trials = study.trials + if len(trials) <= trial_number: + raise KeyError( + "No trial with trial number {} exists in study with study_id {}.".format( + trial_number, study_id + ) + ) + + trial = trials[trial_number] + assert trial.number == trial_number + + return trial._trial_id + + def get_trial_number_from_id(self, trial_id: int) -> int: + with self._lock: + self._check_trial_id(trial_id) + + return self._trial_id_to_study_id_and_number[trial_id][1] + + def get_best_trial(self, study_id: int) -> FrozenTrial: + with self._lock: + self._check_study_id(study_id) + + best_trial_id = self._studies[study_id].best_trial_id + + if best_trial_id is None: + raise ValueError("No trials are completed yet.") + elif len(self._studies[study_id].directions) > 1: + raise RuntimeError( + "Best trial can be obtained only for single-objective optimization." + ) + return self.get_trial(best_trial_id) + + def get_trial_param(self, trial_id: int, param_name: str) -> float: + with self._lock: + trial = self._get_trial(trial_id) + + distribution = trial.distributions[param_name] + return distribution.to_internal_repr(trial.params[param_name]) + + def set_trial_state_values( + self, trial_id: int, state: TrialState, values: Sequence[float] | None = None + ) -> bool: + with self._lock: + trial = copy.copy(self._get_trial(trial_id)) + self.check_trial_is_updatable(trial_id, trial.state) + + if state == TrialState.RUNNING and trial.state != TrialState.WAITING: + return False + + trial.state = state + if values is not None: + trial.values = values + + if state == TrialState.RUNNING: + trial.datetime_start = datetime.now() + + if state.is_finished(): + trial.datetime_complete = datetime.now() + self._set_trial(trial_id, trial) + study_id = self._trial_id_to_study_id_and_number[trial_id][0] + self._update_cache(trial_id, study_id) + else: + self._set_trial(trial_id, trial) + + return True + + def _update_cache(self, trial_id: int, study_id: int) -> None: + trial = self._get_trial(trial_id) + + if trial.state != TrialState.COMPLETE: + return + + best_trial_id = self._studies[study_id].best_trial_id + if best_trial_id is None: + self._studies[study_id].best_trial_id = trial_id + return + + _directions = self.get_study_directions(study_id) + if len(_directions) > 1: + return + direction = _directions[0] + + best_trial = self._get_trial(best_trial_id) + assert best_trial is not None + if best_trial.value is None: + self._studies[study_id].best_trial_id = trial_id + return + # Complete trials do not have `None` values. + assert trial.value is not None + best_value = best_trial.value + new_value = trial.value + + if direction == StudyDirection.MAXIMIZE: + if best_value < new_value: + self._studies[study_id].best_trial_id = trial_id + else: + if best_value > new_value: + self._studies[study_id].best_trial_id = trial_id + + def set_trial_intermediate_value( + self, trial_id: int, step: int, intermediate_value: float + ) -> None: + with self._lock: + trial = self._get_trial(trial_id) + self.check_trial_is_updatable(trial_id, trial.state) + + trial = copy.copy(trial) + trial.intermediate_values = copy.copy(trial.intermediate_values) + trial.intermediate_values[step] = intermediate_value + self._set_trial(trial_id, trial) + + def set_trial_user_attr(self, trial_id: int, key: str, value: Any) -> None: + with self._lock: + self._check_trial_id(trial_id) + trial = self._get_trial(trial_id) + self.check_trial_is_updatable(trial_id, trial.state) + + trial = copy.copy(trial) + trial.user_attrs = copy.copy(trial.user_attrs) + trial.user_attrs[key] = value + self._set_trial(trial_id, trial) + + def set_trial_system_attr(self, trial_id: int, key: str, value: JSONSerializable) -> None: + with self._lock: + trial = self._get_trial(trial_id) + self.check_trial_is_updatable(trial_id, trial.state) + + trial = copy.copy(trial) + trial.system_attrs = copy.copy(trial.system_attrs) + trial.system_attrs[key] = value + self._set_trial(trial_id, trial) + + def get_trial(self, trial_id: int) -> FrozenTrial: + with self._lock: + return self._get_trial(trial_id) + + def _get_trial(self, trial_id: int) -> FrozenTrial: + self._check_trial_id(trial_id) + study_id, trial_number = self._trial_id_to_study_id_and_number[trial_id] + return self._studies[study_id].trials[trial_number] + + def _set_trial(self, trial_id: int, trial: FrozenTrial) -> None: + study_id, trial_number = self._trial_id_to_study_id_and_number[trial_id] + self._studies[study_id].trials[trial_number] = trial + + def get_all_trials( + self, + study_id: int, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + ) -> list[FrozenTrial]: + with self._lock: + self._check_study_id(study_id) + + # Optimized retrieval of trials in the WAITING state to improve performance + # for the call, `get_all_trials(states=(TrialState.WAITING,))`. + if states == (TrialState.WAITING,): + trials: list[FrozenTrial] = [] + for trial in self._studies[study_id].trials[ + self._prev_waiting_trial_number[study_id] : + ]: + if trial.state == TrialState.WAITING: + if not trials: + self._prev_waiting_trial_number[study_id] = trial.number + trials.append(trial) + if not trials: + self._prev_waiting_trial_number[study_id] = len(self._studies[study_id].trials) + + else: + trials = self._studies[study_id].trials + if states is not None: + trials = [t for t in trials if t.state in states] + + if deepcopy: + trials = copy.deepcopy(trials) + else: + # This copy is required for the replacing trick in `set_trial_xxx`. + trials = copy.copy(trials) + + return trials + + def _check_study_id(self, study_id: int) -> None: + if study_id not in self._studies: + raise KeyError("No study with study_id {} exists.".format(study_id)) + + def _check_trial_id(self, trial_id: int) -> None: + if trial_id not in self._trial_id_to_study_id_and_number: + raise KeyError("No trial with trial_id {} exists.".format(trial_id)) + + +class _StudyInfo: + def __init__(self, name: str, directions: list[StudyDirection]) -> None: + self.trials: list[FrozenTrial] = [] + self.param_distribution: dict[str, distributions.BaseDistribution] = {} + self.user_attrs: dict[str, Any] = {} + self.system_attrs: dict[str, Any] = {} + self.name: str = name + self.directions: list[StudyDirection] = directions + self.best_trial_id: int | None = None diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..86346d64b37f62c0fd099cc2f69c40cf0844875c Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/__pycache__/storage.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/__pycache__/storage.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..bbef3d70cb14be4301a049158a6dc81899476584 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/__pycache__/storage.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic.ini b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic.ini new file mode 100644 index 0000000000000000000000000000000000000000..52681f91aacbb835bef6d991229ba33ed8cdebc4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic.ini @@ -0,0 +1,75 @@ +# A generic, single database configuration. + +[alembic] +# path to migration scripts +script_location = alembic + +# template used to generate migration files +# file_template = %%(rev)s_%%(slug)s + +# timezone to use when rendering the date +# within the migration file as well as the filename. +# string value is passed to dateutil.tz.gettz() +# leave blank for localtime +# timezone = + +# max length of characters to apply to the +# "slug" field +#truncate_slug_length = 40 + +# set to 'true' to run the environment during +# the 'revision' command, regardless of autogenerate +# revision_environment = false + +# set to 'true' to allow .pyc and .pyo files without +# a source .py file to be detected as revisions in the +# versions/ directory +# sourceless = false + +# version location specification; this defaults +# to alembic/versions. When using multiple version +# directories, initial revisions must be specified with --version-path +# version_locations = %(here)s/bar %(here)s/bat alembic/versions + +# the output encoding used when revision files +# are written from script.py.mako +# output_encoding = utf-8 + +# NOTE: This URL is only used when generating migration scripts. +sqlalchemy.url = sqlite:///alembic.db + + +# Logging configuration +[loggers] +keys = root,sqlalchemy,alembic + +[handlers] +keys = console + +[formatters] +keys = generic + +[logger_root] +level = WARN +handlers = console +qualname = + +[logger_sqlalchemy] +level = WARN +handlers = +qualname = sqlalchemy.engine + +[logger_alembic] +level = INFO +handlers = +qualname = alembic + +[handler_console] +class = StreamHandler +args = (sys.stderr,) +level = NOTSET +formatter = generic + +[formatter_generic] +format = %(levelname)-5.5s [%(name)s] %(message)s +datefmt = %H:%M:%S diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/env.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/env.py new file mode 100644 index 0000000000000000000000000000000000000000..7b6c569981e85aa5a005417eb0d93351daf33391 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/env.py @@ -0,0 +1,79 @@ +import logging +from logging.config import fileConfig + +from alembic import context +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +import optuna.storages._rdb.models + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. + +if len(logging.getLogger().handlers) == 0: + fileConfig(config.config_file_name) + +# add your model's MetaData object here +# for 'autogenerate' support +# from myapp import mymodel +# target_metadata = mymodel.Base.metadata +target_metadata = optuna.storages._rdb.models.BaseModel.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, target_metadata=target_metadata, literal_binds=True, render_as_batch=True + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, target_metadata=target_metadata, render_as_batch=True + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/script.py.mako b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/script.py.mako new file mode 100644 index 0000000000000000000000000000000000000000..2c0156303a8df3ffdc9de87765bf801bf6bea4a5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v0.9.0.a.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v0.9.0.a.py new file mode 100644 index 0000000000000000000000000000000000000000..229757218eff73678433c48ea157cbf8c9393094 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v0.9.0.a.py @@ -0,0 +1,132 @@ +"""empty message + +Revision ID: v0.9.0.a +Revises: +Create Date: 2019-03-12 12:30:31.178819 + +""" + +from alembic import op +import sqlalchemy as sa + +# revision identifiers, used by Alembic. +revision = "v0.9.0.a" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + # ### commands auto generated by Alembic - please adjust! ### + op.create_table( + "studies", + sa.Column("study_id", sa.Integer(), nullable=False), + sa.Column("study_name", sa.String(length=512), nullable=False), + sa.Column( + "direction", + sa.Enum("NOT_SET", "MINIMIZE", "MAXIMIZE", name="studydirection"), + nullable=False, + ), + sa.PrimaryKeyConstraint("study_id"), + ) + op.create_index(op.f("ix_studies_study_name"), "studies", ["study_name"], unique=True) + op.create_table( + "version_info", + sa.Column("version_info_id", sa.Integer(), autoincrement=False, nullable=False), + sa.Column("schema_version", sa.Integer(), nullable=True), + sa.Column("library_version", sa.String(length=256), nullable=True), + sa.CheckConstraint("version_info_id=1"), + sa.PrimaryKeyConstraint("version_info_id"), + ) + op.create_table( + "study_system_attributes", + sa.Column("study_system_attribute_id", sa.Integer(), nullable=False), + sa.Column("study_id", sa.Integer(), nullable=True), + sa.Column("key", sa.String(length=512), nullable=True), + sa.Column("value_json", sa.String(length=2048), nullable=True), + sa.ForeignKeyConstraint(["study_id"], ["studies.study_id"]), + sa.PrimaryKeyConstraint("study_system_attribute_id"), + sa.UniqueConstraint("study_id", "key"), + ) + op.create_table( + "study_user_attributes", + sa.Column("study_user_attribute_id", sa.Integer(), nullable=False), + sa.Column("study_id", sa.Integer(), nullable=True), + sa.Column("key", sa.String(length=512), nullable=True), + sa.Column("value_json", sa.String(length=2048), nullable=True), + sa.ForeignKeyConstraint(["study_id"], ["studies.study_id"]), + sa.PrimaryKeyConstraint("study_user_attribute_id"), + sa.UniqueConstraint("study_id", "key"), + ) + op.create_table( + "trials", + sa.Column("trial_id", sa.Integer(), nullable=False), + sa.Column("study_id", sa.Integer(), nullable=True), + sa.Column( + "state", + sa.Enum("RUNNING", "COMPLETE", "PRUNED", "FAIL", name="trialstate"), + nullable=False, + ), + sa.Column("value", sa.Float(), nullable=True), + sa.Column("datetime_start", sa.DateTime(), nullable=True), + sa.Column("datetime_complete", sa.DateTime(), nullable=True), + sa.ForeignKeyConstraint(["study_id"], ["studies.study_id"]), + sa.PrimaryKeyConstraint("trial_id"), + ) + op.create_table( + "trial_params", + sa.Column("param_id", sa.Integer(), nullable=False), + sa.Column("trial_id", sa.Integer(), nullable=True), + sa.Column("param_name", sa.String(length=512), nullable=True), + sa.Column("param_value", sa.Float(), nullable=True), + sa.Column("distribution_json", sa.String(length=2048), nullable=True), + sa.ForeignKeyConstraint(["trial_id"], ["trials.trial_id"]), + sa.PrimaryKeyConstraint("param_id"), + sa.UniqueConstraint("trial_id", "param_name"), + ) + op.create_table( + "trial_system_attributes", + sa.Column("trial_system_attribute_id", sa.Integer(), nullable=False), + sa.Column("trial_id", sa.Integer(), nullable=True), + sa.Column("key", sa.String(length=512), nullable=True), + sa.Column("value_json", sa.String(length=2048), nullable=True), + sa.ForeignKeyConstraint(["trial_id"], ["trials.trial_id"]), + sa.PrimaryKeyConstraint("trial_system_attribute_id"), + sa.UniqueConstraint("trial_id", "key"), + ) + op.create_table( + "trial_user_attributes", + sa.Column("trial_user_attribute_id", sa.Integer(), nullable=False), + sa.Column("trial_id", sa.Integer(), nullable=True), + sa.Column("key", sa.String(length=512), nullable=True), + sa.Column("value_json", sa.String(length=2048), nullable=True), + sa.ForeignKeyConstraint(["trial_id"], ["trials.trial_id"]), + sa.PrimaryKeyConstraint("trial_user_attribute_id"), + sa.UniqueConstraint("trial_id", "key"), + ) + op.create_table( + "trial_values", + sa.Column("trial_value_id", sa.Integer(), nullable=False), + sa.Column("trial_id", sa.Integer(), nullable=True), + sa.Column("step", sa.Integer(), nullable=True), + sa.Column("value", sa.Float(), nullable=True), + sa.ForeignKeyConstraint(["trial_id"], ["trials.trial_id"]), + sa.PrimaryKeyConstraint("trial_value_id"), + sa.UniqueConstraint("trial_id", "step"), + ) + # ### end Alembic commands ### + + +def downgrade(): + # ### commands auto generated by Alembic - please adjust! ### + op.drop_table("trial_values") + op.drop_table("trial_user_attributes") + op.drop_table("trial_system_attributes") + op.drop_table("trial_params") + op.drop_table("trials") + op.drop_table("study_user_attributes") + op.drop_table("study_system_attributes") + op.drop_table("version_info") + op.drop_index(op.f("ix_studies_study_name"), table_name="studies") + op.drop_table("studies") + # ### end Alembic commands ### diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v1.2.0.a.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v1.2.0.a.py new file mode 100644 index 0000000000000000000000000000000000000000..b868a45909eb7827b89305af29fcdebd419c440e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v1.2.0.a.py @@ -0,0 +1,37 @@ +"""empty message + +Revision ID: v1.2.0.a +Revises: v0.9.0.a +Create Date: 2020-02-05 15:17:41.458947 + +""" + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision = "v1.2.0.a" +down_revision = "v0.9.0.a" +branch_labels = None +depends_on = None + + +def upgrade(): + with op.batch_alter_table("trials") as batch_op: + batch_op.alter_column( + "state", + type_=sa.Enum("RUNNING", "COMPLETE", "PRUNED", "FAIL", "WAITING", name="trialstate"), + existing_type=sa.Enum("RUNNING", "COMPLETE", "PRUNED", "FAIL", name="trialstate"), + ) + + +def downgrade(): + with op.batch_alter_table("trials") as batch_op: + batch_op.alter_column( + "state", + type_=sa.Enum("RUNNING", "COMPLETE", "PRUNED", "FAIL", name="trialstate"), + existing_type=sa.Enum( + "RUNNING", "COMPLETE", "PRUNED", "FAIL", "WAITING", name="trialstate" + ), + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v1.3.0.a.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v1.3.0.a.py new file mode 100644 index 0000000000000000000000000000000000000000..a24f837a491dcd6e7d420d6694c1ffa4eb95f563 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v1.3.0.a.py @@ -0,0 +1,103 @@ +"""empty message + +Revision ID: v1.3.0.a +Revises: v1.2.0.a +Create Date: 2020-02-14 16:23:04.800808 + +""" + +import json + +from alembic import op +import sqlalchemy as sa + +from sqlalchemy.exc import SQLAlchemyError +from sqlalchemy import orm + +try: + from sqlalchemy.orm import declarative_base +except ImportError: + # TODO(c-bata): Remove this after dropping support for SQLAlchemy v1.3 or prior. + from sqlalchemy.ext.declarative import declarative_base + +# revision identifiers, used by Alembic. +revision = "v1.3.0.a" +down_revision = "v1.2.0.a" +branch_labels = None +depends_on = None + +# Model definition +MAX_INDEXED_STRING_LENGTH = 512 +MAX_STRING_LENGTH = 2048 +BaseModel = declarative_base() + + +class TrialModel(BaseModel): + __tablename__ = "trials" + trial_id = sa.Column(sa.Integer, primary_key=True) + number = sa.Column(sa.Integer) + + +class TrialSystemAttributeModel(BaseModel): + __tablename__ = "trial_system_attributes" + trial_system_attribute_id = sa.Column(sa.Integer, primary_key=True) + trial_id = sa.Column(sa.Integer, sa.ForeignKey("trials.trial_id")) + key = sa.Column(sa.String(MAX_INDEXED_STRING_LENGTH)) + value_json = sa.Column(sa.String(MAX_STRING_LENGTH)) + + +def upgrade(): + bind = op.get_bind() + session = orm.Session(bind=bind) + + with op.batch_alter_table("trials") as batch_op: + batch_op.add_column(sa.Column("number", sa.Integer(), nullable=True, default=None)) + + try: + number_records = ( + session.query(TrialSystemAttributeModel) + .filter(TrialSystemAttributeModel.key == "_number") + .all() + ) + mapping = [ + {"trial_id": r.trial_id, "number": json.loads(r.value_json)} for r in number_records + ] + session.bulk_update_mappings(TrialModel, mapping) + + stmt = ( + sa.delete(TrialSystemAttributeModel) + .where(TrialSystemAttributeModel.key == "_number") + .execution_options(synchronize_session=False) + ) + session.execute(stmt) + session.commit() + except SQLAlchemyError as e: + session.rollback() + raise e + finally: + session.close() + + +def downgrade(): + bind = op.get_bind() + session = orm.Session(bind=bind) + + try: + number_attrs = [] + trials = session.query(TrialModel).all() + for trial in trials: + number_attrs.append( + TrialSystemAttributeModel( + trial_id=trial.trial_id, key="_number", value_json=json.dumps(trial.number) + ) + ) + session.bulk_save_objects(number_attrs) + session.commit() + except SQLAlchemyError as e: + session.rollback() + raise e + finally: + session.close() + + with op.batch_alter_table("trials") as batch_op: + batch_op.drop_column("number") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v2.4.0.a.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v2.4.0.a.py new file mode 100644 index 0000000000000000000000000000000000000000..fdfc0390d292c974730b6e27158f862aaf8503b7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v2.4.0.a.py @@ -0,0 +1,187 @@ +"""empty message + +Revision ID: v2.4.0.a +Revises: v1.3.0.a +Create Date: 2020-11-17 02:16:16.536171 + +""" + +from alembic import op +import sqlalchemy as sa +from typing import Any + +from sqlalchemy import Column +from sqlalchemy import Enum +from sqlalchemy import Float +from sqlalchemy import ForeignKey +from sqlalchemy import Integer +from sqlalchemy import UniqueConstraint +from sqlalchemy.exc import SQLAlchemyError +from sqlalchemy import orm + +from optuna.study import StudyDirection + +try: + from sqlalchemy.orm import declarative_base +except ImportError: + # TODO(c-bata): Remove this after dropping support for SQLAlchemy v1.3 or prior. + from sqlalchemy.ext.declarative import declarative_base + + +# revision identifiers, used by Alembic. +revision = "v2.4.0.a" +down_revision = "v1.3.0.a" +branch_labels = None +depends_on = None + +# Model definition +BaseModel = declarative_base() + + +class StudyModel(BaseModel): + __tablename__ = "studies" + study_id = Column(Integer, primary_key=True) + direction = sa.Column(sa.Enum(StudyDirection)) + + +class StudyDirectionModel(BaseModel): + __tablename__ = "study_directions" + __table_args__: Any = (UniqueConstraint("study_id", "objective"),) + study_direction_id = Column(Integer, primary_key=True) + direction = Column(Enum(StudyDirection), nullable=False) + study_id = Column(Integer, ForeignKey("studies.study_id"), nullable=False) + objective = Column(Integer, nullable=False) + + +class TrialModel(BaseModel): + __tablename__ = "trials" + trial_id = Column(Integer, primary_key=True) + number = Column(Integer) + study_id = Column(Integer, ForeignKey("studies.study_id")) + value = sa.Column(sa.Float) + + +class TrialValueModel(BaseModel): + __tablename__ = "trial_values" + __table_args__: Any = (UniqueConstraint("trial_id", "objective"),) + trial_value_id = Column(Integer, primary_key=True) + trial_id = Column(Integer, ForeignKey("trials.trial_id"), nullable=False) + objective = Column(Integer, nullable=False) + value = Column(Float, nullable=False) + step = sa.Column(sa.Integer) + + +class TrialIntermediateValueModel(BaseModel): + __tablename__ = "trial_intermediate_values" + __table_args__: Any = (UniqueConstraint("trial_id", "step"),) + trial_intermediate_value_id = Column(Integer, primary_key=True) + trial_id = Column(Integer, ForeignKey("trials.trial_id"), nullable=False) + step = Column(Integer, nullable=False) + intermediate_value = Column(Float, nullable=False) + + +def upgrade(): + bind = op.get_bind() + inspector = sa.inspect(bind) + tables = inspector.get_table_names() + + if "study_directions" not in tables: + op.create_table( + "study_directions", + sa.Column("study_direction_id", sa.Integer(), nullable=False), + sa.Column( + "direction", + sa.Enum("NOT_SET", "MINIMIZE", "MAXIMIZE", name="studydirection"), + nullable=False, + ), + sa.Column("study_id", sa.Integer(), nullable=False), + sa.Column("objective", sa.Integer(), nullable=False), + sa.ForeignKeyConstraint( + ["study_id"], + ["studies.study_id"], + ), + sa.PrimaryKeyConstraint("study_direction_id"), + sa.UniqueConstraint("study_id", "objective"), + ) + + if "trial_intermediate_values" not in tables: + op.create_table( + "trial_intermediate_values", + sa.Column("trial_intermediate_value_id", sa.Integer(), nullable=False), + sa.Column("trial_id", sa.Integer(), nullable=False), + sa.Column("step", sa.Integer(), nullable=False), + sa.Column("intermediate_value", sa.Float(), nullable=False), + sa.ForeignKeyConstraint( + ["trial_id"], + ["trials.trial_id"], + ), + sa.PrimaryKeyConstraint("trial_intermediate_value_id"), + sa.UniqueConstraint("trial_id", "step"), + ) + + session = orm.Session(bind=bind) + try: + studies_records = session.query(StudyModel).all() + objects = [ + StudyDirectionModel(study_id=r.study_id, direction=r.direction, objective=0) + for r in studies_records + ] + session.bulk_save_objects(objects) + + intermediate_values_records = session.query( + TrialValueModel.trial_id, TrialValueModel.value, TrialValueModel.step + ).all() + objects = [ + TrialIntermediateValueModel( + trial_id=r.trial_id, intermediate_value=r.value, step=r.step + ) + for r in intermediate_values_records + ] + session.bulk_save_objects(objects) + + session.query(TrialValueModel).delete() + session.commit() + + with op.batch_alter_table("trial_values", schema=None) as batch_op: + batch_op.add_column(sa.Column("objective", sa.Integer(), nullable=False)) + # The name of this constraint is manually determined. + # In the future, the naming convention may be determined based on + # https://alembic.sqlalchemy.org/en/latest/naming.html + batch_op.create_unique_constraint( + "uq_trial_values_trial_id_objective", ["trial_id", "objective"] + ) + + trials_records = session.query(TrialModel).all() + objects = [ + TrialValueModel(trial_id=r.trial_id, value=r.value, objective=0) + for r in trials_records + ] + session.bulk_save_objects(objects) + + session.commit() + except SQLAlchemyError as e: + session.rollback() + raise e + finally: + session.close() + + with op.batch_alter_table("studies", schema=None) as batch_op: + batch_op.drop_column("direction") + + with op.batch_alter_table("trial_values", schema=None) as batch_op: + batch_op.drop_column("step") + + with op.batch_alter_table("trials", schema=None) as batch_op: + batch_op.drop_column("value") + + for c in inspector.get_unique_constraints("trial_values"): + # MySQL changes the uniq constraint of (trial_id, step) to that of trial_id. + if c["column_names"] == ["trial_id"]: + with op.batch_alter_table("trial_values", schema=None) as batch_op: + batch_op.drop_constraint(c["name"], type_="unique") + break + + +# TODO(imamura): Implement downgrade +def downgrade(): + pass diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v2.6.0.a_.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v2.6.0.a_.py new file mode 100644 index 0000000000000000000000000000000000000000..76f7873d4ba403505a62c913b0224b0617e82047 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v2.6.0.a_.py @@ -0,0 +1,45 @@ +"""empty message + +Revision ID: v2.6.0.a +Revises: v2.4.0.a +Create Date: 2021-03-01 11:30:32.214196 + +""" + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision = "v2.6.0.a" +down_revision = "v2.4.0.a" +branch_labels = None +depends_on = None + +MAX_STRING_LENGTH = 2048 + + +def upgrade(): + with op.batch_alter_table("study_user_attributes") as batch_op: + batch_op.alter_column("value_json", type_=sa.TEXT) + with op.batch_alter_table("study_system_attributes") as batch_op: + batch_op.alter_column("value_json", type_=sa.TEXT) + with op.batch_alter_table("trial_user_attributes") as batch_op: + batch_op.alter_column("value_json", type_=sa.TEXT) + with op.batch_alter_table("trial_system_attributes") as batch_op: + batch_op.alter_column("value_json", type_=sa.TEXT) + with op.batch_alter_table("trial_params") as batch_op: + batch_op.alter_column("distribution_json", type_=sa.TEXT) + + +def downgrade(): + with op.batch_alter_table("study_user_attributes") as batch_op: + batch_op.alter_column("value_json", type_=sa.String(MAX_STRING_LENGTH)) + with op.batch_alter_table("study_system_attributes") as batch_op: + batch_op.alter_column("value_json", type_=sa.String(MAX_STRING_LENGTH)) + with op.batch_alter_table("trial_user_attributes") as batch_op: + batch_op.alter_column("value_json", type_=sa.String(MAX_STRING_LENGTH)) + with op.batch_alter_table("trial_system_attributes") as batch_op: + batch_op.alter_column("value_json", type_=sa.String(MAX_STRING_LENGTH)) + with op.batch_alter_table("trial_params") as batch_op: + batch_op.alter_column("distribution_json", type_=sa.String(MAX_STRING_LENGTH)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.a.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.a.py new file mode 100644 index 0000000000000000000000000000000000000000..0e7b21f4d676fa8501d60da434ceeb6727af841f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.a.py @@ -0,0 +1,202 @@ +"""unify existing distributions to {int,float} distribution + +Revision ID: v3.0.0.a +Revises: v2.6.0.a +Create Date: 2021-11-21 23:48:42.424430 + +""" + +from __future__ import annotations + +from typing import Any + +import sqlalchemy as sa +from alembic import op +from sqlalchemy import Column +from sqlalchemy import DateTime +from sqlalchemy import Enum +from sqlalchemy import Float +from sqlalchemy import ForeignKey +from sqlalchemy import Integer +from sqlalchemy import orm +from sqlalchemy import String +from sqlalchemy import Text +from sqlalchemy import UniqueConstraint +from sqlalchemy.exc import SQLAlchemyError + +from optuna.distributions import _convert_old_distribution_to_new_distribution +from optuna.distributions import BaseDistribution +from optuna.distributions import DiscreteUniformDistribution +from optuna.distributions import distribution_to_json +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution +from optuna.distributions import IntLogUniformDistribution +from optuna.distributions import IntUniformDistribution +from optuna.distributions import json_to_distribution +from optuna.distributions import LogUniformDistribution +from optuna.distributions import UniformDistribution +from optuna.trial import TrialState + +try: + from sqlalchemy.orm import declarative_base +except ImportError: + # TODO(c-bata): Remove this after dropping support for SQLAlchemy v1.3 or prior. + from sqlalchemy.ext.declarative import declarative_base + + +# revision identifiers, used by Alembic. +revision = "v3.0.0.a" +down_revision = "v2.6.0.a" +branch_labels = None +depends_on = None + +MAX_INDEXED_STRING_LENGTH = 512 +BATCH_SIZE = 5000 + +BaseModel = declarative_base() + + +class StudyModel(BaseModel): + __tablename__ = "studies" + study_id = Column(Integer, primary_key=True) + study_name = Column(String(MAX_INDEXED_STRING_LENGTH), index=True, unique=True, nullable=False) + + +class TrialModel(BaseModel): + __tablename__ = "trials" + trial_id = Column(Integer, primary_key=True) + number = Column(Integer) + study_id = Column(Integer, ForeignKey("studies.study_id")) + state = Column(Enum(TrialState), nullable=False) + datetime_start = Column(DateTime) + datetime_complete = Column(DateTime) + + +class TrialParamModel(BaseModel): + __tablename__ = "trial_params" + __table_args__: Any = (UniqueConstraint("trial_id", "param_name"),) + param_id = Column(Integer, primary_key=True) + trial_id = Column(Integer, ForeignKey("trials.trial_id")) + param_name = Column(String(MAX_INDEXED_STRING_LENGTH)) + param_value = Column(Float) + distribution_json = Column(Text()) + + +def migrate_new_distribution(distribution_json: str) -> str: + distribution = json_to_distribution(distribution_json) + new_distribution = _convert_old_distribution_to_new_distribution( + distribution, + suppress_warning=True, + ) + return distribution_to_json(new_distribution) + + +def restore_old_distribution(distribution_json: str) -> str: + distribution = json_to_distribution(distribution_json) + old_distribution: BaseDistribution + + # Float distributions. + if isinstance(distribution, FloatDistribution): + if distribution.log: + old_distribution = LogUniformDistribution( + low=distribution.low, + high=distribution.high, + ) + else: + if distribution.step is not None: + old_distribution = DiscreteUniformDistribution( + low=distribution.low, + high=distribution.high, + q=distribution.step, + ) + else: + old_distribution = UniformDistribution( + low=distribution.low, + high=distribution.high, + ) + + # Integer distributions. + elif isinstance(distribution, IntDistribution): + if distribution.log: + old_distribution = IntLogUniformDistribution( + low=distribution.low, + high=distribution.high, + step=distribution.step, + ) + else: + old_distribution = IntUniformDistribution( + low=distribution.low, + high=distribution.high, + step=distribution.step, + ) + + # Categorical distribution. + else: + old_distribution = distribution + + return distribution_to_json(old_distribution) + + +def persist(session: orm.Session, distributions: list[BaseDistribution]) -> None: + if len(distributions) == 0: + return + session.bulk_save_objects(distributions) + session.commit() + + +def upgrade() -> None: + bind = op.get_bind() + inspector = sa.inspect(bind) + tables = inspector.get_table_names() + + assert "trial_params" in tables + + session = orm.Session(bind=bind) + try: + distributions: list[BaseDistribution] = [] + for distribution in session.query(TrialParamModel).yield_per(BATCH_SIZE): + distribution.distribution_json = migrate_new_distribution( + distribution.distribution_json, + ) + distributions.append(distribution) + + if len(distributions) == BATCH_SIZE: + persist(session, distributions) + distributions = [] + + persist(session, distributions) + + except SQLAlchemyError as e: + session.rollback() + raise e + finally: + session.close() + + +def downgrade() -> None: + bind = op.get_bind() + inspector = sa.inspect(bind) + tables = inspector.get_table_names() + + assert "trial_params" in tables + + session = orm.Session(bind=bind) + try: + distributions = [] + for distribution in session.query(TrialParamModel).yield_per(BATCH_SIZE): + distribution.distribution_json = restore_old_distribution( + distribution.distribution_json, + ) + distributions.append(distribution) + + if len(distributions) == BATCH_SIZE: + persist(session, distributions) + distributions = [] + + persist(session, distributions) + + except SQLAlchemyError as e: + session.rollback() + raise e + finally: + session.close() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.b.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.b.py new file mode 100644 index 0000000000000000000000000000000000000000..e7d09c346df6deb8eb5de94fae49fb8819cb9bd7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.b.py @@ -0,0 +1,98 @@ +"""Change floating point precision and make intermediate_value nullable. + +Revision ID: v3.0.0.b +Revises: v3.0.0.a +Create Date: 2022-04-27 16:31:42.012666 + +""" + +import enum + +from alembic import op +from sqlalchemy import and_ +from sqlalchemy import Column +from sqlalchemy import Enum +from sqlalchemy import Float +from sqlalchemy import ForeignKey +from sqlalchemy import Integer +from sqlalchemy.orm import Session + +try: + from sqlalchemy.orm import declarative_base +except ImportError: + # TODO(c-bata): Remove this after dropping support for SQLAlchemy v1.3 or prior. + from sqlalchemy.ext.declarative import declarative_base + + +# revision identifiers, used by Alembic. +revision = "v3.0.0.b" +down_revision = "v3.0.0.a" +branch_labels = None +depends_on = None + +BaseModel = declarative_base() +FLOAT_PRECISION = 53 + + +class TrialState(enum.Enum): + RUNNING = 0 + COMPLETE = 1 + PRUNED = 2 + FAIL = 3 + WAITING = 4 + + +class TrialModel(BaseModel): + __tablename__ = "trials" + trial_id = Column(Integer, primary_key=True) + number = Column(Integer) + state = Column(Enum(TrialState), nullable=False) + + +class TrialValueModel(BaseModel): + __tablename__ = "trial_values" + trial_value_id = Column(Integer, primary_key=True) + trial_id = Column(Integer, ForeignKey("trials.trial_id"), nullable=False) + value = Column(Float, nullable=False) + + +def upgrade(): + bind = op.get_bind() + session = Session(bind=bind) + + if ( + session.query(TrialValueModel) + .join(TrialModel, TrialValueModel.trial_id == TrialModel.trial_id) + .filter(and_(TrialModel.state == TrialState.COMPLETE, TrialValueModel.value.is_(None))) + .count() + ) != 0: + raise ValueError("Found invalid trial_values records (value=None and state='COMPLETE')") + session.query(TrialValueModel).filter(TrialValueModel.value.is_(None)).delete() + + with op.batch_alter_table("trial_intermediate_values") as batch_op: + batch_op.alter_column( + "intermediate_value", + type_=Float(precision=FLOAT_PRECISION), + nullable=True, + ) + with op.batch_alter_table("trial_params") as batch_op: + batch_op.alter_column( + "param_value", + type_=Float(precision=FLOAT_PRECISION), + existing_nullable=True, + ) + with op.batch_alter_table("trial_values") as batch_op: + batch_op.alter_column( + "value", + type_=Float(precision=FLOAT_PRECISION), + nullable=False, + ) + + +def downgrade(): + with op.batch_alter_table("trial_intermediate_values") as batch_op: + batch_op.alter_column("intermediate_value", type_=Float, nullable=False) + with op.batch_alter_table("trial_params") as batch_op: + batch_op.alter_column("param_value", type_=Float, existing_nullable=True) + with op.batch_alter_table("trial_values") as batch_op: + batch_op.alter_column("value", type_=Float, existing_nullable=False) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.c.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.c.py new file mode 100644 index 0000000000000000000000000000000000000000..522d33467840091becde047c9740aad74f578c1e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.c.py @@ -0,0 +1,191 @@ +"""Add intermediate_value_type column to represent +inf and -inf + +Revision ID: v3.0.0.c +Revises: v3.0.0.b +Create Date: 2022-05-16 17:17:28.810792 + +""" + +from __future__ import annotations + +import enum + +import numpy as np +from alembic import op +import sqlalchemy as sa +from sqlalchemy.exc import SQLAlchemyError +from sqlalchemy import orm + +try: + from sqlalchemy.orm import declarative_base +except ImportError: + # TODO(c-bata): Remove this after dropping support for SQLAlchemy v1.3 or prior. + from sqlalchemy.ext.declarative import declarative_base + + +# revision identifiers, used by Alembic. +revision = "v3.0.0.c" +down_revision = "v3.0.0.b" +branch_labels = None +depends_on = None + + +BaseModel = declarative_base() +RDB_MAX_FLOAT = np.finfo(np.float32).max +RDB_MIN_FLOAT = np.finfo(np.float32).min + + +FLOAT_PRECISION = 53 + + +class IntermediateValueModel(BaseModel): + class TrialIntermediateValueType(enum.Enum): + FINITE = 1 + INF_POS = 2 + INF_NEG = 3 + NAN = 4 + + __tablename__ = "trial_intermediate_values" + trial_intermediate_value_id = sa.Column(sa.Integer, primary_key=True) + intermediate_value = sa.Column(sa.Float(precision=FLOAT_PRECISION), nullable=True) + intermediate_value_type = sa.Column(sa.Enum(TrialIntermediateValueType), nullable=False) + + @classmethod + def intermediate_value_to_stored_repr( + cls, + value: float, + ) -> tuple[float | None, TrialIntermediateValueType]: + if np.isnan(value): + return None, cls.TrialIntermediateValueType.NAN + elif value == float("inf"): + return None, cls.TrialIntermediateValueType.INF_POS + elif value == float("-inf"): + return None, cls.TrialIntermediateValueType.INF_NEG + else: + return value, cls.TrialIntermediateValueType.FINITE + + +def upgrade(): + bind = op.get_bind() + inspector = sa.inspect(bind) + column_names = [c["name"] for c in inspector.get_columns("trial_intermediate_values")] + + sa.Enum(IntermediateValueModel.TrialIntermediateValueType).create(bind, checkfirst=True) + + # MySQL and PostgreSQL supports DEFAULT clause like 'ALTER TABLE + # ADD COLUMN ... DEFAULT "FINITE_OR_NAN"', but seemingly Alembic + # does not support such a SQL statement. So first add a column with schema-level + # default value setting, then remove it by `batch_op.alter_column()`. + if "intermediate_value_type" not in column_names: + with op.batch_alter_table("trial_intermediate_values") as batch_op: + batch_op.add_column( + sa.Column( + "intermediate_value_type", + sa.Enum( + "FINITE", "INF_POS", "INF_NEG", "NAN", name="trialintermediatevaluetype" + ), + nullable=False, + server_default="FINITE", + ), + ) + with op.batch_alter_table("trial_intermediate_values") as batch_op: + batch_op.alter_column( + "intermediate_value_type", + existing_type=sa.Enum( + "FINITE", "INF_POS", "INF_NEG", "NAN", name="trialintermediatevaluetype" + ), + existing_nullable=False, + server_default=None, + ) + + session = orm.Session(bind=bind) + try: + records = ( + session.query(IntermediateValueModel) + .filter( + sa.or_( + IntermediateValueModel.intermediate_value > 1e16, + IntermediateValueModel.intermediate_value < -1e16, + IntermediateValueModel.intermediate_value.is_(None), + ) + ) + .all() + ) + mapping = [] + for r in records: + value: float + if r.intermediate_value is None or np.isnan(r.intermediate_value): + value = float("nan") + elif np.isclose(r.intermediate_value, RDB_MAX_FLOAT) or np.isposinf( + r.intermediate_value + ): + value = float("inf") + elif np.isclose(r.intermediate_value, RDB_MIN_FLOAT) or np.isneginf( + r.intermediate_value + ): + value = float("-inf") + else: + value = r.intermediate_value + ( + stored_value, + float_type, + ) = IntermediateValueModel.intermediate_value_to_stored_repr(value) + mapping.append( + { + "trial_intermediate_value_id": r.trial_intermediate_value_id, + "intermediate_value_type": float_type, + "intermediate_value": stored_value, + } + ) + session.bulk_update_mappings(IntermediateValueModel, mapping) + session.commit() + except SQLAlchemyError as e: + session.rollback() + raise e + finally: + session.close() + + +def downgrade(): + bind = op.get_bind() + session = orm.Session(bind=bind) + + try: + records = session.query(IntermediateValueModel).all() + mapping = [] + for r in records: + if ( + r.intermediate_value_type + == IntermediateValueModel.TrialIntermediateValueType.FINITE + or r.intermediate_value_type + == IntermediateValueModel.TrialIntermediateValueType.NAN + ): + continue + + _intermediate_value = r.intermediate_value + if ( + r.intermediate_value_type + == IntermediateValueModel.TrialIntermediateValueType.INF_POS + ): + _intermediate_value = RDB_MAX_FLOAT + else: + _intermediate_value = RDB_MIN_FLOAT + + mapping.append( + { + "trial_intermediate_value_id": r.trial_intermediate_value_id, + "intermediate_value": _intermediate_value, + } + ) + session.bulk_update_mappings(IntermediateValueModel, mapping) + session.commit() + except SQLAlchemyError as e: + session.rollback() + raise e + finally: + session.close() + + with op.batch_alter_table("trial_intermediate_values", schema=None) as batch_op: + batch_op.drop_column("intermediate_value_type") + + sa.Enum(IntermediateValueModel.FloatTypeEnum).drop(bind, checkfirst=True) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.d.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.d.py new file mode 100644 index 0000000000000000000000000000000000000000..c0bb3f611f0632e421631ded7cd102afb1bec4df --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.0.0.d.py @@ -0,0 +1,193 @@ +"""Handle inf/-inf for trial_values table. + +Revision ID: v3.0.0.d +Revises: v3.0.0.c +Create Date: 2022-06-02 09:57:22.818798 + +""" + +from __future__ import annotations + +import enum + +import numpy as np +from alembic import op +import sqlalchemy as sa +from sqlalchemy.exc import SQLAlchemyError +from sqlalchemy import orm + +try: + from sqlalchemy.orm import declarative_base +except ImportError: + # TODO(c-bata): Remove this after dropping support for SQLAlchemy v1.3 or prior. + from sqlalchemy.ext.declarative import declarative_base + + +# revision identifiers, used by Alembic. +revision = "v3.0.0.d" +down_revision = "v3.0.0.c" +branch_labels = None +depends_on = None + + +BaseModel = declarative_base() +RDB_MAX_FLOAT = np.finfo(np.float32).max +RDB_MIN_FLOAT = np.finfo(np.float32).min + + +FLOAT_PRECISION = 53 + + +class TrialValueModel(BaseModel): + class TrialValueType(enum.Enum): + FINITE = 1 + INF_POS = 2 + INF_NEG = 3 + + __tablename__ = "trial_values" + trial_value_id = sa.Column(sa.Integer, primary_key=True) + value = sa.Column(sa.Float(precision=FLOAT_PRECISION), nullable=True) + value_type = sa.Column(sa.Enum(TrialValueType), nullable=False) + + @classmethod + def value_to_stored_repr( + cls, + value: float, + ) -> tuple[float | None, TrialValueType]: + if value == float("inf"): + return None, cls.TrialValueType.INF_POS + elif value == float("-inf"): + return None, cls.TrialValueType.INF_NEG + else: + return value, cls.TrialValueType.FINITE + + @classmethod + def stored_repr_to_value(cls, value: float | None, float_type: TrialValueType) -> float: + if float_type == cls.TrialValueType.INF_POS: + assert value is None + return float("inf") + elif float_type == cls.TrialValueType.INF_NEG: + assert value is None + return float("-inf") + else: + assert float_type == cls.TrialValueType.FINITE + assert value is not None + return value + + +def upgrade(): + bind = op.get_bind() + inspector = sa.inspect(bind) + column_names = [c["name"] for c in inspector.get_columns("trial_values")] + + sa.Enum(TrialValueModel.TrialValueType).create(bind, checkfirst=True) + + # MySQL and PostgreSQL supports DEFAULT clause like 'ALTER TABLE + # ADD COLUMN ... DEFAULT "FINITE"', but seemingly Alembic + # does not support such a SQL statement. So first add a column with schema-level + # default value setting, then remove it by `batch_op.alter_column()`. + if "value_type" not in column_names: + with op.batch_alter_table("trial_values") as batch_op: + batch_op.add_column( + sa.Column( + "value_type", + sa.Enum("FINITE", "INF_POS", "INF_NEG", name="trialvaluetype"), + nullable=False, + server_default="FINITE", + ), + ) + with op.batch_alter_table("trial_values") as batch_op: + batch_op.alter_column( + "value_type", + existing_type=sa.Enum("FINITE", "INF_POS", "INF_NEG", name="trialvaluetype"), + existing_nullable=False, + server_default=None, + ) + batch_op.alter_column( + "value", + existing_type=sa.Float(precision=FLOAT_PRECISION), + nullable=True, + ) + + session = orm.Session(bind=bind) + try: + records = ( + session.query(TrialValueModel) + .filter( + sa.or_( + TrialValueModel.value > 1e16, + TrialValueModel.value < -1e16, + ) + ) + .all() + ) + mapping = [] + for r in records: + value: float + if np.isclose(r.value, RDB_MAX_FLOAT) or np.isposinf(r.value): + value = float("inf") + elif np.isclose(r.value, RDB_MIN_FLOAT) or np.isneginf(r.value): + value = float("-inf") + else: + value = r.value + + ( + stored_value, + float_type, + ) = TrialValueModel.value_to_stored_repr(value) + mapping.append( + { + "trial_value_id": r.trial_value_id, + "value_type": float_type, + "value": stored_value, + } + ) + session.bulk_update_mappings(TrialValueModel, mapping) + session.commit() + except SQLAlchemyError as e: + session.rollback() + raise e + finally: + session.close() + + +def downgrade(): + bind = op.get_bind() + session = orm.Session(bind=bind) + + try: + records = session.query(TrialValueModel).all() + mapping = [] + for r in records: + if r.value_type == TrialValueModel.TrialValueType.FINITE: + continue + + _value = r.value + if r.value_type == TrialValueModel.TrialValueType.INF_POS: + _value = RDB_MAX_FLOAT + else: + _value = RDB_MIN_FLOAT + + mapping.append( + { + "trial_value_id": r.trial_value_id, + "value": _value, + } + ) + session.bulk_update_mappings(TrialValueModel, mapping) + session.commit() + except SQLAlchemyError as e: + session.rollback() + raise e + finally: + session.close() + + with op.batch_alter_table("trial_values", schema=None) as batch_op: + batch_op.drop_column("value_type") + batch_op.alter_column( + "value", + existing_type=sa.Float(precision=FLOAT_PRECISION), + nullable=False, + ) + + sa.Enum(TrialValueModel.TrialValueType).drop(bind, checkfirst=True) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.2.0.a_.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.2.0.a_.py new file mode 100644 index 0000000000000000000000000000000000000000..3e73e20918156abd6181552a9e5bd59e5eb64958 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/alembic/versions/v3.2.0.a_.py @@ -0,0 +1,28 @@ +"""Add index to study_id column in trials table + +Revision ID: v3.2.0.a +Revises: v3.0.0.d +Create Date: 2023-02-25 13:21:00.730272 + +""" + +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "v3.2.0.a" +down_revision = "v3.0.0.d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index(op.f("trials_study_id_key"), "trials", ["study_id"], unique=False) + + +def downgrade(): + # The following operation doesn't work on MySQL due to a foreign key constraint. + # + # mysql> DROP INDEX ix_trials_study_id ON trials; + # ERROR: Cannot drop index 'ix_trials_study_id': needed in a foreign key constraint. + op.drop_index(op.f("trials_study_id_key"), table_name="trials") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/models.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/models.py new file mode 100644 index 0000000000000000000000000000000000000000..d327f07a098753ee308fd1211029def47e4312bf --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/models.py @@ -0,0 +1,592 @@ +from __future__ import annotations + +import enum +import math +from typing import Any + +from sqlalchemy import asc +from sqlalchemy import case +from sqlalchemy import CheckConstraint +from sqlalchemy import DateTime +from sqlalchemy import desc +from sqlalchemy import Enum +from sqlalchemy import Float +from sqlalchemy import ForeignKey +from sqlalchemy import func +from sqlalchemy import Integer +from sqlalchemy import orm +from sqlalchemy import String +from sqlalchemy import Text +from sqlalchemy import UniqueConstraint + +from optuna import distributions +from optuna.study._study_direction import StudyDirection +from optuna.trial import TrialState + + +try: + from sqlalchemy.orm import declarative_base +except ImportError: + # TODO(c-bata): Remove this after dropping support for SQLAlchemy v1.3 or prior. + from sqlalchemy.ext.declarative import declarative_base + +try: + from sqlalchemy.orm import mapped_column + + _Column = mapped_column +except ImportError: + # TODO(Shinichi): Remove this after dropping support for SQLAlchemy<2.0. + from sqlalchemy import Column as _Column # type: ignore[assignment] + +# Don't modify this version number anymore. +# The schema management functionality has been moved to alembic. +SCHEMA_VERSION = 12 + +MAX_INDEXED_STRING_LENGTH = 512 +MAX_VERSION_LENGTH = 256 + +NOT_FOUND_MSG = "Record does not exist." + +FLOAT_PRECISION = 53 + +BaseModel: Any = declarative_base() + + +class StudyModel(BaseModel): + __tablename__ = "studies" + study_id = _Column(Integer, primary_key=True) + study_name = _Column( + String(MAX_INDEXED_STRING_LENGTH), index=True, unique=True, nullable=False + ) + + @classmethod + def find_or_raise_by_id( + cls, study_id: int, session: orm.Session, for_update: bool = False + ) -> "StudyModel": + query = session.query(cls).filter(cls.study_id == study_id) + + if for_update: + query = query.with_for_update() + + study = query.one_or_none() + if study is None: + raise KeyError(NOT_FOUND_MSG) + + return study + + @classmethod + def find_by_name(cls, study_name: str, session: orm.Session) -> "StudyModel" | None: + study = session.query(cls).filter(cls.study_name == study_name).one_or_none() + + return study + + @classmethod + def find_or_raise_by_name(cls, study_name: str, session: orm.Session) -> "StudyModel": + study = cls.find_by_name(study_name, session) + if study is None: + raise KeyError(NOT_FOUND_MSG) + + return study + + +class StudyDirectionModel(BaseModel): + __tablename__ = "study_directions" + __table_args__: Any = (UniqueConstraint("study_id", "objective"),) + study_direction_id = _Column(Integer, primary_key=True) + direction = _Column(Enum(StudyDirection), nullable=False) + study_id = _Column(Integer, ForeignKey("studies.study_id"), nullable=False) + objective = _Column(Integer, nullable=False) + + study = orm.relationship( + StudyModel, backref=orm.backref("directions", cascade="all, delete-orphan") + ) + + @classmethod + def where_study_id(cls, study_id: int, session: orm.Session) -> list["StudyDirectionModel"]: + return session.query(cls).filter(cls.study_id == study_id).all() + + +class StudyUserAttributeModel(BaseModel): + __tablename__ = "study_user_attributes" + __table_args__: Any = (UniqueConstraint("study_id", "key"),) + study_user_attribute_id = _Column(Integer, primary_key=True) + study_id = _Column(Integer, ForeignKey("studies.study_id")) + key = _Column(String(MAX_INDEXED_STRING_LENGTH)) + value_json = _Column(Text()) + + study = orm.relationship( + StudyModel, backref=orm.backref("user_attributes", cascade="all, delete-orphan") + ) + + @classmethod + def find_by_study_and_key( + cls, study: StudyModel, key: str, session: orm.Session + ) -> "StudyUserAttributeModel" | None: + attribute = ( + session.query(cls) + .filter(cls.study_id == study.study_id) + .filter(cls.key == key) + .one_or_none() + ) + + return attribute + + @classmethod + def where_study_id( + cls, study_id: int, session: orm.Session + ) -> list["StudyUserAttributeModel"]: + return session.query(cls).filter(cls.study_id == study_id).all() + + +class StudySystemAttributeModel(BaseModel): + __tablename__ = "study_system_attributes" + __table_args__: Any = (UniqueConstraint("study_id", "key"),) + study_system_attribute_id = _Column(Integer, primary_key=True) + study_id = _Column(Integer, ForeignKey("studies.study_id")) + key = _Column(String(MAX_INDEXED_STRING_LENGTH)) + value_json = _Column(Text()) + + study = orm.relationship( + StudyModel, backref=orm.backref("system_attributes", cascade="all, delete-orphan") + ) + + @classmethod + def find_by_study_and_key( + cls, study: StudyModel, key: str, session: orm.Session + ) -> "StudySystemAttributeModel" | None: + attribute = ( + session.query(cls) + .filter(cls.study_id == study.study_id) + .filter(cls.key == key) + .one_or_none() + ) + + return attribute + + @classmethod + def where_study_id( + cls, study_id: int, session: orm.Session + ) -> list["StudySystemAttributeModel"]: + return session.query(cls).filter(cls.study_id == study_id).all() + + +class TrialModel(BaseModel): + __tablename__ = "trials" + trial_id = _Column(Integer, primary_key=True) + # No `UniqueConstraint` is put on the `number` columns although it in practice is constrained + # to be unique. This is to reduce code complexity as table-level locking would be required + # otherwise. See https://github.com/optuna/optuna/pull/939#discussion_r387447632. + number = _Column(Integer) + study_id = _Column(Integer, ForeignKey("studies.study_id"), index=True) + state = _Column(Enum(TrialState), nullable=False) + datetime_start = _Column(DateTime) + datetime_complete = _Column(DateTime) + + study = orm.relationship( + StudyModel, backref=orm.backref("trials", cascade="all, delete-orphan") + ) + + @classmethod + def find_max_value_trial_id(cls, study_id: int, objective: int, session: orm.Session) -> int: + trial = ( + session.query(cls) + .with_entities(cls.trial_id) + .filter(cls.study_id == study_id) + .filter(cls.state == TrialState.COMPLETE) + .join(TrialValueModel) + .filter(TrialValueModel.objective == objective) + .order_by( + desc( + case( + ( + TrialValueModel.value_type == TrialValueModel.TrialValueType.INF_NEG, + -1, + ), + ( + TrialValueModel.value_type == TrialValueModel.TrialValueType.FINITE, + 0, + ), + ( + TrialValueModel.value_type == TrialValueModel.TrialValueType.INF_POS, + 1, + ), + ) + ), + desc(TrialValueModel.value), + ) + .limit(1) + .one_or_none() + ) + if trial is None: + raise ValueError(NOT_FOUND_MSG) + return trial[0] + + @classmethod + def find_min_value_trial_id(cls, study_id: int, objective: int, session: orm.Session) -> int: + trial = ( + session.query(cls) + .with_entities(cls.trial_id) + .filter(cls.study_id == study_id) + .filter(cls.state == TrialState.COMPLETE) + .join(TrialValueModel) + .filter(TrialValueModel.objective == objective) + .order_by( + asc( + case( + ( + TrialValueModel.value_type == TrialValueModel.TrialValueType.INF_NEG, + -1, + ), + ( + TrialValueModel.value_type == TrialValueModel.TrialValueType.FINITE, + 0, + ), + ( + TrialValueModel.value_type == TrialValueModel.TrialValueType.INF_POS, + 1, + ), + ) + ), + asc(TrialValueModel.value), # Note: asc here + ) + .limit(1) + .one_or_none() + ) + if trial is None: + raise ValueError(NOT_FOUND_MSG) + return trial[0] + + @classmethod + def find_or_raise_by_id( + cls, trial_id: int, session: orm.Session, for_update: bool = False + ) -> "TrialModel": + query = session.query(cls).filter(cls.trial_id == trial_id) + + # "FOR UPDATE" clause is used for row-level locking. + # Please note that SQLite3 doesn't support this clause. + if for_update: + query = query.with_for_update() + + trial = query.one_or_none() + if trial is None: + raise KeyError(NOT_FOUND_MSG) + + return trial + + @classmethod + def count( + cls, session: orm.Session, study: StudyModel | None = None, state: TrialState | None = None + ) -> int: + trial_count = session.query(func.count(cls.trial_id)) + if study is not None: + trial_count = trial_count.filter(cls.study_id == study.study_id) + if state is not None: + trial_count = trial_count.filter(cls.state == state) + + return trial_count.scalar() + + def count_past_trials(self, session: orm.Session) -> int: + trial_count = session.query(func.count(TrialModel.trial_id)).filter( + TrialModel.study_id == self.study_id, TrialModel.trial_id < self.trial_id + ) + return trial_count.scalar() + + +class TrialUserAttributeModel(BaseModel): + __tablename__ = "trial_user_attributes" + __table_args__: Any = (UniqueConstraint("trial_id", "key"),) + trial_user_attribute_id = _Column(Integer, primary_key=True) + trial_id = _Column(Integer, ForeignKey("trials.trial_id")) + key = _Column(String(MAX_INDEXED_STRING_LENGTH)) + value_json = _Column(Text()) + + trial = orm.relationship( + TrialModel, backref=orm.backref("user_attributes", cascade="all, delete-orphan") + ) + + @classmethod + def find_by_trial_and_key( + cls, trial: TrialModel, key: str, session: orm.Session + ) -> "TrialUserAttributeModel" | None: + attribute = ( + session.query(cls) + .filter(cls.trial_id == trial.trial_id) + .filter(cls.key == key) + .one_or_none() + ) + + return attribute + + @classmethod + def where_trial_id( + cls, trial_id: int, session: orm.Session + ) -> list["TrialUserAttributeModel"]: + return session.query(cls).filter(cls.trial_id == trial_id).all() + + +class TrialSystemAttributeModel(BaseModel): + __tablename__ = "trial_system_attributes" + __table_args__: Any = (UniqueConstraint("trial_id", "key"),) + trial_system_attribute_id = _Column(Integer, primary_key=True) + trial_id = _Column(Integer, ForeignKey("trials.trial_id")) + key = _Column(String(MAX_INDEXED_STRING_LENGTH)) + value_json = _Column(Text()) + + trial = orm.relationship( + TrialModel, backref=orm.backref("system_attributes", cascade="all, delete-orphan") + ) + + @classmethod + def find_by_trial_and_key( + cls, trial: TrialModel, key: str, session: orm.Session + ) -> "TrialSystemAttributeModel" | None: + attribute = ( + session.query(cls) + .filter(cls.trial_id == trial.trial_id) + .filter(cls.key == key) + .one_or_none() + ) + + return attribute + + @classmethod + def where_trial_id( + cls, trial_id: int, session: orm.Session + ) -> list["TrialSystemAttributeModel"]: + return session.query(cls).filter(cls.trial_id == trial_id).all() + + +class TrialParamModel(BaseModel): + __tablename__ = "trial_params" + __table_args__: Any = (UniqueConstraint("trial_id", "param_name"),) + param_id = _Column(Integer, primary_key=True) + trial_id = _Column(Integer, ForeignKey("trials.trial_id")) + param_name = _Column(String(MAX_INDEXED_STRING_LENGTH)) + param_value = _Column(Float(precision=FLOAT_PRECISION)) + distribution_json = _Column(Text()) + + trial = orm.relationship( + TrialModel, backref=orm.backref("params", cascade="all, delete-orphan") + ) + + def check_and_add(self, session: orm.Session, study_id: int) -> None: + self._check_compatibility_with_previous_trial_param_distributions(session, study_id) + session.add(self) + + def _check_compatibility_with_previous_trial_param_distributions( + self, session: orm.Session, study_id: int + ) -> None: + previous_record = ( + session.query(TrialParamModel) + .join(TrialModel) + .filter(TrialModel.study_id == study_id) + .filter(TrialParamModel.param_name == self.param_name) + .first() + ) + if previous_record is not None: + distributions.check_distribution_compatibility( + distributions.json_to_distribution(previous_record.distribution_json), + distributions.json_to_distribution(self.distribution_json), + ) + + @classmethod + def find_by_trial_and_param_name( + cls, trial: TrialModel, param_name: str, session: orm.Session + ) -> "TrialParamModel" | None: + param_distribution = ( + session.query(cls) + .filter(cls.trial_id == trial.trial_id) + .filter(cls.param_name == param_name) + .one_or_none() + ) + + return param_distribution + + @classmethod + def find_or_raise_by_trial_and_param_name( + cls, trial: TrialModel, param_name: str, session: orm.Session + ) -> "TrialParamModel": + param_distribution = cls.find_by_trial_and_param_name(trial, param_name, session) + + if param_distribution is None: + raise KeyError(NOT_FOUND_MSG) + + return param_distribution + + @classmethod + def where_trial_id(cls, trial_id: int, session: orm.Session) -> list["TrialParamModel"]: + trial_params = session.query(cls).filter(cls.trial_id == trial_id).all() + + return trial_params + + +class TrialValueModel(BaseModel): + class TrialValueType(enum.Enum): + FINITE = 1 + INF_POS = 2 + INF_NEG = 3 + + __tablename__ = "trial_values" + __table_args__: Any = (UniqueConstraint("trial_id", "objective"),) + trial_value_id = _Column(Integer, primary_key=True) + trial_id = _Column(Integer, ForeignKey("trials.trial_id"), nullable=False) + objective = _Column(Integer, nullable=False) + value = _Column(Float(precision=FLOAT_PRECISION), nullable=True) + value_type = _Column(Enum(TrialValueType), nullable=False) + + trial = orm.relationship( + TrialModel, backref=orm.backref("values", cascade="all, delete-orphan") + ) + + @classmethod + def value_to_stored_repr(cls, value: float) -> tuple[float | None, TrialValueType]: + if value == float("inf"): + return None, cls.TrialValueType.INF_POS + elif value == float("-inf"): + return None, cls.TrialValueType.INF_NEG + else: + return value, cls.TrialValueType.FINITE + + @classmethod + def stored_repr_to_value(cls, value: float | None, float_type: TrialValueType) -> float: + if float_type == cls.TrialValueType.INF_POS: + assert value is None + return float("inf") + elif float_type == cls.TrialValueType.INF_NEG: + assert value is None + return float("-inf") + else: + assert float_type == cls.TrialValueType.FINITE + assert value is not None + return value + + @classmethod + def find_by_trial_and_objective( + cls, trial: TrialModel, objective: int, session: orm.Session + ) -> "TrialValueModel" | None: + trial_value = ( + session.query(cls) + .filter(cls.trial_id == trial.trial_id) + .filter(cls.objective == objective) + .one_or_none() + ) + + return trial_value + + @classmethod + def where_trial_id(cls, trial_id: int, session: orm.Session) -> list["TrialValueModel"]: + trial_values = ( + session.query(cls).filter(cls.trial_id == trial_id).order_by(asc(cls.objective)).all() + ) + + return trial_values + + +class TrialIntermediateValueModel(BaseModel): + class TrialIntermediateValueType(enum.Enum): + FINITE = 1 + INF_POS = 2 + INF_NEG = 3 + NAN = 4 + + __tablename__ = "trial_intermediate_values" + __table_args__: Any = (UniqueConstraint("trial_id", "step"),) + trial_intermediate_value_id = _Column(Integer, primary_key=True) + trial_id = _Column(Integer, ForeignKey("trials.trial_id"), nullable=False) + step = _Column(Integer, nullable=False) + intermediate_value = _Column(Float(precision=FLOAT_PRECISION), nullable=True) + intermediate_value_type = _Column(Enum(TrialIntermediateValueType), nullable=False) + + trial = orm.relationship( + TrialModel, backref=orm.backref("intermediate_values", cascade="all, delete-orphan") + ) + + @classmethod + def intermediate_value_to_stored_repr( + cls, value: float + ) -> tuple[float | None, TrialIntermediateValueType]: + if math.isnan(value): + return None, cls.TrialIntermediateValueType.NAN + elif value == float("inf"): + return None, cls.TrialIntermediateValueType.INF_POS + elif value == float("-inf"): + return None, cls.TrialIntermediateValueType.INF_NEG + else: + return value, cls.TrialIntermediateValueType.FINITE + + @classmethod + def stored_repr_to_intermediate_value( + cls, value: float | None, float_type: TrialIntermediateValueType + ) -> float: + if float_type == cls.TrialIntermediateValueType.NAN: + assert value is None + return float("nan") + elif float_type == cls.TrialIntermediateValueType.INF_POS: + assert value is None + return float("inf") + elif float_type == cls.TrialIntermediateValueType.INF_NEG: + assert value is None + return float("-inf") + else: + assert float_type == cls.TrialIntermediateValueType.FINITE + assert value is not None + return value + + @classmethod + def find_by_trial_and_step( + cls, trial: TrialModel, step: int, session: orm.Session + ) -> "TrialIntermediateValueModel" | None: + trial_intermediate_value = ( + session.query(cls) + .filter(cls.trial_id == trial.trial_id) + .filter(cls.step == step) + .one_or_none() + ) + + return trial_intermediate_value + + @classmethod + def where_trial_id( + cls, trial_id: int, session: orm.Session + ) -> list["TrialIntermediateValueModel"]: + trial_intermediate_values = session.query(cls).filter(cls.trial_id == trial_id).all() + + return trial_intermediate_values + + +class TrialHeartbeatModel(BaseModel): + __tablename__ = "trial_heartbeats" + __table_args__: Any = (UniqueConstraint("trial_id"),) + trial_heartbeat_id = _Column(Integer, primary_key=True) + trial_id = _Column(Integer, ForeignKey("trials.trial_id"), nullable=False) + heartbeat = _Column(DateTime, nullable=False, default=func.current_timestamp()) + + trial = orm.relationship( + TrialModel, backref=orm.backref("heartbeats", cascade="all, delete-orphan") + ) + + @classmethod + def where_trial_id( + cls, trial_id: int, session: orm.Session, for_update: bool = False + ) -> "TrialHeartbeatModel" | None: + + query = session.query(cls).filter(cls.trial_id == trial_id) + + if for_update: + query = query.with_for_update() + + return query.one_or_none() + + +class VersionInfoModel(BaseModel): + __tablename__ = "version_info" + # setting check constraint to ensure the number of rows is at most 1 + __table_args__: Any = (CheckConstraint("version_info_id=1"),) + version_info_id = _Column(Integer, primary_key=True, autoincrement=False, default=1) + schema_version = _Column(Integer) + library_version = _Column(String(MAX_VERSION_LENGTH)) + + @classmethod + def find(cls, session: orm.Session) -> "VersionInfoModel" | None: + version_info = session.query(cls).one_or_none() + return version_info diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/storage.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/storage.py new file mode 100644 index 0000000000000000000000000000000000000000..a8cb11855e3172d924f49ed97e93efb8e207f519 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/_rdb/storage.py @@ -0,0 +1,1175 @@ +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Callable +from collections.abc import Container +from collections.abc import Generator +from collections.abc import Iterable +from collections.abc import Sequence +from contextlib import contextmanager +import copy +from datetime import datetime +from datetime import timedelta +import json +import logging +import os +import random +import sqlite3 +import time +from typing import Any +from typing import TYPE_CHECKING +import uuid + +import optuna +from optuna import distributions +from optuna import version +from optuna._experimental import warn_experimental_argument +from optuna._imports import _LazyImport +from optuna._typing import JSONSerializable +from optuna.storages._base import BaseStorage +from optuna.storages._base import DEFAULT_STUDY_NAME_PREFIX +from optuna.storages._heartbeat import BaseHeartbeat +from optuna.study._frozen import FrozenStudy +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + import alembic.command as alembic_command + import alembic.config as alembic_config + import alembic.migration as alembic_migration + import alembic.script as alembic_script + import sqlalchemy + import sqlalchemy.dialects.mysql as sqlalchemy_dialects_mysql + import sqlalchemy.dialects.sqlite as sqlalchemy_dialects_sqlite + import sqlalchemy.exc as sqlalchemy_exc + import sqlalchemy.orm as sqlalchemy_orm + import sqlalchemy.sql.functions as sqlalchemy_sql_functions + + from optuna.storages._rdb import models +else: + alembic_command = _LazyImport("alembic.command") + alembic_config = _LazyImport("alembic.config") + alembic_migration = _LazyImport("alembic.migration") + alembic_script = _LazyImport("alembic.script") + + sqlalchemy = _LazyImport("sqlalchemy") + sqlalchemy_dialects_mysql = _LazyImport("sqlalchemy.dialects.mysql") + sqlalchemy_dialects_sqlite = _LazyImport("sqlalchemy.dialects.sqlite") + sqlalchemy_exc = _LazyImport("sqlalchemy.exc") + sqlalchemy_orm = _LazyImport("sqlalchemy.orm") + sqlalchemy_sql_functions = _LazyImport("sqlalchemy.sql.functions") + + models = _LazyImport("optuna.storages._rdb.models") + + +_logger = optuna.logging.get_logger(__name__) + + +@contextmanager +def _create_scoped_session( + scoped_session: "sqlalchemy_orm.scoped_session", + ignore_integrity_error: bool = False, +) -> Generator["sqlalchemy_orm.Session", None, None]: + session = scoped_session() + try: + yield session + session.commit() + except sqlalchemy_exc.IntegrityError as e: + session.rollback() + if ignore_integrity_error: + _logger.debug( + "Ignoring {}. This happens due to a timing issue among threads/processes/nodes. " + "Another one might have committed a record with the same key(s).".format(repr(e)) + ) + else: + raise + except sqlalchemy_exc.SQLAlchemyError as e: + session.rollback() + message = ( + "An exception is raised during the commit. " + "This typically happens due to invalid data in the commit, " + "e.g. exceeding max length. " + ) + raise optuna.exceptions.StorageInternalError(message) from e + except Exception: + session.rollback() + raise + finally: + session.close() + + +class RDBStorage(BaseStorage, BaseHeartbeat): + """Storage class for RDB backend. + + Note that library users can instantiate this class, but the attributes + provided by this class are not supposed to be directly accessed by them. + + Example: + + Create an :class:`~optuna.storages.RDBStorage` instance with customized + ``pool_size`` and ``timeout`` settings. + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -100, 100) + return x**2 + + + storage = optuna.storages.RDBStorage( + url="sqlite:///:memory:", + engine_kwargs={"pool_size": 20, "connect_args": {"timeout": 10}}, + ) + + study = optuna.create_study(storage=storage) + study.optimize(objective, n_trials=10) + + Args: + url: + URL of the storage. + engine_kwargs: + A dictionary of keyword arguments that is passed to + `sqlalchemy.engine.create_engine`_ function. + skip_compatibility_check: + Flag to skip schema compatibility check if set to :obj:`True`. + heartbeat_interval: + Interval to record the heartbeat. It is recorded every ``interval`` seconds. + ``heartbeat_interval`` must be :obj:`None` or a positive integer. + + .. note:: + Heartbeat mechanism is experimental. API would change in the future. + + .. note:: + The heartbeat is supposed to be used with :meth:`~optuna.study.Study.optimize`. + If you use :meth:`~optuna.study.Study.ask` and + :meth:`~optuna.study.Study.tell` instead, it will not work. + + grace_period: + Grace period before a running trial is failed from the last heartbeat. + ``grace_period`` must be :obj:`None` or a positive integer. + If it is :obj:`None`, the grace period will be `2 * heartbeat_interval`. + failed_trial_callback: + A callback function that is invoked after failing each stale trial. + The function must accept two parameters with the following types in this order: + :class:`~optuna.study.Study` and :class:`~optuna.trial.FrozenTrial`. + + .. note:: + The procedure to fail existing stale trials is called just before asking the + study for a new trial. + + skip_table_creation: + Flag to skip table creation if set to :obj:`True`. + + .. _sqlalchemy.engine.create_engine: + https://docs.sqlalchemy.org/en/latest/core/engines.html#sqlalchemy.create_engine + + .. note:: + If you use MySQL, `pool_pre_ping`_ will be set to :obj:`True` by default to prevent + connection timeout. You can turn it off with ``engine_kwargs['pool_pre_ping']=False``, but + it is recommended to keep the setting if execution time of your objective function is + longer than the `wait_timeout` of your MySQL configuration. + + .. _pool_pre_ping: + https://docs.sqlalchemy.org/en/13/core/engines.html#sqlalchemy.create_engine.params. + pool_pre_ping + + .. note:: + We would never recommend SQLite3 for parallel optimization. + Please see the FAQ :ref:`sqlite_concurrency` for details. + + .. note:: + Mainly in a cluster environment, running trials are often killed unexpectedly. + If you want to detect a failure of trials, please use the heartbeat + mechanism. Set ``heartbeat_interval``, ``grace_period``, and ``failed_trial_callback`` + appropriately according to your use case. For more details, please refer to the + :ref:`tutorial ` and `Example page + `__. + + .. seealso:: + You can use :class:`~optuna.storages.RetryFailedTrialCallback` to automatically retry + failed trials detected by heartbeat. + + """ + + def __init__( + self, + url: str, + engine_kwargs: dict[str, Any] | None = None, + skip_compatibility_check: bool = False, + *, + heartbeat_interval: int | None = None, + grace_period: int | None = None, + failed_trial_callback: Callable[["optuna.study.Study", FrozenTrial], None] | None = None, + skip_table_creation: bool = False, + ) -> None: + self.engine_kwargs = engine_kwargs or {} + self.url = self._fill_storage_url_template(url) + self.skip_compatibility_check = skip_compatibility_check + if heartbeat_interval is not None: + if heartbeat_interval <= 0: + raise ValueError("The value of `heartbeat_interval` should be a positive integer.") + else: + warn_experimental_argument("heartbeat_interval") + if grace_period is not None and grace_period <= 0: + raise ValueError("The value of `grace_period` should be a positive integer.") + self.heartbeat_interval = heartbeat_interval + self.grace_period = grace_period + self.failed_trial_callback = failed_trial_callback + + self._set_default_engine_kwargs_for_mysql(url, self.engine_kwargs) + + try: + self.engine = sqlalchemy.engine.create_engine(self.url, **self.engine_kwargs) + except ImportError as e: + raise ImportError( + "Failed to import DB access module for the specified storage URL. " + "Please install appropriate one." + ) from e + + self.scoped_session = sqlalchemy_orm.scoped_session( + sqlalchemy_orm.sessionmaker(bind=self.engine) + ) + if not skip_table_creation: + models.BaseModel.metadata.create_all(self.engine) + + self._version_manager = _VersionManager(self.url, self.engine, self.scoped_session) + if not skip_compatibility_check: + self._version_manager.check_table_schema_compatibility() + + def __getstate__(self) -> dict[Any, Any]: + state = self.__dict__.copy() + del state["scoped_session"] + del state["engine"] + del state["_version_manager"] + return state + + def __setstate__(self, state: dict[Any, Any]) -> None: + self.__dict__.update(state) + try: + self.engine = sqlalchemy.engine.create_engine(self.url, **self.engine_kwargs) + except ImportError as e: + raise ImportError( + "Failed to import DB access module for the specified storage URL. " + "Please install appropriate one." + ) from e + + self.scoped_session = sqlalchemy_orm.scoped_session( + sqlalchemy_orm.sessionmaker(bind=self.engine) + ) + models.BaseModel.metadata.create_all(self.engine) + self._version_manager = _VersionManager(self.url, self.engine, self.scoped_session) + if not self.skip_compatibility_check: + self._version_manager.check_table_schema_compatibility() + + def create_new_study( + self, directions: Sequence[StudyDirection], study_name: str | None = None + ) -> int: + try: + with _create_scoped_session(self.scoped_session) as session: + if study_name is None: + study_name = self._create_unique_study_name(session) + + direction_models = [ + models.StudyDirectionModel(objective=objective, direction=d) + for objective, d in enumerate(list(directions)) + ] + + session.add(models.StudyModel(study_name=study_name, directions=direction_models)) + + except sqlalchemy_exc.IntegrityError: + raise optuna.exceptions.DuplicatedStudyError( + "Another study with name '{}' already exists. " + "Please specify a different name, or reuse the existing one " + "by setting `load_if_exists` (for Python API) or " + "`--skip-if-exists` flag (for CLI).".format(study_name) + ) + + _logger.info("A new study created in RDB with name: {}".format(study_name)) + + return self.get_study_id_from_name(study_name) + + def delete_study(self, study_id: int) -> None: + with _create_scoped_session(self.scoped_session, True) as session: + study = models.StudyModel.find_or_raise_by_id(study_id, session) + session.delete(study) + + @staticmethod + def _create_unique_study_name(session: "sqlalchemy_orm.Session") -> str: + while True: + study_uuid = str(uuid.uuid4()) + study_name = DEFAULT_STUDY_NAME_PREFIX + study_uuid + study = models.StudyModel.find_by_name(study_name, session) + if study is None: + break + + return study_name + + def set_study_user_attr(self, study_id: int, key: str, value: Any) -> None: + with _create_scoped_session(self.scoped_session, True) as session: + study = models.StudyModel.find_or_raise_by_id(study_id, session) + attribute = models.StudyUserAttributeModel.find_by_study_and_key(study, key, session) + if attribute is None: + attribute = models.StudyUserAttributeModel( + study_id=study_id, key=key, value_json=json.dumps(value) + ) + session.add(attribute) + else: + attribute.value_json = json.dumps(value) + + def set_study_system_attr(self, study_id: int, key: str, value: JSONSerializable) -> None: + with _create_scoped_session(self.scoped_session, True) as session: + study = models.StudyModel.find_or_raise_by_id(study_id, session) + attribute = models.StudySystemAttributeModel.find_by_study_and_key(study, key, session) + if attribute is None: + attribute = models.StudySystemAttributeModel( + study_id=study_id, key=key, value_json=json.dumps(value) + ) + session.add(attribute) + else: + attribute.value_json = json.dumps(value) + + def get_study_id_from_name(self, study_name: str) -> int: + with _create_scoped_session(self.scoped_session) as session: + study = models.StudyModel.find_or_raise_by_name(study_name, session) + study_id = study.study_id + + return study_id + + def get_study_name_from_id(self, study_id: int) -> str: + with _create_scoped_session(self.scoped_session) as session: + study = models.StudyModel.find_or_raise_by_id(study_id, session) + study_name = study.study_name + + return study_name + + def get_study_directions(self, study_id: int) -> list[StudyDirection]: + with _create_scoped_session(self.scoped_session) as session: + study = models.StudyModel.find_or_raise_by_id(study_id, session) + directions = [d.direction for d in study.directions] + + return directions + + def get_study_user_attrs(self, study_id: int) -> dict[str, Any]: + with _create_scoped_session(self.scoped_session) as session: + # Ensure that that study exists. + models.StudyModel.find_or_raise_by_id(study_id, session) + attributes = models.StudyUserAttributeModel.where_study_id(study_id, session) + user_attrs = {attr.key: json.loads(attr.value_json) for attr in attributes} + + return user_attrs + + def get_study_system_attrs(self, study_id: int) -> dict[str, Any]: + with _create_scoped_session(self.scoped_session) as session: + # Ensure that that study exists. + models.StudyModel.find_or_raise_by_id(study_id, session) + attributes = models.StudySystemAttributeModel.where_study_id(study_id, session) + system_attrs = {attr.key: json.loads(attr.value_json) for attr in attributes} + + return system_attrs + + def get_trial_user_attrs(self, trial_id: int) -> dict[str, Any]: + with _create_scoped_session(self.scoped_session) as session: + # Ensure trial exists. + models.TrialModel.find_or_raise_by_id(trial_id, session) + + attributes = models.TrialUserAttributeModel.where_trial_id(trial_id, session) + user_attrs = {attr.key: json.loads(attr.value_json) for attr in attributes} + + return user_attrs + + def get_trial_system_attrs(self, trial_id: int) -> dict[str, Any]: + with _create_scoped_session(self.scoped_session) as session: + # Ensure trial exists. + models.TrialModel.find_or_raise_by_id(trial_id, session) + + attributes = models.TrialSystemAttributeModel.where_trial_id(trial_id, session) + system_attrs = {attr.key: json.loads(attr.value_json) for attr in attributes} + + return system_attrs + + def get_all_studies(self) -> list[FrozenStudy]: + with _create_scoped_session(self.scoped_session) as session: + studies = ( + session.query( + models.StudyModel.study_id, + models.StudyModel.study_name, + ) + .order_by(models.StudyModel.study_id) + .all() + ) + + _directions = defaultdict(list) + for direction_model in session.query(models.StudyDirectionModel).all(): + _directions[direction_model.study_id].append(direction_model.direction) + + _user_attrs = defaultdict(list) + for attribute_model in session.query(models.StudyUserAttributeModel).all(): + _user_attrs[attribute_model.study_id].append(attribute_model) + + _system_attrs = defaultdict(list) + for attribute_model in session.query(models.StudySystemAttributeModel).all(): + _system_attrs[attribute_model.study_id].append(attribute_model) + + frozen_studies = [] + for study in studies: + directions = _directions[study.study_id] + user_attrs = _user_attrs.get(study.study_id, []) + system_attrs = _system_attrs.get(study.study_id, []) + frozen_studies.append( + FrozenStudy( + study_name=study.study_name, + direction=None, + directions=directions, + user_attrs={i.key: json.loads(i.value_json) for i in user_attrs}, + system_attrs={i.key: json.loads(i.value_json) for i in system_attrs}, + study_id=study.study_id, + ) + ) + + return frozen_studies + + def create_new_trial(self, study_id: int, template_trial: FrozenTrial | None = None) -> int: + return self._create_new_trial(study_id, template_trial)._trial_id + + def _create_new_trial( + self, study_id: int, template_trial: FrozenTrial | None = None + ) -> FrozenTrial: + """Create a new trial and returns a :class:`~optuna.trial.FrozenTrial`. + + Args: + study_id: + Study id. + template_trial: + A :class:`~optuna.trial.FrozenTrial` with default values for trial attributes. + + Returns: + A :class:`~optuna.trial.FrozenTrial` instance. + + """ + + def _create_frozen_trial( + trial: "models.TrialModel", template_trial: FrozenTrial | None + ) -> FrozenTrial: + if template_trial: + frozen = copy.deepcopy(template_trial) + frozen.number = trial.number + frozen.datetime_start = trial.datetime_start + frozen._trial_id = trial.trial_id + return frozen + return FrozenTrial( + number=trial.number, + state=trial.state, + value=None, + values=None, + datetime_start=trial.datetime_start, + datetime_complete=None, + params={}, + distributions={}, + user_attrs={}, + system_attrs={}, + intermediate_values={}, + trial_id=trial.trial_id, + ) + + # Retry maximum five times. Deadlocks may occur in distributed environments. + MAX_RETRIES = 5 + for n_retries in range(1, MAX_RETRIES + 1): + try: + with _create_scoped_session(self.scoped_session) as session: + # This lock is necessary because the trial creation is not an atomic operation + # and the calculation of trial.number is prone to race conditions. + models.StudyModel.find_or_raise_by_id(study_id, session, for_update=True) + trial = self._get_prepared_new_trial(study_id, template_trial, session) + return _create_frozen_trial(trial, template_trial) + # sqlalchemy_exc.OperationalError is converted to ``StorageInternalError``. + except optuna.exceptions.StorageInternalError as e: + # ``OperationalError`` happens either by (1) invalid inputs, e.g., too long string, + # or (2) timeout error, which relates to deadlock. Although Error (1) is not + # intended to be caught here, it must be fixed to use RDBStorage anyways. + if n_retries == MAX_RETRIES: + raise e + + # Optuna defers to the DB administrator to reduce DB server congestion, hence + # Optuna simply uses non-exponential backoff here for retries caused by deadlock. + time.sleep(random.random() * 2.0) + + assert False, "Should not be reached." + + def _get_prepared_new_trial( + self, + study_id: int, + template_trial: FrozenTrial | None, + session: "sqlalchemy_orm.Session", + ) -> "models.TrialModel": + if template_trial is None: + trial = models.TrialModel( + study_id=study_id, + number=None, + state=TrialState.RUNNING, + datetime_start=datetime.now(), + ) + else: + # Because only `RUNNING` trials can be updated, + # we temporarily set the state of the new trial to `RUNNING`. + # After all fields of the trial have been updated, + # the state is set to `template_trial.state`. + temp_state = TrialState.RUNNING + + trial = models.TrialModel( + study_id=study_id, + number=None, + state=temp_state, + datetime_start=template_trial.datetime_start, + datetime_complete=template_trial.datetime_complete, + ) + + session.add(trial) + + # Flush the session cache to reflect the above addition operation to + # the current RDB transaction. + # + # Without flushing, the following operations (e.g, `_set_trial_param_without_commit`) + # will fail because the target trial doesn't exist in the storage yet. + session.flush() + + if template_trial is not None: + if template_trial.values is not None and len(template_trial.values) > 1: + for objective, value in enumerate(template_trial.values): + self._set_trial_value_without_commit(session, trial.trial_id, objective, value) + elif template_trial.value is not None: + self._set_trial_value_without_commit( + session, trial.trial_id, 0, template_trial.value + ) + + for param_name, param_value in template_trial.params.items(): + distribution = template_trial.distributions[param_name] + param_value_in_internal_repr = distribution.to_internal_repr(param_value) + self._set_trial_param_without_commit( + session, trial.trial_id, param_name, param_value_in_internal_repr, distribution + ) + + for key, value in template_trial.user_attrs.items(): + self._set_trial_attr_without_commit( + session, models.TrialUserAttributeModel, trial.trial_id, key, value + ) + + for key, value in template_trial.system_attrs.items(): + self._set_trial_attr_without_commit( + session, models.TrialSystemAttributeModel, trial.trial_id, key, value + ) + + for step, intermediate_value in template_trial.intermediate_values.items(): + self._set_trial_intermediate_value_without_commit( + session, trial.trial_id, step, intermediate_value + ) + + trial.state = template_trial.state + + trial.number = trial.count_past_trials(session) + session.add(trial) + + return trial + + def set_trial_param( + self, + trial_id: int, + param_name: str, + param_value_internal: float, + distribution: distributions.BaseDistribution, + ) -> None: + with _create_scoped_session(self.scoped_session, True) as session: + self._set_trial_param_without_commit( + session, trial_id, param_name, param_value_internal, distribution + ) + + def _set_trial_param_without_commit( + self, + session: "sqlalchemy_orm.Session", + trial_id: int, + param_name: str, + param_value_internal: float, + distribution: distributions.BaseDistribution, + ) -> None: + trial = models.TrialModel.find_or_raise_by_id(trial_id, session) + self.check_trial_is_updatable(trial_id, trial.state) + + trial_param = models.TrialParamModel( + trial_id=trial_id, + param_name=param_name, + param_value=param_value_internal, + distribution_json=distributions.distribution_to_json(distribution), + ) + + trial_param.check_and_add(session, trial.study_id) + + def get_trial_param(self, trial_id: int, param_name: str) -> float: + with _create_scoped_session(self.scoped_session) as session: + trial = models.TrialModel.find_or_raise_by_id(trial_id, session) + trial_param = models.TrialParamModel.find_or_raise_by_trial_and_param_name( + trial, param_name, session + ) + param_value = trial_param.param_value + + return param_value + + def set_trial_state_values( + self, trial_id: int, state: TrialState, values: Sequence[float] | None = None + ) -> bool: + try: + with _create_scoped_session(self.scoped_session) as session: + trial = models.TrialModel.find_or_raise_by_id(trial_id, session, for_update=True) + self.check_trial_is_updatable(trial_id, trial.state) + + if values is not None: + for objective, v in enumerate(values): + self._set_trial_value_without_commit(session, trial_id, objective, v) + + if state == TrialState.RUNNING and trial.state != TrialState.WAITING: + return False + + trial.state = state + + if state == TrialState.RUNNING: + trial.datetime_start = datetime.now() + + if state.is_finished(): + trial.datetime_complete = datetime.now() + except sqlalchemy_exc.IntegrityError: + return False + return True + + def _set_trial_value_without_commit( + self, session: "sqlalchemy_orm.Session", trial_id: int, objective: int, value: float + ) -> None: + trial = models.TrialModel.find_or_raise_by_id(trial_id, session) + self.check_trial_is_updatable(trial_id, trial.state) + stored_value, value_type = models.TrialValueModel.value_to_stored_repr(value) + + trial_value = models.TrialValueModel.find_by_trial_and_objective(trial, objective, session) + if trial_value is None: + trial_value = models.TrialValueModel( + trial_id=trial_id, objective=objective, value=stored_value, value_type=value_type + ) + session.add(trial_value) + else: + trial_value.value = stored_value + trial_value.value_type = value_type + + def set_trial_intermediate_value( + self, trial_id: int, step: int, intermediate_value: float + ) -> None: + with _create_scoped_session(self.scoped_session, True) as session: + self._set_trial_intermediate_value_without_commit( + session, trial_id, step, intermediate_value + ) + + def _set_trial_intermediate_value_without_commit( + self, + session: "sqlalchemy_orm.Session", + trial_id: int, + step: int, + intermediate_value: float, + ) -> None: + trial = models.TrialModel.find_or_raise_by_id(trial_id, session) + self.check_trial_is_updatable(trial_id, trial.state) + + ( + stored_value, + value_type, + ) = models.TrialIntermediateValueModel.intermediate_value_to_stored_repr( + intermediate_value + ) + trial_intermediate_value = models.TrialIntermediateValueModel.find_by_trial_and_step( + trial, step, session + ) + if trial_intermediate_value is None: + trial_intermediate_value = models.TrialIntermediateValueModel( + trial_id=trial_id, + step=step, + intermediate_value=stored_value, + intermediate_value_type=value_type, + ) + session.add(trial_intermediate_value) + else: + trial_intermediate_value.intermediate_value = stored_value + trial_intermediate_value.intermediate_value_type = value_type + + def set_trial_user_attr(self, trial_id: int, key: str, value: Any) -> None: + with _create_scoped_session(self.scoped_session, True) as session: + self._set_trial_attr_without_commit( + session, + models.TrialUserAttributeModel, + trial_id, + key, + value, + ) + + def set_trial_system_attr(self, trial_id: int, key: str, value: JSONSerializable) -> None: + with _create_scoped_session(self.scoped_session, True) as session: + self._set_trial_attr_without_commit( + session, + models.TrialSystemAttributeModel, + trial_id, + key, + value, + ) + + def _set_trial_attr_without_commit( + self, + session: "sqlalchemy_orm.Session", + model_cls: type[models.TrialUserAttributeModel | models.TrialSystemAttributeModel], + trial_id: int, + key: str, + value: Any, + ) -> None: + trial = models.TrialModel.find_or_raise_by_id(trial_id, session) + self.check_trial_is_updatable(trial_id, trial.state) + + if self.engine.name == "mysql": + mysql_insert_stmt = sqlalchemy_dialects_mysql.insert(model_cls).values( + trial_id=trial_id, key=key, value_json=json.dumps(value) + ) + mysql_upsert_stmt = mysql_insert_stmt.on_duplicate_key_update( + value_json=mysql_insert_stmt.inserted.value_json + ) + session.execute(mysql_upsert_stmt) + elif self.engine.name == "sqlite" and sqlite3.sqlite_version_info >= (3, 24, 0): + sqlite_insert_stmt = sqlalchemy_dialects_sqlite.insert(model_cls).values( + trial_id=trial_id, key=key, value_json=json.dumps(value) + ) + sqlite_upsert_stmt = sqlite_insert_stmt.on_conflict_do_update( + index_elements=[model_cls.trial_id, model_cls.key], + set_=dict(value_json=sqlite_insert_stmt.excluded.value_json), + ) + session.execute(sqlite_upsert_stmt) + else: + # TODO(porink0424): Add support for other databases, e.g., PostgreSQL. + attribute = model_cls.find_by_trial_and_key(trial, key, session) + if attribute is None: + attribute = model_cls(trial_id=trial_id, key=key, value_json=json.dumps(value)) + session.add(attribute) + else: + attribute.value_json = json.dumps(value) + + def get_trial_id_from_study_id_trial_number(self, study_id: int, trial_number: int) -> int: + with _create_scoped_session(self.scoped_session) as session: + trial_id = ( + session.query(models.TrialModel.trial_id) + .filter( + models.TrialModel.number == trial_number, + models.TrialModel.study_id == study_id, + ) + .one_or_none() + ) + if trial_id is None: + raise KeyError( + "No trial with trial number {} exists in study with study_id {}.".format( + trial_number, study_id + ) + ) + return trial_id[0] + + def get_trial(self, trial_id: int) -> FrozenTrial: + with _create_scoped_session(self.scoped_session) as session: + trial_model = models.TrialModel.find_or_raise_by_id(trial_id, session) + frozen_trial = self._build_frozen_trial_from_trial_model(trial_model) + + return frozen_trial + + def get_all_trials( + self, + study_id: int, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + ) -> list[FrozenTrial]: + trials = self._get_trials(study_id, states, set(), -1) + + return copy.deepcopy(trials) if deepcopy else trials + + def _get_trials( + self, + study_id: int, + states: Container[TrialState] | None, + included_trial_ids: set[int], + trial_id_greater_than: int, + ) -> list[FrozenTrial]: + included_trial_ids = set( + trial_id for trial_id in included_trial_ids if trial_id <= trial_id_greater_than + ) + + with _create_scoped_session(self.scoped_session) as session: + # Ensure that the study exists. + models.StudyModel.find_or_raise_by_id(study_id, session) + query = ( + session.query(models.TrialModel) + .options(sqlalchemy_orm.selectinload(models.TrialModel.params)) + .options(sqlalchemy_orm.selectinload(models.TrialModel.values)) + .options(sqlalchemy_orm.selectinload(models.TrialModel.user_attributes)) + .options(sqlalchemy_orm.selectinload(models.TrialModel.system_attributes)) + .options(sqlalchemy_orm.selectinload(models.TrialModel.intermediate_values)) + .filter( + models.TrialModel.study_id == study_id, + ) + ) + + if states is not None: + # This assertion is for type checkers, since `states` is required to be Container + # in the base class while `models.TrialModel.state.in_` requires Iterable. + assert isinstance(states, Iterable) + query = query.filter(models.TrialModel.state.in_(states)) + + try: + if len(included_trial_ids) > 0 and trial_id_greater_than > -1: + _query = query.filter( + sqlalchemy.or_( + models.TrialModel.trial_id.in_(included_trial_ids), + models.TrialModel.trial_id > trial_id_greater_than, + ) + ) + elif trial_id_greater_than > -1: + _query = query.filter(models.TrialModel.trial_id > trial_id_greater_than) + else: + _query = query + trial_models = _query.order_by(models.TrialModel.trial_id).all() + except sqlalchemy_exc.OperationalError as e: + # Likely exceeding the number of maximum allowed variables using IN. + # This number differ between database dialects. For SQLite for instance, see + # https://www.sqlite.org/limits.html and the section describing + # SQLITE_MAX_VARIABLE_NUMBER. + + _logger.warning( + "Caught an error from sqlalchemy: {}. Falling back to a slower alternative. " + "".format(str(e)) + ) + + trial_models = query.order_by(models.TrialModel.trial_id).all() + trial_models = [ + t + for t in trial_models + if t.trial_id in included_trial_ids or t.trial_id > trial_id_greater_than + ] + + trials = [self._build_frozen_trial_from_trial_model(trial) for trial in trial_models] + + return trials + + def _build_frozen_trial_from_trial_model(self, trial: "models.TrialModel") -> FrozenTrial: + values: list[float] | None + if trial.values: + values = [0 for _ in trial.values] + for value_model in trial.values: + values[value_model.objective] = models.TrialValueModel.stored_repr_to_value( + value_model.value, value_model.value_type + ) + else: + values = None + + params = sorted(trial.params, key=lambda p: p.param_id) + + return FrozenTrial( + number=trial.number, + state=trial.state, + value=None, + values=values, + datetime_start=trial.datetime_start, + datetime_complete=trial.datetime_complete, + params={ + p.param_name: distributions.json_to_distribution( + p.distribution_json + ).to_external_repr(p.param_value) + for p in params + }, + distributions={ + p.param_name: distributions.json_to_distribution(p.distribution_json) + for p in params + }, + user_attrs={attr.key: json.loads(attr.value_json) for attr in trial.user_attributes}, + system_attrs={ + attr.key: json.loads(attr.value_json) for attr in trial.system_attributes + }, + intermediate_values={ + v.step: models.TrialIntermediateValueModel.stored_repr_to_intermediate_value( + v.intermediate_value, v.intermediate_value_type + ) + for v in trial.intermediate_values + }, + trial_id=trial.trial_id, + ) + + def get_best_trial(self, study_id: int) -> FrozenTrial: + with _create_scoped_session(self.scoped_session) as session: + _directions = self.get_study_directions(study_id) + if len(_directions) > 1: + raise RuntimeError( + "Best trial can be obtained only for single-objective optimization." + ) + direction = _directions[0] + + if direction == StudyDirection.MAXIMIZE: + trial_id = models.TrialModel.find_max_value_trial_id(study_id, 0, session) + else: + trial_id = models.TrialModel.find_min_value_trial_id(study_id, 0, session) + + return self.get_trial(trial_id) + + @staticmethod + def _set_default_engine_kwargs_for_mysql(url: str, engine_kwargs: dict[str, Any]) -> None: + # Skip if RDB is not MySQL. + if not url.startswith("mysql"): + return + + # Do not overwrite value. + if "pool_pre_ping" in engine_kwargs: + return + + # If True, the connection pool checks liveness of connections at every checkout. + # Without this option, trials that take longer than `wait_timeout` may cause connection + # errors. For further details, please refer to the following document: + # https://docs.sqlalchemy.org/en/13/core/pooling.html#pool-disconnects-pessimistic + engine_kwargs["pool_pre_ping"] = True + _logger.debug("pool_pre_ping=True was set to engine_kwargs to prevent connection timeout.") + + @staticmethod + def _fill_storage_url_template(template: str) -> str: + return template.format(SCHEMA_VERSION=models.SCHEMA_VERSION) + + def remove_session(self) -> None: + """Removes the current session. + + A session is stored in SQLAlchemy's ThreadLocalRegistry for each thread. This method + closes and removes the session which is associated to the current thread. Particularly, + under multi-thread use cases, it is important to call this method *from each thread*. + Otherwise, all sessions and their associated DB connections are destructed by a thread + that occasionally invoked the garbage collector. By default, it is not allowed to touch + a SQLite connection from threads other than the thread that created the connection. + Therefore, we need to explicitly close the connection from each thread. + + """ + + self.scoped_session.remove() + + def upgrade(self) -> None: + """Upgrade the storage schema.""" + + self._version_manager.upgrade() + + def get_current_version(self) -> str: + """Return the schema version currently used by this storage.""" + + return self._version_manager.get_current_version() + + def get_head_version(self) -> str: + """Return the latest schema version.""" + + return self._version_manager.get_head_version() + + def get_all_versions(self) -> list[str]: + """Return the schema version list.""" + + return self._version_manager.get_all_versions() + + def record_heartbeat(self, trial_id: int) -> None: + with _create_scoped_session(self.scoped_session, True) as session: + # Fetch heartbeat with read-only. + heartbeat = models.TrialHeartbeatModel.where_trial_id(trial_id, session) + if heartbeat is None: # heartbeat record does not exist. + heartbeat = models.TrialHeartbeatModel(trial_id=trial_id) + session.add(heartbeat) + else: + # Re-fetch the existing heartbeat with the write authorization. + heartbeat = models.TrialHeartbeatModel.where_trial_id(trial_id, session, True) + assert heartbeat is not None + heartbeat.heartbeat = session.execute(sqlalchemy.func.now()).scalar() + + def _get_stale_trial_ids(self, study_id: int) -> list[int]: + assert self.heartbeat_interval is not None + if self.grace_period is None: + grace_period = 2 * self.heartbeat_interval + else: + grace_period = self.grace_period + stale_trial_ids = [] + + with _create_scoped_session(self.scoped_session, True) as session: + current_heartbeat = session.execute(sqlalchemy.func.now()).scalar() + assert current_heartbeat is not None + # Added the following line to prevent mixing of timezone-aware and timezone-naive + # `datetime` in PostgreSQL. See + # https://github.com/optuna/optuna/pull/2190#issuecomment-766605088 for details + current_heartbeat = current_heartbeat.replace(tzinfo=None) + + running_trials = ( + session.query(models.TrialModel) + .options(sqlalchemy_orm.selectinload(models.TrialModel.heartbeats)) + .filter(models.TrialModel.state == TrialState.RUNNING) + .filter(models.TrialModel.study_id == study_id) + .all() + ) + for trial in running_trials: + if len(trial.heartbeats) == 0: + continue + assert len(trial.heartbeats) == 1 + heartbeat = trial.heartbeats[0].heartbeat + if current_heartbeat - heartbeat > timedelta(seconds=grace_period): + stale_trial_ids.append(trial.trial_id) + + return stale_trial_ids + + def get_heartbeat_interval(self) -> int | None: + return self.heartbeat_interval + + def get_failed_trial_callback( + self, + ) -> Callable[["optuna.study.Study", FrozenTrial], None] | None: + return self.failed_trial_callback + + +class _VersionManager: + def __init__( + self, + url: str, + engine: "sqlalchemy.engine.Engine", + scoped_session: "sqlalchemy_orm.scoped_session", + ) -> None: + self.url = url + self.engine = engine + self.scoped_session = scoped_session + self._init_version_info_model() + self._init_alembic() + + def _init_version_info_model(self) -> None: + with _create_scoped_session(self.scoped_session, True) as session: + version_info = models.VersionInfoModel.find(session) + if version_info is not None: + return + + version_info = models.VersionInfoModel( + schema_version=models.SCHEMA_VERSION, + library_version=version.__version__, + ) + session.add(version_info) + + def _init_alembic(self) -> None: + logging.getLogger("alembic").setLevel(logging.WARN) + + with self.engine.connect() as connection: + context = alembic_migration.MigrationContext.configure(connection) + is_initialized = context.get_current_revision() is not None + + if is_initialized: + # The `alembic_version` table already exists and is not empty. + return + + if self._is_alembic_supported(): + revision = self.get_head_version() + else: + # The storage has been created before alembic is introduced. + revision = self._get_base_version() + + self._set_alembic_revision(revision) + + def _set_alembic_revision(self, revision: str) -> None: + with self.engine.connect() as connection: + context = alembic_migration.MigrationContext.configure(connection) + with connection.begin(): + script = self._create_alembic_script() + context.stamp(script, revision) + + def check_table_schema_compatibility(self) -> None: + with _create_scoped_session(self.scoped_session) as session: + # NOTE: After invocation of `_init_version_info_model` method, + # it is ensured that a `VersionInfoModel` entry exists. + version_info = models.VersionInfoModel.find(session) + + assert version_info is not None + + current_version = self.get_current_version() + head_version = self.get_head_version() + if current_version == head_version: + return + + message = ( + "The runtime optuna version {} is no longer compatible with the table schema " + "(set up by optuna {}). ".format(version.__version__, version_info.library_version) + ) + known_versions = self.get_all_versions() + + if current_version in known_versions: + message += ( + "Please execute `$ optuna storage upgrade --storage $STORAGE_URL` " + "for upgrading the storage." + ) + else: + message += ( + "Please try updating optuna to the latest version by `$ pip install -U optuna`." + ) + + raise RuntimeError(message) + + def get_current_version(self) -> str: + with self.engine.connect() as connection: + context = alembic_migration.MigrationContext.configure(connection) + version = context.get_current_revision() + assert version is not None + + return version + + def get_head_version(self) -> str: + script = self._create_alembic_script() + current_head = script.get_current_head() + assert current_head is not None + return current_head + + def _get_base_version(self) -> str: + script = self._create_alembic_script() + base = script.get_base() + assert base is not None, "There should be exactly one base, i.e. v0.9.0.a." + return base + + def get_all_versions(self) -> list[str]: + script = self._create_alembic_script() + return [r.revision for r in script.walk_revisions()] + + def upgrade(self) -> None: + config = self._create_alembic_config() + alembic_command.upgrade(config, "head") + + with _create_scoped_session(self.scoped_session, True) as session: + version_info = models.VersionInfoModel.find(session) + assert version_info is not None + version_info.schema_version = models.SCHEMA_VERSION + version_info.library_version = version.__version__ + + def _is_alembic_supported(self) -> bool: + with _create_scoped_session(self.scoped_session) as session: + version_info = models.VersionInfoModel.find(session) + + if version_info is None: + # `None` means this storage was created just now. + return True + + return version_info.schema_version == models.SCHEMA_VERSION + + def _create_alembic_script(self) -> "alembic_script.ScriptDirectory": + config = self._create_alembic_config() + script = alembic_script.ScriptDirectory.from_config(config) + return script + + def _create_alembic_config(self) -> "alembic_config.Config": + alembic_dir = os.path.join(os.path.dirname(__file__), "alembic") + + config = alembic_config.Config(os.path.join(os.path.dirname(__file__), "alembic.ini")) + config.set_main_option("script_location", escape_alembic_config_value(alembic_dir)) + config.set_main_option("sqlalchemy.url", escape_alembic_config_value(self.url)) + return config + + +def escape_alembic_config_value(value: str) -> str: + # We must escape '%' in a value string because the character + # is regarded as the trigger of variable expansion. + # Please see the documentation of `configparser.BasicInterpolation` for more details. + return value.replace("%", "%%") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..61831b97b8875c325690886057e4c417d954e6b6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/__init__.py @@ -0,0 +1,19 @@ +from optuna.storages.journal._base import BaseJournalBackend +from optuna.storages.journal._file import JournalFileBackend +from optuna.storages.journal._file import JournalFileOpenLock +from optuna.storages.journal._file import JournalFileSymlinkLock +from optuna.storages.journal._redis import JournalRedisBackend +from optuna.storages.journal._storage import JournalStorage + + +# NOTE(nabenabe0928): Do not add objects deprecated at v4.0.0 here, e.g., JournalFileStorage +# because ``optuna/storages/journal`` was added at v4.0.0 and it will be confusing to keep them in +# the non-deprecated directory. +__all__ = [ + "JournalFileBackend", + "BaseJournalBackend", + "JournalFileOpenLock", + "JournalFileSymlinkLock", + "JournalRedisBackend", + "JournalStorage", +] 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b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_base.py @@ -0,0 +1,86 @@ +from __future__ import annotations + +import abc +from typing import Any + +from optuna._deprecated import deprecated_class + + +class BaseJournalBackend(abc.ABC): + """Base class for Journal storages. + + Storage classes implementing this base class must guarantee process safety. This means, + multiple processes might concurrently call ``read_logs`` and ``append_logs``. If the + backend storage does not internally support mutual exclusion mechanisms, such as locks, + you might want to use :class:`~optuna.storages.journal.JournalFileSymlinkLock` or + :class:`~optuna.storages.journal.JournalFileOpenLock` for creating a critical section. + + """ + + @abc.abstractmethod + def read_logs(self, log_number_from: int) -> list[dict[str, Any]]: + """Read logs with a log number greater than or equal to ``log_number_from``. + + If ``log_number_from`` is 0, read all the logs. + + Args: + log_number_from: + A non-negative integer value indicating which logs to read. + + Returns: + Logs with log number greater than or equal to ``log_number_from``. + """ + + raise NotImplementedError + + @abc.abstractmethod + def append_logs(self, logs: list[dict[str, Any]]) -> None: + """Append logs to the backend. + + Args: + logs: + A list that contains json-serializable logs. + """ + + raise NotImplementedError + + +class BaseJournalSnapshot(abc.ABC): + """Optional base class for Journal storages. + + Storage classes implementing this base class may work faster when + constructing the internal state from the large amount of logs. + """ + + @abc.abstractmethod + def save_snapshot(self, snapshot: bytes) -> None: + """Save snapshot to the backend. + + Args: + snapshot: A serialized snapshot (bytes) + """ + raise NotImplementedError + + @abc.abstractmethod + def load_snapshot(self) -> bytes | None: + """Load snapshot from the backend. + + Returns: + A serialized snapshot (bytes) if found, otherwise :obj:`None`. + """ + raise NotImplementedError + + +@deprecated_class( + "4.0.0", "6.0.0", text="Use :class:`~optuna.storages.journal.BaseJournalBackend` instead." +) +class BaseJournalLogStorage(BaseJournalBackend): + """Base class for Journal storages. + + Storage classes implementing this base class must guarantee process safety. This means, + multiple processes might concurrently call ``read_logs`` and ``append_logs``. If the + backend storage does not internally support mutual exclusion mechanisms, such as locks, + you might want to use :class:`~optuna.storages.journal.JournalFileSymlinkLock` or + :class:`~optuna.storages.journal.JournalFileOpenLock` for creating a critical section. + + """ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_file.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_file.py new file mode 100644 index 0000000000000000000000000000000000000000..4449508e46e1c494b533e12c2d7d543049d6619a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_file.py @@ -0,0 +1,319 @@ +from __future__ import annotations + +import abc +from collections.abc import Iterator +from contextlib import contextmanager +import errno +import json +import os +import time +from typing import Any +import uuid +import warnings + +from optuna._deprecated import deprecated_class +from optuna.storages.journal._base import BaseJournalBackend + + +LOCK_FILE_SUFFIX = ".lock" +RENAME_FILE_SUFFIX = ".rename" + + +class JournalFileBackend(BaseJournalBackend): + """File storage class for Journal log backend. + + Compared to SQLite3, the benefit of this backend is that it is more suitable for + environments where the file system does not support ``fcntl()`` file locking. + For example, as written in the `SQLite3 FAQ `__, + SQLite3 might not work on NFS (Network File System) since ``fcntl()`` file locking + is broken on many NFS implementations. In such scenarios, this backend provides + several workarounds for locking files. For more details, refer to the `Medium blog post`_. + + .. _Medium blog post: https://medium.com/optuna/distributed-optimization-via-nfs\ + -using-optunas-new-operation-based-logging-storage-9815f9c3f932 + + It's important to note that, similar to SQLite3, this class doesn't support a high + level of write concurrency, as outlined in the `SQLAlchemy documentation`_. However, + in typical situations where the objective function is computationally expensive, Optuna + users don't need to be concerned about this limitation. The reason being, the write + operations are not the bottleneck as long as the objective function doesn't invoke + :meth:`~optuna.trial.Trial.report` and :meth:`~optuna.trial.Trial.set_user_attr` excessively. + + .. _SQLAlchemy documentation: https://docs.sqlalchemy.org/en/20/dialects/sqlite.html\ + #database-locking-behavior-concurrency + + Args: + file_path: + Path of file to persist the log to. + + lock_obj: + Lock object for process exclusivity. An instance of + :class:`~optuna.storages.journal.JournalFileSymlinkLock` and + :class:`~optuna.storages.journal.JournalFileOpenLock` can be passed. + """ + + def __init__(self, file_path: str, lock_obj: BaseJournalFileLock | None = None) -> None: + self._file_path: str = file_path + self._lock = lock_obj or JournalFileSymlinkLock(self._file_path) + if not os.path.exists(self._file_path): + open(self._file_path, "ab").close() # Create a file if it does not exist. + self._log_number_offset: dict[int, int] = {0: 0} + + def read_logs(self, log_number_from: int) -> list[dict[str, Any]]: + logs = [] + with open(self._file_path, "rb") as f: + # Maintain remaining_log_size to allow writing by another process + # while reading the log. + remaining_log_size = os.stat(self._file_path).st_size + log_number_start = 0 + if log_number_from in self._log_number_offset: + f.seek(self._log_number_offset[log_number_from]) + log_number_start = log_number_from + remaining_log_size -= self._log_number_offset[log_number_from] + + last_decode_error = None + for log_number, line in enumerate(f, start=log_number_start): + byte_len = len(line) + remaining_log_size -= byte_len + if remaining_log_size < 0: + break + if last_decode_error is not None: + raise last_decode_error + if log_number + 1 not in self._log_number_offset: + self._log_number_offset[log_number + 1] = ( + self._log_number_offset[log_number] + byte_len + ) + if log_number < log_number_from: + continue + + # Ensure that each line ends with line separators (\n, \r\n). + if not line.endswith(b"\n"): + last_decode_error = ValueError("Invalid log format.") + del self._log_number_offset[log_number + 1] + continue + try: + logs.append(json.loads(line)) + except json.JSONDecodeError as err: + last_decode_error = err + del self._log_number_offset[log_number + 1] + return logs + + def append_logs(self, logs: list[dict[str, Any]]) -> None: + with get_lock_file(self._lock): + what_to_write = ( + "\n".join([json.dumps(log, separators=(",", ":")) for log in logs]) + "\n" + ) + with open(self._file_path, "ab") as f: + f.write(what_to_write.encode("utf-8")) + f.flush() + os.fsync(f.fileno()) + + +class BaseJournalFileLock(abc.ABC): + @abc.abstractmethod + def acquire(self) -> bool: + raise NotImplementedError + + @abc.abstractmethod + def release(self) -> None: + raise NotImplementedError + + +class JournalFileSymlinkLock(BaseJournalFileLock): + """Lock class for synchronizing processes for NFSv2 or later. + + On acquiring the lock, link system call is called to create an exclusive file. The file is + deleted when the lock is released. In NFS environments prior to NFSv3, use this instead of + :class:`~optuna.storages.journal.JournalFileOpenLock`. + + Args: + filepath: + The path of the file whose race condition must be protected. + grace_period: + Grace period before an existing lock is forcibly released. + """ + + def __init__(self, filepath: str, grace_period: int | None = 30) -> None: + self._lock_target_file = filepath + self._lock_file = filepath + LOCK_FILE_SUFFIX + if grace_period is not None: + if grace_period <= 0: + raise ValueError("The value of `grace_period` should be a positive integer.") + if grace_period < 3: + warnings.warn("The value of `grace_period` might be too small. ") + self.grace_period = grace_period + + def acquire(self) -> bool: + """Acquire a lock in a blocking way by creating a symbolic link of a file. + + Returns: + :obj:`True` if it succeeded in creating a symbolic link of ``self._lock_target_file``. + """ + sleep_secs = 0.001 + last_update_monotonic_time = time.monotonic() + mtime = None + while True: + try: + os.symlink(self._lock_target_file, self._lock_file) + return True + except OSError as err: + if err.errno == errno.EEXIST: + if self.grace_period is not None: + try: + current_mtime = os.stat(self._lock_file).st_mtime + except OSError: + continue + if current_mtime != mtime: + mtime = current_mtime + last_update_monotonic_time = time.monotonic() + + if time.monotonic() - last_update_monotonic_time > self.grace_period: + warnings.warn( + "The existing lock file has not been released " + "for an extended period. Forcibly releasing the lock file." + ) + try: + self.release() + sleep_secs = 0.001 + except RuntimeError: + continue + + time.sleep(sleep_secs) + sleep_secs = min(sleep_secs * 2, 1) + continue + raise err + except BaseException: + self.release() + raise + + def release(self) -> None: + """Release a lock by removing the symbolic link.""" + + lock_rename_file = self._lock_file + str(uuid.uuid4()) + RENAME_FILE_SUFFIX + try: + os.rename(self._lock_file, lock_rename_file) + os.unlink(lock_rename_file) + except OSError: + raise RuntimeError("Error: did not possess lock") + except BaseException: + os.unlink(lock_rename_file) + raise + + +class JournalFileOpenLock(BaseJournalFileLock): + """Lock class for synchronizing processes for NFSv3 or later. + + On acquiring the lock, open system call is called with the O_EXCL option to create an exclusive + file. The file is deleted when the lock is released. This class is only supported when using + NFSv3 or later on kernel 2.6 or later. In prior NFS environments, use + :class:`~optuna.storages.journal.JournalFileSymlinkLock`. + + Args: + filepath: + The path of the file whose race condition must be protected. + grace_period: + Grace period before an existing lock is forcibly released. + """ + + def __init__(self, filepath: str, grace_period: int | None = 30) -> None: + self._lock_file = filepath + LOCK_FILE_SUFFIX + if grace_period is not None: + if grace_period <= 0: + raise ValueError("The value of `grace_period` should be a positive integer.") + if grace_period < 3: + warnings.warn("The value of `grace_period` might be too small. ") + self.grace_period = grace_period + + def acquire(self) -> bool: + """Acquire a lock in a blocking way by creating a lock file. + + Returns: + :obj:`True` if it succeeded in creating a ``self._lock_file``. + + """ + sleep_secs = 0.001 + last_update_monotonic_time = time.monotonic() + mtime = None + while True: + try: + open_flags = os.O_CREAT | os.O_EXCL | os.O_WRONLY + os.close(os.open(self._lock_file, open_flags)) + return True + except OSError as err: + if err.errno == errno.EEXIST: + if self.grace_period is not None: + try: + current_mtime = os.stat(self._lock_file).st_mtime + except OSError: + continue + if current_mtime != mtime: + mtime = current_mtime + last_update_monotonic_time = time.monotonic() + + if time.monotonic() - last_update_monotonic_time > self.grace_period: + warnings.warn( + "The existing lock file has not been released " + "for an extended period. Forcibly releasing the lock file." + ) + try: + self.release() + sleep_secs = 0.001 + except RuntimeError: + continue + + time.sleep(sleep_secs) + sleep_secs = min(sleep_secs * 2, 1) + continue + raise err + except BaseException: + self.release() + raise + + def release(self) -> None: + """Release a lock by removing the created file.""" + + lock_rename_file = self._lock_file + str(uuid.uuid4()) + RENAME_FILE_SUFFIX + try: + os.rename(self._lock_file, lock_rename_file) + os.unlink(lock_rename_file) + except OSError: + raise RuntimeError("Error: did not possess lock") + except BaseException: + os.unlink(lock_rename_file) + raise + + +@contextmanager +def get_lock_file(lock_obj: BaseJournalFileLock) -> Iterator[None]: + lock_obj.acquire() + try: + yield + finally: + lock_obj.release() + + +@deprecated_class( + "4.0.0", "6.0.0", text="Use :class:`~optuna.storages.journal.JournalFileBackend` instead." +) +class JournalFileStorage(JournalFileBackend): + pass + + +@deprecated_class( + deprecated_version="4.0.0", + removed_version="6.0.0", + name="The import path :class:`~optuna.storages.JournalFileOpenLock`", + text="Use :class:`~optuna.storages.journal.JournalFileOpenLock` instead.", +) +class DeprecatedJournalFileOpenLock(JournalFileOpenLock): + pass + + +@deprecated_class( + deprecated_version="4.0.0", + removed_version="6.0.0", + name="The import path :class:`~optuna.storages.JournalFileSymlinkLock`", + text="Use :class:`~optuna.storages.journal.JournalFileSymlinkLock` instead.", +) +class DeprecatedJournalFileSymlinkLock(JournalFileSymlinkLock): + pass diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_redis.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_redis.py new file mode 100644 index 0000000000000000000000000000000000000000..811cdc1eb4cc9f2585402d8d83e7afd99be49bd7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_redis.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +import json +import time +from typing import Any + +from optuna._deprecated import deprecated_class +from optuna._experimental import experimental_class +from optuna._imports import try_import +from optuna.storages.journal._base import BaseJournalBackend +from optuna.storages.journal._base import BaseJournalSnapshot + + +with try_import() as _imports: + import redis + + +@experimental_class("3.1.0") +class JournalRedisBackend(BaseJournalBackend, BaseJournalSnapshot): + """Redis storage class for Journal log backend. + + Args: + url: + URL of the redis storage, password and db are optional. + (ie: ``redis://localhost:6379``) + use_cluster: + Flag whether you use the Redis cluster. If this is :obj:`False`, it is assumed that + you use the standalone Redis server and ensured that a write operation is atomic. This + provides the consistency of the preserved logs. If this is :obj:`True`, it is assumed + that you use the Redis cluster and not ensured that a write operation is atomic. This + means the preserved logs can be inconsistent due to network errors, and may + cause errors. + prefix: + Prefix of the preserved key of logs. This is useful when multiple users work on one + Redis server. + """ + + def __init__(self, url: str, use_cluster: bool = False, prefix: str = "") -> None: + _imports.check() + + self._url = url + self._redis = redis.Redis.from_url(url) + self._use_cluster = use_cluster + self._prefix = prefix + + def __getstate__(self) -> dict[Any, Any]: + state = self.__dict__.copy() + del state["_redis"] + return state + + def __setstate__(self, state: dict[Any, Any]) -> None: + self.__dict__.update(state) + self._redis = redis.Redis.from_url(self._url) + + def read_logs(self, log_number_from: int) -> list[dict[str, Any]]: + max_log_number_bytes = self._redis.get(f"{self._prefix}:log_number") + if max_log_number_bytes is None: + return [] + max_log_number = int(max_log_number_bytes) + + logs = [] + for log_number in range(log_number_from, max_log_number + 1): + sleep_secs = 0.1 + while True: + log = self._redis.get(self._key_log_id(log_number)) + if log is not None: + break + time.sleep(sleep_secs) + sleep_secs = min(sleep_secs * 2, 10) + try: + logs.append(json.loads(log)) + except json.JSONDecodeError as err: + if log_number != max_log_number: + raise err + return logs + + def append_logs(self, logs: list[dict[str, Any]]) -> None: + self._redis.setnx(f"{self._prefix}:log_number", -1) + for log in logs: + if not self._use_cluster: + self._redis.eval( # type: ignore + "local i = redis.call('incr', string.format('%s:log_number', ARGV[1])) " + "redis.call('set', string.format('%s:log:%d', ARGV[1], i), ARGV[2])", + 0, + self._prefix, + json.dumps(log), + ) + else: + log_number = self._redis.incr(f"{self._prefix}:log_number", 1) + self._redis.set(self._key_log_id(log_number), json.dumps(log)) + + def save_snapshot(self, snapshot: bytes) -> None: + self._redis.set(f"{self._prefix}:snapshot", snapshot) + + def load_snapshot(self) -> bytes | None: + snapshot_bytes = self._redis.get(f"{self._prefix}:snapshot") + return snapshot_bytes + + def _key_log_id(self, log_number: int) -> str: + return f"{self._prefix}:log:{log_number}" + + +@deprecated_class( + "4.0.0", "6.0.0", text="Use :class:`~optuna.storages.journal.JournalRedisBackend` instead." +) +class JournalRedisStorage(JournalRedisBackend): + pass diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_storage.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_storage.py new file mode 100644 index 0000000000000000000000000000000000000000..b7ecd37f59d912d4e74730ee3d31bd83282bd28f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/storages/journal/_storage.py @@ -0,0 +1,682 @@ +from __future__ import annotations + +from collections.abc import Container +from collections.abc import Sequence +import copy +import datetime +import enum +import pickle +import threading +from typing import Any +import uuid + +import optuna +from optuna._typing import JSONSerializable +from optuna.distributions import BaseDistribution +from optuna.distributions import check_distribution_compatibility +from optuna.distributions import distribution_to_json +from optuna.distributions import json_to_distribution +from optuna.exceptions import DuplicatedStudyError +from optuna.exceptions import UpdateFinishedTrialError +from optuna.storages import BaseStorage +from optuna.storages._base import DEFAULT_STUDY_NAME_PREFIX +from optuna.storages.journal._base import BaseJournalBackend +from optuna.storages.journal._base import BaseJournalSnapshot +from optuna.study._frozen import FrozenStudy +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +_logger = optuna.logging.get_logger(__name__) + +NOT_FOUND_MSG = "Record does not exist." +UNUPDATABLE_MSG = "Trial#{trial_number} has already finished and can not be updated." +# A heuristic interval number to dump snapshots +SNAPSHOT_INTERVAL = 100 + + +class JournalOperation(enum.IntEnum): + CREATE_STUDY = 0 + DELETE_STUDY = 1 + SET_STUDY_USER_ATTR = 2 + SET_STUDY_SYSTEM_ATTR = 3 + CREATE_TRIAL = 4 + SET_TRIAL_PARAM = 5 + SET_TRIAL_STATE_VALUES = 6 + SET_TRIAL_INTERMEDIATE_VALUE = 7 + SET_TRIAL_USER_ATTR = 8 + SET_TRIAL_SYSTEM_ATTR = 9 + + +class JournalStorage(BaseStorage): + """Storage class for Journal storage backend. + + Note that library users can instantiate this class, but the attributes + provided by this class are not supposed to be directly accessed by them. + + Journal storage writes a record of every operation to the database as it is executed and + at the same time, keeps a latest snapshot of the database in-memory. If the database crashes + for any reason, the storage can re-establish the contents in memory by replaying the + operations stored from the beginning. + + Journal storage has several benefits over the conventional value logging storages. + + 1. The number of IOs can be reduced because of larger granularity of logs. + 2. Journal storage has simpler backend API than value logging storage. + 3. Journal storage keeps a snapshot in-memory so no need to add more cache. + + Example: + + .. code:: + + import optuna + + + def objective(trial): ... + + + storage = optuna.storages.JournalStorage( + optuna.storages.journal.JournalFileBackend("./optuna_journal_storage.log") + ) + + study = optuna.create_study(storage=storage) + study.optimize(objective) + + In a Windows environment, an error message "A required privilege is not held by the + client" may appear. In this case, you can solve the problem with creating storage + by specifying :class:`~optuna.storages.journal.JournalFileOpenLock` as follows. + + .. code:: + + file_path = "./optuna_journal_storage.log" + lock_obj = optuna.storages.journal.JournalFileOpenLock(file_path) + + storage = optuna.storages.JournalStorage( + optuna.storages.journal.JournalFileBackend(file_path, lock_obj=lock_obj), + ) + """ + + def __init__(self, log_storage: BaseJournalBackend) -> None: + self._worker_id_prefix = str(uuid.uuid4()) + "-" + self._backend = log_storage + self._thread_lock = threading.Lock() + self._replay_result = JournalStorageReplayResult(self._worker_id_prefix) + + with self._thread_lock: + if isinstance(self._backend, BaseJournalSnapshot): + snapshot = self._backend.load_snapshot() + if snapshot is not None: + self.restore_replay_result(snapshot) + self._sync_with_backend() + + def __getstate__(self) -> dict[Any, Any]: + state = self.__dict__.copy() + del state["_worker_id_prefix"] + del state["_replay_result"] + del state["_thread_lock"] + return state + + def __setstate__(self, state: dict[Any, Any]) -> None: + self.__dict__.update(state) + self._worker_id_prefix = str(uuid.uuid4()) + "-" + self._replay_result = JournalStorageReplayResult(self._worker_id_prefix) + self._thread_lock = threading.Lock() + + def restore_replay_result(self, snapshot: bytes) -> None: + try: + r: JournalStorageReplayResult | None = pickle.loads(snapshot) + except (pickle.UnpicklingError, KeyError): + _logger.warning("Failed to restore `JournalStorageReplayResult`.") + return + if r is None: + return + if not isinstance(r, JournalStorageReplayResult): + _logger.warning("The restored object is not `JournalStorageReplayResult`.") + return + r._worker_id_prefix = self._worker_id_prefix + r._worker_id_to_owned_trial_id = {} + r._last_created_trial_id_by_this_process = -1 + self._replay_result = r + + def _write_log(self, op_code: int, extra_fields: dict[str, Any]) -> None: + worker_id = self._replay_result.worker_id + self._backend.append_logs([{"op_code": op_code, "worker_id": worker_id, **extra_fields}]) + + def _sync_with_backend(self) -> None: + logs = self._backend.read_logs(self._replay_result.log_number_read) + self._replay_result.apply_logs(logs) + + def create_new_study( + self, directions: Sequence[StudyDirection], study_name: str | None = None + ) -> int: + study_name = study_name or DEFAULT_STUDY_NAME_PREFIX + str(uuid.uuid4()) + + with self._thread_lock: + self._write_log( + JournalOperation.CREATE_STUDY, {"study_name": study_name, "directions": directions} + ) + self._sync_with_backend() + + for frozen_study in self._replay_result.get_all_studies(): + if frozen_study.study_name != study_name: + continue + + _logger.info("A new study created in Journal with name: {}".format(study_name)) + study_id = frozen_study._study_id + + # Dump snapshot here. + if ( + isinstance(self._backend, BaseJournalSnapshot) + and study_id != 0 + and study_id % SNAPSHOT_INTERVAL == 0 + ): + self._backend.save_snapshot(pickle.dumps(self._replay_result)) + + return study_id + assert False, "Should not reach." + + def delete_study(self, study_id: int) -> None: + with self._thread_lock: + self._write_log(JournalOperation.DELETE_STUDY, {"study_id": study_id}) + self._sync_with_backend() + + def set_study_user_attr(self, study_id: int, key: str, value: Any) -> None: + log: dict[str, Any] = {"study_id": study_id, "user_attr": {key: value}} + with self._thread_lock: + self._write_log(JournalOperation.SET_STUDY_USER_ATTR, log) + self._sync_with_backend() + + def set_study_system_attr(self, study_id: int, key: str, value: JSONSerializable) -> None: + log: dict[str, Any] = {"study_id": study_id, "system_attr": {key: value}} + with self._thread_lock: + self._write_log(JournalOperation.SET_STUDY_SYSTEM_ATTR, log) + self._sync_with_backend() + + def get_study_id_from_name(self, study_name: str) -> int: + with self._thread_lock: + self._sync_with_backend() + for study in self._replay_result.get_all_studies(): + if study.study_name == study_name: + return study._study_id + raise KeyError(NOT_FOUND_MSG) + + def get_study_name_from_id(self, study_id: int) -> str: + with self._thread_lock: + self._sync_with_backend() + return self._replay_result.get_study(study_id).study_name + + def get_study_directions(self, study_id: int) -> list[StudyDirection]: + with self._thread_lock: + self._sync_with_backend() + return self._replay_result.get_study(study_id).directions + + def get_study_user_attrs(self, study_id: int) -> dict[str, Any]: + with self._thread_lock: + self._sync_with_backend() + return self._replay_result.get_study(study_id).user_attrs + + def get_study_system_attrs(self, study_id: int) -> dict[str, Any]: + with self._thread_lock: + self._sync_with_backend() + return self._replay_result.get_study(study_id).system_attrs + + def get_all_studies(self) -> list[FrozenStudy]: + with self._thread_lock: + self._sync_with_backend() + return copy.deepcopy(self._replay_result.get_all_studies()) + + # Basic trial manipulation + def create_new_trial(self, study_id: int, template_trial: FrozenTrial | None = None) -> int: + log: dict[str, Any] = { + "study_id": study_id, + "datetime_start": datetime.datetime.now().isoformat(timespec="microseconds"), + } + + if template_trial: + log["state"] = template_trial.state + if template_trial.values is not None and len(template_trial.values) > 1: + log["value"] = None + log["values"] = template_trial.values + else: + log["value"] = template_trial.value + log["values"] = None + if template_trial.datetime_start: + log["datetime_start"] = template_trial.datetime_start.isoformat( + timespec="microseconds" + ) + else: + log["datetime_start"] = None + if template_trial.datetime_complete: + log["datetime_complete"] = template_trial.datetime_complete.isoformat( + timespec="microseconds" + ) + + log["distributions"] = { + k: distribution_to_json(dist) for k, dist in template_trial.distributions.items() + } + log["params"] = { + k: template_trial.distributions[k].to_internal_repr(param) + for k, param in template_trial.params.items() + } + log["user_attrs"] = template_trial.user_attrs + log["system_attrs"] = template_trial.system_attrs + log["intermediate_values"] = template_trial.intermediate_values + + with self._thread_lock: + self._write_log(JournalOperation.CREATE_TRIAL, log) + self._sync_with_backend() + trial_id = self._replay_result._last_created_trial_id_by_this_process + + # Dump snapshot here. + if ( + isinstance(self._backend, BaseJournalSnapshot) + and trial_id != 0 + and trial_id % SNAPSHOT_INTERVAL == 0 + ): + self._backend.save_snapshot(pickle.dumps(self._replay_result)) + return trial_id + + def set_trial_param( + self, + trial_id: int, + param_name: str, + param_value_internal: float, + distribution: BaseDistribution, + ) -> None: + log: dict[str, Any] = { + "trial_id": trial_id, + "param_name": param_name, + "param_value_internal": param_value_internal, + "distribution": distribution_to_json(distribution), + } + + with self._thread_lock: + self._write_log(JournalOperation.SET_TRIAL_PARAM, log) + self._sync_with_backend() + + def get_trial_id_from_study_id_trial_number(self, study_id: int, trial_number: int) -> int: + with self._thread_lock: + self._sync_with_backend() + if len(self._replay_result._study_id_to_trial_ids[study_id]) <= trial_number: + raise KeyError( + "No trial with trial number {} exists in study with study_id {}.".format( + trial_number, study_id + ) + ) + return self._replay_result._study_id_to_trial_ids[study_id][trial_number] + + def set_trial_state_values( + self, trial_id: int, state: TrialState, values: Sequence[float] | None = None + ) -> bool: + log: dict[str, Any] = { + "trial_id": trial_id, + "state": state, + "values": values, + } + + if state == TrialState.RUNNING: + log["datetime_start"] = datetime.datetime.now().isoformat(timespec="microseconds") + elif state.is_finished(): + log["datetime_complete"] = datetime.datetime.now().isoformat(timespec="microseconds") + + with self._thread_lock: + if state == TrialState.RUNNING: + # NOTE(nabenabe): This sync is not necessary because the last + # set_trial_state_values call by the same thread always syncs before the true pop, + # but I keep it here to avoid the confusion. Anyways, this section isn't triggered + # that often because this section is only for enqueue_trial. + self._sync_with_backend() + # NOTE(nabenabe): This section is triggered only when we are using `enqueue_trial` + # and `GrpcProxyStorage` in distributed optimization setups and solves the issue + # https://github.com/optuna/optuna/issues/6084. + # When using gRPC, the current thread may already have popped the trial with + # trial_id for another process, potentially leading to a false positive in the + # return statement of trial_id == _replay_result.owned_trial_id. To eliminate false + # positives, we verify whether another process is already evaluating the trial with + # trial_id. If True, it means this query does not update the trial state. + existing_trial = self._replay_result._trials.get(trial_id) + assert ( + existing_trial is not None + ), "Please report your bug on GitHub if this line fails your script." + if existing_trial.state.is_finished(): + raise UpdateFinishedTrialError( + UNUPDATABLE_MSG.format(trial_number=existing_trial.number) + ) + if existing_trial.state != TrialState.WAITING: + # This line is equivalent to `existing_trial.state == TrialState.RUNNING`. + return False + self._write_log(JournalOperation.SET_TRIAL_STATE_VALUES, log) + self._sync_with_backend() + return state != TrialState.RUNNING or trial_id == self._replay_result.owned_trial_id + + def set_trial_intermediate_value( + self, trial_id: int, step: int, intermediate_value: float + ) -> None: + log: dict[str, Any] = { + "trial_id": trial_id, + "step": step, + "intermediate_value": intermediate_value, + } + + with self._thread_lock: + self._write_log(JournalOperation.SET_TRIAL_INTERMEDIATE_VALUE, log) + self._sync_with_backend() + + def set_trial_user_attr(self, trial_id: int, key: str, value: Any) -> None: + log: dict[str, Any] = { + "trial_id": trial_id, + "user_attr": {key: value}, + } + + with self._thread_lock: + self._write_log(JournalOperation.SET_TRIAL_USER_ATTR, log) + self._sync_with_backend() + + def set_trial_system_attr(self, trial_id: int, key: str, value: JSONSerializable) -> None: + log: dict[str, Any] = { + "trial_id": trial_id, + "system_attr": {key: value}, + } + + with self._thread_lock: + self._write_log(JournalOperation.SET_TRIAL_SYSTEM_ATTR, log) + self._sync_with_backend() + + def get_trial(self, trial_id: int) -> FrozenTrial: + with self._thread_lock: + self._sync_with_backend() + return self._replay_result.get_trial(trial_id) + + def get_all_trials( + self, + study_id: int, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + ) -> list[FrozenTrial]: + with self._thread_lock: + self._sync_with_backend() + frozen_trials = self._replay_result.get_all_trials(study_id, states) + if deepcopy: + return copy.deepcopy(frozen_trials) + return frozen_trials + + +class JournalStorageReplayResult: + def __init__(self, worker_id_prefix: str) -> None: + self.log_number_read = 0 + self._worker_id_prefix = worker_id_prefix + self._studies: dict[int, FrozenStudy] = {} + self._trials: dict[int, FrozenTrial] = {} + + self._study_id_to_trial_ids: dict[int, list[int]] = {} + self._trial_id_to_study_id: dict[int, int] = {} + self._next_study_id: int = 0 + self._worker_id_to_owned_trial_id: dict[str, int] = {} + + def apply_logs(self, logs: list[dict[str, Any]]) -> None: + for log in logs: + self.log_number_read += 1 + op = log["op_code"] + if op == JournalOperation.CREATE_STUDY: + self._apply_create_study(log) + elif op == JournalOperation.DELETE_STUDY: + self._apply_delete_study(log) + elif op == JournalOperation.SET_STUDY_USER_ATTR: + self._apply_set_study_user_attr(log) + elif op == JournalOperation.SET_STUDY_SYSTEM_ATTR: + self._apply_set_study_system_attr(log) + elif op == JournalOperation.CREATE_TRIAL: + self._apply_create_trial(log) + elif op == JournalOperation.SET_TRIAL_PARAM: + self._apply_set_trial_param(log) + elif op == JournalOperation.SET_TRIAL_STATE_VALUES: + self._apply_set_trial_state_values(log) + elif op == JournalOperation.SET_TRIAL_INTERMEDIATE_VALUE: + self._apply_set_trial_intermediate_value(log) + elif op == JournalOperation.SET_TRIAL_USER_ATTR: + self._apply_set_trial_user_attr(log) + elif op == JournalOperation.SET_TRIAL_SYSTEM_ATTR: + self._apply_set_trial_system_attr(log) + else: + assert False, "Should not reach." + + def get_study(self, study_id: int) -> FrozenStudy: + if study_id not in self._studies: + raise KeyError(NOT_FOUND_MSG) + return self._studies[study_id] + + def get_all_studies(self) -> list[FrozenStudy]: + return list(self._studies.values()) + + def get_trial(self, trial_id: int) -> FrozenTrial: + if trial_id not in self._trials: + raise KeyError(NOT_FOUND_MSG) + return self._trials[trial_id] + + def get_all_trials( + self, study_id: int, states: Container[TrialState] | None + ) -> list[FrozenTrial]: + if study_id not in self._studies: + raise KeyError(NOT_FOUND_MSG) + + frozen_trials: list[FrozenTrial] = [] + for trial_id in self._study_id_to_trial_ids[study_id]: + trial = self._trials[trial_id] + if states is None or trial.state in states: + frozen_trials.append(trial) + return frozen_trials + + @property + def worker_id(self) -> str: + return self._worker_id_prefix + str(threading.get_ident()) + + @property + def owned_trial_id(self) -> int | None: + return self._worker_id_to_owned_trial_id.get(self.worker_id) + + def _is_issued_by_this_worker(self, log: dict[str, Any]) -> bool: + return log["worker_id"] == self.worker_id + + def _study_exists(self, study_id: int, log: dict[str, Any]) -> bool: + if study_id in self._studies: + return True + if self._is_issued_by_this_worker(log): + raise KeyError(NOT_FOUND_MSG) + return False + + def _apply_create_study(self, log: dict[str, Any]) -> None: + study_name = log["study_name"] + directions = [StudyDirection(d) for d in log["directions"]] + + if study_name in [s.study_name for s in self._studies.values()]: + if self._is_issued_by_this_worker(log): + raise DuplicatedStudyError( + "Another study with name '{}' already exists. " + "Please specify a different name, or reuse the existing one " + "by setting `load_if_exists` (for Python API) or " + "`--skip-if-exists` flag (for CLI).".format(study_name) + ) + return + + study_id = self._next_study_id + self._next_study_id += 1 + + self._studies[study_id] = FrozenStudy( + study_name=study_name, + direction=None, + user_attrs={}, + system_attrs={}, + study_id=study_id, + directions=directions, + ) + self._study_id_to_trial_ids[study_id] = [] + + def _apply_delete_study(self, log: dict[str, Any]) -> None: + study_id = log["study_id"] + + if self._study_exists(study_id, log): + fs = self._studies.pop(study_id) + assert fs._study_id == study_id + + def _apply_set_study_user_attr(self, log: dict[str, Any]) -> None: + study_id = log["study_id"] + + if self._study_exists(study_id, log): + assert len(log["user_attr"]) == 1 + self._studies[study_id].user_attrs.update(log["user_attr"]) + + def _apply_set_study_system_attr(self, log: dict[str, Any]) -> None: + study_id = log["study_id"] + + if self._study_exists(study_id, log): + assert len(log["system_attr"]) == 1 + self._studies[study_id].system_attrs.update(log["system_attr"]) + + def _apply_create_trial(self, log: dict[str, Any]) -> None: + study_id = log["study_id"] + + if not self._study_exists(study_id, log): + return + + trial_id = len(self._trials) + distributions = {} + if "distributions" in log: + distributions = {k: json_to_distribution(v) for k, v in log["distributions"].items()} + params = {} + if "params" in log: + params = {k: distributions[k].to_external_repr(p) for k, p in log["params"].items()} + if log["datetime_start"] is not None: + datetime_start = datetime.datetime.fromisoformat(log["datetime_start"]) + else: + datetime_start = None + if "datetime_complete" in log: + datetime_complete = datetime.datetime.fromisoformat(log["datetime_complete"]) + else: + datetime_complete = None + + self._trials[trial_id] = FrozenTrial( + trial_id=trial_id, + number=len(self._study_id_to_trial_ids[study_id]), + state=TrialState(log.get("state", TrialState.RUNNING.value)), + params=params, + distributions=distributions, + user_attrs=log.get("user_attrs", {}), + system_attrs=log.get("system_attrs", {}), + value=log.get("value", None), + intermediate_values={int(k): v for k, v in log.get("intermediate_values", {}).items()}, + datetime_start=datetime_start, + datetime_complete=datetime_complete, + values=log.get("values", None), + ) + + self._study_id_to_trial_ids[study_id].append(trial_id) + self._trial_id_to_study_id[trial_id] = study_id + + if self._is_issued_by_this_worker(log): + self._last_created_trial_id_by_this_process = trial_id + if self._trials[trial_id].state == TrialState.RUNNING: + self._worker_id_to_owned_trial_id[self.worker_id] = trial_id + + def _apply_set_trial_param(self, log: dict[str, Any]) -> None: + trial_id = log["trial_id"] + + if not self._trial_exists_and_updatable(trial_id, log): + return + + param_name = log["param_name"] + param_value_internal = log["param_value_internal"] + distribution = json_to_distribution(log["distribution"]) + + study_id = self._trial_id_to_study_id[trial_id] + + for prev_trial_id in self._study_id_to_trial_ids[study_id]: + prev_trial = self._trials[prev_trial_id] + if param_name in prev_trial.params.keys(): + try: + check_distribution_compatibility( + prev_trial.distributions[param_name], distribution + ) + except Exception: + if self._is_issued_by_this_worker(log): + raise + return + break + + trial = copy.copy(self._trials[trial_id]) + trial.params = { + **copy.copy(trial.params), + param_name: distribution.to_external_repr(param_value_internal), + } + trial.distributions = {**copy.copy(trial.distributions), param_name: distribution} + self._trials[trial_id] = trial + + def _apply_set_trial_state_values(self, log: dict[str, Any]) -> None: + trial_id = log["trial_id"] + + if not self._trial_exists_and_updatable(trial_id, log): + return + + state = TrialState(log["state"]) + if state == self._trials[trial_id].state and state == TrialState.RUNNING: + # Reject the operation as the popped trial is already run by another process. + return + + trial = copy.copy(self._trials[trial_id]) + if state == TrialState.RUNNING: + trial.datetime_start = datetime.datetime.fromisoformat(log["datetime_start"]) + if self._is_issued_by_this_worker(log): + self._worker_id_to_owned_trial_id[self.worker_id] = trial_id + if state.is_finished(): + trial.datetime_complete = datetime.datetime.fromisoformat(log["datetime_complete"]) + trial.state = state + if log["values"] is not None: + trial.values = log["values"] + + self._trials[trial_id] = trial + + def _apply_set_trial_intermediate_value(self, log: dict[str, Any]) -> None: + trial_id = log["trial_id"] + + if self._trial_exists_and_updatable(trial_id, log): + trial = copy.copy(self._trials[trial_id]) + trial.intermediate_values = { + **copy.copy(trial.intermediate_values), + log["step"]: log["intermediate_value"], + } + self._trials[trial_id] = trial + + def _apply_set_trial_user_attr(self, log: dict[str, Any]) -> None: + trial_id = log["trial_id"] + + if self._trial_exists_and_updatable(trial_id, log): + assert len(log["user_attr"]) == 1 + trial = copy.copy(self._trials[trial_id]) + trial.user_attrs = {**copy.copy(trial.user_attrs), **log["user_attr"]} + self._trials[trial_id] = trial + + def _apply_set_trial_system_attr(self, log: dict[str, Any]) -> None: + trial_id = log["trial_id"] + + if self._trial_exists_and_updatable(trial_id, log): + assert len(log["system_attr"]) == 1 + trial = copy.copy(self._trials[trial_id]) + trial.system_attrs = { + **copy.copy(trial.system_attrs), + **log["system_attr"], + } + self._trials[trial_id] = trial + + def _trial_exists_and_updatable(self, trial_id: int, log: dict[str, Any]) -> bool: + if trial_id not in self._trials: + if self._is_issued_by_this_worker(log): + raise KeyError(NOT_FOUND_MSG) + return False + elif self._trials[trial_id].state.is_finished(): + if self._is_issued_by_this_worker(log): + raise UpdateFinishedTrialError( + UNUPDATABLE_MSG.format(trial_number=self._trials[trial_id].number) + ) + return False + else: + return True diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7b63b844d0c23b639ca0c87e46e86e6e83a8c1ff --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/__init__.py @@ -0,0 +1,24 @@ +from optuna._callbacks import MaxTrialsCallback +from optuna.study._study_direction import StudyDirection +from optuna.study._study_summary import StudySummary +from optuna.study.study import copy_study +from optuna.study.study import create_study +from optuna.study.study import delete_study +from optuna.study.study import get_all_study_names +from optuna.study.study import get_all_study_summaries +from optuna.study.study import load_study +from optuna.study.study import Study + + +__all__ = [ + "MaxTrialsCallback", + "StudyDirection", + "StudySummary", + "copy_study", + "create_study", + "delete_study", + "get_all_study_names", + "get_all_study_summaries", + "load_study", + "Study", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5169b7af92cc8d81190e02847a7ab581bda7507d Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/__pycache__/_constrained_optimization.cpython-310.pyc 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a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_constrained_optimization.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_constrained_optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..d289e13f55bb2b4459a082670a90211db18a3590 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_constrained_optimization.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +from collections.abc import Sequence + +from optuna.trial import FrozenTrial + + +_CONSTRAINTS_KEY = "constraints" + + +def _get_feasible_trials(trials: Sequence[FrozenTrial]) -> list[FrozenTrial]: + """Return feasible trials from given trials. + + This function assumes that the trials were created in constrained optimization. + Therefore, if there is no violation value in the trial, it is considered infeasible. + + + Returns: + A list of feasible trials. + """ + + feasible_trials = [] + for trial in trials: + constraints = trial.system_attrs.get(_CONSTRAINTS_KEY) + if constraints is not None and all(x <= 0.0 for x in constraints): + feasible_trials.append(trial) + return feasible_trials diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_dataframe.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_dataframe.py new file mode 100644 index 0000000000000000000000000000000000000000..e9f51858f9a0e0db96580af9f688417918314b7a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_dataframe.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +import collections +from typing import Any + +import optuna +from optuna._imports import try_import +from optuna.trial._state import TrialState + + +with try_import() as _imports: + # `Study.trials_dataframe` is disabled if pandas is not available. + import pandas as pd + +# Required for type annotation in `Study.trials_dataframe`. +if not _imports.is_successful(): + pd = object # NOQA + +__all__ = ["pd"] + + +def _create_records_and_aggregate_column( + study: "optuna.Study", attrs: tuple[str, ...] +) -> tuple[list[dict[tuple[str, str], Any]], list[tuple[str, str]]]: + attrs_to_df_columns: dict[str, str] = {} + for attr in attrs: + if attr.startswith("_"): + # Python conventional underscores are omitted in the dataframe. + df_column = attr[1:] + else: + df_column = attr + attrs_to_df_columns[attr] = df_column + + # column_agg is an aggregator of column names. + # Keys of column agg are attributes of `FrozenTrial` such as 'trial_id' and 'params'. + # Values are dataframe columns such as ('trial_id', '') and ('params', 'n_layers'). + column_agg: collections.defaultdict[str, set] = collections.defaultdict(set) + non_nested_attr = "" + + metric_names = study.metric_names + + records = [] + for trial in study.get_trials(deepcopy=False): + record = {} + for attr, df_column in attrs_to_df_columns.items(): + value = getattr(trial, attr) + if isinstance(value, TrialState): + value = value.name + if isinstance(value, dict): + for nested_attr, nested_value in value.items(): + record[(df_column, nested_attr)] = nested_value + column_agg[attr].add((df_column, nested_attr)) + elif attr == "values": + # Expand trial.values. + # trial.values should be None when the trial's state is FAIL or PRUNED. + trial_values = [None] * len(study.directions) if value is None else value + iterator = ( + enumerate(trial_values) + if metric_names is None + else zip(metric_names, trial_values) + ) + for nested_attr, nested_value in iterator: + record[(df_column, nested_attr)] = nested_value + column_agg[attr].add((df_column, nested_attr)) + elif isinstance(value, list): + for nested_attr, nested_value in enumerate(value): + record[(df_column, nested_attr)] = nested_value + column_agg[attr].add((df_column, nested_attr)) + elif attr == "value": + nested_attr = non_nested_attr if metric_names is None else metric_names[0] + record[(df_column, nested_attr)] = value + column_agg[attr].add((df_column, nested_attr)) + else: + record[(df_column, non_nested_attr)] = value + column_agg[attr].add((df_column, non_nested_attr)) + + records.append(record) + + columns: list[tuple[str, str]] = sum( + (sorted(column_agg[k]) for k in attrs if k in column_agg), [] + ) + + return records, columns + + +def _flatten_columns(columns: list[tuple[str, str]]) -> list[str]: + # Flatten the `MultiIndex` columns where names are concatenated with underscores. + # Filtering is required to omit non-nested columns avoiding unwanted trailing underscores. + return ["_".join(filter(lambda c: c, map(lambda c: str(c), col))) for col in columns] + + +def _trials_dataframe( + study: "optuna.Study", attrs: tuple[str, ...], multi_index: bool +) -> "pd.DataFrame": + _imports.check() + + # If no trials, return an empty dataframe. + if len(study.get_trials(deepcopy=False)) == 0: + return pd.DataFrame() + + if "value" in attrs and study._is_multi_objective(): + attrs = tuple("values" if attr == "value" else attr for attr in attrs) + + records, columns = _create_records_and_aggregate_column(study, attrs) + + df = pd.DataFrame(records, columns=pd.MultiIndex.from_tuples(columns)) + + if not multi_index: + df.columns = _flatten_columns(columns) + + return df diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_frozen.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_frozen.py new file mode 100644 index 0000000000000000000000000000000000000000..d74c4529f6d58043b425ba8bdb8f88c49b908626 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_frozen.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any + +from optuna import logging +from optuna.study._study_direction import StudyDirection + + +_logger = logging.get_logger(__name__) + + +class FrozenStudy: + """Basic attributes of a :class:`~optuna.study.Study`. + + This class is private and not referenced by Optuna users. + + Attributes: + study_name: + Name of the :class:`~optuna.study.Study`. + direction: + :class:`~optuna.study.StudyDirection` of the :class:`~optuna.study.Study`. + + .. note:: + This attribute is only available during single-objective optimization. + directions: + A list of :class:`~optuna.study.StudyDirection` objects. + user_attrs: + Dictionary that contains the attributes of the :class:`~optuna.study.Study` set with + :func:`optuna.study.Study.set_user_attr`. + system_attrs: + Dictionary that contains the attributes of the :class:`~optuna.study.Study` internally + set by Optuna. + + """ + + def __init__( + self, + study_name: str, + direction: StudyDirection | None, + user_attrs: dict[str, Any], + system_attrs: dict[str, Any], + study_id: int, + *, + directions: Sequence[StudyDirection] | None = None, + ): + self.study_name = study_name + if direction is None and directions is None: + raise ValueError("Specify one of `direction` and `directions`.") + elif directions is not None: + self._directions = list(directions) + elif direction is not None: + self._directions = [direction] + else: + raise ValueError("Specify only one of `direction` and `directions`.") + self.user_attrs = user_attrs + self.system_attrs = system_attrs + self._study_id = study_id + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, FrozenStudy): + return NotImplemented + + return other.__dict__ == self.__dict__ + + def __lt__(self, other: Any) -> bool: + if not isinstance(other, FrozenStudy): + return NotImplemented + + return self._study_id < other._study_id + + def __le__(self, other: Any) -> bool: + if not isinstance(other, FrozenStudy): + return NotImplemented + + return self._study_id <= other._study_id + + @property + def direction(self) -> StudyDirection: + if len(self._directions) > 1: + raise RuntimeError( + "This attribute is not available during multi-objective optimization." + ) + + return self._directions[0] + + @property + def directions(self) -> list[StudyDirection]: + return self._directions diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_multi_objective.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_multi_objective.py new file mode 100644 index 0000000000000000000000000000000000000000..7a3c03738cdffde6caecc294be86338ba30c0819 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_multi_objective.py @@ -0,0 +1,258 @@ +from __future__ import annotations + +from collections.abc import Sequence + +import numpy as np + +import optuna +from optuna.study._constrained_optimization import _get_feasible_trials +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +def _get_pareto_front_trials_by_trials( + trials: Sequence[FrozenTrial], + directions: Sequence[StudyDirection], + consider_constraint: bool = False, +) -> list[FrozenTrial]: + # NOTE(nabenabe0928): Vectorization relies on all the trials being complete. + trials = [t for t in trials if t.state == TrialState.COMPLETE] + if consider_constraint: + trials = _get_feasible_trials(trials) + if len(trials) == 0: + return [] + + if any(len(t.values) != len(directions) for t in trials): + raise ValueError( + "The number of the values and the number of the objectives must be identical." + ) + + loss_values = np.asarray( + [[_normalize_value(v, d) for v, d in zip(t.values, directions)] for t in trials] + ) + on_front = _is_pareto_front(loss_values, assume_unique_lexsorted=False) + return [t for t, is_pareto in zip(trials, on_front) if is_pareto] + + +def _get_pareto_front_trials( + study: "optuna.study.Study", consider_constraint: bool = False +) -> list[FrozenTrial]: + return _get_pareto_front_trials_by_trials(study.trials, study.directions, consider_constraint) + + +def _fast_non_domination_rank( + loss_values: np.ndarray, *, penalty: np.ndarray | None = None, n_below: int | None = None +) -> np.ndarray: + """Calculate non-domination rank based on the fast non-dominated sort algorithm. + + The fast non-dominated sort algorithm assigns a rank to each trial based on the dominance + relationship of the trials, determined by the objective values and the penalty values. The + algorithm is based on `the constrained NSGA-II algorithm + `__, but the handling of the case when penalty + values are None is different. The algorithm assigns the rank according to the following + rules: + + 1. Feasible trials: First, the algorithm assigns the rank to feasible trials, whose penalty + values are less than or equal to 0, according to unconstrained version of fast non- + dominated sort. + 2. Infeasible trials: Next, the algorithm assigns the rank from the minimum penalty value of to + the maximum penalty value. + 3. Trials with no penalty information (constraints value is None): Finally, The algorithm + assigns the rank to trials with no penalty information according to unconstrained version + of fast non-dominated sort. Note that only this step is different from the original + constrained NSGA-II algorithm. + Plus, the algorithm terminates whenever the number of sorted trials reaches n_below. + + Args: + loss_values: + Objective values, which is better when it is lower, of each trials. + penalty: + Constraints values of each trials. Defaults to None. + n_below: The minimum number of top trials required to be sorted. The algorithm will + terminate when the number of sorted trials reaches n_below. Defaults to None. + + Returns: + An ndarray in the shape of (n_trials,), where each element is the non-domination rank of + each trial. The rank is 0-indexed. This function guarantees the correctness of the ranks + only up to the top-``n_below`` solutions. If a solution's rank is worse than the + top-``n_below`` solution, its rank will be guaranteed to be greater than the rank of + the top-``n_below`` solution. + """ + if len(loss_values) == 0: + return np.array([], dtype=int) + + n_below = n_below or len(loss_values) + assert n_below > 0, "n_below must be a positive integer." + + if penalty is None: + return _calculate_nondomination_rank(loss_values, n_below=n_below) + + if len(penalty) != len(loss_values): + raise ValueError( + "The length of penalty and loss_values must be same, but got " + f"len(penalty)={len(penalty)} and len(loss_values)={len(loss_values)}." + ) + + ranks = np.full(len(loss_values), -1, dtype=int) + is_penalty_nan = np.isnan(penalty) + is_feasible = np.logical_and(~is_penalty_nan, penalty <= 0) + is_infeasible = np.logical_and(~is_penalty_nan, penalty > 0) + + # First, we calculate the domination rank for feasible trials. + ranks[is_feasible] = _calculate_nondomination_rank(loss_values[is_feasible], n_below=n_below) + n_below -= int(np.count_nonzero(is_feasible)) + + # Second, we calculate the domination rank for infeasible trials. + top_rank_infeasible = np.max(ranks[is_feasible], initial=-1) + 1 + ranks[is_infeasible] = top_rank_infeasible + _calculate_nondomination_rank( + penalty[is_infeasible][:, np.newaxis], n_below=n_below + ) + n_below -= int(np.count_nonzero(is_infeasible)) + + # Third, we calculate the domination rank for trials with no penalty information. + top_rank_penalty_nan = np.max(ranks[~is_penalty_nan], initial=-1) + 1 + ranks[is_penalty_nan] = top_rank_penalty_nan + _calculate_nondomination_rank( + loss_values[is_penalty_nan], n_below=n_below + ) + assert np.all(ranks != -1), "All the rank must be updated." + return ranks + + +def _is_pareto_front_nd(unique_lexsorted_loss_values: np.ndarray) -> np.ndarray: + # NOTE(nabenabe0928): I tried the Kung's algorithm below, but it was not really quick. + # https://github.com/optuna/optuna/pull/5302#issuecomment-1988665532 + # As unique_lexsorted_loss_values[:, 0] is sorted, we do not need it to judge dominance. + loss_values = unique_lexsorted_loss_values[:, 1:] + n_trials = loss_values.shape[0] + on_front = np.zeros(n_trials, dtype=bool) + # TODO(nabenabe): Replace with the following once Python 3.8 is dropped. + # remaining_indices: np.ndarray[tuple[int], np.dtype[np.signedinteger]] = ... + remaining_indices: np.ndarray[tuple[int, ...], np.dtype[np.signedinteger]] = np.arange( + n_trials + ) + while len(remaining_indices): + # NOTE: trials[j] cannot dominate trials[i] for i < j because of lexsort. + # Therefore, remaining_indices[0] is always non-dominated. + on_front[(new_nondominated_index := remaining_indices[0])] = True + nondominated_and_not_top = np.any( + loss_values[remaining_indices] < loss_values[new_nondominated_index], axis=1 + ) + # TODO(nabenabe): Replace with the following once Python 3.8 is dropped. + # ... = cast(np.ndarray[tuple[int], np.dtype[np.signedinteger]], ...) + remaining_indices = remaining_indices[nondominated_and_not_top] + + return on_front + + +def _is_pareto_front_2d(unique_lexsorted_loss_values: np.ndarray) -> np.ndarray: + n_trials = unique_lexsorted_loss_values.shape[0] + cummin_value1 = np.minimum.accumulate(unique_lexsorted_loss_values[:, 1]) + on_front = np.ones(n_trials, dtype=bool) + on_front[1:] = cummin_value1[1:] < cummin_value1[:-1] # True if cummin value1 is new minimum. + return on_front + + +def _is_pareto_front_for_unique_sorted(unique_lexsorted_loss_values: np.ndarray) -> np.ndarray: + (n_trials, n_objectives) = unique_lexsorted_loss_values.shape + if n_objectives == 1: + on_front = np.zeros(len(unique_lexsorted_loss_values), dtype=bool) + on_front[0] = True # Only the first element is Pareto optimal. + return on_front + elif n_objectives == 2: + return _is_pareto_front_2d(unique_lexsorted_loss_values) + else: + return _is_pareto_front_nd(unique_lexsorted_loss_values) + + +def _is_pareto_front(loss_values: np.ndarray, assume_unique_lexsorted: bool) -> np.ndarray: + # NOTE(nabenabe): If assume_unique_lexsorted=True, but loss_values is not a unique array, + # Duplicated Pareto solutions will be filtered out except for the earliest occurrences. + # If assume_unique_lexsorted=True and loss_values[:, 0] is not sorted, then the result will be + # incorrect. + if assume_unique_lexsorted: + return _is_pareto_front_for_unique_sorted(loss_values) + + unique_lexsorted_loss_values, order_inv = np.unique(loss_values, axis=0, return_inverse=True) + on_front = _is_pareto_front_for_unique_sorted(unique_lexsorted_loss_values) + # NOTE(nabenabe): We can remove `.reshape(-1)` if ``numpy==2.0.0`` is not used. + # https://github.com/numpy/numpy/issues/26738 + # TODO: Remove `.reshape(-1)` once `numpy==2.0.0` is obsolete. + return on_front[order_inv.reshape(-1)] + + +def _calculate_nondomination_rank( + loss_values: np.ndarray, *, n_below: int | None = None +) -> np.ndarray: + if len(loss_values) == 0 or (n_below is not None and n_below <= 0): + return np.zeros(len(loss_values), dtype=int) + + (n_trials, n_objectives) = loss_values.shape + if n_objectives == 1: + _, ranks = np.unique(loss_values[:, 0], return_inverse=True) + return ranks + + # It ensures that trials[j] will not dominate trials[i] for i < j. + # np.unique does lexsort. + unique_lexsorted_loss_values, order_inv = np.unique(loss_values, return_inverse=True, axis=0) + n_unique = unique_lexsorted_loss_values.shape[0] + # Clip n_below. + n_below = min(n_below or len(unique_lexsorted_loss_values), len(unique_lexsorted_loss_values)) + ranks = np.zeros(n_unique, dtype=int) + rank = 0 + indices = np.arange(n_unique) + while n_unique - indices.size < n_below: + on_front = _is_pareto_front(unique_lexsorted_loss_values, assume_unique_lexsorted=True) + ranks[indices[on_front]] = rank + # Remove the recent Pareto solutions. + indices = indices[~on_front] + unique_lexsorted_loss_values = unique_lexsorted_loss_values[~on_front] + rank += 1 + + ranks[indices] = rank # Rank worse than the top n_below is defined as the worst rank. + # NOTE(nabenabe): We can remove `.reshape(-1)` if ``numpy==2.0.0`` is not used. + # https://github.com/numpy/numpy/issues/26738 + # TODO: Remove `.reshape(-1)` once `numpy==2.0.0` is obsolete. + return ranks[order_inv.reshape(-1)] + + +def _dominates( + trial0: FrozenTrial, trial1: FrozenTrial, directions: Sequence[StudyDirection] +) -> bool: + values0 = trial0.values + values1 = trial1.values + + if trial0.state != TrialState.COMPLETE: + return False + + if trial1.state != TrialState.COMPLETE: + return True + + assert values0 is not None + assert values1 is not None + + if len(values0) != len(values1): + raise ValueError("Trials with different numbers of objectives cannot be compared.") + + if len(values0) != len(directions): + raise ValueError( + "The number of the values and the number of the objectives are mismatched." + ) + + normalized_values0 = [_normalize_value(v, d) for v, d in zip(values0, directions)] + normalized_values1 = [_normalize_value(v, d) for v, d in zip(values1, directions)] + + if normalized_values0 == normalized_values1: + return False + + return all(v0 <= v1 for v0, v1 in zip(normalized_values0, normalized_values1)) + + +def _normalize_value(value: float | None, direction: StudyDirection) -> float: + if value is None: + return float("inf") + + if direction is StudyDirection.MAXIMIZE: + value = -value + + return value diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_optimize.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_optimize.py new file mode 100644 index 0000000000000000000000000000000000000000..6ced4758135425e4a22e0271e93f423d8962da93 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_optimize.py @@ -0,0 +1,276 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Iterable +from collections.abc import Sequence +from concurrent.futures import FIRST_COMPLETED +from concurrent.futures import Future +from concurrent.futures import ThreadPoolExecutor +from concurrent.futures import wait +import copy +import datetime +import gc +import itertools +import os +import sys +from typing import Any +import warnings + +import optuna +from optuna import exceptions +from optuna import logging +from optuna import progress_bar as pbar_module +from optuna.exceptions import ExperimentalWarning +from optuna.storages._heartbeat import get_heartbeat_thread +from optuna.storages._heartbeat import is_heartbeat_enabled +from optuna.study._tell import _tell_with_warning +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +_logger = logging.get_logger(__name__) + + +def _optimize( + study: "optuna.Study", + func: "optuna.study.study.ObjectiveFuncType", + n_trials: int | None = None, + timeout: float | None = None, + n_jobs: int = 1, + catch: tuple[type[Exception], ...] = (), + callbacks: Iterable[Callable[["optuna.Study", FrozenTrial], None]] | None = None, + gc_after_trial: bool = False, + show_progress_bar: bool = False, +) -> None: + if not isinstance(catch, tuple): + raise TypeError( + "The catch argument is of type '{}' but must be a tuple.".format(type(catch).__name__) + ) + + if study._thread_local.in_optimize_loop: + raise RuntimeError("Nested invocation of `Study.optimize` method isn't allowed.") + + if show_progress_bar and n_trials is None and timeout is not None and n_jobs != 1: + warnings.warn("The timeout-based progress bar is not supported with n_jobs != 1.") + show_progress_bar = False + + progress_bar = pbar_module._ProgressBar(show_progress_bar, n_trials, timeout) + + study._stop_flag = False + + try: + if n_jobs == 1: + _optimize_sequential( + study, + func, + n_trials, + timeout, + catch, + callbacks, + gc_after_trial, + reseed_sampler_rng=False, + time_start=None, + progress_bar=progress_bar, + ) + else: + if n_jobs == -1: + n_jobs = os.cpu_count() or 1 + + time_start = datetime.datetime.now() + futures: set[Future] = set() + + with ThreadPoolExecutor(max_workers=n_jobs) as executor: + for n_submitted_trials in itertools.count(): + if study._stop_flag: + break + + if ( + timeout is not None + and (datetime.datetime.now() - time_start).total_seconds() > timeout + ): + break + + if n_trials is not None and n_submitted_trials >= n_trials: + break + + if len(futures) >= n_jobs: + completed, futures = wait(futures, return_when=FIRST_COMPLETED) + # Raise if exception occurred in executing the completed futures. + for f in completed: + f.result() + + futures.add( + executor.submit( + _optimize_sequential, + study, + func, + 1, + timeout, + catch, + callbacks, + gc_after_trial, + True, + time_start, + progress_bar, + ) + ) + finally: + study._thread_local.in_optimize_loop = False + progress_bar.close() + + +def _optimize_sequential( + study: "optuna.Study", + func: "optuna.study.study.ObjectiveFuncType", + n_trials: int | None, + timeout: float | None, + catch: tuple[type[Exception], ...], + callbacks: Iterable[Callable[["optuna.Study", FrozenTrial], None]] | None, + gc_after_trial: bool, + reseed_sampler_rng: bool, + time_start: datetime.datetime | None, + progress_bar: pbar_module._ProgressBar | None, +) -> None: + # Here we set `in_optimize_loop = True`, not at the beginning of the `_optimize()` function. + # Because it is a thread-local object and `n_jobs` option spawns new threads. + study._thread_local.in_optimize_loop = True + if reseed_sampler_rng: + study.sampler.reseed_rng() + + i_trial = 0 + + if time_start is None: + time_start = datetime.datetime.now() + + while True: + if study._stop_flag: + break + + if n_trials is not None: + if i_trial >= n_trials: + break + i_trial += 1 + + if timeout is not None: + elapsed_seconds = (datetime.datetime.now() - time_start).total_seconds() + if elapsed_seconds >= timeout: + break + + try: + frozen_trial_id = _run_trial(study, func, catch) + finally: + # The following line mitigates memory problems that can be occurred in some + # environments (e.g., services that use computing containers such as GitHub Actions). + # Please refer to the following PR for further details: + # https://github.com/optuna/optuna/pull/325. + if gc_after_trial: + gc.collect() + + if callbacks is not None: + frozen_trial = study._storage.get_trial(frozen_trial_id) + for callback in callbacks: + callback(study, copy.deepcopy(frozen_trial)) + + if progress_bar is not None: + elapsed_seconds = (datetime.datetime.now() - time_start).total_seconds() + progress_bar.update(elapsed_seconds, study) + + study._storage.remove_session() + + +def _run_trial( + study: "optuna.Study", + func: "optuna.study.study.ObjectiveFuncType", + catch: tuple[type[Exception], ...], +) -> int: + if is_heartbeat_enabled(study._storage): + with warnings.catch_warnings(): + # Ignore ExperimentalWarning when using fail_stale_trials internally. + warnings.simplefilter("ignore", ExperimentalWarning) + optuna.storages.fail_stale_trials(study) + + trial = study.ask() + + state: TrialState | None = None + value_or_values: float | Sequence[float] | None = None + func_err: Exception | KeyboardInterrupt | None = None + func_err_fail_exc_info: Any | None = None + + with get_heartbeat_thread(trial._trial_id, study._storage): + try: + value_or_values = func(trial) + except exceptions.TrialPruned as e: + # TODO(mamu): Handle multi-objective cases. + state = TrialState.PRUNED + func_err = e + except (Exception, KeyboardInterrupt) as e: + state = TrialState.FAIL + func_err = e + func_err_fail_exc_info = sys.exc_info() + + # `_tell_with_warning` may raise during trial post-processing. + try: + updated_state, values, warning_message = _tell_with_warning( + study=study, + trial=trial, + value_or_values=value_or_values, + state=state, + suppress_warning=True, + ) + except Exception: + frozen_trial = study._storage.get_trial(trial._trial_id) + updated_state = frozen_trial.state + values = frozen_trial.values + warning_message = None + raise + finally: + if updated_state == TrialState.COMPLETE: + assert values is not None + study._log_completed_trial(values, trial.number, trial.params) + elif updated_state == TrialState.PRUNED: + _logger.info("Trial {} pruned. {}".format(trial.number, str(func_err))) + elif updated_state == TrialState.FAIL: + if func_err is not None: + _log_failed_trial( + trial.number, + trial.params, + repr(func_err), + exc_info=func_err_fail_exc_info, + value_or_values=value_or_values, + ) + elif warning_message is not None: + _log_failed_trial( + trial.number, + trial.params, + warning_message, + value_or_values=value_or_values, + ) + else: + assert False, "Should not reach." + else: + assert False, "Should not reach." + + if ( + updated_state == TrialState.FAIL + and func_err is not None + and not isinstance(func_err, catch) + ): + raise func_err + return trial._trial_id + + +def _log_failed_trial( + trial_number: int, + trial_params: dict[str, Any], + message: str | Warning, + exc_info: Any = None, + value_or_values: Any = None, +) -> None: + _logger.warning( + "Trial {} failed with parameters: {} because of the following error: {}.".format( + trial_number, trial_params, message + ), + exc_info=exc_info, + ) + + _logger.warning("Trial {} failed with value {}.".format(trial_number, repr(value_or_values))) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_study_direction.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_study_direction.py new file mode 100644 index 0000000000000000000000000000000000000000..dd2d911953f6711c692f37017948d370200feb22 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_study_direction.py @@ -0,0 +1,18 @@ +import enum + + +class StudyDirection(enum.IntEnum): + """Direction of a :class:`~optuna.study.Study`. + + Attributes: + NOT_SET: + Direction has not been set. + MINIMIZE: + :class:`~optuna.study.Study` minimizes the objective function. + MAXIMIZE: + :class:`~optuna.study.Study` maximizes the objective function. + """ + + NOT_SET = 0 + MINIMIZE = 1 + MAXIMIZE = 2 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_study_summary.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_study_summary.py new file mode 100644 index 0000000000000000000000000000000000000000..ad2a7af2a42e8a33d34c706c5a0f28e116cf4597 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_study_summary.py @@ -0,0 +1,123 @@ +from __future__ import annotations + +from collections.abc import Sequence +import datetime +from typing import Any +import warnings + +from optuna import logging +from optuna import trial +from optuna.study._study_direction import StudyDirection + + +_logger = logging.get_logger(__name__) + + +class StudySummary: + """Basic attributes and aggregated results of a :class:`~optuna.study.Study`. + + See also :func:`optuna.study.get_all_study_summaries`. + + Attributes: + study_name: + Name of the :class:`~optuna.study.Study`. + direction: + :class:`~optuna.study.StudyDirection` of the :class:`~optuna.study.Study`. + + .. note:: + This attribute is only available during single-objective optimization. + directions: + A sequence of :class:`~optuna.study.StudyDirection` objects. + best_trial: + :class:`optuna.trial.FrozenTrial` with best objective value in the + :class:`~optuna.study.Study`. + user_attrs: + Dictionary that contains the attributes of the :class:`~optuna.study.Study` set with + :func:`optuna.study.Study.set_user_attr`. + system_attrs: + Dictionary that contains the attributes of the :class:`~optuna.study.Study` internally + set by Optuna. + + .. warning:: + Deprecated in v3.1.0. ``system_attrs`` argument will be removed in the future. + The removal of this feature is currently scheduled for v5.0.0, + but this schedule is subject to change. + See https://github.com/optuna/optuna/releases/tag/v3.1.0. + n_trials: + The number of trials ran in the :class:`~optuna.study.Study`. + datetime_start: + Datetime where the :class:`~optuna.study.Study` started. + + """ + + def __init__( + self, + study_name: str, + direction: StudyDirection | None, + best_trial: trial.FrozenTrial | None, + user_attrs: dict[str, Any], + system_attrs: dict[str, Any], + n_trials: int, + datetime_start: datetime.datetime | None, + study_id: int, + *, + directions: Sequence[StudyDirection] | None = None, + ): + self.study_name = study_name + if direction is None and directions is None: + raise ValueError("Specify one of `direction` and `directions`.") + elif directions is not None: + self._directions = list(directions) + elif direction is not None: + self._directions = [direction] + else: + raise ValueError("Specify only one of `direction` and `directions`.") + self.best_trial = best_trial + self.user_attrs = user_attrs + self._system_attrs = system_attrs + self.n_trials = n_trials + self.datetime_start = datetime_start + self._study_id = study_id + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, StudySummary): + return NotImplemented + + return other.__dict__ == self.__dict__ + + def __lt__(self, other: Any) -> bool: + if not isinstance(other, StudySummary): + return NotImplemented + + return self._study_id < other._study_id + + def __le__(self, other: Any) -> bool: + if not isinstance(other, StudySummary): + return NotImplemented + + return self._study_id <= other._study_id + + @property + def direction(self) -> StudyDirection: + if len(self._directions) > 1: + raise RuntimeError( + "This attribute is not available during multi-objective optimization." + ) + + return self._directions[0] + + @property + def directions(self) -> Sequence[StudyDirection]: + return self._directions + + @property + def system_attrs(self) -> dict[str, Any]: + warnings.warn( + "`system_attrs` has been deprecated in v3.1.0. " + "The removal of this feature is currently scheduled for v5.0.0, " + "but this schedule is subject to change. " + "See https://github.com/optuna/optuna/releases/tag/v3.1.0.", + FutureWarning, + ) + + return self._system_attrs diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_tell.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_tell.py new file mode 100644 index 0000000000000000000000000000000000000000..0e51d63e04fd8a8b20e52f90f4b779501c387367 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/_tell.py @@ -0,0 +1,177 @@ +from __future__ import annotations + +from collections.abc import Sequence +import math +from typing import TYPE_CHECKING +import warnings + +import optuna +from optuna import logging +from optuna import pruners +from optuna.trial import FrozenTrial +from optuna.trial import TrialState + + +if TYPE_CHECKING: + from optuna import Study + from optuna import Trial + + +_logger = logging.get_logger(__name__) + + +def _get_frozen_trial(study: Study, trial: Trial | int) -> FrozenTrial: + if isinstance(trial, optuna.Trial): + trial_id = trial._trial_id + elif isinstance(trial, int): + trial_number = trial + try: + trial_id = study._storage.get_trial_id_from_study_id_trial_number( + study._study_id, trial_number + ) + except KeyError as e: + raise ValueError( + f"Cannot tell for trial with number {trial_number} since it has not been " + "created." + ) from e + else: + raise TypeError("Trial must be a trial object or trial number.") + + return study._storage.get_trial(trial_id) + + +def _check_state_and_values( + state: TrialState | None, values: float | Sequence[float] | None +) -> None: + if state == TrialState.COMPLETE: + if values is None: + raise ValueError( + "No values were told. Values are required when state is TrialState.COMPLETE." + ) + elif state in (TrialState.PRUNED, TrialState.FAIL): + if values is not None: + raise ValueError( + "Values were told. Values cannot be specified when state is " + "TrialState.PRUNED or TrialState.FAIL." + ) + elif state is not None: + raise ValueError(f"Cannot tell with state {state}.") + + +def _check_values_are_feasible(study: Study, values: Sequence[float]) -> str | None: + for v in values: + # TODO(Imamura): Construct error message taking into account all values and do not early + # return `value` is assumed to be ignored on failure so we can set it to any value. + try: + float(v) + except (ValueError, TypeError): + return f"The value {repr(v)} could not be cast to float" + + if math.isnan(v): + return f"The value {v} is not acceptable" + + if len(study.directions) != len(values): + return ( + f"The number of the values {len(values)} did not match the number of the objectives " + f"{len(study.directions)}" + ) + + return None + + +def _tell_with_warning( + study: Study, + trial: Trial | int, + value_or_values: float | Sequence[float] | None = None, + state: TrialState | None = None, + skip_if_finished: bool = False, + suppress_warning: bool = False, +) -> tuple[TrialState, list[float] | None, str | None]: + """Internal method of :func:`~optuna.study.Study.tell`. + + Refer to the document for :func:`~optuna.study.Study.tell` for the reference. + This method has one additional parameter ``suppress_warning``. + + Args: + suppress_warning: + If :obj:`True`, tell will not show warnings when tell receives an invalid + values. This flag is expected to be :obj:`True` only when it is invoked by + Study.optimize. + """ + + # We must invalidate all trials cache here as it is only valid within a trial. + study._thread_local.cached_all_trials = None + + # Validate the trial argument. + frozen_trial = _get_frozen_trial(study, trial) + if frozen_trial.state.is_finished() and skip_if_finished: + _logger.info( + f"Skipped telling trial {frozen_trial.number} with values " + f"{value_or_values} and state {state} since trial was already finished. " + f"Finished trial has values {frozen_trial.values} and state {frozen_trial.state}." + ) + return frozen_trial.state, frozen_trial.values, None + elif frozen_trial.state != TrialState.RUNNING: + raise ValueError(f"Cannot tell a {frozen_trial.state.name} trial.") + + # Validate the state and values arguments. + values: Sequence[float] | None + if value_or_values is None: + values = None + elif isinstance(value_or_values, Sequence): + values = value_or_values + else: + values = [value_or_values] + + _check_state_and_values(state, values) + + values_conversion_failure_message = None + + if state == TrialState.COMPLETE: + assert values is not None + + values_conversion_failure_message = _check_values_are_feasible(study, values) + if values_conversion_failure_message is not None: + raise ValueError(values_conversion_failure_message) + elif state == TrialState.PRUNED: + # Register the last intermediate value if present as the value of the trial. + # TODO(hvy): Whether a pruned trials should have an actual value can be discussed. + assert values is None + + last_step = frozen_trial.last_step + if last_step is not None: + last_intermediate_value = frozen_trial.intermediate_values[last_step] + # intermediate_values can be unacceptable value, i.e., NaN. + if _check_values_are_feasible(study, [last_intermediate_value]) is None: + values = [last_intermediate_value] + elif state is None: + if values is None: + values_conversion_failure_message = "The value None could not be cast to float." + else: + values_conversion_failure_message = _check_values_are_feasible(study, values) + + if values_conversion_failure_message is None: + state = TrialState.COMPLETE + else: + state = TrialState.FAIL + values = None + if not suppress_warning: + warnings.warn(values_conversion_failure_message) + values_conversion_failure_message = None + + assert state is not None + + # Cast values to list of floats. + if values is not None: + # values have been checked to be castable to floats in _check_values_are_feasible. + values = [float(value) for value in values] + + # Post-processing and storing the trial. + try: + # Sampler defined trial post-processing. + study = pruners._filter_study(study, frozen_trial) + study.sampler.after_trial(study, frozen_trial, state, values) + finally: + study._storage.set_trial_state_values(frozen_trial._trial_id, state, values) + + return state, values, values_conversion_failure_message diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/study.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/study.py new file mode 100644 index 0000000000000000000000000000000000000000..c14de8561029fdc8cd394f3081d67563dccbf827 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/study/study.py @@ -0,0 +1,1728 @@ +from __future__ import annotations + +from collections.abc import Container +from collections.abc import Iterable +from collections.abc import Mapping +import copy +from numbers import Real +import threading +from typing import Any +from typing import Callable +from typing import cast +from typing import Sequence +from typing import TYPE_CHECKING +from typing import Union +import warnings + +import numpy as np + +import optuna +from optuna import exceptions +from optuna import logging +from optuna import pruners +from optuna import samplers +from optuna import storages +from optuna._convert_positional_args import convert_positional_args +from optuna._deprecated import deprecated_func +from optuna._experimental import experimental_func +from optuna._imports import _LazyImport +from optuna._typing import JSONSerializable +from optuna.distributions import _convert_old_distribution_to_new_distribution +from optuna.distributions import BaseDistribution +from optuna.storages._heartbeat import is_heartbeat_enabled +from optuna.study._constrained_optimization import _CONSTRAINTS_KEY +from optuna.study._constrained_optimization import _get_feasible_trials +from optuna.study._multi_objective import _get_pareto_front_trials +from optuna.study._optimize import _optimize +from optuna.study._study_direction import StudyDirection +from optuna.study._study_summary import StudySummary # NOQA +from optuna.study._tell import _get_frozen_trial +from optuna.study._tell import _tell_with_warning +from optuna.trial import create_trial +from optuna.trial import TrialState + + +_dataframe = _LazyImport("optuna.study._dataframe") + +if TYPE_CHECKING: + from optuna.study._dataframe import pd + from optuna.trial import FrozenTrial + from optuna.trial import Trial + + +ObjectiveFuncType = Callable[["Trial"], Union[float, Sequence[float]]] + + +_SYSTEM_ATTR_METRIC_NAMES = "study:metric_names" + + +_logger = logging.get_logger(__name__) + + +class _ThreadLocalStudyAttribute(threading.local): + in_optimize_loop: bool = False + cached_all_trials: list[FrozenTrial] | None = None + + +class Study: + """A study corresponds to an optimization task, i.e., a set of trials. + + This object provides interfaces to run a new :class:`~optuna.trial.Trial`, access trials' + history, set/get user-defined attributes of the study itself. + + Note that the direct use of this constructor is not recommended. + To create and load a study, please refer to the documentation of + :func:`~optuna.study.create_study` and :func:`~optuna.study.load_study` respectively. + + """ + + def __init__( + self, + study_name: str, + storage: str | storages.BaseStorage, + sampler: "samplers.BaseSampler" | None = None, + pruner: pruners.BasePruner | None = None, + ) -> None: + self.study_name = study_name + storage = storages.get_storage(storage) + study_id = storage.get_study_id_from_name(study_name) + self._study_id = study_id + self._storage = storage + self._directions = storage.get_study_directions(study_id) + + self.sampler = sampler or samplers.TPESampler() + self.pruner = pruner or pruners.MedianPruner() + + self._thread_local = _ThreadLocalStudyAttribute() + self._stop_flag = False + + def __getstate__(self) -> dict[Any, Any]: + state = self.__dict__.copy() + del state["_thread_local"] + return state + + def __setstate__(self, state: dict[Any, Any]) -> None: + self.__dict__.update(state) + self._thread_local = _ThreadLocalStudyAttribute() + + @property + def best_params(self) -> dict[str, Any]: + """Return parameters of the best trial in the study. + + .. note:: + This feature can only be used for single-objective optimization. + + Returns: + A dictionary containing parameters of the best trial. + + """ + + return self.best_trial.params + + @property + def best_value(self) -> float: + """Return the best objective value in the study. + + .. note:: + This feature can only be used for single-objective optimization. + + Returns: + A float representing the best objective value. + + """ + + best_value = self.best_trial.value + assert best_value is not None + + return best_value + + @property + def best_trial(self) -> FrozenTrial: + """Return the best trial in the study. + + .. note:: + This feature can only be used for single-objective optimization. + If your study is multi-objective, + use :attr:`~optuna.study.Study.best_trials` instead. + + Returns: + A :class:`~optuna.trial.FrozenTrial` object of the best trial. + + .. seealso:: + The :ref:`reuse_best_trial` tutorial provides a detailed example of how to use this + method. + + """ + return self._get_best_trial(deepcopy=True) + + @property + def best_trials(self) -> list[FrozenTrial]: + """Return trials located at the Pareto front in the study. + + A trial is located at the Pareto front if there are no trials that dominate the trial. + It's called that a trial ``t0`` dominates another trial ``t1`` if + ``all(v0 <= v1) for v0, v1 in zip(t0.values, t1.values)`` and + ``any(v0 < v1) for v0, v1 in zip(t0.values, t1.values)`` are held. + + Returns: + A list of :class:`~optuna.trial.FrozenTrial` objects. + """ + + # Check whether the study is constrained optimization. + trials = self.get_trials(deepcopy=False) + is_constrained = any((_CONSTRAINTS_KEY in trial.system_attrs) for trial in trials) + + return _get_pareto_front_trials(self, consider_constraint=is_constrained) + + @property + def direction(self) -> StudyDirection: + """Return the direction of the study. + + .. note:: + This feature can only be used for single-objective optimization. + If your study is multi-objective, + use :attr:`~optuna.study.Study.directions` instead. + + Returns: + A :class:`~optuna.study.StudyDirection` object. + + """ + + if self._is_multi_objective(): + raise RuntimeError( + "A single direction cannot be retrieved from a multi-objective study. Consider " + "using Study.directions to retrieve a list containing all directions." + ) + + return self.directions[0] + + @property + def directions(self) -> list[StudyDirection]: + """Return the directions of the study. + + Returns: + A list of :class:`~optuna.study.StudyDirection` objects. + """ + + return self._directions + + @property + def trials(self) -> list[FrozenTrial]: + """Return all trials in the study. + + The returned trials are ordered by trial number. + + This is a short form of ``self.get_trials(deepcopy=True, states=None)``. + + Returns: + A list of :class:`~optuna.trial.FrozenTrial` objects. + + .. seealso:: + See :func:`~optuna.study.Study.get_trials` for related method. + + """ + + return self.get_trials(deepcopy=True, states=None) + + def get_trials( + self, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + ) -> list[FrozenTrial]: + """Return all trials in the study. + + The returned trials are ordered by trial number. + + .. seealso:: + See :attr:`~optuna.study.Study.trials` for related property. + + Example: + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -1, 1) + return x**2 + + + study = optuna.create_study() + study.optimize(objective, n_trials=3) + + trials = study.get_trials() + assert len(trials) == 3 + Args: + deepcopy: + Flag to control whether to apply ``copy.deepcopy()`` to the trials. + Note that if you set the flag to :obj:`False`, you shouldn't mutate + any fields of the returned trial. Otherwise the internal state of + the study may corrupt and unexpected behavior may happen. + states: + Trial states to filter on. If :obj:`None`, include all states. + + Returns: + A list of :class:`~optuna.trial.FrozenTrial` objects. + """ + return self._get_trials(deepcopy, states, use_cache=False) + + def _get_trials( + self, + deepcopy: bool = True, + states: Container[TrialState] | None = None, + use_cache: bool = False, + ) -> list[FrozenTrial]: + if use_cache: + if self._thread_local.cached_all_trials is None: + self._thread_local.cached_all_trials = self._storage.get_all_trials( + self._study_id, deepcopy=False + ) + trials = self._thread_local.cached_all_trials + if states is not None: + filtered_trials = [t for t in trials if t.state in states] + else: + filtered_trials = trials + return copy.deepcopy(filtered_trials) if deepcopy else filtered_trials + + return self._storage.get_all_trials(self._study_id, deepcopy=deepcopy, states=states) + + def _get_best_trial(self, deepcopy: bool) -> FrozenTrial: + """Return the best trial in the study. + + Args: + deepcopy: + Flag to control whether to apply ``copy.deepcopy()`` to the trial. + If :obj:`False`, returns the trial without deep copying for better performance. + Note that if you set this to :obj:`False`, you shouldn't mutate any fields + of the returned trial. + + Returns: + A :class:`~optuna.trial.FrozenTrial` object of the best trial. + """ + if self._is_multi_objective(): + raise RuntimeError( + "A single best trial cannot be retrieved from a multi-objective study. Consider " + "using Study.best_trials to retrieve a list containing the best trials." + ) + + best_trial = self._storage.get_best_trial(self._study_id) + + # If the trial with the best value is infeasible, select the best trial from all feasible + # trials. Note that the behavior is undefined when constrained optimization without the + # violation value in the best-valued trial. + constraints = best_trial.system_attrs.get(_CONSTRAINTS_KEY) + if constraints is not None and any([x > 0.0 for x in constraints]): + complete_trials = self.get_trials(deepcopy=False, states=[TrialState.COMPLETE]) + feasible_trials = _get_feasible_trials(complete_trials) + if len(feasible_trials) == 0: + raise ValueError("No feasible trials are completed yet.") + if self.direction == StudyDirection.MAXIMIZE: + best_trial = max(feasible_trials, key=lambda t: cast(float, t.value)) + else: + best_trial = min(feasible_trials, key=lambda t: cast(float, t.value)) + + return copy.deepcopy(best_trial) if deepcopy else best_trial + + @property + def user_attrs(self) -> dict[str, Any]: + """Return user attributes. + + .. seealso:: + + See :func:`~optuna.study.Study.set_user_attr` for related method. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", 0, 1) + y = trial.suggest_float("y", 0, 1) + return x**2 + y**2 + + + study = optuna.create_study() + + study.set_user_attr("objective function", "quadratic function") + study.set_user_attr("dimensions", 2) + study.set_user_attr("contributors", ["Akiba", "Sano"]) + + assert study.user_attrs == { + "objective function": "quadratic function", + "dimensions": 2, + "contributors": ["Akiba", "Sano"], + } + + Returns: + A dictionary containing all user attributes. + """ + + return copy.deepcopy(self._storage.get_study_user_attrs(self._study_id)) + + @property + @deprecated_func("3.1.0", "5.0.0") + def system_attrs(self) -> dict[str, Any]: + """Return system attributes. + + Returns: + A dictionary containing all system attributes. + """ + + return copy.deepcopy(self._storage.get_study_system_attrs(self._study_id)) + + @property + def metric_names(self) -> list[str] | None: + """Return metric names. + + .. note:: + Use :meth:`~optuna.study.Study.set_metric_names` to set the metric names first. + + Returns: + A list with names for each dimension of the returned values of the objective function. + """ + return self._storage.get_study_system_attrs(self._study_id).get(_SYSTEM_ATTR_METRIC_NAMES) + + def optimize( + self, + func: ObjectiveFuncType, + n_trials: int | None = None, + timeout: float | None = None, + n_jobs: int = 1, + catch: Iterable[type[Exception]] | type[Exception] = (), + callbacks: Iterable[Callable[[Study, FrozenTrial], None]] | None = None, + gc_after_trial: bool = False, + show_progress_bar: bool = False, + ) -> None: + """Optimize an objective function. + + Optimization is done by choosing a suitable set of hyperparameter values from a given + range. Uses a sampler which implements the task of value suggestion based on a specified + distribution. The sampler is specified in :func:`~optuna.study.create_study` and the + default choice for the sampler is TPE. + See also :class:`~optuna.samplers.TPESampler` for more details on 'TPE'. + + Optimization will be stopped when receiving a termination signal such as SIGINT and + SIGTERM. Unlike other signals, a trial is automatically and cleanly failed when receiving + SIGINT (Ctrl+C). If ``n_jobs`` is greater than one or if another signal than SIGINT + is used, the interrupted trial state won't be properly updated. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -1, 1) + return x**2 + + + study = optuna.create_study() + study.optimize(objective, n_trials=3) + + Args: + func: + A callable that implements objective function. + n_trials: + The number of trials for each process. :obj:`None` represents no limit in terms of + the number of trials. The study continues to create trials until the number of + trials reaches ``n_trials``, ``timeout`` period elapses, + :func:`~optuna.study.Study.stop` is called, or a termination signal such as + SIGTERM or Ctrl+C is received. + + .. seealso:: + :class:`optuna.study.MaxTrialsCallback` can ensure how many times trials + will be performed across all processes. + timeout: + Stop study after the given number of second(s). :obj:`None` represents no limit in + terms of elapsed time. The study continues to create trials until the number of + trials reaches ``n_trials``, ``timeout`` period elapses, + :func:`~optuna.study.Study.stop` is called or, a termination signal such as + SIGTERM or Ctrl+C is received. + n_jobs: + The number of parallel jobs. If this argument is set to ``-1``, the number is + set to CPU count. + + .. note:: + ``n_jobs`` allows parallelization using :obj:`threading` and may suffer from + `Python's GIL `__. + It is recommended to use :ref:`process-based parallelization` + if ``func`` is CPU bound. + + catch: + A study continues to run even when a trial raises one of the exceptions specified + in this argument. Default is an empty tuple, i.e. the study will stop for any + exception except for :class:`~optuna.exceptions.TrialPruned`. + callbacks: + List of callback functions that are invoked at the end of each trial. Each function + must accept two parameters with the following types in this order: + :class:`~optuna.study.Study` and :class:`~optuna.trial.FrozenTrial`. + + .. seealso:: + + See the tutorial of :ref:`optuna_callback` for how to use and implement + callback functions. + + gc_after_trial: + Flag to determine whether to automatically run garbage collection after each trial. + Set to :obj:`True` to run the garbage collection, :obj:`False` otherwise. + When it runs, it runs a full collection by internally calling :func:`gc.collect`. + If you see an increase in memory consumption over several trials, try setting this + flag to :obj:`True`. + + .. seealso:: + + :ref:`out-of-memory-gc-collect` + + show_progress_bar: + Flag to show progress bars or not. To show progress bar, set this :obj:`True`. + Note that it is disabled when ``n_trials`` is :obj:`None`, + ``timeout`` is not :obj:`None`, and ``n_jobs`` :math:`\\ne 1`. + + Raises: + RuntimeError: + If nested invocation of this method occurs. + """ + _optimize( + study=self, + func=func, + n_trials=n_trials, + timeout=timeout, + n_jobs=n_jobs, + catch=tuple(catch) if isinstance(catch, Iterable) else (catch,), + callbacks=callbacks, + gc_after_trial=gc_after_trial, + show_progress_bar=show_progress_bar, + ) + + def ask(self, fixed_distributions: dict[str, BaseDistribution] | None = None) -> Trial: + """Create a new trial from which hyperparameters can be suggested. + + This method is part of an alternative to :func:`~optuna.study.Study.optimize` that allows + controlling the lifetime of a trial outside the scope of ``func``. Each call to this + method should be followed by a call to :func:`~optuna.study.Study.tell` to finish the + created trial. + + .. seealso:: + + The :ref:`ask_and_tell` tutorial provides use-cases with examples. + + Example: + + Getting the trial object with the :func:`~optuna.study.Study.ask` method. + + .. testcode:: + + import optuna + + + study = optuna.create_study() + + trial = study.ask() + + x = trial.suggest_float("x", -1, 1) + + study.tell(trial, x**2) + + Example: + + Passing previously defined distributions to the :func:`~optuna.study.Study.ask` + method. + + .. testcode:: + + import optuna + + + study = optuna.create_study() + + distributions = { + "optimizer": optuna.distributions.CategoricalDistribution(["adam", "sgd"]), + "lr": optuna.distributions.FloatDistribution(0.0001, 0.1, log=True), + } + + # You can pass the distributions previously defined. + trial = study.ask(fixed_distributions=distributions) + + # `optimizer` and `lr` are already suggested and accessible with `trial.params`. + assert "optimizer" in trial.params + assert "lr" in trial.params + + Args: + fixed_distributions: + A dictionary containing the parameter names and parameter's distributions. Each + parameter in this dictionary is automatically suggested for the returned trial, + even when the suggest method is not explicitly invoked by the user. If this + argument is set to :obj:`None`, no parameter is automatically suggested. + + Returns: + A :class:`~optuna.trial.Trial`. + """ + + if not self._thread_local.in_optimize_loop and is_heartbeat_enabled(self._storage): + warnings.warn("Heartbeat of storage is supposed to be used with Study.optimize.") + + fixed_distributions = fixed_distributions or {} + fixed_distributions = { + key: _convert_old_distribution_to_new_distribution(dist) + for key, dist in fixed_distributions.items() + } + + # Sync storage once every trial. + self._thread_local.cached_all_trials = None + + trial_id = self._pop_waiting_trial_id() + if trial_id is None: + trial_id = self._storage.create_new_trial(self._study_id) + trial = optuna.Trial(self, trial_id) + + for name, param in fixed_distributions.items(): + trial._suggest(name, param) + + return trial + + def tell( + self, + trial: Trial | int, + values: float | Sequence[float] | None = None, + state: TrialState | None = None, + skip_if_finished: bool = False, + ) -> FrozenTrial: + """Finish a trial created with :func:`~optuna.study.Study.ask`. + + .. seealso:: + + The :ref:`ask_and_tell` tutorial provides use-cases with examples. + + Example: + + .. testcode:: + + import optuna + from optuna.trial import TrialState + + + def f(x): + return (x - 2) ** 2 + + + def df(x): + return 2 * x - 4 + + + study = optuna.create_study() + + n_trials = 30 + + for _ in range(n_trials): + trial = study.ask() + + lr = trial.suggest_float("lr", 1e-5, 1e-1, log=True) + + # Iterative gradient descent objective function. + x = 3 # Initial value. + for step in range(128): + y = f(x) + + trial.report(y, step=step) + + if trial.should_prune(): + # Finish the trial with the pruned state. + study.tell(trial, state=TrialState.PRUNED) + break + + gy = df(x) + x -= gy * lr + else: + # Finish the trial with the final value after all iterations. + study.tell(trial, y) + + Args: + trial: + A :class:`~optuna.trial.Trial` object or a trial number. + values: + Optional objective value or a sequence of such values in case the study is used + for multi-objective optimization. Argument must be provided if ``state`` is + :class:`~optuna.trial.TrialState.COMPLETE` and should be :obj:`None` if ``state`` + is :class:`~optuna.trial.TrialState.FAIL` or + :class:`~optuna.trial.TrialState.PRUNED`. + state: + State to be reported. Must be :obj:`None`, + :class:`~optuna.trial.TrialState.COMPLETE`, + :class:`~optuna.trial.TrialState.FAIL` or + :class:`~optuna.trial.TrialState.PRUNED`. + If ``state`` is :obj:`None`, + it will be updated to :class:`~optuna.trial.TrialState.COMPLETE` + or :class:`~optuna.trial.TrialState.FAIL` depending on whether + validation for ``values`` reported succeed or not. + skip_if_finished: + Flag to control whether exception should be raised when values for already + finished trial are told. If :obj:`True`, tell is skipped without any error + when the trial is already finished. + + Returns: + A :class:`~optuna.trial.FrozenTrial` representing the resulting trial. + A returned trial is deep copied thus user can modify it as needed. + """ + + _tell_with_warning( + study=self, + trial=trial, + value_or_values=values, + state=state, + skip_if_finished=skip_if_finished, + ) + return copy.deepcopy(_get_frozen_trial(self, trial)) + + def set_user_attr(self, key: str, value: Any) -> None: + """Set a user attribute to the study. + + .. seealso:: + + See :attr:`~optuna.study.Study.user_attrs` for related attribute. + + .. seealso:: + + See the recipe on :ref:`attributes`. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", 0, 1) + y = trial.suggest_float("y", 0, 1) + return x**2 + y**2 + + + study = optuna.create_study() + + study.set_user_attr("objective function", "quadratic function") + study.set_user_attr("dimensions", 2) + study.set_user_attr("contributors", ["Akiba", "Sano"]) + + assert study.user_attrs == { + "objective function": "quadratic function", + "dimensions": 2, + "contributors": ["Akiba", "Sano"], + } + + Args: + key: A key string of the attribute. + value: A value of the attribute. The value should be JSON serializable. + + """ + + self._storage.set_study_user_attr(self._study_id, key, value) + + @deprecated_func("3.1.0", "5.0.0") + def set_system_attr(self, key: str, value: Any) -> None: + """Set a system attribute to the study. + + Note that Optuna internally uses this method to save system messages. Please use + :func:`~optuna.study.Study.set_user_attr` to set users' attributes. + + Args: + key: A key string of the attribute. + value: A value of the attribute. The value should be JSON serializable. + + """ + + self._storage.set_study_system_attr(self._study_id, key, value) + + def trials_dataframe( + self, + attrs: tuple[str, ...] = ( + "number", + "value", + "datetime_start", + "datetime_complete", + "duration", + "params", + "user_attrs", + "system_attrs", + "state", + ), + multi_index: bool = False, + ) -> "pd.DataFrame": + """Export trials as a pandas DataFrame_. + + The DataFrame_ provides various features to analyze studies. It is also useful to draw a + histogram of objective values and to export trials as a CSV file. + If there are no trials, an empty DataFrame_ is returned. + + Example: + + .. testcode:: + + import optuna + import pandas + + + def objective(trial): + x = trial.suggest_float("x", -1, 1) + return x**2 + + + study = optuna.create_study() + study.optimize(objective, n_trials=3) + + # Create a dataframe from the study. + df = study.trials_dataframe() + assert isinstance(df, pandas.DataFrame) + assert df.shape[0] == 3 # n_trials. + + Args: + attrs: + Specifies field names of :class:`~optuna.trial.FrozenTrial` to include them to a + DataFrame of trials. + multi_index: + Specifies whether the returned DataFrame_ employs MultiIndex_ or not. Columns that + are hierarchical by nature such as ``(params, x)`` will be flattened to + ``params_x`` when set to :obj:`False`. + + Returns: + A pandas DataFrame_ of trials in the :class:`~optuna.study.Study`. + + .. _DataFrame: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html + .. _MultiIndex: https://pandas.pydata.org/pandas-docs/stable/advanced.html + + Note: + If ``value`` is in ``attrs`` during multi-objective optimization, it is implicitly + replaced with ``values``. + + Note: + If :meth:`~optuna.study.Study.set_metric_names` is called, the ``value`` or ``values`` + is implicitly replaced with the dictionary with the objective name as key and the + objective value as value. + """ + return _dataframe._trials_dataframe(self, attrs, multi_index) + + def stop(self) -> None: + """Exit from the current optimization loop after the running trials finish. + + This method lets the running :meth:`~optuna.study.Study.optimize` method return + immediately after all trials which the :meth:`~optuna.study.Study.optimize` method + spawned finishes. + This method does not affect any behaviors of parallel or successive study processes. + This method only works when it is called inside an objective function or callback. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + if trial.number == 4: + trial.study.stop() + x = trial.suggest_float("x", 0, 10) + return x**2 + + + study = optuna.create_study() + study.optimize(objective, n_trials=10) + assert len(study.trials) == 5 + + """ + + if not self._thread_local.in_optimize_loop: + raise RuntimeError( + "`Study.stop` is supposed to be invoked inside an objective function or a " + "callback." + ) + + self._stop_flag = True + + def enqueue_trial( + self, + params: dict[str, Any], + user_attrs: dict[str, Any] | None = None, + skip_if_exists: bool = False, + ) -> None: + """Enqueue a trial with given parameter values. + + You can fix the next sampling parameters which will be evaluated in your + objective function. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", 0, 10) + return x**2 + + + study = optuna.create_study() + study.enqueue_trial({"x": 5}) + study.enqueue_trial({"x": 0}, user_attrs={"memo": "optimal"}) + study.optimize(objective, n_trials=2) + + assert study.trials[0].params == {"x": 5} + assert study.trials[1].params == {"x": 0} + assert study.trials[1].user_attrs == {"memo": "optimal"} + + Args: + params: + Parameter values to pass your objective function. + user_attrs: + A dictionary of user-specific attributes other than ``params``. + skip_if_exists: + When :obj:`True`, prevents duplicate trials from being enqueued again. + + .. note:: + This method might produce duplicated trials if called simultaneously + by multiple processes at the same time with same ``params`` dict. + + .. seealso:: + + Please refer to :ref:`enqueue_trial_tutorial` for the tutorial of specifying + hyperparameters manually. + """ + + if not isinstance(params, dict): + raise TypeError("params must be a dictionary.") + + if skip_if_exists and self._should_skip_enqueue(params): + _logger.info(f"Trial with params {params} already exists. Skipping enqueue.") + return + + self.add_trial( + create_trial( + state=TrialState.WAITING, + system_attrs={"fixed_params": params}, + user_attrs=user_attrs, + ) + ) + + def add_trial(self, trial: FrozenTrial) -> None: + """Add trial to study. + + The trial is validated before being added. + + Example: + + .. testcode:: + + import optuna + from optuna.distributions import FloatDistribution + + + def objective(trial): + x = trial.suggest_float("x", 0, 10) + return x**2 + + + study = optuna.create_study() + assert len(study.trials) == 0 + + trial = optuna.trial.create_trial( + params={"x": 2.0}, + distributions={"x": FloatDistribution(0, 10)}, + value=4.0, + ) + + study.add_trial(trial) + assert len(study.trials) == 1 + + study.optimize(objective, n_trials=3) + assert len(study.trials) == 4 + + other_study = optuna.create_study() + + for trial in study.trials: + other_study.add_trial(trial) + assert len(other_study.trials) == len(study.trials) + + other_study.optimize(objective, n_trials=2) + assert len(other_study.trials) == len(study.trials) + 2 + + .. seealso:: + + This method should in general be used to add already evaluated trials + (``trial.state.is_finished() == True``). To queue trials for evaluation, + please refer to :func:`~optuna.study.Study.enqueue_trial`. + + .. seealso:: + + See :func:`~optuna.trial.create_trial` for how to create trials. + + .. seealso:: + Please refer to :ref:`add_trial_tutorial` for the tutorial of specifying + hyperparameters with the evaluated value manually. + + Args: + trial: Trial to add. + + """ + + trial._validate() + + if trial.values is not None and len(self.directions) != len(trial.values): + raise ValueError( + f"The added trial has {len(trial.values)} values, which is different from the " + f"number of objectives {len(self.directions)} in the study (determined by " + "Study.directions)." + ) + + self._storage.create_new_trial(self._study_id, template_trial=trial) + + def add_trials(self, trials: Iterable[FrozenTrial]) -> None: + """Add trials to study. + + The trials are validated before being added. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", 0, 10) + return x**2 + + + study = optuna.create_study() + study.optimize(objective, n_trials=3) + assert len(study.trials) == 3 + + other_study = optuna.create_study() + other_study.add_trials(study.trials) + assert len(other_study.trials) == len(study.trials) + + other_study.optimize(objective, n_trials=2) + assert len(other_study.trials) == len(study.trials) + 2 + + .. seealso:: + + See :func:`~optuna.study.Study.add_trial` for addition of each trial. + + Args: + trials: Trials to add. + + """ + + for trial in trials: + self.add_trial(trial) + + @experimental_func("3.2.0") + def set_metric_names(self, metric_names: list[str]) -> None: + """Set metric names. + + This method names each dimension of the returned values of the objective function. + It is particularly useful in multi-objective optimization. The metric names are + mainly referenced by the visualization functions. + + Example: + + .. testcode:: + + import optuna + import pandas + + + def objective(trial): + x = trial.suggest_float("x", 0, 10) + return x**2, x + 1 + + + study = optuna.create_study(directions=["minimize", "minimize"]) + study.set_metric_names(["x**2", "x+1"]) + study.optimize(objective, n_trials=3) + + df = study.trials_dataframe(multi_index=True) + assert isinstance(df, pandas.DataFrame) + assert list(df.get("values").keys()) == ["x**2", "x+1"] + + .. seealso:: + The names set by this method are used in :meth:`~optuna.study.Study.trials_dataframe` + and :func:`~optuna.visualization.plot_pareto_front`. + + Args: + metric_names: A list of metric names for the objective function. + """ + if len(self.directions) != len(metric_names): + raise ValueError("The number of objectives must match the length of the metric names.") + + self._storage.set_study_system_attr( + self._study_id, _SYSTEM_ATTR_METRIC_NAMES, metric_names + ) + + def _is_multi_objective(self) -> bool: + """Return :obj:`True` if the study has multiple objectives. + + Returns: + A boolean value indicates if `self.directions` has more than 1 element or not. + """ + + return len(self.directions) > 1 + + def _pop_waiting_trial_id(self) -> int | None: + for trial in self._storage.get_all_trials( + self._study_id, deepcopy=False, states=(TrialState.WAITING,) + ): + # Attempt to set the state to RUNNING. + # - If another process or thread has already changed the state to RUNNING, + # set_trial_state_values returns False. + # - If another process or thread has already finished the trial, + # an UpdateFinishedTrialError is raised. + try: + if not self._storage.set_trial_state_values( + trial._trial_id, + state=TrialState.RUNNING, + ): + continue + except exceptions.UpdateFinishedTrialError: + continue + + _logger.debug("Trial {} popped from the trial queue.".format(trial.number)) + return trial._trial_id + + return None + + def _should_skip_enqueue(self, params: Mapping[str, JSONSerializable]) -> bool: + for trial in self.get_trials(deepcopy=False): + trial_params = trial.system_attrs.get("fixed_params", trial.params) + if trial_params.keys() != params.keys(): + # Can't have repeated trials if different params are suggested. + continue + + repeated_params: list[bool] = [] + for param_name, param_value in params.items(): + existing_param = trial_params[param_name] + if not isinstance(param_value, type(existing_param)): + # Enqueued param has distribution that does not match existing param + # (e.g. trying to enqueue categorical to float param). + # We are not doing anything about it here, since sanitization should + # be handled regardless if `skip_if_exists` is `True`. + repeated_params.append(False) + continue + + is_repeated = ( + np.isnan(float(param_value)) + or np.isclose(float(param_value), float(existing_param), atol=0.0) + if isinstance(param_value, Real) + else param_value == existing_param + ) + repeated_params.append(bool(is_repeated)) + + if all(repeated_params): + return True + + return False + + def _log_completed_trial( + self, values: list[float], number: int, params: dict[str, Any] + ) -> None: + if not _logger.isEnabledFor(logging.INFO): + return + + metric_names = self.metric_names + + if len(values) > 1: + trial_values: list[float] | dict[str, float] + if metric_names is None: + trial_values = values + else: + trial_values = {name: value for name, value in zip(metric_names, values)} + _logger.info( + "Trial {} finished with values: {} and parameters: {}.".format( + number, trial_values, params + ) + ) + elif len(values) == 1: + trial_value: float | dict[str, float] + if metric_names is None: + trial_value = values[0] + else: + trial_value = {metric_names[0]: values[0]} + + message = ( + f"Trial {number} finished with value: {trial_value} and parameters: " f"{params}." + ) + try: + best_trial = self._get_best_trial(deepcopy=False) + message += f" Best is trial {best_trial.number} with value: {best_trial.value}." + except ValueError: + # If no feasible trials are completed yet, study.best_trial raises ValueError. + pass + _logger.info(message) + else: + assert False, "Should not reach." + + +@convert_positional_args( + previous_positional_arg_names=[ + "storage", + "sampler", + "pruner", + "study_name", + "direction", + "load_if_exists", + ], + deprecated_version="3.0.0", + removed_version="5.0.0", +) +def create_study( + *, + storage: str | storages.BaseStorage | None = None, + sampler: "samplers.BaseSampler" | None = None, + pruner: pruners.BasePruner | None = None, + study_name: str | None = None, + direction: str | StudyDirection | None = None, + load_if_exists: bool = False, + directions: Sequence[str | StudyDirection] | None = None, +) -> Study: + """Create a new :class:`~optuna.study.Study`. + + Example: + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", 0, 10) + return x**2 + + + study = optuna.create_study() + study.optimize(objective, n_trials=3) + + Args: + storage: + Database URL. If this argument is set to None, + :class:`~optuna.storages.InMemoryStorage` is used, and the + :class:`~optuna.study.Study` will not be persistent. + + .. note:: + When a database URL is passed, Optuna internally uses `SQLAlchemy`_ to handle + the database. Please refer to `SQLAlchemy's document`_ for further details. + If you want to specify non-default options to `SQLAlchemy Engine`_, you can + instantiate :class:`~optuna.storages.RDBStorage` with your desired options and + pass it to the ``storage`` argument instead of a URL. + + .. _SQLAlchemy: https://www.sqlalchemy.org/ + .. _SQLAlchemy's document: + https://docs.sqlalchemy.org/en/latest/core/engines.html#database-urls + .. _SQLAlchemy Engine: https://docs.sqlalchemy.org/en/latest/core/engines.html + + sampler: + A sampler object that implements background algorithm for value suggestion. + If :obj:`None` is specified, :class:`~optuna.samplers.TPESampler` is used during + single-objective optimization and :class:`~optuna.samplers.NSGAIISampler` during + multi-objective optimization. See also :class:`~optuna.samplers`. + pruner: + A pruner object that decides early stopping of unpromising trials. If :obj:`None` + is specified, :class:`~optuna.pruners.MedianPruner` is used as the default. See + also :class:`~optuna.pruners`. + study_name: + Study's name. If this argument is set to None, a unique name is generated + automatically. + direction: + Direction of optimization. Set ``minimize`` for minimization and ``maximize`` for + maximization. You can also pass the corresponding :class:`~optuna.study.StudyDirection` + object. ``direction`` and ``directions`` must not be specified at the same time. + + .. note:: + If none of `direction` and `directions` are specified, the direction of the study + is set to "minimize". + load_if_exists: + Flag to control the behavior to handle a conflict of study names. + In the case where a study named ``study_name`` already exists in the ``storage``, + a :class:`~optuna.exceptions.DuplicatedStudyError` is raised if ``load_if_exists`` is + set to :obj:`False`. + Otherwise, the creation of the study is skipped, and the existing one is returned. + directions: + A sequence of directions during multi-objective optimization. + ``direction`` and ``directions`` must not be specified at the same time. + + Returns: + A :class:`~optuna.study.Study` object. + + See also: + :func:`optuna.create_study` is an alias of :func:`optuna.study.create_study`. + + See also: + The :ref:`rdb` tutorial provides concrete examples to save and resume optimization using + RDB. + + """ + + if direction is None and directions is None: + directions = ["minimize"] + elif direction is not None and directions is not None: + raise ValueError("Specify only one of `direction` and `directions`.") + elif direction is not None: + if isinstance(direction, Sequence) and not isinstance(direction, str): + raise ValueError( + "Use `directions` instead of `direction` for multi-objective optimization." + ) + directions = [direction] + elif directions is not None: + directions = list(directions) + else: + assert False + + if len(directions) < 1: + raise ValueError("The number of objectives must be greater than 0.") + elif any( + d not in ["minimize", "maximize", StudyDirection.MINIMIZE, StudyDirection.MAXIMIZE] + for d in directions + ): + raise ValueError( + f"`directions` must be a list of `minimize` or `maximize`, but got {directions}. " + "For single-objective optimization, please use `direction` instead of `directions`." + ) + + direction_objects = [ + d if isinstance(d, StudyDirection) else StudyDirection[d.upper()] for d in directions + ] + + storage = storages.get_storage(storage) + try: + study_id = storage.create_new_study(direction_objects, study_name) + except exceptions.DuplicatedStudyError: + if load_if_exists: + assert study_name is not None + + _logger.info( + "Using an existing study with name '{}' instead of " + "creating a new one.".format(study_name) + ) + study_id = storage.get_study_id_from_name(study_name) + else: + raise + + if sampler is None and len(direction_objects) > 1: + sampler = samplers.NSGAIISampler() + + study_name = storage.get_study_name_from_id(study_id) + study = Study(study_name=study_name, storage=storage, sampler=sampler, pruner=pruner) + + return study + + +@convert_positional_args( + previous_positional_arg_names=[ + "study_name", + "storage", + "sampler", + "pruner", + ], + deprecated_version="3.0.0", + removed_version="5.0.0", +) +def load_study( + *, + study_name: str | None, + storage: str | storages.BaseStorage, + sampler: "samplers.BaseSampler" | None = None, + pruner: pruners.BasePruner | None = None, +) -> Study: + """Load the existing :class:`~optuna.study.Study` that has the specified name. + + Example: + + .. testsetup:: + + import os + + if os.path.exists("example.db"): + raise RuntimeError("'example.db' already exists. Please remove it.") + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", 0, 10) + return x**2 + + + study = optuna.create_study(storage="sqlite:///example.db", study_name="my_study") + study.optimize(objective, n_trials=3) + + loaded_study = optuna.load_study(study_name="my_study", storage="sqlite:///example.db") + assert len(loaded_study.trials) == len(study.trials) + + .. testcleanup:: + + os.remove("example.db") + + Args: + study_name: + Study's name. Each study has a unique name as an identifier. If :obj:`None`, checks + whether the storage contains a single study, and if so loads that study. + ``study_name`` is required if there are multiple studies in the storage. + storage: + Database URL such as ``sqlite:///example.db``. Please see also the documentation of + :func:`~optuna.study.create_study` for further details. + sampler: + A sampler object that implements background algorithm for value suggestion. + If :obj:`None` is specified, :class:`~optuna.samplers.TPESampler` is used + as the default. See also :class:`~optuna.samplers`. + pruner: + A pruner object that decides early stopping of unpromising trials. + If :obj:`None` is specified, :class:`~optuna.pruners.MedianPruner` is used + as the default. See also :class:`~optuna.pruners`. + + Returns: + A :class:`~optuna.study.Study` object. + + See also: + :func:`optuna.load_study` is an alias of :func:`optuna.study.load_study`. + + """ + if study_name is None: + study_names = get_all_study_names(storage) + if len(study_names) != 1: + raise ValueError( + f"Could not determine the study name since the storage {storage} does not " + "contain exactly 1 study. Specify `study_name`." + ) + study_name = study_names[0] + _logger.info( + f"Study name was omitted but trying to load '{study_name}' because that was the only " + "study found in the storage." + ) + + study = Study(study_name=study_name, storage=storage, sampler=sampler, pruner=pruner) + if sampler is None and len(study.directions) > 1: + study.sampler = samplers.NSGAIISampler() + return study + + +@convert_positional_args( + previous_positional_arg_names=[ + "study_name", + "storage", + ], + deprecated_version="3.0.0", + removed_version="5.0.0", +) +def delete_study( + *, + study_name: str, + storage: str | storages.BaseStorage, +) -> None: + """Delete a :class:`~optuna.study.Study` object. + + Example: + + .. testsetup:: + + import os + + if os.path.exists("example.db"): + raise RuntimeError("'example.db' already exists. Please remove it.") + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -10, 10) + return (x - 2) ** 2 + + + study = optuna.create_study(study_name="example-study", storage="sqlite:///example.db") + study.optimize(objective, n_trials=3) + + optuna.delete_study(study_name="example-study", storage="sqlite:///example.db") + + .. testcleanup:: + + os.remove("example.db") + + Args: + study_name: + Study's name. + storage: + Database URL such as ``sqlite:///example.db``. Please see also the documentation of + :func:`~optuna.study.create_study` for further details. + + See also: + :func:`optuna.delete_study` is an alias of :func:`optuna.study.delete_study`. + + """ + + storage = storages.get_storage(storage) + study_id = storage.get_study_id_from_name(study_name) + storage.delete_study(study_id) + + +@convert_positional_args( + previous_positional_arg_names=[ + "from_study_name", + "from_storage", + "to_storage", + "to_study_name", + ], + warning_stacklevel=3, + deprecated_version="3.0.0", + removed_version="5.0.0", +) +def copy_study( + *, + from_study_name: str, + from_storage: str | storages.BaseStorage, + to_storage: str | storages.BaseStorage, + to_study_name: str | None = None, +) -> None: + """Copy study from one storage to another. + + The direction(s) of the objective(s) in the study, trials, user attributes and system + attributes are copied. + + .. note:: + :func:`~optuna.copy_study` copies a study even if the optimization is working on. + It means users will get a copied study that contains a trial that is not finished. + + Example: + + .. testsetup:: + + import os + + if os.path.exists("example.db"): + raise RuntimeError("'example.db' already exists. Please remove it.") + if os.path.exists("example_copy.db"): + raise RuntimeError("'example_copy.db' already exists. Please remove it.") + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -10, 10) + return (x - 2) ** 2 + + + study = optuna.create_study( + study_name="example-study", + storage="sqlite:///example.db", + ) + study.optimize(objective, n_trials=3) + + optuna.copy_study( + from_study_name="example-study", + from_storage="sqlite:///example.db", + to_storage="sqlite:///example_copy.db", + ) + + study = optuna.load_study( + study_name=None, + storage="sqlite:///example_copy.db", + ) + + .. testcleanup:: + + os.remove("example.db") + os.remove("example_copy.db") + + Args: + from_study_name: + Name of study. + from_storage: + Source database URL such as ``sqlite:///example.db``. Please see also the + documentation of :func:`~optuna.study.create_study` for further details. + to_storage: + Destination database URL. + to_study_name: + Name of the created study. If omitted, ``from_study_name`` is used. + + Raises: + :class:`~optuna.exceptions.DuplicatedStudyError`: + If a study with a conflicting name already exists in the destination storage. + + """ + + from_study = load_study(study_name=from_study_name, storage=from_storage) + to_study = create_study( + study_name=to_study_name or from_study_name, + storage=to_storage, + directions=from_study.directions, + load_if_exists=False, + ) + + for key, value in from_study._storage.get_study_system_attrs(from_study._study_id).items(): + to_study._storage.set_study_system_attr(to_study._study_id, key, value) + + for key, value in from_study.user_attrs.items(): + to_study.set_user_attr(key, value) + + # Trials are deep copied on `add_trials`. + to_study.add_trials(from_study.get_trials(deepcopy=False)) + + +def get_all_study_summaries( + storage: str | storages.BaseStorage, include_best_trial: bool = True +) -> list[StudySummary]: + """Get all history of studies stored in a specified storage. + + Example: + + .. testsetup:: + + import os + + if os.path.exists("example.db"): + raise RuntimeError("'example.db' already exists. Please remove it.") + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -10, 10) + return (x - 2) ** 2 + + + study = optuna.create_study(study_name="example-study", storage="sqlite:///example.db") + study.optimize(objective, n_trials=3) + + study_summaries = optuna.study.get_all_study_summaries(storage="sqlite:///example.db") + assert len(study_summaries) == 1 + + study_summary = study_summaries[0] + assert study_summary.study_name == "example-study" + + .. testcleanup:: + + os.remove("example.db") + + Args: + storage: + Database URL such as ``sqlite:///example.db``. Please see also the documentation of + :func:`~optuna.study.create_study` for further details. + include_best_trial: + Include the best trials if exist. It potentially increases the number of queries and + may take longer to fetch summaries depending on the storage. + + Returns: + List of study history summarized as :class:`~optuna.study.StudySummary` objects. + + See also: + :func:`optuna.get_all_study_summaries` is an alias of + :func:`optuna.study.get_all_study_summaries`. + + """ + + storage = storages.get_storage(storage) + frozen_studies = storage.get_all_studies() + study_summaries = [] + + for s in frozen_studies: + all_trials = storage.get_all_trials(s._study_id) + completed_trials = [t for t in all_trials if t.state == TrialState.COMPLETE] + + n_trials = len(all_trials) + + if len(s.directions) == 1: + direction = s.direction + directions = None + if include_best_trial and len(completed_trials) != 0: + if direction == StudyDirection.MAXIMIZE: + best_trial = max(completed_trials, key=lambda t: cast(float, t.value)) + else: + best_trial = min(completed_trials, key=lambda t: cast(float, t.value)) + else: + best_trial = None + else: + direction = None + directions = s.directions + best_trial = None + + datetime_start = min( + [t.datetime_start for t in all_trials if t.datetime_start is not None], default=None + ) + + study_summaries.append( + StudySummary( + study_name=s.study_name, + direction=direction, + best_trial=best_trial, + user_attrs=s.user_attrs, + system_attrs=s.system_attrs, + n_trials=n_trials, + datetime_start=datetime_start, + study_id=s._study_id, + directions=directions, + ) + ) + + return study_summaries + + +def get_all_study_names(storage: str | storages.BaseStorage) -> list[str]: + """Get all study names stored in a specified storage. + + Example: + + .. testsetup:: + + import os + + if os.path.exists("example.db"): + raise RuntimeError("'example.db' already exists. Please remove it.") + + .. testcode:: + + import optuna + + + def objective(trial): + x = trial.suggest_float("x", -10, 10) + return (x - 2) ** 2 + + + study = optuna.create_study(study_name="example-study", storage="sqlite:///example.db") + study.optimize(objective, n_trials=3) + + study_names = optuna.study.get_all_study_names(storage="sqlite:///example.db") + assert len(study_names) == 1 + + assert study_names[0] == "example-study" + + .. testcleanup:: + + os.remove("example.db") + + Args: + storage: + Database URL such as ``sqlite:///example.db``. Please see also the documentation of + :func:`~optuna.study.create_study` for further details. + + Returns: + List of all study names in the storage. + + See also: + :func:`optuna.get_all_study_names` is an alias of + :func:`optuna.study.get_all_study_names`. + + """ + + storage = storages.get_storage(storage) + study_names = [study.study_name for study in storage.get_all_studies()] + + return study_names diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/trial/__pycache__/_base.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/trial/__pycache__/_base.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2b7a0cb35bd3f76fa485221e971a432f0cbec667 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/trial/__pycache__/_base.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/version.py 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b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_contour.py @@ -0,0 +1,432 @@ +from __future__ import annotations + +from collections.abc import Callable +import math +from typing import Any +from typing import NamedTuple +import warnings + +import numpy as np + +from optuna.logging import get_logger +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.study import Study +from optuna.study import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports +from optuna.visualization._utils import _check_plot_args +from optuna.visualization._utils import _filter_nonfinite +from optuna.visualization._utils import _is_log_scale +from optuna.visualization._utils import _is_numerical +from optuna.visualization._utils import _is_reverse_scale + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import Contour + from optuna.visualization._plotly_imports import go + from optuna.visualization._plotly_imports import make_subplots + from optuna.visualization._plotly_imports import Scatter + from optuna.visualization._utils import COLOR_SCALE + +_logger = get_logger(__name__) + + +PADDING_RATIO = 0.05 + + +class _AxisInfo(NamedTuple): + name: str + range: tuple[float, float] + is_log: bool + is_cat: bool + indices: list[str | int | float] + values: list[str | float | None] + + +class _SubContourInfo(NamedTuple): + xaxis: _AxisInfo + yaxis: _AxisInfo + z_values: dict[tuple[int, int], float] + constraints: list[bool] = [] + + +class _ContourInfo(NamedTuple): + sorted_params: list[str] + sub_plot_infos: list[list[_SubContourInfo]] + reverse_scale: bool + target_name: str + + +class _PlotValues(NamedTuple): + x: list[Any] + y: list[Any] + + +def plot_contour( + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "go.Figure": + """Plot the parameter relationship as contour plot in a study. + + Note that, if a parameter contains missing values, a trial with missing values is not plotted. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + params: + Parameter list to visualize. The default is all parameters. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the color bar. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + + .. note:: + The colormap is reversed when the ``target`` argument isn't :obj:`None` or ``direction`` + of :class:`~optuna.study.Study` is ``minimize``. + """ + + _imports.check() + info = _get_contour_info(study, params, target, target_name) + return _get_contour_plot(info) + + +def _get_contour_plot(info: _ContourInfo) -> "go.Figure": + layout = go.Layout(title="Contour Plot") + + sorted_params = info.sorted_params + sub_plot_infos = info.sub_plot_infos + reverse_scale = info.reverse_scale + target_name = info.target_name + + if len(sorted_params) <= 1: + return go.Figure(data=[], layout=layout) + + if len(sorted_params) == 2: + x_param = sorted_params[0] + y_param = sorted_params[1] + sub_plot_info = sub_plot_infos[0][0] + sub_plots = _get_contour_subplot(sub_plot_info, reverse_scale, target_name) + figure = go.Figure(data=sub_plots, layout=layout) + figure.update_xaxes(title_text=x_param, range=sub_plot_info.xaxis.range) + figure.update_yaxes(title_text=y_param, range=sub_plot_info.yaxis.range) + + if sub_plot_info.xaxis.is_cat: + figure.update_xaxes(type="category") + if sub_plot_info.yaxis.is_cat: + figure.update_yaxes(type="category") + + if sub_plot_info.xaxis.is_log: + log_range = [math.log10(p) for p in sub_plot_info.xaxis.range] + figure.update_xaxes(range=log_range, type="log") + if sub_plot_info.yaxis.is_log: + log_range = [math.log10(p) for p in sub_plot_info.yaxis.range] + figure.update_yaxes(range=log_range, type="log") + else: + figure = make_subplots( + rows=len(sorted_params), cols=len(sorted_params), shared_xaxes=True, shared_yaxes=True + ) + figure.update_layout(layout) + showscale = True # showscale option only needs to be specified once. + for x_i, x_param in enumerate(sorted_params): + for y_i, y_param in enumerate(sorted_params): + if x_param == y_param: + figure.add_trace(go.Scatter(), row=y_i + 1, col=x_i + 1) + else: + sub_plots = _get_contour_subplot( + sub_plot_infos[y_i][x_i], reverse_scale, target_name + ) + contour = sub_plots[0] + scatter = sub_plots[1] + contour.update(showscale=showscale) # showscale's default is True. + if showscale: + showscale = False + figure.add_trace(contour, row=y_i + 1, col=x_i + 1) + figure.add_trace(scatter, row=y_i + 1, col=x_i + 1) + + xaxis = sub_plot_infos[y_i][x_i].xaxis + yaxis = sub_plot_infos[y_i][x_i].yaxis + figure.update_xaxes(range=xaxis.range, row=y_i + 1, col=x_i + 1) + figure.update_yaxes(range=yaxis.range, row=y_i + 1, col=x_i + 1) + + if xaxis.is_cat: + figure.update_xaxes(type="category", row=y_i + 1, col=x_i + 1) + if yaxis.is_cat: + figure.update_yaxes(type="category", row=y_i + 1, col=x_i + 1) + + if xaxis.is_log: + log_range = [math.log10(p) for p in xaxis.range] + figure.update_xaxes(range=log_range, type="log", row=y_i + 1, col=x_i + 1) + if yaxis.is_log: + log_range = [math.log10(p) for p in yaxis.range] + figure.update_yaxes(range=log_range, type="log", row=y_i + 1, col=x_i + 1) + + if x_i == 0: + figure.update_yaxes(title_text=y_param, row=y_i + 1, col=x_i + 1) + if y_i == len(sorted_params) - 1: + figure.update_xaxes(title_text=x_param, row=y_i + 1, col=x_i + 1) + + return figure + + +def _get_contour_subplot( + info: _SubContourInfo, + reverse_scale: bool, + target_name: str = "Objective Value", +) -> tuple["Contour", "Scatter", "Scatter"]: + x_indices = info.xaxis.indices + y_indices = info.yaxis.indices + + if len(x_indices) < 2 or len(y_indices) < 2: + return go.Contour(), go.Scatter(), go.Scatter() + if len(info.z_values) == 0: + warnings.warn( + f"Contour plot will not be displayed because `{info.xaxis.name}` and " + f"`{info.yaxis.name}` cannot co-exist in `trial.params`." + ) + return go.Contour(), go.Scatter(), go.Scatter() + + feasible = _PlotValues([], []) + infeasible = _PlotValues([], []) + + for x_value, y_value, c in zip(info.xaxis.values, info.yaxis.values, info.constraints): + if x_value is not None and y_value is not None: + if c: + feasible.x.append(x_value) + feasible.y.append(y_value) + else: + infeasible.x.append(x_value) + infeasible.y.append(y_value) + + z_values = np.full((len(y_indices), len(x_indices)), np.nan) + + xys = np.array(list(info.z_values.keys())) + zs = np.array(list(info.z_values.values())) + + z_values[xys[:, 1], xys[:, 0]] = zs + + contour = go.Contour( + x=x_indices, + y=y_indices, + z=z_values, + colorbar={"title": target_name}, + colorscale=COLOR_SCALE, + connectgaps=True, + contours_coloring="heatmap", + hoverinfo="none", + line_smoothing=1.3, + reversescale=reverse_scale, + ) + + return ( + contour, + _create_scatter(feasible.x, feasible.y, is_feasible=True), + _create_scatter(infeasible.x, infeasible.y, is_feasible=False), + ) + + +def _create_scatter(x: list[Any], y: list[Any], is_feasible: bool) -> Scatter: + edge_color = "Gray" + marker_color = "black" if is_feasible else "#cccccc" + name = "Feasible Trial" if is_feasible else "Infeasible Trial" + return go.Scatter( + x=x, + y=y, + marker={ + "line": {"width": 2.0, "color": edge_color}, + "color": marker_color, + }, + mode="markers", + name=name, + showlegend=False, + ) + + +def _get_contour_info( + study: Study, + params: list[str] | None = None, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> _ContourInfo: + _check_plot_args(study, target, target_name) + + trials = _filter_nonfinite( + study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target + ) + + all_params = {p_name for t in trials for p_name in t.params.keys()} + if len(trials) == 0: + _logger.warning("Your study does not have any completed trials.") + sorted_params = [] + elif params is None: + sorted_params = sorted(all_params) + else: + if len(params) <= 1: + _logger.warning("The length of params must be greater than 1.") + + for input_p_name in params: + if input_p_name not in all_params: + raise ValueError("Parameter {} does not exist in your study.".format(input_p_name)) + sorted_params = sorted(set(params)) + + sub_plot_infos: list[list[_SubContourInfo]] + if len(sorted_params) == 2: + x_param = sorted_params[0] + y_param = sorted_params[1] + sub_plot_info = _get_contour_subplot_info(study, trials, x_param, y_param, target) + sub_plot_infos = [[sub_plot_info]] + else: + sub_plot_infos = [] + for i, y_param in enumerate(sorted_params): + sub_plot_infos.append([]) + for x_param in sorted_params: + sub_plot_info = _get_contour_subplot_info(study, trials, x_param, y_param, target) + sub_plot_infos[i].append(sub_plot_info) + + reverse_scale = _is_reverse_scale(study, target) + + return _ContourInfo( + sorted_params=sorted_params, + sub_plot_infos=sub_plot_infos, + reverse_scale=reverse_scale, + target_name=target_name, + ) + + +def _get_contour_subplot_info( + study: Study, + trials: list[FrozenTrial], + x_param: str, + y_param: str, + target: Callable[[FrozenTrial], float] | None, +) -> _SubContourInfo: + xaxis = _get_axis_info(trials, x_param) + yaxis = _get_axis_info(trials, y_param) + + if x_param == y_param: + return _SubContourInfo(xaxis=xaxis, yaxis=yaxis, z_values={}) + + if len(xaxis.indices) < 2: + _logger.warning("Param {} unique value length is less than 2.".format(x_param)) + return _SubContourInfo(xaxis=xaxis, yaxis=yaxis, z_values={}) + if len(yaxis.indices) < 2: + _logger.warning("Param {} unique value length is less than 2.".format(y_param)) + return _SubContourInfo(xaxis=xaxis, yaxis=yaxis, z_values={}) + + z_values: dict[tuple[int, int], float] = {} + for i, trial in enumerate(trials): + if x_param not in trial.params or y_param not in trial.params: + continue + x_value = xaxis.values[i] + y_value = yaxis.values[i] + assert x_value is not None + assert y_value is not None + x_i = xaxis.indices.index(x_value) + y_i = yaxis.indices.index(y_value) + + if target is None: + value = trial.value + else: + value = target(trial) + assert value is not None + + existing = z_values.get((x_i, y_i)) + if existing is None or target is not None: + # When target function is present, we can't be sure what the z-value + # represents and therefore we don't know how to select the best one. + z_values[(x_i, y_i)] = value + else: + z_values[(x_i, y_i)] = ( + min(existing, value) + if study.direction is StudyDirection.MINIMIZE + else max(existing, value) + ) + + return _SubContourInfo( + xaxis=xaxis, + yaxis=yaxis, + z_values=z_values, + constraints=[_satisfy_constraints(t) for t in trials], + ) + + +def _satisfy_constraints(trial: FrozenTrial) -> bool: + constraints = trial.system_attrs.get(_CONSTRAINTS_KEY) + return constraints is None or all([x <= 0.0 for x in constraints]) + + +def _get_axis_info(trials: list[FrozenTrial], param_name: str) -> _AxisInfo: + values: list[str | float | None] + if _is_numerical(trials, param_name): + values = [t.params.get(param_name) for t in trials] + else: + values = [ + str(t.params.get(param_name)) if param_name in t.params else None for t in trials + ] + + min_value = min([v for v in values if v is not None]) + max_value = max([v for v in values if v is not None]) + + if _is_log_scale(trials, param_name): + min_value = float(min_value) + max_value = float(max_value) + padding = (math.log10(max_value) - math.log10(min_value)) * PADDING_RATIO + min_value = math.pow(10, math.log10(min_value) - padding) + max_value = math.pow(10, math.log10(max_value) + padding) + is_log = True + is_cat = False + + elif _is_numerical(trials, param_name): + min_value = float(min_value) + max_value = float(max_value) + padding = (max_value - min_value) * PADDING_RATIO + min_value = min_value - padding + max_value = max_value + padding + is_log = False + is_cat = False + + else: + unique_values = set(values) + span = len(unique_values) - 1 + if None in unique_values: + span -= 1 + padding = span * PADDING_RATIO + min_value = -padding + max_value = span + padding + is_log = False + is_cat = True + + indices = sorted(set([v for v in values if v is not None])) + + if len(indices) < 2: + return _AxisInfo( + name=param_name, + range=(min_value, max_value), + is_log=is_log, + is_cat=is_cat, + indices=indices, + values=values, + ) + + if _is_numerical(trials, param_name): + indices.insert(0, min_value) + indices.append(max_value) + + return _AxisInfo( + name=param_name, + range=(min_value, max_value), + is_log=is_log, + is_cat=is_cat, + indices=indices, + values=values, + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_edf.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_edf.py new file mode 100644 index 0000000000000000000000000000000000000000..4b7da8d4fabf17f796ee539a693eeabbd7b0af10 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_edf.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +from typing import cast +from typing import NamedTuple + +import numpy as np + +from optuna.logging import get_logger +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports +from optuna.visualization._utils import _check_plot_args +from optuna.visualization._utils import _filter_nonfinite + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + +_logger = get_logger(__name__) + + +NUM_SAMPLES_X_AXIS = 100 + + +class _EDFLineInfo(NamedTuple): + study_name: str + y_values: np.ndarray + + +class _EDFInfo(NamedTuple): + lines: list[_EDFLineInfo] + x_values: np.ndarray + + +def plot_edf( + study: Study | Sequence[Study], + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "go.Figure": + """Plot the objective value EDF (empirical distribution function) of a study. + + Note that only the complete trials are considered when plotting the EDF. + + .. note:: + + EDF is useful to analyze and improve search spaces. + For instance, you can see a practical use case of EDF in the paper + `Designing Network Design Spaces + `__. + + .. note:: + + The plotted EDF assumes that the value of the objective function is in + accordance with the uniform distribution over the objective space. + + Args: + study: + A target :class:`~optuna.study.Study` object. + You can pass multiple studies if you want to compare those EDFs. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the axis label. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + """ + + _imports.check() + + layout = go.Layout( + title="Empirical Distribution Function Plot", + xaxis={"title": target_name}, + yaxis={"title": "Cumulative Probability"}, + ) + + info = _get_edf_info(study, target, target_name) + edf_lines = info.lines + + if len(edf_lines) == 0: + return go.Figure(data=[], layout=layout) + + traces = [] + for study_name, y_values in edf_lines: + traces.append(go.Scatter(x=info.x_values, y=y_values, name=study_name, mode="lines")) + + figure = go.Figure(data=traces, layout=layout) + figure.update_yaxes(range=[0, 1]) + + return figure + + +def _get_edf_info( + study: Study | Sequence[Study], + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> _EDFInfo: + if isinstance(study, Study): + studies = [study] + else: + studies = list(study) + + _check_plot_args(studies, target, target_name) + + if len(studies) == 0: + _logger.warning("There are no studies.") + return _EDFInfo(lines=[], x_values=np.array([])) + + if target is None: + + def _target(t: FrozenTrial) -> float: + return cast(float, t.value) + + target = _target + + study_names = [] + all_values: list[np.ndarray] = [] + for study in studies: + trials = _filter_nonfinite( + study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target + ) + + values = np.array([target(trial) for trial in trials]) + all_values.append(values) + study_names.append(study.study_name) + + if all(len(values) == 0 for values in all_values): + _logger.warning("There are no complete trials.") + return _EDFInfo(lines=[], x_values=np.array([])) + + min_x_value = np.min(np.concatenate(all_values)) + max_x_value = np.max(np.concatenate(all_values)) + x_values = np.linspace(min_x_value, max_x_value, NUM_SAMPLES_X_AXIS) + + edf_line_info_list = [] + for study_name, values in zip(study_names, all_values): + y_values = np.sum(values[:, np.newaxis] <= x_values, axis=0) / values.size + edf_line_info_list.append(_EDFLineInfo(study_name=study_name, y_values=y_values)) + + return _EDFInfo(lines=edf_line_info_list, x_values=x_values) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_hypervolume_history.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_hypervolume_history.py new file mode 100644 index 0000000000000000000000000000000000000000..545d0c1434f42103f7731c970c96b39d471bf3a6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_hypervolume_history.py @@ -0,0 +1,138 @@ +from __future__ import annotations + +from collections.abc import Sequence +from typing import NamedTuple + +import numpy as np + +from optuna._experimental import experimental_func +from optuna._hypervolume import compute_hypervolume +from optuna.logging import get_logger +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.study import Study +from optuna.study._study_direction import StudyDirection +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + +_logger = get_logger(__name__) + + +class _HypervolumeHistoryInfo(NamedTuple): + trial_numbers: list[int] + values: list[float] + + +@experimental_func("3.3.0") +def plot_hypervolume_history( + study: Study, + reference_point: Sequence[float], +) -> "go.Figure": + """Plot hypervolume history of all trials in a study. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their hypervolumes. + The number of objectives must be 2 or more. + + reference_point: + A reference point to use for hypervolume computation. + The dimension of the reference point must be the same as the number of objectives. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + """ + + _imports.check() + + if not study._is_multi_objective(): + raise ValueError( + "Study must be multi-objective. For single-objective optimization, " + "please use plot_optimization_history instead." + ) + + if len(reference_point) != len(study.directions): + raise ValueError( + "The dimension of the reference point must be the same as the number of objectives." + ) + + info = _get_hypervolume_history_info(study, np.asarray(reference_point, dtype=np.float64)) + return _get_hypervolume_history_plot(info) + + +def _get_hypervolume_history_plot( + info: _HypervolumeHistoryInfo, +) -> "go.Figure": + layout = go.Layout( + title="Hypervolume History Plot", + xaxis={"title": "Trial"}, + yaxis={"title": "Hypervolume"}, + ) + + data = go.Scatter( + x=info.trial_numbers, + y=info.values, + mode="lines+markers", + ) + return go.Figure(data=data, layout=layout) + + +def _get_hypervolume_history_info( + study: Study, + reference_point: np.ndarray, +) -> _HypervolumeHistoryInfo: + completed_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)) + + if len(completed_trials) == 0: + _logger.warning("Your study does not have any completed trials.") + + # Our hypervolume computation module assumes that all objectives are minimized. + # Here we transform the objective values and the reference point. + signs = np.asarray([1 if d == StudyDirection.MINIMIZE else -1 for d in study.directions]) + minimization_reference_point = signs * reference_point + + # Only feasible trials are considered in hypervolume computation. + trial_numbers = [] + hypervolume_values = [] + best_trials_values_normalized: np.ndarray | None = None + hypervolume = 0.0 + for trial in completed_trials: + trial_numbers.append(trial.number) + + has_constraints = _CONSTRAINTS_KEY in trial.system_attrs + if has_constraints: + constraints_values = trial.system_attrs[_CONSTRAINTS_KEY] + if any(map(lambda x: x > 0.0, constraints_values)): + # The trial is infeasible. + hypervolume_values.append(hypervolume) + continue + + values_normalized = (signs * trial.values)[np.newaxis, :] + if best_trials_values_normalized is not None: + if (best_trials_values_normalized <= values_normalized).all(axis=1).any(axis=0): + # The trial is not on the Pareto front. + hypervolume_values.append(hypervolume) + continue + + if (values_normalized > minimization_reference_point).any(): + hypervolume_values.append(hypervolume) + continue + hypervolume += np.prod(minimization_reference_point - values_normalized) + if best_trials_values_normalized is None: + best_trials_values_normalized = values_normalized + else: + limited_sols = np.maximum(best_trials_values_normalized, values_normalized) + hypervolume -= compute_hypervolume(limited_sols, minimization_reference_point) + is_kept = (best_trials_values_normalized < values_normalized).any(axis=1) + best_trials_values_normalized = np.concatenate( + [best_trials_values_normalized[is_kept, :], values_normalized], axis=0 + ) + hypervolume_values.append(hypervolume) + + if best_trials_values_normalized is None: + _logger.warning("Your study does not have any feasible trials.") + + return _HypervolumeHistoryInfo(trial_numbers, hypervolume_values) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_intermediate_values.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_intermediate_values.py new file mode 100644 index 0000000000000000000000000000000000000000..d79cae6e6ec2ce58bf81486998e70b7941907179 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_intermediate_values.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +from typing import NamedTuple + +from optuna.logging import get_logger +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + +_logger = get_logger(__name__) + + +class _TrialInfo(NamedTuple): + trial_number: int + sorted_intermediate_values: list[tuple[int, float]] + feasible: bool + + +class _IntermediatePlotInfo(NamedTuple): + trial_infos: list[_TrialInfo] + + +def _get_intermediate_plot_info(study: Study) -> _IntermediatePlotInfo: + trials = study.get_trials( + deepcopy=False, states=(TrialState.PRUNED, TrialState.COMPLETE, TrialState.RUNNING) + ) + + def _satisfies_constraints(trial: FrozenTrial) -> bool: + constraints = trial.system_attrs.get(_CONSTRAINTS_KEY) + return constraints is None or all([x <= 0.0 for x in constraints]) + + trial_infos = [ + _TrialInfo( + trial.number, sorted(trial.intermediate_values.items()), _satisfies_constraints(trial) + ) + for trial in trials + if len(trial.intermediate_values) > 0 + ] + + if len(trials) == 0: + _logger.warning("Study instance does not contain trials.") + elif len(trial_infos) == 0: + _logger.warning( + "You need to set up the pruning feature to utilize `plot_intermediate_values()`" + ) + + return _IntermediatePlotInfo(trial_infos) + + +def plot_intermediate_values(study: Study) -> "go.Figure": + """Plot intermediate values of all trials in a study. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their intermediate + values. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + """ + + _imports.check() + return _get_intermediate_plot(_get_intermediate_plot_info(study)) + + +def _get_intermediate_plot(info: _IntermediatePlotInfo) -> "go.Figure": + layout = go.Layout( + title="Intermediate Values Plot", + xaxis={"title": "Step"}, + yaxis={"title": "Intermediate Value"}, + showlegend=False, + ) + + trial_infos = info.trial_infos + + if len(trial_infos) == 0: + return go.Figure(data=[], layout=layout) + + default_marker = {"maxdisplayed": 10} + + traces = [ + go.Scatter( + x=tuple((x for x, _ in tinfo.sorted_intermediate_values)), + y=tuple((y for _, y in tinfo.sorted_intermediate_values)), + mode="lines+markers", + marker=( + default_marker + if tinfo.feasible + else {**default_marker, "color": "#CCCCCC"} # type: ignore[dict-item] + ), + name="Trial{}".format(tinfo.trial_number), + ) + for tinfo in trial_infos + ] + + return go.Figure(data=traces, layout=layout) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_optimization_history.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_optimization_history.py new file mode 100644 index 0000000000000000000000000000000000000000..10557668a7358397e9961a6aacf2ac8b98d43eae --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_optimization_history.py @@ -0,0 +1,299 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +from enum import Enum +import math +from typing import cast +from typing import NamedTuple + +import numpy as np + +from optuna.logging import get_logger +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.study import Study +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports +from optuna.visualization._utils import _check_plot_args + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + +_logger = get_logger(__name__) + + +class _ValueState(Enum): + Feasible = 0 + Infeasible = 1 + Incomplete = 2 + + +class _ValuesInfo(NamedTuple): + values: list[float] + stds: list[float] | None + label_name: str + states: list[_ValueState] + + +class _OptimizationHistoryInfo(NamedTuple): + trial_numbers: list[int] + values_info: _ValuesInfo + best_values_info: _ValuesInfo | None + + +def _get_optimization_history_info_list( + study: Study | Sequence[Study], + target: Callable[[FrozenTrial], float] | None, + target_name: str, + error_bar: bool, +) -> list[_OptimizationHistoryInfo]: + _check_plot_args(study, target, target_name) + if isinstance(study, Study): + studies = [study] + else: + studies = list(study) + + info_list: list[_OptimizationHistoryInfo] = [] + for study in studies: + trials = study.get_trials() + label_name = target_name if len(studies) == 1 else f"{target_name} of {study.study_name}" + values = [] + value_states = [] + for trial in trials: + if trial.state != TrialState.COMPLETE: + values.append(float("nan")) + value_states.append(_ValueState.Incomplete) + continue + constraints = trial.system_attrs.get(_CONSTRAINTS_KEY) + if constraints is None or all([x <= 0.0 for x in constraints]): + value_states.append(_ValueState.Feasible) + else: + value_states.append(_ValueState.Infeasible) + if target is not None: + values.append(target(trial)) + else: + values.append(cast(float, trial.value)) + if target is not None: + # We don't calculate best for user-defined target function since we cannot tell + # which direction is better. + best_values_info: _ValuesInfo | None = None + else: + feasible_best_values = [] + if study.direction == StudyDirection.MINIMIZE: + feasible_best_values = [ + v if s == _ValueState.Feasible else float("inf") + for v, s in zip(values, value_states) + ] + best_values = list(np.minimum.accumulate(feasible_best_values)) + else: + feasible_best_values = [ + v if s == _ValueState.Feasible else -float("inf") + for v, s in zip(values, value_states) + ] + best_values = list(np.maximum.accumulate(feasible_best_values)) + best_label_name = ( + "Best Value" if len(studies) == 1 else f"Best Value of {study.study_name}" + ) + best_values_info = _ValuesInfo(best_values, None, best_label_name, value_states) + info_list.append( + _OptimizationHistoryInfo( + trial_numbers=[t.number for t in trials], + values_info=_ValuesInfo(values, None, label_name, value_states), + best_values_info=best_values_info, + ) + ) + + if len(info_list) == 0: + _logger.warning("There are no studies.") + + feasible_trial_count = sum( + info.values_info.states.count(_ValueState.Feasible) for info in info_list + ) + infeasible_trial_count = sum( + info.values_info.states.count(_ValueState.Infeasible) for info in info_list + ) + if feasible_trial_count + infeasible_trial_count == 0: + _logger.warning("There are no complete trials.") + info_list.clear() + + if not error_bar: + return info_list + + # When error_bar=True, a list of 0 or 1 element is returned. + if len(info_list) == 0: + return [] + if feasible_trial_count == 0: + _logger.warning("There are no feasible trials.") + return [] + + all_trial_numbers = [number for info in info_list for number in info.trial_numbers] + max_num_trial = max(all_trial_numbers) + 1 + + def _aggregate(label_name: str, use_best_value: bool) -> tuple[list[int], _ValuesInfo]: + # Calculate mean and std of values for each trial number. + values: list[list[float]] = [[] for _ in range(max_num_trial)] + states: list[list[_ValueState]] = [[] for _ in range(max_num_trial)] + assert info_list is not None + for trial_numbers, values_info, best_values_info in info_list: + if use_best_value: + assert best_values_info is not None + values_info = best_values_info + for n, v, s in zip(trial_numbers, values_info.values, values_info.states): + if not math.isinf(v): + if not use_best_value and s == _ValueState.Feasible: + values[n].append(v) + elif use_best_value: + values[n].append(v) + states[n].append(s) + trial_numbers_union: list[int] = [] + value_states: list[_ValueState] = [] + value_means: list[float] = [] + value_stds: list[float] = [] + for i in range(max_num_trial): + if len(states[i]) > 0 and _ValueState.Feasible in states[i]: + value_states.append(_ValueState.Feasible) + trial_numbers_union.append(i) + value_means.append(np.mean(values[i]).item()) + value_stds.append(np.std(values[i]).item()) + else: + value_states.append(_ValueState.Infeasible) + return trial_numbers_union, _ValuesInfo(value_means, value_stds, label_name, value_states) + + eb_trial_numbers, eb_values_info = _aggregate(target_name, False) + eb_best_values_info: _ValuesInfo | None = None + if target is None: + _, eb_best_values_info = _aggregate("Best Value", True) + return [_OptimizationHistoryInfo(eb_trial_numbers, eb_values_info, eb_best_values_info)] + + +def plot_optimization_history( + study: Study | Sequence[Study], + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", + error_bar: bool = False, +) -> "go.Figure": + """Plot optimization history of all trials in a study. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + You can pass multiple studies if you want to compare those optimization histories. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the axis label and the legend. + error_bar: + A flag to show the error bar. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + """ + + _imports.check() + + info_list = _get_optimization_history_info_list(study, target, target_name, error_bar) + return _get_optimization_history_plot(info_list, target_name) + + +def _get_optimization_history_plot( + info_list: list[_OptimizationHistoryInfo], + target_name: str, +) -> "go.Figure": + layout = go.Layout( + title="Optimization History Plot", + xaxis={"title": "Trial"}, + yaxis={"title": target_name}, + ) + + traces = [] + for trial_numbers, values_info, best_values_info in info_list: + infeasible_trial_numbers = [ + n for n, s in zip(trial_numbers, values_info.states) if s == _ValueState.Infeasible + ] + if values_info.stds is None: + error_y = None + feasible_trial_numbers = [ + num + for num, s in zip(trial_numbers, values_info.states) + if s == _ValueState.Feasible + ] + feasible_trial_values = [] + for num in feasible_trial_numbers: + feasible_trial_values.append(values_info.values[num]) + infeasible_trial_values = [] + for num in infeasible_trial_numbers: + infeasible_trial_values.append(values_info.values[num]) + else: + if ( + _ValueState.Infeasible in values_info.states + or _ValueState.Incomplete in values_info.states + ): + _logger.warning( + "Your study contains infeasible trials. " + "In optimization history plot, " + "error bars are calculated for only feasible trial values." + ) + error_y = {"type": "data", "array": values_info.stds, "visible": True} + feasible_trial_numbers = trial_numbers + feasible_trial_values = values_info.values + infeasible_trial_values = [] + traces.append( + go.Scatter( + x=feasible_trial_numbers, + y=feasible_trial_values, + error_y=error_y, + mode="markers", + name=values_info.label_name, + ) + ) + if best_values_info is not None: + traces.append( + go.Scatter( + x=trial_numbers, + y=best_values_info.values, + name=best_values_info.label_name, + mode="lines", + ) + ) + if best_values_info.stds is not None: + upper = np.array(best_values_info.values) + np.array(best_values_info.stds) + traces.append( + go.Scatter( + x=trial_numbers, + y=upper, + mode="lines", + line=dict(width=0.01), + showlegend=False, + ) + ) + lower = np.array(best_values_info.values) - np.array(best_values_info.stds) + traces.append( + go.Scatter( + x=trial_numbers, + y=lower, + mode="none", + showlegend=False, + fill="tonexty", + fillcolor="rgba(255,0,0,0.2)", + ) + ) + traces.append( + go.Scatter( + x=infeasible_trial_numbers, + y=infeasible_trial_values, + error_y=error_y, + mode="markers", + name="Infeasible Trial", + marker={"color": "#cccccc"}, + showlegend=False, + ) + ) + return go.Figure(data=traces, layout=layout) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_parallel_coordinate.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_parallel_coordinate.py new file mode 100644 index 0000000000000000000000000000000000000000..3c0b97a45080c9a514d68ad62955231a9263cd84 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_parallel_coordinate.py @@ -0,0 +1,303 @@ +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Callable +import math +from typing import Any +from typing import cast +from typing import NamedTuple + +import numpy as np + +from optuna.distributions import CategoricalDistribution +from optuna.logging import get_logger +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports +from optuna.visualization._utils import _check_plot_args +from optuna.visualization._utils import _filter_nonfinite +from optuna.visualization._utils import _get_skipped_trial_numbers +from optuna.visualization._utils import _is_log_scale +from optuna.visualization._utils import _is_numerical +from optuna.visualization._utils import _is_reverse_scale + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + from optuna.visualization._utils import COLOR_SCALE + +_logger = get_logger(__name__) + + +class _DimensionInfo(NamedTuple): + label: str + values: tuple[float, ...] + range: tuple[float, float] + is_log: bool + is_cat: bool + tickvals: list[int | float] + ticktext: list[str] + + +class _ParallelCoordinateInfo(NamedTuple): + dim_objective: _DimensionInfo + dims_params: list[_DimensionInfo] + reverse_scale: bool + target_name: str + + +def plot_parallel_coordinate( + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "go.Figure": + """Plot the high-dimensional parameter relationships in a study. + + Note that, if a parameter contains missing values, a trial with missing values is not plotted. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + params: + Parameter list to visualize. The default is all parameters. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the axis label and the legend. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + + .. note:: + The colormap is reversed when the ``target`` argument isn't :obj:`None` or ``direction`` + of :class:`~optuna.study.Study` is ``minimize``. + """ + + _imports.check() + info = _get_parallel_coordinate_info(study, params, target, target_name) + return _get_parallel_coordinate_plot(info) + + +def _get_parallel_coordinate_plot(info: _ParallelCoordinateInfo) -> "go.Figure": + layout = go.Layout(title="Parallel Coordinate Plot") + + if len(info.dims_params) == 0 or len(info.dim_objective.values) == 0: + return go.Figure(data=[], layout=layout) + + dims = _get_dims_from_info(info) + reverse_scale = info.reverse_scale + target_name = info.target_name + + traces = [ + go.Parcoords( + dimensions=dims, + labelangle=30, + labelside="bottom", + line={ + "color": dims[0]["values"], + "colorscale": COLOR_SCALE, + "colorbar": {"title": target_name}, + "showscale": True, + "reversescale": reverse_scale, + }, + ) + ] + + figure = go.Figure(data=traces, layout=layout) + + return figure + + +def _get_parallel_coordinate_info( + study: Study, + params: list[str] | None = None, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> _ParallelCoordinateInfo: + _check_plot_args(study, target, target_name) + + reverse_scale = _is_reverse_scale(study, target) + + trials = _filter_nonfinite( + study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target + ) + + all_params = {p_name for t in trials for p_name in t.params.keys()} + if params is not None: + for input_p_name in params: + if input_p_name not in all_params: + raise ValueError("Parameter {} does not exist in your study.".format(input_p_name)) + all_params = set(params) + sorted_params = sorted(all_params) + + if target is None: + + def _target(t: FrozenTrial) -> float: + return cast(float, t.value) + + target = _target + + skipped_trial_numbers = _get_skipped_trial_numbers(trials, sorted_params) + + objectives = tuple([target(t) for t in trials if t.number not in skipped_trial_numbers]) + # The value of (0, 0) is a dummy range. It is ignored when we plot. + objective_range = (min(objectives), max(objectives)) if len(objectives) > 0 else (0, 0) + dim_objective = _DimensionInfo( + label=target_name, + values=objectives, + range=objective_range, + is_log=False, + is_cat=False, + tickvals=[], + ticktext=[], + ) + + if len(trials) == 0: + _logger.warning("Your study does not have any completed trials.") + return _ParallelCoordinateInfo( + dim_objective=dim_objective, + dims_params=[], + reverse_scale=reverse_scale, + target_name=target_name, + ) + + if len(objectives) == 0: + _logger.warning("Your study has only completed trials with missing parameters.") + return _ParallelCoordinateInfo( + dim_objective=dim_objective, + dims_params=[], + reverse_scale=reverse_scale, + target_name=target_name, + ) + + numeric_cat_params_indices: list[int] = [] + dims = [] + for dim_index, p_name in enumerate(sorted_params, start=1): + values = [] + is_categorical = False + for t in trials: + if t.number in skipped_trial_numbers: + continue + if p_name in t.params: + values.append(t.params[p_name]) + is_categorical |= isinstance(t.distributions[p_name], CategoricalDistribution) + if _is_log_scale(trials, p_name): + values = [math.log10(v) for v in values] + min_value = min(values) + max_value = max(values) + tickvals: list[int | float] = list( + range(math.ceil(min_value), math.floor(max_value) + 1) + ) + if min_value not in tickvals: + tickvals = [min_value] + tickvals + if max_value not in tickvals: + tickvals = tickvals + [max_value] + dim = _DimensionInfo( + label=_truncate_label(p_name), + values=tuple(values), + range=(min_value, max_value), + is_log=True, + is_cat=False, + tickvals=tickvals, + ticktext=["{:.3g}".format(math.pow(10, x)) for x in tickvals], + ) + elif is_categorical: + vocab: defaultdict[int | str, int] = defaultdict(lambda: len(vocab)) + + ticktext: list[str] + if _is_numerical(trials, p_name): + _ = [vocab[v] for v in sorted(values)] + values = [vocab[v] for v in values] + ticktext = [str(v) for v in list(sorted(vocab.keys()))] + numeric_cat_params_indices.append(dim_index) + else: + values = [vocab[v] for v in values] + ticktext = [str(v) for v in list(sorted(vocab.keys(), key=lambda x: vocab[x]))] + dim = _DimensionInfo( + label=_truncate_label(p_name), + values=tuple(values), + range=(min(values), max(values)), + is_log=False, + is_cat=True, + tickvals=list(range(len(vocab))), + ticktext=ticktext, + ) + else: + dim = _DimensionInfo( + label=_truncate_label(p_name), + values=tuple(values), + range=(min(values), max(values)), + is_log=False, + is_cat=False, + tickvals=[], + ticktext=[], + ) + + dims.append(dim) + + if numeric_cat_params_indices: + dims.insert(0, dim_objective) + # np.lexsort consumes the sort keys the order from back to front. + # So the values of parameters have to be reversed the order. + idx = np.lexsort([dims[index].values for index in numeric_cat_params_indices][::-1]) + updated_dims = [] + for dim in dims: + # Since the values are mapped to other categories by the index, + # the index will be swapped according to the sorted index of numeric params. + updated_dims.append( + _DimensionInfo( + label=dim.label, + values=tuple(np.array(dim.values)[idx]), + range=dim.range, + is_log=dim.is_log, + is_cat=dim.is_cat, + tickvals=dim.tickvals, + ticktext=dim.ticktext, + ) + ) + dim_objective = updated_dims[0] + dims = updated_dims[1:] + + return _ParallelCoordinateInfo( + dim_objective=dim_objective, + dims_params=dims, + reverse_scale=reverse_scale, + target_name=target_name, + ) + + +def _get_dims_from_info(info: _ParallelCoordinateInfo) -> list[dict[str, Any]]: + dims = [ + { + "label": info.dim_objective.label, + "values": info.dim_objective.values, + "range": info.dim_objective.range, + } + ] + + for dim in info.dims_params: + if dim.is_log or dim.is_cat: + dims.append( + { + "label": dim.label, + "values": dim.values, + "range": dim.range, + "tickvals": dim.tickvals, + "ticktext": dim.ticktext, + } + ) + else: + dims.append({"label": dim.label, "values": dim.values, "range": dim.range}) + + return dims + + +def _truncate_label(label: str) -> str: + return label if len(label) < 20 else "{}...".format(label[:17]) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_param_importances.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_param_importances.py new file mode 100644 index 0000000000000000000000000000000000000000..b3ee4c4df83b76194c787b717e3a9c7a527106e2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_param_importances.py @@ -0,0 +1,219 @@ +from __future__ import annotations + +from collections.abc import Callable +from typing import NamedTuple + +import optuna +from optuna.distributions import BaseDistribution +from optuna.importance._base import BaseImportanceEvaluator +from optuna.logging import get_logger +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports +from optuna.visualization._utils import _check_plot_args +from optuna.visualization._utils import _filter_nonfinite + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + + +logger = get_logger(__name__) + + +class _ImportancesInfo(NamedTuple): + importance_values: list[float] + param_names: list[str] + importance_labels: list[str] + target_name: str + + +def _get_importances_info( + study: Study, + evaluator: BaseImportanceEvaluator | None, + params: list[str] | None, + target: Callable[[FrozenTrial], float] | None, + target_name: str, +) -> _ImportancesInfo: + _check_plot_args(study, target, target_name) + + trials = _filter_nonfinite( + study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target + ) + + if len(trials) == 0: + logger.warning("Study instance does not contain completed trials.") + return _ImportancesInfo( + importance_values=[], + param_names=[], + importance_labels=[], + target_name=target_name, + ) + + importances = optuna.importance.get_param_importances( + study, evaluator=evaluator, params=params, target=target + ) + + importances = dict(reversed(list(importances.items()))) + importance_values = list(importances.values()) + param_names = list(importances.keys()) + importance_labels = [f"{val:.2f}" if val >= 0.01 else "<0.01" for val in importance_values] + + return _ImportancesInfo( + importance_values=importance_values, + param_names=param_names, + importance_labels=importance_labels, + target_name=target_name, + ) + + +def _get_importances_infos( + study: Study, + evaluator: BaseImportanceEvaluator | None, + params: list[str] | None, + target: Callable[[FrozenTrial], float] | None, + target_name: str, +) -> tuple[_ImportancesInfo, ...]: + metric_names = study.metric_names + if target or not study._is_multi_objective(): + target_name = metric_names[0] if metric_names is not None and not target else target_name + importances_infos: tuple[_ImportancesInfo, ...] = ( + _get_importances_info( + study, + evaluator, + params, + target=target, + target_name=target_name, + ), + ) + + else: + n_objectives = len(study.directions) + target_names = ( + metric_names + if metric_names is not None + else (f"{target_name} {objective_id}" for objective_id in range(n_objectives)) + ) + + importances_infos = tuple( + _get_importances_info( + study, + evaluator, + params, + target=lambda t: t.values[objective_id], + target_name=target_name, + ) + for objective_id, target_name in enumerate(target_names) + ) + + return importances_infos + + +def plot_param_importances( + study: Study, + evaluator: BaseImportanceEvaluator | None = None, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "go.Figure": + """Plot hyperparameter importances. + + .. seealso:: + + This function visualizes the results of :func:`optuna.importance.get_param_importances`. + + Args: + study: + An optimized study. + evaluator: + An importance evaluator object that specifies which algorithm to base the importance + assessment on. + Defaults to + :class:`~optuna.importance.FanovaImportanceEvaluator`. + + .. note:: + Although the default importance evaluator in Optuna is + :class:`~optuna.importance.FanovaImportanceEvaluator`, Optuna Dashboard uses a + light-weight evaluator, i.e., + :class:`~optuna.importance.PedAnovaImportanceEvaluator`, for runtime performance + purposes, yielding a different result. + + params: + A list of names of parameters to assess. + If :obj:`None`, all parameters that are present in all of the completed trials are + assessed. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + For multi-objective optimization, all objectives will be plotted if ``target`` + is :obj:`None`. + + .. note:: + This argument can be used to specify which objective to plot if ``study`` is being + used for multi-objective optimization. For example, to get only the hyperparameter + importance of the first objective, use ``target=lambda t: t.values[0]`` for the + target parameter. + target_name: + Target's name to display on the legend. Names set via + :meth:`~optuna.study.Study.set_metric_names` will be used if ``target`` is :obj:`None`, + overriding this argument. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + """ + + _imports.check() + importances_infos = _get_importances_infos(study, evaluator, params, target, target_name) + return _get_importances_plot(importances_infos, study) + + +def _get_importances_plot(infos: tuple[_ImportancesInfo, ...], study: Study) -> "go.Figure": + layout = go.Layout( + title="Hyperparameter Importances", + xaxis={"title": "Hyperparameter Importance"}, + yaxis={"title": "Hyperparameter"}, + ) + + data: list[go.Bar] = [] + for info in infos: + if not info.importance_values: + continue + + data.append( + go.Bar( + x=info.importance_values, + y=info.param_names, + name=info.target_name, + text=info.importance_labels, + textposition="outside", + cliponaxis=False, # Ensure text is not clipped. + hovertemplate=_get_hover_template(info, study), + orientation="h", + ) + ) + + return go.Figure(data, layout) + + +def _get_distribution(param_name: str, study: Study) -> BaseDistribution: + for trial in study.trials: + if param_name in trial.distributions: + return trial.distributions[param_name] + assert False + + +def _make_hovertext(param_name: str, importance: float, study: Study) -> str: + return "{} ({}): {}".format( + param_name, _get_distribution(param_name, study).__class__.__name__, importance + ) + + +def _get_hover_template(importances_info: _ImportancesInfo, study: Study) -> list[str]: + return [ + _make_hovertext(param_name, importance, study) + for param_name, importance in zip( + importances_info.param_names, importances_info.importance_values + ) + ] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_pareto_front.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_pareto_front.py new file mode 100644 index 0000000000000000000000000000000000000000..da427f7919b21af1598fdcfe9bea2dc1ed7dc9d6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_pareto_front.py @@ -0,0 +1,419 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +from typing import Any +from typing import NamedTuple +import warnings + +import optuna +from optuna import _deprecated +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.study import Study +from optuna.study._multi_objective import _get_pareto_front_trials_by_trials +from optuna.trial import FrozenTrial +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports +from optuna.visualization._utils import _make_hovertext + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + +_logger = optuna.logging.get_logger(__name__) + + +class _ParetoFrontInfo(NamedTuple): + n_targets: int + target_names: list[str] + best_trials_with_values: list[tuple[FrozenTrial, list[float]]] + non_best_trials_with_values: list[tuple[FrozenTrial, list[float]]] + infeasible_trials_with_values: list[tuple[FrozenTrial, list[float]]] + axis_order: list[int] + include_dominated_trials: bool + has_constraints: bool + + +def plot_pareto_front( + study: Study, + *, + target_names: list[str] | None = None, + include_dominated_trials: bool = True, + axis_order: list[int] | None = None, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + targets: Callable[[FrozenTrial], Sequence[float]] | None = None, +) -> "go.Figure": + """Plot the Pareto front of a study. + + .. seealso:: + Please refer to :ref:`multi_objective` for the tutorial of the Pareto front visualization. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their objective + values. The number of objectives must be either 2 or 3 when ``targets`` is :obj:`None`. + target_names: + Objective name list used as the axis titles. If :obj:`None` is specified, + "Objective {objective_index}" is used instead. If ``targets`` is specified + for a study that does not contain any completed trial, + ``target_name`` must be specified. + include_dominated_trials: + A flag to include all dominated trial's objective values. + axis_order: + A list of indices indicating the axis order. If :obj:`None` is specified, + default order is used. ``axis_order`` and ``targets`` cannot be used at the same time. + + .. warning:: + Deprecated in v3.0.0. This feature will be removed in the future. The removal of + this feature is currently scheduled for v5.0.0, but this schedule is subject to + change. See https://github.com/optuna/optuna/releases/tag/v3.0.0. + constraints_func: + An optional function that computes the objective constraints. It must take a + :class:`~optuna.trial.FrozenTrial` and return the constraints. The return value must + be a sequence of :obj:`float` s. A value strictly larger than 0 means that a + constraint is violated. A value equal to or smaller than 0 is considered feasible. + This specification is the same as in, for example, + :class:`~optuna.samplers.NSGAIISampler`. + + If given, trials are classified into three categories: feasible and best, feasible but + non-best, and infeasible. Categories are shown in different colors. Here, whether a + trial is best (on Pareto front) or not is determined ignoring all infeasible trials. + + .. warning:: + Deprecated in v4.0.0. This feature will be removed in the future. The removal of + this feature is currently scheduled for v6.0.0, but this schedule is subject to + change. See https://github.com/optuna/optuna/releases/tag/v4.0.0. + targets: + A function that returns targets values to display. + The argument to this function is :class:`~optuna.trial.FrozenTrial`. + ``axis_order`` and ``targets`` cannot be used at the same time. + If ``study.n_objectives`` is neither 2 nor 3, ``targets`` must be specified. + + .. note:: + Added in v3.0.0 as an experimental feature. The interface may change in newer + versions without prior notice. + See https://github.com/optuna/optuna/releases/tag/v3.0.0. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + """ + + _imports.check() + + info = _get_pareto_front_info( + study, target_names, include_dominated_trials, axis_order, constraints_func, targets + ) + return _get_pareto_front_plot(info) + + +def _get_pareto_front_plot(info: _ParetoFrontInfo) -> "go.Figure": + include_dominated_trials = info.include_dominated_trials + has_constraints = info.has_constraints + if not has_constraints: + data = [ + _make_scatter_object( + info.n_targets, + info.axis_order, + include_dominated_trials, + info.non_best_trials_with_values, + hovertemplate="%{text}Trial", + dominated_trials=True, + ), + _make_scatter_object( + info.n_targets, + info.axis_order, + include_dominated_trials, + info.best_trials_with_values, + hovertemplate="%{text}Best Trial", + dominated_trials=False, + ), + ] + else: + data = [ + _make_scatter_object( + info.n_targets, + info.axis_order, + include_dominated_trials, + info.infeasible_trials_with_values, + hovertemplate="%{text}Infeasible Trial", + infeasible=True, + ), + _make_scatter_object( + info.n_targets, + info.axis_order, + include_dominated_trials, + info.non_best_trials_with_values, + hovertemplate="%{text}Feasible Trial", + dominated_trials=True, + ), + _make_scatter_object( + info.n_targets, + info.axis_order, + include_dominated_trials, + info.best_trials_with_values, + hovertemplate="%{text}Best Trial", + dominated_trials=False, + ), + ] + + if info.n_targets == 2: + layout = go.Layout( + title="Pareto-front Plot", + xaxis_title=info.target_names[info.axis_order[0]], + yaxis_title=info.target_names[info.axis_order[1]], + ) + else: + layout = go.Layout( + title="Pareto-front Plot", + scene={ + "xaxis_title": info.target_names[info.axis_order[0]], + "yaxis_title": info.target_names[info.axis_order[1]], + "zaxis_title": info.target_names[info.axis_order[2]], + }, + ) + return go.Figure(data=data, layout=layout) + + +def _get_pareto_front_info( + study: Study, + target_names: list[str] | None = None, + include_dominated_trials: bool = True, + axis_order: list[int] | None = None, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + targets: Callable[[FrozenTrial], Sequence[float]] | None = None, +) -> _ParetoFrontInfo: + if axis_order is not None: + msg = _deprecated._DEPRECATION_WARNING_TEMPLATE.format( + name="`axis_order`", d_ver="3.0.0", r_ver="5.0.0" + ) + warnings.warn(msg, FutureWarning) + + if constraints_func is not None: + msg = _deprecated._DEPRECATION_WARNING_TEMPLATE.format( + name="`constraints_func`", d_ver="4.0.0", r_ver="6.0.0" + ) + warnings.warn(msg, FutureWarning) + + if targets is not None and axis_order is not None: + raise ValueError( + "Using both `targets` and `axis_order` is not supported. " + "Use either `targets` or `axis_order`." + ) + + feasible_trials = [] + infeasible_trials = [] + has_constraints = False + for trial in study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)): + if constraints_func is not None: + # NOTE(nabenabe0928): This part is deprecated. + has_constraints = True + if all(map(lambda x: x <= 0.0, constraints_func(trial))): + feasible_trials.append(trial) + else: + infeasible_trials.append(trial) + continue + + constraints = trial.system_attrs.get(_CONSTRAINTS_KEY) + has_constraints |= constraints is not None + if constraints is None or all(x <= 0.0 for x in constraints): + feasible_trials.append(trial) + else: + infeasible_trials.append(trial) + + best_trials = _get_pareto_front_trials_by_trials(feasible_trials, study.directions) + if include_dominated_trials: + non_best_trials = _get_non_pareto_front_trials(feasible_trials, best_trials) + else: + non_best_trials = [] + + if len(best_trials) == 0: + what_trial = "completed" if has_constraints else "completed and feasible" + _logger.warning(f"Your study does not have any {what_trial} trials. ") + + _targets = targets + if _targets is None: + if len(study.directions) in (2, 3): + _targets = _targets_default + else: + raise ValueError( + "`plot_pareto_front` function only supports 2 or 3 objective" + " studies when using `targets` is `None`. Please use `targets`" + " if your objective studies have more than 3 objectives." + ) + + def _make_trials_with_values( + trials: list[FrozenTrial], + targets: Callable[[FrozenTrial], Sequence[float]], + ) -> list[tuple[FrozenTrial, list[float]]]: + target_values = [targets(trial) for trial in trials] + for v in target_values: + if not isinstance(v, Sequence): + raise ValueError( + "`targets` should return a sequence of target values." + " your `targets` returns {}".format(type(v)) + ) + return [(trial, list(v)) for trial, v in zip(trials, target_values)] + + best_trials_with_values = _make_trials_with_values(best_trials, _targets) + non_best_trials_with_values = _make_trials_with_values(non_best_trials, _targets) + infeasible_trials_with_values = _make_trials_with_values(infeasible_trials, _targets) + + def _infer_n_targets( + trials_with_values: Sequence[tuple[FrozenTrial, Sequence[float]]], + ) -> int | None: + if len(trials_with_values) > 0: + return len(trials_with_values[0][1]) + return None + + # Check for `non_best_trials_with_values` can be skipped, because if `best_trials_with_values` + # is empty, then `non_best_trials_with_values` will also be empty. + n_targets = _infer_n_targets(best_trials_with_values) or _infer_n_targets( + infeasible_trials_with_values + ) + if n_targets is None: + if target_names is not None: + n_targets = len(target_names) + elif targets is None: + n_targets = len(study.directions) + else: + raise ValueError( + "If `targets` is specified for empty studies, `target_names` must be specified." + ) + + if n_targets not in (2, 3): + raise ValueError( + "`plot_pareto_front` function only supports 2 or 3 targets." + " you used {} targets now.".format(n_targets) + ) + + if target_names is None: + metric_names = study.metric_names + if metric_names is None: + target_names = [f"Objective {i}" for i in range(n_targets)] + else: + target_names = metric_names + elif len(target_names) != n_targets: + raise ValueError(f"The length of `target_names` is supposed to be {n_targets}.") + + if axis_order is None: + axis_order = list(range(n_targets)) + else: + if len(axis_order) != n_targets: + raise ValueError( + f"Size of `axis_order` {axis_order}. Expect: {n_targets}, " + f"Actual: {len(axis_order)}." + ) + if len(set(axis_order)) != n_targets: + raise ValueError(f"Elements of given `axis_order` {axis_order} are not unique!.") + if max(axis_order) > n_targets - 1: + raise ValueError( + f"Given `axis_order` {axis_order} contains invalid index {max(axis_order)} " + f"higher than {n_targets - 1}." + ) + if min(axis_order) < 0: + raise ValueError( + f"Given `axis_order` {axis_order} contains invalid index {min(axis_order)} " + "lower than 0." + ) + + return _ParetoFrontInfo( + n_targets=n_targets, + target_names=target_names, + best_trials_with_values=best_trials_with_values, + non_best_trials_with_values=non_best_trials_with_values, + infeasible_trials_with_values=infeasible_trials_with_values, + axis_order=axis_order, + include_dominated_trials=include_dominated_trials, + has_constraints=has_constraints, + ) + + +def _targets_default(trial: FrozenTrial) -> Sequence[float]: + return trial.values + + +def _get_non_pareto_front_trials( + trials: list[FrozenTrial], pareto_trials: list[FrozenTrial] +) -> list[FrozenTrial]: + non_pareto_trials = [] + for trial in trials: + if trial not in pareto_trials: + non_pareto_trials.append(trial) + return non_pareto_trials + + +def _make_scatter_object( + n_targets: int, + axis_order: Sequence[int], + include_dominated_trials: bool, + trials_with_values: Sequence[tuple[FrozenTrial, Sequence[float]]], + hovertemplate: str, + infeasible: bool = False, + dominated_trials: bool = False, +) -> "go.Scatter" | "go.Scatter3d": + trials_with_values = trials_with_values or [] + + marker = _make_marker( + [trial for trial, _ in trials_with_values], + include_dominated_trials, + dominated_trials=dominated_trials, + infeasible=infeasible, + ) + if n_targets == 2: + return go.Scatter( + x=[values[axis_order[0]] for _, values in trials_with_values], + y=[values[axis_order[1]] for _, values in trials_with_values], + text=[_make_hovertext(trial) for trial, _ in trials_with_values], + mode="markers", + hovertemplate=hovertemplate, + marker=marker, + showlegend=False, + ) + elif n_targets == 3: + return go.Scatter3d( + x=[values[axis_order[0]] for _, values in trials_with_values], + y=[values[axis_order[1]] for _, values in trials_with_values], + z=[values[axis_order[2]] for _, values in trials_with_values], + text=[_make_hovertext(trial) for trial, _ in trials_with_values], + mode="markers", + hovertemplate=hovertemplate, + marker=marker, + showlegend=False, + ) + else: + assert False, "Must not reach here" + + +def _make_marker( + trials: Sequence[FrozenTrial], + include_dominated_trials: bool, + dominated_trials: bool = False, + infeasible: bool = False, +) -> dict[str, Any]: + if dominated_trials and not include_dominated_trials: + assert len(trials) == 0 + + if infeasible: + return { + "color": "#cccccc", + } + elif dominated_trials: + return { + "line": {"width": 0.5, "color": "Grey"}, + "color": [t.number for t in trials], + "colorscale": "Blues", + "colorbar": { + "title": "Trial", + }, + } + else: + return { + "line": {"width": 0.5, "color": "Grey"}, + "color": [t.number for t in trials], + "colorscale": "Reds", + "colorbar": { + "title": "Best Trial", + "x": 1.1 if include_dominated_trials else 1, + "xpad": 40, + }, + } diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_plotly_imports.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_plotly_imports.py new file mode 100644 index 0000000000000000000000000000000000000000..32256b29642b7c14107b119487ed3d65028e4fb5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_plotly_imports.py @@ -0,0 +1,23 @@ +from packaging import version + +from optuna._imports import try_import + + +with try_import() as _imports: + import plotly + from plotly import __version__ as plotly_version + import plotly.graph_objects as go + from plotly.graph_objects import Contour + from plotly.graph_objects import Scatter + from plotly.subplots import make_subplots + + if version.parse(plotly_version) < version.parse("4.0.0"): + raise ImportError( + "Your version of Plotly is " + plotly_version + " . " + "Please install plotly version 4.0.0 or higher. " + "Plotly can be installed by executing `$ pip install -U plotly>=4.0.0`. " + "For further information, please refer to the installation guide of plotly. ", + name="plotly", + ) + +__all__ = ["_imports", "plotly", "go", "Contour", "Scatter", "make_subplots"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_rank.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_rank.py new file mode 100644 index 0000000000000000000000000000000000000000..15f74ea6ae616a1322a86e7a173a263a1486452d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_rank.py @@ -0,0 +1,406 @@ +from __future__ import annotations + +from collections.abc import Callable +import math +import typing +from typing import Any +from typing import NamedTuple + +import numpy as np + +from optuna.logging import get_logger +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports +from optuna.visualization._utils import _check_plot_args +from optuna.visualization._utils import _is_log_scale +from optuna.visualization._utils import _is_numerical +from optuna.visualization.matplotlib._matplotlib_imports import _imports as matplotlib_imports + + +plotly_is_available = _imports.is_successful() +if plotly_is_available: + from optuna.visualization._plotly_imports import go + from optuna.visualization._plotly_imports import make_subplots + from optuna.visualization._plotly_imports import plotly + from optuna.visualization._plotly_imports import Scatter +if matplotlib_imports.is_successful(): + # TODO(c-bata): Refactor to remove matplotlib and plotly dependencies in `_get_rank_info()`. + # See https://github.com/optuna/optuna/pull/5133#discussion_r1414761672 for the discussion. + from optuna.visualization.matplotlib._matplotlib_imports import plt as matplotlib_plt + +_logger = get_logger(__name__) + + +PADDING_RATIO = 0.05 + + +class _AxisInfo(NamedTuple): + name: str + range: tuple[float, float] + is_log: bool + is_cat: bool + + +class _RankSubplotInfo(NamedTuple): + xaxis: _AxisInfo + yaxis: _AxisInfo + xs: list[Any] + ys: list[Any] + trials: list[FrozenTrial] + zs: np.ndarray + colors: np.ndarray + + +class _RankPlotInfo(NamedTuple): + params: list[str] + sub_plot_infos: list[list[_RankSubplotInfo]] + target_name: str + zs: np.ndarray + colors: np.ndarray + has_custom_target: bool + + +def plot_rank( + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "go.Figure": + """Plot parameter relations as scatter plots with colors indicating ranks of target value. + + Note that trials missing the specified parameters will not be plotted. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + params: + Parameter list to visualize. The default is all parameters. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the color bar. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + + .. note:: + This function requires plotly >= 5.0.0. + """ + + _imports.check() + info = _get_rank_info(study, params, target, target_name) + return _get_rank_plot(info) + + +def _get_order_with_same_order_averaging(data: np.ndarray) -> np.ndarray: + order = np.zeros_like(data, dtype=float) + data_sorted = np.sort(data) + for i, d in enumerate(data): + indices = np.where(data_sorted == d)[0] + order[i] = sum(indices) / len(indices) + return order + + +def _get_rank_info( + study: Study, + params: list[str] | None, + target: Callable[[FrozenTrial], float] | None, + target_name: str, +) -> _RankPlotInfo: + _check_plot_args(study, target, target_name) + + trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)) + + all_params = {p_name for t in trials for p_name in t.params.keys()} + if len(trials) == 0: + _logger.warning("Your study does not have any completed trials.") + params = [] + elif params is None: + params = sorted(all_params) + else: + for input_p_name in params: + if input_p_name not in all_params: + raise ValueError("Parameter {} does not exist in your study.".format(input_p_name)) + + if len(params) == 0: + _logger.warning("params is an empty list.") + + has_custom_target = True + if target is None: + + def target(trial: FrozenTrial) -> float: + return typing.cast(float, trial.value) + + has_custom_target = False + target_values = np.array([target(trial) for trial in trials]) + raw_ranks = _get_order_with_same_order_averaging(target_values) + color_idxs = raw_ranks / (len(trials) - 1) if len(trials) >= 2 else np.array([0.5]) + colors = _convert_color_idxs_to_scaled_rgb_colors(color_idxs) + + sub_plot_infos: list[list[_RankSubplotInfo]] + if len(params) == 2: + x_param = params[0] + y_param = params[1] + sub_plot_info = _get_rank_subplot_info(trials, target_values, colors, x_param, y_param) + sub_plot_infos = [[sub_plot_info]] + else: + sub_plot_infos = [ + [ + _get_rank_subplot_info(trials, target_values, colors, x_param, y_param) + for x_param in params + ] + for y_param in params + ] + + return _RankPlotInfo( + params=params, + sub_plot_infos=sub_plot_infos, + target_name=target_name, + zs=target_values, + colors=colors, + has_custom_target=has_custom_target, + ) + + +def _get_rank_subplot_info( + trials: list[FrozenTrial], + target_values: np.ndarray, + colors: np.ndarray, + x_param: str, + y_param: str, +) -> _RankSubplotInfo: + xaxis = _get_axis_info(trials, x_param) + yaxis = _get_axis_info(trials, y_param) + + infeasible_trial_ids = [] + filtered_ids = [] + for idx, trial in enumerate(trials): + constraints = trial.system_attrs.get(_CONSTRAINTS_KEY) + if constraints is not None and any([x > 0.0 for x in constraints]): + infeasible_trial_ids.append(idx) + if x_param in trial.params and y_param in trial.params: + filtered_ids.append(idx) + + filtered_trials = [trials[i] for i in filtered_ids] + xs = [trial.params[x_param] for trial in filtered_trials] + ys = [trial.params[y_param] for trial in filtered_trials] + zs = target_values[filtered_ids] + + colors[infeasible_trial_ids] = (204, 204, 204) + colors = colors[filtered_ids] + return _RankSubplotInfo( + xaxis=xaxis, + yaxis=yaxis, + xs=xs, + ys=ys, + trials=filtered_trials, + zs=np.array(zs), + colors=colors, + ) + + +def _get_axis_info(trials: list[FrozenTrial], param_name: str) -> _AxisInfo: + values: list[str | float | None] + is_numerical = _is_numerical(trials, param_name) + if is_numerical: + values = [t.params.get(param_name) for t in trials] + else: + values = [ + str(t.params.get(param_name)) if param_name in t.params else None for t in trials + ] + + min_value = min([v for v in values if v is not None]) + max_value = max([v for v in values if v is not None]) + + if _is_log_scale(trials, param_name): + min_value = float(min_value) + max_value = float(max_value) + padding = (math.log10(max_value) - math.log10(min_value)) * PADDING_RATIO + min_value = math.pow(10, math.log10(min_value) - padding) + max_value = math.pow(10, math.log10(max_value) + padding) + is_log = True + is_cat = False + + elif is_numerical: + min_value = float(min_value) + max_value = float(max_value) + padding = (max_value - min_value) * PADDING_RATIO + min_value = min_value - padding + max_value = max_value + padding + is_log = False + is_cat = False + + else: + unique_values = set(values) + span = len(unique_values) - 1 + if None in unique_values: + span -= 1 + padding = span * PADDING_RATIO + min_value = -padding + max_value = span + padding + is_log = False + is_cat = True + + return _AxisInfo( + name=param_name, + range=(min_value, max_value), + is_log=is_log, + is_cat=is_cat, + ) + + +def _get_rank_subplot( + info: _RankSubplotInfo, target_name: str, print_raw_objectives: bool +) -> "Scatter": + def get_hover_text(trial: FrozenTrial, target_value: float) -> str: + lines = [f"Trial #{trial.number}"] + lines += [f"{k}: {v}" for k, v in trial.params.items()] + lines += [f"{target_name}: {target_value}"] + if print_raw_objectives: + lines += [f"Objective #{i}: {v}" for i, v in enumerate(trial.values)] + return "
".join(lines) + + scatter = go.Scatter( + x=[str(x) for x in info.xs] if info.xaxis.is_cat else info.xs, + y=[str(y) for y in info.ys] if info.yaxis.is_cat else info.ys, + marker={ + "color": list(map(plotly.colors.label_rgb, info.colors)), + "line": {"width": 0.5, "color": "Grey"}, + }, + mode="markers", + showlegend=False, + hovertemplate="%{hovertext}", + hovertext=[ + get_hover_text(trial, target_value) + for trial, target_value in zip(info.trials, info.zs) + ], + ) + return scatter + + +class _TickInfo(NamedTuple): + coloridxs: list[float] + text: list[str] + + +def _get_tick_info(target_values: np.ndarray) -> _TickInfo: + sorted_target_values = np.sort(target_values) + coloridxs = [0, 0.25, 0.5, 0.75, 1] + values = np.quantile(sorted_target_values, coloridxs) + rank_text = ["min.", "25%", "50%", "75%", "max."] + text = [f"{rank_text[i]} ({values[i]:3g})" for i in range(len(values))] + return _TickInfo(coloridxs=coloridxs, text=text) + + +def _get_rank_plot( + info: _RankPlotInfo, +) -> "go.Figure": + params = info.params + sub_plot_infos = info.sub_plot_infos + + layout = go.Layout(title=f"Rank ({info.target_name})") + + if len(params) == 0: + return go.Figure(data=[], layout=layout) + if len(params) == 2: + x_param = params[0] + y_param = params[1] + sub_plot_info = sub_plot_infos[0][0] + sub_plots = _get_rank_subplot(sub_plot_info, info.target_name, info.has_custom_target) + + figure = go.Figure(data=sub_plots, layout=layout) + figure.update_xaxes(title_text=x_param, range=sub_plot_info.xaxis.range) + figure.update_yaxes(title_text=y_param, range=sub_plot_info.yaxis.range) + + if sub_plot_info.xaxis.is_cat: + figure.update_xaxes(type="category") + if sub_plot_info.yaxis.is_cat: + figure.update_yaxes(type="category") + + if sub_plot_info.xaxis.is_log: + log_range = [math.log10(p) for p in sub_plot_info.xaxis.range] + figure.update_xaxes(range=log_range, type="log") + if sub_plot_info.yaxis.is_log: + log_range = [math.log10(p) for p in sub_plot_info.yaxis.range] + figure.update_yaxes(range=log_range, type="log") + else: + figure = make_subplots( + rows=len(params), + cols=len(params), + shared_xaxes=True, + shared_yaxes=True, + horizontal_spacing=0.08 / len(params), + vertical_spacing=0.08 / len(params), + ) + + figure.update_layout(layout) + for x_i, x_param in enumerate(params): + for y_i, y_param in enumerate(params): + scatter = _get_rank_subplot( + sub_plot_infos[y_i][x_i], info.target_name, info.has_custom_target + ) + figure.add_trace(scatter, row=y_i + 1, col=x_i + 1) + + xaxis = sub_plot_infos[y_i][x_i].xaxis + yaxis = sub_plot_infos[y_i][x_i].yaxis + figure.update_xaxes(range=xaxis.range, row=y_i + 1, col=x_i + 1) + figure.update_yaxes(range=yaxis.range, row=y_i + 1, col=x_i + 1) + + if xaxis.is_cat: + figure.update_xaxes(type="category", row=y_i + 1, col=x_i + 1) + if yaxis.is_cat: + figure.update_yaxes(type="category", row=y_i + 1, col=x_i + 1) + + if xaxis.is_log: + log_range = [math.log10(p) for p in xaxis.range] + figure.update_xaxes(range=log_range, type="log", row=y_i + 1, col=x_i + 1) + if yaxis.is_log: + log_range = [math.log10(p) for p in yaxis.range] + figure.update_yaxes(range=log_range, type="log", row=y_i + 1, col=x_i + 1) + + if x_i == 0: + figure.update_yaxes(title_text=y_param, row=y_i + 1, col=x_i + 1) + if y_i == len(params) - 1: + figure.update_xaxes(title_text=x_param, row=y_i + 1, col=x_i + 1) + + tick_info = _get_tick_info(info.zs) + + colormap = "RdYlBu_r" + colorbar_trace = go.Scatter( + x=[None], + y=[None], + mode="markers", + marker=dict( + colorscale=colormap, + showscale=True, + cmin=0, + cmax=1, + colorbar=dict(thickness=10, tickvals=tick_info.coloridxs, ticktext=tick_info.text), + ), + hoverinfo="none", + showlegend=False, + ) + figure.add_trace(colorbar_trace) + return figure + + +def _convert_color_idxs_to_scaled_rgb_colors(color_idxs: np.ndarray) -> np.ndarray: + colormap = "RdYlBu_r" + if plotly_is_available: + # sample_colorscale requires plotly >= 5.0.0. + labeled_colors = plotly.colors.sample_colorscale(colormap, color_idxs) + scaled_rgb_colors = np.array([plotly.colors.unlabel_rgb(cl) for cl in labeled_colors]) + return scaled_rgb_colors + else: + cmap = matplotlib_plt.get_cmap(colormap) + colors = cmap(color_idxs)[:, :3] # Drop alpha values. + rgb_colors = np.asarray(colors * 255, dtype=int) + return rgb_colors diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_slice.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_slice.py new file mode 100644 index 0000000000000000000000000000000000000000..38f014d208d3675242df271a1ce00b65c6c72032 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_slice.py @@ -0,0 +1,272 @@ +from __future__ import annotations + +from collections.abc import Callable +from typing import Any +from typing import cast +from typing import NamedTuple + +from optuna.distributions import CategoricalChoiceType +from optuna.distributions import CategoricalDistribution +from optuna.logging import get_logger +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports +from optuna.visualization._utils import _check_plot_args +from optuna.visualization._utils import _filter_nonfinite +from optuna.visualization._utils import _is_log_scale + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + from optuna.visualization._plotly_imports import make_subplots + from optuna.visualization._plotly_imports import Scatter + from optuna.visualization._utils import COLOR_SCALE + +_logger = get_logger(__name__) + + +class _SliceSubplotInfo(NamedTuple): + param_name: str + x: list[Any] + y: list[float] + trial_numbers: list[int] + is_log: bool + is_numerical: bool + constraints: list[bool] + x_labels: tuple[CategoricalChoiceType, ...] | None + + +class _SlicePlotInfo(NamedTuple): + target_name: str + subplots: list[_SliceSubplotInfo] + + +class _PlotValues(NamedTuple): + x: list[Any] + y: list[float] + trial_numbers: list[int] + + +def _get_slice_subplot_info( + trials: list[FrozenTrial], + param: str, + target: Callable[[FrozenTrial], float] | None, + log_scale: bool, + numerical: bool, + x_labels: tuple[CategoricalChoiceType, ...] | None, +) -> _SliceSubplotInfo: + if target is None: + + def _target(t: FrozenTrial) -> float: + return cast(float, t.value) + + target = _target + + plot_info = _SliceSubplotInfo( + param_name=param, + x=[], + y=[], + trial_numbers=[], + is_log=log_scale, + is_numerical=numerical, + x_labels=x_labels, + constraints=[], + ) + + for t in trials: + if param not in t.params: + continue + plot_info.x.append(t.params[param]) + plot_info.y.append(target(t)) + plot_info.trial_numbers.append(t.number) + constraints = t.system_attrs.get(_CONSTRAINTS_KEY) + plot_info.constraints.append(constraints is None or all([x <= 0.0 for x in constraints])) + + return plot_info + + +def _get_slice_plot_info( + study: Study, + params: list[str] | None, + target: Callable[[FrozenTrial], float] | None, + target_name: str, +) -> _SlicePlotInfo: + _check_plot_args(study, target, target_name) + + trials = _filter_nonfinite( + study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,)), target=target + ) + + if len(trials) == 0: + _logger.warning("Your study does not have any completed trials.") + return _SlicePlotInfo(target_name, []) + + all_params = {p_name for t in trials for p_name in t.params.keys()} + + distributions = {} + for trial in trials: + for param_name, distribution in trial.distributions.items(): + if param_name not in distributions: + distributions[param_name] = distribution + + x_labels = {} + for param_name, distribution in distributions.items(): + if isinstance(distribution, CategoricalDistribution): + x_labels[param_name] = distribution.choices + + if params is None: + sorted_params = sorted(all_params) + else: + for input_p_name in params: + if input_p_name not in all_params: + raise ValueError(f"Parameter {input_p_name} does not exist in your study.") + sorted_params = sorted(set(params)) + + return _SlicePlotInfo( + target_name=target_name, + subplots=[ + _get_slice_subplot_info( + trials=trials, + param=param, + target=target, + log_scale=_is_log_scale(trials, param), + numerical=not isinstance(distributions[param], CategoricalDistribution), + x_labels=x_labels.get(param), + ) + for param in sorted_params + ], + ) + + +def plot_slice( + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "go.Figure": + """Plot the parameter relationship as slice plot in a study. + + Note that, if a parameter contains missing values, a trial with missing values is not plotted. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + params: + Parameter list to visualize. The default is all parameters. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the axis label. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + """ + + _imports.check() + return _get_slice_plot(_get_slice_plot_info(study, params, target, target_name)) + + +def _get_slice_plot(info: _SlicePlotInfo) -> "go.Figure": + layout = go.Layout(title="Slice Plot") + + if len(info.subplots) == 0: + return go.Figure(data=[], layout=layout) + elif len(info.subplots) == 1: + figure = go.Figure(data=_generate_slice_subplot(info.subplots[0]), layout=layout) + figure.update_xaxes(title_text=info.subplots[0].param_name) + figure.update_yaxes(title_text=info.target_name) + if not info.subplots[0].is_numerical: + figure.update_xaxes( + type="category", categoryorder="array", categoryarray=info.subplots[0].x_labels + ) + elif info.subplots[0].is_log: + figure.update_xaxes(type="log") + else: + figure = make_subplots(rows=1, cols=len(info.subplots), shared_yaxes=True) + figure.update_layout(layout) + showscale = True # showscale option only needs to be specified once. + for column_index, subplot_info in enumerate(info.subplots, start=1): + trace = _generate_slice_subplot(subplot_info) + trace[0].update(marker={"showscale": showscale}) # showscale's default is True. + if showscale: + showscale = False + for t in trace: + figure.add_trace(t, row=1, col=column_index) + figure.update_xaxes(title_text=subplot_info.param_name, row=1, col=column_index) + if column_index == 1: + figure.update_yaxes(title_text=info.target_name, row=1, col=column_index) + if not subplot_info.is_numerical: + figure.update_xaxes( + type="category", + categoryorder="array", + categoryarray=subplot_info.x_labels, + row=1, + col=column_index, + ) + elif subplot_info.is_log: + figure.update_xaxes(type="log", row=1, col=column_index) + if len(info.subplots) > 3: + # Ensure that each subplot has a minimum width without relying on autusizing. + figure.update_layout(width=300 * len(info.subplots)) + + return figure + + +def _generate_slice_subplot(subplot_info: _SliceSubplotInfo) -> list[Scatter]: + trace = [] + + feasible = _PlotValues([], [], []) + infeasible = _PlotValues([], [], []) + + for x, y, num, c in zip( + subplot_info.x, subplot_info.y, subplot_info.trial_numbers, subplot_info.constraints + ): + if x is not None or x != "None" or y is not None or y != "None": + if c: + feasible.x.append(x) + feasible.y.append(y) + feasible.trial_numbers.append(num) + else: + infeasible.x.append(x) + infeasible.y.append(y) + trace.append( + go.Scatter( + x=feasible.x, + y=feasible.y, + mode="markers", + name="Feasible Trial", + marker={ + "line": {"width": 0.5, "color": "Grey"}, + "color": feasible.trial_numbers, + "colorscale": COLOR_SCALE, + "colorbar": { + "title": "Trial", + "x": 1.0, # Offset the colorbar position with a fixed width `xpad`. + "xpad": 40, + }, + }, + showlegend=False, + ) + ) + if len(infeasible.x) > 0: + trace.append( + go.Scatter( + x=infeasible.x, + y=infeasible.y, + mode="markers", + name="Infeasible Trial", + marker={ + "color": "#cccccc", + }, + showlegend=False, + ) + ) + + return trace diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_terminator_improvement.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_terminator_improvement.py new file mode 100644 index 0000000000000000000000000000000000000000..e2aeb37ab826ff6c1bce8bf63d0ff65e945d6529 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_terminator_improvement.py @@ -0,0 +1,226 @@ +from __future__ import annotations + +from typing import NamedTuple + +import tqdm + +import optuna +from optuna._experimental import experimental_func +from optuna.logging import get_logger +from optuna.study.study import Study +from optuna.terminator import BaseErrorEvaluator +from optuna.terminator import BaseImprovementEvaluator +from optuna.terminator import CrossValidationErrorEvaluator +from optuna.terminator import RegretBoundEvaluator +from optuna.terminator.erroreval import StaticErrorEvaluator +from optuna.terminator.improvement.evaluator import BestValueStagnationEvaluator +from optuna.terminator.improvement.evaluator import DEFAULT_MIN_N_TRIALS +from optuna.visualization._plotly_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + +_logger = get_logger(__name__) + + +PADDING_RATIO_Y = 0.05 +OPACITY = 0.25 + + +class _ImprovementInfo(NamedTuple): + trial_numbers: list[int] + improvements: list[float] + errors: list[float] | None + + +@experimental_func("3.2.0") +def plot_terminator_improvement( + study: Study, + plot_error: bool = False, + improvement_evaluator: BaseImprovementEvaluator | None = None, + error_evaluator: BaseErrorEvaluator | None = None, + min_n_trials: int = DEFAULT_MIN_N_TRIALS, +) -> "go.Figure": + """Plot the potentials for future objective improvement. + + This function visualizes the objective improvement potentials, evaluated + with ``improvement_evaluator``. + It helps to determine whether we should continue the optimization or not. + You can also plot the error evaluated with + ``error_evaluator`` if the ``plot_error`` argument is set to :obj:`True`. + Note that this function may take some time to compute + the improvement potentials. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted + for their improvement. + plot_error: + A flag to show the error. If it is set to :obj:`True`, errors + evaluated by ``error_evaluator`` are also plotted as line graph. + Defaults to :obj:`False`. + improvement_evaluator: + An object that evaluates the improvement of the objective function. + Defaults to :class:`~optuna.terminator.RegretBoundEvaluator`. + error_evaluator: + An object that evaluates the error inherent in the objective function. + Defaults to :class:`~optuna.terminator.CrossValidationErrorEvaluator`. + min_n_trials: + The minimum number of trials before termination is considered. + Terminator improvements for trials below this value are + shown in a lighter color. Defaults to ``20``. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + """ + _imports.check() + + info = _get_improvement_info(study, plot_error, improvement_evaluator, error_evaluator) + return _get_improvement_plot(info, min_n_trials) + + +def _get_improvement_info( + study: Study, + get_error: bool = False, + improvement_evaluator: BaseImprovementEvaluator | None = None, + error_evaluator: BaseErrorEvaluator | None = None, +) -> _ImprovementInfo: + if study._is_multi_objective(): + raise ValueError("This function does not support multi-objective optimization study.") + + if improvement_evaluator is None: + improvement_evaluator = RegretBoundEvaluator() + if error_evaluator is None: + if isinstance(improvement_evaluator, BestValueStagnationEvaluator): + error_evaluator = StaticErrorEvaluator(constant=0) + else: + error_evaluator = CrossValidationErrorEvaluator() + + trial_numbers = [] + completed_trials = [] + improvements = [] + errors = [] + + for trial in tqdm.tqdm(study.trials): + if trial.state == optuna.trial.TrialState.COMPLETE: + completed_trials.append(trial) + + if len(completed_trials) == 0: + continue + + trial_numbers.append(trial.number) + + improvement = improvement_evaluator.evaluate( + trials=completed_trials, study_direction=study.direction + ) + improvements.append(improvement) + + if get_error: + error = error_evaluator.evaluate( + trials=completed_trials, study_direction=study.direction + ) + errors.append(error) + + if len(errors) == 0: + return _ImprovementInfo( + trial_numbers=trial_numbers, improvements=improvements, errors=None + ) + else: + return _ImprovementInfo( + trial_numbers=trial_numbers, improvements=improvements, errors=errors + ) + + +def _get_improvement_scatter( + trial_numbers: list[int], + improvements: list[float], + opacity: float = 1.0, + showlegend: bool = True, +) -> "go.Scatter": + plotly_blue_with_opacity = f"rgba(99, 110, 250, {opacity})" + return go.Scatter( + x=trial_numbers, + y=improvements, + mode="markers+lines", + marker=dict(color=plotly_blue_with_opacity), + line=dict(color=plotly_blue_with_opacity), + name="Terminator Improvement", + showlegend=showlegend, + legendgroup="improvement", + ) + + +def _get_error_scatter( + trial_numbers: list[int], + errors: list[float] | None, +) -> "go.Scatter": + if errors is None: + return go.Scatter() + + plotly_red = "rgb(239, 85, 59)" + return go.Scatter( + x=trial_numbers, + y=errors, + mode="markers+lines", + name="Error", + marker=dict(color=plotly_red), + line=dict(color=plotly_red), + ) + + +def _get_y_range(info: _ImprovementInfo, min_n_trials: int) -> tuple[float, float]: + min_value = min(info.improvements) + if info.errors is not None: + min_value = min(min_value, min(info.errors)) + + # Determine the display range based on trials after min_n_trials. + if len(info.trial_numbers) > min_n_trials: + max_value = max(info.improvements[min_n_trials:]) + # If there are no trials after min_trials, determine the display range based on all trials. + else: + max_value = max(info.improvements) + + if info.errors is not None: + max_value = max(max_value, max(info.errors)) + + padding = (max_value - min_value) * PADDING_RATIO_Y + return min_value - padding, max_value + padding + + +def _get_improvement_plot(info: _ImprovementInfo, min_n_trials: int) -> "go.Figure": + n_trials = len(info.trial_numbers) + + fig = go.Figure( + layout=go.Layout( + title="Terminator Improvement Plot", + xaxis=dict(title="Trial"), + yaxis=dict(title="Terminator Improvement"), + ) + ) + if n_trials == 0: + _logger.warning("There are no complete trials.") + return fig + + fig.add_trace( + _get_improvement_scatter( + info.trial_numbers[: min_n_trials + 1], + info.improvements[: min_n_trials + 1], + # Plot line with a lighter color until the number of trials reaches min_n_trials. + OPACITY, + n_trials <= min_n_trials, # Avoid showing legend twice. + ) + ) + + if n_trials > min_n_trials: + fig.add_trace( + _get_improvement_scatter( + info.trial_numbers[min_n_trials:], + info.improvements[min_n_trials:], + ) + ) + + fig.add_trace(_get_error_scatter(info.trial_numbers, info.errors)) + + fig.update_yaxes(range=_get_y_range(info, min_n_trials)) + return fig diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_timeline.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_timeline.py new file mode 100644 index 0000000000000000000000000000000000000000..361de7f57306519b81e7a3811f2e995ba647f243 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_timeline.py @@ -0,0 +1,187 @@ +from __future__ import annotations + +import datetime +from typing import NamedTuple + +from optuna.logging import get_logger +from optuna.samplers._base import _CONSTRAINTS_KEY +from optuna.study import Study +from optuna.trial import TrialState +from optuna.visualization._plotly_imports import _imports +from optuna.visualization._utils import _make_hovertext + + +if _imports.is_successful(): + from optuna.visualization._plotly_imports import go + +_logger = get_logger(__name__) + + +class _TimelineBarInfo(NamedTuple): + number: int + start: datetime.datetime + complete: datetime.datetime + state: TrialState + hovertext: str + infeasible: bool + + +class _TimelineInfo(NamedTuple): + bars: list[_TimelineBarInfo] + + +def plot_timeline(study: Study, n_recent_trials: int | None = None) -> "go.Figure": + """Plot the timeline of a study. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted with + their lifetime. + n_recent_trials: + The number of recent trials to plot. If :obj:`None`, all trials are plotted. + If specified, only the most recent ``n_recent_trials`` will be displayed. + Must be a positive integer. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + + Raises: + ValueError: if ``n_recent_trials`` is 0 or negative. + """ + + if n_recent_trials is not None and n_recent_trials <= 0: + raise ValueError("n_recent_trials must be a positive integer or None.") + + _imports.check() + info = _get_timeline_info(study, n_recent_trials=n_recent_trials) + return _get_timeline_plot(info) + + +def _get_max_datetime_complete(study: Study) -> datetime.datetime: + max_run_duration = max( + [ + t.datetime_complete - t.datetime_start + for t in study.trials + if t.datetime_complete is not None and t.datetime_start is not None + ], + default=None, + ) + if _is_running_trials_in_study(study, max_run_duration): + return datetime.datetime.now() + + return max( + [t.datetime_complete for t in study.trials if t.datetime_complete is not None], + default=datetime.datetime.now(), + ) + + +def _is_running_trials_in_study(study: Study, max_run_duration: datetime.timedelta | None) -> bool: + running_trials = study.get_trials(states=(TrialState.RUNNING,), deepcopy=False) + if max_run_duration is None: + return len(running_trials) > 0 + + now = datetime.datetime.now() + # This heuristic is to check whether we have trials that were somehow killed, + # still remain as `RUNNING` in `study`. + return any( + now - t.datetime_start < 5 * max_run_duration + for t in running_trials + # MyPy redefinition: Running trial should have datetime_start. + if t.datetime_start is not None + ) + + +def _get_timeline_info(study: Study, n_recent_trials: int | None = None) -> _TimelineInfo: + bars = [] + + max_datetime = _get_max_datetime_complete(study) + timedelta_for_small_bar = datetime.timedelta(seconds=1) + + trials = study.get_trials(deepcopy=False) + if n_recent_trials is not None: + trials = trials[-n_recent_trials:] + + for trial in trials: + datetime_start = trial.datetime_start or max_datetime + datetime_complete = ( + max_datetime + timedelta_for_small_bar + if trial.state == TrialState.RUNNING + else trial.datetime_complete or datetime_start + timedelta_for_small_bar + ) + infeasible = ( + False + if _CONSTRAINTS_KEY not in trial.system_attrs + else any([x > 0 for x in trial.system_attrs[_CONSTRAINTS_KEY]]) + ) + if datetime_complete < datetime_start: + _logger.warning( + ( + f"The start and end times for Trial {trial.number} seem to be reversed. " + f"The start time is {datetime_start} and the end time is {datetime_complete}." + ) + ) + bars.append( + _TimelineBarInfo( + number=trial.number, + start=datetime_start, + complete=datetime_complete, + state=trial.state, + hovertext=_make_hovertext(trial), + infeasible=infeasible, + ) + ) + + if len(bars) == 0: + _logger.warning("Your study does not have any trials.") + + return _TimelineInfo(bars) + + +def _get_timeline_plot(info: _TimelineInfo) -> "go.Figure": + _cm = { + "COMPLETE": "blue", + "FAIL": "red", + "PRUNED": "orange", + "RUNNING": "green", + "WAITING": "gray", + } + + fig = go.Figure() + for state in sorted(TrialState, key=lambda x: x.name): + if state.name == "COMPLETE": + infeasible_bars = [b for b in info.bars if b.state == state and b.infeasible] + feasible_bars = [b for b in info.bars if b.state == state and not b.infeasible] + _plot_bars(infeasible_bars, "#cccccc", "INFEASIBLE", fig) + _plot_bars(feasible_bars, _cm[state.name], state.name, fig) + else: + bars = [b for b in info.bars if b.state == state] + _plot_bars(bars, _cm[state.name], state.name, fig) + fig.update_xaxes(type="date") + fig.update_layout( + go.Layout( + title="Timeline Plot", + xaxis={"title": "Datetime"}, + yaxis={"title": "Trial"}, + ) + ) + fig.update_layout(showlegend=True) # Draw a legend even if all TrialStates are the same. + return fig + + +def _plot_bars(bars: list[_TimelineBarInfo], color: str, name: str, fig: go.Figure) -> None: + if len(bars) == 0: + return + + fig.add_trace( + go.Bar( + name=name, + x=[(b.complete - b.start).total_seconds() * 1000 for b in bars], + y=[b.number for b in bars], + base=[b.start.isoformat() for b in bars], + text=[b.hovertext for b in bars], + hovertemplate="%{text}" + name + "", + orientation="h", + marker=dict(color=color), + textposition="none", # Avoid drawing hovertext in a bar. + ) + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_utils.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..75864f3781f66092b09ea0ac44df3047975a43ee --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/_utils.py @@ -0,0 +1,201 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +import json +from typing import Any +from typing import cast +import warnings + +import numpy as np + +import optuna +from optuna.distributions import CategoricalDistribution +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution +from optuna.study import Study +from optuna.study._study_direction import StudyDirection +from optuna.trial import FrozenTrial +from optuna.visualization import _plotly_imports + + +__all__ = ["is_available"] +_logger = optuna.logging.get_logger(__name__) + + +def is_available() -> bool: + """Returns whether visualization with plotly is available or not. + + .. note:: + + :mod:`~optuna.visualization` module depends on plotly version 4.0.0 or higher. If a + supported version of plotly isn't installed in your environment, this function will return + :obj:`False`. In such case, please execute ``$ pip install -U plotly>=4.0.0`` to install + plotly. + + Returns: + :obj:`True` if visualization with plotly is available, :obj:`False` otherwise. + """ + + return _plotly_imports._imports.is_successful() + + +if is_available(): + import plotly.colors + + COLOR_SCALE = plotly.colors.sequential.Blues + + +def _check_plot_args( + study: Study | Sequence[Study], + target: Callable[[FrozenTrial], float] | None, + target_name: str, +) -> None: + studies: Sequence[Study] + if isinstance(study, Study): + studies = [study] + else: + studies = study + + if target is None and any(study._is_multi_objective() for study in studies): + raise ValueError( + "If the `study` is being used for multi-objective optimization, " + "please specify the `target`." + ) + + if target is not None and target_name == "Objective Value": + warnings.warn( + "`target` is specified, but `target_name` is the default value, 'Objective Value'." + ) + + +def _is_log_scale(trials: list[FrozenTrial], param: str) -> bool: + for trial in trials: + if param not in trial.params: + continue + dist = trial.distributions[param] + return isinstance(dist, (FloatDistribution, IntDistribution)) and dist.log + return False + + +def _is_numerical(trials: list[FrozenTrial], param: str) -> bool: + for trial in trials: + if param not in trial.params: + continue + dist = trial.distributions[param] + if isinstance(dist, (IntDistribution, FloatDistribution)): + return True + elif isinstance(dist, CategoricalDistribution): + # NOTE: Although it is a bit odd to do so, we keep it as is only for visualization. + return all( + isinstance(v, (int, float)) and not isinstance(v, bool) for v in dist.choices + ) + else: + assert False, "Should not reach." + return True + + +def _get_param_values(trials: list[FrozenTrial], p_name: str) -> list[Any]: + values = [t.params[p_name] for t in trials if p_name in t.params] + if _is_numerical(trials, p_name): + return values + return list(map(str, values)) + + +def _get_skipped_trial_numbers( + trials: list[FrozenTrial], used_param_names: Sequence[str] +) -> set[int]: + """Utility function for ``plot_parallel_coordinate``. + + If trial's parameters do not contain a parameter in ``used_param_names``, + ``plot_parallel_coordinate`` methods do not use such trials. + + Args: + trials: + List of ``FrozenTrial``s. + used_param_names: + The parameter names used in ``plot_parallel_coordinate``. + + Returns: + A set of invalid trial numbers. + """ + + skipped_trial_numbers = set() + for trial in trials: + for used_param in used_param_names: + if used_param not in trial.params.keys(): + skipped_trial_numbers.add(trial.number) + break + return skipped_trial_numbers + + +def _filter_nonfinite( + trials: list[FrozenTrial], + target: Callable[[FrozenTrial], float] | None = None, + with_message: bool = True, +) -> list[FrozenTrial]: + # For multi-objective optimization target must be specified to select + # one of objective values to filter trials by (and plot by later on). + # This function is not raising when target is missing, since we're + # assuming plot args have been sanitized before. + if target is None: + + def _target(t: FrozenTrial) -> float: + return cast(float, t.value) + + target = _target + + filtered_trials: list[FrozenTrial] = [] + for trial in trials: + value = target(trial) + + try: + value = float(value) + except ( + ValueError, + TypeError, + ): + warnings.warn( + f"Trial{trial.number}'s target value {repr(value)} could not be cast to float." + ) + raise + + # Not a Number, positive infinity and negative infinity are considered to be non-finite. + if not np.isfinite(value): + if with_message: + _logger.warning( + f"Trial {trial.number} is omitted in visualization " + "because its objective value is inf or nan." + ) + else: + filtered_trials.append(trial) + + return filtered_trials + + +def _is_reverse_scale(study: Study, target: Callable[[FrozenTrial], float] | None) -> bool: + return target is not None or study.direction == StudyDirection.MINIMIZE + + +def _make_json_compatible(value: Any) -> Any: + try: + json.dumps(value) + return value + except TypeError: + # The value can't be converted to JSON directly, so return a string representation. + return str(value) + + +def _make_hovertext(trial: FrozenTrial) -> str: + user_attrs = {key: _make_json_compatible(value) for key, value in trial.user_attrs.items()} + user_attrs_dict = {"user_attrs": user_attrs} if user_attrs else {} + text = json.dumps( + { + "number": trial.number, + "values": trial.values, + "params": trial.params, + **user_attrs_dict, + }, + indent=2, + ) + return text.replace("\n", "
") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..54368c8e5de73114ac9a311070369042164f56cd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/__init__.py @@ -0,0 +1,30 @@ +from optuna.visualization.matplotlib._contour import plot_contour +from optuna.visualization.matplotlib._edf import plot_edf +from optuna.visualization.matplotlib._hypervolume_history import plot_hypervolume_history +from optuna.visualization.matplotlib._intermediate_values import plot_intermediate_values +from optuna.visualization.matplotlib._optimization_history import plot_optimization_history +from optuna.visualization.matplotlib._parallel_coordinate import plot_parallel_coordinate +from optuna.visualization.matplotlib._param_importances import plot_param_importances +from optuna.visualization.matplotlib._pareto_front import plot_pareto_front +from optuna.visualization.matplotlib._rank import plot_rank +from optuna.visualization.matplotlib._slice import plot_slice +from optuna.visualization.matplotlib._terminator_improvement import plot_terminator_improvement +from optuna.visualization.matplotlib._timeline import plot_timeline +from optuna.visualization.matplotlib._utils import is_available + + +__all__ = [ + "is_available", + "plot_contour", + "plot_edf", + "plot_intermediate_values", + "plot_hypervolume_history", + "plot_optimization_history", + "plot_parallel_coordinate", + "plot_param_importances", + "plot_pareto_front", + "plot_rank", + "plot_slice", + "plot_terminator_improvement", + "plot_timeline", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/__pycache__/__init__.cpython-310.pyc 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__future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence + +import numpy as np + +from optuna._experimental import experimental_func +from optuna._imports import try_import +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.visualization._contour import _AxisInfo +from optuna.visualization._contour import _ContourInfo +from optuna.visualization._contour import _get_contour_info +from optuna.visualization._contour import _PlotValues +from optuna.visualization._contour import _SubContourInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +with try_import() as _optuna_imports: + import scipy + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import Colormap + from optuna.visualization.matplotlib._matplotlib_imports import ContourSet + from optuna.visualization.matplotlib._matplotlib_imports import plt + + +CONTOUR_POINT_NUM = 100 + + +@experimental_func("2.2.0") +def plot_contour( + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "Axes": + """Plot the parameter relationship as contour plot in a study with Matplotlib. + + Note that, if a parameter contains missing values, a trial with missing values is not plotted. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_contour` for an example. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + params: + Parameter list to visualize. The default is all parameters. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the color bar. + + Returns: + A :class:`matplotlib.axes.Axes` object. + + .. note:: + The colormap is reversed when the ``target`` argument isn't :obj:`None` or ``direction`` + of :class:`~optuna.study.Study` is ``minimize``. + """ + + _imports.check() + info = _get_contour_info(study, params, target, target_name) + return _get_contour_plot(info) + + +def _get_contour_plot(info: _ContourInfo) -> "Axes": + sorted_params = info.sorted_params + sub_plot_infos = info.sub_plot_infos + reverse_scale = info.reverse_scale + target_name = info.target_name + + if len(sorted_params) <= 1: + _, ax = plt.subplots() + return ax + n_params = len(sorted_params) + + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + if n_params == 2: + # Set up the graph style. + fig, axs = plt.subplots() + axs.set_title("Contour Plot") + cmap = _set_cmap(reverse_scale) + + cs = _generate_contour_subplot(sub_plot_infos[0][0], axs, cmap) + if isinstance(cs, ContourSet): + axcb = fig.colorbar(cs) + axcb.set_label(target_name) + else: + # Set up the graph style. + fig, axs = plt.subplots(n_params, n_params) + assert isinstance(axs, np.ndarray) + fig.suptitle("Contour Plot") + cmap = _set_cmap(reverse_scale) + + # Prepare data and draw contour plots. + cs_list = [] + for x_i in range(len(sorted_params)): + for y_i in range(len(sorted_params)): + ax = axs[y_i, x_i] + cs = _generate_contour_subplot(sub_plot_infos[y_i][x_i], ax, cmap) + if isinstance(cs, ContourSet): + cs_list.append(cs) + if cs_list: + axcb = fig.colorbar(cs_list[0], ax=axs) + axcb.set_label(target_name) + + return axs + + +def _set_cmap(reverse_scale: bool) -> "Colormap": + cmap = "Blues_r" if not reverse_scale else "Blues" + return plt.get_cmap(cmap) + + +class _LabelEncoder: + def __init__(self) -> None: + self.labels: list[str] = [] + + def fit(self, labels: list[str]) -> "_LabelEncoder": + self.labels = sorted(set(labels)) + return self + + def transform(self, labels: list[str]) -> list[int]: + return [self.labels.index(label) for label in labels] + + def fit_transform(self, labels: list[str]) -> list[int]: + return self.fit(labels).transform(labels) + + def get_labels(self) -> list[str]: + return self.labels + + def get_indices(self) -> list[int]: + return list(range(len(self.labels))) + + +def _filter_missing_values( + xaxis: _AxisInfo, yaxis: _AxisInfo +) -> tuple[list[str | float], list[str | float]]: + x_values = [] + y_values = [] + for x_value, y_value in zip(xaxis.values, yaxis.values): + if x_value is not None and y_value is not None: + x_values.append(x_value) + y_values.append(y_value) + return x_values, y_values + + +def _calculate_axis_data( + axis: _AxisInfo, + values: Sequence[str | float], +) -> tuple[np.ndarray, list[str], list[int], list[int | float]]: + # Convert categorical values to int. + cat_param_labels: list[str] = [] + cat_param_pos: list[int] = [] + returned_values: Sequence[int | float] + if axis.is_cat: + enc = _LabelEncoder() + # Fit LabelEncoder with all the categories in categorical distribution. + enc.fit(list(map(str, filter(lambda value: value is not None, axis.values)))) + # Then transform the values using the fitted label encoder. + # Note that `values` may not include all the categories, + # so we use `axis.values` for fitting. + returned_values = enc.transform(list(map(str, values))) + cat_param_labels = enc.get_labels() + cat_param_pos = enc.get_indices() + else: + returned_values = list(map(lambda x: float(x), values)) + + # For x and y, create 1-D array of evenly spaced coordinates on linear or log scale. + if axis.is_log: + ci = np.logspace(np.log10(axis.range[0]), np.log10(axis.range[1]), CONTOUR_POINT_NUM) + else: + ci = np.linspace(axis.range[0], axis.range[1], CONTOUR_POINT_NUM) + + return ci, cat_param_labels, cat_param_pos, list(returned_values) + + +def _calculate_griddata(info: _SubContourInfo) -> tuple[np.ndarray, _PlotValues, _PlotValues]: + xaxis = info.xaxis + yaxis = info.yaxis + z_values_dict = info.z_values + + x_values = [] + y_values = [] + z_values = [] + for x_value, y_value in zip(xaxis.values, yaxis.values): + if x_value is not None and y_value is not None: + x_values.append(x_value) + y_values.append(y_value) + x_i = xaxis.indices.index(x_value) + y_i = yaxis.indices.index(y_value) + z_values.append(z_values_dict[(x_i, y_i)]) + + # Return empty values when x or y has no value. + if len(x_values) == 0 or len(y_values) == 0: + return np.array([]), _PlotValues([], []), _PlotValues([], []) + + xi, cat_param_labels_x, cat_param_pos_x, transformed_x_values = _calculate_axis_data( + xaxis, + x_values, + ) + yi, cat_param_labels_y, cat_param_pos_y, transformed_y_values = _calculate_axis_data( + yaxis, + y_values, + ) + + # Calculate grid data points. + zi: np.ndarray = np.array([]) + # Create irregularly spaced map of trial values + # and interpolate it with Plotly's interpolation formulation. + if xaxis.name != yaxis.name: + zmap = _create_zmap(transformed_x_values, transformed_y_values, z_values, xi, yi) + zi = _interpolate_zmap(zmap, CONTOUR_POINT_NUM) + + # categorize by constraints + feasible = _PlotValues([], []) + infeasible = _PlotValues([], []) + + for x_value, y_value, c in zip(transformed_x_values, transformed_y_values, info.constraints): + if c: + feasible.x.append(x_value) + feasible.y.append(y_value) + else: + infeasible.x.append(x_value) + infeasible.y.append(y_value) + + return zi, feasible, infeasible + + +def _generate_contour_subplot( + info: _SubContourInfo, ax: "Axes", cmap: "Colormap" +) -> "ContourSet" | None: + ax.label_outer() + + if len(info.xaxis.indices) < 2 or len(info.yaxis.indices) < 2: + return None + + ax.set(xlabel=info.xaxis.name, ylabel=info.yaxis.name) + ax.set_xlim(info.xaxis.range[0], info.xaxis.range[1]) + ax.set_ylim(info.yaxis.range[0], info.yaxis.range[1]) + x_values, y_values = _filter_missing_values(info.xaxis, info.yaxis) + xi, x_cat_param_label, x_cat_param_pos, _ = _calculate_axis_data(info.xaxis, x_values) + yi, y_cat_param_label, y_cat_param_pos, _ = _calculate_axis_data(info.yaxis, y_values) + if info.xaxis.is_cat: + ax.set_xticks(x_cat_param_pos) + ax.set_xticklabels(x_cat_param_label) + else: + ax.set_xscale("log" if info.xaxis.is_log else "linear") + if info.yaxis.is_cat: + ax.set_yticks(y_cat_param_pos) + ax.set_yticklabels(y_cat_param_label) + else: + ax.set_yscale("log" if info.yaxis.is_log else "linear") + + if info.xaxis.name == info.yaxis.name: + return None + + zi, feasible_plot_values, infeasible_plot_values = _calculate_griddata(info) + cs = None + if len(zi) > 0: + # Contour the gridded data. + ax.contour(xi, yi, zi, 15, linewidths=0.5, colors="k") + cs = ax.contourf(xi, yi, zi, 15, cmap=cmap.reversed()) + assert isinstance(cs, ContourSet) + # Plot data points. + ax.scatter( + feasible_plot_values.x, + feasible_plot_values.y, + marker="o", + c="black", + s=20, + edgecolors="grey", + linewidth=2.0, + ) + ax.scatter( + infeasible_plot_values.x, + infeasible_plot_values.y, + marker="o", + c="#cccccc", + s=20, + edgecolors="grey", + linewidth=2.0, + ) + + return cs + + +def _create_zmap( + x_values: Sequence[int | float], + y_values: Sequence[int | float], + z_values: Sequence[float], + xi: np.ndarray, + yi: np.ndarray, +) -> dict[tuple[int, int], float]: + # Creates z-map from trial values and params. + # z-map is represented by hashmap of coordinate and trial value pairs. + # + # Coordinates are represented by tuple of integers, where the first item + # indicates x-axis index and the second item indicates y-axis index + # and refer to a position of trial value on irregular param grid. + # + # Since params were resampled either with linspace or logspace + # original params might not be on the x and y axes anymore + # so we are going with close approximations of trial value positions. + zmap = dict() + for x, y, z in zip(x_values, y_values, z_values): + xindex = int(np.argmin(np.abs(xi - x))) + yindex = int(np.argmin(np.abs(yi - y))) + zmap[(xindex, yindex)] = z + + return zmap + + +def _interpolate_zmap(zmap: dict[tuple[int, int], float], contour_plot_num: int) -> np.ndarray: + # Implements interpolation formulation used in Plotly + # to interpolate heatmaps and contour plots + # https://github.com/plotly/plotly.js/blob/95b3bd1bb19d8dc226627442f8f66bce9576def8/src/traces/heatmap/interp2d.js#L15-L20 + # citing their doc: + # + # > Fill in missing data from a 2D array using an iterative + # > poisson equation solver with zero-derivative BC at edges. + # > Amazingly, this just amounts to repeatedly averaging all the existing + # > nearest neighbors + # + # Plotly's algorithm is equivalent to solve the following linear simultaneous equation. + # It is discretization form of the Poisson equation. + # + # z[x, y] = zmap[(x, y)] (if zmap[(x, y)] is given) + # 4 * z[x, y] = z[x-1, y] + z[x+1, y] + z[x, y-1] + z[x, y+1] (if zmap[(x, y)] is not given) + + a_data = [] + a_row = [] + a_col = [] + b = np.zeros(contour_plot_num**2) + for x in range(contour_plot_num): + for y in range(contour_plot_num): + grid_index = y * contour_plot_num + x + if (x, y) in zmap: + a_data.append(1) + a_row.append(grid_index) + a_col.append(grid_index) + b[grid_index] = zmap[(x, y)] + else: + for dx, dy in ((-1, 0), (1, 0), (0, -1), (0, 1)): + if 0 <= x + dx < contour_plot_num and 0 <= y + dy < contour_plot_num: + a_data.append(1) + a_row.append(grid_index) + a_col.append(grid_index) + a_data.append(-1) + a_row.append(grid_index) + a_col.append(grid_index + dy * contour_plot_num + dx) + + z = scipy.sparse.linalg.spsolve(scipy.sparse.csc_matrix((a_data, (a_row, a_col))), b) + + return z.reshape((contour_plot_num, contour_plot_num)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_edf.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_edf.py new file mode 100644 index 0000000000000000000000000000000000000000..741d0a0fc1594eca312cbc516d1a14806c4a61f2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_edf.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence + +from optuna._experimental import experimental_func +from optuna.logging import get_logger +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.visualization._edf import _get_edf_info +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import plt + +_logger = get_logger(__name__) + + +@experimental_func("2.2.0") +def plot_edf( + study: Study | Sequence[Study], + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "Axes": + """Plot the objective value EDF (empirical distribution function) of a study with Matplotlib. + + Note that only the complete trials are considered when plotting the EDF. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_edf` for an example, + where this function can be replaced with it. + + .. note:: + + Please refer to `matplotlib.pyplot.legend + `_ + to adjust the style of the generated legend. + + Args: + study: + A target :class:`~optuna.study.Study` object. + You can pass multiple studies if you want to compare those EDFs. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the axis label. + + Returns: + A :class:`matplotlib.axes.Axes` object. + """ + + _imports.check() + + # Set up the graph style. + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + _, ax = plt.subplots() + ax.set_title("Empirical Distribution Function Plot") + ax.set_xlabel(target_name) + ax.set_ylabel("Cumulative Probability") + ax.set_ylim(0, 1) + cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots. + + info = _get_edf_info(study, target, target_name) + edf_lines = info.lines + + if len(edf_lines) == 0: + return ax + + for i, (study_name, y_values) in enumerate(edf_lines): + ax.plot(info.x_values, y_values, color=cmap(i), alpha=0.7, label=study_name) + + if len(edf_lines) >= 2: + ax.legend() + + return ax diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_hypervolume_history.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_hypervolume_history.py new file mode 100644 index 0000000000000000000000000000000000000000..d8c230cc9b0405e924b9a9d3b92955c8c118e7cf --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_hypervolume_history.py @@ -0,0 +1,78 @@ +from __future__ import annotations + +from collections.abc import Sequence + +import numpy as np + +from optuna._experimental import experimental_func +from optuna.study import Study +from optuna.visualization._hypervolume_history import _get_hypervolume_history_info +from optuna.visualization._hypervolume_history import _HypervolumeHistoryInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import plt + + +@experimental_func("3.3.0") +def plot_hypervolume_history( + study: Study, + reference_point: Sequence[float], +) -> "Axes": + """Plot hypervolume history of all trials in a study with Matplotlib. + + .. note:: + You need to adjust the size of the plot by yourself using ``plt.tight_layout()`` or + ``plt.savefig(IMAGE_NAME, bbox_inches='tight')``. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their hypervolumes. + The number of objectives must be 2 or more. + + reference_point: + A reference point to use for hypervolume computation. + The dimension of the reference point must be the same as the number of objectives. + + Returns: + A :class:`matplotlib.axes.Axes` object. + """ + + _imports.check() + + if not study._is_multi_objective(): + raise ValueError( + "Study must be multi-objective. For single-objective optimization, " + "please use plot_optimization_history instead." + ) + + if len(reference_point) != len(study.directions): + raise ValueError( + "The dimension of the reference point must be the same as the number of objectives." + ) + + info = _get_hypervolume_history_info(study, np.asarray(reference_point, dtype=np.float64)) + return _get_hypervolume_history_plot(info) + + +def _get_hypervolume_history_plot( + info: _HypervolumeHistoryInfo, +) -> "Axes": + # Set up the graph style. + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + _, ax = plt.subplots() + ax.set_title("Hypervolume History Plot") + ax.set_xlabel("Trial") + ax.set_ylabel("Hypervolume") + cmap = plt.get_cmap("tab10") # Use tab10 colormap for similar outputs to plotly. + + ax.plot( + info.trial_numbers, + info.values, + marker="o", + color=cmap(0), + alpha=0.5, + ) + return ax diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_intermediate_values.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_intermediate_values.py new file mode 100644 index 0000000000000000000000000000000000000000..803e10a9df789fa56f06b71a7f6b605780cb95b6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_intermediate_values.py @@ -0,0 +1,65 @@ +from optuna._experimental import experimental_func +from optuna.logging import get_logger +from optuna.study import Study +from optuna.visualization._intermediate_values import _get_intermediate_plot_info +from optuna.visualization._intermediate_values import _IntermediatePlotInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import plt + +_logger = get_logger(__name__) + + +@experimental_func("2.2.0") +def plot_intermediate_values(study: Study) -> "Axes": + """Plot intermediate values of all trials in a study with Matplotlib. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_intermediate_values` for an example. + + .. note:: + Please refer to `matplotlib.pyplot.legend + `__ + to adjust the style of the generated legend. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their intermediate + values. + + Returns: + A :class:`matplotlib.axes.Axes` object. + """ + + _imports.check() + return _get_intermediate_plot(_get_intermediate_plot_info(study)) + + +def _get_intermediate_plot(info: _IntermediatePlotInfo) -> "Axes": + # Set up the graph style. + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + _, ax = plt.subplots(tight_layout=True) + ax.set_title("Intermediate Values Plot") + ax.set_xlabel("Step") + ax.set_ylabel("Intermediate Value") + cmap = plt.get_cmap("tab20") # Use tab20 colormap for multiple line plots. + + trial_infos = info.trial_infos + + for i, tinfo in enumerate(trial_infos): + ax.plot( + tuple((x for x, _ in tinfo.sorted_intermediate_values)), + tuple((y for _, y in tinfo.sorted_intermediate_values)), + color=cmap(i) if tinfo.feasible else "#CCCCCC", + marker=".", + alpha=0.7, + label="Trial{}".format(tinfo.trial_number), + ) + + if len(trial_infos) >= 2: + ax.legend(bbox_to_anchor=(1.05, 1), loc="upper left", borderaxespad=0.0) + + return ax diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_matplotlib_imports.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_matplotlib_imports.py new file mode 100644 index 0000000000000000000000000000000000000000..1ecece11e5ff1bcbedf7aa140f5a7494d71e8363 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_matplotlib_imports.py @@ -0,0 +1,43 @@ +from packaging import version + +from optuna._imports import try_import + + +with try_import() as _imports: + # TODO(ytknzw): Add specific imports. + import matplotlib + from matplotlib import __version__ as matplotlib_version + from matplotlib import pyplot as plt + from matplotlib.axes._axes import Axes + from matplotlib.collections import LineCollection + from matplotlib.collections import PathCollection + from matplotlib.colors import Colormap + from matplotlib.contour import ContourSet + from matplotlib.dates import DateFormatter + from matplotlib.figure import Figure + from mpl_toolkits.mplot3d.axes3d import Axes3D + + # TODO(ytknzw): Set precise version. + if version.parse(matplotlib_version) < version.parse("3.0.0"): + raise ImportError( + "Your version of Matplotlib is " + matplotlib_version + " . " + "Please install Matplotlib version 3.0.0 or higher. " + "Matplotlib can be installed by executing `$ pip install -U matplotlib>=3.0.0`. " + "For further information, please refer to the installation guide of Matplotlib. ", + name="matplotlib", + ) + +__all__ = [ + "_imports", + "matplotlib", + "matplotlib_version", + "plt", + "Axes", + "Axes3D", + "Colormap", + "ContourSet", + "DateFormatter", + "Figure", + "LineCollection", + "PathCollection", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_optimization_history.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_optimization_history.py new file mode 100644 index 0000000000000000000000000000000000000000..f34aaca00b525599c0b971229efbc38d824f7f58 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_optimization_history.py @@ -0,0 +1,150 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence + +import numpy as np + +from optuna._experimental import experimental_func +from optuna.logging import get_logger +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.visualization._optimization_history import _get_optimization_history_info_list +from optuna.visualization._optimization_history import _OptimizationHistoryInfo +from optuna.visualization._optimization_history import _ValueState +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import plt + +_logger = get_logger(__name__) + + +@experimental_func("2.2.0") +def plot_optimization_history( + study: Study | Sequence[Study], + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", + error_bar: bool = False, +) -> "Axes": + """Plot optimization history of all trials in a study with Matplotlib. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_optimization_history` for an example. + + .. note:: + You need to adjust the size of the plot by yourself using ``plt.tight_layout()`` or + ``plt.savefig(IMAGE_NAME, bbox_inches='tight')``. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + You can pass multiple studies if you want to compare those optimization histories. + + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the axis label and the legend. + + error_bar: + A flag to show the error bar. + + Returns: + A :class:`matplotlib.axes.Axes` object. + """ + + _imports.check() + + info_list = _get_optimization_history_info_list(study, target, target_name, error_bar) + return _get_optimization_history_plot(info_list, target_name) + + +def _get_optimization_history_plot( + info_list: list[_OptimizationHistoryInfo], + target_name: str, +) -> "Axes": + # Set up the graph style. + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + _, ax = plt.subplots() + ax.set_title("Optimization History Plot") + ax.set_xlabel("Trial") + ax.set_ylabel(target_name) + cmap = plt.get_cmap("tab10") # Use tab10 colormap for similar outputs to plotly. + + for i, (trial_numbers, values_info, best_values_info) in enumerate(info_list): + if values_info.stds is not None: + if ( + _ValueState.Infeasible in values_info.states + or _ValueState.Incomplete in values_info.states + ): + _logger.warning( + "Your study contains infeasible trials. " + "In optimization history plot, " + "error bars are calculated for only feasible trial values." + ) + feasible_trial_numbers = trial_numbers + feasible_trial_values = values_info.values + plt.errorbar( + x=feasible_trial_numbers, + y=feasible_trial_values, + yerr=values_info.stds, + capsize=5, + fmt="o", + color="tab:blue", + ) + infeasible_trial_numbers: list[int] = [] + infeasible_trial_values: list[float] = [] + else: + feasible_trial_numbers = [ + n for n, s in zip(trial_numbers, values_info.states) if s == _ValueState.Feasible + ] + infeasible_trial_numbers = [ + n for n, s in zip(trial_numbers, values_info.states) if s == _ValueState.Infeasible + ] + feasible_trial_values = [] + for num in feasible_trial_numbers: + feasible_trial_values.append(values_info.values[num]) + infeasible_trial_values = [] + for num in infeasible_trial_numbers: + infeasible_trial_values.append(values_info.values[num]) + ax.scatter( + x=feasible_trial_numbers, + y=feasible_trial_values, + color=cmap(0) if len(info_list) == 1 else cmap(2 * i), + alpha=1, + label=values_info.label_name, + ) + + if best_values_info is not None: + ax.plot( + trial_numbers, + best_values_info.values, + color=cmap(3) if len(info_list) == 1 else cmap(2 * i + 1), + alpha=0.5, + label=best_values_info.label_name, + ) + if best_values_info.stds is not None: + lower = np.array(best_values_info.values) - np.array(best_values_info.stds) + upper = np.array(best_values_info.values) + np.array(best_values_info.stds) + ax.fill_between( + x=trial_numbers, + y1=lower, + y2=upper, + color="tab:red", + alpha=0.4, + ) + ax.legend() + ax.scatter( + x=infeasible_trial_numbers, + y=infeasible_trial_values, + color="#cccccc", + ) + plt.legend(bbox_to_anchor=(1.05, 1.0), loc="upper left") + return ax diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_parallel_coordinate.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_parallel_coordinate.py new file mode 100644 index 0000000000000000000000000000000000000000..9497364a7469dc9f82122f0d06da21726223a936 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_parallel_coordinate.py @@ -0,0 +1,126 @@ +from __future__ import annotations + +from collections.abc import Callable + +import numpy as np + +from optuna._experimental import experimental_func +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.visualization._parallel_coordinate import _get_parallel_coordinate_info +from optuna.visualization._parallel_coordinate import _ParallelCoordinateInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import LineCollection + from optuna.visualization.matplotlib._matplotlib_imports import plt + + +@experimental_func("2.2.0") +def plot_parallel_coordinate( + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "Axes": + """Plot the high-dimensional parameter relationships in a study with Matplotlib. + + Note that, if a parameter contains missing values, a trial with missing values is not plotted. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_parallel_coordinate` for an example. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + params: + Parameter list to visualize. The default is all parameters. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the axis label and the legend. + + Returns: + A :class:`matplotlib.axes.Axes` object. + + .. note:: + The colormap is reversed when the ``target`` argument isn't :obj:`None` or ``direction`` + of :class:`~optuna.study.Study` is ``minimize``. + """ + + _imports.check() + info = _get_parallel_coordinate_info(study, params, target, target_name) + return _get_parallel_coordinate_plot(info) + + +def _get_parallel_coordinate_plot(info: _ParallelCoordinateInfo) -> "Axes": + reversescale = info.reverse_scale + target_name = info.target_name + + # Set up the graph style. + fig, ax = plt.subplots() + cmap = plt.get_cmap("Blues_r" if reversescale else "Blues") + ax.set_title("Parallel Coordinate Plot") + ax.spines["top"].set_visible(False) + ax.spines["bottom"].set_visible(False) + + # Prepare data for plotting. + if len(info.dims_params) == 0 or len(info.dim_objective.values) == 0: + return ax + + obj_min = info.dim_objective.range[0] + obj_max = info.dim_objective.range[1] + obj_w = obj_max - obj_min + dims_obj_base = [[o] for o in info.dim_objective.values] + for dim in info.dims_params: + p_min = dim.range[0] + p_max = dim.range[1] + p_w = p_max - p_min + + if p_w == 0.0: + center = obj_w / 2 + obj_min + for i in range(len(dim.values)): + dims_obj_base[i].append(center) + else: + for i, v in enumerate(dim.values): + dims_obj_base[i].append((v - p_min) / p_w * obj_w + obj_min) + + # Draw multiple line plots and axes. + # Ref: https://stackoverflow.com/a/50029441 + n_params = len(info.dims_params) + ax.set_xlim(0, n_params) + ax.set_ylim(info.dim_objective.range[0], info.dim_objective.range[1]) + xs = [range(n_params + 1) for _ in range(len(dims_obj_base))] + segments = [np.column_stack([x, y]) for x, y in zip(xs, dims_obj_base)] + lc = LineCollection(segments, cmap=cmap) + lc.set_array(np.asarray(info.dim_objective.values)) + axcb = fig.colorbar(lc, pad=0.1, ax=ax) + axcb.set_label(target_name) + var_names = [info.dim_objective.label] + [dim.label for dim in info.dims_params] + plt.xticks(range(n_params + 1), var_names, rotation=330) + + for i, dim in enumerate(info.dims_params): + ax2 = ax.twinx() + if dim.is_log: + ax2.set_ylim(np.power(10, dim.range[0]), np.power(10, dim.range[1])) + ax2.set_yscale("log") + else: + ax2.set_ylim(dim.range[0], dim.range[1]) + ax2.spines["top"].set_visible(False) + ax2.spines["bottom"].set_visible(False) + ax2.xaxis.set_visible(False) + ax2.spines["right"].set_position(("axes", (i + 1) / n_params)) + if dim.is_cat: + ax2.set_yticks(dim.tickvals) + ax2.set_yticklabels(dim.ticktext) + + ax.add_collection(lc) + + return ax diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_param_importances.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_param_importances.py new file mode 100644 index 0000000000000000000000000000000000000000..e879a77bff0bcde1a6a5daa06d2f1c3fe639621f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_param_importances.py @@ -0,0 +1,130 @@ +from __future__ import annotations + +from collections.abc import Callable + +import numpy as np + +from optuna._experimental import experimental_func +from optuna.importance._base import BaseImportanceEvaluator +from optuna.logging import get_logger +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.visualization._param_importances import _get_importances_infos +from optuna.visualization._param_importances import _ImportancesInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import Figure + from optuna.visualization.matplotlib._matplotlib_imports import plt + + +_logger = get_logger(__name__) + + +AXES_PADDING_RATIO = 1.05 + + +@experimental_func("2.2.0") +def plot_param_importances( + study: Study, + evaluator: BaseImportanceEvaluator | None = None, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "Axes": + """Plot hyperparameter importances with Matplotlib. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_param_importances` for an example. + + Args: + study: + An optimized study. + evaluator: + An importance evaluator object that specifies which algorithm to base the importance + assessment on. + Defaults to + :class:`~optuna.importance.FanovaImportanceEvaluator`. + params: + A list of names of parameters to assess. + If :obj:`None`, all parameters that are present in all of the completed trials are + assessed. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + For multi-objective optimization, all objectives will be plotted if ``target`` + is :obj:`None`. + + .. note:: + This argument can be used to specify which objective to plot if ``study`` is being + used for multi-objective optimization. For example, to get only the hyperparameter + importance of the first objective, use ``target=lambda t: t.values[0]`` for the + target parameter. + target_name: + Target's name to display on the axis label. Names set via + :meth:`~optuna.study.Study.set_metric_names` will be used if ``target`` is :obj:`None`, + overriding this argument. + + Returns: + A :class:`matplotlib.axes.Axes` object. + """ + + _imports.check() + importances_infos = _get_importances_infos(study, evaluator, params, target, target_name) + return _get_importances_plot(importances_infos) + + +def _get_importances_plot(infos: tuple[_ImportancesInfo, ...]) -> "Axes": + # Set up the graph style. + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + fig, ax = plt.subplots() + ax.set_title("Hyperparameter Importances", loc="left") + ax.set_xlabel("Hyperparameter Importance") + ax.set_ylabel("Hyperparameter") + height = 0.8 / len(infos) # Default height split between objectives. + + for objective_id, info in enumerate(infos): + param_names = info.param_names + pos = np.arange(len(param_names)) + offset = height * objective_id + importance_values = info.importance_values + + if not importance_values: + continue + + # Draw horizontal bars. + ax.barh( + pos + offset, + importance_values, + height=height, + align="center", + label=info.target_name, + color=plt.get_cmap("tab20c")(objective_id), + ) + + _set_bar_labels(info, fig, ax, offset) + ax.set_yticks(pos + offset / 2, param_names) + + ax.legend(loc="best") + return ax + + +def _set_bar_labels(info: _ImportancesInfo, fig: "Figure", ax: "Axes", offset: float) -> None: + # Figure canvas does not necessarily have a get_renderer. + assert hasattr(fig.canvas, "get_renderer") + renderer = fig.canvas.get_renderer() + for idx, (val, label) in enumerate(zip(info.importance_values, info.importance_labels)): + text = ax.text(val, idx + offset, label, va="center") + + # Sometimes horizontal axis needs to be re-scaled + # to avoid text going over plot area. + bbox = text.get_window_extent(renderer) + bbox = bbox.transformed(ax.transData.inverted()) + _, plot_xmax = ax.get_xlim() + bbox_xmax = bbox.xmax + + if bbox_xmax > plot_xmax: + ax.set_xlim(xmax=AXES_PADDING_RATIO * bbox_xmax) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_pareto_front.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_pareto_front.py new file mode 100644 index 0000000000000000000000000000000000000000..20bcc4a41e5f6142ffebe536fbf52994d602ce2b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_pareto_front.py @@ -0,0 +1,190 @@ +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence + +from optuna._experimental import experimental_func +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.visualization._pareto_front import _get_pareto_front_info +from optuna.visualization._pareto_front import _ParetoFrontInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import Axes3D + from optuna.visualization.matplotlib._matplotlib_imports import plt + + +@experimental_func("2.8.0") +def plot_pareto_front( + study: Study, + *, + target_names: list[str] | None = None, + include_dominated_trials: bool = True, + axis_order: list[int] | None = None, + constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, + targets: Callable[[FrozenTrial], Sequence[float]] | None = None, +) -> "Axes": + """Plot the Pareto front of a study. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_pareto_front` for an example. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their objective + values. ``study.n_objectives`` must be either 2 or 3 when ``targets`` is :obj:`None`. + target_names: + Objective name list used as the axis titles. If :obj:`None` is specified, + "Objective {objective_index}" is used instead. If ``targets`` is specified + for a study that does not contain any completed trial, + ``target_name`` must be specified. + include_dominated_trials: + A flag to include all dominated trial's objective values. + axis_order: + A list of indices indicating the axis order. If :obj:`None` is specified, + default order is used. ``axis_order`` and ``targets`` cannot be used at the same time. + + .. warning:: + Deprecated in v3.0.0. This feature will be removed in the future. The removal of + this feature is currently scheduled for v5.0.0, but this schedule is subject to + change. See https://github.com/optuna/optuna/releases/tag/v3.0.0. + constraints_func: + An optional function that computes the objective constraints. It must take a + :class:`~optuna.trial.FrozenTrial` and return the constraints. The return value must + be a sequence of :obj:`float` s. A value strictly larger than 0 means that a + constraint is violated. A value equal to or smaller than 0 is considered feasible. + This specification is the same as in, for example, + :class:`~optuna.samplers.NSGAIISampler`. + + If given, trials are classified into three categories: feasible and best, feasible but + non-best, and infeasible. Categories are shown in different colors. Here, whether a + trial is best (on Pareto front) or not is determined ignoring all infeasible trials. + + .. warning:: + Deprecated in v4.0.0. This feature will be removed in the future. The removal of + this feature is currently scheduled for v6.0.0, but this schedule is subject to + change. See https://github.com/optuna/optuna/releases/tag/v4.0.0. + targets: + A function that returns a tuple of target values to display. + The argument to this function is :class:`~optuna.trial.FrozenTrial`. + ``targets`` must be :obj:`None` or return 2 or 3 values. + ``axis_order`` and ``targets`` cannot be used at the same time. + If the number of objectives is neither 2 nor 3, ``targets`` must be specified. + + .. note:: + Added in v3.0.0 as an experimental feature. The interface may change in newer + versions without prior notice. + See https://github.com/optuna/optuna/releases/tag/v3.0.0. + + Returns: + A :class:`matplotlib.axes.Axes` object. + """ + + _imports.check() + + info = _get_pareto_front_info( + study, target_names, include_dominated_trials, axis_order, constraints_func, targets + ) + return _get_pareto_front_plot(info) + + +def _get_pareto_front_plot(info: _ParetoFrontInfo) -> "Axes": + if info.n_targets == 2: + return _get_pareto_front_2d(info) + elif info.n_targets == 3: + return _get_pareto_front_3d(info) + else: + assert False, "Must not reach here" + + +def _get_pareto_front_2d(info: _ParetoFrontInfo) -> "Axes": + # Set up the graph style. + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + _, ax = plt.subplots() + ax.set_title("Pareto-front Plot") + cmap = plt.get_cmap("tab10") # Use tab10 colormap for similar outputs to plotly. + + ax.set_xlabel(info.target_names[info.axis_order[0]]) + ax.set_ylabel(info.target_names[info.axis_order[1]]) + + trial_label: str = "Trial" + if len(info.infeasible_trials_with_values) > 0: + ax.scatter( + x=[values[info.axis_order[0]] for _, values in info.infeasible_trials_with_values], + y=[values[info.axis_order[1]] for _, values in info.infeasible_trials_with_values], + color="#cccccc", + label="Infeasible Trial", + ) + trial_label = "Feasible Trial" + if len(info.non_best_trials_with_values) > 0: + ax.scatter( + x=[values[info.axis_order[0]] for _, values in info.non_best_trials_with_values], + y=[values[info.axis_order[1]] for _, values in info.non_best_trials_with_values], + color=cmap(0), + label=trial_label, + ) + if len(info.best_trials_with_values) > 0: + ax.scatter( + x=[values[info.axis_order[0]] for _, values in info.best_trials_with_values], + y=[values[info.axis_order[1]] for _, values in info.best_trials_with_values], + color=cmap(3), + label="Best Trial", + ) + + if info.non_best_trials_with_values is not None and ax.has_data(): + ax.legend() + + return ax + + +def _get_pareto_front_3d(info: _ParetoFrontInfo) -> "Axes": + # Set up the graph style. + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + fig = plt.figure() + ax: Axes3D = fig.add_subplot(projection="3d") + ax.set_title("Pareto-front Plot") + cmap = plt.get_cmap("tab10") # Use tab10 colormap for similar outputs to plotly. + + ax.set_xlabel(info.target_names[info.axis_order[0]]) + ax.set_ylabel(info.target_names[info.axis_order[1]]) + ax.set_zlabel(info.target_names[info.axis_order[2]]) + + trial_label: str = "Trial" + if ( + info.infeasible_trials_with_values is not None + and len(info.infeasible_trials_with_values) > 0 + ): + ax.scatter( + xs=[values[info.axis_order[0]] for _, values in info.infeasible_trials_with_values], + ys=[values[info.axis_order[1]] for _, values in info.infeasible_trials_with_values], + zs=[values[info.axis_order[2]] for _, values in info.infeasible_trials_with_values], + color="#cccccc", + label="Infeasible Trial", + ) + trial_label = "Feasible Trial" + + if info.non_best_trials_with_values is not None and len(info.non_best_trials_with_values) > 0: + ax.scatter( + xs=[values[info.axis_order[0]] for _, values in info.non_best_trials_with_values], + ys=[values[info.axis_order[1]] for _, values in info.non_best_trials_with_values], + zs=[values[info.axis_order[2]] for _, values in info.non_best_trials_with_values], + color=cmap(0), + label=trial_label, + ) + + if info.best_trials_with_values is not None and len(info.best_trials_with_values): + ax.scatter( + xs=[values[info.axis_order[0]] for _, values in info.best_trials_with_values], + ys=[values[info.axis_order[1]] for _, values in info.best_trials_with_values], + zs=[values[info.axis_order[2]] for _, values in info.best_trials_with_values], + color=cmap(3), + label="Best Trial", + ) + + if info.non_best_trials_with_values is not None and ax.has_data(): + ax.legend() + + return ax diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_rank.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_rank.py new file mode 100644 index 0000000000000000000000000000000000000000..b651f50f164b794b03128209c3301b9fecd9e3d3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_rank.py @@ -0,0 +1,128 @@ +from __future__ import annotations + +from collections.abc import Callable + +from optuna._experimental import experimental_func +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.visualization._rank import _get_rank_info +from optuna.visualization._rank import _get_tick_info +from optuna.visualization._rank import _RankPlotInfo +from optuna.visualization._rank import _RankSubplotInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import PathCollection + from optuna.visualization.matplotlib._matplotlib_imports import plt + + +@experimental_func("3.2.0") +def plot_rank( + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "Axes": + """Plot parameter relations as scatter plots with colors indicating ranks of target value. + + Note that trials missing the specified parameters will not be plotted. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_rank` for an example. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + params: + Parameter list to visualize. The default is all parameters. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the color bar. + + Returns: + A :class:`matplotlib.axes.Axes` object. + """ + + _imports.check() + info = _get_rank_info(study, params, target, target_name) + return _get_rank_plot(info) + + +def _get_rank_plot( + info: _RankPlotInfo, +) -> "Axes": + params = info.params + sub_plot_infos = info.sub_plot_infos + + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + + title = f"Rank ({info.target_name})" + + n_params = len(params) + if n_params == 0: + _, ax = plt.subplots() + ax.set_title(title) + return ax + if n_params == 1 or n_params == 2: + fig, axs = plt.subplots() + axs.set_title(title) + pc = _add_rank_subplot(axs, sub_plot_infos[0][0]) + else: + fig, axs = plt.subplots(n_params, n_params) + fig.suptitle(title) + + for x_i in range(n_params): + for y_i in range(n_params): + ax = axs[x_i, y_i] + # Set the x or y label only if the subplot is in the edge of the overall figure. + pc = _add_rank_subplot( + ax, + sub_plot_infos[x_i][y_i], + set_x_label=x_i == (n_params - 1), + set_y_label=y_i == 0, + ) + + tick_info = _get_tick_info(info.zs) + + pc.set_cmap(plt.get_cmap("RdYlBu_r")) + cbar = fig.colorbar(pc, ax=axs, ticks=tick_info.coloridxs) + cbar.ax.set_yticklabels(tick_info.text) + # NOTE(Alnusjaponica): The class of cbar.outline inherits matplotlib.patches.Patch, + # which has set_edgecolor method. However, mypy does not recognize it. + cbar.outline.set_edgecolor("gray") # type: ignore[operator] + return axs + + +def _add_rank_subplot( + ax: "Axes", info: _RankSubplotInfo, set_x_label: bool = True, set_y_label: bool = True +) -> "PathCollection": + if set_x_label: + ax.set_xlabel(info.xaxis.name) + if set_y_label: + ax.set_ylabel(info.yaxis.name) + + if not info.xaxis.is_cat: + ax.set_xlim(info.xaxis.range[0], info.xaxis.range[1]) + if not info.yaxis.is_cat: + ax.set_ylim(info.yaxis.range[0], info.yaxis.range[1]) + + if info.xaxis.is_log: + ax.set_xscale("log") + + if info.yaxis.is_log: + ax.set_yscale("log") + + return ax.scatter( + x=[str(x) for x in info.xs] if info.xaxis.is_cat else info.xs, + y=[str(y) for y in info.ys] if info.yaxis.is_cat else info.ys, + c=info.colors / 255, + edgecolors="grey", + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_slice.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_slice.py new file mode 100644 index 0000000000000000000000000000000000000000..0747b062f674d213d711fa9eb5d5d100a194b881 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_slice.py @@ -0,0 +1,184 @@ +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Callable +import math +from typing import Any + +from optuna._experimental import experimental_func +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.visualization._slice import _get_slice_plot_info +from optuna.visualization._slice import _PlotValues +from optuna.visualization._slice import _SlicePlotInfo +from optuna.visualization._slice import _SliceSubplotInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import Colormap + from optuna.visualization.matplotlib._matplotlib_imports import matplotlib + from optuna.visualization.matplotlib._matplotlib_imports import PathCollection + from optuna.visualization.matplotlib._matplotlib_imports import plt + + +@experimental_func("2.2.0") +def plot_slice( + study: Study, + params: list[str] | None = None, + *, + target: Callable[[FrozenTrial], float] | None = None, + target_name: str = "Objective Value", +) -> "Axes": + """Plot the parameter relationship as slice plot in a study with Matplotlib. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_slice` for an example. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their target values. + params: + Parameter list to visualize. The default is all parameters. + target: + A function to specify the value to display. If it is :obj:`None` and ``study`` is being + used for single-objective optimization, the objective values are plotted. + + .. note:: + Specify this argument if ``study`` is being used for multi-objective optimization. + target_name: + Target's name to display on the axis label. + + + Returns: + A :class:`matplotlib.axes.Axes` object. + """ + + _imports.check() + return _get_slice_plot(_get_slice_plot_info(study, params, target, target_name)) + + +def _get_slice_plot(info: _SlicePlotInfo) -> "Axes": + if len(info.subplots) == 0: + _, ax = plt.subplots() + return ax + + # Set up the graph style. + cmap = plt.get_cmap("Blues") + padding_ratio = 0.05 + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + + if len(info.subplots) == 1: + # Set up the graph style. + fig, axs = plt.subplots() + axs.set_title("Slice Plot") + + # Draw a scatter plot. + sc = _generate_slice_subplot(info.subplots[0], axs, cmap, padding_ratio, info.target_name) + else: + # Set up the graph style. + min_figwidth = matplotlib.rcParams["figure.figsize"][0] / 2 + fighight = matplotlib.rcParams["figure.figsize"][1] + # Ensure that each subplot has a minimum width without relying on auto-sizing. + fig, axs = plt.subplots( + 1, + len(info.subplots), + sharey=True, + figsize=(min_figwidth * len(info.subplots), fighight), + ) + fig.suptitle("Slice Plot") + + # Draw scatter plots. + for i, subplot in enumerate(info.subplots): + ax = axs[i] + sc = _generate_slice_subplot(subplot, ax, cmap, padding_ratio, info.target_name) + + axcb = fig.colorbar(sc, ax=axs) + axcb.set_label("Trial") + + return axs + + +def _generate_slice_subplot( + subplot_info: _SliceSubplotInfo, + ax: "Axes", + cmap: "Colormap", + padding_ratio: float, + target_name: str, +) -> "PathCollection": + ax.set(xlabel=subplot_info.param_name, ylabel=target_name) + scale = None + + feasible = _PlotValues([], [], []) + infeasible = _PlotValues([], [], []) + for x, y, num, c in zip( + subplot_info.x, subplot_info.y, subplot_info.trial_numbers, subplot_info.constraints + ): + if x is not None or x != "None" or y is not None or y != "None": + if c: + feasible.x.append(x) + feasible.y.append(y) + feasible.trial_numbers.append(num) + else: + infeasible.x.append(x) + infeasible.y.append(y) + infeasible.trial_numbers.append(num) + if subplot_info.is_log: + ax.set_xscale("log") + scale = "log" + if subplot_info.is_numerical: + feasible_x = feasible.x + feasible_y = feasible.y + feasible_c = feasible.trial_numbers + infeasible_x = infeasible.x + infeasible_y = infeasible.y + else: + feasible_x, feasible_y, feasible_c = _get_categorical_plot_values(subplot_info, feasible) + infeasible_x, infeasible_y, _ = _get_categorical_plot_values(subplot_info, infeasible) + scale = "categorical" + xlim = _calc_lim_with_padding(feasible_x + infeasible_x, padding_ratio, scale) + ax.set_xlim(xlim[0], xlim[1]) + sc = ax.scatter(feasible_x, feasible_y, c=feasible_c, cmap=cmap, edgecolors="grey") + ax.scatter(infeasible_x, infeasible_y, c="#cccccc", label="Infeasible Trial") + ax.label_outer() + + return sc + + +def _get_categorical_plot_values( + subplot_info: _SliceSubplotInfo, values: _PlotValues +) -> tuple[list[Any], list[float], list[int]]: + assert subplot_info.x_labels is not None + value_x = [] + value_y = [] + value_c = [] + points_dict = defaultdict(list) + for x, y, number in zip(values.x, values.y, values.trial_numbers): + points_dict[x].append((y, number)) + for x_label in subplot_info.x_labels: + for y, number in points_dict[x_label]: + value_x.append(str(x_label)) + value_y.append(y) + value_c.append(number) + return value_x, value_y, value_c + + +def _calc_lim_with_padding( + values: list[Any], padding_ratio: float, scale: str | None +) -> tuple[float, float]: + value_max = max(values) + value_min = min(values) + if scale == "log": + padding = (math.log10(value_max) - math.log10(value_min)) * padding_ratio + return ( + math.pow(10, math.log10(value_min) - padding), + math.pow(10, math.log10(value_max) + padding), + ) + elif scale == "categorical": + width = len(set(values)) - 1 + padding = width * padding_ratio + return -padding, width + padding + else: + padding = (value_max - value_min) * padding_ratio + return value_min - padding, value_max + padding diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_terminator_improvement.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_terminator_improvement.py new file mode 100644 index 0000000000000000000000000000000000000000..9e2939d304d7ca0af21ae7939ba2e1484d1dc3a7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_terminator_improvement.py @@ -0,0 +1,117 @@ +from __future__ import annotations + +from optuna._experimental import experimental_func +from optuna.logging import get_logger +from optuna.study.study import Study +from optuna.terminator import BaseErrorEvaluator +from optuna.terminator import BaseImprovementEvaluator +from optuna.terminator.improvement.evaluator import DEFAULT_MIN_N_TRIALS +from optuna.visualization._terminator_improvement import _get_improvement_info +from optuna.visualization._terminator_improvement import _get_y_range +from optuna.visualization._terminator_improvement import _ImprovementInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import plt + +_logger = get_logger(__name__) + + +PADDING_RATIO_Y = 0.05 +ALPHA = 0.25 + + +@experimental_func("3.2.0") +def plot_terminator_improvement( + study: Study, + plot_error: bool = False, + improvement_evaluator: BaseImprovementEvaluator | None = None, + error_evaluator: BaseErrorEvaluator | None = None, + min_n_trials: int = DEFAULT_MIN_N_TRIALS, +) -> "Axes": + """Plot the potentials for future objective improvement. + + This function visualizes the objective improvement potentials, evaluated + with ``improvement_evaluator``. + It helps to determine whether we should continue the optimization or not. + You can also plot the error evaluated with + ``error_evaluator`` if the ``plot_error`` argument is set to :obj:`True`. + Note that this function may take some time to compute + the improvement potentials. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_terminator_improvement`. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted for their improvement. + plot_error: + A flag to show the error. If it is set to :obj:`True`, errors + evaluated by ``error_evaluator`` are also plotted as line graph. + Defaults to :obj:`False`. + improvement_evaluator: + An object that evaluates the improvement of the objective function. + Default to :class:`~optuna.terminator.RegretBoundEvaluator`. + error_evaluator: + An object that evaluates the error inherent in the objective function. + Default to :class:`~optuna.terminator.CrossValidationErrorEvaluator`. + min_n_trials: + The minimum number of trials before termination is considered. + Terminator improvements for trials below this value are + shown in a lighter color. Defaults to ``20``. + + Returns: + A :class:`matplotlib.axes.Axes` object. + """ + _imports.check() + + info = _get_improvement_info(study, plot_error, improvement_evaluator, error_evaluator) + return _get_improvement_plot(info, min_n_trials) + + +def _get_improvement_plot(info: _ImprovementInfo, min_n_trials: int) -> "Axes": + n_trials = len(info.trial_numbers) + + # Set up the graph style. + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + _, ax = plt.subplots() + ax.set_title("Terminator Improvement Plot") + ax.set_xlabel("Trial") + ax.set_ylabel("Terminator Improvement") + cmap = plt.get_cmap("tab10") # Use tab10 colormap for similar outputs to plotly. + + if n_trials == 0: + _logger.warning("There are no complete trials.") + return ax + + ax.plot( + info.trial_numbers[: min_n_trials + 1], + info.improvements[: min_n_trials + 1], + marker="o", + color=cmap(0), + alpha=ALPHA, + label="Terminator Improvement" if n_trials <= min_n_trials else None, + ) + + if n_trials > min_n_trials: + ax.plot( + info.trial_numbers[min_n_trials:], + info.improvements[min_n_trials:], + marker="o", + color=cmap(0), + label="Terminator Improvement", + ) + + if info.errors is not None: + ax.plot( + info.trial_numbers, + info.errors, + marker="o", + color=cmap(3), + label="Error", + ) + ax.legend() + ax.set_ylim(_get_y_range(info, min_n_trials)) + return ax diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_timeline.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_timeline.py new file mode 100644 index 0000000000000000000000000000000000000000..6969a34c129192c4b0088c3a01ea1f19f2047ac4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_timeline.py @@ -0,0 +1,113 @@ +from __future__ import annotations + +from optuna._experimental import experimental_func +from optuna.study import Study +from optuna.trial import TrialState +from optuna.visualization._timeline import _get_timeline_info +from optuna.visualization._timeline import _TimelineBarInfo +from optuna.visualization._timeline import _TimelineInfo +from optuna.visualization.matplotlib._matplotlib_imports import _imports + + +if _imports.is_successful(): + from optuna.visualization.matplotlib._matplotlib_imports import Axes + from optuna.visualization.matplotlib._matplotlib_imports import DateFormatter + from optuna.visualization.matplotlib._matplotlib_imports import matplotlib + from optuna.visualization.matplotlib._matplotlib_imports import plt + + +_INFEASIBLE_KEY = "INFEASIBLE" + + +@experimental_func("3.2.0") +def plot_timeline(study: Study, n_recent_trials: int | None = None) -> "Axes": + """Plot the timeline of a study. + + .. seealso:: + Please refer to :func:`optuna.visualization.plot_timeline` for an example. + + Args: + study: + A :class:`~optuna.study.Study` object whose trials are plotted with + their lifetime. + n_recent_trials: + The number of recent trials to plot. If :obj:`None`, all trials are plotted. + If specified, only the most recent ``n_recent_trials`` will be displayed. + Must be a positive integer. + + Returns: + A :class:`plotly.graph_objects.Figure` object. + + Raises: + ValueError: if ``n_recent_trials`` is 0 or negative. + """ + + if n_recent_trials is not None and n_recent_trials <= 0: + raise ValueError("n_recent_trials must be a positive integer or None.") + + _imports.check() + info = _get_timeline_info(study, n_recent_trials) + return _get_timeline_plot(info) + + +def _get_state_name(bar_info: _TimelineBarInfo) -> str: + if bar_info.state == TrialState.COMPLETE and bar_info.infeasible: + return _INFEASIBLE_KEY + else: + return bar_info.state.name + + +def _get_timeline_plot(info: _TimelineInfo) -> "Axes": + _cm = { + TrialState.COMPLETE.name: "tab:blue", + TrialState.FAIL.name: "tab:red", + TrialState.PRUNED.name: "tab:orange", + _INFEASIBLE_KEY: "#CCCCCC", + TrialState.RUNNING.name: "tab:green", + TrialState.WAITING.name: "tab:gray", + } + # Set up the graph style. + plt.style.use("ggplot") # Use ggplot style sheet for similar outputs to plotly. + fig, ax = plt.subplots() + ax.set_title("Timeline Plot") + ax.set_xlabel("Datetime") + ax.set_ylabel("Trial") + + if len(info.bars) == 0: + return ax + + # According to the `ax.barh` docstring, using list[timedelta] as width and list[datetime] as + # left is supported, but mypy does not recognize it. Please refer to the following link for + # more details: + # https://github.com/matplotlib/matplotlib/blob/v3.10.1/lib/matplotlib/axes/_axes.py#L2701-L2836 + ax.barh( + y=[b.number for b in info.bars], + width=[b.complete - b.start for b in info.bars], # type: ignore[arg-type] + left=[b.start for b in info.bars], # type: ignore[arg-type] + color=[_cm[_get_state_name(b)] for b in info.bars], + ) + + # There are 5 types of TrialState in total. + # However, the legend depicts only types present in the arguments. + legend_handles = [] + for state_name, color in _cm.items(): + if any(_get_state_name(b) == state_name for b in info.bars): + legend_handles.append(matplotlib.patches.Patch(color=color, label=state_name)) + ax.legend(handles=legend_handles, loc="upper left", bbox_to_anchor=(1.05, 1.0)) + fig.tight_layout() + + assert len(info.bars) > 0 + first_start_time = min([b.start for b in info.bars]) + last_complete_time = max([b.complete for b in info.bars]) + margin = (last_complete_time - first_start_time) * 0.05 + + # Officially, ax.set_xlim expects arguments right and left to be float, + # but ax.barh() accepts datetime, so we leave the type as datetime. + ax.set_xlim( + right=last_complete_time + margin, # type: ignore[arg-type] + left=first_start_time - margin, # type: ignore[arg-type] + ) + ax.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(integer=True)) + ax.xaxis.set_major_formatter(DateFormatter("%H:%M:%S")) # type: ignore[no-untyped-call] + plt.gcf().autofmt_xdate() + return ax diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_utils.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e52775e842b479cc84059084e2c5cefe72994873 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/optuna/visualization/matplotlib/_utils.py @@ -0,0 +1,58 @@ +from __future__ import annotations + +from optuna._experimental import experimental_func +from optuna.distributions import CategoricalDistribution +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution +from optuna.trial import FrozenTrial +from optuna.visualization.matplotlib import _matplotlib_imports + + +__all__ = ["is_available"] + + +@experimental_func("2.2.0") +def is_available() -> bool: + """Returns whether visualization with Matplotlib is available or not. + + .. note:: + + :mod:`~optuna.visualization.matplotlib` module depends on Matplotlib version 3.0.0 or + higher. If a supported version of Matplotlib isn't installed in your environment, this + function will return :obj:`False`. In such a case, please execute ``$ pip install -U + matplotlib>=3.0.0`` to install Matplotlib. + + Returns: + :obj:`True` if visualization with Matplotlib is available, :obj:`False` otherwise. + """ + + return _matplotlib_imports._imports.is_successful() + + +def _is_log_scale(trials: list[FrozenTrial], param: str) -> bool: + for trial in trials: + if param in trial.params: + dist = trial.distributions[param] + + if isinstance(dist, (FloatDistribution, IntDistribution)): + if dist.log: + return True + + return False + + +def _is_categorical(trials: list[FrozenTrial], param: str) -> bool: + return any( + isinstance(t.distributions[param], CategoricalDistribution) + for t in trials + if param in t.params + ) + + +def _is_numerical(trials: list[FrozenTrial], param: str) -> bool: + return all( + (isinstance(t.params[param], int) or isinstance(t.params[param], float)) + and not isinstance(t.params[param], bool) + for t in trials + if param in t.params + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ca2eba20432924304517be99d5113bc9f57614d2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/__init__.py @@ -0,0 +1,367 @@ +from __future__ import annotations + +import os +import warnings + +__docformat__ = "restructuredtext" + +# Let users know if they're missing any of our hard dependencies +_hard_dependencies = ("numpy", "pytz", "dateutil") +_missing_dependencies = [] + +for _dependency in _hard_dependencies: + try: + __import__(_dependency) + except ImportError as _e: # pragma: no cover + _missing_dependencies.append(f"{_dependency}: {_e}") + +if _missing_dependencies: # pragma: no cover + raise ImportError( + "Unable to import required dependencies:\n" + "\n".join(_missing_dependencies) + ) +del _hard_dependencies, _dependency, _missing_dependencies + +try: + # numpy compat + from pandas.compat import ( + is_numpy_dev as _is_numpy_dev, # pyright: ignore[reportUnusedImport] # noqa: F401 + ) +except ImportError as _err: # pragma: no cover + _module = _err.name + raise ImportError( + f"C extension: {_module} not built. If you want to import " + "pandas from the source directory, you may need to run " + "'python setup.py build_ext' to build the C extensions first." + ) from _err + +from pandas._config import ( + get_option, + set_option, + reset_option, + describe_option, + option_context, + options, +) + +# let init-time option registration happen +import pandas.core.config_init # pyright: ignore[reportUnusedImport] # noqa: F401 + +from pandas.core.api import ( + # dtype + ArrowDtype, + Int8Dtype, + Int16Dtype, + Int32Dtype, + Int64Dtype, + UInt8Dtype, + UInt16Dtype, + UInt32Dtype, + UInt64Dtype, + Float32Dtype, + Float64Dtype, + CategoricalDtype, + PeriodDtype, + IntervalDtype, + DatetimeTZDtype, + StringDtype, + BooleanDtype, + # missing + NA, + isna, + isnull, + notna, + notnull, + # indexes + Index, + CategoricalIndex, + RangeIndex, + MultiIndex, + IntervalIndex, + TimedeltaIndex, + DatetimeIndex, + PeriodIndex, + IndexSlice, + # tseries + NaT, + Period, + period_range, + Timedelta, + timedelta_range, + Timestamp, + date_range, + bdate_range, + Interval, + interval_range, + DateOffset, + # conversion + to_numeric, + to_datetime, + to_timedelta, + # misc + Flags, + Grouper, + factorize, + unique, + value_counts, + NamedAgg, + array, + Categorical, + set_eng_float_format, + Series, + DataFrame, +) + +from pandas.core.dtypes.dtypes import SparseDtype + +from pandas.tseries.api import infer_freq +from pandas.tseries import offsets + +from pandas.core.computation.api import eval + +from pandas.core.reshape.api import ( + concat, + lreshape, + melt, + wide_to_long, + merge, + merge_asof, + merge_ordered, + crosstab, + pivot, + pivot_table, + get_dummies, + from_dummies, + cut, + qcut, +) + +from pandas import api, arrays, errors, io, plotting, tseries +from pandas import testing +from pandas.util._print_versions import show_versions + +from pandas.io.api import ( + # excel + ExcelFile, + ExcelWriter, + read_excel, + # parsers + read_csv, + read_fwf, + read_table, + # pickle + read_pickle, + to_pickle, + # pytables + HDFStore, + read_hdf, + # sql + read_sql, + read_sql_query, + read_sql_table, + # misc + read_clipboard, + read_parquet, + read_orc, + read_feather, + read_gbq, + read_html, + read_xml, + read_json, + read_stata, + read_sas, + read_spss, +) + +from pandas.io.json._normalize import json_normalize + +from pandas.util._tester import test + +# use the closest tagged version if possible +_built_with_meson = False +try: + from pandas._version_meson import ( # pyright: ignore [reportMissingImports] + __version__, + __git_version__, + ) + + _built_with_meson = True +except ImportError: + from pandas._version import get_versions + + v = get_versions() + __version__ = v.get("closest-tag", v["version"]) + __git_version__ = v.get("full-revisionid") + del get_versions, v + +# GH#55043 - deprecation of the data_manager option +if "PANDAS_DATA_MANAGER" in os.environ: + warnings.warn( + "The env variable PANDAS_DATA_MANAGER is set. The data_manager option is " + "deprecated and will be removed in a future version. Only the BlockManager " + "will be available. Unset this environment variable to silence this warning.", + FutureWarning, + stacklevel=2, + ) + +del warnings, os + +# module level doc-string +__doc__ = """ +pandas - a powerful data analysis and manipulation library for Python +===================================================================== + +**pandas** is a Python package providing fast, flexible, and expressive data +structures designed to make working with "relational" or "labeled" data both +easy and intuitive. It aims to be the fundamental high-level building block for +doing practical, **real world** data analysis in Python. Additionally, it has +the broader goal of becoming **the most powerful and flexible open source data +analysis / manipulation tool available in any language**. It is already well on +its way toward this goal. + +Main Features +------------- +Here are just a few of the things that pandas does well: + + - Easy handling of missing data in floating point as well as non-floating + point data. + - Size mutability: columns can be inserted and deleted from DataFrame and + higher dimensional objects + - Automatic and explicit data alignment: objects can be explicitly aligned + to a set of labels, or the user can simply ignore the labels and let + `Series`, `DataFrame`, etc. automatically align the data for you in + computations. + - Powerful, flexible group by functionality to perform split-apply-combine + operations on data sets, for both aggregating and transforming data. + - Make it easy to convert ragged, differently-indexed data in other Python + and NumPy data structures into DataFrame objects. + - Intelligent label-based slicing, fancy indexing, and subsetting of large + data sets. + - Intuitive merging and joining data sets. + - Flexible reshaping and pivoting of data sets. + - Hierarchical labeling of axes (possible to have multiple labels per tick). + - Robust IO tools for loading data from flat files (CSV and delimited), + Excel files, databases, and saving/loading data from the ultrafast HDF5 + format. + - Time series-specific functionality: date range generation and frequency + conversion, moving window statistics, date shifting and lagging. +""" + +# Use __all__ to let type checkers know what is part of the public API. +# Pandas is not (yet) a py.typed library: the public API is determined +# based on the documentation. +__all__ = [ + "ArrowDtype", + "BooleanDtype", + "Categorical", + "CategoricalDtype", + "CategoricalIndex", + "DataFrame", + "DateOffset", + "DatetimeIndex", + "DatetimeTZDtype", + "ExcelFile", + "ExcelWriter", + "Flags", + "Float32Dtype", + "Float64Dtype", + "Grouper", + "HDFStore", + "Index", + "IndexSlice", + "Int16Dtype", + "Int32Dtype", + "Int64Dtype", + "Int8Dtype", + "Interval", + "IntervalDtype", + "IntervalIndex", + "MultiIndex", + "NA", + "NaT", + "NamedAgg", + "Period", + "PeriodDtype", + "PeriodIndex", + "RangeIndex", + "Series", + "SparseDtype", + "StringDtype", + "Timedelta", + "TimedeltaIndex", + "Timestamp", + "UInt16Dtype", + "UInt32Dtype", + "UInt64Dtype", + "UInt8Dtype", + "api", + "array", + "arrays", + "bdate_range", + "concat", + "crosstab", + "cut", + "date_range", + "describe_option", + "errors", + "eval", + "factorize", + "get_dummies", + "from_dummies", + "get_option", + "infer_freq", + "interval_range", + "io", + "isna", + "isnull", + "json_normalize", + "lreshape", + "melt", + "merge", + "merge_asof", + "merge_ordered", + "notna", + "notnull", + "offsets", + "option_context", + "options", + "period_range", + "pivot", + "pivot_table", + "plotting", + "qcut", + "read_clipboard", + "read_csv", + "read_excel", + "read_feather", + "read_fwf", + "read_gbq", + "read_hdf", + "read_html", + "read_json", + "read_orc", + "read_parquet", + "read_pickle", + "read_sas", + "read_spss", + "read_sql", + "read_sql_query", + "read_sql_table", + "read_stata", + "read_table", + "read_xml", + "reset_option", + "set_eng_float_format", + "set_option", + "show_versions", + "test", + "testing", + "timedelta_range", + "to_datetime", + "to_numeric", + "to_pickle", + "to_timedelta", + "tseries", + "unique", + "value_counts", + "wide_to_long", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/_typing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/_typing.py new file mode 100644 index 0000000000000000000000000000000000000000..3df9a47a35fca32547947560a8df1cea1d1863c2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/_typing.py @@ -0,0 +1,525 @@ +from __future__ import annotations + +from collections.abc import ( + Hashable, + Iterator, + Mapping, + MutableMapping, + Sequence, +) +from datetime import ( + date, + datetime, + timedelta, + tzinfo, +) +from os import PathLike +import sys +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Literal, + Optional, + Protocol, + Type as type_t, + TypeVar, + Union, + overload, +) + +import numpy as np + +# To prevent import cycles place any internal imports in the branch below +# and use a string literal forward reference to it in subsequent types +# https://mypy.readthedocs.io/en/latest/common_issues.html#import-cycles +if TYPE_CHECKING: + import numpy.typing as npt + + from pandas._libs import ( + NaTType, + Period, + Timedelta, + Timestamp, + ) + from pandas._libs.tslibs import BaseOffset + + from pandas.core.dtypes.dtypes import ExtensionDtype + + from pandas import Interval + from pandas.arrays import ( + DatetimeArray, + TimedeltaArray, + ) + from pandas.core.arrays.base import ExtensionArray + from pandas.core.frame import DataFrame + from pandas.core.generic import NDFrame + from pandas.core.groupby.generic import ( + DataFrameGroupBy, + GroupBy, + SeriesGroupBy, + ) + from pandas.core.indexes.base import Index + from pandas.core.internals import ( + ArrayManager, + BlockManager, + SingleArrayManager, + SingleBlockManager, + ) + from pandas.core.resample import Resampler + from pandas.core.series import Series + from pandas.core.window.rolling import BaseWindow + + from pandas.io.formats.format import EngFormatter + from pandas.tseries.holiday import AbstractHolidayCalendar + + ScalarLike_co = Union[ + int, + float, + complex, + str, + bytes, + np.generic, + ] + + # numpy compatible types + NumpyValueArrayLike = Union[ScalarLike_co, npt.ArrayLike] + # Name "npt._ArrayLikeInt_co" is not defined [name-defined] + NumpySorter = Optional[npt._ArrayLikeInt_co] # type: ignore[name-defined] + + from typing import SupportsIndex + + if sys.version_info >= (3, 10): + from typing import TypeGuard # pyright: ignore[reportUnusedImport] + else: + from typing_extensions import TypeGuard # pyright: ignore[reportUnusedImport] + + if sys.version_info >= (3, 11): + from typing import Self # pyright: ignore[reportUnusedImport] + else: + from typing_extensions import Self # pyright: ignore[reportUnusedImport] +else: + npt: Any = None + Self: Any = None + TypeGuard: Any = None + +HashableT = TypeVar("HashableT", bound=Hashable) +MutableMappingT = TypeVar("MutableMappingT", bound=MutableMapping) + +# array-like + +ArrayLike = Union["ExtensionArray", np.ndarray] +AnyArrayLike = Union[ArrayLike, "Index", "Series"] +TimeArrayLike = Union["DatetimeArray", "TimedeltaArray"] + +# list-like + +# from https://github.com/hauntsaninja/useful_types +# includes Sequence-like objects but excludes str and bytes +_T_co = TypeVar("_T_co", covariant=True) + + +class SequenceNotStr(Protocol[_T_co]): + @overload + def __getitem__(self, index: SupportsIndex, /) -> _T_co: + ... + + @overload + def __getitem__(self, index: slice, /) -> Sequence[_T_co]: + ... + + def __contains__(self, value: object, /) -> bool: + ... + + def __len__(self) -> int: + ... + + def __iter__(self) -> Iterator[_T_co]: + ... + + def index(self, value: Any, /, start: int = 0, stop: int = ...) -> int: + ... + + def count(self, value: Any, /) -> int: + ... + + def __reversed__(self) -> Iterator[_T_co]: + ... + + +ListLike = Union[AnyArrayLike, SequenceNotStr, range] + +# scalars + +PythonScalar = Union[str, float, bool] +DatetimeLikeScalar = Union["Period", "Timestamp", "Timedelta"] +PandasScalar = Union["Period", "Timestamp", "Timedelta", "Interval"] +Scalar = Union[PythonScalar, PandasScalar, np.datetime64, np.timedelta64, date] +IntStrT = TypeVar("IntStrT", bound=Union[int, str]) + + +# timestamp and timedelta convertible types + +TimestampConvertibleTypes = Union[ + "Timestamp", date, np.datetime64, np.int64, float, str +] +TimestampNonexistent = Union[ + Literal["shift_forward", "shift_backward", "NaT", "raise"], timedelta +] +TimedeltaConvertibleTypes = Union[ + "Timedelta", timedelta, np.timedelta64, np.int64, float, str +] +Timezone = Union[str, tzinfo] + +ToTimestampHow = Literal["s", "e", "start", "end"] + +# NDFrameT is stricter and ensures that the same subclass of NDFrame always is +# used. E.g. `def func(a: NDFrameT) -> NDFrameT: ...` means that if a +# Series is passed into a function, a Series is always returned and if a DataFrame is +# passed in, a DataFrame is always returned. +NDFrameT = TypeVar("NDFrameT", bound="NDFrame") + +NumpyIndexT = TypeVar("NumpyIndexT", np.ndarray, "Index") + +AxisInt = int +Axis = Union[AxisInt, Literal["index", "columns", "rows"]] +IndexLabel = Union[Hashable, Sequence[Hashable]] +Level = Hashable +Shape = tuple[int, ...] +Suffixes = tuple[Optional[str], Optional[str]] +Ordered = Optional[bool] +JSONSerializable = Optional[Union[PythonScalar, list, dict]] +Frequency = Union[str, "BaseOffset"] +Axes = ListLike + +RandomState = Union[ + int, + np.ndarray, + np.random.Generator, + np.random.BitGenerator, + np.random.RandomState, +] + +# dtypes +NpDtype = Union[str, np.dtype, type_t[Union[str, complex, bool, object]]] +Dtype = Union["ExtensionDtype", NpDtype] +AstypeArg = Union["ExtensionDtype", "npt.DTypeLike"] +# DtypeArg specifies all allowable dtypes in a functions its dtype argument +DtypeArg = Union[Dtype, dict[Hashable, Dtype]] +DtypeObj = Union[np.dtype, "ExtensionDtype"] + +# converters +ConvertersArg = dict[Hashable, Callable[[Dtype], Dtype]] + +# parse_dates +ParseDatesArg = Union[ + bool, list[Hashable], list[list[Hashable]], dict[Hashable, list[Hashable]] +] + +# For functions like rename that convert one label to another +Renamer = Union[Mapping[Any, Hashable], Callable[[Any], Hashable]] + +# to maintain type information across generic functions and parametrization +T = TypeVar("T") + +# used in decorators to preserve the signature of the function it decorates +# see https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators +FuncType = Callable[..., Any] +F = TypeVar("F", bound=FuncType) + +# types of vectorized key functions for DataFrame::sort_values and +# DataFrame::sort_index, among others +ValueKeyFunc = Optional[Callable[["Series"], Union["Series", AnyArrayLike]]] +IndexKeyFunc = Optional[Callable[["Index"], Union["Index", AnyArrayLike]]] + +# types of `func` kwarg for DataFrame.aggregate and Series.aggregate +AggFuncTypeBase = Union[Callable, str] +AggFuncTypeDict = MutableMapping[ + Hashable, Union[AggFuncTypeBase, list[AggFuncTypeBase]] +] +AggFuncType = Union[ + AggFuncTypeBase, + list[AggFuncTypeBase], + AggFuncTypeDict, +] +AggObjType = Union[ + "Series", + "DataFrame", + "GroupBy", + "SeriesGroupBy", + "DataFrameGroupBy", + "BaseWindow", + "Resampler", +] + +PythonFuncType = Callable[[Any], Any] + +# filenames and file-like-objects +AnyStr_co = TypeVar("AnyStr_co", str, bytes, covariant=True) +AnyStr_contra = TypeVar("AnyStr_contra", str, bytes, contravariant=True) + + +class BaseBuffer(Protocol): + @property + def mode(self) -> str: + # for _get_filepath_or_buffer + ... + + def seek(self, __offset: int, __whence: int = ...) -> int: + # with one argument: gzip.GzipFile, bz2.BZ2File + # with two arguments: zip.ZipFile, read_sas + ... + + def seekable(self) -> bool: + # for bz2.BZ2File + ... + + def tell(self) -> int: + # for zip.ZipFile, read_stata, to_stata + ... + + +class ReadBuffer(BaseBuffer, Protocol[AnyStr_co]): + def read(self, __n: int = ...) -> AnyStr_co: + # for BytesIOWrapper, gzip.GzipFile, bz2.BZ2File + ... + + +class WriteBuffer(BaseBuffer, Protocol[AnyStr_contra]): + def write(self, __b: AnyStr_contra) -> Any: + # for gzip.GzipFile, bz2.BZ2File + ... + + def flush(self) -> Any: + # for gzip.GzipFile, bz2.BZ2File + ... + + +class ReadPickleBuffer(ReadBuffer[bytes], Protocol): + def readline(self) -> bytes: + ... + + +class WriteExcelBuffer(WriteBuffer[bytes], Protocol): + def truncate(self, size: int | None = ...) -> int: + ... + + +class ReadCsvBuffer(ReadBuffer[AnyStr_co], Protocol): + def __iter__(self) -> Iterator[AnyStr_co]: + # for engine=python + ... + + def fileno(self) -> int: + # for _MMapWrapper + ... + + def readline(self) -> AnyStr_co: + # for engine=python + ... + + @property + def closed(self) -> bool: + # for enine=pyarrow + ... + + +FilePath = Union[str, "PathLike[str]"] + +# for arbitrary kwargs passed during reading/writing files +StorageOptions = Optional[dict[str, Any]] + + +# compression keywords and compression +CompressionDict = dict[str, Any] +CompressionOptions = Optional[ + Union[Literal["infer", "gzip", "bz2", "zip", "xz", "zstd", "tar"], CompressionDict] +] + +# types in DataFrameFormatter +FormattersType = Union[ + list[Callable], tuple[Callable, ...], Mapping[Union[str, int], Callable] +] +ColspaceType = Mapping[Hashable, Union[str, int]] +FloatFormatType = Union[str, Callable, "EngFormatter"] +ColspaceArgType = Union[ + str, int, Sequence[Union[str, int]], Mapping[Hashable, Union[str, int]] +] + +# Arguments for fillna() +FillnaOptions = Literal["backfill", "bfill", "ffill", "pad"] +InterpolateOptions = Literal[ + "linear", + "time", + "index", + "values", + "nearest", + "zero", + "slinear", + "quadratic", + "cubic", + "barycentric", + "polynomial", + "krogh", + "piecewise_polynomial", + "spline", + "pchip", + "akima", + "cubicspline", + "from_derivatives", +] + +# internals +Manager = Union[ + "ArrayManager", "SingleArrayManager", "BlockManager", "SingleBlockManager" +] +SingleManager = Union["SingleArrayManager", "SingleBlockManager"] +Manager2D = Union["ArrayManager", "BlockManager"] + +# indexing +# PositionalIndexer -> valid 1D positional indexer, e.g. can pass +# to ndarray.__getitem__ +# ScalarIndexer is for a single value as the index +# SequenceIndexer is for list like or slices (but not tuples) +# PositionalIndexerTuple is extends the PositionalIndexer for 2D arrays +# These are used in various __getitem__ overloads +# TODO(typing#684): add Ellipsis, see +# https://github.com/python/typing/issues/684#issuecomment-548203158 +# https://bugs.python.org/issue41810 +# Using List[int] here rather than Sequence[int] to disallow tuples. +ScalarIndexer = Union[int, np.integer] +SequenceIndexer = Union[slice, list[int], np.ndarray] +PositionalIndexer = Union[ScalarIndexer, SequenceIndexer] +PositionalIndexerTuple = tuple[PositionalIndexer, PositionalIndexer] +PositionalIndexer2D = Union[PositionalIndexer, PositionalIndexerTuple] +if TYPE_CHECKING: + TakeIndexer = Union[Sequence[int], Sequence[np.integer], npt.NDArray[np.integer]] +else: + TakeIndexer = Any + +# Shared by functions such as drop and astype +IgnoreRaise = Literal["ignore", "raise"] + +# Windowing rank methods +WindowingRankType = Literal["average", "min", "max"] + +# read_csv engines +CSVEngine = Literal["c", "python", "pyarrow", "python-fwf"] + +# read_json engines +JSONEngine = Literal["ujson", "pyarrow"] + +# read_xml parsers +XMLParsers = Literal["lxml", "etree"] + +# read_html flavors +HTMLFlavors = Literal["lxml", "html5lib", "bs4"] + +# Interval closed type +IntervalLeftRight = Literal["left", "right"] +IntervalClosedType = Union[IntervalLeftRight, Literal["both", "neither"]] + +# datetime and NaTType +DatetimeNaTType = Union[datetime, "NaTType"] +DateTimeErrorChoices = Union[IgnoreRaise, Literal["coerce"]] + +# sort_index +SortKind = Literal["quicksort", "mergesort", "heapsort", "stable"] +NaPosition = Literal["first", "last"] + +# Arguments for nsmalles and n_largest +NsmallestNlargestKeep = Literal["first", "last", "all"] + +# quantile interpolation +QuantileInterpolation = Literal["linear", "lower", "higher", "midpoint", "nearest"] + +# plotting +PlottingOrientation = Literal["horizontal", "vertical"] + +# dropna +AnyAll = Literal["any", "all"] + +# merge +MergeHow = Literal["left", "right", "inner", "outer", "cross"] +MergeValidate = Literal[ + "one_to_one", + "1:1", + "one_to_many", + "1:m", + "many_to_one", + "m:1", + "many_to_many", + "m:m", +] + +# join +JoinHow = Literal["left", "right", "inner", "outer"] +JoinValidate = Literal[ + "one_to_one", + "1:1", + "one_to_many", + "1:m", + "many_to_one", + "m:1", + "many_to_many", + "m:m", +] + +# reindex +ReindexMethod = Union[FillnaOptions, Literal["nearest"]] + +MatplotlibColor = Union[str, Sequence[float]] +TimeGrouperOrigin = Union[ + "Timestamp", Literal["epoch", "start", "start_day", "end", "end_day"] +] +TimeAmbiguous = Union[Literal["infer", "NaT", "raise"], "npt.NDArray[np.bool_]"] +TimeNonexistent = Union[ + Literal["shift_forward", "shift_backward", "NaT", "raise"], timedelta +] +DropKeep = Literal["first", "last", False] +CorrelationMethod = Union[ + Literal["pearson", "kendall", "spearman"], Callable[[np.ndarray, np.ndarray], float] +] +AlignJoin = Literal["outer", "inner", "left", "right"] +DtypeBackend = Literal["pyarrow", "numpy_nullable"] + +TimeUnit = Literal["s", "ms", "us", "ns"] +OpenFileErrors = Literal[ + "strict", + "ignore", + "replace", + "surrogateescape", + "xmlcharrefreplace", + "backslashreplace", + "namereplace", +] + +# update +UpdateJoin = Literal["left"] + +# applymap +NaAction = Literal["ignore"] + +# from_dict +FromDictOrient = Literal["columns", "index", "tight"] + +# to_gbc +ToGbqIfexist = Literal["fail", "replace", "append"] + +# to_stata +ToStataByteorder = Literal[">", "<", "little", "big"] + +# ExcelWriter +ExcelWriterIfSheetExists = Literal["error", "new", "replace", "overlay"] + +# Offsets +OffsetCalendar = Union[np.busdaycalendar, "AbstractHolidayCalendar"] + +# read_csv: usecols +UsecolsArgType = Union[ + SequenceNotStr[Hashable], + range, + AnyArrayLike, + Callable[[HashableT], bool], + None, +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/_version.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..1a1e9d1efc55c2f9ac6f36d3b5279aaea6a39c96 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/_version.py @@ -0,0 +1,692 @@ +# This file helps to compute a version number in source trees obtained from +# git-archive tarball (such as those provided by githubs download-from-tag +# feature). Distribution tarballs (built by setup.py sdist) and build +# directories (produced by setup.py build) will contain a much shorter file +# that just contains the computed version number. + +# This file is released into the public domain. +# Generated by versioneer-0.28 +# https://github.com/python-versioneer/python-versioneer + +"""Git implementation of _version.py.""" + +import errno +import functools +import os +import re +import subprocess +import sys +from typing import Callable + + +def get_keywords(): + """Get the keywords needed to look up the version information.""" + # these strings will be replaced by git during git-archive. + # setup.py/versioneer.py will grep for the variable names, so they must + # each be defined on a line of their own. _version.py will just call + # get_keywords(). + git_refnames = " (HEAD, tag: v2.3.2, origin/2.3.x)" + git_full = "4665c10899bc413b639194f6fb8665a5c70f7db5" + git_date = "2025-08-21 10:42:59 +0200" + keywords = {"refnames": git_refnames, "full": git_full, "date": git_date} + return keywords + + +class VersioneerConfig: + """Container for Versioneer configuration parameters.""" + + +def get_config(): + """Create, populate and return the VersioneerConfig() object.""" + # these strings are filled in when 'setup.py versioneer' creates + # _version.py + cfg = VersioneerConfig() + cfg.VCS = "git" + cfg.style = "pep440" + cfg.tag_prefix = "v" + cfg.parentdir_prefix = "pandas-" + cfg.versionfile_source = "pandas/_version.py" + cfg.verbose = False + return cfg + + +class NotThisMethod(Exception): + """Exception raised if a method is not valid for the current scenario.""" + + +LONG_VERSION_PY: dict[str, str] = {} +HANDLERS: dict[str, dict[str, Callable]] = {} + + +def register_vcs_handler(vcs, method): # decorator + """Create decorator to mark a method as the handler of a VCS.""" + + def decorate(f): + """Store f in HANDLERS[vcs][method].""" + if vcs not in HANDLERS: + HANDLERS[vcs] = {} + HANDLERS[vcs][method] = f + return f + + return decorate + + +def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None): + """Call the given command(s).""" + assert isinstance(commands, list) + process = None + + popen_kwargs = {} + if sys.platform == "win32": + # This hides the console window if pythonw.exe is used + startupinfo = subprocess.STARTUPINFO() + startupinfo.dwFlags |= subprocess.STARTF_USESHOWWINDOW + popen_kwargs["startupinfo"] = startupinfo + + for command in commands: + dispcmd = str([command] + args) + try: + # remember shell=False, so use git.cmd on windows, not just git + process = subprocess.Popen( + [command] + args, + cwd=cwd, + env=env, + stdout=subprocess.PIPE, + stderr=(subprocess.PIPE if hide_stderr else None), + **popen_kwargs, + ) + break + except OSError: + e = sys.exc_info()[1] + if e.errno == errno.ENOENT: + continue + if verbose: + print(f"unable to run {dispcmd}") + print(e) + return None, None + else: + if verbose: + print(f"unable to find command, tried {commands}") + return None, None + stdout = process.communicate()[0].strip().decode() + if process.returncode != 0: + if verbose: + print(f"unable to run {dispcmd} (error)") + print(f"stdout was {stdout}") + return None, process.returncode + return stdout, process.returncode + + +def versions_from_parentdir(parentdir_prefix, root, verbose): + """Try to determine the version from the parent directory name. + + Source tarballs conventionally unpack into a directory that includes both + the project name and a version string. We will also support searching up + two directory levels for an appropriately named parent directory + """ + rootdirs = [] + + for _ in range(3): + dirname = os.path.basename(root) + if dirname.startswith(parentdir_prefix): + return { + "version": dirname[len(parentdir_prefix) :], + "full-revisionid": None, + "dirty": False, + "error": None, + "date": None, + } + rootdirs.append(root) + root = os.path.dirname(root) # up a level + + if verbose: + print( + f"Tried directories {str(rootdirs)} \ + but none started with prefix {parentdir_prefix}" + ) + raise NotThisMethod("rootdir doesn't start with parentdir_prefix") + + +@register_vcs_handler("git", "get_keywords") +def git_get_keywords(versionfile_abs): + """Extract version information from the given file.""" + # the code embedded in _version.py can just fetch the value of these + # keywords. When used from setup.py, we don't want to import _version.py, + # so we do it with a regexp instead. This function is not used from + # _version.py. + keywords = {} + try: + with open(versionfile_abs, encoding="utf-8") as fobj: + for line in fobj: + if line.strip().startswith("git_refnames ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["refnames"] = mo.group(1) + if line.strip().startswith("git_full ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["full"] = mo.group(1) + if line.strip().startswith("git_date ="): + mo = re.search(r'=\s*"(.*)"', line) + if mo: + keywords["date"] = mo.group(1) + except OSError: + pass + return keywords + + +@register_vcs_handler("git", "keywords") +def git_versions_from_keywords(keywords, tag_prefix, verbose): + """Get version information from git keywords.""" + if "refnames" not in keywords: + raise NotThisMethod("Short version file found") + date = keywords.get("date") + if date is not None: + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + + # git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant + # datestamp. However we prefer "%ci" (which expands to an "ISO-8601 + # -like" string, which we must then edit to make compliant), because + # it's been around since git-1.5.3, and it's too difficult to + # discover which version we're using, or to work around using an + # older one. + date = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + refnames = keywords["refnames"].strip() + if refnames.startswith("$Format"): + if verbose: + print("keywords are unexpanded, not using") + raise NotThisMethod("unexpanded keywords, not a git-archive tarball") + refs = {r.strip() for r in refnames.strip("()").split(",")} + # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of + # just "foo-1.0". If we see a "tag: " prefix, prefer those. + TAG = "tag: " + tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)} + if not tags: + # Either we're using git < 1.8.3, or there really are no tags. We use + # a heuristic: assume all version tags have a digit. The old git %d + # expansion behaves like git log --decorate=short and strips out the + # refs/heads/ and refs/tags/ prefixes that would let us distinguish + # between branches and tags. By ignoring refnames without digits, we + # filter out many common branch names like "release" and + # "stabilization", as well as "HEAD" and "master". + tags = {r for r in refs if re.search(r"\d", r)} + if verbose: + print(f"discarding '{','.join(refs - tags)}', no digits") + if verbose: + print(f"likely tags: {','.join(sorted(tags))}") + for ref in sorted(tags): + # sorting will prefer e.g. "2.0" over "2.0rc1" + if ref.startswith(tag_prefix): + r = ref[len(tag_prefix) :] + # Filter out refs that exactly match prefix or that don't start + # with a number once the prefix is stripped (mostly a concern + # when prefix is '') + if not re.match(r"\d", r): + continue + if verbose: + print(f"picking {r}") + return { + "version": r, + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": None, + "date": date, + } + # no suitable tags, so version is "0+unknown", but full hex is still there + if verbose: + print("no suitable tags, using unknown + full revision id") + return { + "version": "0+unknown", + "full-revisionid": keywords["full"].strip(), + "dirty": False, + "error": "no suitable tags", + "date": None, + } + + +@register_vcs_handler("git", "pieces_from_vcs") +def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command): + """Get version from 'git describe' in the root of the source tree. + + This only gets called if the git-archive 'subst' keywords were *not* + expanded, and _version.py hasn't already been rewritten with a short + version string, meaning we're inside a checked out source tree. + """ + GITS = ["git"] + if sys.platform == "win32": + GITS = ["git.cmd", "git.exe"] + + # GIT_DIR can interfere with correct operation of Versioneer. + # It may be intended to be passed to the Versioneer-versioned project, + # but that should not change where we get our version from. + env = os.environ.copy() + env.pop("GIT_DIR", None) + runner = functools.partial(runner, env=env) + + _, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root, hide_stderr=not verbose) + if rc != 0: + if verbose: + print(f"Directory {root} not under git control") + raise NotThisMethod("'git rev-parse --git-dir' returned error") + + # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty] + # if there isn't one, this yields HEX[-dirty] (no NUM) + describe_out, rc = runner( + GITS, + [ + "describe", + "--tags", + "--dirty", + "--always", + "--long", + "--match", + f"{tag_prefix}[[:digit:]]*", + ], + cwd=root, + ) + # --long was added in git-1.5.5 + if describe_out is None: + raise NotThisMethod("'git describe' failed") + describe_out = describe_out.strip() + full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root) + if full_out is None: + raise NotThisMethod("'git rev-parse' failed") + full_out = full_out.strip() + + pieces = {} + pieces["long"] = full_out + pieces["short"] = full_out[:7] # maybe improved later + pieces["error"] = None + + branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"], cwd=root) + # --abbrev-ref was added in git-1.6.3 + if rc != 0 or branch_name is None: + raise NotThisMethod("'git rev-parse --abbrev-ref' returned error") + branch_name = branch_name.strip() + + if branch_name == "HEAD": + # If we aren't exactly on a branch, pick a branch which represents + # the current commit. If all else fails, we are on a branchless + # commit. + branches, rc = runner(GITS, ["branch", "--contains"], cwd=root) + # --contains was added in git-1.5.4 + if rc != 0 or branches is None: + raise NotThisMethod("'git branch --contains' returned error") + branches = branches.split("\n") + + # Remove the first line if we're running detached + if "(" in branches[0]: + branches.pop(0) + + # Strip off the leading "* " from the list of branches. + branches = [branch[2:] for branch in branches] + if "master" in branches: + branch_name = "master" + elif not branches: + branch_name = None + else: + # Pick the first branch that is returned. Good or bad. + branch_name = branches[0] + + pieces["branch"] = branch_name + + # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] + # TAG might have hyphens. + git_describe = describe_out + + # look for -dirty suffix + dirty = git_describe.endswith("-dirty") + pieces["dirty"] = dirty + if dirty: + git_describe = git_describe[: git_describe.rindex("-dirty")] + + # now we have TAG-NUM-gHEX or HEX + + if "-" in git_describe: + # TAG-NUM-gHEX + mo = re.search(r"^(.+)-(\d+)-g([0-9a-f]+)$", git_describe) + if not mo: + # unparsable. Maybe git-describe is misbehaving? + pieces["error"] = f"unable to parse git-describe output: '{describe_out}'" + return pieces + + # tag + full_tag = mo.group(1) + if not full_tag.startswith(tag_prefix): + if verbose: + fmt = "tag '%s' doesn't start with prefix '%s'" + print(fmt % (full_tag, tag_prefix)) + pieces[ + "error" + ] = f"tag '{full_tag}' doesn't start with prefix '{tag_prefix}'" + return pieces + pieces["closest-tag"] = full_tag[len(tag_prefix) :] + + # distance: number of commits since tag + pieces["distance"] = int(mo.group(2)) + + # commit: short hex revision ID + pieces["short"] = mo.group(3) + + else: + # HEX: no tags + pieces["closest-tag"] = None + out, rc = runner(GITS, ["rev-list", "HEAD", "--left-right"], cwd=root) + pieces["distance"] = len(out.split()) # total number of commits + + # commit date: see ISO-8601 comment in git_versions_from_keywords() + date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip() + # Use only the last line. Previous lines may contain GPG signature + # information. + date = date.splitlines()[-1] + pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1) + + return pieces + + +def plus_or_dot(pieces) -> str: + """Return a + if we don't already have one, else return a .""" + if "+" in pieces.get("closest-tag", ""): + return "." + return "+" + + +def render_pep440(pieces): + """Build up version string, with post-release "local version identifier". + + Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you + get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty + + Exceptions: + 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += plus_or_dot(pieces) + rendered += f"{pieces['distance']}.g{pieces['short']}" + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = f"0+untagged.{pieces['distance']}.g{pieces['short']}" + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_branch(pieces): + """TAG[[.dev0]+DISTANCE.gHEX[.dirty]] . + + The ".dev0" means not master branch. Note that .dev0 sorts backwards + (a feature branch will appear "older" than the master branch). + + Exceptions: + 1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += f"{pieces['distance']}.g{pieces['short']}" + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = "0" + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += f"+untagged.{pieces['distance']}.g{pieces['short']}" + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def pep440_split_post(ver): + """Split pep440 version string at the post-release segment. + + Returns the release segments before the post-release and the + post-release version number (or -1 if no post-release segment is present). + """ + vc = str.split(ver, ".post") + return vc[0], int(vc[1] or 0) if len(vc) == 2 else None + + +def render_pep440_pre(pieces): + """TAG[.postN.devDISTANCE] -- No -dirty. + + Exceptions: + 1: no tags. 0.post0.devDISTANCE + """ + if pieces["closest-tag"]: + if pieces["distance"]: + # update the post release segment + tag_version, post_version = pep440_split_post(pieces["closest-tag"]) + rendered = tag_version + if post_version is not None: + rendered += f".post{post_version + 1}.dev{pieces['distance']}" + else: + rendered += f".post0.dev{pieces['distance']}" + else: + # no commits, use the tag as the version + rendered = pieces["closest-tag"] + else: + # exception #1 + rendered = f"0.post0.dev{pieces['distance']}" + return rendered + + +def render_pep440_post(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX] . + + The ".dev0" means dirty. Note that .dev0 sorts backwards + (a dirty tree will appear "older" than the corresponding clean one), + but you shouldn't be releasing software with -dirty anyways. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += f".post{pieces['distance']}" + if pieces["dirty"]: + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += f"g{pieces['short']}" + else: + # exception #1 + rendered = f"0.post{pieces['distance']}" + if pieces["dirty"]: + rendered += ".dev0" + rendered += f"+g{pieces['short']}" + return rendered + + +def render_pep440_post_branch(pieces): + """TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] . + + The ".dev0" means not master branch. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += f".post{pieces['distance']}" + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += plus_or_dot(pieces) + rendered += f"g{pieces['short']}" + if pieces["dirty"]: + rendered += ".dirty" + else: + # exception #1 + rendered = f"0.post{pieces['distance']}" + if pieces["branch"] != "master": + rendered += ".dev0" + rendered += f"+g{pieces['short']}" + if pieces["dirty"]: + rendered += ".dirty" + return rendered + + +def render_pep440_old(pieces): + """TAG[.postDISTANCE[.dev0]] . + + The ".dev0" means dirty. + + Exceptions: + 1: no tags. 0.postDISTANCE[.dev0] + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"] or pieces["dirty"]: + rendered += f"0.post{pieces['distance']}" + if pieces["dirty"]: + rendered += ".dev0" + else: + # exception #1 + rendered = f"0.post{pieces['distance']}" + if pieces["dirty"]: + rendered += ".dev0" + return rendered + + +def render_git_describe(pieces): + """TAG[-DISTANCE-gHEX][-dirty]. + + Like 'git describe --tags --dirty --always'. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + if pieces["distance"]: + rendered += f"-{pieces['distance']}-g{pieces['short']}" + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render_git_describe_long(pieces): + """TAG-DISTANCE-gHEX[-dirty]. + + Like 'git describe --tags --dirty --always -long'. + The distance/hash is unconditional. + + Exceptions: + 1: no tags. HEX[-dirty] (note: no 'g' prefix) + """ + if pieces["closest-tag"]: + rendered = pieces["closest-tag"] + rendered += f"-{pieces['distance']}-g{pieces['short']}" + else: + # exception #1 + rendered = pieces["short"] + if pieces["dirty"]: + rendered += "-dirty" + return rendered + + +def render(pieces, style): + """Render the given version pieces into the requested style.""" + if pieces["error"]: + return { + "version": "unknown", + "full-revisionid": pieces.get("long"), + "dirty": None, + "error": pieces["error"], + "date": None, + } + + if not style or style == "default": + style = "pep440" # the default + + if style == "pep440": + rendered = render_pep440(pieces) + elif style == "pep440-branch": + rendered = render_pep440_branch(pieces) + elif style == "pep440-pre": + rendered = render_pep440_pre(pieces) + elif style == "pep440-post": + rendered = render_pep440_post(pieces) + elif style == "pep440-post-branch": + rendered = render_pep440_post_branch(pieces) + elif style == "pep440-old": + rendered = render_pep440_old(pieces) + elif style == "git-describe": + rendered = render_git_describe(pieces) + elif style == "git-describe-long": + rendered = render_git_describe_long(pieces) + else: + raise ValueError(f"unknown style '{style}'") + + return { + "version": rendered, + "full-revisionid": pieces["long"], + "dirty": pieces["dirty"], + "error": None, + "date": pieces.get("date"), + } + + +def get_versions(): + """Get version information or return default if unable to do so.""" + # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have + # __file__, we can work backwards from there to the root. Some + # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which + # case we can only use expanded keywords. + + cfg = get_config() + verbose = cfg.verbose + + try: + return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, verbose) + except NotThisMethod: + pass + + try: + root = os.path.realpath(__file__) + # versionfile_source is the relative path from the top of the source + # tree (where the .git directory might live) to this file. Invert + # this to find the root from __file__. + for _ in cfg.versionfile_source.split("/"): + root = os.path.dirname(root) + except NameError: + return { + "version": "0+unknown", + "full-revisionid": None, + "dirty": None, + "error": "unable to find root of source tree", + "date": None, + } + + try: + pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) + return render(pieces, cfg.style) + except NotThisMethod: + pass + + try: + if cfg.parentdir_prefix: + return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) + except NotThisMethod: + pass + + return { + "version": "0+unknown", + "full-revisionid": None, + "dirty": None, + "error": "unable to compute version", + "date": None, + } diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/_version_meson.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/_version_meson.py new file mode 100644 index 0000000000000000000000000000000000000000..916e91fd8de13853aadc07bbb3463a1487ea1f40 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/_version_meson.py @@ -0,0 +1,2 @@ +__version__="2.3.2" +__git_version__="4665c10899bc413b639194f6fb8665a5c70f7db5" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a0d42b6541fdf8817b996ef9804db8a87a2bcd2c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/__init__.py @@ -0,0 +1,16 @@ +""" public toolkit API """ +from pandas.api import ( + extensions, + indexers, + interchange, + types, + typing, +) + +__all__ = [ + "interchange", + "extensions", + "indexers", + "types", + "typing", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..219fd8c5714815ba7a021d601eb469adfb152f27 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/extensions/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/extensions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ea5f1ba926899f9d11e34e70181ed77cae7ead1d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/extensions/__init__.py @@ -0,0 +1,33 @@ +""" +Public API for extending pandas objects. +""" + +from pandas._libs.lib import no_default + +from pandas.core.dtypes.base import ( + ExtensionDtype, + register_extension_dtype, +) + +from pandas.core.accessor import ( + register_dataframe_accessor, + register_index_accessor, + register_series_accessor, +) +from pandas.core.algorithms import take +from pandas.core.arrays import ( + ExtensionArray, + ExtensionScalarOpsMixin, +) + +__all__ = [ + "no_default", + "ExtensionDtype", + "register_extension_dtype", + "register_dataframe_accessor", + "register_index_accessor", + "register_series_accessor", + "take", + "ExtensionArray", + "ExtensionScalarOpsMixin", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/extensions/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/extensions/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c0f22799a5a361a44062770850d655a97b0b64c2 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/extensions/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/indexers/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/indexers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..78357f11dc3b79f13490b91c69ef5457fbfa9768 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/indexers/__init__.py @@ -0,0 +1,17 @@ +""" +Public API for Rolling Window Indexers. +""" + +from pandas.core.indexers import check_array_indexer +from pandas.core.indexers.objects import ( + BaseIndexer, + FixedForwardWindowIndexer, + VariableOffsetWindowIndexer, +) + +__all__ = [ + "check_array_indexer", + "BaseIndexer", + "FixedForwardWindowIndexer", + "VariableOffsetWindowIndexer", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/indexers/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/indexers/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b8e8d73b3edac37ac83c96df87ce3fcdc1b35af3 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/indexers/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/interchange/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/interchange/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2f3a73bc46b3109c3c13e1a3468a69aed2ffb2e8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/interchange/__init__.py @@ -0,0 +1,8 @@ +""" +Public API for DataFrame interchange protocol. +""" + +from pandas.core.interchange.dataframe_protocol import DataFrame +from pandas.core.interchange.from_dataframe import from_dataframe + +__all__ = ["from_dataframe", "DataFrame"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/interchange/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/interchange/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..13ea5d6831bf848fa971e6306d25cfa203888948 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/interchange/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/types/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c601086bb9f86a633d6a3f2245779fea9428bf95 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/types/__init__.py @@ -0,0 +1,23 @@ +""" +Public toolkit API. +""" + +from pandas._libs.lib import infer_dtype + +from pandas.core.dtypes.api import * # noqa: F403 +from pandas.core.dtypes.concat import union_categoricals +from pandas.core.dtypes.dtypes import ( + CategoricalDtype, + DatetimeTZDtype, + IntervalDtype, + PeriodDtype, +) + +__all__ = [ + "infer_dtype", + "union_categoricals", + "CategoricalDtype", + "DatetimeTZDtype", + "IntervalDtype", + "PeriodDtype", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/types/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/types/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5c4de8d8cfb4d1b9bd006cd623d8c65b065700c2 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/types/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/typing/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/typing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9b5d2cb06b523508b15025be804a66daaaaf7a45 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/typing/__init__.py @@ -0,0 +1,55 @@ +""" +Public API classes that store intermediate results useful for type-hinting. +""" + +from pandas._libs import NaTType +from pandas._libs.missing import NAType + +from pandas.core.groupby import ( + DataFrameGroupBy, + SeriesGroupBy, +) +from pandas.core.resample import ( + DatetimeIndexResamplerGroupby, + PeriodIndexResamplerGroupby, + Resampler, + TimedeltaIndexResamplerGroupby, + TimeGrouper, +) +from pandas.core.window import ( + Expanding, + ExpandingGroupby, + ExponentialMovingWindow, + ExponentialMovingWindowGroupby, + Rolling, + RollingGroupby, + Window, +) + +# TODO: Can't import Styler without importing jinja2 +# from pandas.io.formats.style import Styler +from pandas.io.json._json import JsonReader +from pandas.io.stata import StataReader + +__all__ = [ + "DataFrameGroupBy", + "DatetimeIndexResamplerGroupby", + "Expanding", + "ExpandingGroupby", + "ExponentialMovingWindow", + "ExponentialMovingWindowGroupby", + "JsonReader", + "NaTType", + "NAType", + "PeriodIndexResamplerGroupby", + "Resampler", + "Rolling", + "RollingGroupby", + "SeriesGroupBy", + "StataReader", + # See TODO above + # "Styler", + "TimedeltaIndexResamplerGroupby", + "TimeGrouper", + "Window", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/typing/__pycache__/__init__.cpython-310.pyc b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/typing/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..65c3bd35a6fe6a288f7090a1c71e3f23712b5e33 Binary files /dev/null and b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/api/typing/__pycache__/__init__.cpython-310.pyc differ diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/conftest.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..35fe5cb475cde0e6ea6c181c6326b06cadc763ea --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/conftest.py @@ -0,0 +1,2065 @@ +""" +This file is very long and growing, but it was decided to not split it yet, as +it's still manageable (2020-03-17, ~1.1k LoC). See gh-31989 + +Instead of splitting it was decided to define sections here: +- Configuration / Settings +- Autouse fixtures +- Common arguments +- Missing values & co. +- Classes +- Indices +- Series' +- DataFrames +- Operators & Operations +- Data sets/files +- Time zones +- Dtypes +- Misc +""" +from __future__ import annotations + +from collections import abc +from datetime import ( + date, + datetime, + time, + timedelta, + timezone, +) +from decimal import Decimal +import operator +import os +from typing import ( + TYPE_CHECKING, + Callable, +) + +from dateutil.tz import ( + tzlocal, + tzutc, +) +import hypothesis +from hypothesis import strategies as st +import numpy as np +import pytest +from pytz import ( + FixedOffset, + utc, +) + +from pandas._config.config import _get_option + +import pandas.util._test_decorators as td + +from pandas.core.dtypes.dtypes import ( + DatetimeTZDtype, + IntervalDtype, +) + +import pandas as pd +from pandas import ( + CategoricalIndex, + DataFrame, + Interval, + IntervalIndex, + Period, + RangeIndex, + Series, + Timedelta, + Timestamp, + date_range, + period_range, + timedelta_range, +) +import pandas._testing as tm +from pandas.core import ops +from pandas.core.indexes.api import ( + Index, + MultiIndex, +) +from pandas.util.version import Version + +if TYPE_CHECKING: + from collections.abc import ( + Hashable, + Iterator, + ) + +try: + import pyarrow as pa +except ImportError: + has_pyarrow = False +else: + del pa + has_pyarrow = True + +import zoneinfo + +try: + zoneinfo.ZoneInfo("UTC") +except zoneinfo.ZoneInfoNotFoundError: + zoneinfo = None # type: ignore[assignment] + + +# ---------------------------------------------------------------- +# Configuration / Settings +# ---------------------------------------------------------------- +# pytest + + +def pytest_addoption(parser) -> None: + parser.addoption( + "--no-strict-data-files", + action="store_false", + help="Don't fail if a test is skipped for missing data file.", + ) + + +def ignore_doctest_warning(item: pytest.Item, path: str, message: str) -> None: + """Ignore doctest warning. + + Parameters + ---------- + item : pytest.Item + pytest test item. + path : str + Module path to Python object, e.g. "pandas.core.frame.DataFrame.append". A + warning will be filtered when item.name ends with in given path. So it is + sufficient to specify e.g. "DataFrame.append". + message : str + Message to be filtered. + """ + if item.name.endswith(path): + item.add_marker(pytest.mark.filterwarnings(f"ignore:{message}")) + + +def pytest_collection_modifyitems(items, config) -> None: + is_doctest = config.getoption("--doctest-modules") or config.getoption( + "--doctest-cython", default=False + ) + + # Warnings from doctests that can be ignored; place reason in comment above. + # Each entry specifies (path, message) - see the ignore_doctest_warning function + ignored_doctest_warnings = [ + ("is_int64_dtype", "is_int64_dtype is deprecated"), + ("is_interval_dtype", "is_interval_dtype is deprecated"), + ("is_period_dtype", "is_period_dtype is deprecated"), + ("is_datetime64tz_dtype", "is_datetime64tz_dtype is deprecated"), + ("is_categorical_dtype", "is_categorical_dtype is deprecated"), + ("is_sparse", "is_sparse is deprecated"), + ("DataFrameGroupBy.fillna", "DataFrameGroupBy.fillna is deprecated"), + ("NDFrame.replace", "The 'method' keyword"), + ("NDFrame.replace", "Series.replace without 'value'"), + ("NDFrame.clip", "Downcasting behavior in Series and DataFrame methods"), + ("Series.idxmin", "The behavior of Series.idxmin"), + ("Series.idxmax", "The behavior of Series.idxmax"), + ("SeriesGroupBy.fillna", "SeriesGroupBy.fillna is deprecated"), + ("SeriesGroupBy.idxmin", "The behavior of Series.idxmin"), + ("SeriesGroupBy.idxmax", "The behavior of Series.idxmax"), + # Docstring divides by zero to show behavior difference + ("missing.mask_zero_div_zero", "divide by zero encountered"), + ( + "to_pydatetime", + "The behavior of DatetimeProperties.to_pydatetime is deprecated", + ), + ( + "pandas.core.generic.NDFrame.bool", + "(Series|DataFrame).bool is now deprecated and will be removed " + "in future version of pandas", + ), + ( + "pandas.core.generic.NDFrame.first", + "first is deprecated and will be removed in a future version. " + "Please create a mask and filter using `.loc` instead", + ), + ( + "Resampler.fillna", + "DatetimeIndexResampler.fillna is deprecated", + ), + ( + "DataFrameGroupBy.fillna", + "DataFrameGroupBy.fillna with 'method' is deprecated", + ), + ( + "DataFrameGroupBy.fillna", + "DataFrame.fillna with 'method' is deprecated", + ), + ("read_parquet", "Passing a BlockManager to DataFrame is deprecated"), + ] + + if is_doctest: + for item in items: + for path, message in ignored_doctest_warnings: + ignore_doctest_warning(item, path, message) + + +hypothesis_health_checks = [hypothesis.HealthCheck.too_slow] +if Version(hypothesis.__version__) >= Version("6.83.2"): + hypothesis_health_checks.append(hypothesis.HealthCheck.differing_executors) + +# Hypothesis +hypothesis.settings.register_profile( + "ci", + # Hypothesis timing checks are tuned for scalars by default, so we bump + # them from 200ms to 500ms per test case as the global default. If this + # is too short for a specific test, (a) try to make it faster, and (b) + # if it really is slow add `@settings(deadline=...)` with a working value, + # or `deadline=None` to entirely disable timeouts for that test. + # 2022-02-09: Changed deadline from 500 -> None. Deadline leads to + # non-actionable, flaky CI failures (# GH 24641, 44969, 45118, 44969) + deadline=None, + suppress_health_check=tuple(hypothesis_health_checks), +) +hypothesis.settings.load_profile("ci") + +# Registering these strategies makes them globally available via st.from_type, +# which is use for offsets in tests/tseries/offsets/test_offsets_properties.py +for name in "MonthBegin MonthEnd BMonthBegin BMonthEnd".split(): + cls = getattr(pd.tseries.offsets, name) + st.register_type_strategy( + cls, st.builds(cls, n=st.integers(-99, 99), normalize=st.booleans()) + ) + +for name in "YearBegin YearEnd BYearBegin BYearEnd".split(): + cls = getattr(pd.tseries.offsets, name) + st.register_type_strategy( + cls, + st.builds( + cls, + n=st.integers(-5, 5), + normalize=st.booleans(), + month=st.integers(min_value=1, max_value=12), + ), + ) + +for name in "QuarterBegin QuarterEnd BQuarterBegin BQuarterEnd".split(): + cls = getattr(pd.tseries.offsets, name) + st.register_type_strategy( + cls, + st.builds( + cls, + n=st.integers(-24, 24), + normalize=st.booleans(), + startingMonth=st.integers(min_value=1, max_value=12), + ), + ) + + +# ---------------------------------------------------------------- +# Autouse fixtures +# ---------------------------------------------------------------- + + +# https://github.com/pytest-dev/pytest/issues/11873 +# Would like to avoid autouse=True, but cannot as of pytest 8.0.0 +@pytest.fixture(autouse=True) +def add_doctest_imports(doctest_namespace) -> None: + """ + Make `np` and `pd` names available for doctests. + """ + doctest_namespace["np"] = np + doctest_namespace["pd"] = pd + + +@pytest.fixture(autouse=True) +def configure_tests() -> None: + """ + Configure settings for all tests and test modules. + """ + pd.set_option("chained_assignment", "raise") + + +# ---------------------------------------------------------------- +# Common arguments +# ---------------------------------------------------------------- +@pytest.fixture(params=[0, 1, "index", "columns"], ids=lambda x: f"axis={repr(x)}") +def axis(request): + """ + Fixture for returning the axis numbers of a DataFrame. + """ + return request.param + + +axis_frame = axis + + +@pytest.fixture(params=[1, "columns"], ids=lambda x: f"axis={repr(x)}") +def axis_1(request): + """ + Fixture for returning aliases of axis 1 of a DataFrame. + """ + return request.param + + +@pytest.fixture(params=[True, False, None]) +def observed(request): + """ + Pass in the observed keyword to groupby for [True, False] + This indicates whether categoricals should return values for + values which are not in the grouper [False / None], or only values which + appear in the grouper [True]. [None] is supported for future compatibility + if we decide to change the default (and would need to warn if this + parameter is not passed). + """ + return request.param + + +@pytest.fixture(params=[True, False, None]) +def ordered(request): + """ + Boolean 'ordered' parameter for Categorical. + """ + return request.param + + +@pytest.fixture(params=[True, False]) +def skipna(request): + """ + Boolean 'skipna' parameter. + """ + return request.param + + +@pytest.fixture(params=["first", "last", False]) +def keep(request): + """ + Valid values for the 'keep' parameter used in + .duplicated or .drop_duplicates + """ + return request.param + + +@pytest.fixture(params=["both", "neither", "left", "right"]) +def inclusive_endpoints_fixture(request): + """ + Fixture for trying all interval 'inclusive' parameters. + """ + return request.param + + +@pytest.fixture(params=["left", "right", "both", "neither"]) +def closed(request): + """ + Fixture for trying all interval closed parameters. + """ + return request.param + + +@pytest.fixture(params=["left", "right", "both", "neither"]) +def other_closed(request): + """ + Secondary closed fixture to allow parametrizing over all pairs of closed. + """ + return request.param + + +@pytest.fixture( + params=[ + None, + "gzip", + "bz2", + "zip", + "xz", + "tar", + pytest.param("zstd", marks=td.skip_if_no("zstandard")), + ] +) +def compression(request): + """ + Fixture for trying common compression types in compression tests. + """ + return request.param + + +@pytest.fixture( + params=[ + "gzip", + "bz2", + "zip", + "xz", + "tar", + pytest.param("zstd", marks=td.skip_if_no("zstandard")), + ] +) +def compression_only(request): + """ + Fixture for trying common compression types in compression tests excluding + uncompressed case. + """ + return request.param + + +@pytest.fixture(params=[True, False]) +def writable(request): + """ + Fixture that an array is writable. + """ + return request.param + + +@pytest.fixture(params=["inner", "outer", "left", "right"]) +def join_type(request): + """ + Fixture for trying all types of join operations. + """ + return request.param + + +@pytest.fixture(params=["nlargest", "nsmallest"]) +def nselect_method(request): + """ + Fixture for trying all nselect methods. + """ + return request.param + + +# ---------------------------------------------------------------- +# Missing values & co. +# ---------------------------------------------------------------- +@pytest.fixture(params=tm.NULL_OBJECTS, ids=lambda x: type(x).__name__) +def nulls_fixture(request): + """ + Fixture for each null type in pandas. + """ + return request.param + + +nulls_fixture2 = nulls_fixture # Generate cartesian product of nulls_fixture + + +@pytest.fixture(params=[None, np.nan, pd.NaT]) +def unique_nulls_fixture(request): + """ + Fixture for each null type in pandas, each null type exactly once. + """ + return request.param + + +# Generate cartesian product of unique_nulls_fixture: +unique_nulls_fixture2 = unique_nulls_fixture + + +@pytest.fixture(params=tm.NP_NAT_OBJECTS, ids=lambda x: type(x).__name__) +def np_nat_fixture(request): + """ + Fixture for each NaT type in numpy. + """ + return request.param + + +# Generate cartesian product of np_nat_fixture: +np_nat_fixture2 = np_nat_fixture + + +# ---------------------------------------------------------------- +# Classes +# ---------------------------------------------------------------- + + +@pytest.fixture(params=[DataFrame, Series]) +def frame_or_series(request): + """ + Fixture to parametrize over DataFrame and Series. + """ + return request.param + + +@pytest.fixture(params=[Index, Series], ids=["index", "series"]) +def index_or_series(request): + """ + Fixture to parametrize over Index and Series, made necessary by a mypy + bug, giving an error: + + List item 0 has incompatible type "Type[Series]"; expected "Type[PandasObject]" + + See GH#29725 + """ + return request.param + + +# Generate cartesian product of index_or_series fixture: +index_or_series2 = index_or_series + + +@pytest.fixture(params=[Index, Series, pd.array], ids=["index", "series", "array"]) +def index_or_series_or_array(request): + """ + Fixture to parametrize over Index, Series, and ExtensionArray + """ + return request.param + + +@pytest.fixture(params=[Index, Series, DataFrame, pd.array], ids=lambda x: x.__name__) +def box_with_array(request): + """ + Fixture to test behavior for Index, Series, DataFrame, and pandas Array + classes + """ + return request.param + + +box_with_array2 = box_with_array + + +@pytest.fixture +def dict_subclass() -> type[dict]: + """ + Fixture for a dictionary subclass. + """ + + class TestSubDict(dict): + def __init__(self, *args, **kwargs) -> None: + dict.__init__(self, *args, **kwargs) + + return TestSubDict + + +@pytest.fixture +def non_dict_mapping_subclass() -> type[abc.Mapping]: + """ + Fixture for a non-mapping dictionary subclass. + """ + + class TestNonDictMapping(abc.Mapping): + def __init__(self, underlying_dict) -> None: + self._data = underlying_dict + + def __getitem__(self, key): + return self._data.__getitem__(key) + + def __iter__(self) -> Iterator: + return self._data.__iter__() + + def __len__(self) -> int: + return self._data.__len__() + + return TestNonDictMapping + + +# ---------------------------------------------------------------- +# Indices +# ---------------------------------------------------------------- +@pytest.fixture +def multiindex_year_month_day_dataframe_random_data(): + """ + DataFrame with 3 level MultiIndex (year, month, day) covering + first 100 business days from 2000-01-01 with random data + """ + tdf = DataFrame( + np.random.default_rng(2).standard_normal((100, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=100, freq="B"), + ) + ymd = tdf.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum() + # use int64 Index, to make sure things work + ymd.index = ymd.index.set_levels([lev.astype("i8") for lev in ymd.index.levels]) + ymd.index.set_names(["year", "month", "day"], inplace=True) + return ymd + + +@pytest.fixture +def lexsorted_two_level_string_multiindex() -> MultiIndex: + """ + 2-level MultiIndex, lexsorted, with string names. + """ + return MultiIndex( + levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]], + codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=["first", "second"], + ) + + +@pytest.fixture +def multiindex_dataframe_random_data( + lexsorted_two_level_string_multiindex, +) -> DataFrame: + """DataFrame with 2 level MultiIndex with random data""" + index = lexsorted_two_level_string_multiindex + return DataFrame( + np.random.default_rng(2).standard_normal((10, 3)), + index=index, + columns=Index(["A", "B", "C"], name="exp"), + ) + + +def _create_multiindex(): + """ + MultiIndex used to test the general functionality of this object + """ + + # See Also: tests.multi.conftest.idx + major_axis = Index(["foo", "bar", "baz", "qux"]) + minor_axis = Index(["one", "two"]) + + major_codes = np.array([0, 0, 1, 2, 3, 3]) + minor_codes = np.array([0, 1, 0, 1, 0, 1]) + index_names = ["first", "second"] + return MultiIndex( + levels=[major_axis, minor_axis], + codes=[major_codes, minor_codes], + names=index_names, + verify_integrity=False, + ) + + +def _create_mi_with_dt64tz_level(): + """ + MultiIndex with a level that is a tzaware DatetimeIndex. + """ + # GH#8367 round trip with pickle + return MultiIndex.from_product( + [[1, 2], ["a", "b"], date_range("20130101", periods=3, tz="US/Eastern")], + names=["one", "two", "three"], + ) + + +indices_dict = { + "object": Index([f"pandas_{i}" for i in range(100)], dtype=object), + "string": Index([f"pandas_{i}" for i in range(100)], dtype="str"), + "datetime": date_range("2020-01-01", periods=100), + "datetime-tz": date_range("2020-01-01", periods=100, tz="US/Pacific"), + "period": period_range("2020-01-01", periods=100, freq="D"), + "timedelta": timedelta_range(start="1 day", periods=100, freq="D"), + "range": RangeIndex(100), + "int8": Index(np.arange(100), dtype="int8"), + "int16": Index(np.arange(100), dtype="int16"), + "int32": Index(np.arange(100), dtype="int32"), + "int64": Index(np.arange(100), dtype="int64"), + "uint8": Index(np.arange(100), dtype="uint8"), + "uint16": Index(np.arange(100), dtype="uint16"), + "uint32": Index(np.arange(100), dtype="uint32"), + "uint64": Index(np.arange(100), dtype="uint64"), + "float32": Index(np.arange(100), dtype="float32"), + "float64": Index(np.arange(100), dtype="float64"), + "bool-object": Index([True, False] * 5, dtype=object), + "bool-dtype": Index([True, False] * 5, dtype=bool), + "complex64": Index( + np.arange(100, dtype="complex64") + 1.0j * np.arange(100, dtype="complex64") + ), + "complex128": Index( + np.arange(100, dtype="complex128") + 1.0j * np.arange(100, dtype="complex128") + ), + "categorical": CategoricalIndex(list("abcd") * 25), + "interval": IntervalIndex.from_breaks(np.linspace(0, 100, num=101)), + "empty": Index([]), + "tuples": MultiIndex.from_tuples(zip(["foo", "bar", "baz"], [1, 2, 3])), + "mi-with-dt64tz-level": _create_mi_with_dt64tz_level(), + "multi": _create_multiindex(), + "repeats": Index([0, 0, 1, 1, 2, 2]), + "nullable_int": Index(np.arange(100), dtype="Int64"), + "nullable_uint": Index(np.arange(100), dtype="UInt16"), + "nullable_float": Index(np.arange(100), dtype="Float32"), + "nullable_bool": Index(np.arange(100).astype(bool), dtype="boolean"), + "string-python": Index( + pd.array([f"pandas_{i}" for i in range(100)], dtype="string[python]") + ), +} +if has_pyarrow: + idx = Index(pd.array([f"pandas_{i}" for i in range(100)], dtype="string[pyarrow]")) + indices_dict["string-pyarrow"] = idx + + +@pytest.fixture(params=indices_dict.keys()) +def index(request): + """ + Fixture for many "simple" kinds of indices. + + These indices are unlikely to cover corner cases, e.g. + - no names + - no NaTs/NaNs + - no values near implementation bounds + - ... + """ + # copy to avoid mutation, e.g. setting .name + return indices_dict[request.param].copy() + + +# Needed to generate cartesian product of indices +index_fixture2 = index + + +@pytest.fixture( + params=[ + key for key, value in indices_dict.items() if not isinstance(value, MultiIndex) + ] +) +def index_flat(request): + """ + index fixture, but excluding MultiIndex cases. + """ + key = request.param + return indices_dict[key].copy() + + +# Alias so we can test with cartesian product of index_flat +index_flat2 = index_flat + + +@pytest.fixture( + params=[ + key + for key, value in indices_dict.items() + if not ( + key.startswith(("int", "uint", "float")) + or key in ["range", "empty", "repeats", "bool-dtype"] + ) + and not isinstance(value, MultiIndex) + ] +) +def index_with_missing(request): + """ + Fixture for indices with missing values. + + Integer-dtype and empty cases are excluded because they cannot hold missing + values. + + MultiIndex is excluded because isna() is not defined for MultiIndex. + """ + + # GH 35538. Use deep copy to avoid illusive bug on np-dev + # GHA pipeline that writes into indices_dict despite copy + ind = indices_dict[request.param].copy(deep=True) + vals = ind.values.copy() + if request.param in ["tuples", "mi-with-dt64tz-level", "multi"]: + # For setting missing values in the top level of MultiIndex + vals = ind.tolist() + vals[0] = (None,) + vals[0][1:] + vals[-1] = (None,) + vals[-1][1:] + return MultiIndex.from_tuples(vals) + else: + vals[0] = None + vals[-1] = None + return type(ind)(vals) + + +# ---------------------------------------------------------------- +# Series' +# ---------------------------------------------------------------- +@pytest.fixture +def string_series() -> Series: + """ + Fixture for Series of floats with Index of unique strings + """ + return Series( + np.arange(30, dtype=np.float64) * 1.1, + index=Index([f"i_{i}" for i in range(30)]), + name="series", + ) + + +@pytest.fixture +def object_series() -> Series: + """ + Fixture for Series of dtype object with Index of unique strings + """ + data = [f"foo_{i}" for i in range(30)] + index = Index([f"bar_{i}" for i in range(30)]) + return Series(data, index=index, name="objects", dtype=object) + + +@pytest.fixture +def datetime_series() -> Series: + """ + Fixture for Series of floats with DatetimeIndex + """ + return Series( + np.random.default_rng(2).standard_normal(30), + index=date_range("2000-01-01", periods=30, freq="B"), + name="ts", + ) + + +def _create_series(index): + """Helper for the _series dict""" + size = len(index) + data = np.random.default_rng(2).standard_normal(size) + return Series(data, index=index, name="a", copy=False) + + +_series = { + f"series-with-{index_id}-index": _create_series(index) + for index_id, index in indices_dict.items() +} + + +@pytest.fixture +def series_with_simple_index(index) -> Series: + """ + Fixture for tests on series with changing types of indices. + """ + return _create_series(index) + + +_narrow_series = { + f"{dtype.__name__}-series": Series( + range(30), index=[f"i-{i}" for i in range(30)], name="a", dtype=dtype + ) + for dtype in tm.NARROW_NP_DTYPES +} + + +_index_or_series_objs = {**indices_dict, **_series, **_narrow_series} + + +@pytest.fixture(params=_index_or_series_objs.keys()) +def index_or_series_obj(request): + """ + Fixture for tests on indexes, series and series with a narrow dtype + copy to avoid mutation, e.g. setting .name + """ + return _index_or_series_objs[request.param].copy(deep=True) + + +_typ_objects_series = { + f"{dtype.__name__}-series": Series(dtype) for dtype in tm.PYTHON_DATA_TYPES +} + + +_index_or_series_memory_objs = { + **indices_dict, + **_series, + **_narrow_series, + **_typ_objects_series, +} + + +@pytest.fixture(params=_index_or_series_memory_objs.keys()) +def index_or_series_memory_obj(request): + """ + Fixture for tests on indexes, series, series with a narrow dtype and + series with empty objects type + copy to avoid mutation, e.g. setting .name + """ + return _index_or_series_memory_objs[request.param].copy(deep=True) + + +# ---------------------------------------------------------------- +# DataFrames +# ---------------------------------------------------------------- +@pytest.fixture +def int_frame() -> DataFrame: + """ + Fixture for DataFrame of ints with index of unique strings + + Columns are ['A', 'B', 'C', 'D'] + """ + return DataFrame( + np.ones((30, 4), dtype=np.int64), + index=Index([f"foo_{i}" for i in range(30)]), + columns=Index(list("ABCD")), + ) + + +@pytest.fixture +def float_frame() -> DataFrame: + """ + Fixture for DataFrame of floats with index of unique strings + + Columns are ['A', 'B', 'C', 'D']. + """ + return DataFrame( + np.random.default_rng(2).standard_normal((30, 4)), + index=Index([f"foo_{i}" for i in range(30)]), + columns=Index(list("ABCD")), + ) + + +@pytest.fixture +def rand_series_with_duplicate_datetimeindex() -> Series: + """ + Fixture for Series with a DatetimeIndex that has duplicates. + """ + dates = [ + datetime(2000, 1, 2), + datetime(2000, 1, 2), + datetime(2000, 1, 2), + datetime(2000, 1, 3), + datetime(2000, 1, 3), + datetime(2000, 1, 3), + datetime(2000, 1, 4), + datetime(2000, 1, 4), + datetime(2000, 1, 4), + datetime(2000, 1, 5), + ] + + return Series(np.random.default_rng(2).standard_normal(len(dates)), index=dates) + + +# ---------------------------------------------------------------- +# Scalars +# ---------------------------------------------------------------- +@pytest.fixture( + params=[ + (Interval(left=0, right=5), IntervalDtype("int64", "right")), + (Interval(left=0.1, right=0.5), IntervalDtype("float64", "right")), + (Period("2012-01", freq="M"), "period[M]"), + (Period("2012-02-01", freq="D"), "period[D]"), + ( + Timestamp("2011-01-01", tz="US/Eastern"), + DatetimeTZDtype(unit="s", tz="US/Eastern"), + ), + (Timedelta(seconds=500), "timedelta64[ns]"), + ] +) +def ea_scalar_and_dtype(request): + return request.param + + +# ---------------------------------------------------------------- +# Operators & Operations +# ---------------------------------------------------------------- + + +@pytest.fixture(params=tm.arithmetic_dunder_methods) +def all_arithmetic_operators(request): + """ + Fixture for dunder names for common arithmetic operations. + """ + return request.param + + +@pytest.fixture( + params=[ + operator.add, + ops.radd, + operator.sub, + ops.rsub, + operator.mul, + ops.rmul, + operator.truediv, + ops.rtruediv, + operator.floordiv, + ops.rfloordiv, + operator.mod, + ops.rmod, + operator.pow, + ops.rpow, + operator.eq, + operator.ne, + operator.lt, + operator.le, + operator.gt, + operator.ge, + operator.and_, + ops.rand_, + operator.xor, + ops.rxor, + operator.or_, + ops.ror_, + ] +) +def all_binary_operators(request): + """ + Fixture for operator and roperator arithmetic, comparison, and logical ops. + """ + return request.param + + +@pytest.fixture( + params=[ + operator.add, + ops.radd, + operator.sub, + ops.rsub, + operator.mul, + ops.rmul, + operator.truediv, + ops.rtruediv, + operator.floordiv, + ops.rfloordiv, + operator.mod, + ops.rmod, + operator.pow, + ops.rpow, + ] +) +def all_arithmetic_functions(request): + """ + Fixture for operator and roperator arithmetic functions. + + Notes + ----- + This includes divmod and rdivmod, whereas all_arithmetic_operators + does not. + """ + return request.param + + +_all_numeric_reductions = [ + "count", + "sum", + "max", + "min", + "mean", + "prod", + "std", + "var", + "median", + "kurt", + "skew", + "sem", +] + + +@pytest.fixture(params=_all_numeric_reductions) +def all_numeric_reductions(request): + """ + Fixture for numeric reduction names. + """ + return request.param + + +_all_boolean_reductions = ["all", "any"] + + +@pytest.fixture(params=_all_boolean_reductions) +def all_boolean_reductions(request): + """ + Fixture for boolean reduction names. + """ + return request.param + + +_all_reductions = _all_numeric_reductions + _all_boolean_reductions + + +@pytest.fixture(params=_all_reductions) +def all_reductions(request): + """ + Fixture for all (boolean + numeric) reduction names. + """ + return request.param + + +@pytest.fixture( + params=[ + operator.eq, + operator.ne, + operator.gt, + operator.ge, + operator.lt, + operator.le, + ] +) +def comparison_op(request): + """ + Fixture for operator module comparison functions. + """ + return request.param + + +@pytest.fixture(params=["__le__", "__lt__", "__ge__", "__gt__"]) +def compare_operators_no_eq_ne(request): + """ + Fixture for dunder names for compare operations except == and != + + * >= + * > + * < + * <= + """ + return request.param + + +@pytest.fixture( + params=["__and__", "__rand__", "__or__", "__ror__", "__xor__", "__rxor__"] +) +def all_logical_operators(request): + """ + Fixture for dunder names for common logical operations + + * | + * & + * ^ + """ + return request.param + + +_all_numeric_accumulations = ["cumsum", "cumprod", "cummin", "cummax"] + + +@pytest.fixture(params=_all_numeric_accumulations) +def all_numeric_accumulations(request): + """ + Fixture for numeric accumulation names + """ + return request.param + + +# ---------------------------------------------------------------- +# Data sets/files +# ---------------------------------------------------------------- +@pytest.fixture +def strict_data_files(pytestconfig): + """ + Returns the configuration for the test setting `--no-strict-data-files`. + """ + return pytestconfig.getoption("--no-strict-data-files") + + +@pytest.fixture +def datapath(strict_data_files: str) -> Callable[..., str]: + """ + Get the path to a data file. + + Parameters + ---------- + path : str + Path to the file, relative to ``pandas/tests/`` + + Returns + ------- + path including ``pandas/tests``. + + Raises + ------ + ValueError + If the path doesn't exist and the --no-strict-data-files option is not set. + """ + BASE_PATH = os.path.join(os.path.dirname(__file__), "tests") + + def deco(*args): + path = os.path.join(BASE_PATH, *args) + if not os.path.exists(path): + if strict_data_files: + raise ValueError( + f"Could not find file {path} and --no-strict-data-files is not set." + ) + pytest.skip(f"Could not find {path}.") + return path + + return deco + + +# ---------------------------------------------------------------- +# Time zones +# ---------------------------------------------------------------- +TIMEZONES = [ + None, + "UTC", + "US/Eastern", + "Asia/Tokyo", + "dateutil/US/Pacific", + "dateutil/Asia/Singapore", + "+01:15", + "-02:15", + "UTC+01:15", + "UTC-02:15", + tzutc(), + tzlocal(), + FixedOffset(300), + FixedOffset(0), + FixedOffset(-300), + timezone.utc, + timezone(timedelta(hours=1)), + timezone(timedelta(hours=-1), name="foo"), +] +if zoneinfo is not None: + TIMEZONES.extend( + [ + zoneinfo.ZoneInfo("US/Pacific"), # type: ignore[list-item] + zoneinfo.ZoneInfo("UTC"), # type: ignore[list-item] + ] + ) +TIMEZONE_IDS = [repr(i) for i in TIMEZONES] + + +@td.parametrize_fixture_doc(str(TIMEZONE_IDS)) +@pytest.fixture(params=TIMEZONES, ids=TIMEZONE_IDS) +def tz_naive_fixture(request): + """ + Fixture for trying timezones including default (None): {0} + """ + return request.param + + +@td.parametrize_fixture_doc(str(TIMEZONE_IDS[1:])) +@pytest.fixture(params=TIMEZONES[1:], ids=TIMEZONE_IDS[1:]) +def tz_aware_fixture(request): + """ + Fixture for trying explicit timezones: {0} + """ + return request.param + + +# Generate cartesian product of tz_aware_fixture: +tz_aware_fixture2 = tz_aware_fixture + + +_UTCS = ["utc", "dateutil/UTC", utc, tzutc(), timezone.utc] +if zoneinfo is not None: + _UTCS.append(zoneinfo.ZoneInfo("UTC")) + + +@pytest.fixture(params=_UTCS) +def utc_fixture(request): + """ + Fixture to provide variants of UTC timezone strings and tzinfo objects. + """ + return request.param + + +utc_fixture2 = utc_fixture + + +@pytest.fixture(params=["s", "ms", "us", "ns"]) +def unit(request): + """ + datetime64 units we support. + """ + return request.param + + +unit2 = unit + + +# ---------------------------------------------------------------- +# Dtypes +# ---------------------------------------------------------------- +@pytest.fixture(params=tm.STRING_DTYPES) +def string_dtype(request): + """ + Parametrized fixture for string dtypes. + + * str + * 'str' + * 'U' + """ + return request.param + + +@pytest.fixture( + params=[ + ("python", pd.NA), + pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")), + pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")), + ("python", np.nan), + ], + ids=[ + "string=string[python]", + "string=string[pyarrow]", + "string=str[pyarrow]", + "string=str[python]", + ], +) +def string_dtype_no_object(request): + """ + Parametrized fixture for string dtypes. + * 'string[python]' (NA variant) + * 'string[pyarrow]' (NA variant) + * 'str' (NaN variant, with pyarrow) + * 'str' (NaN variant, without pyarrow) + """ + # need to instantiate the StringDtype here instead of in the params + # to avoid importing pyarrow during test collection + storage, na_value = request.param + return pd.StringDtype(storage, na_value) + + +@pytest.fixture( + params=[ + "string[python]", + pytest.param("string[pyarrow]", marks=td.skip_if_no("pyarrow")), + ] +) +def nullable_string_dtype(request): + """ + Parametrized fixture for string dtypes. + + * 'string[python]' + * 'string[pyarrow]' + """ + return request.param + + +@pytest.fixture( + params=[ + pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")), + pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")), + ] +) +def pyarrow_string_dtype(request): + """ + Parametrized fixture for string dtypes backed by Pyarrow. + + * 'str[pyarrow]' + * 'string[pyarrow]' + """ + return pd.StringDtype(*request.param) + + +@pytest.fixture( + params=[ + "python", + pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")), + ] +) +def string_storage(request): + """ + Parametrized fixture for pd.options.mode.string_storage. + + * 'python' + * 'pyarrow' + """ + return request.param + + +@pytest.fixture( + params=[ + ("python", pd.NA), + pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")), + pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")), + ("python", np.nan), + ], + ids=[ + "string=string[python]", + "string=string[pyarrow]", + "string=str[pyarrow]", + "string=str[python]", + ], +) +def string_dtype_arguments(request): + """ + Parametrized fixture for StringDtype storage and na_value. + + * 'python' + pd.NA + * 'pyarrow' + pd.NA + * 'pyarrow' + np.nan + """ + return request.param + + +@pytest.fixture( + params=[ + "numpy_nullable", + pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")), + ] +) +def dtype_backend(request): + """ + Parametrized fixture for pd.options.mode.string_storage. + + * 'python' + * 'pyarrow' + """ + return request.param + + +# Alias so we can test with cartesian product of string_storage +string_storage2 = string_storage +string_dtype_arguments2 = string_dtype_arguments + + +@pytest.fixture(params=tm.BYTES_DTYPES) +def bytes_dtype(request): + """ + Parametrized fixture for bytes dtypes. + + * bytes + * 'bytes' + """ + return request.param + + +@pytest.fixture(params=tm.OBJECT_DTYPES) +def object_dtype(request): + """ + Parametrized fixture for object dtypes. + + * object + * 'object' + """ + return request.param + + +@pytest.fixture( + params=[ + np.dtype("object"), + ("python", pd.NA), + pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")), + pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")), + ("python", np.nan), + ], + ids=[ + "string=object", + "string=string[python]", + "string=string[pyarrow]", + "string=str[pyarrow]", + "string=str[python]", + ], +) +def any_string_dtype(request): + """ + Parametrized fixture for string dtypes. + * 'object' + * 'string[python]' (NA variant) + * 'string[pyarrow]' (NA variant) + * 'str' (NaN variant, with pyarrow) + * 'str' (NaN variant, without pyarrow) + """ + if isinstance(request.param, np.dtype): + return request.param + else: + # need to instantiate the StringDtype here instead of in the params + # to avoid importing pyarrow during test collection + storage, na_value = request.param + return pd.StringDtype(storage, na_value) + + +@pytest.fixture(params=tm.DATETIME64_DTYPES) +def datetime64_dtype(request): + """ + Parametrized fixture for datetime64 dtypes. + + * 'datetime64[ns]' + * 'M8[ns]' + """ + return request.param + + +@pytest.fixture(params=tm.TIMEDELTA64_DTYPES) +def timedelta64_dtype(request): + """ + Parametrized fixture for timedelta64 dtypes. + + * 'timedelta64[ns]' + * 'm8[ns]' + """ + return request.param + + +@pytest.fixture +def fixed_now_ts() -> Timestamp: + """ + Fixture emits fixed Timestamp.now() + """ + return Timestamp( # pyright: ignore[reportGeneralTypeIssues] + year=2021, month=1, day=1, hour=12, minute=4, second=13, microsecond=22 + ) + + +@pytest.fixture(params=tm.FLOAT_NUMPY_DTYPES) +def float_numpy_dtype(request): + """ + Parameterized fixture for float dtypes. + + * float + * 'float32' + * 'float64' + """ + return request.param + + +@pytest.fixture(params=tm.FLOAT_EA_DTYPES) +def float_ea_dtype(request): + """ + Parameterized fixture for float dtypes. + + * 'Float32' + * 'Float64' + """ + return request.param + + +@pytest.fixture(params=tm.ALL_FLOAT_DTYPES) +def any_float_dtype(request): + """ + Parameterized fixture for float dtypes. + + * float + * 'float32' + * 'float64' + * 'Float32' + * 'Float64' + """ + return request.param + + +@pytest.fixture(params=tm.COMPLEX_DTYPES) +def complex_dtype(request): + """ + Parameterized fixture for complex dtypes. + + * complex + * 'complex64' + * 'complex128' + """ + return request.param + + +@pytest.fixture(params=tm.COMPLEX_FLOAT_DTYPES) +def complex_or_float_dtype(request): + """ + Parameterized fixture for complex and numpy float dtypes. + + * complex + * 'complex64' + * 'complex128' + * float + * 'float32' + * 'float64' + """ + return request.param + + +@pytest.fixture(params=tm.SIGNED_INT_NUMPY_DTYPES) +def any_signed_int_numpy_dtype(request): + """ + Parameterized fixture for signed integer dtypes. + + * int + * 'int8' + * 'int16' + * 'int32' + * 'int64' + """ + return request.param + + +@pytest.fixture(params=tm.UNSIGNED_INT_NUMPY_DTYPES) +def any_unsigned_int_numpy_dtype(request): + """ + Parameterized fixture for unsigned integer dtypes. + + * 'uint8' + * 'uint16' + * 'uint32' + * 'uint64' + """ + return request.param + + +@pytest.fixture(params=tm.ALL_INT_NUMPY_DTYPES) +def any_int_numpy_dtype(request): + """ + Parameterized fixture for any integer dtype. + + * int + * 'int8' + * 'uint8' + * 'int16' + * 'uint16' + * 'int32' + * 'uint32' + * 'int64' + * 'uint64' + """ + return request.param + + +@pytest.fixture(params=tm.ALL_INT_EA_DTYPES) +def any_int_ea_dtype(request): + """ + Parameterized fixture for any nullable integer dtype. + + * 'UInt8' + * 'Int8' + * 'UInt16' + * 'Int16' + * 'UInt32' + * 'Int32' + * 'UInt64' + * 'Int64' + """ + return request.param + + +@pytest.fixture(params=tm.ALL_INT_DTYPES) +def any_int_dtype(request): + """ + Parameterized fixture for any nullable integer dtype. + + * int + * 'int8' + * 'uint8' + * 'int16' + * 'uint16' + * 'int32' + * 'uint32' + * 'int64' + * 'uint64' + * 'UInt8' + * 'Int8' + * 'UInt16' + * 'Int16' + * 'UInt32' + * 'Int32' + * 'UInt64' + * 'Int64' + """ + return request.param + + +@pytest.fixture(params=tm.ALL_INT_EA_DTYPES + tm.FLOAT_EA_DTYPES) +def any_numeric_ea_dtype(request): + """ + Parameterized fixture for any nullable integer dtype and + any float ea dtypes. + + * 'UInt8' + * 'Int8' + * 'UInt16' + * 'Int16' + * 'UInt32' + * 'Int32' + * 'UInt64' + * 'Int64' + * 'Float32' + * 'Float64' + """ + return request.param + + +# Unsupported operand types for + ("List[Union[str, ExtensionDtype, dtype[Any], +# Type[object]]]" and "List[str]") +@pytest.fixture( + params=tm.ALL_INT_EA_DTYPES + + tm.FLOAT_EA_DTYPES + + tm.ALL_INT_PYARROW_DTYPES_STR_REPR + + tm.FLOAT_PYARROW_DTYPES_STR_REPR # type: ignore[operator] +) +def any_numeric_ea_and_arrow_dtype(request): + """ + Parameterized fixture for any nullable integer dtype and + any float ea dtypes. + + * 'UInt8' + * 'Int8' + * 'UInt16' + * 'Int16' + * 'UInt32' + * 'Int32' + * 'UInt64' + * 'Int64' + * 'Float32' + * 'Float64' + * 'uint8[pyarrow]' + * 'int8[pyarrow]' + * 'uint16[pyarrow]' + * 'int16[pyarrow]' + * 'uint32[pyarrow]' + * 'int32[pyarrow]' + * 'uint64[pyarrow]' + * 'int64[pyarrow]' + * 'float32[pyarrow]' + * 'float64[pyarrow]' + """ + return request.param + + +@pytest.fixture(params=tm.SIGNED_INT_EA_DTYPES) +def any_signed_int_ea_dtype(request): + """ + Parameterized fixture for any signed nullable integer dtype. + + * 'Int8' + * 'Int16' + * 'Int32' + * 'Int64' + """ + return request.param + + +@pytest.fixture(params=tm.ALL_REAL_NUMPY_DTYPES) +def any_real_numpy_dtype(request): + """ + Parameterized fixture for any (purely) real numeric dtype. + + * int + * 'int8' + * 'uint8' + * 'int16' + * 'uint16' + * 'int32' + * 'uint32' + * 'int64' + * 'uint64' + * float + * 'float32' + * 'float64' + """ + return request.param + + +@pytest.fixture(params=tm.ALL_REAL_DTYPES) +def any_real_numeric_dtype(request): + """ + Parameterized fixture for any (purely) real numeric dtype. + + * int + * 'int8' + * 'uint8' + * 'int16' + * 'uint16' + * 'int32' + * 'uint32' + * 'int64' + * 'uint64' + * float + * 'float32' + * 'float64' + + and associated ea dtypes. + """ + return request.param + + +@pytest.fixture(params=tm.ALL_NUMPY_DTYPES) +def any_numpy_dtype(request): + """ + Parameterized fixture for all numpy dtypes. + + * bool + * 'bool' + * int + * 'int8' + * 'uint8' + * 'int16' + * 'uint16' + * 'int32' + * 'uint32' + * 'int64' + * 'uint64' + * float + * 'float32' + * 'float64' + * complex + * 'complex64' + * 'complex128' + * str + * 'str' + * 'U' + * bytes + * 'bytes' + * 'datetime64[ns]' + * 'M8[ns]' + * 'timedelta64[ns]' + * 'm8[ns]' + * object + * 'object' + """ + return request.param + + +@pytest.fixture(params=tm.ALL_REAL_NULLABLE_DTYPES) +def any_real_nullable_dtype(request): + """ + Parameterized fixture for all real dtypes that can hold NA. + + * float + * 'float32' + * 'float64' + * 'Float32' + * 'Float64' + * 'UInt8' + * 'UInt16' + * 'UInt32' + * 'UInt64' + * 'Int8' + * 'Int16' + * 'Int32' + * 'Int64' + * 'uint8[pyarrow]' + * 'uint16[pyarrow]' + * 'uint32[pyarrow]' + * 'uint64[pyarrow]' + * 'int8[pyarrow]' + * 'int16[pyarrow]' + * 'int32[pyarrow]' + * 'int64[pyarrow]' + * 'float[pyarrow]' + * 'double[pyarrow]' + """ + return request.param + + +@pytest.fixture(params=tm.ALL_NUMERIC_DTYPES) +def any_numeric_dtype(request): + """ + Parameterized fixture for all numeric dtypes. + + * int + * 'int8' + * 'uint8' + * 'int16' + * 'uint16' + * 'int32' + * 'uint32' + * 'int64' + * 'uint64' + * float + * 'float32' + * 'float64' + * complex + * 'complex64' + * 'complex128' + * 'UInt8' + * 'Int8' + * 'UInt16' + * 'Int16' + * 'UInt32' + * 'Int32' + * 'UInt64' + * 'Int64' + * 'Float32' + * 'Float64' + """ + return request.param + + +# categoricals are handled separately +_any_skipna_inferred_dtype = [ + ("string", ["a", np.nan, "c"]), + ("string", ["a", pd.NA, "c"]), + ("mixed", ["a", pd.NaT, "c"]), # pd.NaT not considered valid by is_string_array + ("bytes", [b"a", np.nan, b"c"]), + ("empty", [np.nan, np.nan, np.nan]), + ("empty", []), + ("mixed-integer", ["a", np.nan, 2]), + ("mixed", ["a", np.nan, 2.0]), + ("floating", [1.0, np.nan, 2.0]), + ("integer", [1, np.nan, 2]), + ("mixed-integer-float", [1, np.nan, 2.0]), + ("decimal", [Decimal(1), np.nan, Decimal(2)]), + ("boolean", [True, np.nan, False]), + ("boolean", [True, pd.NA, False]), + ("datetime64", [np.datetime64("2013-01-01"), np.nan, np.datetime64("2018-01-01")]), + ("datetime", [Timestamp("20130101"), np.nan, Timestamp("20180101")]), + ("date", [date(2013, 1, 1), np.nan, date(2018, 1, 1)]), + ("complex", [1 + 1j, np.nan, 2 + 2j]), + # The following dtype is commented out due to GH 23554 + # ('timedelta64', [np.timedelta64(1, 'D'), + # np.nan, np.timedelta64(2, 'D')]), + ("timedelta", [timedelta(1), np.nan, timedelta(2)]), + ("time", [time(1), np.nan, time(2)]), + ("period", [Period(2013), pd.NaT, Period(2018)]), + ("interval", [Interval(0, 1), np.nan, Interval(0, 2)]), +] +ids, _ = zip(*_any_skipna_inferred_dtype) # use inferred type as fixture-id + + +@pytest.fixture(params=_any_skipna_inferred_dtype, ids=ids) +def any_skipna_inferred_dtype(request): + """ + Fixture for all inferred dtypes from _libs.lib.infer_dtype + + The covered (inferred) types are: + * 'string' + * 'empty' + * 'bytes' + * 'mixed' + * 'mixed-integer' + * 'mixed-integer-float' + * 'floating' + * 'integer' + * 'decimal' + * 'boolean' + * 'datetime64' + * 'datetime' + * 'date' + * 'timedelta' + * 'time' + * 'period' + * 'interval' + + Returns + ------- + inferred_dtype : str + The string for the inferred dtype from _libs.lib.infer_dtype + values : np.ndarray + An array of object dtype that will be inferred to have + `inferred_dtype` + + Examples + -------- + >>> from pandas._libs import lib + >>> + >>> def test_something(any_skipna_inferred_dtype): + ... inferred_dtype, values = any_skipna_inferred_dtype + ... # will pass + ... assert lib.infer_dtype(values, skipna=True) == inferred_dtype + """ + inferred_dtype, values = request.param + values = np.array(values, dtype=object) # object dtype to avoid casting + + # correctness of inference tested in tests/dtypes/test_inference.py + return inferred_dtype, values + + +# ---------------------------------------------------------------- +# Misc +# ---------------------------------------------------------------- +@pytest.fixture +def ip(): + """ + Get an instance of IPython.InteractiveShell. + + Will raise a skip if IPython is not installed. + """ + pytest.importorskip("IPython", minversion="6.0.0") + from IPython.core.interactiveshell import InteractiveShell + + # GH#35711 make sure sqlite history file handle is not leaked + from traitlets.config import Config # isort:skip + + c = Config() + c.HistoryManager.hist_file = ":memory:" + + return InteractiveShell(config=c) + + +@pytest.fixture(params=["bsr", "coo", "csc", "csr", "dia", "dok", "lil"]) +def spmatrix(request): + """ + Yields scipy sparse matrix classes. + """ + sparse = pytest.importorskip("scipy.sparse") + + return getattr(sparse, request.param + "_matrix") + + +@pytest.fixture( + params=[ + getattr(pd.offsets, o) + for o in pd.offsets.__all__ + if issubclass(getattr(pd.offsets, o), pd.offsets.Tick) and o != "Tick" + ] +) +def tick_classes(request): + """ + Fixture for Tick based datetime offsets available for a time series. + """ + return request.param + + +@pytest.fixture(params=[None, lambda x: x]) +def sort_by_key(request): + """ + Simple fixture for testing keys in sorting methods. + Tests None (no key) and the identity key. + """ + return request.param + + +@pytest.fixture( + params=[ + ("foo", None, None), + ("Egon", "Venkman", None), + ("NCC1701D", "NCC1701D", "NCC1701D"), + # possibly-matching NAs + (np.nan, np.nan, np.nan), + (np.nan, pd.NaT, None), + (np.nan, pd.NA, None), + (pd.NA, pd.NA, pd.NA), + ] +) +def names(request) -> tuple[Hashable, Hashable, Hashable]: + """ + A 3-tuple of names, the first two for operands, the last for a result. + """ + return request.param + + +@pytest.fixture(params=[tm.setitem, tm.loc, tm.iloc]) +def indexer_sli(request): + """ + Parametrize over __setitem__, loc.__setitem__, iloc.__setitem__ + """ + return request.param + + +@pytest.fixture(params=[tm.loc, tm.iloc]) +def indexer_li(request): + """ + Parametrize over loc.__getitem__, iloc.__getitem__ + """ + return request.param + + +@pytest.fixture(params=[tm.setitem, tm.iloc]) +def indexer_si(request): + """ + Parametrize over __setitem__, iloc.__setitem__ + """ + return request.param + + +@pytest.fixture(params=[tm.setitem, tm.loc]) +def indexer_sl(request): + """ + Parametrize over __setitem__, loc.__setitem__ + """ + return request.param + + +@pytest.fixture(params=[tm.at, tm.loc]) +def indexer_al(request): + """ + Parametrize over at.__setitem__, loc.__setitem__ + """ + return request.param + + +@pytest.fixture(params=[tm.iat, tm.iloc]) +def indexer_ial(request): + """ + Parametrize over iat.__setitem__, iloc.__setitem__ + """ + return request.param + + +@pytest.fixture +def using_array_manager() -> bool: + """ + Fixture to check if the array manager is being used. + """ + return _get_option("mode.data_manager", silent=True) == "array" + + +@pytest.fixture +def using_copy_on_write() -> bool: + """ + Fixture to check if Copy-on-Write is enabled. + """ + return ( + pd.options.mode.copy_on_write is True + and _get_option("mode.data_manager", silent=True) == "block" + ) + + +@pytest.fixture +def warn_copy_on_write() -> bool: + """ + Fixture to check if Copy-on-Write is in warning mode. + """ + return ( + pd.options.mode.copy_on_write == "warn" + and _get_option("mode.data_manager", silent=True) == "block" + ) + + +@pytest.fixture +def using_infer_string() -> bool: + """ + Fixture to check if infer string option is enabled. + """ + return pd.options.future.infer_string is True + + +warsaws = ["Europe/Warsaw", "dateutil/Europe/Warsaw"] +if zoneinfo is not None: + warsaws.append(zoneinfo.ZoneInfo("Europe/Warsaw")) # type: ignore[arg-type] + + +@pytest.fixture(params=warsaws) +def warsaw(request) -> str: + """ + tzinfo for Europe/Warsaw using pytz, dateutil, or zoneinfo. + """ + return request.param + + +@pytest.fixture() +def arrow_string_storage(): + return ("pyarrow", "pyarrow_numpy") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/pyproject.toml b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..085d5c3bce07a47ba879eec5b0e4c78fc3e28b89 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/pyproject.toml @@ -0,0 +1,818 @@ +[build-system] +# Minimum requirements for the build system to execute. +# See https://github.com/scipy/scipy/pull/12940 for the AIX issue. +requires = [ + "meson-python>=0.13.1", + "meson>=1.2.1,<2", + "wheel", + "Cython<4.0.0a0", # Note: sync with setup.py, environment.yml and asv.conf.json + # Force numpy higher than 2.0rc1, so that built wheels are compatible + # with both numpy 1 and 2 + "numpy>=2.0", + "versioneer[toml]" +] + +build-backend = "mesonpy" + +[project] +name = 'pandas' +dynamic = [ + 'version' +] +description = 'Powerful data structures for data analysis, time series, and statistics' +readme = 'README.md' +authors = [ + { name = 'The Pandas Development Team', email='pandas-dev@python.org' }, +] +license = {file = 'LICENSE'} +requires-python = '>=3.9' +dependencies = [ + "numpy>=1.22.4; python_version<'3.11'", + "numpy>=1.23.2; python_version=='3.11'", + "numpy>=1.26.0; python_version>='3.12'", + "python-dateutil>=2.8.2", + "pytz>=2020.1", + "tzdata>=2022.7" +] +classifiers = [ + 'Development Status :: 5 - Production/Stable', + 'Environment :: Console', + 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: BSD License', + 'Operating System :: OS Independent', + 'Programming Language :: Cython', + 'Programming Language :: Python', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3 :: Only', + 'Programming Language :: Python :: 3.9', + 'Programming Language :: Python :: 3.10', + 'Programming Language :: Python :: 3.11', + 'Programming Language :: Python :: 3.12', + 'Programming Language :: Python :: 3.13', + 'Topic :: Scientific/Engineering' +] + +[project.urls] +homepage = 'https://pandas.pydata.org' +documentation = 'https://pandas.pydata.org/docs/' +repository = 'https://github.com/pandas-dev/pandas' + +[project.entry-points."pandas_plotting_backends"] +matplotlib = "pandas:plotting._matplotlib" + +[project.optional-dependencies] +test = ['hypothesis>=6.46.1', 'pytest>=7.3.2', 'pytest-xdist>=2.2.0'] +pyarrow = ['pyarrow>=10.0.1'] +performance = ['bottleneck>=1.3.6', 'numba>=0.56.4', 'numexpr>=2.8.4'] +computation = ['scipy>=1.10.0', 'xarray>=2022.12.0'] +fss = ['fsspec>=2022.11.0'] +aws = ['s3fs>=2022.11.0'] +gcp = ['gcsfs>=2022.11.0', 'pandas-gbq>=0.19.0'] +excel = ['odfpy>=1.4.1', 'openpyxl>=3.1.0', 'python-calamine>=0.1.7', 'pyxlsb>=1.0.10', 'xlrd>=2.0.1', 'xlsxwriter>=3.0.5'] +parquet = ['pyarrow>=10.0.1'] +feather = ['pyarrow>=10.0.1'] +hdf5 = [# blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297) + #'blosc>=1.20.1', + 'tables>=3.8.0'] +spss = ['pyreadstat>=1.2.0'] +postgresql = ['SQLAlchemy>=2.0.0', 'psycopg2>=2.9.6', 'adbc-driver-postgresql>=0.8.0'] +mysql = ['SQLAlchemy>=2.0.0', 'pymysql>=1.0.2'] +sql-other = ['SQLAlchemy>=2.0.0', 'adbc-driver-postgresql>=0.8.0', 'adbc-driver-sqlite>=0.8.0'] +html = ['beautifulsoup4>=4.11.2', 'html5lib>=1.1', 'lxml>=4.9.2'] +xml = ['lxml>=4.9.2'] +plot = ['matplotlib>=3.6.3'] +output-formatting = ['jinja2>=3.1.2', 'tabulate>=0.9.0'] +clipboard = ['PyQt5>=5.15.9', 'qtpy>=2.3.0'] +compression = ['zstandard>=0.19.0'] +consortium-standard = ['dataframe-api-compat>=0.1.7'] +all = ['adbc-driver-postgresql>=0.8.0', + 'adbc-driver-sqlite>=0.8.0', + 'beautifulsoup4>=4.11.2', + # blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297) + #'blosc>=1.21.3', + 'bottleneck>=1.3.6', + 'dataframe-api-compat>=0.1.7', + 'fastparquet>=2022.12.0', + 'fsspec>=2022.11.0', + 'gcsfs>=2022.11.0', + 'html5lib>=1.1', + 'hypothesis>=6.46.1', + 'jinja2>=3.1.2', + 'lxml>=4.9.2', + 'matplotlib>=3.6.3', + 'numba>=0.56.4', + 'numexpr>=2.8.4', + 'odfpy>=1.4.1', + 'openpyxl>=3.1.0', + 'pandas-gbq>=0.19.0', + 'psycopg2>=2.9.6', + 'pyarrow>=10.0.1', + 'pymysql>=1.0.2', + 'PyQt5>=5.15.9', + 'pyreadstat>=1.2.0', + 'pytest>=7.3.2', + 'pytest-xdist>=2.2.0', + 'python-calamine>=0.1.7', + 'pyxlsb>=1.0.10', + 'qtpy>=2.3.0', + 'scipy>=1.10.0', + 's3fs>=2022.11.0', + 'SQLAlchemy>=2.0.0', + 'tables>=3.8.0', + 'tabulate>=0.9.0', + 'xarray>=2022.12.0', + 'xlrd>=2.0.1', + 'xlsxwriter>=3.0.5', + 'zstandard>=0.19.0'] + +# TODO: Remove after setuptools support is dropped. +[tool.setuptools] +include-package-data = true + +[tool.setuptools.packages.find] +include = ["pandas", "pandas.*"] +namespaces = false + +[tool.setuptools.exclude-package-data] +"*" = ["*.c", "*.h"] + +# See the docstring in versioneer.py for instructions. Note that you must +# re-run 'versioneer.py setup' after changing this section, and commit the +# resulting files. +[tool.versioneer] +VCS = "git" +style = "pep440" +versionfile_source = "pandas/_version.py" +versionfile_build = "pandas/_version.py" +tag_prefix = "v" +parentdir_prefix = "pandas-" + +[tool.meson-python.args] +setup = ['--vsenv'] # For Windows + +[tool.cibuildwheel] +skip = "cp36-* cp37-* cp38-* pp* *_i686 *_ppc64le *_s390x" +build-verbosity = "3" +environment = {LDFLAGS="-Wl,--strip-all"} +# pytz 2024.2 causing some failures +test-requires = "hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytz<2024.2" +test-command = """ + PANDAS_CI='1' python -c 'import pandas as pd; \ + pd.test(extra_args=["-m not clipboard and not single_cpu and not slow and not network and not db", "-n 2", "--no-strict-data-files"]); \ + pd.test(extra_args=["-m not clipboard and single_cpu and not slow and not network and not db", "--no-strict-data-files"]);' \ + """ +enable = ["cpython-freethreading"] +before-build = "PACKAGE_DIR={package} bash {package}/scripts/cibw_before_build.sh" + +[tool.cibuildwheel.windows] +before-build = "pip install delvewheel" +repair-wheel-command = "delvewheel repair -w {dest_dir} {wheel}" + +[[tool.cibuildwheel.overrides]] +select = "*-manylinux_aarch64*" +test-command = """ + PANDAS_CI='1' python -c 'import pandas as pd; \ + pd.test(extra_args=["-m not clipboard and not single_cpu and not slow and not network and not db and not fails_arm_wheels", "-n 2", "--no-strict-data-files"]); \ + pd.test(extra_args=["-m not clipboard and single_cpu and not slow and not network and not db", "--no-strict-data-files"]);' \ + """ + +[[tool.cibuildwheel.overrides]] +select = "*-musllinux*" +before-test = "apk update && apk add musl-locales" + +[[tool.cibuildwheel.overrides]] +select = "*-win*" +# We test separately for Windows, since we use +# the windowsservercore docker image to check if any dlls are +# missing from the wheel +test-command = "" + +[[tool.cibuildwheel.overrides]] +# Don't strip wheels on macOS. +# macOS doesn't support stripping wheels with linker +# https://github.com/MacPython/numpy-wheels/pull/87#issuecomment-624878264 +select = "*-macosx*" +environment = {CFLAGS="-g0"} + +[tool.black] +target-version = ['py39', 'py310'] +required-version = '23.11.0' +exclude = ''' +( + asv_bench/env + | \.egg + | \.git + | \.hg + | \.mypy_cache + | \.nox + | \.tox + | \.venv + | _build + | buck-out + | build + | dist + | setup.py +) +''' + +[tool.ruff] +line-length = 88 +target-version = "py310" +fix = true +unfixable = [] +typing-modules = ["pandas._typing"] + +select = [ + # pyflakes + "F", + # pycodestyle + "E", "W", + # flake8-2020 + "YTT", + # flake8-bugbear + "B", + # flake8-quotes + "Q", + # flake8-debugger + "T10", + # flake8-gettext + "INT", + # pylint + "PL", + # misc lints + "PIE", + # flake8-pyi + "PYI", + # tidy imports + "TID", + # implicit string concatenation + "ISC", + # type-checking imports + "TCH", + # comprehensions + "C4", + # pygrep-hooks + "PGH", + # Ruff-specific rules + "RUF", + # flake8-bandit: exec-builtin + "S102", + # numpy-legacy-random + "NPY002", + # Perflint + "PERF", + # flynt + "FLY", + # flake8-logging-format + "G", + # flake8-future-annotations + "FA", +] + +ignore = [ + ### Intentionally disabled + # space before : (needed for how black formats slicing) + "E203", + # module level import not at top of file + "E402", + # do not assign a lambda expression, use a def + "E731", + # line break before binary operator + # "W503", # not yet implemented + # line break after binary operator + # "W504", # not yet implemented + # controversial + "B006", + # controversial + "B007", + # controversial + "B008", + # setattr is used to side-step mypy + "B009", + # getattr is used to side-step mypy + "B010", + # tests use assert False + "B011", + # tests use comparisons but not their returned value + "B015", + # false positives + "B019", + # Loop control variable overrides iterable it iterates + "B020", + # Function definition does not bind loop variable + "B023", + # Functions defined inside a loop must not use variables redefined in the loop + # "B301", # not yet implemented + # Only works with python >=3.10 + "B905", + # Too many arguments to function call + "PLR0913", + # Too many returns + "PLR0911", + # Too many branches + "PLR0912", + # Too many statements + "PLR0915", + # Redefined loop name + "PLW2901", + # Global statements are discouraged + "PLW0603", + # Docstrings should not be included in stubs + "PYI021", + # Use `typing.NamedTuple` instead of `collections.namedtuple` + "PYI024", + # No builtin `eval()` allowed + "PGH001", + # compare-to-empty-string + "PLC1901", + # while int | float can be shortened to float, the former is more explicit + "PYI041", + # incorrect-dict-iterator, flags valid Series.items usage + "PERF102", + # try-except-in-loop, becomes useless in Python 3.11 + "PERF203", + + + ### TODO: Enable gradually + # Useless statement + "B018", + # Within an except clause, raise exceptions with ... + "B904", + # Magic number + "PLR2004", + # comparison-with-itself + "PLR0124", + # Consider `elif` instead of `else` then `if` to remove indentation level + "PLR5501", + # collection-literal-concatenation + "RUF005", + # pairwise-over-zipped (>=PY310 only) + "RUF007", + # explicit-f-string-type-conversion + "RUF010", + # mutable-class-default + "RUF012" +] + +exclude = [ + "doc/sphinxext/*.py", + "doc/build/*.py", + "doc/temp/*.py", + ".eggs/*.py", + # vendored files + "pandas/util/version/*", + "pandas/io/clipboard/__init__.py", + # exclude asv benchmark environments from linting + "env", +] + +[tool.ruff.per-file-ignores] +# relative imports allowed for asv_bench +"asv_bench/*" = ["TID", "NPY002"] +# to be enabled gradually +"pandas/core/*" = ["PLR5501"] +"pandas/tests/*" = ["B028", "FLY"] +"scripts/*" = ["B028"] +# Keep this one enabled +"pandas/_typing.py" = ["TCH"] + +[tool.pylint.messages_control] +max-line-length = 88 +disable = [ + # intentionally turned off + "bad-mcs-classmethod-argument", + "broad-except", + "c-extension-no-member", + "comparison-with-itself", + "consider-using-enumerate", + "import-error", + "import-outside-toplevel", + "invalid-name", + "invalid-unary-operand-type", + "line-too-long", + "no-else-continue", + "no-else-raise", + "no-else-return", + "no-member", + "no-name-in-module", + "not-an-iterable", + "overridden-final-method", + "pointless-statement", + "redundant-keyword-arg", + "singleton-comparison", + "too-many-ancestors", + "too-many-arguments", + "too-many-boolean-expressions", + "too-many-branches", + "too-many-function-args", + "too-many-instance-attributes", + "too-many-locals", + "too-many-nested-blocks", + "too-many-public-methods", + "too-many-return-statements", + "too-many-statements", + "unexpected-keyword-arg", + "ungrouped-imports", + "unsubscriptable-object", + "unsupported-assignment-operation", + "unsupported-membership-test", + "unused-import", + "use-dict-literal", + "use-implicit-booleaness-not-comparison", + "use-implicit-booleaness-not-len", + "wrong-import-order", + "wrong-import-position", + "redefined-loop-name", + + # misc + "abstract-class-instantiated", + "no-value-for-parameter", + "undefined-variable", + "unpacking-non-sequence", + "used-before-assignment", + + # pylint type "C": convention, for programming standard violation + "missing-class-docstring", + "missing-function-docstring", + "missing-module-docstring", + "superfluous-parens", + "too-many-lines", + "unidiomatic-typecheck", + "unnecessary-dunder-call", + "unnecessary-lambda-assignment", + + # pylint type "R": refactor, for bad code smell + "consider-using-with", + "cyclic-import", + "duplicate-code", + "inconsistent-return-statements", + "redefined-argument-from-local", + "too-few-public-methods", + + # pylint type "W": warning, for python specific problems + "abstract-method", + "arguments-differ", + "arguments-out-of-order", + "arguments-renamed", + "attribute-defined-outside-init", + "broad-exception-raised", + "comparison-with-callable", + "dangerous-default-value", + "deprecated-module", + "eval-used", + "expression-not-assigned", + "fixme", + "global-statement", + "invalid-overridden-method", + "keyword-arg-before-vararg", + "possibly-unused-variable", + "protected-access", + "raise-missing-from", + "redefined-builtin", + "redefined-outer-name", + "self-cls-assignment", + "signature-differs", + "super-init-not-called", + "try-except-raise", + "unnecessary-lambda", + "unused-argument", + "unused-variable", + "using-constant-test", + + # disabled on 2.3.x branch + "consider-using-in", + "simplifiable-if-expression", +] + +[tool.pytest.ini_options] +# sync minversion with pyproject.toml & install.rst +minversion = "7.3.2" +addopts = "--strict-markers --strict-config --capture=no --durations=30 --junitxml=test-data.xml" +empty_parameter_set_mark = "fail_at_collect" +xfail_strict = true +testpaths = "pandas" +doctest_optionflags = [ + "NORMALIZE_WHITESPACE", + "IGNORE_EXCEPTION_DETAIL", + "ELLIPSIS", +] +filterwarnings = [ + "error:::pandas", + "error::ResourceWarning", + "error::pytest.PytestUnraisableExceptionWarning", + # TODO(PY311-minimum): Specify EncodingWarning + # Ignore 3rd party EncodingWarning but raise on pandas' + "ignore:.*encoding.* argument not specified", + "error:.*encoding.* argument not specified::pandas", + "ignore:.*ssl.SSLSocket:pytest.PytestUnraisableExceptionWarning", + "ignore:.*ssl.SSLSocket:ResourceWarning", + # GH 44844: Can remove once minimum matplotlib version >= 3.7 + "ignore:.*FileIO:pytest.PytestUnraisableExceptionWarning", + "ignore:.*BufferedRandom:ResourceWarning", + "ignore::ResourceWarning:asyncio", + # From plotting doctests + "ignore:More than 20 figures have been opened:RuntimeWarning", + # Will be fixed in numba 0.56: https://github.com/numba/numba/issues/7758 + "ignore:`np.MachAr` is deprecated:DeprecationWarning:numba", + "ignore:.*urllib3:DeprecationWarning:botocore", + "ignore:Setuptools is replacing distutils.:UserWarning:_distutils_hack", + # https://github.com/PyTables/PyTables/issues/822 + "ignore:a closed node found in the registry:UserWarning:tables", + "ignore:`np.object` is a deprecated:DeprecationWarning:tables", + "ignore:tostring:DeprecationWarning:tables", + "ignore:distutils Version classes are deprecated:DeprecationWarning:pandas_datareader", + "ignore:distutils Version classes are deprecated:DeprecationWarning:numexpr", + "ignore:distutils Version classes are deprecated:DeprecationWarning:fastparquet", + "ignore:distutils Version classes are deprecated:DeprecationWarning:fsspec", + # Can be removed once https://github.com/numpy/numpy/pull/24794 is merged + "ignore:.*In the future `np.long` will be defined as.*:FutureWarning", + # https://github.com/numpy/numpy/pull/29301 + "ignore:.*align should be passed:", +] +junit_family = "xunit2" +markers = [ + "single_cpu: tests that should run on a single cpu only", + "slow: mark a test as slow", + "network: mark a test as network", + "db: tests requiring a database (mysql or postgres)", + "clipboard: mark a pd.read_clipboard test", + "arm_slow: mark a test as slow for arm64 architecture", + "skip_ubsan: Tests known to fail UBSAN check", + # TODO: someone should investigate this ... + # these tests only fail in the wheel builder and don't fail in regular + # ARM CI + "fails_arm_wheels: Tests that fail in the ARM wheel build only", +] + +[tool.mypy] +# Import discovery +mypy_path = "typings" +files = ["pandas", "typings"] +namespace_packages = false +explicit_package_bases = false +ignore_missing_imports = true +follow_imports = "normal" +follow_imports_for_stubs = false +no_site_packages = false +no_silence_site_packages = false +# Platform configuration +python_version = "3.11" +platform = "linux-64" +# Disallow dynamic typing +disallow_any_unimported = false # TODO +disallow_any_expr = false # TODO +disallow_any_decorated = false # TODO +disallow_any_explicit = false # TODO +disallow_any_generics = false # TODO +disallow_subclassing_any = false # TODO +# Untyped definitions and calls +disallow_untyped_calls = true +disallow_untyped_defs = true +disallow_incomplete_defs = true +check_untyped_defs = true +disallow_untyped_decorators = true +# None and Optional handling +no_implicit_optional = true +strict_optional = true +# Configuring warnings +warn_redundant_casts = true +warn_unused_ignores = true +warn_no_return = true +warn_return_any = false # TODO +warn_unreachable = false # GH#27396 +# Suppressing errors +ignore_errors = false +enable_error_code = "ignore-without-code" +# Miscellaneous strictness flags +allow_untyped_globals = false +allow_redefinition = false +local_partial_types = false +implicit_reexport = true +strict_equality = true +# Configuring error messages +show_error_context = false +show_column_numbers = false +show_error_codes = true + +[[tool.mypy.overrides]] +module = [ + "pandas._config.config", # TODO + "pandas._libs.*", + "pandas._testing.*", # TODO + "pandas.arrays", # TODO + "pandas.compat.numpy.function", # TODO + "pandas.compat._optional", # TODO + "pandas.compat.compressors", # TODO + "pandas.compat.pickle_compat", # TODO + "pandas.core._numba.executor", # TODO + "pandas.core.array_algos.datetimelike_accumulations", # TODO + "pandas.core.array_algos.masked_accumulations", # TODO + "pandas.core.array_algos.masked_reductions", # TODO + "pandas.core.array_algos.putmask", # TODO + "pandas.core.array_algos.quantile", # TODO + "pandas.core.array_algos.replace", # TODO + "pandas.core.array_algos.take", # TODO + "pandas.core.arrays.*", # TODO + "pandas.core.computation.*", # TODO + "pandas.core.dtypes.astype", # TODO + "pandas.core.dtypes.cast", # TODO + "pandas.core.dtypes.common", # TODO + "pandas.core.dtypes.concat", # TODO + "pandas.core.dtypes.dtypes", # TODO + "pandas.core.dtypes.generic", # TODO + "pandas.core.dtypes.inference", # TODO + "pandas.core.dtypes.missing", # TODO + "pandas.core.groupby.categorical", # TODO + "pandas.core.groupby.generic", # TODO + "pandas.core.groupby.grouper", # TODO + "pandas.core.groupby.groupby", # TODO + "pandas.core.groupby.ops", # TODO + "pandas.core.indexers.*", # TODO + "pandas.core.indexes.*", # TODO + "pandas.core.interchange.column", # TODO + "pandas.core.interchange.dataframe_protocol", # TODO + "pandas.core.interchange.from_dataframe", # TODO + "pandas.core.internals.*", # TODO + "pandas.core.methods.*", # TODO + "pandas.core.ops.array_ops", # TODO + "pandas.core.ops.common", # TODO + "pandas.core.ops.invalid", # TODO + "pandas.core.ops.mask_ops", # TODO + "pandas.core.ops.missing", # TODO + "pandas.core.reshape.*", # TODO + "pandas.core.strings.*", # TODO + "pandas.core.tools.*", # TODO + "pandas.core.window.common", # TODO + "pandas.core.window.ewm", # TODO + "pandas.core.window.expanding", # TODO + "pandas.core.window.numba_", # TODO + "pandas.core.window.online", # TODO + "pandas.core.window.rolling", # TODO + "pandas.core.accessor", # TODO + "pandas.core.algorithms", # TODO + "pandas.core.apply", # TODO + "pandas.core.arraylike", # TODO + "pandas.core.base", # TODO + "pandas.core.common", # TODO + "pandas.core.config_init", # TODO + "pandas.core.construction", # TODO + "pandas.core.flags", # TODO + "pandas.core.frame", # TODO + "pandas.core.generic", # TODO + "pandas.core.indexing", # TODO + "pandas.core.missing", # TODO + "pandas.core.nanops", # TODO + "pandas.core.resample", # TODO + "pandas.core.roperator", # TODO + "pandas.core.sample", # TODO + "pandas.core.series", # TODO + "pandas.core.sorting", # TODO + "pandas.errors", # TODO + "pandas.io.clipboard", # TODO + "pandas.io.excel._base", # TODO + "pandas.io.excel._odfreader", # TODO + "pandas.io.excel._odswriter", # TODO + "pandas.io.excel._openpyxl", # TODO + "pandas.io.excel._pyxlsb", # TODO + "pandas.io.excel._xlrd", # TODO + "pandas.io.excel._xlsxwriter", # TODO + "pandas.io.formats.console", # TODO + "pandas.io.formats.css", # TODO + "pandas.io.formats.excel", # TODO + "pandas.io.formats.format", # TODO + "pandas.io.formats.info", # TODO + "pandas.io.formats.printing", # TODO + "pandas.io.formats.style", # TODO + "pandas.io.formats.style_render", # TODO + "pandas.io.formats.xml", # TODO + "pandas.io.json.*", # TODO + "pandas.io.parsers.*", # TODO + "pandas.io.sas.sas_xport", # TODO + "pandas.io.sas.sas7bdat", # TODO + "pandas.io.clipboards", # TODO + "pandas.io.common", # TODO + "pandas.io.gbq", # TODO + "pandas.io.html", # TODO + "pandas.io.gbq", # TODO + "pandas.io.parquet", # TODO + "pandas.io.pytables", # TODO + "pandas.io.sql", # TODO + "pandas.io.stata", # TODO + "pandas.io.xml", # TODO + "pandas.plotting.*", # TODO + "pandas.tests.*", + "pandas.tseries.frequencies", # TODO + "pandas.tseries.holiday", # TODO + "pandas.util._decorators", # TODO + "pandas.util._doctools", # TODO + "pandas.util._print_versions", # TODO + "pandas.util._test_decorators", # TODO + "pandas.util._validators", # TODO + "pandas.util", # TODO + "pandas._version", + "pandas.conftest", + "pandas" +] +disallow_untyped_calls = false +disallow_untyped_defs = false +disallow_incomplete_defs = false + +[[tool.mypy.overrides]] +module = [ + "pandas.tests.*", + "pandas._version", + "pandas.io.clipboard", +] +check_untyped_defs = false + +[[tool.mypy.overrides]] +module = [ + "pandas.tests.apply.test_series_apply", + "pandas.tests.arithmetic.conftest", + "pandas.tests.arrays.sparse.test_combine_concat", + "pandas.tests.dtypes.test_common", + "pandas.tests.frame.methods.test_to_records", + "pandas.tests.groupby.test_rank", + "pandas.tests.groupby.transform.test_transform", + "pandas.tests.indexes.interval.test_interval", + "pandas.tests.indexing.test_categorical", + "pandas.tests.io.excel.test_writers", + "pandas.tests.reductions.test_reductions", + "pandas.tests.test_expressions", +] +ignore_errors = true + +# To be kept consistent with "Import Formatting" section in contributing.rst +[tool.isort] +known_pre_libs = "pandas._config" +known_pre_core = ["pandas._libs", "pandas._typing", "pandas.util._*", "pandas.compat", "pandas.errors"] +known_dtypes = "pandas.core.dtypes" +known_post_core = ["pandas.tseries", "pandas.io", "pandas.plotting"] +sections = ["FUTURE", "STDLIB", "THIRDPARTY" ,"PRE_LIBS" , "PRE_CORE", "DTYPES", "FIRSTPARTY", "POST_CORE", "LOCALFOLDER"] +profile = "black" +combine_as_imports = true +force_grid_wrap = 2 +force_sort_within_sections = true +skip_glob = "env" +skip = "pandas/__init__.py" + +[tool.pyright] +pythonVersion = "3.11" +typeCheckingMode = "basic" +useLibraryCodeForTypes = false +include = ["pandas", "typings"] +exclude = ["pandas/tests", "pandas/io/clipboard", "pandas/util/version", "pandas/core/_numba/extensions.py"] +# enable subset of "strict" +reportDuplicateImport = true +reportInconsistentConstructor = true +reportInvalidStubStatement = true +reportOverlappingOverload = true +reportPropertyTypeMismatch = true +reportUntypedClassDecorator = true +reportUntypedFunctionDecorator = true +reportUntypedNamedTuple = true +reportUnusedImport = true +disableBytesTypePromotions = true +# disable subset of "basic" +reportGeneralTypeIssues = false +reportMissingModuleSource = false +reportOptionalCall = false +reportOptionalIterable = false +reportOptionalMemberAccess = false +reportOptionalOperand = false +reportOptionalSubscript = false +reportPrivateImportUsage = false +reportUnboundVariable = false + +[tool.coverage.run] +branch = true +omit = ["pandas/_typing.py", "pandas/_version.py"] +plugins = ["Cython.Coverage"] +source = ["pandas"] + +[tool.coverage.report] +ignore_errors = false +show_missing = true +omit = ["pandas/_version.py"] +exclude_lines = [ + # Have to re-enable the standard pragma + "pragma: no cover", + # Don't complain about missing debug-only code:s + "def __repr__", + "if self.debug", + # Don't complain if tests don't hit defensive assertion code: + "raise AssertionError", + "raise NotImplementedError", + "AbstractMethodError", + # Don't complain if non-runnable code isn't run: + "if 0:", + "if __name__ == .__main__.:", + "if TYPE_CHECKING:", +] + +[tool.coverage.html] +directory = "coverage_html_report" + +[tool.codespell] +ignore-words-list = "blocs, coo, hist, nd, sav, ser, recuse, nin, timere, expec, expecs" +ignore-regex = 'https://([\w/\.])+' diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/testing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/testing.py new file mode 100644 index 0000000000000000000000000000000000000000..841b55df48556561904b9144a05f747d889ea621 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/testing.py @@ -0,0 +1,18 @@ +""" +Public testing utility functions. +""" + + +from pandas._testing import ( + assert_extension_array_equal, + assert_frame_equal, + assert_index_equal, + assert_series_equal, +) + +__all__ = [ + "assert_extension_array_equal", + "assert_frame_equal", + "assert_series_equal", + "assert_index_equal", +] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_arithmetic.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..9ff690cdc914d7f81d134a2bc93287fb914067e2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_arithmetic.py @@ -0,0 +1,134 @@ +import operator + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.fixture +def data(): + """Fixture returning boolean array with valid and missing values.""" + return pd.array( + [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False], + dtype="boolean", + ) + + +@pytest.fixture +def left_array(): + """Fixture returning boolean array with valid and missing values.""" + return pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + + +@pytest.fixture +def right_array(): + """Fixture returning boolean array with valid and missing values.""" + return pd.array([True, False, None] * 3, dtype="boolean") + + +# Basic test for the arithmetic array ops +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "opname, exp", + [ + ("add", [True, True, None, True, False, None, None, None, None]), + ("mul", [True, False, None, False, False, None, None, None, None]), + ], + ids=["add", "mul"], +) +def test_add_mul(left_array, right_array, opname, exp): + op = getattr(operator, opname) + result = op(left_array, right_array) + expected = pd.array(exp, dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + +def test_sub(left_array, right_array): + msg = ( + r"numpy boolean subtract, the `-` operator, is (?:deprecated|not supported), " + r"use the bitwise_xor, the `\^` operator, or the logical_xor function instead\." + ) + with pytest.raises(TypeError, match=msg): + left_array - right_array + + +def test_div(left_array, right_array): + msg = "operator '.*' not implemented for bool dtypes" + with pytest.raises(NotImplementedError, match=msg): + # check that we are matching the non-masked Series behavior + pd.Series(left_array._data) / pd.Series(right_array._data) + + with pytest.raises(NotImplementedError, match=msg): + left_array / right_array + + +@pytest.mark.parametrize( + "opname", + [ + "floordiv", + "mod", + "pow", + ], +) +def test_op_int8(left_array, right_array, opname): + op = getattr(operator, opname) + if opname != "mod": + msg = "operator '.*' not implemented for bool dtypes" + with pytest.raises(NotImplementedError, match=msg): + result = op(left_array, right_array) + return + result = op(left_array, right_array) + expected = op(left_array.astype("Int8"), right_array.astype("Int8")) + tm.assert_extension_array_equal(result, expected) + + +# Test generic characteristics / errors +# ----------------------------------------------------------------------------- + + +def test_error_invalid_values(data, all_arithmetic_operators): + # invalid ops + op = all_arithmetic_operators + s = pd.Series(data) + ops = getattr(s, op) + + # invalid scalars + msg = ( + "did not contain a loop with signature matching types|" + "BooleanArray cannot perform the operation|" + "not supported for the input types, and the inputs could not be safely coerced " + "to any supported types according to the casting rule ''safe''|" + "not supported for dtype" + ) + with pytest.raises(TypeError, match=msg): + ops("foo") + msg = "|".join( + [ + r"unsupported operand type\(s\) for", + "Concatenation operation is not implemented for NumPy arrays", + "has no kernel", + "not supported for dtype", + ] + ) + with pytest.raises(TypeError, match=msg): + ops(pd.Timestamp("20180101")) + + # invalid array-likes + if op not in ("__mul__", "__rmul__"): + # TODO(extension) numpy's mul with object array sees booleans as numbers + msg = "|".join( + [ + r"unsupported operand type\(s\) for", + "can only concatenate str", + "not all arguments converted during string formatting", + "has no kernel", + "not implemented", + "not supported for dtype", + ] + ) + with pytest.raises(TypeError, match=msg): + ops(pd.Series("foo", index=s.index)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_astype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..8c2672218f273c6ff39ae0a0b9c86f21879e45f3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_astype.py @@ -0,0 +1,59 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +def test_astype(using_infer_string): + # with missing values + arr = pd.array([True, False, None], dtype="boolean") + + with pytest.raises(ValueError, match="cannot convert NA to integer"): + arr.astype("int64") + + with pytest.raises(ValueError, match="cannot convert float NaN to"): + arr.astype("bool") + + result = arr.astype("float64") + expected = np.array([1, 0, np.nan], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + result = arr.astype("str") + if using_infer_string: + expected = pd.array( + ["True", "False", None], dtype=pd.StringDtype(na_value=np.nan) + ) + tm.assert_extension_array_equal(result, expected) + else: + expected = np.array(["True", "False", ""], dtype=f"{tm.ENDIAN}U5") + tm.assert_numpy_array_equal(result, expected) + + # no missing values + arr = pd.array([True, False, True], dtype="boolean") + result = arr.astype("int64") + expected = np.array([1, 0, 1], dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + result = arr.astype("bool") + expected = np.array([True, False, True], dtype="bool") + tm.assert_numpy_array_equal(result, expected) + + +def test_astype_to_boolean_array(): + # astype to BooleanArray + arr = pd.array([True, False, None], dtype="boolean") + + result = arr.astype("boolean") + tm.assert_extension_array_equal(result, arr) + result = arr.astype(pd.BooleanDtype()) + tm.assert_extension_array_equal(result, arr) + + +def test_astype_to_integer_array(): + # astype to IntegerArray + arr = pd.array([True, False, None], dtype="boolean") + + result = arr.astype("Int64") + expected = pd.array([1, 0, None], dtype="Int64") + tm.assert_extension_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_comparison.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_comparison.py new file mode 100644 index 0000000000000000000000000000000000000000..2eeb9da574b1e7973d98390ada40f23f57526203 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_comparison.py @@ -0,0 +1,60 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.arrays import BooleanArray +from pandas.tests.arrays.masked_shared import ComparisonOps + + +@pytest.fixture +def data(): + """Fixture returning boolean array with valid and missing data""" + return pd.array( + [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False], + dtype="boolean", + ) + + +@pytest.fixture +def dtype(): + """Fixture returning BooleanDtype""" + return pd.BooleanDtype() + + +class TestComparisonOps(ComparisonOps): + def test_compare_scalar(self, data, comparison_op): + self._compare_other(data, comparison_op, True) + + def test_compare_array(self, data, comparison_op): + other = pd.array([True] * len(data), dtype="boolean") + self._compare_other(data, comparison_op, other) + other = np.array([True] * len(data)) + self._compare_other(data, comparison_op, other) + other = pd.Series([True] * len(data)) + self._compare_other(data, comparison_op, other) + + @pytest.mark.parametrize("other", [True, False, pd.NA]) + def test_scalar(self, other, comparison_op, dtype): + ComparisonOps.test_scalar(self, other, comparison_op, dtype) + + def test_array(self, comparison_op): + op = comparison_op + a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + b = pd.array([True, False, None] * 3, dtype="boolean") + + result = op(a, b) + + values = op(a._data, b._data) + mask = a._mask | b._mask + expected = BooleanArray(values, mask) + tm.assert_extension_array_equal(result, expected) + + # ensure we haven't mutated anything inplace + result[0] = None + tm.assert_extension_array_equal( + a, pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + ) + tm.assert_extension_array_equal( + b, pd.array([True, False, None] * 3, dtype="boolean") + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_construction.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_construction.py new file mode 100644 index 0000000000000000000000000000000000000000..645e763fbf00cec4f62474fc7d2d2be564a4e4ba --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_construction.py @@ -0,0 +1,325 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.arrays import BooleanArray +from pandas.core.arrays.boolean import coerce_to_array + + +def test_boolean_array_constructor(): + values = np.array([True, False, True, False], dtype="bool") + mask = np.array([False, False, False, True], dtype="bool") + + result = BooleanArray(values, mask) + expected = pd.array([True, False, True, None], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + with pytest.raises(TypeError, match="values should be boolean numpy array"): + BooleanArray(values.tolist(), mask) + + with pytest.raises(TypeError, match="mask should be boolean numpy array"): + BooleanArray(values, mask.tolist()) + + with pytest.raises(TypeError, match="values should be boolean numpy array"): + BooleanArray(values.astype(int), mask) + + with pytest.raises(TypeError, match="mask should be boolean numpy array"): + BooleanArray(values, None) + + with pytest.raises(ValueError, match="values.shape must match mask.shape"): + BooleanArray(values.reshape(1, -1), mask) + + with pytest.raises(ValueError, match="values.shape must match mask.shape"): + BooleanArray(values, mask.reshape(1, -1)) + + +def test_boolean_array_constructor_copy(): + values = np.array([True, False, True, False], dtype="bool") + mask = np.array([False, False, False, True], dtype="bool") + + result = BooleanArray(values, mask) + assert result._data is values + assert result._mask is mask + + result = BooleanArray(values, mask, copy=True) + assert result._data is not values + assert result._mask is not mask + + +def test_to_boolean_array(): + expected = BooleanArray( + np.array([True, False, True]), np.array([False, False, False]) + ) + + result = pd.array([True, False, True], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + result = pd.array(np.array([True, False, True]), dtype="boolean") + tm.assert_extension_array_equal(result, expected) + result = pd.array(np.array([True, False, True], dtype=object), dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + # with missing values + expected = BooleanArray( + np.array([True, False, True]), np.array([False, False, True]) + ) + + result = pd.array([True, False, None], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + result = pd.array(np.array([True, False, None], dtype=object), dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + +def test_to_boolean_array_all_none(): + expected = BooleanArray(np.array([True, True, True]), np.array([True, True, True])) + + result = pd.array([None, None, None], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + result = pd.array(np.array([None, None, None], dtype=object), dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize( + "a, b", + [ + ([True, False, None, np.nan, pd.NA], [True, False, None, None, None]), + ([True, np.nan], [True, None]), + ([True, pd.NA], [True, None]), + ([np.nan, np.nan], [None, None]), + (np.array([np.nan, np.nan], dtype=float), [None, None]), + ], +) +def test_to_boolean_array_missing_indicators(a, b): + result = pd.array(a, dtype="boolean") + expected = pd.array(b, dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize( + "values", + [ + ["foo", "bar"], + ["1", "2"], + # "foo", + [1, 2], + [1.0, 2.0], + pd.date_range("20130101", periods=2), + np.array(["foo"]), + np.array([1, 2]), + np.array([1.0, 2.0]), + [np.nan, {"a": 1}], + ], +) +def test_to_boolean_array_error(values): + # error in converting existing arrays to BooleanArray + msg = "Need to pass bool-like value" + with pytest.raises(TypeError, match=msg): + pd.array(values, dtype="boolean") + + +def test_to_boolean_array_from_integer_array(): + result = pd.array(np.array([1, 0, 1, 0]), dtype="boolean") + expected = pd.array([True, False, True, False], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + # with missing values + result = pd.array(np.array([1, 0, 1, None]), dtype="boolean") + expected = pd.array([True, False, True, None], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + +def test_to_boolean_array_from_float_array(): + result = pd.array(np.array([1.0, 0.0, 1.0, 0.0]), dtype="boolean") + expected = pd.array([True, False, True, False], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + # with missing values + result = pd.array(np.array([1.0, 0.0, 1.0, np.nan]), dtype="boolean") + expected = pd.array([True, False, True, None], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + +def test_to_boolean_array_integer_like(): + # integers of 0's and 1's + result = pd.array([1, 0, 1, 0], dtype="boolean") + expected = pd.array([True, False, True, False], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + # with missing values + result = pd.array([1, 0, 1, None], dtype="boolean") + expected = pd.array([True, False, True, None], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + +def test_coerce_to_array(): + # TODO this is currently not public API + values = np.array([True, False, True, False], dtype="bool") + mask = np.array([False, False, False, True], dtype="bool") + result = BooleanArray(*coerce_to_array(values, mask=mask)) + expected = BooleanArray(values, mask) + tm.assert_extension_array_equal(result, expected) + assert result._data is values + assert result._mask is mask + result = BooleanArray(*coerce_to_array(values, mask=mask, copy=True)) + expected = BooleanArray(values, mask) + tm.assert_extension_array_equal(result, expected) + assert result._data is not values + assert result._mask is not mask + + # mixed missing from values and mask + values = [True, False, None, False] + mask = np.array([False, False, False, True], dtype="bool") + result = BooleanArray(*coerce_to_array(values, mask=mask)) + expected = BooleanArray( + np.array([True, False, True, True]), np.array([False, False, True, True]) + ) + tm.assert_extension_array_equal(result, expected) + result = BooleanArray(*coerce_to_array(np.array(values, dtype=object), mask=mask)) + tm.assert_extension_array_equal(result, expected) + result = BooleanArray(*coerce_to_array(values, mask=mask.tolist())) + tm.assert_extension_array_equal(result, expected) + + # raise errors for wrong dimension + values = np.array([True, False, True, False], dtype="bool") + mask = np.array([False, False, False, True], dtype="bool") + + # passing 2D values is OK as long as no mask + coerce_to_array(values.reshape(1, -1)) + + with pytest.raises(ValueError, match="values.shape and mask.shape must match"): + coerce_to_array(values.reshape(1, -1), mask=mask) + + with pytest.raises(ValueError, match="values.shape and mask.shape must match"): + coerce_to_array(values, mask=mask.reshape(1, -1)) + + +def test_coerce_to_array_from_boolean_array(): + # passing BooleanArray to coerce_to_array + values = np.array([True, False, True, False], dtype="bool") + mask = np.array([False, False, False, True], dtype="bool") + arr = BooleanArray(values, mask) + result = BooleanArray(*coerce_to_array(arr)) + tm.assert_extension_array_equal(result, arr) + # no copy + assert result._data is arr._data + assert result._mask is arr._mask + + result = BooleanArray(*coerce_to_array(arr), copy=True) + tm.assert_extension_array_equal(result, arr) + assert result._data is not arr._data + assert result._mask is not arr._mask + + with pytest.raises(ValueError, match="cannot pass mask for BooleanArray input"): + coerce_to_array(arr, mask=mask) + + +def test_coerce_to_numpy_array(): + # with missing values -> object dtype + arr = pd.array([True, False, None], dtype="boolean") + result = np.array(arr) + expected = np.array([True, False, pd.NA], dtype="object") + tm.assert_numpy_array_equal(result, expected) + + # also with no missing values -> object dtype + arr = pd.array([True, False, True], dtype="boolean") + result = np.array(arr) + expected = np.array([True, False, True], dtype="bool") + tm.assert_numpy_array_equal(result, expected) + + # force bool dtype + result = np.array(arr, dtype="bool") + expected = np.array([True, False, True], dtype="bool") + tm.assert_numpy_array_equal(result, expected) + # with missing values will raise error + arr = pd.array([True, False, None], dtype="boolean") + msg = ( + "cannot convert to 'bool'-dtype NumPy array with missing values. " + "Specify an appropriate 'na_value' for this dtype." + ) + with pytest.raises(ValueError, match=msg): + np.array(arr, dtype="bool") + + +def test_to_boolean_array_from_strings(): + result = BooleanArray._from_sequence_of_strings( + np.array(["True", "False", "1", "1.0", "0", "0.0", np.nan], dtype=object), + dtype="boolean", + ) + expected = BooleanArray( + np.array([True, False, True, True, False, False, False]), + np.array([False, False, False, False, False, False, True]), + ) + + tm.assert_extension_array_equal(result, expected) + + +def test_to_boolean_array_from_strings_invalid_string(): + with pytest.raises(ValueError, match="cannot be cast"): + BooleanArray._from_sequence_of_strings(["donkey"], dtype="boolean") + + +@pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) +def test_to_numpy(box): + con = pd.Series if box else pd.array + # default (with or without missing values) -> object dtype + arr = con([True, False, True], dtype="boolean") + result = arr.to_numpy() + expected = np.array([True, False, True], dtype="bool") + tm.assert_numpy_array_equal(result, expected) + + arr = con([True, False, None], dtype="boolean") + result = arr.to_numpy() + expected = np.array([True, False, pd.NA], dtype="object") + tm.assert_numpy_array_equal(result, expected) + + arr = con([True, False, None], dtype="boolean") + result = arr.to_numpy(dtype="str") + expected = np.array([True, False, pd.NA], dtype=f"{tm.ENDIAN}U5") + tm.assert_numpy_array_equal(result, expected) + + # no missing values -> can convert to bool, otherwise raises + arr = con([True, False, True], dtype="boolean") + result = arr.to_numpy(dtype="bool") + expected = np.array([True, False, True], dtype="bool") + tm.assert_numpy_array_equal(result, expected) + + arr = con([True, False, None], dtype="boolean") + with pytest.raises(ValueError, match="cannot convert to 'bool'-dtype"): + result = arr.to_numpy(dtype="bool") + + # specify dtype and na_value + arr = con([True, False, None], dtype="boolean") + result = arr.to_numpy(dtype=object, na_value=None) + expected = np.array([True, False, None], dtype="object") + tm.assert_numpy_array_equal(result, expected) + + result = arr.to_numpy(dtype=bool, na_value=False) + expected = np.array([True, False, False], dtype="bool") + tm.assert_numpy_array_equal(result, expected) + + result = arr.to_numpy(dtype="int64", na_value=-99) + expected = np.array([1, 0, -99], dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + result = arr.to_numpy(dtype="float64", na_value=np.nan) + expected = np.array([1, 0, np.nan], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + # converting to int or float without specifying na_value raises + with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype"): + arr.to_numpy(dtype="int64") + + +def test_to_numpy_copy(): + # to_numpy can be zero-copy if no missing values + arr = pd.array([True, False, True], dtype="boolean") + result = arr.to_numpy(dtype=bool) + result[0] = False + tm.assert_extension_array_equal( + arr, pd.array([False, False, True], dtype="boolean") + ) + + arr = pd.array([True, False, True], dtype="boolean") + result = arr.to_numpy(dtype=bool, copy=True) + result[0] = False + tm.assert_extension_array_equal(arr, pd.array([True, False, True], dtype="boolean")) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_function.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_function.py new file mode 100644 index 0000000000000000000000000000000000000000..2b3f3d3d16ac6c49d231ac526fa89570975e4bfb --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_function.py @@ -0,0 +1,126 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize( + "ufunc", [np.add, np.logical_or, np.logical_and, np.logical_xor] +) +def test_ufuncs_binary(ufunc): + # two BooleanArrays + a = pd.array([True, False, None], dtype="boolean") + result = ufunc(a, a) + expected = pd.array(ufunc(a._data, a._data), dtype="boolean") + expected[a._mask] = np.nan + tm.assert_extension_array_equal(result, expected) + + s = pd.Series(a) + result = ufunc(s, a) + expected = pd.Series(ufunc(a._data, a._data), dtype="boolean") + expected[a._mask] = np.nan + tm.assert_series_equal(result, expected) + + # Boolean with numpy array + arr = np.array([True, True, False]) + result = ufunc(a, arr) + expected = pd.array(ufunc(a._data, arr), dtype="boolean") + expected[a._mask] = np.nan + tm.assert_extension_array_equal(result, expected) + + result = ufunc(arr, a) + expected = pd.array(ufunc(arr, a._data), dtype="boolean") + expected[a._mask] = np.nan + tm.assert_extension_array_equal(result, expected) + + # BooleanArray with scalar + result = ufunc(a, True) + expected = pd.array(ufunc(a._data, True), dtype="boolean") + expected[a._mask] = np.nan + tm.assert_extension_array_equal(result, expected) + + result = ufunc(True, a) + expected = pd.array(ufunc(True, a._data), dtype="boolean") + expected[a._mask] = np.nan + tm.assert_extension_array_equal(result, expected) + + # not handled types + msg = r"operand type\(s\) all returned NotImplemented from __array_ufunc__" + with pytest.raises(TypeError, match=msg): + ufunc(a, "test") + + +@pytest.mark.parametrize("ufunc", [np.logical_not]) +def test_ufuncs_unary(ufunc): + a = pd.array([True, False, None], dtype="boolean") + result = ufunc(a) + expected = pd.array(ufunc(a._data), dtype="boolean") + expected[a._mask] = np.nan + tm.assert_extension_array_equal(result, expected) + + ser = pd.Series(a) + result = ufunc(ser) + expected = pd.Series(ufunc(a._data), dtype="boolean") + expected[a._mask] = np.nan + tm.assert_series_equal(result, expected) + + +def test_ufunc_numeric(): + # np.sqrt on np.bool_ returns float16, which we upcast to Float32 + # bc we do not have Float16 + arr = pd.array([True, False, None], dtype="boolean") + + res = np.sqrt(arr) + + expected = pd.array([1, 0, None], dtype="Float32") + tm.assert_extension_array_equal(res, expected) + + +@pytest.mark.parametrize("values", [[True, False], [True, None]]) +def test_ufunc_reduce_raises(values): + arr = pd.array(values, dtype="boolean") + + res = np.add.reduce(arr) + if arr[-1] is pd.NA: + expected = pd.NA + else: + expected = arr._data.sum() + tm.assert_almost_equal(res, expected) + + +def test_value_counts_na(): + arr = pd.array([True, False, pd.NA], dtype="boolean") + result = arr.value_counts(dropna=False) + expected = pd.Series([1, 1, 1], index=arr, dtype="Int64", name="count") + assert expected.index.dtype == arr.dtype + tm.assert_series_equal(result, expected) + + result = arr.value_counts(dropna=True) + expected = pd.Series([1, 1], index=arr[:-1], dtype="Int64", name="count") + assert expected.index.dtype == arr.dtype + tm.assert_series_equal(result, expected) + + +def test_value_counts_with_normalize(): + ser = pd.Series([True, False, pd.NA], dtype="boolean") + result = ser.value_counts(normalize=True) + expected = pd.Series([1, 1], index=ser[:-1], dtype="Float64", name="proportion") / 2 + assert expected.index.dtype == "boolean" + tm.assert_series_equal(result, expected) + + +def test_diff(): + a = pd.array( + [True, True, False, False, True, None, True, None, False], dtype="boolean" + ) + result = pd.core.algorithms.diff(a, 1) + expected = pd.array( + [None, False, True, False, True, None, None, None, None], dtype="boolean" + ) + tm.assert_extension_array_equal(result, expected) + + ser = pd.Series(a) + result = ser.diff() + expected = pd.Series(expected) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..6a7daea16963c99fb7c4bbcd4b122d6af53d2576 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_indexing.py @@ -0,0 +1,13 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize("na", [None, np.nan, pd.NA]) +def test_setitem_missing_values(na): + arr = pd.array([True, False, None], dtype="boolean") + expected = pd.array([True, None, None], dtype="boolean") + arr[1] = na + tm.assert_extension_array_equal(arr, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_logical.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_logical.py new file mode 100644 index 0000000000000000000000000000000000000000..66c117ea3fc66cbc5f847cc96c23e80a98329c5d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_logical.py @@ -0,0 +1,254 @@ +import operator + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.arrays import BooleanArray +from pandas.core.ops.mask_ops import ( + kleene_and, + kleene_or, + kleene_xor, +) +from pandas.tests.extension.base import BaseOpsUtil + + +class TestLogicalOps(BaseOpsUtil): + def test_numpy_scalars_ok(self, all_logical_operators): + a = pd.array([True, False, None], dtype="boolean") + op = getattr(a, all_logical_operators) + + tm.assert_extension_array_equal(op(True), op(np.bool_(True))) + tm.assert_extension_array_equal(op(False), op(np.bool_(False))) + + def get_op_from_name(self, op_name): + short_opname = op_name.strip("_") + short_opname = short_opname if "xor" in short_opname else short_opname + "_" + try: + op = getattr(operator, short_opname) + except AttributeError: + # Assume it is the reverse operator + rop = getattr(operator, short_opname[1:]) + op = lambda x, y: rop(y, x) + + return op + + def test_empty_ok(self, all_logical_operators): + a = pd.array([], dtype="boolean") + op_name = all_logical_operators + result = getattr(a, op_name)(True) + tm.assert_extension_array_equal(a, result) + + result = getattr(a, op_name)(False) + tm.assert_extension_array_equal(a, result) + + result = getattr(a, op_name)(pd.NA) + tm.assert_extension_array_equal(a, result) + + @pytest.mark.parametrize( + "other", ["a", pd.Timestamp(2017, 1, 1, 12), np.timedelta64(4)] + ) + def test_eq_mismatched_type(self, other): + # GH-44499 + arr = pd.array([True, False]) + result = arr == other + expected = pd.array([False, False]) + tm.assert_extension_array_equal(result, expected) + + result = arr != other + expected = pd.array([True, True]) + tm.assert_extension_array_equal(result, expected) + + def test_logical_length_mismatch_raises(self, all_logical_operators): + op_name = all_logical_operators + a = pd.array([True, False, None], dtype="boolean") + msg = "Lengths must match" + + with pytest.raises(ValueError, match=msg): + getattr(a, op_name)([True, False]) + + with pytest.raises(ValueError, match=msg): + getattr(a, op_name)(np.array([True, False])) + + with pytest.raises(ValueError, match=msg): + getattr(a, op_name)(pd.array([True, False], dtype="boolean")) + + def test_logical_nan_raises(self, all_logical_operators): + op_name = all_logical_operators + a = pd.array([True, False, None], dtype="boolean") + msg = "Got float instead" + + with pytest.raises(TypeError, match=msg): + getattr(a, op_name)(np.nan) + + @pytest.mark.parametrize("other", ["a", 1]) + def test_non_bool_or_na_other_raises(self, other, all_logical_operators): + a = pd.array([True, False], dtype="boolean") + with pytest.raises(TypeError, match=str(type(other).__name__)): + getattr(a, all_logical_operators)(other) + + def test_kleene_or(self): + # A clear test of behavior. + a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + b = pd.array([True, False, None] * 3, dtype="boolean") + result = a | b + expected = pd.array( + [True, True, True, True, False, None, True, None, None], dtype="boolean" + ) + tm.assert_extension_array_equal(result, expected) + + result = b | a + tm.assert_extension_array_equal(result, expected) + + # ensure we haven't mutated anything inplace + tm.assert_extension_array_equal( + a, pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + ) + tm.assert_extension_array_equal( + b, pd.array([True, False, None] * 3, dtype="boolean") + ) + + @pytest.mark.parametrize( + "other, expected", + [ + (pd.NA, [True, None, None]), + (True, [True, True, True]), + (np.bool_(True), [True, True, True]), + (False, [True, False, None]), + (np.bool_(False), [True, False, None]), + ], + ) + def test_kleene_or_scalar(self, other, expected): + # TODO: test True & False + a = pd.array([True, False, None], dtype="boolean") + result = a | other + expected = pd.array(expected, dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + result = other | a + tm.assert_extension_array_equal(result, expected) + + # ensure we haven't mutated anything inplace + tm.assert_extension_array_equal( + a, pd.array([True, False, None], dtype="boolean") + ) + + def test_kleene_and(self): + # A clear test of behavior. + a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + b = pd.array([True, False, None] * 3, dtype="boolean") + result = a & b + expected = pd.array( + [True, False, None, False, False, False, None, False, None], dtype="boolean" + ) + tm.assert_extension_array_equal(result, expected) + + result = b & a + tm.assert_extension_array_equal(result, expected) + + # ensure we haven't mutated anything inplace + tm.assert_extension_array_equal( + a, pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + ) + tm.assert_extension_array_equal( + b, pd.array([True, False, None] * 3, dtype="boolean") + ) + + @pytest.mark.parametrize( + "other, expected", + [ + (pd.NA, [None, False, None]), + (True, [True, False, None]), + (False, [False, False, False]), + (np.bool_(True), [True, False, None]), + (np.bool_(False), [False, False, False]), + ], + ) + def test_kleene_and_scalar(self, other, expected): + a = pd.array([True, False, None], dtype="boolean") + result = a & other + expected = pd.array(expected, dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + result = other & a + tm.assert_extension_array_equal(result, expected) + + # ensure we haven't mutated anything inplace + tm.assert_extension_array_equal( + a, pd.array([True, False, None], dtype="boolean") + ) + + def test_kleene_xor(self): + a = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + b = pd.array([True, False, None] * 3, dtype="boolean") + result = a ^ b + expected = pd.array( + [False, True, None, True, False, None, None, None, None], dtype="boolean" + ) + tm.assert_extension_array_equal(result, expected) + + result = b ^ a + tm.assert_extension_array_equal(result, expected) + + # ensure we haven't mutated anything inplace + tm.assert_extension_array_equal( + a, pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + ) + tm.assert_extension_array_equal( + b, pd.array([True, False, None] * 3, dtype="boolean") + ) + + @pytest.mark.parametrize( + "other, expected", + [ + (pd.NA, [None, None, None]), + (True, [False, True, None]), + (np.bool_(True), [False, True, None]), + (np.bool_(False), [True, False, None]), + ], + ) + def test_kleene_xor_scalar(self, other, expected): + a = pd.array([True, False, None], dtype="boolean") + result = a ^ other + expected = pd.array(expected, dtype="boolean") + tm.assert_extension_array_equal(result, expected) + + result = other ^ a + tm.assert_extension_array_equal(result, expected) + + # ensure we haven't mutated anything inplace + tm.assert_extension_array_equal( + a, pd.array([True, False, None], dtype="boolean") + ) + + @pytest.mark.parametrize("other", [True, False, pd.NA, [True, False, None] * 3]) + def test_no_masked_assumptions(self, other, all_logical_operators): + # The logical operations should not assume that masked values are False! + a = pd.arrays.BooleanArray( + np.array([True, True, True, False, False, False, True, False, True]), + np.array([False] * 6 + [True, True, True]), + ) + b = pd.array([True] * 3 + [False] * 3 + [None] * 3, dtype="boolean") + if isinstance(other, list): + other = pd.array(other, dtype="boolean") + + result = getattr(a, all_logical_operators)(other) + expected = getattr(b, all_logical_operators)(other) + tm.assert_extension_array_equal(result, expected) + + if isinstance(other, BooleanArray): + other._data[other._mask] = True + a._data[a._mask] = False + + result = getattr(a, all_logical_operators)(other) + expected = getattr(b, all_logical_operators)(other) + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize("operation", [kleene_or, kleene_xor, kleene_and]) +def test_error_both_scalar(operation): + msg = r"Either `left` or `right` need to be a np\.ndarray." + with pytest.raises(TypeError, match=msg): + # masks need to be non-None, otherwise it ends up in an infinite recursion + operation(True, True, np.zeros(1), np.zeros(1)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_ops.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..95ebe8528c2e5fec1a580b00bd79e0617fe7609f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_ops.py @@ -0,0 +1,27 @@ +import pandas as pd +import pandas._testing as tm + + +class TestUnaryOps: + def test_invert(self): + a = pd.array([True, False, None], dtype="boolean") + expected = pd.array([False, True, None], dtype="boolean") + tm.assert_extension_array_equal(~a, expected) + + expected = pd.Series(expected, index=["a", "b", "c"], name="name") + result = ~pd.Series(a, index=["a", "b", "c"], name="name") + tm.assert_series_equal(result, expected) + + df = pd.DataFrame({"A": a, "B": [True, False, False]}, index=["a", "b", "c"]) + result = ~df + expected = pd.DataFrame( + {"A": expected, "B": [False, True, True]}, index=["a", "b", "c"] + ) + tm.assert_frame_equal(result, expected) + + def test_abs(self): + # matching numpy behavior, abs is the identity function + arr = pd.array([True, False, None], dtype="boolean") + result = abs(arr) + + tm.assert_extension_array_equal(result, arr) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_reduction.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_reduction.py new file mode 100644 index 0000000000000000000000000000000000000000..dd8c3eda9ed05b6844c90024631a1e90f755069e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_reduction.py @@ -0,0 +1,62 @@ +import numpy as np +import pytest + +import pandas as pd + + +@pytest.fixture +def data(): + """Fixture returning boolean array, with valid and missing values.""" + return pd.array( + [True, False] * 4 + [np.nan] + [True, False] * 44 + [np.nan] + [True, False], + dtype="boolean", + ) + + +@pytest.mark.parametrize( + "values, exp_any, exp_all, exp_any_noskip, exp_all_noskip", + [ + ([True, pd.NA], True, True, True, pd.NA), + ([False, pd.NA], False, False, pd.NA, False), + ([pd.NA], False, True, pd.NA, pd.NA), + ([], False, True, False, True), + # GH-33253: all True / all False values buggy with skipna=False + ([True, True], True, True, True, True), + ([False, False], False, False, False, False), + ], +) +def test_any_all(values, exp_any, exp_all, exp_any_noskip, exp_all_noskip): + # the methods return numpy scalars + exp_any = pd.NA if exp_any is pd.NA else np.bool_(exp_any) + exp_all = pd.NA if exp_all is pd.NA else np.bool_(exp_all) + exp_any_noskip = pd.NA if exp_any_noskip is pd.NA else np.bool_(exp_any_noskip) + exp_all_noskip = pd.NA if exp_all_noskip is pd.NA else np.bool_(exp_all_noskip) + + for con in [pd.array, pd.Series]: + a = con(values, dtype="boolean") + assert a.any() is exp_any + assert a.all() is exp_all + assert a.any(skipna=False) is exp_any_noskip + assert a.all(skipna=False) is exp_all_noskip + + assert np.any(a.any()) is exp_any + assert np.all(a.all()) is exp_all + + +@pytest.mark.parametrize("dropna", [True, False]) +def test_reductions_return_types(dropna, data, all_numeric_reductions): + op = all_numeric_reductions + s = pd.Series(data) + if dropna: + s = s.dropna() + + if op in ("sum", "prod"): + assert isinstance(getattr(s, op)(), np.int_) + elif op == "count": + # Oddly on the 32 bit build (but not Windows), this is intc (!= intp) + assert isinstance(getattr(s, op)(), np.integer) + elif op in ("min", "max"): + assert isinstance(getattr(s, op)(), np.bool_) + else: + # "mean", "std", "var", "median", "kurt", "skew" + assert isinstance(getattr(s, op)(), np.float64) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_repr.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_repr.py new file mode 100644 index 0000000000000000000000000000000000000000..0ee904b18cc9ec6197ed3ad009fae1da593c5219 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/boolean/test_repr.py @@ -0,0 +1,13 @@ +import pandas as pd + + +def test_repr(): + df = pd.DataFrame({"A": pd.array([True, False, None], dtype="boolean")}) + expected = " A\n0 True\n1 False\n2 " + assert repr(df) == expected + + expected = "0 True\n1 False\n2 \nName: A, dtype: boolean" + assert repr(df.A) == expected + + expected = "\n[True, False, ]\nLength: 3, dtype: boolean" + assert repr(df.A.array) == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_algos.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_algos.py new file mode 100644 index 0000000000000000000000000000000000000000..d4c19a4970135cfb1865eaa0fae0845dc7d17971 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_algos.py @@ -0,0 +1,89 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize("ordered", [True, False]) +@pytest.mark.parametrize("categories", [["b", "a", "c"], ["a", "b", "c", "d"]]) +def test_factorize(categories, ordered): + cat = pd.Categorical( + ["b", "b", "a", "c", None], categories=categories, ordered=ordered + ) + codes, uniques = pd.factorize(cat) + expected_codes = np.array([0, 0, 1, 2, -1], dtype=np.intp) + expected_uniques = pd.Categorical( + ["b", "a", "c"], categories=categories, ordered=ordered + ) + + tm.assert_numpy_array_equal(codes, expected_codes) + tm.assert_categorical_equal(uniques, expected_uniques) + + +def test_factorized_sort(): + cat = pd.Categorical(["b", "b", None, "a"]) + codes, uniques = pd.factorize(cat, sort=True) + expected_codes = np.array([1, 1, -1, 0], dtype=np.intp) + expected_uniques = pd.Categorical(["a", "b"]) + + tm.assert_numpy_array_equal(codes, expected_codes) + tm.assert_categorical_equal(uniques, expected_uniques) + + +def test_factorized_sort_ordered(): + cat = pd.Categorical( + ["b", "b", None, "a"], categories=["c", "b", "a"], ordered=True + ) + + codes, uniques = pd.factorize(cat, sort=True) + expected_codes = np.array([0, 0, -1, 1], dtype=np.intp) + expected_uniques = pd.Categorical( + ["b", "a"], categories=["c", "b", "a"], ordered=True + ) + + tm.assert_numpy_array_equal(codes, expected_codes) + tm.assert_categorical_equal(uniques, expected_uniques) + + +def test_isin_cats(): + # GH2003 + cat = pd.Categorical(["a", "b", np.nan]) + + result = cat.isin(["a", np.nan]) + expected = np.array([True, False, True], dtype=bool) + tm.assert_numpy_array_equal(expected, result) + + result = cat.isin(["a", "c"]) + expected = np.array([True, False, False], dtype=bool) + tm.assert_numpy_array_equal(expected, result) + + +@pytest.mark.parametrize("value", [[""], [None, ""], [pd.NaT, ""]]) +def test_isin_cats_corner_cases(value): + # GH36550 + cat = pd.Categorical([""]) + result = cat.isin(value) + expected = np.array([True], dtype=bool) + tm.assert_numpy_array_equal(expected, result) + + +@pytest.mark.parametrize("empty", [[], pd.Series(dtype=object), np.array([])]) +def test_isin_empty(empty): + s = pd.Categorical(["a", "b"]) + expected = np.array([False, False], dtype=bool) + + result = s.isin(empty) + tm.assert_numpy_array_equal(expected, result) + + +def test_diff(): + ser = pd.Series([1, 2, 3], dtype="category") + + msg = "Convert to a suitable dtype" + with pytest.raises(TypeError, match=msg): + ser.diff() + + df = ser.to_frame(name="A") + with pytest.raises(TypeError, match=msg): + df.diff() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_analytics.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_analytics.py new file mode 100644 index 0000000000000000000000000000000000000000..9a0356cbc422bc534b0cef5aad79ba304e7a77b2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_analytics.py @@ -0,0 +1,355 @@ +import re +import sys + +import numpy as np +import pytest + +from pandas.compat import PYPY + +from pandas import ( + Categorical, + CategoricalDtype, + DataFrame, + Index, + NaT, + Series, + date_range, +) +import pandas._testing as tm +from pandas.api.types import is_scalar + + +class TestCategoricalAnalytics: + @pytest.mark.parametrize("aggregation", ["min", "max"]) + def test_min_max_not_ordered_raises(self, aggregation): + # unordered cats have no min/max + cat = Categorical(["a", "b", "c", "d"], ordered=False) + msg = f"Categorical is not ordered for operation {aggregation}" + agg_func = getattr(cat, aggregation) + + with pytest.raises(TypeError, match=msg): + agg_func() + + ufunc = np.minimum if aggregation == "min" else np.maximum + with pytest.raises(TypeError, match=msg): + ufunc.reduce(cat) + + def test_min_max_ordered(self, index_or_series_or_array): + cat = Categorical(["a", "b", "c", "d"], ordered=True) + obj = index_or_series_or_array(cat) + _min = obj.min() + _max = obj.max() + assert _min == "a" + assert _max == "d" + + assert np.minimum.reduce(obj) == "a" + assert np.maximum.reduce(obj) == "d" + # TODO: raises if we pass axis=0 (on Index and Categorical, not Series) + + cat = Categorical( + ["a", "b", "c", "d"], categories=["d", "c", "b", "a"], ordered=True + ) + obj = index_or_series_or_array(cat) + _min = obj.min() + _max = obj.max() + assert _min == "d" + assert _max == "a" + assert np.minimum.reduce(obj) == "d" + assert np.maximum.reduce(obj) == "a" + + def test_min_max_reduce(self): + # GH52788 + cat = Categorical(["a", "b", "c", "d"], ordered=True) + df = DataFrame(cat) + + result_max = df.agg("max") + expected_max = Series(Categorical(["d"], dtype=cat.dtype)) + tm.assert_series_equal(result_max, expected_max) + + result_min = df.agg("min") + expected_min = Series(Categorical(["a"], dtype=cat.dtype)) + tm.assert_series_equal(result_min, expected_min) + + @pytest.mark.parametrize( + "categories,expected", + [ + (list("ABC"), np.nan), + ([1, 2, 3], np.nan), + pytest.param( + Series(date_range("2020-01-01", periods=3), dtype="category"), + NaT, + marks=pytest.mark.xfail( + reason="https://github.com/pandas-dev/pandas/issues/29962" + ), + ), + ], + ) + @pytest.mark.parametrize("aggregation", ["min", "max"]) + def test_min_max_ordered_empty(self, categories, expected, aggregation): + # GH 30227 + cat = Categorical([], categories=categories, ordered=True) + + agg_func = getattr(cat, aggregation) + result = agg_func() + assert result is expected + + @pytest.mark.parametrize( + "values, categories", + [(["a", "b", "c", np.nan], list("cba")), ([1, 2, 3, np.nan], [3, 2, 1])], + ) + @pytest.mark.parametrize("skipna", [True, False]) + @pytest.mark.parametrize("function", ["min", "max"]) + def test_min_max_with_nan(self, values, categories, function, skipna): + # GH 25303 + cat = Categorical(values, categories=categories, ordered=True) + result = getattr(cat, function)(skipna=skipna) + + if skipna is False: + assert result is np.nan + else: + expected = categories[0] if function == "min" else categories[2] + assert result == expected + + @pytest.mark.parametrize("function", ["min", "max"]) + @pytest.mark.parametrize("skipna", [True, False]) + def test_min_max_only_nan(self, function, skipna): + # https://github.com/pandas-dev/pandas/issues/33450 + cat = Categorical([np.nan], categories=[1, 2], ordered=True) + result = getattr(cat, function)(skipna=skipna) + assert result is np.nan + + @pytest.mark.parametrize("method", ["min", "max"]) + def test_numeric_only_min_max_raises(self, method): + # GH 25303 + cat = Categorical( + [np.nan, 1, 2, np.nan], categories=[5, 4, 3, 2, 1], ordered=True + ) + with pytest.raises(TypeError, match=".* got an unexpected keyword"): + getattr(cat, method)(numeric_only=True) + + @pytest.mark.parametrize("method", ["min", "max"]) + def test_numpy_min_max_raises(self, method): + cat = Categorical(["a", "b", "c", "b"], ordered=False) + msg = ( + f"Categorical is not ordered for operation {method}\n" + "you can use .as_ordered() to change the Categorical to an ordered one" + ) + method = getattr(np, method) + with pytest.raises(TypeError, match=re.escape(msg)): + method(cat) + + @pytest.mark.parametrize("kwarg", ["axis", "out", "keepdims"]) + @pytest.mark.parametrize("method", ["min", "max"]) + def test_numpy_min_max_unsupported_kwargs_raises(self, method, kwarg): + cat = Categorical(["a", "b", "c", "b"], ordered=True) + msg = ( + f"the '{kwarg}' parameter is not supported in the pandas implementation " + f"of {method}" + ) + if kwarg == "axis": + msg = r"`axis` must be fewer than the number of dimensions \(1\)" + kwargs = {kwarg: 42} + method = getattr(np, method) + with pytest.raises(ValueError, match=msg): + method(cat, **kwargs) + + @pytest.mark.parametrize("method, expected", [("min", "a"), ("max", "c")]) + def test_numpy_min_max_axis_equals_none(self, method, expected): + cat = Categorical(["a", "b", "c", "b"], ordered=True) + method = getattr(np, method) + result = method(cat, axis=None) + assert result == expected + + @pytest.mark.parametrize( + "values,categories,exp_mode", + [ + ([1, 1, 2, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5]), + ([1, 1, 1, 4, 5, 5, 5], [5, 4, 3, 2, 1], [5, 1]), + ([1, 2, 3, 4, 5], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1]), + ([np.nan, np.nan, np.nan, 4, 5], [5, 4, 3, 2, 1], [5, 4]), + ([np.nan, np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]), + ([np.nan, np.nan, 4, 5, 4], [5, 4, 3, 2, 1], [4]), + ], + ) + def test_mode(self, values, categories, exp_mode): + cat = Categorical(values, categories=categories, ordered=True) + res = Series(cat).mode()._values + exp = Categorical(exp_mode, categories=categories, ordered=True) + tm.assert_categorical_equal(res, exp) + + def test_searchsorted(self, ordered): + # https://github.com/pandas-dev/pandas/issues/8420 + # https://github.com/pandas-dev/pandas/issues/14522 + + cat = Categorical( + ["cheese", "milk", "apple", "bread", "bread"], + categories=["cheese", "milk", "apple", "bread"], + ordered=ordered, + ) + ser = Series(cat) + + # Searching for single item argument, side='left' (default) + res_cat = cat.searchsorted("apple") + assert res_cat == 2 + assert is_scalar(res_cat) + + res_ser = ser.searchsorted("apple") + assert res_ser == 2 + assert is_scalar(res_ser) + + # Searching for single item array, side='left' (default) + res_cat = cat.searchsorted(["bread"]) + res_ser = ser.searchsorted(["bread"]) + exp = np.array([3], dtype=np.intp) + tm.assert_numpy_array_equal(res_cat, exp) + tm.assert_numpy_array_equal(res_ser, exp) + + # Searching for several items array, side='right' + res_cat = cat.searchsorted(["apple", "bread"], side="right") + res_ser = ser.searchsorted(["apple", "bread"], side="right") + exp = np.array([3, 5], dtype=np.intp) + tm.assert_numpy_array_equal(res_cat, exp) + tm.assert_numpy_array_equal(res_ser, exp) + + # Searching for a single value that is not from the Categorical + with pytest.raises(TypeError, match="cucumber"): + cat.searchsorted("cucumber") + with pytest.raises(TypeError, match="cucumber"): + ser.searchsorted("cucumber") + + # Searching for multiple values one of each is not from the Categorical + msg = ( + "Cannot setitem on a Categorical with a new category, " + "set the categories first" + ) + with pytest.raises(TypeError, match=msg): + cat.searchsorted(["bread", "cucumber"]) + with pytest.raises(TypeError, match=msg): + ser.searchsorted(["bread", "cucumber"]) + + def test_unique(self, ordered): + # GH38140 + dtype = CategoricalDtype(["a", "b", "c"], ordered=ordered) + + # categories are reordered based on value when ordered=False + cat = Categorical(["a", "b", "c"], dtype=dtype) + res = cat.unique() + tm.assert_categorical_equal(res, cat) + + cat = Categorical(["a", "b", "a", "a"], dtype=dtype) + res = cat.unique() + tm.assert_categorical_equal(res, Categorical(["a", "b"], dtype=dtype)) + + cat = Categorical(["c", "a", "b", "a", "a"], dtype=dtype) + res = cat.unique() + exp_cat = Categorical(["c", "a", "b"], dtype=dtype) + tm.assert_categorical_equal(res, exp_cat) + + # nan must be removed + cat = Categorical(["b", np.nan, "b", np.nan, "a"], dtype=dtype) + res = cat.unique() + exp_cat = Categorical(["b", np.nan, "a"], dtype=dtype) + tm.assert_categorical_equal(res, exp_cat) + + def test_unique_index_series(self, ordered): + # GH38140 + dtype = CategoricalDtype([3, 2, 1], ordered=ordered) + + c = Categorical([3, 1, 2, 2, 1], dtype=dtype) + # Categorical.unique sorts categories by appearance order + # if ordered=False + exp = Categorical([3, 1, 2], dtype=dtype) + tm.assert_categorical_equal(c.unique(), exp) + + tm.assert_index_equal(Index(c).unique(), Index(exp)) + tm.assert_categorical_equal(Series(c).unique(), exp) + + c = Categorical([1, 1, 2, 2], dtype=dtype) + exp = Categorical([1, 2], dtype=dtype) + tm.assert_categorical_equal(c.unique(), exp) + tm.assert_index_equal(Index(c).unique(), Index(exp)) + tm.assert_categorical_equal(Series(c).unique(), exp) + + def test_shift(self): + # GH 9416 + cat = Categorical(["a", "b", "c", "d", "a"]) + + # shift forward + sp1 = cat.shift(1) + xp1 = Categorical([np.nan, "a", "b", "c", "d"]) + tm.assert_categorical_equal(sp1, xp1) + tm.assert_categorical_equal(cat[:-1], sp1[1:]) + + # shift back + sn2 = cat.shift(-2) + xp2 = Categorical( + ["c", "d", "a", np.nan, np.nan], categories=["a", "b", "c", "d"] + ) + tm.assert_categorical_equal(sn2, xp2) + tm.assert_categorical_equal(cat[2:], sn2[:-2]) + + # shift by zero + tm.assert_categorical_equal(cat, cat.shift(0)) + + def test_nbytes(self): + cat = Categorical([1, 2, 3]) + exp = 3 + 3 * 8 # 3 int8s for values + 3 int64s for categories + assert cat.nbytes == exp + + def test_memory_usage(self, using_infer_string): + cat = Categorical([1, 2, 3]) + + # .categories is an index, so we include the hashtable + assert 0 < cat.nbytes <= cat.memory_usage() + assert 0 < cat.nbytes <= cat.memory_usage(deep=True) + + cat = Categorical(["foo", "foo", "bar"]) + if using_infer_string: + if cat.categories.dtype.storage == "python": + assert cat.memory_usage(deep=True) > cat.nbytes + else: + assert cat.memory_usage(deep=True) >= cat.nbytes + else: + assert cat.memory_usage(deep=True) > cat.nbytes + + if not PYPY: + # sys.getsizeof will call the .memory_usage with + # deep=True, and add on some GC overhead + diff = cat.memory_usage(deep=True) - sys.getsizeof(cat) + assert abs(diff) < 100 + + def test_map(self): + c = Categorical(list("ABABC"), categories=list("CBA"), ordered=True) + result = c.map(lambda x: x.lower(), na_action=None) + exp = Categorical(list("ababc"), categories=list("cba"), ordered=True) + tm.assert_categorical_equal(result, exp) + + c = Categorical(list("ABABC"), categories=list("ABC"), ordered=False) + result = c.map(lambda x: x.lower(), na_action=None) + exp = Categorical(list("ababc"), categories=list("abc"), ordered=False) + tm.assert_categorical_equal(result, exp) + + result = c.map(lambda x: 1, na_action=None) + # GH 12766: Return an index not an array + tm.assert_index_equal(result, Index(np.array([1] * 5, dtype=np.int64))) + + @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0]) + def test_validate_inplace_raises(self, value): + cat = Categorical(["A", "B", "B", "C", "A"]) + msg = ( + 'For argument "inplace" expected type bool, ' + f"received type {type(value).__name__}" + ) + + with pytest.raises(ValueError, match=msg): + cat.sort_values(inplace=value) + + def test_quantile_empty(self): + # make sure we have correct itemsize on resulting codes + cat = Categorical(["A", "B"]) + idx = Index([0.0, 0.5]) + result = cat[:0]._quantile(idx, interpolation="linear") + assert result._codes.dtype == np.int8 + + expected = cat.take([-1, -1], allow_fill=True) + tm.assert_extension_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_api.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..a939ee5f6f53f805211d46773c625c8361203991 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_api.py @@ -0,0 +1,501 @@ +import re + +import numpy as np +import pytest + +from pandas.compat import PY311 + +from pandas import ( + Categorical, + CategoricalIndex, + DataFrame, + Index, + Series, + StringDtype, +) +import pandas._testing as tm +from pandas.core.arrays.categorical import recode_for_categories + + +class TestCategoricalAPI: + def test_to_list_deprecated(self): + # GH#51254 + cat1 = Categorical(list("acb"), ordered=False) + msg = "Categorical.to_list is deprecated and will be removed" + with tm.assert_produces_warning(FutureWarning, match=msg): + cat1.to_list() + + def test_ordered_api(self): + # GH 9347 + cat1 = Categorical(list("acb"), ordered=False) + tm.assert_index_equal(cat1.categories, Index(["a", "b", "c"])) + assert not cat1.ordered + + cat2 = Categorical(list("acb"), categories=list("bca"), ordered=False) + tm.assert_index_equal(cat2.categories, Index(["b", "c", "a"])) + assert not cat2.ordered + + cat3 = Categorical(list("acb"), ordered=True) + tm.assert_index_equal(cat3.categories, Index(["a", "b", "c"])) + assert cat3.ordered + + cat4 = Categorical(list("acb"), categories=list("bca"), ordered=True) + tm.assert_index_equal(cat4.categories, Index(["b", "c", "a"])) + assert cat4.ordered + + def test_set_ordered(self): + cat = Categorical(["a", "b", "c", "a"], ordered=True) + cat2 = cat.as_unordered() + assert not cat2.ordered + cat2 = cat.as_ordered() + assert cat2.ordered + + assert cat2.set_ordered(True).ordered + assert not cat2.set_ordered(False).ordered + + # removed in 0.19.0 + msg = ( + "property 'ordered' of 'Categorical' object has no setter" + if PY311 + else "can't set attribute" + ) + with pytest.raises(AttributeError, match=msg): + cat.ordered = True + with pytest.raises(AttributeError, match=msg): + cat.ordered = False + + def test_rename_categories(self): + cat = Categorical(["a", "b", "c", "a"]) + + # inplace=False: the old one must not be changed + res = cat.rename_categories([1, 2, 3]) + tm.assert_numpy_array_equal( + res.__array__(), np.array([1, 2, 3, 1], dtype=np.int64) + ) + tm.assert_index_equal(res.categories, Index([1, 2, 3])) + + exp_cat = np.array(["a", "b", "c", "a"], dtype=np.object_) + tm.assert_numpy_array_equal(cat.__array__(), exp_cat) + + exp_cat = Index(["a", "b", "c"]) + tm.assert_index_equal(cat.categories, exp_cat) + + # GH18862 (let rename_categories take callables) + result = cat.rename_categories(lambda x: x.upper()) + expected = Categorical(["A", "B", "C", "A"]) + tm.assert_categorical_equal(result, expected) + + @pytest.mark.parametrize("new_categories", [[1, 2, 3, 4], [1, 2]]) + def test_rename_categories_wrong_length_raises(self, new_categories): + cat = Categorical(["a", "b", "c", "a"]) + msg = ( + "new categories need to have the same number of items as the " + "old categories!" + ) + with pytest.raises(ValueError, match=msg): + cat.rename_categories(new_categories) + + def test_rename_categories_series(self): + # https://github.com/pandas-dev/pandas/issues/17981 + c = Categorical(["a", "b"]) + result = c.rename_categories(Series([0, 1], index=["a", "b"])) + expected = Categorical([0, 1]) + tm.assert_categorical_equal(result, expected) + + def test_rename_categories_dict(self): + # GH 17336 + cat = Categorical(["a", "b", "c", "d"]) + res = cat.rename_categories({"a": 4, "b": 3, "c": 2, "d": 1}) + expected = Index([4, 3, 2, 1]) + tm.assert_index_equal(res.categories, expected) + + # Test for dicts of smaller length + cat = Categorical(["a", "b", "c", "d"]) + res = cat.rename_categories({"a": 1, "c": 3}) + + expected = Index([1, "b", 3, "d"]) + tm.assert_index_equal(res.categories, expected) + + # Test for dicts with bigger length + cat = Categorical(["a", "b", "c", "d"]) + res = cat.rename_categories({"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6}) + expected = Index([1, 2, 3, 4]) + tm.assert_index_equal(res.categories, expected) + + # Test for dicts with no items from old categories + cat = Categorical(["a", "b", "c", "d"]) + res = cat.rename_categories({"f": 1, "g": 3}) + + expected = Index(["a", "b", "c", "d"]) + tm.assert_index_equal(res.categories, expected) + + def test_reorder_categories(self): + cat = Categorical(["a", "b", "c", "a"], ordered=True) + old = cat.copy() + new = Categorical( + ["a", "b", "c", "a"], categories=["c", "b", "a"], ordered=True + ) + + res = cat.reorder_categories(["c", "b", "a"]) + # cat must be the same as before + tm.assert_categorical_equal(cat, old) + # only res is changed + tm.assert_categorical_equal(res, new) + + @pytest.mark.parametrize( + "new_categories", + [ + ["a"], # not all "old" included in "new" + ["a", "b", "d"], # still not all "old" in "new" + ["a", "b", "c", "d"], # all "old" included in "new", but too long + ], + ) + def test_reorder_categories_raises(self, new_categories): + cat = Categorical(["a", "b", "c", "a"], ordered=True) + msg = "items in new_categories are not the same as in old categories" + with pytest.raises(ValueError, match=msg): + cat.reorder_categories(new_categories) + + def test_add_categories(self): + cat = Categorical(["a", "b", "c", "a"], ordered=True) + old = cat.copy() + new = Categorical( + ["a", "b", "c", "a"], categories=["a", "b", "c", "d"], ordered=True + ) + + res = cat.add_categories("d") + tm.assert_categorical_equal(cat, old) + tm.assert_categorical_equal(res, new) + + res = cat.add_categories(["d"]) + tm.assert_categorical_equal(cat, old) + tm.assert_categorical_equal(res, new) + + # GH 9927 + cat = Categorical(list("abc"), ordered=True) + expected = Categorical(list("abc"), categories=list("abcde"), ordered=True) + # test with Series, np.array, index, list + res = cat.add_categories(Series(["d", "e"])) + tm.assert_categorical_equal(res, expected) + res = cat.add_categories(np.array(["d", "e"])) + tm.assert_categorical_equal(res, expected) + res = cat.add_categories(Index(["d", "e"])) + tm.assert_categorical_equal(res, expected) + res = cat.add_categories(["d", "e"]) + tm.assert_categorical_equal(res, expected) + + def test_add_categories_existing_raises(self): + # new is in old categories + cat = Categorical(["a", "b", "c", "d"], ordered=True) + msg = re.escape("new categories must not include old categories: {'d'}") + with pytest.raises(ValueError, match=msg): + cat.add_categories(["d"]) + + def test_add_categories_losing_dtype_information(self): + # GH#48812 + cat = Categorical(Series([1, 2], dtype="Int64")) + ser = Series([4], dtype="Int64") + result = cat.add_categories(ser) + expected = Categorical( + Series([1, 2], dtype="Int64"), categories=Series([1, 2, 4], dtype="Int64") + ) + tm.assert_categorical_equal(result, expected) + + cat = Categorical(Series(["a", "b", "a"], dtype=StringDtype())) + ser = Series(["d"], dtype=StringDtype()) + result = cat.add_categories(ser) + expected = Categorical( + Series(["a", "b", "a"], dtype=StringDtype()), + categories=Series(["a", "b", "d"], dtype=StringDtype()), + ) + tm.assert_categorical_equal(result, expected) + + def test_set_categories(self): + cat = Categorical(["a", "b", "c", "a"], ordered=True) + exp_categories = Index(["c", "b", "a"]) + exp_values = np.array(["a", "b", "c", "a"], dtype=np.object_) + + cat = cat.set_categories(["c", "b", "a"]) + res = cat.set_categories(["a", "b", "c"]) + # cat must be the same as before + tm.assert_index_equal(cat.categories, exp_categories) + tm.assert_numpy_array_equal(cat.__array__(), exp_values) + # only res is changed + exp_categories_back = Index(["a", "b", "c"]) + tm.assert_index_equal(res.categories, exp_categories_back) + tm.assert_numpy_array_equal(res.__array__(), exp_values) + + # not all "old" included in "new" -> all not included ones are now + # np.nan + cat = Categorical(["a", "b", "c", "a"], ordered=True) + res = cat.set_categories(["a"]) + tm.assert_numpy_array_equal(res.codes, np.array([0, -1, -1, 0], dtype=np.int8)) + + # still not all "old" in "new" + res = cat.set_categories(["a", "b", "d"]) + tm.assert_numpy_array_equal(res.codes, np.array([0, 1, -1, 0], dtype=np.int8)) + tm.assert_index_equal(res.categories, Index(["a", "b", "d"])) + + # all "old" included in "new" + cat = cat.set_categories(["a", "b", "c", "d"]) + exp_categories = Index(["a", "b", "c", "d"]) + tm.assert_index_equal(cat.categories, exp_categories) + + # internals... + c = Categorical([1, 2, 3, 4, 1], categories=[1, 2, 3, 4], ordered=True) + tm.assert_numpy_array_equal(c._codes, np.array([0, 1, 2, 3, 0], dtype=np.int8)) + tm.assert_index_equal(c.categories, Index([1, 2, 3, 4])) + + exp = np.array([1, 2, 3, 4, 1], dtype=np.int64) + tm.assert_numpy_array_equal(np.asarray(c), exp) + + # all "pointers" to '4' must be changed from 3 to 0,... + c = c.set_categories([4, 3, 2, 1]) + + # positions are changed + tm.assert_numpy_array_equal(c._codes, np.array([3, 2, 1, 0, 3], dtype=np.int8)) + + # categories are now in new order + tm.assert_index_equal(c.categories, Index([4, 3, 2, 1])) + + # output is the same + exp = np.array([1, 2, 3, 4, 1], dtype=np.int64) + tm.assert_numpy_array_equal(np.asarray(c), exp) + assert c.min() == 4 + assert c.max() == 1 + + # set_categories should set the ordering if specified + c2 = c.set_categories([4, 3, 2, 1], ordered=False) + assert not c2.ordered + + tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2)) + + # set_categories should pass thru the ordering + c2 = c.set_ordered(False).set_categories([4, 3, 2, 1]) + assert not c2.ordered + + tm.assert_numpy_array_equal(np.asarray(c), np.asarray(c2)) + + @pytest.mark.parametrize( + "values, categories, new_categories", + [ + # No NaNs, same cats, same order + (["a", "b", "a"], ["a", "b"], ["a", "b"]), + # No NaNs, same cats, different order + (["a", "b", "a"], ["a", "b"], ["b", "a"]), + # Same, unsorted + (["b", "a", "a"], ["a", "b"], ["a", "b"]), + # No NaNs, same cats, different order + (["b", "a", "a"], ["a", "b"], ["b", "a"]), + # NaNs + (["a", "b", "c"], ["a", "b"], ["a", "b"]), + (["a", "b", "c"], ["a", "b"], ["b", "a"]), + (["b", "a", "c"], ["a", "b"], ["a", "b"]), + (["b", "a", "c"], ["a", "b"], ["a", "b"]), + # Introduce NaNs + (["a", "b", "c"], ["a", "b"], ["a"]), + (["a", "b", "c"], ["a", "b"], ["b"]), + (["b", "a", "c"], ["a", "b"], ["a"]), + (["b", "a", "c"], ["a", "b"], ["a"]), + # No overlap + (["a", "b", "c"], ["a", "b"], ["d", "e"]), + ], + ) + @pytest.mark.parametrize("ordered", [True, False]) + def test_set_categories_many(self, values, categories, new_categories, ordered): + c = Categorical(values, categories) + expected = Categorical(values, new_categories, ordered) + result = c.set_categories(new_categories, ordered=ordered) + tm.assert_categorical_equal(result, expected) + + def test_set_categories_rename_less(self): + # GH 24675 + cat = Categorical(["A", "B"]) + result = cat.set_categories(["A"], rename=True) + expected = Categorical(["A", np.nan]) + tm.assert_categorical_equal(result, expected) + + def test_set_categories_private(self): + cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"]) + cat._set_categories(["a", "c", "d", "e"]) + expected = Categorical(["a", "c", "d"], categories=list("acde")) + tm.assert_categorical_equal(cat, expected) + + # fastpath + cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"]) + cat._set_categories(["a", "c", "d", "e"], fastpath=True) + expected = Categorical(["a", "c", "d"], categories=list("acde")) + tm.assert_categorical_equal(cat, expected) + + def test_remove_categories(self): + cat = Categorical(["a", "b", "c", "a"], ordered=True) + old = cat.copy() + new = Categorical(["a", "b", np.nan, "a"], categories=["a", "b"], ordered=True) + + res = cat.remove_categories("c") + tm.assert_categorical_equal(cat, old) + tm.assert_categorical_equal(res, new) + + res = cat.remove_categories(["c"]) + tm.assert_categorical_equal(cat, old) + tm.assert_categorical_equal(res, new) + + @pytest.mark.parametrize("removals", [["c"], ["c", np.nan], "c", ["c", "c"]]) + def test_remove_categories_raises(self, removals): + cat = Categorical(["a", "b", "a"]) + message = re.escape("removals must all be in old categories: {'c'}") + + with pytest.raises(ValueError, match=message): + cat.remove_categories(removals) + + def test_remove_unused_categories(self): + c = Categorical(["a", "b", "c", "d", "a"], categories=["a", "b", "c", "d", "e"]) + exp_categories_all = Index(["a", "b", "c", "d", "e"]) + exp_categories_dropped = Index(["a", "b", "c", "d"]) + + tm.assert_index_equal(c.categories, exp_categories_all) + + res = c.remove_unused_categories() + tm.assert_index_equal(res.categories, exp_categories_dropped) + tm.assert_index_equal(c.categories, exp_categories_all) + + # with NaN values (GH11599) + c = Categorical(["a", "b", "c", np.nan], categories=["a", "b", "c", "d", "e"]) + res = c.remove_unused_categories() + tm.assert_index_equal(res.categories, Index(np.array(["a", "b", "c"]))) + exp_codes = np.array([0, 1, 2, -1], dtype=np.int8) + tm.assert_numpy_array_equal(res.codes, exp_codes) + tm.assert_index_equal(c.categories, exp_categories_all) + + val = ["F", np.nan, "D", "B", "D", "F", np.nan] + cat = Categorical(values=val, categories=list("ABCDEFG")) + out = cat.remove_unused_categories() + tm.assert_index_equal(out.categories, Index(["B", "D", "F"])) + exp_codes = np.array([2, -1, 1, 0, 1, 2, -1], dtype=np.int8) + tm.assert_numpy_array_equal(out.codes, exp_codes) + assert out.tolist() == val + + alpha = list("abcdefghijklmnopqrstuvwxyz") + val = np.random.default_rng(2).choice(alpha[::2], 10000).astype("object") + val[np.random.default_rng(2).choice(len(val), 100)] = np.nan + + cat = Categorical(values=val, categories=alpha) + out = cat.remove_unused_categories() + assert out.tolist() == val.tolist() + + +class TestCategoricalAPIWithFactor: + def test_describe(self): + factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True) + # string type + desc = factor.describe() + assert factor.ordered + exp_index = CategoricalIndex( + ["a", "b", "c"], name="categories", ordered=factor.ordered + ) + expected = DataFrame( + {"counts": [3, 2, 3], "freqs": [3 / 8.0, 2 / 8.0, 3 / 8.0]}, index=exp_index + ) + tm.assert_frame_equal(desc, expected) + + # check unused categories + cat = factor.copy() + cat = cat.set_categories(["a", "b", "c", "d"]) + desc = cat.describe() + + exp_index = CategoricalIndex( + list("abcd"), ordered=factor.ordered, name="categories" + ) + expected = DataFrame( + {"counts": [3, 2, 3, 0], "freqs": [3 / 8.0, 2 / 8.0, 3 / 8.0, 0]}, + index=exp_index, + ) + tm.assert_frame_equal(desc, expected) + + # check an integer one + cat = Categorical([1, 2, 3, 1, 2, 3, 3, 2, 1, 1, 1]) + desc = cat.describe() + exp_index = CategoricalIndex([1, 2, 3], ordered=cat.ordered, name="categories") + expected = DataFrame( + {"counts": [5, 3, 3], "freqs": [5 / 11.0, 3 / 11.0, 3 / 11.0]}, + index=exp_index, + ) + tm.assert_frame_equal(desc, expected) + + # https://github.com/pandas-dev/pandas/issues/3678 + # describe should work with NaN + cat = Categorical([np.nan, 1, 2, 2]) + desc = cat.describe() + expected = DataFrame( + {"counts": [1, 2, 1], "freqs": [1 / 4.0, 2 / 4.0, 1 / 4.0]}, + index=CategoricalIndex( + [1, 2, np.nan], categories=[1, 2], name="categories" + ), + ) + tm.assert_frame_equal(desc, expected) + + +class TestPrivateCategoricalAPI: + def test_codes_immutable(self): + # Codes should be read only + c = Categorical(["a", "b", "c", "a", np.nan]) + exp = np.array([0, 1, 2, 0, -1], dtype="int8") + tm.assert_numpy_array_equal(c.codes, exp) + + # Assignments to codes should raise + msg = ( + "property 'codes' of 'Categorical' object has no setter" + if PY311 + else "can't set attribute" + ) + with pytest.raises(AttributeError, match=msg): + c.codes = np.array([0, 1, 2, 0, 1], dtype="int8") + + # changes in the codes array should raise + codes = c.codes + + with pytest.raises(ValueError, match="assignment destination is read-only"): + codes[4] = 1 + + # But even after getting the codes, the original array should still be + # writeable! + c[4] = "a" + exp = np.array([0, 1, 2, 0, 0], dtype="int8") + tm.assert_numpy_array_equal(c.codes, exp) + c._codes[4] = 2 + exp = np.array([0, 1, 2, 0, 2], dtype="int8") + tm.assert_numpy_array_equal(c.codes, exp) + + @pytest.mark.parametrize( + "codes, old, new, expected", + [ + ([0, 1], ["a", "b"], ["a", "b"], [0, 1]), + ([0, 1], ["b", "a"], ["b", "a"], [0, 1]), + ([0, 1], ["a", "b"], ["b", "a"], [1, 0]), + ([0, 1], ["b", "a"], ["a", "b"], [1, 0]), + ([0, 1, 0, 1], ["a", "b"], ["a", "b", "c"], [0, 1, 0, 1]), + ([0, 1, 2, 2], ["a", "b", "c"], ["a", "b"], [0, 1, -1, -1]), + ([0, 1, -1], ["a", "b", "c"], ["a", "b", "c"], [0, 1, -1]), + ([0, 1, -1], ["a", "b", "c"], ["b"], [-1, 0, -1]), + ([0, 1, -1], ["a", "b", "c"], ["d"], [-1, -1, -1]), + ([0, 1, -1], ["a", "b", "c"], [], [-1, -1, -1]), + ([-1, -1], [], ["a", "b"], [-1, -1]), + ([1, 0], ["b", "a"], ["a", "b"], [0, 1]), + ], + ) + def test_recode_to_categories(self, codes, old, new, expected): + codes = np.asanyarray(codes, dtype=np.int8) + expected = np.asanyarray(expected, dtype=np.int8) + old = Index(old) + new = Index(new) + result = recode_for_categories(codes, old, new) + tm.assert_numpy_array_equal(result, expected) + + def test_recode_to_categories_large(self): + N = 1000 + codes = np.arange(N) + old = Index(codes) + expected = np.arange(N - 1, -1, -1, dtype=np.int16) + new = Index(expected) + result = recode_for_categories(codes, old, new) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_astype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..ee930ac84aaf246c6e79cb1c7eb7a7dfe179ff8c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_astype.py @@ -0,0 +1,155 @@ +import numpy as np +import pytest + +from pandas import ( + Categorical, + CategoricalDtype, + CategoricalIndex, + DatetimeIndex, + Interval, + NaT, + Period, + Timestamp, + array, + to_datetime, +) +import pandas._testing as tm + + +class TestAstype: + @pytest.mark.parametrize("cls", [Categorical, CategoricalIndex]) + @pytest.mark.parametrize("values", [[1, np.nan], [Timestamp("2000"), NaT]]) + def test_astype_nan_to_int(self, cls, values): + # GH#28406 + obj = cls(values) + + msg = "Cannot (cast|convert)" + with pytest.raises((ValueError, TypeError), match=msg): + obj.astype(int) + + @pytest.mark.parametrize( + "expected", + [ + array(["2019", "2020"], dtype="datetime64[ns, UTC]"), + array([0, 0], dtype="timedelta64[ns]"), + array([Period("2019"), Period("2020")], dtype="period[Y-DEC]"), + array([Interval(0, 1), Interval(1, 2)], dtype="interval"), + array([1, np.nan], dtype="Int64"), + ], + ) + def test_astype_category_to_extension_dtype(self, expected): + # GH#28668 + result = expected.astype("category").astype(expected.dtype) + + tm.assert_extension_array_equal(result, expected) + + @pytest.mark.parametrize( + "dtype, expected", + [ + ( + "datetime64[ns]", + np.array(["2015-01-01T00:00:00.000000000"], dtype="datetime64[ns]"), + ), + ( + "datetime64[ns, MET]", + DatetimeIndex([Timestamp("2015-01-01 00:00:00+0100", tz="MET")]).array, + ), + ], + ) + def test_astype_to_datetime64(self, dtype, expected): + # GH#28448 + result = Categorical(["2015-01-01"]).astype(dtype) + assert result == expected + + def test_astype_str_int_categories_to_nullable_int(self): + # GH#39616 + dtype = CategoricalDtype([str(i) for i in range(5)]) + codes = np.random.default_rng(2).integers(5, size=20) + arr = Categorical.from_codes(codes, dtype=dtype) + + res = arr.astype("Int64") + expected = array(codes, dtype="Int64") + tm.assert_extension_array_equal(res, expected) + + def test_astype_str_int_categories_to_nullable_float(self): + # GH#39616 + dtype = CategoricalDtype([str(i / 2) for i in range(5)]) + codes = np.random.default_rng(2).integers(5, size=20) + arr = Categorical.from_codes(codes, dtype=dtype) + + res = arr.astype("Float64") + expected = array(codes, dtype="Float64") / 2 + tm.assert_extension_array_equal(res, expected) + + @pytest.mark.parametrize("ordered", [True, False]) + def test_astype(self, ordered): + # string + cat = Categorical(list("abbaaccc"), ordered=ordered) + result = cat.astype(object) + expected = np.array(cat) + tm.assert_numpy_array_equal(result, expected) + + msg = r"Cannot cast object|str dtype to float64" + with pytest.raises(ValueError, match=msg): + cat.astype(float) + + # numeric + cat = Categorical([0, 1, 2, 2, 1, 0, 1, 0, 2], ordered=ordered) + result = cat.astype(object) + expected = np.array(cat, dtype=object) + tm.assert_numpy_array_equal(result, expected) + + result = cat.astype(int) + expected = np.array(cat, dtype="int") + tm.assert_numpy_array_equal(result, expected) + + result = cat.astype(float) + expected = np.array(cat, dtype=float) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("dtype_ordered", [True, False]) + @pytest.mark.parametrize("cat_ordered", [True, False]) + def test_astype_category(self, dtype_ordered, cat_ordered): + # GH#10696/GH#18593 + data = list("abcaacbab") + cat = Categorical(data, categories=list("bac"), ordered=cat_ordered) + + # standard categories + dtype = CategoricalDtype(ordered=dtype_ordered) + result = cat.astype(dtype) + expected = Categorical(data, categories=cat.categories, ordered=dtype_ordered) + tm.assert_categorical_equal(result, expected) + + # non-standard categories + dtype = CategoricalDtype(list("adc"), dtype_ordered) + result = cat.astype(dtype) + expected = Categorical(data, dtype=dtype) + tm.assert_categorical_equal(result, expected) + + if dtype_ordered is False: + # dtype='category' can't specify ordered, so only test once + result = cat.astype("category") + expected = cat + tm.assert_categorical_equal(result, expected) + + def test_astype_object_datetime_categories(self): + # GH#40754 + cat = Categorical(to_datetime(["2021-03-27", NaT])) + result = cat.astype(object) + expected = np.array([Timestamp("2021-03-27 00:00:00"), NaT], dtype="object") + tm.assert_numpy_array_equal(result, expected) + + def test_astype_object_timestamp_categories(self): + # GH#18024 + cat = Categorical([Timestamp("2014-01-01")]) + result = cat.astype(object) + expected = np.array([Timestamp("2014-01-01 00:00:00")], dtype="object") + tm.assert_numpy_array_equal(result, expected) + + def test_astype_category_readonly_mask_values(self): + # GH#53658 + arr = array([0, 1, 2], dtype="Int64") + arr._mask.flags["WRITEABLE"] = False + result = arr.astype("category") + expected = array([0, 1, 2], dtype="Int64").astype("category") + tm.assert_extension_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_constructors.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..8ac479cf8a0a4a7fb0c11f94f04bad496f7a6343 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_constructors.py @@ -0,0 +1,787 @@ +from datetime import ( + date, + datetime, +) + +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +from pandas.compat import HAS_PYARROW + +from pandas.core.dtypes.common import ( + is_float_dtype, + is_integer_dtype, +) +from pandas.core.dtypes.dtypes import CategoricalDtype + +import pandas as pd +from pandas import ( + Categorical, + CategoricalIndex, + DatetimeIndex, + Index, + Interval, + IntervalIndex, + MultiIndex, + NaT, + Series, + Timestamp, + date_range, + period_range, + timedelta_range, +) +import pandas._testing as tm + + +class TestCategoricalConstructors: + def test_fastpath_deprecated(self): + codes = np.array([1, 2, 3]) + dtype = CategoricalDtype(categories=["a", "b", "c", "d"], ordered=False) + msg = "The 'fastpath' keyword in Categorical is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + Categorical(codes, dtype=dtype, fastpath=True) + + def test_categorical_from_cat_and_dtype_str_preserve_ordered(self): + # GH#49309 we should preserve orderedness in `res` + cat = Categorical([3, 1], categories=[3, 2, 1], ordered=True) + + res = Categorical(cat, dtype="category") + assert res.dtype.ordered + + def test_categorical_disallows_scalar(self): + # GH#38433 + with pytest.raises(TypeError, match="Categorical input must be list-like"): + Categorical("A", categories=["A", "B"]) + + def test_categorical_1d_only(self): + # ndim > 1 + msg = "> 1 ndim Categorical are not supported at this time" + with pytest.raises(NotImplementedError, match=msg): + Categorical(np.array([list("abcd")])) + + def test_validate_ordered(self): + # see gh-14058 + exp_msg = "'ordered' must either be 'True' or 'False'" + exp_err = TypeError + + # This should be a boolean. + ordered = np.array([0, 1, 2]) + + with pytest.raises(exp_err, match=exp_msg): + Categorical([1, 2, 3], ordered=ordered) + + with pytest.raises(exp_err, match=exp_msg): + Categorical.from_codes( + [0, 0, 1], categories=["a", "b", "c"], ordered=ordered + ) + + def test_constructor_empty(self): + # GH 17248 + c = Categorical([]) + expected = Index([]) + tm.assert_index_equal(c.categories, expected) + + c = Categorical([], categories=[1, 2, 3]) + expected = Index([1, 2, 3], dtype=np.int64) + tm.assert_index_equal(c.categories, expected) + + def test_constructor_empty_boolean(self): + # see gh-22702 + cat = Categorical([], categories=[True, False]) + categories = sorted(cat.categories.tolist()) + assert categories == [False, True] + + def test_constructor_tuples(self): + values = np.array([(1,), (1, 2), (1,), (1, 2)], dtype=object) + result = Categorical(values) + expected = Index([(1,), (1, 2)], tupleize_cols=False) + tm.assert_index_equal(result.categories, expected) + assert result.ordered is False + + def test_constructor_tuples_datetimes(self): + # numpy will auto reshape when all of the tuples are the + # same len, so add an extra one with 2 items and slice it off + values = np.array( + [ + (Timestamp("2010-01-01"),), + (Timestamp("2010-01-02"),), + (Timestamp("2010-01-01"),), + (Timestamp("2010-01-02"),), + ("a", "b"), + ], + dtype=object, + )[:-1] + result = Categorical(values) + expected = Index( + [(Timestamp("2010-01-01"),), (Timestamp("2010-01-02"),)], + tupleize_cols=False, + ) + tm.assert_index_equal(result.categories, expected) + + def test_constructor_unsortable(self): + # it works! + arr = np.array([1, 2, 3, datetime.now()], dtype="O") + factor = Categorical(arr, ordered=False) + assert not factor.ordered + + # this however will raise as cannot be sorted + msg = ( + "'values' is not ordered, please explicitly specify the " + "categories order by passing in a categories argument." + ) + with pytest.raises(TypeError, match=msg): + Categorical(arr, ordered=True) + + def test_constructor_interval(self): + result = Categorical( + [Interval(1, 2), Interval(2, 3), Interval(3, 6)], ordered=True + ) + ii = IntervalIndex([Interval(1, 2), Interval(2, 3), Interval(3, 6)]) + exp = Categorical(ii, ordered=True) + tm.assert_categorical_equal(result, exp) + tm.assert_index_equal(result.categories, ii) + + def test_constructor(self): + exp_arr = np.array(["a", "b", "c", "a", "b", "c"], dtype=np.object_) + c1 = Categorical(exp_arr) + tm.assert_numpy_array_equal(c1.__array__(), exp_arr) + c2 = Categorical(exp_arr, categories=["a", "b", "c"]) + tm.assert_numpy_array_equal(c2.__array__(), exp_arr) + c2 = Categorical(exp_arr, categories=["c", "b", "a"]) + tm.assert_numpy_array_equal(c2.__array__(), exp_arr) + + # categories must be unique + msg = "Categorical categories must be unique" + with pytest.raises(ValueError, match=msg): + Categorical([1, 2], [1, 2, 2]) + + with pytest.raises(ValueError, match=msg): + Categorical(["a", "b"], ["a", "b", "b"]) + + # The default should be unordered + c1 = Categorical(["a", "b", "c", "a"]) + assert not c1.ordered + + # Categorical as input + c1 = Categorical(["a", "b", "c", "a"]) + c2 = Categorical(c1) + tm.assert_categorical_equal(c1, c2) + + c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"]) + c2 = Categorical(c1) + tm.assert_categorical_equal(c1, c2) + + c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"]) + c2 = Categorical(c1) + tm.assert_categorical_equal(c1, c2) + + c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"]) + c2 = Categorical(c1, categories=["a", "b", "c"]) + tm.assert_numpy_array_equal(c1.__array__(), c2.__array__()) + tm.assert_index_equal(c2.categories, Index(["a", "b", "c"])) + + # Series of dtype category + c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"]) + c2 = Categorical(Series(c1)) + tm.assert_categorical_equal(c1, c2) + + c1 = Categorical(["a", "b", "c", "a"], categories=["a", "c", "b"]) + c2 = Categorical(Series(c1)) + tm.assert_categorical_equal(c1, c2) + + # Series + c1 = Categorical(["a", "b", "c", "a"]) + c2 = Categorical(Series(["a", "b", "c", "a"])) + tm.assert_categorical_equal(c1, c2) + + c1 = Categorical(["a", "b", "c", "a"], categories=["a", "b", "c", "d"]) + c2 = Categorical(Series(["a", "b", "c", "a"]), categories=["a", "b", "c", "d"]) + tm.assert_categorical_equal(c1, c2) + + # This should result in integer categories, not float! + cat = Categorical([1, 2, 3, np.nan], categories=[1, 2, 3]) + assert is_integer_dtype(cat.categories) + + # https://github.com/pandas-dev/pandas/issues/3678 + cat = Categorical([np.nan, 1, 2, 3]) + assert is_integer_dtype(cat.categories) + + # this should result in floats + cat = Categorical([np.nan, 1, 2.0, 3]) + assert is_float_dtype(cat.categories) + + cat = Categorical([np.nan, 1.0, 2.0, 3.0]) + assert is_float_dtype(cat.categories) + + # This doesn't work -> this would probably need some kind of "remember + # the original type" feature to try to cast the array interface result + # to... + + # vals = np.asarray(cat[cat.notna()]) + # assert is_integer_dtype(vals) + + # corner cases + cat = Categorical([1]) + assert len(cat.categories) == 1 + assert cat.categories[0] == 1 + assert len(cat.codes) == 1 + assert cat.codes[0] == 0 + + cat = Categorical(["a"]) + assert len(cat.categories) == 1 + assert cat.categories[0] == "a" + assert len(cat.codes) == 1 + assert cat.codes[0] == 0 + + # two arrays + # - when the first is an integer dtype and the second is not + # - when the resulting codes are all -1/NaN + with tm.assert_produces_warning(None): + Categorical([0, 1, 2, 0, 1, 2], categories=["a", "b", "c"]) + + with tm.assert_produces_warning(None): + Categorical([0, 1, 2, 0, 1, 2], categories=[3, 4, 5]) + + # the next one are from the old docs + with tm.assert_produces_warning(None): + Categorical([0, 1, 2, 0, 1, 2], [1, 2, 3]) + cat = Categorical([1, 2], categories=[1, 2, 3]) + + # this is a legitimate constructor + with tm.assert_produces_warning(None): + Categorical(np.array([], dtype="int64"), categories=[3, 2, 1], ordered=True) + + def test_constructor_with_existing_categories(self): + # GH25318: constructing with pd.Series used to bogusly skip recoding + # categories + c0 = Categorical(["a", "b", "c", "a"]) + c1 = Categorical(["a", "b", "c", "a"], categories=["b", "c"]) + + c2 = Categorical(c0, categories=c1.categories) + tm.assert_categorical_equal(c1, c2) + + c3 = Categorical(Series(c0), categories=c1.categories) + tm.assert_categorical_equal(c1, c3) + + def test_constructor_not_sequence(self): + # https://github.com/pandas-dev/pandas/issues/16022 + msg = r"^Parameter 'categories' must be list-like, was" + with pytest.raises(TypeError, match=msg): + Categorical(["a", "b"], categories="a") + + def test_constructor_with_null(self): + # Cannot have NaN in categories + msg = "Categorical categories cannot be null" + with pytest.raises(ValueError, match=msg): + Categorical([np.nan, "a", "b", "c"], categories=[np.nan, "a", "b", "c"]) + + with pytest.raises(ValueError, match=msg): + Categorical([None, "a", "b", "c"], categories=[None, "a", "b", "c"]) + + with pytest.raises(ValueError, match=msg): + Categorical( + DatetimeIndex(["nat", "20160101"]), + categories=[NaT, Timestamp("20160101")], + ) + + def test_constructor_with_index(self): + ci = CategoricalIndex(list("aabbca"), categories=list("cab")) + tm.assert_categorical_equal(ci.values, Categorical(ci)) + + ci = CategoricalIndex(list("aabbca"), categories=list("cab")) + tm.assert_categorical_equal( + ci.values, Categorical(ci.astype(object), categories=ci.categories) + ) + + def test_constructor_with_generator(self): + # This was raising an Error in isna(single_val).any() because isna + # returned a scalar for a generator + + exp = Categorical([0, 1, 2]) + cat = Categorical(x for x in [0, 1, 2]) + tm.assert_categorical_equal(cat, exp) + cat = Categorical(range(3)) + tm.assert_categorical_equal(cat, exp) + + MultiIndex.from_product([range(5), ["a", "b", "c"]]) + + # check that categories accept generators and sequences + cat = Categorical([0, 1, 2], categories=(x for x in [0, 1, 2])) + tm.assert_categorical_equal(cat, exp) + cat = Categorical([0, 1, 2], categories=range(3)) + tm.assert_categorical_equal(cat, exp) + + def test_constructor_with_rangeindex(self): + # RangeIndex is preserved in Categories + rng = Index(range(3)) + + cat = Categorical(rng) + tm.assert_index_equal(cat.categories, rng, exact=True) + + cat = Categorical([1, 2, 0], categories=rng) + tm.assert_index_equal(cat.categories, rng, exact=True) + + @pytest.mark.parametrize( + "dtl", + [ + date_range("1995-01-01 00:00:00", periods=5, freq="s"), + date_range("1995-01-01 00:00:00", periods=5, freq="s", tz="US/Eastern"), + timedelta_range("1 day", periods=5, freq="s"), + ], + ) + def test_constructor_with_datetimelike(self, dtl): + # see gh-12077 + # constructor with a datetimelike and NaT + + s = Series(dtl) + c = Categorical(s) + + expected = type(dtl)(s) + expected._data.freq = None + + tm.assert_index_equal(c.categories, expected) + tm.assert_numpy_array_equal(c.codes, np.arange(5, dtype="int8")) + + # with NaT + s2 = s.copy() + s2.iloc[-1] = NaT + c = Categorical(s2) + + expected = type(dtl)(s2.dropna()) + expected._data.freq = None + + tm.assert_index_equal(c.categories, expected) + + exp = np.array([0, 1, 2, 3, -1], dtype=np.int8) + tm.assert_numpy_array_equal(c.codes, exp) + + result = repr(c) + assert "NaT" in result + + def test_constructor_from_index_series_datetimetz(self): + idx = date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern") + idx = idx._with_freq(None) # freq not preserved in result.categories + result = Categorical(idx) + tm.assert_index_equal(result.categories, idx) + + result = Categorical(Series(idx)) + tm.assert_index_equal(result.categories, idx) + + def test_constructor_date_objects(self): + # we dont cast date objects to timestamps, matching Index constructor + v = date.today() + + cat = Categorical([v, v]) + assert cat.categories.dtype == object + assert type(cat.categories[0]) is date + + def test_constructor_from_index_series_timedelta(self): + idx = timedelta_range("1 days", freq="D", periods=3) + idx = idx._with_freq(None) # freq not preserved in result.categories + result = Categorical(idx) + tm.assert_index_equal(result.categories, idx) + + result = Categorical(Series(idx)) + tm.assert_index_equal(result.categories, idx) + + def test_constructor_from_index_series_period(self): + idx = period_range("2015-01-01", freq="D", periods=3) + result = Categorical(idx) + tm.assert_index_equal(result.categories, idx) + + result = Categorical(Series(idx)) + tm.assert_index_equal(result.categories, idx) + + @pytest.mark.parametrize( + "values", + [ + np.array([1.0, 1.2, 1.8, np.nan]), + np.array([1, 2, 3], dtype="int64"), + ["a", "b", "c", np.nan], + [pd.Period("2014-01"), pd.Period("2014-02"), NaT], + [Timestamp("2014-01-01"), Timestamp("2014-01-02"), NaT], + [ + Timestamp("2014-01-01", tz="US/Eastern"), + Timestamp("2014-01-02", tz="US/Eastern"), + NaT, + ], + ], + ) + def test_constructor_invariant(self, values): + # GH 14190 + c = Categorical(values) + c2 = Categorical(c) + tm.assert_categorical_equal(c, c2) + + @pytest.mark.parametrize("ordered", [True, False]) + def test_constructor_with_dtype(self, ordered): + categories = ["b", "a", "c"] + dtype = CategoricalDtype(categories, ordered=ordered) + result = Categorical(["a", "b", "a", "c"], dtype=dtype) + expected = Categorical( + ["a", "b", "a", "c"], categories=categories, ordered=ordered + ) + tm.assert_categorical_equal(result, expected) + assert result.ordered is ordered + + def test_constructor_dtype_and_others_raises(self): + dtype = CategoricalDtype(["a", "b"], ordered=True) + msg = "Cannot specify `categories` or `ordered` together with `dtype`." + with pytest.raises(ValueError, match=msg): + Categorical(["a", "b"], categories=["a", "b"], dtype=dtype) + + with pytest.raises(ValueError, match=msg): + Categorical(["a", "b"], ordered=True, dtype=dtype) + + with pytest.raises(ValueError, match=msg): + Categorical(["a", "b"], ordered=False, dtype=dtype) + + @pytest.mark.parametrize("categories", [None, ["a", "b"], ["a", "c"]]) + @pytest.mark.parametrize("ordered", [True, False]) + def test_constructor_str_category(self, categories, ordered): + result = Categorical( + ["a", "b"], categories=categories, ordered=ordered, dtype="category" + ) + expected = Categorical(["a", "b"], categories=categories, ordered=ordered) + tm.assert_categorical_equal(result, expected) + + def test_constructor_str_unknown(self): + with pytest.raises(ValueError, match="Unknown dtype"): + Categorical([1, 2], dtype="foo") + + @pytest.mark.xfail( + using_string_dtype() and HAS_PYARROW, reason="Can't be NumPy strings" + ) + def test_constructor_np_strs(self): + # GH#31499 Hashtable.map_locations needs to work on np.str_ objects + cat = Categorical(["1", "0", "1"], [np.str_("0"), np.str_("1")]) + assert all(isinstance(x, np.str_) for x in cat.categories) + + def test_constructor_from_categorical_with_dtype(self): + dtype = CategoricalDtype(["a", "b", "c"], ordered=True) + values = Categorical(["a", "b", "d"]) + result = Categorical(values, dtype=dtype) + # We use dtype.categories, not values.categories + expected = Categorical( + ["a", "b", "d"], categories=["a", "b", "c"], ordered=True + ) + tm.assert_categorical_equal(result, expected) + + def test_constructor_from_categorical_with_unknown_dtype(self): + dtype = CategoricalDtype(None, ordered=True) + values = Categorical(["a", "b", "d"]) + result = Categorical(values, dtype=dtype) + # We use values.categories, not dtype.categories + expected = Categorical( + ["a", "b", "d"], categories=["a", "b", "d"], ordered=True + ) + tm.assert_categorical_equal(result, expected) + + def test_constructor_from_categorical_string(self): + values = Categorical(["a", "b", "d"]) + # use categories, ordered + result = Categorical( + values, categories=["a", "b", "c"], ordered=True, dtype="category" + ) + expected = Categorical( + ["a", "b", "d"], categories=["a", "b", "c"], ordered=True + ) + tm.assert_categorical_equal(result, expected) + + # No string + result = Categorical(values, categories=["a", "b", "c"], ordered=True) + tm.assert_categorical_equal(result, expected) + + def test_constructor_with_categorical_categories(self): + # GH17884 + expected = Categorical(["a", "b"], categories=["a", "b", "c"]) + + result = Categorical(["a", "b"], categories=Categorical(["a", "b", "c"])) + tm.assert_categorical_equal(result, expected) + + result = Categorical(["a", "b"], categories=CategoricalIndex(["a", "b", "c"])) + tm.assert_categorical_equal(result, expected) + + @pytest.mark.parametrize("klass", [lambda x: np.array(x, dtype=object), list]) + def test_construction_with_null(self, klass, nulls_fixture): + # https://github.com/pandas-dev/pandas/issues/31927 + values = klass(["a", nulls_fixture, "b"]) + result = Categorical(values) + + dtype = CategoricalDtype(["a", "b"]) + codes = [0, -1, 1] + expected = Categorical.from_codes(codes=codes, dtype=dtype) + + tm.assert_categorical_equal(result, expected) + + @pytest.mark.parametrize("validate", [True, False]) + def test_from_codes_nullable_int_categories(self, any_numeric_ea_dtype, validate): + # GH#39649 + cats = pd.array(range(5), dtype=any_numeric_ea_dtype) + codes = np.random.default_rng(2).integers(5, size=3) + dtype = CategoricalDtype(cats) + arr = Categorical.from_codes(codes, dtype=dtype, validate=validate) + assert arr.categories.dtype == cats.dtype + tm.assert_index_equal(arr.categories, Index(cats)) + + def test_from_codes_empty(self): + cat = ["a", "b", "c"] + result = Categorical.from_codes([], categories=cat) + expected = Categorical([], categories=cat) + + tm.assert_categorical_equal(result, expected) + + @pytest.mark.parametrize("validate", [True, False]) + def test_from_codes_validate(self, validate): + # GH53122 + dtype = CategoricalDtype(["a", "b"]) + if validate: + with pytest.raises(ValueError, match="codes need to be between "): + Categorical.from_codes([4, 5], dtype=dtype, validate=validate) + else: + # passes, though has incorrect codes, but that's the user responsibility + Categorical.from_codes([4, 5], dtype=dtype, validate=validate) + + def test_from_codes_too_few_categories(self): + dtype = CategoricalDtype(categories=[1, 2]) + msg = "codes need to be between " + with pytest.raises(ValueError, match=msg): + Categorical.from_codes([1, 2], categories=dtype.categories) + with pytest.raises(ValueError, match=msg): + Categorical.from_codes([1, 2], dtype=dtype) + + def test_from_codes_non_int_codes(self): + dtype = CategoricalDtype(categories=[1, 2]) + msg = "codes need to be array-like integers" + with pytest.raises(ValueError, match=msg): + Categorical.from_codes(["a"], categories=dtype.categories) + with pytest.raises(ValueError, match=msg): + Categorical.from_codes(["a"], dtype=dtype) + + def test_from_codes_non_unique_categories(self): + with pytest.raises(ValueError, match="Categorical categories must be unique"): + Categorical.from_codes([0, 1, 2], categories=["a", "a", "b"]) + + def test_from_codes_nan_cat_included(self): + with pytest.raises(ValueError, match="Categorical categories cannot be null"): + Categorical.from_codes([0, 1, 2], categories=["a", "b", np.nan]) + + def test_from_codes_too_negative(self): + dtype = CategoricalDtype(categories=["a", "b", "c"]) + msg = r"codes need to be between -1 and len\(categories\)-1" + with pytest.raises(ValueError, match=msg): + Categorical.from_codes([-2, 1, 2], categories=dtype.categories) + with pytest.raises(ValueError, match=msg): + Categorical.from_codes([-2, 1, 2], dtype=dtype) + + def test_from_codes(self): + dtype = CategoricalDtype(categories=["a", "b", "c"]) + exp = Categorical(["a", "b", "c"], ordered=False) + res = Categorical.from_codes([0, 1, 2], categories=dtype.categories) + tm.assert_categorical_equal(exp, res) + + res = Categorical.from_codes([0, 1, 2], dtype=dtype) + tm.assert_categorical_equal(exp, res) + + @pytest.mark.parametrize("klass", [Categorical, CategoricalIndex]) + def test_from_codes_with_categorical_categories(self, klass): + # GH17884 + expected = Categorical(["a", "b"], categories=["a", "b", "c"]) + + result = Categorical.from_codes([0, 1], categories=klass(["a", "b", "c"])) + tm.assert_categorical_equal(result, expected) + + @pytest.mark.parametrize("klass", [Categorical, CategoricalIndex]) + def test_from_codes_with_non_unique_categorical_categories(self, klass): + with pytest.raises(ValueError, match="Categorical categories must be unique"): + Categorical.from_codes([0, 1], klass(["a", "b", "a"])) + + def test_from_codes_with_nan_code(self): + # GH21767 + codes = [1, 2, np.nan] + dtype = CategoricalDtype(categories=["a", "b", "c"]) + with pytest.raises(ValueError, match="codes need to be array-like integers"): + Categorical.from_codes(codes, categories=dtype.categories) + with pytest.raises(ValueError, match="codes need to be array-like integers"): + Categorical.from_codes(codes, dtype=dtype) + + @pytest.mark.parametrize("codes", [[1.0, 2.0, 0], [1.1, 2.0, 0]]) + def test_from_codes_with_float(self, codes): + # GH21767 + # float codes should raise even if values are equal to integers + dtype = CategoricalDtype(categories=["a", "b", "c"]) + + msg = "codes need to be array-like integers" + with pytest.raises(ValueError, match=msg): + Categorical.from_codes(codes, dtype.categories) + with pytest.raises(ValueError, match=msg): + Categorical.from_codes(codes, dtype=dtype) + + def test_from_codes_with_dtype_raises(self): + msg = "Cannot specify" + with pytest.raises(ValueError, match=msg): + Categorical.from_codes( + [0, 1], categories=["a", "b"], dtype=CategoricalDtype(["a", "b"]) + ) + + with pytest.raises(ValueError, match=msg): + Categorical.from_codes( + [0, 1], ordered=True, dtype=CategoricalDtype(["a", "b"]) + ) + + def test_from_codes_neither(self): + msg = "Both were None" + with pytest.raises(ValueError, match=msg): + Categorical.from_codes([0, 1]) + + def test_from_codes_with_nullable_int(self): + codes = pd.array([0, 1], dtype="Int64") + categories = ["a", "b"] + + result = Categorical.from_codes(codes, categories=categories) + expected = Categorical.from_codes(codes.to_numpy(int), categories=categories) + + tm.assert_categorical_equal(result, expected) + + def test_from_codes_with_nullable_int_na_raises(self): + codes = pd.array([0, None], dtype="Int64") + categories = ["a", "b"] + + msg = "codes cannot contain NA values" + with pytest.raises(ValueError, match=msg): + Categorical.from_codes(codes, categories=categories) + + @pytest.mark.parametrize("dtype", [None, "category"]) + def test_from_inferred_categories(self, dtype): + cats = ["a", "b"] + codes = np.array([0, 0, 1, 1], dtype="i8") + result = Categorical._from_inferred_categories(cats, codes, dtype) + expected = Categorical.from_codes(codes, cats) + tm.assert_categorical_equal(result, expected) + + @pytest.mark.parametrize("dtype", [None, "category"]) + def test_from_inferred_categories_sorts(self, dtype): + cats = ["b", "a"] + codes = np.array([0, 1, 1, 1], dtype="i8") + result = Categorical._from_inferred_categories(cats, codes, dtype) + expected = Categorical.from_codes([1, 0, 0, 0], ["a", "b"]) + tm.assert_categorical_equal(result, expected) + + def test_from_inferred_categories_dtype(self): + cats = ["a", "b", "d"] + codes = np.array([0, 1, 0, 2], dtype="i8") + dtype = CategoricalDtype(["c", "b", "a"], ordered=True) + result = Categorical._from_inferred_categories(cats, codes, dtype) + expected = Categorical( + ["a", "b", "a", "d"], categories=["c", "b", "a"], ordered=True + ) + tm.assert_categorical_equal(result, expected) + + def test_from_inferred_categories_coerces(self): + cats = ["1", "2", "bad"] + codes = np.array([0, 0, 1, 2], dtype="i8") + dtype = CategoricalDtype([1, 2]) + result = Categorical._from_inferred_categories(cats, codes, dtype) + expected = Categorical([1, 1, 2, np.nan]) + tm.assert_categorical_equal(result, expected) + + @pytest.mark.parametrize("ordered", [None, True, False]) + def test_construction_with_ordered(self, ordered): + # GH 9347, 9190 + cat = Categorical([0, 1, 2], ordered=ordered) + assert cat.ordered == bool(ordered) + + def test_constructor_imaginary(self): + values = [1, 2, 3 + 1j] + c1 = Categorical(values) + tm.assert_index_equal(c1.categories, Index(values)) + tm.assert_numpy_array_equal(np.array(c1), np.array(values)) + + def test_constructor_string_and_tuples(self): + # GH 21416 + c = Categorical(np.array(["c", ("a", "b"), ("b", "a"), "c"], dtype=object)) + expected_index = Index([("a", "b"), ("b", "a"), "c"]) + assert c.categories.equals(expected_index) + + def test_interval(self): + idx = pd.interval_range(0, 10, periods=10) + cat = Categorical(idx, categories=idx) + expected_codes = np.arange(10, dtype="int8") + tm.assert_numpy_array_equal(cat.codes, expected_codes) + tm.assert_index_equal(cat.categories, idx) + + # infer categories + cat = Categorical(idx) + tm.assert_numpy_array_equal(cat.codes, expected_codes) + tm.assert_index_equal(cat.categories, idx) + + # list values + cat = Categorical(list(idx)) + tm.assert_numpy_array_equal(cat.codes, expected_codes) + tm.assert_index_equal(cat.categories, idx) + + # list values, categories + cat = Categorical(list(idx), categories=list(idx)) + tm.assert_numpy_array_equal(cat.codes, expected_codes) + tm.assert_index_equal(cat.categories, idx) + + # shuffled + values = idx.take([1, 2, 0]) + cat = Categorical(values, categories=idx) + tm.assert_numpy_array_equal(cat.codes, np.array([1, 2, 0], dtype="int8")) + tm.assert_index_equal(cat.categories, idx) + + # extra + values = pd.interval_range(8, 11, periods=3) + cat = Categorical(values, categories=idx) + expected_codes = np.array([8, 9, -1], dtype="int8") + tm.assert_numpy_array_equal(cat.codes, expected_codes) + tm.assert_index_equal(cat.categories, idx) + + # overlapping + idx = IntervalIndex([Interval(0, 2), Interval(0, 1)]) + cat = Categorical(idx, categories=idx) + expected_codes = np.array([0, 1], dtype="int8") + tm.assert_numpy_array_equal(cat.codes, expected_codes) + tm.assert_index_equal(cat.categories, idx) + + def test_categorical_extension_array_nullable(self, nulls_fixture): + # GH: + arr = pd.arrays.StringArray._from_sequence( + [nulls_fixture] * 2, dtype=pd.StringDtype() + ) + result = Categorical(arr) + assert arr.dtype == result.categories.dtype + expected = Categorical(Series([pd.NA, pd.NA], dtype=arr.dtype)) + tm.assert_categorical_equal(result, expected) + + def test_from_sequence_copy(self): + cat = Categorical(np.arange(5).repeat(2)) + result = Categorical._from_sequence(cat, dtype=cat.dtype, copy=False) + + # more generally, we'd be OK with a view + assert result._codes is cat._codes + + result = Categorical._from_sequence(cat, dtype=cat.dtype, copy=True) + + assert not tm.shares_memory(result, cat) + + def test_constructor_datetime64_non_nano(self): + categories = np.arange(10).view("M8[D]") + values = categories[::2].copy() + + cat = Categorical(values, categories=categories) + assert (cat == values).all() + + def test_constructor_preserves_freq(self): + # GH33830 freq retention in categorical + dti = date_range("2016-01-01", periods=5) + + expected = dti.freq + + cat = Categorical(dti) + result = cat.categories.freq + + assert expected == result diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_dtypes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..525663cad1745880bc5e683e7302afdc2c06a527 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_dtypes.py @@ -0,0 +1,139 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.dtypes import CategoricalDtype + +from pandas import ( + Categorical, + CategoricalIndex, + Index, + IntervalIndex, + Series, + Timestamp, +) +import pandas._testing as tm + + +class TestCategoricalDtypes: + def test_categories_match_up_to_permutation(self): + # test dtype comparisons between cats + + c1 = Categorical(list("aabca"), categories=list("abc"), ordered=False) + c2 = Categorical(list("aabca"), categories=list("cab"), ordered=False) + c3 = Categorical(list("aabca"), categories=list("cab"), ordered=True) + assert c1._categories_match_up_to_permutation(c1) + assert c2._categories_match_up_to_permutation(c2) + assert c3._categories_match_up_to_permutation(c3) + assert c1._categories_match_up_to_permutation(c2) + assert not c1._categories_match_up_to_permutation(c3) + assert not c1._categories_match_up_to_permutation(Index(list("aabca"))) + assert not c1._categories_match_up_to_permutation(c1.astype(object)) + assert c1._categories_match_up_to_permutation(CategoricalIndex(c1)) + assert c1._categories_match_up_to_permutation( + CategoricalIndex(c1, categories=list("cab")) + ) + assert not c1._categories_match_up_to_permutation( + CategoricalIndex(c1, ordered=True) + ) + + # GH 16659 + s1 = Series(c1) + s2 = Series(c2) + s3 = Series(c3) + assert c1._categories_match_up_to_permutation(s1) + assert c2._categories_match_up_to_permutation(s2) + assert c3._categories_match_up_to_permutation(s3) + assert c1._categories_match_up_to_permutation(s2) + assert not c1._categories_match_up_to_permutation(s3) + assert not c1._categories_match_up_to_permutation(s1.astype(object)) + + def test_set_dtype_same(self): + c = Categorical(["a", "b", "c"]) + result = c._set_dtype(CategoricalDtype(["a", "b", "c"])) + tm.assert_categorical_equal(result, c) + + def test_set_dtype_new_categories(self): + c = Categorical(["a", "b", "c"]) + result = c._set_dtype(CategoricalDtype(list("abcd"))) + tm.assert_numpy_array_equal(result.codes, c.codes) + tm.assert_index_equal(result.dtype.categories, Index(list("abcd"))) + + @pytest.mark.parametrize( + "values, categories, new_categories", + [ + # No NaNs, same cats, same order + (["a", "b", "a"], ["a", "b"], ["a", "b"]), + # No NaNs, same cats, different order + (["a", "b", "a"], ["a", "b"], ["b", "a"]), + # Same, unsorted + (["b", "a", "a"], ["a", "b"], ["a", "b"]), + # No NaNs, same cats, different order + (["b", "a", "a"], ["a", "b"], ["b", "a"]), + # NaNs + (["a", "b", "c"], ["a", "b"], ["a", "b"]), + (["a", "b", "c"], ["a", "b"], ["b", "a"]), + (["b", "a", "c"], ["a", "b"], ["a", "b"]), + (["b", "a", "c"], ["a", "b"], ["a", "b"]), + # Introduce NaNs + (["a", "b", "c"], ["a", "b"], ["a"]), + (["a", "b", "c"], ["a", "b"], ["b"]), + (["b", "a", "c"], ["a", "b"], ["a"]), + (["b", "a", "c"], ["a", "b"], ["a"]), + # No overlap + (["a", "b", "c"], ["a", "b"], ["d", "e"]), + ], + ) + @pytest.mark.parametrize("ordered", [True, False]) + def test_set_dtype_many(self, values, categories, new_categories, ordered): + c = Categorical(values, categories) + expected = Categorical(values, new_categories, ordered) + result = c._set_dtype(expected.dtype) + tm.assert_categorical_equal(result, expected) + + def test_set_dtype_no_overlap(self): + c = Categorical(["a", "b", "c"], ["d", "e"]) + result = c._set_dtype(CategoricalDtype(["a", "b"])) + expected = Categorical([None, None, None], categories=["a", "b"]) + tm.assert_categorical_equal(result, expected) + + def test_codes_dtypes(self): + # GH 8453 + result = Categorical(["foo", "bar", "baz"]) + assert result.codes.dtype == "int8" + + result = Categorical([f"foo{i:05d}" for i in range(400)]) + assert result.codes.dtype == "int16" + + result = Categorical([f"foo{i:05d}" for i in range(40000)]) + assert result.codes.dtype == "int32" + + # adding cats + result = Categorical(["foo", "bar", "baz"]) + assert result.codes.dtype == "int8" + result = result.add_categories([f"foo{i:05d}" for i in range(400)]) + assert result.codes.dtype == "int16" + + # removing cats + result = result.remove_categories([f"foo{i:05d}" for i in range(300)]) + assert result.codes.dtype == "int8" + + def test_iter_python_types(self): + # GH-19909 + cat = Categorical([1, 2]) + assert isinstance(next(iter(cat)), int) + assert isinstance(cat.tolist()[0], int) + + def test_iter_python_types_datetime(self): + cat = Categorical([Timestamp("2017-01-01"), Timestamp("2017-01-02")]) + assert isinstance(next(iter(cat)), Timestamp) + assert isinstance(cat.tolist()[0], Timestamp) + + def test_interval_index_category(self): + # GH 38316 + index = IntervalIndex.from_breaks(np.arange(3, dtype="uint64")) + + result = CategoricalIndex(index).dtype.categories + expected = IntervalIndex.from_arrays( + [0, 1], [1, 2], dtype="interval[uint64, right]" + ) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..5e1c5c64fa660f501d2b9d77c9181f47e013267f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_indexing.py @@ -0,0 +1,388 @@ +import math + +import numpy as np +import pytest + +from pandas import ( + NA, + Categorical, + CategoricalIndex, + Index, + Interval, + IntervalIndex, + NaT, + PeriodIndex, + Series, + Timedelta, + Timestamp, +) +import pandas._testing as tm +import pandas.core.common as com + + +class TestCategoricalIndexingWithFactor: + def test_getitem(self): + factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True) + assert factor[0] == "a" + assert factor[-1] == "c" + + subf = factor[[0, 1, 2]] + tm.assert_numpy_array_equal(subf._codes, np.array([0, 1, 1], dtype=np.int8)) + + subf = factor[np.asarray(factor) == "c"] + tm.assert_numpy_array_equal(subf._codes, np.array([2, 2, 2], dtype=np.int8)) + + def test_setitem(self): + factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True) + # int/positional + c = factor.copy() + c[0] = "b" + assert c[0] == "b" + c[-1] = "a" + assert c[-1] == "a" + + # boolean + c = factor.copy() + indexer = np.zeros(len(c), dtype="bool") + indexer[0] = True + indexer[-1] = True + c[indexer] = "c" + expected = Categorical(["c", "b", "b", "a", "a", "c", "c", "c"], ordered=True) + + tm.assert_categorical_equal(c, expected) + + @pytest.mark.parametrize( + "other", + [Categorical(["b", "a"]), Categorical(["b", "a"], categories=["b", "a"])], + ) + def test_setitem_same_but_unordered(self, other): + # GH-24142 + target = Categorical(["a", "b"], categories=["a", "b"]) + mask = np.array([True, False]) + target[mask] = other[mask] + expected = Categorical(["b", "b"], categories=["a", "b"]) + tm.assert_categorical_equal(target, expected) + + @pytest.mark.parametrize( + "other", + [ + Categorical(["b", "a"], categories=["b", "a", "c"]), + Categorical(["b", "a"], categories=["a", "b", "c"]), + Categorical(["a", "a"], categories=["a"]), + Categorical(["b", "b"], categories=["b"]), + ], + ) + def test_setitem_different_unordered_raises(self, other): + # GH-24142 + target = Categorical(["a", "b"], categories=["a", "b"]) + mask = np.array([True, False]) + msg = "Cannot set a Categorical with another, without identical categories" + with pytest.raises(TypeError, match=msg): + target[mask] = other[mask] + + @pytest.mark.parametrize( + "other", + [ + Categorical(["b", "a"]), + Categorical(["b", "a"], categories=["b", "a"], ordered=True), + Categorical(["b", "a"], categories=["a", "b", "c"], ordered=True), + ], + ) + def test_setitem_same_ordered_raises(self, other): + # Gh-24142 + target = Categorical(["a", "b"], categories=["a", "b"], ordered=True) + mask = np.array([True, False]) + msg = "Cannot set a Categorical with another, without identical categories" + with pytest.raises(TypeError, match=msg): + target[mask] = other[mask] + + def test_setitem_tuple(self): + # GH#20439 + cat = Categorical([(0, 1), (0, 2), (0, 1)]) + + # This should not raise + cat[1] = cat[0] + assert cat[1] == (0, 1) + + def test_setitem_listlike(self): + # GH#9469 + # properly coerce the input indexers + + cat = Categorical( + np.random.default_rng(2).integers(0, 5, size=150000).astype(np.int8) + ).add_categories([-1000]) + indexer = np.array([100000]).astype(np.int64) + cat[indexer] = -1000 + + # we are asserting the code result here + # which maps to the -1000 category + result = cat.codes[np.array([100000]).astype(np.int64)] + tm.assert_numpy_array_equal(result, np.array([5], dtype="int8")) + + +class TestCategoricalIndexing: + def test_getitem_slice(self): + cat = Categorical(["a", "b", "c", "d", "a", "b", "c"]) + sliced = cat[3] + assert sliced == "d" + + sliced = cat[3:5] + expected = Categorical(["d", "a"], categories=["a", "b", "c", "d"]) + tm.assert_categorical_equal(sliced, expected) + + def test_getitem_listlike(self): + # GH 9469 + # properly coerce the input indexers + + c = Categorical( + np.random.default_rng(2).integers(0, 5, size=150000).astype(np.int8) + ) + result = c.codes[np.array([100000]).astype(np.int64)] + expected = c[np.array([100000]).astype(np.int64)].codes + tm.assert_numpy_array_equal(result, expected) + + def test_periodindex(self): + idx1 = PeriodIndex( + ["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"], + freq="M", + ) + + cat1 = Categorical(idx1) + str(cat1) + exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.int8) + exp_idx = PeriodIndex(["2014-01", "2014-02", "2014-03"], freq="M") + tm.assert_numpy_array_equal(cat1._codes, exp_arr) + tm.assert_index_equal(cat1.categories, exp_idx) + + idx2 = PeriodIndex( + ["2014-03", "2014-03", "2014-02", "2014-01", "2014-03", "2014-01"], + freq="M", + ) + cat2 = Categorical(idx2, ordered=True) + str(cat2) + exp_arr = np.array([2, 2, 1, 0, 2, 0], dtype=np.int8) + exp_idx2 = PeriodIndex(["2014-01", "2014-02", "2014-03"], freq="M") + tm.assert_numpy_array_equal(cat2._codes, exp_arr) + tm.assert_index_equal(cat2.categories, exp_idx2) + + idx3 = PeriodIndex( + [ + "2013-12", + "2013-11", + "2013-10", + "2013-09", + "2013-08", + "2013-07", + "2013-05", + ], + freq="M", + ) + cat3 = Categorical(idx3, ordered=True) + exp_arr = np.array([6, 5, 4, 3, 2, 1, 0], dtype=np.int8) + exp_idx = PeriodIndex( + [ + "2013-05", + "2013-07", + "2013-08", + "2013-09", + "2013-10", + "2013-11", + "2013-12", + ], + freq="M", + ) + tm.assert_numpy_array_equal(cat3._codes, exp_arr) + tm.assert_index_equal(cat3.categories, exp_idx) + + @pytest.mark.parametrize( + "null_val", + [None, np.nan, NaT, NA, math.nan, "NaT", "nat", "NAT", "nan", "NaN", "NAN"], + ) + def test_periodindex_on_null_types(self, null_val): + # GH 46673 + result = PeriodIndex(["2022-04-06", "2022-04-07", null_val], freq="D") + expected = PeriodIndex(["2022-04-06", "2022-04-07", "NaT"], dtype="period[D]") + assert result[2] is NaT + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("new_categories", [[1, 2, 3, 4], [1, 2]]) + def test_categories_assignments_wrong_length_raises(self, new_categories): + cat = Categorical(["a", "b", "c", "a"]) + msg = ( + "new categories need to have the same number of items " + "as the old categories!" + ) + with pytest.raises(ValueError, match=msg): + cat.rename_categories(new_categories) + + # Combinations of sorted/unique: + @pytest.mark.parametrize( + "idx_values", [[1, 2, 3, 4], [1, 3, 2, 4], [1, 3, 3, 4], [1, 2, 2, 4]] + ) + # Combinations of missing/unique + @pytest.mark.parametrize("key_values", [[1, 2], [1, 5], [1, 1], [5, 5]]) + @pytest.mark.parametrize("key_class", [Categorical, CategoricalIndex]) + @pytest.mark.parametrize("dtype", [None, "category", "key"]) + def test_get_indexer_non_unique(self, idx_values, key_values, key_class, dtype): + # GH 21448 + key = key_class(key_values, categories=range(1, 5)) + + if dtype == "key": + dtype = key.dtype + + # Test for flat index and CategoricalIndex with same/different cats: + idx = Index(idx_values, dtype=dtype) + expected, exp_miss = idx.get_indexer_non_unique(key_values) + result, res_miss = idx.get_indexer_non_unique(key) + + tm.assert_numpy_array_equal(expected, result) + tm.assert_numpy_array_equal(exp_miss, res_miss) + + exp_unique = idx.unique().get_indexer(key_values) + res_unique = idx.unique().get_indexer(key) + tm.assert_numpy_array_equal(res_unique, exp_unique) + + def test_where_unobserved_nan(self): + ser = Series(Categorical(["a", "b"])) + result = ser.where([True, False]) + expected = Series(Categorical(["a", None], categories=["a", "b"])) + tm.assert_series_equal(result, expected) + + # all NA + ser = Series(Categorical(["a", "b"])) + result = ser.where([False, False]) + expected = Series(Categorical([None, None], categories=["a", "b"])) + tm.assert_series_equal(result, expected) + + def test_where_unobserved_categories(self): + ser = Series(Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"])) + result = ser.where([True, True, False], other="b") + expected = Series(Categorical(["a", "b", "b"], categories=ser.cat.categories)) + tm.assert_series_equal(result, expected) + + def test_where_other_categorical(self): + ser = Series(Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"])) + other = Categorical(["b", "c", "a"], categories=["a", "c", "b", "d"]) + result = ser.where([True, False, True], other) + expected = Series(Categorical(["a", "c", "c"], dtype=ser.dtype)) + tm.assert_series_equal(result, expected) + + def test_where_new_category_raises(self): + ser = Series(Categorical(["a", "b", "c"])) + msg = "Cannot setitem on a Categorical with a new category" + with pytest.raises(TypeError, match=msg): + ser.where([True, False, True], "d") + + def test_where_ordered_differs_rasies(self): + ser = Series( + Categorical(["a", "b", "c"], categories=["d", "c", "b", "a"], ordered=True) + ) + other = Categorical( + ["b", "c", "a"], categories=["a", "c", "b", "d"], ordered=True + ) + with pytest.raises(TypeError, match="without identical categories"): + ser.where([True, False, True], other) + + +class TestContains: + def test_contains(self): + # GH#21508 + cat = Categorical(list("aabbca"), categories=list("cab")) + + assert "b" in cat + assert "z" not in cat + assert np.nan not in cat + with pytest.raises(TypeError, match="unhashable type: 'list'"): + assert [1] in cat + + # assert codes NOT in index + assert 0 not in cat + assert 1 not in cat + + cat = Categorical(list("aabbca") + [np.nan], categories=list("cab")) + assert np.nan in cat + + @pytest.mark.parametrize( + "item, expected", + [ + (Interval(0, 1), True), + (1.5, True), + (Interval(0.5, 1.5), False), + ("a", False), + (Timestamp(1), False), + (Timedelta(1), False), + ], + ids=str, + ) + def test_contains_interval(self, item, expected): + # GH#23705 + cat = Categorical(IntervalIndex.from_breaks(range(3))) + result = item in cat + assert result is expected + + def test_contains_list(self): + # GH#21729 + cat = Categorical([1, 2, 3]) + + assert "a" not in cat + + with pytest.raises(TypeError, match="unhashable type"): + ["a"] in cat + + with pytest.raises(TypeError, match="unhashable type"): + ["a", "b"] in cat + + +@pytest.mark.parametrize("index", [True, False]) +def test_mask_with_boolean(index): + ser = Series(range(3)) + idx = Categorical([True, False, True]) + if index: + idx = CategoricalIndex(idx) + + assert com.is_bool_indexer(idx) + result = ser[idx] + expected = ser[idx.astype("object")] + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("index", [True, False]) +def test_mask_with_boolean_na_treated_as_false(index): + # https://github.com/pandas-dev/pandas/issues/31503 + ser = Series(range(3)) + idx = Categorical([True, False, None]) + if index: + idx = CategoricalIndex(idx) + + result = ser[idx] + expected = ser[idx.fillna(False)] + + tm.assert_series_equal(result, expected) + + +@pytest.fixture +def non_coercible_categorical(monkeypatch): + """ + Monkeypatch Categorical.__array__ to ensure no implicit conversion. + + Raises + ------ + ValueError + When Categorical.__array__ is called. + """ + + # TODO(Categorical): identify other places where this may be + # useful and move to a conftest.py + def array(self, dtype=None): + raise ValueError("I cannot be converted.") + + with monkeypatch.context() as m: + m.setattr(Categorical, "__array__", array) + yield + + +def test_series_at(): + arr = Categorical(["a", "b", "c"]) + ser = Series(arr) + result = ser.at[0] + assert result == "a" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_map.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_map.py new file mode 100644 index 0000000000000000000000000000000000000000..3d41b7cc7094d237fa8d31501ce90a99b04fe4e6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_map.py @@ -0,0 +1,154 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Categorical, + Index, + Series, +) +import pandas._testing as tm + + +@pytest.fixture(params=[None, "ignore"]) +def na_action(request): + return request.param + + +@pytest.mark.parametrize( + "data, categories", + [ + (list("abcbca"), list("cab")), + (pd.interval_range(0, 3).repeat(3), pd.interval_range(0, 3)), + ], + ids=["string", "interval"], +) +def test_map_str(data, categories, ordered, na_action): + # GH 31202 - override base class since we want to maintain categorical/ordered + cat = Categorical(data, categories=categories, ordered=ordered) + result = cat.map(str, na_action=na_action) + expected = Categorical( + map(str, data), categories=map(str, categories), ordered=ordered + ) + tm.assert_categorical_equal(result, expected) + + +def test_map(na_action): + cat = Categorical(list("ABABC"), categories=list("CBA"), ordered=True) + result = cat.map(lambda x: x.lower(), na_action=na_action) + exp = Categorical(list("ababc"), categories=list("cba"), ordered=True) + tm.assert_categorical_equal(result, exp) + + cat = Categorical(list("ABABC"), categories=list("BAC"), ordered=False) + result = cat.map(lambda x: x.lower(), na_action=na_action) + exp = Categorical(list("ababc"), categories=list("bac"), ordered=False) + tm.assert_categorical_equal(result, exp) + + # GH 12766: Return an index not an array + result = cat.map(lambda x: 1, na_action=na_action) + exp = Index(np.array([1] * 5, dtype=np.int64)) + tm.assert_index_equal(result, exp) + + # change categories dtype + cat = Categorical(list("ABABC"), categories=list("BAC"), ordered=False) + + def f(x): + return {"A": 10, "B": 20, "C": 30}.get(x) + + result = cat.map(f, na_action=na_action) + exp = Categorical([10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False) + tm.assert_categorical_equal(result, exp) + + mapper = Series([10, 20, 30], index=["A", "B", "C"]) + result = cat.map(mapper, na_action=na_action) + tm.assert_categorical_equal(result, exp) + + result = cat.map({"A": 10, "B": 20, "C": 30}, na_action=na_action) + tm.assert_categorical_equal(result, exp) + + +@pytest.mark.parametrize( + ("data", "f", "expected"), + ( + ([1, 1, np.nan], pd.isna, Index([False, False, True])), + ([1, 2, np.nan], pd.isna, Index([False, False, True])), + ([1, 1, np.nan], {1: False}, Categorical([False, False, np.nan])), + ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])), + ( + [1, 1, np.nan], + Series([False, False]), + Categorical([False, False, np.nan]), + ), + ( + [1, 2, np.nan], + Series([False] * 3), + Index([False, False, np.nan]), + ), + ), +) +def test_map_with_nan_none(data, f, expected): # GH 24241 + values = Categorical(data) + result = values.map(f, na_action=None) + if isinstance(expected, Categorical): + tm.assert_categorical_equal(result, expected) + else: + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize( + ("data", "f", "expected"), + ( + ([1, 1, np.nan], pd.isna, Categorical([False, False, np.nan])), + ([1, 2, np.nan], pd.isna, Index([False, False, np.nan])), + ([1, 1, np.nan], {1: False}, Categorical([False, False, np.nan])), + ([1, 2, np.nan], {1: False, 2: False}, Index([False, False, np.nan])), + ( + [1, 1, np.nan], + Series([False, False]), + Categorical([False, False, np.nan]), + ), + ( + [1, 2, np.nan], + Series([False, False, False]), + Index([False, False, np.nan]), + ), + ), +) +def test_map_with_nan_ignore(data, f, expected): # GH 24241 + values = Categorical(data) + result = values.map(f, na_action="ignore") + if data[1] == 1: + tm.assert_categorical_equal(result, expected) + else: + tm.assert_index_equal(result, expected) + + +def test_map_with_dict_or_series(na_action): + orig_values = ["a", "B", 1, "a"] + new_values = ["one", 2, 3.0, "one"] + cat = Categorical(orig_values) + + mapper = Series(new_values[:-1], index=orig_values[:-1]) + result = cat.map(mapper, na_action=na_action) + + # Order of categories in result can be different + expected = Categorical(new_values, categories=[3.0, 2, "one"]) + tm.assert_categorical_equal(result, expected) + + mapper = dict(zip(orig_values[:-1], new_values[:-1])) + result = cat.map(mapper, na_action=na_action) + # Order of categories in result can be different + tm.assert_categorical_equal(result, expected) + + +def test_map_na_action_no_default_deprecated(): + # GH51645 + cat = Categorical(["a", "b", "c"]) + msg = ( + "The default value of 'ignore' for the `na_action` parameter in " + "pandas.Categorical.map is deprecated and will be " + "changed to 'None' in a future version. Please set na_action to the " + "desired value to avoid seeing this warning" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + cat.map(lambda x: x) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_missing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_missing.py new file mode 100644 index 0000000000000000000000000000000000000000..0eeb01b74608890daf81fef083adb29e797e57ce --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_missing.py @@ -0,0 +1,216 @@ +import collections + +import numpy as np +import pytest + +from pandas.core.dtypes.dtypes import CategoricalDtype + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + Index, + Series, + isna, +) +import pandas._testing as tm + + +class TestCategoricalMissing: + def test_isna(self): + exp = np.array([False, False, True]) + cat = Categorical(["a", "b", np.nan]) + res = cat.isna() + + tm.assert_numpy_array_equal(res, exp) + + def test_na_flags_int_categories(self): + # #1457 + + categories = list(range(10)) + labels = np.random.default_rng(2).integers(0, 10, 20) + labels[::5] = -1 + + cat = Categorical(labels, categories) + repr(cat) + + tm.assert_numpy_array_equal(isna(cat), labels == -1) + + def test_nan_handling(self): + # Nans are represented as -1 in codes + c = Categorical(["a", "b", np.nan, "a"]) + tm.assert_index_equal(c.categories, Index(["a", "b"])) + tm.assert_numpy_array_equal(c._codes, np.array([0, 1, -1, 0], dtype=np.int8)) + c[1] = np.nan + tm.assert_index_equal(c.categories, Index(["a", "b"])) + tm.assert_numpy_array_equal(c._codes, np.array([0, -1, -1, 0], dtype=np.int8)) + + # Adding nan to categories should make assigned nan point to the + # category! + c = Categorical(["a", "b", np.nan, "a"]) + tm.assert_index_equal(c.categories, Index(["a", "b"])) + tm.assert_numpy_array_equal(c._codes, np.array([0, 1, -1, 0], dtype=np.int8)) + + def test_set_dtype_nans(self): + c = Categorical(["a", "b", np.nan]) + result = c._set_dtype(CategoricalDtype(["a", "c"])) + tm.assert_numpy_array_equal(result.codes, np.array([0, -1, -1], dtype="int8")) + + def test_set_item_nan(self): + cat = Categorical([1, 2, 3]) + cat[1] = np.nan + + exp = Categorical([1, np.nan, 3], categories=[1, 2, 3]) + tm.assert_categorical_equal(cat, exp) + + @pytest.mark.parametrize( + "fillna_kwargs, msg", + [ + ( + {"value": 1, "method": "ffill"}, + "Cannot specify both 'value' and 'method'.", + ), + ({}, "Must specify a fill 'value' or 'method'."), + ({"method": "bad"}, "Invalid fill method. Expecting .* bad"), + ( + {"value": Series([1, 2, 3, 4, "a"])}, + "Cannot setitem on a Categorical with a new category", + ), + ], + ) + def test_fillna_raises(self, fillna_kwargs, msg): + # https://github.com/pandas-dev/pandas/issues/19682 + # https://github.com/pandas-dev/pandas/issues/13628 + cat = Categorical([1, 2, 3, None, None]) + + if len(fillna_kwargs) == 1 and "value" in fillna_kwargs: + err = TypeError + else: + err = ValueError + + with pytest.raises(err, match=msg): + cat.fillna(**fillna_kwargs) + + @pytest.mark.parametrize("named", [True, False]) + def test_fillna_iterable_category(self, named): + # https://github.com/pandas-dev/pandas/issues/21097 + if named: + Point = collections.namedtuple("Point", "x y") + else: + Point = lambda *args: args # tuple + cat = Categorical(np.array([Point(0, 0), Point(0, 1), None], dtype=object)) + result = cat.fillna(Point(0, 0)) + expected = Categorical([Point(0, 0), Point(0, 1), Point(0, 0)]) + + tm.assert_categorical_equal(result, expected) + + # Case where the Point is not among our categories; we want ValueError, + # not NotImplementedError GH#41914 + cat = Categorical(np.array([Point(1, 0), Point(0, 1), None], dtype=object)) + msg = "Cannot setitem on a Categorical with a new category" + with pytest.raises(TypeError, match=msg): + cat.fillna(Point(0, 0)) + + def test_fillna_array(self): + # accept Categorical or ndarray value if it holds appropriate values + cat = Categorical(["A", "B", "C", None, None]) + + other = cat.fillna("C") + result = cat.fillna(other) + tm.assert_categorical_equal(result, other) + assert isna(cat[-1]) # didn't modify original inplace + + other = np.array(["A", "B", "C", "B", "A"]) + result = cat.fillna(other) + expected = Categorical(["A", "B", "C", "B", "A"], dtype=cat.dtype) + tm.assert_categorical_equal(result, expected) + assert isna(cat[-1]) # didn't modify original inplace + + @pytest.mark.parametrize( + "values, expected", + [ + ([1, 2, 3], np.array([False, False, False])), + ([1, 2, np.nan], np.array([False, False, True])), + ([1, 2, np.inf], np.array([False, False, True])), + ([1, 2, pd.NA], np.array([False, False, True])), + ], + ) + def test_use_inf_as_na(self, values, expected): + # https://github.com/pandas-dev/pandas/issues/33594 + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("mode.use_inf_as_na", True): + cat = Categorical(values) + result = cat.isna() + tm.assert_numpy_array_equal(result, expected) + + result = Series(cat).isna() + expected = Series(expected) + tm.assert_series_equal(result, expected) + + result = DataFrame(cat).isna() + expected = DataFrame(expected) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "values, expected", + [ + ([1, 2, 3], np.array([False, False, False])), + ([1, 2, np.nan], np.array([False, False, True])), + ([1, 2, np.inf], np.array([False, False, True])), + ([1, 2, pd.NA], np.array([False, False, True])), + ], + ) + def test_use_inf_as_na_outside_context(self, values, expected): + # https://github.com/pandas-dev/pandas/issues/33594 + # Using isna directly for Categorical will fail in general here + cat = Categorical(values) + + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("mode.use_inf_as_na", True): + result = isna(cat) + tm.assert_numpy_array_equal(result, expected) + + result = isna(Series(cat)) + expected = Series(expected) + tm.assert_series_equal(result, expected) + + result = isna(DataFrame(cat)) + expected = DataFrame(expected) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "a1, a2, categories", + [ + (["a", "b", "c"], [np.nan, "a", "b"], ["a", "b", "c"]), + ([1, 2, 3], [np.nan, 1, 2], [1, 2, 3]), + ], + ) + def test_compare_categorical_with_missing(self, a1, a2, categories): + # GH 28384 + cat_type = CategoricalDtype(categories) + + # != + result = Series(a1, dtype=cat_type) != Series(a2, dtype=cat_type) + expected = Series(a1) != Series(a2) + tm.assert_series_equal(result, expected) + + # == + result = Series(a1, dtype=cat_type) == Series(a2, dtype=cat_type) + expected = Series(a1) == Series(a2) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "na_value, dtype", + [ + (pd.NaT, "datetime64[ns]"), + (None, "float64"), + (np.nan, "float64"), + (pd.NA, "float64"), + ], + ) + def test_categorical_only_missing_values_no_cast(self, na_value, dtype): + # GH#44900 + result = Categorical([na_value, na_value]) + tm.assert_index_equal(result.categories, Index([], dtype=dtype)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_operators.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_operators.py new file mode 100644 index 0000000000000000000000000000000000000000..4174d2adc810b872e7ec0b1e3ca820e3d2c3920d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_operators.py @@ -0,0 +1,414 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + + +class TestCategoricalOpsWithFactor: + def test_categories_none_comparisons(self): + factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True) + tm.assert_categorical_equal(factor, factor) + + def test_comparisons(self): + factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True) + result = factor[factor == "a"] + expected = factor[np.asarray(factor) == "a"] + tm.assert_categorical_equal(result, expected) + + result = factor[factor != "a"] + expected = factor[np.asarray(factor) != "a"] + tm.assert_categorical_equal(result, expected) + + result = factor[factor < "c"] + expected = factor[np.asarray(factor) < "c"] + tm.assert_categorical_equal(result, expected) + + result = factor[factor > "a"] + expected = factor[np.asarray(factor) > "a"] + tm.assert_categorical_equal(result, expected) + + result = factor[factor >= "b"] + expected = factor[np.asarray(factor) >= "b"] + tm.assert_categorical_equal(result, expected) + + result = factor[factor <= "b"] + expected = factor[np.asarray(factor) <= "b"] + tm.assert_categorical_equal(result, expected) + + n = len(factor) + + other = factor[np.random.default_rng(2).permutation(n)] + result = factor == other + expected = np.asarray(factor) == np.asarray(other) + tm.assert_numpy_array_equal(result, expected) + + result = factor == "d" + expected = np.zeros(len(factor), dtype=bool) + tm.assert_numpy_array_equal(result, expected) + + # comparisons with categoricals + cat_rev = Categorical(["a", "b", "c"], categories=["c", "b", "a"], ordered=True) + cat_rev_base = Categorical( + ["b", "b", "b"], categories=["c", "b", "a"], ordered=True + ) + cat = Categorical(["a", "b", "c"], ordered=True) + cat_base = Categorical(["b", "b", "b"], categories=cat.categories, ordered=True) + + # comparisons need to take categories ordering into account + res_rev = cat_rev > cat_rev_base + exp_rev = np.array([True, False, False]) + tm.assert_numpy_array_equal(res_rev, exp_rev) + + res_rev = cat_rev < cat_rev_base + exp_rev = np.array([False, False, True]) + tm.assert_numpy_array_equal(res_rev, exp_rev) + + res = cat > cat_base + exp = np.array([False, False, True]) + tm.assert_numpy_array_equal(res, exp) + + # Only categories with same categories can be compared + msg = "Categoricals can only be compared if 'categories' are the same" + with pytest.raises(TypeError, match=msg): + cat > cat_rev + + cat_rev_base2 = Categorical(["b", "b", "b"], categories=["c", "b", "a", "d"]) + + with pytest.raises(TypeError, match=msg): + cat_rev > cat_rev_base2 + + # Only categories with same ordering information can be compared + cat_unordered = cat.set_ordered(False) + assert not (cat > cat).any() + + with pytest.raises(TypeError, match=msg): + cat > cat_unordered + + # comparison (in both directions) with Series will raise + s = Series(["b", "b", "b"], dtype=object) + msg = ( + "Cannot compare a Categorical for op __gt__ with type " + r"" + ) + with pytest.raises(TypeError, match=msg): + cat > s + with pytest.raises(TypeError, match=msg): + cat_rev > s + with pytest.raises(TypeError, match=msg): + s < cat + with pytest.raises(TypeError, match=msg): + s < cat_rev + + # comparison with numpy.array will raise in both direction, but only on + # newer numpy versions + a = np.array(["b", "b", "b"], dtype=object) + with pytest.raises(TypeError, match=msg): + cat > a + with pytest.raises(TypeError, match=msg): + cat_rev > a + + # Make sure that unequal comparison take the categories order in + # account + cat_rev = Categorical(list("abc"), categories=list("cba"), ordered=True) + exp = np.array([True, False, False]) + res = cat_rev > "b" + tm.assert_numpy_array_equal(res, exp) + + # check that zero-dim array gets unboxed + res = cat_rev > np.array("b") + tm.assert_numpy_array_equal(res, exp) + + +class TestCategoricalOps: + @pytest.mark.parametrize( + "categories", + [["a", "b"], [0, 1], [Timestamp("2019"), Timestamp("2020")]], + ) + def test_not_equal_with_na(self, categories): + # https://github.com/pandas-dev/pandas/issues/32276 + c1 = Categorical.from_codes([-1, 0], categories=categories) + c2 = Categorical.from_codes([0, 1], categories=categories) + + result = c1 != c2 + + assert result.all() + + def test_compare_frame(self): + # GH#24282 check that Categorical.__cmp__(DataFrame) defers to frame + data = ["a", "b", 2, "a"] + cat = Categorical(data) + + df = DataFrame(cat) + + result = cat == df.T + expected = DataFrame([[True, True, True, True]]) + tm.assert_frame_equal(result, expected) + + result = cat[::-1] != df.T + expected = DataFrame([[False, True, True, False]]) + tm.assert_frame_equal(result, expected) + + def test_compare_frame_raises(self, comparison_op): + # alignment raises unless we transpose + op = comparison_op + cat = Categorical(["a", "b", 2, "a"]) + df = DataFrame(cat) + msg = "Unable to coerce to Series, length must be 1: given 4" + with pytest.raises(ValueError, match=msg): + op(cat, df) + + def test_datetime_categorical_comparison(self): + dt_cat = Categorical(date_range("2014-01-01", periods=3), ordered=True) + tm.assert_numpy_array_equal(dt_cat > dt_cat[0], np.array([False, True, True])) + tm.assert_numpy_array_equal(dt_cat[0] < dt_cat, np.array([False, True, True])) + + def test_reflected_comparison_with_scalars(self): + # GH8658 + cat = Categorical([1, 2, 3], ordered=True) + tm.assert_numpy_array_equal(cat > cat[0], np.array([False, True, True])) + tm.assert_numpy_array_equal(cat[0] < cat, np.array([False, True, True])) + + def test_comparison_with_unknown_scalars(self): + # https://github.com/pandas-dev/pandas/issues/9836#issuecomment-92123057 + # and following comparisons with scalars not in categories should raise + # for unequal comps, but not for equal/not equal + cat = Categorical([1, 2, 3], ordered=True) + + msg = "Invalid comparison between dtype=category and int" + with pytest.raises(TypeError, match=msg): + cat < 4 + with pytest.raises(TypeError, match=msg): + cat > 4 + with pytest.raises(TypeError, match=msg): + 4 < cat + with pytest.raises(TypeError, match=msg): + 4 > cat + + tm.assert_numpy_array_equal(cat == 4, np.array([False, False, False])) + tm.assert_numpy_array_equal(cat != 4, np.array([True, True, True])) + + def test_comparison_with_tuple(self): + cat = Categorical(np.array(["foo", (0, 1), 3, (0, 1)], dtype=object)) + + result = cat == "foo" + expected = np.array([True, False, False, False], dtype=bool) + tm.assert_numpy_array_equal(result, expected) + + result = cat == (0, 1) + expected = np.array([False, True, False, True], dtype=bool) + tm.assert_numpy_array_equal(result, expected) + + result = cat != (0, 1) + tm.assert_numpy_array_equal(result, ~expected) + + @pytest.mark.filterwarnings("ignore::RuntimeWarning") + def test_comparison_of_ordered_categorical_with_nan_to_scalar( + self, compare_operators_no_eq_ne + ): + # https://github.com/pandas-dev/pandas/issues/26504 + # BUG: fix ordered categorical comparison with missing values (#26504 ) + # and following comparisons with scalars in categories with missing + # values should be evaluated as False + + cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True) + scalar = 2 + expected = getattr(np.array(cat), compare_operators_no_eq_ne)(scalar) + actual = getattr(cat, compare_operators_no_eq_ne)(scalar) + tm.assert_numpy_array_equal(actual, expected) + + @pytest.mark.filterwarnings("ignore::RuntimeWarning") + def test_comparison_of_ordered_categorical_with_nan_to_listlike( + self, compare_operators_no_eq_ne + ): + # https://github.com/pandas-dev/pandas/issues/26504 + # and following comparisons of missing values in ordered Categorical + # with listlike should be evaluated as False + + cat = Categorical([1, 2, 3, None], categories=[1, 2, 3], ordered=True) + other = Categorical([2, 2, 2, 2], categories=[1, 2, 3], ordered=True) + expected = getattr(np.array(cat), compare_operators_no_eq_ne)(2) + actual = getattr(cat, compare_operators_no_eq_ne)(other) + tm.assert_numpy_array_equal(actual, expected) + + @pytest.mark.parametrize( + "data,reverse,base", + [(list("abc"), list("cba"), list("bbb")), ([1, 2, 3], [3, 2, 1], [2, 2, 2])], + ) + def test_comparisons(self, data, reverse, base): + cat_rev = Series(Categorical(data, categories=reverse, ordered=True)) + cat_rev_base = Series(Categorical(base, categories=reverse, ordered=True)) + cat = Series(Categorical(data, ordered=True)) + cat_base = Series( + Categorical(base, categories=cat.cat.categories, ordered=True) + ) + s = Series(base, dtype=object if base == list("bbb") else None) + a = np.array(base) + + # comparisons need to take categories ordering into account + res_rev = cat_rev > cat_rev_base + exp_rev = Series([True, False, False]) + tm.assert_series_equal(res_rev, exp_rev) + + res_rev = cat_rev < cat_rev_base + exp_rev = Series([False, False, True]) + tm.assert_series_equal(res_rev, exp_rev) + + res = cat > cat_base + exp = Series([False, False, True]) + tm.assert_series_equal(res, exp) + + scalar = base[1] + res = cat > scalar + exp = Series([False, False, True]) + exp2 = cat.values > scalar + tm.assert_series_equal(res, exp) + tm.assert_numpy_array_equal(res.values, exp2) + res_rev = cat_rev > scalar + exp_rev = Series([True, False, False]) + exp_rev2 = cat_rev.values > scalar + tm.assert_series_equal(res_rev, exp_rev) + tm.assert_numpy_array_equal(res_rev.values, exp_rev2) + + # Only categories with same categories can be compared + msg = "Categoricals can only be compared if 'categories' are the same" + with pytest.raises(TypeError, match=msg): + cat > cat_rev + + # categorical cannot be compared to Series or numpy array, and also + # not the other way around + msg = ( + "Cannot compare a Categorical for op __gt__ with type " + r"" + ) + with pytest.raises(TypeError, match=msg): + cat > s + with pytest.raises(TypeError, match=msg): + cat_rev > s + with pytest.raises(TypeError, match=msg): + cat > a + with pytest.raises(TypeError, match=msg): + cat_rev > a + + with pytest.raises(TypeError, match=msg): + s < cat + with pytest.raises(TypeError, match=msg): + s < cat_rev + + with pytest.raises(TypeError, match=msg): + a < cat + with pytest.raises(TypeError, match=msg): + a < cat_rev + + @pytest.mark.parametrize( + "ctor", + [ + lambda *args, **kwargs: Categorical(*args, **kwargs), + lambda *args, **kwargs: Series(Categorical(*args, **kwargs)), + ], + ) + def test_unordered_different_order_equal(self, ctor): + # https://github.com/pandas-dev/pandas/issues/16014 + c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False) + c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False) + assert (c1 == c2).all() + + c1 = ctor(["a", "b"], categories=["a", "b"], ordered=False) + c2 = ctor(["b", "a"], categories=["b", "a"], ordered=False) + assert (c1 != c2).all() + + c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False) + c2 = ctor(["b", "b"], categories=["b", "a"], ordered=False) + assert (c1 != c2).all() + + c1 = ctor(["a", "a"], categories=["a", "b"], ordered=False) + c2 = ctor(["a", "b"], categories=["b", "a"], ordered=False) + result = c1 == c2 + tm.assert_numpy_array_equal(np.array(result), np.array([True, False])) + + def test_unordered_different_categories_raises(self): + c1 = Categorical(["a", "b"], categories=["a", "b"], ordered=False) + c2 = Categorical(["a", "c"], categories=["c", "a"], ordered=False) + + with pytest.raises(TypeError, match=("Categoricals can only be compared")): + c1 == c2 + + def test_compare_different_lengths(self): + c1 = Categorical([], categories=["a", "b"]) + c2 = Categorical([], categories=["a"]) + + msg = "Categoricals can only be compared if 'categories' are the same." + with pytest.raises(TypeError, match=msg): + c1 == c2 + + def test_compare_unordered_different_order(self): + # https://github.com/pandas-dev/pandas/issues/16603#issuecomment- + # 349290078 + a = Categorical(["a"], categories=["a", "b"]) + b = Categorical(["b"], categories=["b", "a"]) + assert not a.equals(b) + + def test_numeric_like_ops(self): + df = DataFrame({"value": np.random.default_rng(2).integers(0, 10000, 100)}) + labels = [f"{i} - {i + 499}" for i in range(0, 10000, 500)] + cat_labels = Categorical(labels, labels) + + df = df.sort_values(by=["value"], ascending=True) + df["value_group"] = pd.cut( + df.value, range(0, 10500, 500), right=False, labels=cat_labels + ) + + # numeric ops should not succeed + for op, str_rep in [ + ("__add__", r"\+"), + ("__sub__", "-"), + ("__mul__", r"\*"), + ("__truediv__", "/"), + ]: + msg = f"Series cannot perform the operation {str_rep}|unsupported operand" + with pytest.raises(TypeError, match=msg): + getattr(df, op)(df) + + # reduction ops should not succeed (unless specifically defined, e.g. + # min/max) + s = df["value_group"] + for op in ["kurt", "skew", "var", "std", "mean", "sum", "median"]: + msg = f"does not support reduction '{op}'" + with pytest.raises(TypeError, match=msg): + getattr(s, op)(numeric_only=False) + + def test_numeric_like_ops_series(self): + # numpy ops + s = Series(Categorical([1, 2, 3, 4])) + with pytest.raises(TypeError, match="does not support reduction 'sum'"): + np.sum(s) + + @pytest.mark.parametrize( + "op, str_rep", + [ + ("__add__", r"\+"), + ("__sub__", "-"), + ("__mul__", r"\*"), + ("__truediv__", "/"), + ], + ) + def test_numeric_like_ops_series_arith(self, op, str_rep): + # numeric ops on a Series + s = Series(Categorical([1, 2, 3, 4])) + msg = f"Series cannot perform the operation {str_rep}|unsupported operand" + with pytest.raises(TypeError, match=msg): + getattr(s, op)(2) + + def test_numeric_like_ops_series_invalid(self): + # invalid ufunc + s = Series(Categorical([1, 2, 3, 4])) + msg = "Object with dtype category cannot perform the numpy op log" + with pytest.raises(TypeError, match=msg): + np.log(s) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_replace.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_replace.py new file mode 100644 index 0000000000000000000000000000000000000000..3c677142846d73f7cfd08c6681ff0d7814b55bd1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_replace.py @@ -0,0 +1,111 @@ +import pytest + +import pandas as pd +from pandas import Categorical +import pandas._testing as tm + + +@pytest.mark.parametrize( + "to_replace,value,expected,flip_categories", + [ + # one-to-one + (1, 2, [2, 2, 3], False), + (1, 4, [4, 2, 3], False), + (4, 1, [1, 2, 3], False), + (5, 6, [1, 2, 3], False), + # many-to-one + ([1], 2, [2, 2, 3], False), + ([1, 2], 3, [3, 3, 3], False), + ([1, 2], 4, [4, 4, 3], False), + ((1, 2, 4), 5, [5, 5, 3], False), + ((5, 6), 2, [1, 2, 3], False), + ([1], [2], [2, 2, 3], False), + ([1, 4], [5, 2], [5, 2, 3], False), + # GH49404: overlap between to_replace and value + ([1, 2, 3], [2, 3, 4], [2, 3, 4], False), + # GH50872, GH46884: replace with null + (1, None, [None, 2, 3], False), + (1, pd.NA, [None, 2, 3], False), + # check_categorical sorts categories, which crashes on mixed dtypes + (3, "4", [1, 2, "4"], False), + ([1, 2, "3"], "5", ["5", "5", 3], True), + ], +) +@pytest.mark.filterwarnings( + "ignore:.*with CategoricalDtype is deprecated:FutureWarning" +) +def test_replace_categorical_series(to_replace, value, expected, flip_categories): + # GH 31720 + + ser = pd.Series([1, 2, 3], dtype="category") + result = ser.replace(to_replace, value) + expected = pd.Series(expected, dtype="category") + ser.replace(to_replace, value, inplace=True) + + if flip_categories: + expected = expected.cat.set_categories(expected.cat.categories[::-1]) + + tm.assert_series_equal(expected, result, check_category_order=False) + tm.assert_series_equal(expected, ser, check_category_order=False) + + +@pytest.mark.parametrize( + "to_replace, value, result, expected_error_msg", + [ + ("b", "c", ["a", "c"], "Categorical.categories are different"), + ("c", "d", ["a", "b"], None), + # https://github.com/pandas-dev/pandas/issues/33288 + ("a", "a", ["a", "b"], None), + ("b", None, ["a", None], "Categorical.categories length are different"), + ], +) +def test_replace_categorical(to_replace, value, result, expected_error_msg): + # GH#26988 + cat = Categorical(["a", "b"]) + expected = Categorical(result) + msg = ( + r"The behavior of Series\.replace \(and DataFrame.replace\) " + "with CategoricalDtype" + ) + warn = FutureWarning if expected_error_msg is not None else None + with tm.assert_produces_warning(warn, match=msg): + result = pd.Series(cat, copy=False).replace(to_replace, value)._values + + tm.assert_categorical_equal(result, expected) + if to_replace == "b": # the "c" test is supposed to be unchanged + with pytest.raises(AssertionError, match=expected_error_msg): + # ensure non-inplace call does not affect original + tm.assert_categorical_equal(cat, expected) + + ser = pd.Series(cat, copy=False) + with tm.assert_produces_warning(warn, match=msg): + ser.replace(to_replace, value, inplace=True) + tm.assert_categorical_equal(cat, expected) + + +def test_replace_categorical_ea_dtype(): + # GH49404 + cat = Categorical(pd.array(["a", "b"], dtype="string")) + msg = ( + r"The behavior of Series\.replace \(and DataFrame.replace\) " + "with CategoricalDtype" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = pd.Series(cat).replace(["a", "b"], ["c", pd.NA])._values + expected = Categorical(pd.array(["c", pd.NA], dtype="string")) + tm.assert_categorical_equal(result, expected) + + +def test_replace_maintain_ordering(): + # GH51016 + dtype = pd.CategoricalDtype([0, 1, 2], ordered=True) + ser = pd.Series([0, 1, 2], dtype=dtype) + msg = ( + r"The behavior of Series\.replace \(and DataFrame.replace\) " + "with CategoricalDtype" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = ser.replace(0, 2) + expected_dtype = pd.CategoricalDtype([1, 2], ordered=True) + expected = pd.Series([2, 1, 2], dtype=expected_dtype) + tm.assert_series_equal(expected, result, check_category_order=True) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_repr.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_repr.py new file mode 100644 index 0000000000000000000000000000000000000000..7929dfc9270342f188493cc9d51cc8ac013a43b5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_repr.py @@ -0,0 +1,545 @@ +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +from pandas import ( + Categorical, + CategoricalDtype, + CategoricalIndex, + Index, + Series, + date_range, + option_context, + period_range, + timedelta_range, +) + + +class TestCategoricalReprWithFactor: + def test_print(self, using_infer_string): + factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"], ordered=True) + dtype = "str" if using_infer_string else "object" + expected = [ + "['a', 'b', 'b', 'a', 'a', 'c', 'c', 'c']", + f"Categories (3, {dtype}): ['a' < 'b' < 'c']", + ] + expected = "\n".join(expected) + actual = repr(factor) + assert actual == expected + + +class TestCategoricalRepr: + def test_big_print(self): + codes = np.array([0, 1, 2, 0, 1, 2] * 100) + dtype = CategoricalDtype(categories=Index(["a", "b", "c"], dtype=object)) + factor = Categorical.from_codes(codes, dtype=dtype) + expected = [ + "['a', 'b', 'c', 'a', 'b', ..., 'b', 'c', 'a', 'b', 'c']", + "Length: 600", + "Categories (3, object): ['a', 'b', 'c']", + ] + expected = "\n".join(expected) + + actual = repr(factor) + + assert actual == expected + + def test_empty_print(self): + factor = Categorical([], Index(["a", "b", "c"], dtype=object)) + expected = "[], Categories (3, object): ['a', 'b', 'c']" + actual = repr(factor) + assert actual == expected + + assert expected == actual + factor = Categorical([], Index(["a", "b", "c"], dtype=object), ordered=True) + expected = "[], Categories (3, object): ['a' < 'b' < 'c']" + actual = repr(factor) + assert expected == actual + + factor = Categorical([], []) + expected = "[], Categories (0, object): []" + assert expected == repr(factor) + + def test_print_none_width(self): + # GH10087 + a = Series(Categorical([1, 2, 3, 4])) + exp = ( + "0 1\n1 2\n2 3\n3 4\n" + "dtype: category\nCategories (4, int64): [1, 2, 3, 4]" + ) + + with option_context("display.width", None): + assert exp == repr(a) + + @pytest.mark.skipif( + using_string_dtype(), + reason="Change once infer_string is set to True by default", + ) + def test_unicode_print(self): + c = Categorical(["aaaaa", "bb", "cccc"] * 20) + expected = """\ +['aaaaa', 'bb', 'cccc', 'aaaaa', 'bb', ..., 'bb', 'cccc', 'aaaaa', 'bb', 'cccc'] +Length: 60 +Categories (3, object): ['aaaaa', 'bb', 'cccc']""" + + assert repr(c) == expected + + c = Categorical(["ああああ", "いいいいい", "ううううううう"] * 20) + expected = """\ +['ああああ', 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', ..., 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', 'ううううううう'] +Length: 60 +Categories (3, object): ['ああああ', 'いいいいい', 'ううううううう']""" # noqa: E501 + + assert repr(c) == expected + + # unicode option should not affect to Categorical, as it doesn't care + # the repr width + with option_context("display.unicode.east_asian_width", True): + c = Categorical(["ああああ", "いいいいい", "ううううううう"] * 20) + expected = """['ああああ', 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', ..., 'いいいいい', 'ううううううう', 'ああああ', 'いいいいい', 'ううううううう'] +Length: 60 +Categories (3, object): ['ああああ', 'いいいいい', 'ううううううう']""" # noqa: E501 + + assert repr(c) == expected + + def test_categorical_repr(self): + c = Categorical([1, 2, 3]) + exp = """[1, 2, 3] +Categories (3, int64): [1, 2, 3]""" + + assert repr(c) == exp + + c = Categorical([1, 2, 3, 1, 2, 3], categories=[1, 2, 3]) + exp = """[1, 2, 3, 1, 2, 3] +Categories (3, int64): [1, 2, 3]""" + + assert repr(c) == exp + + c = Categorical([1, 2, 3, 4, 5] * 10) + exp = """[1, 2, 3, 4, 5, ..., 1, 2, 3, 4, 5] +Length: 50 +Categories (5, int64): [1, 2, 3, 4, 5]""" + + assert repr(c) == exp + + c = Categorical(np.arange(20, dtype=np.int64)) + exp = """[0, 1, 2, 3, 4, ..., 15, 16, 17, 18, 19] +Length: 20 +Categories (20, int64): [0, 1, 2, 3, ..., 16, 17, 18, 19]""" + + assert repr(c) == exp + + def test_categorical_repr_ordered(self): + c = Categorical([1, 2, 3], ordered=True) + exp = """[1, 2, 3] +Categories (3, int64): [1 < 2 < 3]""" + + assert repr(c) == exp + + c = Categorical([1, 2, 3, 1, 2, 3], categories=[1, 2, 3], ordered=True) + exp = """[1, 2, 3, 1, 2, 3] +Categories (3, int64): [1 < 2 < 3]""" + + assert repr(c) == exp + + c = Categorical([1, 2, 3, 4, 5] * 10, ordered=True) + exp = """[1, 2, 3, 4, 5, ..., 1, 2, 3, 4, 5] +Length: 50 +Categories (5, int64): [1 < 2 < 3 < 4 < 5]""" + + assert repr(c) == exp + + c = Categorical(np.arange(20, dtype=np.int64), ordered=True) + exp = """[0, 1, 2, 3, 4, ..., 15, 16, 17, 18, 19] +Length: 20 +Categories (20, int64): [0 < 1 < 2 < 3 ... 16 < 17 < 18 < 19]""" + + assert repr(c) == exp + + def test_categorical_repr_datetime(self): + idx = date_range("2011-01-01 09:00", freq="h", periods=5) + c = Categorical(idx) + + exp = ( + "[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, " + "2011-01-01 12:00:00, 2011-01-01 13:00:00]\n" + "Categories (5, datetime64[ns]): [2011-01-01 09:00:00, " + "2011-01-01 10:00:00, 2011-01-01 11:00:00,\n" + " 2011-01-01 12:00:00, " + "2011-01-01 13:00:00]" + "" + ) + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx) + exp = ( + "[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, " + "2011-01-01 12:00:00, 2011-01-01 13:00:00, 2011-01-01 09:00:00, " + "2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, " + "2011-01-01 13:00:00]\n" + "Categories (5, datetime64[ns]): [2011-01-01 09:00:00, " + "2011-01-01 10:00:00, 2011-01-01 11:00:00,\n" + " 2011-01-01 12:00:00, " + "2011-01-01 13:00:00]" + ) + + assert repr(c) == exp + + idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern") + c = Categorical(idx) + exp = ( + "[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, " + "2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, " + "2011-01-01 13:00:00-05:00]\n" + "Categories (5, datetime64[ns, US/Eastern]): " + "[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00,\n" + " " + "2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00,\n" + " " + "2011-01-01 13:00:00-05:00]" + ) + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx) + exp = ( + "[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, " + "2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, " + "2011-01-01 13:00:00-05:00, 2011-01-01 09:00:00-05:00, " + "2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, " + "2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]\n" + "Categories (5, datetime64[ns, US/Eastern]): " + "[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00,\n" + " " + "2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00,\n" + " " + "2011-01-01 13:00:00-05:00]" + ) + + assert repr(c) == exp + + def test_categorical_repr_datetime_ordered(self): + idx = date_range("2011-01-01 09:00", freq="h", periods=5) + c = Categorical(idx, ordered=True) + exp = """[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00] +Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 < + 2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa: E501 + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx, ordered=True) + exp = """[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00, 2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00] +Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 < + 2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa: E501 + + assert repr(c) == exp + + idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern") + c = Categorical(idx, ordered=True) + exp = """[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00] +Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 < + 2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 < + 2011-01-01 13:00:00-05:00]""" # noqa: E501 + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx, ordered=True) + exp = """[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00, 2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00] +Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 < + 2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 < + 2011-01-01 13:00:00-05:00]""" # noqa: E501 + + assert repr(c) == exp + + def test_categorical_repr_int_with_nan(self): + c = Categorical([1, 2, np.nan]) + c_exp = """[1, 2, NaN]\nCategories (2, int64): [1, 2]""" + assert repr(c) == c_exp + + s = Series([1, 2, np.nan], dtype="object").astype("category") + s_exp = """0 1\n1 2\n2 NaN +dtype: category +Categories (2, int64): [1, 2]""" + assert repr(s) == s_exp + + def test_categorical_repr_period(self): + idx = period_range("2011-01-01 09:00", freq="h", periods=5) + c = Categorical(idx) + exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00] +Categories (5, period[h]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, + 2011-01-01 13:00]""" # noqa: E501 + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx) + exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00, 2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00] +Categories (5, period[h]): [2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, + 2011-01-01 13:00]""" # noqa: E501 + + assert repr(c) == exp + + idx = period_range("2011-01", freq="M", periods=5) + c = Categorical(idx) + exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05] +Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]""" + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx) + exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05, 2011-01, 2011-02, 2011-03, 2011-04, 2011-05] +Categories (5, period[M]): [2011-01, 2011-02, 2011-03, 2011-04, 2011-05]""" # noqa: E501 + + assert repr(c) == exp + + def test_categorical_repr_period_ordered(self): + idx = period_range("2011-01-01 09:00", freq="h", periods=5) + c = Categorical(idx, ordered=True) + exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00] +Categories (5, period[h]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 < + 2011-01-01 13:00]""" # noqa: E501 + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx, ordered=True) + exp = """[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00, 2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00] +Categories (5, period[h]): [2011-01-01 09:00 < 2011-01-01 10:00 < 2011-01-01 11:00 < 2011-01-01 12:00 < + 2011-01-01 13:00]""" # noqa: E501 + + assert repr(c) == exp + + idx = period_range("2011-01", freq="M", periods=5) + c = Categorical(idx, ordered=True) + exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05] +Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]""" + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx, ordered=True) + exp = """[2011-01, 2011-02, 2011-03, 2011-04, 2011-05, 2011-01, 2011-02, 2011-03, 2011-04, 2011-05] +Categories (5, period[M]): [2011-01 < 2011-02 < 2011-03 < 2011-04 < 2011-05]""" # noqa: E501 + + assert repr(c) == exp + + def test_categorical_repr_timedelta(self): + idx = timedelta_range("1 days", periods=5) + c = Categorical(idx) + exp = """[1 days, 2 days, 3 days, 4 days, 5 days] +Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]""" + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx) + exp = """[1 days, 2 days, 3 days, 4 days, 5 days, 1 days, 2 days, 3 days, 4 days, 5 days] +Categories (5, timedelta64[ns]): [1 days, 2 days, 3 days, 4 days, 5 days]""" # noqa: E501 + + assert repr(c) == exp + + idx = timedelta_range("1 hours", periods=20) + c = Categorical(idx) + exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00] +Length: 20 +Categories (20, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, + 3 days 01:00:00, ..., 16 days 01:00:00, 17 days 01:00:00, + 18 days 01:00:00, 19 days 01:00:00]""" # noqa: E501 + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx) + exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00] +Length: 40 +Categories (20, timedelta64[ns]): [0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, + 3 days 01:00:00, ..., 16 days 01:00:00, 17 days 01:00:00, + 18 days 01:00:00, 19 days 01:00:00]""" # noqa: E501 + + assert repr(c) == exp + + def test_categorical_repr_timedelta_ordered(self): + idx = timedelta_range("1 days", periods=5) + c = Categorical(idx, ordered=True) + exp = """[1 days, 2 days, 3 days, 4 days, 5 days] +Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]""" + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx, ordered=True) + exp = """[1 days, 2 days, 3 days, 4 days, 5 days, 1 days, 2 days, 3 days, 4 days, 5 days] +Categories (5, timedelta64[ns]): [1 days < 2 days < 3 days < 4 days < 5 days]""" # noqa: E501 + + assert repr(c) == exp + + idx = timedelta_range("1 hours", periods=20) + c = Categorical(idx, ordered=True) + exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00] +Length: 20 +Categories (20, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 < + 3 days 01:00:00 ... 16 days 01:00:00 < 17 days 01:00:00 < + 18 days 01:00:00 < 19 days 01:00:00]""" # noqa: E501 + + assert repr(c) == exp + + c = Categorical(idx.append(idx), categories=idx, ordered=True) + exp = """[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, 4 days 01:00:00, ..., 15 days 01:00:00, 16 days 01:00:00, 17 days 01:00:00, 18 days 01:00:00, 19 days 01:00:00] +Length: 40 +Categories (20, timedelta64[ns]): [0 days 01:00:00 < 1 days 01:00:00 < 2 days 01:00:00 < + 3 days 01:00:00 ... 16 days 01:00:00 < 17 days 01:00:00 < + 18 days 01:00:00 < 19 days 01:00:00]""" # noqa: E501 + + assert repr(c) == exp + + def test_categorical_index_repr(self): + idx = CategoricalIndex(Categorical([1, 2, 3])) + exp = """CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=False, dtype='category')""" # noqa: E501 + assert repr(idx) == exp + + i = CategoricalIndex(Categorical(np.arange(10, dtype=np.int64))) + exp = """CategoricalIndex([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], categories=[0, 1, 2, 3, ..., 6, 7, 8, 9], ordered=False, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + def test_categorical_index_repr_ordered(self): + i = CategoricalIndex(Categorical([1, 2, 3], ordered=True)) + exp = """CategoricalIndex([1, 2, 3], categories=[1, 2, 3], ordered=True, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + i = CategoricalIndex(Categorical(np.arange(10, dtype=np.int64), ordered=True)) + exp = """CategoricalIndex([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], categories=[0, 1, 2, 3, ..., 6, 7, 8, 9], ordered=True, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + def test_categorical_index_repr_datetime(self): + idx = date_range("2011-01-01 09:00", freq="h", periods=5) + i = CategoricalIndex(Categorical(idx)) + exp = """CategoricalIndex(['2011-01-01 09:00:00', '2011-01-01 10:00:00', + '2011-01-01 11:00:00', '2011-01-01 12:00:00', + '2011-01-01 13:00:00'], + categories=[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00], ordered=False, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern") + i = CategoricalIndex(Categorical(idx)) + exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00', + '2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00', + '2011-01-01 13:00:00-05:00'], + categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=False, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + def test_categorical_index_repr_datetime_ordered(self): + idx = date_range("2011-01-01 09:00", freq="h", periods=5) + i = CategoricalIndex(Categorical(idx, ordered=True)) + exp = """CategoricalIndex(['2011-01-01 09:00:00', '2011-01-01 10:00:00', + '2011-01-01 11:00:00', '2011-01-01 12:00:00', + '2011-01-01 13:00:00'], + categories=[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00], ordered=True, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + idx = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern") + i = CategoricalIndex(Categorical(idx, ordered=True)) + exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00', + '2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00', + '2011-01-01 13:00:00-05:00'], + categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=True, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + i = CategoricalIndex(Categorical(idx.append(idx), ordered=True)) + exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00', + '2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00', + '2011-01-01 13:00:00-05:00', '2011-01-01 09:00:00-05:00', + '2011-01-01 10:00:00-05:00', '2011-01-01 11:00:00-05:00', + '2011-01-01 12:00:00-05:00', '2011-01-01 13:00:00-05:00'], + categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=True, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + def test_categorical_index_repr_period(self): + # test all length + idx = period_range("2011-01-01 09:00", freq="h", periods=1) + i = CategoricalIndex(Categorical(idx)) + exp = """CategoricalIndex(['2011-01-01 09:00'], categories=[2011-01-01 09:00], ordered=False, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + idx = period_range("2011-01-01 09:00", freq="h", periods=2) + i = CategoricalIndex(Categorical(idx)) + exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00'], categories=[2011-01-01 09:00, 2011-01-01 10:00], ordered=False, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + idx = period_range("2011-01-01 09:00", freq="h", periods=3) + i = CategoricalIndex(Categorical(idx)) + exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00'], categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00], ordered=False, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + idx = period_range("2011-01-01 09:00", freq="h", periods=5) + i = CategoricalIndex(Categorical(idx)) + exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00', + '2011-01-01 12:00', '2011-01-01 13:00'], + categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=False, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + i = CategoricalIndex(Categorical(idx.append(idx))) + exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00', + '2011-01-01 12:00', '2011-01-01 13:00', '2011-01-01 09:00', + '2011-01-01 10:00', '2011-01-01 11:00', '2011-01-01 12:00', + '2011-01-01 13:00'], + categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=False, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + idx = period_range("2011-01", freq="M", periods=5) + i = CategoricalIndex(Categorical(idx)) + exp = """CategoricalIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05'], categories=[2011-01, 2011-02, 2011-03, 2011-04, 2011-05], ordered=False, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + def test_categorical_index_repr_period_ordered(self): + idx = period_range("2011-01-01 09:00", freq="h", periods=5) + i = CategoricalIndex(Categorical(idx, ordered=True)) + exp = """CategoricalIndex(['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00', + '2011-01-01 12:00', '2011-01-01 13:00'], + categories=[2011-01-01 09:00, 2011-01-01 10:00, 2011-01-01 11:00, 2011-01-01 12:00, 2011-01-01 13:00], ordered=True, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + idx = period_range("2011-01", freq="M", periods=5) + i = CategoricalIndex(Categorical(idx, ordered=True)) + exp = """CategoricalIndex(['2011-01', '2011-02', '2011-03', '2011-04', '2011-05'], categories=[2011-01, 2011-02, 2011-03, 2011-04, 2011-05], ordered=True, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + def test_categorical_index_repr_timedelta(self): + idx = timedelta_range("1 days", periods=5) + i = CategoricalIndex(Categorical(idx)) + exp = """CategoricalIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], categories=[1 days, 2 days, 3 days, 4 days, 5 days], ordered=False, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + idx = timedelta_range("1 hours", periods=10) + i = CategoricalIndex(Categorical(idx)) + exp = """CategoricalIndex(['0 days 01:00:00', '1 days 01:00:00', '2 days 01:00:00', + '3 days 01:00:00', '4 days 01:00:00', '5 days 01:00:00', + '6 days 01:00:00', '7 days 01:00:00', '8 days 01:00:00', + '9 days 01:00:00'], + categories=[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00, 8 days 01:00:00, 9 days 01:00:00], ordered=False, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + def test_categorical_index_repr_timedelta_ordered(self): + idx = timedelta_range("1 days", periods=5) + i = CategoricalIndex(Categorical(idx, ordered=True)) + exp = """CategoricalIndex(['1 days', '2 days', '3 days', '4 days', '5 days'], categories=[1 days, 2 days, 3 days, 4 days, 5 days], ordered=True, dtype='category')""" # noqa: E501 + assert repr(i) == exp + + idx = timedelta_range("1 hours", periods=10) + i = CategoricalIndex(Categorical(idx, ordered=True)) + exp = """CategoricalIndex(['0 days 01:00:00', '1 days 01:00:00', '2 days 01:00:00', + '3 days 01:00:00', '4 days 01:00:00', '5 days 01:00:00', + '6 days 01:00:00', '7 days 01:00:00', '8 days 01:00:00', + '9 days 01:00:00'], + categories=[0 days 01:00:00, 1 days 01:00:00, 2 days 01:00:00, 3 days 01:00:00, ..., 6 days 01:00:00, 7 days 01:00:00, 8 days 01:00:00, 9 days 01:00:00], ordered=True, dtype='category')""" # noqa: E501 + + assert repr(i) == exp + + def test_categorical_str_repr(self): + # GH 33676 + result = repr(Categorical([1, "2", 3, 4])) + expected = "[1, '2', 3, 4]\nCategories (4, object): [1, 3, 4, '2']" + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_sorting.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_sorting.py new file mode 100644 index 0000000000000000000000000000000000000000..ae527065b3fb970263609881d217f5c6d2761231 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_sorting.py @@ -0,0 +1,128 @@ +import numpy as np +import pytest + +from pandas import ( + Categorical, + Index, +) +import pandas._testing as tm + + +class TestCategoricalSort: + def test_argsort(self): + c = Categorical([5, 3, 1, 4, 2], ordered=True) + + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal( + c.argsort(ascending=True), expected, check_dtype=False + ) + + expected = expected[::-1] + tm.assert_numpy_array_equal( + c.argsort(ascending=False), expected, check_dtype=False + ) + + def test_numpy_argsort(self): + c = Categorical([5, 3, 1, 4, 2], ordered=True) + + expected = np.array([2, 4, 1, 3, 0]) + tm.assert_numpy_array_equal(np.argsort(c), expected, check_dtype=False) + + tm.assert_numpy_array_equal( + np.argsort(c, kind="mergesort"), expected, check_dtype=False + ) + + msg = "the 'axis' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.argsort(c, axis=0) + + msg = "the 'order' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.argsort(c, order="C") + + def test_sort_values(self): + # unordered cats are sortable + cat = Categorical(["a", "b", "b", "a"], ordered=False) + cat.sort_values() + + cat = Categorical(["a", "c", "b", "d"], ordered=True) + + # sort_values + res = cat.sort_values() + exp = np.array(["a", "b", "c", "d"], dtype=object) + tm.assert_numpy_array_equal(res.__array__(), exp) + tm.assert_index_equal(res.categories, cat.categories) + + cat = Categorical( + ["a", "c", "b", "d"], categories=["a", "b", "c", "d"], ordered=True + ) + res = cat.sort_values() + exp = np.array(["a", "b", "c", "d"], dtype=object) + tm.assert_numpy_array_equal(res.__array__(), exp) + tm.assert_index_equal(res.categories, cat.categories) + + res = cat.sort_values(ascending=False) + exp = np.array(["d", "c", "b", "a"], dtype=object) + tm.assert_numpy_array_equal(res.__array__(), exp) + tm.assert_index_equal(res.categories, cat.categories) + + # sort (inplace order) + cat1 = cat.copy() + orig_codes = cat1._codes + cat1.sort_values(inplace=True) + assert cat1._codes is orig_codes + exp = np.array(["a", "b", "c", "d"], dtype=object) + tm.assert_numpy_array_equal(cat1.__array__(), exp) + tm.assert_index_equal(res.categories, cat.categories) + + # reverse + cat = Categorical(["a", "c", "c", "b", "d"], ordered=True) + res = cat.sort_values(ascending=False) + exp_val = np.array(["d", "c", "c", "b", "a"], dtype=object) + exp_categories = Index(["a", "b", "c", "d"]) + tm.assert_numpy_array_equal(res.__array__(), exp_val) + tm.assert_index_equal(res.categories, exp_categories) + + def test_sort_values_na_position(self): + # see gh-12882 + cat = Categorical([5, 2, np.nan, 2, np.nan], ordered=True) + exp_categories = Index([2, 5]) + + exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) + res = cat.sort_values() # default arguments + tm.assert_numpy_array_equal(res.__array__(), exp) + tm.assert_index_equal(res.categories, exp_categories) + + exp = np.array([np.nan, np.nan, 2.0, 2.0, 5.0]) + res = cat.sort_values(ascending=True, na_position="first") + tm.assert_numpy_array_equal(res.__array__(), exp) + tm.assert_index_equal(res.categories, exp_categories) + + exp = np.array([np.nan, np.nan, 5.0, 2.0, 2.0]) + res = cat.sort_values(ascending=False, na_position="first") + tm.assert_numpy_array_equal(res.__array__(), exp) + tm.assert_index_equal(res.categories, exp_categories) + + exp = np.array([2.0, 2.0, 5.0, np.nan, np.nan]) + res = cat.sort_values(ascending=True, na_position="last") + tm.assert_numpy_array_equal(res.__array__(), exp) + tm.assert_index_equal(res.categories, exp_categories) + + exp = np.array([5.0, 2.0, 2.0, np.nan, np.nan]) + res = cat.sort_values(ascending=False, na_position="last") + tm.assert_numpy_array_equal(res.__array__(), exp) + tm.assert_index_equal(res.categories, exp_categories) + + cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) + res = cat.sort_values(ascending=False, na_position="last") + exp_val = np.array(["d", "c", "b", "a", np.nan], dtype=object) + exp_categories = Index(["a", "b", "c", "d"]) + tm.assert_numpy_array_equal(res.__array__(), exp_val) + tm.assert_index_equal(res.categories, exp_categories) + + cat = Categorical(["a", "c", "b", "d", np.nan], ordered=True) + res = cat.sort_values(ascending=False, na_position="first") + exp_val = np.array([np.nan, "d", "c", "b", "a"], dtype=object) + exp_categories = Index(["a", "b", "c", "d"]) + tm.assert_numpy_array_equal(res.__array__(), exp_val) + tm.assert_index_equal(res.categories, exp_categories) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_subclass.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_subclass.py new file mode 100644 index 0000000000000000000000000000000000000000..5b0c0a44e655d5dd943f95415336204aa12f0b67 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_subclass.py @@ -0,0 +1,26 @@ +from pandas import Categorical +import pandas._testing as tm + + +class SubclassedCategorical(Categorical): + pass + + +class TestCategoricalSubclassing: + def test_constructor(self): + sc = SubclassedCategorical(["a", "b", "c"]) + assert isinstance(sc, SubclassedCategorical) + tm.assert_categorical_equal(sc, Categorical(["a", "b", "c"])) + + def test_from_codes(self): + sc = SubclassedCategorical.from_codes([1, 0, 2], ["a", "b", "c"]) + assert isinstance(sc, SubclassedCategorical) + exp = Categorical.from_codes([1, 0, 2], ["a", "b", "c"]) + tm.assert_categorical_equal(sc, exp) + + def test_map(self): + sc = SubclassedCategorical(["a", "b", "c"]) + res = sc.map(lambda x: x.upper(), na_action=None) + assert isinstance(res, SubclassedCategorical) + exp = Categorical(["A", "B", "C"]) + tm.assert_categorical_equal(res, exp) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_take.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_take.py new file mode 100644 index 0000000000000000000000000000000000000000..373f1b30a13c2daff23e14a3e0640e7a716cceb3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_take.py @@ -0,0 +1,89 @@ +import numpy as np +import pytest + +from pandas import Categorical +import pandas._testing as tm + + +@pytest.fixture(params=[True, False]) +def allow_fill(request): + """Boolean 'allow_fill' parameter for Categorical.take""" + return request.param + + +class TestTake: + # https://github.com/pandas-dev/pandas/issues/20664 + + def test_take_default_allow_fill(self): + cat = Categorical(["a", "b"]) + with tm.assert_produces_warning(None): + result = cat.take([0, -1]) + + assert result.equals(cat) + + def test_take_positive_no_warning(self): + cat = Categorical(["a", "b"]) + with tm.assert_produces_warning(None): + cat.take([0, 0]) + + def test_take_bounds(self, allow_fill): + # https://github.com/pandas-dev/pandas/issues/20664 + cat = Categorical(["a", "b", "a"]) + if allow_fill: + msg = "indices are out-of-bounds" + else: + msg = "index 4 is out of bounds for( axis 0 with)? size 3" + with pytest.raises(IndexError, match=msg): + cat.take([4, 5], allow_fill=allow_fill) + + def test_take_empty(self, allow_fill): + # https://github.com/pandas-dev/pandas/issues/20664 + cat = Categorical([], categories=["a", "b"]) + if allow_fill: + msg = "indices are out-of-bounds" + else: + msg = "cannot do a non-empty take from an empty axes" + with pytest.raises(IndexError, match=msg): + cat.take([0], allow_fill=allow_fill) + + def test_positional_take(self, ordered): + cat = Categorical(["a", "a", "b", "b"], categories=["b", "a"], ordered=ordered) + result = cat.take([0, 1, 2], allow_fill=False) + expected = Categorical( + ["a", "a", "b"], categories=cat.categories, ordered=ordered + ) + tm.assert_categorical_equal(result, expected) + + def test_positional_take_unobserved(self, ordered): + cat = Categorical(["a", "b"], categories=["a", "b", "c"], ordered=ordered) + result = cat.take([1, 0], allow_fill=False) + expected = Categorical(["b", "a"], categories=cat.categories, ordered=ordered) + tm.assert_categorical_equal(result, expected) + + def test_take_allow_fill(self): + # https://github.com/pandas-dev/pandas/issues/23296 + cat = Categorical(["a", "a", "b"]) + result = cat.take([0, -1, -1], allow_fill=True) + expected = Categorical(["a", np.nan, np.nan], categories=["a", "b"]) + tm.assert_categorical_equal(result, expected) + + def test_take_fill_with_negative_one(self): + # -1 was a category + cat = Categorical([-1, 0, 1]) + result = cat.take([0, -1, 1], allow_fill=True, fill_value=-1) + expected = Categorical([-1, -1, 0], categories=[-1, 0, 1]) + tm.assert_categorical_equal(result, expected) + + def test_take_fill_value(self): + # https://github.com/pandas-dev/pandas/issues/23296 + cat = Categorical(["a", "b", "c"]) + result = cat.take([0, 1, -1], fill_value="a", allow_fill=True) + expected = Categorical(["a", "b", "a"], categories=["a", "b", "c"]) + tm.assert_categorical_equal(result, expected) + + def test_take_fill_value_new_raises(self): + # https://github.com/pandas-dev/pandas/issues/23296 + cat = Categorical(["a", "b", "c"]) + xpr = r"Cannot setitem on a Categorical with a new category \(d\)" + with pytest.raises(TypeError, match=xpr): + cat.take([0, 1, -1], fill_value="d", allow_fill=True) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_warnings.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_warnings.py new file mode 100644 index 0000000000000000000000000000000000000000..68c59706a6c3bf93908108c337b51c8da187cbb4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/categorical/test_warnings.py @@ -0,0 +1,19 @@ +import pytest + +import pandas._testing as tm + + +class TestCategoricalWarnings: + def test_tab_complete_warning(self, ip): + # https://github.com/pandas-dev/pandas/issues/16409 + pytest.importorskip("IPython", minversion="6.0.0") + from IPython.core.completer import provisionalcompleter + + code = "import pandas as pd; c = pd.Categorical([])" + ip.run_cell(code) + + # GH 31324 newer jedi version raises Deprecation warning; + # appears resolved 2021-02-02 + with tm.assert_produces_warning(None, raise_on_extra_warnings=False): + with provisionalcompleter("ignore"): + list(ip.Completer.completions("c.", 1)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_constructors.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..3652b5fec46bbe7a519dd2c3a196ac87bd74784f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_constructors.py @@ -0,0 +1,284 @@ +import numpy as np +import pytest + +from pandas._libs import iNaT + +from pandas.core.dtypes.dtypes import DatetimeTZDtype + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import DatetimeArray + + +class TestDatetimeArrayConstructor: + def test_from_sequence_invalid_type(self): + mi = pd.MultiIndex.from_product([np.arange(5), np.arange(5)]) + with pytest.raises(TypeError, match="Cannot create a DatetimeArray"): + DatetimeArray._from_sequence(mi, dtype="M8[ns]") + + def test_only_1dim_accepted(self): + arr = np.array([0, 1, 2, 3], dtype="M8[h]").astype("M8[ns]") + + depr_msg = "DatetimeArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="Only 1-dimensional"): + # 3-dim, we allow 2D to sneak in for ops purposes GH#29853 + DatetimeArray(arr.reshape(2, 2, 1)) + + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="Only 1-dimensional"): + # 0-dim + DatetimeArray(arr[[0]].squeeze()) + + def test_freq_validation(self): + # GH#24623 check that invalid instances cannot be created with the + # public constructor + arr = np.arange(5, dtype=np.int64) * 3600 * 10**9 + + msg = ( + "Inferred frequency h from passed values does not " + "conform to passed frequency W-SUN" + ) + depr_msg = "DatetimeArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match=msg): + DatetimeArray(arr, freq="W") + + @pytest.mark.parametrize( + "meth", + [ + DatetimeArray._from_sequence, + pd.to_datetime, + pd.DatetimeIndex, + ], + ) + def test_mixing_naive_tzaware_raises(self, meth): + # GH#24569 + arr = np.array([pd.Timestamp("2000"), pd.Timestamp("2000", tz="CET")]) + + msg = ( + "Cannot mix tz-aware with tz-naive values|" + "Tz-aware datetime.datetime cannot be converted " + "to datetime64 unless utc=True" + ) + + for obj in [arr, arr[::-1]]: + # check that we raise regardless of whether naive is found + # before aware or vice-versa + with pytest.raises(ValueError, match=msg): + meth(obj) + + def test_from_pandas_array(self): + arr = pd.array(np.arange(5, dtype=np.int64)) * 3600 * 10**9 + + result = DatetimeArray._from_sequence(arr, dtype="M8[ns]")._with_freq("infer") + + expected = pd.date_range("1970-01-01", periods=5, freq="h")._data + tm.assert_datetime_array_equal(result, expected) + + def test_mismatched_timezone_raises(self): + depr_msg = "DatetimeArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + arr = DatetimeArray( + np.array(["2000-01-01T06:00:00"], dtype="M8[ns]"), + dtype=DatetimeTZDtype(tz="US/Central"), + ) + dtype = DatetimeTZDtype(tz="US/Eastern") + msg = r"dtype=datetime64\[ns.*\] does not match data dtype datetime64\[ns.*\]" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(TypeError, match=msg): + DatetimeArray(arr, dtype=dtype) + + # also with mismatched tzawareness + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(TypeError, match=msg): + DatetimeArray(arr, dtype=np.dtype("M8[ns]")) + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(TypeError, match=msg): + DatetimeArray(arr.tz_localize(None), dtype=arr.dtype) + + def test_non_array_raises(self): + depr_msg = "DatetimeArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="list"): + DatetimeArray([1, 2, 3]) + + def test_bool_dtype_raises(self): + arr = np.array([1, 2, 3], dtype="bool") + + depr_msg = "DatetimeArray.__init__ is deprecated" + msg = "Unexpected value for 'dtype': 'bool'. Must be" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match=msg): + DatetimeArray(arr) + + msg = r"dtype bool cannot be converted to datetime64\[ns\]" + with pytest.raises(TypeError, match=msg): + DatetimeArray._from_sequence(arr, dtype="M8[ns]") + + with pytest.raises(TypeError, match=msg): + pd.DatetimeIndex(arr) + + with pytest.raises(TypeError, match=msg): + pd.to_datetime(arr) + + def test_incorrect_dtype_raises(self): + depr_msg = "DatetimeArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="Unexpected value for 'dtype'."): + DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="category") + + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="Unexpected value for 'dtype'."): + DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="m8[s]") + + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="Unexpected value for 'dtype'."): + DatetimeArray(np.array([1, 2, 3], dtype="i8"), dtype="M8[D]") + + def test_mismatched_values_dtype_units(self): + arr = np.array([1, 2, 3], dtype="M8[s]") + dtype = np.dtype("M8[ns]") + msg = "Values resolution does not match dtype." + depr_msg = "DatetimeArray.__init__ is deprecated" + + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match=msg): + DatetimeArray(arr, dtype=dtype) + + dtype2 = DatetimeTZDtype(tz="UTC", unit="ns") + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match=msg): + DatetimeArray(arr, dtype=dtype2) + + def test_freq_infer_raises(self): + depr_msg = "DatetimeArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="Frequency inference"): + DatetimeArray(np.array([1, 2, 3], dtype="i8"), freq="infer") + + def test_copy(self): + data = np.array([1, 2, 3], dtype="M8[ns]") + arr = DatetimeArray._from_sequence(data, copy=False) + assert arr._ndarray is data + + arr = DatetimeArray._from_sequence(data, copy=True) + assert arr._ndarray is not data + + @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"]) + def test_numpy_datetime_unit(self, unit): + data = np.array([1, 2, 3], dtype=f"M8[{unit}]") + arr = DatetimeArray._from_sequence(data) + assert arr.unit == unit + assert arr[0].unit == unit + + +class TestSequenceToDT64NS: + def test_tz_dtype_mismatch_raises(self): + arr = DatetimeArray._from_sequence( + ["2000"], dtype=DatetimeTZDtype(tz="US/Central") + ) + with pytest.raises(TypeError, match="data is already tz-aware"): + DatetimeArray._from_sequence(arr, dtype=DatetimeTZDtype(tz="UTC")) + + def test_tz_dtype_matches(self): + dtype = DatetimeTZDtype(tz="US/Central") + arr = DatetimeArray._from_sequence(["2000"], dtype=dtype) + result = DatetimeArray._from_sequence(arr, dtype=dtype) + tm.assert_equal(arr, result) + + @pytest.mark.parametrize("order", ["F", "C"]) + def test_2d(self, order): + dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific") + arr = np.array(dti, dtype=object).reshape(3, 2) + if order == "F": + arr = arr.T + + res = DatetimeArray._from_sequence(arr, dtype=dti.dtype) + expected = DatetimeArray._from_sequence(arr.ravel(), dtype=dti.dtype).reshape( + arr.shape + ) + tm.assert_datetime_array_equal(res, expected) + + +# ---------------------------------------------------------------------------- +# Arrow interaction + + +EXTREME_VALUES = [0, 123456789, None, iNaT, 2**63 - 1, -(2**63) + 1] +FINE_TO_COARSE_SAFE = [123_000_000_000, None, -123_000_000_000] +COARSE_TO_FINE_SAFE = [123, None, -123] + + +@pytest.mark.parametrize( + ("pa_unit", "pd_unit", "pa_tz", "pd_tz", "data"), + [ + ("s", "s", "UTC", "UTC", EXTREME_VALUES), + ("ms", "ms", "UTC", "Europe/Berlin", EXTREME_VALUES), + ("us", "us", "US/Eastern", "UTC", EXTREME_VALUES), + ("ns", "ns", "US/Central", "Asia/Kolkata", EXTREME_VALUES), + ("ns", "s", "UTC", "UTC", FINE_TO_COARSE_SAFE), + ("us", "ms", "UTC", "Europe/Berlin", FINE_TO_COARSE_SAFE), + ("ms", "us", "US/Eastern", "UTC", COARSE_TO_FINE_SAFE), + ("s", "ns", "US/Central", "Asia/Kolkata", COARSE_TO_FINE_SAFE), + ], +) +def test_from_arrow_with_different_units_and_timezones_with( + pa_unit, pd_unit, pa_tz, pd_tz, data +): + pa = pytest.importorskip("pyarrow") + + pa_type = pa.timestamp(pa_unit, tz=pa_tz) + arr = pa.array(data, type=pa_type) + dtype = DatetimeTZDtype(unit=pd_unit, tz=pd_tz) + + result = dtype.__from_arrow__(arr) + expected = DatetimeArray._from_sequence(data, dtype=f"M8[{pa_unit}, UTC]").astype( + dtype, copy=False + ) + tm.assert_extension_array_equal(result, expected) + + result = dtype.__from_arrow__(pa.chunked_array([arr])) + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize( + ("unit", "tz"), + [ + ("s", "UTC"), + ("ms", "Europe/Berlin"), + ("us", "US/Eastern"), + ("ns", "Asia/Kolkata"), + ("ns", "UTC"), + ], +) +def test_from_arrow_from_empty(unit, tz): + pa = pytest.importorskip("pyarrow") + + data = [] + arr = pa.array(data) + dtype = DatetimeTZDtype(unit=unit, tz=tz) + + result = dtype.__from_arrow__(arr) + expected = DatetimeArray._from_sequence(np.array(data, dtype=f"datetime64[{unit}]")) + expected = expected.tz_localize(tz=tz) + tm.assert_extension_array_equal(result, expected) + + result = dtype.__from_arrow__(pa.chunked_array([arr])) + tm.assert_extension_array_equal(result, expected) + + +def test_from_arrow_from_integers(): + pa = pytest.importorskip("pyarrow") + + data = [0, 123456789, None, 2**63 - 1, iNaT, -123456789] + arr = pa.array(data) + dtype = DatetimeTZDtype(unit="ns", tz="UTC") + + result = dtype.__from_arrow__(arr) + expected = DatetimeArray._from_sequence(np.array(data, dtype="datetime64[ns]")) + expected = expected.tz_localize("UTC") + tm.assert_extension_array_equal(result, expected) + + result = dtype.__from_arrow__(pa.chunked_array([arr])) + tm.assert_extension_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_cumulative.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_cumulative.py new file mode 100644 index 0000000000000000000000000000000000000000..e9d2dfdd0048a42a3f23e41be1d45a89aae11d23 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_cumulative.py @@ -0,0 +1,44 @@ +import pytest + +import pandas._testing as tm +from pandas.core.arrays import DatetimeArray + + +class TestAccumulator: + def test_accumulators_freq(self): + # GH#50297 + arr = DatetimeArray._from_sequence( + [ + "2000-01-01", + "2000-01-02", + "2000-01-03", + ], + dtype="M8[ns]", + )._with_freq("infer") + result = arr._accumulate("cummin") + expected = DatetimeArray._from_sequence(["2000-01-01"] * 3, dtype="M8[ns]") + tm.assert_datetime_array_equal(result, expected) + + result = arr._accumulate("cummax") + expected = DatetimeArray._from_sequence( + [ + "2000-01-01", + "2000-01-02", + "2000-01-03", + ], + dtype="M8[ns]", + ) + tm.assert_datetime_array_equal(result, expected) + + @pytest.mark.parametrize("func", ["cumsum", "cumprod"]) + def test_accumulators_disallowed(self, func): + # GH#50297 + arr = DatetimeArray._from_sequence( + [ + "2000-01-01", + "2000-01-02", + ], + dtype="M8[ns]", + )._with_freq("infer") + with pytest.raises(TypeError, match=f"Accumulation {func}"): + arr._accumulate(func) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_reductions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_reductions.py new file mode 100644 index 0000000000000000000000000000000000000000..a941546b13a567b705f61a3a667119cd55a2f0e4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/datetimes/test_reductions.py @@ -0,0 +1,183 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.dtypes import DatetimeTZDtype + +import pandas as pd +from pandas import NaT +import pandas._testing as tm +from pandas.core.arrays import DatetimeArray + + +class TestReductions: + @pytest.fixture(params=["s", "ms", "us", "ns"]) + def unit(self, request): + return request.param + + @pytest.fixture + def arr1d(self, tz_naive_fixture): + """Fixture returning DatetimeArray with parametrized timezones""" + tz = tz_naive_fixture + dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]") + arr = DatetimeArray._from_sequence( + [ + "2000-01-03", + "2000-01-03", + "NaT", + "2000-01-02", + "2000-01-05", + "2000-01-04", + ], + dtype=dtype, + ) + return arr + + def test_min_max(self, arr1d, unit): + arr = arr1d + arr = arr.as_unit(unit) + tz = arr.tz + + result = arr.min() + expected = pd.Timestamp("2000-01-02", tz=tz).as_unit(unit) + assert result == expected + assert result.unit == expected.unit + + result = arr.max() + expected = pd.Timestamp("2000-01-05", tz=tz).as_unit(unit) + assert result == expected + assert result.unit == expected.unit + + result = arr.min(skipna=False) + assert result is NaT + + result = arr.max(skipna=False) + assert result is NaT + + @pytest.mark.parametrize("tz", [None, "US/Central"]) + @pytest.mark.parametrize("skipna", [True, False]) + def test_min_max_empty(self, skipna, tz): + dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]") + arr = DatetimeArray._from_sequence([], dtype=dtype) + result = arr.min(skipna=skipna) + assert result is NaT + + result = arr.max(skipna=skipna) + assert result is NaT + + @pytest.mark.parametrize("tz", [None, "US/Central"]) + @pytest.mark.parametrize("skipna", [True, False]) + def test_median_empty(self, skipna, tz): + dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]") + arr = DatetimeArray._from_sequence([], dtype=dtype) + result = arr.median(skipna=skipna) + assert result is NaT + + arr = arr.reshape(0, 3) + result = arr.median(axis=0, skipna=skipna) + expected = type(arr)._from_sequence([NaT, NaT, NaT], dtype=arr.dtype) + tm.assert_equal(result, expected) + + result = arr.median(axis=1, skipna=skipna) + expected = type(arr)._from_sequence([], dtype=arr.dtype) + tm.assert_equal(result, expected) + + def test_median(self, arr1d): + arr = arr1d + + result = arr.median() + assert result == arr[0] + result = arr.median(skipna=False) + assert result is NaT + + result = arr.dropna().median(skipna=False) + assert result == arr[0] + + result = arr.median(axis=0) + assert result == arr[0] + + def test_median_axis(self, arr1d): + arr = arr1d + assert arr.median(axis=0) == arr.median() + assert arr.median(axis=0, skipna=False) is NaT + + msg = r"abs\(axis\) must be less than ndim" + with pytest.raises(ValueError, match=msg): + arr.median(axis=1) + + @pytest.mark.filterwarnings("ignore:All-NaN slice encountered:RuntimeWarning") + def test_median_2d(self, arr1d): + arr = arr1d.reshape(1, -1) + + # axis = None + assert arr.median() == arr1d.median() + assert arr.median(skipna=False) is NaT + + # axis = 0 + result = arr.median(axis=0) + expected = arr1d + tm.assert_equal(result, expected) + + # Since column 3 is all-NaT, we get NaT there with or without skipna + result = arr.median(axis=0, skipna=False) + expected = arr1d + tm.assert_equal(result, expected) + + # axis = 1 + result = arr.median(axis=1) + expected = type(arr)._from_sequence([arr1d.median()], dtype=arr.dtype) + tm.assert_equal(result, expected) + + result = arr.median(axis=1, skipna=False) + expected = type(arr)._from_sequence([NaT], dtype=arr.dtype) + tm.assert_equal(result, expected) + + def test_mean(self, arr1d): + arr = arr1d + + # manually verified result + expected = arr[0] + 0.4 * pd.Timedelta(days=1) + + result = arr.mean() + assert result == expected + result = arr.mean(skipna=False) + assert result is NaT + + result = arr.dropna().mean(skipna=False) + assert result == expected + + result = arr.mean(axis=0) + assert result == expected + + def test_mean_2d(self): + dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific") + dta = dti._data.reshape(3, 2) + + result = dta.mean(axis=0) + expected = dta[1] + tm.assert_datetime_array_equal(result, expected) + + result = dta.mean(axis=1) + expected = dta[:, 0] + pd.Timedelta(hours=12) + tm.assert_datetime_array_equal(result, expected) + + result = dta.mean(axis=None) + expected = dti.mean() + assert result == expected + + @pytest.mark.parametrize("skipna", [True, False]) + def test_mean_empty(self, arr1d, skipna): + arr = arr1d[:0] + + assert arr.mean(skipna=skipna) is NaT + + arr2d = arr.reshape(0, 3) + result = arr2d.mean(axis=0, skipna=skipna) + expected = DatetimeArray._from_sequence([NaT, NaT, NaT], dtype=arr.dtype) + tm.assert_datetime_array_equal(result, expected) + + result = arr2d.mean(axis=1, skipna=skipna) + expected = arr # i.e. 1D, empty + tm.assert_datetime_array_equal(result, expected) + + result = arr2d.mean(axis=None, skipna=skipna) + assert result is NaT diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/conftest.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..5e971c66029d5ba90ecaa5eb3437246f1548557a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/conftest.py @@ -0,0 +1,48 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas.core.arrays.floating import ( + Float32Dtype, + Float64Dtype, +) + + +@pytest.fixture(params=[Float32Dtype, Float64Dtype]) +def dtype(request): + """Parametrized fixture returning a float 'dtype'""" + return request.param() + + +@pytest.fixture +def data(dtype): + """Fixture returning 'data' array according to parametrized float 'dtype'""" + return pd.array( + list(np.arange(0.1, 0.9, 0.1)) + + [pd.NA] + + list(np.arange(1, 9.8, 0.1)) + + [pd.NA] + + [9.9, 10.0], + dtype=dtype, + ) + + +@pytest.fixture +def data_missing(dtype): + """ + Fixture returning array with missing data according to parametrized float + 'dtype'. + """ + return pd.array([np.nan, 0.1], dtype=dtype) + + +@pytest.fixture(params=["data", "data_missing"]) +def all_data(request, data, data_missing): + """Parametrized fixture returning 'data' or 'data_missing' float arrays. + + Used to test dtype conversion with and without missing values. + """ + if request.param == "data": + return data + elif request.param == "data_missing": + return data_missing diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_arithmetic.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..009fac4c2f5ed4af079024aea35f60337f85989b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_arithmetic.py @@ -0,0 +1,240 @@ +import operator + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import FloatingArray + +# Basic test for the arithmetic array ops +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "opname, exp", + [ + ("add", [1.1, 2.2, None, None, 5.5]), + ("mul", [0.1, 0.4, None, None, 2.5]), + ("sub", [0.9, 1.8, None, None, 4.5]), + ("truediv", [10.0, 10.0, None, None, 10.0]), + ("floordiv", [9.0, 9.0, None, None, 10.0]), + ("mod", [0.1, 0.2, None, None, 0.0]), + ], + ids=["add", "mul", "sub", "div", "floordiv", "mod"], +) +def test_array_op(dtype, opname, exp): + a = pd.array([1.0, 2.0, None, 4.0, 5.0], dtype=dtype) + b = pd.array([0.1, 0.2, 0.3, None, 0.5], dtype=dtype) + + op = getattr(operator, opname) + + result = op(a, b) + expected = pd.array(exp, dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize("zero, negative", [(0, False), (0.0, False), (-0.0, True)]) +def test_divide_by_zero(dtype, zero, negative): + # TODO pending NA/NaN discussion + # https://github.com/pandas-dev/pandas/issues/32265/ + a = pd.array([0, 1, -1, None], dtype=dtype) + result = a / zero + expected = FloatingArray( + np.array([np.nan, np.inf, -np.inf, np.nan], dtype=dtype.numpy_dtype), + np.array([False, False, False, True]), + ) + if negative: + expected *= -1 + tm.assert_extension_array_equal(result, expected) + + +def test_pow_scalar(dtype): + a = pd.array([-1, 0, 1, None, 2], dtype=dtype) + result = a**0 + expected = pd.array([1, 1, 1, 1, 1], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = a**1 + expected = pd.array([-1, 0, 1, None, 2], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = a**pd.NA + expected = pd.array([None, None, 1, None, None], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = a**np.nan + # TODO np.nan should be converted to pd.NA / missing before operation? + expected = FloatingArray( + np.array([np.nan, np.nan, 1, np.nan, np.nan], dtype=dtype.numpy_dtype), + mask=a._mask, + ) + tm.assert_extension_array_equal(result, expected) + + # reversed + a = a[1:] # Can't raise integers to negative powers. + + result = 0**a + expected = pd.array([1, 0, None, 0], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = 1**a + expected = pd.array([1, 1, 1, 1], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = pd.NA**a + expected = pd.array([1, None, None, None], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = np.nan**a + expected = FloatingArray( + np.array([1, np.nan, np.nan, np.nan], dtype=dtype.numpy_dtype), mask=a._mask + ) + tm.assert_extension_array_equal(result, expected) + + +def test_pow_array(dtype): + a = pd.array([0, 0, 0, 1, 1, 1, None, None, None], dtype=dtype) + b = pd.array([0, 1, None, 0, 1, None, 0, 1, None], dtype=dtype) + result = a**b + expected = pd.array([1, 0, None, 1, 1, 1, 1, None, None], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_rpow_one_to_na(): + # https://github.com/pandas-dev/pandas/issues/22022 + # https://github.com/pandas-dev/pandas/issues/29997 + arr = pd.array([np.nan, np.nan], dtype="Float64") + result = np.array([1.0, 2.0]) ** arr + expected = pd.array([1.0, np.nan], dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize("other", [0, 0.5]) +def test_arith_zero_dim_ndarray(other): + arr = pd.array([1, None, 2], dtype="Float64") + result = arr + np.array(other) + expected = arr + other + tm.assert_equal(result, expected) + + +# Test generic characteristics / errors +# ----------------------------------------------------------------------------- + + +def test_error_invalid_values(data, all_arithmetic_operators): + op = all_arithmetic_operators + s = pd.Series(data) + ops = getattr(s, op) + + # invalid scalars + msg = "|".join( + [ + r"can only perform ops with numeric values", + r"FloatingArray cannot perform the operation mod", + "unsupported operand type", + "not all arguments converted during string formatting", + "can't multiply sequence by non-int of type 'float'", + "ufunc 'subtract' cannot use operands with types dtype", + r"can only concatenate str \(not \"float\"\) to str", + "ufunc '.*' not supported for the input types, and the inputs could not", + "ufunc '.*' did not contain a loop with signature matching types", + "Concatenation operation is not implemented for NumPy arrays", + "has no kernel", + "not implemented", + "not supported for dtype", + "Can only string multiply by an integer", + ] + ) + with pytest.raises(TypeError, match=msg): + ops("foo") + with pytest.raises(TypeError, match=msg): + ops(pd.Timestamp("20180101")) + + # invalid array-likes + with pytest.raises(TypeError, match=msg): + ops(pd.Series("foo", index=s.index)) + + msg = "|".join( + [ + "can only perform ops with numeric values", + "cannot perform .* with this index type: DatetimeArray", + "Addition/subtraction of integers and integer-arrays " + "with DatetimeArray is no longer supported. *", + "unsupported operand type", + "not all arguments converted during string formatting", + "can't multiply sequence by non-int of type 'float'", + "ufunc 'subtract' cannot use operands with types dtype", + ( + "ufunc 'add' cannot use operands with types " + rf"dtype\('{tm.ENDIAN}M8\[ns\]'\)" + ), + r"ufunc 'add' cannot use operands with types dtype\('float\d{2}'\)", + "cannot subtract DatetimeArray from ndarray", + "has no kernel", + "not implemented", + "not supported for dtype", + ] + ) + with pytest.raises(TypeError, match=msg): + ops(pd.Series(pd.date_range("20180101", periods=len(s)))) + + +# Various +# ----------------------------------------------------------------------------- + + +def test_cross_type_arithmetic(): + df = pd.DataFrame( + { + "A": pd.array([1, 2, np.nan], dtype="Float64"), + "B": pd.array([1, np.nan, 3], dtype="Float32"), + "C": np.array([1, 2, 3], dtype="float64"), + } + ) + + result = df.A + df.C + expected = pd.Series([2, 4, np.nan], dtype="Float64") + tm.assert_series_equal(result, expected) + + result = (df.A + df.C) * 3 == 12 + expected = pd.Series([False, True, None], dtype="boolean") + tm.assert_series_equal(result, expected) + + result = df.A + df.B + expected = pd.Series([2, np.nan, np.nan], dtype="Float64") + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "source, neg_target, abs_target", + [ + ([1.1, 2.2, 3.3], [-1.1, -2.2, -3.3], [1.1, 2.2, 3.3]), + ([1.1, 2.2, None], [-1.1, -2.2, None], [1.1, 2.2, None]), + ([-1.1, 0.0, 1.1], [1.1, 0.0, -1.1], [1.1, 0.0, 1.1]), + ], +) +def test_unary_float_operators(float_ea_dtype, source, neg_target, abs_target): + # GH38794 + dtype = float_ea_dtype + arr = pd.array(source, dtype=dtype) + neg_result, pos_result, abs_result = -arr, +arr, abs(arr) + neg_target = pd.array(neg_target, dtype=dtype) + abs_target = pd.array(abs_target, dtype=dtype) + + tm.assert_extension_array_equal(neg_result, neg_target) + tm.assert_extension_array_equal(pos_result, arr) + assert not tm.shares_memory(pos_result, arr) + tm.assert_extension_array_equal(abs_result, abs_target) + + +def test_bitwise(dtype): + left = pd.array([1, None, 3, 4], dtype=dtype) + right = pd.array([None, 3, 5, 4], dtype=dtype) + + with pytest.raises(TypeError, match="unsupported operand type"): + left | right + with pytest.raises(TypeError, match="unsupported operand type"): + left & right + with pytest.raises(TypeError, match="unsupported operand type"): + left ^ right diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_astype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..752ebe194ffcfdccf491d22320a8edcae5a8adab --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_astype.py @@ -0,0 +1,135 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +def test_astype(): + # with missing values + arr = pd.array([0.1, 0.2, None], dtype="Float64") + + with pytest.raises(ValueError, match="cannot convert NA to integer"): + arr.astype("int64") + + with pytest.raises(ValueError, match="cannot convert float NaN to bool"): + arr.astype("bool") + + result = arr.astype("float64") + expected = np.array([0.1, 0.2, np.nan], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + # no missing values + arr = pd.array([0.0, 1.0, 0.5], dtype="Float64") + result = arr.astype("int64") + expected = np.array([0, 1, 0], dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + result = arr.astype("bool") + expected = np.array([False, True, True], dtype="bool") + tm.assert_numpy_array_equal(result, expected) + + +def test_astype_to_floating_array(): + # astype to FloatingArray + arr = pd.array([0.0, 1.0, None], dtype="Float64") + + result = arr.astype("Float64") + tm.assert_extension_array_equal(result, arr) + result = arr.astype(pd.Float64Dtype()) + tm.assert_extension_array_equal(result, arr) + result = arr.astype("Float32") + expected = pd.array([0.0, 1.0, None], dtype="Float32") + tm.assert_extension_array_equal(result, expected) + + +def test_astype_to_boolean_array(): + # astype to BooleanArray + arr = pd.array([0.0, 1.0, None], dtype="Float64") + + result = arr.astype("boolean") + expected = pd.array([False, True, None], dtype="boolean") + tm.assert_extension_array_equal(result, expected) + result = arr.astype(pd.BooleanDtype()) + tm.assert_extension_array_equal(result, expected) + + +def test_astype_to_integer_array(): + # astype to IntegerArray + arr = pd.array([0.0, 1.5, None], dtype="Float64") + + result = arr.astype("Int64") + expected = pd.array([0, 1, None], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + +def test_astype_str(using_infer_string): + a = pd.array([0.1, 0.2, None], dtype="Float64") + + if using_infer_string: + expected = pd.array(["0.1", "0.2", None], dtype=pd.StringDtype(na_value=np.nan)) + + tm.assert_extension_array_equal(a.astype(str), expected) + tm.assert_extension_array_equal(a.astype("str"), expected) + else: + expected = np.array(["0.1", "0.2", ""], dtype="U32") + + tm.assert_numpy_array_equal(a.astype(str), expected) + tm.assert_numpy_array_equal(a.astype("str"), expected) + + +def test_astype_copy(): + arr = pd.array([0.1, 0.2, None], dtype="Float64") + orig = pd.array([0.1, 0.2, None], dtype="Float64") + + # copy=True -> ensure both data and mask are actual copies + result = arr.astype("Float64", copy=True) + assert result is not arr + assert not tm.shares_memory(result, arr) + result[0] = 10 + tm.assert_extension_array_equal(arr, orig) + result[0] = pd.NA + tm.assert_extension_array_equal(arr, orig) + + # copy=False + result = arr.astype("Float64", copy=False) + assert result is arr + assert np.shares_memory(result._data, arr._data) + assert np.shares_memory(result._mask, arr._mask) + result[0] = 10 + assert arr[0] == 10 + result[0] = pd.NA + assert arr[0] is pd.NA + + # astype to different dtype -> always needs a copy -> even with copy=False + # we need to ensure that also the mask is actually copied + arr = pd.array([0.1, 0.2, None], dtype="Float64") + orig = pd.array([0.1, 0.2, None], dtype="Float64") + + result = arr.astype("Float32", copy=False) + assert not tm.shares_memory(result, arr) + result[0] = 10 + tm.assert_extension_array_equal(arr, orig) + result[0] = pd.NA + tm.assert_extension_array_equal(arr, orig) + + +def test_astype_object(dtype): + arr = pd.array([1.0, pd.NA], dtype=dtype) + + result = arr.astype(object) + expected = np.array([1.0, pd.NA], dtype=object) + tm.assert_numpy_array_equal(result, expected) + # check exact element types + assert isinstance(result[0], float) + assert result[1] is pd.NA + + +def test_Float64_conversion(): + # GH#40729 + testseries = pd.Series(["1", "2", "3", "4"], dtype="object") + result = testseries.astype(pd.Float64Dtype()) + + expected = pd.Series([1.0, 2.0, 3.0, 4.0], dtype=pd.Float64Dtype()) + + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_comparison.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_comparison.py new file mode 100644 index 0000000000000000000000000000000000000000..a429649f1ce1dc10fc9610faa73a81dd94255b37 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_comparison.py @@ -0,0 +1,65 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import FloatingArray +from pandas.tests.arrays.masked_shared import ( + ComparisonOps, + NumericOps, +) + + +class TestComparisonOps(NumericOps, ComparisonOps): + @pytest.mark.parametrize("other", [True, False, pd.NA, -1.0, 0.0, 1]) + def test_scalar(self, other, comparison_op, dtype): + ComparisonOps.test_scalar(self, other, comparison_op, dtype) + + def test_compare_with_integerarray(self, comparison_op): + op = comparison_op + a = pd.array([0, 1, None] * 3, dtype="Int64") + b = pd.array([0] * 3 + [1] * 3 + [None] * 3, dtype="Float64") + other = b.astype("Int64") + expected = op(a, other) + result = op(a, b) + tm.assert_extension_array_equal(result, expected) + expected = op(other, a) + result = op(b, a) + tm.assert_extension_array_equal(result, expected) + + +def test_equals(): + # GH-30652 + # equals is generally tested in /tests/extension/base/methods, but this + # specifically tests that two arrays of the same class but different dtype + # do not evaluate equal + a1 = pd.array([1, 2, None], dtype="Float64") + a2 = pd.array([1, 2, None], dtype="Float32") + assert a1.equals(a2) is False + + +def test_equals_nan_vs_na(): + # GH#44382 + + mask = np.zeros(3, dtype=bool) + data = np.array([1.0, np.nan, 3.0], dtype=np.float64) + + left = FloatingArray(data, mask) + assert left.equals(left) + tm.assert_extension_array_equal(left, left) + + assert left.equals(left.copy()) + assert left.equals(FloatingArray(data.copy(), mask.copy())) + + mask2 = np.array([False, True, False], dtype=bool) + data2 = np.array([1.0, 2.0, 3.0], dtype=np.float64) + right = FloatingArray(data2, mask2) + assert right.equals(right) + tm.assert_extension_array_equal(right, right) + + assert not left.equals(right) + + # with mask[1] = True, the only difference is data[1], which should + # not matter for equals + mask[1] = True + assert left.equals(right) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_concat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_concat.py new file mode 100644 index 0000000000000000000000000000000000000000..2174a834aa959b88d899971f83247258a94476e3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_concat.py @@ -0,0 +1,20 @@ +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize( + "to_concat_dtypes, result_dtype", + [ + (["Float64", "Float64"], "Float64"), + (["Float32", "Float64"], "Float64"), + (["Float32", "Float32"], "Float32"), + ], +) +def test_concat_series(to_concat_dtypes, result_dtype): + result = pd.concat([pd.Series([1, 2, pd.NA], dtype=t) for t in to_concat_dtypes]) + expected = pd.concat([pd.Series([1, 2, pd.NA], dtype=object)] * 2).astype( + result_dtype + ) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_construction.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_construction.py new file mode 100644 index 0000000000000000000000000000000000000000..4007ee6b415c9b0f21f580f6240ed85ba1889781 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_construction.py @@ -0,0 +1,204 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import FloatingArray +from pandas.core.arrays.floating import ( + Float32Dtype, + Float64Dtype, +) + + +def test_uses_pandas_na(): + a = pd.array([1, None], dtype=Float64Dtype()) + assert a[1] is pd.NA + + +def test_floating_array_constructor(): + values = np.array([1, 2, 3, 4], dtype="float64") + mask = np.array([False, False, False, True], dtype="bool") + + result = FloatingArray(values, mask) + expected = pd.array([1, 2, 3, np.nan], dtype="Float64") + tm.assert_extension_array_equal(result, expected) + tm.assert_numpy_array_equal(result._data, values) + tm.assert_numpy_array_equal(result._mask, mask) + + msg = r".* should be .* numpy array. Use the 'pd.array' function instead" + with pytest.raises(TypeError, match=msg): + FloatingArray(values.tolist(), mask) + + with pytest.raises(TypeError, match=msg): + FloatingArray(values, mask.tolist()) + + with pytest.raises(TypeError, match=msg): + FloatingArray(values.astype(int), mask) + + msg = r"__init__\(\) missing 1 required positional argument: 'mask'" + with pytest.raises(TypeError, match=msg): + FloatingArray(values) + + +def test_floating_array_disallows_float16(): + # GH#44715 + arr = np.array([1, 2], dtype=np.float16) + mask = np.array([False, False]) + + msg = "FloatingArray does not support np.float16 dtype" + with pytest.raises(TypeError, match=msg): + FloatingArray(arr, mask) + + +def test_floating_array_disallows_Float16_dtype(request): + # GH#44715 + with pytest.raises(TypeError, match="data type 'Float16' not understood"): + pd.array([1.0, 2.0], dtype="Float16") + + +def test_floating_array_constructor_copy(): + values = np.array([1, 2, 3, 4], dtype="float64") + mask = np.array([False, False, False, True], dtype="bool") + + result = FloatingArray(values, mask) + assert result._data is values + assert result._mask is mask + + result = FloatingArray(values, mask, copy=True) + assert result._data is not values + assert result._mask is not mask + + +def test_to_array(): + result = pd.array([0.1, 0.2, 0.3, 0.4]) + expected = pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize( + "a, b", + [ + ([1, None], [1, pd.NA]), + ([None], [pd.NA]), + ([None, np.nan], [pd.NA, pd.NA]), + ([1, np.nan], [1, pd.NA]), + ([np.nan], [pd.NA]), + ], +) +def test_to_array_none_is_nan(a, b): + result = pd.array(a, dtype="Float64") + expected = pd.array(b, dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + +def test_to_array_mixed_integer_float(): + result = pd.array([1, 2.0]) + expected = pd.array([1.0, 2.0], dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + result = pd.array([1, None, 2.0]) + expected = pd.array([1.0, None, 2.0], dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize( + "values", + [ + ["foo", "bar"], + "foo", + 1, + 1.0, + pd.date_range("20130101", periods=2), + np.array(["foo"]), + [[1, 2], [3, 4]], + [np.nan, {"a": 1}], + # GH#44514 all-NA case used to get quietly swapped out before checking ndim + np.array([pd.NA] * 6, dtype=object).reshape(3, 2), + ], +) +def test_to_array_error(values): + # error in converting existing arrays to FloatingArray + msg = "|".join( + [ + "cannot be converted to FloatingDtype", + "values must be a 1D list-like", + "Cannot pass scalar", + r"float\(\) argument must be a string or a (real )?number, not 'dict'", + "could not convert string to float: 'foo'", + r"could not convert string to float: np\.str_\('foo'\)", + ] + ) + with pytest.raises((TypeError, ValueError), match=msg): + pd.array(values, dtype="Float64") + + +@pytest.mark.parametrize("values", [["1", "2", None], ["1.5", "2", None]]) +def test_construct_from_float_strings(values): + # see also test_to_integer_array_str + expected = pd.array([float(values[0]), 2, None], dtype="Float64") + + res = pd.array(values, dtype="Float64") + tm.assert_extension_array_equal(res, expected) + + res = FloatingArray._from_sequence(values) + tm.assert_extension_array_equal(res, expected) + + +def test_to_array_inferred_dtype(): + # if values has dtype -> respect it + result = pd.array(np.array([1, 2], dtype="float32")) + assert result.dtype == Float32Dtype() + + # if values have no dtype -> always float64 + result = pd.array([1.0, 2.0]) + assert result.dtype == Float64Dtype() + + +def test_to_array_dtype_keyword(): + result = pd.array([1, 2], dtype="Float32") + assert result.dtype == Float32Dtype() + + # if values has dtype -> override it + result = pd.array(np.array([1, 2], dtype="float32"), dtype="Float64") + assert result.dtype == Float64Dtype() + + +def test_to_array_integer(): + result = pd.array([1, 2], dtype="Float64") + expected = pd.array([1.0, 2.0], dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + # for integer dtypes, the itemsize is not preserved + # TODO can we specify "floating" in general? + result = pd.array(np.array([1, 2], dtype="int32"), dtype="Float64") + assert result.dtype == Float64Dtype() + + +@pytest.mark.parametrize( + "bool_values, values, target_dtype, expected_dtype", + [ + ([False, True], [0, 1], Float64Dtype(), Float64Dtype()), + ([False, True], [0, 1], "Float64", Float64Dtype()), + ([False, True, np.nan], [0, 1, np.nan], Float64Dtype(), Float64Dtype()), + ], +) +def test_to_array_bool(bool_values, values, target_dtype, expected_dtype): + result = pd.array(bool_values, dtype=target_dtype) + assert result.dtype == expected_dtype + expected = pd.array(values, dtype=target_dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_series_from_float(data): + # construct from our dtype & string dtype + dtype = data.dtype + + # from float + expected = pd.Series(data) + result = pd.Series(data.to_numpy(na_value=np.nan, dtype="float"), dtype=str(dtype)) + tm.assert_series_equal(result, expected) + + # from list + expected = pd.Series(data) + result = pd.Series(np.array(data).tolist(), dtype=str(dtype)) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_contains.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_contains.py new file mode 100644 index 0000000000000000000000000000000000000000..956642697bf3285e5c661c43047a5f0dafa83144 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_contains.py @@ -0,0 +1,12 @@ +import numpy as np + +import pandas as pd + + +def test_contains_nan(): + # GH#52840 + arr = pd.array(range(5)) / 0 + + assert np.isnan(arr._data[0]) + assert not arr.isna()[0] + assert np.nan in arr diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_function.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_function.py new file mode 100644 index 0000000000000000000000000000000000000000..40fd66fd049a621138c2cda074a08a1a94967bb5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_function.py @@ -0,0 +1,194 @@ +import numpy as np +import pytest + +from pandas.compat import IS64 + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize("ufunc", [np.abs, np.sign]) +# np.sign emits a warning with nans, +@pytest.mark.filterwarnings("ignore:invalid value encountered in sign:RuntimeWarning") +def test_ufuncs_single(ufunc): + a = pd.array([1, 2, -3, np.nan], dtype="Float64") + result = ufunc(a) + expected = pd.array(ufunc(a.astype(float)), dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + s = pd.Series(a) + result = ufunc(s) + expected = pd.Series(expected) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt]) +def test_ufuncs_single_float(ufunc): + a = pd.array([1.0, 0.2, 3.0, np.nan], dtype="Float64") + with np.errstate(invalid="ignore"): + result = ufunc(a) + expected = pd.array(ufunc(a.astype(float)), dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + s = pd.Series(a) + with np.errstate(invalid="ignore"): + result = ufunc(s) + expected = pd.Series(ufunc(s.astype(float)), dtype="Float64") + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("ufunc", [np.add, np.subtract]) +def test_ufuncs_binary_float(ufunc): + # two FloatingArrays + a = pd.array([1, 0.2, -3, np.nan], dtype="Float64") + result = ufunc(a, a) + expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + # FloatingArray with numpy array + arr = np.array([1, 2, 3, 4]) + result = ufunc(a, arr) + expected = pd.array(ufunc(a.astype(float), arr), dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + result = ufunc(arr, a) + expected = pd.array(ufunc(arr, a.astype(float)), dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + # FloatingArray with scalar + result = ufunc(a, 1) + expected = pd.array(ufunc(a.astype(float), 1), dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + result = ufunc(1, a) + expected = pd.array(ufunc(1, a.astype(float)), dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize("values", [[0, 1], [0, None]]) +def test_ufunc_reduce_raises(values): + arr = pd.array(values, dtype="Float64") + + res = np.add.reduce(arr) + expected = arr.sum(skipna=False) + tm.assert_almost_equal(res, expected) + + +@pytest.mark.skipif(not IS64, reason="GH 36579: fail on 32-bit system") +@pytest.mark.parametrize( + "pandasmethname, kwargs", + [ + ("var", {"ddof": 0}), + ("var", {"ddof": 1}), + ("std", {"ddof": 0}), + ("std", {"ddof": 1}), + ("kurtosis", {}), + ("skew", {}), + ("sem", {}), + ], +) +def test_stat_method(pandasmethname, kwargs): + s = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, np.nan, np.nan], dtype="Float64") + pandasmeth = getattr(s, pandasmethname) + result = pandasmeth(**kwargs) + s2 = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype="float64") + pandasmeth = getattr(s2, pandasmethname) + expected = pandasmeth(**kwargs) + assert expected == result + + +def test_value_counts_na(): + arr = pd.array([0.1, 0.2, 0.1, pd.NA], dtype="Float64") + result = arr.value_counts(dropna=False) + idx = pd.Index([0.1, 0.2, pd.NA], dtype=arr.dtype) + assert idx.dtype == arr.dtype + expected = pd.Series([2, 1, 1], index=idx, dtype="Int64", name="count") + tm.assert_series_equal(result, expected) + + result = arr.value_counts(dropna=True) + expected = pd.Series([2, 1], index=idx[:-1], dtype="Int64", name="count") + tm.assert_series_equal(result, expected) + + +def test_value_counts_empty(): + ser = pd.Series([], dtype="Float64") + result = ser.value_counts() + idx = pd.Index([], dtype="Float64") + assert idx.dtype == "Float64" + expected = pd.Series([], index=idx, dtype="Int64", name="count") + tm.assert_series_equal(result, expected) + + +def test_value_counts_with_normalize(): + ser = pd.Series([0.1, 0.2, 0.1, pd.NA], dtype="Float64") + result = ser.value_counts(normalize=True) + expected = pd.Series([2, 1], index=ser[:2], dtype="Float64", name="proportion") / 3 + assert expected.index.dtype == ser.dtype + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("skipna", [True, False]) +@pytest.mark.parametrize("min_count", [0, 4]) +def test_floating_array_sum(skipna, min_count, dtype): + arr = pd.array([1, 2, 3, None], dtype=dtype) + result = arr.sum(skipna=skipna, min_count=min_count) + if skipna and min_count == 0: + assert result == 6.0 + else: + assert result is pd.NA + + +@pytest.mark.parametrize( + "values, expected", [([1, 2, 3], 6.0), ([1, 2, 3, None], 6.0), ([None], 0.0)] +) +def test_floating_array_numpy_sum(values, expected): + arr = pd.array(values, dtype="Float64") + result = np.sum(arr) + assert result == expected + + +@pytest.mark.parametrize("op", ["sum", "min", "max", "prod"]) +def test_preserve_dtypes(op): + df = pd.DataFrame( + { + "A": ["a", "b", "b"], + "B": [1, None, 3], + "C": pd.array([0.1, None, 3.0], dtype="Float64"), + } + ) + + # op + result = getattr(df.C, op)() + assert isinstance(result, np.float64) + + # groupby + result = getattr(df.groupby("A"), op)() + + expected = pd.DataFrame( + {"B": np.array([1.0, 3.0]), "C": pd.array([0.1, 3], dtype="Float64")}, + index=pd.Index(["a", "b"], name="A"), + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("skipna", [True, False]) +@pytest.mark.parametrize("method", ["min", "max"]) +def test_floating_array_min_max(skipna, method, dtype): + arr = pd.array([0.0, 1.0, None], dtype=dtype) + func = getattr(arr, method) + result = func(skipna=skipna) + if skipna: + assert result == (0 if method == "min" else 1) + else: + assert result is pd.NA + + +@pytest.mark.parametrize("skipna", [True, False]) +@pytest.mark.parametrize("min_count", [0, 9]) +def test_floating_array_prod(skipna, min_count, dtype): + arr = pd.array([1.0, 2.0, None], dtype=dtype) + result = arr.prod(skipna=skipna, min_count=min_count) + if skipna and min_count == 0: + assert result == 2 + else: + assert result is pd.NA diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_repr.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_repr.py new file mode 100644 index 0000000000000000000000000000000000000000..ea2cdd4fab86ada36d6d5804204c4a479a3e1603 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_repr.py @@ -0,0 +1,47 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas.core.arrays.floating import ( + Float32Dtype, + Float64Dtype, +) + + +def test_dtypes(dtype): + # smoke tests on auto dtype construction + + np.dtype(dtype.type).kind == "f" + assert dtype.name is not None + + +@pytest.mark.parametrize( + "dtype, expected", + [(Float32Dtype(), "Float32Dtype()"), (Float64Dtype(), "Float64Dtype()")], +) +def test_repr_dtype(dtype, expected): + assert repr(dtype) == expected + + +def test_repr_array(): + result = repr(pd.array([1.0, None, 3.0])) + expected = "\n[1.0, , 3.0]\nLength: 3, dtype: Float64" + assert result == expected + + +def test_repr_array_long(): + data = pd.array([1.0, 2.0, None] * 1000) + expected = """ +[ 1.0, 2.0, , 1.0, 2.0, , 1.0, 2.0, , 1.0, + ... + , 1.0, 2.0, , 1.0, 2.0, , 1.0, 2.0, ] +Length: 3000, dtype: Float64""" + result = repr(data) + assert result == expected + + +def test_frame_repr(data_missing): + df = pd.DataFrame({"A": data_missing}) + result = repr(df) + expected = " A\n0 \n1 0.1" + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_to_numpy.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_to_numpy.py new file mode 100644 index 0000000000000000000000000000000000000000..e954cecba417afd71059a35f7506c650eb780373 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/floating/test_to_numpy.py @@ -0,0 +1,132 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import FloatingArray + + +@pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) +def test_to_numpy(box): + con = pd.Series if box else pd.array + + # default (with or without missing values) -> object dtype + arr = con([0.1, 0.2, 0.3], dtype="Float64") + result = arr.to_numpy() + expected = np.array([0.1, 0.2, 0.3], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + arr = con([0.1, 0.2, None], dtype="Float64") + result = arr.to_numpy() + expected = np.array([0.1, 0.2, np.nan], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) +def test_to_numpy_float(box): + con = pd.Series if box else pd.array + + # no missing values -> can convert to float, otherwise raises + arr = con([0.1, 0.2, 0.3], dtype="Float64") + result = arr.to_numpy(dtype="float64") + expected = np.array([0.1, 0.2, 0.3], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + arr = con([0.1, 0.2, None], dtype="Float64") + result = arr.to_numpy(dtype="float64") + expected = np.array([0.1, 0.2, np.nan], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + result = arr.to_numpy(dtype="float64", na_value=np.nan) + expected = np.array([0.1, 0.2, np.nan], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) +def test_to_numpy_int(box): + con = pd.Series if box else pd.array + + # no missing values -> can convert to int, otherwise raises + arr = con([1.0, 2.0, 3.0], dtype="Float64") + result = arr.to_numpy(dtype="int64") + expected = np.array([1, 2, 3], dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + arr = con([1.0, 2.0, None], dtype="Float64") + with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype"): + result = arr.to_numpy(dtype="int64") + + # automatic casting (floors the values) + arr = con([0.1, 0.9, 1.1], dtype="Float64") + result = arr.to_numpy(dtype="int64") + expected = np.array([0, 0, 1], dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) +def test_to_numpy_na_value(box): + con = pd.Series if box else pd.array + + arr = con([0.0, 1.0, None], dtype="Float64") + result = arr.to_numpy(dtype=object, na_value=None) + expected = np.array([0.0, 1.0, None], dtype="object") + tm.assert_numpy_array_equal(result, expected) + + result = arr.to_numpy(dtype=bool, na_value=False) + expected = np.array([False, True, False], dtype="bool") + tm.assert_numpy_array_equal(result, expected) + + result = arr.to_numpy(dtype="int64", na_value=-99) + expected = np.array([0, 1, -99], dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + +def test_to_numpy_na_value_with_nan(): + # array with both NaN and NA -> only fill NA with `na_value` + arr = FloatingArray(np.array([0.0, np.nan, 0.0]), np.array([False, False, True])) + result = arr.to_numpy(dtype="float64", na_value=-1) + expected = np.array([0.0, np.nan, -1.0], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("dtype", ["float64", "float32", "int32", "int64", "bool"]) +@pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) +def test_to_numpy_dtype(box, dtype): + con = pd.Series if box else pd.array + arr = con([0.0, 1.0], dtype="Float64") + + result = arr.to_numpy(dtype=dtype) + expected = np.array([0, 1], dtype=dtype) + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("dtype", ["int32", "int64", "bool"]) +@pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) +def test_to_numpy_na_raises(box, dtype): + con = pd.Series if box else pd.array + arr = con([0.0, 1.0, None], dtype="Float64") + with pytest.raises(ValueError, match=dtype): + arr.to_numpy(dtype=dtype) + + +@pytest.mark.parametrize("box", [True, False], ids=["series", "array"]) +def test_to_numpy_string(box, dtype): + con = pd.Series if box else pd.array + arr = con([0.0, 1.0, None], dtype="Float64") + + result = arr.to_numpy(dtype="str") + expected = np.array([0.0, 1.0, pd.NA], dtype=f"{tm.ENDIAN}U32") + tm.assert_numpy_array_equal(result, expected) + + +def test_to_numpy_copy(): + # to_numpy can be zero-copy if no missing values + arr = pd.array([0.1, 0.2, 0.3], dtype="Float64") + result = arr.to_numpy(dtype="float64") + result[0] = 10 + tm.assert_extension_array_equal(arr, pd.array([10, 0.2, 0.3], dtype="Float64")) + + arr = pd.array([0.1, 0.2, 0.3], dtype="Float64") + result = arr.to_numpy(dtype="float64", copy=True) + result[0] = 10 + tm.assert_extension_array_equal(arr, pd.array([0.1, 0.2, 0.3], dtype="Float64")) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/conftest.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..f73400dfe689e91c4c2b457c4be1a0a41380fd6a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/conftest.py @@ -0,0 +1,68 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas.core.arrays.integer import ( + Int8Dtype, + Int16Dtype, + Int32Dtype, + Int64Dtype, + UInt8Dtype, + UInt16Dtype, + UInt32Dtype, + UInt64Dtype, +) + + +@pytest.fixture( + params=[ + Int8Dtype, + Int16Dtype, + Int32Dtype, + Int64Dtype, + UInt8Dtype, + UInt16Dtype, + UInt32Dtype, + UInt64Dtype, + ] +) +def dtype(request): + """Parametrized fixture returning integer 'dtype'""" + return request.param() + + +@pytest.fixture +def data(dtype): + """ + Fixture returning 'data' array with valid and missing values according to + parametrized integer 'dtype'. + + Used to test dtype conversion with and without missing values. + """ + return pd.array( + list(range(8)) + [np.nan] + list(range(10, 98)) + [np.nan] + [99, 100], + dtype=dtype, + ) + + +@pytest.fixture +def data_missing(dtype): + """ + Fixture returning array with exactly one NaN and one valid integer, + according to parametrized integer 'dtype'. + + Used to test dtype conversion with and without missing values. + """ + return pd.array([np.nan, 1], dtype=dtype) + + +@pytest.fixture(params=["data", "data_missing"]) +def all_data(request, data, data_missing): + """Parametrized fixture returning 'data' or 'data_missing' integer arrays. + + Used to test dtype conversion with and without missing values. + """ + if request.param == "data": + return data + elif request.param == "data_missing": + return data_missing diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_arithmetic.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..9fbea2022c87b36a44acdab9e42ac36f0018ae06 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_arithmetic.py @@ -0,0 +1,345 @@ +import operator + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core import ops +from pandas.core.arrays import FloatingArray + +# Basic test for the arithmetic array ops +# ----------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "opname, exp", + [("add", [1, 3, None, None, 9]), ("mul", [0, 2, None, None, 20])], + ids=["add", "mul"], +) +def test_add_mul(dtype, opname, exp): + a = pd.array([0, 1, None, 3, 4], dtype=dtype) + b = pd.array([1, 2, 3, None, 5], dtype=dtype) + + # array / array + expected = pd.array(exp, dtype=dtype) + + op = getattr(operator, opname) + result = op(a, b) + tm.assert_extension_array_equal(result, expected) + + op = getattr(ops, "r" + opname) + result = op(a, b) + tm.assert_extension_array_equal(result, expected) + + +def test_sub(dtype): + a = pd.array([1, 2, 3, None, 5], dtype=dtype) + b = pd.array([0, 1, None, 3, 4], dtype=dtype) + + result = a - b + expected = pd.array([1, 1, None, None, 1], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_div(dtype): + a = pd.array([1, 2, 3, None, 5], dtype=dtype) + b = pd.array([0, 1, None, 3, 4], dtype=dtype) + + result = a / b + expected = pd.array([np.inf, 2, None, None, 1.25], dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize("zero, negative", [(0, False), (0.0, False), (-0.0, True)]) +def test_divide_by_zero(zero, negative): + # https://github.com/pandas-dev/pandas/issues/27398, GH#22793 + a = pd.array([0, 1, -1, None], dtype="Int64") + result = a / zero + expected = FloatingArray( + np.array([np.nan, np.inf, -np.inf, 1], dtype="float64"), + np.array([False, False, False, True]), + ) + if negative: + expected *= -1 + tm.assert_extension_array_equal(result, expected) + + +def test_floordiv(dtype): + a = pd.array([1, 2, 3, None, 5], dtype=dtype) + b = pd.array([0, 1, None, 3, 4], dtype=dtype) + + result = a // b + # Series op sets 1//0 to np.inf, which IntegerArray does not do (yet) + expected = pd.array([0, 2, None, None, 1], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_floordiv_by_int_zero_no_mask(any_int_ea_dtype): + # GH 48223: Aligns with non-masked floordiv + # but differs from numpy + # https://github.com/pandas-dev/pandas/issues/30188#issuecomment-564452740 + ser = pd.Series([0, 1], dtype=any_int_ea_dtype) + result = 1 // ser + expected = pd.Series([np.inf, 1.0], dtype="Float64") + tm.assert_series_equal(result, expected) + + ser_non_nullable = ser.astype(ser.dtype.numpy_dtype) + result = 1 // ser_non_nullable + expected = expected.astype(np.float64) + tm.assert_series_equal(result, expected) + + +def test_mod(dtype): + a = pd.array([1, 2, 3, None, 5], dtype=dtype) + b = pd.array([0, 1, None, 3, 4], dtype=dtype) + + result = a % b + expected = pd.array([0, 0, None, None, 1], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_pow_scalar(): + a = pd.array([-1, 0, 1, None, 2], dtype="Int64") + result = a**0 + expected = pd.array([1, 1, 1, 1, 1], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + result = a**1 + expected = pd.array([-1, 0, 1, None, 2], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + result = a**pd.NA + expected = pd.array([None, None, 1, None, None], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + result = a**np.nan + expected = FloatingArray( + np.array([np.nan, np.nan, 1, np.nan, np.nan], dtype="float64"), + np.array([False, False, False, True, False]), + ) + tm.assert_extension_array_equal(result, expected) + + # reversed + a = a[1:] # Can't raise integers to negative powers. + + result = 0**a + expected = pd.array([1, 0, None, 0], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + result = 1**a + expected = pd.array([1, 1, 1, 1], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + result = pd.NA**a + expected = pd.array([1, None, None, None], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + result = np.nan**a + expected = FloatingArray( + np.array([1, np.nan, np.nan, np.nan], dtype="float64"), + np.array([False, False, True, False]), + ) + tm.assert_extension_array_equal(result, expected) + + +def test_pow_array(): + a = pd.array([0, 0, 0, 1, 1, 1, None, None, None]) + b = pd.array([0, 1, None, 0, 1, None, 0, 1, None]) + result = a**b + expected = pd.array([1, 0, None, 1, 1, 1, 1, None, None]) + tm.assert_extension_array_equal(result, expected) + + +def test_rpow_one_to_na(): + # https://github.com/pandas-dev/pandas/issues/22022 + # https://github.com/pandas-dev/pandas/issues/29997 + arr = pd.array([np.nan, np.nan], dtype="Int64") + result = np.array([1.0, 2.0]) ** arr + expected = pd.array([1.0, np.nan], dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize("other", [0, 0.5]) +def test_numpy_zero_dim_ndarray(other): + arr = pd.array([1, None, 2]) + result = arr + np.array(other) + expected = arr + other + tm.assert_equal(result, expected) + + +# Test generic characteristics / errors +# ----------------------------------------------------------------------------- + + +def test_error_invalid_values(data, all_arithmetic_operators): + op = all_arithmetic_operators + s = pd.Series(data) + ops = getattr(s, op) + + # invalid scalars + with tm.external_error_raised(TypeError): + ops("foo") + with tm.external_error_raised(TypeError): + ops(pd.Timestamp("20180101")) + + # invalid array-likes + str_ser = pd.Series("foo", index=s.index) + # with pytest.raises(TypeError, match=msg): + if all_arithmetic_operators in [ + "__mul__", + "__rmul__", + ]: # (data[~data.isna()] >= 0).all(): + res = ops(str_ser) + expected = pd.Series(["foo" * x for x in data], index=s.index) + expected = expected.fillna(np.nan) + # TODO: doing this fillna to keep tests passing as we make + # assert_almost_equal stricter, but the expected with pd.NA seems + # more-correct than np.nan here. + tm.assert_series_equal(res, expected) + else: + with tm.external_error_raised(TypeError): + ops(str_ser) + + with tm.external_error_raised(TypeError): + ops(pd.Series(pd.date_range("20180101", periods=len(s)))) + + +# Various +# ----------------------------------------------------------------------------- + + +# TODO test unsigned overflow + + +def test_arith_coerce_scalar(data, all_arithmetic_operators): + op = tm.get_op_from_name(all_arithmetic_operators) + s = pd.Series(data) + other = 0.01 + + result = op(s, other) + expected = op(s.astype(float), other) + expected = expected.astype("Float64") + + # rmod results in NaN that wasn't NA in original nullable Series -> unmask it + if all_arithmetic_operators == "__rmod__": + mask = (s == 0).fillna(False).to_numpy(bool) + expected.array._mask[mask] = False + + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("other", [1.0, np.array(1.0)]) +def test_arithmetic_conversion(all_arithmetic_operators, other): + # if we have a float operand we should have a float result + # if that is equal to an integer + op = tm.get_op_from_name(all_arithmetic_operators) + + s = pd.Series([1, 2, 3], dtype="Int64") + result = op(s, other) + assert result.dtype == "Float64" + + +def test_cross_type_arithmetic(): + df = pd.DataFrame( + { + "A": pd.Series([1, 2, np.nan], dtype="Int64"), + "B": pd.Series([1, np.nan, 3], dtype="UInt8"), + "C": [1, 2, 3], + } + ) + + result = df.A + df.C + expected = pd.Series([2, 4, np.nan], dtype="Int64") + tm.assert_series_equal(result, expected) + + result = (df.A + df.C) * 3 == 12 + expected = pd.Series([False, True, None], dtype="boolean") + tm.assert_series_equal(result, expected) + + result = df.A + df.B + expected = pd.Series([2, np.nan, np.nan], dtype="Int64") + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("op", ["mean"]) +def test_reduce_to_float(op): + # some reduce ops always return float, even if the result + # is a rounded number + df = pd.DataFrame( + { + "A": ["a", "b", "b"], + "B": [1, None, 3], + "C": pd.array([1, None, 3], dtype="Int64"), + } + ) + + # op + result = getattr(df.C, op)() + assert isinstance(result, float) + + # groupby + result = getattr(df.groupby("A"), op)() + + expected = pd.DataFrame( + {"B": np.array([1.0, 3.0]), "C": pd.array([1, 3], dtype="Float64")}, + index=pd.Index(["a", "b"], name="A"), + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "source, neg_target, abs_target", + [ + ([1, 2, 3], [-1, -2, -3], [1, 2, 3]), + ([1, 2, None], [-1, -2, None], [1, 2, None]), + ([-1, 0, 1], [1, 0, -1], [1, 0, 1]), + ], +) +def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target): + dtype = any_signed_int_ea_dtype + arr = pd.array(source, dtype=dtype) + neg_result, pos_result, abs_result = -arr, +arr, abs(arr) + neg_target = pd.array(neg_target, dtype=dtype) + abs_target = pd.array(abs_target, dtype=dtype) + + tm.assert_extension_array_equal(neg_result, neg_target) + tm.assert_extension_array_equal(pos_result, arr) + assert not tm.shares_memory(pos_result, arr) + tm.assert_extension_array_equal(abs_result, abs_target) + + +def test_values_multiplying_large_series_by_NA(): + # GH#33701 + + result = pd.NA * pd.Series(np.zeros(10001)) + expected = pd.Series([pd.NA] * 10001) + + tm.assert_series_equal(result, expected) + + +def test_bitwise(dtype): + left = pd.array([1, None, 3, 4], dtype=dtype) + right = pd.array([None, 3, 5, 4], dtype=dtype) + + result = left | right + expected = pd.array([None, None, 3 | 5, 4 | 4], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = left & right + expected = pd.array([None, None, 3 & 5, 4 & 4], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = left ^ right + expected = pd.array([None, None, 3 ^ 5, 4 ^ 4], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + # TODO: desired behavior when operating with boolean? defer? + + floats = right.astype("Float64") + with pytest.raises(TypeError, match="unsupported operand type"): + left | floats + with pytest.raises(TypeError, match="unsupported operand type"): + left & floats + with pytest.raises(TypeError, match="unsupported operand type"): + left ^ floats diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_comparison.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_comparison.py new file mode 100644 index 0000000000000000000000000000000000000000..568b0b087bf1db9610960dba12ea2e0bab8f1729 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_comparison.py @@ -0,0 +1,39 @@ +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.tests.arrays.masked_shared import ( + ComparisonOps, + NumericOps, +) + + +class TestComparisonOps(NumericOps, ComparisonOps): + @pytest.mark.parametrize("other", [True, False, pd.NA, -1, 0, 1]) + def test_scalar(self, other, comparison_op, dtype): + ComparisonOps.test_scalar(self, other, comparison_op, dtype) + + def test_compare_to_int(self, dtype, comparison_op): + # GH 28930 + op_name = f"__{comparison_op.__name__}__" + s1 = pd.Series([1, None, 3], dtype=dtype) + s2 = pd.Series([1, None, 3], dtype="float") + + method = getattr(s1, op_name) + result = method(2) + + method = getattr(s2, op_name) + expected = method(2).astype("boolean") + expected[s2.isna()] = pd.NA + + tm.assert_series_equal(result, expected) + + +def test_equals(): + # GH-30652 + # equals is generally tested in /tests/extension/base/methods, but this + # specifically tests that two arrays of the same class but different dtype + # do not evaluate equal + a1 = pd.array([1, 2, None], dtype="Int64") + a2 = pd.array([1, 2, None], dtype="Int32") + assert a1.equals(a2) is False diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_concat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_concat.py new file mode 100644 index 0000000000000000000000000000000000000000..feba574da548fd597c25103f67821145bccec9ed --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_concat.py @@ -0,0 +1,69 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize( + "to_concat_dtypes, result_dtype", + [ + (["Int64", "Int64"], "Int64"), + (["UInt64", "UInt64"], "UInt64"), + (["Int8", "Int8"], "Int8"), + (["Int8", "Int16"], "Int16"), + (["UInt8", "Int8"], "Int16"), + (["Int32", "UInt32"], "Int64"), + (["Int64", "UInt64"], "Float64"), + (["Int64", "boolean"], "object"), + (["UInt8", "boolean"], "object"), + ], +) +def test_concat_series(to_concat_dtypes, result_dtype): + # we expect the same dtypes as we would get with non-masked inputs, + # just masked where available. + + result = pd.concat([pd.Series([0, 1, pd.NA], dtype=t) for t in to_concat_dtypes]) + expected = pd.concat([pd.Series([0, 1, pd.NA], dtype=object)] * 2).astype( + result_dtype + ) + tm.assert_series_equal(result, expected) + + # order doesn't matter for result + result = pd.concat( + [pd.Series([0, 1, pd.NA], dtype=t) for t in to_concat_dtypes[::-1]] + ) + expected = pd.concat([pd.Series([0, 1, pd.NA], dtype=object)] * 2).astype( + result_dtype + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "to_concat_dtypes, result_dtype", + [ + (["Int64", "int64"], "Int64"), + (["UInt64", "uint64"], "UInt64"), + (["Int8", "int8"], "Int8"), + (["Int8", "int16"], "Int16"), + (["UInt8", "int8"], "Int16"), + (["Int32", "uint32"], "Int64"), + (["Int64", "uint64"], "Float64"), + (["Int64", "bool"], "object"), + (["UInt8", "bool"], "object"), + ], +) +def test_concat_series_with_numpy(to_concat_dtypes, result_dtype): + # we expect the same dtypes as we would get with non-masked inputs, + # just masked where available. + + s1 = pd.Series([0, 1, pd.NA], dtype=to_concat_dtypes[0]) + s2 = pd.Series(np.array([0, 1], dtype=to_concat_dtypes[1])) + result = pd.concat([s1, s2], ignore_index=True) + expected = pd.Series([0, 1, pd.NA, 0, 1], dtype=object).astype(result_dtype) + tm.assert_series_equal(result, expected) + + # order doesn't matter for result + result = pd.concat([s2, s1], ignore_index=True) + expected = pd.Series([0, 1, 0, 1, pd.NA], dtype=object).astype(result_dtype) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_construction.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_construction.py new file mode 100644 index 0000000000000000000000000000000000000000..64fe40e53a9d287b6240a908b7ed9d0cf7ec1396 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_construction.py @@ -0,0 +1,245 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.api.types import is_integer +from pandas.core.arrays import IntegerArray +from pandas.core.arrays.integer import ( + Int8Dtype, + Int32Dtype, + Int64Dtype, +) + + +@pytest.fixture(params=[pd.array, IntegerArray._from_sequence]) +def constructor(request): + """Fixture returning parametrized IntegerArray from given sequence. + + Used to test dtype conversions. + """ + return request.param + + +def test_uses_pandas_na(): + a = pd.array([1, None], dtype=Int64Dtype()) + assert a[1] is pd.NA + + +def test_from_dtype_from_float(data): + # construct from our dtype & string dtype + dtype = data.dtype + + # from float + expected = pd.Series(data) + result = pd.Series(data.to_numpy(na_value=np.nan, dtype="float"), dtype=str(dtype)) + tm.assert_series_equal(result, expected) + + # from int / list + expected = pd.Series(data) + result = pd.Series(np.array(data).tolist(), dtype=str(dtype)) + tm.assert_series_equal(result, expected) + + # from int / array + expected = pd.Series(data).dropna().reset_index(drop=True) + dropped = np.array(data.dropna()).astype(np.dtype(dtype.type)) + result = pd.Series(dropped, dtype=str(dtype)) + tm.assert_series_equal(result, expected) + + +def test_conversions(data_missing): + # astype to object series + df = pd.DataFrame({"A": data_missing}) + result = df["A"].astype("object") + expected = pd.Series(np.array([pd.NA, 1], dtype=object), name="A") + tm.assert_series_equal(result, expected) + + # convert to object ndarray + # we assert that we are exactly equal + # including type conversions of scalars + result = df["A"].astype("object").values + expected = np.array([pd.NA, 1], dtype=object) + tm.assert_numpy_array_equal(result, expected) + + for r, e in zip(result, expected): + if pd.isnull(r): + assert pd.isnull(e) + elif is_integer(r): + assert r == e + assert is_integer(e) + else: + assert r == e + assert type(r) == type(e) + + +def test_integer_array_constructor(): + values = np.array([1, 2, 3, 4], dtype="int64") + mask = np.array([False, False, False, True], dtype="bool") + + result = IntegerArray(values, mask) + expected = pd.array([1, 2, 3, np.nan], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + msg = r".* should be .* numpy array. Use the 'pd.array' function instead" + with pytest.raises(TypeError, match=msg): + IntegerArray(values.tolist(), mask) + + with pytest.raises(TypeError, match=msg): + IntegerArray(values, mask.tolist()) + + with pytest.raises(TypeError, match=msg): + IntegerArray(values.astype(float), mask) + msg = r"__init__\(\) missing 1 required positional argument: 'mask'" + with pytest.raises(TypeError, match=msg): + IntegerArray(values) + + +def test_integer_array_constructor_copy(): + values = np.array([1, 2, 3, 4], dtype="int64") + mask = np.array([False, False, False, True], dtype="bool") + + result = IntegerArray(values, mask) + assert result._data is values + assert result._mask is mask + + result = IntegerArray(values, mask, copy=True) + assert result._data is not values + assert result._mask is not mask + + +@pytest.mark.parametrize( + "a, b", + [ + ([1, None], [1, np.nan]), + ([None], [np.nan]), + ([None, np.nan], [np.nan, np.nan]), + ([np.nan, np.nan], [np.nan, np.nan]), + ], +) +def test_to_integer_array_none_is_nan(a, b): + result = pd.array(a, dtype="Int64") + expected = pd.array(b, dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize( + "values", + [ + ["foo", "bar"], + "foo", + 1, + 1.0, + pd.date_range("20130101", periods=2), + np.array(["foo"]), + [[1, 2], [3, 4]], + [np.nan, {"a": 1}], + ], +) +def test_to_integer_array_error(values): + # error in converting existing arrays to IntegerArrays + msg = "|".join( + [ + r"cannot be converted to IntegerDtype", + r"invalid literal for int\(\) with base 10:", + r"values must be a 1D list-like", + r"Cannot pass scalar", + r"int\(\) argument must be a string", + ] + ) + with pytest.raises((ValueError, TypeError), match=msg): + pd.array(values, dtype="Int64") + + with pytest.raises((ValueError, TypeError), match=msg): + IntegerArray._from_sequence(values) + + +def test_to_integer_array_inferred_dtype(constructor): + # if values has dtype -> respect it + result = constructor(np.array([1, 2], dtype="int8")) + assert result.dtype == Int8Dtype() + result = constructor(np.array([1, 2], dtype="int32")) + assert result.dtype == Int32Dtype() + + # if values have no dtype -> always int64 + result = constructor([1, 2]) + assert result.dtype == Int64Dtype() + + +def test_to_integer_array_dtype_keyword(constructor): + result = constructor([1, 2], dtype="Int8") + assert result.dtype == Int8Dtype() + + # if values has dtype -> override it + result = constructor(np.array([1, 2], dtype="int8"), dtype="Int32") + assert result.dtype == Int32Dtype() + + +def test_to_integer_array_float(): + result = IntegerArray._from_sequence([1.0, 2.0], dtype="Int64") + expected = pd.array([1, 2], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + with pytest.raises(TypeError, match="cannot safely cast non-equivalent"): + IntegerArray._from_sequence([1.5, 2.0], dtype="Int64") + + # for float dtypes, the itemsize is not preserved + result = IntegerArray._from_sequence( + np.array([1.0, 2.0], dtype="float32"), dtype="Int64" + ) + assert result.dtype == Int64Dtype() + + +def test_to_integer_array_str(): + result = IntegerArray._from_sequence(["1", "2", None], dtype="Int64") + expected = pd.array([1, 2, np.nan], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + with pytest.raises( + ValueError, match=r"invalid literal for int\(\) with base 10: .*" + ): + IntegerArray._from_sequence(["1", "2", ""], dtype="Int64") + + with pytest.raises( + ValueError, match=r"invalid literal for int\(\) with base 10: .*" + ): + IntegerArray._from_sequence(["1.5", "2.0"], dtype="Int64") + + +@pytest.mark.parametrize( + "bool_values, int_values, target_dtype, expected_dtype", + [ + ([False, True], [0, 1], Int64Dtype(), Int64Dtype()), + ([False, True], [0, 1], "Int64", Int64Dtype()), + ([False, True, np.nan], [0, 1, np.nan], Int64Dtype(), Int64Dtype()), + ], +) +def test_to_integer_array_bool( + constructor, bool_values, int_values, target_dtype, expected_dtype +): + result = constructor(bool_values, dtype=target_dtype) + assert result.dtype == expected_dtype + expected = pd.array(int_values, dtype=target_dtype) + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize( + "values, to_dtype, result_dtype", + [ + (np.array([1], dtype="int64"), None, Int64Dtype), + (np.array([1, np.nan]), None, Int64Dtype), + (np.array([1, np.nan]), "int8", Int8Dtype), + ], +) +def test_to_integer_array(values, to_dtype, result_dtype): + # convert existing arrays to IntegerArrays + result = IntegerArray._from_sequence(values, dtype=to_dtype) + assert result.dtype == result_dtype() + expected = pd.array(values, dtype=result_dtype()) + tm.assert_extension_array_equal(result, expected) + + +def test_integer_array_from_boolean(): + # GH31104 + expected = pd.array(np.array([True, False]), dtype="Int64") + result = pd.array(np.array([True, False], dtype=object), dtype="Int64") + tm.assert_extension_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_dtypes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..90879d8bd30633246415acccd2d4d94e7c46124c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_dtypes.py @@ -0,0 +1,301 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.generic import ABCIndex + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays.integer import ( + Int8Dtype, + UInt32Dtype, +) + + +def test_dtypes(dtype): + # smoke tests on auto dtype construction + + if dtype.is_signed_integer: + assert np.dtype(dtype.type).kind == "i" + else: + assert np.dtype(dtype.type).kind == "u" + assert dtype.name is not None + + +@pytest.mark.parametrize("op", ["sum", "min", "max", "prod"]) +def test_preserve_dtypes(op): + # for ops that enable (mean would actually work here + # but generally it is a float return value) + df = pd.DataFrame( + { + "A": ["a", "b", "b"], + "B": [1, None, 3], + "C": pd.array([1, None, 3], dtype="Int64"), + } + ) + + # op + result = getattr(df.C, op)() + if op in {"sum", "prod", "min", "max"}: + assert isinstance(result, np.int64) + else: + assert isinstance(result, int) + + # groupby + result = getattr(df.groupby("A"), op)() + + expected = pd.DataFrame( + {"B": np.array([1.0, 3.0]), "C": pd.array([1, 3], dtype="Int64")}, + index=pd.Index(["a", "b"], name="A"), + ) + tm.assert_frame_equal(result, expected) + + +def test_astype_nansafe(): + # see gh-22343 + arr = pd.array([np.nan, 1, 2], dtype="Int8") + msg = "cannot convert NA to integer" + + with pytest.raises(ValueError, match=msg): + arr.astype("uint32") + + +@pytest.mark.parametrize("dropna", [True, False]) +def test_construct_index(all_data, dropna): + # ensure that we do not coerce to different Index dtype or non-index + + all_data = all_data[:10] + if dropna: + other = np.array(all_data[~all_data.isna()]) + else: + other = all_data + + result = pd.Index(pd.array(other, dtype=all_data.dtype)) + expected = pd.Index(other, dtype=all_data.dtype) + assert all_data.dtype == expected.dtype # dont coerce to object + + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("dropna", [True, False]) +def test_astype_index(all_data, dropna): + # as an int/uint index to Index + + all_data = all_data[:10] + if dropna: + other = all_data[~all_data.isna()] + else: + other = all_data + + dtype = all_data.dtype + idx = pd.Index(np.array(other)) + assert isinstance(idx, ABCIndex) + + result = idx.astype(dtype) + expected = idx.astype(object).astype(dtype) + tm.assert_index_equal(result, expected) + + +def test_astype(all_data): + all_data = all_data[:10] + + ints = all_data[~all_data.isna()] + mixed = all_data + dtype = Int8Dtype() + + # coerce to same type - ints + s = pd.Series(ints) + result = s.astype(all_data.dtype) + expected = pd.Series(ints) + tm.assert_series_equal(result, expected) + + # coerce to same other - ints + s = pd.Series(ints) + result = s.astype(dtype) + expected = pd.Series(ints, dtype=dtype) + tm.assert_series_equal(result, expected) + + # coerce to same numpy_dtype - ints + s = pd.Series(ints) + result = s.astype(all_data.dtype.numpy_dtype) + expected = pd.Series(ints._data.astype(all_data.dtype.numpy_dtype)) + tm.assert_series_equal(result, expected) + + # coerce to same type - mixed + s = pd.Series(mixed) + result = s.astype(all_data.dtype) + expected = pd.Series(mixed) + tm.assert_series_equal(result, expected) + + # coerce to same other - mixed + s = pd.Series(mixed) + result = s.astype(dtype) + expected = pd.Series(mixed, dtype=dtype) + tm.assert_series_equal(result, expected) + + # coerce to same numpy_dtype - mixed + s = pd.Series(mixed) + msg = "cannot convert NA to integer" + with pytest.raises(ValueError, match=msg): + s.astype(all_data.dtype.numpy_dtype) + + # coerce to object + s = pd.Series(mixed) + result = s.astype("object") + expected = pd.Series(np.asarray(mixed, dtype=object)) + tm.assert_series_equal(result, expected) + + +def test_astype_copy(): + arr = pd.array([1, 2, 3, None], dtype="Int64") + orig = pd.array([1, 2, 3, None], dtype="Int64") + + # copy=True -> ensure both data and mask are actual copies + result = arr.astype("Int64", copy=True) + assert result is not arr + assert not tm.shares_memory(result, arr) + result[0] = 10 + tm.assert_extension_array_equal(arr, orig) + result[0] = pd.NA + tm.assert_extension_array_equal(arr, orig) + + # copy=False + result = arr.astype("Int64", copy=False) + assert result is arr + assert np.shares_memory(result._data, arr._data) + assert np.shares_memory(result._mask, arr._mask) + result[0] = 10 + assert arr[0] == 10 + result[0] = pd.NA + assert arr[0] is pd.NA + + # astype to different dtype -> always needs a copy -> even with copy=False + # we need to ensure that also the mask is actually copied + arr = pd.array([1, 2, 3, None], dtype="Int64") + orig = pd.array([1, 2, 3, None], dtype="Int64") + + result = arr.astype("Int32", copy=False) + assert not tm.shares_memory(result, arr) + result[0] = 10 + tm.assert_extension_array_equal(arr, orig) + result[0] = pd.NA + tm.assert_extension_array_equal(arr, orig) + + +def test_astype_to_larger_numpy(): + a = pd.array([1, 2], dtype="Int32") + result = a.astype("int64") + expected = np.array([1, 2], dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + a = pd.array([1, 2], dtype="UInt32") + result = a.astype("uint64") + expected = np.array([1, 2], dtype="uint64") + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("dtype", [Int8Dtype(), "Int8", UInt32Dtype(), "UInt32"]) +def test_astype_specific_casting(dtype): + s = pd.Series([1, 2, 3], dtype="Int64") + result = s.astype(dtype) + expected = pd.Series([1, 2, 3], dtype=dtype) + tm.assert_series_equal(result, expected) + + s = pd.Series([1, 2, 3, None], dtype="Int64") + result = s.astype(dtype) + expected = pd.Series([1, 2, 3, None], dtype=dtype) + tm.assert_series_equal(result, expected) + + +def test_astype_floating(): + arr = pd.array([1, 2, None], dtype="Int64") + result = arr.astype("Float64") + expected = pd.array([1.0, 2.0, None], dtype="Float64") + tm.assert_extension_array_equal(result, expected) + + +def test_astype_dt64(): + # GH#32435 + arr = pd.array([1, 2, 3, pd.NA]) * 10**9 + + result = arr.astype("datetime64[ns]") + + expected = np.array([1, 2, 3, "NaT"], dtype="M8[s]").astype("M8[ns]") + tm.assert_numpy_array_equal(result, expected) + + +def test_construct_cast_invalid(dtype): + msg = "cannot safely" + arr = [1.2, 2.3, 3.7] + with pytest.raises(TypeError, match=msg): + pd.array(arr, dtype=dtype) + + with pytest.raises(TypeError, match=msg): + pd.Series(arr).astype(dtype) + + arr = [1.2, 2.3, 3.7, np.nan] + with pytest.raises(TypeError, match=msg): + pd.array(arr, dtype=dtype) + + with pytest.raises(TypeError, match=msg): + pd.Series(arr).astype(dtype) + + +@pytest.mark.parametrize("in_series", [True, False]) +def test_to_numpy_na_nan(in_series): + a = pd.array([0, 1, None], dtype="Int64") + if in_series: + a = pd.Series(a) + + result = a.to_numpy(dtype="float64", na_value=np.nan) + expected = np.array([0.0, 1.0, np.nan], dtype="float64") + tm.assert_numpy_array_equal(result, expected) + + result = a.to_numpy(dtype="int64", na_value=-1) + expected = np.array([0, 1, -1], dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + result = a.to_numpy(dtype="bool", na_value=False) + expected = np.array([False, True, False], dtype="bool") + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("in_series", [True, False]) +@pytest.mark.parametrize("dtype", ["int32", "int64", "bool"]) +def test_to_numpy_dtype(dtype, in_series): + a = pd.array([0, 1], dtype="Int64") + if in_series: + a = pd.Series(a) + + result = a.to_numpy(dtype=dtype) + expected = np.array([0, 1], dtype=dtype) + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("dtype", ["int64", "bool"]) +def test_to_numpy_na_raises(dtype): + a = pd.array([0, 1, None], dtype="Int64") + with pytest.raises(ValueError, match=dtype): + a.to_numpy(dtype=dtype) + + +def test_astype_str(using_infer_string): + a = pd.array([1, 2, None], dtype="Int64") + + if using_infer_string: + expected = pd.array(["1", "2", None], dtype=pd.StringDtype(na_value=np.nan)) + + tm.assert_extension_array_equal(a.astype(str), expected) + tm.assert_extension_array_equal(a.astype("str"), expected) + else: + expected = np.array(["1", "2", ""], dtype=f"{tm.ENDIAN}U21") + + tm.assert_numpy_array_equal(a.astype(str), expected) + tm.assert_numpy_array_equal(a.astype("str"), expected) + + +def test_astype_boolean(): + # https://github.com/pandas-dev/pandas/issues/31102 + a = pd.array([1, 0, -1, 2, None], dtype="Int64") + result = a.astype("boolean") + expected = pd.array([True, False, True, True, None], dtype="boolean") + tm.assert_extension_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_function.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_function.py new file mode 100644 index 0000000000000000000000000000000000000000..d48b636a98feb94c5eaeee9eefbf5730fe9c6f79 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_function.py @@ -0,0 +1,203 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import FloatingArray + + +@pytest.mark.parametrize("ufunc", [np.abs, np.sign]) +# np.sign emits a warning with nans, +@pytest.mark.filterwarnings("ignore:invalid value encountered in sign:RuntimeWarning") +def test_ufuncs_single_int(ufunc): + a = pd.array([1, 2, -3, np.nan]) + result = ufunc(a) + expected = pd.array(ufunc(a.astype(float)), dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + s = pd.Series(a) + result = ufunc(s) + expected = pd.Series(pd.array(ufunc(a.astype(float)), dtype="Int64")) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt]) +def test_ufuncs_single_float(ufunc): + a = pd.array([1, 2, -3, np.nan]) + with np.errstate(invalid="ignore"): + result = ufunc(a) + expected = FloatingArray(ufunc(a.astype(float)), mask=a._mask) + tm.assert_extension_array_equal(result, expected) + + s = pd.Series(a) + with np.errstate(invalid="ignore"): + result = ufunc(s) + expected = pd.Series(expected) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("ufunc", [np.add, np.subtract]) +def test_ufuncs_binary_int(ufunc): + # two IntegerArrays + a = pd.array([1, 2, -3, np.nan]) + result = ufunc(a, a) + expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + # IntegerArray with numpy array + arr = np.array([1, 2, 3, 4]) + result = ufunc(a, arr) + expected = pd.array(ufunc(a.astype(float), arr), dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + result = ufunc(arr, a) + expected = pd.array(ufunc(arr, a.astype(float)), dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + # IntegerArray with scalar + result = ufunc(a, 1) + expected = pd.array(ufunc(a.astype(float), 1), dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + result = ufunc(1, a) + expected = pd.array(ufunc(1, a.astype(float)), dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + +def test_ufunc_binary_output(): + a = pd.array([1, 2, np.nan]) + result = np.modf(a) + expected = np.modf(a.to_numpy(na_value=np.nan, dtype="float")) + expected = (pd.array(expected[0]), pd.array(expected[1])) + + assert isinstance(result, tuple) + assert len(result) == 2 + + for x, y in zip(result, expected): + tm.assert_extension_array_equal(x, y) + + +@pytest.mark.parametrize("values", [[0, 1], [0, None]]) +def test_ufunc_reduce_raises(values): + arr = pd.array(values) + + res = np.add.reduce(arr) + expected = arr.sum(skipna=False) + tm.assert_almost_equal(res, expected) + + +@pytest.mark.parametrize( + "pandasmethname, kwargs", + [ + ("var", {"ddof": 0}), + ("var", {"ddof": 1}), + ("std", {"ddof": 0}), + ("std", {"ddof": 1}), + ("kurtosis", {}), + ("skew", {}), + ("sem", {}), + ], +) +def test_stat_method(pandasmethname, kwargs): + s = pd.Series(data=[1, 2, 3, 4, 5, 6, np.nan, np.nan], dtype="Int64") + pandasmeth = getattr(s, pandasmethname) + result = pandasmeth(**kwargs) + s2 = pd.Series(data=[1, 2, 3, 4, 5, 6], dtype="Int64") + pandasmeth = getattr(s2, pandasmethname) + expected = pandasmeth(**kwargs) + assert expected == result + + +def test_value_counts_na(): + arr = pd.array([1, 2, 1, pd.NA], dtype="Int64") + result = arr.value_counts(dropna=False) + ex_index = pd.Index([1, 2, pd.NA], dtype="Int64") + assert ex_index.dtype == "Int64" + expected = pd.Series([2, 1, 1], index=ex_index, dtype="Int64", name="count") + tm.assert_series_equal(result, expected) + + result = arr.value_counts(dropna=True) + expected = pd.Series([2, 1], index=arr[:2], dtype="Int64", name="count") + assert expected.index.dtype == arr.dtype + tm.assert_series_equal(result, expected) + + +def test_value_counts_empty(): + # https://github.com/pandas-dev/pandas/issues/33317 + ser = pd.Series([], dtype="Int64") + result = ser.value_counts() + idx = pd.Index([], dtype=ser.dtype) + assert idx.dtype == ser.dtype + expected = pd.Series([], index=idx, dtype="Int64", name="count") + tm.assert_series_equal(result, expected) + + +def test_value_counts_with_normalize(): + # GH 33172 + ser = pd.Series([1, 2, 1, pd.NA], dtype="Int64") + result = ser.value_counts(normalize=True) + expected = pd.Series([2, 1], index=ser[:2], dtype="Float64", name="proportion") / 3 + assert expected.index.dtype == ser.dtype + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("skipna", [True, False]) +@pytest.mark.parametrize("min_count", [0, 4]) +def test_integer_array_sum(skipna, min_count, any_int_ea_dtype): + dtype = any_int_ea_dtype + arr = pd.array([1, 2, 3, None], dtype=dtype) + result = arr.sum(skipna=skipna, min_count=min_count) + if skipna and min_count == 0: + assert result == 6 + else: + assert result is pd.NA + + +@pytest.mark.parametrize("skipna", [True, False]) +@pytest.mark.parametrize("method", ["min", "max"]) +def test_integer_array_min_max(skipna, method, any_int_ea_dtype): + dtype = any_int_ea_dtype + arr = pd.array([0, 1, None], dtype=dtype) + func = getattr(arr, method) + result = func(skipna=skipna) + if skipna: + assert result == (0 if method == "min" else 1) + else: + assert result is pd.NA + + +@pytest.mark.parametrize("skipna", [True, False]) +@pytest.mark.parametrize("min_count", [0, 9]) +def test_integer_array_prod(skipna, min_count, any_int_ea_dtype): + dtype = any_int_ea_dtype + arr = pd.array([1, 2, None], dtype=dtype) + result = arr.prod(skipna=skipna, min_count=min_count) + if skipna and min_count == 0: + assert result == 2 + else: + assert result is pd.NA + + +@pytest.mark.parametrize( + "values, expected", [([1, 2, 3], 6), ([1, 2, 3, None], 6), ([None], 0)] +) +def test_integer_array_numpy_sum(values, expected): + arr = pd.array(values, dtype="Int64") + result = np.sum(arr) + assert result == expected + + +@pytest.mark.parametrize("op", ["sum", "prod", "min", "max"]) +def test_dataframe_reductions(op): + # https://github.com/pandas-dev/pandas/pull/32867 + # ensure the integers are not cast to float during reductions + df = pd.DataFrame({"a": pd.array([1, 2], dtype="Int64")}) + result = df.max() + assert isinstance(result["a"], np.int64) + + +# TODO(jreback) - these need testing / are broken + +# shift + +# set_index (destroys type) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..4b953d699108b2aed1c992cf3a33f3013b298254 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_indexing.py @@ -0,0 +1,19 @@ +import pandas as pd +import pandas._testing as tm + + +def test_array_setitem_nullable_boolean_mask(): + # GH 31446 + ser = pd.Series([1, 2], dtype="Int64") + result = ser.where(ser > 1) + expected = pd.Series([pd.NA, 2], dtype="Int64") + tm.assert_series_equal(result, expected) + + +def test_array_setitem(): + # GH 31446 + arr = pd.Series([1, 2], dtype="Int64").array + arr[arr > 1] = 1 + + expected = pd.array([1, 1], dtype="Int64") + tm.assert_extension_array_equal(arr, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_reduction.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_reduction.py new file mode 100644 index 0000000000000000000000000000000000000000..1c91cd25ba69ce207c6be3f6dd438a4ae4f37980 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_reduction.py @@ -0,0 +1,123 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Series, + array, +) +import pandas._testing as tm + + +@pytest.mark.parametrize( + "op, expected", + [ + ["sum", np.int64(3)], + ["prod", np.int64(2)], + ["min", np.int64(1)], + ["max", np.int64(2)], + ["mean", np.float64(1.5)], + ["median", np.float64(1.5)], + ["var", np.float64(0.5)], + ["std", np.float64(0.5**0.5)], + ["skew", pd.NA], + ["kurt", pd.NA], + ["any", True], + ["all", True], + ], +) +def test_series_reductions(op, expected): + ser = Series([1, 2], dtype="Int64") + result = getattr(ser, op)() + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "op, expected", + [ + ["sum", Series([3], index=["a"], dtype="Int64")], + ["prod", Series([2], index=["a"], dtype="Int64")], + ["min", Series([1], index=["a"], dtype="Int64")], + ["max", Series([2], index=["a"], dtype="Int64")], + ["mean", Series([1.5], index=["a"], dtype="Float64")], + ["median", Series([1.5], index=["a"], dtype="Float64")], + ["var", Series([0.5], index=["a"], dtype="Float64")], + ["std", Series([0.5**0.5], index=["a"], dtype="Float64")], + ["skew", Series([pd.NA], index=["a"], dtype="Float64")], + ["kurt", Series([pd.NA], index=["a"], dtype="Float64")], + ["any", Series([True], index=["a"], dtype="boolean")], + ["all", Series([True], index=["a"], dtype="boolean")], + ], +) +def test_dataframe_reductions(op, expected): + df = DataFrame({"a": array([1, 2], dtype="Int64")}) + result = getattr(df, op)() + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "op, expected", + [ + ["sum", array([1, 3], dtype="Int64")], + ["prod", array([1, 3], dtype="Int64")], + ["min", array([1, 3], dtype="Int64")], + ["max", array([1, 3], dtype="Int64")], + ["mean", array([1, 3], dtype="Float64")], + ["median", array([1, 3], dtype="Float64")], + ["var", array([pd.NA], dtype="Float64")], + ["std", array([pd.NA], dtype="Float64")], + ["skew", array([pd.NA], dtype="Float64")], + ["any", array([True, True], dtype="boolean")], + ["all", array([True, True], dtype="boolean")], + ], +) +def test_groupby_reductions(op, expected): + df = DataFrame( + { + "A": ["a", "b", "b"], + "B": array([1, None, 3], dtype="Int64"), + } + ) + result = getattr(df.groupby("A"), op)() + expected = DataFrame(expected, index=pd.Index(["a", "b"], name="A"), columns=["B"]) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "op, expected", + [ + ["sum", Series([4, 4], index=["B", "C"], dtype="Float64")], + ["prod", Series([3, 3], index=["B", "C"], dtype="Float64")], + ["min", Series([1, 1], index=["B", "C"], dtype="Float64")], + ["max", Series([3, 3], index=["B", "C"], dtype="Float64")], + ["mean", Series([2, 2], index=["B", "C"], dtype="Float64")], + ["median", Series([2, 2], index=["B", "C"], dtype="Float64")], + ["var", Series([2, 2], index=["B", "C"], dtype="Float64")], + ["std", Series([2**0.5, 2**0.5], index=["B", "C"], dtype="Float64")], + ["skew", Series([pd.NA, pd.NA], index=["B", "C"], dtype="Float64")], + ["kurt", Series([pd.NA, pd.NA], index=["B", "C"], dtype="Float64")], + ["any", Series([True, True, True], index=["A", "B", "C"], dtype="boolean")], + ["all", Series([True, True, True], index=["A", "B", "C"], dtype="boolean")], + ], +) +def test_mixed_reductions(op, expected): + df = DataFrame( + { + "A": ["a", "b", "b"], + "B": [1, None, 3], + "C": array([1, None, 3], dtype="Int64"), + } + ) + + # series + result = getattr(df.C, op)() + tm.assert_equal(result, expected["C"]) + + # frame + if op in ["any", "all"]: + result = getattr(df, op)() + else: + result = getattr(df, op)(numeric_only=True) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_repr.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_repr.py new file mode 100644 index 0000000000000000000000000000000000000000..168210eed5d06a461bbf42dd1e1fae3db0fd851c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/integer/test_repr.py @@ -0,0 +1,67 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas.core.arrays.integer import ( + Int8Dtype, + Int16Dtype, + Int32Dtype, + Int64Dtype, + UInt8Dtype, + UInt16Dtype, + UInt32Dtype, + UInt64Dtype, +) + + +def test_dtypes(dtype): + # smoke tests on auto dtype construction + + if dtype.is_signed_integer: + assert np.dtype(dtype.type).kind == "i" + else: + assert np.dtype(dtype.type).kind == "u" + assert dtype.name is not None + + +@pytest.mark.parametrize( + "dtype, expected", + [ + (Int8Dtype(), "Int8Dtype()"), + (Int16Dtype(), "Int16Dtype()"), + (Int32Dtype(), "Int32Dtype()"), + (Int64Dtype(), "Int64Dtype()"), + (UInt8Dtype(), "UInt8Dtype()"), + (UInt16Dtype(), "UInt16Dtype()"), + (UInt32Dtype(), "UInt32Dtype()"), + (UInt64Dtype(), "UInt64Dtype()"), + ], +) +def test_repr_dtype(dtype, expected): + assert repr(dtype) == expected + + +def test_repr_array(): + result = repr(pd.array([1, None, 3])) + expected = "\n[1, , 3]\nLength: 3, dtype: Int64" + assert result == expected + + +def test_repr_array_long(): + data = pd.array([1, 2, None] * 1000) + expected = ( + "\n" + "[ 1, 2, , 1, 2, , 1, 2, , 1,\n" + " ...\n" + " , 1, 2, , 1, 2, , 1, 2, ]\n" + "Length: 3000, dtype: Int64" + ) + result = repr(data) + assert result == expected + + +def test_frame_repr(data_missing): + df = pd.DataFrame({"A": data_missing}) + result = repr(df) + expected = " A\n0 \n1 1" + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_astype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..d7a2140f817f3a8e5689d001768cf5642118b105 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_astype.py @@ -0,0 +1,28 @@ +import pytest + +from pandas import ( + Categorical, + CategoricalDtype, + Index, + IntervalIndex, +) +import pandas._testing as tm + + +class TestAstype: + @pytest.mark.parametrize("ordered", [True, False]) + def test_astype_categorical_retains_ordered(self, ordered): + index = IntervalIndex.from_breaks(range(5)) + arr = index._data + + dtype = CategoricalDtype(None, ordered=ordered) + + expected = Categorical(list(arr), ordered=ordered) + result = arr.astype(dtype) + assert result.ordered is ordered + tm.assert_categorical_equal(result, expected) + + # test IntervalIndex.astype while we're at it. + result = index.astype(dtype) + expected = Index(expected) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_formats.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..535efee51937473071c9490330eb68364769d5aa --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_formats.py @@ -0,0 +1,13 @@ +from pandas.core.arrays import IntervalArray + + +def test_repr(): + # GH#25022 + arr = IntervalArray.from_tuples([(0, 1), (1, 2)]) + result = repr(arr) + expected = ( + "\n" + "[(0, 1], (1, 2]]\n" + "Length: 2, dtype: interval[int64, right]" + ) + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval.py new file mode 100644 index 0000000000000000000000000000000000000000..be4b2c3e7e74cddfaf8b2efa08879d3a6d8f1757 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval.py @@ -0,0 +1,231 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Index, + Interval, + IntervalIndex, + Timedelta, + Timestamp, + date_range, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.arrays import IntervalArray + + +@pytest.fixture( + params=[ + (Index([0, 2, 4]), Index([1, 3, 5])), + (Index([0.0, 1.0, 2.0]), Index([1.0, 2.0, 3.0])), + (timedelta_range("0 days", periods=3), timedelta_range("1 day", periods=3)), + (date_range("20170101", periods=3), date_range("20170102", periods=3)), + ( + date_range("20170101", periods=3, tz="US/Eastern"), + date_range("20170102", periods=3, tz="US/Eastern"), + ), + ], + ids=lambda x: str(x[0].dtype), +) +def left_right_dtypes(request): + """ + Fixture for building an IntervalArray from various dtypes + """ + return request.param + + +class TestAttributes: + @pytest.mark.parametrize( + "left, right", + [ + (0, 1), + (Timedelta("0 days"), Timedelta("1 day")), + (Timestamp("2018-01-01"), Timestamp("2018-01-02")), + ( + Timestamp("2018-01-01", tz="US/Eastern"), + Timestamp("2018-01-02", tz="US/Eastern"), + ), + ], + ) + @pytest.mark.parametrize("constructor", [IntervalArray, IntervalIndex]) + def test_is_empty(self, constructor, left, right, closed): + # GH27219 + tuples = [(left, left), (left, right), np.nan] + expected = np.array([closed != "both", False, False]) + result = constructor.from_tuples(tuples, closed=closed).is_empty + tm.assert_numpy_array_equal(result, expected) + + +class TestMethods: + @pytest.mark.parametrize("new_closed", ["left", "right", "both", "neither"]) + def test_set_closed(self, closed, new_closed): + # GH 21670 + array = IntervalArray.from_breaks(range(10), closed=closed) + result = array.set_closed(new_closed) + expected = IntervalArray.from_breaks(range(10), closed=new_closed) + tm.assert_extension_array_equal(result, expected) + + @pytest.mark.parametrize( + "other", + [ + Interval(0, 1, closed="right"), + IntervalArray.from_breaks([1, 2, 3, 4], closed="right"), + ], + ) + def test_where_raises(self, other): + # GH#45768 The IntervalArray methods raises; the Series method coerces + ser = pd.Series(IntervalArray.from_breaks([1, 2, 3, 4], closed="left")) + mask = np.array([True, False, True]) + match = "'value.closed' is 'right', expected 'left'." + with pytest.raises(ValueError, match=match): + ser.array._where(mask, other) + + res = ser.where(mask, other=other) + expected = ser.astype(object).where(mask, other) + tm.assert_series_equal(res, expected) + + def test_shift(self): + # https://github.com/pandas-dev/pandas/issues/31495, GH#22428, GH#31502 + a = IntervalArray.from_breaks([1, 2, 3]) + result = a.shift() + # int -> float + expected = IntervalArray.from_tuples([(np.nan, np.nan), (1.0, 2.0)]) + tm.assert_interval_array_equal(result, expected) + + msg = "can only insert Interval objects and NA into an IntervalArray" + with pytest.raises(TypeError, match=msg): + a.shift(1, fill_value=pd.NaT) + + def test_shift_datetime(self): + # GH#31502, GH#31504 + a = IntervalArray.from_breaks(date_range("2000", periods=4)) + result = a.shift(2) + expected = a.take([-1, -1, 0], allow_fill=True) + tm.assert_interval_array_equal(result, expected) + + result = a.shift(-1) + expected = a.take([1, 2, -1], allow_fill=True) + tm.assert_interval_array_equal(result, expected) + + msg = "can only insert Interval objects and NA into an IntervalArray" + with pytest.raises(TypeError, match=msg): + a.shift(1, fill_value=np.timedelta64("NaT", "ns")) + + +class TestSetitem: + def test_set_na(self, left_right_dtypes): + left, right = left_right_dtypes + left = left.copy(deep=True) + right = right.copy(deep=True) + result = IntervalArray.from_arrays(left, right) + + if result.dtype.subtype.kind not in ["m", "M"]: + msg = "'value' should be an interval type, got <.*NaTType'> instead." + with pytest.raises(TypeError, match=msg): + result[0] = pd.NaT + if result.dtype.subtype.kind in ["i", "u"]: + msg = "Cannot set float NaN to integer-backed IntervalArray" + # GH#45484 TypeError, not ValueError, matches what we get with + # non-NA un-holdable value. + with pytest.raises(TypeError, match=msg): + result[0] = np.nan + return + + result[0] = np.nan + + expected_left = Index([left._na_value] + list(left[1:])) + expected_right = Index([right._na_value] + list(right[1:])) + expected = IntervalArray.from_arrays(expected_left, expected_right) + + tm.assert_extension_array_equal(result, expected) + + def test_setitem_mismatched_closed(self): + arr = IntervalArray.from_breaks(range(4)) + orig = arr.copy() + other = arr.set_closed("both") + + msg = "'value.closed' is 'both', expected 'right'" + with pytest.raises(ValueError, match=msg): + arr[0] = other[0] + with pytest.raises(ValueError, match=msg): + arr[:1] = other[:1] + with pytest.raises(ValueError, match=msg): + arr[:0] = other[:0] + with pytest.raises(ValueError, match=msg): + arr[:] = other[::-1] + with pytest.raises(ValueError, match=msg): + arr[:] = list(other[::-1]) + with pytest.raises(ValueError, match=msg): + arr[:] = other[::-1].astype(object) + with pytest.raises(ValueError, match=msg): + arr[:] = other[::-1].astype("category") + + # empty list should be no-op + arr[:0] = [] + tm.assert_interval_array_equal(arr, orig) + + +class TestReductions: + def test_min_max_invalid_axis(self, left_right_dtypes): + left, right = left_right_dtypes + left = left.copy(deep=True) + right = right.copy(deep=True) + arr = IntervalArray.from_arrays(left, right) + + msg = "`axis` must be fewer than the number of dimensions" + for axis in [-2, 1]: + with pytest.raises(ValueError, match=msg): + arr.min(axis=axis) + with pytest.raises(ValueError, match=msg): + arr.max(axis=axis) + + msg = "'>=' not supported between" + with pytest.raises(TypeError, match=msg): + arr.min(axis="foo") + with pytest.raises(TypeError, match=msg): + arr.max(axis="foo") + + def test_min_max(self, left_right_dtypes, index_or_series_or_array): + # GH#44746 + left, right = left_right_dtypes + left = left.copy(deep=True) + right = right.copy(deep=True) + arr = IntervalArray.from_arrays(left, right) + + # The expected results below are only valid if monotonic + assert left.is_monotonic_increasing + assert Index(arr).is_monotonic_increasing + + MIN = arr[0] + MAX = arr[-1] + + indexer = np.arange(len(arr)) + np.random.default_rng(2).shuffle(indexer) + arr = arr.take(indexer) + + arr_na = arr.insert(2, np.nan) + + arr = index_or_series_or_array(arr) + arr_na = index_or_series_or_array(arr_na) + + for skipna in [True, False]: + res = arr.min(skipna=skipna) + assert res == MIN + assert type(res) == type(MIN) + + res = arr.max(skipna=skipna) + assert res == MAX + assert type(res) == type(MAX) + + res = arr_na.min(skipna=False) + assert np.isnan(res) + res = arr_na.max(skipna=False) + assert np.isnan(res) + + res = arr_na.min(skipna=True) + assert res == MIN + assert type(res) == type(MIN) + res = arr_na.max(skipna=True) + assert res == MAX + assert type(res) == type(MAX) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval_pyarrow.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval_pyarrow.py new file mode 100644 index 0000000000000000000000000000000000000000..ef8701be81e2b9248c29fc4e901161fd18d72bbe --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_interval_pyarrow.py @@ -0,0 +1,160 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import IntervalArray + + +def test_arrow_extension_type(): + pa = pytest.importorskip("pyarrow") + + from pandas.core.arrays.arrow.extension_types import ArrowIntervalType + + p1 = ArrowIntervalType(pa.int64(), "left") + p2 = ArrowIntervalType(pa.int64(), "left") + p3 = ArrowIntervalType(pa.int64(), "right") + + assert p1.closed == "left" + assert p1 == p2 + assert p1 != p3 + assert hash(p1) == hash(p2) + assert hash(p1) != hash(p3) + + +def test_arrow_array(): + pa = pytest.importorskip("pyarrow") + + from pandas.core.arrays.arrow.extension_types import ArrowIntervalType + + intervals = pd.interval_range(1, 5, freq=1).array + + result = pa.array(intervals) + assert isinstance(result.type, ArrowIntervalType) + assert result.type.closed == intervals.closed + assert result.type.subtype == pa.int64() + assert result.storage.field("left").equals(pa.array([1, 2, 3, 4], type="int64")) + assert result.storage.field("right").equals(pa.array([2, 3, 4, 5], type="int64")) + + expected = pa.array([{"left": i, "right": i + 1} for i in range(1, 5)]) + assert result.storage.equals(expected) + + # convert to its storage type + result = pa.array(intervals, type=expected.type) + assert result.equals(expected) + + # unsupported conversions + with pytest.raises(TypeError, match="Not supported to convert IntervalArray"): + pa.array(intervals, type="float64") + + with pytest.raises(TypeError, match="Not supported to convert IntervalArray"): + pa.array(intervals, type=ArrowIntervalType(pa.float64(), "left")) + + +def test_arrow_array_missing(): + pa = pytest.importorskip("pyarrow") + + from pandas.core.arrays.arrow.extension_types import ArrowIntervalType + + arr = IntervalArray.from_breaks([0.0, 1.0, 2.0, 3.0]) + arr[1] = None + + result = pa.array(arr) + assert isinstance(result.type, ArrowIntervalType) + assert result.type.closed == arr.closed + assert result.type.subtype == pa.float64() + + # fields have missing values (not NaN) + left = pa.array([0.0, None, 2.0], type="float64") + right = pa.array([1.0, None, 3.0], type="float64") + assert result.storage.field("left").equals(left) + assert result.storage.field("right").equals(right) + + # structarray itself also has missing values on the array level + vals = [ + {"left": 0.0, "right": 1.0}, + {"left": None, "right": None}, + {"left": 2.0, "right": 3.0}, + ] + expected = pa.StructArray.from_pandas(vals, mask=np.array([False, True, False])) + assert result.storage.equals(expected) + + +@pytest.mark.filterwarnings( + "ignore:Passing a BlockManager to DataFrame:DeprecationWarning" +) +@pytest.mark.parametrize( + "breaks", + [[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")], + ids=["float", "datetime64[ns]"], +) +def test_arrow_table_roundtrip(breaks): + pa = pytest.importorskip("pyarrow") + + from pandas.core.arrays.arrow.extension_types import ArrowIntervalType + + arr = IntervalArray.from_breaks(breaks) + arr[1] = None + df = pd.DataFrame({"a": arr}) + + table = pa.table(df) + assert isinstance(table.field("a").type, ArrowIntervalType) + result = table.to_pandas() + assert isinstance(result["a"].dtype, pd.IntervalDtype) + tm.assert_frame_equal(result, df) + + table2 = pa.concat_tables([table, table]) + result = table2.to_pandas() + expected = pd.concat([df, df], ignore_index=True) + tm.assert_frame_equal(result, expected) + + # GH#41040 + table = pa.table( + [pa.chunked_array([], type=table.column(0).type)], schema=table.schema + ) + result = table.to_pandas() + tm.assert_frame_equal(result, expected[0:0]) + + +@pytest.mark.filterwarnings( + "ignore:Passing a BlockManager to DataFrame:DeprecationWarning" +) +@pytest.mark.parametrize( + "breaks", + [[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")], + ids=["float", "datetime64[ns]"], +) +def test_arrow_table_roundtrip_without_metadata(breaks): + pa = pytest.importorskip("pyarrow") + + arr = IntervalArray.from_breaks(breaks) + arr[1] = None + df = pd.DataFrame({"a": arr}) + + table = pa.table(df) + # remove the metadata + table = table.replace_schema_metadata() + assert table.schema.metadata is None + + result = table.to_pandas() + assert isinstance(result["a"].dtype, pd.IntervalDtype) + tm.assert_frame_equal(result, df) + + +def test_from_arrow_from_raw_struct_array(): + # in case pyarrow lost the Interval extension type (eg on parquet roundtrip + # with datetime64[ns] subtype, see GH-45881), still allow conversion + # from arrow to IntervalArray + pa = pytest.importorskip("pyarrow") + + arr = pa.array([{"left": 0, "right": 1}, {"left": 1, "right": 2}]) + dtype = pd.IntervalDtype(np.dtype("int64"), closed="neither") + + result = dtype.__from_arrow__(arr) + expected = IntervalArray.from_breaks( + np.array([0, 1, 2], dtype="int64"), closed="neither" + ) + tm.assert_extension_array_equal(result, expected) + + result = dtype.__from_arrow__(pa.chunked_array([arr])) + tm.assert_extension_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_overlaps.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_overlaps.py new file mode 100644 index 0000000000000000000000000000000000000000..4853bec51106c05781a4c11921f0e082934147ec --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/interval/test_overlaps.py @@ -0,0 +1,93 @@ +"""Tests for Interval-Interval operations, such as overlaps, contains, etc.""" +import numpy as np +import pytest + +from pandas import ( + Interval, + IntervalIndex, + Timedelta, + Timestamp, +) +import pandas._testing as tm +from pandas.core.arrays import IntervalArray + + +@pytest.fixture(params=[IntervalArray, IntervalIndex]) +def constructor(request): + """ + Fixture for testing both interval container classes. + """ + return request.param + + +@pytest.fixture( + params=[ + (Timedelta("0 days"), Timedelta("1 day")), + (Timestamp("2018-01-01"), Timedelta("1 day")), + (0, 1), + ], + ids=lambda x: type(x[0]).__name__, +) +def start_shift(request): + """ + Fixture for generating intervals of different types from a start value + and a shift value that can be added to start to generate an endpoint. + """ + return request.param + + +class TestOverlaps: + def test_overlaps_interval(self, constructor, start_shift, closed, other_closed): + start, shift = start_shift + interval = Interval(start, start + 3 * shift, other_closed) + + # intervals: identical, nested, spanning, partial, adjacent, disjoint + tuples = [ + (start, start + 3 * shift), + (start + shift, start + 2 * shift), + (start - shift, start + 4 * shift), + (start + 2 * shift, start + 4 * shift), + (start + 3 * shift, start + 4 * shift), + (start + 4 * shift, start + 5 * shift), + ] + interval_container = constructor.from_tuples(tuples, closed) + + adjacent = interval.closed_right and interval_container.closed_left + expected = np.array([True, True, True, True, adjacent, False]) + result = interval_container.overlaps(interval) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("other_constructor", [IntervalArray, IntervalIndex]) + def test_overlaps_interval_container(self, constructor, other_constructor): + # TODO: modify this test when implemented + interval_container = constructor.from_breaks(range(5)) + other_container = other_constructor.from_breaks(range(5)) + with pytest.raises(NotImplementedError, match="^$"): + interval_container.overlaps(other_container) + + def test_overlaps_na(self, constructor, start_shift): + """NA values are marked as False""" + start, shift = start_shift + interval = Interval(start, start + shift) + + tuples = [ + (start, start + shift), + np.nan, + (start + 2 * shift, start + 3 * shift), + ] + interval_container = constructor.from_tuples(tuples) + + expected = np.array([True, False, False]) + result = interval_container.overlaps(interval) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize( + "other", + [10, True, "foo", Timedelta("1 day"), Timestamp("2018-01-01")], + ids=lambda x: type(x).__name__, + ) + def test_overlaps_invalid_type(self, constructor, other): + interval_container = constructor.from_breaks(range(5)) + msg = f"`other` must be Interval-like, got {type(other).__name__}" + with pytest.raises(TypeError, match=msg): + interval_container.overlaps(other) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arithmetic.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..f4b571ca627b3da34e943972ba70b03bae74417a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arithmetic.py @@ -0,0 +1,248 @@ +from __future__ import annotations + +from typing import Any + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + +# integer dtypes +arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES] +scalars: list[Any] = [2] * len(arrays) +# floating dtypes +arrays += [pd.array([0.1, 0.2, 0.3, None], dtype=dtype) for dtype in tm.FLOAT_EA_DTYPES] +scalars += [0.2, 0.2] +# boolean +arrays += [pd.array([True, False, True, None], dtype="boolean")] +scalars += [False] + + +@pytest.fixture(params=zip(arrays, scalars), ids=[a.dtype.name for a in arrays]) +def data(request): + """Fixture returning parametrized (array, scalar) tuple. + + Used to test equivalence of scalars, numpy arrays with array ops, and the + equivalence of DataFrame and Series ops. + """ + return request.param + + +def check_skip(data, op_name): + if isinstance(data.dtype, pd.BooleanDtype) and "sub" in op_name: + pytest.skip("subtract not implemented for boolean") + + +def is_bool_not_implemented(data, op_name): + # match non-masked behavior + return data.dtype.kind == "b" and op_name.strip("_").lstrip("r") in [ + "pow", + "truediv", + "floordiv", + ] + + +# Test equivalence of scalars, numpy arrays with array ops +# ----------------------------------------------------------------------------- + + +def test_array_scalar_like_equivalence(data, all_arithmetic_operators): + data, scalar = data + op = tm.get_op_from_name(all_arithmetic_operators) + check_skip(data, all_arithmetic_operators) + + scalar_array = pd.array([scalar] * len(data), dtype=data.dtype) + + # TODO also add len-1 array (np.array([scalar], dtype=data.dtype.numpy_dtype)) + for scalar in [scalar, data.dtype.type(scalar)]: + if is_bool_not_implemented(data, all_arithmetic_operators): + msg = "operator '.*' not implemented for bool dtypes" + with pytest.raises(NotImplementedError, match=msg): + op(data, scalar) + with pytest.raises(NotImplementedError, match=msg): + op(data, scalar_array) + else: + result = op(data, scalar) + expected = op(data, scalar_array) + tm.assert_extension_array_equal(result, expected) + + +def test_array_NA(data, all_arithmetic_operators): + data, _ = data + op = tm.get_op_from_name(all_arithmetic_operators) + check_skip(data, all_arithmetic_operators) + + scalar = pd.NA + scalar_array = pd.array([pd.NA] * len(data), dtype=data.dtype) + + mask = data._mask.copy() + + if is_bool_not_implemented(data, all_arithmetic_operators): + msg = "operator '.*' not implemented for bool dtypes" + with pytest.raises(NotImplementedError, match=msg): + op(data, scalar) + # GH#45421 check op doesn't alter data._mask inplace + tm.assert_numpy_array_equal(mask, data._mask) + return + + result = op(data, scalar) + # GH#45421 check op doesn't alter data._mask inplace + tm.assert_numpy_array_equal(mask, data._mask) + + expected = op(data, scalar_array) + tm.assert_numpy_array_equal(mask, data._mask) + + tm.assert_extension_array_equal(result, expected) + + +def test_numpy_array_equivalence(data, all_arithmetic_operators): + data, scalar = data + op = tm.get_op_from_name(all_arithmetic_operators) + check_skip(data, all_arithmetic_operators) + + numpy_array = np.array([scalar] * len(data), dtype=data.dtype.numpy_dtype) + pd_array = pd.array(numpy_array, dtype=data.dtype) + + if is_bool_not_implemented(data, all_arithmetic_operators): + msg = "operator '.*' not implemented for bool dtypes" + with pytest.raises(NotImplementedError, match=msg): + op(data, numpy_array) + with pytest.raises(NotImplementedError, match=msg): + op(data, pd_array) + return + + result = op(data, numpy_array) + expected = op(data, pd_array) + tm.assert_extension_array_equal(result, expected) + + +# Test equivalence with Series and DataFrame ops +# ----------------------------------------------------------------------------- + + +def test_frame(data, all_arithmetic_operators): + data, scalar = data + op = tm.get_op_from_name(all_arithmetic_operators) + check_skip(data, all_arithmetic_operators) + + # DataFrame with scalar + df = pd.DataFrame({"A": data}) + + if is_bool_not_implemented(data, all_arithmetic_operators): + msg = "operator '.*' not implemented for bool dtypes" + with pytest.raises(NotImplementedError, match=msg): + op(df, scalar) + with pytest.raises(NotImplementedError, match=msg): + op(data, scalar) + return + + result = op(df, scalar) + expected = pd.DataFrame({"A": op(data, scalar)}) + tm.assert_frame_equal(result, expected) + + +def test_series(data, all_arithmetic_operators): + data, scalar = data + op = tm.get_op_from_name(all_arithmetic_operators) + check_skip(data, all_arithmetic_operators) + + ser = pd.Series(data) + + others = [ + scalar, + np.array([scalar] * len(data), dtype=data.dtype.numpy_dtype), + pd.array([scalar] * len(data), dtype=data.dtype), + pd.Series([scalar] * len(data), dtype=data.dtype), + ] + + for other in others: + if is_bool_not_implemented(data, all_arithmetic_operators): + msg = "operator '.*' not implemented for bool dtypes" + with pytest.raises(NotImplementedError, match=msg): + op(ser, other) + + else: + result = op(ser, other) + expected = pd.Series(op(data, other)) + tm.assert_series_equal(result, expected) + + +# Test generic characteristics / errors +# ----------------------------------------------------------------------------- + + +def test_error_invalid_object(data, all_arithmetic_operators): + data, _ = data + + op = all_arithmetic_operators + opa = getattr(data, op) + + # 2d -> return NotImplemented + result = opa(pd.DataFrame({"A": data})) + assert result is NotImplemented + + msg = r"can only perform ops with 1-d structures" + with pytest.raises(NotImplementedError, match=msg): + opa(np.arange(len(data)).reshape(-1, len(data))) + + +def test_error_len_mismatch(data, all_arithmetic_operators): + # operating with a list-like with non-matching length raises + data, scalar = data + op = tm.get_op_from_name(all_arithmetic_operators) + + other = [scalar] * (len(data) - 1) + + err = ValueError + msg = "|".join( + [ + r"operands could not be broadcast together with shapes \(3,\) \(4,\)", + r"operands could not be broadcast together with shapes \(4,\) \(3,\)", + ] + ) + if data.dtype.kind == "b" and all_arithmetic_operators.strip("_") in [ + "sub", + "rsub", + ]: + err = TypeError + msg = ( + r"numpy boolean subtract, the `\-` operator, is not supported, use " + r"the bitwise_xor, the `\^` operator, or the logical_xor function instead" + ) + elif is_bool_not_implemented(data, all_arithmetic_operators): + msg = "operator '.*' not implemented for bool dtypes" + err = NotImplementedError + + for other in [other, np.array(other)]: + with pytest.raises(err, match=msg): + op(data, other) + + s = pd.Series(data) + with pytest.raises(err, match=msg): + op(s, other) + + +@pytest.mark.parametrize("op", ["__neg__", "__abs__", "__invert__"]) +def test_unary_op_does_not_propagate_mask(data, op): + # https://github.com/pandas-dev/pandas/issues/39943 + data, _ = data + ser = pd.Series(data) + + if op == "__invert__" and data.dtype.kind == "f": + # we follow numpy in raising + msg = "ufunc 'invert' not supported for the input types" + with pytest.raises(TypeError, match=msg): + getattr(ser, op)() + with pytest.raises(TypeError, match=msg): + getattr(data, op)() + with pytest.raises(TypeError, match=msg): + # Check that this is still the numpy behavior + getattr(data._data, op)() + + return + + result = getattr(ser, op)() + expected = result.copy(deep=True) + ser[0] = None + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arrow_compat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arrow_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..293ee4095d02e1b35ace33458e38c61d87181373 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_arrow_compat.py @@ -0,0 +1,210 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + +pytestmark = pytest.mark.filterwarnings( + "ignore:Passing a BlockManager to DataFrame:DeprecationWarning" +) + + +pa = pytest.importorskip("pyarrow") + +from pandas.core.arrays.arrow._arrow_utils import pyarrow_array_to_numpy_and_mask + +arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES] +arrays += [pd.array([0.1, 0.2, 0.3, None], dtype=dtype) for dtype in tm.FLOAT_EA_DTYPES] +arrays += [pd.array([True, False, True, None], dtype="boolean")] + + +@pytest.fixture(params=arrays, ids=[a.dtype.name for a in arrays]) +def data(request): + """ + Fixture returning parametrized array from given dtype, including integer, + float and boolean + """ + return request.param + + +def test_arrow_array(data): + arr = pa.array(data) + expected = pa.array( + data.to_numpy(object, na_value=None), + type=pa.from_numpy_dtype(data.dtype.numpy_dtype), + ) + assert arr.equals(expected) + + +def test_arrow_roundtrip(data): + df = pd.DataFrame({"a": data}) + table = pa.table(df) + assert table.field("a").type == str(data.dtype.numpy_dtype) + + result = table.to_pandas() + assert result["a"].dtype == data.dtype + tm.assert_frame_equal(result, df) + + +def test_dataframe_from_arrow_types_mapper(): + def types_mapper(arrow_type): + if pa.types.is_boolean(arrow_type): + return pd.BooleanDtype() + elif pa.types.is_integer(arrow_type): + return pd.Int64Dtype() + + bools_array = pa.array([True, None, False], type=pa.bool_()) + ints_array = pa.array([1, None, 2], type=pa.int64()) + small_ints_array = pa.array([-1, 0, 7], type=pa.int8()) + record_batch = pa.RecordBatch.from_arrays( + [bools_array, ints_array, small_ints_array], ["bools", "ints", "small_ints"] + ) + result = record_batch.to_pandas(types_mapper=types_mapper) + bools = pd.Series([True, None, False], dtype="boolean") + ints = pd.Series([1, None, 2], dtype="Int64") + small_ints = pd.Series([-1, 0, 7], dtype="Int64") + expected = pd.DataFrame({"bools": bools, "ints": ints, "small_ints": small_ints}) + tm.assert_frame_equal(result, expected) + + +def test_arrow_load_from_zero_chunks(data): + # GH-41040 + + df = pd.DataFrame({"a": data[0:0]}) + table = pa.table(df) + assert table.field("a").type == str(data.dtype.numpy_dtype) + table = pa.table( + [pa.chunked_array([], type=table.field("a").type)], schema=table.schema + ) + result = table.to_pandas() + assert result["a"].dtype == data.dtype + tm.assert_frame_equal(result, df) + + +def test_arrow_from_arrow_uint(): + # https://github.com/pandas-dev/pandas/issues/31896 + # possible mismatch in types + + dtype = pd.UInt32Dtype() + result = dtype.__from_arrow__(pa.array([1, 2, 3, 4, None], type="int64")) + expected = pd.array([1, 2, 3, 4, None], dtype="UInt32") + + tm.assert_extension_array_equal(result, expected) + + +def test_arrow_sliced(data): + # https://github.com/pandas-dev/pandas/issues/38525 + + df = pd.DataFrame({"a": data}) + table = pa.table(df) + result = table.slice(2, None).to_pandas() + expected = df.iloc[2:].reset_index(drop=True) + tm.assert_frame_equal(result, expected) + + # no missing values + df2 = df.fillna(data[0]) + table = pa.table(df2) + result = table.slice(2, None).to_pandas() + expected = df2.iloc[2:].reset_index(drop=True) + tm.assert_frame_equal(result, expected) + + +@pytest.fixture +def np_dtype_to_arrays(any_real_numpy_dtype): + """ + Fixture returning actual and expected dtype, pandas and numpy arrays and + mask from a given numpy dtype + """ + np_dtype = np.dtype(any_real_numpy_dtype) + pa_type = pa.from_numpy_dtype(np_dtype) + + # None ensures the creation of a bitmask buffer. + pa_array = pa.array([0, 1, 2, None], type=pa_type) + # Since masked Arrow buffer slots are not required to contain a specific + # value, assert only the first three values of the created np.array + np_expected = np.array([0, 1, 2], dtype=np_dtype) + mask_expected = np.array([True, True, True, False]) + return np_dtype, pa_array, np_expected, mask_expected + + +def test_pyarrow_array_to_numpy_and_mask(np_dtype_to_arrays): + """ + Test conversion from pyarrow array to numpy array. + + Modifies the pyarrow buffer to contain padding and offset, which are + considered valid buffers by pyarrow. + + Also tests empty pyarrow arrays with non empty buffers. + See https://github.com/pandas-dev/pandas/issues/40896 + """ + np_dtype, pa_array, np_expected, mask_expected = np_dtype_to_arrays + data, mask = pyarrow_array_to_numpy_and_mask(pa_array, np_dtype) + tm.assert_numpy_array_equal(data[:3], np_expected) + tm.assert_numpy_array_equal(mask, mask_expected) + + mask_buffer = pa_array.buffers()[0] + data_buffer = pa_array.buffers()[1] + data_buffer_bytes = pa_array.buffers()[1].to_pybytes() + + # Add trailing padding to the buffer. + data_buffer_trail = pa.py_buffer(data_buffer_bytes + b"\x00") + pa_array_trail = pa.Array.from_buffers( + type=pa_array.type, + length=len(pa_array), + buffers=[mask_buffer, data_buffer_trail], + offset=pa_array.offset, + ) + pa_array_trail.validate() + data, mask = pyarrow_array_to_numpy_and_mask(pa_array_trail, np_dtype) + tm.assert_numpy_array_equal(data[:3], np_expected) + tm.assert_numpy_array_equal(mask, mask_expected) + + # Add offset to the buffer. + offset = b"\x00" * (pa_array.type.bit_width // 8) + data_buffer_offset = pa.py_buffer(offset + data_buffer_bytes) + mask_buffer_offset = pa.py_buffer(b"\x0E") + pa_array_offset = pa.Array.from_buffers( + type=pa_array.type, + length=len(pa_array), + buffers=[mask_buffer_offset, data_buffer_offset], + offset=pa_array.offset + 1, + ) + pa_array_offset.validate() + data, mask = pyarrow_array_to_numpy_and_mask(pa_array_offset, np_dtype) + tm.assert_numpy_array_equal(data[:3], np_expected) + tm.assert_numpy_array_equal(mask, mask_expected) + + # Empty array + np_expected_empty = np.array([], dtype=np_dtype) + mask_expected_empty = np.array([], dtype=np.bool_) + + pa_array_offset = pa.Array.from_buffers( + type=pa_array.type, + length=0, + buffers=[mask_buffer, data_buffer], + offset=pa_array.offset, + ) + pa_array_offset.validate() + data, mask = pyarrow_array_to_numpy_and_mask(pa_array_offset, np_dtype) + tm.assert_numpy_array_equal(data[:3], np_expected_empty) + tm.assert_numpy_array_equal(mask, mask_expected_empty) + + +@pytest.mark.parametrize( + "arr", [pa.nulls(10), pa.chunked_array([pa.nulls(4), pa.nulls(6)])] +) +def test_from_arrow_null(data, arr): + res = data.dtype.__from_arrow__(arr) + assert res.isna().all() + assert len(res) == 10 + + +def test_from_arrow_type_error(data): + # ensure that __from_arrow__ returns a TypeError when getting a wrong + # array type + + arr = pa.array(data).cast("string") + with pytest.raises(TypeError, match=None): + # we don't test the exact error message, only the fact that it raises + # a TypeError is relevant + data.dtype.__from_arrow__(arr) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_function.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_function.py new file mode 100644 index 0000000000000000000000000000000000000000..b259018cd6121c53c767e36e3c757211643262d6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_function.py @@ -0,0 +1,74 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.common import is_integer_dtype + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import BaseMaskedArray + +arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES] +arrays += [ + pd.array([0.141, -0.268, 5.895, None], dtype=dtype) for dtype in tm.FLOAT_EA_DTYPES +] + + +@pytest.fixture(params=arrays, ids=[a.dtype.name for a in arrays]) +def data(request): + """ + Fixture returning parametrized 'data' array with different integer and + floating point types + """ + return request.param + + +@pytest.fixture() +def numpy_dtype(data): + """ + Fixture returning numpy dtype from 'data' input array. + """ + # For integer dtype, the numpy conversion must be done to float + if is_integer_dtype(data): + numpy_dtype = float + else: + numpy_dtype = data.dtype.type + return numpy_dtype + + +def test_round(data, numpy_dtype): + # No arguments + result = data.round() + expected = pd.array( + np.round(data.to_numpy(dtype=numpy_dtype, na_value=None)), dtype=data.dtype + ) + tm.assert_extension_array_equal(result, expected) + + # Decimals argument + result = data.round(decimals=2) + expected = pd.array( + np.round(data.to_numpy(dtype=numpy_dtype, na_value=None), decimals=2), + dtype=data.dtype, + ) + tm.assert_extension_array_equal(result, expected) + + +def test_tolist(data): + result = data.tolist() + expected = list(data) + tm.assert_equal(result, expected) + + +def test_to_numpy(): + # GH#56991 + + class MyStringArray(BaseMaskedArray): + dtype = pd.StringDtype() + _dtype_cls = pd.StringDtype + _internal_fill_value = pd.NA + + arr = MyStringArray( + values=np.array(["a", "b", "c"]), mask=np.array([False, True, False]) + ) + result = arr.to_numpy() + expected = np.array(["a", pd.NA, "c"]) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..753d562c87ffa86bbbf665bf3dbbd409eddfdd88 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked/test_indexing.py @@ -0,0 +1,60 @@ +import re + +import numpy as np +import pytest + +import pandas as pd + + +class TestSetitemValidation: + def _check_setitem_invalid(self, arr, invalid): + msg = f"Invalid value '{invalid!s}' for dtype '{arr.dtype}'" + msg = re.escape(msg) + with pytest.raises(TypeError, match=msg): + arr[0] = invalid + + with pytest.raises(TypeError, match=msg): + arr[:] = invalid + + with pytest.raises(TypeError, match=msg): + arr[[0]] = invalid + + # FIXME: don't leave commented-out + # with pytest.raises(TypeError): + # arr[[0]] = [invalid] + + # with pytest.raises(TypeError): + # arr[[0]] = np.array([invalid], dtype=object) + + # Series non-coercion, behavior subject to change + ser = pd.Series(arr) + with pytest.raises(TypeError, match=msg): + ser[0] = invalid + # TODO: so, so many other variants of this... + + _invalid_scalars = [ + 1 + 2j, + "True", + "1", + "1.0", + pd.NaT, + np.datetime64("NaT"), + np.timedelta64("NaT"), + ] + + @pytest.mark.parametrize( + "invalid", _invalid_scalars + [1, 1.0, np.int64(1), np.float64(1)] + ) + def test_setitem_validation_scalar_bool(self, invalid): + arr = pd.array([True, False, None], dtype="boolean") + self._check_setitem_invalid(arr, invalid) + + @pytest.mark.parametrize("invalid", _invalid_scalars + [True, 1.5, np.float64(1.5)]) + def test_setitem_validation_scalar_int(self, invalid, any_int_ea_dtype): + arr = pd.array([1, 2, None], dtype=any_int_ea_dtype) + self._check_setitem_invalid(arr, invalid) + + @pytest.mark.parametrize("invalid", _invalid_scalars + [True]) + def test_setitem_validation_scalar_float(self, invalid, float_ea_dtype): + arr = pd.array([1, 2, None], dtype=float_ea_dtype) + self._check_setitem_invalid(arr, invalid) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked_shared.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked_shared.py new file mode 100644 index 0000000000000000000000000000000000000000..3e74402263cf9c119ec344c5da48dd8598970f69 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/masked_shared.py @@ -0,0 +1,154 @@ +""" +Tests shared by MaskedArray subclasses. +""" +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.tests.extension.base import BaseOpsUtil + + +class ComparisonOps(BaseOpsUtil): + def _compare_other(self, data, op, other): + # array + result = pd.Series(op(data, other)) + expected = pd.Series(op(data._data, other), dtype="boolean") + + # fill the nan locations + expected[data._mask] = pd.NA + + tm.assert_series_equal(result, expected) + + # series + ser = pd.Series(data) + result = op(ser, other) + + # Set nullable dtype here to avoid upcasting when setting to pd.NA below + expected = op(pd.Series(data._data), other).astype("boolean") + + # fill the nan locations + expected[data._mask] = pd.NA + + tm.assert_series_equal(result, expected) + + # subclass will override to parametrize 'other' + def test_scalar(self, other, comparison_op, dtype): + op = comparison_op + left = pd.array([1, 0, None], dtype=dtype) + + result = op(left, other) + + if other is pd.NA: + expected = pd.array([None, None, None], dtype="boolean") + else: + values = op(left._data, other) + expected = pd.arrays.BooleanArray(values, left._mask, copy=True) + tm.assert_extension_array_equal(result, expected) + + # ensure we haven't mutated anything inplace + result[0] = pd.NA + tm.assert_extension_array_equal(left, pd.array([1, 0, None], dtype=dtype)) + + +class NumericOps: + # Shared by IntegerArray and FloatingArray, not BooleanArray + + def test_searchsorted_nan(self, dtype): + # The base class casts to object dtype, for which searchsorted returns + # 0 from the left and 10 from the right. + arr = pd.array(range(10), dtype=dtype) + + assert arr.searchsorted(np.nan, side="left") == 10 + assert arr.searchsorted(np.nan, side="right") == 10 + + def test_no_shared_mask(self, data): + result = data + 1 + assert not tm.shares_memory(result, data) + + def test_array(self, comparison_op, dtype): + op = comparison_op + + left = pd.array([0, 1, 2, None, None, None], dtype=dtype) + right = pd.array([0, 1, None, 0, 1, None], dtype=dtype) + + result = op(left, right) + values = op(left._data, right._data) + mask = left._mask | right._mask + + expected = pd.arrays.BooleanArray(values, mask) + tm.assert_extension_array_equal(result, expected) + + # ensure we haven't mutated anything inplace + result[0] = pd.NA + tm.assert_extension_array_equal( + left, pd.array([0, 1, 2, None, None, None], dtype=dtype) + ) + tm.assert_extension_array_equal( + right, pd.array([0, 1, None, 0, 1, None], dtype=dtype) + ) + + def test_compare_with_booleanarray(self, comparison_op, dtype): + op = comparison_op + + left = pd.array([True, False, None] * 3, dtype="boolean") + right = pd.array([0] * 3 + [1] * 3 + [None] * 3, dtype=dtype) + other = pd.array([False] * 3 + [True] * 3 + [None] * 3, dtype="boolean") + + expected = op(left, other) + result = op(left, right) + tm.assert_extension_array_equal(result, expected) + + # reversed op + expected = op(other, left) + result = op(right, left) + tm.assert_extension_array_equal(result, expected) + + def test_compare_to_string(self, dtype): + # GH#28930 + ser = pd.Series([1, None], dtype=dtype) + result = ser == "a" + expected = pd.Series([False, pd.NA], dtype="boolean") + + tm.assert_series_equal(result, expected) + + def test_ufunc_with_out(self, dtype): + arr = pd.array([1, 2, 3], dtype=dtype) + arr2 = pd.array([1, 2, pd.NA], dtype=dtype) + + mask = arr == arr + mask2 = arr2 == arr2 + + result = np.zeros(3, dtype=bool) + result |= mask + # If MaskedArray.__array_ufunc__ handled "out" appropriately, + # `result` should still be an ndarray. + assert isinstance(result, np.ndarray) + assert result.all() + + # result |= mask worked because mask could be cast losslessly to + # boolean ndarray. mask2 can't, so this raises + result = np.zeros(3, dtype=bool) + msg = "Specify an appropriate 'na_value' for this dtype" + with pytest.raises(ValueError, match=msg): + result |= mask2 + + # addition + res = np.add(arr, arr2) + expected = pd.array([2, 4, pd.NA], dtype=dtype) + tm.assert_extension_array_equal(res, expected) + + # when passing out=arr, we will modify 'arr' inplace. + res = np.add(arr, arr2, out=arr) + assert res is arr + tm.assert_extension_array_equal(res, expected) + tm.assert_extension_array_equal(arr, expected) + + def test_mul_td64_array(self, dtype): + # GH#45622 + arr = pd.array([1, 2, pd.NA], dtype=dtype) + other = np.arange(3, dtype=np.int64).view("m8[ns]") + + result = arr * other + expected = pd.array([pd.Timedelta(0), pd.Timedelta(2), pd.NaT]) + tm.assert_extension_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..225d64ad7d2580f877505f0ac3a459e2ea4f0f53 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_indexing.py @@ -0,0 +1,41 @@ +import numpy as np + +from pandas.core.dtypes.common import is_scalar + +import pandas as pd +import pandas._testing as tm + + +class TestSearchsorted: + def test_searchsorted_string(self, string_dtype): + arr = pd.array(["a", "b", "c"], dtype=string_dtype) + + result = arr.searchsorted("a", side="left") + assert is_scalar(result) + assert result == 0 + + result = arr.searchsorted("a", side="right") + assert is_scalar(result) + assert result == 1 + + def test_searchsorted_numeric_dtypes_scalar(self, any_real_numpy_dtype): + arr = pd.array([1, 3, 90], dtype=any_real_numpy_dtype) + result = arr.searchsorted(30) + assert is_scalar(result) + assert result == 2 + + result = arr.searchsorted([30]) + expected = np.array([2], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + def test_searchsorted_numeric_dtypes_vector(self, any_real_numpy_dtype): + arr = pd.array([1, 3, 90], dtype=any_real_numpy_dtype) + result = arr.searchsorted([2, 30]) + expected = np.array([1, 2], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + def test_searchsorted_sorter(self, any_real_numpy_dtype): + arr = pd.array([3, 1, 2], dtype=any_real_numpy_dtype) + result = arr.searchsorted([0, 3], sorter=np.argsort(arr)) + expected = np.array([0, 2], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_numpy.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_numpy.py new file mode 100644 index 0000000000000000000000000000000000000000..f21fb4ccfba075acf2771ac5c9d1056aa50274bd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/numpy_/test_numpy.py @@ -0,0 +1,351 @@ +""" +Additional tests for NumpyExtensionArray that aren't covered by +the interface tests. +""" +import numpy as np +import pytest + +from pandas.core.dtypes.dtypes import NumpyEADtype + +import pandas as pd +import pandas._testing as tm +from pandas.arrays import NumpyExtensionArray + + +@pytest.fixture( + params=[ + np.array(["a", "b"], dtype=object), + np.array([0, 1], dtype=float), + np.array([0, 1], dtype=int), + np.array([0, 1 + 2j], dtype=complex), + np.array([True, False], dtype=bool), + np.array([0, 1], dtype="datetime64[ns]"), + np.array([0, 1], dtype="timedelta64[ns]"), + ], +) +def any_numpy_array(request): + """ + Parametrized fixture for NumPy arrays with different dtypes. + + This excludes string and bytes. + """ + return request.param.copy() + + +# ---------------------------------------------------------------------------- +# NumpyEADtype + + +@pytest.mark.parametrize( + "dtype, expected", + [ + ("bool", True), + ("int", True), + ("uint", True), + ("float", True), + ("complex", True), + ("str", False), + ("bytes", False), + ("datetime64[ns]", False), + ("object", False), + ("void", False), + ], +) +def test_is_numeric(dtype, expected): + dtype = NumpyEADtype(dtype) + assert dtype._is_numeric is expected + + +@pytest.mark.parametrize( + "dtype, expected", + [ + ("bool", True), + ("int", False), + ("uint", False), + ("float", False), + ("complex", False), + ("str", False), + ("bytes", False), + ("datetime64[ns]", False), + ("object", False), + ("void", False), + ], +) +def test_is_boolean(dtype, expected): + dtype = NumpyEADtype(dtype) + assert dtype._is_boolean is expected + + +def test_repr(): + dtype = NumpyEADtype(np.dtype("int64")) + assert repr(dtype) == "NumpyEADtype('int64')" + + +def test_constructor_from_string(): + result = NumpyEADtype.construct_from_string("int64") + expected = NumpyEADtype(np.dtype("int64")) + assert result == expected + + +def test_dtype_idempotent(any_numpy_dtype): + dtype = NumpyEADtype(any_numpy_dtype) + + result = NumpyEADtype(dtype) + assert result == dtype + + +# ---------------------------------------------------------------------------- +# Construction + + +def test_constructor_no_coercion(): + with pytest.raises(ValueError, match="NumPy array"): + NumpyExtensionArray([1, 2, 3]) + + +def test_series_constructor_with_copy(): + ndarray = np.array([1, 2, 3]) + ser = pd.Series(NumpyExtensionArray(ndarray), copy=True) + + assert ser.values is not ndarray + + +def test_series_constructor_with_astype(): + ndarray = np.array([1, 2, 3]) + result = pd.Series(NumpyExtensionArray(ndarray), dtype="float64") + expected = pd.Series([1.0, 2.0, 3.0], dtype="float64") + tm.assert_series_equal(result, expected) + + +def test_from_sequence_dtype(): + arr = np.array([1, 2, 3], dtype="int64") + result = NumpyExtensionArray._from_sequence(arr, dtype="uint64") + expected = NumpyExtensionArray(np.array([1, 2, 3], dtype="uint64")) + tm.assert_extension_array_equal(result, expected) + + +def test_constructor_copy(): + arr = np.array([0, 1]) + result = NumpyExtensionArray(arr, copy=True) + + assert not tm.shares_memory(result, arr) + + +def test_constructor_with_data(any_numpy_array): + nparr = any_numpy_array + arr = NumpyExtensionArray(nparr) + assert arr.dtype.numpy_dtype == nparr.dtype + + +# ---------------------------------------------------------------------------- +# Conversion + + +def test_to_numpy(): + arr = NumpyExtensionArray(np.array([1, 2, 3])) + result = arr.to_numpy() + assert result is arr._ndarray + + result = arr.to_numpy(copy=True) + assert result is not arr._ndarray + + result = arr.to_numpy(dtype="f8") + expected = np.array([1, 2, 3], dtype="f8") + tm.assert_numpy_array_equal(result, expected) + + +# ---------------------------------------------------------------------------- +# Setitem + + +def test_setitem_series(): + ser = pd.Series([1, 2, 3]) + ser.array[0] = 10 + expected = pd.Series([10, 2, 3]) + tm.assert_series_equal(ser, expected) + + +def test_setitem(any_numpy_array): + nparr = any_numpy_array + arr = NumpyExtensionArray(nparr, copy=True) + + arr[0] = arr[1] + nparr[0] = nparr[1] + + tm.assert_numpy_array_equal(arr.to_numpy(), nparr) + + +# ---------------------------------------------------------------------------- +# Reductions + + +def test_bad_reduce_raises(): + arr = np.array([1, 2, 3], dtype="int64") + arr = NumpyExtensionArray(arr) + msg = "cannot perform not_a_method with type int" + with pytest.raises(TypeError, match=msg): + arr._reduce(msg) + + +def test_validate_reduction_keyword_args(): + arr = NumpyExtensionArray(np.array([1, 2, 3])) + msg = "the 'keepdims' parameter is not supported .*all" + with pytest.raises(ValueError, match=msg): + arr.all(keepdims=True) + + +def test_np_max_nested_tuples(): + # case where checking in ufunc.nout works while checking for tuples + # does not + vals = [ + (("j", "k"), ("l", "m")), + (("l", "m"), ("o", "p")), + (("o", "p"), ("j", "k")), + ] + ser = pd.Series(vals) + arr = ser.array + + assert arr.max() is arr[2] + assert ser.max() is arr[2] + + result = np.maximum.reduce(arr) + assert result == arr[2] + + result = np.maximum.reduce(ser) + assert result == arr[2] + + +def test_np_reduce_2d(): + raw = np.arange(12).reshape(4, 3) + arr = NumpyExtensionArray(raw) + + res = np.maximum.reduce(arr, axis=0) + tm.assert_extension_array_equal(res, arr[-1]) + + alt = arr.max(axis=0) + tm.assert_extension_array_equal(alt, arr[-1]) + + +# ---------------------------------------------------------------------------- +# Ops + + +@pytest.mark.parametrize("ufunc", [np.abs, np.negative, np.positive]) +def test_ufunc_unary(ufunc): + arr = NumpyExtensionArray(np.array([-1.0, 0.0, 1.0])) + result = ufunc(arr) + expected = NumpyExtensionArray(ufunc(arr._ndarray)) + tm.assert_extension_array_equal(result, expected) + + # same thing but with the 'out' keyword + out = NumpyExtensionArray(np.array([-9.0, -9.0, -9.0])) + ufunc(arr, out=out) + tm.assert_extension_array_equal(out, expected) + + +def test_ufunc(): + arr = NumpyExtensionArray(np.array([-1.0, 0.0, 1.0])) + + r1, r2 = np.divmod(arr, np.add(arr, 2)) + e1, e2 = np.divmod(arr._ndarray, np.add(arr._ndarray, 2)) + e1 = NumpyExtensionArray(e1) + e2 = NumpyExtensionArray(e2) + tm.assert_extension_array_equal(r1, e1) + tm.assert_extension_array_equal(r2, e2) + + +def test_basic_binop(): + # Just a basic smoke test. The EA interface tests exercise this + # more thoroughly. + x = NumpyExtensionArray(np.array([1, 2, 3])) + result = x + x + expected = NumpyExtensionArray(np.array([2, 4, 6])) + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize("dtype", [None, object]) +def test_setitem_object_typecode(dtype): + arr = NumpyExtensionArray(np.array(["a", "b", "c"], dtype=dtype)) + arr[0] = "t" + expected = NumpyExtensionArray(np.array(["t", "b", "c"], dtype=dtype)) + tm.assert_extension_array_equal(arr, expected) + + +def test_setitem_no_coercion(): + # https://github.com/pandas-dev/pandas/issues/28150 + arr = NumpyExtensionArray(np.array([1, 2, 3])) + with pytest.raises(ValueError, match="int"): + arr[0] = "a" + + # With a value that we do coerce, check that we coerce the value + # and not the underlying array. + arr[0] = 2.5 + assert isinstance(arr[0], (int, np.integer)), type(arr[0]) + + +def test_setitem_preserves_views(): + # GH#28150, see also extension test of the same name + arr = NumpyExtensionArray(np.array([1, 2, 3])) + view1 = arr.view() + view2 = arr[:] + view3 = np.asarray(arr) + + arr[0] = 9 + assert view1[0] == 9 + assert view2[0] == 9 + assert view3[0] == 9 + + arr[-1] = 2.5 + view1[-1] = 5 + assert arr[-1] == 5 + + +@pytest.mark.parametrize("dtype", [np.int64, np.uint64]) +def test_quantile_empty(dtype): + # we should get back np.nans, not -1s + arr = NumpyExtensionArray(np.array([], dtype=dtype)) + idx = pd.Index([0.0, 0.5]) + + result = arr._quantile(idx, interpolation="linear") + expected = NumpyExtensionArray(np.array([np.nan, np.nan])) + tm.assert_extension_array_equal(result, expected) + + +def test_factorize_unsigned(): + # don't raise when calling factorize on unsigned int NumpyExtensionArray + arr = np.array([1, 2, 3], dtype=np.uint64) + obj = NumpyExtensionArray(arr) + + res_codes, res_unique = obj.factorize() + exp_codes, exp_unique = pd.factorize(arr) + + tm.assert_numpy_array_equal(res_codes, exp_codes) + + tm.assert_extension_array_equal(res_unique, NumpyExtensionArray(exp_unique)) + + +# ---------------------------------------------------------------------------- +# Output formatting + + +def test_array_repr(any_numpy_array): + # GH#61085 + nparray = any_numpy_array + arr = NumpyExtensionArray(nparray) + if nparray.dtype == "object": + values = "['a', 'b']" + elif nparray.dtype == "float64": + values = "[0.0, 1.0]" + elif str(nparray.dtype).startswith("int"): + values = "[0, 1]" + elif nparray.dtype == "complex128": + values = "[0j, (1+2j)]" + elif nparray.dtype == "bool": + values = "[True, False]" + elif nparray.dtype == "datetime64[ns]": + values = "[1970-01-01T00:00:00.000000000, 1970-01-01T00:00:00.000000001]" + elif nparray.dtype == "timedelta64[ns]": + values = "[0 nanoseconds, 1 nanoseconds]" + expected = f"\n{values}\nLength: 2, dtype: {nparray.dtype}" + result = repr(arr) + assert result == expected, f"{result} vs {expected}" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_arrow_compat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_arrow_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..431309aca0df21dbe885ae015b10c3c21f0134a2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_arrow_compat.py @@ -0,0 +1,130 @@ +import pytest + +from pandas.compat.pyarrow import pa_version_under10p1 + +from pandas.core.dtypes.dtypes import PeriodDtype + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import ( + PeriodArray, + period_array, +) + +pytestmark = pytest.mark.filterwarnings( + "ignore:Passing a BlockManager to DataFrame:DeprecationWarning" +) + + +pa = pytest.importorskip("pyarrow") + + +def test_arrow_extension_type(): + from pandas.core.arrays.arrow.extension_types import ArrowPeriodType + + p1 = ArrowPeriodType("D") + p2 = ArrowPeriodType("D") + p3 = ArrowPeriodType("M") + + assert p1.freq == "D" + assert p1 == p2 + assert p1 != p3 + assert hash(p1) == hash(p2) + assert hash(p1) != hash(p3) + + +@pytest.mark.xfail(not pa_version_under10p1, reason="Wrong behavior with pyarrow 10") +@pytest.mark.parametrize( + "data, freq", + [ + (pd.date_range("2017", periods=3), "D"), + (pd.date_range("2017", periods=3, freq="YE"), "Y-DEC"), + ], +) +def test_arrow_array(data, freq): + from pandas.core.arrays.arrow.extension_types import ArrowPeriodType + + periods = period_array(data, freq=freq) + result = pa.array(periods) + assert isinstance(result.type, ArrowPeriodType) + assert result.type.freq == freq + expected = pa.array(periods.asi8, type="int64") + assert result.storage.equals(expected) + + # convert to its storage type + result = pa.array(periods, type=pa.int64()) + assert result.equals(expected) + + # unsupported conversions + msg = "Not supported to convert PeriodArray to 'double' type" + with pytest.raises(TypeError, match=msg): + pa.array(periods, type="float64") + + with pytest.raises(TypeError, match="different 'freq'"): + pa.array(periods, type=ArrowPeriodType("T")) + + +def test_arrow_array_missing(): + from pandas.core.arrays.arrow.extension_types import ArrowPeriodType + + arr = PeriodArray([1, 2, 3], dtype="period[D]") + arr[1] = pd.NaT + + result = pa.array(arr) + assert isinstance(result.type, ArrowPeriodType) + assert result.type.freq == "D" + expected = pa.array([1, None, 3], type="int64") + assert result.storage.equals(expected) + + +def test_arrow_table_roundtrip(): + from pandas.core.arrays.arrow.extension_types import ArrowPeriodType + + arr = PeriodArray([1, 2, 3], dtype="period[D]") + arr[1] = pd.NaT + df = pd.DataFrame({"a": arr}) + + table = pa.table(df) + assert isinstance(table.field("a").type, ArrowPeriodType) + result = table.to_pandas() + assert isinstance(result["a"].dtype, PeriodDtype) + tm.assert_frame_equal(result, df) + + table2 = pa.concat_tables([table, table]) + result = table2.to_pandas() + expected = pd.concat([df, df], ignore_index=True) + tm.assert_frame_equal(result, expected) + + +def test_arrow_load_from_zero_chunks(): + # GH-41040 + + from pandas.core.arrays.arrow.extension_types import ArrowPeriodType + + arr = PeriodArray([], dtype="period[D]") + df = pd.DataFrame({"a": arr}) + + table = pa.table(df) + assert isinstance(table.field("a").type, ArrowPeriodType) + table = pa.table( + [pa.chunked_array([], type=table.column(0).type)], schema=table.schema + ) + + result = table.to_pandas() + assert isinstance(result["a"].dtype, PeriodDtype) + tm.assert_frame_equal(result, df) + + +def test_arrow_table_roundtrip_without_metadata(): + arr = PeriodArray([1, 2, 3], dtype="period[h]") + arr[1] = pd.NaT + df = pd.DataFrame({"a": arr}) + + table = pa.table(df) + # remove the metadata + table = table.replace_schema_metadata() + assert table.schema.metadata is None + + result = table.to_pandas() + assert isinstance(result["a"].dtype, PeriodDtype) + tm.assert_frame_equal(result, df) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_astype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..9976c3a32580da0b5b237eaa2b839b2337363f51 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_astype.py @@ -0,0 +1,67 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.dtypes import PeriodDtype + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import period_array + + +@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"]) +def test_astype_int(dtype): + # We choose to ignore the sign and size of integers for + # Period/Datetime/Timedelta astype + arr = period_array(["2000", "2001", None], freq="D") + + if np.dtype(dtype) != np.int64: + with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"): + arr.astype(dtype) + return + + result = arr.astype(dtype) + expected = arr._ndarray.view("i8") + tm.assert_numpy_array_equal(result, expected) + + +def test_astype_copies(): + arr = period_array(["2000", "2001", None], freq="D") + result = arr.astype(np.int64, copy=False) + + # Add the `.base`, since we now use `.asi8` which returns a view. + # We could maybe override it in PeriodArray to return ._ndarray directly. + assert result.base is arr._ndarray + + result = arr.astype(np.int64, copy=True) + assert result is not arr._ndarray + tm.assert_numpy_array_equal(result, arr._ndarray.view("i8")) + + +def test_astype_categorical(): + arr = period_array(["2000", "2001", "2001", None], freq="D") + result = arr.astype("category") + categories = pd.PeriodIndex(["2000", "2001"], freq="D") + expected = pd.Categorical.from_codes([0, 1, 1, -1], categories=categories) + tm.assert_categorical_equal(result, expected) + + +def test_astype_period(): + arr = period_array(["2000", "2001", None], freq="D") + result = arr.astype(PeriodDtype("M")) + expected = period_array(["2000", "2001", None], freq="M") + tm.assert_period_array_equal(result, expected) + + +@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"]) +def test_astype_datetime(dtype): + arr = period_array(["2000", "2001", None], freq="D") + # slice off the [ns] so that the regex matches. + if dtype == "timedelta64[ns]": + with pytest.raises(TypeError, match=dtype[:-4]): + arr.astype(dtype) + + else: + # GH#45038 allow period->dt64 because we allow dt64->period + result = arr.astype(dtype) + expected = pd.DatetimeIndex(["2000", "2001", pd.NaT], dtype=dtype)._data + tm.assert_datetime_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_constructors.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..d034162f1b46e11bd06204de7707c7343fd9b1b2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_constructors.py @@ -0,0 +1,156 @@ +import numpy as np +import pytest + +from pandas._libs.tslibs import iNaT +from pandas._libs.tslibs.offsets import MonthEnd +from pandas._libs.tslibs.period import IncompatibleFrequency + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import ( + PeriodArray, + period_array, +) + + +@pytest.mark.parametrize( + "data, freq, expected", + [ + ([pd.Period("2017", "D")], None, [17167]), + ([pd.Period("2017", "D")], "D", [17167]), + ([2017], "D", [17167]), + (["2017"], "D", [17167]), + ([pd.Period("2017", "D")], pd.tseries.offsets.Day(), [17167]), + ([pd.Period("2017", "D"), None], None, [17167, iNaT]), + (pd.Series(pd.date_range("2017", periods=3)), None, [17167, 17168, 17169]), + (pd.date_range("2017", periods=3), None, [17167, 17168, 17169]), + (pd.period_range("2017", periods=4, freq="Q"), None, [188, 189, 190, 191]), + ], +) +def test_period_array_ok(data, freq, expected): + result = period_array(data, freq=freq).asi8 + expected = np.asarray(expected, dtype=np.int64) + tm.assert_numpy_array_equal(result, expected) + + +def test_period_array_readonly_object(): + # https://github.com/pandas-dev/pandas/issues/25403 + pa = period_array([pd.Period("2019-01-01")]) + arr = np.asarray(pa, dtype="object") + arr.setflags(write=False) + + result = period_array(arr) + tm.assert_period_array_equal(result, pa) + + result = pd.Series(arr) + tm.assert_series_equal(result, pd.Series(pa)) + + result = pd.DataFrame({"A": arr}) + tm.assert_frame_equal(result, pd.DataFrame({"A": pa})) + + +def test_from_datetime64_freq_changes(): + # https://github.com/pandas-dev/pandas/issues/23438 + arr = pd.date_range("2017", periods=3, freq="D") + result = PeriodArray._from_datetime64(arr, freq="M") + expected = period_array(["2017-01-01", "2017-01-01", "2017-01-01"], freq="M") + tm.assert_period_array_equal(result, expected) + + +@pytest.mark.parametrize("freq", ["2M", MonthEnd(2)]) +def test_from_datetime64_freq_2M(freq): + arr = np.array( + ["2020-01-01T00:00:00", "2020-01-02T00:00:00"], dtype="datetime64[ns]" + ) + result = PeriodArray._from_datetime64(arr, freq) + expected = period_array(["2020-01", "2020-01"], freq=freq) + tm.assert_period_array_equal(result, expected) + + +@pytest.mark.parametrize( + "data, freq, msg", + [ + ( + [pd.Period("2017", "D"), pd.Period("2017", "Y")], + None, + "Input has different freq", + ), + ([pd.Period("2017", "D")], "Y", "Input has different freq"), + ], +) +def test_period_array_raises(data, freq, msg): + with pytest.raises(IncompatibleFrequency, match=msg): + period_array(data, freq) + + +def test_period_array_non_period_series_raies(): + ser = pd.Series([1, 2, 3]) + with pytest.raises(TypeError, match="dtype"): + PeriodArray(ser, dtype="period[D]") + + +def test_period_array_freq_mismatch(): + arr = period_array(["2000", "2001"], freq="D") + with pytest.raises(IncompatibleFrequency, match="freq"): + PeriodArray(arr, dtype="period[M]") + + dtype = pd.PeriodDtype(pd.tseries.offsets.MonthEnd()) + with pytest.raises(IncompatibleFrequency, match="freq"): + PeriodArray(arr, dtype=dtype) + + +def test_from_sequence_disallows_i8(): + arr = period_array(["2000", "2001"], freq="D") + + msg = str(arr[0].ordinal) + with pytest.raises(TypeError, match=msg): + PeriodArray._from_sequence(arr.asi8, dtype=arr.dtype) + + with pytest.raises(TypeError, match=msg): + PeriodArray._from_sequence(list(arr.asi8), dtype=arr.dtype) + + +def test_from_td64nat_sequence_raises(): + # GH#44507 + td = pd.NaT.to_numpy("m8[ns]") + + dtype = pd.period_range("2005-01-01", periods=3, freq="D").dtype + + arr = np.array([None], dtype=object) + arr[0] = td + + msg = "Value must be Period, string, integer, or datetime" + with pytest.raises(ValueError, match=msg): + PeriodArray._from_sequence(arr, dtype=dtype) + + with pytest.raises(ValueError, match=msg): + pd.PeriodIndex(arr, dtype=dtype) + with pytest.raises(ValueError, match=msg): + pd.Index(arr, dtype=dtype) + with pytest.raises(ValueError, match=msg): + pd.array(arr, dtype=dtype) + with pytest.raises(ValueError, match=msg): + pd.Series(arr, dtype=dtype) + with pytest.raises(ValueError, match=msg): + pd.DataFrame(arr, dtype=dtype) + + +def test_freq_deprecated(): + # GH#52462 + data = np.arange(5).astype(np.int64) + msg = "The 'freq' keyword in the PeriodArray constructor is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = PeriodArray(data, freq="M") + + expected = PeriodArray(data, dtype="period[M]") + tm.assert_equal(res, expected) + + +def test_period_array_from_datetime64(): + arr = np.array( + ["2020-01-01T00:00:00", "2020-02-02T00:00:00"], dtype="datetime64[ns]" + ) + result = PeriodArray._from_datetime64(arr, freq=MonthEnd(2)) + + expected = period_array(["2020-01-01", "2020-02-01"], freq=MonthEnd(2)) + tm.assert_period_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_reductions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_reductions.py new file mode 100644 index 0000000000000000000000000000000000000000..2889cc786dd71583ca345ad206553907af3a13fa --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/period/test_reductions.py @@ -0,0 +1,42 @@ +import pytest + +import pandas as pd +from pandas.core.arrays import period_array + + +class TestReductions: + def test_min_max(self): + arr = period_array( + [ + "2000-01-03", + "2000-01-03", + "NaT", + "2000-01-02", + "2000-01-05", + "2000-01-04", + ], + freq="D", + ) + + result = arr.min() + expected = pd.Period("2000-01-02", freq="D") + assert result == expected + + result = arr.max() + expected = pd.Period("2000-01-05", freq="D") + assert result == expected + + result = arr.min(skipna=False) + assert result is pd.NaT + + result = arr.max(skipna=False) + assert result is pd.NaT + + @pytest.mark.parametrize("skipna", [True, False]) + def test_min_max_empty(self, skipna): + arr = period_array([], freq="D") + result = arr.min(skipna=skipna) + assert result is pd.NaT + + result = arr.max(skipna=skipna) + assert result is pd.NaT diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_accessor.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_accessor.py new file mode 100644 index 0000000000000000000000000000000000000000..87eb7bcfa9cee3e92386ad0f148b896c0e682b07 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_accessor.py @@ -0,0 +1,253 @@ +import string + +import numpy as np +import pytest + +import pandas as pd +from pandas import SparseDtype +import pandas._testing as tm +from pandas.core.arrays.sparse import SparseArray + + +class TestSeriesAccessor: + def test_to_dense(self): + ser = pd.Series([0, 1, 0, 10], dtype="Sparse[int64]") + result = ser.sparse.to_dense() + expected = pd.Series([0, 1, 0, 10]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("attr", ["npoints", "density", "fill_value", "sp_values"]) + def test_get_attributes(self, attr): + arr = SparseArray([0, 1]) + ser = pd.Series(arr) + + result = getattr(ser.sparse, attr) + expected = getattr(arr, attr) + assert result == expected + + def test_from_coo(self): + scipy_sparse = pytest.importorskip("scipy.sparse") + + row = [0, 3, 1, 0] + col = [0, 3, 1, 2] + data = [4, 5, 7, 9] + + sp_array = scipy_sparse.coo_matrix((data, (row, col))) + result = pd.Series.sparse.from_coo(sp_array) + + index = pd.MultiIndex.from_arrays( + [ + np.array([0, 0, 1, 3], dtype=np.int32), + np.array([0, 2, 1, 3], dtype=np.int32), + ], + ) + expected = pd.Series([4, 9, 7, 5], index=index, dtype="Sparse[int]") + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "sort_labels, expected_rows, expected_cols, expected_values_pos", + [ + ( + False, + [("b", 2), ("a", 2), ("b", 1), ("a", 1)], + [("z", 1), ("z", 2), ("x", 2), ("z", 0)], + {1: (1, 0), 3: (3, 3)}, + ), + ( + True, + [("a", 1), ("a", 2), ("b", 1), ("b", 2)], + [("x", 2), ("z", 0), ("z", 1), ("z", 2)], + {1: (1, 2), 3: (0, 1)}, + ), + ], + ) + def test_to_coo( + self, sort_labels, expected_rows, expected_cols, expected_values_pos + ): + sp_sparse = pytest.importorskip("scipy.sparse") + + values = SparseArray([0, np.nan, 1, 0, None, 3], fill_value=0) + index = pd.MultiIndex.from_tuples( + [ + ("b", 2, "z", 1), + ("a", 2, "z", 2), + ("a", 2, "z", 1), + ("a", 2, "x", 2), + ("b", 1, "z", 1), + ("a", 1, "z", 0), + ] + ) + ss = pd.Series(values, index=index) + + expected_A = np.zeros((4, 4)) + for value, (row, col) in expected_values_pos.items(): + expected_A[row, col] = value + + A, rows, cols = ss.sparse.to_coo( + row_levels=(0, 1), column_levels=(2, 3), sort_labels=sort_labels + ) + assert isinstance(A, sp_sparse.coo_matrix) + tm.assert_numpy_array_equal(A.toarray(), expected_A) + assert rows == expected_rows + assert cols == expected_cols + + def test_non_sparse_raises(self): + ser = pd.Series([1, 2, 3]) + with pytest.raises(AttributeError, match=".sparse"): + ser.sparse.density + + +class TestFrameAccessor: + def test_accessor_raises(self): + df = pd.DataFrame({"A": [0, 1]}) + with pytest.raises(AttributeError, match="sparse"): + df.sparse + + @pytest.mark.parametrize("format", ["csc", "csr", "coo"]) + @pytest.mark.parametrize("labels", [None, list(string.ascii_letters[:10])]) + @pytest.mark.parametrize("dtype", ["float64", "int64"]) + def test_from_spmatrix(self, format, labels, dtype): + sp_sparse = pytest.importorskip("scipy.sparse") + + sp_dtype = SparseDtype(dtype, np.array(0, dtype=dtype).item()) + + mat = sp_sparse.eye(10, format=format, dtype=dtype) + result = pd.DataFrame.sparse.from_spmatrix(mat, index=labels, columns=labels) + expected = pd.DataFrame( + np.eye(10, dtype=dtype), index=labels, columns=labels + ).astype(sp_dtype) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("format", ["csc", "csr", "coo"]) + def test_from_spmatrix_including_explicit_zero(self, format): + sp_sparse = pytest.importorskip("scipy.sparse") + + mat = sp_sparse.random(10, 2, density=0.5, format=format) + mat.data[0] = 0 + result = pd.DataFrame.sparse.from_spmatrix(mat) + dtype = SparseDtype("float64", 0.0) + expected = pd.DataFrame(mat.todense()).astype(dtype) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "columns", + [["a", "b"], pd.MultiIndex.from_product([["A"], ["a", "b"]]), ["a", "a"]], + ) + def test_from_spmatrix_columns(self, columns): + sp_sparse = pytest.importorskip("scipy.sparse") + + dtype = SparseDtype("float64", 0.0) + + mat = sp_sparse.random(10, 2, density=0.5) + result = pd.DataFrame.sparse.from_spmatrix(mat, columns=columns) + expected = pd.DataFrame(mat.toarray(), columns=columns).astype(dtype) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "colnames", [("A", "B"), (1, 2), (1, pd.NA), (0.1, 0.2), ("x", "x"), (0, 0)] + ) + def test_to_coo(self, colnames): + sp_sparse = pytest.importorskip("scipy.sparse") + + df = pd.DataFrame( + {colnames[0]: [0, 1, 0], colnames[1]: [1, 0, 0]}, dtype="Sparse[int64, 0]" + ) + result = df.sparse.to_coo() + expected = sp_sparse.coo_matrix(np.asarray(df)) + assert (result != expected).nnz == 0 + + @pytest.mark.parametrize("fill_value", [1, np.nan]) + def test_to_coo_nonzero_fill_val_raises(self, fill_value): + pytest.importorskip("scipy") + df = pd.DataFrame( + { + "A": SparseArray( + [fill_value, fill_value, fill_value, 2], fill_value=fill_value + ), + "B": SparseArray( + [fill_value, 2, fill_value, fill_value], fill_value=fill_value + ), + } + ) + with pytest.raises(ValueError, match="fill value must be 0"): + df.sparse.to_coo() + + def test_to_coo_midx_categorical(self): + # GH#50996 + sp_sparse = pytest.importorskip("scipy.sparse") + + midx = pd.MultiIndex.from_arrays( + [ + pd.CategoricalIndex(list("ab"), name="x"), + pd.CategoricalIndex([0, 1], name="y"), + ] + ) + + ser = pd.Series(1, index=midx, dtype="Sparse[int]") + result = ser.sparse.to_coo(row_levels=["x"], column_levels=["y"])[0] + expected = sp_sparse.coo_matrix( + (np.array([1, 1]), (np.array([0, 1]), np.array([0, 1]))), shape=(2, 2) + ) + assert (result != expected).nnz == 0 + + def test_to_dense(self): + df = pd.DataFrame( + { + "A": SparseArray([1, 0], dtype=SparseDtype("int64", 0)), + "B": SparseArray([1, 0], dtype=SparseDtype("int64", 1)), + "C": SparseArray([1.0, 0.0], dtype=SparseDtype("float64", 0.0)), + }, + index=["b", "a"], + ) + result = df.sparse.to_dense() + expected = pd.DataFrame( + {"A": [1, 0], "B": [1, 0], "C": [1.0, 0.0]}, index=["b", "a"] + ) + tm.assert_frame_equal(result, expected) + + def test_density(self): + df = pd.DataFrame( + { + "A": SparseArray([1, 0, 2, 1], fill_value=0), + "B": SparseArray([0, 1, 1, 1], fill_value=0), + } + ) + res = df.sparse.density + expected = 0.75 + assert res == expected + + @pytest.mark.parametrize("dtype", ["int64", "float64"]) + @pytest.mark.parametrize("dense_index", [True, False]) + def test_series_from_coo(self, dtype, dense_index): + sp_sparse = pytest.importorskip("scipy.sparse") + + A = sp_sparse.eye(3, format="coo", dtype=dtype) + result = pd.Series.sparse.from_coo(A, dense_index=dense_index) + + index = pd.MultiIndex.from_tuples( + [ + np.array([0, 0], dtype=np.int32), + np.array([1, 1], dtype=np.int32), + np.array([2, 2], dtype=np.int32), + ], + ) + expected = pd.Series(SparseArray(np.array([1, 1, 1], dtype=dtype)), index=index) + if dense_index: + expected = expected.reindex(pd.MultiIndex.from_product(index.levels)) + + tm.assert_series_equal(result, expected) + + def test_series_from_coo_incorrect_format_raises(self): + # gh-26554 + sp_sparse = pytest.importorskip("scipy.sparse") + + m = sp_sparse.csr_matrix(np.array([[0, 1], [0, 0]])) + with pytest.raises( + TypeError, match="Expected coo_matrix. Got csr_matrix instead." + ): + pd.Series.sparse.from_coo(m) + + def test_with_column_named_sparse(self): + # https://github.com/pandas-dev/pandas/issues/30758 + df = pd.DataFrame({"sparse": pd.arrays.SparseArray([1, 2])}) + assert isinstance(df.sparse, pd.core.arrays.sparse.accessor.SparseFrameAccessor) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_arithmetics.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_arithmetics.py new file mode 100644 index 0000000000000000000000000000000000000000..ffc93b4e4f176385ac7b2b8a0b51027cb0bad9f6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_arithmetics.py @@ -0,0 +1,514 @@ +import operator + +import numpy as np +import pytest + +import pandas as pd +from pandas import SparseDtype +import pandas._testing as tm +from pandas.core.arrays.sparse import SparseArray + + +@pytest.fixture(params=["integer", "block"]) +def kind(request): + """kind kwarg to pass to SparseArray""" + return request.param + + +@pytest.fixture(params=[True, False]) +def mix(request): + """ + Fixture returning True or False, determining whether to operate + op(sparse, dense) instead of op(sparse, sparse) + """ + return request.param + + +class TestSparseArrayArithmetics: + def _assert(self, a, b): + # We have to use tm.assert_sp_array_equal. See GH #45126 + tm.assert_numpy_array_equal(a, b) + + def _check_numeric_ops(self, a, b, a_dense, b_dense, mix: bool, op): + # Check that arithmetic behavior matches non-Sparse Series arithmetic + + if isinstance(a_dense, np.ndarray): + expected = op(pd.Series(a_dense), b_dense).values + elif isinstance(b_dense, np.ndarray): + expected = op(a_dense, pd.Series(b_dense)).values + else: + raise NotImplementedError + + with np.errstate(invalid="ignore", divide="ignore"): + if mix: + result = op(a, b_dense).to_dense() + else: + result = op(a, b).to_dense() + + self._assert(result, expected) + + def _check_bool_result(self, res): + assert isinstance(res, SparseArray) + assert isinstance(res.dtype, SparseDtype) + assert res.dtype.subtype == np.bool_ + assert isinstance(res.fill_value, bool) + + def _check_comparison_ops(self, a, b, a_dense, b_dense): + with np.errstate(invalid="ignore"): + # Unfortunately, trying to wrap the computation of each expected + # value is with np.errstate() is too tedious. + # + # sparse & sparse + self._check_bool_result(a == b) + self._assert((a == b).to_dense(), a_dense == b_dense) + + self._check_bool_result(a != b) + self._assert((a != b).to_dense(), a_dense != b_dense) + + self._check_bool_result(a >= b) + self._assert((a >= b).to_dense(), a_dense >= b_dense) + + self._check_bool_result(a <= b) + self._assert((a <= b).to_dense(), a_dense <= b_dense) + + self._check_bool_result(a > b) + self._assert((a > b).to_dense(), a_dense > b_dense) + + self._check_bool_result(a < b) + self._assert((a < b).to_dense(), a_dense < b_dense) + + # sparse & dense + self._check_bool_result(a == b_dense) + self._assert((a == b_dense).to_dense(), a_dense == b_dense) + + self._check_bool_result(a != b_dense) + self._assert((a != b_dense).to_dense(), a_dense != b_dense) + + self._check_bool_result(a >= b_dense) + self._assert((a >= b_dense).to_dense(), a_dense >= b_dense) + + self._check_bool_result(a <= b_dense) + self._assert((a <= b_dense).to_dense(), a_dense <= b_dense) + + self._check_bool_result(a > b_dense) + self._assert((a > b_dense).to_dense(), a_dense > b_dense) + + self._check_bool_result(a < b_dense) + self._assert((a < b_dense).to_dense(), a_dense < b_dense) + + def _check_logical_ops(self, a, b, a_dense, b_dense): + # sparse & sparse + self._check_bool_result(a & b) + self._assert((a & b).to_dense(), a_dense & b_dense) + + self._check_bool_result(a | b) + self._assert((a | b).to_dense(), a_dense | b_dense) + # sparse & dense + self._check_bool_result(a & b_dense) + self._assert((a & b_dense).to_dense(), a_dense & b_dense) + + self._check_bool_result(a | b_dense) + self._assert((a | b_dense).to_dense(), a_dense | b_dense) + + @pytest.mark.parametrize("scalar", [0, 1, 3]) + @pytest.mark.parametrize("fill_value", [None, 0, 2]) + def test_float_scalar( + self, kind, mix, all_arithmetic_functions, fill_value, scalar, request + ): + op = all_arithmetic_functions + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + a = SparseArray(values, kind=kind, fill_value=fill_value) + self._check_numeric_ops(a, scalar, values, scalar, mix, op) + + def test_float_scalar_comparison(self, kind): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + + a = SparseArray(values, kind=kind) + self._check_comparison_ops(a, 1, values, 1) + self._check_comparison_ops(a, 0, values, 0) + self._check_comparison_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=0) + self._check_comparison_ops(a, 1, values, 1) + self._check_comparison_ops(a, 0, values, 0) + self._check_comparison_ops(a, 3, values, 3) + + a = SparseArray(values, kind=kind, fill_value=2) + self._check_comparison_ops(a, 1, values, 1) + self._check_comparison_ops(a, 0, values, 0) + self._check_comparison_ops(a, 3, values, 3) + + def test_float_same_index_without_nans(self, kind, mix, all_arithmetic_functions): + # when sp_index are the same + op = all_arithmetic_functions + + values = np.array([0.0, 1.0, 2.0, 6.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0]) + rvalues = np.array([0.0, 2.0, 3.0, 4.0, 0.0, 0.0, 1.0, 3.0, 2.0, 0.0]) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + def test_float_same_index_with_nans( + self, kind, mix, all_arithmetic_functions, request + ): + # when sp_index are the same + op = all_arithmetic_functions + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan]) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + def test_float_same_index_comparison(self, kind): + # when sp_index are the same + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([np.nan, 2, 3, 4, np.nan, 0, 1, 3, 2, np.nan]) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + + values = np.array([0.0, 1.0, 2.0, 6.0, 0.0, 0.0, 1.0, 2.0, 1.0, 0.0]) + rvalues = np.array([0.0, 2.0, 3.0, 4.0, 0.0, 0.0, 1.0, 3.0, 2.0, 0.0]) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_comparison_ops(a, b, values, rvalues) + + def test_float_array(self, kind, mix, all_arithmetic_functions): + op = all_arithmetic_functions + + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + a = SparseArray(values, kind=kind, fill_value=1) + b = SparseArray(rvalues, kind=kind, fill_value=2) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + def test_float_array_different_kind(self, mix, all_arithmetic_functions): + op = all_arithmetic_functions + + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) + + a = SparseArray(values, kind="integer") + b = SparseArray(rvalues, kind="block") + self._check_numeric_ops(a, b, values, rvalues, mix, op) + self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op) + + a = SparseArray(values, kind="integer", fill_value=0) + b = SparseArray(rvalues, kind="block") + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + a = SparseArray(values, kind="integer", fill_value=0) + b = SparseArray(rvalues, kind="block", fill_value=0) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + a = SparseArray(values, kind="integer", fill_value=1) + b = SparseArray(rvalues, kind="block", fill_value=2) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + def test_float_array_comparison(self, kind): + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, np.nan, 2, 3, np.nan, 0, 1, 5, 2, np.nan]) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + self._check_comparison_ops(a, b * 0, values, rvalues * 0) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=1) + b = SparseArray(rvalues, kind=kind, fill_value=2) + self._check_comparison_ops(a, b, values, rvalues) + + def test_int_array(self, kind, mix, all_arithmetic_functions): + op = all_arithmetic_functions + + # have to specify dtype explicitly until fixing GH 667 + dtype = np.int64 + + values = np.array([0, 1, 2, 0, 0, 0, 1, 2, 1, 0], dtype=dtype) + rvalues = np.array([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=dtype) + + a = SparseArray(values, dtype=dtype, kind=kind) + assert a.dtype == SparseDtype(dtype) + b = SparseArray(rvalues, dtype=dtype, kind=kind) + assert b.dtype == SparseDtype(dtype) + + self._check_numeric_ops(a, b, values, rvalues, mix, op) + self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op) + + a = SparseArray(values, fill_value=0, dtype=dtype, kind=kind) + assert a.dtype == SparseDtype(dtype) + b = SparseArray(rvalues, dtype=dtype, kind=kind) + assert b.dtype == SparseDtype(dtype) + + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + a = SparseArray(values, fill_value=0, dtype=dtype, kind=kind) + assert a.dtype == SparseDtype(dtype) + b = SparseArray(rvalues, fill_value=0, dtype=dtype, kind=kind) + assert b.dtype == SparseDtype(dtype) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + a = SparseArray(values, fill_value=1, dtype=dtype, kind=kind) + assert a.dtype == SparseDtype(dtype, fill_value=1) + b = SparseArray(rvalues, fill_value=2, dtype=dtype, kind=kind) + assert b.dtype == SparseDtype(dtype, fill_value=2) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + def test_int_array_comparison(self, kind): + dtype = "int64" + # int32 NI ATM + + values = np.array([0, 1, 2, 0, 0, 0, 1, 2, 1, 0], dtype=dtype) + rvalues = np.array([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=dtype) + + a = SparseArray(values, dtype=dtype, kind=kind) + b = SparseArray(rvalues, dtype=dtype, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + self._check_comparison_ops(a, b * 0, values, rvalues * 0) + + a = SparseArray(values, dtype=dtype, kind=kind, fill_value=0) + b = SparseArray(rvalues, dtype=dtype, kind=kind) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, dtype=dtype, kind=kind, fill_value=0) + b = SparseArray(rvalues, dtype=dtype, kind=kind, fill_value=0) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, dtype=dtype, kind=kind, fill_value=1) + b = SparseArray(rvalues, dtype=dtype, kind=kind, fill_value=2) + self._check_comparison_ops(a, b, values, rvalues) + + @pytest.mark.parametrize("fill_value", [True, False, np.nan]) + def test_bool_same_index(self, kind, fill_value): + # GH 14000 + # when sp_index are the same + values = np.array([True, False, True, True], dtype=np.bool_) + rvalues = np.array([True, False, True, True], dtype=np.bool_) + + a = SparseArray(values, kind=kind, dtype=np.bool_, fill_value=fill_value) + b = SparseArray(rvalues, kind=kind, dtype=np.bool_, fill_value=fill_value) + self._check_logical_ops(a, b, values, rvalues) + + @pytest.mark.parametrize("fill_value", [True, False, np.nan]) + def test_bool_array_logical(self, kind, fill_value): + # GH 14000 + # when sp_index are the same + values = np.array([True, False, True, False, True, True], dtype=np.bool_) + rvalues = np.array([True, False, False, True, False, True], dtype=np.bool_) + + a = SparseArray(values, kind=kind, dtype=np.bool_, fill_value=fill_value) + b = SparseArray(rvalues, kind=kind, dtype=np.bool_, fill_value=fill_value) + self._check_logical_ops(a, b, values, rvalues) + + def test_mixed_array_float_int(self, kind, mix, all_arithmetic_functions, request): + op = all_arithmetic_functions + rdtype = "int64" + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=rdtype) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + assert b.dtype == SparseDtype(rdtype) + + self._check_numeric_ops(a, b, values, rvalues, mix, op) + self._check_numeric_ops(a, b * 0, values, rvalues * 0, mix, op) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind) + assert b.dtype == SparseDtype(rdtype) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + assert b.dtype == SparseDtype(rdtype) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + a = SparseArray(values, kind=kind, fill_value=1) + b = SparseArray(rvalues, kind=kind, fill_value=2) + assert b.dtype == SparseDtype(rdtype, fill_value=2) + self._check_numeric_ops(a, b, values, rvalues, mix, op) + + def test_mixed_array_comparison(self, kind): + rdtype = "int64" + # int32 NI ATM + + values = np.array([np.nan, 1, 2, 0, np.nan, 0, 1, 2, 1, np.nan]) + rvalues = np.array([2, 0, 2, 3, 0, 0, 1, 5, 2, 0], dtype=rdtype) + + a = SparseArray(values, kind=kind) + b = SparseArray(rvalues, kind=kind) + assert b.dtype == SparseDtype(rdtype) + + self._check_comparison_ops(a, b, values, rvalues) + self._check_comparison_ops(a, b * 0, values, rvalues * 0) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind) + assert b.dtype == SparseDtype(rdtype) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=0) + b = SparseArray(rvalues, kind=kind, fill_value=0) + assert b.dtype == SparseDtype(rdtype) + self._check_comparison_ops(a, b, values, rvalues) + + a = SparseArray(values, kind=kind, fill_value=1) + b = SparseArray(rvalues, kind=kind, fill_value=2) + assert b.dtype == SparseDtype(rdtype, fill_value=2) + self._check_comparison_ops(a, b, values, rvalues) + + def test_xor(self): + s = SparseArray([True, True, False, False]) + t = SparseArray([True, False, True, False]) + result = s ^ t + sp_index = pd.core.arrays.sparse.IntIndex(4, np.array([0, 1, 2], dtype="int32")) + expected = SparseArray([False, True, True], sparse_index=sp_index) + tm.assert_sp_array_equal(result, expected) + + +@pytest.mark.parametrize("op", [operator.eq, operator.add]) +def test_with_list(op): + arr = SparseArray([0, 1], fill_value=0) + result = op(arr, [0, 1]) + expected = op(arr, SparseArray([0, 1])) + tm.assert_sp_array_equal(result, expected) + + +def test_with_dataframe(): + # GH#27910 + arr = SparseArray([0, 1], fill_value=0) + df = pd.DataFrame([[1, 2], [3, 4]]) + result = arr.__add__(df) + assert result is NotImplemented + + +def test_with_zerodim_ndarray(): + # GH#27910 + arr = SparseArray([0, 1], fill_value=0) + + result = arr * np.array(2) + expected = arr * 2 + tm.assert_sp_array_equal(result, expected) + + +@pytest.mark.parametrize("ufunc", [np.abs, np.exp]) +@pytest.mark.parametrize( + "arr", [SparseArray([0, 0, -1, 1]), SparseArray([None, None, -1, 1])] +) +def test_ufuncs(ufunc, arr): + result = ufunc(arr) + fill_value = ufunc(arr.fill_value) + expected = SparseArray(ufunc(np.asarray(arr)), fill_value=fill_value) + tm.assert_sp_array_equal(result, expected) + + +@pytest.mark.parametrize( + "a, b", + [ + (SparseArray([0, 0, 0]), np.array([0, 1, 2])), + (SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])), + (SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])), + (SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])), + (SparseArray([0, 0, 0], fill_value=1), np.array([0, 1, 2])), + ], +) +@pytest.mark.parametrize("ufunc", [np.add, np.greater]) +def test_binary_ufuncs(ufunc, a, b): + # can't say anything about fill value here. + result = ufunc(a, b) + expected = ufunc(np.asarray(a), np.asarray(b)) + assert isinstance(result, SparseArray) + tm.assert_numpy_array_equal(np.asarray(result), expected) + + +def test_ndarray_inplace(): + sparray = SparseArray([0, 2, 0, 0]) + ndarray = np.array([0, 1, 2, 3]) + ndarray += sparray + expected = np.array([0, 3, 2, 3]) + tm.assert_numpy_array_equal(ndarray, expected) + + +def test_sparray_inplace(): + sparray = SparseArray([0, 2, 0, 0]) + ndarray = np.array([0, 1, 2, 3]) + sparray += ndarray + expected = SparseArray([0, 3, 2, 3], fill_value=0) + tm.assert_sp_array_equal(sparray, expected) + + +@pytest.mark.parametrize("cons", [list, np.array, SparseArray]) +def test_mismatched_length_cmp_op(cons): + left = SparseArray([True, True]) + right = cons([True, True, True]) + with pytest.raises(ValueError, match="operands have mismatched length"): + left & right + + +@pytest.mark.parametrize("op", ["add", "sub", "mul", "truediv", "floordiv", "pow"]) +@pytest.mark.parametrize("fill_value", [np.nan, 3]) +def test_binary_operators(op, fill_value): + op = getattr(operator, op) + data1 = np.random.default_rng(2).standard_normal(20) + data2 = np.random.default_rng(2).standard_normal(20) + + data1[::2] = fill_value + data2[::3] = fill_value + + first = SparseArray(data1, fill_value=fill_value) + second = SparseArray(data2, fill_value=fill_value) + + with np.errstate(all="ignore"): + res = op(first, second) + exp = SparseArray( + op(first.to_dense(), second.to_dense()), fill_value=first.fill_value + ) + assert isinstance(res, SparseArray) + tm.assert_almost_equal(res.to_dense(), exp.to_dense()) + + res2 = op(first, second.to_dense()) + assert isinstance(res2, SparseArray) + tm.assert_sp_array_equal(res, res2) + + res3 = op(first.to_dense(), second) + assert isinstance(res3, SparseArray) + tm.assert_sp_array_equal(res, res3) + + res4 = op(first, 4) + assert isinstance(res4, SparseArray) + + # Ignore this if the actual op raises (e.g. pow). + try: + exp = op(first.to_dense(), 4) + exp_fv = op(first.fill_value, 4) + except ValueError: + pass + else: + tm.assert_almost_equal(res4.fill_value, exp_fv) + tm.assert_almost_equal(res4.to_dense(), exp) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_array.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_array.py new file mode 100644 index 0000000000000000000000000000000000000000..b2a570b14df3c980a6fe5f32773bf4e8ed02da60 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_array.py @@ -0,0 +1,511 @@ +import re + +import numpy as np +import pytest + +from pandas._libs.sparse import IntIndex +from pandas.compat.numpy import np_version_gt2 + +import pandas as pd +from pandas import ( + SparseDtype, + isna, +) +import pandas._testing as tm +from pandas.core.arrays.sparse import SparseArray + + +@pytest.fixture +def arr_data(): + """Fixture returning numpy array with valid and missing entries""" + return np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6]) + + +@pytest.fixture +def arr(arr_data): + """Fixture returning SparseArray from 'arr_data'""" + return SparseArray(arr_data) + + +@pytest.fixture +def zarr(): + """Fixture returning SparseArray with integer entries and 'fill_value=0'""" + return SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0) + + +class TestSparseArray: + @pytest.mark.parametrize("fill_value", [0, None, np.nan]) + def test_shift_fill_value(self, fill_value): + # GH #24128 + sparse = SparseArray(np.array([1, 0, 0, 3, 0]), fill_value=8.0) + res = sparse.shift(1, fill_value=fill_value) + if isna(fill_value): + fill_value = res.dtype.na_value + exp = SparseArray(np.array([fill_value, 1, 0, 0, 3]), fill_value=8.0) + tm.assert_sp_array_equal(res, exp) + + def test_set_fill_value(self): + arr = SparseArray([1.0, np.nan, 2.0], fill_value=np.nan) + arr.fill_value = 2 + assert arr.fill_value == 2 + + arr = SparseArray([1, 0, 2], fill_value=0, dtype=np.int64) + arr.fill_value = 2 + assert arr.fill_value == 2 + + msg = "Allowing arbitrary scalar fill_value in SparseDtype is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + arr.fill_value = 3.1 + assert arr.fill_value == 3.1 + + arr.fill_value = np.nan + assert np.isnan(arr.fill_value) + + arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_) + arr.fill_value = True + assert arr.fill_value is True + + with tm.assert_produces_warning(FutureWarning, match=msg): + arr.fill_value = 0 + + arr.fill_value = np.nan + assert np.isnan(arr.fill_value) + + @pytest.mark.parametrize("val", [[1, 2, 3], np.array([1, 2]), (1, 2, 3)]) + def test_set_fill_invalid_non_scalar(self, val): + arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_) + msg = "fill_value must be a scalar" + + with pytest.raises(ValueError, match=msg): + arr.fill_value = val + + def test_copy(self, arr): + arr2 = arr.copy() + assert arr2.sp_values is not arr.sp_values + assert arr2.sp_index is arr.sp_index + + def test_values_asarray(self, arr_data, arr): + tm.assert_almost_equal(arr.to_dense(), arr_data) + + @pytest.mark.parametrize( + "data,shape,dtype", + [ + ([0, 0, 0, 0, 0], (5,), None), + ([], (0,), None), + ([0], (1,), None), + (["A", "A", np.nan, "B"], (4,), object), + ], + ) + def test_shape(self, data, shape, dtype): + # GH 21126 + out = SparseArray(data, dtype=dtype) + assert out.shape == shape + + @pytest.mark.parametrize( + "vals", + [ + [np.nan, np.nan, np.nan, np.nan, np.nan], + [1, np.nan, np.nan, 3, np.nan], + [1, np.nan, 0, 3, 0], + ], + ) + @pytest.mark.parametrize("fill_value", [None, 0]) + def test_dense_repr(self, vals, fill_value): + vals = np.array(vals) + arr = SparseArray(vals, fill_value=fill_value) + + res = arr.to_dense() + tm.assert_numpy_array_equal(res, vals) + + @pytest.mark.parametrize("fix", ["arr", "zarr"]) + def test_pickle(self, fix, request): + obj = request.getfixturevalue(fix) + unpickled = tm.round_trip_pickle(obj) + tm.assert_sp_array_equal(unpickled, obj) + + def test_generator_warnings(self): + sp_arr = SparseArray([1, 2, 3]) + with tm.assert_produces_warning(None): + for _ in sp_arr: + pass + + def test_where_retain_fill_value(self): + # GH#45691 don't lose fill_value on _where + arr = SparseArray([np.nan, 1.0], fill_value=0) + + mask = np.array([True, False]) + + res = arr._where(~mask, 1) + exp = SparseArray([1, 1.0], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + ser = pd.Series(arr) + res = ser.where(~mask, 1) + tm.assert_series_equal(res, pd.Series(exp)) + + def test_fillna(self): + s = SparseArray([1, np.nan, np.nan, 3, np.nan]) + res = s.fillna(-1) + exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1, dtype=np.float64) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([1, -1, -1, 3, -1], fill_value=0, dtype=np.float64) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([1, np.nan, 0, 3, 0]) + res = s.fillna(-1) + exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1, dtype=np.float64) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([1, -1, 0, 3, 0], fill_value=0, dtype=np.float64) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([np.nan, np.nan, np.nan, np.nan]) + res = s.fillna(-1) + exp = SparseArray([-1, -1, -1, -1], fill_value=-1, dtype=np.float64) + tm.assert_sp_array_equal(res, exp) + + s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0) + res = s.fillna(-1) + exp = SparseArray([-1, -1, -1, -1], fill_value=0, dtype=np.float64) + tm.assert_sp_array_equal(res, exp) + + # float dtype's fill_value is np.nan, replaced by -1 + s = SparseArray([0.0, 0.0, 0.0, 0.0]) + res = s.fillna(-1) + exp = SparseArray([0.0, 0.0, 0.0, 0.0], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + # int dtype shouldn't have missing. No changes. + s = SparseArray([0, 0, 0, 0]) + assert s.dtype == SparseDtype(np.int64) + assert s.fill_value == 0 + res = s.fillna(-1) + tm.assert_sp_array_equal(res, s) + + s = SparseArray([0, 0, 0, 0], fill_value=0) + assert s.dtype == SparseDtype(np.int64) + assert s.fill_value == 0 + res = s.fillna(-1) + exp = SparseArray([0, 0, 0, 0], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + # fill_value can be nan if there is no missing hole. + # only fill_value will be changed + s = SparseArray([0, 0, 0, 0], fill_value=np.nan) + assert s.dtype == SparseDtype(np.int64, fill_value=np.nan) + assert np.isnan(s.fill_value) + res = s.fillna(-1) + exp = SparseArray([0, 0, 0, 0], fill_value=-1) + tm.assert_sp_array_equal(res, exp) + + def test_fillna_overlap(self): + s = SparseArray([1, np.nan, np.nan, 3, np.nan]) + # filling with existing value doesn't replace existing value with + # fill_value, i.e. existing 3 remains in sp_values + res = s.fillna(3) + exp = np.array([1, 3, 3, 3, 3], dtype=np.float64) + tm.assert_numpy_array_equal(res.to_dense(), exp) + + s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0) + res = s.fillna(3) + exp = SparseArray([1, 3, 3, 3, 3], fill_value=0, dtype=np.float64) + tm.assert_sp_array_equal(res, exp) + + def test_nonzero(self): + # Tests regression #21172. + sa = SparseArray([float("nan"), float("nan"), 1, 0, 0, 2, 0, 0, 0, 3, 0, 0]) + expected = np.array([2, 5, 9], dtype=np.int32) + (result,) = sa.nonzero() + tm.assert_numpy_array_equal(expected, result) + + sa = SparseArray([0, 0, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0]) + (result,) = sa.nonzero() + tm.assert_numpy_array_equal(expected, result) + + +class TestSparseArrayAnalytics: + @pytest.mark.parametrize( + "data,expected", + [ + ( + np.array([1, 2, 3, 4, 5], dtype=float), # non-null data + SparseArray(np.array([1.0, 3.0, 6.0, 10.0, 15.0])), + ), + ( + np.array([1, 2, np.nan, 4, 5], dtype=float), # null data + SparseArray(np.array([1.0, 3.0, np.nan, 7.0, 12.0])), + ), + ], + ) + @pytest.mark.parametrize("numpy", [True, False]) + def test_cumsum(self, data, expected, numpy): + cumsum = np.cumsum if numpy else lambda s: s.cumsum() + + out = cumsum(SparseArray(data)) + tm.assert_sp_array_equal(out, expected) + + out = cumsum(SparseArray(data, fill_value=np.nan)) + tm.assert_sp_array_equal(out, expected) + + out = cumsum(SparseArray(data, fill_value=2)) + tm.assert_sp_array_equal(out, expected) + + if numpy: # numpy compatibility checks. + msg = "the 'dtype' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.cumsum(SparseArray(data), dtype=np.int64) + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.cumsum(SparseArray(data), out=out) + else: + axis = 1 # SparseArray currently 1-D, so only axis = 0 is valid. + msg = re.escape(f"axis(={axis}) out of bounds") + with pytest.raises(ValueError, match=msg): + SparseArray(data).cumsum(axis=axis) + + def test_ufunc(self): + # GH 13853 make sure ufunc is applied to fill_value + sparse = SparseArray([1, np.nan, 2, np.nan, -2]) + result = SparseArray([1, np.nan, 2, np.nan, 2]) + tm.assert_sp_array_equal(abs(sparse), result) + tm.assert_sp_array_equal(np.abs(sparse), result) + + sparse = SparseArray([1, -1, 2, -2], fill_value=1) + result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index, fill_value=1) + tm.assert_sp_array_equal(abs(sparse), result) + tm.assert_sp_array_equal(np.abs(sparse), result) + + sparse = SparseArray([1, -1, 2, -2], fill_value=-1) + exp = SparseArray([1, 1, 2, 2], fill_value=1) + tm.assert_sp_array_equal(abs(sparse), exp) + tm.assert_sp_array_equal(np.abs(sparse), exp) + + sparse = SparseArray([1, np.nan, 2, np.nan, -2]) + result = SparseArray(np.sin([1, np.nan, 2, np.nan, -2])) + tm.assert_sp_array_equal(np.sin(sparse), result) + + sparse = SparseArray([1, -1, 2, -2], fill_value=1) + result = SparseArray(np.sin([1, -1, 2, -2]), fill_value=np.sin(1)) + tm.assert_sp_array_equal(np.sin(sparse), result) + + sparse = SparseArray([1, -1, 0, -2], fill_value=0) + result = SparseArray(np.sin([1, -1, 0, -2]), fill_value=np.sin(0)) + tm.assert_sp_array_equal(np.sin(sparse), result) + + def test_ufunc_args(self): + # GH 13853 make sure ufunc is applied to fill_value, including its arg + sparse = SparseArray([1, np.nan, 2, np.nan, -2]) + result = SparseArray([2, np.nan, 3, np.nan, -1]) + tm.assert_sp_array_equal(np.add(sparse, 1), result) + + sparse = SparseArray([1, -1, 2, -2], fill_value=1) + result = SparseArray([2, 0, 3, -1], fill_value=2) + tm.assert_sp_array_equal(np.add(sparse, 1), result) + + sparse = SparseArray([1, -1, 0, -2], fill_value=0) + result = SparseArray([2, 0, 1, -1], fill_value=1) + tm.assert_sp_array_equal(np.add(sparse, 1), result) + + @pytest.mark.parametrize("fill_value", [0.0, np.nan]) + def test_modf(self, fill_value): + # https://github.com/pandas-dev/pandas/issues/26946 + sparse = SparseArray([fill_value] * 10 + [1.1, 2.2], fill_value=fill_value) + r1, r2 = np.modf(sparse) + e1, e2 = np.modf(np.asarray(sparse)) + tm.assert_sp_array_equal(r1, SparseArray(e1, fill_value=fill_value)) + tm.assert_sp_array_equal(r2, SparseArray(e2, fill_value=fill_value)) + + def test_nbytes_integer(self): + arr = SparseArray([1, 0, 0, 0, 2], kind="integer") + result = arr.nbytes + # (2 * 8) + 2 * 4 + assert result == 24 + + def test_nbytes_block(self): + arr = SparseArray([1, 2, 0, 0, 0], kind="block") + result = arr.nbytes + # (2 * 8) + 4 + 4 + # sp_values, blocs, blengths + assert result == 24 + + def test_asarray_datetime64(self): + s = SparseArray(pd.to_datetime(["2012", None, None, "2013"])) + np.asarray(s) + + def test_density(self): + arr = SparseArray([0, 1]) + assert arr.density == 0.5 + + def test_npoints(self): + arr = SparseArray([0, 1]) + assert arr.npoints == 1 + + +def test_setting_fill_value_fillna_still_works(): + # This is why letting users update fill_value / dtype is bad + # astype has the same problem. + arr = SparseArray([1.0, np.nan, 1.0], fill_value=0.0) + arr.fill_value = np.nan + result = arr.isna() + # Can't do direct comparison, since the sp_index will be different + # So let's convert to ndarray and check there. + result = np.asarray(result) + + expected = np.array([False, True, False]) + tm.assert_numpy_array_equal(result, expected) + + +def test_setting_fill_value_updates(): + arr = SparseArray([0.0, np.nan], fill_value=0) + arr.fill_value = np.nan + # use private constructor to get the index right + # otherwise both nans would be un-stored. + expected = SparseArray._simple_new( + sparse_array=np.array([np.nan]), + sparse_index=IntIndex(2, [1]), + dtype=SparseDtype(float, np.nan), + ) + tm.assert_sp_array_equal(arr, expected) + + +@pytest.mark.parametrize( + "arr,fill_value,loc", + [ + ([None, 1, 2], None, 0), + ([0, None, 2], None, 1), + ([0, 1, None], None, 2), + ([0, 1, 1, None, None], None, 3), + ([1, 1, 1, 2], None, -1), + ([], None, -1), + ([None, 1, 0, 0, None, 2], None, 0), + ([None, 1, 0, 0, None, 2], 1, 1), + ([None, 1, 0, 0, None, 2], 2, 5), + ([None, 1, 0, 0, None, 2], 3, -1), + ([None, 0, 0, 1, 2, 1], 0, 1), + ([None, 0, 0, 1, 2, 1], 1, 3), + ], +) +def test_first_fill_value_loc(arr, fill_value, loc): + result = SparseArray(arr, fill_value=fill_value)._first_fill_value_loc() + assert result == loc + + +@pytest.mark.parametrize( + "arr", + [ + [1, 2, np.nan, np.nan], + [1, np.nan, 2, np.nan], + [1, 2, np.nan], + [np.nan, 1, 0, 0, np.nan, 2], + [np.nan, 0, 0, 1, 2, 1], + ], +) +@pytest.mark.parametrize("fill_value", [np.nan, 0, 1]) +def test_unique_na_fill(arr, fill_value): + a = SparseArray(arr, fill_value=fill_value).unique() + b = pd.Series(arr).unique() + assert isinstance(a, SparseArray) + a = np.asarray(a) + tm.assert_numpy_array_equal(a, b) + + +def test_unique_all_sparse(): + # https://github.com/pandas-dev/pandas/issues/23168 + arr = SparseArray([0, 0]) + result = arr.unique() + expected = SparseArray([0]) + tm.assert_sp_array_equal(result, expected) + + +def test_map(): + arr = SparseArray([0, 1, 2]) + expected = SparseArray([10, 11, 12], fill_value=10) + + # dict + result = arr.map({0: 10, 1: 11, 2: 12}) + tm.assert_sp_array_equal(result, expected) + + # series + result = arr.map(pd.Series({0: 10, 1: 11, 2: 12})) + tm.assert_sp_array_equal(result, expected) + + # function + result = arr.map(pd.Series({0: 10, 1: 11, 2: 12})) + expected = SparseArray([10, 11, 12], fill_value=10) + tm.assert_sp_array_equal(result, expected) + + +def test_map_missing(): + arr = SparseArray([0, 1, 2]) + expected = SparseArray([10, 11, None], fill_value=10) + + result = arr.map({0: 10, 1: 11}) + tm.assert_sp_array_equal(result, expected) + + +@pytest.mark.parametrize("fill_value", [np.nan, 1]) +def test_dropna(fill_value): + # GH-28287 + arr = SparseArray([np.nan, 1], fill_value=fill_value) + exp = SparseArray([1.0], fill_value=fill_value) + tm.assert_sp_array_equal(arr.dropna(), exp) + + df = pd.DataFrame({"a": [0, 1], "b": arr}) + expected_df = pd.DataFrame({"a": [1], "b": exp}, index=pd.Index([1])) + tm.assert_equal(df.dropna(), expected_df) + + +def test_drop_duplicates_fill_value(): + # GH 11726 + df = pd.DataFrame(np.zeros((5, 5))).apply(lambda x: SparseArray(x, fill_value=0)) + result = df.drop_duplicates() + expected = pd.DataFrame({i: SparseArray([0.0], fill_value=0) for i in range(5)}) + tm.assert_frame_equal(result, expected) + + +def test_zero_sparse_column(): + # GH 27781 + df1 = pd.DataFrame({"A": SparseArray([0, 0, 0]), "B": [1, 2, 3]}) + df2 = pd.DataFrame({"A": SparseArray([0, 1, 0]), "B": [1, 2, 3]}) + result = df1.loc[df1["B"] != 2] + expected = df2.loc[df2["B"] != 2] + tm.assert_frame_equal(result, expected) + + expected = pd.DataFrame({"A": SparseArray([0, 0]), "B": [1, 3]}, index=[0, 2]) + tm.assert_frame_equal(result, expected) + + +def test_array_interface(arr_data, arr): + # https://github.com/pandas-dev/pandas/pull/60046 + result = np.asarray(arr) + tm.assert_numpy_array_equal(result, arr_data) + + # it always gives a copy by default + result_copy1 = np.asarray(arr) + result_copy2 = np.asarray(arr) + assert not np.may_share_memory(result_copy1, result_copy2) + + # or with explicit copy=True + result_copy1 = np.array(arr, copy=True) + result_copy2 = np.array(arr, copy=True) + assert not np.may_share_memory(result_copy1, result_copy2) + + if not np_version_gt2: + # copy=False semantics are only supported in NumPy>=2. + return + + msg = "Starting with NumPy 2.0, the behavior of the 'copy' keyword has changed" + with tm.assert_produces_warning(FutureWarning, match=msg): + np.array(arr, copy=False) + + # except when there are actually no sparse filled values + arr2 = SparseArray(np.array([1, 2, 3])) + result_nocopy1 = np.array(arr2, copy=False) + result_nocopy2 = np.array(arr2, copy=False) + assert np.may_share_memory(result_nocopy1, result_nocopy2) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_astype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..e6e4a11a0f5ab4056606a127f8ed61ee3a4456a8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_astype.py @@ -0,0 +1,133 @@ +import numpy as np +import pytest + +from pandas._libs.sparse import IntIndex + +from pandas import ( + SparseDtype, + Timestamp, +) +import pandas._testing as tm +from pandas.core.arrays.sparse import SparseArray + + +class TestAstype: + def test_astype(self): + # float -> float + arr = SparseArray([None, None, 0, 2]) + result = arr.astype("Sparse[float32]") + expected = SparseArray([None, None, 0, 2], dtype=np.dtype("float32")) + tm.assert_sp_array_equal(result, expected) + + dtype = SparseDtype("float64", fill_value=0) + result = arr.astype(dtype) + expected = SparseArray._simple_new( + np.array([0.0, 2.0], dtype=dtype.subtype), IntIndex(4, [2, 3]), dtype + ) + tm.assert_sp_array_equal(result, expected) + + dtype = SparseDtype("int64", 0) + result = arr.astype(dtype) + expected = SparseArray._simple_new( + np.array([0, 2], dtype=np.int64), IntIndex(4, [2, 3]), dtype + ) + tm.assert_sp_array_equal(result, expected) + + arr = SparseArray([0, np.nan, 0, 1], fill_value=0) + with pytest.raises(ValueError, match="NA"): + arr.astype("Sparse[i8]") + + def test_astype_bool(self): + a = SparseArray([1, 0, 0, 1], dtype=SparseDtype(int, 0)) + result = a.astype(bool) + expected = np.array([1, 0, 0, 1], dtype=bool) + tm.assert_numpy_array_equal(result, expected) + + # update fill value + result = a.astype(SparseDtype(bool, False)) + expected = SparseArray( + [True, False, False, True], dtype=SparseDtype(bool, False) + ) + tm.assert_sp_array_equal(result, expected) + + def test_astype_all(self, any_real_numpy_dtype): + vals = np.array([1, 2, 3]) + arr = SparseArray(vals, fill_value=1) + typ = np.dtype(any_real_numpy_dtype) + res = arr.astype(typ) + tm.assert_numpy_array_equal(res, vals.astype(any_real_numpy_dtype)) + + @pytest.mark.parametrize( + "arr, dtype, expected", + [ + ( + SparseArray([0, 1]), + "float", + SparseArray([0.0, 1.0], dtype=SparseDtype(float, 0.0)), + ), + (SparseArray([0, 1]), bool, SparseArray([False, True])), + ( + SparseArray([0, 1], fill_value=1), + bool, + SparseArray([False, True], dtype=SparseDtype(bool, True)), + ), + pytest.param( + SparseArray([0, 1]), + "datetime64[ns]", + SparseArray( + np.array([0, 1], dtype="datetime64[ns]"), + dtype=SparseDtype("datetime64[ns]", Timestamp("1970")), + ), + ), + ( + SparseArray([0, 1, 10]), + np.str_, + SparseArray(["0", "1", "10"], dtype=SparseDtype(np.str_, "0")), + ), + (SparseArray(["10", "20"]), float, SparseArray([10.0, 20.0])), + ( + SparseArray([0, 1, 0]), + object, + SparseArray([0, 1, 0], dtype=SparseDtype(object, 0)), + ), + ], + ) + def test_astype_more(self, arr, dtype, expected): + result = arr.astype(arr.dtype.update_dtype(dtype)) + tm.assert_sp_array_equal(result, expected) + + def test_astype_nan_raises(self): + arr = SparseArray([1.0, np.nan]) + with pytest.raises(ValueError, match="Cannot convert non-finite"): + arr.astype(int) + + def test_astype_copy_false(self): + # GH#34456 bug caused by using .view instead of .astype in astype_nansafe + arr = SparseArray([1, 2, 3]) + + dtype = SparseDtype(float, 0) + + result = arr.astype(dtype, copy=False) + expected = SparseArray([1.0, 2.0, 3.0], fill_value=0.0) + tm.assert_sp_array_equal(result, expected) + + def test_astype_dt64_to_int64(self): + # GH#49631 match non-sparse behavior + values = np.array(["NaT", "2016-01-02", "2016-01-03"], dtype="M8[ns]") + + arr = SparseArray(values) + result = arr.astype("int64") + expected = values.astype("int64") + tm.assert_numpy_array_equal(result, expected) + + # we should also be able to cast to equivalent Sparse[int64] + dtype_int64 = SparseDtype("int64", np.iinfo(np.int64).min) + result2 = arr.astype(dtype_int64) + tm.assert_numpy_array_equal(result2.to_numpy(), expected) + + # GH#50087 we should match the non-sparse behavior regardless of + # if we have a fill_value other than NaT + dtype = SparseDtype("datetime64[ns]", values[1]) + arr3 = SparseArray(values, dtype=dtype) + result3 = arr3.astype("int64") + tm.assert_numpy_array_equal(result3, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_combine_concat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_combine_concat.py new file mode 100644 index 0000000000000000000000000000000000000000..0f09af269148bc6fec712b9b1df63cca6f44d248 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_combine_concat.py @@ -0,0 +1,62 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays.sparse import SparseArray + + +class TestSparseArrayConcat: + @pytest.mark.parametrize("kind", ["integer", "block"]) + def test_basic(self, kind): + a = SparseArray([1, 0, 0, 2], kind=kind) + b = SparseArray([1, 0, 2, 2], kind=kind) + + result = SparseArray._concat_same_type([a, b]) + # Can't make any assertions about the sparse index itself + # since we aren't don't merge sparse blocs across arrays + # in to_concat + expected = np.array([1, 2, 1, 2, 2], dtype="int64") + tm.assert_numpy_array_equal(result.sp_values, expected) + assert result.kind == kind + + @pytest.mark.parametrize("kind", ["integer", "block"]) + def test_uses_first_kind(self, kind): + other = "integer" if kind == "block" else "block" + a = SparseArray([1, 0, 0, 2], kind=kind) + b = SparseArray([1, 0, 2, 2], kind=other) + + result = SparseArray._concat_same_type([a, b]) + expected = np.array([1, 2, 1, 2, 2], dtype="int64") + tm.assert_numpy_array_equal(result.sp_values, expected) + assert result.kind == kind + + +@pytest.mark.parametrize( + "other, expected_dtype", + [ + # compatible dtype -> preserve sparse + (pd.Series([3, 4, 5], dtype="int64"), pd.SparseDtype("int64", 0)), + # (pd.Series([3, 4, 5], dtype="Int64"), pd.SparseDtype("int64", 0)), + # incompatible dtype -> Sparse[common dtype] + (pd.Series([1.5, 2.5, 3.5], dtype="float64"), pd.SparseDtype("float64", 0)), + # incompatible dtype -> Sparse[object] dtype + (pd.Series(["a", "b", "c"], dtype=object), pd.SparseDtype(object, 0)), + # categorical with compatible categories -> dtype of the categories + (pd.Series([3, 4, 5], dtype="category"), np.dtype("int64")), + (pd.Series([1.5, 2.5, 3.5], dtype="category"), np.dtype("float64")), + # categorical with incompatible categories -> object dtype + (pd.Series(["a", "b", "c"], dtype="category"), np.dtype(object)), + ], +) +def test_concat_with_non_sparse(other, expected_dtype): + # https://github.com/pandas-dev/pandas/issues/34336 + s_sparse = pd.Series([1, 0, 2], dtype=pd.SparseDtype("int64", 0)) + + result = pd.concat([s_sparse, other], ignore_index=True) + expected = pd.Series(list(s_sparse) + list(other)).astype(expected_dtype) + tm.assert_series_equal(result, expected) + + result = pd.concat([other, s_sparse], ignore_index=True) + expected = pd.Series(list(other) + list(s_sparse)).astype(expected_dtype) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_constructors.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..2831c8abdaf137b6454ea8f73bff7e94a3ec1b2b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_constructors.py @@ -0,0 +1,285 @@ +import numpy as np +import pytest + +from pandas._libs.sparse import IntIndex + +import pandas as pd +from pandas import ( + SparseDtype, + isna, +) +import pandas._testing as tm +from pandas.core.arrays.sparse import SparseArray + + +class TestConstructors: + def test_constructor_dtype(self): + arr = SparseArray([np.nan, 1, 2, np.nan]) + assert arr.dtype == SparseDtype(np.float64, np.nan) + assert arr.dtype.subtype == np.float64 + assert np.isnan(arr.fill_value) + + arr = SparseArray([np.nan, 1, 2, np.nan], fill_value=0) + assert arr.dtype == SparseDtype(np.float64, 0) + assert arr.fill_value == 0 + + arr = SparseArray([0, 1, 2, 4], dtype=np.float64) + assert arr.dtype == SparseDtype(np.float64, np.nan) + assert np.isnan(arr.fill_value) + + arr = SparseArray([0, 1, 2, 4], dtype=np.int64) + assert arr.dtype == SparseDtype(np.int64, 0) + assert arr.fill_value == 0 + + arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=np.int64) + assert arr.dtype == SparseDtype(np.int64, 0) + assert arr.fill_value == 0 + + arr = SparseArray([0, 1, 2, 4], dtype=None) + assert arr.dtype == SparseDtype(np.int64, 0) + assert arr.fill_value == 0 + + arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=None) + assert arr.dtype == SparseDtype(np.int64, 0) + assert arr.fill_value == 0 + + def test_constructor_dtype_str(self): + result = SparseArray([1, 2, 3], dtype="int") + expected = SparseArray([1, 2, 3], dtype=int) + tm.assert_sp_array_equal(result, expected) + + def test_constructor_sparse_dtype(self): + result = SparseArray([1, 0, 0, 1], dtype=SparseDtype("int64", -1)) + expected = SparseArray([1, 0, 0, 1], fill_value=-1, dtype=np.int64) + tm.assert_sp_array_equal(result, expected) + assert result.sp_values.dtype == np.dtype("int64") + + def test_constructor_sparse_dtype_str(self): + result = SparseArray([1, 0, 0, 1], dtype="Sparse[int32]") + expected = SparseArray([1, 0, 0, 1], dtype=np.int32) + tm.assert_sp_array_equal(result, expected) + assert result.sp_values.dtype == np.dtype("int32") + + def test_constructor_object_dtype(self): + # GH#11856 + arr = SparseArray(["A", "A", np.nan, "B"], dtype=object) + assert arr.dtype == SparseDtype(object) + assert np.isnan(arr.fill_value) + + arr = SparseArray(["A", "A", np.nan, "B"], dtype=object, fill_value="A") + assert arr.dtype == SparseDtype(object, "A") + assert arr.fill_value == "A" + + def test_constructor_object_dtype_bool_fill(self): + # GH#17574 + data = [False, 0, 100.0, 0.0] + arr = SparseArray(data, dtype=object, fill_value=False) + assert arr.dtype == SparseDtype(object, False) + assert arr.fill_value is False + arr_expected = np.array(data, dtype=object) + it = (type(x) == type(y) and x == y for x, y in zip(arr, arr_expected)) + assert np.fromiter(it, dtype=np.bool_).all() + + @pytest.mark.parametrize("dtype", [SparseDtype(int, 0), int]) + def test_constructor_na_dtype(self, dtype): + with pytest.raises(ValueError, match="Cannot convert"): + SparseArray([0, 1, np.nan], dtype=dtype) + + def test_constructor_warns_when_losing_timezone(self): + # GH#32501 warn when losing timezone information + dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific") + + expected = SparseArray(np.asarray(dti, dtype="datetime64[ns]")) + + with tm.assert_produces_warning(UserWarning): + result = SparseArray(dti) + + tm.assert_sp_array_equal(result, expected) + + with tm.assert_produces_warning(UserWarning): + result = SparseArray(pd.Series(dti)) + + tm.assert_sp_array_equal(result, expected) + + def test_constructor_spindex_dtype(self): + arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2])) + # TODO: actionable? + # XXX: Behavior change: specifying SparseIndex no longer changes the + # fill_value + expected = SparseArray([0, 1, 2, 0], kind="integer") + tm.assert_sp_array_equal(arr, expected) + assert arr.dtype == SparseDtype(np.int64) + assert arr.fill_value == 0 + + arr = SparseArray( + data=[1, 2, 3], + sparse_index=IntIndex(4, [1, 2, 3]), + dtype=np.int64, + fill_value=0, + ) + exp = SparseArray([0, 1, 2, 3], dtype=np.int64, fill_value=0) + tm.assert_sp_array_equal(arr, exp) + assert arr.dtype == SparseDtype(np.int64) + assert arr.fill_value == 0 + + arr = SparseArray( + data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=np.int64 + ) + exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=np.int64) + tm.assert_sp_array_equal(arr, exp) + assert arr.dtype == SparseDtype(np.int64) + assert arr.fill_value == 0 + + arr = SparseArray( + data=[1, 2, 3], + sparse_index=IntIndex(4, [1, 2, 3]), + dtype=None, + fill_value=0, + ) + exp = SparseArray([0, 1, 2, 3], dtype=None) + tm.assert_sp_array_equal(arr, exp) + assert arr.dtype == SparseDtype(np.int64) + assert arr.fill_value == 0 + + @pytest.mark.parametrize("sparse_index", [None, IntIndex(1, [0])]) + def test_constructor_spindex_dtype_scalar(self, sparse_index): + # scalar input + msg = "Constructing SparseArray with scalar data is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + arr = SparseArray(data=1, sparse_index=sparse_index, dtype=None) + exp = SparseArray([1], dtype=None) + tm.assert_sp_array_equal(arr, exp) + assert arr.dtype == SparseDtype(np.int64) + assert arr.fill_value == 0 + + with tm.assert_produces_warning(FutureWarning, match=msg): + arr = SparseArray(data=1, sparse_index=IntIndex(1, [0]), dtype=None) + exp = SparseArray([1], dtype=None) + tm.assert_sp_array_equal(arr, exp) + assert arr.dtype == SparseDtype(np.int64) + assert arr.fill_value == 0 + + def test_constructor_spindex_dtype_scalar_broadcasts(self): + arr = SparseArray( + data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=None + ) + exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None) + tm.assert_sp_array_equal(arr, exp) + assert arr.dtype == SparseDtype(np.int64) + assert arr.fill_value == 0 + + @pytest.mark.parametrize( + "data, fill_value", + [ + (np.array([1, 2]), 0), + (np.array([1.0, 2.0]), np.nan), + ([True, False], False), + ([pd.Timestamp("2017-01-01")], pd.NaT), + ], + ) + def test_constructor_inferred_fill_value(self, data, fill_value): + result = SparseArray(data).fill_value + + if isna(fill_value): + assert isna(result) + else: + assert result == fill_value + + @pytest.mark.parametrize("format", ["coo", "csc", "csr"]) + @pytest.mark.parametrize("size", [0, 10]) + def test_from_spmatrix(self, size, format): + sp_sparse = pytest.importorskip("scipy.sparse") + + mat = sp_sparse.random(size, 1, density=0.5, format=format) + result = SparseArray.from_spmatrix(mat) + + result = np.asarray(result) + expected = mat.toarray().ravel() + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("format", ["coo", "csc", "csr"]) + def test_from_spmatrix_including_explicit_zero(self, format): + sp_sparse = pytest.importorskip("scipy.sparse") + + mat = sp_sparse.random(10, 1, density=0.5, format=format) + mat.data[0] = 0 + result = SparseArray.from_spmatrix(mat) + + result = np.asarray(result) + expected = mat.toarray().ravel() + tm.assert_numpy_array_equal(result, expected) + + def test_from_spmatrix_raises(self): + sp_sparse = pytest.importorskip("scipy.sparse") + + mat = sp_sparse.eye(5, 4, format="csc") + + with pytest.raises(ValueError, match="not '4'"): + SparseArray.from_spmatrix(mat) + + def test_constructor_from_too_large_array(self): + with pytest.raises(TypeError, match="expected dimension <= 1 data"): + SparseArray(np.arange(10).reshape((2, 5))) + + def test_constructor_from_sparse(self): + zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0) + res = SparseArray(zarr) + assert res.fill_value == 0 + tm.assert_almost_equal(res.sp_values, zarr.sp_values) + + def test_constructor_copy(self): + arr_data = np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6]) + arr = SparseArray(arr_data) + + cp = SparseArray(arr, copy=True) + cp.sp_values[:3] = 0 + assert not (arr.sp_values[:3] == 0).any() + + not_copy = SparseArray(arr) + not_copy.sp_values[:3] = 0 + assert (arr.sp_values[:3] == 0).all() + + def test_constructor_bool(self): + # GH#10648 + data = np.array([False, False, True, True, False, False]) + arr = SparseArray(data, fill_value=False, dtype=bool) + + assert arr.dtype == SparseDtype(bool) + tm.assert_numpy_array_equal(arr.sp_values, np.array([True, True])) + # Behavior change: np.asarray densifies. + # tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr)) + tm.assert_numpy_array_equal(arr.sp_index.indices, np.array([2, 3], np.int32)) + + dense = arr.to_dense() + assert dense.dtype == bool + tm.assert_numpy_array_equal(dense, data) + + def test_constructor_bool_fill_value(self): + arr = SparseArray([True, False, True], dtype=None) + assert arr.dtype == SparseDtype(np.bool_) + assert not arr.fill_value + + arr = SparseArray([True, False, True], dtype=np.bool_) + assert arr.dtype == SparseDtype(np.bool_) + assert not arr.fill_value + + arr = SparseArray([True, False, True], dtype=np.bool_, fill_value=True) + assert arr.dtype == SparseDtype(np.bool_, True) + assert arr.fill_value + + def test_constructor_float32(self): + # GH#10648 + data = np.array([1.0, np.nan, 3], dtype=np.float32) + arr = SparseArray(data, dtype=np.float32) + + assert arr.dtype == SparseDtype(np.float32) + tm.assert_numpy_array_equal(arr.sp_values, np.array([1, 3], dtype=np.float32)) + # Behavior change: np.asarray densifies. + # tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr)) + tm.assert_numpy_array_equal( + arr.sp_index.indices, np.array([0, 2], dtype=np.int32) + ) + + dense = arr.to_dense() + assert dense.dtype == np.float32 + tm.assert_numpy_array_equal(dense, data) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_dtype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_dtype.py new file mode 100644 index 0000000000000000000000000000000000000000..149c28341ba3d8b83b490361f7cfb4f9df0994ea --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_dtype.py @@ -0,0 +1,224 @@ +import re +import warnings + +import numpy as np +import pytest + +import pandas as pd +from pandas import SparseDtype + + +@pytest.mark.parametrize( + "dtype, fill_value", + [ + ("int", 0), + ("float", np.nan), + ("bool", False), + ("object", np.nan), + ("datetime64[ns]", np.datetime64("NaT", "ns")), + ("timedelta64[ns]", np.timedelta64("NaT", "ns")), + ], +) +def test_inferred_dtype(dtype, fill_value): + sparse_dtype = SparseDtype(dtype) + result = sparse_dtype.fill_value + if pd.isna(fill_value): + assert pd.isna(result) and type(result) == type(fill_value) + else: + assert result == fill_value + + +def test_from_sparse_dtype(): + dtype = SparseDtype("float", 0) + result = SparseDtype(dtype) + assert result.fill_value == 0 + + +def test_from_sparse_dtype_fill_value(): + dtype = SparseDtype("int", 1) + result = SparseDtype(dtype, fill_value=2) + expected = SparseDtype("int", 2) + assert result == expected + + +@pytest.mark.parametrize( + "dtype, fill_value", + [ + ("int", None), + ("float", None), + ("bool", None), + ("object", None), + ("datetime64[ns]", None), + ("timedelta64[ns]", None), + ("int", np.nan), + ("float", 0), + ], +) +def test_equal(dtype, fill_value): + a = SparseDtype(dtype, fill_value) + b = SparseDtype(dtype, fill_value) + assert a == b + assert b == a + + +def test_nans_equal(): + a = SparseDtype(float, float("nan")) + b = SparseDtype(float, np.nan) + assert a == b + assert b == a + + +with warnings.catch_warnings(): + msg = "Allowing arbitrary scalar fill_value in SparseDtype is deprecated" + warnings.filterwarnings("ignore", msg, category=FutureWarning) + + tups = [ + (SparseDtype("float64"), SparseDtype("float32")), + (SparseDtype("float64"), SparseDtype("float64", 0)), + (SparseDtype("float64"), SparseDtype("datetime64[ns]", np.nan)), + (SparseDtype(int, pd.NaT), SparseDtype(float, pd.NaT)), + (SparseDtype("float64"), np.dtype("float64")), + ] + + +@pytest.mark.parametrize( + "a, b", + tups, +) +def test_not_equal(a, b): + assert a != b + + +def test_construct_from_string_raises(): + with pytest.raises( + TypeError, match="Cannot construct a 'SparseDtype' from 'not a dtype'" + ): + SparseDtype.construct_from_string("not a dtype") + + +@pytest.mark.parametrize( + "dtype, expected", + [ + (SparseDtype(int), True), + (SparseDtype(float), True), + (SparseDtype(bool), True), + (SparseDtype(object), False), + (SparseDtype(str), False), + ], +) +def test_is_numeric(dtype, expected): + assert dtype._is_numeric is expected + + +def test_str_uses_object(): + result = SparseDtype(str).subtype + assert result == np.dtype("object") + + +@pytest.mark.parametrize( + "string, expected", + [ + ("Sparse[float64]", SparseDtype(np.dtype("float64"))), + ("Sparse[float32]", SparseDtype(np.dtype("float32"))), + ("Sparse[int]", SparseDtype(np.dtype("int"))), + ("Sparse[str]", SparseDtype(np.dtype("str"))), + ("Sparse[datetime64[ns]]", SparseDtype(np.dtype("datetime64[ns]"))), + ("Sparse", SparseDtype(np.dtype("float"), np.nan)), + ], +) +def test_construct_from_string(string, expected): + result = SparseDtype.construct_from_string(string) + assert result == expected + + +@pytest.mark.parametrize( + "a, b, expected", + [ + (SparseDtype(float, 0.0), SparseDtype(np.dtype("float"), 0.0), True), + (SparseDtype(int, 0), SparseDtype(int, 0), True), + (SparseDtype(float, float("nan")), SparseDtype(float, np.nan), True), + (SparseDtype(float, 0), SparseDtype(float, np.nan), False), + (SparseDtype(int, 0.0), SparseDtype(float, 0.0), False), + ], +) +def test_hash_equal(a, b, expected): + result = a == b + assert result is expected + + result = hash(a) == hash(b) + assert result is expected + + +@pytest.mark.parametrize( + "string, expected", + [ + ("Sparse[int]", "int"), + ("Sparse[int, 0]", "int"), + ("Sparse[int64]", "int64"), + ("Sparse[int64, 0]", "int64"), + ("Sparse[datetime64[ns], 0]", "datetime64[ns]"), + ], +) +def test_parse_subtype(string, expected): + subtype, _ = SparseDtype._parse_subtype(string) + assert subtype == expected + + +@pytest.mark.parametrize( + "string", ["Sparse[int, 1]", "Sparse[float, 0.0]", "Sparse[bool, True]"] +) +def test_construct_from_string_fill_value_raises(string): + with pytest.raises(TypeError, match="fill_value in the string is not"): + SparseDtype.construct_from_string(string) + + +@pytest.mark.parametrize( + "original, dtype, expected", + [ + (SparseDtype(int, 0), float, SparseDtype(float, 0.0)), + (SparseDtype(int, 1), float, SparseDtype(float, 1.0)), + (SparseDtype(int, 1), np.str_, SparseDtype(object, "1")), + (SparseDtype(float, 1.5), int, SparseDtype(int, 1)), + ], +) +def test_update_dtype(original, dtype, expected): + result = original.update_dtype(dtype) + assert result == expected + + +@pytest.mark.parametrize( + "original, dtype, expected_error_msg", + [ + ( + SparseDtype(float, np.nan), + int, + re.escape("Cannot convert non-finite values (NA or inf) to integer"), + ), + ( + SparseDtype(str, "abc"), + int, + r"invalid literal for int\(\) with base 10: ('abc'|np\.str_\('abc'\))", + ), + ], +) +def test_update_dtype_raises(original, dtype, expected_error_msg): + with pytest.raises(ValueError, match=expected_error_msg): + original.update_dtype(dtype) + + +def test_repr(): + # GH-34352 + result = str(SparseDtype("int64", fill_value=0)) + expected = "Sparse[int64, 0]" + assert result == expected + + result = str(SparseDtype(object, fill_value="0")) + expected = "Sparse[object, '0']" + assert result == expected + + +def test_sparse_dtype_subtype_must_be_numpy_dtype(): + # GH#53160 + msg = "SparseDtype subtype must be a numpy dtype" + with pytest.raises(TypeError, match=msg): + SparseDtype("category", fill_value="c") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..60029ac06ddb47ef0ad4ee35a75fd09ca12f7f53 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_indexing.py @@ -0,0 +1,302 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import SparseDtype +import pandas._testing as tm +from pandas.core.arrays.sparse import SparseArray + + +@pytest.fixture +def arr_data(): + return np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6]) + + +@pytest.fixture +def arr(arr_data): + return SparseArray(arr_data) + + +class TestGetitem: + def test_getitem(self, arr): + dense = arr.to_dense() + for i, value in enumerate(arr): + tm.assert_almost_equal(value, dense[i]) + tm.assert_almost_equal(arr[-i], dense[-i]) + + def test_getitem_arraylike_mask(self, arr): + arr = SparseArray([0, 1, 2]) + result = arr[[True, False, True]] + expected = SparseArray([0, 2]) + tm.assert_sp_array_equal(result, expected) + + @pytest.mark.parametrize( + "slc", + [ + np.s_[:], + np.s_[1:10], + np.s_[1:100], + np.s_[10:1], + np.s_[:-3], + np.s_[-5:-4], + np.s_[:-12], + np.s_[-12:], + np.s_[2:], + np.s_[2::3], + np.s_[::2], + np.s_[::-1], + np.s_[::-2], + np.s_[1:6:2], + np.s_[:-6:-2], + ], + ) + @pytest.mark.parametrize( + "as_dense", [[np.nan] * 10, [1] * 10, [np.nan] * 5 + [1] * 5, []] + ) + def test_getslice(self, slc, as_dense): + as_dense = np.array(as_dense) + arr = SparseArray(as_dense) + + result = arr[slc] + expected = SparseArray(as_dense[slc]) + + tm.assert_sp_array_equal(result, expected) + + def test_getslice_tuple(self): + dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0]) + + sparse = SparseArray(dense) + res = sparse[(slice(4, None),)] + exp = SparseArray(dense[4:]) + tm.assert_sp_array_equal(res, exp) + + sparse = SparseArray(dense, fill_value=0) + res = sparse[(slice(4, None),)] + exp = SparseArray(dense[4:], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + msg = "too many indices for array" + with pytest.raises(IndexError, match=msg): + sparse[4:, :] + + with pytest.raises(IndexError, match=msg): + # check numpy compat + dense[4:, :] + + def test_boolean_slice_empty(self): + arr = SparseArray([0, 1, 2]) + res = arr[[False, False, False]] + assert res.dtype == arr.dtype + + def test_getitem_bool_sparse_array(self, arr): + # GH 23122 + spar_bool = SparseArray([False, True] * 5, dtype=np.bool_, fill_value=True) + exp = SparseArray([np.nan, 2, np.nan, 5, 6]) + tm.assert_sp_array_equal(arr[spar_bool], exp) + + spar_bool = ~spar_bool + res = arr[spar_bool] + exp = SparseArray([np.nan, 1, 3, 4, np.nan]) + tm.assert_sp_array_equal(res, exp) + + spar_bool = SparseArray( + [False, True, np.nan] * 3, dtype=np.bool_, fill_value=np.nan + ) + res = arr[spar_bool] + exp = SparseArray([np.nan, 3, 5]) + tm.assert_sp_array_equal(res, exp) + + def test_getitem_bool_sparse_array_as_comparison(self): + # GH 45110 + arr = SparseArray([1, 2, 3, 4, np.nan, np.nan], fill_value=np.nan) + res = arr[arr > 2] + exp = SparseArray([3.0, 4.0], fill_value=np.nan) + tm.assert_sp_array_equal(res, exp) + + def test_get_item(self, arr): + zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0) + + assert np.isnan(arr[1]) + assert arr[2] == 1 + assert arr[7] == 5 + + assert zarr[0] == 0 + assert zarr[2] == 1 + assert zarr[7] == 5 + + errmsg = "must be an integer between -10 and 10" + + with pytest.raises(IndexError, match=errmsg): + arr[11] + + with pytest.raises(IndexError, match=errmsg): + arr[-11] + + assert arr[-1] == arr[len(arr) - 1] + + +class TestSetitem: + def test_set_item(self, arr_data): + arr = SparseArray(arr_data).copy() + + def setitem(): + arr[5] = 3 + + def setslice(): + arr[1:5] = 2 + + with pytest.raises(TypeError, match="assignment via setitem"): + setitem() + + with pytest.raises(TypeError, match="assignment via setitem"): + setslice() + + +class TestTake: + def test_take_scalar_raises(self, arr): + msg = "'indices' must be an array, not a scalar '2'." + with pytest.raises(ValueError, match=msg): + arr.take(2) + + def test_take(self, arr_data, arr): + exp = SparseArray(np.take(arr_data, [2, 3])) + tm.assert_sp_array_equal(arr.take([2, 3]), exp) + + exp = SparseArray(np.take(arr_data, [0, 1, 2])) + tm.assert_sp_array_equal(arr.take([0, 1, 2]), exp) + + def test_take_all_empty(self): + sparse = pd.array([0, 0], dtype=SparseDtype("int64")) + result = sparse.take([0, 1], allow_fill=True, fill_value=np.nan) + tm.assert_sp_array_equal(sparse, result) + + def test_take_different_fill_value(self): + # Take with a different fill value shouldn't overwrite the original + sparse = pd.array([0.0], dtype=SparseDtype("float64", fill_value=0.0)) + result = sparse.take([0, -1], allow_fill=True, fill_value=np.nan) + expected = pd.array([0, np.nan], dtype=sparse.dtype) + tm.assert_sp_array_equal(expected, result) + + def test_take_fill_value(self): + data = np.array([1, np.nan, 0, 3, 0]) + sparse = SparseArray(data, fill_value=0) + + exp = SparseArray(np.take(data, [0]), fill_value=0) + tm.assert_sp_array_equal(sparse.take([0]), exp) + + exp = SparseArray(np.take(data, [1, 3, 4]), fill_value=0) + tm.assert_sp_array_equal(sparse.take([1, 3, 4]), exp) + + def test_take_negative(self, arr_data, arr): + exp = SparseArray(np.take(arr_data, [-1])) + tm.assert_sp_array_equal(arr.take([-1]), exp) + + exp = SparseArray(np.take(arr_data, [-4, -3, -2])) + tm.assert_sp_array_equal(arr.take([-4, -3, -2]), exp) + + def test_bad_take(self, arr): + with pytest.raises(IndexError, match="bounds"): + arr.take([11]) + + def test_take_filling(self): + # similar tests as GH 12631 + sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4]) + result = sparse.take(np.array([1, 0, -1])) + expected = SparseArray([np.nan, np.nan, 4]) + tm.assert_sp_array_equal(result, expected) + + # TODO: actionable? + # XXX: test change: fill_value=True -> allow_fill=True + result = sparse.take(np.array([1, 0, -1]), allow_fill=True) + expected = SparseArray([np.nan, np.nan, np.nan]) + tm.assert_sp_array_equal(result, expected) + + # allow_fill=False + result = sparse.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) + expected = SparseArray([np.nan, np.nan, 4]) + tm.assert_sp_array_equal(result, expected) + + msg = "Invalid value in 'indices'" + with pytest.raises(ValueError, match=msg): + sparse.take(np.array([1, 0, -2]), allow_fill=True) + + with pytest.raises(ValueError, match=msg): + sparse.take(np.array([1, 0, -5]), allow_fill=True) + + msg = "out of bounds value in 'indices'" + with pytest.raises(IndexError, match=msg): + sparse.take(np.array([1, -6])) + with pytest.raises(IndexError, match=msg): + sparse.take(np.array([1, 5])) + with pytest.raises(IndexError, match=msg): + sparse.take(np.array([1, 5]), allow_fill=True) + + def test_take_filling_fill_value(self): + # same tests as GH#12631 + sparse = SparseArray([np.nan, 0, 1, 0, 4], fill_value=0) + result = sparse.take(np.array([1, 0, -1])) + expected = SparseArray([0, np.nan, 4], fill_value=0) + tm.assert_sp_array_equal(result, expected) + + # fill_value + result = sparse.take(np.array([1, 0, -1]), allow_fill=True) + # TODO: actionable? + # XXX: behavior change. + # the old way of filling self.fill_value doesn't follow EA rules. + # It's supposed to be self.dtype.na_value (nan in this case) + expected = SparseArray([0, np.nan, np.nan], fill_value=0) + tm.assert_sp_array_equal(result, expected) + + # allow_fill=False + result = sparse.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) + expected = SparseArray([0, np.nan, 4], fill_value=0) + tm.assert_sp_array_equal(result, expected) + + msg = "Invalid value in 'indices'." + with pytest.raises(ValueError, match=msg): + sparse.take(np.array([1, 0, -2]), allow_fill=True) + with pytest.raises(ValueError, match=msg): + sparse.take(np.array([1, 0, -5]), allow_fill=True) + + msg = "out of bounds value in 'indices'" + with pytest.raises(IndexError, match=msg): + sparse.take(np.array([1, -6])) + with pytest.raises(IndexError, match=msg): + sparse.take(np.array([1, 5])) + with pytest.raises(IndexError, match=msg): + sparse.take(np.array([1, 5]), fill_value=True) + + @pytest.mark.parametrize("kind", ["block", "integer"]) + def test_take_filling_all_nan(self, kind): + sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan], kind=kind) + result = sparse.take(np.array([1, 0, -1])) + expected = SparseArray([np.nan, np.nan, np.nan], kind=kind) + tm.assert_sp_array_equal(result, expected) + + result = sparse.take(np.array([1, 0, -1]), fill_value=True) + expected = SparseArray([np.nan, np.nan, np.nan], kind=kind) + tm.assert_sp_array_equal(result, expected) + + msg = "out of bounds value in 'indices'" + with pytest.raises(IndexError, match=msg): + sparse.take(np.array([1, -6])) + with pytest.raises(IndexError, match=msg): + sparse.take(np.array([1, 5])) + with pytest.raises(IndexError, match=msg): + sparse.take(np.array([1, 5]), fill_value=True) + + +class TestWhere: + def test_where_retain_fill_value(self): + # GH#45691 don't lose fill_value on _where + arr = SparseArray([np.nan, 1.0], fill_value=0) + + mask = np.array([True, False]) + + res = arr._where(~mask, 1) + exp = SparseArray([1, 1.0], fill_value=0) + tm.assert_sp_array_equal(res, exp) + + ser = pd.Series(arr) + res = ser.where(~mask, 1) + tm.assert_series_equal(res, pd.Series(exp)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_libsparse.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_libsparse.py new file mode 100644 index 0000000000000000000000000000000000000000..7a77a2064e7e097f924a3901994d131d98164ad6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_libsparse.py @@ -0,0 +1,551 @@ +import operator + +import numpy as np +import pytest + +import pandas._libs.sparse as splib +import pandas.util._test_decorators as td + +from pandas import Series +import pandas._testing as tm +from pandas.core.arrays.sparse import ( + BlockIndex, + IntIndex, + make_sparse_index, +) + + +@pytest.fixture +def test_length(): + return 20 + + +@pytest.fixture( + params=[ + [ + [0, 7, 15], + [3, 5, 5], + [2, 9, 14], + [2, 3, 5], + [2, 9, 15], + [1, 3, 4], + ], + [ + [0, 5], + [4, 4], + [1], + [4], + [1], + [3], + ], + [ + [0], + [10], + [0, 5], + [3, 7], + [0, 5], + [3, 5], + ], + [ + [10], + [5], + [0, 12], + [5, 3], + [12], + [3], + ], + [ + [0, 10], + [4, 6], + [5, 17], + [4, 2], + [], + [], + ], + [ + [0], + [5], + [], + [], + [], + [], + ], + ], + ids=[ + "plain_case", + "delete_blocks", + "split_blocks", + "skip_block", + "no_intersect", + "one_empty", + ], +) +def cases(request): + return request.param + + +class TestSparseIndexUnion: + @pytest.mark.parametrize( + "xloc, xlen, yloc, ylen, eloc, elen", + [ + [[0], [5], [5], [4], [0], [9]], + [[0, 10], [5, 5], [2, 17], [5, 2], [0, 10, 17], [7, 5, 2]], + [[1], [5], [3], [5], [1], [7]], + [[2, 10], [4, 4], [4], [8], [2], [12]], + [[0, 5], [3, 5], [0], [7], [0], [10]], + [[2, 10], [4, 4], [4, 13], [8, 4], [2], [15]], + [[2], [15], [4, 9, 14], [3, 2, 2], [2], [15]], + [[0, 10], [3, 3], [5, 15], [2, 2], [0, 5, 10, 15], [3, 2, 3, 2]], + ], + ) + def test_index_make_union(self, xloc, xlen, yloc, ylen, eloc, elen, test_length): + # Case 1 + # x: ---- + # y: ---- + # r: -------- + # Case 2 + # x: ----- ----- + # y: ----- -- + # Case 3 + # x: ------ + # y: ------- + # r: ---------- + # Case 4 + # x: ------ ----- + # y: ------- + # r: ------------- + # Case 5 + # x: --- ----- + # y: ------- + # r: ------------- + # Case 6 + # x: ------ ----- + # y: ------- --- + # r: ------------- + # Case 7 + # x: ---------------------- + # y: ---- ---- --- + # r: ---------------------- + # Case 8 + # x: ---- --- + # y: --- --- + xindex = BlockIndex(test_length, xloc, xlen) + yindex = BlockIndex(test_length, yloc, ylen) + bresult = xindex.make_union(yindex) + assert isinstance(bresult, BlockIndex) + tm.assert_numpy_array_equal(bresult.blocs, np.array(eloc, dtype=np.int32)) + tm.assert_numpy_array_equal(bresult.blengths, np.array(elen, dtype=np.int32)) + + ixindex = xindex.to_int_index() + iyindex = yindex.to_int_index() + iresult = ixindex.make_union(iyindex) + assert isinstance(iresult, IntIndex) + tm.assert_numpy_array_equal(iresult.indices, bresult.to_int_index().indices) + + def test_int_index_make_union(self): + a = IntIndex(5, np.array([0, 3, 4], dtype=np.int32)) + b = IntIndex(5, np.array([0, 2], dtype=np.int32)) + res = a.make_union(b) + exp = IntIndex(5, np.array([0, 2, 3, 4], np.int32)) + assert res.equals(exp) + + a = IntIndex(5, np.array([], dtype=np.int32)) + b = IntIndex(5, np.array([0, 2], dtype=np.int32)) + res = a.make_union(b) + exp = IntIndex(5, np.array([0, 2], np.int32)) + assert res.equals(exp) + + a = IntIndex(5, np.array([], dtype=np.int32)) + b = IntIndex(5, np.array([], dtype=np.int32)) + res = a.make_union(b) + exp = IntIndex(5, np.array([], np.int32)) + assert res.equals(exp) + + a = IntIndex(5, np.array([0, 1, 2, 3, 4], dtype=np.int32)) + b = IntIndex(5, np.array([0, 1, 2, 3, 4], dtype=np.int32)) + res = a.make_union(b) + exp = IntIndex(5, np.array([0, 1, 2, 3, 4], np.int32)) + assert res.equals(exp) + + a = IntIndex(5, np.array([0, 1], dtype=np.int32)) + b = IntIndex(4, np.array([0, 1], dtype=np.int32)) + + msg = "Indices must reference same underlying length" + with pytest.raises(ValueError, match=msg): + a.make_union(b) + + +class TestSparseIndexIntersect: + @td.skip_if_windows + def test_intersect(self, cases, test_length): + xloc, xlen, yloc, ylen, eloc, elen = cases + xindex = BlockIndex(test_length, xloc, xlen) + yindex = BlockIndex(test_length, yloc, ylen) + expected = BlockIndex(test_length, eloc, elen) + longer_index = BlockIndex(test_length + 1, yloc, ylen) + + result = xindex.intersect(yindex) + assert result.equals(expected) + result = xindex.to_int_index().intersect(yindex.to_int_index()) + assert result.equals(expected.to_int_index()) + + msg = "Indices must reference same underlying length" + with pytest.raises(Exception, match=msg): + xindex.intersect(longer_index) + with pytest.raises(Exception, match=msg): + xindex.to_int_index().intersect(longer_index.to_int_index()) + + def test_intersect_empty(self): + xindex = IntIndex(4, np.array([], dtype=np.int32)) + yindex = IntIndex(4, np.array([2, 3], dtype=np.int32)) + assert xindex.intersect(yindex).equals(xindex) + assert yindex.intersect(xindex).equals(xindex) + + xindex = xindex.to_block_index() + yindex = yindex.to_block_index() + assert xindex.intersect(yindex).equals(xindex) + assert yindex.intersect(xindex).equals(xindex) + + @pytest.mark.parametrize( + "case", + [ + # Argument 2 to "IntIndex" has incompatible type "ndarray[Any, + # dtype[signedinteger[_32Bit]]]"; expected "Sequence[int]" + IntIndex(5, np.array([1, 2], dtype=np.int32)), # type: ignore[arg-type] + IntIndex(5, np.array([0, 2, 4], dtype=np.int32)), # type: ignore[arg-type] + IntIndex(0, np.array([], dtype=np.int32)), # type: ignore[arg-type] + IntIndex(5, np.array([], dtype=np.int32)), # type: ignore[arg-type] + ], + ) + def test_intersect_identical(self, case): + assert case.intersect(case).equals(case) + case = case.to_block_index() + assert case.intersect(case).equals(case) + + +class TestSparseIndexCommon: + def test_int_internal(self): + idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind="integer") + assert isinstance(idx, IntIndex) + assert idx.npoints == 2 + tm.assert_numpy_array_equal(idx.indices, np.array([2, 3], dtype=np.int32)) + + idx = make_sparse_index(4, np.array([], dtype=np.int32), kind="integer") + assert isinstance(idx, IntIndex) + assert idx.npoints == 0 + tm.assert_numpy_array_equal(idx.indices, np.array([], dtype=np.int32)) + + idx = make_sparse_index( + 4, np.array([0, 1, 2, 3], dtype=np.int32), kind="integer" + ) + assert isinstance(idx, IntIndex) + assert idx.npoints == 4 + tm.assert_numpy_array_equal(idx.indices, np.array([0, 1, 2, 3], dtype=np.int32)) + + def test_block_internal(self): + idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind="block") + assert isinstance(idx, BlockIndex) + assert idx.npoints == 2 + tm.assert_numpy_array_equal(idx.blocs, np.array([2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, np.array([2], dtype=np.int32)) + + idx = make_sparse_index(4, np.array([], dtype=np.int32), kind="block") + assert isinstance(idx, BlockIndex) + assert idx.npoints == 0 + tm.assert_numpy_array_equal(idx.blocs, np.array([], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, np.array([], dtype=np.int32)) + + idx = make_sparse_index(4, np.array([0, 1, 2, 3], dtype=np.int32), kind="block") + assert isinstance(idx, BlockIndex) + assert idx.npoints == 4 + tm.assert_numpy_array_equal(idx.blocs, np.array([0], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, np.array([4], dtype=np.int32)) + + idx = make_sparse_index(4, np.array([0, 2, 3], dtype=np.int32), kind="block") + assert isinstance(idx, BlockIndex) + assert idx.npoints == 3 + tm.assert_numpy_array_equal(idx.blocs, np.array([0, 2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, np.array([1, 2], dtype=np.int32)) + + @pytest.mark.parametrize("kind", ["integer", "block"]) + def test_lookup(self, kind): + idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind=kind) + assert idx.lookup(-1) == -1 + assert idx.lookup(0) == -1 + assert idx.lookup(1) == -1 + assert idx.lookup(2) == 0 + assert idx.lookup(3) == 1 + assert idx.lookup(4) == -1 + + idx = make_sparse_index(4, np.array([], dtype=np.int32), kind=kind) + + for i in range(-1, 5): + assert idx.lookup(i) == -1 + + idx = make_sparse_index(4, np.array([0, 1, 2, 3], dtype=np.int32), kind=kind) + assert idx.lookup(-1) == -1 + assert idx.lookup(0) == 0 + assert idx.lookup(1) == 1 + assert idx.lookup(2) == 2 + assert idx.lookup(3) == 3 + assert idx.lookup(4) == -1 + + idx = make_sparse_index(4, np.array([0, 2, 3], dtype=np.int32), kind=kind) + assert idx.lookup(-1) == -1 + assert idx.lookup(0) == 0 + assert idx.lookup(1) == -1 + assert idx.lookup(2) == 1 + assert idx.lookup(3) == 2 + assert idx.lookup(4) == -1 + + @pytest.mark.parametrize("kind", ["integer", "block"]) + def test_lookup_array(self, kind): + idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind=kind) + + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, -1, 0], dtype=np.int32) + tm.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 0, -1, 1], dtype=np.int32) + tm.assert_numpy_array_equal(res, exp) + + idx = make_sparse_index(4, np.array([], dtype=np.int32), kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2, 4], dtype=np.int32)) + exp = np.array([-1, -1, -1, -1], dtype=np.int32) + tm.assert_numpy_array_equal(res, exp) + + idx = make_sparse_index(4, np.array([0, 1, 2, 3], dtype=np.int32), kind=kind) + res = idx.lookup_array(np.array([-1, 0, 2], dtype=np.int32)) + exp = np.array([-1, 0, 2], dtype=np.int32) + tm.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([4, 2, 1, 3], dtype=np.int32)) + exp = np.array([-1, 2, 1, 3], dtype=np.int32) + tm.assert_numpy_array_equal(res, exp) + + idx = make_sparse_index(4, np.array([0, 2, 3], dtype=np.int32), kind=kind) + res = idx.lookup_array(np.array([2, 1, 3, 0], dtype=np.int32)) + exp = np.array([1, -1, 2, 0], dtype=np.int32) + tm.assert_numpy_array_equal(res, exp) + + res = idx.lookup_array(np.array([1, 4, 2, 5], dtype=np.int32)) + exp = np.array([-1, -1, 1, -1], dtype=np.int32) + tm.assert_numpy_array_equal(res, exp) + + @pytest.mark.parametrize( + "idx, expected", + [ + [0, -1], + [5, 0], + [7, 2], + [8, -1], + [9, -1], + [10, -1], + [11, -1], + [12, 3], + [17, 8], + [18, -1], + ], + ) + def test_lookup_basics(self, idx, expected): + bindex = BlockIndex(20, [5, 12], [3, 6]) + assert bindex.lookup(idx) == expected + + iindex = bindex.to_int_index() + assert iindex.lookup(idx) == expected + + +class TestBlockIndex: + def test_block_internal(self): + idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind="block") + assert isinstance(idx, BlockIndex) + assert idx.npoints == 2 + tm.assert_numpy_array_equal(idx.blocs, np.array([2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, np.array([2], dtype=np.int32)) + + idx = make_sparse_index(4, np.array([], dtype=np.int32), kind="block") + assert isinstance(idx, BlockIndex) + assert idx.npoints == 0 + tm.assert_numpy_array_equal(idx.blocs, np.array([], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, np.array([], dtype=np.int32)) + + idx = make_sparse_index(4, np.array([0, 1, 2, 3], dtype=np.int32), kind="block") + assert isinstance(idx, BlockIndex) + assert idx.npoints == 4 + tm.assert_numpy_array_equal(idx.blocs, np.array([0], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, np.array([4], dtype=np.int32)) + + idx = make_sparse_index(4, np.array([0, 2, 3], dtype=np.int32), kind="block") + assert isinstance(idx, BlockIndex) + assert idx.npoints == 3 + tm.assert_numpy_array_equal(idx.blocs, np.array([0, 2], dtype=np.int32)) + tm.assert_numpy_array_equal(idx.blengths, np.array([1, 2], dtype=np.int32)) + + @pytest.mark.parametrize("i", [5, 10, 100, 101]) + def test_make_block_boundary(self, i): + idx = make_sparse_index(i, np.arange(0, i, 2, dtype=np.int32), kind="block") + + exp = np.arange(0, i, 2, dtype=np.int32) + tm.assert_numpy_array_equal(idx.blocs, exp) + tm.assert_numpy_array_equal(idx.blengths, np.ones(len(exp), dtype=np.int32)) + + def test_equals(self): + index = BlockIndex(10, [0, 4], [2, 5]) + + assert index.equals(index) + assert not index.equals(BlockIndex(10, [0, 4], [2, 6])) + + def test_check_integrity(self): + locs = [] + lengths = [] + + # 0-length OK + BlockIndex(0, locs, lengths) + + # also OK even though empty + BlockIndex(1, locs, lengths) + + msg = "Block 0 extends beyond end" + with pytest.raises(ValueError, match=msg): + BlockIndex(10, [5], [10]) + + msg = "Block 0 overlaps" + with pytest.raises(ValueError, match=msg): + BlockIndex(10, [2, 5], [5, 3]) + + def test_to_int_index(self): + locs = [0, 10] + lengths = [4, 6] + exp_inds = [0, 1, 2, 3, 10, 11, 12, 13, 14, 15] + + block = BlockIndex(20, locs, lengths) + dense = block.to_int_index() + + tm.assert_numpy_array_equal(dense.indices, np.array(exp_inds, dtype=np.int32)) + + def test_to_block_index(self): + index = BlockIndex(10, [0, 5], [4, 5]) + assert index.to_block_index() is index + + +class TestIntIndex: + def test_check_integrity(self): + # Too many indices than specified in self.length + msg = "Too many indices" + + with pytest.raises(ValueError, match=msg): + IntIndex(length=1, indices=[1, 2, 3]) + + # No index can be negative. + msg = "No index can be less than zero" + + with pytest.raises(ValueError, match=msg): + IntIndex(length=5, indices=[1, -2, 3]) + + # No index can be negative. + msg = "No index can be less than zero" + + with pytest.raises(ValueError, match=msg): + IntIndex(length=5, indices=[1, -2, 3]) + + # All indices must be less than the length. + msg = "All indices must be less than the length" + + with pytest.raises(ValueError, match=msg): + IntIndex(length=5, indices=[1, 2, 5]) + + with pytest.raises(ValueError, match=msg): + IntIndex(length=5, indices=[1, 2, 6]) + + # Indices must be strictly ascending. + msg = "Indices must be strictly increasing" + + with pytest.raises(ValueError, match=msg): + IntIndex(length=5, indices=[1, 3, 2]) + + with pytest.raises(ValueError, match=msg): + IntIndex(length=5, indices=[1, 3, 3]) + + def test_int_internal(self): + idx = make_sparse_index(4, np.array([2, 3], dtype=np.int32), kind="integer") + assert isinstance(idx, IntIndex) + assert idx.npoints == 2 + tm.assert_numpy_array_equal(idx.indices, np.array([2, 3], dtype=np.int32)) + + idx = make_sparse_index(4, np.array([], dtype=np.int32), kind="integer") + assert isinstance(idx, IntIndex) + assert idx.npoints == 0 + tm.assert_numpy_array_equal(idx.indices, np.array([], dtype=np.int32)) + + idx = make_sparse_index( + 4, np.array([0, 1, 2, 3], dtype=np.int32), kind="integer" + ) + assert isinstance(idx, IntIndex) + assert idx.npoints == 4 + tm.assert_numpy_array_equal(idx.indices, np.array([0, 1, 2, 3], dtype=np.int32)) + + def test_equals(self): + index = IntIndex(10, [0, 1, 2, 3, 4]) + assert index.equals(index) + assert not index.equals(IntIndex(10, [0, 1, 2, 3])) + + def test_to_block_index(self, cases, test_length): + xloc, xlen, yloc, ylen, _, _ = cases + xindex = BlockIndex(test_length, xloc, xlen) + yindex = BlockIndex(test_length, yloc, ylen) + + # see if survive the round trip + xbindex = xindex.to_int_index().to_block_index() + ybindex = yindex.to_int_index().to_block_index() + assert isinstance(xbindex, BlockIndex) + assert xbindex.equals(xindex) + assert ybindex.equals(yindex) + + def test_to_int_index(self): + index = IntIndex(10, [2, 3, 4, 5, 6]) + assert index.to_int_index() is index + + +class TestSparseOperators: + @pytest.mark.parametrize("opname", ["add", "sub", "mul", "truediv", "floordiv"]) + def test_op(self, opname, cases, test_length): + xloc, xlen, yloc, ylen, _, _ = cases + sparse_op = getattr(splib, f"sparse_{opname}_float64") + python_op = getattr(operator, opname) + + xindex = BlockIndex(test_length, xloc, xlen) + yindex = BlockIndex(test_length, yloc, ylen) + + xdindex = xindex.to_int_index() + ydindex = yindex.to_int_index() + + x = np.arange(xindex.npoints) * 10.0 + 1 + y = np.arange(yindex.npoints) * 100.0 + 1 + + xfill = 0 + yfill = 2 + + result_block_vals, rb_index, bfill = sparse_op( + x, xindex, xfill, y, yindex, yfill + ) + result_int_vals, ri_index, ifill = sparse_op( + x, xdindex, xfill, y, ydindex, yfill + ) + + assert rb_index.to_int_index().equals(ri_index) + tm.assert_numpy_array_equal(result_block_vals, result_int_vals) + assert bfill == ifill + + # check versus Series... + xseries = Series(x, xdindex.indices) + xseries = xseries.reindex(np.arange(test_length)).fillna(xfill) + + yseries = Series(y, ydindex.indices) + yseries = yseries.reindex(np.arange(test_length)).fillna(yfill) + + series_result = python_op(xseries, yseries) + series_result = series_result.reindex(ri_index.indices) + + tm.assert_numpy_array_equal(result_block_vals, series_result.values) + tm.assert_numpy_array_equal(result_int_vals, series_result.values) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_reductions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_reductions.py new file mode 100644 index 0000000000000000000000000000000000000000..f44423d5e635c3f74725db219d48fcaca27c4d53 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_reductions.py @@ -0,0 +1,306 @@ +import numpy as np +import pytest + +from pandas import ( + NaT, + SparseDtype, + Timestamp, + isna, +) +from pandas.core.arrays.sparse import SparseArray + + +class TestReductions: + @pytest.mark.parametrize( + "data,pos,neg", + [ + ([True, True, True], True, False), + ([1, 2, 1], 1, 0), + ([1.0, 2.0, 1.0], 1.0, 0.0), + ], + ) + def test_all(self, data, pos, neg): + # GH#17570 + out = SparseArray(data).all() + assert out + + out = SparseArray(data, fill_value=pos).all() + assert out + + data[1] = neg + out = SparseArray(data).all() + assert not out + + out = SparseArray(data, fill_value=pos).all() + assert not out + + @pytest.mark.parametrize( + "data,pos,neg", + [ + ([True, True, True], True, False), + ([1, 2, 1], 1, 0), + ([1.0, 2.0, 1.0], 1.0, 0.0), + ], + ) + def test_numpy_all(self, data, pos, neg): + # GH#17570 + out = np.all(SparseArray(data)) + assert out + + out = np.all(SparseArray(data, fill_value=pos)) + assert out + + data[1] = neg + out = np.all(SparseArray(data)) + assert not out + + out = np.all(SparseArray(data, fill_value=pos)) + assert not out + + # raises with a different message on py2. + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.all(SparseArray(data), out=np.array([])) + + @pytest.mark.parametrize( + "data,pos,neg", + [ + ([False, True, False], True, False), + ([0, 2, 0], 2, 0), + ([0.0, 2.0, 0.0], 2.0, 0.0), + ], + ) + def test_any(self, data, pos, neg): + # GH#17570 + out = SparseArray(data).any() + assert out + + out = SparseArray(data, fill_value=pos).any() + assert out + + data[1] = neg + out = SparseArray(data).any() + assert not out + + out = SparseArray(data, fill_value=pos).any() + assert not out + + @pytest.mark.parametrize( + "data,pos,neg", + [ + ([False, True, False], True, False), + ([0, 2, 0], 2, 0), + ([0.0, 2.0, 0.0], 2.0, 0.0), + ], + ) + def test_numpy_any(self, data, pos, neg): + # GH#17570 + out = np.any(SparseArray(data)) + assert out + + out = np.any(SparseArray(data, fill_value=pos)) + assert out + + data[1] = neg + out = np.any(SparseArray(data)) + assert not out + + out = np.any(SparseArray(data, fill_value=pos)) + assert not out + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.any(SparseArray(data), out=out) + + def test_sum(self): + data = np.arange(10).astype(float) + out = SparseArray(data).sum() + assert out == 45.0 + + data[5] = np.nan + out = SparseArray(data, fill_value=2).sum() + assert out == 40.0 + + out = SparseArray(data, fill_value=np.nan).sum() + assert out == 40.0 + + @pytest.mark.parametrize( + "arr", + [np.array([0, 1, np.nan, 1]), np.array([0, 1, 1])], + ) + @pytest.mark.parametrize("fill_value", [0, 1, np.nan]) + @pytest.mark.parametrize("min_count, expected", [(3, 2), (4, np.nan)]) + def test_sum_min_count(self, arr, fill_value, min_count, expected): + # GH#25777 + sparray = SparseArray(arr, fill_value=fill_value) + result = sparray.sum(min_count=min_count) + if np.isnan(expected): + assert np.isnan(result) + else: + assert result == expected + + def test_bool_sum_min_count(self): + spar_bool = SparseArray([False, True] * 5, dtype=np.bool_, fill_value=True) + res = spar_bool.sum(min_count=1) + assert res == 5 + res = spar_bool.sum(min_count=11) + assert isna(res) + + def test_numpy_sum(self): + data = np.arange(10).astype(float) + out = np.sum(SparseArray(data)) + assert out == 45.0 + + data[5] = np.nan + out = np.sum(SparseArray(data, fill_value=2)) + assert out == 40.0 + + out = np.sum(SparseArray(data, fill_value=np.nan)) + assert out == 40.0 + + msg = "the 'dtype' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.sum(SparseArray(data), dtype=np.int64) + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.sum(SparseArray(data), out=out) + + def test_mean(self): + data = np.arange(10).astype(float) + out = SparseArray(data).mean() + assert out == 4.5 + + data[5] = np.nan + out = SparseArray(data).mean() + assert out == 40.0 / 9 + + def test_numpy_mean(self): + data = np.arange(10).astype(float) + out = np.mean(SparseArray(data)) + assert out == 4.5 + + data[5] = np.nan + out = np.mean(SparseArray(data)) + assert out == 40.0 / 9 + + msg = "the 'dtype' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.mean(SparseArray(data), dtype=np.int64) + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.mean(SparseArray(data), out=out) + + +class TestMinMax: + @pytest.mark.parametrize( + "raw_data,max_expected,min_expected", + [ + (np.arange(5.0), [4], [0]), + (-np.arange(5.0), [0], [-4]), + (np.array([0, 1, 2, np.nan, 4]), [4], [0]), + (np.array([np.nan] * 5), [np.nan], [np.nan]), + (np.array([]), [np.nan], [np.nan]), + ], + ) + def test_nan_fill_value(self, raw_data, max_expected, min_expected): + arr = SparseArray(raw_data) + max_result = arr.max() + min_result = arr.min() + assert max_result in max_expected + assert min_result in min_expected + + max_result = arr.max(skipna=False) + min_result = arr.min(skipna=False) + if np.isnan(raw_data).any(): + assert np.isnan(max_result) + assert np.isnan(min_result) + else: + assert max_result in max_expected + assert min_result in min_expected + + @pytest.mark.parametrize( + "fill_value,max_expected,min_expected", + [ + (100, 100, 0), + (-100, 1, -100), + ], + ) + def test_fill_value(self, fill_value, max_expected, min_expected): + arr = SparseArray( + np.array([fill_value, 0, 1]), dtype=SparseDtype("int", fill_value) + ) + max_result = arr.max() + assert max_result == max_expected + + min_result = arr.min() + assert min_result == min_expected + + def test_only_fill_value(self): + fv = 100 + arr = SparseArray(np.array([fv, fv, fv]), dtype=SparseDtype("int", fv)) + assert len(arr._valid_sp_values) == 0 + + assert arr.max() == fv + assert arr.min() == fv + assert arr.max(skipna=False) == fv + assert arr.min(skipna=False) == fv + + @pytest.mark.parametrize("func", ["min", "max"]) + @pytest.mark.parametrize("data", [np.array([]), np.array([np.nan, np.nan])]) + @pytest.mark.parametrize( + "dtype,expected", + [ + (SparseDtype(np.float64, np.nan), np.nan), + (SparseDtype(np.float64, 5.0), np.nan), + (SparseDtype("datetime64[ns]", NaT), NaT), + (SparseDtype("datetime64[ns]", Timestamp("2018-05-05")), NaT), + ], + ) + def test_na_value_if_no_valid_values(self, func, data, dtype, expected): + arr = SparseArray(data, dtype=dtype) + result = getattr(arr, func)() + if expected is NaT: + # TODO: pin down whether we wrap datetime64("NaT") + assert result is NaT or np.isnat(result) + else: + assert np.isnan(result) + + +class TestArgmaxArgmin: + @pytest.mark.parametrize( + "arr,argmax_expected,argmin_expected", + [ + (SparseArray([1, 2, 0, 1, 2]), 1, 2), + (SparseArray([-1, -2, 0, -1, -2]), 2, 1), + (SparseArray([np.nan, 1, 0, 0, np.nan, -1]), 1, 5), + (SparseArray([np.nan, 1, 0, 0, np.nan, 2]), 5, 2), + (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=-1), 5, 2), + (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=0), 5, 2), + (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=1), 5, 2), + (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=2), 5, 2), + (SparseArray([np.nan, 1, 0, 0, np.nan, 2], fill_value=3), 5, 2), + (SparseArray([0] * 10 + [-1], fill_value=0), 0, 10), + (SparseArray([0] * 10 + [-1], fill_value=-1), 0, 10), + (SparseArray([0] * 10 + [-1], fill_value=1), 0, 10), + (SparseArray([-1] + [0] * 10, fill_value=0), 1, 0), + (SparseArray([1] + [0] * 10, fill_value=0), 0, 1), + (SparseArray([-1] + [0] * 10, fill_value=-1), 1, 0), + (SparseArray([1] + [0] * 10, fill_value=1), 0, 1), + ], + ) + def test_argmax_argmin(self, arr, argmax_expected, argmin_expected): + argmax_result = arr.argmax() + argmin_result = arr.argmin() + assert argmax_result == argmax_expected + assert argmin_result == argmin_expected + + @pytest.mark.parametrize( + "arr,method", + [(SparseArray([]), "argmax"), (SparseArray([]), "argmin")], + ) + def test_empty_array(self, arr, method): + msg = f"attempt to get {method} of an empty sequence" + with pytest.raises(ValueError, match=msg): + arr.argmax() if method == "argmax" else arr.argmin() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_unary.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_unary.py new file mode 100644 index 0000000000000000000000000000000000000000..c00a73773fdd4795e3d5d7f030a591a060dc3bfc --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/sparse/test_unary.py @@ -0,0 +1,79 @@ +import operator + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import SparseArray + + +@pytest.mark.filterwarnings("ignore:invalid value encountered in cast:RuntimeWarning") +@pytest.mark.parametrize("fill_value", [0, np.nan]) +@pytest.mark.parametrize("op", [operator.pos, operator.neg]) +def test_unary_op(op, fill_value): + arr = np.array([0, 1, np.nan, 2]) + sparray = SparseArray(arr, fill_value=fill_value) + result = op(sparray) + expected = SparseArray(op(arr), fill_value=op(fill_value)) + tm.assert_sp_array_equal(result, expected) + + +@pytest.mark.parametrize("fill_value", [True, False]) +def test_invert(fill_value): + arr = np.array([True, False, False, True]) + sparray = SparseArray(arr, fill_value=fill_value) + result = ~sparray + expected = SparseArray(~arr, fill_value=not fill_value) + tm.assert_sp_array_equal(result, expected) + + result = ~pd.Series(sparray) + expected = pd.Series(expected) + tm.assert_series_equal(result, expected) + + result = ~pd.DataFrame({"A": sparray}) + expected = pd.DataFrame({"A": expected}) + tm.assert_frame_equal(result, expected) + + +class TestUnaryMethods: + @pytest.mark.filterwarnings( + "ignore:invalid value encountered in cast:RuntimeWarning" + ) + def test_neg_operator(self): + arr = SparseArray([-1, -2, np.nan, 3], fill_value=np.nan, dtype=np.int8) + res = -arr + exp = SparseArray([1, 2, np.nan, -3], fill_value=np.nan, dtype=np.int8) + tm.assert_sp_array_equal(exp, res) + + arr = SparseArray([-1, -2, 1, 3], fill_value=-1, dtype=np.int8) + res = -arr + exp = SparseArray([1, 2, -1, -3], fill_value=1, dtype=np.int8) + tm.assert_sp_array_equal(exp, res) + + @pytest.mark.filterwarnings( + "ignore:invalid value encountered in cast:RuntimeWarning" + ) + def test_abs_operator(self): + arr = SparseArray([-1, -2, np.nan, 3], fill_value=np.nan, dtype=np.int8) + res = abs(arr) + exp = SparseArray([1, 2, np.nan, 3], fill_value=np.nan, dtype=np.int8) + tm.assert_sp_array_equal(exp, res) + + arr = SparseArray([-1, -2, 1, 3], fill_value=-1, dtype=np.int8) + res = abs(arr) + exp = SparseArray([1, 2, 1, 3], fill_value=1, dtype=np.int8) + tm.assert_sp_array_equal(exp, res) + + def test_invert_operator(self): + arr = SparseArray([False, True, False, True], fill_value=False, dtype=np.bool_) + exp = SparseArray( + np.invert([False, True, False, True]), fill_value=True, dtype=np.bool_ + ) + res = ~arr + tm.assert_sp_array_equal(exp, res) + + arr = SparseArray([0, 1, 0, 2, 3, 0], fill_value=0, dtype=np.int32) + res = ~arr + exp = SparseArray([-1, -2, -1, -3, -4, -1], fill_value=-1, dtype=np.int32) + tm.assert_sp_array_equal(exp, res) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_concat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_concat.py new file mode 100644 index 0000000000000000000000000000000000000000..320d700b2b6c340d1cb52e708aa8defcad7e60bb --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_concat.py @@ -0,0 +1,73 @@ +import numpy as np +import pytest + +from pandas.compat import HAS_PYARROW + +from pandas.core.dtypes.cast import find_common_type + +import pandas as pd +import pandas._testing as tm +from pandas.util.version import Version + + +@pytest.mark.parametrize( + "to_concat_dtypes, result_dtype", + [ + # same types + ([("pyarrow", pd.NA), ("pyarrow", pd.NA)], ("pyarrow", pd.NA)), + ([("pyarrow", np.nan), ("pyarrow", np.nan)], ("pyarrow", np.nan)), + ([("python", pd.NA), ("python", pd.NA)], ("python", pd.NA)), + ([("python", np.nan), ("python", np.nan)], ("python", np.nan)), + # pyarrow preference + ([("pyarrow", pd.NA), ("python", pd.NA)], ("pyarrow", pd.NA)), + # NA preference + ([("python", pd.NA), ("python", np.nan)], ("python", pd.NA)), + ], +) +def test_concat_series(request, to_concat_dtypes, result_dtype): + if any(storage == "pyarrow" for storage, _ in to_concat_dtypes) and not HAS_PYARROW: + pytest.skip("Could not import 'pyarrow'") + + ser_list = [ + pd.Series(["a", "b", None], dtype=pd.StringDtype(storage, na_value)) + for storage, na_value in to_concat_dtypes + ] + + result = pd.concat(ser_list, ignore_index=True) + expected = pd.Series( + ["a", "b", None, "a", "b", None], dtype=pd.StringDtype(*result_dtype) + ) + tm.assert_series_equal(result, expected) + + # order doesn't matter for result + result = pd.concat(ser_list[::1], ignore_index=True) + tm.assert_series_equal(result, expected) + + +def test_concat_with_object(string_dtype_arguments): + # _get_common_dtype cannot inspect values, so object dtype with strings still + # results in object dtype + result = pd.concat( + [ + pd.Series(["a", "b", None], dtype=pd.StringDtype(*string_dtype_arguments)), + pd.Series(["a", "b", None], dtype=object), + ] + ) + assert result.dtype == np.dtype("object") + + +def test_concat_with_numpy(string_dtype_arguments): + # common type with a numpy string dtype always preserves the pandas string dtype + dtype = pd.StringDtype(*string_dtype_arguments) + assert find_common_type([dtype, np.dtype("U")]) == dtype + assert find_common_type([np.dtype("U"), dtype]) == dtype + assert find_common_type([dtype, np.dtype("U10")]) == dtype + assert find_common_type([np.dtype("U10"), dtype]) == dtype + + # with any other numpy dtype -> object + assert find_common_type([dtype, np.dtype("S")]) == np.dtype("object") + assert find_common_type([dtype, np.dtype("int64")]) == np.dtype("object") + + if Version(np.__version__) >= Version("2"): + assert find_common_type([dtype, np.dtypes.StringDType()]) == dtype + assert find_common_type([np.dtypes.StringDType(), dtype]) == dtype diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string.py new file mode 100644 index 0000000000000000000000000000000000000000..b4516da3883149c399921c1ae669c7b8a154524d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string.py @@ -0,0 +1,854 @@ +""" +This module tests the functionality of StringArray and ArrowStringArray. +Tests for the str accessors are in pandas/tests/strings/test_string_array.py +""" +import operator + +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +from pandas.compat import HAS_PYARROW +from pandas.compat.pyarrow import ( + pa_version_under12p0, + pa_version_under19p0, +) +import pandas.util._test_decorators as td + +from pandas.core.dtypes.common import is_dtype_equal + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays.string_ import StringArrayNumpySemantics +from pandas.core.arrays.string_arrow import ( + ArrowStringArray, + ArrowStringArrayNumpySemantics, +) + + +@pytest.fixture +def dtype(string_dtype_arguments): + """Fixture giving StringDtype from parametrized storage and na_value arguments""" + storage, na_value = string_dtype_arguments + return pd.StringDtype(storage=storage, na_value=na_value) + + +@pytest.fixture +def dtype2(string_dtype_arguments2): + storage, na_value = string_dtype_arguments2 + return pd.StringDtype(storage=storage, na_value=na_value) + + +@pytest.fixture +def cls(dtype): + """Fixture giving array type from parametrized 'dtype'""" + return dtype.construct_array_type() + + +def string_dtype_highest_priority(dtype1, dtype2): + if HAS_PYARROW: + DTYPE_HIERARCHY = [ + pd.StringDtype("python", na_value=np.nan), + pd.StringDtype("pyarrow", na_value=np.nan), + pd.StringDtype("python", na_value=pd.NA), + pd.StringDtype("pyarrow", na_value=pd.NA), + ] + else: + DTYPE_HIERARCHY = [ + pd.StringDtype("python", na_value=np.nan), + pd.StringDtype("python", na_value=pd.NA), + ] + + h1 = DTYPE_HIERARCHY.index(dtype1) + h2 = DTYPE_HIERARCHY.index(dtype2) + return DTYPE_HIERARCHY[max(h1, h2)] + + +def test_dtype_constructor(): + pytest.importorskip("pyarrow") + + with tm.assert_produces_warning(FutureWarning): + dtype = pd.StringDtype("pyarrow_numpy") + assert dtype == pd.StringDtype("pyarrow", na_value=np.nan) + + +def test_dtype_equality(): + pytest.importorskip("pyarrow") + + dtype1 = pd.StringDtype("python") + dtype2 = pd.StringDtype("pyarrow") + dtype3 = pd.StringDtype("pyarrow", na_value=np.nan) + + assert dtype1 == pd.StringDtype("python", na_value=pd.NA) + assert dtype1 != dtype2 + assert dtype1 != dtype3 + + assert dtype2 == pd.StringDtype("pyarrow", na_value=pd.NA) + assert dtype2 != dtype1 + assert dtype2 != dtype3 + + assert dtype3 == pd.StringDtype("pyarrow", na_value=np.nan) + assert dtype3 == pd.StringDtype("pyarrow", na_value=float("nan")) + assert dtype3 != dtype1 + assert dtype3 != dtype2 + + +def test_repr(dtype): + df = pd.DataFrame({"A": pd.array(["a", pd.NA, "b"], dtype=dtype)}) + if dtype.na_value is np.nan: + expected = " A\n0 a\n1 NaN\n2 b" + else: + expected = " A\n0 a\n1 \n2 b" + assert repr(df) == expected + + if dtype.na_value is np.nan: + expected = "0 a\n1 NaN\n2 b\nName: A, dtype: str" + else: + expected = "0 a\n1 \n2 b\nName: A, dtype: string" + assert repr(df.A) == expected + + if dtype.storage == "pyarrow" and dtype.na_value is pd.NA: + arr_name = "ArrowStringArray" + expected = f"<{arr_name}>\n['a', , 'b']\nLength: 3, dtype: string" + elif dtype.storage == "pyarrow" and dtype.na_value is np.nan: + arr_name = "ArrowStringArrayNumpySemantics" + expected = f"<{arr_name}>\n['a', nan, 'b']\nLength: 3, dtype: str" + elif dtype.storage == "python" and dtype.na_value is np.nan: + arr_name = "StringArrayNumpySemantics" + expected = f"<{arr_name}>\n['a', nan, 'b']\nLength: 3, dtype: str" + else: + arr_name = "StringArray" + expected = f"<{arr_name}>\n['a', , 'b']\nLength: 3, dtype: string" + assert repr(df.A.array) == expected + + +def test_none_to_nan(cls, dtype): + a = cls._from_sequence(["a", None, "b"], dtype=dtype) + assert a[1] is not None + assert a[1] is a.dtype.na_value + + +def test_setitem_validates(cls, dtype): + arr = cls._from_sequence(["a", "b"], dtype=dtype) + + msg = "Invalid value '10' for dtype 'str" + with pytest.raises(TypeError, match=msg): + arr[0] = 10 + + msg = "Invalid value for dtype 'str" + with pytest.raises(TypeError, match=msg): + arr[:] = np.array([1, 2]) + + +def test_setitem_with_scalar_string(dtype): + # is_float_dtype considers some strings, like 'd', to be floats + # which can cause issues. + arr = pd.array(["a", "c"], dtype=dtype) + arr[0] = "d" + expected = pd.array(["d", "c"], dtype=dtype) + tm.assert_extension_array_equal(arr, expected) + + +def test_setitem_with_array_with_missing(dtype): + # ensure that when setting with an array of values, we don't mutate the + # array `value` in __setitem__(self, key, value) + arr = pd.array(["a", "b", "c"], dtype=dtype) + value = np.array(["A", None]) + value_orig = value.copy() + arr[[0, 1]] = value + + expected = pd.array(["A", pd.NA, "c"], dtype=dtype) + tm.assert_extension_array_equal(arr, expected) + tm.assert_numpy_array_equal(value, value_orig) + + +def test_astype_roundtrip(dtype): + ser = pd.Series(pd.date_range("2000", periods=12)) + ser[0] = None + + casted = ser.astype(dtype) + assert is_dtype_equal(casted.dtype, dtype) + + result = casted.astype("datetime64[ns]") + tm.assert_series_equal(result, ser) + + # GH#38509 same thing for timedelta64 + ser2 = ser - ser.iloc[-1] + casted2 = ser2.astype(dtype) + assert is_dtype_equal(casted2.dtype, dtype) + + result2 = casted2.astype(ser2.dtype) + tm.assert_series_equal(result2, ser2) + + +def test_add(dtype): + a = pd.Series(["a", "b", "c", None, None], dtype=dtype) + b = pd.Series(["x", "y", None, "z", None], dtype=dtype) + + result = a + b + expected = pd.Series(["ax", "by", None, None, None], dtype=dtype) + tm.assert_series_equal(result, expected) + + result = a.add(b) + tm.assert_series_equal(result, expected) + + result = a.radd(b) + expected = pd.Series(["xa", "yb", None, None, None], dtype=dtype) + tm.assert_series_equal(result, expected) + + result = a.add(b, fill_value="-") + expected = pd.Series(["ax", "by", "c-", "-z", None], dtype=dtype) + tm.assert_series_equal(result, expected) + + +def test_add_2d(dtype, request): + if dtype.storage == "pyarrow": + reason = "Failed: DID NOT RAISE " + mark = pytest.mark.xfail(raises=None, reason=reason) + request.applymarker(mark) + + a = pd.array(["a", "b", "c"], dtype=dtype) + b = np.array([["a", "b", "c"]], dtype=object) + with pytest.raises(ValueError, match="3 != 1"): + a + b + + s = pd.Series(a) + with pytest.raises(ValueError, match="3 != 1"): + s + b + + +def test_add_sequence(dtype): + a = pd.array(["a", "b", None, None], dtype=dtype) + other = ["x", None, "y", None] + + result = a + other + expected = pd.array(["ax", None, None, None], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = other + a + expected = pd.array(["xa", None, None, None], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_mul(dtype): + a = pd.array(["a", "b", None], dtype=dtype) + result = a * 2 + expected = pd.array(["aa", "bb", None], dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + result = 2 * a + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.xfail(reason="GH-28527") +def test_add_strings(dtype): + arr = pd.array(["a", "b", "c", "d"], dtype=dtype) + df = pd.DataFrame([["t", "y", "v", "w"]], dtype=object) + assert arr.__add__(df) is NotImplemented + + result = arr + df + expected = pd.DataFrame([["at", "by", "cv", "dw"]]).astype(dtype) + tm.assert_frame_equal(result, expected) + + result = df + arr + expected = pd.DataFrame([["ta", "yb", "vc", "wd"]]).astype(dtype) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.xfail(reason="GH-28527") +def test_add_frame(dtype): + arr = pd.array(["a", "b", np.nan, np.nan], dtype=dtype) + df = pd.DataFrame([["x", np.nan, "y", np.nan]]) + + assert arr.__add__(df) is NotImplemented + + result = arr + df + expected = pd.DataFrame([["ax", np.nan, np.nan, np.nan]]).astype(dtype) + tm.assert_frame_equal(result, expected) + + result = df + arr + expected = pd.DataFrame([["xa", np.nan, np.nan, np.nan]]).astype(dtype) + tm.assert_frame_equal(result, expected) + + +def test_comparison_methods_scalar(comparison_op, dtype): + op_name = f"__{comparison_op.__name__}__" + a = pd.array(["a", None, "c"], dtype=dtype) + other = "a" + result = getattr(a, op_name)(other) + if dtype.na_value is np.nan: + expected = np.array([getattr(item, op_name)(other) for item in a]) + if comparison_op == operator.ne: + expected[1] = True + else: + expected[1] = False + tm.assert_numpy_array_equal(result, expected.astype(np.bool_)) + else: + expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean" + expected = np.array([getattr(item, op_name)(other) for item in a], dtype=object) + expected = pd.array(expected, dtype=expected_dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_comparison_methods_scalar_pd_na(comparison_op, dtype): + op_name = f"__{comparison_op.__name__}__" + a = pd.array(["a", None, "c"], dtype=dtype) + result = getattr(a, op_name)(pd.NA) + + if dtype.na_value is np.nan: + if operator.ne == comparison_op: + expected = np.array([True, True, True]) + else: + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(result, expected) + else: + expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean" + expected = pd.array([None, None, None], dtype=expected_dtype) + tm.assert_extension_array_equal(result, expected) + tm.assert_extension_array_equal(result, expected) + + +def test_comparison_methods_scalar_not_string(comparison_op, dtype): + op_name = f"__{comparison_op.__name__}__" + + a = pd.array(["a", None, "c"], dtype=dtype) + other = 42 + + if op_name not in ["__eq__", "__ne__"]: + with pytest.raises(TypeError, match="Invalid comparison|not supported between"): + getattr(a, op_name)(other) + + return + + result = getattr(a, op_name)(other) + + if dtype.na_value is np.nan: + expected_data = { + "__eq__": [False, False, False], + "__ne__": [True, True, True], + }[op_name] + expected = np.array(expected_data) + tm.assert_numpy_array_equal(result, expected) + else: + expected_data = {"__eq__": [False, None, False], "__ne__": [True, None, True]}[ + op_name + ] + expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean" + expected = pd.array(expected_data, dtype=expected_dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_comparison_methods_array(comparison_op, dtype, dtype2): + op_name = f"__{comparison_op.__name__}__" + + a = pd.array(["a", None, "c"], dtype=dtype) + other = pd.array([None, None, "c"], dtype=dtype2) + result = comparison_op(a, other) + + # ensure operation is commutative + result2 = comparison_op(other, a) + tm.assert_equal(result, result2) + + if dtype.na_value is np.nan and dtype2.na_value is np.nan: + if operator.ne == comparison_op: + expected = np.array([True, True, False]) + else: + expected = np.array([False, False, False]) + expected[-1] = getattr(other[-1], op_name)(a[-1]) + tm.assert_numpy_array_equal(result, expected) + + else: + max_dtype = string_dtype_highest_priority(dtype, dtype2) + if max_dtype.storage == "python": + expected_dtype = "boolean" + else: + expected_dtype = "bool[pyarrow]" + + expected = np.full(len(a), fill_value=None, dtype="object") + expected[-1] = getattr(other[-1], op_name)(a[-1]) + expected = pd.array(expected, dtype=expected_dtype) + tm.assert_extension_array_equal(result, expected) + + +@td.skip_if_no("pyarrow") +def test_comparison_methods_array_arrow_extension(comparison_op, dtype2): + # Test pd.ArrowDtype(pa.string()) against other string arrays + import pyarrow as pa + + op_name = f"__{comparison_op.__name__}__" + dtype = pd.ArrowDtype(pa.string()) + a = pd.array(["a", None, "c"], dtype=dtype) + other = pd.array([None, None, "c"], dtype=dtype2) + result = comparison_op(a, other) + + # ensure operation is commutative + result2 = comparison_op(other, a) + tm.assert_equal(result, result2) + + expected = pd.array([None, None, True], dtype="bool[pyarrow]") + expected[-1] = getattr(other[-1], op_name)(a[-1]) + tm.assert_extension_array_equal(result, expected) + + +def test_comparison_methods_list(comparison_op, dtype): + op_name = f"__{comparison_op.__name__}__" + + a = pd.array(["a", None, "c"], dtype=dtype) + other = [None, None, "c"] + result = comparison_op(a, other) + + # ensure operation is commutative + result2 = comparison_op(other, a) + tm.assert_equal(result, result2) + + if dtype.na_value is np.nan: + if operator.ne == comparison_op: + expected = np.array([True, True, False]) + else: + expected = np.array([False, False, False]) + expected[-1] = getattr(other[-1], op_name)(a[-1]) + tm.assert_numpy_array_equal(result, expected) + + else: + expected_dtype = "boolean[pyarrow]" if dtype.storage == "pyarrow" else "boolean" + expected = np.full(len(a), fill_value=None, dtype="object") + expected[-1] = getattr(other[-1], op_name)(a[-1]) + expected = pd.array(expected, dtype=expected_dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_constructor_raises(cls): + if cls is pd.arrays.StringArray: + msg = "StringArray requires a sequence of strings or pandas.NA" + elif cls is StringArrayNumpySemantics: + msg = "StringArrayNumpySemantics requires a sequence of strings or NaN" + else: + msg = "Unsupported type '' for ArrowExtensionArray" + + with pytest.raises(ValueError, match=msg): + cls(np.array(["a", "b"], dtype="S1")) + + with pytest.raises(ValueError, match=msg): + cls(np.array([])) + + if cls is pd.arrays.StringArray or cls is StringArrayNumpySemantics: + # GH#45057 np.nan and None do NOT raise, as they are considered valid NAs + # for string dtype + cls(np.array(["a", np.nan], dtype=object)) + cls(np.array(["a", None], dtype=object)) + else: + with pytest.raises(ValueError, match=msg): + cls(np.array(["a", np.nan], dtype=object)) + with pytest.raises(ValueError, match=msg): + cls(np.array(["a", None], dtype=object)) + + with pytest.raises(ValueError, match=msg): + cls(np.array(["a", pd.NaT], dtype=object)) + + with pytest.raises(ValueError, match=msg): + cls(np.array(["a", np.datetime64("NaT", "ns")], dtype=object)) + + with pytest.raises(ValueError, match=msg): + cls(np.array(["a", np.timedelta64("NaT", "ns")], dtype=object)) + + +@pytest.mark.parametrize("na", [np.nan, np.float64("nan"), float("nan"), None, pd.NA]) +def test_constructor_nan_like(na): + expected = pd.arrays.StringArray(np.array(["a", pd.NA])) + tm.assert_extension_array_equal( + pd.arrays.StringArray(np.array(["a", na], dtype="object")), expected + ) + + +@pytest.mark.parametrize("copy", [True, False]) +def test_from_sequence_no_mutate(copy, cls, dtype): + nan_arr = np.array(["a", np.nan], dtype=object) + expected_input = nan_arr.copy() + na_arr = np.array(["a", pd.NA], dtype=object) + + result = cls._from_sequence(nan_arr, dtype=dtype, copy=copy) + + if cls in (ArrowStringArray, ArrowStringArrayNumpySemantics): + import pyarrow as pa + + expected = cls(pa.array(na_arr, type=pa.string(), from_pandas=True)) + elif cls is StringArrayNumpySemantics: + expected = cls(nan_arr) + else: + expected = cls(na_arr) + + tm.assert_extension_array_equal(result, expected) + tm.assert_numpy_array_equal(nan_arr, expected_input) + + +def test_astype_int(dtype): + arr = pd.array(["1", "2", "3"], dtype=dtype) + result = arr.astype("int64") + expected = np.array([1, 2, 3], dtype="int64") + tm.assert_numpy_array_equal(result, expected) + + arr = pd.array(["1", pd.NA, "3"], dtype=dtype) + if dtype.na_value is np.nan: + err = ValueError + msg = "cannot convert float NaN to integer" + else: + err = TypeError + msg = ( + r"int\(\) argument must be a string, a bytes-like " + r"object or a( real)? number" + ) + with pytest.raises(err, match=msg): + arr.astype("int64") + + +def test_astype_nullable_int(dtype): + arr = pd.array(["1", pd.NA, "3"], dtype=dtype) + + result = arr.astype("Int64") + expected = pd.array([1, pd.NA, 3], dtype="Int64") + tm.assert_extension_array_equal(result, expected) + + +def test_astype_float(dtype, any_float_dtype): + # Don't compare arrays (37974) + ser = pd.Series(["1.1", pd.NA, "3.3"], dtype=dtype) + result = ser.astype(any_float_dtype) + expected = pd.Series([1.1, np.nan, 3.3], dtype=any_float_dtype) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("skipna", [True, False]) +def test_reduce(skipna, dtype): + arr = pd.Series(["a", "b", "c"], dtype=dtype) + result = arr.sum(skipna=skipna) + assert result == "abc" + + +@pytest.mark.parametrize("skipna", [True, False]) +def test_reduce_missing(skipna, dtype): + arr = pd.Series([None, "a", None, "b", "c", None], dtype=dtype) + result = arr.sum(skipna=skipna) + if skipna: + assert result == "abc" + else: + assert pd.isna(result) + + +@pytest.mark.parametrize("method", ["min", "max"]) +@pytest.mark.parametrize("skipna", [True, False]) +def test_min_max(method, skipna, dtype): + arr = pd.Series(["a", "b", "c", None], dtype=dtype) + result = getattr(arr, method)(skipna=skipna) + if skipna: + expected = "a" if method == "min" else "c" + assert result == expected + else: + assert result is arr.dtype.na_value + + +@pytest.mark.parametrize("method", ["min", "max"]) +@pytest.mark.parametrize("box", [pd.Series, pd.array]) +def test_min_max_numpy(method, box, dtype, request): + if dtype.storage == "pyarrow" and box is pd.array: + if box is pd.array: + reason = "'<=' not supported between instances of 'str' and 'NoneType'" + else: + reason = "'ArrowStringArray' object has no attribute 'max'" + mark = pytest.mark.xfail(raises=TypeError, reason=reason) + request.applymarker(mark) + + arr = box(["a", "b", "c", None], dtype=dtype) + result = getattr(np, method)(arr) + expected = "a" if method == "min" else "c" + assert result == expected + + +def test_fillna_args(dtype): + # GH 37987 + + arr = pd.array(["a", pd.NA], dtype=dtype) + + res = arr.fillna(value="b") + expected = pd.array(["a", "b"], dtype=dtype) + tm.assert_extension_array_equal(res, expected) + + res = arr.fillna(value=np.str_("b")) + expected = pd.array(["a", "b"], dtype=dtype) + tm.assert_extension_array_equal(res, expected) + + msg = "Invalid value '1' for dtype 'str" + with pytest.raises(TypeError, match=msg): + arr.fillna(value=1) + + +def test_arrow_array(dtype): + # protocol added in 0.15.0 + pa = pytest.importorskip("pyarrow") + import pyarrow.compute as pc + + data = pd.array(["a", "b", "c"], dtype=dtype) + arr = pa.array(data) + expected = pa.array(list(data), type=pa.large_string(), from_pandas=True) + if dtype.storage == "pyarrow" and pa_version_under12p0: + expected = pa.chunked_array(expected) + if dtype.storage == "python": + expected = pc.cast(expected, pa.string()) + assert arr.equals(expected) + + +@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning") +def test_arrow_roundtrip(dtype, string_storage, using_infer_string): + # roundtrip possible from arrow 1.0.0 + pa = pytest.importorskip("pyarrow") + + data = pd.array(["a", "b", None], dtype=dtype) + df = pd.DataFrame({"a": data}) + table = pa.table(df) + if dtype.storage == "python": + assert table.field("a").type == "string" + else: + assert table.field("a").type == "large_string" + with pd.option_context("string_storage", string_storage): + result = table.to_pandas() + if dtype.na_value is np.nan and not using_infer_string: + assert result["a"].dtype == "object" + else: + assert isinstance(result["a"].dtype, pd.StringDtype) + expected = df.astype(pd.StringDtype(string_storage, na_value=dtype.na_value)) + if using_infer_string: + expected.columns = expected.columns.astype( + pd.StringDtype(string_storage, na_value=np.nan) + ) + tm.assert_frame_equal(result, expected) + # ensure the missing value is represented by NA and not np.nan or None + assert result.loc[2, "a"] is result["a"].dtype.na_value + + +@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning") +def test_arrow_from_string(using_infer_string): + # not roundtrip, but starting with pyarrow table without pandas metadata + pa = pytest.importorskip("pyarrow") + table = pa.table({"a": pa.array(["a", "b", None], type=pa.string())}) + + result = table.to_pandas() + + if using_infer_string and not pa_version_under19p0: + expected = pd.DataFrame({"a": ["a", "b", None]}, dtype="str") + else: + expected = pd.DataFrame({"a": ["a", "b", None]}, dtype="object") + tm.assert_frame_equal(result, expected) + + +@pytest.mark.filterwarnings("ignore:Passing a BlockManager:DeprecationWarning") +def test_arrow_load_from_zero_chunks(dtype, string_storage, using_infer_string): + # GH-41040 + pa = pytest.importorskip("pyarrow") + + data = pd.array([], dtype=dtype) + df = pd.DataFrame({"a": data}) + table = pa.table(df) + if dtype.storage == "python": + assert table.field("a").type == "string" + else: + assert table.field("a").type == "large_string" + # Instantiate the same table with no chunks at all + table = pa.table([pa.chunked_array([], type=pa.string())], schema=table.schema) + with pd.option_context("string_storage", string_storage): + result = table.to_pandas() + + if dtype.na_value is np.nan and not using_string_dtype(): + assert result["a"].dtype == "object" + else: + assert isinstance(result["a"].dtype, pd.StringDtype) + expected = df.astype(pd.StringDtype(string_storage, na_value=dtype.na_value)) + if using_infer_string: + expected.columns = expected.columns.astype( + pd.StringDtype(string_storage, na_value=np.nan) + ) + tm.assert_frame_equal(result, expected) + + +def test_value_counts_na(dtype): + if dtype.na_value is np.nan: + exp_dtype = "int64" + elif dtype.storage == "pyarrow": + exp_dtype = "int64[pyarrow]" + else: + exp_dtype = "Int64" + arr = pd.array(["a", "b", "a", pd.NA], dtype=dtype) + result = arr.value_counts(dropna=False) + expected = pd.Series([2, 1, 1], index=arr[[0, 1, 3]], dtype=exp_dtype, name="count") + tm.assert_series_equal(result, expected) + + result = arr.value_counts(dropna=True) + expected = pd.Series([2, 1], index=arr[:2], dtype=exp_dtype, name="count") + tm.assert_series_equal(result, expected) + + +def test_value_counts_with_normalize(dtype): + if dtype.na_value is np.nan: + exp_dtype = np.float64 + elif dtype.storage == "pyarrow": + exp_dtype = "double[pyarrow]" + else: + exp_dtype = "Float64" + ser = pd.Series(["a", "b", "a", pd.NA], dtype=dtype) + result = ser.value_counts(normalize=True) + expected = pd.Series([2, 1], index=ser[:2], dtype=exp_dtype, name="proportion") / 3 + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "values, expected", + [ + (["a", "b", "c"], np.array([False, False, False])), + (["a", "b", None], np.array([False, False, True])), + ], +) +def test_use_inf_as_na(values, expected, dtype): + # https://github.com/pandas-dev/pandas/issues/33655 + values = pd.array(values, dtype=dtype) + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with pd.option_context("mode.use_inf_as_na", True): + result = values.isna() + tm.assert_numpy_array_equal(result, expected) + + result = pd.Series(values).isna() + expected = pd.Series(expected) + tm.assert_series_equal(result, expected) + + result = pd.DataFrame(values).isna() + expected = pd.DataFrame(expected) + tm.assert_frame_equal(result, expected) + + +def test_value_counts_sort_false(dtype): + if dtype.na_value is np.nan: + exp_dtype = "int64" + elif dtype.storage == "pyarrow": + exp_dtype = "int64[pyarrow]" + else: + exp_dtype = "Int64" + ser = pd.Series(["a", "b", "c", "b"], dtype=dtype) + result = ser.value_counts(sort=False) + expected = pd.Series([1, 2, 1], index=ser[:3], dtype=exp_dtype, name="count") + tm.assert_series_equal(result, expected) + + +def test_memory_usage(dtype): + # GH 33963 + + if dtype.storage == "pyarrow": + pytest.skip(f"not applicable for {dtype.storage}") + + series = pd.Series(["a", "b", "c"], dtype=dtype) + + assert 0 < series.nbytes <= series.memory_usage() < series.memory_usage(deep=True) + + +@pytest.mark.parametrize("float_dtype", [np.float16, np.float32, np.float64]) +def test_astype_from_float_dtype(float_dtype, dtype): + # https://github.com/pandas-dev/pandas/issues/36451 + ser = pd.Series([0.1], dtype=float_dtype) + result = ser.astype(dtype) + expected = pd.Series(["0.1"], dtype=dtype) + tm.assert_series_equal(result, expected) + + +def test_to_numpy_returns_pdna_default(dtype): + arr = pd.array(["a", pd.NA, "b"], dtype=dtype) + result = np.array(arr) + expected = np.array(["a", dtype.na_value, "b"], dtype=object) + tm.assert_numpy_array_equal(result, expected) + + +def test_to_numpy_na_value(dtype, nulls_fixture): + na_value = nulls_fixture + arr = pd.array(["a", pd.NA, "b"], dtype=dtype) + result = arr.to_numpy(na_value=na_value) + expected = np.array(["a", na_value, "b"], dtype=object) + tm.assert_numpy_array_equal(result, expected) + + +def test_isin(dtype, fixed_now_ts): + s = pd.Series(["a", "b", None], dtype=dtype) + + result = s.isin(["a", "c"]) + expected = pd.Series([True, False, False]) + tm.assert_series_equal(result, expected) + + result = s.isin(["a", pd.NA]) + expected = pd.Series([True, False, True]) + tm.assert_series_equal(result, expected) + + result = s.isin([]) + expected = pd.Series([False, False, False]) + tm.assert_series_equal(result, expected) + + result = s.isin(["a", fixed_now_ts]) + expected = pd.Series([True, False, False]) + tm.assert_series_equal(result, expected) + + result = s.isin([fixed_now_ts]) + expected = pd.Series([False, False, False]) + tm.assert_series_equal(result, expected) + + +def test_isin_string_array(dtype, dtype2): + s = pd.Series(["a", "b", None], dtype=dtype) + + result = s.isin(pd.array(["a", "c"], dtype=dtype2)) + expected = pd.Series([True, False, False]) + tm.assert_series_equal(result, expected) + + result = s.isin(pd.array(["a", None], dtype=dtype2)) + expected = pd.Series([True, False, True]) + tm.assert_series_equal(result, expected) + + +def test_isin_arrow_string_array(dtype): + pa = pytest.importorskip("pyarrow") + s = pd.Series(["a", "b", None], dtype=dtype) + + result = s.isin(pd.array(["a", "c"], dtype=pd.ArrowDtype(pa.string()))) + expected = pd.Series([True, False, False]) + tm.assert_series_equal(result, expected) + + result = s.isin(pd.array(["a", None], dtype=pd.ArrowDtype(pa.string()))) + expected = pd.Series([True, False, True]) + tm.assert_series_equal(result, expected) + + +def test_setitem_scalar_with_mask_validation(dtype): + # https://github.com/pandas-dev/pandas/issues/47628 + # setting None with a boolean mask (through _putmaks) should still result + # in pd.NA values in the underlying array + ser = pd.Series(["a", "b", "c"], dtype=dtype) + mask = np.array([False, True, False]) + + ser[mask] = None + assert ser.array[1] is ser.dtype.na_value + + # for other non-string we should also raise an error + ser = pd.Series(["a", "b", "c"], dtype=dtype) + msg = "Invalid value '1' for dtype 'str" + with pytest.raises(TypeError, match=msg): + ser[mask] = 1 + + +def test_from_numpy_str(dtype): + vals = ["a", "b", "c"] + arr = np.array(vals, dtype=np.str_) + result = pd.array(arr, dtype=dtype) + expected = pd.array(vals, dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + +def test_tolist(dtype): + vals = ["a", "b", "c"] + arr = pd.array(vals, dtype=dtype) + result = arr.tolist() + expected = vals + tm.assert_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string_arrow.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string_arrow.py new file mode 100644 index 0000000000000000000000000000000000000000..aa87f5fc0f49a07b40174ef7aad5b12349d28a5b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/string_/test_string_arrow.py @@ -0,0 +1,282 @@ +import pickle +import re + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays.string_ import ( + StringArray, + StringDtype, +) +from pandas.core.arrays.string_arrow import ( + ArrowStringArray, + ArrowStringArrayNumpySemantics, +) + + +def test_eq_all_na(): + pytest.importorskip("pyarrow") + a = pd.array([pd.NA, pd.NA], dtype=StringDtype("pyarrow")) + result = a == a + expected = pd.array([pd.NA, pd.NA], dtype="boolean[pyarrow]") + tm.assert_extension_array_equal(result, expected) + + +def test_config(string_storage, using_infer_string): + # with the default string_storage setting + # always "python" at the moment + assert StringDtype().storage == "python" + + with pd.option_context("string_storage", string_storage): + assert StringDtype().storage == string_storage + result = pd.array(["a", "b"]) + assert result.dtype.storage == string_storage + + # pd.array(..) by default always returns the NA-variant + dtype = StringDtype(string_storage, na_value=pd.NA) + expected = dtype.construct_array_type()._from_sequence(["a", "b"], dtype=dtype) + tm.assert_equal(result, expected) + + +def test_config_bad_storage_raises(): + msg = re.escape("Value must be one of python|pyarrow") + with pytest.raises(ValueError, match=msg): + pd.options.mode.string_storage = "foo" + + +@pytest.mark.parametrize("chunked", [True, False]) +@pytest.mark.parametrize("array_lib", ["numpy", "pyarrow"]) +def test_constructor_not_string_type_raises(array_lib, chunked): + pa = pytest.importorskip("pyarrow") + + array_lib = pa if array_lib == "pyarrow" else np + + arr = array_lib.array([1, 2, 3]) + if chunked: + if array_lib is np: + pytest.skip("chunked not applicable to numpy array") + arr = pa.chunked_array(arr) + if array_lib is np: + msg = "Unsupported type '' for ArrowExtensionArray" + else: + msg = re.escape( + "ArrowStringArray requires a PyArrow (chunked) array of large_string type" + ) + with pytest.raises(ValueError, match=msg): + ArrowStringArray(arr) + + +@pytest.mark.parametrize("chunked", [True, False]) +def test_constructor_not_string_type_value_dictionary_raises(chunked): + pa = pytest.importorskip("pyarrow") + + arr = pa.array([1, 2, 3], pa.dictionary(pa.int32(), pa.int32())) + if chunked: + arr = pa.chunked_array(arr) + + msg = re.escape( + "ArrowStringArray requires a PyArrow (chunked) array of large_string type" + ) + with pytest.raises(ValueError, match=msg): + ArrowStringArray(arr) + + +@pytest.mark.parametrize("string_type", ["string", "large_string"]) +@pytest.mark.parametrize("chunked", [True, False]) +def test_constructor_valid_string_type_value_dictionary(string_type, chunked): + pa = pytest.importorskip("pyarrow") + + arr = pa.array(["1", "2", "3"], getattr(pa, string_type)()).dictionary_encode() + if chunked: + arr = pa.chunked_array(arr) + + arr = ArrowStringArray(arr) + # dictionary type get converted to dense large string array + assert pa.types.is_large_string(arr._pa_array.type) + + +@pytest.mark.parametrize("chunked", [True, False]) +def test_constructor_valid_string_view(chunked): + # requires pyarrow>=18 for casting string_view to string + pa = pytest.importorskip("pyarrow", minversion="18") + + arr = pa.array(["1", "2", "3"], pa.string_view()) + if chunked: + arr = pa.chunked_array(arr) + + arr = ArrowStringArray(arr) + # dictionary type get converted to dense large string array + assert pa.types.is_large_string(arr._pa_array.type) + + +def test_constructor_from_list(): + # GH#27673 + pytest.importorskip("pyarrow") + result = pd.Series(["E"], dtype=StringDtype(storage="pyarrow")) + assert isinstance(result.dtype, StringDtype) + assert result.dtype.storage == "pyarrow" + + +def test_from_sequence_wrong_dtype_raises(using_infer_string): + pytest.importorskip("pyarrow") + with pd.option_context("string_storage", "python"): + ArrowStringArray._from_sequence(["a", None, "c"], dtype="string") + + with pd.option_context("string_storage", "pyarrow"): + ArrowStringArray._from_sequence(["a", None, "c"], dtype="string") + + with pytest.raises(AssertionError, match=None): + ArrowStringArray._from_sequence(["a", None, "c"], dtype="string[python]") + + ArrowStringArray._from_sequence(["a", None, "c"], dtype="string[pyarrow]") + + if not using_infer_string: + with pytest.raises(AssertionError, match=None): + with pd.option_context("string_storage", "python"): + ArrowStringArray._from_sequence(["a", None, "c"], dtype=StringDtype()) + + with pd.option_context("string_storage", "pyarrow"): + ArrowStringArray._from_sequence(["a", None, "c"], dtype=StringDtype()) + + if not using_infer_string: + with pytest.raises(AssertionError, match=None): + ArrowStringArray._from_sequence( + ["a", None, "c"], dtype=StringDtype("python") + ) + + ArrowStringArray._from_sequence(["a", None, "c"], dtype=StringDtype("pyarrow")) + + with pd.option_context("string_storage", "python"): + StringArray._from_sequence(["a", None, "c"], dtype="string") + + with pd.option_context("string_storage", "pyarrow"): + StringArray._from_sequence(["a", None, "c"], dtype="string") + + StringArray._from_sequence(["a", None, "c"], dtype="string[python]") + + with pytest.raises(AssertionError, match=None): + StringArray._from_sequence(["a", None, "c"], dtype="string[pyarrow]") + + if not using_infer_string: + with pd.option_context("string_storage", "python"): + StringArray._from_sequence(["a", None, "c"], dtype=StringDtype()) + + if not using_infer_string: + with pytest.raises(AssertionError, match=None): + with pd.option_context("string_storage", "pyarrow"): + StringArray._from_sequence(["a", None, "c"], dtype=StringDtype()) + + StringArray._from_sequence(["a", None, "c"], dtype=StringDtype("python")) + + with pytest.raises(AssertionError, match=None): + StringArray._from_sequence(["a", None, "c"], dtype=StringDtype("pyarrow")) + + +@td.skip_if_installed("pyarrow") +def test_pyarrow_not_installed_raises(): + msg = re.escape("pyarrow>=10.0.1 is required for PyArrow backed") + + with pytest.raises(ImportError, match=msg): + StringDtype(storage="pyarrow") + + with pytest.raises(ImportError, match=msg): + ArrowStringArray([]) + + with pytest.raises(ImportError, match=msg): + ArrowStringArrayNumpySemantics([]) + + with pytest.raises(ImportError, match=msg): + ArrowStringArray._from_sequence(["a", None, "b"]) + + +@pytest.mark.parametrize("multiple_chunks", [False, True]) +@pytest.mark.parametrize( + "key, value, expected", + [ + (-1, "XX", ["a", "b", "c", "d", "XX"]), + (1, "XX", ["a", "XX", "c", "d", "e"]), + (1, None, ["a", None, "c", "d", "e"]), + (1, pd.NA, ["a", None, "c", "d", "e"]), + ([1, 3], "XX", ["a", "XX", "c", "XX", "e"]), + ([1, 3], ["XX", "YY"], ["a", "XX", "c", "YY", "e"]), + ([1, 3], ["XX", None], ["a", "XX", "c", None, "e"]), + ([1, 3], ["XX", pd.NA], ["a", "XX", "c", None, "e"]), + ([0, -1], ["XX", "YY"], ["XX", "b", "c", "d", "YY"]), + ([-1, 0], ["XX", "YY"], ["YY", "b", "c", "d", "XX"]), + (slice(3, None), "XX", ["a", "b", "c", "XX", "XX"]), + (slice(2, 4), ["XX", "YY"], ["a", "b", "XX", "YY", "e"]), + (slice(3, 1, -1), ["XX", "YY"], ["a", "b", "YY", "XX", "e"]), + (slice(None), "XX", ["XX", "XX", "XX", "XX", "XX"]), + ([False, True, False, True, False], ["XX", "YY"], ["a", "XX", "c", "YY", "e"]), + ], +) +def test_setitem(multiple_chunks, key, value, expected): + pa = pytest.importorskip("pyarrow") + + result = pa.array(list("abcde")) + expected = pa.array(expected) + + if multiple_chunks: + result = pa.chunked_array([result[:3], result[3:]]) + expected = pa.chunked_array([expected[:3], expected[3:]]) + + result = ArrowStringArray(result) + expected = ArrowStringArray(expected) + + result[key] = value + tm.assert_equal(result, expected) + + +def test_setitem_invalid_indexer_raises(): + pa = pytest.importorskip("pyarrow") + + arr = ArrowStringArray(pa.array(list("abcde"))) + + with pytest.raises(IndexError, match=None): + arr[5] = "foo" + + with pytest.raises(IndexError, match=None): + arr[-6] = "foo" + + with pytest.raises(IndexError, match=None): + arr[[0, 5]] = "foo" + + with pytest.raises(IndexError, match=None): + arr[[0, -6]] = "foo" + + with pytest.raises(IndexError, match=None): + arr[[True, True, False]] = "foo" + + with pytest.raises(ValueError, match=None): + arr[[0, 1]] = ["foo", "bar", "baz"] + + +@pytest.mark.parametrize("na_value", [pd.NA, np.nan]) +def test_pickle_roundtrip(na_value): + # GH 42600 + pytest.importorskip("pyarrow") + dtype = StringDtype("pyarrow", na_value=na_value) + expected = pd.Series(range(10), dtype=dtype) + expected_sliced = expected.head(2) + full_pickled = pickle.dumps(expected) + sliced_pickled = pickle.dumps(expected_sliced) + + assert len(full_pickled) > len(sliced_pickled) + + result = pickle.loads(full_pickled) + tm.assert_series_equal(result, expected) + + result_sliced = pickle.loads(sliced_pickled) + tm.assert_series_equal(result_sliced, expected_sliced) + + +def test_string_dtype_error_message(): + # GH#55051 + pytest.importorskip("pyarrow") + msg = "Storage must be 'python' or 'pyarrow'." + with pytest.raises(ValueError, match=msg): + StringDtype("bla") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_array.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_array.py new file mode 100644 index 0000000000000000000000000000000000000000..158a963845b066139868de6905f45c83da1ca4bb --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_array.py @@ -0,0 +1,519 @@ +import datetime +import decimal +import re + +import numpy as np +import pytest +import pytz + +from pandas._config import using_string_dtype + +import pandas as pd +import pandas._testing as tm +from pandas.api.extensions import register_extension_dtype +from pandas.arrays import ( + BooleanArray, + DatetimeArray, + FloatingArray, + IntegerArray, + IntervalArray, + SparseArray, + TimedeltaArray, +) +from pandas.core.arrays import ( + NumpyExtensionArray, + period_array, +) +from pandas.tests.extension.decimal import ( + DecimalArray, + DecimalDtype, + to_decimal, +) + + +@pytest.mark.parametrize("dtype_unit", ["M8[h]", "M8[m]", "m8[h]", "M8[m]"]) +def test_dt64_array(dtype_unit): + # PR 53817 + dtype_var = np.dtype(dtype_unit) + msg = ( + r"datetime64 and timedelta64 dtype resolutions other than " + r"'s', 'ms', 'us', and 'ns' are deprecated. " + r"In future releases passing unsupported resolutions will " + r"raise an exception." + ) + with tm.assert_produces_warning(FutureWarning, match=re.escape(msg)): + pd.array([], dtype=dtype_var) + + +@pytest.mark.parametrize( + "data, dtype, expected", + [ + # Basic NumPy defaults. + ([], None, FloatingArray._from_sequence([], dtype="Float64")), + ([1, 2], None, IntegerArray._from_sequence([1, 2], dtype="Int64")), + ([1, 2], object, NumpyExtensionArray(np.array([1, 2], dtype=object))), + ( + [1, 2], + np.dtype("float32"), + NumpyExtensionArray(np.array([1.0, 2.0], dtype=np.dtype("float32"))), + ), + ( + np.array([], dtype=object), + None, + NumpyExtensionArray(np.array([], dtype=object)), + ), + ( + np.array([1, 2], dtype="int64"), + None, + IntegerArray._from_sequence([1, 2], dtype="Int64"), + ), + ( + np.array([1.0, 2.0], dtype="float64"), + None, + FloatingArray._from_sequence([1.0, 2.0], dtype="Float64"), + ), + # String alias passes through to NumPy + ([1, 2], "float32", NumpyExtensionArray(np.array([1, 2], dtype="float32"))), + ([1, 2], "int64", NumpyExtensionArray(np.array([1, 2], dtype=np.int64))), + # GH#44715 FloatingArray does not support float16, so fall + # back to NumpyExtensionArray + ( + np.array([1, 2], dtype=np.float16), + None, + NumpyExtensionArray(np.array([1, 2], dtype=np.float16)), + ), + # idempotency with e.g. pd.array(pd.array([1, 2], dtype="int64")) + ( + NumpyExtensionArray(np.array([1, 2], dtype=np.int32)), + None, + NumpyExtensionArray(np.array([1, 2], dtype=np.int32)), + ), + # Period alias + ( + [pd.Period("2000", "D"), pd.Period("2001", "D")], + "Period[D]", + period_array(["2000", "2001"], freq="D"), + ), + # Period dtype + ( + [pd.Period("2000", "D")], + pd.PeriodDtype("D"), + period_array(["2000"], freq="D"), + ), + # Datetime (naive) + ( + [1, 2], + np.dtype("datetime64[ns]"), + DatetimeArray._from_sequence( + np.array([1, 2], dtype="M8[ns]"), dtype="M8[ns]" + ), + ), + ( + [1, 2], + np.dtype("datetime64[s]"), + DatetimeArray._from_sequence( + np.array([1, 2], dtype="M8[s]"), dtype="M8[s]" + ), + ), + ( + np.array([1, 2], dtype="datetime64[ns]"), + None, + DatetimeArray._from_sequence( + np.array([1, 2], dtype="M8[ns]"), dtype="M8[ns]" + ), + ), + ( + pd.DatetimeIndex(["2000", "2001"]), + np.dtype("datetime64[ns]"), + DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"), + ), + ( + pd.DatetimeIndex(["2000", "2001"]), + None, + DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"), + ), + ( + ["2000", "2001"], + np.dtype("datetime64[ns]"), + DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"), + ), + # Datetime (tz-aware) + ( + ["2000", "2001"], + pd.DatetimeTZDtype(tz="CET"), + DatetimeArray._from_sequence( + ["2000", "2001"], dtype=pd.DatetimeTZDtype(tz="CET") + ), + ), + # Timedelta + ( + ["1h", "2h"], + np.dtype("timedelta64[ns]"), + TimedeltaArray._from_sequence(["1h", "2h"], dtype="m8[ns]"), + ), + ( + pd.TimedeltaIndex(["1h", "2h"]), + np.dtype("timedelta64[ns]"), + TimedeltaArray._from_sequence(["1h", "2h"], dtype="m8[ns]"), + ), + ( + np.array([1, 2], dtype="m8[s]"), + np.dtype("timedelta64[s]"), + TimedeltaArray._from_sequence( + np.array([1, 2], dtype="m8[s]"), dtype="m8[s]" + ), + ), + ( + pd.TimedeltaIndex(["1h", "2h"]), + None, + TimedeltaArray._from_sequence(["1h", "2h"], dtype="m8[ns]"), + ), + ( + # preserve non-nano, i.e. don't cast to NumpyExtensionArray + TimedeltaArray._simple_new( + np.arange(5, dtype=np.int64).view("m8[s]"), dtype=np.dtype("m8[s]") + ), + None, + TimedeltaArray._simple_new( + np.arange(5, dtype=np.int64).view("m8[s]"), dtype=np.dtype("m8[s]") + ), + ), + ( + # preserve non-nano, i.e. don't cast to NumpyExtensionArray + TimedeltaArray._simple_new( + np.arange(5, dtype=np.int64).view("m8[s]"), dtype=np.dtype("m8[s]") + ), + np.dtype("m8[s]"), + TimedeltaArray._simple_new( + np.arange(5, dtype=np.int64).view("m8[s]"), dtype=np.dtype("m8[s]") + ), + ), + # Category + (["a", "b"], "category", pd.Categorical(["a", "b"])), + ( + ["a", "b"], + pd.CategoricalDtype(None, ordered=True), + pd.Categorical(["a", "b"], ordered=True), + ), + # Interval + ( + [pd.Interval(1, 2), pd.Interval(3, 4)], + "interval", + IntervalArray.from_tuples([(1, 2), (3, 4)]), + ), + # Sparse + ([0, 1], "Sparse[int64]", SparseArray([0, 1], dtype="int64")), + # IntegerNA + ([1, None], "Int16", pd.array([1, None], dtype="Int16")), + ( + pd.Series([1, 2]), + None, + NumpyExtensionArray(np.array([1, 2], dtype=np.int64)), + ), + # String + ( + ["a", None], + "string", + pd.StringDtype() + .construct_array_type() + ._from_sequence(["a", None], dtype=pd.StringDtype()), + ), + ( + ["a", None], + "str", + pd.StringDtype(na_value=np.nan) + .construct_array_type() + ._from_sequence(["a", None], dtype=pd.StringDtype(na_value=np.nan)) + if using_string_dtype() + else NumpyExtensionArray(np.array(["a", "None"])), + ), + ( + ["a", None], + pd.StringDtype(), + pd.StringDtype() + .construct_array_type() + ._from_sequence(["a", None], dtype=pd.StringDtype()), + ), + ( + ["a", None], + pd.StringDtype(na_value=np.nan), + pd.StringDtype(na_value=np.nan) + .construct_array_type() + ._from_sequence(["a", None], dtype=pd.StringDtype(na_value=np.nan)), + ), + ( + # numpy array with string dtype + np.array(["a", "b"], dtype=str), + pd.StringDtype(), + pd.StringDtype() + .construct_array_type() + ._from_sequence(["a", "b"], dtype=pd.StringDtype()), + ), + ( + # numpy array with string dtype + np.array(["a", "b"], dtype=str), + pd.StringDtype(na_value=np.nan), + pd.StringDtype(na_value=np.nan) + .construct_array_type() + ._from_sequence(["a", "b"], dtype=pd.StringDtype(na_value=np.nan)), + ), + # Boolean + ( + [True, None], + "boolean", + BooleanArray._from_sequence([True, None], dtype="boolean"), + ), + ( + [True, None], + pd.BooleanDtype(), + BooleanArray._from_sequence([True, None], dtype="boolean"), + ), + # Index + (pd.Index([1, 2]), None, NumpyExtensionArray(np.array([1, 2], dtype=np.int64))), + # Series[EA] returns the EA + ( + pd.Series(pd.Categorical(["a", "b"], categories=["a", "b", "c"])), + None, + pd.Categorical(["a", "b"], categories=["a", "b", "c"]), + ), + # "3rd party" EAs work + ([decimal.Decimal(0), decimal.Decimal(1)], "decimal", to_decimal([0, 1])), + # pass an ExtensionArray, but a different dtype + ( + period_array(["2000", "2001"], freq="D"), + "category", + pd.Categorical([pd.Period("2000", "D"), pd.Period("2001", "D")]), + ), + ], +) +def test_array(data, dtype, expected): + result = pd.array(data, dtype=dtype) + tm.assert_equal(result, expected) + + +def test_array_copy(): + a = np.array([1, 2]) + # default is to copy + b = pd.array(a, dtype=a.dtype) + assert not tm.shares_memory(a, b) + + # copy=True + b = pd.array(a, dtype=a.dtype, copy=True) + assert not tm.shares_memory(a, b) + + # copy=False + b = pd.array(a, dtype=a.dtype, copy=False) + assert tm.shares_memory(a, b) + + +cet = pytz.timezone("CET") + + +@pytest.mark.parametrize( + "data, expected", + [ + # period + ( + [pd.Period("2000", "D"), pd.Period("2001", "D")], + period_array(["2000", "2001"], freq="D"), + ), + # interval + ([pd.Interval(0, 1), pd.Interval(1, 2)], IntervalArray.from_breaks([0, 1, 2])), + # datetime + ( + [pd.Timestamp("2000"), pd.Timestamp("2001")], + DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"), + ), + ( + [datetime.datetime(2000, 1, 1), datetime.datetime(2001, 1, 1)], + DatetimeArray._from_sequence(["2000", "2001"], dtype="M8[ns]"), + ), + ( + np.array([1, 2], dtype="M8[ns]"), + DatetimeArray._from_sequence(np.array([1, 2], dtype="M8[ns]")), + ), + ( + np.array([1, 2], dtype="M8[us]"), + DatetimeArray._simple_new( + np.array([1, 2], dtype="M8[us]"), dtype=np.dtype("M8[us]") + ), + ), + # datetimetz + ( + [pd.Timestamp("2000", tz="CET"), pd.Timestamp("2001", tz="CET")], + DatetimeArray._from_sequence( + ["2000", "2001"], dtype=pd.DatetimeTZDtype(tz="CET", unit="ns") + ), + ), + ( + [ + datetime.datetime(2000, 1, 1, tzinfo=cet), + datetime.datetime(2001, 1, 1, tzinfo=cet), + ], + DatetimeArray._from_sequence( + ["2000", "2001"], dtype=pd.DatetimeTZDtype(tz=cet, unit="ns") + ), + ), + # timedelta + ( + [pd.Timedelta("1h"), pd.Timedelta("2h")], + TimedeltaArray._from_sequence(["1h", "2h"], dtype="m8[ns]"), + ), + ( + np.array([1, 2], dtype="m8[ns]"), + TimedeltaArray._from_sequence(np.array([1, 2], dtype="m8[ns]")), + ), + ( + np.array([1, 2], dtype="m8[us]"), + TimedeltaArray._from_sequence(np.array([1, 2], dtype="m8[us]")), + ), + # integer + ([1, 2], IntegerArray._from_sequence([1, 2], dtype="Int64")), + ([1, None], IntegerArray._from_sequence([1, None], dtype="Int64")), + ([1, pd.NA], IntegerArray._from_sequence([1, pd.NA], dtype="Int64")), + ([1, np.nan], IntegerArray._from_sequence([1, np.nan], dtype="Int64")), + # float + ([0.1, 0.2], FloatingArray._from_sequence([0.1, 0.2], dtype="Float64")), + ([0.1, None], FloatingArray._from_sequence([0.1, pd.NA], dtype="Float64")), + ([0.1, np.nan], FloatingArray._from_sequence([0.1, pd.NA], dtype="Float64")), + ([0.1, pd.NA], FloatingArray._from_sequence([0.1, pd.NA], dtype="Float64")), + # integer-like float + ([1.0, 2.0], FloatingArray._from_sequence([1.0, 2.0], dtype="Float64")), + ([1.0, None], FloatingArray._from_sequence([1.0, pd.NA], dtype="Float64")), + ([1.0, np.nan], FloatingArray._from_sequence([1.0, pd.NA], dtype="Float64")), + ([1.0, pd.NA], FloatingArray._from_sequence([1.0, pd.NA], dtype="Float64")), + # mixed-integer-float + ([1, 2.0], FloatingArray._from_sequence([1.0, 2.0], dtype="Float64")), + ( + [1, np.nan, 2.0], + FloatingArray._from_sequence([1.0, None, 2.0], dtype="Float64"), + ), + # string + ( + ["a", "b"], + pd.StringDtype() + .construct_array_type() + ._from_sequence(["a", "b"], dtype=pd.StringDtype()), + ), + ( + ["a", None], + pd.StringDtype() + .construct_array_type() + ._from_sequence(["a", None], dtype=pd.StringDtype()), + ), + ( + # numpy array with string dtype + np.array(["a", "b"], dtype=str), + pd.StringDtype() + .construct_array_type() + ._from_sequence(["a", "b"], dtype=pd.StringDtype()), + ), + # Boolean + ([True, False], BooleanArray._from_sequence([True, False], dtype="boolean")), + ([True, None], BooleanArray._from_sequence([True, None], dtype="boolean")), + ], +) +def test_array_inference(data, expected): + result = pd.array(data) + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "data", + [ + # mix of frequencies + [pd.Period("2000", "D"), pd.Period("2001", "Y")], + # mix of closed + [pd.Interval(0, 1, closed="left"), pd.Interval(1, 2, closed="right")], + # Mix of timezones + [pd.Timestamp("2000", tz="CET"), pd.Timestamp("2000", tz="UTC")], + # Mix of tz-aware and tz-naive + [pd.Timestamp("2000", tz="CET"), pd.Timestamp("2000")], + np.array([pd.Timestamp("2000"), pd.Timestamp("2000", tz="CET")]), + ], +) +def test_array_inference_fails(data): + result = pd.array(data) + expected = NumpyExtensionArray(np.array(data, dtype=object)) + tm.assert_extension_array_equal(result, expected) + + +@pytest.mark.parametrize("data", [np.array(0)]) +def test_nd_raises(data): + with pytest.raises(ValueError, match="NumpyExtensionArray must be 1-dimensional"): + pd.array(data, dtype="int64") + + +def test_scalar_raises(): + with pytest.raises(ValueError, match="Cannot pass scalar '1'"): + pd.array(1) + + +def test_dataframe_raises(): + # GH#51167 don't accidentally cast to StringArray by doing inference on columns + df = pd.DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) + msg = "Cannot pass DataFrame to 'pandas.array'" + with pytest.raises(TypeError, match=msg): + pd.array(df) + + +def test_bounds_check(): + # GH21796 + with pytest.raises( + TypeError, match=r"cannot safely cast non-equivalent int(32|64) to uint16" + ): + pd.array([-1, 2, 3], dtype="UInt16") + + +# --------------------------------------------------------------------------- +# A couple dummy classes to ensure that Series and Indexes are unboxed before +# getting to the EA classes. + + +@register_extension_dtype +class DecimalDtype2(DecimalDtype): + name = "decimal2" + + @classmethod + def construct_array_type(cls): + """ + Return the array type associated with this dtype. + + Returns + ------- + type + """ + return DecimalArray2 + + +class DecimalArray2(DecimalArray): + @classmethod + def _from_sequence(cls, scalars, *, dtype=None, copy=False): + if isinstance(scalars, (pd.Series, pd.Index)): + raise TypeError("scalars should not be of type pd.Series or pd.Index") + + return super()._from_sequence(scalars, dtype=dtype, copy=copy) + + +def test_array_unboxes(index_or_series): + box = index_or_series + + data = box([decimal.Decimal("1"), decimal.Decimal("2")]) + dtype = DecimalDtype2() + # make sure it works + with pytest.raises( + TypeError, match="scalars should not be of type pd.Series or pd.Index" + ): + DecimalArray2._from_sequence(data, dtype=dtype) + + result = pd.array(data, dtype="decimal2") + expected = DecimalArray2._from_sequence(data.values, dtype=dtype) + tm.assert_equal(result, expected) + + +def test_array_to_numpy_na(): + # GH#40638 + arr = pd.array([pd.NA, 1], dtype="string[python]") + result = arr.to_numpy(na_value=True, dtype=bool) + expected = np.array([True, True]) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimelike.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimelike.py new file mode 100644 index 0000000000000000000000000000000000000000..0397913b69b26833c394fb25c427130cea098674 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimelike.py @@ -0,0 +1,1360 @@ +from __future__ import annotations + +import re +import warnings + +import numpy as np +import pytest + +from pandas._libs import ( + NaT, + OutOfBoundsDatetime, + Timestamp, +) +from pandas._libs.tslibs.dtypes import freq_to_period_freqstr +from pandas.compat.numpy import np_version_gt2 + +import pandas as pd +from pandas import ( + DatetimeIndex, + Period, + PeriodIndex, + TimedeltaIndex, +) +import pandas._testing as tm +from pandas.core.arrays import ( + DatetimeArray, + NumpyExtensionArray, + PeriodArray, + TimedeltaArray, +) + + +# TODO: more freq variants +@pytest.fixture(params=["D", "B", "W", "ME", "QE", "YE"]) +def freqstr(request): + """Fixture returning parametrized frequency in string format.""" + return request.param + + +@pytest.fixture +def period_index(freqstr): + """ + A fixture to provide PeriodIndex objects with different frequencies. + + Most PeriodArray behavior is already tested in PeriodIndex tests, + so here we just test that the PeriodArray behavior matches + the PeriodIndex behavior. + """ + # TODO: non-monotone indexes; NaTs, different start dates + with warnings.catch_warnings(): + # suppress deprecation of Period[B] + warnings.filterwarnings( + "ignore", message="Period with BDay freq", category=FutureWarning + ) + freqstr = freq_to_period_freqstr(1, freqstr) + pi = pd.period_range(start=Timestamp("2000-01-01"), periods=100, freq=freqstr) + return pi + + +@pytest.fixture +def datetime_index(freqstr): + """ + A fixture to provide DatetimeIndex objects with different frequencies. + + Most DatetimeArray behavior is already tested in DatetimeIndex tests, + so here we just test that the DatetimeArray behavior matches + the DatetimeIndex behavior. + """ + # TODO: non-monotone indexes; NaTs, different start dates, timezones + dti = pd.date_range(start=Timestamp("2000-01-01"), periods=100, freq=freqstr) + return dti + + +@pytest.fixture +def timedelta_index(): + """ + A fixture to provide TimedeltaIndex objects with different frequencies. + Most TimedeltaArray behavior is already tested in TimedeltaIndex tests, + so here we just test that the TimedeltaArray behavior matches + the TimedeltaIndex behavior. + """ + # TODO: flesh this out + return TimedeltaIndex(["1 Day", "3 Hours", "NaT"]) + + +class SharedTests: + index_cls: type[DatetimeIndex | PeriodIndex | TimedeltaIndex] + + @pytest.fixture + def arr1d(self): + """Fixture returning DatetimeArray with daily frequency.""" + data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + if self.array_cls is PeriodArray: + arr = self.array_cls(data, freq="D") + else: + arr = self.index_cls(data, freq="D")._data + return arr + + def test_compare_len1_raises(self, arr1d): + # make sure we raise when comparing with different lengths, specific + # to the case where one has length-1, which numpy would broadcast + arr = arr1d + idx = self.index_cls(arr) + + with pytest.raises(ValueError, match="Lengths must match"): + arr == arr[:1] + + # test the index classes while we're at it, GH#23078 + with pytest.raises(ValueError, match="Lengths must match"): + idx <= idx[[0]] + + @pytest.mark.parametrize( + "result", + [ + pd.date_range("2020", periods=3), + pd.date_range("2020", periods=3, tz="UTC"), + pd.timedelta_range("0 days", periods=3), + pd.period_range("2020Q1", periods=3, freq="Q"), + ], + ) + def test_compare_with_Categorical(self, result): + expected = pd.Categorical(result) + assert all(result == expected) + assert not any(result != expected) + + @pytest.mark.parametrize("reverse", [True, False]) + @pytest.mark.parametrize("as_index", [True, False]) + def test_compare_categorical_dtype(self, arr1d, as_index, reverse, ordered): + other = pd.Categorical(arr1d, ordered=ordered) + if as_index: + other = pd.CategoricalIndex(other) + + left, right = arr1d, other + if reverse: + left, right = right, left + + ones = np.ones(arr1d.shape, dtype=bool) + zeros = ~ones + + result = left == right + tm.assert_numpy_array_equal(result, ones) + + result = left != right + tm.assert_numpy_array_equal(result, zeros) + + if not reverse and not as_index: + # Otherwise Categorical raises TypeError bc it is not ordered + # TODO: we should probably get the same behavior regardless? + result = left < right + tm.assert_numpy_array_equal(result, zeros) + + result = left <= right + tm.assert_numpy_array_equal(result, ones) + + result = left > right + tm.assert_numpy_array_equal(result, zeros) + + result = left >= right + tm.assert_numpy_array_equal(result, ones) + + def test_take(self): + data = np.arange(100, dtype="i8") * 24 * 3600 * 10**9 + np.random.default_rng(2).shuffle(data) + + if self.array_cls is PeriodArray: + arr = PeriodArray(data, dtype="period[D]") + else: + arr = self.index_cls(data)._data + idx = self.index_cls._simple_new(arr) + + takers = [1, 4, 94] + result = arr.take(takers) + expected = idx.take(takers) + + tm.assert_index_equal(self.index_cls(result), expected) + + takers = np.array([1, 4, 94]) + result = arr.take(takers) + expected = idx.take(takers) + + tm.assert_index_equal(self.index_cls(result), expected) + + @pytest.mark.parametrize("fill_value", [2, 2.0, Timestamp(2021, 1, 1, 12).time]) + def test_take_fill_raises(self, fill_value, arr1d): + msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got" + with pytest.raises(TypeError, match=msg): + arr1d.take([0, 1], allow_fill=True, fill_value=fill_value) + + def test_take_fill(self, arr1d): + arr = arr1d + + result = arr.take([-1, 1], allow_fill=True, fill_value=None) + assert result[0] is NaT + + result = arr.take([-1, 1], allow_fill=True, fill_value=np.nan) + assert result[0] is NaT + + result = arr.take([-1, 1], allow_fill=True, fill_value=NaT) + assert result[0] is NaT + + @pytest.mark.filterwarnings( + "ignore:Period with BDay freq is deprecated:FutureWarning" + ) + def test_take_fill_str(self, arr1d): + # Cast str fill_value matching other fill_value-taking methods + result = arr1d.take([-1, 1], allow_fill=True, fill_value=str(arr1d[-1])) + expected = arr1d[[-1, 1]] + tm.assert_equal(result, expected) + + msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got" + with pytest.raises(TypeError, match=msg): + arr1d.take([-1, 1], allow_fill=True, fill_value="foo") + + def test_concat_same_type(self, arr1d): + arr = arr1d + idx = self.index_cls(arr) + idx = idx.insert(0, NaT) + arr = arr1d + + result = arr._concat_same_type([arr[:-1], arr[1:], arr]) + arr2 = arr.astype(object) + expected = self.index_cls(np.concatenate([arr2[:-1], arr2[1:], arr2])) + + tm.assert_index_equal(self.index_cls(result), expected) + + def test_unbox_scalar(self, arr1d): + result = arr1d._unbox_scalar(arr1d[0]) + expected = arr1d._ndarray.dtype.type + assert isinstance(result, expected) + + result = arr1d._unbox_scalar(NaT) + assert isinstance(result, expected) + + msg = f"'value' should be a {self.scalar_type.__name__}." + with pytest.raises(ValueError, match=msg): + arr1d._unbox_scalar("foo") + + def test_check_compatible_with(self, arr1d): + arr1d._check_compatible_with(arr1d[0]) + arr1d._check_compatible_with(arr1d[:1]) + arr1d._check_compatible_with(NaT) + + def test_scalar_from_string(self, arr1d): + result = arr1d._scalar_from_string(str(arr1d[0])) + assert result == arr1d[0] + + def test_reduce_invalid(self, arr1d): + msg = "does not support reduction 'not a method'" + with pytest.raises(TypeError, match=msg): + arr1d._reduce("not a method") + + @pytest.mark.parametrize("method", ["pad", "backfill"]) + def test_fillna_method_doesnt_change_orig(self, method): + data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + if self.array_cls is PeriodArray: + arr = self.array_cls(data, dtype="period[D]") + else: + arr = self.array_cls._from_sequence(data) + arr[4] = NaT + + fill_value = arr[3] if method == "pad" else arr[5] + + result = arr._pad_or_backfill(method=method) + assert result[4] == fill_value + + # check that the original was not changed + assert arr[4] is NaT + + def test_searchsorted(self): + data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + if self.array_cls is PeriodArray: + arr = self.array_cls(data, dtype="period[D]") + else: + arr = self.array_cls._from_sequence(data) + + # scalar + result = arr.searchsorted(arr[1]) + assert result == 1 + + result = arr.searchsorted(arr[2], side="right") + assert result == 3 + + # own-type + result = arr.searchsorted(arr[1:3]) + expected = np.array([1, 2], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + result = arr.searchsorted(arr[1:3], side="right") + expected = np.array([2, 3], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + # GH#29884 match numpy convention on whether NaT goes + # at the end or the beginning + result = arr.searchsorted(NaT) + assert result == 10 + + @pytest.mark.parametrize("box", [None, "index", "series"]) + def test_searchsorted_castable_strings(self, arr1d, box, string_storage): + arr = arr1d + if box is None: + pass + elif box == "index": + # Test the equivalent Index.searchsorted method while we're here + arr = self.index_cls(arr) + else: + # Test the equivalent Series.searchsorted method while we're here + arr = pd.Series(arr) + + # scalar + result = arr.searchsorted(str(arr[1])) + assert result == 1 + + result = arr.searchsorted(str(arr[2]), side="right") + assert result == 3 + + result = arr.searchsorted([str(x) for x in arr[1:3]]) + expected = np.array([1, 2], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + with pytest.raises( + TypeError, + match=re.escape( + f"value should be a '{arr1d._scalar_type.__name__}', 'NaT', " + "or array of those. Got 'str' instead." + ), + ): + arr.searchsorted("foo") + + with pd.option_context("string_storage", string_storage): + with pytest.raises( + TypeError, + match=re.escape( + f"value should be a '{arr1d._scalar_type.__name__}', 'NaT', " + "or array of those. Got string array instead." + ), + ): + arr.searchsorted([str(arr[1]), "baz"]) + + def test_getitem_near_implementation_bounds(self): + # We only check tz-naive for DTA bc the bounds are slightly different + # for other tzs + i8vals = np.asarray([NaT._value + n for n in range(1, 5)], dtype="i8") + if self.array_cls is PeriodArray: + arr = self.array_cls(i8vals, dtype="period[ns]") + else: + arr = self.index_cls(i8vals, freq="ns")._data + arr[0] # should not raise OutOfBoundsDatetime + + index = pd.Index(arr) + index[0] # should not raise OutOfBoundsDatetime + + ser = pd.Series(arr) + ser[0] # should not raise OutOfBoundsDatetime + + def test_getitem_2d(self, arr1d): + # 2d slicing on a 1D array + expected = type(arr1d)._simple_new( + arr1d._ndarray[:, np.newaxis], dtype=arr1d.dtype + ) + result = arr1d[:, np.newaxis] + tm.assert_equal(result, expected) + + # Lookup on a 2D array + arr2d = expected + expected = type(arr2d)._simple_new(arr2d._ndarray[:3, 0], dtype=arr2d.dtype) + result = arr2d[:3, 0] + tm.assert_equal(result, expected) + + # Scalar lookup + result = arr2d[-1, 0] + expected = arr1d[-1] + assert result == expected + + def test_iter_2d(self, arr1d): + data2d = arr1d._ndarray[:3, np.newaxis] + arr2d = type(arr1d)._simple_new(data2d, dtype=arr1d.dtype) + result = list(arr2d) + assert len(result) == 3 + for x in result: + assert isinstance(x, type(arr1d)) + assert x.ndim == 1 + assert x.dtype == arr1d.dtype + + def test_repr_2d(self, arr1d): + data2d = arr1d._ndarray[:3, np.newaxis] + arr2d = type(arr1d)._simple_new(data2d, dtype=arr1d.dtype) + + result = repr(arr2d) + + if isinstance(arr2d, TimedeltaArray): + expected = ( + f"<{type(arr2d).__name__}>\n" + "[\n" + f"['{arr1d[0]._repr_base()}'],\n" + f"['{arr1d[1]._repr_base()}'],\n" + f"['{arr1d[2]._repr_base()}']\n" + "]\n" + f"Shape: (3, 1), dtype: {arr1d.dtype}" + ) + else: + expected = ( + f"<{type(arr2d).__name__}>\n" + "[\n" + f"['{arr1d[0]}'],\n" + f"['{arr1d[1]}'],\n" + f"['{arr1d[2]}']\n" + "]\n" + f"Shape: (3, 1), dtype: {arr1d.dtype}" + ) + + assert result == expected + + def test_setitem(self): + data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + if self.array_cls is PeriodArray: + arr = self.array_cls(data, dtype="period[D]") + else: + arr = self.index_cls(data, freq="D")._data + + arr[0] = arr[1] + expected = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + expected[0] = expected[1] + + tm.assert_numpy_array_equal(arr.asi8, expected) + + arr[:2] = arr[-2:] + expected[:2] = expected[-2:] + tm.assert_numpy_array_equal(arr.asi8, expected) + + @pytest.mark.parametrize( + "box", + [ + pd.Index, + pd.Series, + np.array, + list, + NumpyExtensionArray, + ], + ) + def test_setitem_object_dtype(self, box, arr1d): + expected = arr1d.copy()[::-1] + if expected.dtype.kind in ["m", "M"]: + expected = expected._with_freq(None) + + vals = expected + if box is list: + vals = list(vals) + elif box is np.array: + # if we do np.array(x).astype(object) then dt64 and td64 cast to ints + vals = np.array(vals.astype(object)) + elif box is NumpyExtensionArray: + vals = box(np.asarray(vals, dtype=object)) + else: + vals = box(vals).astype(object) + + arr1d[:] = vals + + tm.assert_equal(arr1d, expected) + + def test_setitem_strs(self, arr1d): + # Check that we parse strs in both scalar and listlike + + # Setting list-like of strs + expected = arr1d.copy() + expected[[0, 1]] = arr1d[-2:] + + result = arr1d.copy() + result[:2] = [str(x) for x in arr1d[-2:]] + tm.assert_equal(result, expected) + + # Same thing but now for just a scalar str + expected = arr1d.copy() + expected[0] = arr1d[-1] + + result = arr1d.copy() + result[0] = str(arr1d[-1]) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("as_index", [True, False]) + def test_setitem_categorical(self, arr1d, as_index): + expected = arr1d.copy()[::-1] + if not isinstance(expected, PeriodArray): + expected = expected._with_freq(None) + + cat = pd.Categorical(arr1d) + if as_index: + cat = pd.CategoricalIndex(cat) + + arr1d[:] = cat[::-1] + + tm.assert_equal(arr1d, expected) + + def test_setitem_raises(self, arr1d): + arr = arr1d[:10] + val = arr[0] + + with pytest.raises(IndexError, match="index 12 is out of bounds"): + arr[12] = val + + with pytest.raises(TypeError, match="value should be a.* 'object'"): + arr[0] = object() + + msg = "cannot set using a list-like indexer with a different length" + with pytest.raises(ValueError, match=msg): + # GH#36339 + arr[[]] = [arr[1]] + + msg = "cannot set using a slice indexer with a different length than" + with pytest.raises(ValueError, match=msg): + # GH#36339 + arr[1:1] = arr[:3] + + @pytest.mark.parametrize("box", [list, np.array, pd.Index, pd.Series]) + def test_setitem_numeric_raises(self, arr1d, box): + # We dont case e.g. int64 to our own dtype for setitem + + msg = ( + f"value should be a '{arr1d._scalar_type.__name__}', " + "'NaT', or array of those. Got" + ) + with pytest.raises(TypeError, match=msg): + arr1d[:2] = box([0, 1]) + + with pytest.raises(TypeError, match=msg): + arr1d[:2] = box([0.0, 1.0]) + + def test_inplace_arithmetic(self): + # GH#24115 check that iadd and isub are actually in-place + data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + if self.array_cls is PeriodArray: + arr = self.array_cls(data, dtype="period[D]") + else: + arr = self.index_cls(data, freq="D")._data + + expected = arr + pd.Timedelta(days=1) + arr += pd.Timedelta(days=1) + tm.assert_equal(arr, expected) + + expected = arr - pd.Timedelta(days=1) + arr -= pd.Timedelta(days=1) + tm.assert_equal(arr, expected) + + def test_shift_fill_int_deprecated(self, arr1d): + # GH#31971, enforced in 2.0 + with pytest.raises(TypeError, match="value should be a"): + arr1d.shift(1, fill_value=1) + + def test_median(self, arr1d): + arr = arr1d + if len(arr) % 2 == 0: + # make it easier to define `expected` + arr = arr[:-1] + + expected = arr[len(arr) // 2] + + result = arr.median() + assert type(result) is type(expected) + assert result == expected + + arr[len(arr) // 2] = NaT + if not isinstance(expected, Period): + expected = arr[len(arr) // 2 - 1 : len(arr) // 2 + 2].mean() + + assert arr.median(skipna=False) is NaT + + result = arr.median() + assert type(result) is type(expected) + assert result == expected + + assert arr[:0].median() is NaT + assert arr[:0].median(skipna=False) is NaT + + # 2d Case + arr2 = arr.reshape(-1, 1) + + result = arr2.median(axis=None) + assert type(result) is type(expected) + assert result == expected + + assert arr2.median(axis=None, skipna=False) is NaT + + result = arr2.median(axis=0) + expected2 = type(arr)._from_sequence([expected], dtype=arr.dtype) + tm.assert_equal(result, expected2) + + result = arr2.median(axis=0, skipna=False) + expected2 = type(arr)._from_sequence([NaT], dtype=arr.dtype) + tm.assert_equal(result, expected2) + + result = arr2.median(axis=1) + tm.assert_equal(result, arr) + + result = arr2.median(axis=1, skipna=False) + tm.assert_equal(result, arr) + + def test_from_integer_array(self): + arr = np.array([1, 2, 3], dtype=np.int64) + data = pd.array(arr, dtype="Int64") + if self.array_cls is PeriodArray: + expected = self.array_cls(arr, dtype=self.example_dtype) + result = self.array_cls(data, dtype=self.example_dtype) + else: + expected = self.array_cls._from_sequence(arr, dtype=self.example_dtype) + result = self.array_cls._from_sequence(data, dtype=self.example_dtype) + + tm.assert_extension_array_equal(result, expected) + + +class TestDatetimeArray(SharedTests): + index_cls = DatetimeIndex + array_cls = DatetimeArray + scalar_type = Timestamp + example_dtype = "M8[ns]" + + @pytest.fixture + def arr1d(self, tz_naive_fixture, freqstr): + """ + Fixture returning DatetimeArray with parametrized frequency and + timezones + """ + tz = tz_naive_fixture + dti = pd.date_range("2016-01-01 01:01:00", periods=5, freq=freqstr, tz=tz) + dta = dti._data + return dta + + def test_round(self, arr1d): + # GH#24064 + dti = self.index_cls(arr1d) + + result = dti.round(freq="2min") + expected = dti - pd.Timedelta(minutes=1) + expected = expected._with_freq(None) + tm.assert_index_equal(result, expected) + + dta = dti._data + result = dta.round(freq="2min") + expected = expected._data._with_freq(None) + tm.assert_datetime_array_equal(result, expected) + + def test_array_interface(self, datetime_index): + arr = datetime_index._data + copy_false = None if np_version_gt2 else False + + # default asarray gives the same underlying data (for tz naive) + result = np.asarray(arr) + expected = arr._ndarray + assert result is expected + tm.assert_numpy_array_equal(result, expected) + result = np.array(arr, copy=copy_false) + assert result is expected + tm.assert_numpy_array_equal(result, expected) + + # specifying M8[ns] gives the same result as default + result = np.asarray(arr, dtype="datetime64[ns]") + expected = arr._ndarray + assert result is expected + tm.assert_numpy_array_equal(result, expected) + result = np.array(arr, dtype="datetime64[ns]", copy=copy_false) + assert result is expected + tm.assert_numpy_array_equal(result, expected) + result = np.array(arr, dtype="datetime64[ns]") + if not np_version_gt2: + # TODO: GH 57739 + assert result is not expected + tm.assert_numpy_array_equal(result, expected) + + # to object dtype + result = np.asarray(arr, dtype=object) + expected = np.array(list(arr), dtype=object) + tm.assert_numpy_array_equal(result, expected) + + # to other dtype always copies + result = np.asarray(arr, dtype="int64") + assert result is not arr.asi8 + assert not np.may_share_memory(arr, result) + expected = arr.asi8.copy() + tm.assert_numpy_array_equal(result, expected) + + # other dtypes handled by numpy + for dtype in ["float64", str]: + result = np.asarray(arr, dtype=dtype) + expected = np.asarray(arr).astype(dtype) + tm.assert_numpy_array_equal(result, expected) + + def test_array_object_dtype(self, arr1d): + # GH#23524 + arr = arr1d + dti = self.index_cls(arr1d) + + expected = np.array(list(dti)) + + result = np.array(arr, dtype=object) + tm.assert_numpy_array_equal(result, expected) + + # also test the DatetimeIndex method while we're at it + result = np.array(dti, dtype=object) + tm.assert_numpy_array_equal(result, expected) + + def test_array_tz(self, arr1d): + # GH#23524 + arr = arr1d + dti = self.index_cls(arr1d) + copy_false = None if np_version_gt2 else False + + expected = dti.asi8.view("M8[ns]") + result = np.array(arr, dtype="M8[ns]") + tm.assert_numpy_array_equal(result, expected) + + result = np.array(arr, dtype="datetime64[ns]") + tm.assert_numpy_array_equal(result, expected) + + # check that we are not making copies when setting copy=copy_false + result = np.array(arr, dtype="M8[ns]", copy=copy_false) + assert result.base is expected.base + assert result.base is not None + result = np.array(arr, dtype="datetime64[ns]", copy=copy_false) + assert result.base is expected.base + assert result.base is not None + + def test_array_i8_dtype(self, arr1d): + arr = arr1d + dti = self.index_cls(arr1d) + copy_false = None if np_version_gt2 else False + + expected = dti.asi8 + result = np.array(arr, dtype="i8") + tm.assert_numpy_array_equal(result, expected) + + result = np.array(arr, dtype=np.int64) + tm.assert_numpy_array_equal(result, expected) + + # check that we are still making copies when setting copy=copy_false + result = np.array(arr, dtype="i8", copy=copy_false) + assert result.base is not expected.base + assert result.base is None + + def test_from_array_keeps_base(self): + # Ensure that DatetimeArray._ndarray.base isn't lost. + arr = np.array(["2000-01-01", "2000-01-02"], dtype="M8[ns]") + dta = DatetimeArray._from_sequence(arr) + + assert dta._ndarray is arr + dta = DatetimeArray._from_sequence(arr[:0]) + assert dta._ndarray.base is arr + + def test_from_dti(self, arr1d): + arr = arr1d + dti = self.index_cls(arr1d) + assert list(dti) == list(arr) + + # Check that Index.__new__ knows what to do with DatetimeArray + dti2 = pd.Index(arr) + assert isinstance(dti2, DatetimeIndex) + assert list(dti2) == list(arr) + + def test_astype_object(self, arr1d): + arr = arr1d + dti = self.index_cls(arr1d) + + asobj = arr.astype("O") + assert isinstance(asobj, np.ndarray) + assert asobj.dtype == "O" + assert list(asobj) == list(dti) + + @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning") + def test_to_period(self, datetime_index, freqstr): + dti = datetime_index + arr = dti._data + + freqstr = freq_to_period_freqstr(1, freqstr) + expected = dti.to_period(freq=freqstr) + result = arr.to_period(freq=freqstr) + assert isinstance(result, PeriodArray) + + tm.assert_equal(result, expected._data) + + def test_to_period_2d(self, arr1d): + arr2d = arr1d.reshape(1, -1) + + warn = None if arr1d.tz is None else UserWarning + with tm.assert_produces_warning(warn): + result = arr2d.to_period("D") + expected = arr1d.to_period("D").reshape(1, -1) + tm.assert_period_array_equal(result, expected) + + @pytest.mark.parametrize("propname", DatetimeArray._bool_ops) + def test_bool_properties(self, arr1d, propname): + # in this case _bool_ops is just `is_leap_year` + dti = self.index_cls(arr1d) + arr = arr1d + assert dti.freq == arr.freq + + result = getattr(arr, propname) + expected = np.array(getattr(dti, propname), dtype=result.dtype) + + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("propname", DatetimeArray._field_ops) + def test_int_properties(self, arr1d, propname): + dti = self.index_cls(arr1d) + arr = arr1d + + result = getattr(arr, propname) + expected = np.array(getattr(dti, propname), dtype=result.dtype) + + tm.assert_numpy_array_equal(result, expected) + + def test_take_fill_valid(self, arr1d, fixed_now_ts): + arr = arr1d + dti = self.index_cls(arr1d) + + now = fixed_now_ts.tz_localize(dti.tz) + result = arr.take([-1, 1], allow_fill=True, fill_value=now) + assert result[0] == now + + msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got" + with pytest.raises(TypeError, match=msg): + # fill_value Timedelta invalid + arr.take([-1, 1], allow_fill=True, fill_value=now - now) + + with pytest.raises(TypeError, match=msg): + # fill_value Period invalid + arr.take([-1, 1], allow_fill=True, fill_value=Period("2014Q1")) + + tz = None if dti.tz is not None else "US/Eastern" + now = fixed_now_ts.tz_localize(tz) + msg = "Cannot compare tz-naive and tz-aware datetime-like objects" + with pytest.raises(TypeError, match=msg): + # Timestamp with mismatched tz-awareness + arr.take([-1, 1], allow_fill=True, fill_value=now) + + value = NaT._value + msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got" + with pytest.raises(TypeError, match=msg): + # require NaT, not iNaT, as it could be confused with an integer + arr.take([-1, 1], allow_fill=True, fill_value=value) + + value = np.timedelta64("NaT", "ns") + with pytest.raises(TypeError, match=msg): + # require appropriate-dtype if we have a NA value + arr.take([-1, 1], allow_fill=True, fill_value=value) + + if arr.tz is not None: + # GH#37356 + # Assuming here that arr1d fixture does not include Australia/Melbourne + value = fixed_now_ts.tz_localize("Australia/Melbourne") + result = arr.take([-1, 1], allow_fill=True, fill_value=value) + + expected = arr.take( + [-1, 1], + allow_fill=True, + fill_value=value.tz_convert(arr.dtype.tz), + ) + tm.assert_equal(result, expected) + + def test_concat_same_type_invalid(self, arr1d): + # different timezones + arr = arr1d + + if arr.tz is None: + other = arr.tz_localize("UTC") + else: + other = arr.tz_localize(None) + + with pytest.raises(ValueError, match="to_concat must have the same"): + arr._concat_same_type([arr, other]) + + def test_concat_same_type_different_freq(self, unit): + # we *can* concatenate DTI with different freqs. + a = pd.date_range("2000", periods=2, freq="D", tz="US/Central", unit=unit)._data + b = pd.date_range("2000", periods=2, freq="h", tz="US/Central", unit=unit)._data + result = DatetimeArray._concat_same_type([a, b]) + expected = ( + pd.to_datetime( + [ + "2000-01-01 00:00:00", + "2000-01-02 00:00:00", + "2000-01-01 00:00:00", + "2000-01-01 01:00:00", + ] + ) + .tz_localize("US/Central") + .as_unit(unit) + ._data + ) + + tm.assert_datetime_array_equal(result, expected) + + def test_strftime(self, arr1d, using_infer_string): + arr = arr1d + + result = arr.strftime("%Y %b") + expected = np.array([ts.strftime("%Y %b") for ts in arr], dtype=object) + if using_infer_string: + expected = pd.array(expected, dtype=pd.StringDtype(na_value=np.nan)) + tm.assert_equal(result, expected) + + def test_strftime_nat(self, using_infer_string): + # GH 29578 + arr = DatetimeIndex(["2019-01-01", NaT])._data + + result = arr.strftime("%Y-%m-%d") + expected = np.array(["2019-01-01", np.nan], dtype=object) + if using_infer_string: + expected = pd.array(expected, dtype=pd.StringDtype(na_value=np.nan)) + tm.assert_equal(result, expected) + + +class TestTimedeltaArray(SharedTests): + index_cls = TimedeltaIndex + array_cls = TimedeltaArray + scalar_type = pd.Timedelta + example_dtype = "m8[ns]" + + def test_from_tdi(self): + tdi = TimedeltaIndex(["1 Day", "3 Hours"]) + arr = tdi._data + assert list(arr) == list(tdi) + + # Check that Index.__new__ knows what to do with TimedeltaArray + tdi2 = pd.Index(arr) + assert isinstance(tdi2, TimedeltaIndex) + assert list(tdi2) == list(arr) + + def test_astype_object(self): + tdi = TimedeltaIndex(["1 Day", "3 Hours"]) + arr = tdi._data + asobj = arr.astype("O") + assert isinstance(asobj, np.ndarray) + assert asobj.dtype == "O" + assert list(asobj) == list(tdi) + + def test_to_pytimedelta(self, timedelta_index): + tdi = timedelta_index + arr = tdi._data + + expected = tdi.to_pytimedelta() + result = arr.to_pytimedelta() + + tm.assert_numpy_array_equal(result, expected) + + def test_total_seconds(self, timedelta_index): + tdi = timedelta_index + arr = tdi._data + + expected = tdi.total_seconds() + result = arr.total_seconds() + + tm.assert_numpy_array_equal(result, expected.values) + + @pytest.mark.parametrize("propname", TimedeltaArray._field_ops) + def test_int_properties(self, timedelta_index, propname): + tdi = timedelta_index + arr = tdi._data + + result = getattr(arr, propname) + expected = np.array(getattr(tdi, propname), dtype=result.dtype) + + tm.assert_numpy_array_equal(result, expected) + + def test_array_interface(self, timedelta_index): + arr = timedelta_index._data + copy_false = None if np_version_gt2 else False + + # default asarray gives the same underlying data + result = np.asarray(arr) + expected = arr._ndarray + assert result is expected + tm.assert_numpy_array_equal(result, expected) + result = np.array(arr, copy=copy_false) + assert result is expected + tm.assert_numpy_array_equal(result, expected) + + # specifying m8[ns] gives the same result as default + result = np.asarray(arr, dtype="timedelta64[ns]") + expected = arr._ndarray + assert result is expected + tm.assert_numpy_array_equal(result, expected) + result = np.array(arr, dtype="timedelta64[ns]", copy=copy_false) + assert result is expected + tm.assert_numpy_array_equal(result, expected) + result = np.array(arr, dtype="timedelta64[ns]") + if not np_version_gt2: + # TODO: GH 57739 + assert result is not expected + tm.assert_numpy_array_equal(result, expected) + + # to object dtype + result = np.asarray(arr, dtype=object) + expected = np.array(list(arr), dtype=object) + tm.assert_numpy_array_equal(result, expected) + + # to other dtype always copies + result = np.asarray(arr, dtype="int64") + assert result is not arr.asi8 + assert not np.may_share_memory(arr, result) + expected = arr.asi8.copy() + tm.assert_numpy_array_equal(result, expected) + + # other dtypes handled by numpy + for dtype in ["float64", str]: + result = np.asarray(arr, dtype=dtype) + expected = np.asarray(arr).astype(dtype) + tm.assert_numpy_array_equal(result, expected) + + def test_take_fill_valid(self, timedelta_index, fixed_now_ts): + tdi = timedelta_index + arr = tdi._data + + td1 = pd.Timedelta(days=1) + result = arr.take([-1, 1], allow_fill=True, fill_value=td1) + assert result[0] == td1 + + value = fixed_now_ts + msg = f"value should be a '{arr._scalar_type.__name__}' or 'NaT'. Got" + with pytest.raises(TypeError, match=msg): + # fill_value Timestamp invalid + arr.take([0, 1], allow_fill=True, fill_value=value) + + value = fixed_now_ts.to_period("D") + with pytest.raises(TypeError, match=msg): + # fill_value Period invalid + arr.take([0, 1], allow_fill=True, fill_value=value) + + value = np.datetime64("NaT", "ns") + with pytest.raises(TypeError, match=msg): + # require appropriate-dtype if we have a NA value + arr.take([-1, 1], allow_fill=True, fill_value=value) + + +@pytest.mark.filterwarnings(r"ignore:Period with BDay freq is deprecated:FutureWarning") +@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning") +class TestPeriodArray(SharedTests): + index_cls = PeriodIndex + array_cls = PeriodArray + scalar_type = Period + example_dtype = PeriodIndex([], freq="W").dtype + + @pytest.fixture + def arr1d(self, period_index): + """ + Fixture returning DatetimeArray from parametrized PeriodIndex objects + """ + return period_index._data + + def test_from_pi(self, arr1d): + pi = self.index_cls(arr1d) + arr = arr1d + assert list(arr) == list(pi) + + # Check that Index.__new__ knows what to do with PeriodArray + pi2 = pd.Index(arr) + assert isinstance(pi2, PeriodIndex) + assert list(pi2) == list(arr) + + def test_astype_object(self, arr1d): + pi = self.index_cls(arr1d) + arr = arr1d + asobj = arr.astype("O") + assert isinstance(asobj, np.ndarray) + assert asobj.dtype == "O" + assert list(asobj) == list(pi) + + def test_take_fill_valid(self, arr1d): + arr = arr1d + + value = NaT._value + msg = f"value should be a '{arr1d._scalar_type.__name__}' or 'NaT'. Got" + with pytest.raises(TypeError, match=msg): + # require NaT, not iNaT, as it could be confused with an integer + arr.take([-1, 1], allow_fill=True, fill_value=value) + + value = np.timedelta64("NaT", "ns") + with pytest.raises(TypeError, match=msg): + # require appropriate-dtype if we have a NA value + arr.take([-1, 1], allow_fill=True, fill_value=value) + + @pytest.mark.parametrize("how", ["S", "E"]) + def test_to_timestamp(self, how, arr1d): + pi = self.index_cls(arr1d) + arr = arr1d + + expected = DatetimeIndex(pi.to_timestamp(how=how))._data + result = arr.to_timestamp(how=how) + assert isinstance(result, DatetimeArray) + + tm.assert_equal(result, expected) + + def test_to_timestamp_roundtrip_bday(self): + # Case where infer_freq inside would choose "D" instead of "B" + dta = pd.date_range("2021-10-18", periods=3, freq="B")._data + parr = dta.to_period() + result = parr.to_timestamp() + assert result.freq == "B" + tm.assert_extension_array_equal(result, dta) + + dta2 = dta[::2] + parr2 = dta2.to_period() + result2 = parr2.to_timestamp() + assert result2.freq == "2B" + tm.assert_extension_array_equal(result2, dta2) + + parr3 = dta.to_period("2B") + result3 = parr3.to_timestamp() + assert result3.freq == "B" + tm.assert_extension_array_equal(result3, dta) + + def test_to_timestamp_out_of_bounds(self): + # GH#19643 previously overflowed silently + pi = pd.period_range("1500", freq="Y", periods=3) + msg = "Out of bounds nanosecond timestamp: 1500-01-01 00:00:00" + with pytest.raises(OutOfBoundsDatetime, match=msg): + pi.to_timestamp() + + with pytest.raises(OutOfBoundsDatetime, match=msg): + pi._data.to_timestamp() + + @pytest.mark.parametrize("propname", PeriodArray._bool_ops) + def test_bool_properties(self, arr1d, propname): + # in this case _bool_ops is just `is_leap_year` + pi = self.index_cls(arr1d) + arr = arr1d + + result = getattr(arr, propname) + expected = np.array(getattr(pi, propname)) + + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("propname", PeriodArray._field_ops) + def test_int_properties(self, arr1d, propname): + pi = self.index_cls(arr1d) + arr = arr1d + + result = getattr(arr, propname) + expected = np.array(getattr(pi, propname)) + + tm.assert_numpy_array_equal(result, expected) + + def test_array_interface(self, arr1d): + arr = arr1d + + # default asarray gives objects + result = np.asarray(arr) + expected = np.array(list(arr), dtype=object) + tm.assert_numpy_array_equal(result, expected) + + # to object dtype (same as default) + result = np.asarray(arr, dtype=object) + tm.assert_numpy_array_equal(result, expected) + + # to int64 gives the underlying representation + result = np.asarray(arr, dtype="int64") + tm.assert_numpy_array_equal(result, arr.asi8) + + result2 = np.asarray(arr, dtype="int64") + assert np.may_share_memory(result, result2) + + result_copy1 = np.array(arr, dtype="int64", copy=True) + result_copy2 = np.array(arr, dtype="int64", copy=True) + assert not np.may_share_memory(result_copy1, result_copy2) + + # to other dtypes + msg = r"float\(\) argument must be a string or a( real)? number, not 'Period'" + with pytest.raises(TypeError, match=msg): + np.asarray(arr, dtype="float64") + + result = np.asarray(arr, dtype="S20") + expected = np.asarray(arr).astype("S20") + tm.assert_numpy_array_equal(result, expected) + + def test_strftime(self, arr1d, using_infer_string): + arr = arr1d + + result = arr.strftime("%Y") + expected = np.array([per.strftime("%Y") for per in arr], dtype=object) + if using_infer_string: + expected = pd.array(expected, dtype=pd.StringDtype(na_value=np.nan)) + tm.assert_equal(result, expected) + + def test_strftime_nat(self, using_infer_string): + # GH 29578 + arr = PeriodArray(PeriodIndex(["2019-01-01", NaT], dtype="period[D]")) + + result = arr.strftime("%Y-%m-%d") + expected = np.array(["2019-01-01", np.nan], dtype=object) + if using_infer_string: + expected = pd.array(expected, dtype=pd.StringDtype(na_value=np.nan)) + tm.assert_equal(result, expected) + + +@pytest.mark.parametrize( + "arr,casting_nats", + [ + ( + TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data, + (NaT, np.timedelta64("NaT", "ns")), + ), + ( + pd.date_range("2000-01-01", periods=3, freq="D")._data, + (NaT, np.datetime64("NaT", "ns")), + ), + (pd.period_range("2000-01-01", periods=3, freq="D")._data, (NaT,)), + ], + ids=lambda x: type(x).__name__, +) +def test_casting_nat_setitem_array(arr, casting_nats): + expected = type(arr)._from_sequence([NaT, arr[1], arr[2]], dtype=arr.dtype) + + for nat in casting_nats: + arr = arr.copy() + arr[0] = nat + tm.assert_equal(arr, expected) + + +@pytest.mark.parametrize( + "arr,non_casting_nats", + [ + ( + TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data, + (np.datetime64("NaT", "ns"), NaT._value), + ), + ( + pd.date_range("2000-01-01", periods=3, freq="D")._data, + (np.timedelta64("NaT", "ns"), NaT._value), + ), + ( + pd.period_range("2000-01-01", periods=3, freq="D")._data, + (np.datetime64("NaT", "ns"), np.timedelta64("NaT", "ns"), NaT._value), + ), + ], + ids=lambda x: type(x).__name__, +) +def test_invalid_nat_setitem_array(arr, non_casting_nats): + msg = ( + "value should be a '(Timestamp|Timedelta|Period)', 'NaT', or array of those. " + "Got '(timedelta64|datetime64|int)' instead." + ) + + for nat in non_casting_nats: + with pytest.raises(TypeError, match=msg): + arr[0] = nat + + +@pytest.mark.parametrize( + "arr", + [ + pd.date_range("2000", periods=4).array, + pd.timedelta_range("2000", periods=4).array, + ], +) +def test_to_numpy_extra(arr): + arr[0] = NaT + original = arr.copy() + + result = arr.to_numpy() + assert np.isnan(result[0]) + + result = arr.to_numpy(dtype="int64") + assert result[0] == -9223372036854775808 + + result = arr.to_numpy(dtype="int64", na_value=0) + assert result[0] == 0 + + result = arr.to_numpy(na_value=arr[1].to_numpy()) + assert result[0] == result[1] + + result = arr.to_numpy(na_value=arr[1].to_numpy(copy=False)) + assert result[0] == result[1] + + tm.assert_equal(arr, original) + + +@pytest.mark.parametrize("as_index", [True, False]) +@pytest.mark.parametrize( + "values", + [ + pd.to_datetime(["2020-01-01", "2020-02-01"]), + pd.to_timedelta([1, 2], unit="D"), + PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"), + ], +) +@pytest.mark.parametrize( + "klass", + [ + list, + np.array, + pd.array, + pd.Series, + pd.Index, + pd.Categorical, + pd.CategoricalIndex, + ], +) +def test_searchsorted_datetimelike_with_listlike(values, klass, as_index): + # https://github.com/pandas-dev/pandas/issues/32762 + if not as_index: + values = values._data + + result = values.searchsorted(klass(values)) + expected = np.array([0, 1], dtype=result.dtype) + + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize( + "values", + [ + pd.to_datetime(["2020-01-01", "2020-02-01"]), + pd.to_timedelta([1, 2], unit="D"), + PeriodIndex(["2020-01-01", "2020-02-01"], freq="D"), + ], +) +@pytest.mark.parametrize( + "arg", [[1, 2], ["a", "b"], [Timestamp("2020-01-01", tz="Europe/London")] * 2] +) +def test_searchsorted_datetimelike_with_listlike_invalid_dtype(values, arg): + # https://github.com/pandas-dev/pandas/issues/32762 + msg = "[Unexpected type|Cannot compare]" + with pytest.raises(TypeError, match=msg): + values.searchsorted(arg) + + +@pytest.mark.parametrize("klass", [list, tuple, np.array, pd.Series]) +def test_period_index_construction_from_strings(klass): + # https://github.com/pandas-dev/pandas/issues/26109 + strings = ["2020Q1", "2020Q2"] * 2 + data = klass(strings) + result = PeriodIndex(data, freq="Q") + expected = PeriodIndex([Period(s) for s in strings]) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"]) +def test_from_pandas_array(dtype): + # GH#24615 + data = np.array([1, 2, 3], dtype=dtype) + arr = NumpyExtensionArray(data) + + cls = {"M8[ns]": DatetimeArray, "m8[ns]": TimedeltaArray}[dtype] + + depr_msg = f"{cls.__name__}.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + result = cls(arr) + expected = cls(data) + tm.assert_extension_array_equal(result, expected) + + result = cls._from_sequence(arr, dtype=dtype) + expected = cls._from_sequence(data, dtype=dtype) + tm.assert_extension_array_equal(result, expected) + + func = {"M8[ns]": pd.to_datetime, "m8[ns]": pd.to_timedelta}[dtype] + result = func(arr).array + expected = func(data).array + tm.assert_equal(result, expected) + + # Let's check the Indexes while we're here + idx_cls = {"M8[ns]": DatetimeIndex, "m8[ns]": TimedeltaIndex}[dtype] + result = idx_cls(arr) + expected = idx_cls(data) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimes.py new file mode 100644 index 0000000000000000000000000000000000000000..8f0576cc65a2787edacdb1e377a02287d1caaff1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_datetimes.py @@ -0,0 +1,840 @@ +""" +Tests for DatetimeArray +""" +from __future__ import annotations + +from datetime import timedelta +import operator + +try: + from zoneinfo import ZoneInfo +except ImportError: + # Cannot assign to a type + ZoneInfo = None # type: ignore[misc, assignment] + +import numpy as np +import pytest + +from pandas._libs.tslibs import tz_compare + +from pandas.core.dtypes.dtypes import DatetimeTZDtype + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import ( + DatetimeArray, + TimedeltaArray, +) + + +class TestNonNano: + @pytest.fixture(params=["s", "ms", "us"]) + def unit(self, request): + """Fixture returning parametrized time units""" + return request.param + + @pytest.fixture + def dtype(self, unit, tz_naive_fixture): + tz = tz_naive_fixture + if tz is None: + return np.dtype(f"datetime64[{unit}]") + else: + return DatetimeTZDtype(unit=unit, tz=tz) + + @pytest.fixture + def dta_dti(self, unit, dtype): + tz = getattr(dtype, "tz", None) + + dti = pd.date_range("2016-01-01", periods=55, freq="D", tz=tz) + if tz is None: + arr = np.asarray(dti).astype(f"M8[{unit}]") + else: + arr = np.asarray(dti.tz_convert("UTC").tz_localize(None)).astype( + f"M8[{unit}]" + ) + + dta = DatetimeArray._simple_new(arr, dtype=dtype) + return dta, dti + + @pytest.fixture + def dta(self, dta_dti): + dta, dti = dta_dti + return dta + + def test_non_nano(self, unit, dtype): + arr = np.arange(5, dtype=np.int64).view(f"M8[{unit}]") + dta = DatetimeArray._simple_new(arr, dtype=dtype) + + assert dta.dtype == dtype + assert dta[0].unit == unit + assert tz_compare(dta.tz, dta[0].tz) + assert (dta[0] == dta[:1]).all() + + @pytest.mark.parametrize( + "field", DatetimeArray._field_ops + DatetimeArray._bool_ops + ) + def test_fields(self, unit, field, dtype, dta_dti): + dta, dti = dta_dti + + assert (dti == dta).all() + + res = getattr(dta, field) + expected = getattr(dti._data, field) + tm.assert_numpy_array_equal(res, expected) + + def test_normalize(self, unit): + dti = pd.date_range("2016-01-01 06:00:00", periods=55, freq="D") + arr = np.asarray(dti).astype(f"M8[{unit}]") + + dta = DatetimeArray._simple_new(arr, dtype=arr.dtype) + + assert not dta.is_normalized + + # TODO: simplify once we can just .astype to other unit + exp = np.asarray(dti.normalize()).astype(f"M8[{unit}]") + expected = DatetimeArray._simple_new(exp, dtype=exp.dtype) + + res = dta.normalize() + tm.assert_extension_array_equal(res, expected) + + def test_simple_new_requires_match(self, unit): + arr = np.arange(5, dtype=np.int64).view(f"M8[{unit}]") + dtype = DatetimeTZDtype(unit, "UTC") + + dta = DatetimeArray._simple_new(arr, dtype=dtype) + assert dta.dtype == dtype + + wrong = DatetimeTZDtype("ns", "UTC") + with pytest.raises(AssertionError, match=""): + DatetimeArray._simple_new(arr, dtype=wrong) + + def test_std_non_nano(self, unit): + dti = pd.date_range("2016-01-01", periods=55, freq="D") + arr = np.asarray(dti).astype(f"M8[{unit}]") + + dta = DatetimeArray._simple_new(arr, dtype=arr.dtype) + + # we should match the nano-reso std, but floored to our reso. + res = dta.std() + assert res._creso == dta._creso + assert res == dti.std().floor(unit) + + @pytest.mark.filterwarnings("ignore:Converting to PeriodArray.*:UserWarning") + def test_to_period(self, dta_dti): + dta, dti = dta_dti + result = dta.to_period("D") + expected = dti._data.to_period("D") + + tm.assert_extension_array_equal(result, expected) + + def test_iter(self, dta): + res = next(iter(dta)) + expected = dta[0] + + assert type(res) is pd.Timestamp + assert res._value == expected._value + assert res._creso == expected._creso + assert res == expected + + def test_astype_object(self, dta): + result = dta.astype(object) + assert all(x._creso == dta._creso for x in result) + assert all(x == y for x, y in zip(result, dta)) + + def test_to_pydatetime(self, dta_dti): + dta, dti = dta_dti + + result = dta.to_pydatetime() + expected = dti.to_pydatetime() + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("meth", ["time", "timetz", "date"]) + def test_time_date(self, dta_dti, meth): + dta, dti = dta_dti + + result = getattr(dta, meth) + expected = getattr(dti, meth) + tm.assert_numpy_array_equal(result, expected) + + def test_format_native_types(self, unit, dtype, dta_dti): + # In this case we should get the same formatted values with our nano + # version dti._data as we do with the non-nano dta + dta, dti = dta_dti + + res = dta._format_native_types() + exp = dti._data._format_native_types() + tm.assert_numpy_array_equal(res, exp) + + def test_repr(self, dta_dti, unit): + dta, dti = dta_dti + + assert repr(dta) == repr(dti._data).replace("[ns", f"[{unit}") + + # TODO: tests with td64 + def test_compare_mismatched_resolutions(self, comparison_op): + # comparison that numpy gets wrong bc of silent overflows + op = comparison_op + + iinfo = np.iinfo(np.int64) + vals = np.array([iinfo.min, iinfo.min + 1, iinfo.max], dtype=np.int64) + + # Construct so that arr2[1] < arr[1] < arr[2] < arr2[2] + arr = np.array(vals).view("M8[ns]") + arr2 = arr.view("M8[s]") + + left = DatetimeArray._simple_new(arr, dtype=arr.dtype) + right = DatetimeArray._simple_new(arr2, dtype=arr2.dtype) + + if comparison_op is operator.eq: + expected = np.array([False, False, False]) + elif comparison_op is operator.ne: + expected = np.array([True, True, True]) + elif comparison_op in [operator.lt, operator.le]: + expected = np.array([False, False, True]) + else: + expected = np.array([False, True, False]) + + result = op(left, right) + tm.assert_numpy_array_equal(result, expected) + + result = op(left[1], right) + tm.assert_numpy_array_equal(result, expected) + + if op not in [operator.eq, operator.ne]: + # check that numpy still gets this wrong; if it is fixed we may be + # able to remove compare_mismatched_resolutions + np_res = op(left._ndarray, right._ndarray) + tm.assert_numpy_array_equal(np_res[1:], ~expected[1:]) + + def test_add_mismatched_reso_doesnt_downcast(self): + # https://github.com/pandas-dev/pandas/pull/48748#issuecomment-1260181008 + td = pd.Timedelta(microseconds=1) + dti = pd.date_range("2016-01-01", periods=3) - td + dta = dti._data.as_unit("us") + + res = dta + td.as_unit("us") + # even though the result is an even number of days + # (so we _could_ downcast to unit="s"), we do not. + assert res.unit == "us" + + @pytest.mark.parametrize( + "scalar", + [ + timedelta(hours=2), + pd.Timedelta(hours=2), + np.timedelta64(2, "h"), + np.timedelta64(2 * 3600 * 1000, "ms"), + pd.offsets.Minute(120), + pd.offsets.Hour(2), + ], + ) + def test_add_timedeltalike_scalar_mismatched_reso(self, dta_dti, scalar): + dta, dti = dta_dti + + td = pd.Timedelta(scalar) + exp_unit = tm.get_finest_unit(dta.unit, td.unit) + + expected = (dti + td)._data.as_unit(exp_unit) + result = dta + scalar + tm.assert_extension_array_equal(result, expected) + + result = scalar + dta + tm.assert_extension_array_equal(result, expected) + + expected = (dti - td)._data.as_unit(exp_unit) + result = dta - scalar + tm.assert_extension_array_equal(result, expected) + + def test_sub_datetimelike_scalar_mismatch(self): + dti = pd.date_range("2016-01-01", periods=3) + dta = dti._data.as_unit("us") + + ts = dta[0].as_unit("s") + + result = dta - ts + expected = (dti - dti[0])._data.as_unit("us") + assert result.dtype == "m8[us]" + tm.assert_extension_array_equal(result, expected) + + def test_sub_datetime64_reso_mismatch(self): + dti = pd.date_range("2016-01-01", periods=3) + left = dti._data.as_unit("s") + right = left.as_unit("ms") + + result = left - right + exp_values = np.array([0, 0, 0], dtype="m8[ms]") + expected = TimedeltaArray._simple_new( + exp_values, + dtype=exp_values.dtype, + ) + tm.assert_extension_array_equal(result, expected) + result2 = right - left + tm.assert_extension_array_equal(result2, expected) + + +class TestDatetimeArrayComparisons: + # TODO: merge this into tests/arithmetic/test_datetime64 once it is + # sufficiently robust + + def test_cmp_dt64_arraylike_tznaive(self, comparison_op): + # arbitrary tz-naive DatetimeIndex + op = comparison_op + + dti = pd.date_range("2016-01-1", freq="MS", periods=9, tz=None) + arr = dti._data + assert arr.freq == dti.freq + assert arr.tz == dti.tz + + right = dti + + expected = np.ones(len(arr), dtype=bool) + if comparison_op.__name__ in ["ne", "gt", "lt"]: + # for these the comparisons should be all-False + expected = ~expected + + result = op(arr, arr) + tm.assert_numpy_array_equal(result, expected) + for other in [ + right, + np.array(right), + list(right), + tuple(right), + right.astype(object), + ]: + result = op(arr, other) + tm.assert_numpy_array_equal(result, expected) + + result = op(other, arr) + tm.assert_numpy_array_equal(result, expected) + + +class TestDatetimeArray: + def test_astype_ns_to_ms_near_bounds(self): + # GH#55979 + ts = pd.Timestamp("1677-09-21 00:12:43.145225") + target = ts.as_unit("ms") + + dta = DatetimeArray._from_sequence([ts], dtype="M8[ns]") + assert (dta.view("i8") == ts.as_unit("ns").value).all() + + result = dta.astype("M8[ms]") + assert result[0] == target + + expected = DatetimeArray._from_sequence([ts], dtype="M8[ms]") + assert (expected.view("i8") == target._value).all() + + tm.assert_datetime_array_equal(result, expected) + + def test_astype_non_nano_tznaive(self): + dti = pd.date_range("2016-01-01", periods=3) + + res = dti.astype("M8[s]") + assert res.dtype == "M8[s]" + + dta = dti._data + res = dta.astype("M8[s]") + assert res.dtype == "M8[s]" + assert isinstance(res, pd.core.arrays.DatetimeArray) # used to be ndarray + + def test_astype_non_nano_tzaware(self): + dti = pd.date_range("2016-01-01", periods=3, tz="UTC") + + res = dti.astype("M8[s, US/Pacific]") + assert res.dtype == "M8[s, US/Pacific]" + + dta = dti._data + res = dta.astype("M8[s, US/Pacific]") + assert res.dtype == "M8[s, US/Pacific]" + + # from non-nano to non-nano, preserving reso + res2 = res.astype("M8[s, UTC]") + assert res2.dtype == "M8[s, UTC]" + assert not tm.shares_memory(res2, res) + + res3 = res.astype("M8[s, UTC]", copy=False) + assert res2.dtype == "M8[s, UTC]" + assert tm.shares_memory(res3, res) + + def test_astype_to_same(self): + arr = DatetimeArray._from_sequence( + ["2000"], dtype=DatetimeTZDtype(tz="US/Central") + ) + result = arr.astype(DatetimeTZDtype(tz="US/Central"), copy=False) + assert result is arr + + @pytest.mark.parametrize("dtype", ["datetime64[ns]", "datetime64[ns, UTC]"]) + @pytest.mark.parametrize( + "other", ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, CET]"] + ) + def test_astype_copies(self, dtype, other): + # https://github.com/pandas-dev/pandas/pull/32490 + ser = pd.Series([1, 2], dtype=dtype) + orig = ser.copy() + + err = False + if (dtype == "datetime64[ns]") ^ (other == "datetime64[ns]"): + # deprecated in favor of tz_localize + err = True + + if err: + if dtype == "datetime64[ns]": + msg = "Use obj.tz_localize instead or series.dt.tz_localize instead" + else: + msg = "from timezone-aware dtype to timezone-naive dtype" + with pytest.raises(TypeError, match=msg): + ser.astype(other) + else: + t = ser.astype(other) + t[:] = pd.NaT + tm.assert_series_equal(ser, orig) + + @pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"]) + def test_astype_int(self, dtype): + arr = DatetimeArray._from_sequence( + [pd.Timestamp("2000"), pd.Timestamp("2001")], dtype="M8[ns]" + ) + + if np.dtype(dtype) != np.int64: + with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"): + arr.astype(dtype) + return + + result = arr.astype(dtype) + expected = arr._ndarray.view("i8") + tm.assert_numpy_array_equal(result, expected) + + def test_astype_to_sparse_dt64(self): + # GH#50082 + dti = pd.date_range("2016-01-01", periods=4) + dta = dti._data + result = dta.astype("Sparse[datetime64[ns]]") + + assert result.dtype == "Sparse[datetime64[ns]]" + assert (result == dta).all() + + def test_tz_setter_raises(self): + arr = DatetimeArray._from_sequence( + ["2000"], dtype=DatetimeTZDtype(tz="US/Central") + ) + with pytest.raises(AttributeError, match="tz_localize"): + arr.tz = "UTC" + + def test_setitem_str_impute_tz(self, tz_naive_fixture): + # Like for getitem, if we are passed a naive-like string, we impute + # our own timezone. + tz = tz_naive_fixture + + data = np.array([1, 2, 3], dtype="M8[ns]") + dtype = data.dtype if tz is None else DatetimeTZDtype(tz=tz) + arr = DatetimeArray._from_sequence(data, dtype=dtype) + expected = arr.copy() + + ts = pd.Timestamp("2020-09-08 16:50").tz_localize(tz) + setter = str(ts.tz_localize(None)) + + # Setting a scalar tznaive string + expected[0] = ts + arr[0] = setter + tm.assert_equal(arr, expected) + + # Setting a listlike of tznaive strings + expected[1] = ts + arr[:2] = [setter, setter] + tm.assert_equal(arr, expected) + + def test_setitem_different_tz_raises(self): + # pre-2.0 we required exact tz match, in 2.0 we require only + # tzawareness-match + data = np.array([1, 2, 3], dtype="M8[ns]") + arr = DatetimeArray._from_sequence( + data, copy=False, dtype=DatetimeTZDtype(tz="US/Central") + ) + with pytest.raises(TypeError, match="Cannot compare tz-naive and tz-aware"): + arr[0] = pd.Timestamp("2000") + + ts = pd.Timestamp("2000", tz="US/Eastern") + arr[0] = ts + assert arr[0] == ts.tz_convert("US/Central") + + def test_setitem_clears_freq(self): + a = pd.date_range("2000", periods=2, freq="D", tz="US/Central")._data + a[0] = pd.Timestamp("2000", tz="US/Central") + assert a.freq is None + + @pytest.mark.parametrize( + "obj", + [ + pd.Timestamp("2021-01-01"), + pd.Timestamp("2021-01-01").to_datetime64(), + pd.Timestamp("2021-01-01").to_pydatetime(), + ], + ) + def test_setitem_objects(self, obj): + # make sure we accept datetime64 and datetime in addition to Timestamp + dti = pd.date_range("2000", periods=2, freq="D") + arr = dti._data + + arr[0] = obj + assert arr[0] == obj + + def test_repeat_preserves_tz(self): + dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central") + arr = dti._data + + repeated = arr.repeat([1, 1]) + + # preserves tz and values, but not freq + expected = DatetimeArray._from_sequence(arr.asi8, dtype=arr.dtype) + tm.assert_equal(repeated, expected) + + def test_value_counts_preserves_tz(self): + dti = pd.date_range("2000", periods=2, freq="D", tz="US/Central") + arr = dti._data.repeat([4, 3]) + + result = arr.value_counts() + + # Note: not tm.assert_index_equal, since `freq`s do not match + assert result.index.equals(dti) + + arr[-2] = pd.NaT + result = arr.value_counts(dropna=False) + expected = pd.Series([4, 2, 1], index=[dti[0], dti[1], pd.NaT], name="count") + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("method", ["pad", "backfill"]) + def test_fillna_preserves_tz(self, method): + dti = pd.date_range("2000-01-01", periods=5, freq="D", tz="US/Central") + arr = DatetimeArray._from_sequence(dti, copy=True) + arr[2] = pd.NaT + + fill_val = dti[1] if method == "pad" else dti[3] + expected = DatetimeArray._from_sequence( + [dti[0], dti[1], fill_val, dti[3], dti[4]], + dtype=DatetimeTZDtype(tz="US/Central"), + ) + + result = arr._pad_or_backfill(method=method) + tm.assert_extension_array_equal(result, expected) + + # assert that arr and dti were not modified in-place + assert arr[2] is pd.NaT + assert dti[2] == pd.Timestamp("2000-01-03", tz="US/Central") + + def test_fillna_2d(self): + dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific") + dta = dti._data.reshape(3, 2).copy() + dta[0, 1] = pd.NaT + dta[1, 0] = pd.NaT + + res1 = dta._pad_or_backfill(method="pad") + expected1 = dta.copy() + expected1[1, 0] = dta[0, 0] + tm.assert_extension_array_equal(res1, expected1) + + res2 = dta._pad_or_backfill(method="backfill") + expected2 = dta.copy() + expected2 = dta.copy() + expected2[1, 0] = dta[2, 0] + expected2[0, 1] = dta[1, 1] + tm.assert_extension_array_equal(res2, expected2) + + # with different ordering for underlying ndarray; behavior should + # be unchanged + dta2 = dta._from_backing_data(dta._ndarray.copy(order="F")) + assert dta2._ndarray.flags["F_CONTIGUOUS"] + assert not dta2._ndarray.flags["C_CONTIGUOUS"] + tm.assert_extension_array_equal(dta, dta2) + + res3 = dta2._pad_or_backfill(method="pad") + tm.assert_extension_array_equal(res3, expected1) + + res4 = dta2._pad_or_backfill(method="backfill") + tm.assert_extension_array_equal(res4, expected2) + + # test the DataFrame method while we're here + df = pd.DataFrame(dta) + res = df.ffill() + expected = pd.DataFrame(expected1) + tm.assert_frame_equal(res, expected) + + res = df.bfill() + expected = pd.DataFrame(expected2) + tm.assert_frame_equal(res, expected) + + def test_array_interface_tz(self): + tz = "US/Central" + data = pd.date_range("2017", periods=2, tz=tz)._data + result = np.asarray(data) + + expected = np.array( + [ + pd.Timestamp("2017-01-01T00:00:00", tz=tz), + pd.Timestamp("2017-01-02T00:00:00", tz=tz), + ], + dtype=object, + ) + tm.assert_numpy_array_equal(result, expected) + + result = np.asarray(data, dtype=object) + tm.assert_numpy_array_equal(result, expected) + + result = np.asarray(data, dtype="M8[ns]") + + expected = np.array( + ["2017-01-01T06:00:00", "2017-01-02T06:00:00"], dtype="M8[ns]" + ) + tm.assert_numpy_array_equal(result, expected) + + def test_array_interface(self): + data = pd.date_range("2017", periods=2)._data + expected = np.array( + ["2017-01-01T00:00:00", "2017-01-02T00:00:00"], dtype="datetime64[ns]" + ) + + result = np.asarray(data) + tm.assert_numpy_array_equal(result, expected) + + result = np.asarray(data, dtype=object) + expected = np.array( + [pd.Timestamp("2017-01-01T00:00:00"), pd.Timestamp("2017-01-02T00:00:00")], + dtype=object, + ) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("index", [True, False]) + def test_searchsorted_different_tz(self, index): + data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + arr = pd.DatetimeIndex(data, freq="D")._data.tz_localize("Asia/Tokyo") + if index: + arr = pd.Index(arr) + + expected = arr.searchsorted(arr[2]) + result = arr.searchsorted(arr[2].tz_convert("UTC")) + assert result == expected + + expected = arr.searchsorted(arr[2:6]) + result = arr.searchsorted(arr[2:6].tz_convert("UTC")) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("index", [True, False]) + def test_searchsorted_tzawareness_compat(self, index): + data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + arr = pd.DatetimeIndex(data, freq="D")._data + if index: + arr = pd.Index(arr) + + mismatch = arr.tz_localize("Asia/Tokyo") + + msg = "Cannot compare tz-naive and tz-aware datetime-like objects" + with pytest.raises(TypeError, match=msg): + arr.searchsorted(mismatch[0]) + with pytest.raises(TypeError, match=msg): + arr.searchsorted(mismatch) + + with pytest.raises(TypeError, match=msg): + mismatch.searchsorted(arr[0]) + with pytest.raises(TypeError, match=msg): + mismatch.searchsorted(arr) + + @pytest.mark.parametrize( + "other", + [ + 1, + np.int64(1), + 1.0, + np.timedelta64("NaT"), + pd.Timedelta(days=2), + "invalid", + np.arange(10, dtype="i8") * 24 * 3600 * 10**9, + np.arange(10).view("timedelta64[ns]") * 24 * 3600 * 10**9, + pd.Timestamp("2021-01-01").to_period("D"), + ], + ) + @pytest.mark.parametrize("index", [True, False]) + def test_searchsorted_invalid_types(self, other, index): + data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + arr = pd.DatetimeIndex(data, freq="D")._data + if index: + arr = pd.Index(arr) + + msg = "|".join( + [ + "searchsorted requires compatible dtype or scalar", + "value should be a 'Timestamp', 'NaT', or array of those. Got", + ] + ) + with pytest.raises(TypeError, match=msg): + arr.searchsorted(other) + + def test_shift_fill_value(self): + dti = pd.date_range("2016-01-01", periods=3) + + dta = dti._data + expected = DatetimeArray._from_sequence(np.roll(dta._ndarray, 1)) + + fv = dta[-1] + for fill_value in [fv, fv.to_pydatetime(), fv.to_datetime64()]: + result = dta.shift(1, fill_value=fill_value) + tm.assert_datetime_array_equal(result, expected) + + dta = dta.tz_localize("UTC") + expected = expected.tz_localize("UTC") + fv = dta[-1] + for fill_value in [fv, fv.to_pydatetime()]: + result = dta.shift(1, fill_value=fill_value) + tm.assert_datetime_array_equal(result, expected) + + def test_shift_value_tzawareness_mismatch(self): + dti = pd.date_range("2016-01-01", periods=3) + + dta = dti._data + + fv = dta[-1].tz_localize("UTC") + for invalid in [fv, fv.to_pydatetime()]: + with pytest.raises(TypeError, match="Cannot compare"): + dta.shift(1, fill_value=invalid) + + dta = dta.tz_localize("UTC") + fv = dta[-1].tz_localize(None) + for invalid in [fv, fv.to_pydatetime(), fv.to_datetime64()]: + with pytest.raises(TypeError, match="Cannot compare"): + dta.shift(1, fill_value=invalid) + + def test_shift_requires_tzmatch(self): + # pre-2.0 we required exact tz match, in 2.0 we require just + # matching tzawareness + dti = pd.date_range("2016-01-01", periods=3, tz="UTC") + dta = dti._data + + fill_value = pd.Timestamp("2020-10-18 18:44", tz="US/Pacific") + + result = dta.shift(1, fill_value=fill_value) + expected = dta.shift(1, fill_value=fill_value.tz_convert("UTC")) + tm.assert_equal(result, expected) + + def test_tz_localize_t2d(self): + dti = pd.date_range("1994-05-12", periods=12, tz="US/Pacific") + dta = dti._data.reshape(3, 4) + result = dta.tz_localize(None) + + expected = dta.ravel().tz_localize(None).reshape(dta.shape) + tm.assert_datetime_array_equal(result, expected) + + roundtrip = expected.tz_localize("US/Pacific") + tm.assert_datetime_array_equal(roundtrip, dta) + + easts = ["US/Eastern", "dateutil/US/Eastern"] + if ZoneInfo is not None: + try: + tz = ZoneInfo("US/Eastern") + except KeyError: + # no tzdata + pass + else: + # Argument 1 to "append" of "list" has incompatible type "ZoneInfo"; + # expected "str" + easts.append(tz) # type: ignore[arg-type] + + @pytest.mark.parametrize("tz", easts) + def test_iter_zoneinfo_fold(self, tz): + # GH#49684 + utc_vals = np.array( + [1320552000, 1320555600, 1320559200, 1320562800], dtype=np.int64 + ) + utc_vals *= 1_000_000_000 + + dta = DatetimeArray._from_sequence(utc_vals).tz_localize("UTC").tz_convert(tz) + + left = dta[2] + right = list(dta)[2] + assert str(left) == str(right) + # previously there was a bug where with non-pytz right would be + # Timestamp('2011-11-06 01:00:00-0400', tz='US/Eastern') + # while left would be + # Timestamp('2011-11-06 01:00:00-0500', tz='US/Eastern') + # The .value's would match (so they would compare as equal), + # but the folds would not + assert left.utcoffset() == right.utcoffset() + + # The same bug in ints_to_pydatetime affected .astype, so we test + # that here. + right2 = dta.astype(object)[2] + assert str(left) == str(right2) + assert left.utcoffset() == right2.utcoffset() + + @pytest.mark.parametrize( + "freq, freq_depr", + [ + ("2ME", "2M"), + ("2SME", "2SM"), + ("2SME", "2sm"), + ("2QE", "2Q"), + ("2QE-SEP", "2Q-SEP"), + ("1YE", "1Y"), + ("2YE-MAR", "2Y-MAR"), + ("1YE", "1A"), + ("2YE-MAR", "2A-MAR"), + ("2ME", "2m"), + ("2QE-SEP", "2q-sep"), + ("2YE-MAR", "2a-mar"), + ("2YE", "2y"), + ], + ) + def test_date_range_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr): + # GH#9586, GH#54275 + depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed " + f"in a future version, please use '{freq[1:]}' instead." + + expected = pd.date_range("1/1/2000", periods=4, freq=freq) + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + result = pd.date_range("1/1/2000", periods=4, freq=freq_depr) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("freq_depr", ["2H", "2CBH", "2MIN", "2S", "2mS", "2Us"]) + def test_date_range_uppercase_frequency_deprecated(self, freq_depr): + # GH#9586, GH#54939 + depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed in a " + f"future version. Please use '{freq_depr.lower()[1:]}' instead." + + expected = pd.date_range("1/1/2000", periods=4, freq=freq_depr.lower()) + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + result = pd.date_range("1/1/2000", periods=4, freq=freq_depr) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "freq_depr", + [ + "2ye-mar", + "2ys", + "2qe", + "2qs-feb", + "2bqs", + "2sms", + "2bms", + "2cbme", + "2me", + "2w", + ], + ) + def test_date_range_lowercase_frequency_deprecated(self, freq_depr): + # GH#9586, GH#54939 + depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed in a " + f"future version, please use '{freq_depr.upper()[1:]}' instead." + + expected = pd.date_range("1/1/2000", periods=4, freq=freq_depr.upper()) + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + result = pd.date_range("1/1/2000", periods=4, freq=freq_depr) + tm.assert_index_equal(result, expected) + + +def test_factorize_sort_without_freq(): + dta = DatetimeArray._from_sequence([0, 2, 1], dtype="M8[ns]") + + msg = r"call pd.factorize\(obj, sort=True\) instead" + with pytest.raises(NotImplementedError, match=msg): + dta.factorize(sort=True) + + # Do TimedeltaArray while we're here + tda = dta - dta[0] + with pytest.raises(NotImplementedError, match=msg): + tda.factorize(sort=True) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_ndarray_backed.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_ndarray_backed.py new file mode 100644 index 0000000000000000000000000000000000000000..1fe7cc9b03e8a6cef04558958ed949a0239a96cc --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_ndarray_backed.py @@ -0,0 +1,75 @@ +""" +Tests for subclasses of NDArrayBackedExtensionArray +""" +import numpy as np + +from pandas import ( + CategoricalIndex, + date_range, +) +from pandas.core.arrays import ( + Categorical, + DatetimeArray, + NumpyExtensionArray, + TimedeltaArray, +) + + +class TestEmpty: + def test_empty_categorical(self): + ci = CategoricalIndex(["a", "b", "c"], ordered=True) + dtype = ci.dtype + + # case with int8 codes + shape = (4,) + result = Categorical._empty(shape, dtype=dtype) + assert isinstance(result, Categorical) + assert result.shape == shape + assert result._ndarray.dtype == np.int8 + + # case where repr would segfault if we didn't override base implementation + result = Categorical._empty((4096,), dtype=dtype) + assert isinstance(result, Categorical) + assert result.shape == (4096,) + assert result._ndarray.dtype == np.int8 + repr(result) + + # case with int16 codes + ci = CategoricalIndex(list(range(512)) * 4, ordered=False) + dtype = ci.dtype + result = Categorical._empty(shape, dtype=dtype) + assert isinstance(result, Categorical) + assert result.shape == shape + assert result._ndarray.dtype == np.int16 + + def test_empty_dt64tz(self): + dti = date_range("2016-01-01", periods=2, tz="Asia/Tokyo") + dtype = dti.dtype + + shape = (0,) + result = DatetimeArray._empty(shape, dtype=dtype) + assert result.dtype == dtype + assert isinstance(result, DatetimeArray) + assert result.shape == shape + + def test_empty_dt64(self): + shape = (3, 9) + result = DatetimeArray._empty(shape, dtype="datetime64[ns]") + assert isinstance(result, DatetimeArray) + assert result.shape == shape + + def test_empty_td64(self): + shape = (3, 9) + result = TimedeltaArray._empty(shape, dtype="m8[ns]") + assert isinstance(result, TimedeltaArray) + assert result.shape == shape + + def test_empty_pandas_array(self): + arr = NumpyExtensionArray(np.array([1, 2])) + dtype = arr.dtype + + shape = (3, 9) + result = NumpyExtensionArray._empty(shape, dtype=dtype) + assert isinstance(result, NumpyExtensionArray) + assert result.dtype == dtype + assert result.shape == shape diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_period.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_period.py new file mode 100644 index 0000000000000000000000000000000000000000..48453ba19e9a1f6971a2e56872ec42f1856d1dd0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_period.py @@ -0,0 +1,184 @@ +import numpy as np +import pytest + +from pandas._libs.tslibs import iNaT +from pandas._libs.tslibs.period import IncompatibleFrequency + +from pandas.core.dtypes.base import _registry as registry +from pandas.core.dtypes.dtypes import PeriodDtype + +import pandas as pd +import pandas._testing as tm +from pandas.core.arrays import PeriodArray + +# ---------------------------------------------------------------------------- +# Dtype + + +def test_registered(): + assert PeriodDtype in registry.dtypes + result = registry.find("Period[D]") + expected = PeriodDtype("D") + assert result == expected + + +# ---------------------------------------------------------------------------- +# period_array + + +def test_asi8(): + result = PeriodArray._from_sequence(["2000", "2001", None], dtype="period[D]").asi8 + expected = np.array([10957, 11323, iNaT]) + tm.assert_numpy_array_equal(result, expected) + + +def test_take_raises(): + arr = PeriodArray._from_sequence(["2000", "2001"], dtype="period[D]") + with pytest.raises(IncompatibleFrequency, match="freq"): + arr.take([0, -1], allow_fill=True, fill_value=pd.Period("2000", freq="W")) + + msg = "value should be a 'Period' or 'NaT'. Got 'str' instead" + with pytest.raises(TypeError, match=msg): + arr.take([0, -1], allow_fill=True, fill_value="foo") + + +def test_fillna_raises(): + arr = PeriodArray._from_sequence(["2000", "2001", "2002"], dtype="period[D]") + with pytest.raises(ValueError, match="Length"): + arr.fillna(arr[:2]) + + +def test_fillna_copies(): + arr = PeriodArray._from_sequence(["2000", "2001", "2002"], dtype="period[D]") + result = arr.fillna(pd.Period("2000", "D")) + assert result is not arr + + +# ---------------------------------------------------------------------------- +# setitem + + +@pytest.mark.parametrize( + "key, value, expected", + [ + ([0], pd.Period("2000", "D"), [10957, 1, 2]), + ([0], None, [iNaT, 1, 2]), + ([0], np.nan, [iNaT, 1, 2]), + ([0, 1, 2], pd.Period("2000", "D"), [10957] * 3), + ( + [0, 1, 2], + [pd.Period("2000", "D"), pd.Period("2001", "D"), pd.Period("2002", "D")], + [10957, 11323, 11688], + ), + ], +) +def test_setitem(key, value, expected): + arr = PeriodArray(np.arange(3), dtype="period[D]") + expected = PeriodArray(expected, dtype="period[D]") + arr[key] = value + tm.assert_period_array_equal(arr, expected) + + +def test_setitem_raises_incompatible_freq(): + arr = PeriodArray(np.arange(3), dtype="period[D]") + with pytest.raises(IncompatibleFrequency, match="freq"): + arr[0] = pd.Period("2000", freq="Y") + + other = PeriodArray._from_sequence(["2000", "2001"], dtype="period[Y]") + with pytest.raises(IncompatibleFrequency, match="freq"): + arr[[0, 1]] = other + + +def test_setitem_raises_length(): + arr = PeriodArray(np.arange(3), dtype="period[D]") + with pytest.raises(ValueError, match="length"): + arr[[0, 1]] = [pd.Period("2000", freq="D")] + + +def test_setitem_raises_type(): + arr = PeriodArray(np.arange(3), dtype="period[D]") + with pytest.raises(TypeError, match="int"): + arr[0] = 1 + + +# ---------------------------------------------------------------------------- +# Ops + + +def test_sub_period(): + arr = PeriodArray._from_sequence(["2000", "2001"], dtype="period[D]") + other = pd.Period("2000", freq="M") + with pytest.raises(IncompatibleFrequency, match="freq"): + arr - other + + +def test_sub_period_overflow(): + # GH#47538 + dti = pd.date_range("1677-09-22", periods=2, freq="D") + pi = dti.to_period("ns") + + per = pd.Period._from_ordinal(10**14, pi.freq) + + with pytest.raises(OverflowError, match="Overflow in int64 addition"): + pi - per + + with pytest.raises(OverflowError, match="Overflow in int64 addition"): + per - pi + + +# ---------------------------------------------------------------------------- +# Methods + + +@pytest.mark.parametrize( + "other", + [ + pd.Period("2000", freq="h"), + PeriodArray._from_sequence(["2000", "2001", "2000"], dtype="period[h]"), + ], +) +def test_where_different_freq_raises(other): + # GH#45768 The PeriodArray method raises, the Series method coerces + ser = pd.Series( + PeriodArray._from_sequence(["2000", "2001", "2002"], dtype="period[D]") + ) + cond = np.array([True, False, True]) + + with pytest.raises(IncompatibleFrequency, match="freq"): + ser.array._where(cond, other) + + res = ser.where(cond, other) + expected = ser.astype(object).where(cond, other) + tm.assert_series_equal(res, expected) + + +# ---------------------------------------------------------------------------- +# Printing + + +def test_repr_small(): + arr = PeriodArray._from_sequence(["2000", "2001"], dtype="period[D]") + result = str(arr) + expected = ( + "\n['2000-01-01', '2001-01-01']\nLength: 2, dtype: period[D]" + ) + assert result == expected + + +def test_repr_large(): + arr = PeriodArray._from_sequence(["2000", "2001"] * 500, dtype="period[D]") + result = str(arr) + expected = ( + "\n" + "['2000-01-01', '2001-01-01', '2000-01-01', '2001-01-01', " + "'2000-01-01',\n" + " '2001-01-01', '2000-01-01', '2001-01-01', '2000-01-01', " + "'2001-01-01',\n" + " ...\n" + " '2000-01-01', '2001-01-01', '2000-01-01', '2001-01-01', " + "'2000-01-01',\n" + " '2001-01-01', '2000-01-01', '2001-01-01', '2000-01-01', " + "'2001-01-01']\n" + "Length: 1000, dtype: period[D]" + ) + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_timedeltas.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_timedeltas.py new file mode 100644 index 0000000000000000000000000000000000000000..a3f15467feb144ee21883a0a2a777e3b5e0cdf42 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/test_timedeltas.py @@ -0,0 +1,313 @@ +from datetime import timedelta + +import numpy as np +import pytest + +import pandas as pd +from pandas import Timedelta +import pandas._testing as tm +from pandas.core.arrays import ( + DatetimeArray, + TimedeltaArray, +) + + +class TestNonNano: + @pytest.fixture(params=["s", "ms", "us"]) + def unit(self, request): + return request.param + + @pytest.fixture + def tda(self, unit): + arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]") + return TimedeltaArray._simple_new(arr, dtype=arr.dtype) + + def test_non_nano(self, unit): + arr = np.arange(5, dtype=np.int64).view(f"m8[{unit}]") + tda = TimedeltaArray._simple_new(arr, dtype=arr.dtype) + + assert tda.dtype == arr.dtype + assert tda[0].unit == unit + + def test_as_unit_raises(self, tda): + # GH#50616 + with pytest.raises(ValueError, match="Supported units"): + tda.as_unit("D") + + tdi = pd.Index(tda) + with pytest.raises(ValueError, match="Supported units"): + tdi.as_unit("D") + + @pytest.mark.parametrize("field", TimedeltaArray._field_ops) + def test_fields(self, tda, field): + as_nano = tda._ndarray.astype("m8[ns]") + tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype) + + result = getattr(tda, field) + expected = getattr(tda_nano, field) + tm.assert_numpy_array_equal(result, expected) + + def test_to_pytimedelta(self, tda): + as_nano = tda._ndarray.astype("m8[ns]") + tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype) + + result = tda.to_pytimedelta() + expected = tda_nano.to_pytimedelta() + tm.assert_numpy_array_equal(result, expected) + + def test_total_seconds(self, unit, tda): + as_nano = tda._ndarray.astype("m8[ns]") + tda_nano = TimedeltaArray._simple_new(as_nano, dtype=as_nano.dtype) + + result = tda.total_seconds() + expected = tda_nano.total_seconds() + tm.assert_numpy_array_equal(result, expected) + + def test_timedelta_array_total_seconds(self): + # GH34290 + expected = Timedelta("2 min").total_seconds() + + result = pd.array([Timedelta("2 min")]).total_seconds()[0] + assert result == expected + + def test_total_seconds_nanoseconds(self): + # issue #48521 + start_time = pd.Series(["2145-11-02 06:00:00"]).astype("datetime64[ns]") + end_time = pd.Series(["2145-11-02 07:06:00"]).astype("datetime64[ns]") + expected = (end_time - start_time).values / np.timedelta64(1, "s") + result = (end_time - start_time).dt.total_seconds().values + assert result == expected + + @pytest.mark.parametrize( + "nat", [np.datetime64("NaT", "ns"), np.datetime64("NaT", "us")] + ) + def test_add_nat_datetimelike_scalar(self, nat, tda): + result = tda + nat + assert isinstance(result, DatetimeArray) + assert result._creso == tda._creso + assert result.isna().all() + + result = nat + tda + assert isinstance(result, DatetimeArray) + assert result._creso == tda._creso + assert result.isna().all() + + def test_add_pdnat(self, tda): + result = tda + pd.NaT + assert isinstance(result, TimedeltaArray) + assert result._creso == tda._creso + assert result.isna().all() + + result = pd.NaT + tda + assert isinstance(result, TimedeltaArray) + assert result._creso == tda._creso + assert result.isna().all() + + # TODO: 2022-07-11 this is the only test that gets to DTA.tz_convert + # or tz_localize with non-nano; implement tests specific to that. + def test_add_datetimelike_scalar(self, tda, tz_naive_fixture): + ts = pd.Timestamp("2016-01-01", tz=tz_naive_fixture).as_unit("ns") + + expected = tda.as_unit("ns") + ts + res = tda + ts + tm.assert_extension_array_equal(res, expected) + res = ts + tda + tm.assert_extension_array_equal(res, expected) + + ts += Timedelta(1) # case where we can't cast losslessly + + exp_values = tda._ndarray + ts.asm8 + expected = ( + DatetimeArray._simple_new(exp_values, dtype=exp_values.dtype) + .tz_localize("UTC") + .tz_convert(ts.tz) + ) + + result = tda + ts + tm.assert_extension_array_equal(result, expected) + + result = ts + tda + tm.assert_extension_array_equal(result, expected) + + def test_mul_scalar(self, tda): + other = 2 + result = tda * other + expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype) + tm.assert_extension_array_equal(result, expected) + assert result._creso == tda._creso + + def test_mul_listlike(self, tda): + other = np.arange(len(tda)) + result = tda * other + expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype) + tm.assert_extension_array_equal(result, expected) + assert result._creso == tda._creso + + def test_mul_listlike_object(self, tda): + other = np.arange(len(tda)) + result = tda * other.astype(object) + expected = TimedeltaArray._simple_new(tda._ndarray * other, dtype=tda.dtype) + tm.assert_extension_array_equal(result, expected) + assert result._creso == tda._creso + + def test_div_numeric_scalar(self, tda): + other = 2 + result = tda / other + expected = TimedeltaArray._simple_new(tda._ndarray / other, dtype=tda.dtype) + tm.assert_extension_array_equal(result, expected) + assert result._creso == tda._creso + + def test_div_td_scalar(self, tda): + other = timedelta(seconds=1) + result = tda / other + expected = tda._ndarray / np.timedelta64(1, "s") + tm.assert_numpy_array_equal(result, expected) + + def test_div_numeric_array(self, tda): + other = np.arange(len(tda)) + result = tda / other + expected = TimedeltaArray._simple_new(tda._ndarray / other, dtype=tda.dtype) + tm.assert_extension_array_equal(result, expected) + assert result._creso == tda._creso + + def test_div_td_array(self, tda): + other = tda._ndarray + tda._ndarray[-1] + result = tda / other + expected = tda._ndarray / other + tm.assert_numpy_array_equal(result, expected) + + def test_add_timedeltaarraylike(self, tda): + tda_nano = tda.astype("m8[ns]") + + expected = tda_nano * 2 + res = tda_nano + tda + tm.assert_extension_array_equal(res, expected) + res = tda + tda_nano + tm.assert_extension_array_equal(res, expected) + + expected = tda_nano * 0 + res = tda - tda_nano + tm.assert_extension_array_equal(res, expected) + + res = tda_nano - tda + tm.assert_extension_array_equal(res, expected) + + +class TestTimedeltaArray: + @pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"]) + def test_astype_int(self, dtype): + arr = TimedeltaArray._from_sequence( + [Timedelta("1h"), Timedelta("2h")], dtype="m8[ns]" + ) + + if np.dtype(dtype) != np.int64: + with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"): + arr.astype(dtype) + return + + result = arr.astype(dtype) + expected = arr._ndarray.view("i8") + tm.assert_numpy_array_equal(result, expected) + + def test_setitem_clears_freq(self): + a = pd.timedelta_range("1h", periods=2, freq="h")._data + a[0] = Timedelta("1h") + assert a.freq is None + + @pytest.mark.parametrize( + "obj", + [ + Timedelta(seconds=1), + Timedelta(seconds=1).to_timedelta64(), + Timedelta(seconds=1).to_pytimedelta(), + ], + ) + def test_setitem_objects(self, obj): + # make sure we accept timedelta64 and timedelta in addition to Timedelta + tdi = pd.timedelta_range("2 Days", periods=4, freq="h") + arr = tdi._data + + arr[0] = obj + assert arr[0] == Timedelta(seconds=1) + + @pytest.mark.parametrize( + "other", + [ + 1, + np.int64(1), + 1.0, + np.datetime64("NaT"), + pd.Timestamp("2021-01-01"), + "invalid", + np.arange(10, dtype="i8") * 24 * 3600 * 10**9, + (np.arange(10) * 24 * 3600 * 10**9).view("datetime64[ns]"), + pd.Timestamp("2021-01-01").to_period("D"), + ], + ) + @pytest.mark.parametrize("index", [True, False]) + def test_searchsorted_invalid_types(self, other, index): + data = np.arange(10, dtype="i8") * 24 * 3600 * 10**9 + arr = pd.TimedeltaIndex(data, freq="D")._data + if index: + arr = pd.Index(arr) + + msg = "|".join( + [ + "searchsorted requires compatible dtype or scalar", + "value should be a 'Timedelta', 'NaT', or array of those. Got", + ] + ) + with pytest.raises(TypeError, match=msg): + arr.searchsorted(other) + + +class TestUnaryOps: + def test_abs(self): + vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]") + arr = TimedeltaArray._from_sequence(vals) + + evals = np.array([3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]") + expected = TimedeltaArray._from_sequence(evals) + + result = abs(arr) + tm.assert_timedelta_array_equal(result, expected) + + result2 = np.abs(arr) + tm.assert_timedelta_array_equal(result2, expected) + + def test_pos(self): + vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]") + arr = TimedeltaArray._from_sequence(vals) + + result = +arr + tm.assert_timedelta_array_equal(result, arr) + assert not tm.shares_memory(result, arr) + + result2 = np.positive(arr) + tm.assert_timedelta_array_equal(result2, arr) + assert not tm.shares_memory(result2, arr) + + def test_neg(self): + vals = np.array([-3600 * 10**9, "NaT", 7200 * 10**9], dtype="m8[ns]") + arr = TimedeltaArray._from_sequence(vals) + + evals = np.array([3600 * 10**9, "NaT", -7200 * 10**9], dtype="m8[ns]") + expected = TimedeltaArray._from_sequence(evals) + + result = -arr + tm.assert_timedelta_array_equal(result, expected) + + result2 = np.negative(arr) + tm.assert_timedelta_array_equal(result2, expected) + + def test_neg_freq(self): + tdi = pd.timedelta_range("2 Days", periods=4, freq="h") + arr = tdi._data + + expected = -tdi._data + + result = -arr + tm.assert_timedelta_array_equal(result, expected) + + result2 = np.negative(arr) + tm.assert_timedelta_array_equal(result2, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_constructors.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..91b6f7fa222f9a668092a99a8371753e914008c8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_constructors.py @@ -0,0 +1,103 @@ +import numpy as np +import pytest + +import pandas._testing as tm +from pandas.core.arrays import TimedeltaArray + + +class TestTimedeltaArrayConstructor: + def test_only_1dim_accepted(self): + # GH#25282 + arr = np.array([0, 1, 2, 3], dtype="m8[h]").astype("m8[ns]") + + depr_msg = "TimedeltaArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="Only 1-dimensional"): + # 3-dim, we allow 2D to sneak in for ops purposes GH#29853 + TimedeltaArray(arr.reshape(2, 2, 1)) + + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="Only 1-dimensional"): + # 0-dim + TimedeltaArray(arr[[0]].squeeze()) + + def test_freq_validation(self): + # ensure that the public constructor cannot create an invalid instance + arr = np.array([0, 0, 1], dtype=np.int64) * 3600 * 10**9 + + msg = ( + "Inferred frequency None from passed values does not " + "conform to passed frequency D" + ) + depr_msg = "TimedeltaArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match=msg): + TimedeltaArray(arr.view("timedelta64[ns]"), freq="D") + + def test_non_array_raises(self): + depr_msg = "TimedeltaArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match="list"): + TimedeltaArray([1, 2, 3]) + + def test_other_type_raises(self): + msg = r"dtype bool cannot be converted to timedelta64\[ns\]" + with pytest.raises(TypeError, match=msg): + TimedeltaArray._from_sequence(np.array([1, 2, 3], dtype="bool")) + + def test_incorrect_dtype_raises(self): + msg = "dtype 'category' is invalid, should be np.timedelta64 dtype" + with pytest.raises(ValueError, match=msg): + TimedeltaArray._from_sequence( + np.array([1, 2, 3], dtype="i8"), dtype="category" + ) + + msg = "dtype 'int64' is invalid, should be np.timedelta64 dtype" + with pytest.raises(ValueError, match=msg): + TimedeltaArray._from_sequence( + np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("int64") + ) + + msg = r"dtype 'datetime64\[ns\]' is invalid, should be np.timedelta64 dtype" + with pytest.raises(ValueError, match=msg): + TimedeltaArray._from_sequence( + np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("M8[ns]") + ) + + msg = ( + r"dtype 'datetime64\[us, UTC\]' is invalid, should be np.timedelta64 dtype" + ) + with pytest.raises(ValueError, match=msg): + TimedeltaArray._from_sequence( + np.array([1, 2, 3], dtype="i8"), dtype="M8[us, UTC]" + ) + + msg = "Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns'" + with pytest.raises(ValueError, match=msg): + TimedeltaArray._from_sequence( + np.array([1, 2, 3], dtype="i8"), dtype=np.dtype("m8[Y]") + ) + + def test_mismatched_values_dtype_units(self): + arr = np.array([1, 2, 3], dtype="m8[s]") + dtype = np.dtype("m8[ns]") + msg = r"Values resolution does not match dtype" + depr_msg = "TimedeltaArray.__init__ is deprecated" + + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + with pytest.raises(ValueError, match=msg): + TimedeltaArray(arr, dtype=dtype) + + def test_copy(self): + data = np.array([1, 2, 3], dtype="m8[ns]") + arr = TimedeltaArray._from_sequence(data, copy=False) + assert arr._ndarray is data + + arr = TimedeltaArray._from_sequence(data, copy=True) + assert arr._ndarray is not data + assert arr._ndarray.base is not data + + def test_from_sequence_dtype(self): + msg = "dtype 'object' is invalid, should be np.timedelta64 dtype" + with pytest.raises(ValueError, match=msg): + TimedeltaArray._from_sequence([], dtype=object) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_cumulative.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_cumulative.py new file mode 100644 index 0000000000000000000000000000000000000000..2d8fe65f807e431d788e526eee058780b5bf979c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_cumulative.py @@ -0,0 +1,20 @@ +import pytest + +import pandas._testing as tm +from pandas.core.arrays import TimedeltaArray + + +class TestAccumulator: + def test_accumulators_disallowed(self): + # GH#50297 + arr = TimedeltaArray._from_sequence(["1D", "2D"], dtype="m8[ns]") + with pytest.raises(TypeError, match="cumprod not supported"): + arr._accumulate("cumprod") + + def test_cumsum(self, unit): + # GH#50297 + dtype = f"m8[{unit}]" + arr = TimedeltaArray._from_sequence(["1D", "2D"], dtype=dtype) + result = arr._accumulate("cumsum") + expected = TimedeltaArray._from_sequence(["1D", "3D"], dtype=dtype) + tm.assert_timedelta_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_reductions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_reductions.py new file mode 100644 index 0000000000000000000000000000000000000000..991dbf41c808794f9a53a3f3351a9b6e79a7c6fd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/arrays/timedeltas/test_reductions.py @@ -0,0 +1,218 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import Timedelta +import pandas._testing as tm +from pandas.core import nanops +from pandas.core.arrays import TimedeltaArray + + +class TestReductions: + @pytest.mark.parametrize("name", ["std", "min", "max", "median", "mean"]) + @pytest.mark.parametrize("skipna", [True, False]) + def test_reductions_empty(self, name, skipna): + tdi = pd.TimedeltaIndex([]) + arr = tdi.array + + result = getattr(tdi, name)(skipna=skipna) + assert result is pd.NaT + + result = getattr(arr, name)(skipna=skipna) + assert result is pd.NaT + + @pytest.mark.parametrize("skipna", [True, False]) + def test_sum_empty(self, skipna): + tdi = pd.TimedeltaIndex([]) + arr = tdi.array + + result = tdi.sum(skipna=skipna) + assert isinstance(result, Timedelta) + assert result == Timedelta(0) + + result = arr.sum(skipna=skipna) + assert isinstance(result, Timedelta) + assert result == Timedelta(0) + + def test_min_max(self, unit): + dtype = f"m8[{unit}]" + arr = TimedeltaArray._from_sequence( + ["3h", "3h", "NaT", "2h", "5h", "4h"], dtype=dtype + ) + + result = arr.min() + expected = Timedelta("2h") + assert result == expected + + result = arr.max() + expected = Timedelta("5h") + assert result == expected + + result = arr.min(skipna=False) + assert result is pd.NaT + + result = arr.max(skipna=False) + assert result is pd.NaT + + def test_sum(self): + tdi = pd.TimedeltaIndex(["3h", "3h", "NaT", "2h", "5h", "4h"]) + arr = tdi.array + + result = arr.sum(skipna=True) + expected = Timedelta(hours=17) + assert isinstance(result, Timedelta) + assert result == expected + + result = tdi.sum(skipna=True) + assert isinstance(result, Timedelta) + assert result == expected + + result = arr.sum(skipna=False) + assert result is pd.NaT + + result = tdi.sum(skipna=False) + assert result is pd.NaT + + result = arr.sum(min_count=9) + assert result is pd.NaT + + result = tdi.sum(min_count=9) + assert result is pd.NaT + + result = arr.sum(min_count=1) + assert isinstance(result, Timedelta) + assert result == expected + + result = tdi.sum(min_count=1) + assert isinstance(result, Timedelta) + assert result == expected + + def test_npsum(self): + # GH#25282, GH#25335 np.sum should return a Timedelta, not timedelta64 + tdi = pd.TimedeltaIndex(["3h", "3h", "2h", "5h", "4h"]) + arr = tdi.array + + result = np.sum(tdi) + expected = Timedelta(hours=17) + assert isinstance(result, Timedelta) + assert result == expected + + result = np.sum(arr) + assert isinstance(result, Timedelta) + assert result == expected + + def test_sum_2d_skipna_false(self): + arr = np.arange(8).astype(np.int64).view("m8[s]").astype("m8[ns]").reshape(4, 2) + arr[-1, -1] = "Nat" + + tda = TimedeltaArray._from_sequence(arr) + + result = tda.sum(skipna=False) + assert result is pd.NaT + + result = tda.sum(axis=0, skipna=False) + expected = pd.TimedeltaIndex([Timedelta(seconds=12), pd.NaT])._values + tm.assert_timedelta_array_equal(result, expected) + + result = tda.sum(axis=1, skipna=False) + expected = pd.TimedeltaIndex( + [ + Timedelta(seconds=1), + Timedelta(seconds=5), + Timedelta(seconds=9), + pd.NaT, + ] + )._values + tm.assert_timedelta_array_equal(result, expected) + + # Adding a Timestamp makes this a test for DatetimeArray.std + @pytest.mark.parametrize( + "add", + [ + Timedelta(0), + pd.Timestamp("2021-01-01"), + pd.Timestamp("2021-01-01", tz="UTC"), + pd.Timestamp("2021-01-01", tz="Asia/Tokyo"), + ], + ) + def test_std(self, add): + tdi = pd.TimedeltaIndex(["0h", "4h", "NaT", "4h", "0h", "2h"]) + add + arr = tdi.array + + result = arr.std(skipna=True) + expected = Timedelta(hours=2) + assert isinstance(result, Timedelta) + assert result == expected + + result = tdi.std(skipna=True) + assert isinstance(result, Timedelta) + assert result == expected + + if getattr(arr, "tz", None) is None: + result = nanops.nanstd(np.asarray(arr), skipna=True) + assert isinstance(result, np.timedelta64) + assert result == expected + + result = arr.std(skipna=False) + assert result is pd.NaT + + result = tdi.std(skipna=False) + assert result is pd.NaT + + if getattr(arr, "tz", None) is None: + result = nanops.nanstd(np.asarray(arr), skipna=False) + assert isinstance(result, np.timedelta64) + assert np.isnat(result) + + def test_median(self): + tdi = pd.TimedeltaIndex(["0h", "3h", "NaT", "5h06m", "0h", "2h"]) + arr = tdi.array + + result = arr.median(skipna=True) + expected = Timedelta(hours=2) + assert isinstance(result, Timedelta) + assert result == expected + + result = tdi.median(skipna=True) + assert isinstance(result, Timedelta) + assert result == expected + + result = arr.median(skipna=False) + assert result is pd.NaT + + result = tdi.median(skipna=False) + assert result is pd.NaT + + def test_mean(self): + tdi = pd.TimedeltaIndex(["0h", "3h", "NaT", "5h06m", "0h", "2h"]) + arr = tdi._data + + # manually verified result + expected = Timedelta(arr.dropna()._ndarray.mean()) + + result = arr.mean() + assert result == expected + result = arr.mean(skipna=False) + assert result is pd.NaT + + result = arr.dropna().mean(skipna=False) + assert result == expected + + result = arr.mean(axis=0) + assert result == expected + + def test_mean_2d(self): + tdi = pd.timedelta_range("14 days", periods=6) + tda = tdi._data.reshape(3, 2) + + result = tda.mean(axis=0) + expected = tda[1] + tm.assert_timedelta_array_equal(result, expected) + + result = tda.mean(axis=1) + expected = tda[:, 0] + Timedelta(hours=12) + tm.assert_timedelta_array_equal(result, expected) + + result = tda.mean(axis=None) + expected = tdi.mean() + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/computation/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/computation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/computation/test_compat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/computation/test_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..856a5b3a22a95d35cc577050f52d762b065e3ddf --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/computation/test_compat.py @@ -0,0 +1,32 @@ +import pytest + +from pandas.compat._optional import VERSIONS + +import pandas as pd +from pandas.core.computation import expr +from pandas.core.computation.engines import ENGINES +from pandas.util.version import Version + + +def test_compat(): + # test we have compat with our version of numexpr + + from pandas.core.computation.check import NUMEXPR_INSTALLED + + ne = pytest.importorskip("numexpr") + + ver = ne.__version__ + if Version(ver) < Version(VERSIONS["numexpr"]): + assert not NUMEXPR_INSTALLED + else: + assert NUMEXPR_INSTALLED + + +@pytest.mark.parametrize("engine", ENGINES) +@pytest.mark.parametrize("parser", expr.PARSERS) +def test_invalid_numexpr_version(engine, parser): + if engine == "numexpr": + pytest.importorskip("numexpr") + a, b = 1, 2 # noqa: F841 + res = pd.eval("a + b", engine=engine, parser=parser) + assert res == 3 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/computation/test_eval.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/computation/test_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..cf3e50094ac97cbc857f68e5fe73dd2ad7bf066f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/computation/test_eval.py @@ -0,0 +1,2000 @@ +from __future__ import annotations + +from functools import reduce +from itertools import product +import operator + +import numpy as np +import pytest + +from pandas.compat import PY312 +from pandas.errors import ( + NumExprClobberingError, + PerformanceWarning, + UndefinedVariableError, +) +import pandas.util._test_decorators as td + +from pandas.core.dtypes.common import ( + is_bool, + is_float, + is_list_like, + is_scalar, +) + +import pandas as pd +from pandas import ( + DataFrame, + Index, + Series, + date_range, + period_range, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.computation import ( + expr, + pytables, +) +from pandas.core.computation.engines import ENGINES +from pandas.core.computation.expr import ( + BaseExprVisitor, + PandasExprVisitor, + PythonExprVisitor, +) +from pandas.core.computation.expressions import ( + NUMEXPR_INSTALLED, + USE_NUMEXPR, +) +from pandas.core.computation.ops import ( + ARITH_OPS_SYMS, + SPECIAL_CASE_ARITH_OPS_SYMS, + _binary_math_ops, + _binary_ops_dict, + _unary_math_ops, +) +from pandas.core.computation.scope import DEFAULT_GLOBALS + + +@pytest.fixture( + params=( + pytest.param( + engine, + marks=[ + pytest.mark.skipif( + engine == "numexpr" and not USE_NUMEXPR, + reason=f"numexpr enabled->{USE_NUMEXPR}, " + f"installed->{NUMEXPR_INSTALLED}", + ), + td.skip_if_no("numexpr"), + ], + ) + for engine in ENGINES + ) +) +def engine(request): + return request.param + + +@pytest.fixture(params=expr.PARSERS) +def parser(request): + return request.param + + +def _eval_single_bin(lhs, cmp1, rhs, engine): + c = _binary_ops_dict[cmp1] + if ENGINES[engine].has_neg_frac: + try: + return c(lhs, rhs) + except ValueError as e: + if str(e).startswith( + "negative number cannot be raised to a fractional power" + ): + return np.nan + raise + return c(lhs, rhs) + + +# TODO: using range(5) here is a kludge +@pytest.fixture( + params=list(range(5)), + ids=["DataFrame", "Series", "SeriesNaN", "DataFrameNaN", "float"], +) +def lhs(request): + nan_df1 = DataFrame(np.random.default_rng(2).standard_normal((10, 5))) + nan_df1[nan_df1 > 0.5] = np.nan + + opts = ( + DataFrame(np.random.default_rng(2).standard_normal((10, 5))), + Series(np.random.default_rng(2).standard_normal(5)), + Series([1, 2, np.nan, np.nan, 5]), + nan_df1, + np.random.default_rng(2).standard_normal(), + ) + return opts[request.param] + + +rhs = lhs +midhs = lhs + + +@pytest.fixture +def idx_func_dict(): + return { + "i": lambda n: Index(np.arange(n), dtype=np.int64), + "f": lambda n: Index(np.arange(n), dtype=np.float64), + "s": lambda n: Index([f"{i}_{chr(i)}" for i in range(97, 97 + n)]), + "dt": lambda n: date_range("2020-01-01", periods=n), + "td": lambda n: timedelta_range("1 day", periods=n), + "p": lambda n: period_range("2020-01-01", periods=n, freq="D"), + } + + +class TestEval: + @pytest.mark.parametrize( + "cmp1", + ["!=", "==", "<=", ">=", "<", ">"], + ids=["ne", "eq", "le", "ge", "lt", "gt"], + ) + @pytest.mark.parametrize("cmp2", [">", "<"], ids=["gt", "lt"]) + @pytest.mark.parametrize("binop", expr.BOOL_OPS_SYMS) + def test_complex_cmp_ops(self, cmp1, cmp2, binop, lhs, rhs, engine, parser): + if parser == "python" and binop in ["and", "or"]: + msg = "'BoolOp' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)" + pd.eval(ex, engine=engine, parser=parser) + return + + lhs_new = _eval_single_bin(lhs, cmp1, rhs, engine) + rhs_new = _eval_single_bin(lhs, cmp2, rhs, engine) + expected = _eval_single_bin(lhs_new, binop, rhs_new, engine) + + ex = f"(lhs {cmp1} rhs) {binop} (lhs {cmp2} rhs)" + result = pd.eval(ex, engine=engine, parser=parser) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("cmp_op", expr.CMP_OPS_SYMS) + def test_simple_cmp_ops(self, cmp_op, lhs, rhs, engine, parser): + lhs = lhs < 0 + rhs = rhs < 0 + + if parser == "python" and cmp_op in ["in", "not in"]: + msg = "'(In|NotIn)' nodes are not implemented" + + with pytest.raises(NotImplementedError, match=msg): + ex = f"lhs {cmp_op} rhs" + pd.eval(ex, engine=engine, parser=parser) + return + + ex = f"lhs {cmp_op} rhs" + msg = "|".join( + [ + r"only list-like( or dict-like)? objects are allowed to be " + r"passed to (DataFrame\.)?isin\(\), you passed a " + r"(`|')bool(`|')", + "argument of type 'bool' is not iterable", + ] + ) + if cmp_op in ("in", "not in") and not is_list_like(rhs): + with pytest.raises(TypeError, match=msg): + pd.eval( + ex, + engine=engine, + parser=parser, + local_dict={"lhs": lhs, "rhs": rhs}, + ) + else: + expected = _eval_single_bin(lhs, cmp_op, rhs, engine) + result = pd.eval(ex, engine=engine, parser=parser) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("op", expr.CMP_OPS_SYMS) + def test_compound_invert_op(self, op, lhs, rhs, request, engine, parser): + if parser == "python" and op in ["in", "not in"]: + msg = "'(In|NotIn)' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + ex = f"~(lhs {op} rhs)" + pd.eval(ex, engine=engine, parser=parser) + return + + if ( + is_float(lhs) + and not is_float(rhs) + and op in ["in", "not in"] + and engine == "python" + and parser == "pandas" + ): + mark = pytest.mark.xfail( + reason="Looks like expected is negative, unclear whether " + "expected is incorrect or result is incorrect" + ) + request.applymarker(mark) + skip_these = ["in", "not in"] + ex = f"~(lhs {op} rhs)" + + msg = "|".join( + [ + r"only list-like( or dict-like)? objects are allowed to be " + r"passed to (DataFrame\.)?isin\(\), you passed a " + r"(`|')float(`|')", + "argument of type 'float' is not iterable", + ] + ) + if is_scalar(rhs) and op in skip_these: + with pytest.raises(TypeError, match=msg): + pd.eval( + ex, + engine=engine, + parser=parser, + local_dict={"lhs": lhs, "rhs": rhs}, + ) + else: + # compound + if is_scalar(lhs) and is_scalar(rhs): + lhs, rhs = (np.array([x]) for x in (lhs, rhs)) + expected = _eval_single_bin(lhs, op, rhs, engine) + if is_scalar(expected): + expected = not expected + else: + expected = ~expected + result = pd.eval(ex, engine=engine, parser=parser) + tm.assert_almost_equal(expected, result) + + @pytest.mark.parametrize("cmp1", ["<", ">"]) + @pytest.mark.parametrize("cmp2", ["<", ">"]) + def test_chained_cmp_op(self, cmp1, cmp2, lhs, midhs, rhs, engine, parser): + mid = midhs + if parser == "python": + ex1 = f"lhs {cmp1} mid {cmp2} rhs" + msg = "'BoolOp' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + pd.eval(ex1, engine=engine, parser=parser) + return + + lhs_new = _eval_single_bin(lhs, cmp1, mid, engine) + rhs_new = _eval_single_bin(mid, cmp2, rhs, engine) + + if lhs_new is not None and rhs_new is not None: + ex1 = f"lhs {cmp1} mid {cmp2} rhs" + ex2 = f"lhs {cmp1} mid and mid {cmp2} rhs" + ex3 = f"(lhs {cmp1} mid) & (mid {cmp2} rhs)" + expected = _eval_single_bin(lhs_new, "&", rhs_new, engine) + + for ex in (ex1, ex2, ex3): + result = pd.eval(ex, engine=engine, parser=parser) + + tm.assert_almost_equal(result, expected) + + @pytest.mark.parametrize( + "arith1", sorted(set(ARITH_OPS_SYMS).difference(SPECIAL_CASE_ARITH_OPS_SYMS)) + ) + def test_binary_arith_ops(self, arith1, lhs, rhs, engine, parser): + ex = f"lhs {arith1} rhs" + result = pd.eval(ex, engine=engine, parser=parser) + expected = _eval_single_bin(lhs, arith1, rhs, engine) + + tm.assert_almost_equal(result, expected) + ex = f"lhs {arith1} rhs {arith1} rhs" + result = pd.eval(ex, engine=engine, parser=parser) + nlhs = _eval_single_bin(lhs, arith1, rhs, engine) + try: + nlhs, ghs = nlhs.align(rhs) + except (ValueError, TypeError, AttributeError): + # ValueError: series frame or frame series align + # TypeError, AttributeError: series or frame with scalar align + return + else: + if engine == "numexpr": + import numexpr as ne + + # direct numpy comparison + expected = ne.evaluate(f"nlhs {arith1} ghs") + # Update assert statement due to unreliable numerical + # precision component (GH37328) + # TODO: update testing code so that assert_almost_equal statement + # can be replaced again by the assert_numpy_array_equal statement + tm.assert_almost_equal(result.values, expected) + else: + expected = eval(f"nlhs {arith1} ghs") + tm.assert_almost_equal(result, expected) + + # modulus, pow, and floor division require special casing + + def test_modulus(self, lhs, rhs, engine, parser): + ex = r"lhs % rhs" + result = pd.eval(ex, engine=engine, parser=parser) + expected = lhs % rhs + tm.assert_almost_equal(result, expected) + + if engine == "numexpr": + import numexpr as ne + + expected = ne.evaluate(r"expected % rhs") + if isinstance(result, (DataFrame, Series)): + tm.assert_almost_equal(result.values, expected) + else: + tm.assert_almost_equal(result, expected.item()) + else: + expected = _eval_single_bin(expected, "%", rhs, engine) + tm.assert_almost_equal(result, expected) + + def test_floor_division(self, lhs, rhs, engine, parser): + ex = "lhs // rhs" + + if engine == "python": + res = pd.eval(ex, engine=engine, parser=parser) + expected = lhs // rhs + tm.assert_equal(res, expected) + else: + msg = ( + r"unsupported operand type\(s\) for //: 'VariableNode' and " + "'VariableNode'" + ) + with pytest.raises(TypeError, match=msg): + pd.eval( + ex, + local_dict={"lhs": lhs, "rhs": rhs}, + engine=engine, + parser=parser, + ) + + @td.skip_if_windows + def test_pow(self, lhs, rhs, engine, parser): + # odd failure on win32 platform, so skip + ex = "lhs ** rhs" + expected = _eval_single_bin(lhs, "**", rhs, engine) + result = pd.eval(ex, engine=engine, parser=parser) + + if ( + is_scalar(lhs) + and is_scalar(rhs) + and isinstance(expected, (complex, np.complexfloating)) + and np.isnan(result) + ): + msg = "(DataFrame.columns|numpy array) are different" + with pytest.raises(AssertionError, match=msg): + tm.assert_numpy_array_equal(result, expected) + else: + tm.assert_almost_equal(result, expected) + + ex = "(lhs ** rhs) ** rhs" + result = pd.eval(ex, engine=engine, parser=parser) + + middle = _eval_single_bin(lhs, "**", rhs, engine) + expected = _eval_single_bin(middle, "**", rhs, engine) + tm.assert_almost_equal(result, expected) + + def test_check_single_invert_op(self, lhs, engine, parser): + # simple + try: + elb = lhs.astype(bool) + except AttributeError: + elb = np.array([bool(lhs)]) + expected = ~elb + result = pd.eval("~elb", engine=engine, parser=parser) + tm.assert_almost_equal(expected, result) + + def test_frame_invert(self, engine, parser): + expr = "~lhs" + + # ~ ## + # frame + # float always raises + lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2))) + if engine == "numexpr": + msg = "couldn't find matching opcode for 'invert_dd'" + with pytest.raises(NotImplementedError, match=msg): + pd.eval(expr, engine=engine, parser=parser) + else: + msg = "ufunc 'invert' not supported for the input types" + with pytest.raises(TypeError, match=msg): + pd.eval(expr, engine=engine, parser=parser) + + # int raises on numexpr + lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2))) + if engine == "numexpr": + msg = "couldn't find matching opcode for 'invert" + with pytest.raises(NotImplementedError, match=msg): + pd.eval(expr, engine=engine, parser=parser) + else: + expect = ~lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_frame_equal(expect, result) + + # bool always works + lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5) + expect = ~lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_frame_equal(expect, result) + + # object raises + lhs = DataFrame( + {"b": ["a", 1, 2.0], "c": np.random.default_rng(2).standard_normal(3) > 0.5} + ) + if engine == "numexpr": + with pytest.raises(ValueError, match="unknown type object"): + pd.eval(expr, engine=engine, parser=parser) + else: + msg = "bad operand type for unary ~: 'str'" + with pytest.raises(TypeError, match=msg): + pd.eval(expr, engine=engine, parser=parser) + + def test_series_invert(self, engine, parser): + # ~ #### + expr = "~lhs" + + # series + # float raises + lhs = Series(np.random.default_rng(2).standard_normal(5)) + if engine == "numexpr": + msg = "couldn't find matching opcode for 'invert_dd'" + with pytest.raises(NotImplementedError, match=msg): + result = pd.eval(expr, engine=engine, parser=parser) + else: + msg = "ufunc 'invert' not supported for the input types" + with pytest.raises(TypeError, match=msg): + pd.eval(expr, engine=engine, parser=parser) + + # int raises on numexpr + lhs = Series(np.random.default_rng(2).integers(5, size=5)) + if engine == "numexpr": + msg = "couldn't find matching opcode for 'invert" + with pytest.raises(NotImplementedError, match=msg): + pd.eval(expr, engine=engine, parser=parser) + else: + expect = ~lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_series_equal(expect, result) + + # bool + lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5) + expect = ~lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_series_equal(expect, result) + + # float + # int + # bool + + # object + lhs = Series(["a", 1, 2.0]) + if engine == "numexpr": + with pytest.raises(ValueError, match="unknown type object"): + pd.eval(expr, engine=engine, parser=parser) + else: + msg = "bad operand type for unary ~: 'str'" + with pytest.raises(TypeError, match=msg): + pd.eval(expr, engine=engine, parser=parser) + + def test_frame_negate(self, engine, parser): + expr = "-lhs" + + # float + lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2))) + expect = -lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_frame_equal(expect, result) + + # int + lhs = DataFrame(np.random.default_rng(2).integers(5, size=(5, 2))) + expect = -lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_frame_equal(expect, result) + + # bool doesn't work with numexpr but works elsewhere + lhs = DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5) + if engine == "numexpr": + msg = "couldn't find matching opcode for 'neg_bb'" + with pytest.raises(NotImplementedError, match=msg): + pd.eval(expr, engine=engine, parser=parser) + else: + expect = -lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_frame_equal(expect, result) + + def test_series_negate(self, engine, parser): + expr = "-lhs" + + # float + lhs = Series(np.random.default_rng(2).standard_normal(5)) + expect = -lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_series_equal(expect, result) + + # int + lhs = Series(np.random.default_rng(2).integers(5, size=5)) + expect = -lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_series_equal(expect, result) + + # bool doesn't work with numexpr but works elsewhere + lhs = Series(np.random.default_rng(2).standard_normal(5) > 0.5) + if engine == "numexpr": + msg = "couldn't find matching opcode for 'neg_bb'" + with pytest.raises(NotImplementedError, match=msg): + pd.eval(expr, engine=engine, parser=parser) + else: + expect = -lhs + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_series_equal(expect, result) + + @pytest.mark.parametrize( + "lhs", + [ + # Float + DataFrame(np.random.default_rng(2).standard_normal((5, 2))), + # Int + DataFrame(np.random.default_rng(2).integers(5, size=(5, 2))), + # bool doesn't work with numexpr but works elsewhere + DataFrame(np.random.default_rng(2).standard_normal((5, 2)) > 0.5), + ], + ) + def test_frame_pos(self, lhs, engine, parser): + expr = "+lhs" + expect = lhs + + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_frame_equal(expect, result) + + @pytest.mark.parametrize( + "lhs", + [ + # Float + Series(np.random.default_rng(2).standard_normal(5)), + # Int + Series(np.random.default_rng(2).integers(5, size=5)), + # bool doesn't work with numexpr but works elsewhere + Series(np.random.default_rng(2).standard_normal(5) > 0.5), + ], + ) + def test_series_pos(self, lhs, engine, parser): + expr = "+lhs" + expect = lhs + + result = pd.eval(expr, engine=engine, parser=parser) + tm.assert_series_equal(expect, result) + + def test_scalar_unary(self, engine, parser): + msg = "bad operand type for unary ~: 'float'" + warn = None + if PY312 and not (engine == "numexpr" and parser == "pandas"): + warn = DeprecationWarning + with pytest.raises(TypeError, match=msg): + pd.eval("~1.0", engine=engine, parser=parser) + + assert pd.eval("-1.0", parser=parser, engine=engine) == -1.0 + assert pd.eval("+1.0", parser=parser, engine=engine) == +1.0 + assert pd.eval("~1", parser=parser, engine=engine) == ~1 + assert pd.eval("-1", parser=parser, engine=engine) == -1 + assert pd.eval("+1", parser=parser, engine=engine) == +1 + with tm.assert_produces_warning( + warn, match="Bitwise inversion", check_stacklevel=False + ): + assert pd.eval("~True", parser=parser, engine=engine) == ~True + with tm.assert_produces_warning( + warn, match="Bitwise inversion", check_stacklevel=False + ): + assert pd.eval("~False", parser=parser, engine=engine) == ~False + assert pd.eval("-True", parser=parser, engine=engine) == -True + assert pd.eval("-False", parser=parser, engine=engine) == -False + assert pd.eval("+True", parser=parser, engine=engine) == +True + assert pd.eval("+False", parser=parser, engine=engine) == +False + + def test_unary_in_array(self): + # GH 11235 + # TODO: 2022-01-29: result return list with numexpr 2.7.3 in CI + # but cannot reproduce locally + result = np.array( + pd.eval("[-True, True, +True, -False, False, +False, -37, 37, ~37, +37]"), + dtype=np.object_, + ) + expected = np.array( + [ + -True, + True, + +True, + -False, + False, + +False, + -37, + 37, + ~37, + +37, + ], + dtype=np.object_, + ) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("expr", ["x < -0.1", "-5 > x"]) + def test_float_comparison_bin_op(self, float_numpy_dtype, expr): + # GH 16363 + df = DataFrame({"x": np.array([0], dtype=float_numpy_dtype)}) + res = df.eval(expr) + assert res.values == np.array([False]) + + def test_unary_in_function(self): + # GH 46471 + df = DataFrame({"x": [0, 1, np.nan]}) + + result = df.eval("x.fillna(-1)") + expected = df.x.fillna(-1) + # column name becomes None if using numexpr + # only check names when the engine is not numexpr + tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR) + + result = df.eval("x.shift(1, fill_value=-1)") + expected = df.x.shift(1, fill_value=-1) + tm.assert_series_equal(result, expected, check_names=not USE_NUMEXPR) + + @pytest.mark.parametrize( + "ex", + ( + "1 or 2", + "1 and 2", + "a and b", + "a or b", + "1 or 2 and (3 + 2) > 3", + "2 * x > 2 or 1 and 2", + "2 * df > 3 and 1 or a", + ), + ) + def test_disallow_scalar_bool_ops(self, ex, engine, parser): + x, a, b = np.random.default_rng(2).standard_normal(3), 1, 2 # noqa: F841 + df = DataFrame(np.random.default_rng(2).standard_normal((3, 2))) # noqa: F841 + + msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not" + with pytest.raises(NotImplementedError, match=msg): + pd.eval(ex, engine=engine, parser=parser) + + def test_identical(self, engine, parser): + # see gh-10546 + x = 1 + result = pd.eval("x", engine=engine, parser=parser) + assert result == 1 + assert is_scalar(result) + + x = 1.5 + result = pd.eval("x", engine=engine, parser=parser) + assert result == 1.5 + assert is_scalar(result) + + x = False + result = pd.eval("x", engine=engine, parser=parser) + assert not result + assert is_bool(result) + assert is_scalar(result) + + x = np.array([1]) + result = pd.eval("x", engine=engine, parser=parser) + tm.assert_numpy_array_equal(result, np.array([1])) + assert result.shape == (1,) + + x = np.array([1.5]) + result = pd.eval("x", engine=engine, parser=parser) + tm.assert_numpy_array_equal(result, np.array([1.5])) + assert result.shape == (1,) + + x = np.array([False]) # noqa: F841 + result = pd.eval("x", engine=engine, parser=parser) + tm.assert_numpy_array_equal(result, np.array([False])) + assert result.shape == (1,) + + def test_line_continuation(self, engine, parser): + # GH 11149 + exp = """1 + 2 * \ + 5 - 1 + 2 """ + result = pd.eval(exp, engine=engine, parser=parser) + assert result == 12 + + def test_float_truncation(self, engine, parser): + # GH 14241 + exp = "1000000000.006" + result = pd.eval(exp, engine=engine, parser=parser) + expected = np.float64(exp) + assert result == expected + + df = DataFrame({"A": [1000000000.0009, 1000000000.0011, 1000000000.0015]}) + cutoff = 1000000000.0006 + result = df.query(f"A < {cutoff:.4f}") + assert result.empty + + cutoff = 1000000000.0010 + result = df.query(f"A > {cutoff:.4f}") + expected = df.loc[[1, 2], :] + tm.assert_frame_equal(expected, result) + + exact = 1000000000.0011 + result = df.query(f"A == {exact:.4f}") + expected = df.loc[[1], :] + tm.assert_frame_equal(expected, result) + + def test_disallow_python_keywords(self): + # GH 18221 + df = DataFrame([[0, 0, 0]], columns=["foo", "bar", "class"]) + msg = "Python keyword not valid identifier in numexpr query" + with pytest.raises(SyntaxError, match=msg): + df.query("class == 0") + + df = DataFrame() + df.index.name = "lambda" + with pytest.raises(SyntaxError, match=msg): + df.query("lambda == 0") + + def test_true_false_logic(self): + # GH 25823 + # This behavior is deprecated in Python 3.12 + with tm.maybe_produces_warning( + DeprecationWarning, PY312, check_stacklevel=False + ): + assert pd.eval("not True") == -2 + assert pd.eval("not False") == -1 + assert pd.eval("True and not True") == 0 + + def test_and_logic_string_match(self): + # GH 25823 + event = Series({"a": "hello"}) + assert pd.eval(f"{event.str.match('hello').a}") + assert pd.eval(f"{event.str.match('hello').a and event.str.match('hello').a}") + + +# ------------------------------------- +# gh-12388: Typecasting rules consistency with python + + +class TestTypeCasting: + @pytest.mark.parametrize("op", ["+", "-", "*", "**", "/"]) + # maybe someday... numexpr has too many upcasting rules now + # chain(*(np.core.sctypes[x] for x in ['uint', 'int', 'float'])) + @pytest.mark.parametrize("left_right", [("df", "3"), ("3", "df")]) + def test_binop_typecasting( + self, engine, parser, op, complex_or_float_dtype, left_right, request + ): + # GH#21374 + dtype = complex_or_float_dtype + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)), dtype=dtype) + left, right = left_right + s = f"{left} {op} {right}" + res = pd.eval(s, engine=engine, parser=parser) + if dtype == "complex64" and engine == "numexpr": + mark = pytest.mark.xfail( + reason="numexpr issue with complex that are upcast " + "to complex 128 " + "https://github.com/pydata/numexpr/issues/492" + ) + request.applymarker(mark) + assert df.values.dtype == dtype + assert res.values.dtype == dtype + tm.assert_frame_equal(res, eval(s), check_exact=False) + + +# ------------------------------------- +# Basic and complex alignment + + +def should_warn(*args): + not_mono = not any(map(operator.attrgetter("is_monotonic_increasing"), args)) + only_one_dt = reduce( + operator.xor, (issubclass(x.dtype.type, np.datetime64) for x in args) + ) + return not_mono and only_one_dt + + +class TestAlignment: + index_types = ["i", "s", "dt"] + lhs_index_types = index_types + ["s"] # 'p' + + def test_align_nested_unary_op(self, engine, parser): + s = "df * ~2" + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + res = pd.eval(s, engine=engine, parser=parser) + tm.assert_frame_equal(res, df * ~2) + + @pytest.mark.filterwarnings("always::RuntimeWarning") + @pytest.mark.parametrize("lr_idx_type", lhs_index_types) + @pytest.mark.parametrize("rr_idx_type", index_types) + @pytest.mark.parametrize("c_idx_type", index_types) + def test_basic_frame_alignment( + self, engine, parser, lr_idx_type, rr_idx_type, c_idx_type, idx_func_dict + ): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 10)), + index=idx_func_dict[lr_idx_type](10), + columns=idx_func_dict[c_idx_type](10), + ) + df2 = DataFrame( + np.random.default_rng(2).standard_normal((20, 10)), + index=idx_func_dict[rr_idx_type](20), + columns=idx_func_dict[c_idx_type](10), + ) + # only warns if not monotonic and not sortable + if should_warn(df.index, df2.index): + with tm.assert_produces_warning(RuntimeWarning): + res = pd.eval("df + df2", engine=engine, parser=parser) + else: + res = pd.eval("df + df2", engine=engine, parser=parser) + tm.assert_frame_equal(res, df + df2) + + @pytest.mark.parametrize("r_idx_type", lhs_index_types) + @pytest.mark.parametrize("c_idx_type", lhs_index_types) + def test_frame_comparison( + self, engine, parser, r_idx_type, c_idx_type, idx_func_dict + ): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 10)), + index=idx_func_dict[r_idx_type](10), + columns=idx_func_dict[c_idx_type](10), + ) + res = pd.eval("df < 2", engine=engine, parser=parser) + tm.assert_frame_equal(res, df < 2) + + df3 = DataFrame( + np.random.default_rng(2).standard_normal(df.shape), + index=df.index, + columns=df.columns, + ) + res = pd.eval("df < df3", engine=engine, parser=parser) + tm.assert_frame_equal(res, df < df3) + + @pytest.mark.filterwarnings("ignore::RuntimeWarning") + @pytest.mark.parametrize("r1", lhs_index_types) + @pytest.mark.parametrize("c1", index_types) + @pytest.mark.parametrize("r2", index_types) + @pytest.mark.parametrize("c2", index_types) + def test_medium_complex_frame_alignment( + self, engine, parser, r1, c1, r2, c2, idx_func_dict + ): + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 2)), + index=idx_func_dict[r1](3), + columns=idx_func_dict[c1](2), + ) + df2 = DataFrame( + np.random.default_rng(2).standard_normal((4, 2)), + index=idx_func_dict[r2](4), + columns=idx_func_dict[c2](2), + ) + df3 = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), + index=idx_func_dict[r2](5), + columns=idx_func_dict[c2](2), + ) + if should_warn(df.index, df2.index, df3.index): + with tm.assert_produces_warning(RuntimeWarning): + res = pd.eval("df + df2 + df3", engine=engine, parser=parser) + else: + res = pd.eval("df + df2 + df3", engine=engine, parser=parser) + tm.assert_frame_equal(res, df + df2 + df3) + + @pytest.mark.filterwarnings("ignore::RuntimeWarning") + @pytest.mark.parametrize("index_name", ["index", "columns"]) + @pytest.mark.parametrize("c_idx_type", index_types) + @pytest.mark.parametrize("r_idx_type", lhs_index_types) + def test_basic_frame_series_alignment( + self, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict + ): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 10)), + index=idx_func_dict[r_idx_type](10), + columns=idx_func_dict[c_idx_type](10), + ) + index = getattr(df, index_name) + s = Series(np.random.default_rng(2).standard_normal(5), index[:5]) + + if should_warn(df.index, s.index): + with tm.assert_produces_warning(RuntimeWarning): + res = pd.eval("df + s", engine=engine, parser=parser) + else: + res = pd.eval("df + s", engine=engine, parser=parser) + + if r_idx_type == "dt" or c_idx_type == "dt": + expected = df.add(s) if engine == "numexpr" else df + s + else: + expected = df + s + tm.assert_frame_equal(res, expected) + + @pytest.mark.parametrize("index_name", ["index", "columns"]) + @pytest.mark.parametrize( + "r_idx_type, c_idx_type", + list(product(["i", "s"], ["i", "s"])) + [("dt", "dt")], + ) + @pytest.mark.filterwarnings("ignore::RuntimeWarning") + def test_basic_series_frame_alignment( + self, request, engine, parser, index_name, r_idx_type, c_idx_type, idx_func_dict + ): + if ( + engine == "numexpr" + and parser in ("pandas", "python") + and index_name == "index" + and r_idx_type == "i" + and c_idx_type == "s" + ): + reason = ( + f"Flaky column ordering when engine={engine}, " + f"parser={parser}, index_name={index_name}, " + f"r_idx_type={r_idx_type}, c_idx_type={c_idx_type}" + ) + request.applymarker(pytest.mark.xfail(reason=reason, strict=False)) + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 7)), + index=idx_func_dict[r_idx_type](10), + columns=idx_func_dict[c_idx_type](7), + ) + index = getattr(df, index_name) + s = Series(np.random.default_rng(2).standard_normal(5), index[:5]) + if should_warn(s.index, df.index): + with tm.assert_produces_warning(RuntimeWarning): + res = pd.eval("s + df", engine=engine, parser=parser) + else: + res = pd.eval("s + df", engine=engine, parser=parser) + + if r_idx_type == "dt" or c_idx_type == "dt": + expected = df.add(s) if engine == "numexpr" else s + df + else: + expected = s + df + tm.assert_frame_equal(res, expected) + + @pytest.mark.filterwarnings("ignore::RuntimeWarning") + @pytest.mark.parametrize("c_idx_type", index_types) + @pytest.mark.parametrize("r_idx_type", lhs_index_types) + @pytest.mark.parametrize("index_name", ["index", "columns"]) + @pytest.mark.parametrize("op", ["+", "*"]) + def test_series_frame_commutativity( + self, engine, parser, index_name, op, r_idx_type, c_idx_type, idx_func_dict + ): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 10)), + index=idx_func_dict[r_idx_type](10), + columns=idx_func_dict[c_idx_type](10), + ) + index = getattr(df, index_name) + s = Series(np.random.default_rng(2).standard_normal(5), index[:5]) + + lhs = f"s {op} df" + rhs = f"df {op} s" + if should_warn(df.index, s.index): + with tm.assert_produces_warning(RuntimeWarning): + a = pd.eval(lhs, engine=engine, parser=parser) + with tm.assert_produces_warning(RuntimeWarning): + b = pd.eval(rhs, engine=engine, parser=parser) + else: + a = pd.eval(lhs, engine=engine, parser=parser) + b = pd.eval(rhs, engine=engine, parser=parser) + + if r_idx_type != "dt" and c_idx_type != "dt": + if engine == "numexpr": + tm.assert_frame_equal(a, b) + + @pytest.mark.filterwarnings("always::RuntimeWarning") + @pytest.mark.parametrize("r1", lhs_index_types) + @pytest.mark.parametrize("c1", index_types) + @pytest.mark.parametrize("r2", index_types) + @pytest.mark.parametrize("c2", index_types) + def test_complex_series_frame_alignment( + self, engine, parser, r1, c1, r2, c2, idx_func_dict + ): + n = 3 + m1 = 5 + m2 = 2 * m1 + df = DataFrame( + np.random.default_rng(2).standard_normal((m1, n)), + index=idx_func_dict[r1](m1), + columns=idx_func_dict[c1](n), + ) + df2 = DataFrame( + np.random.default_rng(2).standard_normal((m2, n)), + index=idx_func_dict[r2](m2), + columns=idx_func_dict[c2](n), + ) + index = df2.columns + ser = Series(np.random.default_rng(2).standard_normal(n), index[:n]) + + if r2 == "dt" or c2 == "dt": + if engine == "numexpr": + expected2 = df2.add(ser) + else: + expected2 = df2 + ser + else: + expected2 = df2 + ser + + if r1 == "dt" or c1 == "dt": + if engine == "numexpr": + expected = expected2.add(df) + else: + expected = expected2 + df + else: + expected = expected2 + df + + if should_warn(df2.index, ser.index, df.index): + with tm.assert_produces_warning(RuntimeWarning): + res = pd.eval("df2 + ser + df", engine=engine, parser=parser) + else: + res = pd.eval("df2 + ser + df", engine=engine, parser=parser) + assert res.shape == expected.shape + tm.assert_frame_equal(res, expected) + + def test_performance_warning_for_poor_alignment(self, engine, parser): + df = DataFrame(np.random.default_rng(2).standard_normal((1000, 10))) + s = Series(np.random.default_rng(2).standard_normal(10000)) + if engine == "numexpr": + seen = PerformanceWarning + else: + seen = False + + with tm.assert_produces_warning(seen): + pd.eval("df + s", engine=engine, parser=parser) + + s = Series(np.random.default_rng(2).standard_normal(1000)) + with tm.assert_produces_warning(False): + pd.eval("df + s", engine=engine, parser=parser) + + df = DataFrame(np.random.default_rng(2).standard_normal((10, 10000))) + s = Series(np.random.default_rng(2).standard_normal(10000)) + with tm.assert_produces_warning(False): + pd.eval("df + s", engine=engine, parser=parser) + + df = DataFrame(np.random.default_rng(2).standard_normal((10, 10))) + s = Series(np.random.default_rng(2).standard_normal(10000)) + + is_python_engine = engine == "python" + + if not is_python_engine: + wrn = PerformanceWarning + else: + wrn = False + + with tm.assert_produces_warning(wrn) as w: + pd.eval("df + s", engine=engine, parser=parser) + + if not is_python_engine: + assert len(w) == 1 + msg = str(w[0].message) + logged = np.log10(s.size - df.shape[1]) + expected = ( + f"Alignment difference on axis 1 is larger " + f"than an order of magnitude on term 'df', " + f"by more than {logged:.4g}; performance may suffer." + ) + assert msg == expected + + +# ------------------------------------ +# Slightly more complex ops + + +class TestOperations: + def eval(self, *args, **kwargs): + kwargs["level"] = kwargs.pop("level", 0) + 1 + return pd.eval(*args, **kwargs) + + def test_simple_arith_ops(self, engine, parser): + exclude_arith = [] + if parser == "python": + exclude_arith = ["in", "not in"] + + arith_ops = [ + op + for op in expr.ARITH_OPS_SYMS + expr.CMP_OPS_SYMS + if op not in exclude_arith + ] + + ops = (op for op in arith_ops if op != "//") + + for op in ops: + ex = f"1 {op} 1" + ex2 = f"x {op} 1" + ex3 = f"1 {op} (x + 1)" + + if op in ("in", "not in"): + msg = "argument of type 'int' is not iterable" + with pytest.raises(TypeError, match=msg): + pd.eval(ex, engine=engine, parser=parser) + else: + expec = _eval_single_bin(1, op, 1, engine) + x = self.eval(ex, engine=engine, parser=parser) + assert x == expec + + expec = _eval_single_bin(x, op, 1, engine) + y = self.eval(ex2, local_dict={"x": x}, engine=engine, parser=parser) + assert y == expec + + expec = _eval_single_bin(1, op, x + 1, engine) + y = self.eval(ex3, local_dict={"x": x}, engine=engine, parser=parser) + assert y == expec + + @pytest.mark.parametrize("rhs", [True, False]) + @pytest.mark.parametrize("lhs", [True, False]) + @pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS) + def test_simple_bool_ops(self, rhs, lhs, op): + ex = f"{lhs} {op} {rhs}" + + if parser == "python" and op in ["and", "or"]: + msg = "'BoolOp' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + self.eval(ex) + return + + res = self.eval(ex) + exp = eval(ex) + assert res == exp + + @pytest.mark.parametrize("rhs", [True, False]) + @pytest.mark.parametrize("lhs", [True, False]) + @pytest.mark.parametrize("op", expr.BOOL_OPS_SYMS) + def test_bool_ops_with_constants(self, rhs, lhs, op): + ex = f"{lhs} {op} {rhs}" + + if parser == "python" and op in ["and", "or"]: + msg = "'BoolOp' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + self.eval(ex) + return + + res = self.eval(ex) + exp = eval(ex) + assert res == exp + + def test_4d_ndarray_fails(self): + x = np.random.default_rng(2).standard_normal((3, 4, 5, 6)) + y = Series(np.random.default_rng(2).standard_normal(10)) + msg = "N-dimensional objects, where N > 2, are not supported with eval" + with pytest.raises(NotImplementedError, match=msg): + self.eval("x + y", local_dict={"x": x, "y": y}) + + def test_constant(self): + x = self.eval("1") + assert x == 1 + + def test_single_variable(self): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) + df2 = self.eval("df", local_dict={"df": df}) + tm.assert_frame_equal(df, df2) + + def test_failing_subscript_with_name_error(self): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) # noqa: F841 + with pytest.raises(NameError, match="name 'x' is not defined"): + self.eval("df[x > 2] > 2") + + def test_lhs_expression_subscript(self): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + result = self.eval("(df + 1)[df > 2]", local_dict={"df": df}) + expected = (df + 1)[df > 2] + tm.assert_frame_equal(result, expected) + + def test_attr_expression(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc") + ) + expr1 = "df.a < df.b" + expec1 = df.a < df.b + expr2 = "df.a + df.b + df.c" + expec2 = df.a + df.b + df.c + expr3 = "df.a + df.b + df.c[df.b < 0]" + expec3 = df.a + df.b + df.c[df.b < 0] + exprs = expr1, expr2, expr3 + expecs = expec1, expec2, expec3 + for e, expec in zip(exprs, expecs): + tm.assert_series_equal(expec, self.eval(e, local_dict={"df": df})) + + def test_assignment_fails(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), columns=list("abc") + ) + df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + expr1 = "df = df2" + msg = "cannot assign without a target object" + with pytest.raises(ValueError, match=msg): + self.eval(expr1, local_dict={"df": df, "df2": df2}) + + def test_assignment_column_multiple_raise(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + # multiple assignees + with pytest.raises(SyntaxError, match="invalid syntax"): + df.eval("d c = a + b") + + def test_assignment_column_invalid_assign(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + # invalid assignees + msg = "left hand side of an assignment must be a single name" + with pytest.raises(SyntaxError, match=msg): + df.eval("d,c = a + b") + + def test_assignment_column_invalid_assign_function_call(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + msg = "cannot assign to function call" + with pytest.raises(SyntaxError, match=msg): + df.eval('Timestamp("20131001") = a + b') + + def test_assignment_single_assign_existing(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + # single assignment - existing variable + expected = df.copy() + expected["a"] = expected["a"] + expected["b"] + df.eval("a = a + b", inplace=True) + tm.assert_frame_equal(df, expected) + + def test_assignment_single_assign_new(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + # single assignment - new variable + expected = df.copy() + expected["c"] = expected["a"] + expected["b"] + df.eval("c = a + b", inplace=True) + tm.assert_frame_equal(df, expected) + + def test_assignment_single_assign_local_overlap(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + df = df.copy() + a = 1 # noqa: F841 + df.eval("a = 1 + b", inplace=True) + + expected = df.copy() + expected["a"] = 1 + expected["b"] + tm.assert_frame_equal(df, expected) + + def test_assignment_single_assign_name(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + + a = 1 # noqa: F841 + old_a = df.a.copy() + df.eval("a = a + b", inplace=True) + result = old_a + df.b + tm.assert_series_equal(result, df.a, check_names=False) + assert result.name is None + + def test_assignment_multiple_raises(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + # multiple assignment + df.eval("c = a + b", inplace=True) + msg = "can only assign a single expression" + with pytest.raises(SyntaxError, match=msg): + df.eval("c = a = b") + + def test_assignment_explicit(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + # explicit targets + self.eval("c = df.a + df.b", local_dict={"df": df}, target=df, inplace=True) + expected = df.copy() + expected["c"] = expected["a"] + expected["b"] + tm.assert_frame_equal(df, expected) + + def test_column_in(self): + # GH 11235 + df = DataFrame({"a": [11], "b": [-32]}) + result = df.eval("a in [11, -32]") + expected = Series([True]) + # TODO: 2022-01-29: Name check failed with numexpr 2.7.3 in CI + # but cannot reproduce locally + tm.assert_series_equal(result, expected, check_names=False) + + @pytest.mark.xfail(reason="Unknown: Omitted test_ in name prior.") + def test_assignment_not_inplace(self): + # see gh-9297 + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("ab") + ) + + actual = df.eval("c = a + b", inplace=False) + assert actual is not None + + expected = df.copy() + expected["c"] = expected["a"] + expected["b"] + tm.assert_frame_equal(df, expected) + + def test_multi_line_expression(self, warn_copy_on_write): + # GH 11149 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + expected = df.copy() + + expected["c"] = expected["a"] + expected["b"] + expected["d"] = expected["c"] + expected["b"] + answer = df.eval( + """ + c = a + b + d = c + b""", + inplace=True, + ) + tm.assert_frame_equal(expected, df) + assert answer is None + + expected["a"] = expected["a"] - 1 + expected["e"] = expected["a"] + 2 + answer = df.eval( + """ + a = a - 1 + e = a + 2""", + inplace=True, + ) + tm.assert_frame_equal(expected, df) + assert answer is None + + # multi-line not valid if not all assignments + msg = "Multi-line expressions are only valid if all expressions contain" + with pytest.raises(ValueError, match=msg): + df.eval( + """ + a = b + 2 + b - 2""", + inplace=False, + ) + + def test_multi_line_expression_not_inplace(self): + # GH 11149 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + expected = df.copy() + + expected["c"] = expected["a"] + expected["b"] + expected["d"] = expected["c"] + expected["b"] + df = df.eval( + """ + c = a + b + d = c + b""", + inplace=False, + ) + tm.assert_frame_equal(expected, df) + + expected["a"] = expected["a"] - 1 + expected["e"] = expected["a"] + 2 + df = df.eval( + """ + a = a - 1 + e = a + 2""", + inplace=False, + ) + tm.assert_frame_equal(expected, df) + + def test_multi_line_expression_local_variable(self): + # GH 15342 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + expected = df.copy() + + local_var = 7 + expected["c"] = expected["a"] * local_var + expected["d"] = expected["c"] + local_var + answer = df.eval( + """ + c = a * @local_var + d = c + @local_var + """, + inplace=True, + ) + tm.assert_frame_equal(expected, df) + assert answer is None + + def test_multi_line_expression_callable_local_variable(self): + # 26426 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + + def local_func(a, b): + return b + + expected = df.copy() + expected["c"] = expected["a"] * local_func(1, 7) + expected["d"] = expected["c"] + local_func(1, 7) + answer = df.eval( + """ + c = a * @local_func(1, 7) + d = c + @local_func(1, 7) + """, + inplace=True, + ) + tm.assert_frame_equal(expected, df) + assert answer is None + + def test_multi_line_expression_callable_local_variable_with_kwargs(self): + # 26426 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + + def local_func(a, b): + return b + + expected = df.copy() + expected["c"] = expected["a"] * local_func(b=7, a=1) + expected["d"] = expected["c"] + local_func(b=7, a=1) + answer = df.eval( + """ + c = a * @local_func(b=7, a=1) + d = c + @local_func(b=7, a=1) + """, + inplace=True, + ) + tm.assert_frame_equal(expected, df) + assert answer is None + + def test_assignment_in_query(self): + # GH 8664 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + df_orig = df.copy() + msg = "cannot assign without a target object" + with pytest.raises(ValueError, match=msg): + df.query("a = 1") + tm.assert_frame_equal(df, df_orig) + + def test_query_inplace(self): + # see gh-11149 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + expected = df.copy() + expected = expected[expected["a"] == 2] + df.query("a == 2", inplace=True) + tm.assert_frame_equal(expected, df) + + df = {} + expected = {"a": 3} + + self.eval("a = 1 + 2", target=df, inplace=True) + tm.assert_dict_equal(df, expected) + + @pytest.mark.parametrize("invalid_target", [1, "cat", [1, 2], np.array([]), (1, 3)]) + def test_cannot_item_assign(self, invalid_target): + msg = "Cannot assign expression output to target" + expression = "a = 1 + 2" + + with pytest.raises(ValueError, match=msg): + self.eval(expression, target=invalid_target, inplace=True) + + if hasattr(invalid_target, "copy"): + with pytest.raises(ValueError, match=msg): + self.eval(expression, target=invalid_target, inplace=False) + + @pytest.mark.parametrize("invalid_target", [1, "cat", (1, 3)]) + def test_cannot_copy_item(self, invalid_target): + msg = "Cannot return a copy of the target" + expression = "a = 1 + 2" + + with pytest.raises(ValueError, match=msg): + self.eval(expression, target=invalid_target, inplace=False) + + @pytest.mark.parametrize("target", [1, "cat", [1, 2], np.array([]), (1, 3), {1: 2}]) + def test_inplace_no_assignment(self, target): + expression = "1 + 2" + + assert self.eval(expression, target=target, inplace=False) == 3 + + msg = "Cannot operate inplace if there is no assignment" + with pytest.raises(ValueError, match=msg): + self.eval(expression, target=target, inplace=True) + + def test_basic_period_index_boolean_expression(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((2, 2)), + columns=period_range("2020-01-01", freq="D", periods=2), + ) + e = df < 2 + r = self.eval("df < 2", local_dict={"df": df}) + x = df < 2 + + tm.assert_frame_equal(r, e) + tm.assert_frame_equal(x, e) + + def test_basic_period_index_subscript_expression(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((2, 2)), + columns=period_range("2020-01-01", freq="D", periods=2), + ) + r = self.eval("df[df < 2 + 3]", local_dict={"df": df}) + e = df[df < 2 + 3] + tm.assert_frame_equal(r, e) + + def test_nested_period_index_subscript_expression(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((2, 2)), + columns=period_range("2020-01-01", freq="D", periods=2), + ) + r = self.eval("df[df[df < 2] < 2] + df * 2", local_dict={"df": df}) + e = df[df[df < 2] < 2] + df * 2 + tm.assert_frame_equal(r, e) + + def test_date_boolean(self, engine, parser): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + df["dates1"] = date_range("1/1/2012", periods=5) + res = self.eval( + "df.dates1 < 20130101", + local_dict={"df": df}, + engine=engine, + parser=parser, + ) + expec = df.dates1 < "20130101" + tm.assert_series_equal(res, expec, check_names=False) + + def test_simple_in_ops(self, engine, parser): + if parser != "python": + res = pd.eval("1 in [1, 2]", engine=engine, parser=parser) + assert res + + res = pd.eval("2 in (1, 2)", engine=engine, parser=parser) + assert res + + res = pd.eval("3 in (1, 2)", engine=engine, parser=parser) + assert not res + + res = pd.eval("3 not in (1, 2)", engine=engine, parser=parser) + assert res + + res = pd.eval("[3] not in (1, 2)", engine=engine, parser=parser) + assert res + + res = pd.eval("[3] in ([3], 2)", engine=engine, parser=parser) + assert res + + res = pd.eval("[[3]] in [[[3]], 2]", engine=engine, parser=parser) + assert res + + res = pd.eval("(3,) in [(3,), 2]", engine=engine, parser=parser) + assert res + + res = pd.eval("(3,) not in [(3,), 2]", engine=engine, parser=parser) + assert not res + + res = pd.eval("[(3,)] in [[(3,)], 2]", engine=engine, parser=parser) + assert res + else: + msg = "'In' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + pd.eval("1 in [1, 2]", engine=engine, parser=parser) + with pytest.raises(NotImplementedError, match=msg): + pd.eval("2 in (1, 2)", engine=engine, parser=parser) + with pytest.raises(NotImplementedError, match=msg): + pd.eval("3 in (1, 2)", engine=engine, parser=parser) + with pytest.raises(NotImplementedError, match=msg): + pd.eval("[(3,)] in (1, 2, [(3,)])", engine=engine, parser=parser) + msg = "'NotIn' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + pd.eval("3 not in (1, 2)", engine=engine, parser=parser) + with pytest.raises(NotImplementedError, match=msg): + pd.eval("[3] not in (1, 2, [[3]])", engine=engine, parser=parser) + + def test_check_many_exprs(self, engine, parser): + a = 1 # noqa: F841 + expr = " * ".join("a" * 33) + expected = 1 + res = pd.eval(expr, engine=engine, parser=parser) + assert res == expected + + @pytest.mark.parametrize( + "expr", + [ + "df > 2 and df > 3", + "df > 2 or df > 3", + "not df > 2", + ], + ) + def test_fails_and_or_not(self, expr, engine, parser): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + if parser == "python": + msg = "'BoolOp' nodes are not implemented" + if "not" in expr: + msg = "'Not' nodes are not implemented" + + with pytest.raises(NotImplementedError, match=msg): + pd.eval( + expr, + local_dict={"df": df}, + parser=parser, + engine=engine, + ) + else: + # smoke-test, should not raise + pd.eval( + expr, + local_dict={"df": df}, + parser=parser, + engine=engine, + ) + + @pytest.mark.parametrize("char", ["|", "&"]) + def test_fails_ampersand_pipe(self, char, engine, parser): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) # noqa: F841 + ex = f"(df + 2)[df > 1] > 0 {char} (df > 0)" + if parser == "python": + msg = "cannot evaluate scalar only bool ops" + with pytest.raises(NotImplementedError, match=msg): + pd.eval(ex, parser=parser, engine=engine) + else: + # smoke-test, should not raise + pd.eval(ex, parser=parser, engine=engine) + + +class TestMath: + def eval(self, *args, **kwargs): + kwargs["level"] = kwargs.pop("level", 0) + 1 + return pd.eval(*args, **kwargs) + + @pytest.mark.skipif( + not NUMEXPR_INSTALLED, reason="Unary ops only implemented for numexpr" + ) + @pytest.mark.parametrize("fn", _unary_math_ops) + def test_unary_functions(self, fn): + df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)}) + a = df.a + + expr = f"{fn}(a)" + got = self.eval(expr) + with np.errstate(all="ignore"): + expect = getattr(np, fn)(a) + tm.assert_series_equal(got, expect, check_names=False) + + @pytest.mark.parametrize("fn", _binary_math_ops) + def test_binary_functions(self, fn): + df = DataFrame( + { + "a": np.random.default_rng(2).standard_normal(10), + "b": np.random.default_rng(2).standard_normal(10), + } + ) + a = df.a + b = df.b + + expr = f"{fn}(a, b)" + got = self.eval(expr) + with np.errstate(all="ignore"): + expect = getattr(np, fn)(a, b) + tm.assert_almost_equal(got, expect, check_names=False) + + def test_df_use_case(self, engine, parser): + df = DataFrame( + { + "a": np.random.default_rng(2).standard_normal(10), + "b": np.random.default_rng(2).standard_normal(10), + } + ) + df.eval( + "e = arctan2(sin(a), b)", + engine=engine, + parser=parser, + inplace=True, + ) + got = df.e + expect = np.arctan2(np.sin(df.a), df.b) + tm.assert_series_equal(got, expect, check_names=False) + + def test_df_arithmetic_subexpression(self, engine, parser): + df = DataFrame( + { + "a": np.random.default_rng(2).standard_normal(10), + "b": np.random.default_rng(2).standard_normal(10), + } + ) + df.eval("e = sin(a + b)", engine=engine, parser=parser, inplace=True) + got = df.e + expect = np.sin(df.a + df.b) + tm.assert_series_equal(got, expect, check_names=False) + + @pytest.mark.parametrize( + "dtype, expect_dtype", + [ + (np.int32, np.float64), + (np.int64, np.float64), + (np.float32, np.float32), + (np.float64, np.float64), + pytest.param(np.complex128, np.complex128, marks=td.skip_if_windows), + ], + ) + def test_result_types(self, dtype, expect_dtype, engine, parser): + # xref https://github.com/pandas-dev/pandas/issues/12293 + # this fails on Windows, apparently a floating point precision issue + + # Did not test complex64 because DataFrame is converting it to + # complex128. Due to https://github.com/pandas-dev/pandas/issues/10952 + df = DataFrame( + {"a": np.random.default_rng(2).standard_normal(10).astype(dtype)} + ) + assert df.a.dtype == dtype + df.eval("b = sin(a)", engine=engine, parser=parser, inplace=True) + got = df.b + expect = np.sin(df.a) + assert expect.dtype == got.dtype + assert expect_dtype == got.dtype + tm.assert_series_equal(got, expect, check_names=False) + + def test_undefined_func(self, engine, parser): + df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)}) + msg = '"mysin" is not a supported function' + + with pytest.raises(ValueError, match=msg): + df.eval("mysin(a)", engine=engine, parser=parser) + + def test_keyword_arg(self, engine, parser): + df = DataFrame({"a": np.random.default_rng(2).standard_normal(10)}) + msg = 'Function "sin" does not support keyword arguments' + + with pytest.raises(TypeError, match=msg): + df.eval("sin(x=a)", engine=engine, parser=parser) + + +_var_s = np.random.default_rng(2).standard_normal(10) + + +class TestScope: + def test_global_scope(self, engine, parser): + e = "_var_s * 2" + tm.assert_numpy_array_equal( + _var_s * 2, pd.eval(e, engine=engine, parser=parser) + ) + + def test_no_new_locals(self, engine, parser): + x = 1 + lcls = locals().copy() + pd.eval("x + 1", local_dict=lcls, engine=engine, parser=parser) + lcls2 = locals().copy() + lcls2.pop("lcls") + assert lcls == lcls2 + + def test_no_new_globals(self, engine, parser): + x = 1 # noqa: F841 + gbls = globals().copy() + pd.eval("x + 1", engine=engine, parser=parser) + gbls2 = globals().copy() + assert gbls == gbls2 + + def test_empty_locals(self, engine, parser): + # GH 47084 + x = 1 # noqa: F841 + msg = "name 'x' is not defined" + with pytest.raises(UndefinedVariableError, match=msg): + pd.eval("x + 1", engine=engine, parser=parser, local_dict={}) + + def test_empty_globals(self, engine, parser): + # GH 47084 + msg = "name '_var_s' is not defined" + e = "_var_s * 2" + with pytest.raises(UndefinedVariableError, match=msg): + pd.eval(e, engine=engine, parser=parser, global_dict={}) + + +@td.skip_if_no("numexpr") +def test_invalid_engine(): + msg = "Invalid engine 'asdf' passed" + with pytest.raises(KeyError, match=msg): + pd.eval("x + y", local_dict={"x": 1, "y": 2}, engine="asdf") + + +@td.skip_if_no("numexpr") +@pytest.mark.parametrize( + ("use_numexpr", "expected"), + ( + (True, "numexpr"), + (False, "python"), + ), +) +def test_numexpr_option_respected(use_numexpr, expected): + # GH 32556 + from pandas.core.computation.eval import _check_engine + + with pd.option_context("compute.use_numexpr", use_numexpr): + result = _check_engine(None) + assert result == expected + + +@td.skip_if_no("numexpr") +def test_numexpr_option_incompatible_op(): + # GH 32556 + with pd.option_context("compute.use_numexpr", False): + df = DataFrame( + {"A": [True, False, True, False, None, None], "B": [1, 2, 3, 4, 5, 6]} + ) + result = df.query("A.isnull()") + expected = DataFrame({"A": [None, None], "B": [5, 6]}, index=[4, 5]) + tm.assert_frame_equal(result, expected) + + +@td.skip_if_no("numexpr") +def test_invalid_parser(): + msg = "Invalid parser 'asdf' passed" + with pytest.raises(KeyError, match=msg): + pd.eval("x + y", local_dict={"x": 1, "y": 2}, parser="asdf") + + +_parsers: dict[str, type[BaseExprVisitor]] = { + "python": PythonExprVisitor, + "pytables": pytables.PyTablesExprVisitor, + "pandas": PandasExprVisitor, +} + + +@pytest.mark.parametrize("engine", ENGINES) +@pytest.mark.parametrize("parser", _parsers) +def test_disallowed_nodes(engine, parser): + VisitorClass = _parsers[parser] + inst = VisitorClass("x + 1", engine, parser) + + for ops in VisitorClass.unsupported_nodes: + msg = "nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + getattr(inst, ops)() + + +def test_syntax_error_exprs(engine, parser): + e = "s +" + with pytest.raises(SyntaxError, match="invalid syntax"): + pd.eval(e, engine=engine, parser=parser) + + +def test_name_error_exprs(engine, parser): + e = "s + t" + msg = "name 's' is not defined" + with pytest.raises(NameError, match=msg): + pd.eval(e, engine=engine, parser=parser) + + +@pytest.mark.parametrize("express", ["a + @b", "@a + b", "@a + @b"]) +def test_invalid_local_variable_reference(engine, parser, express): + a, b = 1, 2 # noqa: F841 + + if parser != "pandas": + with pytest.raises(SyntaxError, match="The '@' prefix is only"): + pd.eval(express, engine=engine, parser=parser) + else: + with pytest.raises(SyntaxError, match="The '@' prefix is not"): + pd.eval(express, engine=engine, parser=parser) + + +def test_numexpr_builtin_raises(engine, parser): + sin, dotted_line = 1, 2 + if engine == "numexpr": + msg = "Variables in expression .+" + with pytest.raises(NumExprClobberingError, match=msg): + pd.eval("sin + dotted_line", engine=engine, parser=parser) + else: + res = pd.eval("sin + dotted_line", engine=engine, parser=parser) + assert res == sin + dotted_line + + +def test_bad_resolver_raises(engine, parser): + cannot_resolve = 42, 3.0 + with pytest.raises(TypeError, match="Resolver of type .+"): + pd.eval("1 + 2", resolvers=cannot_resolve, engine=engine, parser=parser) + + +def test_empty_string_raises(engine, parser): + # GH 13139 + with pytest.raises(ValueError, match="expr cannot be an empty string"): + pd.eval("", engine=engine, parser=parser) + + +def test_more_than_one_expression_raises(engine, parser): + with pytest.raises(SyntaxError, match="only a single expression is allowed"): + pd.eval("1 + 1; 2 + 2", engine=engine, parser=parser) + + +@pytest.mark.parametrize("cmp", ("and", "or")) +@pytest.mark.parametrize("lhs", (int, float)) +@pytest.mark.parametrize("rhs", (int, float)) +def test_bool_ops_fails_on_scalars(lhs, cmp, rhs, engine, parser): + gen = { + int: lambda: np.random.default_rng(2).integers(10), + float: np.random.default_rng(2).standard_normal, + } + + mid = gen[lhs]() # noqa: F841 + lhs = gen[lhs]() + rhs = gen[rhs]() + + ex1 = f"lhs {cmp} mid {cmp} rhs" + ex2 = f"lhs {cmp} mid and mid {cmp} rhs" + ex3 = f"(lhs {cmp} mid) & (mid {cmp} rhs)" + for ex in (ex1, ex2, ex3): + msg = "cannot evaluate scalar only bool ops|'BoolOp' nodes are not" + with pytest.raises(NotImplementedError, match=msg): + pd.eval(ex, engine=engine, parser=parser) + + +@pytest.mark.parametrize( + "other", + [ + "'x'", + "...", + ], +) +def test_equals_various(other): + df = DataFrame({"A": ["a", "b", "c"]}, dtype=object) + result = df.eval(f"A == {other}") + expected = Series([False, False, False], name="A") + if USE_NUMEXPR: + # https://github.com/pandas-dev/pandas/issues/10239 + # lose name with numexpr engine. Remove when that's fixed. + expected.name = None + tm.assert_series_equal(result, expected) + + +def test_inf(engine, parser): + s = "inf + 1" + expected = np.inf + result = pd.eval(s, engine=engine, parser=parser) + assert result == expected + + +@pytest.mark.parametrize("column", ["Temp(°C)", "Capacitance(μF)"]) +def test_query_token(engine, column): + # See: https://github.com/pandas-dev/pandas/pull/42826 + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=[column, "b"] + ) + expected = df[df[column] > 5] + query_string = f"`{column}` > 5" + result = df.query(query_string, engine=engine) + tm.assert_frame_equal(result, expected) + + +def test_negate_lt_eq_le(engine, parser): + df = DataFrame([[0, 10], [1, 20]], columns=["cat", "count"]) + expected = df[~(df.cat > 0)] + + result = df.query("~(cat > 0)", engine=engine, parser=parser) + tm.assert_frame_equal(result, expected) + + if parser == "python": + msg = "'Not' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + df.query("not (cat > 0)", engine=engine, parser=parser) + else: + result = df.query("not (cat > 0)", engine=engine, parser=parser) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "column", + DEFAULT_GLOBALS.keys(), +) +def test_eval_no_support_column_name(request, column): + # GH 44603 + if column in ["True", "False", "inf", "Inf"]: + request.applymarker( + pytest.mark.xfail( + raises=KeyError, + reason=f"GH 47859 DataFrame eval not supported with {column}", + ) + ) + + df = DataFrame( + np.random.default_rng(2).integers(0, 100, size=(10, 2)), + columns=[column, "col1"], + ) + expected = df[df[column] > 6] + result = df.query(f"{column}>6") + + tm.assert_frame_equal(result, expected) + + +def test_set_inplace(using_copy_on_write, warn_copy_on_write): + # https://github.com/pandas-dev/pandas/issues/47449 + # Ensure we don't only update the DataFrame inplace, but also the actual + # column values, such that references to this column also get updated + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + result_view = df[:] + ser = df["A"] + with tm.assert_cow_warning(warn_copy_on_write): + df.eval("A = B + C", inplace=True) + expected = DataFrame({"A": [11, 13, 15], "B": [4, 5, 6], "C": [7, 8, 9]}) + tm.assert_frame_equal(df, expected) + if not using_copy_on_write: + tm.assert_series_equal(ser, expected["A"]) + tm.assert_series_equal(result_view["A"], expected["A"]) + else: + expected = Series([1, 2, 3], name="A") + tm.assert_series_equal(ser, expected) + tm.assert_series_equal(result_view["A"], expected) + + +class TestValidate: + @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0]) + def test_validate_bool_args(self, value): + msg = 'For argument "inplace" expected type bool, received type' + with pytest.raises(ValueError, match=msg): + pd.eval("2+2", inplace=value) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_can_hold_element.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_can_hold_element.py new file mode 100644 index 0000000000000000000000000000000000000000..3b7d76ead119a1bad784ca3fda3303c7a9e23244 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_can_hold_element.py @@ -0,0 +1,79 @@ +import numpy as np + +from pandas.core.dtypes.cast import can_hold_element + + +def test_can_hold_element_range(any_int_numpy_dtype): + # GH#44261 + dtype = np.dtype(any_int_numpy_dtype) + arr = np.array([], dtype=dtype) + + rng = range(2, 127) + assert can_hold_element(arr, rng) + + # negatives -> can't be held by uint dtypes + rng = range(-2, 127) + if dtype.kind == "i": + assert can_hold_element(arr, rng) + else: + assert not can_hold_element(arr, rng) + + rng = range(2, 255) + if dtype == "int8": + assert not can_hold_element(arr, rng) + else: + assert can_hold_element(arr, rng) + + rng = range(-255, 65537) + if dtype.kind == "u": + assert not can_hold_element(arr, rng) + elif dtype.itemsize < 4: + assert not can_hold_element(arr, rng) + else: + assert can_hold_element(arr, rng) + + # empty + rng = range(-(10**10), -(10**10)) + assert len(rng) == 0 + # assert can_hold_element(arr, rng) + + rng = range(10**10, 10**10) + assert len(rng) == 0 + assert can_hold_element(arr, rng) + + +def test_can_hold_element_int_values_float_ndarray(): + arr = np.array([], dtype=np.int64) + + element = np.array([1.0, 2.0]) + assert can_hold_element(arr, element) + + assert not can_hold_element(arr, element + 0.5) + + # integer but not losslessly castable to int64 + element = np.array([3, 2**65], dtype=np.float64) + assert not can_hold_element(arr, element) + + +def test_can_hold_element_int8_int(): + arr = np.array([], dtype=np.int8) + + element = 2 + assert can_hold_element(arr, element) + assert can_hold_element(arr, np.int8(element)) + assert can_hold_element(arr, np.uint8(element)) + assert can_hold_element(arr, np.int16(element)) + assert can_hold_element(arr, np.uint16(element)) + assert can_hold_element(arr, np.int32(element)) + assert can_hold_element(arr, np.uint32(element)) + assert can_hold_element(arr, np.int64(element)) + assert can_hold_element(arr, np.uint64(element)) + + element = 2**9 + assert not can_hold_element(arr, element) + assert not can_hold_element(arr, np.int16(element)) + assert not can_hold_element(arr, np.uint16(element)) + assert not can_hold_element(arr, np.int32(element)) + assert not can_hold_element(arr, np.uint32(element)) + assert not can_hold_element(arr, np.int64(element)) + assert not can_hold_element(arr, np.uint64(element)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_from_scalar.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_from_scalar.py new file mode 100644 index 0000000000000000000000000000000000000000..0ce04ce2e64cda1d3fc7c48390baa91ee2b06525 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_from_scalar.py @@ -0,0 +1,55 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.cast import construct_1d_arraylike_from_scalar +from pandas.core.dtypes.dtypes import CategoricalDtype + +from pandas import ( + Categorical, + Timedelta, +) +import pandas._testing as tm + + +def test_cast_1d_array_like_from_scalar_categorical(): + # see gh-19565 + # + # Categorical result from scalar did not maintain + # categories and ordering of the passed dtype. + cats = ["a", "b", "c"] + cat_type = CategoricalDtype(categories=cats, ordered=False) + expected = Categorical(["a", "a"], categories=cats) + + result = construct_1d_arraylike_from_scalar("a", len(expected), cat_type) + tm.assert_categorical_equal(result, expected) + + +def test_cast_1d_array_like_from_timestamp(fixed_now_ts): + # check we dont lose nanoseconds + ts = fixed_now_ts + Timedelta(1) + res = construct_1d_arraylike_from_scalar(ts, 2, np.dtype("M8[ns]")) + assert res[0] == ts + + +def test_cast_1d_array_like_from_timedelta(): + # check we dont lose nanoseconds + td = Timedelta(1) + res = construct_1d_arraylike_from_scalar(td, 2, np.dtype("m8[ns]")) + assert res[0] == td + + +def test_cast_1d_array_like_mismatched_datetimelike(): + td = np.timedelta64("NaT", "ns") + dt = np.datetime64("NaT", "ns") + + with pytest.raises(TypeError, match="Cannot cast"): + construct_1d_arraylike_from_scalar(td, 2, dt.dtype) + + with pytest.raises(TypeError, match="Cannot cast"): + construct_1d_arraylike_from_scalar(np.timedelta64(4, "ns"), 2, dt.dtype) + + with pytest.raises(TypeError, match="Cannot cast"): + construct_1d_arraylike_from_scalar(dt, 2, td.dtype) + + with pytest.raises(TypeError, match="Cannot cast"): + construct_1d_arraylike_from_scalar(np.datetime64(4, "ns"), 2, td.dtype) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_ndarray.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_ndarray.py new file mode 100644 index 0000000000000000000000000000000000000000..6b9b2dfda6e8b81a0f1f29d3ba97589b9d385600 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_ndarray.py @@ -0,0 +1,36 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.core.construction import sanitize_array + + +@pytest.mark.parametrize( + "values, dtype, expected", + [ + ([1, 2, 3], None, np.array([1, 2, 3], dtype=np.int64)), + (np.array([1, 2, 3]), None, np.array([1, 2, 3])), + (["1", "2", None], None, np.array(["1", "2", None])), + (["1", "2", None], np.dtype("str"), np.array(["1", "2", None])), + ([1, 2, None], np.dtype("str"), np.array(["1", "2", None])), + ], +) +def test_construct_1d_ndarray_preserving_na( + values, dtype, expected, using_infer_string +): + result = sanitize_array(values, index=None, dtype=dtype) + if using_infer_string and expected.dtype == object and dtype is None: + tm.assert_extension_array_equal(result, pd.array(expected, dtype="str")) + else: + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("dtype", ["m8[ns]", "M8[ns]"]) +def test_construct_1d_ndarray_preserving_na_datetimelike(dtype): + arr = np.arange(5, dtype=np.int64).view(dtype) + expected = np.array(list(arr), dtype=object) + assert all(isinstance(x, type(arr[0])) for x in expected) + + result = sanitize_array(arr, index=None, dtype=np.dtype(object)) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_object_arr.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_object_arr.py new file mode 100644 index 0000000000000000000000000000000000000000..cb44f91f34dec80c090d3ce3fc9a2dbd4578bb57 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_construct_object_arr.py @@ -0,0 +1,20 @@ +import pytest + +from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike + + +@pytest.mark.parametrize("datum1", [1, 2.0, "3", (4, 5), [6, 7], None]) +@pytest.mark.parametrize("datum2", [8, 9.0, "10", (11, 12), [13, 14], None]) +def test_cast_1d_array(datum1, datum2): + data = [datum1, datum2] + result = construct_1d_object_array_from_listlike(data) + + # Direct comparison fails: https://github.com/numpy/numpy/issues/10218 + assert result.dtype == "object" + assert list(result) == data + + +@pytest.mark.parametrize("val", [1, 2.0, None]) +def test_cast_1d_array_invalid_scalar(val): + with pytest.raises(TypeError, match="has no len()"): + construct_1d_object_array_from_listlike(val) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_dict_compat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_dict_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..13dc82d779f953fbea54323785bdcadc3e24dfd8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_dict_compat.py @@ -0,0 +1,14 @@ +import numpy as np + +from pandas.core.dtypes.cast import dict_compat + +from pandas import Timestamp + + +def test_dict_compat(): + data_datetime64 = {np.datetime64("1990-03-15"): 1, np.datetime64("2015-03-15"): 2} + data_unchanged = {1: 2, 3: 4, 5: 6} + expected = {Timestamp("1990-3-15"): 1, Timestamp("2015-03-15"): 2} + assert dict_compat(data_datetime64) == expected + assert dict_compat(expected) == expected + assert dict_compat(data_unchanged) == data_unchanged diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_downcast.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_downcast.py new file mode 100644 index 0000000000000000000000000000000000000000..9430ba2c478ae40a4a21bcc6dc034783cdf9543c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_downcast.py @@ -0,0 +1,97 @@ +import decimal + +import numpy as np +import pytest + +from pandas.core.dtypes.cast import maybe_downcast_to_dtype + +from pandas import ( + Series, + Timedelta, +) +import pandas._testing as tm + + +@pytest.mark.parametrize( + "arr,dtype,expected", + [ + ( + np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]), + "infer", + np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]), + ), + ( + np.array([8.0, 8.0, 8.0, 8.0, 8.9999999999995]), + "infer", + np.array([8, 8, 8, 8, 9], dtype=np.int64), + ), + ( + np.array([8.0, 8.0, 8.0, 8.0, 9.0000000000005]), + "infer", + np.array([8, 8, 8, 8, 9], dtype=np.int64), + ), + ( + # This is a judgement call, but we do _not_ downcast Decimal + # objects + np.array([decimal.Decimal(0.0)]), + "int64", + np.array([decimal.Decimal(0.0)]), + ), + ( + # GH#45837 + np.array([Timedelta(days=1), Timedelta(days=2)], dtype=object), + "infer", + np.array([1, 2], dtype="m8[D]").astype("m8[ns]"), + ), + # TODO: similar for dt64, dt64tz, Period, Interval? + ], +) +def test_downcast(arr, expected, dtype): + result = maybe_downcast_to_dtype(arr, dtype) + tm.assert_numpy_array_equal(result, expected) + + +def test_downcast_booleans(): + # see gh-16875: coercing of booleans. + ser = Series([True, True, False]) + result = maybe_downcast_to_dtype(ser, np.dtype(np.float64)) + + expected = ser.values + tm.assert_numpy_array_equal(result, expected) + + +def test_downcast_conversion_no_nan(any_real_numpy_dtype): + dtype = any_real_numpy_dtype + expected = np.array([1, 2]) + arr = np.array([1.0, 2.0], dtype=dtype) + + result = maybe_downcast_to_dtype(arr, "infer") + tm.assert_almost_equal(result, expected, check_dtype=False) + + +def test_downcast_conversion_nan(float_numpy_dtype): + dtype = float_numpy_dtype + data = [1.0, 2.0, np.nan] + + expected = np.array(data, dtype=dtype) + arr = np.array(data, dtype=dtype) + + result = maybe_downcast_to_dtype(arr, "infer") + tm.assert_almost_equal(result, expected) + + +def test_downcast_conversion_empty(any_real_numpy_dtype): + dtype = any_real_numpy_dtype + arr = np.array([], dtype=dtype) + result = maybe_downcast_to_dtype(arr, np.dtype("int64")) + tm.assert_numpy_array_equal(result, np.array([], dtype=np.int64)) + + +@pytest.mark.parametrize("klass", [np.datetime64, np.timedelta64]) +def test_datetime_likes_nan(klass): + dtype = klass.__name__ + "[ns]" + arr = np.array([1, 2, np.nan]) + + exp = np.array([1, 2, klass("NaT")], dtype) + res = maybe_downcast_to_dtype(arr, dtype) + tm.assert_numpy_array_equal(res, exp) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_find_common_type.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_find_common_type.py new file mode 100644 index 0000000000000000000000000000000000000000..83ef7382fbe8a27ad96511a3675c51b9eadc2331 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_find_common_type.py @@ -0,0 +1,175 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.cast import find_common_type +from pandas.core.dtypes.common import pandas_dtype +from pandas.core.dtypes.dtypes import ( + CategoricalDtype, + DatetimeTZDtype, + IntervalDtype, + PeriodDtype, +) + +from pandas import ( + Categorical, + Index, +) + + +@pytest.mark.parametrize( + "source_dtypes,expected_common_dtype", + [ + ((np.int64,), np.int64), + ((np.uint64,), np.uint64), + ((np.float32,), np.float32), + ((object,), object), + # Into ints. + ((np.int16, np.int64), np.int64), + ((np.int32, np.uint32), np.int64), + ((np.uint16, np.uint64), np.uint64), + # Into floats. + ((np.float16, np.float32), np.float32), + ((np.float16, np.int16), np.float32), + ((np.float32, np.int16), np.float32), + ((np.uint64, np.int64), np.float64), + ((np.int16, np.float64), np.float64), + ((np.float16, np.int64), np.float64), + # Into others. + ((np.complex128, np.int32), np.complex128), + ((object, np.float32), object), + ((object, np.int16), object), + # Bool with int. + ((np.dtype("bool"), np.int64), object), + ((np.dtype("bool"), np.int32), object), + ((np.dtype("bool"), np.int16), object), + ((np.dtype("bool"), np.int8), object), + ((np.dtype("bool"), np.uint64), object), + ((np.dtype("bool"), np.uint32), object), + ((np.dtype("bool"), np.uint16), object), + ((np.dtype("bool"), np.uint8), object), + # Bool with float. + ((np.dtype("bool"), np.float64), object), + ((np.dtype("bool"), np.float32), object), + ( + (np.dtype("datetime64[ns]"), np.dtype("datetime64[ns]")), + np.dtype("datetime64[ns]"), + ), + ( + (np.dtype("timedelta64[ns]"), np.dtype("timedelta64[ns]")), + np.dtype("timedelta64[ns]"), + ), + ( + (np.dtype("datetime64[ns]"), np.dtype("datetime64[ms]")), + np.dtype("datetime64[ns]"), + ), + ( + (np.dtype("timedelta64[ms]"), np.dtype("timedelta64[ns]")), + np.dtype("timedelta64[ns]"), + ), + ((np.dtype("datetime64[ns]"), np.dtype("timedelta64[ns]")), object), + ((np.dtype("datetime64[ns]"), np.int64), object), + ], +) +def test_numpy_dtypes(source_dtypes, expected_common_dtype): + source_dtypes = [pandas_dtype(x) for x in source_dtypes] + assert find_common_type(source_dtypes) == expected_common_dtype + + +def test_raises_empty_input(): + with pytest.raises(ValueError, match="no types given"): + find_common_type([]) + + +@pytest.mark.parametrize( + "dtypes,exp_type", + [ + ([CategoricalDtype()], "category"), + ([object, CategoricalDtype()], object), + ([CategoricalDtype(), CategoricalDtype()], "category"), + ], +) +def test_categorical_dtype(dtypes, exp_type): + assert find_common_type(dtypes) == exp_type + + +def test_datetimetz_dtype_match(): + dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern") + assert find_common_type([dtype, dtype]) == "datetime64[ns, US/Eastern]" + + +@pytest.mark.parametrize( + "dtype2", + [ + DatetimeTZDtype(unit="ns", tz="Asia/Tokyo"), + np.dtype("datetime64[ns]"), + object, + np.int64, + ], +) +def test_datetimetz_dtype_mismatch(dtype2): + dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern") + assert find_common_type([dtype, dtype2]) == object + assert find_common_type([dtype2, dtype]) == object + + +def test_period_dtype_match(): + dtype = PeriodDtype(freq="D") + assert find_common_type([dtype, dtype]) == "period[D]" + + +@pytest.mark.parametrize( + "dtype2", + [ + DatetimeTZDtype(unit="ns", tz="Asia/Tokyo"), + PeriodDtype(freq="2D"), + PeriodDtype(freq="h"), + np.dtype("datetime64[ns]"), + object, + np.int64, + ], +) +def test_period_dtype_mismatch(dtype2): + dtype = PeriodDtype(freq="D") + assert find_common_type([dtype, dtype2]) == object + assert find_common_type([dtype2, dtype]) == object + + +interval_dtypes = [ + IntervalDtype(np.int64, "right"), + IntervalDtype(np.float64, "right"), + IntervalDtype(np.uint64, "right"), + IntervalDtype(DatetimeTZDtype(unit="ns", tz="US/Eastern"), "right"), + IntervalDtype("M8[ns]", "right"), + IntervalDtype("m8[ns]", "right"), +] + + +@pytest.mark.parametrize("left", interval_dtypes) +@pytest.mark.parametrize("right", interval_dtypes) +def test_interval_dtype(left, right): + result = find_common_type([left, right]) + + if left is right: + assert result is left + + elif left.subtype.kind in ["i", "u", "f"]: + # i.e. numeric + if right.subtype.kind in ["i", "u", "f"]: + # both numeric -> common numeric subtype + expected = IntervalDtype(np.float64, "right") + assert result == expected + else: + assert result == object + + else: + assert result == object + + +@pytest.mark.parametrize("dtype", interval_dtypes) +def test_interval_dtype_with_categorical(dtype): + obj = Index([], dtype=dtype) + + cat = Categorical([], categories=obj) + + result = find_common_type([dtype, cat.dtype]) + assert result == dtype diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_datetimelike.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_datetimelike.py new file mode 100644 index 0000000000000000000000000000000000000000..3c3844e69586d2f49377e77910627ee42fef9bb2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_datetimelike.py @@ -0,0 +1,28 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + NaT, + Series, + Timestamp, +) + + +@pytest.mark.parametrize( + "data,exp_size", + [ + # see gh-16362. + ([[NaT, "a", "b", 0], [NaT, "b", "c", 1]], 8), + ([[NaT, "a", 0], [NaT, "b", 1]], 6), + ], +) +def test_maybe_infer_to_datetimelike_df_construct(data, exp_size): + result = DataFrame(np.array(data)) + assert result.size == exp_size + + +def test_maybe_infer_to_datetimelike_ser_construct(): + # see gh-19671. + result = Series(["M1701", Timestamp("20130101")]) + assert result.dtype.kind == "O" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_dtype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_dtype.py new file mode 100644 index 0000000000000000000000000000000000000000..679031a625c2da1386af78059b5e2986975a73ab --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_infer_dtype.py @@ -0,0 +1,216 @@ +from datetime import ( + date, + datetime, + timedelta, +) + +import numpy as np +import pytest + +from pandas.core.dtypes.cast import ( + infer_dtype_from, + infer_dtype_from_array, + infer_dtype_from_scalar, +) +from pandas.core.dtypes.common import is_dtype_equal + +from pandas import ( + Categorical, + Interval, + Period, + Series, + Timedelta, + Timestamp, + date_range, +) + + +def test_infer_dtype_from_int_scalar(any_int_numpy_dtype): + # Test that infer_dtype_from_scalar is + # returning correct dtype for int and float. + data = np.dtype(any_int_numpy_dtype).type(12) + dtype, val = infer_dtype_from_scalar(data) + assert dtype == type(data) + + +def test_infer_dtype_from_float_scalar(float_numpy_dtype): + float_numpy_dtype = np.dtype(float_numpy_dtype).type + data = float_numpy_dtype(12) + + dtype, val = infer_dtype_from_scalar(data) + assert dtype == float_numpy_dtype + + +@pytest.mark.parametrize( + "data,exp_dtype", [(12, np.int64), (np.float64(12), np.float64)] +) +def test_infer_dtype_from_python_scalar(data, exp_dtype): + dtype, val = infer_dtype_from_scalar(data) + assert dtype == exp_dtype + + +@pytest.mark.parametrize("bool_val", [True, False]) +def test_infer_dtype_from_boolean(bool_val): + dtype, val = infer_dtype_from_scalar(bool_val) + assert dtype == np.bool_ + + +def test_infer_dtype_from_complex(complex_dtype): + data = np.dtype(complex_dtype).type(1) + dtype, val = infer_dtype_from_scalar(data) + assert dtype == np.complex128 + + +def test_infer_dtype_from_datetime(): + dt64 = np.datetime64(1, "ns") + dtype, val = infer_dtype_from_scalar(dt64) + assert dtype == "M8[ns]" + + ts = Timestamp(1) + dtype, val = infer_dtype_from_scalar(ts) + assert dtype == "M8[ns]" + + dt = datetime(2000, 1, 1, 0, 0) + dtype, val = infer_dtype_from_scalar(dt) + assert dtype == "M8[us]" + + +def test_infer_dtype_from_timedelta(): + td64 = np.timedelta64(1, "ns") + dtype, val = infer_dtype_from_scalar(td64) + assert dtype == "m8[ns]" + + pytd = timedelta(1) + dtype, val = infer_dtype_from_scalar(pytd) + assert dtype == "m8[us]" + + td = Timedelta(1) + dtype, val = infer_dtype_from_scalar(td) + assert dtype == "m8[ns]" + + +@pytest.mark.parametrize("freq", ["M", "D"]) +def test_infer_dtype_from_period(freq): + p = Period("2011-01-01", freq=freq) + dtype, val = infer_dtype_from_scalar(p) + + exp_dtype = f"period[{freq}]" + + assert dtype == exp_dtype + assert val == p + + +def test_infer_dtype_misc(): + dt = date(2000, 1, 1) + dtype, val = infer_dtype_from_scalar(dt) + assert dtype == np.object_ + + ts = Timestamp(1, tz="US/Eastern") + dtype, val = infer_dtype_from_scalar(ts) + assert dtype == "datetime64[ns, US/Eastern]" + + +@pytest.mark.parametrize("tz", ["UTC", "US/Eastern", "Asia/Tokyo"]) +def test_infer_from_scalar_tz(tz): + dt = Timestamp(1, tz=tz) + dtype, val = infer_dtype_from_scalar(dt) + + exp_dtype = f"datetime64[ns, {tz}]" + + assert dtype == exp_dtype + assert val == dt + + +@pytest.mark.parametrize( + "left, right, subtype", + [ + (0, 1, "int64"), + (0.0, 1.0, "float64"), + (Timestamp(0), Timestamp(1), "datetime64[ns]"), + (Timestamp(0, tz="UTC"), Timestamp(1, tz="UTC"), "datetime64[ns, UTC]"), + (Timedelta(0), Timedelta(1), "timedelta64[ns]"), + ], +) +def test_infer_from_interval(left, right, subtype, closed): + # GH 30337 + interval = Interval(left, right, closed) + result_dtype, result_value = infer_dtype_from_scalar(interval) + expected_dtype = f"interval[{subtype}, {closed}]" + assert result_dtype == expected_dtype + assert result_value == interval + + +def test_infer_dtype_from_scalar_errors(): + msg = "invalid ndarray passed to infer_dtype_from_scalar" + + with pytest.raises(ValueError, match=msg): + infer_dtype_from_scalar(np.array([1])) + + +@pytest.mark.parametrize( + "value, expected", + [ + ("foo", np.object_), + (b"foo", np.object_), + (1, np.int64), + (1.5, np.float64), + (np.datetime64("2016-01-01"), np.dtype("M8[s]")), + (Timestamp("20160101"), np.dtype("M8[s]")), + (Timestamp("20160101", tz="UTC"), "datetime64[s, UTC]"), + ], +) +def test_infer_dtype_from_scalar(value, expected, using_infer_string): + dtype, _ = infer_dtype_from_scalar(value) + if using_infer_string and value == "foo": + expected = "string" + assert is_dtype_equal(dtype, expected) + + with pytest.raises(TypeError, match="must be list-like"): + infer_dtype_from_array(value) + + +@pytest.mark.parametrize( + "arr, expected", + [ + ([1], np.dtype(int)), + (np.array([1], dtype=np.int64), np.int64), + ([np.nan, 1, ""], np.object_), + (np.array([[1.0, 2.0]]), np.float64), + (Categorical(list("aabc")), "category"), + (Categorical([1, 2, 3]), "category"), + (date_range("20160101", periods=3), np.dtype("=M8[ns]")), + ( + date_range("20160101", periods=3, tz="US/Eastern"), + "datetime64[ns, US/Eastern]", + ), + (Series([1.0, 2, 3]), np.float64), + (Series(list("abc")), np.object_), + ( + Series(date_range("20160101", periods=3, tz="US/Eastern")), + "datetime64[ns, US/Eastern]", + ), + ], +) +def test_infer_dtype_from_array(arr, expected, using_infer_string): + dtype, _ = infer_dtype_from_array(arr) + if ( + using_infer_string + and isinstance(arr, Series) + and arr.tolist() == ["a", "b", "c"] + ): + expected = "string" + assert is_dtype_equal(dtype, expected) + + +@pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64]) +def test_infer_dtype_from_scalar_zerodim_datetimelike(cls): + # ndarray.item() can incorrectly return int instead of td64/dt64 + val = cls(1234, "ns") + arr = np.array(val) + + dtype, res = infer_dtype_from_scalar(arr) + assert dtype.type is cls + assert isinstance(res, cls) + + dtype, res = infer_dtype_from(arr) + assert dtype.type is cls diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_maybe_box_native.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_maybe_box_native.py new file mode 100644 index 0000000000000000000000000000000000000000..3f62f31dac2191a15d7df8db028a9286262d0080 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_maybe_box_native.py @@ -0,0 +1,40 @@ +from datetime import datetime + +import numpy as np +import pytest + +from pandas.core.dtypes.cast import maybe_box_native + +from pandas import ( + Interval, + Period, + Timedelta, + Timestamp, +) + + +@pytest.mark.parametrize( + "obj,expected_dtype", + [ + (b"\x00\x10", bytes), + (int(4), int), + (np.uint(4), int), + (np.int32(-4), int), + (np.uint8(4), int), + (float(454.98), float), + (np.float16(0.4), float), + (np.float64(1.4), float), + (np.bool_(False), bool), + (datetime(2005, 2, 25), datetime), + (np.datetime64("2005-02-25"), Timestamp), + (Timestamp("2005-02-25"), Timestamp), + (np.timedelta64(1, "D"), Timedelta), + (Timedelta(1, "D"), Timedelta), + (Interval(0, 1), Interval), + (Period("4Q2005"), Period), + ], +) +def test_maybe_box_native(obj, expected_dtype): + boxed_obj = maybe_box_native(obj) + result_dtype = type(boxed_obj) + assert result_dtype is expected_dtype diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_promote.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_promote.py new file mode 100644 index 0000000000000000000000000000000000000000..021107724bef73d998191d65b55fb29848fc8b9a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/cast/test_promote.py @@ -0,0 +1,530 @@ +""" +These test the method maybe_promote from core/dtypes/cast.py +""" + +import datetime +from decimal import Decimal + +import numpy as np +import pytest + +from pandas._libs.tslibs import NaT + +from pandas.core.dtypes.cast import maybe_promote +from pandas.core.dtypes.common import is_scalar +from pandas.core.dtypes.dtypes import DatetimeTZDtype +from pandas.core.dtypes.missing import isna + +import pandas as pd + + +def _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar=None): + """ + Auxiliary function to unify testing of scalar/array promotion. + + Parameters + ---------- + dtype : dtype + The value to pass on as the first argument to maybe_promote. + fill_value : scalar + The value to pass on as the second argument to maybe_promote as + a scalar. + expected_dtype : dtype + The expected dtype returned by maybe_promote (by design this is the + same regardless of whether fill_value was passed as a scalar or in an + array!). + exp_val_for_scalar : scalar + The expected value for the (potentially upcast) fill_value returned by + maybe_promote. + """ + assert is_scalar(fill_value) + + # here, we pass on fill_value as a scalar directly; the expected value + # returned from maybe_promote is fill_value, potentially upcast to the + # returned dtype. + result_dtype, result_fill_value = maybe_promote(dtype, fill_value) + expected_fill_value = exp_val_for_scalar + + assert result_dtype == expected_dtype + _assert_match(result_fill_value, expected_fill_value) + + +def _assert_match(result_fill_value, expected_fill_value): + # GH#23982/25425 require the same type in addition to equality/NA-ness + res_type = type(result_fill_value) + ex_type = type(expected_fill_value) + + if hasattr(result_fill_value, "dtype"): + # Compare types in a way that is robust to platform-specific + # idiosyncrasies where e.g. sometimes we get "ulonglong" as an alias + # for "uint64" or "intc" as an alias for "int32" + assert result_fill_value.dtype.kind == expected_fill_value.dtype.kind + assert result_fill_value.dtype.itemsize == expected_fill_value.dtype.itemsize + else: + # On some builds, type comparison fails, e.g. np.int32 != np.int32 + assert res_type == ex_type or res_type.__name__ == ex_type.__name__ + + match_value = result_fill_value == expected_fill_value + if match_value is pd.NA: + match_value = False + + # Note: type check above ensures that we have the _same_ NA value + # for missing values, None == None (which is checked + # through match_value above), but np.nan != np.nan and pd.NaT != pd.NaT + match_missing = isna(result_fill_value) and isna(expected_fill_value) + + assert match_value or match_missing + + +@pytest.mark.parametrize( + "dtype, fill_value, expected_dtype", + [ + # size 8 + ("int8", 1, "int8"), + ("int8", np.iinfo("int8").max + 1, "int16"), + ("int8", np.iinfo("int16").max + 1, "int32"), + ("int8", np.iinfo("int32").max + 1, "int64"), + ("int8", np.iinfo("int64").max + 1, "object"), + ("int8", -1, "int8"), + ("int8", np.iinfo("int8").min - 1, "int16"), + ("int8", np.iinfo("int16").min - 1, "int32"), + ("int8", np.iinfo("int32").min - 1, "int64"), + ("int8", np.iinfo("int64").min - 1, "object"), + # keep signed-ness as long as possible + ("uint8", 1, "uint8"), + ("uint8", np.iinfo("int8").max + 1, "uint8"), + ("uint8", np.iinfo("uint8").max + 1, "uint16"), + ("uint8", np.iinfo("int16").max + 1, "uint16"), + ("uint8", np.iinfo("uint16").max + 1, "uint32"), + ("uint8", np.iinfo("int32").max + 1, "uint32"), + ("uint8", np.iinfo("uint32").max + 1, "uint64"), + ("uint8", np.iinfo("int64").max + 1, "uint64"), + ("uint8", np.iinfo("uint64").max + 1, "object"), + # max of uint8 cannot be contained in int8 + ("uint8", -1, "int16"), + ("uint8", np.iinfo("int8").min - 1, "int16"), + ("uint8", np.iinfo("int16").min - 1, "int32"), + ("uint8", np.iinfo("int32").min - 1, "int64"), + ("uint8", np.iinfo("int64").min - 1, "object"), + # size 16 + ("int16", 1, "int16"), + ("int16", np.iinfo("int8").max + 1, "int16"), + ("int16", np.iinfo("int16").max + 1, "int32"), + ("int16", np.iinfo("int32").max + 1, "int64"), + ("int16", np.iinfo("int64").max + 1, "object"), + ("int16", -1, "int16"), + ("int16", np.iinfo("int8").min - 1, "int16"), + ("int16", np.iinfo("int16").min - 1, "int32"), + ("int16", np.iinfo("int32").min - 1, "int64"), + ("int16", np.iinfo("int64").min - 1, "object"), + ("uint16", 1, "uint16"), + ("uint16", np.iinfo("int8").max + 1, "uint16"), + ("uint16", np.iinfo("uint8").max + 1, "uint16"), + ("uint16", np.iinfo("int16").max + 1, "uint16"), + ("uint16", np.iinfo("uint16").max + 1, "uint32"), + ("uint16", np.iinfo("int32").max + 1, "uint32"), + ("uint16", np.iinfo("uint32").max + 1, "uint64"), + ("uint16", np.iinfo("int64").max + 1, "uint64"), + ("uint16", np.iinfo("uint64").max + 1, "object"), + ("uint16", -1, "int32"), + ("uint16", np.iinfo("int8").min - 1, "int32"), + ("uint16", np.iinfo("int16").min - 1, "int32"), + ("uint16", np.iinfo("int32").min - 1, "int64"), + ("uint16", np.iinfo("int64").min - 1, "object"), + # size 32 + ("int32", 1, "int32"), + ("int32", np.iinfo("int8").max + 1, "int32"), + ("int32", np.iinfo("int16").max + 1, "int32"), + ("int32", np.iinfo("int32").max + 1, "int64"), + ("int32", np.iinfo("int64").max + 1, "object"), + ("int32", -1, "int32"), + ("int32", np.iinfo("int8").min - 1, "int32"), + ("int32", np.iinfo("int16").min - 1, "int32"), + ("int32", np.iinfo("int32").min - 1, "int64"), + ("int32", np.iinfo("int64").min - 1, "object"), + ("uint32", 1, "uint32"), + ("uint32", np.iinfo("int8").max + 1, "uint32"), + ("uint32", np.iinfo("uint8").max + 1, "uint32"), + ("uint32", np.iinfo("int16").max + 1, "uint32"), + ("uint32", np.iinfo("uint16").max + 1, "uint32"), + ("uint32", np.iinfo("int32").max + 1, "uint32"), + ("uint32", np.iinfo("uint32").max + 1, "uint64"), + ("uint32", np.iinfo("int64").max + 1, "uint64"), + ("uint32", np.iinfo("uint64").max + 1, "object"), + ("uint32", -1, "int64"), + ("uint32", np.iinfo("int8").min - 1, "int64"), + ("uint32", np.iinfo("int16").min - 1, "int64"), + ("uint32", np.iinfo("int32").min - 1, "int64"), + ("uint32", np.iinfo("int64").min - 1, "object"), + # size 64 + ("int64", 1, "int64"), + ("int64", np.iinfo("int8").max + 1, "int64"), + ("int64", np.iinfo("int16").max + 1, "int64"), + ("int64", np.iinfo("int32").max + 1, "int64"), + ("int64", np.iinfo("int64").max + 1, "object"), + ("int64", -1, "int64"), + ("int64", np.iinfo("int8").min - 1, "int64"), + ("int64", np.iinfo("int16").min - 1, "int64"), + ("int64", np.iinfo("int32").min - 1, "int64"), + ("int64", np.iinfo("int64").min - 1, "object"), + ("uint64", 1, "uint64"), + ("uint64", np.iinfo("int8").max + 1, "uint64"), + ("uint64", np.iinfo("uint8").max + 1, "uint64"), + ("uint64", np.iinfo("int16").max + 1, "uint64"), + ("uint64", np.iinfo("uint16").max + 1, "uint64"), + ("uint64", np.iinfo("int32").max + 1, "uint64"), + ("uint64", np.iinfo("uint32").max + 1, "uint64"), + ("uint64", np.iinfo("int64").max + 1, "uint64"), + ("uint64", np.iinfo("uint64").max + 1, "object"), + ("uint64", -1, "object"), + ("uint64", np.iinfo("int8").min - 1, "object"), + ("uint64", np.iinfo("int16").min - 1, "object"), + ("uint64", np.iinfo("int32").min - 1, "object"), + ("uint64", np.iinfo("int64").min - 1, "object"), + ], +) +def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype): + dtype = np.dtype(dtype) + expected_dtype = np.dtype(expected_dtype) + + # output is not a generic int, but corresponds to expected_dtype + exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0] + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_int_with_float(any_int_numpy_dtype, float_numpy_dtype): + dtype = np.dtype(any_int_numpy_dtype) + fill_dtype = np.dtype(float_numpy_dtype) + + # create array of given dtype; casts "1" to correct dtype + fill_value = np.array([1], dtype=fill_dtype)[0] + + # filling int with float always upcasts to float64 + expected_dtype = np.float64 + # fill_value can be different float type + exp_val_for_scalar = np.float64(fill_value) + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_float_with_int(float_numpy_dtype, any_int_numpy_dtype): + dtype = np.dtype(float_numpy_dtype) + fill_dtype = np.dtype(any_int_numpy_dtype) + + # create array of given dtype; casts "1" to correct dtype + fill_value = np.array([1], dtype=fill_dtype)[0] + + # filling float with int always keeps float dtype + # because: np.finfo('float32').max > np.iinfo('uint64').max + expected_dtype = dtype + # output is not a generic float, but corresponds to expected_dtype + exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0] + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +@pytest.mark.parametrize( + "dtype, fill_value, expected_dtype", + [ + # float filled with float + ("float32", 1, "float32"), + ("float32", float(np.finfo("float32").max) * 1.1, "float64"), + ("float64", 1, "float64"), + ("float64", float(np.finfo("float32").max) * 1.1, "float64"), + # complex filled with float + ("complex64", 1, "complex64"), + ("complex64", float(np.finfo("float32").max) * 1.1, "complex128"), + ("complex128", 1, "complex128"), + ("complex128", float(np.finfo("float32").max) * 1.1, "complex128"), + # float filled with complex + ("float32", 1 + 1j, "complex64"), + ("float32", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"), + ("float64", 1 + 1j, "complex128"), + ("float64", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"), + # complex filled with complex + ("complex64", 1 + 1j, "complex64"), + ("complex64", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"), + ("complex128", 1 + 1j, "complex128"), + ("complex128", float(np.finfo("float32").max) * (1.1 + 1j), "complex128"), + ], +) +def test_maybe_promote_float_with_float(dtype, fill_value, expected_dtype): + dtype = np.dtype(dtype) + expected_dtype = np.dtype(expected_dtype) + + # output is not a generic float, but corresponds to expected_dtype + exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0] + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_bool_with_any(any_numpy_dtype): + dtype = np.dtype(bool) + fill_dtype = np.dtype(any_numpy_dtype) + + # create array of given dtype; casts "1" to correct dtype + fill_value = np.array([1], dtype=fill_dtype)[0] + + # filling bool with anything but bool casts to object + expected_dtype = np.dtype(object) if fill_dtype != bool else fill_dtype + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_any_with_bool(any_numpy_dtype): + dtype = np.dtype(any_numpy_dtype) + fill_value = True + + # filling anything but bool with bool casts to object + expected_dtype = np.dtype(object) if dtype != bool else dtype + # output is not a generic bool, but corresponds to expected_dtype + exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0] + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_bytes_with_any(bytes_dtype, any_numpy_dtype): + dtype = np.dtype(bytes_dtype) + fill_dtype = np.dtype(any_numpy_dtype) + + # create array of given dtype; casts "1" to correct dtype + fill_value = np.array([1], dtype=fill_dtype)[0] + + # we never use bytes dtype internally, always promote to object + expected_dtype = np.dtype(np.object_) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_any_with_bytes(any_numpy_dtype): + dtype = np.dtype(any_numpy_dtype) + + # create array of given dtype + fill_value = b"abc" + + # we never use bytes dtype internally, always promote to object + expected_dtype = np.dtype(np.object_) + # output is not a generic bytes, but corresponds to expected_dtype + exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0] + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_datetime64_with_any(datetime64_dtype, any_numpy_dtype): + dtype = np.dtype(datetime64_dtype) + fill_dtype = np.dtype(any_numpy_dtype) + + # create array of given dtype; casts "1" to correct dtype + fill_value = np.array([1], dtype=fill_dtype)[0] + + # filling datetime with anything but datetime casts to object + if fill_dtype.kind == "M": + expected_dtype = dtype + # for datetime dtypes, scalar values get cast to to_datetime64 + exp_val_for_scalar = pd.Timestamp(fill_value).to_datetime64() + else: + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +@pytest.mark.parametrize( + "fill_value", + [ + pd.Timestamp("now"), + np.datetime64("now"), + datetime.datetime.now(), + datetime.date.today(), + ], + ids=["pd.Timestamp", "np.datetime64", "datetime.datetime", "datetime.date"], +) +def test_maybe_promote_any_with_datetime64(any_numpy_dtype, fill_value): + dtype = np.dtype(any_numpy_dtype) + + # filling datetime with anything but datetime casts to object + if dtype.kind == "M": + expected_dtype = dtype + # for datetime dtypes, scalar values get cast to pd.Timestamp.value + exp_val_for_scalar = pd.Timestamp(fill_value).to_datetime64() + else: + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + if type(fill_value) is datetime.date and dtype.kind == "M": + # Casting date to dt64 is deprecated, in 2.0 enforced to cast to object + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +@pytest.mark.parametrize( + "fill_value", + [ + pd.Timestamp(2023, 1, 1), + np.datetime64("2023-01-01"), + datetime.datetime(2023, 1, 1), + datetime.date(2023, 1, 1), + ], + ids=["pd.Timestamp", "np.datetime64", "datetime.datetime", "datetime.date"], +) +def test_maybe_promote_any_numpy_dtype_with_datetimetz( + any_numpy_dtype, tz_aware_fixture, fill_value +): + dtype = np.dtype(any_numpy_dtype) + fill_dtype = DatetimeTZDtype(tz=tz_aware_fixture) + + fill_value = pd.Series([fill_value], dtype=fill_dtype)[0] + + # filling any numpy dtype with datetimetz casts to object + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_timedelta64_with_any(timedelta64_dtype, any_numpy_dtype): + dtype = np.dtype(timedelta64_dtype) + fill_dtype = np.dtype(any_numpy_dtype) + + # create array of given dtype; casts "1" to correct dtype + fill_value = np.array([1], dtype=fill_dtype)[0] + + # filling timedelta with anything but timedelta casts to object + if fill_dtype.kind == "m": + expected_dtype = dtype + # for timedelta dtypes, scalar values get cast to pd.Timedelta.value + exp_val_for_scalar = pd.Timedelta(fill_value).to_timedelta64() + else: + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +@pytest.mark.parametrize( + "fill_value", + [pd.Timedelta(days=1), np.timedelta64(24, "h"), datetime.timedelta(1)], + ids=["pd.Timedelta", "np.timedelta64", "datetime.timedelta"], +) +def test_maybe_promote_any_with_timedelta64(any_numpy_dtype, fill_value): + dtype = np.dtype(any_numpy_dtype) + + # filling anything but timedelta with timedelta casts to object + if dtype.kind == "m": + expected_dtype = dtype + # for timedelta dtypes, scalar values get cast to pd.Timedelta.value + exp_val_for_scalar = pd.Timedelta(fill_value).to_timedelta64() + else: + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_string_with_any(string_dtype, any_numpy_dtype): + dtype = np.dtype(string_dtype) + fill_dtype = np.dtype(any_numpy_dtype) + + # create array of given dtype; casts "1" to correct dtype + fill_value = np.array([1], dtype=fill_dtype)[0] + + # filling string with anything casts to object + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_any_with_string(any_numpy_dtype): + dtype = np.dtype(any_numpy_dtype) + + # create array of given dtype + fill_value = "abc" + + # filling anything with a string casts to object + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_object_with_any(object_dtype, any_numpy_dtype): + dtype = np.dtype(object_dtype) + fill_dtype = np.dtype(any_numpy_dtype) + + # create array of given dtype; casts "1" to correct dtype + fill_value = np.array([1], dtype=fill_dtype)[0] + + # filling object with anything stays object + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_any_with_object(any_numpy_dtype): + dtype = np.dtype(any_numpy_dtype) + + # create array of object dtype from a scalar value (i.e. passing + # dtypes.common.is_scalar), which can however not be cast to int/float etc. + fill_value = pd.DateOffset(1) + + # filling object with anything stays object + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) + + +def test_maybe_promote_any_numpy_dtype_with_na(any_numpy_dtype, nulls_fixture): + fill_value = nulls_fixture + dtype = np.dtype(any_numpy_dtype) + + if isinstance(fill_value, Decimal): + # Subject to change, but ATM (When Decimal(NAN) is being added to nulls_fixture) + # this is the existing behavior in maybe_promote, + # hinges on is_valid_na_for_dtype + if dtype.kind in "iufc": + if dtype.kind in "iu": + expected_dtype = np.dtype(np.float64) + else: + expected_dtype = dtype + exp_val_for_scalar = np.nan + else: + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + elif dtype.kind in "iu" and fill_value is not NaT: + # integer + other missing value (np.nan / None) casts to float + expected_dtype = np.float64 + exp_val_for_scalar = np.nan + elif dtype == object and fill_value is NaT: + # inserting into object does not cast the value + # but *does* cast None to np.nan + expected_dtype = np.dtype(object) + exp_val_for_scalar = fill_value + elif dtype.kind in "mM": + # datetime / timedelta cast all missing values to dtyped-NaT + expected_dtype = dtype + exp_val_for_scalar = dtype.type("NaT", "ns") + elif fill_value is NaT: + # NaT upcasts everything that's not datetime/timedelta to object + expected_dtype = np.dtype(object) + exp_val_for_scalar = NaT + elif dtype.kind in "fc": + # float / complex + missing value (!= NaT) stays the same + expected_dtype = dtype + exp_val_for_scalar = np.nan + else: + # all other cases cast to object, and use np.nan as missing value + expected_dtype = np.dtype(object) + if fill_value is pd.NA: + exp_val_for_scalar = pd.NA + else: + exp_val_for_scalar = np.nan + + _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/test_common.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/test_common.py new file mode 100644 index 0000000000000000000000000000000000000000..579f5636922dc3fb4ed652e1fa374607ec57501a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/test_common.py @@ -0,0 +1,865 @@ +from __future__ import annotations + +import numpy as np +import pytest + +from pandas.compat import HAS_PYARROW +import pandas.util._test_decorators as td + +from pandas.core.dtypes.astype import astype_array +import pandas.core.dtypes.common as com +from pandas.core.dtypes.dtypes import ( + CategoricalDtype, + CategoricalDtypeType, + DatetimeTZDtype, + ExtensionDtype, + IntervalDtype, + PeriodDtype, +) +from pandas.core.dtypes.missing import isna + +import pandas as pd +import pandas._testing as tm +from pandas.api.types import pandas_dtype +from pandas.arrays import SparseArray +from pandas.util.version import Version + + +# EA & Actual Dtypes +def to_ea_dtypes(dtypes): + """convert list of string dtypes to EA dtype""" + return [getattr(pd, dt + "Dtype") for dt in dtypes] + + +def to_numpy_dtypes(dtypes): + """convert list of string dtypes to numpy dtype""" + return [getattr(np, dt) for dt in dtypes if isinstance(dt, str)] + + +class TestNumpyEADtype: + # Passing invalid dtype, both as a string or object, must raise TypeError + # Per issue GH15520 + @pytest.mark.parametrize("box", [pd.Timestamp, "pd.Timestamp", list]) + def test_invalid_dtype_error(self, box): + with pytest.raises(TypeError, match="not understood"): + com.pandas_dtype(box) + + @pytest.mark.parametrize( + "dtype", + [ + object, + "float64", + np.object_, + np.dtype("object"), + "O", + np.float64, + float, + np.dtype("float64"), + "object_", + ], + ) + def test_pandas_dtype_valid(self, dtype): + assert com.pandas_dtype(dtype) == dtype + + @pytest.mark.parametrize( + "dtype", ["M8[ns]", "m8[ns]", "object", "float64", "int64"] + ) + def test_numpy_dtype(self, dtype): + assert com.pandas_dtype(dtype) == np.dtype(dtype) + + def test_numpy_string_dtype(self): + # do not parse freq-like string as period dtype + assert com.pandas_dtype("U") == np.dtype("U") + assert com.pandas_dtype("S") == np.dtype("S") + + @pytest.mark.parametrize( + "dtype", + [ + "datetime64[ns, US/Eastern]", + "datetime64[ns, Asia/Tokyo]", + "datetime64[ns, UTC]", + # GH#33885 check that the M8 alias is understood + "M8[ns, US/Eastern]", + "M8[ns, Asia/Tokyo]", + "M8[ns, UTC]", + ], + ) + def test_datetimetz_dtype(self, dtype): + assert com.pandas_dtype(dtype) == DatetimeTZDtype.construct_from_string(dtype) + assert com.pandas_dtype(dtype) == dtype + + def test_categorical_dtype(self): + assert com.pandas_dtype("category") == CategoricalDtype() + + @pytest.mark.parametrize( + "dtype", + [ + "period[D]", + "period[3M]", + "period[us]", + "Period[D]", + "Period[3M]", + "Period[us]", + ], + ) + def test_period_dtype(self, dtype): + assert com.pandas_dtype(dtype) is not PeriodDtype(dtype) + assert com.pandas_dtype(dtype) == PeriodDtype(dtype) + assert com.pandas_dtype(dtype) == dtype + + +dtypes = { + "datetime_tz": com.pandas_dtype("datetime64[ns, US/Eastern]"), + "datetime": com.pandas_dtype("datetime64[ns]"), + "timedelta": com.pandas_dtype("timedelta64[ns]"), + "period": PeriodDtype("D"), + "integer": np.dtype(np.int64), + "float": np.dtype(np.float64), + "object": np.dtype(object), + "category": com.pandas_dtype("category"), + "string": pd.StringDtype(), +} + + +@pytest.mark.parametrize("name1,dtype1", list(dtypes.items()), ids=lambda x: str(x)) +@pytest.mark.parametrize("name2,dtype2", list(dtypes.items()), ids=lambda x: str(x)) +def test_dtype_equal(name1, dtype1, name2, dtype2): + # match equal to self, but not equal to other + assert com.is_dtype_equal(dtype1, dtype1) + if name1 != name2: + assert not com.is_dtype_equal(dtype1, dtype2) + + +@pytest.mark.parametrize("name,dtype", list(dtypes.items()), ids=lambda x: str(x)) +def test_pyarrow_string_import_error(name, dtype): + # GH-44276 + assert not com.is_dtype_equal(dtype, "string[pyarrow]") + + +@pytest.mark.parametrize( + "dtype1,dtype2", + [ + (np.int8, np.int64), + (np.int16, np.int64), + (np.int32, np.int64), + (np.float32, np.float64), + (PeriodDtype("D"), PeriodDtype("2D")), # PeriodType + ( + com.pandas_dtype("datetime64[ns, US/Eastern]"), + com.pandas_dtype("datetime64[ns, CET]"), + ), # Datetime + (None, None), # gh-15941: no exception should be raised. + ], +) +def test_dtype_equal_strict(dtype1, dtype2): + assert not com.is_dtype_equal(dtype1, dtype2) + + +def get_is_dtype_funcs(): + """ + Get all functions in pandas.core.dtypes.common that + begin with 'is_' and end with 'dtype' + + """ + fnames = [f for f in dir(com) if (f.startswith("is_") and f.endswith("dtype"))] + fnames.remove("is_string_or_object_np_dtype") # fastpath requires np.dtype obj + return [getattr(com, fname) for fname in fnames] + + +@pytest.mark.filterwarnings( + "ignore:is_categorical_dtype is deprecated:DeprecationWarning" +) +@pytest.mark.parametrize("func", get_is_dtype_funcs(), ids=lambda x: x.__name__) +def test_get_dtype_error_catch(func): + # see gh-15941 + # + # No exception should be raised. + + msg = f"{func.__name__} is deprecated" + warn = None + if ( + func is com.is_int64_dtype + or func is com.is_interval_dtype + or func is com.is_datetime64tz_dtype + or func is com.is_categorical_dtype + or func is com.is_period_dtype + ): + warn = DeprecationWarning + + with tm.assert_produces_warning(warn, match=msg): + assert not func(None) + + +def test_is_object(): + assert com.is_object_dtype(object) + assert com.is_object_dtype(np.array([], dtype=object)) + + assert not com.is_object_dtype(int) + assert not com.is_object_dtype(np.array([], dtype=int)) + assert not com.is_object_dtype([1, 2, 3]) + + +@pytest.mark.parametrize( + "check_scipy", [False, pytest.param(True, marks=td.skip_if_no("scipy"))] +) +def test_is_sparse(check_scipy): + msg = "is_sparse is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert com.is_sparse(SparseArray([1, 2, 3])) + + assert not com.is_sparse(np.array([1, 2, 3])) + + if check_scipy: + import scipy.sparse + + assert not com.is_sparse(scipy.sparse.bsr_matrix([1, 2, 3])) + + +def test_is_scipy_sparse(): + sp_sparse = pytest.importorskip("scipy.sparse") + + assert com.is_scipy_sparse(sp_sparse.bsr_matrix([1, 2, 3])) + + assert not com.is_scipy_sparse(SparseArray([1, 2, 3])) + + +def test_is_datetime64_dtype(): + assert not com.is_datetime64_dtype(object) + assert not com.is_datetime64_dtype([1, 2, 3]) + assert not com.is_datetime64_dtype(np.array([], dtype=int)) + + assert com.is_datetime64_dtype(np.datetime64) + assert com.is_datetime64_dtype(np.array([], dtype=np.datetime64)) + + +def test_is_datetime64tz_dtype(): + msg = "is_datetime64tz_dtype is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert not com.is_datetime64tz_dtype(object) + assert not com.is_datetime64tz_dtype([1, 2, 3]) + assert not com.is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3])) + assert com.is_datetime64tz_dtype(pd.DatetimeIndex(["2000"], tz="US/Eastern")) + + +def test_custom_ea_kind_M_not_datetime64tz(): + # GH 34986 + class NotTZDtype(ExtensionDtype): + @property + def kind(self) -> str: + return "M" + + not_tz_dtype = NotTZDtype() + msg = "is_datetime64tz_dtype is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert not com.is_datetime64tz_dtype(not_tz_dtype) + assert not com.needs_i8_conversion(not_tz_dtype) + + +def test_is_timedelta64_dtype(): + assert not com.is_timedelta64_dtype(object) + assert not com.is_timedelta64_dtype(None) + assert not com.is_timedelta64_dtype([1, 2, 3]) + assert not com.is_timedelta64_dtype(np.array([], dtype=np.datetime64)) + assert not com.is_timedelta64_dtype("0 days") + assert not com.is_timedelta64_dtype("0 days 00:00:00") + assert not com.is_timedelta64_dtype(["0 days 00:00:00"]) + assert not com.is_timedelta64_dtype("NO DATE") + + assert com.is_timedelta64_dtype(np.timedelta64) + assert com.is_timedelta64_dtype(pd.Series([], dtype="timedelta64[ns]")) + assert com.is_timedelta64_dtype(pd.to_timedelta(["0 days", "1 days"])) + + +def test_is_period_dtype(): + msg = "is_period_dtype is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert not com.is_period_dtype(object) + assert not com.is_period_dtype([1, 2, 3]) + assert not com.is_period_dtype(pd.Period("2017-01-01")) + + assert com.is_period_dtype(PeriodDtype(freq="D")) + assert com.is_period_dtype(pd.PeriodIndex([], freq="Y")) + + +def test_is_interval_dtype(): + msg = "is_interval_dtype is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert not com.is_interval_dtype(object) + assert not com.is_interval_dtype([1, 2, 3]) + + assert com.is_interval_dtype(IntervalDtype()) + + interval = pd.Interval(1, 2, closed="right") + assert not com.is_interval_dtype(interval) + assert com.is_interval_dtype(pd.IntervalIndex([interval])) + + +def test_is_categorical_dtype(): + msg = "is_categorical_dtype is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert not com.is_categorical_dtype(object) + assert not com.is_categorical_dtype([1, 2, 3]) + + assert com.is_categorical_dtype(CategoricalDtype()) + assert com.is_categorical_dtype(pd.Categorical([1, 2, 3])) + assert com.is_categorical_dtype(pd.CategoricalIndex([1, 2, 3])) + + +@pytest.mark.parametrize( + "dtype, expected", + [ + (int, False), + (pd.Series([1, 2]), False), + (str, True), + (object, True), + (np.array(["a", "b"]), True), + (pd.StringDtype(), True), + (pd.Index([], dtype="O"), True), + ], +) +def test_is_string_dtype(dtype, expected): + # GH#54661 + + result = com.is_string_dtype(dtype) + assert result is expected + + +@pytest.mark.parametrize( + "data", + [[(0, 1), (1, 1)], pd.Categorical([1, 2, 3]), np.array([1, 2], dtype=object)], +) +def test_is_string_dtype_arraylike_with_object_elements_not_strings(data): + # GH 15585 + assert not com.is_string_dtype(pd.Series(data)) + + +def test_is_string_dtype_nullable(nullable_string_dtype): + assert com.is_string_dtype(pd.array(["a", "b"], dtype=nullable_string_dtype)) + + +integer_dtypes: list = [] + + +@pytest.mark.parametrize( + "dtype", + integer_dtypes + + [pd.Series([1, 2])] + + tm.ALL_INT_NUMPY_DTYPES + + to_numpy_dtypes(tm.ALL_INT_NUMPY_DTYPES) + + tm.ALL_INT_EA_DTYPES + + to_ea_dtypes(tm.ALL_INT_EA_DTYPES), +) +def test_is_integer_dtype(dtype): + assert com.is_integer_dtype(dtype) + + +@pytest.mark.parametrize( + "dtype", + [ + str, + float, + np.datetime64, + np.timedelta64, + pd.Index([1, 2.0]), + np.array(["a", "b"]), + np.array([], dtype=np.timedelta64), + ], +) +def test_is_not_integer_dtype(dtype): + assert not com.is_integer_dtype(dtype) + + +signed_integer_dtypes: list = [] + + +@pytest.mark.parametrize( + "dtype", + signed_integer_dtypes + + [pd.Series([1, 2])] + + tm.SIGNED_INT_NUMPY_DTYPES + + to_numpy_dtypes(tm.SIGNED_INT_NUMPY_DTYPES) + + tm.SIGNED_INT_EA_DTYPES + + to_ea_dtypes(tm.SIGNED_INT_EA_DTYPES), +) +def test_is_signed_integer_dtype(dtype): + assert com.is_integer_dtype(dtype) + + +@pytest.mark.parametrize( + "dtype", + [ + str, + float, + np.datetime64, + np.timedelta64, + pd.Index([1, 2.0]), + np.array(["a", "b"]), + np.array([], dtype=np.timedelta64), + ] + + tm.UNSIGNED_INT_NUMPY_DTYPES + + to_numpy_dtypes(tm.UNSIGNED_INT_NUMPY_DTYPES) + + tm.UNSIGNED_INT_EA_DTYPES + + to_ea_dtypes(tm.UNSIGNED_INT_EA_DTYPES), +) +def test_is_not_signed_integer_dtype(dtype): + assert not com.is_signed_integer_dtype(dtype) + + +unsigned_integer_dtypes: list = [] + + +@pytest.mark.parametrize( + "dtype", + unsigned_integer_dtypes + + [pd.Series([1, 2], dtype=np.uint32)] + + tm.UNSIGNED_INT_NUMPY_DTYPES + + to_numpy_dtypes(tm.UNSIGNED_INT_NUMPY_DTYPES) + + tm.UNSIGNED_INT_EA_DTYPES + + to_ea_dtypes(tm.UNSIGNED_INT_EA_DTYPES), +) +def test_is_unsigned_integer_dtype(dtype): + assert com.is_unsigned_integer_dtype(dtype) + + +@pytest.mark.parametrize( + "dtype", + [ + str, + float, + np.datetime64, + np.timedelta64, + pd.Index([1, 2.0]), + np.array(["a", "b"]), + np.array([], dtype=np.timedelta64), + ] + + tm.SIGNED_INT_NUMPY_DTYPES + + to_numpy_dtypes(tm.SIGNED_INT_NUMPY_DTYPES) + + tm.SIGNED_INT_EA_DTYPES + + to_ea_dtypes(tm.SIGNED_INT_EA_DTYPES), +) +def test_is_not_unsigned_integer_dtype(dtype): + assert not com.is_unsigned_integer_dtype(dtype) + + +@pytest.mark.parametrize( + "dtype", [np.int64, np.array([1, 2], dtype=np.int64), "Int64", pd.Int64Dtype] +) +def test_is_int64_dtype(dtype): + msg = "is_int64_dtype is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert com.is_int64_dtype(dtype) + + +def test_type_comparison_with_numeric_ea_dtype(any_numeric_ea_dtype): + # GH#43038 + assert pandas_dtype(any_numeric_ea_dtype) == any_numeric_ea_dtype + + +def test_type_comparison_with_real_numpy_dtype(any_real_numpy_dtype): + # GH#43038 + assert pandas_dtype(any_real_numpy_dtype) == any_real_numpy_dtype + + +def test_type_comparison_with_signed_int_ea_dtype_and_signed_int_numpy_dtype( + any_signed_int_ea_dtype, any_signed_int_numpy_dtype +): + # GH#43038 + assert not pandas_dtype(any_signed_int_ea_dtype) == any_signed_int_numpy_dtype + + +@pytest.mark.parametrize( + "dtype", + [ + str, + float, + np.int32, + np.uint64, + pd.Index([1, 2.0]), + np.array(["a", "b"]), + np.array([1, 2], dtype=np.uint32), + "int8", + "Int8", + pd.Int8Dtype, + ], +) +def test_is_not_int64_dtype(dtype): + msg = "is_int64_dtype is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert not com.is_int64_dtype(dtype) + + +def test_is_datetime64_any_dtype(): + assert not com.is_datetime64_any_dtype(int) + assert not com.is_datetime64_any_dtype(str) + assert not com.is_datetime64_any_dtype(np.array([1, 2])) + assert not com.is_datetime64_any_dtype(np.array(["a", "b"])) + + assert com.is_datetime64_any_dtype(np.datetime64) + assert com.is_datetime64_any_dtype(np.array([], dtype=np.datetime64)) + assert com.is_datetime64_any_dtype(DatetimeTZDtype("ns", "US/Eastern")) + assert com.is_datetime64_any_dtype( + pd.DatetimeIndex([1, 2, 3], dtype="datetime64[ns]") + ) + + +def test_is_datetime64_ns_dtype(): + assert not com.is_datetime64_ns_dtype(int) + assert not com.is_datetime64_ns_dtype(str) + assert not com.is_datetime64_ns_dtype(np.datetime64) + assert not com.is_datetime64_ns_dtype(np.array([1, 2])) + assert not com.is_datetime64_ns_dtype(np.array(["a", "b"])) + assert not com.is_datetime64_ns_dtype(np.array([], dtype=np.datetime64)) + + # This datetime array has the wrong unit (ps instead of ns) + assert not com.is_datetime64_ns_dtype(np.array([], dtype="datetime64[ps]")) + + assert com.is_datetime64_ns_dtype(DatetimeTZDtype("ns", "US/Eastern")) + assert com.is_datetime64_ns_dtype( + pd.DatetimeIndex([1, 2, 3], dtype=np.dtype("datetime64[ns]")) + ) + + # non-nano dt64tz + assert not com.is_datetime64_ns_dtype(DatetimeTZDtype("us", "US/Eastern")) + + +def test_is_timedelta64_ns_dtype(): + assert not com.is_timedelta64_ns_dtype(np.dtype("m8[ps]")) + assert not com.is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64)) + + assert com.is_timedelta64_ns_dtype(np.dtype("m8[ns]")) + assert com.is_timedelta64_ns_dtype(np.array([1, 2], dtype="m8[ns]")) + + +def test_is_numeric_v_string_like(): + assert not com.is_numeric_v_string_like(np.array([1]), 1) + assert not com.is_numeric_v_string_like(np.array([1]), np.array([2])) + assert not com.is_numeric_v_string_like(np.array(["foo"]), np.array(["foo"])) + + assert com.is_numeric_v_string_like(np.array([1]), "foo") + assert com.is_numeric_v_string_like(np.array([1, 2]), np.array(["foo"])) + assert com.is_numeric_v_string_like(np.array(["foo"]), np.array([1, 2])) + + +def test_needs_i8_conversion(): + assert not com.needs_i8_conversion(str) + assert not com.needs_i8_conversion(np.int64) + assert not com.needs_i8_conversion(pd.Series([1, 2])) + assert not com.needs_i8_conversion(np.array(["a", "b"])) + + assert not com.needs_i8_conversion(np.datetime64) + assert com.needs_i8_conversion(np.dtype(np.datetime64)) + assert not com.needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]")) + assert com.needs_i8_conversion(pd.Series([], dtype="timedelta64[ns]").dtype) + assert not com.needs_i8_conversion(pd.DatetimeIndex(["2000"], tz="US/Eastern")) + assert com.needs_i8_conversion(pd.DatetimeIndex(["2000"], tz="US/Eastern").dtype) + + +def test_is_numeric_dtype(): + assert not com.is_numeric_dtype(str) + assert not com.is_numeric_dtype(np.datetime64) + assert not com.is_numeric_dtype(np.timedelta64) + assert not com.is_numeric_dtype(np.array(["a", "b"])) + assert not com.is_numeric_dtype(np.array([], dtype=np.timedelta64)) + + assert com.is_numeric_dtype(int) + assert com.is_numeric_dtype(float) + assert com.is_numeric_dtype(np.uint64) + assert com.is_numeric_dtype(pd.Series([1, 2])) + assert com.is_numeric_dtype(pd.Index([1, 2.0])) + + class MyNumericDType(ExtensionDtype): + @property + def type(self): + return str + + @property + def name(self): + raise NotImplementedError + + @classmethod + def construct_array_type(cls): + raise NotImplementedError + + def _is_numeric(self) -> bool: + return True + + assert com.is_numeric_dtype(MyNumericDType()) + + +def test_is_any_real_numeric_dtype(): + assert not com.is_any_real_numeric_dtype(str) + assert not com.is_any_real_numeric_dtype(bool) + assert not com.is_any_real_numeric_dtype(complex) + assert not com.is_any_real_numeric_dtype(object) + assert not com.is_any_real_numeric_dtype(np.datetime64) + assert not com.is_any_real_numeric_dtype(np.array(["a", "b", complex(1, 2)])) + assert not com.is_any_real_numeric_dtype(pd.DataFrame([complex(1, 2), True])) + + assert com.is_any_real_numeric_dtype(int) + assert com.is_any_real_numeric_dtype(float) + assert com.is_any_real_numeric_dtype(np.array([1, 2.5])) + + +def test_is_float_dtype(): + assert not com.is_float_dtype(str) + assert not com.is_float_dtype(int) + assert not com.is_float_dtype(pd.Series([1, 2])) + assert not com.is_float_dtype(np.array(["a", "b"])) + + assert com.is_float_dtype(float) + assert com.is_float_dtype(pd.Index([1, 2.0])) + + +def test_is_bool_dtype(): + assert not com.is_bool_dtype(int) + assert not com.is_bool_dtype(str) + assert not com.is_bool_dtype(pd.Series([1, 2])) + assert not com.is_bool_dtype(pd.Series(["a", "b"], dtype="category")) + assert not com.is_bool_dtype(np.array(["a", "b"])) + assert not com.is_bool_dtype(pd.Index(["a", "b"])) + assert not com.is_bool_dtype("Int64") + + assert com.is_bool_dtype(bool) + assert com.is_bool_dtype(np.bool_) + assert com.is_bool_dtype(pd.Series([True, False], dtype="category")) + assert com.is_bool_dtype(np.array([True, False])) + assert com.is_bool_dtype(pd.Index([True, False])) + + assert com.is_bool_dtype(pd.BooleanDtype()) + assert com.is_bool_dtype(pd.array([True, False, None], dtype="boolean")) + assert com.is_bool_dtype("boolean") + + +def test_is_bool_dtype_numpy_error(): + # GH39010 + assert not com.is_bool_dtype("0 - Name") + + +@pytest.mark.parametrize( + "check_scipy", [False, pytest.param(True, marks=td.skip_if_no("scipy"))] +) +def test_is_extension_array_dtype(check_scipy): + assert not com.is_extension_array_dtype([1, 2, 3]) + assert not com.is_extension_array_dtype(np.array([1, 2, 3])) + assert not com.is_extension_array_dtype(pd.DatetimeIndex([1, 2, 3])) + + cat = pd.Categorical([1, 2, 3]) + assert com.is_extension_array_dtype(cat) + assert com.is_extension_array_dtype(pd.Series(cat)) + assert com.is_extension_array_dtype(SparseArray([1, 2, 3])) + assert com.is_extension_array_dtype(pd.DatetimeIndex(["2000"], tz="US/Eastern")) + + dtype = DatetimeTZDtype("ns", tz="US/Eastern") + s = pd.Series([], dtype=dtype) + assert com.is_extension_array_dtype(s) + + if check_scipy: + import scipy.sparse + + assert not com.is_extension_array_dtype(scipy.sparse.bsr_matrix([1, 2, 3])) + + +def test_is_complex_dtype(): + assert not com.is_complex_dtype(int) + assert not com.is_complex_dtype(str) + assert not com.is_complex_dtype(pd.Series([1, 2])) + assert not com.is_complex_dtype(np.array(["a", "b"])) + + assert com.is_complex_dtype(np.complex128) + assert com.is_complex_dtype(complex) + assert com.is_complex_dtype(np.array([1 + 1j, 5])) + + +@pytest.mark.parametrize( + "input_param,result", + [ + (int, np.dtype(int)), + ("int32", np.dtype("int32")), + (float, np.dtype(float)), + ("float64", np.dtype("float64")), + (np.dtype("float64"), np.dtype("float64")), + (str, np.dtype(str)), + (pd.Series([1, 2], dtype=np.dtype("int16")), np.dtype("int16")), + (pd.Series(["a", "b"], dtype=object), np.dtype(object)), + (pd.Index([1, 2]), np.dtype("int64")), + (pd.Index(["a", "b"], dtype=object), np.dtype(object)), + ("category", "category"), + (pd.Categorical(["a", "b"]).dtype, CategoricalDtype(["a", "b"])), + (pd.Categorical(["a", "b"]), CategoricalDtype(["a", "b"])), + (pd.CategoricalIndex(["a", "b"]).dtype, CategoricalDtype(["a", "b"])), + (pd.CategoricalIndex(["a", "b"]), CategoricalDtype(["a", "b"])), + (CategoricalDtype(), CategoricalDtype()), + (pd.DatetimeIndex([1, 2]), np.dtype("=M8[ns]")), + (pd.DatetimeIndex([1, 2]).dtype, np.dtype("=M8[ns]")), + (" df.two.sum() + + with tm.assert_produces_warning(None): + # successfully modify column in place + # this should not raise a warning + df.one += 1 + assert df.one.iloc[0] == 2 + + with tm.assert_produces_warning(None): + # successfully add an attribute to a series + # this should not raise a warning + df.two.not_an_index = [1, 2] + + with tm.assert_produces_warning(UserWarning): + # warn when setting column to nonexistent name + df.four = df.two + 2 + assert df.four.sum() > df.two.sum() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/test_inference.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/test_inference.py new file mode 100644 index 0000000000000000000000000000000000000000..79b7e6ff092b6efc519d8b29ad134c8019c6602f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/test_inference.py @@ -0,0 +1,2072 @@ +""" +These the test the public routines exposed in types/common.py +related to inference and not otherwise tested in types/test_common.py + +""" +import collections +from collections import namedtuple +from collections.abc import Iterator +from datetime import ( + date, + datetime, + time, + timedelta, +) +from decimal import Decimal +from fractions import Fraction +from io import StringIO +import itertools +from numbers import Number +import re +import sys +from typing import ( + Generic, + TypeVar, +) + +import numpy as np +import pytest +import pytz + +from pandas._libs import ( + lib, + missing as libmissing, + ops as libops, +) +from pandas.compat.numpy import np_version_gt2 + +from pandas.core.dtypes import inference +from pandas.core.dtypes.cast import find_result_type +from pandas.core.dtypes.common import ( + ensure_int32, + is_bool, + is_complex, + is_datetime64_any_dtype, + is_datetime64_dtype, + is_datetime64_ns_dtype, + is_datetime64tz_dtype, + is_float, + is_integer, + is_number, + is_scalar, + is_scipy_sparse, + is_timedelta64_dtype, + is_timedelta64_ns_dtype, +) + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + DateOffset, + DatetimeIndex, + Index, + Interval, + Period, + PeriodIndex, + Series, + Timedelta, + TimedeltaIndex, + Timestamp, +) +import pandas._testing as tm +from pandas.core.arrays import ( + BooleanArray, + FloatingArray, + IntegerArray, +) + + +@pytest.fixture(params=[True, False], ids=str) +def coerce(request): + return request.param + + +class MockNumpyLikeArray: + """ + A class which is numpy-like (e.g. Pint's Quantity) but not actually numpy + + The key is that it is not actually a numpy array so + ``util.is_array(mock_numpy_like_array_instance)`` returns ``False``. Other + important properties are that the class defines a :meth:`__iter__` method + (so that ``isinstance(abc.Iterable)`` returns ``True``) and has a + :meth:`ndim` property, as pandas special-cases 0-dimensional arrays in some + cases. + + We expect pandas to behave with respect to such duck arrays exactly as + with real numpy arrays. In particular, a 0-dimensional duck array is *NOT* + a scalar (`is_scalar(np.array(1)) == False`), but it is not list-like either. + """ + + def __init__(self, values) -> None: + self._values = values + + def __iter__(self) -> Iterator: + iter_values = iter(self._values) + + def it_outer(): + yield from iter_values + + return it_outer() + + def __len__(self) -> int: + return len(self._values) + + def __array__(self, dtype=None, copy=None): + return np.asarray(self._values, dtype=dtype) + + @property + def ndim(self): + return self._values.ndim + + @property + def dtype(self): + return self._values.dtype + + @property + def size(self): + return self._values.size + + @property + def shape(self): + return self._values.shape + + +# collect all objects to be tested for list-like-ness; use tuples of objects, +# whether they are list-like or not (special casing for sets), and their ID +ll_params = [ + ([1], True, "list"), + ([], True, "list-empty"), + ((1,), True, "tuple"), + ((), True, "tuple-empty"), + ({"a": 1}, True, "dict"), + ({}, True, "dict-empty"), + ({"a", 1}, "set", "set"), + (set(), "set", "set-empty"), + (frozenset({"a", 1}), "set", "frozenset"), + (frozenset(), "set", "frozenset-empty"), + (iter([1, 2]), True, "iterator"), + (iter([]), True, "iterator-empty"), + ((x for x in [1, 2]), True, "generator"), + ((_ for _ in []), True, "generator-empty"), + (Series([1]), True, "Series"), + (Series([], dtype=object), True, "Series-empty"), + # Series.str will still raise a TypeError if iterated + (Series(["a"]).str, True, "StringMethods"), + (Series([], dtype="O").str, True, "StringMethods-empty"), + (Index([1]), True, "Index"), + (Index([]), True, "Index-empty"), + (DataFrame([[1]]), True, "DataFrame"), + (DataFrame(), True, "DataFrame-empty"), + (np.ndarray((2,) * 1), True, "ndarray-1d"), + (np.array([]), True, "ndarray-1d-empty"), + (np.ndarray((2,) * 2), True, "ndarray-2d"), + (np.array([[]]), True, "ndarray-2d-empty"), + (np.ndarray((2,) * 3), True, "ndarray-3d"), + (np.array([[[]]]), True, "ndarray-3d-empty"), + (np.ndarray((2,) * 4), True, "ndarray-4d"), + (np.array([[[[]]]]), True, "ndarray-4d-empty"), + (np.array(2), False, "ndarray-0d"), + (MockNumpyLikeArray(np.ndarray((2,) * 1)), True, "duck-ndarray-1d"), + (MockNumpyLikeArray(np.array([])), True, "duck-ndarray-1d-empty"), + (MockNumpyLikeArray(np.ndarray((2,) * 2)), True, "duck-ndarray-2d"), + (MockNumpyLikeArray(np.array([[]])), True, "duck-ndarray-2d-empty"), + (MockNumpyLikeArray(np.ndarray((2,) * 3)), True, "duck-ndarray-3d"), + (MockNumpyLikeArray(np.array([[[]]])), True, "duck-ndarray-3d-empty"), + (MockNumpyLikeArray(np.ndarray((2,) * 4)), True, "duck-ndarray-4d"), + (MockNumpyLikeArray(np.array([[[[]]]])), True, "duck-ndarray-4d-empty"), + (MockNumpyLikeArray(np.array(2)), False, "duck-ndarray-0d"), + (1, False, "int"), + (b"123", False, "bytes"), + (b"", False, "bytes-empty"), + ("123", False, "string"), + ("", False, "string-empty"), + (str, False, "string-type"), + (object(), False, "object"), + (np.nan, False, "NaN"), + (None, False, "None"), +] +objs, expected, ids = zip(*ll_params) + + +@pytest.fixture(params=zip(objs, expected), ids=ids) +def maybe_list_like(request): + return request.param + + +def test_is_list_like(maybe_list_like): + obj, expected = maybe_list_like + expected = True if expected == "set" else expected + assert inference.is_list_like(obj) == expected + + +def test_is_list_like_disallow_sets(maybe_list_like): + obj, expected = maybe_list_like + expected = False if expected == "set" else expected + assert inference.is_list_like(obj, allow_sets=False) == expected + + +def test_is_list_like_recursion(): + # GH 33721 + # interpreter would crash with SIGABRT + def list_like(): + inference.is_list_like([]) + list_like() + + rec_limit = sys.getrecursionlimit() + try: + # Limit to avoid stack overflow on Windows CI + sys.setrecursionlimit(100) + with tm.external_error_raised(RecursionError): + list_like() + finally: + sys.setrecursionlimit(rec_limit) + + +def test_is_list_like_iter_is_none(): + # GH 43373 + # is_list_like was yielding false positives with __iter__ == None + class NotListLike: + def __getitem__(self, item): + return self + + __iter__ = None + + assert not inference.is_list_like(NotListLike()) + + +def test_is_list_like_generic(): + # GH 49649 + # is_list_like was yielding false positives for Generic classes in python 3.11 + T = TypeVar("T") + + class MyDataFrame(DataFrame, Generic[T]): + ... + + tstc = MyDataFrame[int] + tst = MyDataFrame[int]({"x": [1, 2, 3]}) + + assert not inference.is_list_like(tstc) + assert isinstance(tst, DataFrame) + assert inference.is_list_like(tst) + + +def test_is_sequence(): + is_seq = inference.is_sequence + assert is_seq((1, 2)) + assert is_seq([1, 2]) + assert not is_seq("abcd") + assert not is_seq(np.int64) + + class A: + def __getitem__(self, item): + return 1 + + assert not is_seq(A()) + + +def test_is_array_like(): + assert inference.is_array_like(Series([], dtype=object)) + assert inference.is_array_like(Series([1, 2])) + assert inference.is_array_like(np.array(["a", "b"])) + assert inference.is_array_like(Index(["2016-01-01"])) + assert inference.is_array_like(np.array([2, 3])) + assert inference.is_array_like(MockNumpyLikeArray(np.array([2, 3]))) + + class DtypeList(list): + dtype = "special" + + assert inference.is_array_like(DtypeList()) + + assert not inference.is_array_like([1, 2, 3]) + assert not inference.is_array_like(()) + assert not inference.is_array_like("foo") + assert not inference.is_array_like(123) + + +@pytest.mark.parametrize( + "inner", + [ + [], + [1], + (1,), + (1, 2), + {"a": 1}, + {1, "a"}, + Series([1]), + Series([], dtype=object), + Series(["a"]).str, + (x for x in range(5)), + ], +) +@pytest.mark.parametrize("outer", [list, Series, np.array, tuple]) +def test_is_nested_list_like_passes(inner, outer): + result = outer([inner for _ in range(5)]) + assert inference.is_list_like(result) + + +@pytest.mark.parametrize( + "obj", + [ + "abc", + [], + [1], + (1,), + ["a"], + "a", + {"a"}, + [1, 2, 3], + Series([1]), + DataFrame({"A": [1]}), + ([1, 2] for _ in range(5)), + ], +) +def test_is_nested_list_like_fails(obj): + assert not inference.is_nested_list_like(obj) + + +@pytest.mark.parametrize("ll", [{}, {"A": 1}, Series([1]), collections.defaultdict()]) +def test_is_dict_like_passes(ll): + assert inference.is_dict_like(ll) + + +@pytest.mark.parametrize( + "ll", + [ + "1", + 1, + [1, 2], + (1, 2), + range(2), + Index([1]), + dict, + collections.defaultdict, + Series, + ], +) +def test_is_dict_like_fails(ll): + assert not inference.is_dict_like(ll) + + +@pytest.mark.parametrize("has_keys", [True, False]) +@pytest.mark.parametrize("has_getitem", [True, False]) +@pytest.mark.parametrize("has_contains", [True, False]) +def test_is_dict_like_duck_type(has_keys, has_getitem, has_contains): + class DictLike: + def __init__(self, d) -> None: + self.d = d + + if has_keys: + + def keys(self): + return self.d.keys() + + if has_getitem: + + def __getitem__(self, key): + return self.d.__getitem__(key) + + if has_contains: + + def __contains__(self, key) -> bool: + return self.d.__contains__(key) + + d = DictLike({1: 2}) + result = inference.is_dict_like(d) + expected = has_keys and has_getitem and has_contains + + assert result is expected + + +def test_is_file_like(): + class MockFile: + pass + + is_file = inference.is_file_like + + data = StringIO("data") + assert is_file(data) + + # No read / write attributes + # No iterator attributes + m = MockFile() + assert not is_file(m) + + MockFile.write = lambda self: 0 + + # Write attribute but not an iterator + m = MockFile() + assert not is_file(m) + + # gh-16530: Valid iterator just means we have the + # __iter__ attribute for our purposes. + MockFile.__iter__ = lambda self: self + + # Valid write-only file + m = MockFile() + assert is_file(m) + + del MockFile.write + MockFile.read = lambda self: 0 + + # Valid read-only file + m = MockFile() + assert is_file(m) + + # Iterator but no read / write attributes + data = [1, 2, 3] + assert not is_file(data) + + +test_tuple = collections.namedtuple("test_tuple", ["a", "b", "c"]) + + +@pytest.mark.parametrize("ll", [test_tuple(1, 2, 3)]) +def test_is_names_tuple_passes(ll): + assert inference.is_named_tuple(ll) + + +@pytest.mark.parametrize("ll", [(1, 2, 3), "a", Series({"pi": 3.14})]) +def test_is_names_tuple_fails(ll): + assert not inference.is_named_tuple(ll) + + +def test_is_hashable(): + # all new-style classes are hashable by default + class HashableClass: + pass + + class UnhashableClass1: + __hash__ = None + + class UnhashableClass2: + def __hash__(self): + raise TypeError("Not hashable") + + hashable = (1, 3.14, np.float64(3.14), "a", (), (1,), HashableClass()) + not_hashable = ([], UnhashableClass1()) + abc_hashable_not_really_hashable = (([],), UnhashableClass2()) + + for i in hashable: + assert inference.is_hashable(i) + for i in not_hashable: + assert not inference.is_hashable(i) + for i in abc_hashable_not_really_hashable: + assert not inference.is_hashable(i) + + # numpy.array is no longer collections.abc.Hashable as of + # https://github.com/numpy/numpy/pull/5326, just test + # is_hashable() + assert not inference.is_hashable(np.array([])) + + +@pytest.mark.parametrize("ll", [re.compile("ad")]) +def test_is_re_passes(ll): + assert inference.is_re(ll) + + +@pytest.mark.parametrize("ll", ["x", 2, 3, object()]) +def test_is_re_fails(ll): + assert not inference.is_re(ll) + + +@pytest.mark.parametrize( + "ll", [r"a", "x", r"asdf", re.compile("adsf"), r"\u2233\s*", re.compile(r"")] +) +def test_is_recompilable_passes(ll): + assert inference.is_re_compilable(ll) + + +@pytest.mark.parametrize("ll", [1, [], object()]) +def test_is_recompilable_fails(ll): + assert not inference.is_re_compilable(ll) + + +class TestInference: + @pytest.mark.parametrize( + "arr", + [ + np.array(list("abc"), dtype="S1"), + np.array(list("abc"), dtype="S1").astype(object), + [b"a", np.nan, b"c"], + ], + ) + def test_infer_dtype_bytes(self, arr): + result = lib.infer_dtype(arr, skipna=True) + assert result == "bytes" + + @pytest.mark.parametrize( + "value, expected", + [ + (float("inf"), True), + (np.inf, True), + (-np.inf, False), + (1, False), + ("a", False), + ], + ) + def test_isposinf_scalar(self, value, expected): + # GH 11352 + result = libmissing.isposinf_scalar(value) + assert result is expected + + @pytest.mark.parametrize( + "value, expected", + [ + (float("-inf"), True), + (-np.inf, True), + (np.inf, False), + (1, False), + ("a", False), + ], + ) + def test_isneginf_scalar(self, value, expected): + result = libmissing.isneginf_scalar(value) + assert result is expected + + @pytest.mark.parametrize( + "convert_to_masked_nullable, exp", + [ + ( + True, + BooleanArray( + np.array([True, False], dtype="bool"), np.array([False, True]) + ), + ), + (False, np.array([True, np.nan], dtype="object")), + ], + ) + def test_maybe_convert_nullable_boolean(self, convert_to_masked_nullable, exp): + # GH 40687 + arr = np.array([True, np.nan], dtype=object) + result = libops.maybe_convert_bool( + arr, set(), convert_to_masked_nullable=convert_to_masked_nullable + ) + if convert_to_masked_nullable: + tm.assert_extension_array_equal(BooleanArray(*result), exp) + else: + result = result[0] + tm.assert_numpy_array_equal(result, exp) + + @pytest.mark.parametrize("convert_to_masked_nullable", [True, False]) + @pytest.mark.parametrize("coerce_numeric", [True, False]) + @pytest.mark.parametrize( + "infinity", ["inf", "inF", "iNf", "Inf", "iNF", "InF", "INf", "INF"] + ) + @pytest.mark.parametrize("prefix", ["", "-", "+"]) + def test_maybe_convert_numeric_infinities( + self, coerce_numeric, infinity, prefix, convert_to_masked_nullable + ): + # see gh-13274 + result, _ = lib.maybe_convert_numeric( + np.array([prefix + infinity], dtype=object), + na_values={"", "NULL", "nan"}, + coerce_numeric=coerce_numeric, + convert_to_masked_nullable=convert_to_masked_nullable, + ) + expected = np.array([np.inf if prefix in ["", "+"] else -np.inf]) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("convert_to_masked_nullable", [True, False]) + def test_maybe_convert_numeric_infinities_raises(self, convert_to_masked_nullable): + msg = "Unable to parse string" + with pytest.raises(ValueError, match=msg): + lib.maybe_convert_numeric( + np.array(["foo_inf"], dtype=object), + na_values={"", "NULL", "nan"}, + coerce_numeric=False, + convert_to_masked_nullable=convert_to_masked_nullable, + ) + + @pytest.mark.parametrize("convert_to_masked_nullable", [True, False]) + def test_maybe_convert_numeric_post_floatify_nan( + self, coerce, convert_to_masked_nullable + ): + # see gh-13314 + data = np.array(["1.200", "-999.000", "4.500"], dtype=object) + expected = np.array([1.2, np.nan, 4.5], dtype=np.float64) + nan_values = {-999, -999.0} + + out = lib.maybe_convert_numeric( + data, + nan_values, + coerce, + convert_to_masked_nullable=convert_to_masked_nullable, + ) + if convert_to_masked_nullable: + expected = FloatingArray(expected, np.isnan(expected)) + tm.assert_extension_array_equal(expected, FloatingArray(*out)) + else: + out = out[0] + tm.assert_numpy_array_equal(out, expected) + + def test_convert_infs(self): + arr = np.array(["inf", "inf", "inf"], dtype="O") + result, _ = lib.maybe_convert_numeric(arr, set(), False) + assert result.dtype == np.float64 + + arr = np.array(["-inf", "-inf", "-inf"], dtype="O") + result, _ = lib.maybe_convert_numeric(arr, set(), False) + assert result.dtype == np.float64 + + def test_scientific_no_exponent(self): + # See PR 12215 + arr = np.array(["42E", "2E", "99e", "6e"], dtype="O") + result, _ = lib.maybe_convert_numeric(arr, set(), False, True) + assert np.all(np.isnan(result)) + + def test_convert_non_hashable(self): + # GH13324 + # make sure that we are handing non-hashables + arr = np.array([[10.0, 2], 1.0, "apple"], dtype=object) + result, _ = lib.maybe_convert_numeric(arr, set(), False, True) + tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan])) + + def test_convert_numeric_uint64(self): + arr = np.array([2**63], dtype=object) + exp = np.array([2**63], dtype=np.uint64) + tm.assert_numpy_array_equal(lib.maybe_convert_numeric(arr, set())[0], exp) + + arr = np.array([str(2**63)], dtype=object) + exp = np.array([2**63], dtype=np.uint64) + tm.assert_numpy_array_equal(lib.maybe_convert_numeric(arr, set())[0], exp) + + arr = np.array([np.uint64(2**63)], dtype=object) + exp = np.array([2**63], dtype=np.uint64) + tm.assert_numpy_array_equal(lib.maybe_convert_numeric(arr, set())[0], exp) + + @pytest.mark.parametrize( + "arr", + [ + np.array([2**63, np.nan], dtype=object), + np.array([str(2**63), np.nan], dtype=object), + np.array([np.nan, 2**63], dtype=object), + np.array([np.nan, str(2**63)], dtype=object), + ], + ) + def test_convert_numeric_uint64_nan(self, coerce, arr): + expected = arr.astype(float) if coerce else arr.copy() + result, _ = lib.maybe_convert_numeric(arr, set(), coerce_numeric=coerce) + tm.assert_almost_equal(result, expected) + + @pytest.mark.parametrize("convert_to_masked_nullable", [True, False]) + def test_convert_numeric_uint64_nan_values( + self, coerce, convert_to_masked_nullable + ): + arr = np.array([2**63, 2**63 + 1], dtype=object) + na_values = {2**63} + + expected = ( + np.array([np.nan, 2**63 + 1], dtype=float) if coerce else arr.copy() + ) + result = lib.maybe_convert_numeric( + arr, + na_values, + coerce_numeric=coerce, + convert_to_masked_nullable=convert_to_masked_nullable, + ) + if convert_to_masked_nullable and coerce: + expected = IntegerArray( + np.array([0, 2**63 + 1], dtype="u8"), + np.array([True, False], dtype="bool"), + ) + result = IntegerArray(*result) + else: + result = result[0] # discard mask + tm.assert_almost_equal(result, expected) + + @pytest.mark.parametrize( + "case", + [ + np.array([2**63, -1], dtype=object), + np.array([str(2**63), -1], dtype=object), + np.array([str(2**63), str(-1)], dtype=object), + np.array([-1, 2**63], dtype=object), + np.array([-1, str(2**63)], dtype=object), + np.array([str(-1), str(2**63)], dtype=object), + ], + ) + @pytest.mark.parametrize("convert_to_masked_nullable", [True, False]) + def test_convert_numeric_int64_uint64( + self, case, coerce, convert_to_masked_nullable + ): + expected = case.astype(float) if coerce else case.copy() + result, _ = lib.maybe_convert_numeric( + case, + set(), + coerce_numeric=coerce, + convert_to_masked_nullable=convert_to_masked_nullable, + ) + + tm.assert_almost_equal(result, expected) + + @pytest.mark.parametrize("convert_to_masked_nullable", [True, False]) + def test_convert_numeric_string_uint64(self, convert_to_masked_nullable): + # GH32394 + result = lib.maybe_convert_numeric( + np.array(["uint64"], dtype=object), + set(), + coerce_numeric=True, + convert_to_masked_nullable=convert_to_masked_nullable, + ) + if convert_to_masked_nullable: + result = FloatingArray(*result) + else: + result = result[0] + assert np.isnan(result) + + @pytest.mark.parametrize("value", [-(2**63) - 1, 2**64]) + def test_convert_int_overflow(self, value): + # see gh-18584 + arr = np.array([value], dtype=object) + result = lib.maybe_convert_objects(arr) + tm.assert_numpy_array_equal(arr, result) + + @pytest.mark.parametrize("val", [None, np.nan, float("nan")]) + @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"]) + def test_maybe_convert_objects_nat_inference(self, val, dtype): + dtype = np.dtype(dtype) + vals = np.array([pd.NaT, val], dtype=object) + result = lib.maybe_convert_objects( + vals, + convert_non_numeric=True, + dtype_if_all_nat=dtype, + ) + assert result.dtype == dtype + assert np.isnat(result).all() + + result = lib.maybe_convert_objects( + vals[::-1], + convert_non_numeric=True, + dtype_if_all_nat=dtype, + ) + assert result.dtype == dtype + assert np.isnat(result).all() + + @pytest.mark.parametrize( + "value, expected_dtype", + [ + # see gh-4471 + ([2**63], np.uint64), + # NumPy bug: can't compare uint64 to int64, as that + # results in both casting to float64, so we should + # make sure that this function is robust against it + ([np.uint64(2**63)], np.uint64), + ([2, -1], np.int64), + ([2**63, -1], object), + # GH#47294 + ([np.uint8(1)], np.uint8), + ([np.uint16(1)], np.uint16), + ([np.uint32(1)], np.uint32), + ([np.uint64(1)], np.uint64), + ([np.uint8(2), np.uint16(1)], np.uint16), + ([np.uint32(2), np.uint16(1)], np.uint32), + ([np.uint32(2), -1], object), + ([np.uint32(2), 1], np.uint64), + ([np.uint32(2), np.int32(1)], object), + ], + ) + def test_maybe_convert_objects_uint(self, value, expected_dtype): + arr = np.array(value, dtype=object) + exp = np.array(value, dtype=expected_dtype) + tm.assert_numpy_array_equal(lib.maybe_convert_objects(arr), exp) + + def test_maybe_convert_objects_datetime(self): + # GH27438 + arr = np.array( + [np.datetime64("2000-01-01"), np.timedelta64(1, "s")], dtype=object + ) + exp = arr.copy() + out = lib.maybe_convert_objects(arr, convert_non_numeric=True) + tm.assert_numpy_array_equal(out, exp) + + arr = np.array([pd.NaT, np.timedelta64(1, "s")], dtype=object) + exp = np.array([np.timedelta64("NaT"), np.timedelta64(1, "s")], dtype="m8[ns]") + out = lib.maybe_convert_objects(arr, convert_non_numeric=True) + tm.assert_numpy_array_equal(out, exp) + + # with convert_non_numeric=True, the nan is a valid NA value for td64 + arr = np.array([np.timedelta64(1, "s"), np.nan], dtype=object) + exp = exp[::-1] + out = lib.maybe_convert_objects(arr, convert_non_numeric=True) + tm.assert_numpy_array_equal(out, exp) + + def test_maybe_convert_objects_dtype_if_all_nat(self): + arr = np.array([pd.NaT, pd.NaT], dtype=object) + out = lib.maybe_convert_objects(arr, convert_non_numeric=True) + # no dtype_if_all_nat passed -> we dont guess + tm.assert_numpy_array_equal(out, arr) + + out = lib.maybe_convert_objects( + arr, + convert_non_numeric=True, + dtype_if_all_nat=np.dtype("timedelta64[ns]"), + ) + exp = np.array(["NaT", "NaT"], dtype="timedelta64[ns]") + tm.assert_numpy_array_equal(out, exp) + + out = lib.maybe_convert_objects( + arr, + convert_non_numeric=True, + dtype_if_all_nat=np.dtype("datetime64[ns]"), + ) + exp = np.array(["NaT", "NaT"], dtype="datetime64[ns]") + tm.assert_numpy_array_equal(out, exp) + + def test_maybe_convert_objects_dtype_if_all_nat_invalid(self): + # we accept datetime64[ns], timedelta64[ns], and EADtype + arr = np.array([pd.NaT, pd.NaT], dtype=object) + + with pytest.raises(ValueError, match="int64"): + lib.maybe_convert_objects( + arr, + convert_non_numeric=True, + dtype_if_all_nat=np.dtype("int64"), + ) + + @pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"]) + def test_maybe_convert_objects_datetime_overflow_safe(self, dtype): + stamp = datetime(2363, 10, 4) # Enterprise-D launch date + if dtype == "timedelta64[ns]": + stamp = stamp - datetime(1970, 1, 1) + arr = np.array([stamp], dtype=object) + + out = lib.maybe_convert_objects(arr, convert_non_numeric=True) + # no OutOfBoundsDatetime/OutOfBoundsTimedeltas + tm.assert_numpy_array_equal(out, arr) + + def test_maybe_convert_objects_mixed_datetimes(self): + ts = Timestamp("now") + vals = [ts, ts.to_pydatetime(), ts.to_datetime64(), pd.NaT, np.nan, None] + + for data in itertools.permutations(vals): + data = np.array(list(data), dtype=object) + expected = DatetimeIndex(data)._data._ndarray + result = lib.maybe_convert_objects(data, convert_non_numeric=True) + tm.assert_numpy_array_equal(result, expected) + + def test_maybe_convert_objects_timedelta64_nat(self): + obj = np.timedelta64("NaT", "ns") + arr = np.array([obj], dtype=object) + assert arr[0] is obj + + result = lib.maybe_convert_objects(arr, convert_non_numeric=True) + + expected = np.array([obj], dtype="m8[ns]") + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize( + "exp", + [ + IntegerArray(np.array([2, 0], dtype="i8"), np.array([False, True])), + IntegerArray(np.array([2, 0], dtype="int64"), np.array([False, True])), + ], + ) + def test_maybe_convert_objects_nullable_integer(self, exp): + # GH27335 + arr = np.array([2, np.nan], dtype=object) + result = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True) + + tm.assert_extension_array_equal(result, exp) + + @pytest.mark.parametrize( + "dtype, val", [("int64", 1), ("uint64", np.iinfo(np.int64).max + 1)] + ) + def test_maybe_convert_objects_nullable_none(self, dtype, val): + # GH#50043 + arr = np.array([val, None, 3], dtype="object") + result = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True) + expected = IntegerArray( + np.array([val, 0, 3], dtype=dtype), np.array([False, True, False]) + ) + tm.assert_extension_array_equal(result, expected) + + @pytest.mark.parametrize( + "convert_to_masked_nullable, exp", + [ + (True, IntegerArray(np.array([2, 0], dtype="i8"), np.array([False, True]))), + (False, np.array([2, np.nan], dtype="float64")), + ], + ) + def test_maybe_convert_numeric_nullable_integer( + self, convert_to_masked_nullable, exp + ): + # GH 40687 + arr = np.array([2, np.nan], dtype=object) + result = lib.maybe_convert_numeric( + arr, set(), convert_to_masked_nullable=convert_to_masked_nullable + ) + if convert_to_masked_nullable: + result = IntegerArray(*result) + tm.assert_extension_array_equal(result, exp) + else: + result = result[0] + tm.assert_numpy_array_equal(result, exp) + + @pytest.mark.parametrize( + "convert_to_masked_nullable, exp", + [ + ( + True, + FloatingArray( + np.array([2.0, 0.0], dtype="float64"), np.array([False, True]) + ), + ), + (False, np.array([2.0, np.nan], dtype="float64")), + ], + ) + def test_maybe_convert_numeric_floating_array( + self, convert_to_masked_nullable, exp + ): + # GH 40687 + arr = np.array([2.0, np.nan], dtype=object) + result = lib.maybe_convert_numeric( + arr, set(), convert_to_masked_nullable=convert_to_masked_nullable + ) + if convert_to_masked_nullable: + tm.assert_extension_array_equal(FloatingArray(*result), exp) + else: + result = result[0] + tm.assert_numpy_array_equal(result, exp) + + def test_maybe_convert_objects_bool_nan(self): + # GH32146 + ind = Index([True, False, np.nan], dtype=object) + exp = np.array([True, False, np.nan], dtype=object) + out = lib.maybe_convert_objects(ind.values, safe=1) + tm.assert_numpy_array_equal(out, exp) + + def test_maybe_convert_objects_nullable_boolean(self): + # GH50047 + arr = np.array([True, False], dtype=object) + exp = np.array([True, False]) + out = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True) + tm.assert_numpy_array_equal(out, exp) + + arr = np.array([True, False, pd.NaT], dtype=object) + exp = np.array([True, False, pd.NaT], dtype=object) + out = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True) + tm.assert_numpy_array_equal(out, exp) + + @pytest.mark.parametrize("val", [None, np.nan]) + def test_maybe_convert_objects_nullable_boolean_na(self, val): + # GH50047 + arr = np.array([True, False, val], dtype=object) + exp = BooleanArray( + np.array([True, False, False]), np.array([False, False, True]) + ) + out = lib.maybe_convert_objects(arr, convert_to_nullable_dtype=True) + tm.assert_extension_array_equal(out, exp) + + @pytest.mark.parametrize( + "data0", + [ + True, + 1, + 1.0, + 1.0 + 1.0j, + np.int8(1), + np.int16(1), + np.int32(1), + np.int64(1), + np.float16(1), + np.float32(1), + np.float64(1), + np.complex64(1), + np.complex128(1), + ], + ) + @pytest.mark.parametrize( + "data1", + [ + True, + 1, + 1.0, + 1.0 + 1.0j, + np.int8(1), + np.int16(1), + np.int32(1), + np.int64(1), + np.float16(1), + np.float32(1), + np.float64(1), + np.complex64(1), + np.complex128(1), + ], + ) + def test_maybe_convert_objects_itemsize(self, data0, data1): + # GH 40908 + data = [data0, data1] + arr = np.array(data, dtype="object") + + common_kind = np.result_type(type(data0), type(data1)).kind + kind0 = "python" if not hasattr(data0, "dtype") else data0.dtype.kind + kind1 = "python" if not hasattr(data1, "dtype") else data1.dtype.kind + if kind0 != "python" and kind1 != "python": + kind = common_kind + itemsize = max(data0.dtype.itemsize, data1.dtype.itemsize) + elif is_bool(data0) or is_bool(data1): + kind = "bool" if (is_bool(data0) and is_bool(data1)) else "object" + itemsize = "" + elif is_complex(data0) or is_complex(data1): + kind = common_kind + itemsize = 16 + else: + kind = common_kind + itemsize = 8 + + expected = np.array(data, dtype=f"{kind}{itemsize}") + result = lib.maybe_convert_objects(arr) + tm.assert_numpy_array_equal(result, expected) + + def test_mixed_dtypes_remain_object_array(self): + # GH14956 + arr = np.array([datetime(2015, 1, 1, tzinfo=pytz.utc), 1], dtype=object) + result = lib.maybe_convert_objects(arr, convert_non_numeric=True) + tm.assert_numpy_array_equal(result, arr) + + @pytest.mark.parametrize( + "idx", + [ + pd.IntervalIndex.from_breaks(range(5), closed="both"), + pd.period_range("2016-01-01", periods=3, freq="D"), + ], + ) + def test_maybe_convert_objects_ea(self, idx): + result = lib.maybe_convert_objects( + np.array(idx, dtype=object), + convert_non_numeric=True, + ) + tm.assert_extension_array_equal(result, idx._data) + + +class TestTypeInference: + # Dummy class used for testing with Python objects + class Dummy: + pass + + def test_inferred_dtype_fixture(self, any_skipna_inferred_dtype): + # see pandas/conftest.py + inferred_dtype, values = any_skipna_inferred_dtype + + # make sure the inferred dtype of the fixture is as requested + assert inferred_dtype == lib.infer_dtype(values, skipna=True) + + @pytest.mark.parametrize("skipna", [True, False]) + def test_length_zero(self, skipna): + result = lib.infer_dtype(np.array([], dtype="i4"), skipna=skipna) + assert result == "integer" + + result = lib.infer_dtype([], skipna=skipna) + assert result == "empty" + + # GH 18004 + arr = np.array([np.array([], dtype=object), np.array([], dtype=object)]) + result = lib.infer_dtype(arr, skipna=skipna) + assert result == "empty" + + def test_integers(self): + arr = np.array([1, 2, 3, np.int64(4), np.int32(5)], dtype="O") + result = lib.infer_dtype(arr, skipna=True) + assert result == "integer" + + arr = np.array([1, 2, 3, np.int64(4), np.int32(5), "foo"], dtype="O") + result = lib.infer_dtype(arr, skipna=True) + assert result == "mixed-integer" + + arr = np.array([1, 2, 3, 4, 5], dtype="i4") + result = lib.infer_dtype(arr, skipna=True) + assert result == "integer" + + @pytest.mark.parametrize( + "arr, skipna", + [ + (np.array([1, 2, np.nan, np.nan, 3], dtype="O"), False), + (np.array([1, 2, np.nan, np.nan, 3], dtype="O"), True), + (np.array([1, 2, 3, np.int64(4), np.int32(5), np.nan], dtype="O"), False), + (np.array([1, 2, 3, np.int64(4), np.int32(5), np.nan], dtype="O"), True), + ], + ) + def test_integer_na(self, arr, skipna): + # GH 27392 + result = lib.infer_dtype(arr, skipna=skipna) + expected = "integer" if skipna else "integer-na" + assert result == expected + + def test_infer_dtype_skipna_default(self): + # infer_dtype `skipna` default deprecated in GH#24050, + # changed to True in GH#29876 + arr = np.array([1, 2, 3, np.nan], dtype=object) + + result = lib.infer_dtype(arr) + assert result == "integer" + + def test_bools(self): + arr = np.array([True, False, True, True, True], dtype="O") + result = lib.infer_dtype(arr, skipna=True) + assert result == "boolean" + + arr = np.array([np.bool_(True), np.bool_(False)], dtype="O") + result = lib.infer_dtype(arr, skipna=True) + assert result == "boolean" + + arr = np.array([True, False, True, "foo"], dtype="O") + result = lib.infer_dtype(arr, skipna=True) + assert result == "mixed" + + arr = np.array([True, False, True], dtype=bool) + result = lib.infer_dtype(arr, skipna=True) + assert result == "boolean" + + arr = np.array([True, np.nan, False], dtype="O") + result = lib.infer_dtype(arr, skipna=True) + assert result == "boolean" + + result = lib.infer_dtype(arr, skipna=False) + assert result == "mixed" + + def test_floats(self): + arr = np.array([1.0, 2.0, 3.0, np.float64(4), np.float32(5)], dtype="O") + result = lib.infer_dtype(arr, skipna=True) + assert result == "floating" + + arr = np.array([1, 2, 3, np.float64(4), np.float32(5), "foo"], dtype="O") + result = lib.infer_dtype(arr, skipna=True) + assert result == "mixed-integer" + + arr = np.array([1, 2, 3, 4, 5], dtype="f4") + result = lib.infer_dtype(arr, skipna=True) + assert result == "floating" + + arr = np.array([1, 2, 3, 4, 5], dtype="f8") + result = lib.infer_dtype(arr, skipna=True) + assert result == "floating" + + def test_decimals(self): + # GH15690 + arr = np.array([Decimal(1), Decimal(2), Decimal(3)]) + result = lib.infer_dtype(arr, skipna=True) + assert result == "decimal" + + arr = np.array([1.0, 2.0, Decimal(3)]) + result = lib.infer_dtype(arr, skipna=True) + assert result == "mixed" + + result = lib.infer_dtype(arr[::-1], skipna=True) + assert result == "mixed" + + arr = np.array([Decimal(1), Decimal("NaN"), Decimal(3)]) + result = lib.infer_dtype(arr, skipna=True) + assert result == "decimal" + + arr = np.array([Decimal(1), np.nan, Decimal(3)], dtype="O") + result = lib.infer_dtype(arr, skipna=True) + assert result == "decimal" + + # complex is compatible with nan, so skipna has no effect + @pytest.mark.parametrize("skipna", [True, False]) + def test_complex(self, skipna): + # gets cast to complex on array construction + arr = np.array([1.0, 2.0, 1 + 1j]) + result = lib.infer_dtype(arr, skipna=skipna) + assert result == "complex" + + arr = np.array([1.0, 2.0, 1 + 1j], dtype="O") + result = lib.infer_dtype(arr, skipna=skipna) + assert result == "mixed" + + result = lib.infer_dtype(arr[::-1], skipna=skipna) + assert result == "mixed" + + # gets cast to complex on array construction + arr = np.array([1, np.nan, 1 + 1j]) + result = lib.infer_dtype(arr, skipna=skipna) + assert result == "complex" + + arr = np.array([1.0, np.nan, 1 + 1j], dtype="O") + result = lib.infer_dtype(arr, skipna=skipna) + assert result == "mixed" + + # complex with nans stays complex + arr = np.array([1 + 1j, np.nan, 3 + 3j], dtype="O") + result = lib.infer_dtype(arr, skipna=skipna) + assert result == "complex" + + # test smaller complex dtype; will pass through _try_infer_map fastpath + arr = np.array([1 + 1j, np.nan, 3 + 3j], dtype=np.complex64) + result = lib.infer_dtype(arr, skipna=skipna) + assert result == "complex" + + def test_string(self): + pass + + def test_unicode(self): + arr = ["a", np.nan, "c"] + result = lib.infer_dtype(arr, skipna=False) + # This currently returns "mixed", but it's not clear that's optimal. + # This could also return "string" or "mixed-string" + assert result == "mixed" + + # even though we use skipna, we are only skipping those NAs that are + # considered matching by is_string_array + arr = ["a", np.nan, "c"] + result = lib.infer_dtype(arr, skipna=True) + assert result == "string" + + arr = ["a", pd.NA, "c"] + result = lib.infer_dtype(arr, skipna=True) + assert result == "string" + + arr = ["a", pd.NaT, "c"] + result = lib.infer_dtype(arr, skipna=True) + assert result == "mixed" + + arr = ["a", "c"] + result = lib.infer_dtype(arr, skipna=False) + assert result == "string" + + @pytest.mark.parametrize( + "dtype, missing, skipna, expected", + [ + (float, np.nan, False, "floating"), + (float, np.nan, True, "floating"), + (object, np.nan, False, "floating"), + (object, np.nan, True, "empty"), + (object, None, False, "mixed"), + (object, None, True, "empty"), + ], + ) + @pytest.mark.parametrize("box", [Series, np.array]) + def test_object_empty(self, box, missing, dtype, skipna, expected): + # GH 23421 + arr = box([missing, missing], dtype=dtype) + + result = lib.infer_dtype(arr, skipna=skipna) + assert result == expected + + def test_datetime(self): + dates = [datetime(2012, 1, x) for x in range(1, 20)] + index = Index(dates) + assert index.inferred_type == "datetime64" + + def test_infer_dtype_datetime64(self): + arr = np.array( + [np.datetime64("2011-01-01"), np.datetime64("2011-01-01")], dtype=object + ) + assert lib.infer_dtype(arr, skipna=True) == "datetime64" + + @pytest.mark.parametrize("na_value", [pd.NaT, np.nan]) + def test_infer_dtype_datetime64_with_na(self, na_value): + # starts with nan + arr = np.array([na_value, np.datetime64("2011-01-02")]) + assert lib.infer_dtype(arr, skipna=True) == "datetime64" + + arr = np.array([na_value, np.datetime64("2011-01-02"), na_value]) + assert lib.infer_dtype(arr, skipna=True) == "datetime64" + + @pytest.mark.parametrize( + "arr", + [ + np.array( + [np.timedelta64("nat"), np.datetime64("2011-01-02")], dtype=object + ), + np.array( + [np.datetime64("2011-01-02"), np.timedelta64("nat")], dtype=object + ), + np.array([np.datetime64("2011-01-01"), Timestamp("2011-01-02")]), + np.array([Timestamp("2011-01-02"), np.datetime64("2011-01-01")]), + np.array([np.nan, Timestamp("2011-01-02"), 1.1]), + np.array([np.nan, "2011-01-01", Timestamp("2011-01-02")], dtype=object), + np.array([np.datetime64("nat"), np.timedelta64(1, "D")], dtype=object), + np.array([np.timedelta64(1, "D"), np.datetime64("nat")], dtype=object), + ], + ) + def test_infer_datetimelike_dtype_mixed(self, arr): + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + def test_infer_dtype_mixed_integer(self): + arr = np.array([np.nan, Timestamp("2011-01-02"), 1]) + assert lib.infer_dtype(arr, skipna=True) == "mixed-integer" + + @pytest.mark.parametrize( + "arr", + [ + np.array([Timestamp("2011-01-01"), Timestamp("2011-01-02")]), + np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)]), + np.array([datetime(2011, 1, 1), Timestamp("2011-01-02")]), + ], + ) + def test_infer_dtype_datetime(self, arr): + assert lib.infer_dtype(arr, skipna=True) == "datetime" + + @pytest.mark.parametrize("na_value", [pd.NaT, np.nan]) + @pytest.mark.parametrize( + "time_stamp", [Timestamp("2011-01-01"), datetime(2011, 1, 1)] + ) + def test_infer_dtype_datetime_with_na(self, na_value, time_stamp): + # starts with nan + arr = np.array([na_value, time_stamp]) + assert lib.infer_dtype(arr, skipna=True) == "datetime" + + arr = np.array([na_value, time_stamp, na_value]) + assert lib.infer_dtype(arr, skipna=True) == "datetime" + + @pytest.mark.parametrize( + "arr", + [ + np.array([Timedelta("1 days"), Timedelta("2 days")]), + np.array([np.timedelta64(1, "D"), np.timedelta64(2, "D")], dtype=object), + np.array([timedelta(1), timedelta(2)]), + ], + ) + def test_infer_dtype_timedelta(self, arr): + assert lib.infer_dtype(arr, skipna=True) == "timedelta" + + @pytest.mark.parametrize("na_value", [pd.NaT, np.nan]) + @pytest.mark.parametrize( + "delta", [Timedelta("1 days"), np.timedelta64(1, "D"), timedelta(1)] + ) + def test_infer_dtype_timedelta_with_na(self, na_value, delta): + # starts with nan + arr = np.array([na_value, delta]) + assert lib.infer_dtype(arr, skipna=True) == "timedelta" + + arr = np.array([na_value, delta, na_value]) + assert lib.infer_dtype(arr, skipna=True) == "timedelta" + + def test_infer_dtype_period(self): + # GH 13664 + arr = np.array([Period("2011-01", freq="D"), Period("2011-02", freq="D")]) + assert lib.infer_dtype(arr, skipna=True) == "period" + + # non-homogeneous freqs -> mixed + arr = np.array([Period("2011-01", freq="D"), Period("2011-02", freq="M")]) + assert lib.infer_dtype(arr, skipna=True) == "mixed" + + @pytest.mark.parametrize("klass", [pd.array, Series, Index]) + @pytest.mark.parametrize("skipna", [True, False]) + def test_infer_dtype_period_array(self, klass, skipna): + # https://github.com/pandas-dev/pandas/issues/23553 + values = klass( + [ + Period("2011-01-01", freq="D"), + Period("2011-01-02", freq="D"), + pd.NaT, + ] + ) + assert lib.infer_dtype(values, skipna=skipna) == "period" + + # periods but mixed freq + values = klass( + [ + Period("2011-01-01", freq="D"), + Period("2011-01-02", freq="M"), + pd.NaT, + ] + ) + # with pd.array this becomes NumpyExtensionArray which ends up + # as "unknown-array" + exp = "unknown-array" if klass is pd.array else "mixed" + assert lib.infer_dtype(values, skipna=skipna) == exp + + def test_infer_dtype_period_mixed(self): + arr = np.array( + [Period("2011-01", freq="M"), np.datetime64("nat")], dtype=object + ) + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + arr = np.array( + [np.datetime64("nat"), Period("2011-01", freq="M")], dtype=object + ) + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + @pytest.mark.parametrize("na_value", [pd.NaT, np.nan]) + def test_infer_dtype_period_with_na(self, na_value): + # starts with nan + arr = np.array([na_value, Period("2011-01", freq="D")]) + assert lib.infer_dtype(arr, skipna=True) == "period" + + arr = np.array([na_value, Period("2011-01", freq="D"), na_value]) + assert lib.infer_dtype(arr, skipna=True) == "period" + + def test_infer_dtype_all_nan_nat_like(self): + arr = np.array([np.nan, np.nan]) + assert lib.infer_dtype(arr, skipna=True) == "floating" + + # nan and None mix are result in mixed + arr = np.array([np.nan, np.nan, None]) + assert lib.infer_dtype(arr, skipna=True) == "empty" + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + arr = np.array([None, np.nan, np.nan]) + assert lib.infer_dtype(arr, skipna=True) == "empty" + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + # pd.NaT + arr = np.array([pd.NaT]) + assert lib.infer_dtype(arr, skipna=False) == "datetime" + + arr = np.array([pd.NaT, np.nan]) + assert lib.infer_dtype(arr, skipna=False) == "datetime" + + arr = np.array([np.nan, pd.NaT]) + assert lib.infer_dtype(arr, skipna=False) == "datetime" + + arr = np.array([np.nan, pd.NaT, np.nan]) + assert lib.infer_dtype(arr, skipna=False) == "datetime" + + arr = np.array([None, pd.NaT, None]) + assert lib.infer_dtype(arr, skipna=False) == "datetime" + + # np.datetime64(nat) + arr = np.array([np.datetime64("nat")]) + assert lib.infer_dtype(arr, skipna=False) == "datetime64" + + for n in [np.nan, pd.NaT, None]: + arr = np.array([n, np.datetime64("nat"), n]) + assert lib.infer_dtype(arr, skipna=False) == "datetime64" + + arr = np.array([pd.NaT, n, np.datetime64("nat"), n]) + assert lib.infer_dtype(arr, skipna=False) == "datetime64" + + arr = np.array([np.timedelta64("nat")], dtype=object) + assert lib.infer_dtype(arr, skipna=False) == "timedelta" + + for n in [np.nan, pd.NaT, None]: + arr = np.array([n, np.timedelta64("nat"), n]) + assert lib.infer_dtype(arr, skipna=False) == "timedelta" + + arr = np.array([pd.NaT, n, np.timedelta64("nat"), n]) + assert lib.infer_dtype(arr, skipna=False) == "timedelta" + + # datetime / timedelta mixed + arr = np.array([pd.NaT, np.datetime64("nat"), np.timedelta64("nat"), np.nan]) + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + arr = np.array([np.timedelta64("nat"), np.datetime64("nat")], dtype=object) + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + def test_is_datetimelike_array_all_nan_nat_like(self): + arr = np.array([np.nan, pd.NaT, np.datetime64("nat")]) + assert lib.is_datetime_array(arr) + assert lib.is_datetime64_array(arr) + assert not lib.is_timedelta_or_timedelta64_array(arr) + + arr = np.array([np.nan, pd.NaT, np.timedelta64("nat")]) + assert not lib.is_datetime_array(arr) + assert not lib.is_datetime64_array(arr) + assert lib.is_timedelta_or_timedelta64_array(arr) + + arr = np.array([np.nan, pd.NaT, np.datetime64("nat"), np.timedelta64("nat")]) + assert not lib.is_datetime_array(arr) + assert not lib.is_datetime64_array(arr) + assert not lib.is_timedelta_or_timedelta64_array(arr) + + arr = np.array([np.nan, pd.NaT]) + assert lib.is_datetime_array(arr) + assert lib.is_datetime64_array(arr) + assert lib.is_timedelta_or_timedelta64_array(arr) + + arr = np.array([np.nan, np.nan], dtype=object) + assert not lib.is_datetime_array(arr) + assert not lib.is_datetime64_array(arr) + assert not lib.is_timedelta_or_timedelta64_array(arr) + + assert lib.is_datetime_with_singletz_array( + np.array( + [ + Timestamp("20130101", tz="US/Eastern"), + Timestamp("20130102", tz="US/Eastern"), + ], + dtype=object, + ) + ) + assert not lib.is_datetime_with_singletz_array( + np.array( + [ + Timestamp("20130101", tz="US/Eastern"), + Timestamp("20130102", tz="CET"), + ], + dtype=object, + ) + ) + + @pytest.mark.parametrize( + "func", + [ + "is_datetime_array", + "is_datetime64_array", + "is_bool_array", + "is_timedelta_or_timedelta64_array", + "is_date_array", + "is_time_array", + "is_interval_array", + ], + ) + def test_other_dtypes_for_array(self, func): + func = getattr(lib, func) + arr = np.array(["foo", "bar"]) + assert not func(arr) + assert not func(arr.reshape(2, 1)) + + arr = np.array([1, 2]) + assert not func(arr) + assert not func(arr.reshape(2, 1)) + + def test_date(self): + dates = [date(2012, 1, day) for day in range(1, 20)] + index = Index(dates) + assert index.inferred_type == "date" + + dates = [date(2012, 1, day) for day in range(1, 20)] + [np.nan] + result = lib.infer_dtype(dates, skipna=False) + assert result == "mixed" + + result = lib.infer_dtype(dates, skipna=True) + assert result == "date" + + @pytest.mark.parametrize( + "values", + [ + [date(2020, 1, 1), Timestamp("2020-01-01")], + [Timestamp("2020-01-01"), date(2020, 1, 1)], + [date(2020, 1, 1), pd.NaT], + [pd.NaT, date(2020, 1, 1)], + ], + ) + @pytest.mark.parametrize("skipna", [True, False]) + def test_infer_dtype_date_order_invariant(self, values, skipna): + # https://github.com/pandas-dev/pandas/issues/33741 + result = lib.infer_dtype(values, skipna=skipna) + assert result == "date" + + def test_is_numeric_array(self): + assert lib.is_float_array(np.array([1, 2.0])) + assert lib.is_float_array(np.array([1, 2.0, np.nan])) + assert not lib.is_float_array(np.array([1, 2])) + + assert lib.is_integer_array(np.array([1, 2])) + assert not lib.is_integer_array(np.array([1, 2.0])) + + def test_is_string_array(self): + # We should only be accepting pd.NA, np.nan, + # other floating point nans e.g. float('nan')] + # when skipna is True. + assert lib.is_string_array(np.array(["foo", "bar"])) + assert not lib.is_string_array( + np.array(["foo", "bar", pd.NA], dtype=object), skipna=False + ) + assert lib.is_string_array( + np.array(["foo", "bar", pd.NA], dtype=object), skipna=True + ) + # we allow NaN/None in the StringArray constructor, so its allowed here + assert lib.is_string_array( + np.array(["foo", "bar", None], dtype=object), skipna=True + ) + assert lib.is_string_array( + np.array(["foo", "bar", np.nan], dtype=object), skipna=True + ) + # But not e.g. datetimelike or Decimal NAs + assert not lib.is_string_array( + np.array(["foo", "bar", pd.NaT], dtype=object), skipna=True + ) + assert not lib.is_string_array( + np.array(["foo", "bar", np.datetime64("NaT")], dtype=object), skipna=True + ) + assert not lib.is_string_array( + np.array(["foo", "bar", Decimal("NaN")], dtype=object), skipna=True + ) + + assert not lib.is_string_array( + np.array(["foo", "bar", None], dtype=object), skipna=False + ) + assert not lib.is_string_array( + np.array(["foo", "bar", np.nan], dtype=object), skipna=False + ) + assert not lib.is_string_array(np.array([1, 2])) + + @pytest.mark.parametrize( + "func", + [ + "is_bool_array", + "is_date_array", + "is_datetime_array", + "is_datetime64_array", + "is_float_array", + "is_integer_array", + "is_interval_array", + "is_string_array", + "is_time_array", + "is_timedelta_or_timedelta64_array", + ], + ) + def test_is_dtype_array_empty_obj(self, func): + # https://github.com/pandas-dev/pandas/pull/60796 + func = getattr(lib, func) + + arr = np.empty((2, 0), dtype=object) + assert not func(arr) + + arr = np.empty((0, 2), dtype=object) + assert not func(arr) + + def test_to_object_array_tuples(self): + r = (5, 6) + values = [r] + lib.to_object_array_tuples(values) + + # make sure record array works + record = namedtuple("record", "x y") + r = record(5, 6) + values = [r] + lib.to_object_array_tuples(values) + + def test_object(self): + # GH 7431 + # cannot infer more than this as only a single element + arr = np.array([None], dtype="O") + result = lib.infer_dtype(arr, skipna=False) + assert result == "mixed" + result = lib.infer_dtype(arr, skipna=True) + assert result == "empty" + + def test_to_object_array_width(self): + # see gh-13320 + rows = [[1, 2, 3], [4, 5, 6]] + + expected = np.array(rows, dtype=object) + out = lib.to_object_array(rows) + tm.assert_numpy_array_equal(out, expected) + + expected = np.array(rows, dtype=object) + out = lib.to_object_array(rows, min_width=1) + tm.assert_numpy_array_equal(out, expected) + + expected = np.array( + [[1, 2, 3, None, None], [4, 5, 6, None, None]], dtype=object + ) + out = lib.to_object_array(rows, min_width=5) + tm.assert_numpy_array_equal(out, expected) + + def test_is_period(self): + # GH#55264 + msg = "is_period is deprecated and will be removed in a future version" + with tm.assert_produces_warning(FutureWarning, match=msg): + assert lib.is_period(Period("2011-01", freq="M")) + assert not lib.is_period(PeriodIndex(["2011-01"], freq="M")) + assert not lib.is_period(Timestamp("2011-01")) + assert not lib.is_period(1) + assert not lib.is_period(np.nan) + + def test_is_interval(self): + # GH#55264 + msg = "is_interval is deprecated and will be removed in a future version" + item = Interval(1, 2) + with tm.assert_produces_warning(FutureWarning, match=msg): + assert lib.is_interval(item) + assert not lib.is_interval(pd.IntervalIndex([item])) + assert not lib.is_interval(pd.IntervalIndex([item])._engine) + + def test_categorical(self): + # GH 8974 + arr = Categorical(list("abc")) + result = lib.infer_dtype(arr, skipna=True) + assert result == "categorical" + + result = lib.infer_dtype(Series(arr), skipna=True) + assert result == "categorical" + + arr = Categorical(list("abc"), categories=["cegfab"], ordered=True) + result = lib.infer_dtype(arr, skipna=True) + assert result == "categorical" + + result = lib.infer_dtype(Series(arr), skipna=True) + assert result == "categorical" + + @pytest.mark.parametrize("asobject", [True, False]) + def test_interval(self, asobject): + idx = pd.IntervalIndex.from_breaks(range(5), closed="both") + if asobject: + idx = idx.astype(object) + + inferred = lib.infer_dtype(idx, skipna=False) + assert inferred == "interval" + + inferred = lib.infer_dtype(idx._data, skipna=False) + assert inferred == "interval" + + inferred = lib.infer_dtype(Series(idx, dtype=idx.dtype), skipna=False) + assert inferred == "interval" + + @pytest.mark.parametrize("value", [Timestamp(0), Timedelta(0), 0, 0.0]) + def test_interval_mismatched_closed(self, value): + first = Interval(value, value, closed="left") + second = Interval(value, value, closed="right") + + # if closed match, we should infer "interval" + arr = np.array([first, first], dtype=object) + assert lib.infer_dtype(arr, skipna=False) == "interval" + + # if closed dont match, we should _not_ get "interval" + arr2 = np.array([first, second], dtype=object) + assert lib.infer_dtype(arr2, skipna=False) == "mixed" + + def test_interval_mismatched_subtype(self): + first = Interval(0, 1, closed="left") + second = Interval(Timestamp(0), Timestamp(1), closed="left") + third = Interval(Timedelta(0), Timedelta(1), closed="left") + + arr = np.array([first, second]) + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + arr = np.array([second, third]) + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + arr = np.array([first, third]) + assert lib.infer_dtype(arr, skipna=False) == "mixed" + + # float vs int subdtype are compatible + flt_interval = Interval(1.5, 2.5, closed="left") + arr = np.array([first, flt_interval], dtype=object) + assert lib.infer_dtype(arr, skipna=False) == "interval" + + @pytest.mark.parametrize("klass", [pd.array, Series]) + @pytest.mark.parametrize("skipna", [True, False]) + @pytest.mark.parametrize("data", [["a", "b", "c"], ["a", "b", pd.NA]]) + def test_string_dtype(self, data, skipna, klass, nullable_string_dtype): + # StringArray + val = klass(data, dtype=nullable_string_dtype) + inferred = lib.infer_dtype(val, skipna=skipna) + assert inferred == "string" + + @pytest.mark.parametrize("klass", [pd.array, Series]) + @pytest.mark.parametrize("skipna", [True, False]) + @pytest.mark.parametrize("data", [[True, False, True], [True, False, pd.NA]]) + def test_boolean_dtype(self, data, skipna, klass): + # BooleanArray + val = klass(data, dtype="boolean") + inferred = lib.infer_dtype(val, skipna=skipna) + assert inferred == "boolean" + + +class TestNumberScalar: + def test_is_number(self): + assert is_number(True) + assert is_number(1) + assert is_number(1.1) + assert is_number(1 + 3j) + assert is_number(np.int64(1)) + assert is_number(np.float64(1.1)) + assert is_number(np.complex128(1 + 3j)) + assert is_number(np.nan) + + assert not is_number(None) + assert not is_number("x") + assert not is_number(datetime(2011, 1, 1)) + assert not is_number(np.datetime64("2011-01-01")) + assert not is_number(Timestamp("2011-01-01")) + assert not is_number(Timestamp("2011-01-01", tz="US/Eastern")) + assert not is_number(timedelta(1000)) + assert not is_number(Timedelta("1 days")) + + # questionable + assert not is_number(np.bool_(False)) + assert is_number(np.timedelta64(1, "D")) + + def test_is_bool(self): + assert is_bool(True) + assert is_bool(False) + assert is_bool(np.bool_(False)) + + assert not is_bool(1) + assert not is_bool(1.1) + assert not is_bool(1 + 3j) + assert not is_bool(np.int64(1)) + assert not is_bool(np.float64(1.1)) + assert not is_bool(np.complex128(1 + 3j)) + assert not is_bool(np.nan) + assert not is_bool(None) + assert not is_bool("x") + assert not is_bool(datetime(2011, 1, 1)) + assert not is_bool(np.datetime64("2011-01-01")) + assert not is_bool(Timestamp("2011-01-01")) + assert not is_bool(Timestamp("2011-01-01", tz="US/Eastern")) + assert not is_bool(timedelta(1000)) + assert not is_bool(np.timedelta64(1, "D")) + assert not is_bool(Timedelta("1 days")) + + def test_is_integer(self): + assert is_integer(1) + assert is_integer(np.int64(1)) + + assert not is_integer(True) + assert not is_integer(1.1) + assert not is_integer(1 + 3j) + assert not is_integer(False) + assert not is_integer(np.bool_(False)) + assert not is_integer(np.float64(1.1)) + assert not is_integer(np.complex128(1 + 3j)) + assert not is_integer(np.nan) + assert not is_integer(None) + assert not is_integer("x") + assert not is_integer(datetime(2011, 1, 1)) + assert not is_integer(np.datetime64("2011-01-01")) + assert not is_integer(Timestamp("2011-01-01")) + assert not is_integer(Timestamp("2011-01-01", tz="US/Eastern")) + assert not is_integer(timedelta(1000)) + assert not is_integer(Timedelta("1 days")) + assert not is_integer(np.timedelta64(1, "D")) + + def test_is_float(self): + assert is_float(1.1) + assert is_float(np.float64(1.1)) + assert is_float(np.nan) + + assert not is_float(True) + assert not is_float(1) + assert not is_float(1 + 3j) + assert not is_float(False) + assert not is_float(np.bool_(False)) + assert not is_float(np.int64(1)) + assert not is_float(np.complex128(1 + 3j)) + assert not is_float(None) + assert not is_float("x") + assert not is_float(datetime(2011, 1, 1)) + assert not is_float(np.datetime64("2011-01-01")) + assert not is_float(Timestamp("2011-01-01")) + assert not is_float(Timestamp("2011-01-01", tz="US/Eastern")) + assert not is_float(timedelta(1000)) + assert not is_float(np.timedelta64(1, "D")) + assert not is_float(Timedelta("1 days")) + + def test_is_datetime_dtypes(self): + ts = pd.date_range("20130101", periods=3) + tsa = pd.date_range("20130101", periods=3, tz="US/Eastern") + + msg = "is_datetime64tz_dtype is deprecated" + + assert is_datetime64_dtype("datetime64") + assert is_datetime64_dtype("datetime64[ns]") + assert is_datetime64_dtype(ts) + assert not is_datetime64_dtype(tsa) + + assert not is_datetime64_ns_dtype("datetime64") + assert is_datetime64_ns_dtype("datetime64[ns]") + assert is_datetime64_ns_dtype(ts) + assert is_datetime64_ns_dtype(tsa) + + assert is_datetime64_any_dtype("datetime64") + assert is_datetime64_any_dtype("datetime64[ns]") + assert is_datetime64_any_dtype(ts) + assert is_datetime64_any_dtype(tsa) + + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert not is_datetime64tz_dtype("datetime64") + assert not is_datetime64tz_dtype("datetime64[ns]") + assert not is_datetime64tz_dtype(ts) + assert is_datetime64tz_dtype(tsa) + + @pytest.mark.parametrize("tz", ["US/Eastern", "UTC"]) + def test_is_datetime_dtypes_with_tz(self, tz): + dtype = f"datetime64[ns, {tz}]" + assert not is_datetime64_dtype(dtype) + + msg = "is_datetime64tz_dtype is deprecated" + with tm.assert_produces_warning(DeprecationWarning, match=msg): + assert is_datetime64tz_dtype(dtype) + assert is_datetime64_ns_dtype(dtype) + assert is_datetime64_any_dtype(dtype) + + def test_is_timedelta(self): + assert is_timedelta64_dtype("timedelta64") + assert is_timedelta64_dtype("timedelta64[ns]") + assert not is_timedelta64_ns_dtype("timedelta64") + assert is_timedelta64_ns_dtype("timedelta64[ns]") + + tdi = TimedeltaIndex([1e14, 2e14], dtype="timedelta64[ns]") + assert is_timedelta64_dtype(tdi) + assert is_timedelta64_ns_dtype(tdi) + assert is_timedelta64_ns_dtype(tdi.astype("timedelta64[ns]")) + + assert not is_timedelta64_ns_dtype(Index([], dtype=np.float64)) + assert not is_timedelta64_ns_dtype(Index([], dtype=np.int64)) + + +class TestIsScalar: + def test_is_scalar_builtin_scalars(self): + assert is_scalar(None) + assert is_scalar(True) + assert is_scalar(False) + assert is_scalar(Fraction()) + assert is_scalar(0.0) + assert is_scalar(1) + assert is_scalar(complex(2)) + assert is_scalar(float("NaN")) + assert is_scalar(np.nan) + assert is_scalar("foobar") + assert is_scalar(b"foobar") + assert is_scalar(datetime(2014, 1, 1)) + assert is_scalar(date(2014, 1, 1)) + assert is_scalar(time(12, 0)) + assert is_scalar(timedelta(hours=1)) + assert is_scalar(pd.NaT) + assert is_scalar(pd.NA) + + def test_is_scalar_builtin_nonscalars(self): + assert not is_scalar({}) + assert not is_scalar([]) + assert not is_scalar([1]) + assert not is_scalar(()) + assert not is_scalar((1,)) + assert not is_scalar(slice(None)) + assert not is_scalar(Ellipsis) + + def test_is_scalar_numpy_array_scalars(self): + assert is_scalar(np.int64(1)) + assert is_scalar(np.float64(1.0)) + assert is_scalar(np.int32(1)) + assert is_scalar(np.complex64(2)) + assert is_scalar(np.object_("foobar")) + assert is_scalar(np.str_("foobar")) + assert is_scalar(np.bytes_(b"foobar")) + assert is_scalar(np.datetime64("2014-01-01")) + assert is_scalar(np.timedelta64(1, "h")) + + @pytest.mark.parametrize( + "zerodim", + [ + np.array(1), + np.array("foobar"), + np.array(np.datetime64("2014-01-01")), + np.array(np.timedelta64(1, "h")), + np.array(np.datetime64("NaT")), + ], + ) + def test_is_scalar_numpy_zerodim_arrays(self, zerodim): + assert not is_scalar(zerodim) + assert is_scalar(lib.item_from_zerodim(zerodim)) + + @pytest.mark.parametrize("arr", [np.array([]), np.array([[]])]) + def test_is_scalar_numpy_arrays(self, arr): + assert not is_scalar(arr) + assert not is_scalar(MockNumpyLikeArray(arr)) + + def test_is_scalar_pandas_scalars(self): + assert is_scalar(Timestamp("2014-01-01")) + assert is_scalar(Timedelta(hours=1)) + assert is_scalar(Period("2014-01-01")) + assert is_scalar(Interval(left=0, right=1)) + assert is_scalar(DateOffset(days=1)) + assert is_scalar(pd.offsets.Minute(3)) + + def test_is_scalar_pandas_containers(self): + assert not is_scalar(Series(dtype=object)) + assert not is_scalar(Series([1])) + assert not is_scalar(DataFrame()) + assert not is_scalar(DataFrame([[1]])) + assert not is_scalar(Index([])) + assert not is_scalar(Index([1])) + assert not is_scalar(Categorical([])) + assert not is_scalar(DatetimeIndex([])._data) + assert not is_scalar(TimedeltaIndex([])._data) + assert not is_scalar(DatetimeIndex([])._data.to_period("D")) + assert not is_scalar(pd.array([1, 2, 3])) + + def test_is_scalar_number(self): + # Number() is not recognied by PyNumber_Check, so by extension + # is not recognized by is_scalar, but instances of non-abstract + # subclasses are. + + class Numeric(Number): + def __init__(self, value) -> None: + self.value = value + + def __int__(self) -> int: + return self.value + + num = Numeric(1) + assert is_scalar(num) + + +@pytest.mark.parametrize("unit", ["ms", "us", "ns"]) +def test_datetimeindex_from_empty_datetime64_array(unit): + idx = DatetimeIndex(np.array([], dtype=f"datetime64[{unit}]")) + assert len(idx) == 0 + + +def test_nan_to_nat_conversions(): + df = DataFrame( + {"A": np.asarray(range(10), dtype="float64"), "B": Timestamp("20010101")} + ) + df.iloc[3:6, :] = np.nan + result = df.loc[4, "B"] + assert result is pd.NaT + + s = df["B"].copy() + s[8:9] = np.nan + assert s[8] is pd.NaT + + +@pytest.mark.filterwarnings("ignore::PendingDeprecationWarning") +def test_is_scipy_sparse(spmatrix): + pytest.importorskip("scipy") + assert is_scipy_sparse(spmatrix([[0, 1]])) + assert not is_scipy_sparse(np.array([1])) + + +def test_ensure_int32(): + values = np.arange(10, dtype=np.int32) + result = ensure_int32(values) + assert result.dtype == np.int32 + + values = np.arange(10, dtype=np.int64) + result = ensure_int32(values) + assert result.dtype == np.int32 + + +@pytest.mark.parametrize( + "right,result", + [ + (0, np.uint8), + (-1, np.int16), + (300, np.uint16), + # For floats, we just upcast directly to float64 instead of trying to + # find a smaller floating dtype + (300.0, np.uint16), # for integer floats, we convert them to ints + (300.1, np.float64), + (np.int16(300), np.int16 if np_version_gt2 else np.uint16), + ], +) +def test_find_result_type_uint_int(right, result): + left_dtype = np.dtype("uint8") + assert find_result_type(left_dtype, right) == result + + +@pytest.mark.parametrize( + "right,result", + [ + (0, np.int8), + (-1, np.int8), + (300, np.int16), + # For floats, we just upcast directly to float64 instead of trying to + # find a smaller floating dtype + (300.0, np.int16), # for integer floats, we convert them to ints + (300.1, np.float64), + (np.int16(300), np.int16), + ], +) +def test_find_result_type_int_int(right, result): + left_dtype = np.dtype("int8") + assert find_result_type(left_dtype, right) == result + + +@pytest.mark.parametrize( + "right,result", + [ + (300.0, np.float64), + (np.float32(300), np.float32), + ], +) +def test_find_result_type_floats(right, result): + left_dtype = np.dtype("float16") + assert find_result_type(left_dtype, right) == result diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/test_missing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/test_missing.py new file mode 100644 index 0000000000000000000000000000000000000000..e3d3e98ae2b93f0a182ea15a3a11d6af39fd2f05 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/dtypes/test_missing.py @@ -0,0 +1,923 @@ +from contextlib import nullcontext +from datetime import datetime +from decimal import Decimal + +import numpy as np +import pytest + +from pandas._config import config as cf + +from pandas._libs import missing as libmissing +from pandas._libs.tslibs import iNaT +from pandas.compat.numpy import np_version_gte1p25 + +from pandas.core.dtypes.common import ( + is_float, + is_scalar, + pandas_dtype, +) +from pandas.core.dtypes.dtypes import ( + CategoricalDtype, + DatetimeTZDtype, + IntervalDtype, + PeriodDtype, +) +from pandas.core.dtypes.missing import ( + array_equivalent, + is_valid_na_for_dtype, + isna, + isnull, + na_value_for_dtype, + notna, + notnull, +) + +import pandas as pd +from pandas import ( + DatetimeIndex, + Index, + NaT, + Series, + TimedeltaIndex, + date_range, + period_range, +) +import pandas._testing as tm + +fix_now = pd.Timestamp("2021-01-01") +fix_utcnow = pd.Timestamp("2021-01-01", tz="UTC") + + +@pytest.mark.parametrize("notna_f", [notna, notnull]) +def test_notna_notnull(notna_f): + assert notna_f(1.0) + assert not notna_f(None) + assert not notna_f(np.nan) + + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with cf.option_context("mode.use_inf_as_na", False): + assert notna_f(np.inf) + assert notna_f(-np.inf) + + arr = np.array([1.5, np.inf, 3.5, -np.inf]) + result = notna_f(arr) + assert result.all() + + with tm.assert_produces_warning(FutureWarning, match=msg): + with cf.option_context("mode.use_inf_as_na", True): + assert not notna_f(np.inf) + assert not notna_f(-np.inf) + + arr = np.array([1.5, np.inf, 3.5, -np.inf]) + result = notna_f(arr) + assert result.sum() == 2 + + +@pytest.mark.parametrize("null_func", [notna, notnull, isna, isnull]) +@pytest.mark.parametrize( + "ser", + [ + Series( + [str(i) for i in range(5)], + index=Index([str(i) for i in range(5)], dtype=object), + dtype=object, + ), + Series(range(5), date_range("2020-01-01", periods=5)), + Series(range(5), period_range("2020-01-01", periods=5)), + ], +) +def test_null_check_is_series(null_func, ser): + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with cf.option_context("mode.use_inf_as_na", False): + assert isinstance(null_func(ser), Series) + + +class TestIsNA: + def test_0d_array(self): + assert isna(np.array(np.nan)) + assert not isna(np.array(0.0)) + assert not isna(np.array(0)) + # test object dtype + assert isna(np.array(np.nan, dtype=object)) + assert not isna(np.array(0.0, dtype=object)) + assert not isna(np.array(0, dtype=object)) + + @pytest.mark.parametrize("shape", [(4, 0), (4,)]) + def test_empty_object(self, shape): + arr = np.empty(shape=shape, dtype=object) + result = isna(arr) + expected = np.ones(shape=shape, dtype=bool) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("isna_f", [isna, isnull]) + def test_isna_isnull(self, isna_f): + assert not isna_f(1.0) + assert isna_f(None) + assert isna_f(np.nan) + assert float("nan") + assert not isna_f(np.inf) + assert not isna_f(-np.inf) + + # type + assert not isna_f(type(Series(dtype=object))) + assert not isna_f(type(Series(dtype=np.float64))) + assert not isna_f(type(pd.DataFrame())) + + @pytest.mark.parametrize("isna_f", [isna, isnull]) + @pytest.mark.parametrize( + "data", + [ + np.arange(4, dtype=float), + [0.0, 1.0, 0.0, 1.0], + Series(list("abcd")), + date_range("2020-01-01", periods=4), + ], + ) + @pytest.mark.parametrize( + "index", + [ + date_range("2020-01-01", periods=4), + range(4), + period_range("2020-01-01", periods=4), + ], + ) + def test_isna_isnull_frame(self, isna_f, data, index): + # frame + df = pd.DataFrame(data, index=index) + result = isna_f(df) + expected = df.apply(isna_f) + tm.assert_frame_equal(result, expected) + + def test_isna_lists(self): + result = isna([[False]]) + exp = np.array([[False]]) + tm.assert_numpy_array_equal(result, exp) + + result = isna([[1], [2]]) + exp = np.array([[False], [False]]) + tm.assert_numpy_array_equal(result, exp) + + # list of strings / unicode + result = isna(["foo", "bar"]) + exp = np.array([False, False]) + tm.assert_numpy_array_equal(result, exp) + + result = isna(["foo", "bar"]) + exp = np.array([False, False]) + tm.assert_numpy_array_equal(result, exp) + + # GH20675 + result = isna([np.nan, "world"]) + exp = np.array([True, False]) + tm.assert_numpy_array_equal(result, exp) + + def test_isna_nat(self): + result = isna([NaT]) + exp = np.array([True]) + tm.assert_numpy_array_equal(result, exp) + + result = isna(np.array([NaT], dtype=object)) + exp = np.array([True]) + tm.assert_numpy_array_equal(result, exp) + + def test_isna_numpy_nat(self): + arr = np.array( + [ + NaT, + np.datetime64("NaT"), + np.timedelta64("NaT"), + np.datetime64("NaT", "s"), + ] + ) + result = isna(arr) + expected = np.array([True] * 4) + tm.assert_numpy_array_equal(result, expected) + + def test_isna_datetime(self): + assert not isna(datetime.now()) + assert notna(datetime.now()) + + idx = date_range("1/1/1990", periods=20) + exp = np.ones(len(idx), dtype=bool) + tm.assert_numpy_array_equal(notna(idx), exp) + + idx = np.asarray(idx) + idx[0] = iNaT + idx = DatetimeIndex(idx) + mask = isna(idx) + assert mask[0] + exp = np.array([True] + [False] * (len(idx) - 1), dtype=bool) + tm.assert_numpy_array_equal(mask, exp) + + # GH 9129 + pidx = idx.to_period(freq="M") + mask = isna(pidx) + assert mask[0] + exp = np.array([True] + [False] * (len(idx) - 1), dtype=bool) + tm.assert_numpy_array_equal(mask, exp) + + mask = isna(pidx[1:]) + exp = np.zeros(len(mask), dtype=bool) + tm.assert_numpy_array_equal(mask, exp) + + def test_isna_old_datetimelike(self): + # isna_old should work for dt64tz, td64, and period, not just tznaive + dti = date_range("2016-01-01", periods=3) + dta = dti._data + dta[-1] = NaT + expected = np.array([False, False, True], dtype=bool) + + objs = [dta, dta.tz_localize("US/Eastern"), dta - dta, dta.to_period("D")] + + for obj in objs: + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with cf.option_context("mode.use_inf_as_na", True): + result = isna(obj) + + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize( + "value, expected", + [ + (np.complex128(np.nan), True), + (np.float64(1), False), + (np.array([1, 1 + 0j, np.nan, 3]), np.array([False, False, True, False])), + ( + np.array([1, 1 + 0j, np.nan, 3], dtype=object), + np.array([False, False, True, False]), + ), + ( + np.array([1, 1 + 0j, np.nan, 3]).astype(object), + np.array([False, False, True, False]), + ), + ], + ) + def test_complex(self, value, expected): + result = isna(value) + if is_scalar(result): + assert result is expected + else: + tm.assert_numpy_array_equal(result, expected) + + def test_datetime_other_units(self): + idx = DatetimeIndex(["2011-01-01", "NaT", "2011-01-02"]) + exp = np.array([False, True, False]) + tm.assert_numpy_array_equal(isna(idx), exp) + tm.assert_numpy_array_equal(notna(idx), ~exp) + tm.assert_numpy_array_equal(isna(idx.values), exp) + tm.assert_numpy_array_equal(notna(idx.values), ~exp) + + @pytest.mark.parametrize( + "dtype", + [ + "datetime64[D]", + "datetime64[h]", + "datetime64[m]", + "datetime64[s]", + "datetime64[ms]", + "datetime64[us]", + "datetime64[ns]", + ], + ) + def test_datetime_other_units_astype(self, dtype): + idx = DatetimeIndex(["2011-01-01", "NaT", "2011-01-02"]) + values = idx.values.astype(dtype) + + exp = np.array([False, True, False]) + tm.assert_numpy_array_equal(isna(values), exp) + tm.assert_numpy_array_equal(notna(values), ~exp) + + exp = Series([False, True, False]) + s = Series(values) + tm.assert_series_equal(isna(s), exp) + tm.assert_series_equal(notna(s), ~exp) + s = Series(values, dtype=object) + tm.assert_series_equal(isna(s), exp) + tm.assert_series_equal(notna(s), ~exp) + + def test_timedelta_other_units(self): + idx = TimedeltaIndex(["1 days", "NaT", "2 days"]) + exp = np.array([False, True, False]) + tm.assert_numpy_array_equal(isna(idx), exp) + tm.assert_numpy_array_equal(notna(idx), ~exp) + tm.assert_numpy_array_equal(isna(idx.values), exp) + tm.assert_numpy_array_equal(notna(idx.values), ~exp) + + @pytest.mark.parametrize( + "dtype", + [ + "timedelta64[D]", + "timedelta64[h]", + "timedelta64[m]", + "timedelta64[s]", + "timedelta64[ms]", + "timedelta64[us]", + "timedelta64[ns]", + ], + ) + def test_timedelta_other_units_dtype(self, dtype): + idx = TimedeltaIndex(["1 days", "NaT", "2 days"]) + values = idx.values.astype(dtype) + + exp = np.array([False, True, False]) + tm.assert_numpy_array_equal(isna(values), exp) + tm.assert_numpy_array_equal(notna(values), ~exp) + + exp = Series([False, True, False]) + s = Series(values) + tm.assert_series_equal(isna(s), exp) + tm.assert_series_equal(notna(s), ~exp) + s = Series(values, dtype=object) + tm.assert_series_equal(isna(s), exp) + tm.assert_series_equal(notna(s), ~exp) + + def test_period(self): + idx = pd.PeriodIndex(["2011-01", "NaT", "2012-01"], freq="M") + exp = np.array([False, True, False]) + tm.assert_numpy_array_equal(isna(idx), exp) + tm.assert_numpy_array_equal(notna(idx), ~exp) + + exp = Series([False, True, False]) + s = Series(idx) + tm.assert_series_equal(isna(s), exp) + tm.assert_series_equal(notna(s), ~exp) + s = Series(idx, dtype=object) + tm.assert_series_equal(isna(s), exp) + tm.assert_series_equal(notna(s), ~exp) + + def test_decimal(self): + # scalars GH#23530 + a = Decimal(1.0) + assert isna(a) is False + assert notna(a) is True + + b = Decimal("NaN") + assert isna(b) is True + assert notna(b) is False + + # array + arr = np.array([a, b]) + expected = np.array([False, True]) + result = isna(arr) + tm.assert_numpy_array_equal(result, expected) + + result = notna(arr) + tm.assert_numpy_array_equal(result, ~expected) + + # series + ser = Series(arr) + expected = Series(expected) + result = isna(ser) + tm.assert_series_equal(result, expected) + + result = notna(ser) + tm.assert_series_equal(result, ~expected) + + # index + idx = Index(arr) + expected = np.array([False, True]) + result = isna(idx) + tm.assert_numpy_array_equal(result, expected) + + result = notna(idx) + tm.assert_numpy_array_equal(result, ~expected) + + +@pytest.mark.parametrize("dtype_equal", [True, False]) +def test_array_equivalent(dtype_equal): + assert array_equivalent( + np.array([np.nan, np.nan]), np.array([np.nan, np.nan]), dtype_equal=dtype_equal + ) + assert array_equivalent( + np.array([np.nan, 1, np.nan]), + np.array([np.nan, 1, np.nan]), + dtype_equal=dtype_equal, + ) + assert array_equivalent( + np.array([np.nan, None], dtype="object"), + np.array([np.nan, None], dtype="object"), + dtype_equal=dtype_equal, + ) + # Check the handling of nested arrays in array_equivalent_object + assert array_equivalent( + np.array([np.array([np.nan, None], dtype="object"), None], dtype="object"), + np.array([np.array([np.nan, None], dtype="object"), None], dtype="object"), + dtype_equal=dtype_equal, + ) + assert array_equivalent( + np.array([np.nan, 1 + 1j], dtype="complex"), + np.array([np.nan, 1 + 1j], dtype="complex"), + dtype_equal=dtype_equal, + ) + assert not array_equivalent( + np.array([np.nan, 1 + 1j], dtype="complex"), + np.array([np.nan, 1 + 2j], dtype="complex"), + dtype_equal=dtype_equal, + ) + assert not array_equivalent( + np.array([np.nan, 1, np.nan]), + np.array([np.nan, 2, np.nan]), + dtype_equal=dtype_equal, + ) + assert not array_equivalent( + np.array(["a", "b", "c", "d"]), np.array(["e", "e"]), dtype_equal=dtype_equal + ) + assert array_equivalent( + Index([0, np.nan]), Index([0, np.nan]), dtype_equal=dtype_equal + ) + assert not array_equivalent( + Index([0, np.nan]), Index([1, np.nan]), dtype_equal=dtype_equal + ) + + +@pytest.mark.parametrize("dtype_equal", [True, False]) +def test_array_equivalent_tdi(dtype_equal): + assert array_equivalent( + TimedeltaIndex([0, np.nan]), + TimedeltaIndex([0, np.nan]), + dtype_equal=dtype_equal, + ) + assert not array_equivalent( + TimedeltaIndex([0, np.nan]), + TimedeltaIndex([1, np.nan]), + dtype_equal=dtype_equal, + ) + + +@pytest.mark.parametrize("dtype_equal", [True, False]) +def test_array_equivalent_dti(dtype_equal): + assert array_equivalent( + DatetimeIndex([0, np.nan]), DatetimeIndex([0, np.nan]), dtype_equal=dtype_equal + ) + assert not array_equivalent( + DatetimeIndex([0, np.nan]), DatetimeIndex([1, np.nan]), dtype_equal=dtype_equal + ) + + dti1 = DatetimeIndex([0, np.nan], tz="US/Eastern") + dti2 = DatetimeIndex([0, np.nan], tz="CET") + dti3 = DatetimeIndex([1, np.nan], tz="US/Eastern") + + assert array_equivalent( + dti1, + dti1, + dtype_equal=dtype_equal, + ) + assert not array_equivalent( + dti1, + dti3, + dtype_equal=dtype_equal, + ) + # The rest are not dtype_equal + assert not array_equivalent(DatetimeIndex([0, np.nan]), dti1) + assert array_equivalent( + dti2, + dti1, + ) + + assert not array_equivalent(DatetimeIndex([0, np.nan]), TimedeltaIndex([0, np.nan])) + + +@pytest.mark.parametrize( + "val", [1, 1.1, 1 + 1j, True, "abc", [1, 2], (1, 2), {1, 2}, {"a": 1}, None] +) +def test_array_equivalent_series(val): + arr = np.array([1, 2]) + msg = "elementwise comparison failed" + cm = ( + # stacklevel is chosen to make sense when called from .equals + tm.assert_produces_warning(FutureWarning, match=msg, check_stacklevel=False) + if isinstance(val, str) and not np_version_gte1p25 + else nullcontext() + ) + with cm: + assert not array_equivalent(Series([arr, arr]), Series([arr, val])) + + +def test_array_equivalent_array_mismatched_shape(): + # to trigger the motivating bug, the first N elements of the arrays need + # to match + first = np.array([1, 2, 3]) + second = np.array([1, 2]) + + left = Series([first, "a"], dtype=object) + right = Series([second, "a"], dtype=object) + assert not array_equivalent(left, right) + + +def test_array_equivalent_array_mismatched_dtype(): + # same shape, different dtype can still be equivalent + first = np.array([1, 2], dtype=np.float64) + second = np.array([1, 2]) + + left = Series([first, "a"], dtype=object) + right = Series([second, "a"], dtype=object) + assert array_equivalent(left, right) + + +def test_array_equivalent_different_dtype_but_equal(): + # Unclear if this is exposed anywhere in the public-facing API + assert array_equivalent(np.array([1, 2]), np.array([1.0, 2.0])) + + +@pytest.mark.parametrize( + "lvalue, rvalue", + [ + # There are 3 variants for each of lvalue and rvalue. We include all + # three for the tz-naive `now` and exclude the datetim64 variant + # for utcnow because it drops tzinfo. + (fix_now, fix_utcnow), + (fix_now.to_datetime64(), fix_utcnow), + (fix_now.to_pydatetime(), fix_utcnow), + (fix_now, fix_utcnow), + (fix_now.to_datetime64(), fix_utcnow.to_pydatetime()), + (fix_now.to_pydatetime(), fix_utcnow.to_pydatetime()), + ], +) +def test_array_equivalent_tzawareness(lvalue, rvalue): + # we shouldn't raise if comparing tzaware and tznaive datetimes + left = np.array([lvalue], dtype=object) + right = np.array([rvalue], dtype=object) + + assert not array_equivalent(left, right, strict_nan=True) + assert not array_equivalent(left, right, strict_nan=False) + + +def test_array_equivalent_compat(): + # see gh-13388 + m = np.array([(1, 2), (3, 4)], dtype=[("a", int), ("b", float)]) + n = np.array([(1, 2), (3, 4)], dtype=[("a", int), ("b", float)]) + assert array_equivalent(m, n, strict_nan=True) + assert array_equivalent(m, n, strict_nan=False) + + m = np.array([(1, 2), (3, 4)], dtype=[("a", int), ("b", float)]) + n = np.array([(1, 2), (4, 3)], dtype=[("a", int), ("b", float)]) + assert not array_equivalent(m, n, strict_nan=True) + assert not array_equivalent(m, n, strict_nan=False) + + m = np.array([(1, 2), (3, 4)], dtype=[("a", int), ("b", float)]) + n = np.array([(1, 2), (3, 4)], dtype=[("b", int), ("a", float)]) + assert not array_equivalent(m, n, strict_nan=True) + assert not array_equivalent(m, n, strict_nan=False) + + +@pytest.mark.parametrize("dtype", ["O", "S", "U"]) +def test_array_equivalent_str(dtype): + assert array_equivalent( + np.array(["A", "B"], dtype=dtype), np.array(["A", "B"], dtype=dtype) + ) + assert not array_equivalent( + np.array(["A", "B"], dtype=dtype), np.array(["A", "X"], dtype=dtype) + ) + + +@pytest.mark.parametrize("strict_nan", [True, False]) +def test_array_equivalent_nested(strict_nan): + # reached in groupby aggregations, make sure we use np.any when checking + # if the comparison is truthy + left = np.array([np.array([50, 70, 90]), np.array([20, 30])], dtype=object) + right = np.array([np.array([50, 70, 90]), np.array([20, 30])], dtype=object) + + assert array_equivalent(left, right, strict_nan=strict_nan) + assert not array_equivalent(left, right[::-1], strict_nan=strict_nan) + + left = np.empty(2, dtype=object) + left[:] = [np.array([50, 70, 90]), np.array([20, 30, 40])] + right = np.empty(2, dtype=object) + right[:] = [np.array([50, 70, 90]), np.array([20, 30, 40])] + assert array_equivalent(left, right, strict_nan=strict_nan) + assert not array_equivalent(left, right[::-1], strict_nan=strict_nan) + + left = np.array([np.array([50, 50, 50]), np.array([40, 40])], dtype=object) + right = np.array([50, 40]) + assert not array_equivalent(left, right, strict_nan=strict_nan) + + +@pytest.mark.filterwarnings("ignore:elementwise comparison failed:DeprecationWarning") +@pytest.mark.parametrize("strict_nan", [True, False]) +def test_array_equivalent_nested2(strict_nan): + # more than one level of nesting + left = np.array( + [ + np.array([np.array([50, 70]), np.array([90])], dtype=object), + np.array([np.array([20, 30])], dtype=object), + ], + dtype=object, + ) + right = np.array( + [ + np.array([np.array([50, 70]), np.array([90])], dtype=object), + np.array([np.array([20, 30])], dtype=object), + ], + dtype=object, + ) + assert array_equivalent(left, right, strict_nan=strict_nan) + assert not array_equivalent(left, right[::-1], strict_nan=strict_nan) + + left = np.array([np.array([np.array([50, 50, 50])], dtype=object)], dtype=object) + right = np.array([50]) + assert not array_equivalent(left, right, strict_nan=strict_nan) + + +@pytest.mark.parametrize("strict_nan", [True, False]) +def test_array_equivalent_nested_list(strict_nan): + left = np.array([[50, 70, 90], [20, 30]], dtype=object) + right = np.array([[50, 70, 90], [20, 30]], dtype=object) + + assert array_equivalent(left, right, strict_nan=strict_nan) + assert not array_equivalent(left, right[::-1], strict_nan=strict_nan) + + left = np.array([[50, 50, 50], [40, 40]], dtype=object) + right = np.array([50, 40]) + assert not array_equivalent(left, right, strict_nan=strict_nan) + + +@pytest.mark.filterwarnings("ignore:elementwise comparison failed:DeprecationWarning") +@pytest.mark.xfail(reason="failing") +@pytest.mark.parametrize("strict_nan", [True, False]) +def test_array_equivalent_nested_mixed_list(strict_nan): + # mixed arrays / lists in left and right + # https://github.com/pandas-dev/pandas/issues/50360 + left = np.array([np.array([1, 2, 3]), np.array([4, 5])], dtype=object) + right = np.array([[1, 2, 3], [4, 5]], dtype=object) + + assert array_equivalent(left, right, strict_nan=strict_nan) + assert not array_equivalent(left, right[::-1], strict_nan=strict_nan) + + # multiple levels of nesting + left = np.array( + [ + np.array([np.array([1, 2, 3]), np.array([4, 5])], dtype=object), + np.array([np.array([6]), np.array([7, 8]), np.array([9])], dtype=object), + ], + dtype=object, + ) + right = np.array([[[1, 2, 3], [4, 5]], [[6], [7, 8], [9]]], dtype=object) + assert array_equivalent(left, right, strict_nan=strict_nan) + assert not array_equivalent(left, right[::-1], strict_nan=strict_nan) + + # same-length lists + subarr = np.empty(2, dtype=object) + subarr[:] = [ + np.array([None, "b"], dtype=object), + np.array(["c", "d"], dtype=object), + ] + left = np.array([subarr, None], dtype=object) + right = np.array([[[None, "b"], ["c", "d"]], None], dtype=object) + assert array_equivalent(left, right, strict_nan=strict_nan) + assert not array_equivalent(left, right[::-1], strict_nan=strict_nan) + + +@pytest.mark.xfail(reason="failing") +@pytest.mark.parametrize("strict_nan", [True, False]) +def test_array_equivalent_nested_dicts(strict_nan): + left = np.array([{"f1": 1, "f2": np.array(["a", "b"], dtype=object)}], dtype=object) + right = np.array( + [{"f1": 1, "f2": np.array(["a", "b"], dtype=object)}], dtype=object + ) + assert array_equivalent(left, right, strict_nan=strict_nan) + assert not array_equivalent(left, right[::-1], strict_nan=strict_nan) + + right2 = np.array([{"f1": 1, "f2": ["a", "b"]}], dtype=object) + assert array_equivalent(left, right2, strict_nan=strict_nan) + assert not array_equivalent(left, right2[::-1], strict_nan=strict_nan) + + +def test_array_equivalent_index_with_tuples(): + # GH#48446 + idx1 = Index(np.array([(pd.NA, 4), (1, 1)], dtype="object")) + idx2 = Index(np.array([(1, 1), (pd.NA, 4)], dtype="object")) + assert not array_equivalent(idx1, idx2) + assert not idx1.equals(idx2) + assert not array_equivalent(idx2, idx1) + assert not idx2.equals(idx1) + + idx1 = Index(np.array([(4, pd.NA), (1, 1)], dtype="object")) + idx2 = Index(np.array([(1, 1), (4, pd.NA)], dtype="object")) + assert not array_equivalent(idx1, idx2) + assert not idx1.equals(idx2) + assert not array_equivalent(idx2, idx1) + assert not idx2.equals(idx1) + + +@pytest.mark.parametrize( + "dtype, na_value", + [ + # Datetime-like + (np.dtype("M8[ns]"), np.datetime64("NaT", "ns")), + (np.dtype("m8[ns]"), np.timedelta64("NaT", "ns")), + (DatetimeTZDtype.construct_from_string("datetime64[ns, US/Eastern]"), NaT), + (PeriodDtype("M"), NaT), + # Integer + ("u1", 0), + ("u2", 0), + ("u4", 0), + ("u8", 0), + ("i1", 0), + ("i2", 0), + ("i4", 0), + ("i8", 0), + # Bool + ("bool", False), + # Float + ("f2", np.nan), + ("f4", np.nan), + ("f8", np.nan), + # Object + ("O", np.nan), + # Interval + (IntervalDtype(), np.nan), + ], +) +def test_na_value_for_dtype(dtype, na_value): + result = na_value_for_dtype(pandas_dtype(dtype)) + # identify check doesn't work for datetime64/timedelta64("NaT") bc they + # are not singletons + assert result is na_value or ( + isna(result) and isna(na_value) and type(result) is type(na_value) + ) + + +class TestNAObj: + def _check_behavior(self, arr, expected): + result = libmissing.isnaobj(arr) + tm.assert_numpy_array_equal(result, expected) + result = libmissing.isnaobj(arr, inf_as_na=True) + tm.assert_numpy_array_equal(result, expected) + + arr = np.atleast_2d(arr) + expected = np.atleast_2d(expected) + + result = libmissing.isnaobj(arr) + tm.assert_numpy_array_equal(result, expected) + result = libmissing.isnaobj(arr, inf_as_na=True) + tm.assert_numpy_array_equal(result, expected) + + # Test fortran order + arr = arr.copy(order="F") + result = libmissing.isnaobj(arr) + tm.assert_numpy_array_equal(result, expected) + result = libmissing.isnaobj(arr, inf_as_na=True) + tm.assert_numpy_array_equal(result, expected) + + def test_basic(self): + arr = np.array([1, None, "foo", -5.1, NaT, np.nan]) + expected = np.array([False, True, False, False, True, True]) + + self._check_behavior(arr, expected) + + def test_non_obj_dtype(self): + arr = np.array([1, 3, np.nan, 5], dtype=float) + expected = np.array([False, False, True, False]) + + self._check_behavior(arr, expected) + + def test_empty_arr(self): + arr = np.array([]) + expected = np.array([], dtype=bool) + + self._check_behavior(arr, expected) + + def test_empty_str_inp(self): + arr = np.array([""]) # empty but not na + expected = np.array([False]) + + self._check_behavior(arr, expected) + + def test_empty_like(self): + # see gh-13717: no segfaults! + arr = np.empty_like([None]) + expected = np.array([True]) + + self._check_behavior(arr, expected) + + +m8_units = ["as", "ps", "ns", "us", "ms", "s", "m", "h", "D", "W", "M", "Y"] + +na_vals = ( + [ + None, + NaT, + float("NaN"), + complex("NaN"), + np.nan, + np.float64("NaN"), + np.float32("NaN"), + np.complex64(np.nan), + np.complex128(np.nan), + np.datetime64("NaT"), + np.timedelta64("NaT"), + ] + + [np.datetime64("NaT", unit) for unit in m8_units] + + [np.timedelta64("NaT", unit) for unit in m8_units] +) + +inf_vals = [ + float("inf"), + float("-inf"), + complex("inf"), + complex("-inf"), + np.inf, + -np.inf, +] + +int_na_vals = [ + # Values that match iNaT, which we treat as null in specific cases + np.int64(NaT._value), + int(NaT._value), +] + +sometimes_na_vals = [Decimal("NaN")] + +never_na_vals = [ + # float/complex values that when viewed as int64 match iNaT + -0.0, + np.float64("-0.0"), + -0j, + np.complex64(-0j), +] + + +class TestLibMissing: + @pytest.mark.parametrize("func", [libmissing.checknull, isna]) + @pytest.mark.parametrize( + "value", na_vals + sometimes_na_vals # type: ignore[operator] + ) + def test_checknull_na_vals(self, func, value): + assert func(value) + + @pytest.mark.parametrize("func", [libmissing.checknull, isna]) + @pytest.mark.parametrize("value", inf_vals) + def test_checknull_inf_vals(self, func, value): + assert not func(value) + + @pytest.mark.parametrize("func", [libmissing.checknull, isna]) + @pytest.mark.parametrize("value", int_na_vals) + def test_checknull_intna_vals(self, func, value): + assert not func(value) + + @pytest.mark.parametrize("func", [libmissing.checknull, isna]) + @pytest.mark.parametrize("value", never_na_vals) + def test_checknull_never_na_vals(self, func, value): + assert not func(value) + + @pytest.mark.parametrize( + "value", na_vals + sometimes_na_vals # type: ignore[operator] + ) + def test_checknull_old_na_vals(self, value): + assert libmissing.checknull(value, inf_as_na=True) + + @pytest.mark.parametrize("value", inf_vals) + def test_checknull_old_inf_vals(self, value): + assert libmissing.checknull(value, inf_as_na=True) + + @pytest.mark.parametrize("value", int_na_vals) + def test_checknull_old_intna_vals(self, value): + assert not libmissing.checknull(value, inf_as_na=True) + + @pytest.mark.parametrize("value", int_na_vals) + def test_checknull_old_never_na_vals(self, value): + assert not libmissing.checknull(value, inf_as_na=True) + + def test_is_matching_na(self, nulls_fixture, nulls_fixture2): + left = nulls_fixture + right = nulls_fixture2 + + assert libmissing.is_matching_na(left, left) + + if left is right: + assert libmissing.is_matching_na(left, right) + elif is_float(left) and is_float(right): + # np.nan vs float("NaN") we consider as matching + assert libmissing.is_matching_na(left, right) + elif type(left) is type(right): + # e.g. both Decimal("NaN") + assert libmissing.is_matching_na(left, right) + else: + assert not libmissing.is_matching_na(left, right) + + def test_is_matching_na_nan_matches_none(self): + assert not libmissing.is_matching_na(None, np.nan) + assert not libmissing.is_matching_na(np.nan, None) + + assert libmissing.is_matching_na(None, np.nan, nan_matches_none=True) + assert libmissing.is_matching_na(np.nan, None, nan_matches_none=True) + + +class TestIsValidNAForDtype: + def test_is_valid_na_for_dtype_interval(self): + dtype = IntervalDtype("int64", "left") + assert not is_valid_na_for_dtype(NaT, dtype) + + dtype = IntervalDtype("datetime64[ns]", "both") + assert not is_valid_na_for_dtype(NaT, dtype) + + def test_is_valid_na_for_dtype_categorical(self): + dtype = CategoricalDtype(categories=[0, 1, 2]) + assert is_valid_na_for_dtype(np.nan, dtype) + + assert not is_valid_na_for_dtype(NaT, dtype) + assert not is_valid_na_for_dtype(np.datetime64("NaT", "ns"), dtype) + assert not is_valid_na_for_dtype(np.timedelta64("NaT", "ns"), dtype) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/common.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/common.py new file mode 100644 index 0000000000000000000000000000000000000000..fc41d7907a240f0dd9dc19e0ae1296bee86be421 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/common.py @@ -0,0 +1,63 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING + +from pandas import ( + DataFrame, + concat, +) + +if TYPE_CHECKING: + from pandas._typing import AxisInt + + +def _check_mixed_float(df, dtype=None): + # float16 are most likely to be upcasted to float32 + dtypes = {"A": "float32", "B": "float32", "C": "float16", "D": "float64"} + if isinstance(dtype, str): + dtypes = {k: dtype for k, v in dtypes.items()} + elif isinstance(dtype, dict): + dtypes.update(dtype) + if dtypes.get("A"): + assert df.dtypes["A"] == dtypes["A"] + if dtypes.get("B"): + assert df.dtypes["B"] == dtypes["B"] + if dtypes.get("C"): + assert df.dtypes["C"] == dtypes["C"] + if dtypes.get("D"): + assert df.dtypes["D"] == dtypes["D"] + + +def _check_mixed_int(df, dtype=None): + dtypes = {"A": "int32", "B": "uint64", "C": "uint8", "D": "int64"} + if isinstance(dtype, str): + dtypes = {k: dtype for k, v in dtypes.items()} + elif isinstance(dtype, dict): + dtypes.update(dtype) + if dtypes.get("A"): + assert df.dtypes["A"] == dtypes["A"] + if dtypes.get("B"): + assert df.dtypes["B"] == dtypes["B"] + if dtypes.get("C"): + assert df.dtypes["C"] == dtypes["C"] + if dtypes.get("D"): + assert df.dtypes["D"] == dtypes["D"] + + +def zip_frames(frames: list[DataFrame], axis: AxisInt = 1) -> DataFrame: + """ + take a list of frames, zip them together under the + assumption that these all have the first frames' index/columns. + + Returns + ------- + new_frame : DataFrame + """ + if axis == 1: + columns = frames[0].columns + zipped = [f.loc[:, c] for c in columns for f in frames] + return concat(zipped, axis=1) + else: + index = frames[0].index + zipped = [f.loc[i, :] for i in index for f in frames] + return DataFrame(zipped) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/conftest.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..b7293946d38c9de9ef4b406d0d8fc933d33abfdd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/conftest.py @@ -0,0 +1,100 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + NaT, + date_range, +) + + +@pytest.fixture +def datetime_frame() -> DataFrame: + """ + Fixture for DataFrame of floats with DatetimeIndex + + Columns are ['A', 'B', 'C', 'D'] + """ + return DataFrame( + np.random.default_rng(2).standard_normal((100, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=100, freq="B"), + ) + + +@pytest.fixture +def float_string_frame(): + """ + Fixture for DataFrame of floats and strings with index of unique strings + + Columns are ['A', 'B', 'C', 'D', 'foo']. + """ + df = DataFrame( + np.random.default_rng(2).standard_normal((30, 4)), + index=Index([f"foo_{i}" for i in range(30)], dtype=object), + columns=Index(list("ABCD")), + ) + df["foo"] = "bar" + return df + + +@pytest.fixture +def mixed_float_frame(): + """ + Fixture for DataFrame of different float types with index of unique strings + + Columns are ['A', 'B', 'C', 'D']. + """ + df = DataFrame( + { + col: np.random.default_rng(2).random(30, dtype=dtype) + for col, dtype in zip( + list("ABCD"), ["float32", "float32", "float32", "float64"] + ) + }, + index=Index([f"foo_{i}" for i in range(30)], dtype=object), + ) + # not supported by numpy random + df["C"] = df["C"].astype("float16") + return df + + +@pytest.fixture +def mixed_int_frame(): + """ + Fixture for DataFrame of different int types with index of unique strings + + Columns are ['A', 'B', 'C', 'D']. + """ + return DataFrame( + { + col: np.ones(30, dtype=dtype) + for col, dtype in zip(list("ABCD"), ["int32", "uint64", "uint8", "int64"]) + }, + index=Index([f"foo_{i}" for i in range(30)], dtype=object), + ) + + +@pytest.fixture +def timezone_frame(): + """ + Fixture for DataFrame of date_range Series with different time zones + + Columns are ['A', 'B', 'C']; some entries are missing + + A B C + 0 2013-01-01 2013-01-01 00:00:00-05:00 2013-01-01 00:00:00+01:00 + 1 2013-01-02 NaT NaT + 2 2013-01-03 2013-01-03 00:00:00-05:00 2013-01-03 00:00:00+01:00 + """ + df = DataFrame( + { + "A": date_range("20130101", periods=3), + "B": date_range("20130101", periods=3, tz="US/Eastern"), + "C": date_range("20130101", periods=3, tz="CET"), + } + ) + df.iloc[1, 1] = NaT + df.iloc[1, 2] = NaT + return df diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_dict.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_dict.py new file mode 100644 index 0000000000000000000000000000000000000000..845174bbf600e0211d4514ff6b713e0b3b40d756 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_dict.py @@ -0,0 +1,223 @@ +from collections import OrderedDict + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + MultiIndex, + RangeIndex, + Series, +) +import pandas._testing as tm + + +class TestFromDict: + # Note: these tests are specific to the from_dict method, not for + # passing dictionaries to DataFrame.__init__ + + def test_constructor_list_of_odicts(self): + data = [ + OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]), + OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]), + OrderedDict([["a", 1.5], ["d", 6]]), + OrderedDict(), + OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]), + OrderedDict([["b", 3], ["c", 4], ["d", 6]]), + ] + + result = DataFrame(data) + expected = DataFrame.from_dict( + dict(zip(range(len(data)), data)), orient="index" + ) + tm.assert_frame_equal(result, expected.reindex(result.index)) + + def test_constructor_single_row(self): + data = [OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]])] + + result = DataFrame(data) + expected = DataFrame.from_dict(dict(zip([0], data)), orient="index").reindex( + result.index + ) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_series(self): + data = [ + OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]), + OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]), + ] + sdict = OrderedDict(zip(["x", "y"], data)) + idx = Index(["a", "b", "c"]) + + # all named + data2 = [ + Series([1.5, 3, 4], idx, dtype="O", name="x"), + Series([1.5, 3, 6], idx, name="y"), + ] + result = DataFrame(data2) + expected = DataFrame.from_dict(sdict, orient="index") + tm.assert_frame_equal(result, expected) + + # some unnamed + data2 = [ + Series([1.5, 3, 4], idx, dtype="O", name="x"), + Series([1.5, 3, 6], idx), + ] + result = DataFrame(data2) + + sdict = OrderedDict(zip(["x", "Unnamed 0"], data)) + expected = DataFrame.from_dict(sdict, orient="index") + tm.assert_frame_equal(result, expected) + + # none named + data = [ + OrderedDict([["a", 1.5], ["b", 3], ["c", 4], ["d", 6]]), + OrderedDict([["a", 1.5], ["b", 3], ["d", 6]]), + OrderedDict([["a", 1.5], ["d", 6]]), + OrderedDict(), + OrderedDict([["a", 1.5], ["b", 3], ["c", 4]]), + OrderedDict([["b", 3], ["c", 4], ["d", 6]]), + ] + data = [Series(d) for d in data] + + result = DataFrame(data) + sdict = OrderedDict(zip(range(len(data)), data)) + expected = DataFrame.from_dict(sdict, orient="index") + tm.assert_frame_equal(result, expected.reindex(result.index)) + + result2 = DataFrame(data, index=np.arange(6, dtype=np.int64)) + tm.assert_frame_equal(result, result2) + + result = DataFrame([Series(dtype=object)]) + expected = DataFrame(index=[0]) + tm.assert_frame_equal(result, expected) + + data = [ + OrderedDict([["a", 1.5], ["b", 3.0], ["c", 4.0]]), + OrderedDict([["a", 1.5], ["b", 3.0], ["c", 6.0]]), + ] + sdict = OrderedDict(zip(range(len(data)), data)) + + idx = Index(["a", "b", "c"]) + data2 = [Series([1.5, 3, 4], idx, dtype="O"), Series([1.5, 3, 6], idx)] + result = DataFrame(data2) + expected = DataFrame.from_dict(sdict, orient="index") + tm.assert_frame_equal(result, expected) + + def test_constructor_orient(self, float_string_frame): + data_dict = float_string_frame.T._series + recons = DataFrame.from_dict(data_dict, orient="index") + expected = float_string_frame.reindex(index=recons.index) + tm.assert_frame_equal(recons, expected) + + # dict of sequence + a = {"hi": [32, 3, 3], "there": [3, 5, 3]} + rs = DataFrame.from_dict(a, orient="index") + xp = DataFrame.from_dict(a).T.reindex(list(a.keys())) + tm.assert_frame_equal(rs, xp) + + def test_constructor_from_ordered_dict(self): + # GH#8425 + a = OrderedDict( + [ + ("one", OrderedDict([("col_a", "foo1"), ("col_b", "bar1")])), + ("two", OrderedDict([("col_a", "foo2"), ("col_b", "bar2")])), + ("three", OrderedDict([("col_a", "foo3"), ("col_b", "bar3")])), + ] + ) + expected = DataFrame.from_dict(a, orient="columns").T + result = DataFrame.from_dict(a, orient="index") + tm.assert_frame_equal(result, expected) + + def test_from_dict_columns_parameter(self): + # GH#18529 + # Test new columns parameter for from_dict that was added to make + # from_items(..., orient='index', columns=[...]) easier to replicate + result = DataFrame.from_dict( + OrderedDict([("A", [1, 2]), ("B", [4, 5])]), + orient="index", + columns=["one", "two"], + ) + expected = DataFrame([[1, 2], [4, 5]], index=["A", "B"], columns=["one", "two"]) + tm.assert_frame_equal(result, expected) + + msg = "cannot use columns parameter with orient='columns'" + with pytest.raises(ValueError, match=msg): + DataFrame.from_dict( + {"A": [1, 2], "B": [4, 5]}, + orient="columns", + columns=["one", "two"], + ) + with pytest.raises(ValueError, match=msg): + DataFrame.from_dict({"A": [1, 2], "B": [4, 5]}, columns=["one", "two"]) + + @pytest.mark.parametrize( + "data_dict, orient, expected", + [ + ({}, "index", RangeIndex(0)), + ( + [{("a",): 1}, {("a",): 2}], + "columns", + Index([("a",)], tupleize_cols=False), + ), + ( + [OrderedDict([(("a",), 1), (("b",), 2)])], + "columns", + Index([("a",), ("b",)], tupleize_cols=False), + ), + ([{("a", "b"): 1}], "columns", Index([("a", "b")], tupleize_cols=False)), + ], + ) + def test_constructor_from_dict_tuples(self, data_dict, orient, expected): + # GH#16769 + df = DataFrame.from_dict(data_dict, orient) + result = df.columns + tm.assert_index_equal(result, expected) + + def test_frame_dict_constructor_empty_series(self): + s1 = Series( + [1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (2, 2), (2, 4)]) + ) + s2 = Series( + [1, 2, 3, 4], index=MultiIndex.from_tuples([(1, 2), (1, 3), (3, 2), (3, 4)]) + ) + s3 = Series(dtype=object) + + # it works! + DataFrame({"foo": s1, "bar": s2, "baz": s3}) + DataFrame.from_dict({"foo": s1, "baz": s3, "bar": s2}) + + def test_from_dict_scalars_requires_index(self): + msg = "If using all scalar values, you must pass an index" + with pytest.raises(ValueError, match=msg): + DataFrame.from_dict(OrderedDict([("b", 8), ("a", 5), ("a", 6)])) + + def test_from_dict_orient_invalid(self): + msg = ( + "Expected 'index', 'columns' or 'tight' for orient parameter. " + "Got 'abc' instead" + ) + with pytest.raises(ValueError, match=msg): + DataFrame.from_dict({"foo": 1, "baz": 3, "bar": 2}, orient="abc") + + def test_from_dict_order_with_single_column(self): + data = { + "alpha": { + "value2": 123, + "value1": 532, + "animal": 222, + "plant": False, + "name": "test", + } + } + result = DataFrame.from_dict( + data, + orient="columns", + ) + expected = DataFrame( + [[123], [532], [222], [False], ["test"]], + index=["value2", "value1", "animal", "plant", "name"], + columns=["alpha"], + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_records.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_records.py new file mode 100644 index 0000000000000000000000000000000000000000..58e47ba48f89445ee888c61055ad5a397a192f4a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/constructors/test_from_records.py @@ -0,0 +1,503 @@ +from collections.abc import Iterator +from datetime import datetime +from decimal import Decimal + +import numpy as np +import pytest +import pytz + +from pandas._config import using_string_dtype + +from pandas.compat import is_platform_little_endian + +from pandas import ( + CategoricalIndex, + DataFrame, + Index, + Interval, + RangeIndex, + Series, + date_range, +) +import pandas._testing as tm + + +class TestFromRecords: + def test_from_records_dt64tz_frame(self): + # GH#51162 don't lose tz when calling from_records with DataFrame input + dti = date_range("2016-01-01", periods=10, tz="US/Pacific") + df = DataFrame({i: dti for i in range(4)}) + with tm.assert_produces_warning(FutureWarning): + res = DataFrame.from_records(df) + tm.assert_frame_equal(res, df) + + def test_from_records_with_datetimes(self): + # this may fail on certain platforms because of a numpy issue + # related GH#6140 + if not is_platform_little_endian(): + pytest.skip("known failure of test on non-little endian") + + # construction with a null in a recarray + # GH#6140 + expected = DataFrame({"EXPIRY": [datetime(2005, 3, 1, 0, 0), None]}) + + arrdata = [np.array([datetime(2005, 3, 1, 0, 0), None])] + dtypes = [("EXPIRY", " None: + self.args = args + + def __getitem__(self, i): + return self.args[i] + + def __iter__(self) -> Iterator: + return iter(self.args) + + recs = [Record(1, 2, 3), Record(4, 5, 6), Record(7, 8, 9)] + tups = [tuple(rec) for rec in recs] + + result = DataFrame.from_records(recs) + expected = DataFrame.from_records(tups) + tm.assert_frame_equal(result, expected) + + def test_from_records_len0_with_columns(self): + # GH#2633 + result = DataFrame.from_records([], index="foo", columns=["foo", "bar"]) + expected = Index(["bar"]) + + assert len(result) == 0 + assert result.index.name == "foo" + tm.assert_index_equal(result.columns, expected) + + def test_from_records_series_list_dict(self): + # GH#27358 + expected = DataFrame([[{"a": 1, "b": 2}, {"a": 3, "b": 4}]]).T + data = Series([[{"a": 1, "b": 2}], [{"a": 3, "b": 4}]]) + result = DataFrame.from_records(data) + tm.assert_frame_equal(result, expected) + + def test_from_records_series_categorical_index(self): + # GH#32805 + index = CategoricalIndex( + [Interval(-20, -10), Interval(-10, 0), Interval(0, 10)] + ) + series_of_dicts = Series([{"a": 1}, {"a": 2}, {"b": 3}], index=index) + frame = DataFrame.from_records(series_of_dicts, index=index) + expected = DataFrame( + {"a": [1, 2, np.nan], "b": [np.nan, np.nan, 3]}, index=index + ) + tm.assert_frame_equal(frame, expected) + + def test_frame_from_records_utc(self): + rec = {"datum": 1.5, "begin_time": datetime(2006, 4, 27, tzinfo=pytz.utc)} + + # it works + DataFrame.from_records([rec], index="begin_time") + + def test_from_records_to_records(self): + # from numpy documentation + arr = np.zeros((2,), dtype=("i4,f4,S10")) + arr[:] = [(1, 2.0, "Hello"), (2, 3.0, "World")] + + DataFrame.from_records(arr) + + index = Index(np.arange(len(arr))[::-1]) + indexed_frame = DataFrame.from_records(arr, index=index) + tm.assert_index_equal(indexed_frame.index, index) + + # without names, it should go to last ditch + arr2 = np.zeros((2, 3)) + tm.assert_frame_equal(DataFrame.from_records(arr2), DataFrame(arr2)) + + # wrong length + msg = "|".join( + [ + r"Length of values \(2\) does not match length of index \(1\)", + ] + ) + with pytest.raises(ValueError, match=msg): + DataFrame.from_records(arr, index=index[:-1]) + + indexed_frame = DataFrame.from_records(arr, index="f1") + + # what to do? + records = indexed_frame.to_records() + assert len(records.dtype.names) == 3 + + records = indexed_frame.to_records(index=False) + assert len(records.dtype.names) == 2 + assert "index" not in records.dtype.names + + def test_from_records_nones(self): + tuples = [(1, 2, None, 3), (1, 2, None, 3), (None, 2, 5, 3)] + + df = DataFrame.from_records(tuples, columns=["a", "b", "c", "d"]) + assert np.isnan(df["c"][0]) + + def test_from_records_iterator(self): + arr = np.array( + [(1.0, 1.0, 2, 2), (3.0, 3.0, 4, 4), (5.0, 5.0, 6, 6), (7.0, 7.0, 8, 8)], + dtype=[ + ("x", np.float64), + ("u", np.float32), + ("y", np.int64), + ("z", np.int32), + ], + ) + df = DataFrame.from_records(iter(arr), nrows=2) + xp = DataFrame( + { + "x": np.array([1.0, 3.0], dtype=np.float64), + "u": np.array([1.0, 3.0], dtype=np.float32), + "y": np.array([2, 4], dtype=np.int64), + "z": np.array([2, 4], dtype=np.int32), + } + ) + tm.assert_frame_equal(df.reindex_like(xp), xp) + + # no dtypes specified here, so just compare with the default + arr = [(1.0, 2), (3.0, 4), (5.0, 6), (7.0, 8)] + df = DataFrame.from_records(iter(arr), columns=["x", "y"], nrows=2) + tm.assert_frame_equal(df, xp.reindex(columns=["x", "y"]), check_dtype=False) + + def test_from_records_tuples_generator(self): + def tuple_generator(length): + for i in range(length): + letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + yield (i, letters[i % len(letters)], i / length) + + columns_names = ["Integer", "String", "Float"] + columns = [ + [i[j] for i in tuple_generator(10)] for j in range(len(columns_names)) + ] + data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]} + expected = DataFrame(data, columns=columns_names) + + generator = tuple_generator(10) + result = DataFrame.from_records(generator, columns=columns_names) + tm.assert_frame_equal(result, expected) + + def test_from_records_lists_generator(self): + def list_generator(length): + for i in range(length): + letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + yield [i, letters[i % len(letters)], i / length] + + columns_names = ["Integer", "String", "Float"] + columns = [ + [i[j] for i in list_generator(10)] for j in range(len(columns_names)) + ] + data = {"Integer": columns[0], "String": columns[1], "Float": columns[2]} + expected = DataFrame(data, columns=columns_names) + + generator = list_generator(10) + result = DataFrame.from_records(generator, columns=columns_names) + tm.assert_frame_equal(result, expected) + + def test_from_records_columns_not_modified(self): + tuples = [(1, 2, 3), (1, 2, 3), (2, 5, 3)] + + columns = ["a", "b", "c"] + original_columns = list(columns) + + DataFrame.from_records(tuples, columns=columns, index="a") + + assert columns == original_columns + + def test_from_records_decimal(self): + tuples = [(Decimal("1.5"),), (Decimal("2.5"),), (None,)] + + df = DataFrame.from_records(tuples, columns=["a"]) + assert df["a"].dtype == object + + df = DataFrame.from_records(tuples, columns=["a"], coerce_float=True) + assert df["a"].dtype == np.float64 + assert np.isnan(df["a"].values[-1]) + + def test_from_records_duplicates(self): + result = DataFrame.from_records([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"]) + + expected = DataFrame([(1, 2, 3), (4, 5, 6)], columns=["a", "b", "a"]) + + tm.assert_frame_equal(result, expected) + + def test_from_records_set_index_name(self): + def create_dict(order_id): + return { + "order_id": order_id, + "quantity": np.random.default_rng(2).integers(1, 10), + "price": np.random.default_rng(2).integers(1, 10), + } + + documents = [create_dict(i) for i in range(10)] + # demo missing data + documents.append({"order_id": 10, "quantity": 5}) + + result = DataFrame.from_records(documents, index="order_id") + assert result.index.name == "order_id" + + # MultiIndex + result = DataFrame.from_records(documents, index=["order_id", "quantity"]) + assert result.index.names == ("order_id", "quantity") + + def test_from_records_misc_brokenness(self): + # GH#2179 + + data = {1: ["foo"], 2: ["bar"]} + + result = DataFrame.from_records(data, columns=["a", "b"]) + exp = DataFrame(data, columns=["a", "b"]) + tm.assert_frame_equal(result, exp) + + # overlap in index/index_names + + data = {"a": [1, 2, 3], "b": [4, 5, 6]} + + result = DataFrame.from_records(data, index=["a", "b", "c"]) + exp = DataFrame(data, index=["a", "b", "c"]) + tm.assert_frame_equal(result, exp) + + def test_from_records_misc_brokenness2(self): + # GH#2623 + rows = [] + rows.append([datetime(2010, 1, 1), 1]) + rows.append([datetime(2010, 1, 2), "hi"]) # test col upconverts to obj + result = DataFrame.from_records(rows, columns=["date", "test"]) + expected = DataFrame( + {"date": [row[0] for row in rows], "test": [row[1] for row in rows]} + ) + tm.assert_frame_equal(result, expected) + assert result.dtypes["test"] == np.dtype(object) + + def test_from_records_misc_brokenness3(self): + rows = [] + rows.append([datetime(2010, 1, 1), 1]) + rows.append([datetime(2010, 1, 2), 1]) + result = DataFrame.from_records(rows, columns=["date", "test"]) + expected = DataFrame( + {"date": [row[0] for row in rows], "test": [row[1] for row in rows]} + ) + tm.assert_frame_equal(result, expected) + + def test_from_records_empty(self): + # GH#3562 + result = DataFrame.from_records([], columns=["a", "b", "c"]) + expected = DataFrame(columns=["a", "b", "c"]) + tm.assert_frame_equal(result, expected) + + result = DataFrame.from_records([], columns=["a", "b", "b"]) + expected = DataFrame(columns=["a", "b", "b"]) + tm.assert_frame_equal(result, expected) + + def test_from_records_empty_with_nonempty_fields_gh3682(self): + a = np.array([(1, 2)], dtype=[("id", np.int64), ("value", np.int64)]) + df = DataFrame.from_records(a, index="id") + + ex_index = Index([1], name="id") + expected = DataFrame({"value": [2]}, index=ex_index, columns=["value"]) + tm.assert_frame_equal(df, expected) + + b = a[:0] + df2 = DataFrame.from_records(b, index="id") + tm.assert_frame_equal(df2, df.iloc[:0]) + + def test_from_records_empty2(self): + # GH#42456 + dtype = [("prop", int)] + shape = (0, len(dtype)) + arr = np.empty(shape, dtype=dtype) + + result = DataFrame.from_records(arr) + expected = DataFrame({"prop": np.array([], dtype=int)}) + tm.assert_frame_equal(result, expected) + + alt = DataFrame(arr) + tm.assert_frame_equal(alt, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_coercion.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_coercion.py new file mode 100644 index 0000000000000000000000000000000000000000..6186dcd2868cfc6a3e1ca4f3afb1bc2151284963 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_coercion.py @@ -0,0 +1,209 @@ +""" +Tests for values coercion in setitem-like operations on DataFrame. + +For the most part, these should be multi-column DataFrames, otherwise +we would share the tests with Series. +""" +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + MultiIndex, + NaT, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameSetitemCoercion: + @pytest.mark.parametrize("consolidate", [True, False]) + def test_loc_setitem_multiindex_columns(self, consolidate): + # GH#18415 Setting values in a single column preserves dtype, + # while setting them in multiple columns did unwanted cast. + + # Note that A here has 2 blocks, below we do the same thing + # with a consolidated frame. + A = DataFrame(np.zeros((6, 5), dtype=np.float32)) + A = pd.concat([A, A], axis=1, keys=[1, 2]) + if consolidate: + A = A._consolidate() + + A.loc[2:3, (1, slice(2, 3))] = np.ones((2, 2), dtype=np.float32) + assert (A.dtypes == np.float32).all() + + A.loc[0:5, (1, slice(2, 3))] = np.ones((6, 2), dtype=np.float32) + + assert (A.dtypes == np.float32).all() + + A.loc[:, (1, slice(2, 3))] = np.ones((6, 2), dtype=np.float32) + assert (A.dtypes == np.float32).all() + + # TODO: i think this isn't about MultiIndex and could be done with iloc? + + +def test_37477(): + # fixed by GH#45121 + orig = DataFrame({"A": [1, 2, 3], "B": [3, 4, 5]}) + expected = DataFrame({"A": [1, 2, 3], "B": [3, 1.2, 5]}) + + df = orig.copy() + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + df.at[1, "B"] = 1.2 + tm.assert_frame_equal(df, expected) + + df = orig.copy() + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + df.loc[1, "B"] = 1.2 + tm.assert_frame_equal(df, expected) + + df = orig.copy() + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + df.iat[1, 1] = 1.2 + tm.assert_frame_equal(df, expected) + + df = orig.copy() + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + df.iloc[1, 1] = 1.2 + tm.assert_frame_equal(df, expected) + + +def test_6942(indexer_al): + # check that the .at __setitem__ after setting "Live" actually sets the data + start = Timestamp("2014-04-01") + t1 = Timestamp("2014-04-23 12:42:38.883082") + t2 = Timestamp("2014-04-24 01:33:30.040039") + + dti = date_range(start, periods=1) + orig = DataFrame(index=dti, columns=["timenow", "Live"]) + + df = orig.copy() + indexer_al(df)[start, "timenow"] = t1 + + df["Live"] = True + + df.at[start, "timenow"] = t2 + assert df.iloc[0, 0] == t2 + + +def test_26395(indexer_al): + # .at case fixed by GH#45121 (best guess) + df = DataFrame(index=["A", "B", "C"]) + df["D"] = 0 + + indexer_al(df)["C", "D"] = 2 + expected = DataFrame({"D": [0, 0, 2]}, index=["A", "B", "C"], dtype=np.int64) + tm.assert_frame_equal(df, expected) + + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + indexer_al(df)["C", "D"] = 44.5 + expected = DataFrame( + {"D": [0, 0, 44.5]}, + index=["A", "B", "C"], + columns=["D"], + dtype=np.float64, + ) + tm.assert_frame_equal(df, expected) + + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + indexer_al(df)["C", "D"] = "hello" + expected = DataFrame( + {"D": [0, 0, "hello"]}, + index=["A", "B", "C"], + columns=["D"], + dtype=object, + ) + tm.assert_frame_equal(df, expected) + + +@pytest.mark.xfail(reason="unwanted upcast") +def test_15231(): + df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"]) + df.loc[2] = Series({"a": 5, "b": 6}) + assert (df.dtypes == np.int64).all() + + df.loc[3] = Series({"a": 7}) + + # df["a"] doesn't have any NaNs, should not have been cast + exp_dtypes = Series([np.int64, np.float64], dtype=object, index=["a", "b"]) + tm.assert_series_equal(df.dtypes, exp_dtypes) + + +def test_iloc_setitem_unnecesssary_float_upcasting(): + # GH#12255 + df = DataFrame( + { + 0: np.array([1, 3], dtype=np.float32), + 1: np.array([2, 4], dtype=np.float32), + 2: ["a", "b"], + } + ) + orig = df.copy() + + values = df[0].values.reshape(2, 1) + df.iloc[:, 0:1] = values + + tm.assert_frame_equal(df, orig) + + +@pytest.mark.xfail(reason="unwanted casting to dt64") +def test_12499(): + # TODO: OP in GH#12499 used np.datetim64("NaT") instead of pd.NaT, + # which has consequences for the expected df["two"] (though i think at + # the time it might not have because of a separate bug). See if it makes + # a difference which one we use here. + ts = Timestamp("2016-03-01 03:13:22.98986", tz="UTC") + + data = [{"one": 0, "two": ts}] + orig = DataFrame(data) + df = orig.copy() + df.loc[1] = [np.nan, NaT] + + expected = DataFrame( + {"one": [0, np.nan], "two": Series([ts, NaT], dtype="datetime64[ns, UTC]")} + ) + tm.assert_frame_equal(df, expected) + + data = [{"one": 0, "two": ts}] + df = orig.copy() + df.loc[1, :] = [np.nan, NaT] + tm.assert_frame_equal(df, expected) + + +def test_20476(): + mi = MultiIndex.from_product([["A", "B"], ["a", "b", "c"]]) + df = DataFrame(-1, index=range(3), columns=mi) + filler = DataFrame([[1, 2, 3.0]] * 3, index=range(3), columns=["a", "b", "c"]) + df["A"] = filler + + expected = DataFrame( + { + 0: [1, 1, 1], + 1: [2, 2, 2], + 2: [3.0, 3.0, 3.0], + 3: [-1, -1, -1], + 4: [-1, -1, -1], + 5: [-1, -1, -1], + } + ) + expected.columns = mi + exp_dtypes = Series( + [np.dtype(np.int64)] * 2 + [np.dtype(np.float64)] + [np.dtype(np.int64)] * 3, + index=mi, + ) + tm.assert_series_equal(df.dtypes, exp_dtypes) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_delitem.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_delitem.py new file mode 100644 index 0000000000000000000000000000000000000000..daec991b7a8dbf8de0221f041e767cb9ce58ae29 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_delitem.py @@ -0,0 +1,60 @@ +import re + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + MultiIndex, +) + + +class TestDataFrameDelItem: + def test_delitem(self, float_frame): + del float_frame["A"] + assert "A" not in float_frame + + def test_delitem_multiindex(self): + midx = MultiIndex.from_product([["A", "B"], [1, 2]]) + df = DataFrame(np.random.default_rng(2).standard_normal((4, 4)), columns=midx) + assert len(df.columns) == 4 + assert ("A",) in df.columns + assert "A" in df.columns + + result = df["A"] + assert isinstance(result, DataFrame) + del df["A"] + + assert len(df.columns) == 2 + + # A still in the levels, BUT get a KeyError if trying + # to delete + assert ("A",) not in df.columns + with pytest.raises(KeyError, match=re.escape("('A',)")): + del df[("A",)] + + # behavior of dropped/deleted MultiIndex levels changed from + # GH 2770 to GH 19027: MultiIndex no longer '.__contains__' + # levels which are dropped/deleted + assert "A" not in df.columns + with pytest.raises(KeyError, match=re.escape("('A',)")): + del df["A"] + + def test_delitem_corner(self, float_frame): + f = float_frame.copy() + del f["D"] + assert len(f.columns) == 3 + with pytest.raises(KeyError, match=r"^'D'$"): + del f["D"] + del f["B"] + assert len(f.columns) == 2 + + def test_delitem_col_still_multiindex(self): + arrays = [["a", "b", "c", "top"], ["", "", "", "OD"], ["", "", "", "wx"]] + + tuples = sorted(zip(*arrays)) + index = MultiIndex.from_tuples(tuples) + + df = DataFrame(np.random.default_rng(2).standard_normal((3, 4)), columns=index) + del df[("a", "", "")] + assert isinstance(df.columns, MultiIndex) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get.py new file mode 100644 index 0000000000000000000000000000000000000000..5f2651eec683c10097fb623728048b64778c87e8 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get.py @@ -0,0 +1,27 @@ +import pytest + +from pandas import DataFrame +import pandas._testing as tm + + +class TestGet: + def test_get(self, float_frame): + b = float_frame.get("B") + tm.assert_series_equal(b, float_frame["B"]) + + assert float_frame.get("foo") is None + tm.assert_series_equal( + float_frame.get("foo", float_frame["B"]), float_frame["B"] + ) + + @pytest.mark.parametrize( + "df", + [ + DataFrame(), + DataFrame(columns=list("AB")), + DataFrame(columns=list("AB"), index=range(3)), + ], + ) + def test_get_none(self, df): + # see gh-5652 + assert df.get(None) is None diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get_value.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get_value.py new file mode 100644 index 0000000000000000000000000000000000000000..65a1c64a1578ad0cadd9ed6470ab60a2087ffec5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_get_value.py @@ -0,0 +1,22 @@ +import pytest + +from pandas import ( + DataFrame, + MultiIndex, +) + + +class TestGetValue: + def test_get_set_value_no_partial_indexing(self): + # partial w/ MultiIndex raise exception + index = MultiIndex.from_tuples([(0, 1), (0, 2), (1, 1), (1, 2)]) + df = DataFrame(index=index, columns=range(4)) + with pytest.raises(KeyError, match=r"^0$"): + df._get_value(0, 1) + + def test_get_value(self, float_frame): + for idx in float_frame.index: + for col in float_frame.columns: + result = float_frame._get_value(idx, col) + expected = float_frame[col][idx] + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_getitem.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_getitem.py new file mode 100644 index 0000000000000000000000000000000000000000..a36b0c0e850b3d0994349065cc5390c9f40cbf60 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_getitem.py @@ -0,0 +1,472 @@ +import re + +import numpy as np +import pytest + +from pandas import ( + Categorical, + CategoricalDtype, + CategoricalIndex, + DataFrame, + DateOffset, + DatetimeIndex, + Index, + MultiIndex, + Series, + Timestamp, + concat, + date_range, + get_dummies, + period_range, +) +import pandas._testing as tm +from pandas.core.arrays import SparseArray + + +class TestGetitem: + def test_getitem_unused_level_raises(self): + # GH#20410 + mi = MultiIndex( + levels=[["a_lot", "onlyone", "notevenone"], [1970, ""]], + codes=[[1, 0], [1, 0]], + ) + df = DataFrame(-1, index=range(3), columns=mi) + + with pytest.raises(KeyError, match="notevenone"): + df["notevenone"] + + def test_getitem_periodindex(self): + rng = period_range("1/1/2000", periods=5) + df = DataFrame(np.random.default_rng(2).standard_normal((10, 5)), columns=rng) + + ts = df[rng[0]] + tm.assert_series_equal(ts, df.iloc[:, 0]) + + ts = df["1/1/2000"] + tm.assert_series_equal(ts, df.iloc[:, 0]) + + def test_getitem_list_of_labels_categoricalindex_cols(self): + # GH#16115 + cats = Categorical([Timestamp("12-31-1999"), Timestamp("12-31-2000")]) + + expected = DataFrame([[1, 0], [0, 1]], dtype="bool", index=[0, 1], columns=cats) + dummies = get_dummies(cats) + result = dummies[list(dummies.columns)] + tm.assert_frame_equal(result, expected) + + def test_getitem_sparse_column_return_type_and_dtype(self): + # https://github.com/pandas-dev/pandas/issues/23559 + data = SparseArray([0, 1]) + df = DataFrame({"A": data}) + expected = Series(data, name="A") + result = df["A"] + tm.assert_series_equal(result, expected) + + # Also check iloc and loc while we're here + result = df.iloc[:, 0] + tm.assert_series_equal(result, expected) + + result = df.loc[:, "A"] + tm.assert_series_equal(result, expected) + + def test_getitem_string_columns(self): + # GH#46185 + df = DataFrame([[1, 2]], columns=Index(["A", "B"], dtype="string")) + result = df.A + expected = df["A"] + tm.assert_series_equal(result, expected) + + +class TestGetitemListLike: + def test_getitem_list_missing_key(self): + # GH#13822, incorrect error string with non-unique columns when missing + # column is accessed + df = DataFrame({"x": [1.0], "y": [2.0], "z": [3.0]}) + df.columns = ["x", "x", "z"] + + # Check that we get the correct value in the KeyError + with pytest.raises(KeyError, match=r"\['y'\] not in index"): + df[["x", "y", "z"]] + + def test_getitem_list_duplicates(self): + # GH#1943 + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), columns=list("AABC") + ) + df.columns.name = "foo" + + result = df[["B", "C"]] + assert result.columns.name == "foo" + + expected = df.iloc[:, 2:] + tm.assert_frame_equal(result, expected) + + def test_getitem_dupe_cols(self): + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"]) + msg = "\"None of [Index(['baf'], dtype=" + with pytest.raises(KeyError, match=re.escape(msg)): + df[["baf"]] + + @pytest.mark.parametrize( + "idx_type", + [ + list, + iter, + Index, + set, + lambda keys: dict(zip(keys, range(len(keys)))), + lambda keys: dict(zip(keys, range(len(keys)))).keys(), + ], + ids=["list", "iter", "Index", "set", "dict", "dict_keys"], + ) + @pytest.mark.parametrize("levels", [1, 2]) + def test_getitem_listlike(self, idx_type, levels, float_frame): + # GH#21294 + + if levels == 1: + frame, missing = float_frame, "food" + else: + # MultiIndex columns + frame = DataFrame( + np.random.default_rng(2).standard_normal((8, 3)), + columns=Index( + [("foo", "bar"), ("baz", "qux"), ("peek", "aboo")], + name=("sth", "sth2"), + ), + ) + missing = ("good", "food") + + keys = [frame.columns[1], frame.columns[0]] + idx = idx_type(keys) + idx_check = list(idx_type(keys)) + + if isinstance(idx, (set, dict)): + with pytest.raises(TypeError, match="as an indexer is not supported"): + frame[idx] + + return + else: + result = frame[idx] + + expected = frame.loc[:, idx_check] + expected.columns.names = frame.columns.names + + tm.assert_frame_equal(result, expected) + + idx = idx_type(keys + [missing]) + with pytest.raises(KeyError, match="not in index"): + frame[idx] + + def test_getitem_iloc_generator(self): + # GH#39614 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + indexer = (x for x in [1, 2]) + result = df.iloc[indexer] + expected = DataFrame({"a": [2, 3], "b": [5, 6]}, index=[1, 2]) + tm.assert_frame_equal(result, expected) + + def test_getitem_iloc_two_dimensional_generator(self): + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + indexer = (x for x in [1, 2]) + result = df.iloc[indexer, 1] + expected = Series([5, 6], name="b", index=[1, 2]) + tm.assert_series_equal(result, expected) + + def test_getitem_iloc_dateoffset_days(self): + # GH 46671 + df = DataFrame( + list(range(10)), + index=date_range("01-01-2022", periods=10, freq=DateOffset(days=1)), + ) + result = df.loc["2022-01-01":"2022-01-03"] + expected = DataFrame( + [0, 1, 2], + index=DatetimeIndex( + ["2022-01-01", "2022-01-02", "2022-01-03"], + dtype="datetime64[ns]", + freq=DateOffset(days=1), + ), + ) + tm.assert_frame_equal(result, expected) + + df = DataFrame( + list(range(10)), + index=date_range( + "01-01-2022", periods=10, freq=DateOffset(days=1, hours=2) + ), + ) + result = df.loc["2022-01-01":"2022-01-03"] + expected = DataFrame( + [0, 1, 2], + index=DatetimeIndex( + ["2022-01-01 00:00:00", "2022-01-02 02:00:00", "2022-01-03 04:00:00"], + dtype="datetime64[ns]", + freq=DateOffset(days=1, hours=2), + ), + ) + tm.assert_frame_equal(result, expected) + + df = DataFrame( + list(range(10)), + index=date_range("01-01-2022", periods=10, freq=DateOffset(minutes=3)), + ) + result = df.loc["2022-01-01":"2022-01-03"] + tm.assert_frame_equal(result, df) + + +class TestGetitemCallable: + def test_getitem_callable(self, float_frame): + # GH#12533 + result = float_frame[lambda x: "A"] + expected = float_frame.loc[:, "A"] + tm.assert_series_equal(result, expected) + + result = float_frame[lambda x: ["A", "B"]] + expected = float_frame.loc[:, ["A", "B"]] + tm.assert_frame_equal(result, float_frame.loc[:, ["A", "B"]]) + + df = float_frame[:3] + result = df[lambda x: [True, False, True]] + expected = float_frame.iloc[[0, 2], :] + tm.assert_frame_equal(result, expected) + + def test_loc_multiindex_columns_one_level(self): + # GH#29749 + df = DataFrame([[1, 2]], columns=[["a", "b"]]) + expected = DataFrame([1], columns=[["a"]]) + + result = df["a"] + tm.assert_frame_equal(result, expected) + + result = df.loc[:, "a"] + tm.assert_frame_equal(result, expected) + + +class TestGetitemBooleanMask: + def test_getitem_bool_mask_categorical_index(self): + df3 = DataFrame( + { + "A": np.arange(6, dtype="int64"), + }, + index=CategoricalIndex( + [1, 1, 2, 1, 3, 2], + dtype=CategoricalDtype([3, 2, 1], ordered=True), + name="B", + ), + ) + df4 = DataFrame( + { + "A": np.arange(6, dtype="int64"), + }, + index=CategoricalIndex( + [1, 1, 2, 1, 3, 2], + dtype=CategoricalDtype([3, 2, 1], ordered=False), + name="B", + ), + ) + + result = df3[df3.index == "a"] + expected = df3.iloc[[]] + tm.assert_frame_equal(result, expected) + + result = df4[df4.index == "a"] + expected = df4.iloc[[]] + tm.assert_frame_equal(result, expected) + + result = df3[df3.index == 1] + expected = df3.iloc[[0, 1, 3]] + tm.assert_frame_equal(result, expected) + + result = df4[df4.index == 1] + expected = df4.iloc[[0, 1, 3]] + tm.assert_frame_equal(result, expected) + + # since we have an ordered categorical + + # CategoricalIndex([1, 1, 2, 1, 3, 2], + # categories=[3, 2, 1], + # ordered=True, + # name='B') + result = df3[df3.index < 2] + expected = df3.iloc[[4]] + tm.assert_frame_equal(result, expected) + + result = df3[df3.index > 1] + expected = df3.iloc[[]] + tm.assert_frame_equal(result, expected) + + # unordered + # cannot be compared + + # CategoricalIndex([1, 1, 2, 1, 3, 2], + # categories=[3, 2, 1], + # ordered=False, + # name='B') + msg = "Unordered Categoricals can only compare equality or not" + with pytest.raises(TypeError, match=msg): + df4[df4.index < 2] + with pytest.raises(TypeError, match=msg): + df4[df4.index > 1] + + @pytest.mark.parametrize( + "data1,data2,expected_data", + ( + ( + [[1, 2], [3, 4]], + [[0.5, 6], [7, 8]], + [[np.nan, 3.0], [np.nan, 4.0], [np.nan, 7.0], [6.0, 8.0]], + ), + ( + [[1, 2], [3, 4]], + [[5, 6], [7, 8]], + [[np.nan, 3.0], [np.nan, 4.0], [5, 7], [6, 8]], + ), + ), + ) + def test_getitem_bool_mask_duplicate_columns_mixed_dtypes( + self, + data1, + data2, + expected_data, + ): + # GH#31954 + + df1 = DataFrame(np.array(data1)) + df2 = DataFrame(np.array(data2)) + df = concat([df1, df2], axis=1) + + result = df[df > 2] + + exdict = {i: np.array(col) for i, col in enumerate(expected_data)} + expected = DataFrame(exdict).rename(columns={2: 0, 3: 1}) + tm.assert_frame_equal(result, expected) + + @pytest.fixture + def df_dup_cols(self): + dups = ["A", "A", "C", "D"] + df = DataFrame(np.arange(12).reshape(3, 4), columns=dups, dtype="float64") + return df + + def test_getitem_boolean_frame_unaligned_with_duplicate_columns(self, df_dup_cols): + # `df.A > 6` is a DataFrame with a different shape from df + + # boolean with the duplicate raises + df = df_dup_cols + msg = "cannot reindex on an axis with duplicate labels" + with pytest.raises(ValueError, match=msg): + df[df.A > 6] + + def test_getitem_boolean_series_with_duplicate_columns(self, df_dup_cols): + # boolean indexing + # GH#4879 + df = DataFrame( + np.arange(12).reshape(3, 4), columns=["A", "B", "C", "D"], dtype="float64" + ) + expected = df[df.C > 6] + expected.columns = df_dup_cols.columns + + df = df_dup_cols + result = df[df.C > 6] + + tm.assert_frame_equal(result, expected) + + def test_getitem_boolean_frame_with_duplicate_columns(self, df_dup_cols): + # where + df = DataFrame( + np.arange(12).reshape(3, 4), columns=["A", "B", "C", "D"], dtype="float64" + ) + # `df > 6` is a DataFrame with the same shape+alignment as df + expected = df[df > 6] + expected.columns = df_dup_cols.columns + + df = df_dup_cols + result = df[df > 6] + + tm.assert_frame_equal(result, expected) + + def test_getitem_empty_frame_with_boolean(self): + # Test for issue GH#11859 + + df = DataFrame() + df2 = df[df > 0] + tm.assert_frame_equal(df, df2) + + def test_getitem_returns_view_when_column_is_unique_in_df( + self, using_copy_on_write, warn_copy_on_write + ): + # GH#45316 + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"]) + df_orig = df.copy() + view = df["b"] + with tm.assert_cow_warning(warn_copy_on_write): + view.loc[:] = 100 + if using_copy_on_write: + expected = df_orig + else: + expected = DataFrame([[1, 2, 100], [4, 5, 100]], columns=["a", "a", "b"]) + tm.assert_frame_equal(df, expected) + + def test_getitem_frozenset_unique_in_column(self): + # GH#41062 + df = DataFrame([[1, 2, 3, 4]], columns=[frozenset(["KEY"]), "B", "C", "C"]) + result = df[frozenset(["KEY"])] + expected = Series([1], name=frozenset(["KEY"])) + tm.assert_series_equal(result, expected) + + +class TestGetitemSlice: + def test_getitem_slice_float64(self, frame_or_series): + values = np.arange(10.0, 50.0, 2) + index = Index(values) + + start, end = values[[5, 15]] + + data = np.random.default_rng(2).standard_normal((20, 3)) + if frame_or_series is not DataFrame: + data = data[:, 0] + + obj = frame_or_series(data, index=index) + + result = obj[start:end] + expected = obj.iloc[5:16] + tm.assert_equal(result, expected) + + result = obj.loc[start:end] + tm.assert_equal(result, expected) + + def test_getitem_datetime_slice(self): + # GH#43223 + df = DataFrame( + {"a": 0}, + index=DatetimeIndex( + [ + "11.01.2011 22:00", + "11.01.2011 23:00", + "12.01.2011 00:00", + "2011-01-13 00:00", + ] + ), + ) + with pytest.raises( + KeyError, match="Value based partial slicing on non-monotonic" + ): + df["2011-01-01":"2011-11-01"] + + def test_getitem_slice_same_dim_only_one_axis(self): + # GH#54622 + df = DataFrame(np.random.default_rng(2).standard_normal((10, 8))) + result = df.iloc[(slice(None, None, 2),)] + assert result.shape == (5, 8) + expected = df.iloc[slice(None, None, 2), slice(None)] + tm.assert_frame_equal(result, expected) + + +class TestGetitemDeprecatedIndexers: + @pytest.mark.parametrize("key", [{"a", "b"}, {"a": "a"}]) + def test_getitem_dict_and_set_deprecated(self, key): + # GH#42825 enforced in 2.0 + df = DataFrame( + [[1, 2], [3, 4]], columns=MultiIndex.from_tuples([("a", 1), ("b", 2)]) + ) + with pytest.raises(TypeError, match="as an indexer is not supported"): + df[key] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..93f4c2c6e327390dd792f2e25cefe72b990c31ff --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_indexing.py @@ -0,0 +1,2028 @@ +from collections import namedtuple +from datetime import ( + datetime, + timedelta, +) +from decimal import Decimal +import re + +import numpy as np +import pytest + +from pandas._config import using_string_dtype + +from pandas._libs import iNaT +from pandas.errors import ( + InvalidIndexError, + PerformanceWarning, + SettingWithCopyError, +) +import pandas.util._test_decorators as td + +from pandas.core.dtypes.common import is_integer + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + DatetimeIndex, + Index, + MultiIndex, + Series, + Timestamp, + date_range, + isna, + notna, + to_datetime, +) +import pandas._testing as tm + +# We pass through a TypeError raised by numpy +_slice_msg = "slice indices must be integers or None or have an __index__ method" + + +class TestDataFrameIndexing: + def test_getitem(self, float_frame): + # Slicing + sl = float_frame[:20] + assert len(sl.index) == 20 + + # Column access + for _, series in sl.items(): + assert len(series.index) == 20 + tm.assert_index_equal(series.index, sl.index) + + for key, _ in float_frame._series.items(): + assert float_frame[key] is not None + + assert "random" not in float_frame + with pytest.raises(KeyError, match="random"): + float_frame["random"] + + def test_getitem_numeric_should_not_fallback_to_positional(self, any_numeric_dtype): + # GH51053 + dtype = any_numeric_dtype + idx = Index([1, 0, 1], dtype=dtype) + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=idx) + result = df[1] + expected = DataFrame([[1, 3], [4, 6]], columns=Index([1, 1], dtype=dtype)) + tm.assert_frame_equal(result, expected, check_exact=True) + + def test_getitem2(self, float_frame): + df = float_frame.copy() + df["$10"] = np.random.default_rng(2).standard_normal(len(df)) + + ad = np.random.default_rng(2).standard_normal(len(df)) + df["@awesome_domain"] = ad + + with pytest.raises(KeyError, match=re.escape("'df[\"$10\"]'")): + df.__getitem__('df["$10"]') + + res = df["@awesome_domain"] + tm.assert_numpy_array_equal(ad, res.values) + + def test_setitem_numeric_should_not_fallback_to_positional(self, any_numeric_dtype): + # GH51053 + dtype = any_numeric_dtype + idx = Index([1, 0, 1], dtype=dtype) + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=idx) + df[1] = 10 + expected = DataFrame([[10, 2, 10], [10, 5, 10]], columns=idx) + tm.assert_frame_equal(df, expected, check_exact=True) + + def test_setitem_list(self, float_frame): + float_frame["E"] = "foo" + data = float_frame[["A", "B"]] + float_frame[["B", "A"]] = data + + tm.assert_series_equal(float_frame["B"], data["A"], check_names=False) + tm.assert_series_equal(float_frame["A"], data["B"], check_names=False) + + msg = "Columns must be same length as key" + with pytest.raises(ValueError, match=msg): + data[["A"]] = float_frame[["A", "B"]] + newcolumndata = range(len(data.index) - 1) + msg = ( + rf"Length of values \({len(newcolumndata)}\) " + rf"does not match length of index \({len(data)}\)" + ) + with pytest.raises(ValueError, match=msg): + data["A"] = newcolumndata + + def test_setitem_list2(self): + df = DataFrame(0, index=range(3), columns=["tt1", "tt2"], dtype=int) + df.loc[1, ["tt1", "tt2"]] = [1, 2] + + result = df.loc[df.index[1], ["tt1", "tt2"]] + expected = Series([1, 2], df.columns, dtype=int, name=1) + tm.assert_series_equal(result, expected) + + df["tt1"] = df["tt2"] = "0" + df.loc[df.index[1], ["tt1", "tt2"]] = ["1", "2"] + result = df.loc[df.index[1], ["tt1", "tt2"]] + expected = Series(["1", "2"], df.columns, name=1) + tm.assert_series_equal(result, expected) + + def test_getitem_boolean(self, mixed_float_frame, mixed_int_frame, datetime_frame): + # boolean indexing + d = datetime_frame.index[10] + indexer = datetime_frame.index > d + indexer_obj = indexer.astype(object) + + subindex = datetime_frame.index[indexer] + subframe = datetime_frame[indexer] + + tm.assert_index_equal(subindex, subframe.index) + with pytest.raises(ValueError, match="Item wrong length"): + datetime_frame[indexer[:-1]] + + subframe_obj = datetime_frame[indexer_obj] + tm.assert_frame_equal(subframe_obj, subframe) + + with pytest.raises(ValueError, match="Boolean array expected"): + datetime_frame[datetime_frame] + + # test that Series work + indexer_obj = Series(indexer_obj, datetime_frame.index) + + subframe_obj = datetime_frame[indexer_obj] + tm.assert_frame_equal(subframe_obj, subframe) + + # test that Series indexers reindex + # we are producing a warning that since the passed boolean + # key is not the same as the given index, we will reindex + # not sure this is really necessary + with tm.assert_produces_warning(UserWarning): + indexer_obj = indexer_obj.reindex(datetime_frame.index[::-1]) + subframe_obj = datetime_frame[indexer_obj] + tm.assert_frame_equal(subframe_obj, subframe) + + # test df[df > 0] + for df in [ + datetime_frame, + mixed_float_frame, + mixed_int_frame, + ]: + data = df._get_numeric_data() + bif = df[df > 0] + bifw = DataFrame( + {c: np.where(data[c] > 0, data[c], np.nan) for c in data.columns}, + index=data.index, + columns=data.columns, + ) + + # add back other columns to compare + for c in df.columns: + if c not in bifw: + bifw[c] = df[c] + bifw = bifw.reindex(columns=df.columns) + + tm.assert_frame_equal(bif, bifw, check_dtype=False) + for c in df.columns: + if bif[c].dtype != bifw[c].dtype: + assert bif[c].dtype == df[c].dtype + + def test_getitem_boolean_casting(self, datetime_frame): + # don't upcast if we don't need to + df = datetime_frame.copy() + df["E"] = 1 + df["E"] = df["E"].astype("int32") + df["E1"] = df["E"].copy() + df["F"] = 1 + df["F"] = df["F"].astype("int64") + df["F1"] = df["F"].copy() + + casted = df[df > 0] + result = casted.dtypes + expected = Series( + [np.dtype("float64")] * 4 + + [np.dtype("int32")] * 2 + + [np.dtype("int64")] * 2, + index=["A", "B", "C", "D", "E", "E1", "F", "F1"], + ) + tm.assert_series_equal(result, expected) + + # int block splitting + df.loc[df.index[1:3], ["E1", "F1"]] = 0 + casted = df[df > 0] + result = casted.dtypes + expected = Series( + [np.dtype("float64")] * 4 + + [np.dtype("int32")] + + [np.dtype("float64")] + + [np.dtype("int64")] + + [np.dtype("float64")], + index=["A", "B", "C", "D", "E", "E1", "F", "F1"], + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "lst", [[True, False, True], [True, True, True], [False, False, False]] + ) + def test_getitem_boolean_list(self, lst): + df = DataFrame(np.arange(12).reshape(3, 4)) + result = df[lst] + expected = df.loc[df.index[lst]] + tm.assert_frame_equal(result, expected) + + def test_getitem_boolean_iadd(self): + arr = np.random.default_rng(2).standard_normal((5, 5)) + + df = DataFrame(arr.copy(), columns=["A", "B", "C", "D", "E"]) + + df[df < 0] += 1 + arr[arr < 0] += 1 + + tm.assert_almost_equal(df.values, arr) + + def test_boolean_index_empty_corner(self): + # #2096 + blah = DataFrame(np.empty([0, 1]), columns=["A"], index=DatetimeIndex([])) + + # both of these should succeed trivially + k = np.array([], bool) + + blah[k] + blah[k] = 0 + + def test_getitem_ix_mixed_integer(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 3)), + index=[1, 10, "C", "E"], + columns=[1, 2, 3], + ) + + result = df.iloc[:-1] + expected = df.loc[df.index[:-1]] + tm.assert_frame_equal(result, expected) + + result = df.loc[[1, 10]] + expected = df.loc[Index([1, 10])] + tm.assert_frame_equal(result, expected) + + def test_getitem_ix_mixed_integer2(self): + # 11320 + df = DataFrame( + { + "rna": (1.5, 2.2, 3.2, 4.5), + -1000: [11, 21, 36, 40], + 0: [10, 22, 43, 34], + 1000: [0, 10, 20, 30], + }, + columns=["rna", -1000, 0, 1000], + ) + result = df[[1000]] + expected = df.iloc[:, [3]] + tm.assert_frame_equal(result, expected) + result = df[[-1000]] + expected = df.iloc[:, [1]] + tm.assert_frame_equal(result, expected) + + def test_getattr(self, float_frame): + tm.assert_series_equal(float_frame.A, float_frame["A"]) + msg = "'DataFrame' object has no attribute 'NONEXISTENT_NAME'" + with pytest.raises(AttributeError, match=msg): + float_frame.NONEXISTENT_NAME + + def test_setattr_column(self): + df = DataFrame({"foobar": 1}, index=range(10)) + + df.foobar = 5 + assert (df.foobar == 5).all() + + def test_setitem( + self, float_frame, using_copy_on_write, warn_copy_on_write, using_infer_string + ): + # not sure what else to do here + series = float_frame["A"][::2] + float_frame["col5"] = series + assert "col5" in float_frame + + assert len(series) == 15 + assert len(float_frame) == 30 + + exp = np.ravel(np.column_stack((series.values, [np.nan] * 15))) + exp = Series(exp, index=float_frame.index, name="col5") + tm.assert_series_equal(float_frame["col5"], exp) + + series = float_frame["A"] + float_frame["col6"] = series + tm.assert_series_equal(series, float_frame["col6"], check_names=False) + + # set ndarray + arr = np.random.default_rng(2).standard_normal(len(float_frame)) + float_frame["col9"] = arr + assert (float_frame["col9"] == arr).all() + + float_frame["col7"] = 5 + assert (float_frame["col7"] == 5).all() + + float_frame["col0"] = 3.14 + assert (float_frame["col0"] == 3.14).all() + + float_frame["col8"] = "foo" + assert (float_frame["col8"] == "foo").all() + + # this is partially a view (e.g. some blocks are view) + # so raise/warn + smaller = float_frame[:2] + + msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame" + if using_copy_on_write or warn_copy_on_write: + # With CoW, adding a new column doesn't raise a warning + smaller["col10"] = ["1", "2"] + else: + with pytest.raises(SettingWithCopyError, match=msg): + smaller["col10"] = ["1", "2"] + + if using_infer_string: + assert smaller["col10"].dtype == "str" + else: + assert smaller["col10"].dtype == np.object_ + assert (smaller["col10"] == ["1", "2"]).all() + + def test_setitem2(self): + # dtype changing GH4204 + df = DataFrame([[0, 0]]) + df.iloc[0] = np.nan + expected = DataFrame([[np.nan, np.nan]]) + tm.assert_frame_equal(df, expected) + + df = DataFrame([[0, 0]]) + df.loc[0] = np.nan + tm.assert_frame_equal(df, expected) + + def test_setitem_boolean(self, float_frame): + df = float_frame.copy() + values = float_frame.values.copy() + + df[df["A"] > 0] = 4 + values[values[:, 0] > 0] = 4 + tm.assert_almost_equal(df.values, values) + + # test that column reindexing works + series = df["A"] == 4 + series = series.reindex(df.index[::-1]) + df[series] = 1 + values[values[:, 0] == 4] = 1 + tm.assert_almost_equal(df.values, values) + + df[df > 0] = 5 + values[values > 0] = 5 + tm.assert_almost_equal(df.values, values) + + df[df == 5] = 0 + values[values == 5] = 0 + tm.assert_almost_equal(df.values, values) + + # a df that needs alignment first + df[df[:-1] < 0] = 2 + np.putmask(values[:-1], values[:-1] < 0, 2) + tm.assert_almost_equal(df.values, values) + + # indexed with same shape but rows-reversed df + df[df[::-1] == 2] = 3 + values[values == 2] = 3 + tm.assert_almost_equal(df.values, values) + + msg = "Must pass DataFrame or 2-d ndarray with boolean values only" + with pytest.raises(TypeError, match=msg): + df[df * 0] = 2 + + # index with DataFrame + df_orig = df.copy() + mask = df > np.abs(df) + df[df > np.abs(df)] = np.nan + values = df_orig.values.copy() + values[mask.values] = np.nan + expected = DataFrame(values, index=df_orig.index, columns=df_orig.columns) + tm.assert_frame_equal(df, expected) + + # set from DataFrame + df[df > np.abs(df)] = df * 2 + np.putmask(values, mask.values, df.values * 2) + expected = DataFrame(values, index=df_orig.index, columns=df_orig.columns) + tm.assert_frame_equal(df, expected) + + def test_setitem_cast(self, float_frame): + float_frame["D"] = float_frame["D"].astype("i8") + assert float_frame["D"].dtype == np.int64 + + # #669, should not cast? + # this is now set to int64, which means a replacement of the column to + # the value dtype (and nothing to do with the existing dtype) + float_frame["B"] = 0 + assert float_frame["B"].dtype == np.int64 + + # cast if pass array of course + float_frame["B"] = np.arange(len(float_frame)) + assert issubclass(float_frame["B"].dtype.type, np.integer) + + float_frame["foo"] = "bar" + float_frame["foo"] = 0 + assert float_frame["foo"].dtype == np.int64 + + float_frame["foo"] = "bar" + float_frame["foo"] = 2.5 + assert float_frame["foo"].dtype == np.float64 + + float_frame["something"] = 0 + assert float_frame["something"].dtype == np.int64 + float_frame["something"] = 2 + assert float_frame["something"].dtype == np.int64 + float_frame["something"] = 2.5 + assert float_frame["something"].dtype == np.float64 + + def test_setitem_corner(self, float_frame, using_infer_string): + # corner case + df = DataFrame({"B": [1.0, 2.0, 3.0], "C": ["a", "b", "c"]}, index=np.arange(3)) + del df["B"] + df["B"] = [1.0, 2.0, 3.0] + assert "B" in df + assert len(df.columns) == 2 + + df["A"] = "beginning" + df["E"] = "foo" + df["D"] = "bar" + df[datetime.now()] = "date" + df[datetime.now()] = 5.0 + + # what to do when empty frame with index + dm = DataFrame(index=float_frame.index) + dm["A"] = "foo" + dm["B"] = "bar" + assert len(dm.columns) == 2 + assert dm.values.dtype == np.object_ + + # upcast + dm["C"] = 1 + assert dm["C"].dtype == np.int64 + + dm["E"] = 1.0 + assert dm["E"].dtype == np.float64 + + # set existing column + dm["A"] = "bar" + assert "bar" == dm["A"].iloc[0] + + dm = DataFrame(index=np.arange(3)) + dm["A"] = 1 + dm["foo"] = "bar" + del dm["foo"] + dm["foo"] = "bar" + if using_infer_string: + assert dm["foo"].dtype == "str" + else: + assert dm["foo"].dtype == np.object_ + + dm["coercible"] = ["1", "2", "3"] + if using_infer_string: + assert dm["coercible"].dtype == "str" + else: + assert dm["coercible"].dtype == np.object_ + + def test_setitem_corner2(self): + data = { + "title": ["foobar", "bar", "foobar"] + ["foobar"] * 17, + "cruft": np.random.default_rng(2).random(20), + } + + df = DataFrame(data) + ix = df[df["title"] == "bar"].index + + df.loc[ix, ["title"]] = "foobar" + df.loc[ix, ["cruft"]] = 0 + + assert df.loc[1, "title"] == "foobar" + assert df.loc[1, "cruft"] == 0 + + def test_setitem_ambig(self, using_infer_string): + # Difficulties with mixed-type data + # Created as float type + dm = DataFrame(index=range(3), columns=range(3)) + + coercable_series = Series([Decimal(1) for _ in range(3)], index=range(3)) + uncoercable_series = Series(["foo", "bzr", "baz"], index=range(3)) + + dm[0] = np.ones(3) + assert len(dm.columns) == 3 + + dm[1] = coercable_series + assert len(dm.columns) == 3 + + dm[2] = uncoercable_series + assert len(dm.columns) == 3 + if using_infer_string: + assert dm[2].dtype == "str" + else: + assert dm[2].dtype == np.object_ + + def test_setitem_None(self, float_frame): + # GH #766 + float_frame[None] = float_frame["A"] + tm.assert_series_equal( + float_frame.iloc[:, -1], float_frame["A"], check_names=False + ) + tm.assert_series_equal( + float_frame.loc[:, None], float_frame["A"], check_names=False + ) + tm.assert_series_equal(float_frame[None], float_frame["A"], check_names=False) + + def test_loc_setitem_boolean_mask_allfalse(self): + # GH 9596 + df = DataFrame( + {"a": ["1", "2", "3"], "b": ["11", "22", "33"], "c": ["111", "222", "333"]} + ) + + result = df.copy() + result.loc[result.b.isna(), "a"] = result.a.copy() + tm.assert_frame_equal(result, df) + + def test_getitem_fancy_slice_integers_step(self): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 5))) + + # this is OK + df.iloc[:8:2] + df.iloc[:8:2] = np.nan + assert isna(df.iloc[:8:2]).values.all() + + def test_getitem_setitem_integer_slice_keyerrors(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 5)), index=range(0, 20, 2) + ) + + # this is OK + cp = df.copy() + cp.iloc[4:10] = 0 + assert (cp.iloc[4:10] == 0).values.all() + + # so is this + cp = df.copy() + cp.iloc[3:11] = 0 + assert (cp.iloc[3:11] == 0).values.all() + + result = df.iloc[2:6] + result2 = df.loc[3:11] + expected = df.reindex([4, 6, 8, 10]) + + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result2, expected) + + # non-monotonic, raise KeyError + df2 = df.iloc[list(range(5)) + list(range(5, 10))[::-1]] + with pytest.raises(KeyError, match=r"^3$"): + df2.loc[3:11] + with pytest.raises(KeyError, match=r"^3$"): + df2.loc[3:11] = 0 + + @td.skip_array_manager_invalid_test # already covered in test_iloc_col_slice_view + def test_fancy_getitem_slice_mixed( + self, float_frame, float_string_frame, using_copy_on_write, warn_copy_on_write + ): + sliced = float_string_frame.iloc[:, -3:] + assert sliced["D"].dtype == np.float64 + + # get view with single block + # setting it triggers setting with copy + original = float_frame.copy() + sliced = float_frame.iloc[:, -3:] + + assert np.shares_memory(sliced["C"]._values, float_frame["C"]._values) + + with tm.assert_cow_warning(warn_copy_on_write): + sliced.loc[:, "C"] = 4.0 + if not using_copy_on_write: + assert (float_frame["C"] == 4).all() + + # with the enforcement of GH#45333 in 2.0, this remains a view + np.shares_memory(sliced["C"]._values, float_frame["C"]._values) + else: + tm.assert_frame_equal(float_frame, original) + + def test_getitem_setitem_non_ix_labels(self): + df = DataFrame(range(20), index=date_range("2020-01-01", periods=20)) + + start, end = df.index[[5, 10]] + + result = df.loc[start:end] + result2 = df[start:end] + expected = df[5:11] + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result2, expected) + + result = df.copy() + result.loc[start:end] = 0 + result2 = df.copy() + result2[start:end] = 0 + expected = df.copy() + expected[5:11] = 0 + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result2, expected) + + def test_ix_multi_take(self): + df = DataFrame(np.random.default_rng(2).standard_normal((3, 2))) + rs = df.loc[df.index == 0, :] + xp = df.reindex([0]) + tm.assert_frame_equal(rs, xp) + + # GH#1321 + df = DataFrame(np.random.default_rng(2).standard_normal((3, 2))) + rs = df.loc[df.index == 0, df.columns == 1] + xp = df.reindex(index=[0], columns=[1]) + tm.assert_frame_equal(rs, xp) + + def test_getitem_fancy_scalar(self, float_frame): + f = float_frame + ix = f.loc + + # individual value + for col in f.columns: + ts = f[col] + for idx in f.index[::5]: + assert ix[idx, col] == ts[idx] + + @td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite not using .values + def test_setitem_fancy_scalar(self, float_frame): + f = float_frame + expected = float_frame.copy() + ix = f.loc + + # individual value + for j, col in enumerate(f.columns): + f[col] + for idx in f.index[::5]: + i = f.index.get_loc(idx) + val = np.random.default_rng(2).standard_normal() + expected.iloc[i, j] = val + + ix[idx, col] = val + tm.assert_frame_equal(f, expected) + + def test_getitem_fancy_boolean(self, float_frame): + f = float_frame + ix = f.loc + + expected = f.reindex(columns=["B", "D"]) + result = ix[:, [False, True, False, True]] + tm.assert_frame_equal(result, expected) + + expected = f.reindex(index=f.index[5:10], columns=["B", "D"]) + result = ix[f.index[5:10], [False, True, False, True]] + tm.assert_frame_equal(result, expected) + + boolvec = f.index > f.index[7] + expected = f.reindex(index=f.index[boolvec]) + result = ix[boolvec] + tm.assert_frame_equal(result, expected) + result = ix[boolvec, :] + tm.assert_frame_equal(result, expected) + + result = ix[boolvec, f.columns[2:]] + expected = f.reindex(index=f.index[boolvec], columns=["C", "D"]) + tm.assert_frame_equal(result, expected) + + @td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite not using .values + def test_setitem_fancy_boolean(self, float_frame): + # from 2d, set with booleans + frame = float_frame.copy() + expected = float_frame.copy() + values = expected.values.copy() + + mask = frame["A"] > 0 + frame.loc[mask] = 0.0 + values[mask.values] = 0.0 + expected = DataFrame(values, index=expected.index, columns=expected.columns) + tm.assert_frame_equal(frame, expected) + + frame = float_frame.copy() + expected = float_frame.copy() + values = expected.values.copy() + frame.loc[mask, ["A", "B"]] = 0.0 + values[mask.values, :2] = 0.0 + expected = DataFrame(values, index=expected.index, columns=expected.columns) + tm.assert_frame_equal(frame, expected) + + def test_getitem_fancy_ints(self, float_frame): + result = float_frame.iloc[[1, 4, 7]] + expected = float_frame.loc[float_frame.index[[1, 4, 7]]] + tm.assert_frame_equal(result, expected) + + result = float_frame.iloc[:, [2, 0, 1]] + expected = float_frame.loc[:, float_frame.columns[[2, 0, 1]]] + tm.assert_frame_equal(result, expected) + + def test_getitem_setitem_boolean_misaligned(self, float_frame): + # boolean index misaligned labels + mask = float_frame["A"][::-1] > 1 + + result = float_frame.loc[mask] + expected = float_frame.loc[mask[::-1]] + tm.assert_frame_equal(result, expected) + + cp = float_frame.copy() + expected = float_frame.copy() + cp.loc[mask] = 0 + expected.loc[mask] = 0 + tm.assert_frame_equal(cp, expected) + + def test_getitem_setitem_boolean_multi(self): + df = DataFrame(np.random.default_rng(2).standard_normal((3, 2))) + + # get + k1 = np.array([True, False, True]) + k2 = np.array([False, True]) + result = df.loc[k1, k2] + expected = df.loc[[0, 2], [1]] + tm.assert_frame_equal(result, expected) + + expected = df.copy() + df.loc[np.array([True, False, True]), np.array([False, True])] = 5 + expected.loc[[0, 2], [1]] = 5 + tm.assert_frame_equal(df, expected) + + def test_getitem_setitem_float_labels(self, using_array_manager): + index = Index([1.5, 2, 3, 4, 5]) + df = DataFrame(np.random.default_rng(2).standard_normal((5, 5)), index=index) + + result = df.loc[1.5:4] + expected = df.reindex([1.5, 2, 3, 4]) + tm.assert_frame_equal(result, expected) + assert len(result) == 4 + + result = df.loc[4:5] + expected = df.reindex([4, 5]) # reindex with int + tm.assert_frame_equal(result, expected, check_index_type=False) + assert len(result) == 2 + + result = df.loc[4:5] + expected = df.reindex([4.0, 5.0]) # reindex with float + tm.assert_frame_equal(result, expected) + assert len(result) == 2 + + # loc_float changes this to work properly + result = df.loc[1:2] + expected = df.iloc[0:2] + tm.assert_frame_equal(result, expected) + + expected = df.iloc[0:2] + msg = r"The behavior of obj\[i:j\] with a float-dtype index" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df[1:2] + tm.assert_frame_equal(result, expected) + + # #2727 + index = Index([1.0, 2.5, 3.5, 4.5, 5.0]) + df = DataFrame(np.random.default_rng(2).standard_normal((5, 5)), index=index) + + # positional slicing only via iloc! + msg = ( + "cannot do positional indexing on Index with " + r"these indexers \[1.0\] of type float" + ) + with pytest.raises(TypeError, match=msg): + df.iloc[1.0:5] + + result = df.iloc[4:5] + expected = df.reindex([5.0]) + tm.assert_frame_equal(result, expected) + assert len(result) == 1 + + cp = df.copy() + + with pytest.raises(TypeError, match=_slice_msg): + cp.iloc[1.0:5] = 0 + + with pytest.raises(TypeError, match=msg): + result = cp.iloc[1.0:5] == 0 + + assert result.values.all() + assert (cp.iloc[0:1] == df.iloc[0:1]).values.all() + + cp = df.copy() + cp.iloc[4:5] = 0 + assert (cp.iloc[4:5] == 0).values.all() + assert (cp.iloc[0:4] == df.iloc[0:4]).values.all() + + # float slicing + result = df.loc[1.0:5] + expected = df + tm.assert_frame_equal(result, expected) + assert len(result) == 5 + + result = df.loc[1.1:5] + expected = df.reindex([2.5, 3.5, 4.5, 5.0]) + tm.assert_frame_equal(result, expected) + assert len(result) == 4 + + result = df.loc[4.51:5] + expected = df.reindex([5.0]) + tm.assert_frame_equal(result, expected) + assert len(result) == 1 + + result = df.loc[1.0:5.0] + expected = df.reindex([1.0, 2.5, 3.5, 4.5, 5.0]) + tm.assert_frame_equal(result, expected) + assert len(result) == 5 + + cp = df.copy() + cp.loc[1.0:5.0] = 0 + result = cp.loc[1.0:5.0] + assert (result == 0).values.all() + + def test_setitem_single_column_mixed_datetime(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + index=["a", "b", "c", "d", "e"], + columns=["foo", "bar", "baz"], + ) + + df["timestamp"] = Timestamp("20010102") + + # check our dtypes + result = df.dtypes + expected = Series( + [np.dtype("float64")] * 3 + [np.dtype("datetime64[s]")], + index=["foo", "bar", "baz", "timestamp"], + ) + tm.assert_series_equal(result, expected) + + # GH#16674 iNaT is treated as an integer when given by the user + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + df.loc["b", "timestamp"] = iNaT + assert not isna(df.loc["b", "timestamp"]) + assert df["timestamp"].dtype == np.object_ + assert df.loc["b", "timestamp"] == iNaT + + # allow this syntax (as of GH#3216) + df.loc["c", "timestamp"] = np.nan + assert isna(df.loc["c", "timestamp"]) + + # allow this syntax + df.loc["d", :] = np.nan + assert not isna(df.loc["c", :]).all() + + def test_setitem_mixed_datetime(self): + # GH 9336 + expected = DataFrame( + { + "a": [0, 0, 0, 0, 13, 14], + "b": [ + datetime(2012, 1, 1), + 1, + "x", + "y", + datetime(2013, 1, 1), + datetime(2014, 1, 1), + ], + } + ) + df = DataFrame(0, columns=list("ab"), index=range(6)) + df["b"] = pd.NaT + df.loc[0, "b"] = datetime(2012, 1, 1) + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + df.loc[1, "b"] = 1 + df.loc[[2, 3], "b"] = "x", "y" + A = np.array( + [ + [13, np.datetime64("2013-01-01T00:00:00")], + [14, np.datetime64("2014-01-01T00:00:00")], + ] + ) + df.loc[[4, 5], ["a", "b"]] = A + tm.assert_frame_equal(df, expected) + + def test_setitem_frame_float(self, float_frame): + piece = float_frame.loc[float_frame.index[:2], ["A", "B"]] + float_frame.loc[float_frame.index[-2] :, ["A", "B"]] = piece.values + result = float_frame.loc[float_frame.index[-2:], ["A", "B"]].values + expected = piece.values + tm.assert_almost_equal(result, expected) + + # dtype inference + @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)") + def test_setitem_frame_mixed(self, float_string_frame): + # GH 3216 + + # already aligned + f = float_string_frame.copy() + piece = DataFrame( + [[1.0, 2.0], [3.0, 4.0]], index=f.index[0:2], columns=["A", "B"] + ) + key = (f.index[slice(None, 2)], ["A", "B"]) + f.loc[key] = piece + tm.assert_almost_equal(f.loc[f.index[0:2], ["A", "B"]].values, piece.values) + + # dtype inference + @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)") + def test_setitem_frame_mixed_rows_unaligned(self, float_string_frame): + # GH#3216 rows unaligned + f = float_string_frame.copy() + piece = DataFrame( + [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]], + index=list(f.index[0:2]) + ["foo", "bar"], + columns=["A", "B"], + ) + key = (f.index[slice(None, 2)], ["A", "B"]) + f.loc[key] = piece + tm.assert_almost_equal( + f.loc[f.index[0:2:], ["A", "B"]].values, piece.values[0:2] + ) + + # dtype inference + @pytest.mark.xfail(using_string_dtype(), reason="TODO(infer_string)") + def test_setitem_frame_mixed_key_unaligned(self, float_string_frame): + # GH#3216 key is unaligned with values + f = float_string_frame.copy() + piece = f.loc[f.index[:2], ["A"]] + piece.index = f.index[-2:] + key = (f.index[slice(-2, None)], ["A", "B"]) + f.loc[key] = piece + piece["B"] = np.nan + tm.assert_almost_equal(f.loc[f.index[-2:], ["A", "B"]].values, piece.values) + + def test_setitem_frame_mixed_ndarray(self, float_string_frame): + # GH#3216 ndarray + f = float_string_frame.copy() + piece = float_string_frame.loc[f.index[:2], ["A", "B"]] + key = (f.index[slice(-2, None)], ["A", "B"]) + f.loc[key] = piece.values + tm.assert_almost_equal(f.loc[f.index[-2:], ["A", "B"]].values, piece.values) + + def test_setitem_frame_upcast(self): + # needs upcasting + df = DataFrame([[1, 2, "foo"], [3, 4, "bar"]], columns=["A", "B", "C"]) + df2 = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + df2.loc[:, ["A", "B"]] = df.loc[:, ["A", "B"]] + 0.5 + expected = df.reindex(columns=["A", "B"]) + expected += 0.5 + expected["C"] = df["C"] + tm.assert_frame_equal(df2, expected) + + def test_setitem_frame_align(self, float_frame): + piece = float_frame.loc[float_frame.index[:2], ["A", "B"]] + piece.index = float_frame.index[-2:] + piece.columns = ["A", "B"] + float_frame.loc[float_frame.index[-2:], ["A", "B"]] = piece + result = float_frame.loc[float_frame.index[-2:], ["A", "B"]].values + expected = piece.values + tm.assert_almost_equal(result, expected) + + def test_getitem_setitem_ix_duplicates(self): + # #1201 + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + index=["foo", "foo", "bar", "baz", "bar"], + ) + + result = df.loc["foo"] + expected = df[:2] + tm.assert_frame_equal(result, expected) + + result = df.loc["bar"] + expected = df.iloc[[2, 4]] + tm.assert_frame_equal(result, expected) + + result = df.loc["baz"] + expected = df.iloc[3] + tm.assert_series_equal(result, expected) + + def test_getitem_ix_boolean_duplicates_multiple(self): + # #1201 + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + index=["foo", "foo", "bar", "baz", "bar"], + ) + + result = df.loc[["bar"]] + exp = df.iloc[[2, 4]] + tm.assert_frame_equal(result, exp) + + result = df.loc[df[1] > 0] + exp = df[df[1] > 0] + tm.assert_frame_equal(result, exp) + + result = df.loc[df[0] > 0] + exp = df[df[0] > 0] + tm.assert_frame_equal(result, exp) + + @pytest.mark.parametrize("bool_value", [True, False]) + def test_getitem_setitem_ix_bool_keyerror(self, bool_value): + # #2199 + df = DataFrame({"a": [1, 2, 3]}) + message = f"{bool_value}: boolean label can not be used without a boolean index" + with pytest.raises(KeyError, match=message): + df.loc[bool_value] + + msg = "cannot use a single bool to index into setitem" + with pytest.raises(KeyError, match=msg): + df.loc[bool_value] = 0 + + # TODO: rename? remove? + def test_single_element_ix_dont_upcast(self, float_frame): + float_frame["E"] = 1 + assert issubclass(float_frame["E"].dtype.type, (int, np.integer)) + + result = float_frame.loc[float_frame.index[5], "E"] + assert is_integer(result) + + # GH 11617 + df = DataFrame({"a": [1.23]}) + df["b"] = 666 + + result = df.loc[0, "b"] + assert is_integer(result) + + expected = Series([666], [0], name="b") + result = df.loc[[0], "b"] + tm.assert_series_equal(result, expected) + + def test_iloc_callable_tuple_return_value(self): + # GH53769 + df = DataFrame(np.arange(40).reshape(10, 4), index=range(0, 20, 2)) + msg = "callable with iloc" + with tm.assert_produces_warning(FutureWarning, match=msg): + df.iloc[lambda _: (0,)] + with tm.assert_produces_warning(FutureWarning, match=msg): + df.iloc[lambda _: (0,)] = 1 + + def test_iloc_row(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), index=range(0, 20, 2) + ) + + result = df.iloc[1] + exp = df.loc[2] + tm.assert_series_equal(result, exp) + + result = df.iloc[2] + exp = df.loc[4] + tm.assert_series_equal(result, exp) + + # slice + result = df.iloc[slice(4, 8)] + expected = df.loc[8:14] + tm.assert_frame_equal(result, expected) + + # list of integers + result = df.iloc[[1, 2, 4, 6]] + expected = df.reindex(df.index[[1, 2, 4, 6]]) + tm.assert_frame_equal(result, expected) + + def test_iloc_row_slice_view(self, using_copy_on_write, warn_copy_on_write): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), index=range(0, 20, 2) + ) + original = df.copy() + + # verify slice is view + # setting it makes it raise/warn + subset = df.iloc[slice(4, 8)] + + assert np.shares_memory(df[2], subset[2]) + + exp_col = original[2].copy() + with tm.assert_cow_warning(warn_copy_on_write): + subset.loc[:, 2] = 0.0 + if not using_copy_on_write: + exp_col._values[4:8] = 0.0 + + # With the enforcement of GH#45333 in 2.0, this remains a view + assert np.shares_memory(df[2], subset[2]) + tm.assert_series_equal(df[2], exp_col) + + def test_iloc_col(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 10)), columns=range(0, 20, 2) + ) + + result = df.iloc[:, 1] + exp = df.loc[:, 2] + tm.assert_series_equal(result, exp) + + result = df.iloc[:, 2] + exp = df.loc[:, 4] + tm.assert_series_equal(result, exp) + + # slice + result = df.iloc[:, slice(4, 8)] + expected = df.loc[:, 8:14] + tm.assert_frame_equal(result, expected) + + # list of integers + result = df.iloc[:, [1, 2, 4, 6]] + expected = df.reindex(columns=df.columns[[1, 2, 4, 6]]) + tm.assert_frame_equal(result, expected) + + def test_iloc_col_slice_view( + self, using_array_manager, using_copy_on_write, warn_copy_on_write + ): + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 10)), columns=range(0, 20, 2) + ) + original = df.copy() + subset = df.iloc[:, slice(4, 8)] + + if not using_array_manager and not using_copy_on_write: + # verify slice is view + assert np.shares_memory(df[8]._values, subset[8]._values) + + with tm.assert_cow_warning(warn_copy_on_write): + subset.loc[:, 8] = 0.0 + + assert (df[8] == 0).all() + + # with the enforcement of GH#45333 in 2.0, this remains a view + assert np.shares_memory(df[8]._values, subset[8]._values) + else: + if using_copy_on_write: + # verify slice is view + assert np.shares_memory(df[8]._values, subset[8]._values) + subset[8] = 0.0 + # subset changed + assert (subset[8] == 0).all() + # but df itself did not change (setitem replaces full column) + tm.assert_frame_equal(df, original) + + def test_loc_duplicates(self): + # gh-17105 + + # insert a duplicate element to the index + trange = date_range( + start=Timestamp(year=2017, month=1, day=1), + end=Timestamp(year=2017, month=1, day=5), + ) + + trange = trange.insert(loc=5, item=Timestamp(year=2017, month=1, day=5)) + + df = DataFrame(0, index=trange, columns=["A", "B"]) + bool_idx = np.array([False, False, False, False, False, True]) + + # assignment + df.loc[trange[bool_idx], "A"] = 6 + + expected = DataFrame( + {"A": [0, 0, 0, 0, 6, 6], "B": [0, 0, 0, 0, 0, 0]}, index=trange + ) + tm.assert_frame_equal(df, expected) + + # in-place + df = DataFrame(0, index=trange, columns=["A", "B"]) + df.loc[trange[bool_idx], "A"] += 6 + tm.assert_frame_equal(df, expected) + + def test_setitem_with_unaligned_tz_aware_datetime_column(self): + # GH 12981 + # Assignment of unaligned offset-aware datetime series. + # Make sure timezone isn't lost + column = Series(date_range("2015-01-01", periods=3, tz="utc"), name="dates") + df = DataFrame({"dates": column}) + df["dates"] = column[[1, 0, 2]] + tm.assert_series_equal(df["dates"], column) + + df = DataFrame({"dates": column}) + df.loc[[0, 1, 2], "dates"] = column[[1, 0, 2]] + tm.assert_series_equal(df["dates"], column) + + def test_loc_setitem_datetimelike_with_inference(self): + # GH 7592 + # assignment of timedeltas with NaT + + one_hour = timedelta(hours=1) + df = DataFrame(index=date_range("20130101", periods=4)) + df["A"] = np.array([1 * one_hour] * 4, dtype="m8[ns]") + df.loc[:, "B"] = np.array([2 * one_hour] * 4, dtype="m8[ns]") + df.loc[df.index[:3], "C"] = np.array([3 * one_hour] * 3, dtype="m8[ns]") + df.loc[:, "D"] = np.array([4 * one_hour] * 4, dtype="m8[ns]") + df.loc[df.index[:3], "E"] = np.array([5 * one_hour] * 3, dtype="m8[ns]") + df["F"] = np.timedelta64("NaT") + df.loc[df.index[:-1], "F"] = np.array([6 * one_hour] * 3, dtype="m8[ns]") + df.loc[df.index[-3] :, "G"] = date_range("20130101", periods=3) + df["H"] = np.datetime64("NaT") + result = df.dtypes + expected = Series( + [np.dtype("timedelta64[ns]")] * 6 + [np.dtype("datetime64[ns]")] * 2, + index=list("ABCDEFGH"), + ) + tm.assert_series_equal(result, expected) + + def test_getitem_boolean_indexing_mixed(self): + df = DataFrame( + { + 0: {35: np.nan, 40: np.nan, 43: np.nan, 49: np.nan, 50: np.nan}, + 1: { + 35: np.nan, + 40: 0.32632316859446198, + 43: np.nan, + 49: 0.32632316859446198, + 50: 0.39114724480578139, + }, + 2: { + 35: np.nan, + 40: np.nan, + 43: 0.29012581014105987, + 49: np.nan, + 50: np.nan, + }, + 3: {35: np.nan, 40: np.nan, 43: np.nan, 49: np.nan, 50: np.nan}, + 4: { + 35: 0.34215328467153283, + 40: np.nan, + 43: np.nan, + 49: np.nan, + 50: np.nan, + }, + "y": {35: 0, 40: 0, 43: 0, 49: 0, 50: 1}, + } + ) + + # mixed int/float ok + df2 = df.copy() + df2[df2 > 0.3] = 1 + expected = df.copy() + expected.loc[40, 1] = 1 + expected.loc[49, 1] = 1 + expected.loc[50, 1] = 1 + expected.loc[35, 4] = 1 + tm.assert_frame_equal(df2, expected) + + df["foo"] = "test" + msg = "not supported between instances|unorderable types|Invalid comparison" + + with pytest.raises(TypeError, match=msg): + df[df > 0.3] = 1 + + def test_type_error_multiindex(self): + # See gh-12218 + mi = MultiIndex.from_product([["x", "y"], [0, 1]], names=[None, "c"]) + dg = DataFrame( + [[1, 1, 2, 2], [3, 3, 4, 4]], columns=mi, index=Index([0, 1], name="i") + ) + with pytest.raises(InvalidIndexError, match="slice"): + dg[:, 0] + + index = Index(range(2), name="i") + columns = MultiIndex( + levels=[["x", "y"], [0, 1]], codes=[[0, 1], [0, 0]], names=[None, "c"] + ) + expected = DataFrame([[1, 2], [3, 4]], columns=columns, index=index) + + result = dg.loc[:, (slice(None), 0)] + tm.assert_frame_equal(result, expected) + + name = ("x", 0) + index = Index(range(2), name="i") + expected = Series([1, 3], index=index, name=name) + + result = dg["x", 0] + tm.assert_series_equal(result, expected) + + def test_getitem_interval_index_partial_indexing(self): + # GH#36490 + df = DataFrame( + np.ones((3, 4)), columns=pd.IntervalIndex.from_breaks(np.arange(5)) + ) + + expected = df.iloc[:, 0] + + res = df[0.5] + tm.assert_series_equal(res, expected) + + res = df.loc[:, 0.5] + tm.assert_series_equal(res, expected) + + def test_setitem_array_as_cell_value(self): + # GH#43422 + df = DataFrame(columns=["a", "b"], dtype=object) + df.loc[0] = {"a": np.zeros((2,)), "b": np.zeros((2, 2))} + expected = DataFrame({"a": [np.zeros((2,))], "b": [np.zeros((2, 2))]}) + tm.assert_frame_equal(df, expected) + + def test_iloc_setitem_nullable_2d_values(self): + df = DataFrame({"A": [1, 2, 3]}, dtype="Int64") + orig = df.copy() + + df.loc[:] = df.values[:, ::-1] + tm.assert_frame_equal(df, orig) + + df.loc[:] = pd.core.arrays.NumpyExtensionArray(df.values[:, ::-1]) + tm.assert_frame_equal(df, orig) + + df.iloc[:] = df.iloc[:, :].copy() + tm.assert_frame_equal(df, orig) + + def test_getitem_segfault_with_empty_like_object(self): + # GH#46848 + df = DataFrame(np.empty((1, 1), dtype=object)) + df[0] = np.empty_like(df[0]) + # this produces the segfault + df[[0]] + + @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning") + @pytest.mark.parametrize( + "null", [pd.NaT, pd.NaT.to_numpy("M8[ns]"), pd.NaT.to_numpy("m8[ns]")] + ) + def test_setting_mismatched_na_into_nullable_fails( + self, null, any_numeric_ea_dtype + ): + # GH#44514 don't cast mismatched nulls to pd.NA + df = DataFrame({"A": [1, 2, 3]}, dtype=any_numeric_ea_dtype) + ser = df["A"].copy() + arr = ser._values + + msg = "|".join( + [ + r"timedelta64\[ns\] cannot be converted to (Floating|Integer)Dtype", + r"datetime64\[ns\] cannot be converted to (Floating|Integer)Dtype", + "'values' contains non-numeric NA", + r"Invalid value '.*' for dtype '(U?Int|Float)\d{1,2}'", + ] + ) + with pytest.raises(TypeError, match=msg): + arr[0] = null + + with pytest.raises(TypeError, match=msg): + arr[:2] = [null, null] + + with pytest.raises(TypeError, match=msg): + ser[0] = null + + with pytest.raises(TypeError, match=msg): + ser[:2] = [null, null] + + with pytest.raises(TypeError, match=msg): + ser.iloc[0] = null + + with pytest.raises(TypeError, match=msg): + ser.iloc[:2] = [null, null] + + with pytest.raises(TypeError, match=msg): + df.iloc[0, 0] = null + + with pytest.raises(TypeError, match=msg): + df.iloc[:2, 0] = [null, null] + + # Multi-Block + df2 = df.copy() + df2["B"] = ser.copy() + with pytest.raises(TypeError, match=msg): + df2.iloc[0, 0] = null + + with pytest.raises(TypeError, match=msg): + df2.iloc[:2, 0] = [null, null] + + def test_loc_expand_empty_frame_keep_index_name(self): + # GH#45621 + df = DataFrame(columns=["b"], index=Index([], name="a")) + df.loc[0] = 1 + expected = DataFrame({"b": [1]}, index=Index([0], name="a")) + tm.assert_frame_equal(df, expected) + + def test_loc_expand_empty_frame_keep_midx_names(self): + # GH#46317 + df = DataFrame( + columns=["d"], index=MultiIndex.from_tuples([], names=["a", "b", "c"]) + ) + df.loc[(1, 2, 3)] = "foo" + expected = DataFrame( + {"d": ["foo"]}, + index=MultiIndex.from_tuples([(1, 2, 3)], names=["a", "b", "c"]), + ) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "val, idxr", + [ + ("x", "a"), + ("x", ["a"]), + (1, "a"), + (1, ["a"]), + ], + ) + def test_loc_setitem_rhs_frame(self, idxr, val): + # GH#47578 + df = DataFrame({"a": [1, 2]}) + + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + df.loc[:, idxr] = DataFrame({"a": [val, 11]}, index=[1, 2]) + expected = DataFrame({"a": [np.nan, val]}) + tm.assert_frame_equal(df, expected) + + @td.skip_array_manager_invalid_test + def test_iloc_setitem_enlarge_no_warning(self, warn_copy_on_write): + # GH#47381 + df = DataFrame(columns=["a", "b"]) + expected = df.copy() + view = df[:] + df.iloc[:, 0] = np.array([1, 2], dtype=np.float64) + tm.assert_frame_equal(view, expected) + + def test_loc_internals_not_updated_correctly(self): + # GH#47867 all steps are necessary to reproduce the initial bug + df = DataFrame( + {"bool_col": True, "a": 1, "b": 2.5}, + index=MultiIndex.from_arrays([[1, 2], [1, 2]], names=["idx1", "idx2"]), + ) + idx = [(1, 1)] + + df["c"] = 3 + df.loc[idx, "c"] = 0 + + df.loc[idx, "c"] + df.loc[idx, ["a", "b"]] + + df.loc[idx, "c"] = 15 + result = df.loc[idx, "c"] + expected = df = Series( + 15, + index=MultiIndex.from_arrays([[1], [1]], names=["idx1", "idx2"]), + name="c", + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("val", [None, [None], pd.NA, [pd.NA]]) + def test_iloc_setitem_string_list_na(self, val): + # GH#45469 + df = DataFrame({"a": ["a", "b", "c"]}, dtype="string") + df.iloc[[0], :] = val + expected = DataFrame({"a": [pd.NA, "b", "c"]}, dtype="string") + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("val", [None, pd.NA]) + def test_iloc_setitem_string_na(self, val): + # GH#45469 + df = DataFrame({"a": ["a", "b", "c"]}, dtype="string") + df.iloc[0, :] = val + expected = DataFrame({"a": [pd.NA, "b", "c"]}, dtype="string") + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("func", [list, Series, np.array]) + def test_iloc_setitem_ea_null_slice_length_one_list(self, func): + # GH#48016 + df = DataFrame({"a": [1, 2, 3]}, dtype="Int64") + df.iloc[:, func([0])] = 5 + expected = DataFrame({"a": [5, 5, 5]}, dtype="Int64") + tm.assert_frame_equal(df, expected) + + def test_loc_named_tuple_for_midx(self): + # GH#48124 + df = DataFrame( + index=MultiIndex.from_product( + [["A", "B"], ["a", "b", "c"]], names=["first", "second"] + ) + ) + indexer_tuple = namedtuple("Indexer", df.index.names) + idxr = indexer_tuple(first="A", second=["a", "b"]) + result = df.loc[idxr, :] + expected = DataFrame( + index=MultiIndex.from_tuples( + [("A", "a"), ("A", "b")], names=["first", "second"] + ) + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("indexer", [["a"], "a"]) + @pytest.mark.parametrize("col", [{}, {"b": 1}]) + def test_set_2d_casting_date_to_int(self, col, indexer): + # GH#49159 + df = DataFrame( + {"a": [Timestamp("2022-12-29"), Timestamp("2022-12-30")], **col}, + ) + df.loc[[1], indexer] = df["a"] + pd.Timedelta(days=1) + expected = DataFrame( + {"a": [Timestamp("2022-12-29"), Timestamp("2022-12-31")], **col}, + ) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("col", [{}, {"name": "a"}]) + def test_loc_setitem_reordering_with_all_true_indexer(self, col): + # GH#48701 + n = 17 + df = DataFrame({**col, "x": range(n), "y": range(n)}) + expected = df.copy() + df.loc[n * [True], ["x", "y"]] = df[["x", "y"]] + tm.assert_frame_equal(df, expected) + + def test_loc_rhs_empty_warning(self): + # GH48480 + df = DataFrame(columns=["a", "b"]) + expected = df.copy() + rhs = DataFrame(columns=["a"]) + with tm.assert_produces_warning(None): + df.loc[:, "a"] = rhs + tm.assert_frame_equal(df, expected) + + def test_iloc_ea_series_indexer(self): + # GH#49521 + df = DataFrame([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) + indexer = Series([0, 1], dtype="Int64") + row_indexer = Series([1], dtype="Int64") + result = df.iloc[row_indexer, indexer] + expected = DataFrame([[5, 6]], index=[1]) + tm.assert_frame_equal(result, expected) + + result = df.iloc[row_indexer.values, indexer.values] + tm.assert_frame_equal(result, expected) + + def test_iloc_ea_series_indexer_with_na(self): + # GH#49521 + df = DataFrame([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) + indexer = Series([0, pd.NA], dtype="Int64") + msg = "cannot convert" + with pytest.raises(ValueError, match=msg): + df.iloc[:, indexer] + with pytest.raises(ValueError, match=msg): + df.iloc[:, indexer.values] + + @pytest.mark.parametrize("indexer", [True, (True,)]) + @pytest.mark.parametrize("dtype", [bool, "boolean"]) + def test_loc_bool_multiindex(self, dtype, indexer): + # GH#47687 + midx = MultiIndex.from_arrays( + [ + Series([True, True, False, False], dtype=dtype), + Series([True, False, True, False], dtype=dtype), + ], + names=["a", "b"], + ) + df = DataFrame({"c": [1, 2, 3, 4]}, index=midx) + with tm.maybe_produces_warning(PerformanceWarning, isinstance(indexer, tuple)): + result = df.loc[indexer] + expected = DataFrame( + {"c": [1, 2]}, index=Index([True, False], name="b", dtype=dtype) + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("utc", [False, True]) + @pytest.mark.parametrize("indexer", ["date", ["date"]]) + def test_loc_datetime_assignment_dtype_does_not_change(self, utc, indexer): + # GH#49837 + df = DataFrame( + { + "date": to_datetime( + [datetime(2022, 1, 20), datetime(2022, 1, 22)], utc=utc + ), + "update": [True, False], + } + ) + expected = df.copy(deep=True) + + update_df = df[df["update"]] + + df.loc[df["update"], indexer] = update_df["date"] + + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("indexer, idx", [(tm.loc, 1), (tm.iloc, 2)]) + def test_setitem_value_coercing_dtypes(self, indexer, idx): + # GH#50467 + df = DataFrame([["1", np.nan], ["2", np.nan], ["3", np.nan]], dtype=object) + rhs = DataFrame([[1, np.nan], [2, np.nan]]) + indexer(df)[:idx, :] = rhs + expected = DataFrame([[1, np.nan], [2, np.nan], ["3", np.nan]], dtype=object) + tm.assert_frame_equal(df, expected) + + +class TestDataFrameIndexingUInt64: + def test_setitem(self): + df = DataFrame( + {"A": np.arange(3), "B": [2**63, 2**63 + 5, 2**63 + 10]}, + dtype=np.uint64, + ) + idx = df["A"].rename("foo") + + # setitem + assert "C" not in df.columns + df["C"] = idx + tm.assert_series_equal(df["C"], Series(idx, name="C")) + + assert "D" not in df.columns + df["D"] = "foo" + df["D"] = idx + tm.assert_series_equal(df["D"], Series(idx, name="D")) + del df["D"] + + # With NaN: because uint64 has no NaN element, + # the column should be cast to object. + df2 = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + df2.iloc[1, 1] = pd.NaT + df2.iloc[1, 2] = pd.NaT + result = df2["B"] + tm.assert_series_equal(notna(result), Series([True, False, True], name="B")) + tm.assert_series_equal( + df2.dtypes, + Series( + [np.dtype("uint64"), np.dtype("O"), np.dtype("O")], + index=["A", "B", "C"], + ), + ) + + +def test_object_casting_indexing_wraps_datetimelike(using_array_manager): + # GH#31649, check the indexing methods all the way down the stack + df = DataFrame( + { + "A": [1, 2], + "B": date_range("2000", periods=2), + "C": pd.timedelta_range("1 Day", periods=2), + } + ) + + ser = df.loc[0] + assert isinstance(ser.values[1], Timestamp) + assert isinstance(ser.values[2], pd.Timedelta) + + ser = df.iloc[0] + assert isinstance(ser.values[1], Timestamp) + assert isinstance(ser.values[2], pd.Timedelta) + + ser = df.xs(0, axis=0) + assert isinstance(ser.values[1], Timestamp) + assert isinstance(ser.values[2], pd.Timedelta) + + if using_array_manager: + # remainder of the test checking BlockManager internals + return + + mgr = df._mgr + mgr._rebuild_blknos_and_blklocs() + arr = mgr.fast_xs(0).array + assert isinstance(arr[1], Timestamp) + assert isinstance(arr[2], pd.Timedelta) + + blk = mgr.blocks[mgr.blknos[1]] + assert blk.dtype == "M8[ns]" # we got the right block + val = blk.iget((0, 0)) + assert isinstance(val, Timestamp) + + blk = mgr.blocks[mgr.blknos[2]] + assert blk.dtype == "m8[ns]" # we got the right block + val = blk.iget((0, 0)) + assert isinstance(val, pd.Timedelta) + + +msg1 = r"Cannot setitem on a Categorical with a new category( \(.*\))?, set the" +msg2 = "Cannot set a Categorical with another, without identical categories" + + +class TestLocILocDataFrameCategorical: + @pytest.fixture + def orig(self): + cats = Categorical(["a", "a", "a", "a", "a", "a", "a"], categories=["a", "b"]) + idx = Index(["h", "i", "j", "k", "l", "m", "n"]) + values = [1, 1, 1, 1, 1, 1, 1] + orig = DataFrame({"cats": cats, "values": values}, index=idx) + return orig + + @pytest.fixture + def exp_single_row(self): + # The expected values if we change a single row + cats1 = Categorical(["a", "a", "b", "a", "a", "a", "a"], categories=["a", "b"]) + idx1 = Index(["h", "i", "j", "k", "l", "m", "n"]) + values1 = [1, 1, 2, 1, 1, 1, 1] + exp_single_row = DataFrame({"cats": cats1, "values": values1}, index=idx1) + return exp_single_row + + @pytest.fixture + def exp_multi_row(self): + # assign multiple rows (mixed values) (-> array) -> exp_multi_row + # changed multiple rows + cats2 = Categorical(["a", "a", "b", "b", "a", "a", "a"], categories=["a", "b"]) + idx2 = Index(["h", "i", "j", "k", "l", "m", "n"]) + values2 = [1, 1, 2, 2, 1, 1, 1] + exp_multi_row = DataFrame({"cats": cats2, "values": values2}, index=idx2) + return exp_multi_row + + @pytest.fixture + def exp_parts_cats_col(self): + # changed part of the cats column + cats3 = Categorical(["a", "a", "b", "b", "a", "a", "a"], categories=["a", "b"]) + idx3 = Index(["h", "i", "j", "k", "l", "m", "n"]) + values3 = [1, 1, 1, 1, 1, 1, 1] + exp_parts_cats_col = DataFrame({"cats": cats3, "values": values3}, index=idx3) + return exp_parts_cats_col + + @pytest.fixture + def exp_single_cats_value(self): + # changed single value in cats col + cats4 = Categorical(["a", "a", "b", "a", "a", "a", "a"], categories=["a", "b"]) + idx4 = Index(["h", "i", "j", "k", "l", "m", "n"]) + values4 = [1, 1, 1, 1, 1, 1, 1] + exp_single_cats_value = DataFrame( + {"cats": cats4, "values": values4}, index=idx4 + ) + return exp_single_cats_value + + @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc]) + def test_loc_iloc_setitem_list_of_lists(self, orig, exp_multi_row, indexer): + # - assign multiple rows (mixed values) -> exp_multi_row + df = orig.copy() + + key = slice(2, 4) + if indexer is tm.loc: + key = slice("j", "k") + + indexer(df)[key, :] = [["b", 2], ["b", 2]] + tm.assert_frame_equal(df, exp_multi_row) + + df = orig.copy() + with pytest.raises(TypeError, match=msg1): + indexer(df)[key, :] = [["c", 2], ["c", 2]] + + @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc, tm.at, tm.iat]) + def test_loc_iloc_at_iat_setitem_single_value_in_categories( + self, orig, exp_single_cats_value, indexer + ): + # - assign a single value -> exp_single_cats_value + df = orig.copy() + + key = (2, 0) + if indexer in [tm.loc, tm.at]: + key = (df.index[2], df.columns[0]) + + # "b" is among the categories for df["cat"}] + indexer(df)[key] = "b" + tm.assert_frame_equal(df, exp_single_cats_value) + + # "c" is not among the categories for df["cat"] + with pytest.raises(TypeError, match=msg1): + indexer(df)[key] = "c" + + @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc]) + def test_loc_iloc_setitem_mask_single_value_in_categories( + self, orig, exp_single_cats_value, indexer + ): + # mask with single True + df = orig.copy() + + mask = df.index == "j" + key = 0 + if indexer is tm.loc: + key = df.columns[key] + + indexer(df)[mask, key] = "b" + tm.assert_frame_equal(df, exp_single_cats_value) + + @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc]) + def test_loc_iloc_setitem_full_row_non_categorical_rhs( + self, orig, exp_single_row, indexer + ): + # - assign a complete row (mixed values) -> exp_single_row + df = orig.copy() + + key = 2 + if indexer is tm.loc: + key = df.index[2] + + # not categorical dtype, but "b" _is_ among the categories for df["cat"] + indexer(df)[key, :] = ["b", 2] + tm.assert_frame_equal(df, exp_single_row) + + # "c" is not among the categories for df["cat"] + with pytest.raises(TypeError, match=msg1): + indexer(df)[key, :] = ["c", 2] + + @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc]) + def test_loc_iloc_setitem_partial_col_categorical_rhs( + self, orig, exp_parts_cats_col, indexer + ): + # assign a part of a column with dtype == categorical -> + # exp_parts_cats_col + df = orig.copy() + + key = (slice(2, 4), 0) + if indexer is tm.loc: + key = (slice("j", "k"), df.columns[0]) + + # same categories as we currently have in df["cats"] + compat = Categorical(["b", "b"], categories=["a", "b"]) + indexer(df)[key] = compat + tm.assert_frame_equal(df, exp_parts_cats_col) + + # categories do not match df["cat"]'s, but "b" is among them + semi_compat = Categorical(list("bb"), categories=list("abc")) + with pytest.raises(TypeError, match=msg2): + # different categories but holdable values + # -> not sure if this should fail or pass + indexer(df)[key] = semi_compat + + # categories do not match df["cat"]'s, and "c" is not among them + incompat = Categorical(list("cc"), categories=list("abc")) + with pytest.raises(TypeError, match=msg2): + # different values + indexer(df)[key] = incompat + + @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc]) + def test_loc_iloc_setitem_non_categorical_rhs( + self, orig, exp_parts_cats_col, indexer + ): + # assign a part of a column with dtype != categorical -> exp_parts_cats_col + df = orig.copy() + + key = (slice(2, 4), 0) + if indexer is tm.loc: + key = (slice("j", "k"), df.columns[0]) + + # "b" is among the categories for df["cat"] + indexer(df)[key] = ["b", "b"] + tm.assert_frame_equal(df, exp_parts_cats_col) + + # "c" not part of the categories + with pytest.raises(TypeError, match=msg1): + indexer(df)[key] = ["c", "c"] + + @pytest.mark.parametrize("indexer", [tm.getitem, tm.loc, tm.iloc]) + def test_getitem_preserve_object_index_with_dates(self, indexer): + # https://github.com/pandas-dev/pandas/pull/42950 - when selecting a column + # from dataframe, don't try to infer object dtype index on Series construction + idx = date_range("2012", periods=3).astype(object) + df = DataFrame({0: [1, 2, 3]}, index=idx) + assert df.index.dtype == object + + if indexer is tm.getitem: + ser = indexer(df)[0] + else: + ser = indexer(df)[:, 0] + + assert ser.index.dtype == object + + def test_loc_on_multiindex_one_level(self): + # GH#45779 + df = DataFrame( + data=[[0], [1]], + index=MultiIndex.from_tuples([("a",), ("b",)], names=["first"]), + ) + expected = DataFrame( + data=[[0]], index=MultiIndex.from_tuples([("a",)], names=["first"]) + ) + result = df.loc["a"] + tm.assert_frame_equal(result, expected) + + +class TestDeprecatedIndexers: + @pytest.mark.parametrize( + "key", [{1}, {1: 1}, ({1}, "a"), ({1: 1}, "a"), (1, {"a"}), (1, {"a": "a"})] + ) + def test_getitem_dict_and_set_deprecated(self, key): + # GH#42825 enforced in 2.0 + df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"]) + with pytest.raises(TypeError, match="as an indexer is not supported"): + df.loc[key] + + @pytest.mark.parametrize( + "key", + [ + {1}, + {1: 1}, + (({1}, 2), "a"), + (({1: 1}, 2), "a"), + ((1, 2), {"a"}), + ((1, 2), {"a": "a"}), + ], + ) + def test_getitem_dict_and_set_deprecated_multiindex(self, key): + # GH#42825 enforced in 2.0 + df = DataFrame( + [[1, 2], [3, 4]], + columns=["a", "b"], + index=MultiIndex.from_tuples([(1, 2), (3, 4)]), + ) + with pytest.raises(TypeError, match="as an indexer is not supported"): + df.loc[key] + + @pytest.mark.parametrize( + "key", [{1}, {1: 1}, ({1}, "a"), ({1: 1}, "a"), (1, {"a"}), (1, {"a": "a"})] + ) + def test_setitem_dict_and_set_disallowed(self, key): + # GH#42825 enforced in 2.0 + df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"]) + with pytest.raises(TypeError, match="as an indexer is not supported"): + df.loc[key] = 1 + + @pytest.mark.parametrize( + "key", + [ + {1}, + {1: 1}, + (({1}, 2), "a"), + (({1: 1}, 2), "a"), + ((1, 2), {"a"}), + ((1, 2), {"a": "a"}), + ], + ) + def test_setitem_dict_and_set_disallowed_multiindex(self, key): + # GH#42825 enforced in 2.0 + df = DataFrame( + [[1, 2], [3, 4]], + columns=["a", "b"], + index=MultiIndex.from_tuples([(1, 2), (3, 4)]), + ) + with pytest.raises(TypeError, match="as an indexer is not supported"): + df.loc[key] = 1 + + +def test_adding_new_conditional_column() -> None: + # https://github.com/pandas-dev/pandas/issues/55025 + df = DataFrame({"x": [1]}) + df.loc[df["x"] == 1, "y"] = "1" + expected = DataFrame({"x": [1], "y": ["1"]}) + tm.assert_frame_equal(df, expected) + + df = DataFrame({"x": [1]}) + # try inserting something which numpy would store as 'object' + value = lambda x: x + df.loc[df["x"] == 1, "y"] = value + expected = DataFrame({"x": [1], "y": [value]}) + tm.assert_frame_equal(df, expected) + + +@pytest.mark.parametrize( + ("dtype", "infer_string"), + [ + (object, False), + (pd.StringDtype(na_value=np.nan), True), + ], +) +def test_adding_new_conditional_column_with_string(dtype, infer_string) -> None: + # https://github.com/pandas-dev/pandas/issues/56204 + df = DataFrame({"a": [1, 2], "b": [3, 4]}) + with pd.option_context("future.infer_string", infer_string): + df.loc[df["a"] == 1, "c"] = "1" + expected = DataFrame({"a": [1, 2], "b": [3, 4], "c": ["1", float("nan")]}).astype( + {"a": "int64", "b": "int64", "c": dtype} + ) + tm.assert_frame_equal(df, expected) + + +def test_add_new_column_infer_string(): + # GH#55366 + df = DataFrame({"x": [1]}) + with pd.option_context("future.infer_string", True): + df.loc[df["x"] == 1, "y"] = "1" + expected = DataFrame( + {"x": [1], "y": Series(["1"], dtype=pd.StringDtype(na_value=np.nan))}, + columns=Index(["x", "y"], dtype="str"), + ) + tm.assert_frame_equal(df, expected) + + +class TestSetitemValidation: + # This is adapted from pandas/tests/arrays/masked/test_indexing.py + # but checks for warnings instead of errors. + def _check_setitem_invalid(self, df, invalid, indexer, warn): + msg = "Setting an item of incompatible dtype is deprecated" + msg = re.escape(msg) + + orig_df = df.copy() + + # iloc + with tm.assert_produces_warning(warn, match=msg): + df.iloc[indexer, 0] = invalid + df = orig_df.copy() + + # loc + with tm.assert_produces_warning(warn, match=msg): + df.loc[indexer, "a"] = invalid + df = orig_df.copy() + + _invalid_scalars = [ + 1 + 2j, + "True", + "1", + "1.0", + pd.NaT, + np.datetime64("NaT"), + np.timedelta64("NaT"), + ] + _indexers = [0, [0], slice(0, 1), [True, False, False], slice(None, None, None)] + + @pytest.mark.parametrize( + "invalid", _invalid_scalars + [1, 1.0, np.int64(1), np.float64(1)] + ) + @pytest.mark.parametrize("indexer", _indexers) + def test_setitem_validation_scalar_bool(self, invalid, indexer): + df = DataFrame({"a": [True, False, False]}, dtype="bool") + self._check_setitem_invalid(df, invalid, indexer, FutureWarning) + + @pytest.mark.parametrize("invalid", _invalid_scalars + [True, 1.5, np.float64(1.5)]) + @pytest.mark.parametrize("indexer", _indexers) + def test_setitem_validation_scalar_int(self, invalid, any_int_numpy_dtype, indexer): + df = DataFrame({"a": [1, 2, 3]}, dtype=any_int_numpy_dtype) + if isna(invalid) and invalid is not pd.NaT and not np.isnat(invalid): + warn = None + else: + warn = FutureWarning + self._check_setitem_invalid(df, invalid, indexer, warn) + + @pytest.mark.parametrize("invalid", _invalid_scalars + [True]) + @pytest.mark.parametrize("indexer", _indexers) + def test_setitem_validation_scalar_float(self, invalid, float_numpy_dtype, indexer): + df = DataFrame({"a": [1, 2, None]}, dtype=float_numpy_dtype) + self._check_setitem_invalid(df, invalid, indexer, FutureWarning) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_insert.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_insert.py new file mode 100644 index 0000000000000000000000000000000000000000..7e702bdc993bd1444dc48f85e016c768dadd042f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_insert.py @@ -0,0 +1,120 @@ +""" +test_insert is specifically for the DataFrame.insert method; not to be +confused with tests with "insert" in their names that are really testing +__setitem__. +""" +import numpy as np +import pytest + +from pandas.errors import PerformanceWarning + +from pandas import ( + DataFrame, + Index, +) +import pandas._testing as tm + + +class TestDataFrameInsert: + def test_insert(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + index=np.arange(5), + columns=["c", "b", "a"], + ) + + df.insert(0, "foo", df["a"]) + tm.assert_index_equal(df.columns, Index(["foo", "c", "b", "a"])) + tm.assert_series_equal(df["a"], df["foo"], check_names=False) + + df.insert(2, "bar", df["c"]) + tm.assert_index_equal(df.columns, Index(["foo", "c", "bar", "b", "a"])) + tm.assert_almost_equal(df["c"], df["bar"], check_names=False) + + with pytest.raises(ValueError, match="already exists"): + df.insert(1, "a", df["b"]) + + msg = "cannot insert c, already exists" + with pytest.raises(ValueError, match=msg): + df.insert(1, "c", df["b"]) + + df.columns.name = "some_name" + # preserve columns name field + df.insert(0, "baz", df["c"]) + assert df.columns.name == "some_name" + + def test_insert_column_bug_4032(self): + # GH#4032, inserting a column and renaming causing errors + df = DataFrame({"b": [1.1, 2.2]}) + + df = df.rename(columns={}) + df.insert(0, "a", [1, 2]) + result = df.rename(columns={}) + + expected = DataFrame([[1, 1.1], [2, 2.2]], columns=["a", "b"]) + tm.assert_frame_equal(result, expected) + + df.insert(0, "c", [1.3, 2.3]) + result = df.rename(columns={}) + + expected = DataFrame([[1.3, 1, 1.1], [2.3, 2, 2.2]], columns=["c", "a", "b"]) + tm.assert_frame_equal(result, expected) + + def test_insert_with_columns_dups(self): + # GH#14291 + df = DataFrame() + df.insert(0, "A", ["g", "h", "i"], allow_duplicates=True) + df.insert(0, "A", ["d", "e", "f"], allow_duplicates=True) + df.insert(0, "A", ["a", "b", "c"], allow_duplicates=True) + exp = DataFrame( + [["a", "d", "g"], ["b", "e", "h"], ["c", "f", "i"]], columns=["A", "A", "A"] + ) + tm.assert_frame_equal(df, exp) + + def test_insert_item_cache(self, using_array_manager, using_copy_on_write): + df = DataFrame(np.random.default_rng(2).standard_normal((4, 3))) + ser = df[0] + + if using_array_manager: + expected_warning = None + else: + # with BlockManager warn about high fragmentation of single dtype + expected_warning = PerformanceWarning + + with tm.assert_produces_warning(expected_warning): + for n in range(100): + df[n + 3] = df[1] * n + + if using_copy_on_write: + ser.iloc[0] = 99 + assert df.iloc[0, 0] == df[0][0] + assert df.iloc[0, 0] != 99 + else: + ser.values[0] = 99 + assert df.iloc[0, 0] == df[0][0] + assert df.iloc[0, 0] == 99 + + def test_insert_EA_no_warning(self): + # PerformanceWarning about fragmented frame should not be raised when + # using EAs (https://github.com/pandas-dev/pandas/issues/44098) + df = DataFrame( + np.random.default_rng(2).integers(0, 100, size=(3, 100)), dtype="Int64" + ) + with tm.assert_produces_warning(None): + df["a"] = np.array([1, 2, 3]) + + def test_insert_frame(self): + # GH#42403 + df = DataFrame({"col1": [1, 2], "col2": [3, 4]}) + + msg = ( + "Expected a one-dimensional object, got a DataFrame with 2 columns instead." + ) + with pytest.raises(ValueError, match=msg): + df.insert(1, "newcol", df) + + def test_insert_int64_loc(self): + # GH#53193 + df = DataFrame({"a": [1, 2]}) + df.insert(np.int64(0), "b", 0) + tm.assert_frame_equal(df, DataFrame({"b": [0, 0], "a": [1, 2]})) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_mask.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..264e27c9c122ebb6d59c5b16531ebbdc8ce51320 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_mask.py @@ -0,0 +1,152 @@ +""" +Tests for DataFrame.mask; tests DataFrame.where as a side-effect. +""" + +import numpy as np + +from pandas import ( + NA, + DataFrame, + Float64Dtype, + Series, + StringDtype, + Timedelta, + isna, +) +import pandas._testing as tm + + +class TestDataFrameMask: + def test_mask(self): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + cond = df > 0 + + rs = df.where(cond, np.nan) + tm.assert_frame_equal(rs, df.mask(df <= 0)) + tm.assert_frame_equal(rs, df.mask(~cond)) + + other = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + rs = df.where(cond, other) + tm.assert_frame_equal(rs, df.mask(df <= 0, other)) + tm.assert_frame_equal(rs, df.mask(~cond, other)) + + def test_mask2(self): + # see GH#21891 + df = DataFrame([1, 2]) + res = df.mask([[True], [False]]) + + exp = DataFrame([np.nan, 2]) + tm.assert_frame_equal(res, exp) + + def test_mask_inplace(self): + # GH#8801 + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + cond = df > 0 + + rdf = df.copy() + + return_value = rdf.where(cond, inplace=True) + assert return_value is None + tm.assert_frame_equal(rdf, df.where(cond)) + tm.assert_frame_equal(rdf, df.mask(~cond)) + + rdf = df.copy() + return_value = rdf.where(cond, -df, inplace=True) + assert return_value is None + tm.assert_frame_equal(rdf, df.where(cond, -df)) + tm.assert_frame_equal(rdf, df.mask(~cond, -df)) + + def test_mask_edge_case_1xN_frame(self): + # GH#4071 + df = DataFrame([[1, 2]]) + res = df.mask(DataFrame([[True, False]])) + expec = DataFrame([[np.nan, 2]]) + tm.assert_frame_equal(res, expec) + + def test_mask_callable(self): + # GH#12533 + df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + result = df.mask(lambda x: x > 4, lambda x: x + 1) + exp = DataFrame([[1, 2, 3], [4, 6, 7], [8, 9, 10]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.mask(df > 4, df + 1)) + + # return ndarray and scalar + result = df.mask(lambda x: (x % 2 == 0).values, lambda x: 99) + exp = DataFrame([[1, 99, 3], [99, 5, 99], [7, 99, 9]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.mask(df % 2 == 0, 99)) + + # chain + result = (df + 2).mask(lambda x: x > 8, lambda x: x + 10) + exp = DataFrame([[3, 4, 5], [6, 7, 8], [19, 20, 21]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, (df + 2).mask((df + 2) > 8, (df + 2) + 10)) + + def test_mask_dtype_bool_conversion(self): + # GH#3733 + df = DataFrame(data=np.random.default_rng(2).standard_normal((100, 50))) + df = df.where(df > 0) # create nans + bools = df > 0 + mask = isna(df) + expected = bools.astype(object).mask(mask) + result = bools.mask(mask) + tm.assert_frame_equal(result, expected) + + +def test_mask_stringdtype(frame_or_series): + # GH 40824 + obj = DataFrame( + {"A": ["foo", "bar", "baz", NA]}, + index=["id1", "id2", "id3", "id4"], + dtype=StringDtype(), + ) + filtered_obj = DataFrame( + {"A": ["this", "that"]}, index=["id2", "id3"], dtype=StringDtype() + ) + expected = DataFrame( + {"A": [NA, "this", "that", NA]}, + index=["id1", "id2", "id3", "id4"], + dtype=StringDtype(), + ) + if frame_or_series is Series: + obj = obj["A"] + filtered_obj = filtered_obj["A"] + expected = expected["A"] + + filter_ser = Series([False, True, True, False]) + result = obj.mask(filter_ser, filtered_obj) + + tm.assert_equal(result, expected) + + +def test_mask_where_dtype_timedelta(): + # https://github.com/pandas-dev/pandas/issues/39548 + df = DataFrame([Timedelta(i, unit="d") for i in range(5)]) + + expected = DataFrame(np.full(5, np.nan, dtype="timedelta64[ns]")) + tm.assert_frame_equal(df.mask(df.notna()), expected) + + expected = DataFrame( + [np.nan, np.nan, np.nan, Timedelta("3 day"), Timedelta("4 day")] + ) + tm.assert_frame_equal(df.where(df > Timedelta(2, unit="d")), expected) + + +def test_mask_return_dtype(): + # GH#50488 + ser = Series([0.0, 1.0, 2.0, 3.0], dtype=Float64Dtype()) + cond = ~ser.isna() + other = Series([True, False, True, False]) + excepted = Series([1.0, 0.0, 1.0, 0.0], dtype=ser.dtype) + result = ser.mask(cond, other) + tm.assert_series_equal(result, excepted) + + +def test_mask_inplace_no_other(): + # GH#51685 + df = DataFrame({"a": [1.0, 2.0], "b": ["x", "y"]}) + cond = DataFrame({"a": [True, False], "b": [False, True]}) + df.mask(cond, inplace=True) + expected = DataFrame({"a": [np.nan, 2], "b": ["x", np.nan]}) + tm.assert_frame_equal(df, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_set_value.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_set_value.py new file mode 100644 index 0000000000000000000000000000000000000000..3d23e13264911c52dabd58c12ed133f8cf1766a6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_set_value.py @@ -0,0 +1,77 @@ +import numpy as np + +from pandas.core.dtypes.common import is_float_dtype + +from pandas import ( + DataFrame, + isna, +) +import pandas._testing as tm + + +class TestSetValue: + def test_set_value(self, float_frame): + for idx in float_frame.index: + for col in float_frame.columns: + float_frame._set_value(idx, col, 1) + assert float_frame[col][idx] == 1 + + def test_set_value_resize(self, float_frame, using_infer_string): + res = float_frame._set_value("foobar", "B", 0) + assert res is None + assert float_frame.index[-1] == "foobar" + assert float_frame._get_value("foobar", "B") == 0 + + float_frame.loc["foobar", "qux"] = 0 + assert float_frame._get_value("foobar", "qux") == 0 + + res = float_frame.copy() + res._set_value("foobar", "baz", "sam") + if using_infer_string: + assert res["baz"].dtype == "str" + else: + assert res["baz"].dtype == np.object_ + res = float_frame.copy() + res._set_value("foobar", "baz", True) + assert res["baz"].dtype == np.object_ + + res = float_frame.copy() + res._set_value("foobar", "baz", 5) + assert is_float_dtype(res["baz"]) + assert isna(res["baz"].drop(["foobar"])).all() + + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + res._set_value("foobar", "baz", "sam") + assert res.loc["foobar", "baz"] == "sam" + + def test_set_value_with_index_dtype_change(self): + df_orig = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + index=range(3), + columns=list("ABC"), + ) + + # this is actually ambiguous as the 2 is interpreted as a positional + # so column is not created + df = df_orig.copy() + df._set_value("C", 2, 1.0) + assert list(df.index) == list(df_orig.index) + ["C"] + # assert list(df.columns) == list(df_orig.columns) + [2] + + df = df_orig.copy() + df.loc["C", 2] = 1.0 + assert list(df.index) == list(df_orig.index) + ["C"] + # assert list(df.columns) == list(df_orig.columns) + [2] + + # create both new + df = df_orig.copy() + df._set_value("C", "D", 1.0) + assert list(df.index) == list(df_orig.index) + ["C"] + assert list(df.columns) == list(df_orig.columns) + ["D"] + + df = df_orig.copy() + df.loc["C", "D"] = 1.0 + assert list(df.index) == list(df_orig.index) + ["C"] + assert list(df.columns) == list(df_orig.columns) + ["D"] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_setitem.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_setitem.py new file mode 100644 index 0000000000000000000000000000000000000000..b3fe538d938f49f63e986a8ff5cfaa95130a1b90 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_setitem.py @@ -0,0 +1,1437 @@ +from datetime import datetime + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas.core.dtypes.base import _registry as ea_registry +from pandas.core.dtypes.common import is_object_dtype +from pandas.core.dtypes.dtypes import ( + CategoricalDtype, + DatetimeTZDtype, + IntervalDtype, + PeriodDtype, +) + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + DatetimeIndex, + Index, + Interval, + IntervalIndex, + MultiIndex, + NaT, + Period, + PeriodIndex, + Series, + Timestamp, + cut, + date_range, + notna, + period_range, +) +import pandas._testing as tm +from pandas.core.arrays import SparseArray + +from pandas.tseries.offsets import BDay + + +class TestDataFrameSetItem: + def test_setitem_str_subclass(self): + # GH#37366 + class mystring(str): + pass + + data = ["2020-10-22 01:21:00+00:00"] + index = DatetimeIndex(data) + df = DataFrame({"a": [1]}, index=index) + df["b"] = 2 + df[mystring("c")] = 3 + expected = DataFrame({"a": [1], "b": [2], mystring("c"): [3]}, index=index) + tm.assert_equal(df, expected) + + @pytest.mark.parametrize( + "dtype", ["int32", "int64", "uint32", "uint64", "float32", "float64"] + ) + def test_setitem_dtype(self, dtype, float_frame): + # Use integers since casting negative floats to uints is undefined + arr = np.random.default_rng(2).integers(1, 10, len(float_frame)) + + float_frame[dtype] = np.array(arr, dtype=dtype) + assert float_frame[dtype].dtype.name == dtype + + def test_setitem_list_not_dataframe(self, float_frame): + data = np.random.default_rng(2).standard_normal((len(float_frame), 2)) + float_frame[["A", "B"]] = data + tm.assert_almost_equal(float_frame[["A", "B"]].values, data) + + def test_setitem_error_msmgs(self): + # GH 7432 + df = DataFrame( + {"bar": [1, 2, 3], "baz": ["d", "e", "f"]}, + index=Index(["a", "b", "c"], name="foo"), + ) + ser = Series( + ["g", "h", "i", "j"], + index=Index(["a", "b", "c", "a"], name="foo"), + name="fiz", + ) + msg = "cannot reindex on an axis with duplicate labels" + with pytest.raises(ValueError, match=msg): + df["newcol"] = ser + + # GH 4107, more descriptive error message + df = DataFrame( + np.random.default_rng(2).integers(0, 2, (4, 4)), + columns=["a", "b", "c", "d"], + ) + + msg = "Cannot set a DataFrame with multiple columns to the single column gr" + with pytest.raises(ValueError, match=msg): + df["gr"] = df.groupby(["b", "c"]).count() + + # GH 55956, specific message for zero columns + msg = "Cannot set a DataFrame without columns to the column gr" + with pytest.raises(ValueError, match=msg): + df["gr"] = DataFrame() + + def test_setitem_benchmark(self): + # from the vb_suite/frame_methods/frame_insert_columns + N = 10 + K = 5 + df = DataFrame(index=range(N)) + new_col = np.random.default_rng(2).standard_normal(N) + for i in range(K): + df[i] = new_col + expected = DataFrame(np.repeat(new_col, K).reshape(N, K), index=range(N)) + tm.assert_frame_equal(df, expected) + + def test_setitem_different_dtype(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + index=np.arange(5), + columns=["c", "b", "a"], + ) + df.insert(0, "foo", df["a"]) + df.insert(2, "bar", df["c"]) + + # diff dtype + + # new item + df["x"] = df["a"].astype("float32") + result = df.dtypes + expected = Series( + [np.dtype("float64")] * 5 + [np.dtype("float32")], + index=["foo", "c", "bar", "b", "a", "x"], + ) + tm.assert_series_equal(result, expected) + + # replacing current (in different block) + df["a"] = df["a"].astype("float32") + result = df.dtypes + expected = Series( + [np.dtype("float64")] * 4 + [np.dtype("float32")] * 2, + index=["foo", "c", "bar", "b", "a", "x"], + ) + tm.assert_series_equal(result, expected) + + df["y"] = df["a"].astype("int32") + result = df.dtypes + expected = Series( + [np.dtype("float64")] * 4 + [np.dtype("float32")] * 2 + [np.dtype("int32")], + index=["foo", "c", "bar", "b", "a", "x", "y"], + ) + tm.assert_series_equal(result, expected) + + def test_setitem_overwrite_index(self): + # GH 13522 - assign the index as a column and then overwrite the values + # -> should not affect the index + df = DataFrame(index=["A", "B", "C"]) + df["X"] = df.index + df["X"] = ["x", "y", "z"] + exp = DataFrame( + data={"X": ["x", "y", "z"]}, index=["A", "B", "C"], columns=["X"] + ) + tm.assert_frame_equal(df, exp) + + def test_setitem_empty_columns(self): + # Starting from an empty DataFrame and setting a column should result + # in a default string dtype for the columns' Index + # https://github.com/pandas-dev/pandas/issues/60338 + + df = DataFrame() + df["foo"] = [1, 2, 3] + expected = DataFrame({"foo": [1, 2, 3]}) + tm.assert_frame_equal(df, expected) + + df = DataFrame(columns=Index([])) + df["foo"] = [1, 2, 3] + expected = DataFrame({"foo": [1, 2, 3]}) + tm.assert_frame_equal(df, expected) + + def test_setitem_dt64_index_empty_columns(self): + rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s") + df = DataFrame(index=np.arange(len(rng))) + + df["A"] = rng + assert df["A"].dtype == np.dtype("M8[ns]") + + def test_setitem_timestamp_empty_columns(self): + # GH#19843 + df = DataFrame(index=range(3)) + df["now"] = Timestamp("20130101", tz="UTC").as_unit("ns") + + expected = DataFrame( + [[Timestamp("20130101", tz="UTC")]] * 3, index=range(3), columns=["now"] + ) + tm.assert_frame_equal(df, expected) + + def test_setitem_wrong_length_categorical_dtype_raises(self): + # GH#29523 + cat = Categorical.from_codes([0, 1, 1, 0, 1, 2], ["a", "b", "c"]) + df = DataFrame(range(10), columns=["bar"]) + + msg = ( + rf"Length of values \({len(cat)}\) " + rf"does not match length of index \({len(df)}\)" + ) + with pytest.raises(ValueError, match=msg): + df["foo"] = cat + + def test_setitem_with_sparse_value(self): + # GH#8131 + df = DataFrame({"c_1": ["a", "b", "c"], "n_1": [1.0, 2.0, 3.0]}) + sp_array = SparseArray([0, 0, 1]) + df["new_column"] = sp_array + + expected = Series(sp_array, name="new_column") + tm.assert_series_equal(df["new_column"], expected) + + def test_setitem_with_unaligned_sparse_value(self): + df = DataFrame({"c_1": ["a", "b", "c"], "n_1": [1.0, 2.0, 3.0]}) + sp_series = Series(SparseArray([0, 0, 1]), index=[2, 1, 0]) + + df["new_column"] = sp_series + expected = Series(SparseArray([1, 0, 0]), name="new_column") + tm.assert_series_equal(df["new_column"], expected) + + def test_setitem_period_preserves_dtype(self): + # GH: 26861 + data = [Period("2003-12", "D")] + result = DataFrame([]) + result["a"] = data + + expected = DataFrame({"a": data}, columns=["a"]) + + tm.assert_frame_equal(result, expected) + + def test_setitem_dict_preserves_dtypes(self): + # https://github.com/pandas-dev/pandas/issues/34573 + expected = DataFrame( + { + "a": Series([0, 1, 2], dtype="int64"), + "b": Series([1, 2, 3], dtype=float), + "c": Series([1, 2, 3], dtype=float), + "d": Series([1, 2, 3], dtype="uint32"), + } + ) + df = DataFrame( + { + "a": Series([], dtype="int64"), + "b": Series([], dtype=float), + "c": Series([], dtype=float), + "d": Series([], dtype="uint32"), + } + ) + for idx, b in enumerate([1, 2, 3]): + df.loc[df.shape[0]] = { + "a": int(idx), + "b": float(b), + "c": float(b), + "d": np.uint32(b), + } + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "obj,dtype", + [ + (Period("2020-01"), PeriodDtype("M")), + (Interval(left=0, right=5), IntervalDtype("int64", "right")), + ( + Timestamp("2011-01-01", tz="US/Eastern"), + DatetimeTZDtype(unit="s", tz="US/Eastern"), + ), + ], + ) + def test_setitem_extension_types(self, obj, dtype): + # GH: 34832 + expected = DataFrame({"idx": [1, 2, 3], "obj": Series([obj] * 3, dtype=dtype)}) + + df = DataFrame({"idx": [1, 2, 3]}) + df["obj"] = obj + + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "ea_name", + [ + dtype.name + for dtype in ea_registry.dtypes + # property would require instantiation + if not isinstance(dtype.name, property) + ] + + ["datetime64[ns, UTC]", "period[D]"], + ) + def test_setitem_with_ea_name(self, ea_name): + # GH 38386 + result = DataFrame([0]) + result[ea_name] = [1] + expected = DataFrame({0: [0], ea_name: [1]}) + tm.assert_frame_equal(result, expected) + + def test_setitem_dt64_ndarray_with_NaT_and_diff_time_units(self): + # GH#7492 + data_ns = np.array([1, "nat"], dtype="datetime64[ns]") + result = Series(data_ns).to_frame() + result["new"] = data_ns + expected = DataFrame({0: [1, None], "new": [1, None]}, dtype="datetime64[ns]") + tm.assert_frame_equal(result, expected) + + # OutOfBoundsDatetime error shouldn't occur; as of 2.0 we preserve "M8[s]" + data_s = np.array([1, "nat"], dtype="datetime64[s]") + result["new"] = data_s + tm.assert_series_equal(result[0], expected[0]) + tm.assert_numpy_array_equal(result["new"].to_numpy(), data_s) + + @pytest.mark.parametrize("unit", ["h", "m", "s", "ms", "D", "M", "Y"]) + def test_frame_setitem_datetime64_col_other_units(self, unit): + # Check that non-nano dt64 values get cast to dt64 on setitem + # into a not-yet-existing column + n = 100 + + dtype = np.dtype(f"M8[{unit}]") + vals = np.arange(n, dtype=np.int64).view(dtype) + if unit in ["s", "ms"]: + # supported unit + ex_vals = vals + else: + # we get the nearest supported units, i.e. "s" + ex_vals = vals.astype("datetime64[s]") + + df = DataFrame({"ints": np.arange(n)}, index=np.arange(n)) + df[unit] = vals + + assert df[unit].dtype == ex_vals.dtype + assert (df[unit].values == ex_vals).all() + + @pytest.mark.parametrize("unit", ["h", "m", "s", "ms", "D", "M", "Y"]) + def test_frame_setitem_existing_datetime64_col_other_units(self, unit): + # Check that non-nano dt64 values get cast to dt64 on setitem + # into an already-existing dt64 column + n = 100 + + dtype = np.dtype(f"M8[{unit}]") + vals = np.arange(n, dtype=np.int64).view(dtype) + ex_vals = vals.astype("datetime64[ns]") + + df = DataFrame({"ints": np.arange(n)}, index=np.arange(n)) + df["dates"] = np.arange(n, dtype=np.int64).view("M8[ns]") + + # We overwrite existing dt64 column with new, non-nano dt64 vals + df["dates"] = vals + assert (df["dates"].values == ex_vals).all() + + def test_setitem_dt64tz(self, timezone_frame, using_copy_on_write): + df = timezone_frame + idx = df["B"].rename("foo") + + # setitem + df["C"] = idx + tm.assert_series_equal(df["C"], Series(idx, name="C")) + + df["D"] = "foo" + df["D"] = idx + tm.assert_series_equal(df["D"], Series(idx, name="D")) + del df["D"] + + # assert that A & C are not sharing the same base (e.g. they + # are copies) + # Note: This does not hold with Copy on Write (because of lazy copying) + v1 = df._mgr.arrays[1] + v2 = df._mgr.arrays[2] + tm.assert_extension_array_equal(v1, v2) + v1base = v1._ndarray.base + v2base = v2._ndarray.base + if not using_copy_on_write: + assert v1base is None or (id(v1base) != id(v2base)) + else: + assert id(v1base) == id(v2base) + + # with nan + df2 = df.copy() + df2.iloc[1, 1] = NaT + df2.iloc[1, 2] = NaT + result = df2["B"] + tm.assert_series_equal(notna(result), Series([True, False, True], name="B")) + tm.assert_series_equal(df2.dtypes, df.dtypes) + + def test_setitem_periodindex(self): + rng = period_range("1/1/2000", periods=5, name="index") + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)), index=rng) + + df["Index"] = rng + rs = Index(df["Index"]) + tm.assert_index_equal(rs, rng, check_names=False) + assert rs.name == "Index" + assert rng.name == "index" + + rs = df.reset_index().set_index("index") + assert isinstance(rs.index, PeriodIndex) + tm.assert_index_equal(rs.index, rng) + + def test_setitem_complete_column_with_array(self): + # GH#37954 + df = DataFrame({"a": ["one", "two", "three"], "b": [1, 2, 3]}) + arr = np.array([[1, 1], [3, 1], [5, 1]]) + df[["c", "d"]] = arr + expected = DataFrame( + { + "a": ["one", "two", "three"], + "b": [1, 2, 3], + "c": [1, 3, 5], + "d": [1, 1, 1], + } + ) + expected["c"] = expected["c"].astype(arr.dtype) + expected["d"] = expected["d"].astype(arr.dtype) + assert expected["c"].dtype == arr.dtype + assert expected["d"].dtype == arr.dtype + tm.assert_frame_equal(df, expected) + + def test_setitem_period_d_dtype(self): + # GH 39763 + rng = period_range("2016-01-01", periods=9, freq="D", name="A") + result = DataFrame(rng) + expected = DataFrame( + {"A": ["NaT", "NaT", "NaT", "NaT", "NaT", "NaT", "NaT", "NaT", "NaT"]}, + dtype="period[D]", + ) + result.iloc[:] = rng._na_value + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dtype", ["f8", "i8", "u8"]) + def test_setitem_bool_with_numeric_index(self, dtype): + # GH#36319 + cols = Index([1, 2, 3], dtype=dtype) + df = DataFrame(np.random.default_rng(2).standard_normal((3, 3)), columns=cols) + + df[False] = ["a", "b", "c"] + + expected_cols = Index([1, 2, 3, False], dtype=object) + if dtype == "f8": + expected_cols = Index([1.0, 2.0, 3.0, False], dtype=object) + + tm.assert_index_equal(df.columns, expected_cols) + + @pytest.mark.parametrize("indexer", ["B", ["B"]]) + def test_setitem_frame_length_0_str_key(self, indexer): + # GH#38831 + df = DataFrame(columns=["A", "B"]) + other = DataFrame({"B": [1, 2]}) + df[indexer] = other + expected = DataFrame({"A": [np.nan] * 2, "B": [1, 2]}) + expected["A"] = expected["A"].astype("object") + tm.assert_frame_equal(df, expected) + + def test_setitem_frame_duplicate_columns(self): + # GH#15695 + cols = ["A", "B", "C"] * 2 + df = DataFrame(index=range(3), columns=cols) + df.loc[0, "A"] = (0, 3) + df.loc[:, "B"] = (1, 4) + df["C"] = (2, 5) + expected = DataFrame( + [ + [0, 1, 2, 3, 4, 5], + [np.nan, 1, 2, np.nan, 4, 5], + [np.nan, 1, 2, np.nan, 4, 5], + ], + dtype="object", + ) + + # set these with unique columns to be extra-unambiguous + expected[2] = expected[2].astype(np.int64) + expected[5] = expected[5].astype(np.int64) + expected.columns = cols + + tm.assert_frame_equal(df, expected) + + def test_setitem_frame_duplicate_columns_size_mismatch(self): + # GH#39510 + cols = ["A", "B", "C"] * 2 + df = DataFrame(index=range(3), columns=cols) + with pytest.raises(ValueError, match="Columns must be same length as key"): + df[["A"]] = (0, 3, 5) + + df2 = df.iloc[:, :3] # unique columns + with pytest.raises(ValueError, match="Columns must be same length as key"): + df2[["A"]] = (0, 3, 5) + + @pytest.mark.parametrize("cols", [["a", "b", "c"], ["a", "a", "a"]]) + def test_setitem_df_wrong_column_number(self, cols): + # GH#38604 + df = DataFrame([[1, 2, 3]], columns=cols) + rhs = DataFrame([[10, 11]], columns=["d", "e"]) + msg = "Columns must be same length as key" + with pytest.raises(ValueError, match=msg): + df["a"] = rhs + + def test_setitem_listlike_indexer_duplicate_columns(self): + # GH#38604 + df = DataFrame([[1, 2, 3]], columns=["a", "b", "b"]) + rhs = DataFrame([[10, 11, 12]], columns=["a", "b", "b"]) + df[["a", "b"]] = rhs + expected = DataFrame([[10, 11, 12]], columns=["a", "b", "b"]) + tm.assert_frame_equal(df, expected) + + df[["c", "b"]] = rhs + expected = DataFrame([[10, 11, 12, 10]], columns=["a", "b", "b", "c"]) + tm.assert_frame_equal(df, expected) + + def test_setitem_listlike_indexer_duplicate_columns_not_equal_length(self): + # GH#39403 + df = DataFrame([[1, 2, 3]], columns=["a", "b", "b"]) + rhs = DataFrame([[10, 11]], columns=["a", "b"]) + msg = "Columns must be same length as key" + with pytest.raises(ValueError, match=msg): + df[["a", "b"]] = rhs + + def test_setitem_intervals(self): + df = DataFrame({"A": range(10)}) + ser = cut(df["A"], 5) + assert isinstance(ser.cat.categories, IntervalIndex) + + # B & D end up as Categoricals + # the remainder are converted to in-line objects + # containing an IntervalIndex.values + df["B"] = ser + df["C"] = np.array(ser) + df["D"] = ser.values + df["E"] = np.array(ser.values) + df["F"] = ser.astype(object) + + assert isinstance(df["B"].dtype, CategoricalDtype) + assert isinstance(df["B"].cat.categories.dtype, IntervalDtype) + assert isinstance(df["D"].dtype, CategoricalDtype) + assert isinstance(df["D"].cat.categories.dtype, IntervalDtype) + + # These go through the Series constructor and so get inferred back + # to IntervalDtype + assert isinstance(df["C"].dtype, IntervalDtype) + assert isinstance(df["E"].dtype, IntervalDtype) + + # But the Series constructor doesn't do inference on Series objects, + # so setting df["F"] doesn't get cast back to IntervalDtype + assert is_object_dtype(df["F"]) + + # they compare equal as Index + # when converted to numpy objects + c = lambda x: Index(np.array(x)) + tm.assert_index_equal(c(df.B), c(df.B)) + tm.assert_index_equal(c(df.B), c(df.C), check_names=False) + tm.assert_index_equal(c(df.B), c(df.D), check_names=False) + tm.assert_index_equal(c(df.C), c(df.D), check_names=False) + + # B & D are the same Series + tm.assert_series_equal(df["B"], df["B"]) + tm.assert_series_equal(df["B"], df["D"], check_names=False) + + # C & E are the same Series + tm.assert_series_equal(df["C"], df["C"]) + tm.assert_series_equal(df["C"], df["E"], check_names=False) + + def test_setitem_categorical(self): + # GH#35369 + df = DataFrame({"h": Series(list("mn")).astype("category")}) + df.h = df.h.cat.reorder_categories(["n", "m"]) + expected = DataFrame( + {"h": Categorical(["m", "n"]).reorder_categories(["n", "m"])} + ) + tm.assert_frame_equal(df, expected) + + def test_setitem_with_empty_listlike(self): + # GH#17101 + index = Index([], name="idx") + result = DataFrame(columns=["A"], index=index) + result["A"] = [] + expected = DataFrame(columns=["A"], index=index) + tm.assert_index_equal(result.index, expected.index) + + @pytest.mark.parametrize( + "cols, values, expected", + [ + (["C", "D", "D", "a"], [1, 2, 3, 4], 4), # with duplicates + (["D", "C", "D", "a"], [1, 2, 3, 4], 4), # mixed order + (["C", "B", "B", "a"], [1, 2, 3, 4], 4), # other duplicate cols + (["C", "B", "a"], [1, 2, 3], 3), # no duplicates + (["B", "C", "a"], [3, 2, 1], 1), # alphabetical order + (["C", "a", "B"], [3, 2, 1], 2), # in the middle + ], + ) + def test_setitem_same_column(self, cols, values, expected): + # GH#23239 + df = DataFrame([values], columns=cols) + df["a"] = df["a"] + result = df["a"].values[0] + assert result == expected + + def test_setitem_multi_index(self): + # GH#7655, test that assigning to a sub-frame of a frame + # with multi-index columns aligns both rows and columns + it = ["jim", "joe", "jolie"], ["first", "last"], ["left", "center", "right"] + + cols = MultiIndex.from_product(it) + index = date_range("20141006", periods=20) + vals = np.random.default_rng(2).integers(1, 1000, (len(index), len(cols))) + df = DataFrame(vals, columns=cols, index=index) + + i, j = df.index.values.copy(), it[-1][:] + + np.random.default_rng(2).shuffle(i) + df["jim"] = df["jolie"].loc[i, ::-1] + tm.assert_frame_equal(df["jim"], df["jolie"]) + + np.random.default_rng(2).shuffle(j) + df[("joe", "first")] = df[("jolie", "last")].loc[i, j] + tm.assert_frame_equal(df[("joe", "first")], df[("jolie", "last")]) + + np.random.default_rng(2).shuffle(j) + df[("joe", "last")] = df[("jolie", "first")].loc[i, j] + tm.assert_frame_equal(df[("joe", "last")], df[("jolie", "first")]) + + @pytest.mark.parametrize( + "columns,box,expected", + [ + ( + ["A", "B", "C", "D"], + 7, + DataFrame( + [[7, 7, 7, 7], [7, 7, 7, 7], [7, 7, 7, 7]], + columns=["A", "B", "C", "D"], + ), + ), + ( + ["C", "D"], + [7, 8], + DataFrame( + [[1, 2, 7, 8], [3, 4, 7, 8], [5, 6, 7, 8]], + columns=["A", "B", "C", "D"], + ), + ), + ( + ["A", "B", "C"], + np.array([7, 8, 9], dtype=np.int64), + DataFrame([[7, 8, 9], [7, 8, 9], [7, 8, 9]], columns=["A", "B", "C"]), + ), + ( + ["B", "C", "D"], + [[7, 8, 9], [10, 11, 12], [13, 14, 15]], + DataFrame( + [[1, 7, 8, 9], [3, 10, 11, 12], [5, 13, 14, 15]], + columns=["A", "B", "C", "D"], + ), + ), + ( + ["C", "A", "D"], + np.array([[7, 8, 9], [10, 11, 12], [13, 14, 15]], dtype=np.int64), + DataFrame( + [[8, 2, 7, 9], [11, 4, 10, 12], [14, 6, 13, 15]], + columns=["A", "B", "C", "D"], + ), + ), + ( + ["A", "C"], + DataFrame([[7, 8], [9, 10], [11, 12]], columns=["A", "C"]), + DataFrame( + [[7, 2, 8], [9, 4, 10], [11, 6, 12]], columns=["A", "B", "C"] + ), + ), + ], + ) + def test_setitem_list_missing_columns(self, columns, box, expected): + # GH#29334 + df = DataFrame([[1, 2], [3, 4], [5, 6]], columns=["A", "B"]) + df[columns] = box + tm.assert_frame_equal(df, expected) + + def test_setitem_list_of_tuples(self, float_frame): + tuples = list(zip(float_frame["A"], float_frame["B"])) + float_frame["tuples"] = tuples + + result = float_frame["tuples"] + expected = Series(tuples, index=float_frame.index, name="tuples") + tm.assert_series_equal(result, expected) + + def test_setitem_iloc_generator(self): + # GH#39614 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + indexer = (x for x in [1, 2]) + df.iloc[indexer] = 1 + expected = DataFrame({"a": [1, 1, 1], "b": [4, 1, 1]}) + tm.assert_frame_equal(df, expected) + + def test_setitem_iloc_two_dimensional_generator(self): + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + indexer = (x for x in [1, 2]) + df.iloc[indexer, 1] = 1 + expected = DataFrame({"a": [1, 2, 3], "b": [4, 1, 1]}) + tm.assert_frame_equal(df, expected) + + def test_setitem_dtypes_bytes_type_to_object(self): + # GH 20734 + index = Series(name="id", dtype="S24") + df = DataFrame(index=index, columns=Index([], dtype="str")) + df["a"] = Series(name="a", index=index, dtype=np.uint32) + df["b"] = Series(name="b", index=index, dtype="S64") + df["c"] = Series(name="c", index=index, dtype="S64") + df["d"] = Series(name="d", index=index, dtype=np.uint8) + result = df.dtypes + expected = Series([np.uint32, object, object, np.uint8], index=list("abcd")) + tm.assert_series_equal(result, expected) + + def test_boolean_mask_nullable_int64(self): + # GH 28928 + result = DataFrame({"a": [3, 4], "b": [5, 6]}).astype( + {"a": "int64", "b": "Int64"} + ) + mask = Series(False, index=result.index) + result.loc[mask, "a"] = result["a"] + result.loc[mask, "b"] = result["b"] + expected = DataFrame({"a": [3, 4], "b": [5, 6]}).astype( + {"a": "int64", "b": "Int64"} + ) + tm.assert_frame_equal(result, expected) + + def test_setitem_ea_dtype_rhs_series(self): + # GH#47425 + df = DataFrame({"a": [1, 2]}) + df["a"] = Series([1, 2], dtype="Int64") + expected = DataFrame({"a": [1, 2]}, dtype="Int64") + tm.assert_frame_equal(df, expected) + + # TODO(ArrayManager) set column with 2d column array, see #44788 + @td.skip_array_manager_not_yet_implemented + def test_setitem_npmatrix_2d(self): + # GH#42376 + # for use-case df["x"] = sparse.random((10, 10)).mean(axis=1) + expected = DataFrame( + {"np-array": np.ones(10), "np-matrix": np.ones(10)}, index=np.arange(10) + ) + + a = np.ones((10, 1)) + df = DataFrame(index=np.arange(10), columns=Index([], dtype="str")) + df["np-array"] = a + + # Instantiation of `np.matrix` gives PendingDeprecationWarning + with tm.assert_produces_warning(PendingDeprecationWarning): + df["np-matrix"] = np.matrix(a) + + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("vals", [{}, {"d": "a"}]) + def test_setitem_aligning_dict_with_index(self, vals): + # GH#47216 + df = DataFrame({"a": [1, 2], "b": [3, 4], **vals}) + df.loc[:, "a"] = {1: 100, 0: 200} + df.loc[:, "c"] = {0: 5, 1: 6} + df.loc[:, "e"] = {1: 5} + expected = DataFrame( + {"a": [200, 100], "b": [3, 4], **vals, "c": [5, 6], "e": [np.nan, 5]} + ) + tm.assert_frame_equal(df, expected) + + def test_setitem_rhs_dataframe(self): + # GH#47578 + df = DataFrame({"a": [1, 2]}) + df["a"] = DataFrame({"a": [10, 11]}, index=[1, 2]) + expected = DataFrame({"a": [np.nan, 10]}) + tm.assert_frame_equal(df, expected) + + df = DataFrame({"a": [1, 2]}) + df.isetitem(0, DataFrame({"a": [10, 11]}, index=[1, 2])) + tm.assert_frame_equal(df, expected) + + def test_setitem_frame_overwrite_with_ea_dtype(self, any_numeric_ea_dtype): + # GH#46896 + df = DataFrame(columns=["a", "b"], data=[[1, 2], [3, 4]]) + df["a"] = DataFrame({"a": [10, 11]}, dtype=any_numeric_ea_dtype) + expected = DataFrame( + { + "a": Series([10, 11], dtype=any_numeric_ea_dtype), + "b": [2, 4], + } + ) + tm.assert_frame_equal(df, expected) + + def test_setitem_string_option_object_index(self): + # GH#55638 + pytest.importorskip("pyarrow") + df = DataFrame({"a": [1, 2]}) + with pd.option_context("future.infer_string", True): + df["b"] = Index(["a", "b"], dtype=object) + expected = DataFrame({"a": [1, 2], "b": Series(["a", "b"], dtype=object)}) + tm.assert_frame_equal(df, expected) + + def test_setitem_frame_midx_columns(self): + # GH#49121 + df = DataFrame({("a", "b"): [10]}) + expected = df.copy() + col_name = ("a", "b") + df[col_name] = df[[col_name]] + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_ea_dtype(self): + # GH#55604 + df = DataFrame({"a": np.array([10], dtype="i8")}) + df.loc[:, "a"] = Series([11], dtype="Int64") + expected = DataFrame({"a": np.array([11], dtype="i8")}) + tm.assert_frame_equal(df, expected) + + df = DataFrame({"a": np.array([10], dtype="i8")}) + df.iloc[:, 0] = Series([11], dtype="Int64") + tm.assert_frame_equal(df, expected) + + def test_setitem_object_inferring(self): + # GH#56102 + idx = Index([Timestamp("2019-12-31")], dtype=object) + df = DataFrame({"a": [1]}) + with tm.assert_produces_warning(FutureWarning, match="infer"): + df.loc[:, "b"] = idx + with tm.assert_produces_warning(FutureWarning, match="infer"): + df["c"] = idx + + expected = DataFrame( + { + "a": [1], + "b": Series([Timestamp("2019-12-31")], dtype="datetime64[ns]"), + "c": Series([Timestamp("2019-12-31")], dtype="datetime64[ns]"), + } + ) + tm.assert_frame_equal(df, expected) + + +class TestSetitemTZAwareValues: + @pytest.fixture + def idx(self): + naive = DatetimeIndex(["2013-1-1 13:00", "2013-1-2 14:00"], name="B") + idx = naive.tz_localize("US/Pacific") + return idx + + @pytest.fixture + def expected(self, idx): + expected = Series(np.array(idx.tolist(), dtype="object"), name="B") + assert expected.dtype == idx.dtype + return expected + + def test_setitem_dt64series(self, idx, expected): + # convert to utc + df = DataFrame(np.random.default_rng(2).standard_normal((2, 1)), columns=["A"]) + df["B"] = idx + df["B"] = idx.to_series(index=[0, 1]).dt.tz_convert(None) + + result = df["B"] + comp = Series(idx.tz_convert("UTC").tz_localize(None), name="B") + tm.assert_series_equal(result, comp) + + def test_setitem_datetimeindex(self, idx, expected): + # setting a DataFrame column with a tzaware DTI retains the dtype + df = DataFrame(np.random.default_rng(2).standard_normal((2, 1)), columns=["A"]) + + # assign to frame + df["B"] = idx + result = df["B"] + tm.assert_series_equal(result, expected) + + def test_setitem_object_array_of_tzaware_datetimes(self, idx, expected): + # setting a DataFrame column with a tzaware DTI retains the dtype + df = DataFrame(np.random.default_rng(2).standard_normal((2, 1)), columns=["A"]) + + # object array of datetimes with a tz + df["B"] = idx.to_pydatetime() + result = df["B"] + tm.assert_series_equal(result, expected) + + +class TestDataFrameSetItemWithExpansion: + def test_setitem_listlike_views(self, using_copy_on_write, warn_copy_on_write): + # GH#38148 + df = DataFrame({"a": [1, 2, 3], "b": [4, 4, 6]}) + + # get one column as a view of df + ser = df["a"] + + # add columns with list-like indexer + df[["c", "d"]] = np.array([[0.1, 0.2], [0.3, 0.4], [0.4, 0.5]]) + + # edit in place the first column to check view semantics + with tm.assert_cow_warning(warn_copy_on_write): + df.iloc[0, 0] = 100 + + if using_copy_on_write: + expected = Series([1, 2, 3], name="a") + else: + expected = Series([100, 2, 3], name="a") + tm.assert_series_equal(ser, expected) + + def test_setitem_string_column_numpy_dtype_raising(self): + # GH#39010 + df = DataFrame([[1, 2], [3, 4]]) + df["0 - Name"] = [5, 6] + expected = DataFrame([[1, 2, 5], [3, 4, 6]], columns=[0, 1, "0 - Name"]) + tm.assert_frame_equal(df, expected) + + def test_setitem_empty_df_duplicate_columns(self, using_copy_on_write): + # GH#38521 + df = DataFrame(columns=["a", "b", "b"], dtype="float64") + df.loc[:, "a"] = list(range(2)) + expected = DataFrame( + [[0, np.nan, np.nan], [1, np.nan, np.nan]], columns=["a", "b", "b"] + ) + tm.assert_frame_equal(df, expected) + + def test_setitem_with_expansion_categorical_dtype(self): + # assignment + df = DataFrame( + { + "value": np.array( + np.random.default_rng(2).integers(0, 10000, 100), dtype="int32" + ) + } + ) + labels = Categorical([f"{i} - {i + 499}" for i in range(0, 10000, 500)]) + + df = df.sort_values(by=["value"], ascending=True) + ser = cut(df.value, range(0, 10500, 500), right=False, labels=labels) + cat = ser.values + + # setting with a Categorical + df["D"] = cat + result = df.dtypes + expected = Series( + [np.dtype("int32"), CategoricalDtype(categories=labels, ordered=False)], + index=["value", "D"], + ) + tm.assert_series_equal(result, expected) + + # setting with a Series + df["E"] = ser + result = df.dtypes + expected = Series( + [ + np.dtype("int32"), + CategoricalDtype(categories=labels, ordered=False), + CategoricalDtype(categories=labels, ordered=False), + ], + index=["value", "D", "E"], + ) + tm.assert_series_equal(result, expected) + + result1 = df["D"] + result2 = df["E"] + tm.assert_categorical_equal(result1._mgr.array, cat) + + # sorting + ser.name = "E" + tm.assert_series_equal(result2.sort_index(), ser.sort_index()) + + def test_setitem_scalars_no_index(self): + # GH#16823 / GH#17894 + df = DataFrame() + df["foo"] = 1 + expected = DataFrame(columns=["foo"]).astype(np.int64) + tm.assert_frame_equal(df, expected) + + def test_setitem_newcol_tuple_key(self, float_frame): + assert ( + "A", + "B", + ) not in float_frame.columns + float_frame["A", "B"] = float_frame["A"] + assert ("A", "B") in float_frame.columns + + result = float_frame["A", "B"] + expected = float_frame["A"] + tm.assert_series_equal(result, expected, check_names=False) + + def test_frame_setitem_newcol_timestamp(self): + # GH#2155 + columns = date_range(start="1/1/2012", end="2/1/2012", freq=BDay()) + data = DataFrame(columns=columns, index=range(10)) + t = datetime(2012, 11, 1) + ts = Timestamp(t) + data[ts] = np.nan # works, mostly a smoke-test + assert np.isnan(data[ts]).all() + + def test_frame_setitem_rangeindex_into_new_col(self): + # GH#47128 + df = DataFrame({"a": ["a", "b"]}) + df["b"] = df.index + df.loc[[False, True], "b"] = 100 + result = df.loc[[1], :] + expected = DataFrame({"a": ["b"], "b": [100]}, index=[1]) + tm.assert_frame_equal(result, expected) + + def test_setitem_frame_keep_ea_dtype(self, any_numeric_ea_dtype): + # GH#46896 + df = DataFrame(columns=["a", "b"], data=[[1, 2], [3, 4]]) + df["c"] = DataFrame({"a": [10, 11]}, dtype=any_numeric_ea_dtype) + expected = DataFrame( + { + "a": [1, 3], + "b": [2, 4], + "c": Series([10, 11], dtype=any_numeric_ea_dtype), + } + ) + tm.assert_frame_equal(df, expected) + + def test_loc_expansion_with_timedelta_type(self): + result = DataFrame(columns=list("abc")) + result.loc[0] = { + "a": pd.to_timedelta(5, unit="s"), + "b": pd.to_timedelta(72, unit="s"), + "c": "23", + } + expected = DataFrame( + [[pd.Timedelta("0 days 00:00:05"), pd.Timedelta("0 days 00:01:12"), "23"]], + index=Index([0]), + columns=(["a", "b", "c"]), + ) + tm.assert_frame_equal(result, expected) + + +class TestDataFrameSetItemSlicing: + def test_setitem_slice_position(self): + # GH#31469 + df = DataFrame(np.zeros((100, 1))) + df[-4:] = 1 + arr = np.zeros((100, 1)) + arr[-4:] = 1 + expected = DataFrame(arr) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("indexer", [tm.setitem, tm.iloc]) + @pytest.mark.parametrize("box", [Series, np.array, list, pd.array]) + @pytest.mark.parametrize("n", [1, 2, 3]) + def test_setitem_slice_indexer_broadcasting_rhs(self, n, box, indexer): + # GH#40440 + df = DataFrame([[1, 3, 5]] + [[2, 4, 6]] * n, columns=["a", "b", "c"]) + indexer(df)[1:] = box([10, 11, 12]) + expected = DataFrame([[1, 3, 5]] + [[10, 11, 12]] * n, columns=["a", "b", "c"]) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("box", [Series, np.array, list, pd.array]) + @pytest.mark.parametrize("n", [1, 2, 3]) + def test_setitem_list_indexer_broadcasting_rhs(self, n, box): + # GH#40440 + df = DataFrame([[1, 3, 5]] + [[2, 4, 6]] * n, columns=["a", "b", "c"]) + df.iloc[list(range(1, n + 1))] = box([10, 11, 12]) + expected = DataFrame([[1, 3, 5]] + [[10, 11, 12]] * n, columns=["a", "b", "c"]) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("indexer", [tm.setitem, tm.iloc]) + @pytest.mark.parametrize("box", [Series, np.array, list, pd.array]) + @pytest.mark.parametrize("n", [1, 2, 3]) + def test_setitem_slice_broadcasting_rhs_mixed_dtypes(self, n, box, indexer): + # GH#40440 + df = DataFrame( + [[1, 3, 5], ["x", "y", "z"]] + [[2, 4, 6]] * n, columns=["a", "b", "c"] + ) + indexer(df)[1:] = box([10, 11, 12]) + expected = DataFrame( + [[1, 3, 5]] + [[10, 11, 12]] * (n + 1), + columns=["a", "b", "c"], + dtype="object", + ) + tm.assert_frame_equal(df, expected) + + +class TestDataFrameSetItemCallable: + def test_setitem_callable(self): + # GH#12533 + df = DataFrame({"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]}) + df[lambda x: "A"] = [11, 12, 13, 14] + + exp = DataFrame({"A": [11, 12, 13, 14], "B": [5, 6, 7, 8]}) + tm.assert_frame_equal(df, exp) + + def test_setitem_other_callable(self): + # GH#13299 + def inc(x): + return x + 1 + + # Set dtype object straight away to avoid upcast when setting inc below + df = DataFrame([[-1, 1], [1, -1]], dtype=object) + df[df > 0] = inc + + expected = DataFrame([[-1, inc], [inc, -1]]) + tm.assert_frame_equal(df, expected) + + +class TestDataFrameSetItemBooleanMask: + @td.skip_array_manager_invalid_test # TODO(ArrayManager) rewrite not using .values + @pytest.mark.parametrize( + "mask_type", + [lambda df: df > np.abs(df) / 2, lambda df: (df > np.abs(df) / 2).values], + ids=["dataframe", "array"], + ) + def test_setitem_boolean_mask(self, mask_type, float_frame): + # Test for issue #18582 + df = float_frame.copy() + mask = mask_type(df) + + # index with boolean mask + result = df.copy() + result[mask] = np.nan + + expected = df.values.copy() + expected[np.array(mask)] = np.nan + expected = DataFrame(expected, index=df.index, columns=df.columns) + tm.assert_frame_equal(result, expected) + + @pytest.mark.xfail(reason="Currently empty indexers are treated as all False") + @pytest.mark.parametrize("box", [list, np.array, Series]) + def test_setitem_loc_empty_indexer_raises_with_non_empty_value(self, box): + # GH#37672 + df = DataFrame({"a": ["a"], "b": [1], "c": [1]}) + if box == Series: + indexer = box([], dtype="object") + else: + indexer = box([]) + msg = "Must have equal len keys and value when setting with an iterable" + with pytest.raises(ValueError, match=msg): + df.loc[indexer, ["b"]] = [1] + + @pytest.mark.parametrize("box", [list, np.array, Series]) + def test_setitem_loc_only_false_indexer_dtype_changed(self, box): + # GH#37550 + # Dtype is only changed when value to set is a Series and indexer is + # empty/bool all False + df = DataFrame({"a": ["a"], "b": [1], "c": [1]}) + indexer = box([False]) + df.loc[indexer, ["b"]] = 10 - df["c"] + expected = DataFrame({"a": ["a"], "b": [1], "c": [1]}) + tm.assert_frame_equal(df, expected) + + df.loc[indexer, ["b"]] = 9 + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("indexer", [tm.setitem, tm.loc]) + def test_setitem_boolean_mask_aligning(self, indexer): + # GH#39931 + df = DataFrame({"a": [1, 4, 2, 3], "b": [5, 6, 7, 8]}) + expected = df.copy() + mask = df["a"] >= 3 + indexer(df)[mask] = indexer(df)[mask].sort_values("a") + tm.assert_frame_equal(df, expected) + + def test_setitem_mask_categorical(self): + # assign multiple rows (mixed values) (-> array) -> exp_multi_row + # changed multiple rows + cats2 = Categorical(["a", "a", "b", "b", "a", "a", "a"], categories=["a", "b"]) + idx2 = Index(["h", "i", "j", "k", "l", "m", "n"]) + values2 = [1, 1, 2, 2, 1, 1, 1] + exp_multi_row = DataFrame({"cats": cats2, "values": values2}, index=idx2) + + catsf = Categorical( + ["a", "a", "c", "c", "a", "a", "a"], categories=["a", "b", "c"] + ) + idxf = Index(["h", "i", "j", "k", "l", "m", "n"]) + valuesf = [1, 1, 3, 3, 1, 1, 1] + df = DataFrame({"cats": catsf, "values": valuesf}, index=idxf) + + exp_fancy = exp_multi_row.copy() + exp_fancy["cats"] = exp_fancy["cats"].cat.set_categories(["a", "b", "c"]) + + mask = df["cats"] == "c" + df[mask] = ["b", 2] + # category c is kept in .categories + tm.assert_frame_equal(df, exp_fancy) + + @pytest.mark.parametrize("dtype", ["float", "int64"]) + @pytest.mark.parametrize("kwargs", [{}, {"index": [1]}, {"columns": ["A"]}]) + def test_setitem_empty_frame_with_boolean(self, dtype, kwargs): + # see GH#10126 + kwargs["dtype"] = dtype + df = DataFrame(**kwargs) + + df2 = df.copy() + df[df > df2] = 47 + tm.assert_frame_equal(df, df2) + + def test_setitem_boolean_indexing(self): + idx = list(range(3)) + cols = ["A", "B", "C"] + df1 = DataFrame( + index=idx, + columns=cols, + data=np.array( + [[0.0, 0.5, 1.0], [1.5, 2.0, 2.5], [3.0, 3.5, 4.0]], dtype=float + ), + ) + df2 = DataFrame(index=idx, columns=cols, data=np.ones((len(idx), len(cols)))) + + expected = DataFrame( + index=idx, + columns=cols, + data=np.array([[0.0, 0.5, 1.0], [1.5, 2.0, -1], [-1, -1, -1]], dtype=float), + ) + + df1[df1 > 2.0 * df2] = -1 + tm.assert_frame_equal(df1, expected) + with pytest.raises(ValueError, match="Item wrong length"): + df1[df1.index[:-1] > 2] = -1 + + def test_loc_setitem_all_false_boolean_two_blocks(self): + # GH#40885 + df = DataFrame({"a": [1, 2], "b": [3, 4], "c": "a"}) + expected = df.copy() + indexer = Series([False, False], name="c") + df.loc[indexer, ["b"]] = DataFrame({"b": [5, 6]}, index=[0, 1]) + tm.assert_frame_equal(df, expected) + + def test_setitem_ea_boolean_mask(self): + # GH#47125 + df = DataFrame([[-1, 2], [3, -4]]) + expected = DataFrame([[0, 2], [3, 0]]) + boolean_indexer = DataFrame( + { + 0: Series([True, False], dtype="boolean"), + 1: Series([pd.NA, True], dtype="boolean"), + } + ) + df[boolean_indexer] = 0 + tm.assert_frame_equal(df, expected) + + +class TestDataFrameSetitemCopyViewSemantics: + def test_setitem_always_copy(self, float_frame): + assert "E" not in float_frame.columns + s = float_frame["A"].copy() + float_frame["E"] = s + + float_frame.iloc[5:10, float_frame.columns.get_loc("E")] = np.nan + assert notna(s[5:10]).all() + + @pytest.mark.parametrize("consolidate", [True, False]) + def test_setitem_partial_column_inplace( + self, consolidate, using_array_manager, using_copy_on_write + ): + # This setting should be in-place, regardless of whether frame is + # single-block or multi-block + # GH#304 this used to be incorrectly not-inplace, in which case + # we needed to ensure _item_cache was cleared. + + df = DataFrame( + {"x": [1.1, 2.1, 3.1, 4.1], "y": [5.1, 6.1, 7.1, 8.1]}, index=[0, 1, 2, 3] + ) + df.insert(2, "z", np.nan) + if not using_array_manager: + if consolidate: + df._consolidate_inplace() + assert len(df._mgr.blocks) == 1 + else: + assert len(df._mgr.blocks) == 2 + + zvals = df["z"]._values + + df.loc[2:, "z"] = 42 + + expected = Series([np.nan, np.nan, 42, 42], index=df.index, name="z") + tm.assert_series_equal(df["z"], expected) + + # check setting occurred in-place + if not using_copy_on_write: + tm.assert_numpy_array_equal(zvals, expected.values) + assert np.shares_memory(zvals, df["z"]._values) + + def test_setitem_duplicate_columns_not_inplace(self): + # GH#39510 + cols = ["A", "B"] * 2 + df = DataFrame(0.0, index=[0], columns=cols) + df_copy = df.copy() + df_view = df[:] + df["B"] = (2, 5) + + expected = DataFrame([[0.0, 2, 0.0, 5]], columns=cols) + tm.assert_frame_equal(df_view, df_copy) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "value", [1, np.array([[1], [1]], dtype="int64"), [[1], [1]]] + ) + def test_setitem_same_dtype_not_inplace(self, value, using_array_manager): + # GH#39510 + cols = ["A", "B"] + df = DataFrame(0, index=[0, 1], columns=cols) + df_copy = df.copy() + df_view = df[:] + df[["B"]] = value + + expected = DataFrame([[0, 1], [0, 1]], columns=cols) + tm.assert_frame_equal(df, expected) + tm.assert_frame_equal(df_view, df_copy) + + @pytest.mark.parametrize("value", [1.0, np.array([[1.0], [1.0]]), [[1.0], [1.0]]]) + def test_setitem_listlike_key_scalar_value_not_inplace(self, value): + # GH#39510 + cols = ["A", "B"] + df = DataFrame(0, index=[0, 1], columns=cols) + df_copy = df.copy() + df_view = df[:] + df[["B"]] = value + + expected = DataFrame([[0, 1.0], [0, 1.0]], columns=cols) + tm.assert_frame_equal(df_view, df_copy) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "indexer", + [ + "a", + ["a"], + pytest.param( + [True, False], + marks=pytest.mark.xfail( + reason="Boolean indexer incorrectly setting inplace", + strict=False, # passing on some builds, no obvious pattern + ), + ), + ], + ) + @pytest.mark.parametrize( + "value, set_value", + [ + (1, 5), + (1.0, 5.0), + (Timestamp("2020-12-31"), Timestamp("2021-12-31")), + ("a", "b"), + ], + ) + def test_setitem_not_operating_inplace(self, value, set_value, indexer): + # GH#43406 + df = DataFrame({"a": value}, index=[0, 1]) + expected = df.copy() + view = df[:] + df[indexer] = set_value + tm.assert_frame_equal(view, expected) + + @td.skip_array_manager_invalid_test + def test_setitem_column_update_inplace( + self, using_copy_on_write, warn_copy_on_write + ): + # https://github.com/pandas-dev/pandas/issues/47172 + + labels = [f"c{i}" for i in range(10)] + df = DataFrame({col: np.zeros(len(labels)) for col in labels}, index=labels) + values = df._mgr.blocks[0].values + + with tm.raises_chained_assignment_error(): + for label in df.columns: + df[label][label] = 1 + if not using_copy_on_write: + # diagonal values all updated + assert np.all(values[np.arange(10), np.arange(10)] == 1) + else: + # original dataframe not updated + assert np.all(values[np.arange(10), np.arange(10)] == 0) + + def test_setitem_column_frame_as_category(self): + # GH31581 + df = DataFrame([1, 2, 3]) + df["col1"] = DataFrame([1, 2, 3], dtype="category") + df["col2"] = Series([1, 2, 3], dtype="category") + + expected_types = Series( + ["int64", "category", "category"], index=[0, "col1", "col2"], dtype=object + ) + tm.assert_series_equal(df.dtypes, expected_types) + + @pytest.mark.parametrize("dtype", ["int64", "Int64"]) + def test_setitem_iloc_with_numpy_array(self, dtype): + # GH-33828 + df = DataFrame({"a": np.ones(3)}, dtype=dtype) + df.iloc[np.array([0]), np.array([0])] = np.array([[2]]) + + expected = DataFrame({"a": [2, 1, 1]}, dtype=dtype) + tm.assert_frame_equal(df, expected) + + def test_setitem_frame_dup_cols_dtype(self): + # GH#53143 + df = DataFrame([[1, 2, 3, 4], [4, 5, 6, 7]], columns=["a", "b", "a", "c"]) + rhs = DataFrame([[0, 1.5], [2, 2.5]], columns=["a", "a"]) + df["a"] = rhs + expected = DataFrame( + [[0, 2, 1.5, 4], [2, 5, 2.5, 7]], columns=["a", "b", "a", "c"] + ) + tm.assert_frame_equal(df, expected) + + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"]) + rhs = DataFrame([[0, 1.5], [2, 2.5]], columns=["a", "a"]) + df["a"] = rhs + expected = DataFrame([[0, 1.5, 3], [2, 2.5, 6]], columns=["a", "a", "b"]) + tm.assert_frame_equal(df, expected) + + def test_frame_setitem_empty_dataframe(self): + # GH#28871 + dti = DatetimeIndex(["2000-01-01"], dtype="M8[ns]", name="date") + df = DataFrame({"date": dti}).set_index("date") + df = df[0:0].copy() + + df["3010"] = None + df["2010"] = None + + expected = DataFrame( + [], + columns=["3010", "2010"], + index=dti[:0], + ) + tm.assert_frame_equal(df, expected) + + +def test_full_setter_loc_incompatible_dtype(): + # https://github.com/pandas-dev/pandas/issues/55791 + df = DataFrame({"a": [1, 2]}) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + df.loc[:, "a"] = True + expected = DataFrame({"a": [True, True]}) + tm.assert_frame_equal(df, expected) + + df = DataFrame({"a": [1, 2]}) + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + df.loc[:, "a"] = {0: 3.5, 1: 4.5} + expected = DataFrame({"a": [3.5, 4.5]}) + tm.assert_frame_equal(df, expected) + + df = DataFrame({"a": [1, 2]}) + df.loc[:, "a"] = {0: 3, 1: 4} + expected = DataFrame({"a": [3, 4]}) + tm.assert_frame_equal(df, expected) + + +def test_setitem_partial_row_multiple_columns(): + # https://github.com/pandas-dev/pandas/issues/56503 + df = DataFrame({"A": [1, 2, 3], "B": [4.0, 5, 6]}) + # should not warn + df.loc[df.index <= 1, ["F", "G"]] = (1, "abc") + expected = DataFrame( + { + "A": [1, 2, 3], + "B": [4.0, 5, 6], + "F": [1.0, 1, float("nan")], + "G": ["abc", "abc", float("nan")], + } + ) + tm.assert_frame_equal(df, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_take.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_take.py new file mode 100644 index 0000000000000000000000000000000000000000..8c172314409171bc102e599bb26ca0d1e0b12078 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_take.py @@ -0,0 +1,92 @@ +import pytest + +import pandas._testing as tm + + +class TestDataFrameTake: + def test_take_slices_deprecated(self, float_frame): + # GH#51539 + df = float_frame + + slc = slice(0, 4, 1) + with tm.assert_produces_warning(FutureWarning): + df.take(slc, axis=0) + with tm.assert_produces_warning(FutureWarning): + df.take(slc, axis=1) + + def test_take(self, float_frame): + # homogeneous + order = [3, 1, 2, 0] + for df in [float_frame]: + result = df.take(order, axis=0) + expected = df.reindex(df.index.take(order)) + tm.assert_frame_equal(result, expected) + + # axis = 1 + result = df.take(order, axis=1) + expected = df.loc[:, ["D", "B", "C", "A"]] + tm.assert_frame_equal(result, expected, check_names=False) + + # negative indices + order = [2, 1, -1] + for df in [float_frame]: + result = df.take(order, axis=0) + expected = df.reindex(df.index.take(order)) + tm.assert_frame_equal(result, expected) + + result = df.take(order, axis=0) + tm.assert_frame_equal(result, expected) + + # axis = 1 + result = df.take(order, axis=1) + expected = df.loc[:, ["C", "B", "D"]] + tm.assert_frame_equal(result, expected, check_names=False) + + # illegal indices + msg = "indices are out-of-bounds" + with pytest.raises(IndexError, match=msg): + df.take([3, 1, 2, 30], axis=0) + with pytest.raises(IndexError, match=msg): + df.take([3, 1, 2, -31], axis=0) + with pytest.raises(IndexError, match=msg): + df.take([3, 1, 2, 5], axis=1) + with pytest.raises(IndexError, match=msg): + df.take([3, 1, 2, -5], axis=1) + + def test_take_mixed_type(self, float_string_frame): + # mixed-dtype + order = [4, 1, 2, 0, 3] + for df in [float_string_frame]: + result = df.take(order, axis=0) + expected = df.reindex(df.index.take(order)) + tm.assert_frame_equal(result, expected) + + # axis = 1 + result = df.take(order, axis=1) + expected = df.loc[:, ["foo", "B", "C", "A", "D"]] + tm.assert_frame_equal(result, expected) + + # negative indices + order = [4, 1, -2] + for df in [float_string_frame]: + result = df.take(order, axis=0) + expected = df.reindex(df.index.take(order)) + tm.assert_frame_equal(result, expected) + + # axis = 1 + result = df.take(order, axis=1) + expected = df.loc[:, ["foo", "B", "D"]] + tm.assert_frame_equal(result, expected) + + def test_take_mixed_numeric(self, mixed_float_frame, mixed_int_frame): + # by dtype + order = [1, 2, 0, 3] + for df in [mixed_float_frame, mixed_int_frame]: + result = df.take(order, axis=0) + expected = df.reindex(df.index.take(order)) + tm.assert_frame_equal(result, expected) + + # axis = 1 + result = df.take(order, axis=1) + expected = df.loc[:, ["B", "C", "A", "D"]] + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_where.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_where.py new file mode 100644 index 0000000000000000000000000000000000000000..356257bbfec9804336159becf86212d2afeeed0d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_where.py @@ -0,0 +1,1104 @@ +from datetime import datetime + +from hypothesis import given +import numpy as np +import pytest + +from pandas.core.dtypes.common import is_scalar + +import pandas as pd +from pandas import ( + DataFrame, + DatetimeIndex, + Index, + Series, + StringDtype, + Timestamp, + date_range, + isna, +) +import pandas._testing as tm +from pandas._testing._hypothesis import OPTIONAL_ONE_OF_ALL + + +@pytest.fixture(params=["default", "float_string", "mixed_float", "mixed_int"]) +def where_frame(request, float_string_frame, mixed_float_frame, mixed_int_frame): + if request.param == "default": + return DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), columns=["A", "B", "C"] + ) + if request.param == "float_string": + return float_string_frame + if request.param == "mixed_float": + return mixed_float_frame + if request.param == "mixed_int": + return mixed_int_frame + + +def _safe_add(df): + # only add to the numeric items + def is_ok(s): + return ( + issubclass(s.dtype.type, (np.integer, np.floating)) and s.dtype != "uint8" + ) + + return DataFrame(dict((c, s + 1) if is_ok(s) else (c, s) for c, s in df.items())) + + +class TestDataFrameIndexingWhere: + def test_where_get(self, where_frame, float_string_frame): + def _check_get(df, cond, check_dtypes=True): + other1 = _safe_add(df) + rs = df.where(cond, other1) + rs2 = df.where(cond.values, other1) + for k, v in rs.items(): + exp = Series(np.where(cond[k], df[k], other1[k]), index=v.index) + tm.assert_series_equal(v, exp, check_names=False) + tm.assert_frame_equal(rs, rs2) + + # dtypes + if check_dtypes: + assert (rs.dtypes == df.dtypes).all() + + # check getting + df = where_frame + if df is float_string_frame: + msg = ( + "'>' not supported between instances of 'str' and 'int'" + "|Invalid comparison" + ) + with pytest.raises(TypeError, match=msg): + df > 0 + return + cond = df > 0 + _check_get(df, cond) + + def test_where_upcasting(self): + # upcasting case (GH # 2794) + df = DataFrame( + { + c: Series([1] * 3, dtype=c) + for c in ["float32", "float64", "int32", "int64"] + } + ) + df.iloc[1, :] = 0 + result = df.dtypes + expected = Series( + [ + np.dtype("float32"), + np.dtype("float64"), + np.dtype("int32"), + np.dtype("int64"), + ], + index=["float32", "float64", "int32", "int64"], + ) + + # when we don't preserve boolean casts + # + # expected = Series({ 'float32' : 1, 'float64' : 3 }) + + tm.assert_series_equal(result, expected) + + @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning") + def test_where_alignment(self, where_frame, float_string_frame): + # aligning + def _check_align(df, cond, other, check_dtypes=True): + rs = df.where(cond, other) + for i, k in enumerate(rs.columns): + result = rs[k] + d = df[k].values + c = cond[k].reindex(df[k].index).fillna(False).values + + if is_scalar(other): + o = other + elif isinstance(other, np.ndarray): + o = Series(other[:, i], index=result.index).values + else: + o = other[k].values + + new_values = d if c.all() else np.where(c, d, o) + expected = Series(new_values, index=result.index, name=k) + + # since we can't always have the correct numpy dtype + # as numpy doesn't know how to downcast, don't check + tm.assert_series_equal(result, expected, check_dtype=False) + + # dtypes + # can't check dtype when other is an ndarray + + if check_dtypes and not isinstance(other, np.ndarray): + assert (rs.dtypes == df.dtypes).all() + + df = where_frame + if df is float_string_frame: + msg = ( + "'>' not supported between instances of 'str' and 'int'" + "|Invalid comparison" + ) + with pytest.raises(TypeError, match=msg): + df > 0 + return + + # other is a frame + cond = (df > 0)[1:] + _check_align(df, cond, _safe_add(df)) + + # check other is ndarray + cond = df > 0 + _check_align(df, cond, (_safe_add(df).values)) + + # integers are upcast, so don't check the dtypes + cond = df > 0 + check_dtypes = all(not issubclass(s.type, np.integer) for s in df.dtypes) + _check_align(df, cond, np.nan, check_dtypes=check_dtypes) + + # Ignore deprecation warning in Python 3.12 for inverting a bool + @pytest.mark.filterwarnings("ignore::DeprecationWarning") + def test_where_invalid(self): + # invalid conditions + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), columns=["A", "B", "C"] + ) + cond = df > 0 + + err1 = (df + 1).values[0:2, :] + msg = "other must be the same shape as self when an ndarray" + with pytest.raises(ValueError, match=msg): + df.where(cond, err1) + + err2 = cond.iloc[:2, :].values + other1 = _safe_add(df) + msg = "Array conditional must be same shape as self" + with pytest.raises(ValueError, match=msg): + df.where(err2, other1) + + with pytest.raises(ValueError, match=msg): + df.mask(True) + with pytest.raises(ValueError, match=msg): + df.mask(0) + + @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning") + def test_where_set(self, where_frame, float_string_frame, mixed_int_frame): + # where inplace + + def _check_set(df, cond, check_dtypes=True): + dfi = df.copy() + econd = cond.reindex_like(df).fillna(True).infer_objects(copy=False) + expected = dfi.mask(~econd) + + return_value = dfi.where(cond, np.nan, inplace=True) + assert return_value is None + tm.assert_frame_equal(dfi, expected) + + # dtypes (and confirm upcasts)x + if check_dtypes: + for k, v in df.dtypes.items(): + if issubclass(v.type, np.integer) and not cond[k].all(): + v = np.dtype("float64") + assert dfi[k].dtype == v + + df = where_frame + if df is float_string_frame: + msg = ( + "'>' not supported between instances of 'str' and 'int'" + "|Invalid comparison" + ) + with pytest.raises(TypeError, match=msg): + df > 0 + return + if df is mixed_int_frame: + df = df.astype("float64") + + cond = df > 0 + _check_set(df, cond) + + cond = df >= 0 + _check_set(df, cond) + + # aligning + cond = (df >= 0)[1:] + _check_set(df, cond) + + def test_where_series_slicing(self): + # GH 10218 + # test DataFrame.where with Series slicing + df = DataFrame({"a": range(3), "b": range(4, 7)}) + result = df.where(df["a"] == 1) + expected = df[df["a"] == 1].reindex(df.index) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("klass", [list, tuple, np.array]) + def test_where_array_like(self, klass): + # see gh-15414 + df = DataFrame({"a": [1, 2, 3]}) + cond = [[False], [True], [True]] + expected = DataFrame({"a": [np.nan, 2, 3]}) + + result = df.where(klass(cond)) + tm.assert_frame_equal(result, expected) + + df["b"] = 2 + expected["b"] = [2, np.nan, 2] + cond = [[False, True], [True, False], [True, True]] + + result = df.where(klass(cond)) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "cond", + [ + [[1], [0], [1]], + Series([[2], [5], [7]]), + DataFrame({"a": [2, 5, 7]}), + [["True"], ["False"], ["True"]], + [[Timestamp("2017-01-01")], [pd.NaT], [Timestamp("2017-01-02")]], + ], + ) + def test_where_invalid_input_single(self, cond): + # see gh-15414: only boolean arrays accepted + df = DataFrame({"a": [1, 2, 3]}) + msg = "Boolean array expected for the condition" + + with pytest.raises(ValueError, match=msg): + df.where(cond) + + @pytest.mark.parametrize( + "cond", + [ + [[0, 1], [1, 0], [1, 1]], + Series([[0, 2], [5, 0], [4, 7]]), + [["False", "True"], ["True", "False"], ["True", "True"]], + DataFrame({"a": [2, 5, 7], "b": [4, 8, 9]}), + [ + [pd.NaT, Timestamp("2017-01-01")], + [Timestamp("2017-01-02"), pd.NaT], + [Timestamp("2017-01-03"), Timestamp("2017-01-03")], + ], + ], + ) + def test_where_invalid_input_multiple(self, cond): + # see gh-15414: only boolean arrays accepted + df = DataFrame({"a": [1, 2, 3], "b": [2, 2, 2]}) + msg = "Boolean array expected for the condition" + + with pytest.raises(ValueError, match=msg): + df.where(cond) + + def test_where_dataframe_col_match(self): + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + cond = DataFrame([[True, False, True], [False, False, True]]) + + result = df.where(cond) + expected = DataFrame([[1.0, np.nan, 3], [np.nan, np.nan, 6]]) + tm.assert_frame_equal(result, expected) + + # this *does* align, though has no matching columns + cond.columns = ["a", "b", "c"] + result = df.where(cond) + expected = DataFrame(np.nan, index=df.index, columns=df.columns) + tm.assert_frame_equal(result, expected) + + def test_where_ndframe_align(self): + msg = "Array conditional must be same shape as self" + df = DataFrame([[1, 2, 3], [4, 5, 6]]) + + cond = [True] + with pytest.raises(ValueError, match=msg): + df.where(cond) + + expected = DataFrame([[1, 2, 3], [np.nan, np.nan, np.nan]]) + + out = df.where(Series(cond)) + tm.assert_frame_equal(out, expected) + + cond = np.array([False, True, False, True]) + with pytest.raises(ValueError, match=msg): + df.where(cond) + + expected = DataFrame([[np.nan, np.nan, np.nan], [4, 5, 6]]) + + out = df.where(Series(cond)) + tm.assert_frame_equal(out, expected) + + def test_where_bug(self): + # see gh-2793 + df = DataFrame( + {"a": [1.0, 2.0, 3.0, 4.0], "b": [4.0, 3.0, 2.0, 1.0]}, dtype="float64" + ) + expected = DataFrame( + {"a": [np.nan, np.nan, 3.0, 4.0], "b": [4.0, 3.0, np.nan, np.nan]}, + dtype="float64", + ) + result = df.where(df > 2, np.nan) + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(result > 2, np.nan, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_bug_mixed(self, any_signed_int_numpy_dtype): + # see gh-2793 + df = DataFrame( + { + "a": np.array([1, 2, 3, 4], dtype=any_signed_int_numpy_dtype), + "b": np.array([4.0, 3.0, 2.0, 1.0], dtype="float64"), + } + ) + + expected = DataFrame( + {"a": [-1, -1, 3, 4], "b": [4.0, 3.0, -1, -1]}, + ).astype({"a": any_signed_int_numpy_dtype, "b": "float64"}) + + result = df.where(df > 2, -1) + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(result > 2, -1, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_bug_transposition(self): + # see gh-7506 + a = DataFrame({0: [1, 2], 1: [3, 4], 2: [5, 6]}) + b = DataFrame({0: [np.nan, 8], 1: [9, np.nan], 2: [np.nan, np.nan]}) + do_not_replace = b.isna() | (a > b) + + expected = a.copy() + expected[~do_not_replace] = b + + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = a.where(do_not_replace, b) + tm.assert_frame_equal(result, expected) + + a = DataFrame({0: [4, 6], 1: [1, 0]}) + b = DataFrame({0: [np.nan, 3], 1: [3, np.nan]}) + do_not_replace = b.isna() | (a > b) + + expected = a.copy() + expected[~do_not_replace] = b + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = a.where(do_not_replace, b) + tm.assert_frame_equal(result, expected) + + def test_where_datetime(self): + # GH 3311 + df = DataFrame( + { + "A": date_range("20130102", periods=5), + "B": date_range("20130104", periods=5), + "C": np.random.default_rng(2).standard_normal(5), + } + ) + + stamp = datetime(2013, 1, 3) + msg = "'>' not supported between instances of 'float' and 'datetime.datetime'" + with pytest.raises(TypeError, match=msg): + df > stamp + + result = df[df.iloc[:, :-1] > stamp] + + expected = df.copy() + expected.loc[[0, 1], "A"] = np.nan + + expected.loc[:, "C"] = np.nan + tm.assert_frame_equal(result, expected) + + def test_where_none(self): + # GH 4667 + # setting with None changes dtype + df = DataFrame({"series": Series(range(10))}).astype(float) + df[df > 7] = None + expected = DataFrame( + {"series": Series([0, 1, 2, 3, 4, 5, 6, 7, np.nan, np.nan])} + ) + tm.assert_frame_equal(df, expected) + + # GH 7656 + df = DataFrame( + [ + {"A": 1, "B": np.nan, "C": "Test"}, + {"A": np.nan, "B": "Test", "C": np.nan}, + ] + ) + + orig = df.copy() + + mask = ~isna(df) + df.where(mask, None, inplace=True) + expected = DataFrame( + { + "A": [1.0, np.nan], + "B": [None, "Test"], + "C": ["Test", None], + } + ) + tm.assert_frame_equal(df, expected) + + df = orig.copy() + df[~mask] = None + tm.assert_frame_equal(df, expected) + + def test_where_empty_df_and_empty_cond_having_non_bool_dtypes(self): + # see gh-21947 + df = DataFrame(columns=["a"]) + cond = df + assert (cond.dtypes == object).all() + + result = df.where(cond) + tm.assert_frame_equal(result, df) + + def test_where_align(self): + def create(): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 3))) + df.iloc[3:5, 0] = np.nan + df.iloc[4:6, 1] = np.nan + df.iloc[5:8, 2] = np.nan + return df + + # series + df = create() + expected = df.fillna(df.mean()) + result = df.where(pd.notna(df), df.mean(), axis="columns") + tm.assert_frame_equal(result, expected) + + return_value = df.where(pd.notna(df), df.mean(), inplace=True, axis="columns") + assert return_value is None + tm.assert_frame_equal(df, expected) + + df = create().fillna(0) + expected = df.apply(lambda x, y: x.where(x > 0, y), y=df[0]) + result = df.where(df > 0, df[0], axis="index") + tm.assert_frame_equal(result, expected) + result = df.where(df > 0, df[0], axis="rows") + tm.assert_frame_equal(result, expected) + + # frame + df = create() + expected = df.fillna(1) + result = df.where( + pd.notna(df), DataFrame(1, index=df.index, columns=df.columns) + ) + tm.assert_frame_equal(result, expected) + + def test_where_complex(self): + # GH 6345 + expected = DataFrame([[1 + 1j, 2], [np.nan, 4 + 1j]], columns=["a", "b"]) + df = DataFrame([[1 + 1j, 2], [5 + 1j, 4 + 1j]], columns=["a", "b"]) + df[df.abs() >= 5] = np.nan + tm.assert_frame_equal(df, expected) + + def test_where_axis(self): + # GH 9736 + df = DataFrame(np.random.default_rng(2).standard_normal((2, 2))) + mask = DataFrame([[False, False], [False, False]]) + ser = Series([0, 1]) + + expected = DataFrame([[0, 0], [1, 1]], dtype="float64") + result = df.where(mask, ser, axis="index") + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(mask, ser, axis="index", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + expected = DataFrame([[0, 1], [0, 1]], dtype="float64") + result = df.where(mask, ser, axis="columns") + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(mask, ser, axis="columns", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_axis_with_upcast(self): + # Upcast needed + df = DataFrame([[1, 2], [3, 4]], dtype="int64") + mask = DataFrame([[False, False], [False, False]]) + ser = Series([0, np.nan]) + + expected = DataFrame([[0, 0], [np.nan, np.nan]], dtype="float64") + result = df.where(mask, ser, axis="index") + tm.assert_frame_equal(result, expected) + + result = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + return_value = result.where(mask, ser, axis="index", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + expected = DataFrame([[0, np.nan], [0, np.nan]]) + result = df.where(mask, ser, axis="columns") + tm.assert_frame_equal(result, expected) + + expected = DataFrame( + { + 0: np.array([0, 0], dtype="int64"), + 1: np.array([np.nan, np.nan], dtype="float64"), + } + ) + result = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + return_value = result.where(mask, ser, axis="columns", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_axis_multiple_dtypes(self): + # Multiple dtypes (=> multiple Blocks) + df = pd.concat( + [ + DataFrame(np.random.default_rng(2).standard_normal((10, 2))), + DataFrame( + np.random.default_rng(2).integers(0, 10, size=(10, 2)), + dtype="int64", + ), + ], + ignore_index=True, + axis=1, + ) + mask = DataFrame(False, columns=df.columns, index=df.index) + s1 = Series(1, index=df.columns) + s2 = Series(2, index=df.index) + + result = df.where(mask, s1, axis="columns") + expected = DataFrame(1.0, columns=df.columns, index=df.index) + expected[2] = expected[2].astype("int64") + expected[3] = expected[3].astype("int64") + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(mask, s1, axis="columns", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + result = df.where(mask, s2, axis="index") + expected = DataFrame(2.0, columns=df.columns, index=df.index) + expected[2] = expected[2].astype("int64") + expected[3] = expected[3].astype("int64") + tm.assert_frame_equal(result, expected) + + result = df.copy() + return_value = result.where(mask, s2, axis="index", inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + + # DataFrame vs DataFrame + d1 = df.copy().drop(1, axis=0) + # Explicit cast to avoid implicit cast when setting value to np.nan + expected = df.copy().astype("float") + expected.loc[1, :] = np.nan + + result = df.where(mask, d1) + tm.assert_frame_equal(result, expected) + result = df.where(mask, d1, axis="index") + tm.assert_frame_equal(result, expected) + result = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + return_value = result.where(mask, d1, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + result = df.copy() + with tm.assert_produces_warning(FutureWarning, match="incompatible dtype"): + return_value = result.where(mask, d1, inplace=True, axis="index") + assert return_value is None + tm.assert_frame_equal(result, expected) + + d2 = df.copy().drop(1, axis=1) + expected = df.copy() + expected.loc[:, 1] = np.nan + + result = df.where(mask, d2) + tm.assert_frame_equal(result, expected) + result = df.where(mask, d2, axis="columns") + tm.assert_frame_equal(result, expected) + result = df.copy() + return_value = result.where(mask, d2, inplace=True) + assert return_value is None + tm.assert_frame_equal(result, expected) + result = df.copy() + return_value = result.where(mask, d2, inplace=True, axis="columns") + assert return_value is None + tm.assert_frame_equal(result, expected) + + def test_where_callable(self): + # GH 12533 + df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + result = df.where(lambda x: x > 4, lambda x: x + 1) + exp = DataFrame([[2, 3, 4], [5, 5, 6], [7, 8, 9]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.where(df > 4, df + 1)) + + # return ndarray and scalar + result = df.where(lambda x: (x % 2 == 0).values, lambda x: 99) + exp = DataFrame([[99, 2, 99], [4, 99, 6], [99, 8, 99]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, df.where(df % 2 == 0, 99)) + + # chain + result = (df + 2).where(lambda x: x > 8, lambda x: x + 10) + exp = DataFrame([[13, 14, 15], [16, 17, 18], [9, 10, 11]]) + tm.assert_frame_equal(result, exp) + tm.assert_frame_equal(result, (df + 2).where((df + 2) > 8, (df + 2) + 10)) + + def test_where_tz_values(self, tz_naive_fixture, frame_or_series): + obj1 = DataFrame( + DatetimeIndex(["20150101", "20150102", "20150103"], tz=tz_naive_fixture), + columns=["date"], + ) + obj2 = DataFrame( + DatetimeIndex(["20150103", "20150104", "20150105"], tz=tz_naive_fixture), + columns=["date"], + ) + mask = DataFrame([True, True, False], columns=["date"]) + exp = DataFrame( + DatetimeIndex(["20150101", "20150102", "20150105"], tz=tz_naive_fixture), + columns=["date"], + ) + if frame_or_series is Series: + obj1 = obj1["date"] + obj2 = obj2["date"] + mask = mask["date"] + exp = exp["date"] + + result = obj1.where(mask, obj2) + tm.assert_equal(exp, result) + + def test_df_where_change_dtype(self): + # GH#16979 + df = DataFrame(np.arange(2 * 3).reshape(2, 3), columns=list("ABC")) + mask = np.array([[True, False, False], [False, False, True]]) + + result = df.where(mask) + expected = DataFrame( + [[0, np.nan, np.nan], [np.nan, np.nan, 5]], columns=list("ABC") + ) + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("kwargs", [{}, {"other": None}]) + def test_df_where_with_category(self, kwargs): + # GH#16979 + data = np.arange(2 * 3, dtype=np.int64).reshape(2, 3) + df = DataFrame(data, columns=list("ABC")) + mask = np.array([[True, False, False], [False, False, True]]) + + # change type to category + df.A = df.A.astype("category") + df.B = df.B.astype("category") + df.C = df.C.astype("category") + + result = df.where(mask, **kwargs) + A = pd.Categorical([0, np.nan], categories=[0, 3]) + B = pd.Categorical([np.nan, np.nan], categories=[1, 4]) + C = pd.Categorical([np.nan, 5], categories=[2, 5]) + expected = DataFrame({"A": A, "B": B, "C": C}) + + tm.assert_frame_equal(result, expected) + + # Check Series.where while we're here + result = df.A.where(mask[:, 0], **kwargs) + expected = Series(A, name="A") + + tm.assert_series_equal(result, expected) + + def test_where_categorical_filtering(self): + # GH#22609 Verify filtering operations on DataFrames with categorical Series + df = DataFrame(data=[[0, 0], [1, 1]], columns=["a", "b"]) + df["b"] = df["b"].astype("category") + + result = df.where(df["a"] > 0) + # Explicitly cast to 'float' to avoid implicit cast when setting np.nan + expected = df.copy().astype({"a": "float"}) + expected.loc[0, :] = np.nan + + tm.assert_equal(result, expected) + + def test_where_ea_other(self): + # GH#38729/GH#38742 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + arr = pd.array([7, pd.NA, 9]) + ser = Series(arr) + mask = np.ones(df.shape, dtype=bool) + mask[1, :] = False + + # TODO: ideally we would get Int64 instead of object + result = df.where(mask, ser, axis=0) + expected = DataFrame({"A": [1, np.nan, 3], "B": [4, np.nan, 6]}) + tm.assert_frame_equal(result, expected) + + ser2 = Series(arr[:2], index=["A", "B"]) + expected = DataFrame({"A": [1, 7, 3], "B": [4, np.nan, 6]}) + result = df.where(mask, ser2, axis=1) + tm.assert_frame_equal(result, expected) + + def test_where_interval_noop(self): + # GH#44181 + df = DataFrame([pd.Interval(0, 0)]) + res = df.where(df.notna()) + tm.assert_frame_equal(res, df) + + ser = df[0] + res = ser.where(ser.notna()) + tm.assert_series_equal(res, ser) + + def test_where_interval_fullop_downcast(self, frame_or_series): + # GH#45768 + obj = frame_or_series([pd.Interval(0, 0)] * 2) + other = frame_or_series([1.0, 2.0]) + + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = obj.where(~obj.notna(), other) + + # since all entries are being changed, we will downcast result + # from object to ints (not floats) + tm.assert_equal(res, other.astype(np.int64)) + + # unlike where, Block.putmask does not downcast + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + obj.mask(obj.notna(), other, inplace=True) + tm.assert_equal(obj, other.astype(object)) + + @pytest.mark.parametrize( + "dtype", + [ + "timedelta64[ns]", + "datetime64[ns]", + "datetime64[ns, Asia/Tokyo]", + "Period[D]", + ], + ) + def test_where_datetimelike_noop(self, dtype): + # GH#45135, analogue to GH#44181 for Period don't raise on no-op + # For td64/dt64/dt64tz we already don't raise, but also are + # checking that we don't unnecessarily upcast to object. + with tm.assert_produces_warning(FutureWarning, match="is deprecated"): + ser = Series(np.arange(3) * 10**9, dtype=np.int64).view(dtype) + df = ser.to_frame() + mask = np.array([False, False, False]) + + res = ser.where(~mask, "foo") + tm.assert_series_equal(res, ser) + + mask2 = mask.reshape(-1, 1) + res2 = df.where(~mask2, "foo") + tm.assert_frame_equal(res2, df) + + res3 = ser.mask(mask, "foo") + tm.assert_series_equal(res3, ser) + + res4 = df.mask(mask2, "foo") + tm.assert_frame_equal(res4, df) + + # opposite case where we are replacing *all* values -> we downcast + # from object dtype # GH#45768 + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + with tm.assert_produces_warning(FutureWarning, match=msg): + res5 = df.where(mask2, 4) + expected = DataFrame(4, index=df.index, columns=df.columns) + tm.assert_frame_equal(res5, expected) + + # unlike where, Block.putmask does not downcast + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + df.mask(~mask2, 4, inplace=True) + tm.assert_frame_equal(df, expected.astype(object)) + + +def test_where_int_downcasting_deprecated(): + # GH#44597 + arr = np.arange(6).astype(np.int16).reshape(3, 2) + df = DataFrame(arr) + + mask = np.zeros(arr.shape, dtype=bool) + mask[:, 0] = True + + res = df.where(mask, 2**17) + + expected = DataFrame({0: arr[:, 0], 1: np.array([2**17] * 3, dtype=np.int32)}) + tm.assert_frame_equal(res, expected) + + +def test_where_copies_with_noop(frame_or_series): + # GH-39595 + result = frame_or_series([1, 2, 3, 4]) + expected = result.copy() + col = result[0] if frame_or_series is DataFrame else result + + where_res = result.where(col < 5) + where_res *= 2 + + tm.assert_equal(result, expected) + + where_res = result.where(col > 5, [1, 2, 3, 4]) + where_res *= 2 + + tm.assert_equal(result, expected) + + +def test_where_string_dtype(frame_or_series): + # GH40824 + obj = frame_or_series( + ["a", "b", "c", "d"], index=["id1", "id2", "id3", "id4"], dtype=StringDtype() + ) + filtered_obj = frame_or_series( + ["b", "c"], index=["id2", "id3"], dtype=StringDtype() + ) + filter_ser = Series([False, True, True, False]) + + result = obj.where(filter_ser, filtered_obj) + expected = frame_or_series( + [pd.NA, "b", "c", pd.NA], + index=["id1", "id2", "id3", "id4"], + dtype=StringDtype(), + ) + tm.assert_equal(result, expected) + + result = obj.mask(~filter_ser, filtered_obj) + tm.assert_equal(result, expected) + + obj.mask(~filter_ser, filtered_obj, inplace=True) + tm.assert_equal(result, expected) + + +def test_where_bool_comparison(): + # GH 10336 + df_mask = DataFrame( + {"AAA": [True] * 4, "BBB": [False] * 4, "CCC": [True, False, True, False]} + ) + result = df_mask.where(df_mask == False) # noqa: E712 + expected = DataFrame( + { + "AAA": np.array([np.nan] * 4, dtype=object), + "BBB": [False] * 4, + "CCC": [np.nan, False, np.nan, False], + } + ) + tm.assert_frame_equal(result, expected) + + +def test_where_none_nan_coerce(): + # GH 15613 + expected = DataFrame( + { + "A": [Timestamp("20130101"), pd.NaT, Timestamp("20130103")], + "B": [1, 2, np.nan], + } + ) + result = expected.where(expected.notnull(), None) + tm.assert_frame_equal(result, expected) + + +def test_where_duplicate_axes_mixed_dtypes(): + # GH 25399, verify manually masking is not affected anymore by dtype of column for + # duplicate axes. + result = DataFrame(data=[[0, np.nan]], columns=Index(["A", "A"])) + index, columns = result.axes + mask = DataFrame(data=[[True, True]], columns=columns, index=index) + a = result.astype(object).where(mask) + b = result.astype("f8").where(mask) + c = result.T.where(mask.T).T + d = result.where(mask) # used to fail with "cannot reindex from a duplicate axis" + tm.assert_frame_equal(a.astype("f8"), b.astype("f8")) + tm.assert_frame_equal(b.astype("f8"), c.astype("f8")) + tm.assert_frame_equal(c.astype("f8"), d.astype("f8")) + + +def test_where_columns_casting(): + # GH 42295 + + df = DataFrame({"a": [1.0, 2.0], "b": [3, np.nan]}) + expected = df.copy() + result = df.where(pd.notnull(df), None) + # make sure dtypes don't change + tm.assert_frame_equal(expected, result) + + +@pytest.mark.parametrize("as_cat", [True, False]) +def test_where_period_invalid_na(frame_or_series, as_cat, request): + # GH#44697 + idx = pd.period_range("2016-01-01", periods=3, freq="D") + if as_cat: + idx = idx.astype("category") + obj = frame_or_series(idx) + + # NA value that we should *not* cast to Period dtype + tdnat = pd.NaT.to_numpy("m8[ns]") + + mask = np.array([True, True, False], ndmin=obj.ndim).T + + if as_cat: + msg = ( + r"Cannot setitem on a Categorical with a new category \(NaT\), " + "set the categories first" + ) + else: + msg = "value should be a 'Period'" + + if as_cat: + with pytest.raises(TypeError, match=msg): + obj.where(mask, tdnat) + + with pytest.raises(TypeError, match=msg): + obj.mask(mask, tdnat) + + with pytest.raises(TypeError, match=msg): + obj.mask(mask, tdnat, inplace=True) + + else: + # With PeriodDtype, ser[i] = tdnat coerces instead of raising, + # so for consistency, ser[mask] = tdnat must as well + expected = obj.astype(object).where(mask, tdnat) + result = obj.where(mask, tdnat) + tm.assert_equal(result, expected) + + expected = obj.astype(object).mask(mask, tdnat) + result = obj.mask(mask, tdnat) + tm.assert_equal(result, expected) + + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + obj.mask(mask, tdnat, inplace=True) + tm.assert_equal(obj, expected) + + +def test_where_nullable_invalid_na(frame_or_series, any_numeric_ea_dtype): + # GH#44697 + arr = pd.array([1, 2, 3], dtype=any_numeric_ea_dtype) + obj = frame_or_series(arr) + + mask = np.array([True, True, False], ndmin=obj.ndim).T + + msg = r"Invalid value '.*' for dtype '(U?Int|Float)\d{1,2}'" + + for null in tm.NP_NAT_OBJECTS + [pd.NaT]: + # NaT is an NA value that we should *not* cast to pd.NA dtype + with pytest.raises(TypeError, match=msg): + obj.where(mask, null) + + with pytest.raises(TypeError, match=msg): + obj.mask(mask, null) + + +@given(data=OPTIONAL_ONE_OF_ALL) +def test_where_inplace_casting(data): + # GH 22051 + df = DataFrame({"a": data}) + df_copy = df.where(pd.notnull(df), None).copy() + df.where(pd.notnull(df), None, inplace=True) + tm.assert_equal(df, df_copy) + + +def test_where_downcast_to_td64(): + ser = Series([1, 2, 3]) + + mask = np.array([False, False, False]) + + td = pd.Timedelta(days=1) + + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + with tm.assert_produces_warning(FutureWarning, match=msg): + res = ser.where(mask, td) + expected = Series([td, td, td], dtype="m8[ns]") + tm.assert_series_equal(res, expected) + + with pd.option_context("future.no_silent_downcasting", True): + with tm.assert_produces_warning(None, match=msg): + res2 = ser.where(mask, td) + expected2 = expected.astype(object) + tm.assert_series_equal(res2, expected2) + + +def _check_where_equivalences(df, mask, other, expected): + # similar to tests.series.indexing.test_setitem.SetitemCastingEquivalences + # but with DataFrame in mind and less fleshed-out + res = df.where(mask, other) + tm.assert_frame_equal(res, expected) + + res = df.mask(~mask, other) + tm.assert_frame_equal(res, expected) + + # Note: frame.mask(~mask, other, inplace=True) takes some more work bc + # Block.putmask does *not* downcast. The change to 'expected' here + # is specific to the cases in test_where_dt64_2d. + df = df.copy() + df.mask(~mask, other, inplace=True) + if not mask.all(): + # with mask.all(), Block.putmask is a no-op, so does not downcast + expected = expected.copy() + expected["A"] = expected["A"].astype(object) + tm.assert_frame_equal(df, expected) + + +def test_where_dt64_2d(): + dti = date_range("2016-01-01", periods=6) + dta = dti._data.reshape(3, 2) + other = dta - dta[0, 0] + + df = DataFrame(dta, columns=["A", "B"]) + + mask = np.asarray(df.isna()).copy() + mask[:, 1] = True + + # setting all of one column, none of the other + expected = DataFrame({"A": other[:, 0], "B": dta[:, 1]}) + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + _check_where_equivalences(df, mask, other, expected) + + # setting part of one column, none of the other + mask[1, 0] = True + expected = DataFrame( + { + "A": np.array([other[0, 0], dta[1, 0], other[2, 0]], dtype=object), + "B": dta[:, 1], + } + ) + with tm.assert_produces_warning( + FutureWarning, match="Setting an item of incompatible dtype" + ): + _check_where_equivalences(df, mask, other, expected) + + # setting nothing in either column + mask[:] = True + expected = df + _check_where_equivalences(df, mask, other, expected) + + +def test_where_producing_ea_cond_for_np_dtype(): + # GH#44014 + df = DataFrame({"a": Series([1, pd.NA, 2], dtype="Int64"), "b": [1, 2, 3]}) + result = df.where(lambda x: x.apply(lambda y: y > 1, axis=1)) + expected = DataFrame( + {"a": Series([pd.NA, pd.NA, 2], dtype="Int64"), "b": [np.nan, 2, 3]} + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "replacement", [0.001, True, "snake", None, datetime(2022, 5, 4)] +) +def test_where_int_overflow(replacement): + # GH 31687 + df = DataFrame([[1.0, 2e25, "nine"], [np.nan, 0.1, None]]) + result = df.where(pd.notnull(df), replacement) + expected = DataFrame([[1.0, 2e25, "nine"], [replacement, 0.1, replacement]]) + + tm.assert_frame_equal(result, expected) + + +def test_where_inplace_no_other(): + # GH#51685 + df = DataFrame({"a": [1.0, 2.0], "b": ["x", "y"]}) + cond = DataFrame({"a": [True, False], "b": [False, True]}) + df.where(cond, inplace=True) + expected = DataFrame({"a": [1, np.nan], "b": [np.nan, "y"]}) + tm.assert_frame_equal(df, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_xs.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_xs.py new file mode 100644 index 0000000000000000000000000000000000000000..2aa27d1d6a5489933459bb877fbbbba1d5a1d98b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/indexing/test_xs.py @@ -0,0 +1,444 @@ +import re + +import numpy as np +import pytest + +from pandas.errors import SettingWithCopyError + +from pandas import ( + DataFrame, + Index, + IndexSlice, + MultiIndex, + Series, + concat, +) +import pandas._testing as tm + +from pandas.tseries.offsets import BDay + + +@pytest.fixture +def four_level_index_dataframe(): + arr = np.array( + [ + [-0.5109, -2.3358, -0.4645, 0.05076, 0.364], + [0.4473, 1.4152, 0.2834, 1.00661, 0.1744], + [-0.6662, -0.5243, -0.358, 0.89145, 2.5838], + ] + ) + index = MultiIndex( + levels=[["a", "x"], ["b", "q"], [10.0032, 20.0, 30.0], [3, 4, 5]], + codes=[[0, 0, 1], [0, 1, 1], [0, 1, 2], [2, 1, 0]], + names=["one", "two", "three", "four"], + ) + return DataFrame(arr, index=index, columns=list("ABCDE")) + + +class TestXS: + def test_xs( + self, float_frame, datetime_frame, using_copy_on_write, warn_copy_on_write + ): + float_frame_orig = float_frame.copy() + idx = float_frame.index[5] + xs = float_frame.xs(idx) + for item, value in xs.items(): + if np.isnan(value): + assert np.isnan(float_frame[item][idx]) + else: + assert value == float_frame[item][idx] + + # mixed-type xs + test_data = {"A": {"1": 1, "2": 2}, "B": {"1": "1", "2": "2", "3": "3"}} + frame = DataFrame(test_data) + xs = frame.xs("1") + assert xs.dtype == np.object_ + assert xs["A"] == 1 + assert xs["B"] == "1" + + with pytest.raises( + KeyError, match=re.escape("Timestamp('1999-12-31 00:00:00')") + ): + datetime_frame.xs(datetime_frame.index[0] - BDay()) + + # xs get column + series = float_frame.xs("A", axis=1) + expected = float_frame["A"] + tm.assert_series_equal(series, expected) + + # view is returned if possible + series = float_frame.xs("A", axis=1) + with tm.assert_cow_warning(warn_copy_on_write): + series[:] = 5 + if using_copy_on_write: + # but with CoW the view shouldn't propagate mutations + tm.assert_series_equal(float_frame["A"], float_frame_orig["A"]) + assert not (expected == 5).all() + else: + assert (expected == 5).all() + + def test_xs_corner(self): + # pathological mixed-type reordering case + df = DataFrame(index=[0], columns=Index([], dtype="str")) + df["A"] = 1.0 + df["B"] = "foo" + df["C"] = 2.0 + df["D"] = "bar" + df["E"] = 3.0 + + xs = df.xs(0) + exp = Series([1.0, "foo", 2.0, "bar", 3.0], index=list("ABCDE"), name=0) + tm.assert_series_equal(xs, exp) + + # no columns but Index(dtype=object) + df = DataFrame(index=["a", "b", "c"]) + result = df.xs("a") + expected = Series([], name="a", dtype=np.float64) + tm.assert_series_equal(result, expected) + + def test_xs_duplicates(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), + index=["b", "b", "c", "b", "a"], + ) + + cross = df.xs("c") + exp = df.iloc[2] + tm.assert_series_equal(cross, exp) + + def test_xs_keep_level(self): + df = DataFrame( + { + "day": {0: "sat", 1: "sun"}, + "flavour": {0: "strawberry", 1: "strawberry"}, + "sales": {0: 10, 1: 12}, + "year": {0: 2008, 1: 2008}, + } + ).set_index(["year", "flavour", "day"]) + result = df.xs("sat", level="day", drop_level=False) + expected = df[:1] + tm.assert_frame_equal(result, expected) + + result = df.xs((2008, "sat"), level=["year", "day"], drop_level=False) + tm.assert_frame_equal(result, expected) + + def test_xs_view( + self, using_array_manager, using_copy_on_write, warn_copy_on_write + ): + # in 0.14 this will return a view if possible a copy otherwise, but + # this is numpy dependent + + dm = DataFrame(np.arange(20.0).reshape(4, 5), index=range(4), columns=range(5)) + df_orig = dm.copy() + + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + dm.xs(2)[:] = 20 + tm.assert_frame_equal(dm, df_orig) + elif using_array_manager: + # INFO(ArrayManager) with ArrayManager getting a row as a view is + # not possible + msg = r"\nA value is trying to be set on a copy of a slice from a DataFrame" + with pytest.raises(SettingWithCopyError, match=msg): + dm.xs(2)[:] = 20 + assert not (dm.xs(2) == 20).any() + else: + with tm.raises_chained_assignment_error(): + dm.xs(2)[:] = 20 + assert (dm.xs(2) == 20).all() + + +class TestXSWithMultiIndex: + def test_xs_doc_example(self): + # TODO: more descriptive name + # based on example in advanced.rst + arrays = [ + ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], + ["one", "two", "one", "two", "one", "two", "one", "two"], + ] + tuples = list(zip(*arrays)) + + index = MultiIndex.from_tuples(tuples, names=["first", "second"]) + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 8)), + index=["A", "B", "C"], + columns=index, + ) + + result = df.xs(("one", "bar"), level=("second", "first"), axis=1) + + expected = df.iloc[:, [0]] + tm.assert_frame_equal(result, expected) + + def test_xs_integer_key(self): + # see GH#2107 + dates = range(20111201, 20111205) + ids = list("abcde") + index = MultiIndex.from_product([dates, ids], names=["date", "secid"]) + df = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 3)), + index, + ["X", "Y", "Z"], + ) + + result = df.xs(20111201, level="date") + expected = df.loc[20111201, :] + tm.assert_frame_equal(result, expected) + + def test_xs_level(self, multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + result = df.xs("two", level="second") + expected = df[df.index.get_level_values(1) == "two"] + expected.index = Index(["foo", "bar", "baz", "qux"], name="first") + tm.assert_frame_equal(result, expected) + + def test_xs_level_eq_2(self): + arr = np.random.default_rng(2).standard_normal((3, 5)) + index = MultiIndex( + levels=[["a", "p", "x"], ["b", "q", "y"], ["c", "r", "z"]], + codes=[[2, 0, 1], [2, 0, 1], [2, 0, 1]], + ) + df = DataFrame(arr, index=index) + expected = DataFrame(arr[1:2], index=[["a"], ["b"]]) + result = df.xs("c", level=2) + tm.assert_frame_equal(result, expected) + + def test_xs_setting_with_copy_error( + self, + multiindex_dataframe_random_data, + using_copy_on_write, + warn_copy_on_write, + ): + # this is a copy in 0.14 + df = multiindex_dataframe_random_data + df_orig = df.copy() + result = df.xs("two", level="second") + + if using_copy_on_write or warn_copy_on_write: + result[:] = 10 + else: + # setting this will give a SettingWithCopyError + # as we are trying to write a view + msg = "A value is trying to be set on a copy of a slice from a DataFrame" + with pytest.raises(SettingWithCopyError, match=msg): + result[:] = 10 + tm.assert_frame_equal(df, df_orig) + + def test_xs_setting_with_copy_error_multiple( + self, four_level_index_dataframe, using_copy_on_write, warn_copy_on_write + ): + # this is a copy in 0.14 + df = four_level_index_dataframe + df_orig = df.copy() + result = df.xs(("a", 4), level=["one", "four"]) + + if using_copy_on_write or warn_copy_on_write: + result[:] = 10 + else: + # setting this will give a SettingWithCopyError + # as we are trying to write a view + msg = "A value is trying to be set on a copy of a slice from a DataFrame" + with pytest.raises(SettingWithCopyError, match=msg): + result[:] = 10 + tm.assert_frame_equal(df, df_orig) + + @pytest.mark.parametrize("key, level", [("one", "second"), (["one"], ["second"])]) + def test_xs_with_duplicates(self, key, level, multiindex_dataframe_random_data): + # see GH#13719 + frame = multiindex_dataframe_random_data + df = concat([frame] * 2) + assert df.index.is_unique is False + expected = concat([frame.xs("one", level="second")] * 2) + + if isinstance(key, list): + result = df.xs(tuple(key), level=level) + else: + result = df.xs(key, level=level) + tm.assert_frame_equal(result, expected) + + def test_xs_missing_values_in_index(self): + # see GH#6574 + # missing values in returned index should be preserved + acc = [ + ("a", "abcde", 1), + ("b", "bbcde", 2), + ("y", "yzcde", 25), + ("z", "xbcde", 24), + ("z", None, 26), + ("z", "zbcde", 25), + ("z", "ybcde", 26), + ] + df = DataFrame(acc, columns=["a1", "a2", "cnt"]).set_index(["a1", "a2"]) + expected = DataFrame( + {"cnt": [24, 26, 25, 26]}, + index=Index(["xbcde", np.nan, "zbcde", "ybcde"], name="a2"), + ) + + result = df.xs("z", level="a1") + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "key, level, exp_arr, exp_index", + [ + ("a", "lvl0", lambda x: x[:, 0:2], Index(["bar", "foo"], name="lvl1")), + ("foo", "lvl1", lambda x: x[:, 1:2], Index(["a"], name="lvl0")), + ], + ) + def test_xs_named_levels_axis_eq_1(self, key, level, exp_arr, exp_index): + # see GH#2903 + arr = np.random.default_rng(2).standard_normal((4, 4)) + index = MultiIndex( + levels=[["a", "b"], ["bar", "foo", "hello", "world"]], + codes=[[0, 0, 1, 1], [0, 1, 2, 3]], + names=["lvl0", "lvl1"], + ) + df = DataFrame(arr, columns=index) + result = df.xs(key, level=level, axis=1) + expected = DataFrame(exp_arr(arr), columns=exp_index) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "indexer", + [ + lambda df: df.xs(("a", 4), level=["one", "four"]), + lambda df: df.xs("a").xs(4, level="four"), + ], + ) + def test_xs_level_multiple(self, indexer, four_level_index_dataframe): + df = four_level_index_dataframe + expected_values = [[0.4473, 1.4152, 0.2834, 1.00661, 0.1744]] + expected_index = MultiIndex( + levels=[["q"], [20.0]], codes=[[0], [0]], names=["two", "three"] + ) + expected = DataFrame( + expected_values, index=expected_index, columns=list("ABCDE") + ) + result = indexer(df) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "indexer", [lambda df: df.xs("a", level=0), lambda df: df.xs("a")] + ) + def test_xs_level0(self, indexer, four_level_index_dataframe): + df = four_level_index_dataframe + expected_values = [ + [-0.5109, -2.3358, -0.4645, 0.05076, 0.364], + [0.4473, 1.4152, 0.2834, 1.00661, 0.1744], + ] + expected_index = MultiIndex( + levels=[["b", "q"], [10.0032, 20.0], [4, 5]], + codes=[[0, 1], [0, 1], [1, 0]], + names=["two", "three", "four"], + ) + expected = DataFrame( + expected_values, index=expected_index, columns=list("ABCDE") + ) + + result = indexer(df) + tm.assert_frame_equal(result, expected) + + def test_xs_values(self, multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + result = df.xs(("bar", "two")).values + expected = df.values[4] + tm.assert_almost_equal(result, expected) + + def test_xs_loc_equality(self, multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + result = df.xs(("bar", "two")) + expected = df.loc[("bar", "two")] + tm.assert_series_equal(result, expected) + + def test_xs_IndexSlice_argument_not_implemented(self, frame_or_series): + # GH#35301 + + index = MultiIndex( + levels=[[("foo", "bar", 0), ("foo", "baz", 0), ("foo", "qux", 0)], [0, 1]], + codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]], + ) + + obj = DataFrame(np.random.default_rng(2).standard_normal((6, 4)), index=index) + if frame_or_series is Series: + obj = obj[0] + + expected = obj.iloc[-2:].droplevel(0) + + result = obj.xs(IndexSlice[("foo", "qux", 0), :]) + tm.assert_equal(result, expected) + + result = obj.loc[IndexSlice[("foo", "qux", 0), :]] + tm.assert_equal(result, expected) + + def test_xs_levels_raises(self, frame_or_series): + obj = DataFrame({"A": [1, 2, 3]}) + if frame_or_series is Series: + obj = obj["A"] + + msg = "Index must be a MultiIndex" + with pytest.raises(TypeError, match=msg): + obj.xs(0, level="as") + + def test_xs_multiindex_droplevel_false(self): + # GH#19056 + mi = MultiIndex.from_tuples( + [("a", "x"), ("a", "y"), ("b", "x")], names=["level1", "level2"] + ) + df = DataFrame([[1, 2, 3]], columns=mi) + result = df.xs("a", axis=1, drop_level=False) + expected = DataFrame( + [[1, 2]], + columns=MultiIndex.from_tuples( + [("a", "x"), ("a", "y")], names=["level1", "level2"] + ), + ) + tm.assert_frame_equal(result, expected) + + def test_xs_droplevel_false(self): + # GH#19056 + df = DataFrame([[1, 2, 3]], columns=Index(["a", "b", "c"])) + result = df.xs("a", axis=1, drop_level=False) + expected = DataFrame({"a": [1]}) + tm.assert_frame_equal(result, expected) + + def test_xs_droplevel_false_view( + self, using_array_manager, using_copy_on_write, warn_copy_on_write + ): + # GH#37832 + df = DataFrame([[1, 2, 3]], columns=Index(["a", "b", "c"])) + result = df.xs("a", axis=1, drop_level=False) + # check that result still views the same data as df + assert np.shares_memory(result.iloc[:, 0]._values, df.iloc[:, 0]._values) + + with tm.assert_cow_warning(warn_copy_on_write): + df.iloc[0, 0] = 2 + if using_copy_on_write: + # with copy on write the subset is never modified + expected = DataFrame({"a": [1]}) + else: + # modifying original df also modifies result when having a single block + expected = DataFrame({"a": [2]}) + tm.assert_frame_equal(result, expected) + + # with mixed dataframe, modifying the parent doesn't modify result + # TODO the "split" path behaves differently here as with single block + df = DataFrame([[1, 2.5, "a"]], columns=Index(["a", "b", "c"])) + result = df.xs("a", axis=1, drop_level=False) + df.iloc[0, 0] = 2 + if using_copy_on_write: + # with copy on write the subset is never modified + expected = DataFrame({"a": [1]}) + elif using_array_manager: + # Here the behavior is consistent + expected = DataFrame({"a": [2]}) + else: + # FIXME: iloc does not update the array inplace using + # "split" path + expected = DataFrame({"a": [1]}) + tm.assert_frame_equal(result, expected) + + def test_xs_list_indexer_droplevel_false(self): + # GH#41760 + mi = MultiIndex.from_tuples([("x", "m", "a"), ("x", "n", "b"), ("y", "o", "c")]) + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=mi) + with pytest.raises(KeyError, match="y"): + df.xs(("x", "y"), drop_level=False, axis=1) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..245594bfdc9e72ff5cb3a4799e9055c7cd6b5a3e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/__init__.py @@ -0,0 +1,7 @@ +""" +Test files dedicated to individual (stand-alone) DataFrame methods + +Ideally these files/tests should correspond 1-to-1 with tests.series.methods + +These may also present opportunities for sharing/de-duplicating test code. +""" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_add_prefix_suffix.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_add_prefix_suffix.py new file mode 100644 index 0000000000000000000000000000000000000000..92d7cdd7990e168721610b7f52f653a69ac1e078 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_add_prefix_suffix.py @@ -0,0 +1,49 @@ +import pytest + +from pandas import Index +import pandas._testing as tm + + +def test_add_prefix_suffix(float_frame): + with_prefix = float_frame.add_prefix("foo#") + expected = Index([f"foo#{c}" for c in float_frame.columns]) + tm.assert_index_equal(with_prefix.columns, expected) + + with_suffix = float_frame.add_suffix("#foo") + expected = Index([f"{c}#foo" for c in float_frame.columns]) + tm.assert_index_equal(with_suffix.columns, expected) + + with_pct_prefix = float_frame.add_prefix("%") + expected = Index([f"%{c}" for c in float_frame.columns]) + tm.assert_index_equal(with_pct_prefix.columns, expected) + + with_pct_suffix = float_frame.add_suffix("%") + expected = Index([f"{c}%" for c in float_frame.columns]) + tm.assert_index_equal(with_pct_suffix.columns, expected) + + +def test_add_prefix_suffix_axis(float_frame): + # GH 47819 + with_prefix = float_frame.add_prefix("foo#", axis=0) + expected = Index([f"foo#{c}" for c in float_frame.index]) + tm.assert_index_equal(with_prefix.index, expected) + + with_prefix = float_frame.add_prefix("foo#", axis=1) + expected = Index([f"foo#{c}" for c in float_frame.columns]) + tm.assert_index_equal(with_prefix.columns, expected) + + with_pct_suffix = float_frame.add_suffix("#foo", axis=0) + expected = Index([f"{c}#foo" for c in float_frame.index]) + tm.assert_index_equal(with_pct_suffix.index, expected) + + with_pct_suffix = float_frame.add_suffix("#foo", axis=1) + expected = Index([f"{c}#foo" for c in float_frame.columns]) + tm.assert_index_equal(with_pct_suffix.columns, expected) + + +def test_add_prefix_suffix_invalid_axis(float_frame): + with pytest.raises(ValueError, match="No axis named 2 for object type DataFrame"): + float_frame.add_prefix("foo#", axis=2) + + with pytest.raises(ValueError, match="No axis named 2 for object type DataFrame"): + float_frame.add_suffix("foo#", axis=2) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_align.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_align.py new file mode 100644 index 0000000000000000000000000000000000000000..5a9c47866dae8102fdf51541aaae5c61eeb7e84c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_align.py @@ -0,0 +1,484 @@ +from datetime import timezone + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + Series, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameAlign: + def test_align_asfreq_method_raises(self): + df = DataFrame({"A": [1, np.nan, 2]}) + msg = "Invalid fill method" + msg2 = "The 'method', 'limit', and 'fill_axis' keywords" + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=msg2): + df.align(df.iloc[::-1], method="asfreq") + + def test_frame_align_aware(self): + idx1 = date_range("2001", periods=5, freq="h", tz="US/Eastern") + idx2 = date_range("2001", periods=5, freq="2h", tz="US/Eastern") + df1 = DataFrame(np.random.default_rng(2).standard_normal((len(idx1), 3)), idx1) + df2 = DataFrame(np.random.default_rng(2).standard_normal((len(idx2), 3)), idx2) + new1, new2 = df1.align(df2) + assert df1.index.tz == new1.index.tz + assert df2.index.tz == new2.index.tz + + # different timezones convert to UTC + + # frame with frame + df1_central = df1.tz_convert("US/Central") + new1, new2 = df1.align(df1_central) + assert new1.index.tz is timezone.utc + assert new2.index.tz is timezone.utc + + # frame with Series + new1, new2 = df1.align(df1_central[0], axis=0) + assert new1.index.tz is timezone.utc + assert new2.index.tz is timezone.utc + + df1[0].align(df1_central, axis=0) + assert new1.index.tz is timezone.utc + assert new2.index.tz is timezone.utc + + def test_align_float(self, float_frame, using_copy_on_write): + af, bf = float_frame.align(float_frame) + assert af._mgr is not float_frame._mgr + + af, bf = float_frame.align(float_frame, copy=False) + if not using_copy_on_write: + assert af._mgr is float_frame._mgr + else: + assert af._mgr is not float_frame._mgr + + # axis = 0 + other = float_frame.iloc[:-5, :3] + af, bf = float_frame.align(other, axis=0, fill_value=-1) + + tm.assert_index_equal(bf.columns, other.columns) + + # test fill value + join_idx = float_frame.index.join(other.index) + diff_a = float_frame.index.difference(join_idx) + diff_a_vals = af.reindex(diff_a).values + assert (diff_a_vals == -1).all() + + af, bf = float_frame.align(other, join="right", axis=0) + tm.assert_index_equal(bf.columns, other.columns) + tm.assert_index_equal(bf.index, other.index) + tm.assert_index_equal(af.index, other.index) + + # axis = 1 + other = float_frame.iloc[:-5, :3].copy() + af, bf = float_frame.align(other, axis=1) + tm.assert_index_equal(bf.columns, float_frame.columns) + tm.assert_index_equal(bf.index, other.index) + + # test fill value + join_idx = float_frame.index.join(other.index) + diff_a = float_frame.index.difference(join_idx) + diff_a_vals = af.reindex(diff_a).values + + assert (diff_a_vals == -1).all() + + af, bf = float_frame.align(other, join="inner", axis=1) + tm.assert_index_equal(bf.columns, other.columns) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = float_frame.align(other, join="inner", axis=1, method="pad") + tm.assert_index_equal(bf.columns, other.columns) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = float_frame.align( + other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=None + ) + tm.assert_index_equal(bf.index, Index([]).astype(bf.index.dtype)) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = float_frame.align( + other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0 + ) + tm.assert_index_equal(bf.index, Index([]).astype(bf.index.dtype)) + + # Try to align DataFrame to Series along bad axis + msg = "No axis named 2 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + float_frame.align(af.iloc[0, :3], join="inner", axis=2) + + def test_align_frame_with_series(self, float_frame): + # align dataframe to series with broadcast or not + idx = float_frame.index + s = Series(range(len(idx)), index=idx) + + left, right = float_frame.align(s, axis=0) + tm.assert_index_equal(left.index, float_frame.index) + tm.assert_index_equal(right.index, float_frame.index) + assert isinstance(right, Series) + + msg = "The 'broadcast_axis' keyword in DataFrame.align is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + left, right = float_frame.align(s, broadcast_axis=1) + tm.assert_index_equal(left.index, float_frame.index) + expected = {c: s for c in float_frame.columns} + expected = DataFrame( + expected, index=float_frame.index, columns=float_frame.columns + ) + tm.assert_frame_equal(right, expected) + + def test_align_series_condition(self): + # see gh-9558 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + result = df[df["a"] == 2] + expected = DataFrame([[2, 5]], index=[1], columns=["a", "b"]) + tm.assert_frame_equal(result, expected) + + result = df.where(df["a"] == 2, 0) + expected = DataFrame({"a": [0, 2, 0], "b": [0, 5, 0]}) + tm.assert_frame_equal(result, expected) + + def test_align_int(self, int_frame): + # test other non-float types + other = DataFrame(index=range(5), columns=["A", "B", "C"]) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = int_frame.align(other, join="inner", axis=1, method="pad") + tm.assert_index_equal(bf.columns, other.columns) + + def test_align_mixed_type(self, float_string_frame): + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = float_string_frame.align( + float_string_frame, join="inner", axis=1, method="pad" + ) + tm.assert_index_equal(bf.columns, float_string_frame.columns) + + def test_align_mixed_float(self, mixed_float_frame): + # mixed floats/ints + other = DataFrame(index=range(5), columns=["A", "B", "C"]) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = mixed_float_frame.align( + other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0 + ) + tm.assert_index_equal(bf.index, Index([])) + + def test_align_mixed_int(self, mixed_int_frame): + other = DataFrame(index=range(5), columns=["A", "B", "C"]) + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + af, bf = mixed_int_frame.align( + other.iloc[:, 0], join="inner", axis=1, method=None, fill_value=0 + ) + tm.assert_index_equal(bf.index, Index([])) + + @pytest.mark.parametrize( + "l_ordered,r_ordered,expected", + [ + [True, True, pd.CategoricalIndex], + [True, False, Index], + [False, True, Index], + [False, False, pd.CategoricalIndex], + ], + ) + def test_align_categorical(self, l_ordered, r_ordered, expected): + # GH-28397 + df_1 = DataFrame( + { + "A": np.arange(6, dtype="int64"), + "B": Series(list("aabbca")).astype( + pd.CategoricalDtype(list("cab"), ordered=l_ordered) + ), + } + ).set_index("B") + df_2 = DataFrame( + { + "A": np.arange(5, dtype="int64"), + "B": Series(list("babca")).astype( + pd.CategoricalDtype(list("cab"), ordered=r_ordered) + ), + } + ).set_index("B") + + aligned_1, aligned_2 = df_1.align(df_2) + assert isinstance(aligned_1.index, expected) + assert isinstance(aligned_2.index, expected) + tm.assert_index_equal(aligned_1.index, aligned_2.index) + + def test_align_multiindex(self): + # GH#10665 + # same test cases as test_align_multiindex in test_series.py + + midx = pd.MultiIndex.from_product( + [range(2), range(3), range(2)], names=("a", "b", "c") + ) + idx = Index(range(2), name="b") + df1 = DataFrame(np.arange(12, dtype="int64"), index=midx) + df2 = DataFrame(np.arange(2, dtype="int64"), index=idx) + + # these must be the same results (but flipped) + res1l, res1r = df1.align(df2, join="left") + res2l, res2r = df2.align(df1, join="right") + + expl = df1 + tm.assert_frame_equal(expl, res1l) + tm.assert_frame_equal(expl, res2r) + expr = DataFrame([0, 0, 1, 1, np.nan, np.nan] * 2, index=midx) + tm.assert_frame_equal(expr, res1r) + tm.assert_frame_equal(expr, res2l) + + res1l, res1r = df1.align(df2, join="right") + res2l, res2r = df2.align(df1, join="left") + + exp_idx = pd.MultiIndex.from_product( + [range(2), range(2), range(2)], names=("a", "b", "c") + ) + expl = DataFrame([0, 1, 2, 3, 6, 7, 8, 9], index=exp_idx) + tm.assert_frame_equal(expl, res1l) + tm.assert_frame_equal(expl, res2r) + expr = DataFrame([0, 0, 1, 1] * 2, index=exp_idx) + tm.assert_frame_equal(expr, res1r) + tm.assert_frame_equal(expr, res2l) + + def test_align_series_combinations(self): + df = DataFrame({"a": [1, 3, 5], "b": [1, 3, 5]}, index=list("ACE")) + s = Series([1, 2, 4], index=list("ABD"), name="x") + + # frame + series + res1, res2 = df.align(s, axis=0) + exp1 = DataFrame( + {"a": [1, np.nan, 3, np.nan, 5], "b": [1, np.nan, 3, np.nan, 5]}, + index=list("ABCDE"), + ) + exp2 = Series([1, 2, np.nan, 4, np.nan], index=list("ABCDE"), name="x") + + tm.assert_frame_equal(res1, exp1) + tm.assert_series_equal(res2, exp2) + + # series + frame + res1, res2 = s.align(df) + tm.assert_series_equal(res1, exp2) + tm.assert_frame_equal(res2, exp1) + + def test_multiindex_align_to_series_with_common_index_level(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 2], name="bar") + + series = Series([1, 2], index=bar_index, name="foo_series") + df = DataFrame( + {"col": np.arange(6)}, + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + + expected_r = Series([1, 2] * 3, index=df.index, name="foo_series") + result_l, result_r = df.align(series, axis=0) + + tm.assert_frame_equal(result_l, df) + tm.assert_series_equal(result_r, expected_r) + + def test_multiindex_align_to_series_with_common_index_level_missing_in_left(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 2], name="bar") + + series = Series( + [1, 2, 3, 4], index=Index([1, 2, 3, 4], name="bar"), name="foo_series" + ) + df = DataFrame( + {"col": np.arange(6)}, + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + + expected_r = Series([1, 2] * 3, index=df.index, name="foo_series") + result_l, result_r = df.align(series, axis=0) + + tm.assert_frame_equal(result_l, df) + tm.assert_series_equal(result_r, expected_r) + + def test_multiindex_align_to_series_with_common_index_level_missing_in_right(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 2, 3, 4], name="bar") + + series = Series([1, 2], index=Index([1, 2], name="bar"), name="foo_series") + df = DataFrame( + {"col": np.arange(12)}, + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + + expected_r = Series( + [1, 2, np.nan, np.nan] * 3, index=df.index, name="foo_series" + ) + result_l, result_r = df.align(series, axis=0) + + tm.assert_frame_equal(result_l, df) + tm.assert_series_equal(result_r, expected_r) + + def test_multiindex_align_to_series_with_common_index_level_missing_in_both(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 3, 4], name="bar") + + series = Series( + [1, 2, 3], index=Index([1, 2, 4], name="bar"), name="foo_series" + ) + df = DataFrame( + {"col": np.arange(9)}, + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + + expected_r = Series([1, np.nan, 3] * 3, index=df.index, name="foo_series") + result_l, result_r = df.align(series, axis=0) + + tm.assert_frame_equal(result_l, df) + tm.assert_series_equal(result_r, expected_r) + + def test_multiindex_align_to_series_with_common_index_level_non_unique_cols(self): + # GH-46001 + foo_index = Index([1, 2, 3], name="foo") + bar_index = Index([1, 2], name="bar") + + series = Series([1, 2], index=bar_index, name="foo_series") + df = DataFrame( + np.arange(18).reshape(6, 3), + index=pd.MultiIndex.from_product([foo_index, bar_index]), + ) + df.columns = ["cfoo", "cbar", "cfoo"] + + expected = Series([1, 2] * 3, index=df.index, name="foo_series") + result_left, result_right = df.align(series, axis=0) + + tm.assert_series_equal(result_right, expected) + tm.assert_index_equal(result_left.columns, df.columns) + + def test_missing_axis_specification_exception(self): + df = DataFrame(np.arange(50).reshape((10, 5))) + series = Series(np.arange(5)) + + with pytest.raises(ValueError, match=r"axis=0 or 1"): + df.align(series) + + @pytest.mark.parametrize("method", ["pad", "bfill"]) + @pytest.mark.parametrize("axis", [0, 1, None]) + @pytest.mark.parametrize("fill_axis", [0, 1]) + @pytest.mark.parametrize("how", ["inner", "outer", "left", "right"]) + @pytest.mark.parametrize( + "left_slice", + [ + [slice(4), slice(10)], + [slice(0), slice(0)], + ], + ) + @pytest.mark.parametrize( + "right_slice", + [ + [slice(2, None), slice(6, None)], + [slice(0), slice(0)], + ], + ) + @pytest.mark.parametrize("limit", [1, None]) + def test_align_fill_method( + self, how, method, axis, fill_axis, float_frame, left_slice, right_slice, limit + ): + frame = float_frame + left = frame.iloc[left_slice[0], left_slice[1]] + right = frame.iloc[right_slice[0], right_slice[1]] + + msg = ( + "The 'method', 'limit', and 'fill_axis' keywords in DataFrame.align " + "are deprecated" + ) + + with tm.assert_produces_warning(FutureWarning, match=msg): + aa, ab = left.align( + right, + axis=axis, + join=how, + method=method, + limit=limit, + fill_axis=fill_axis, + ) + + join_index, join_columns = None, None + + ea, eb = left, right + if axis is None or axis == 0: + join_index = left.index.join(right.index, how=how) + ea = ea.reindex(index=join_index) + eb = eb.reindex(index=join_index) + + if axis is None or axis == 1: + join_columns = left.columns.join(right.columns, how=how) + ea = ea.reindex(columns=join_columns) + eb = eb.reindex(columns=join_columns) + + msg = "DataFrame.fillna with 'method' is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + ea = ea.fillna(axis=fill_axis, method=method, limit=limit) + eb = eb.fillna(axis=fill_axis, method=method, limit=limit) + + tm.assert_frame_equal(aa, ea) + tm.assert_frame_equal(ab, eb) + + def test_align_series_check_copy(self): + # GH# + df = DataFrame({0: [1, 2]}) + ser = Series([1], name=0) + expected = ser.copy() + result, other = df.align(ser, axis=1) + ser.iloc[0] = 100 + tm.assert_series_equal(other, expected) + + def test_align_identical_different_object(self): + # GH#51032 + df = DataFrame({"a": [1, 2]}) + ser = Series([3, 4]) + result, result2 = df.align(ser, axis=0) + tm.assert_frame_equal(result, df) + tm.assert_series_equal(result2, ser) + assert df is not result + assert ser is not result2 + + def test_align_identical_different_object_columns(self): + # GH#51032 + df = DataFrame({"a": [1, 2]}) + ser = Series([1], index=["a"]) + result, result2 = df.align(ser, axis=1) + tm.assert_frame_equal(result, df) + tm.assert_series_equal(result2, ser) + assert df is not result + assert ser is not result2 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asfreq.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asfreq.py new file mode 100644 index 0000000000000000000000000000000000000000..ef72ca1ac86b9a6eb395a5a64bbfb99aef76a02a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asfreq.py @@ -0,0 +1,263 @@ +from datetime import datetime + +import numpy as np +import pytest + +from pandas._libs.tslibs.offsets import MonthEnd + +from pandas import ( + DataFrame, + DatetimeIndex, + Series, + date_range, + period_range, + to_datetime, +) +import pandas._testing as tm + +from pandas.tseries import offsets + + +class TestAsFreq: + @pytest.fixture(params=["s", "ms", "us", "ns"]) + def unit(self, request): + return request.param + + def test_asfreq2(self, frame_or_series): + ts = frame_or_series( + [0.0, 1.0, 2.0], + index=DatetimeIndex( + [ + datetime(2009, 10, 30), + datetime(2009, 11, 30), + datetime(2009, 12, 31), + ], + dtype="M8[ns]", + freq="BME", + ), + ) + + daily_ts = ts.asfreq("B") + monthly_ts = daily_ts.asfreq("BME") + tm.assert_equal(monthly_ts, ts) + + daily_ts = ts.asfreq("B", method="pad") + monthly_ts = daily_ts.asfreq("BME") + tm.assert_equal(monthly_ts, ts) + + daily_ts = ts.asfreq(offsets.BDay()) + monthly_ts = daily_ts.asfreq(offsets.BMonthEnd()) + tm.assert_equal(monthly_ts, ts) + + result = ts[:0].asfreq("ME") + assert len(result) == 0 + assert result is not ts + + if frame_or_series is Series: + daily_ts = ts.asfreq("D", fill_value=-1) + result = daily_ts.value_counts().sort_index() + expected = Series( + [60, 1, 1, 1], index=[-1.0, 2.0, 1.0, 0.0], name="count" + ).sort_index() + tm.assert_series_equal(result, expected) + + def test_asfreq_datetimeindex_empty(self, frame_or_series): + # GH#14320 + index = DatetimeIndex(["2016-09-29 11:00"]) + expected = frame_or_series(index=index, dtype=object).asfreq("h") + result = frame_or_series([3], index=index.copy()).asfreq("h") + tm.assert_index_equal(expected.index, result.index) + + @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"]) + def test_tz_aware_asfreq_smoke(self, tz, frame_or_series): + dr = date_range("2011-12-01", "2012-07-20", freq="D", tz=tz) + + obj = frame_or_series( + np.random.default_rng(2).standard_normal(len(dr)), index=dr + ) + + # it works! + obj.asfreq("min") + + def test_asfreq_normalize(self, frame_or_series): + rng = date_range("1/1/2000 09:30", periods=20) + norm = date_range("1/1/2000", periods=20) + + vals = np.random.default_rng(2).standard_normal((20, 3)) + + obj = DataFrame(vals, index=rng) + expected = DataFrame(vals, index=norm) + if frame_or_series is Series: + obj = obj[0] + expected = expected[0] + + result = obj.asfreq("D", normalize=True) + tm.assert_equal(result, expected) + + def test_asfreq_keep_index_name(self, frame_or_series): + # GH#9854 + index_name = "bar" + index = date_range("20130101", periods=20, name=index_name) + obj = DataFrame(list(range(20)), columns=["foo"], index=index) + obj = tm.get_obj(obj, frame_or_series) + + assert index_name == obj.index.name + assert index_name == obj.asfreq("10D").index.name + + def test_asfreq_ts(self, frame_or_series): + index = period_range(freq="Y", start="1/1/2001", end="12/31/2010") + obj = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 3)), index=index + ) + obj = tm.get_obj(obj, frame_or_series) + + result = obj.asfreq("D", how="end") + exp_index = index.asfreq("D", how="end") + assert len(result) == len(obj) + tm.assert_index_equal(result.index, exp_index) + + result = obj.asfreq("D", how="start") + exp_index = index.asfreq("D", how="start") + assert len(result) == len(obj) + tm.assert_index_equal(result.index, exp_index) + + def test_asfreq_resample_set_correct_freq(self, frame_or_series): + # GH#5613 + # we test if .asfreq() and .resample() set the correct value for .freq + dti = to_datetime(["2012-01-01", "2012-01-02", "2012-01-03"]) + obj = DataFrame({"col": [1, 2, 3]}, index=dti) + obj = tm.get_obj(obj, frame_or_series) + + # testing the settings before calling .asfreq() and .resample() + assert obj.index.freq is None + assert obj.index.inferred_freq == "D" + + # does .asfreq() set .freq correctly? + assert obj.asfreq("D").index.freq == "D" + + # does .resample() set .freq correctly? + assert obj.resample("D").asfreq().index.freq == "D" + + def test_asfreq_empty(self, datetime_frame): + # test does not blow up on length-0 DataFrame + zero_length = datetime_frame.reindex([]) + result = zero_length.asfreq("BME") + assert result is not zero_length + + def test_asfreq(self, datetime_frame): + offset_monthly = datetime_frame.asfreq(offsets.BMonthEnd()) + rule_monthly = datetime_frame.asfreq("BME") + + tm.assert_frame_equal(offset_monthly, rule_monthly) + + rule_monthly.asfreq("B", method="pad") + # TODO: actually check that this worked. + + # don't forget! + rule_monthly.asfreq("B", method="pad") + + def test_asfreq_datetimeindex(self): + df = DataFrame( + {"A": [1, 2, 3]}, + index=[datetime(2011, 11, 1), datetime(2011, 11, 2), datetime(2011, 11, 3)], + ) + df = df.asfreq("B") + assert isinstance(df.index, DatetimeIndex) + + ts = df["A"].asfreq("B") + assert isinstance(ts.index, DatetimeIndex) + + def test_asfreq_fillvalue(self): + # test for fill value during upsampling, related to issue 3715 + + # setup + rng = date_range("1/1/2016", periods=10, freq="2s") + # Explicit cast to 'float' to avoid implicit cast when setting None + ts = Series(np.arange(len(rng)), index=rng, dtype="float") + df = DataFrame({"one": ts}) + + # insert pre-existing missing value + df.loc["2016-01-01 00:00:08", "one"] = None + + actual_df = df.asfreq(freq="1s", fill_value=9.0) + expected_df = df.asfreq(freq="1s").fillna(9.0) + expected_df.loc["2016-01-01 00:00:08", "one"] = None + tm.assert_frame_equal(expected_df, actual_df) + + expected_series = ts.asfreq(freq="1s").fillna(9.0) + actual_series = ts.asfreq(freq="1s", fill_value=9.0) + tm.assert_series_equal(expected_series, actual_series) + + def test_asfreq_with_date_object_index(self, frame_or_series): + rng = date_range("1/1/2000", periods=20) + ts = frame_or_series(np.random.default_rng(2).standard_normal(20), index=rng) + + ts2 = ts.copy() + ts2.index = [x.date() for x in ts2.index] + + result = ts2.asfreq("4h", method="ffill") + expected = ts.asfreq("4h", method="ffill") + tm.assert_equal(result, expected) + + def test_asfreq_with_unsorted_index(self, frame_or_series): + # GH#39805 + # Test that rows are not dropped when the datetime index is out of order + index = to_datetime(["2021-01-04", "2021-01-02", "2021-01-03", "2021-01-01"]) + result = frame_or_series(range(4), index=index) + + expected = result.reindex(sorted(index)) + expected.index = expected.index._with_freq("infer") + + result = result.asfreq("D") + tm.assert_equal(result, expected) + + def test_asfreq_after_normalize(self, unit): + # https://github.com/pandas-dev/pandas/issues/50727 + result = DatetimeIndex( + date_range("2000", periods=2).as_unit(unit).normalize(), freq="D" + ) + expected = DatetimeIndex(["2000-01-01", "2000-01-02"], freq="D").as_unit(unit) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "freq, freq_half", + [ + ("2ME", "ME"), + (MonthEnd(2), MonthEnd(1)), + ], + ) + def test_asfreq_2ME(self, freq, freq_half): + index = date_range("1/1/2000", periods=6, freq=freq_half) + df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0, 4.0, 5.0], index=index)}) + expected = df.asfreq(freq=freq) + + index = date_range("1/1/2000", periods=3, freq=freq) + result = DataFrame({"s": Series([0.0, 2.0, 4.0], index=index)}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "freq, freq_depr", + [ + ("2ME", "2M"), + ("2QE", "2Q"), + ("2QE-SEP", "2Q-SEP"), + ("1BQE", "1BQ"), + ("2BQE-SEP", "2BQ-SEP"), + ("1YE", "1Y"), + ("2YE-MAR", "2Y-MAR"), + ("1YE", "1A"), + ("2YE-MAR", "2A-MAR"), + ("2BYE-MAR", "2BA-MAR"), + ], + ) + def test_asfreq_frequency_M_Q_Y_A_deprecated(self, freq, freq_depr): + # GH#9586, #55978 + depr_msg = f"'{freq_depr[1:]}' is deprecated and will be removed " + f"in a future version, please use '{freq[1:]}' instead." + + index = date_range("1/1/2000", periods=4, freq=f"{freq[1:]}") + df = DataFrame({"s": Series([0.0, 1.0, 2.0, 3.0], index=index)}) + expected = df.asfreq(freq=freq) + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + result = df.asfreq(freq=freq_depr) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asof.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asof.py new file mode 100644 index 0000000000000000000000000000000000000000..4a8adf89b3aef83001f6bb7669d8a9eae12529ea --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_asof.py @@ -0,0 +1,198 @@ +import numpy as np +import pytest + +from pandas._libs.tslibs import IncompatibleFrequency + +from pandas import ( + DataFrame, + Period, + Series, + Timestamp, + date_range, + period_range, + to_datetime, +) +import pandas._testing as tm + + +@pytest.fixture +def date_range_frame(): + """ + Fixture for DataFrame of ints with date_range index + + Columns are ['A', 'B']. + """ + N = 50 + rng = date_range("1/1/1990", periods=N, freq="53s") + return DataFrame({"A": np.arange(N), "B": np.arange(N)}, index=rng) + + +class TestFrameAsof: + def test_basic(self, date_range_frame): + # Explicitly cast to float to avoid implicit cast when setting np.nan + df = date_range_frame.astype({"A": "float"}) + N = 50 + df.loc[df.index[15:30], "A"] = np.nan + dates = date_range("1/1/1990", periods=N * 3, freq="25s") + + result = df.asof(dates) + assert result.notna().all(1).all() + lb = df.index[14] + ub = df.index[30] + + dates = list(dates) + + result = df.asof(dates) + assert result.notna().all(1).all() + + mask = (result.index >= lb) & (result.index < ub) + rs = result[mask] + assert (rs == 14).all(1).all() + + def test_subset(self, date_range_frame): + N = 10 + # explicitly cast to float to avoid implicit upcast when setting to np.nan + df = date_range_frame.iloc[:N].copy().astype({"A": "float"}) + df.loc[df.index[4:8], "A"] = np.nan + dates = date_range("1/1/1990", periods=N * 3, freq="25s") + + # with a subset of A should be the same + result = df.asof(dates, subset="A") + expected = df.asof(dates) + tm.assert_frame_equal(result, expected) + + # same with A/B + result = df.asof(dates, subset=["A", "B"]) + expected = df.asof(dates) + tm.assert_frame_equal(result, expected) + + # B gives df.asof + result = df.asof(dates, subset="B") + expected = df.resample("25s", closed="right").ffill().reindex(dates) + expected.iloc[20:] = 9 + # no "missing", so "B" can retain int dtype (df["A"].dtype platform-dependent) + expected["B"] = expected["B"].astype(df["B"].dtype) + + tm.assert_frame_equal(result, expected) + + def test_missing(self, date_range_frame): + # GH 15118 + # no match found - `where` value before earliest date in index + N = 10 + # Cast to 'float64' to avoid upcast when introducing nan in df.asof + df = date_range_frame.iloc[:N].copy().astype("float64") + + result = df.asof("1989-12-31") + + expected = Series( + index=["A", "B"], name=Timestamp("1989-12-31"), dtype=np.float64 + ) + tm.assert_series_equal(result, expected) + + result = df.asof(to_datetime(["1989-12-31"])) + expected = DataFrame( + index=to_datetime(["1989-12-31"]), columns=["A", "B"], dtype="float64" + ) + tm.assert_frame_equal(result, expected) + + # Check that we handle PeriodIndex correctly, dont end up with + # period.ordinal for series name + df = df.to_period("D") + result = df.asof("1989-12-31") + assert isinstance(result.name, Period) + + def test_asof_all_nans(self, frame_or_series): + # GH 15713 + # DataFrame/Series is all nans + result = frame_or_series([np.nan]).asof([0]) + expected = frame_or_series([np.nan]) + tm.assert_equal(result, expected) + + def test_all_nans(self, date_range_frame): + # GH 15713 + # DataFrame is all nans + + # testing non-default indexes, multiple inputs + N = 150 + rng = date_range_frame.index + dates = date_range("1/1/1990", periods=N, freq="25s") + result = DataFrame(np.nan, index=rng, columns=["A"]).asof(dates) + expected = DataFrame(np.nan, index=dates, columns=["A"]) + tm.assert_frame_equal(result, expected) + + # testing multiple columns + dates = date_range("1/1/1990", periods=N, freq="25s") + result = DataFrame(np.nan, index=rng, columns=["A", "B", "C"]).asof(dates) + expected = DataFrame(np.nan, index=dates, columns=["A", "B", "C"]) + tm.assert_frame_equal(result, expected) + + # testing scalar input + result = DataFrame(np.nan, index=[1, 2], columns=["A", "B"]).asof([3]) + expected = DataFrame(np.nan, index=[3], columns=["A", "B"]) + tm.assert_frame_equal(result, expected) + + result = DataFrame(np.nan, index=[1, 2], columns=["A", "B"]).asof(3) + expected = Series(np.nan, index=["A", "B"], name=3) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "stamp,expected", + [ + ( + Timestamp("2018-01-01 23:22:43.325+00:00"), + Series(2, name=Timestamp("2018-01-01 23:22:43.325+00:00")), + ), + ( + Timestamp("2018-01-01 22:33:20.682+01:00"), + Series(1, name=Timestamp("2018-01-01 22:33:20.682+01:00")), + ), + ], + ) + def test_time_zone_aware_index(self, stamp, expected): + # GH21194 + # Testing awareness of DataFrame index considering different + # UTC and timezone + df = DataFrame( + data=[1, 2], + index=[ + Timestamp("2018-01-01 21:00:05.001+00:00"), + Timestamp("2018-01-01 22:35:10.550+00:00"), + ], + ) + + result = df.asof(stamp) + tm.assert_series_equal(result, expected) + + def test_is_copy(self, date_range_frame): + # GH-27357, GH-30784: ensure the result of asof is an actual copy and + # doesn't track the parent dataframe / doesn't give SettingWithCopy warnings + df = date_range_frame.astype({"A": "float"}) + N = 50 + df.loc[df.index[15:30], "A"] = np.nan + dates = date_range("1/1/1990", periods=N * 3, freq="25s") + + result = df.asof(dates) + + with tm.assert_produces_warning(None): + result["C"] = 1 + + def test_asof_periodindex_mismatched_freq(self): + N = 50 + rng = period_range("1/1/1990", periods=N, freq="h") + df = DataFrame(np.random.default_rng(2).standard_normal(N), index=rng) + + # Mismatched freq + msg = "Input has different freq" + with pytest.raises(IncompatibleFrequency, match=msg): + df.asof(rng.asfreq("D")) + + def test_asof_preserves_bool_dtype(self): + # GH#16063 was casting bools to floats + dti = date_range("2017-01-01", freq="MS", periods=4) + ser = Series([True, False, True], index=dti[:-1]) + + ts = dti[-1] + res = ser.asof([ts]) + + expected = Series([True], index=[ts]) + tm.assert_series_equal(res, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_assign.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_assign.py new file mode 100644 index 0000000000000000000000000000000000000000..0ae501d43e74252a420acf96b9428ab4b8f5f211 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_assign.py @@ -0,0 +1,84 @@ +import pytest + +from pandas import DataFrame +import pandas._testing as tm + + +class TestAssign: + def test_assign(self): + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + original = df.copy() + result = df.assign(C=df.B / df.A) + expected = df.copy() + expected["C"] = [4, 2.5, 2] + tm.assert_frame_equal(result, expected) + + # lambda syntax + result = df.assign(C=lambda x: x.B / x.A) + tm.assert_frame_equal(result, expected) + + # original is unmodified + tm.assert_frame_equal(df, original) + + # Non-Series array-like + result = df.assign(C=[4, 2.5, 2]) + tm.assert_frame_equal(result, expected) + # original is unmodified + tm.assert_frame_equal(df, original) + + result = df.assign(B=df.B / df.A) + expected = expected.drop("B", axis=1).rename(columns={"C": "B"}) + tm.assert_frame_equal(result, expected) + + # overwrite + result = df.assign(A=df.A + df.B) + expected = df.copy() + expected["A"] = [5, 7, 9] + tm.assert_frame_equal(result, expected) + + # lambda + result = df.assign(A=lambda x: x.A + x.B) + tm.assert_frame_equal(result, expected) + + def test_assign_multiple(self): + df = DataFrame([[1, 4], [2, 5], [3, 6]], columns=["A", "B"]) + result = df.assign(C=[7, 8, 9], D=df.A, E=lambda x: x.B) + expected = DataFrame( + [[1, 4, 7, 1, 4], [2, 5, 8, 2, 5], [3, 6, 9, 3, 6]], columns=list("ABCDE") + ) + tm.assert_frame_equal(result, expected) + + def test_assign_order(self): + # GH 9818 + df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) + result = df.assign(D=df.A + df.B, C=df.A - df.B) + + expected = DataFrame([[1, 2, 3, -1], [3, 4, 7, -1]], columns=list("ABDC")) + tm.assert_frame_equal(result, expected) + result = df.assign(C=df.A - df.B, D=df.A + df.B) + + expected = DataFrame([[1, 2, -1, 3], [3, 4, -1, 7]], columns=list("ABCD")) + + tm.assert_frame_equal(result, expected) + + def test_assign_bad(self): + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + + # non-keyword argument + msg = r"assign\(\) takes 1 positional argument but 2 were given" + with pytest.raises(TypeError, match=msg): + df.assign(lambda x: x.A) + msg = "'DataFrame' object has no attribute 'C'" + with pytest.raises(AttributeError, match=msg): + df.assign(C=df.A, D=df.A + df.C) + + def test_assign_dependent(self): + df = DataFrame({"A": [1, 2], "B": [3, 4]}) + + result = df.assign(C=df.A, D=lambda x: x["A"] + x["C"]) + expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD")) + tm.assert_frame_equal(result, expected) + + result = df.assign(C=lambda df: df.A, D=lambda df: df["A"] + df["C"]) + expected = DataFrame([[1, 3, 1, 2], [2, 4, 2, 4]], columns=list("ABCD")) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_at_time.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_at_time.py new file mode 100644 index 0000000000000000000000000000000000000000..4c1434bd66aff127e07c4ff3fce90a22721b2035 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_at_time.py @@ -0,0 +1,132 @@ +from datetime import time + +import numpy as np +import pytest +import pytz + +from pandas._libs.tslibs import timezones + +from pandas import ( + DataFrame, + date_range, +) +import pandas._testing as tm + + +class TestAtTime: + @pytest.mark.parametrize("tzstr", ["US/Eastern", "dateutil/US/Eastern"]) + def test_localized_at_time(self, tzstr, frame_or_series): + tz = timezones.maybe_get_tz(tzstr) + + rng = date_range("4/16/2012", "5/1/2012", freq="h") + ts = frame_or_series( + np.random.default_rng(2).standard_normal(len(rng)), index=rng + ) + + ts_local = ts.tz_localize(tzstr) + + result = ts_local.at_time(time(10, 0)) + expected = ts.at_time(time(10, 0)).tz_localize(tzstr) + tm.assert_equal(result, expected) + assert timezones.tz_compare(result.index.tz, tz) + + def test_at_time(self, frame_or_series): + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + ts = DataFrame( + np.random.default_rng(2).standard_normal((len(rng), 2)), index=rng + ) + ts = tm.get_obj(ts, frame_or_series) + rs = ts.at_time(rng[1]) + assert (rs.index.hour == rng[1].hour).all() + assert (rs.index.minute == rng[1].minute).all() + assert (rs.index.second == rng[1].second).all() + + result = ts.at_time("9:30") + expected = ts.at_time(time(9, 30)) + tm.assert_equal(result, expected) + + def test_at_time_midnight(self, frame_or_series): + # midnight, everything + rng = date_range("1/1/2000", "1/31/2000") + ts = DataFrame( + np.random.default_rng(2).standard_normal((len(rng), 3)), index=rng + ) + ts = tm.get_obj(ts, frame_or_series) + + result = ts.at_time(time(0, 0)) + tm.assert_equal(result, ts) + + def test_at_time_nonexistent(self, frame_or_series): + # time doesn't exist + rng = date_range("1/1/2012", freq="23Min", periods=384) + ts = DataFrame(np.random.default_rng(2).standard_normal(len(rng)), rng) + ts = tm.get_obj(ts, frame_or_series) + rs = ts.at_time("16:00") + assert len(rs) == 0 + + @pytest.mark.parametrize( + "hour", ["1:00", "1:00AM", time(1), time(1, tzinfo=pytz.UTC)] + ) + def test_at_time_errors(self, hour): + # GH#24043 + dti = date_range("2018", periods=3, freq="h") + df = DataFrame(list(range(len(dti))), index=dti) + if getattr(hour, "tzinfo", None) is None: + result = df.at_time(hour) + expected = df.iloc[1:2] + tm.assert_frame_equal(result, expected) + else: + with pytest.raises(ValueError, match="Index must be timezone"): + df.at_time(hour) + + def test_at_time_tz(self): + # GH#24043 + dti = date_range("2018", periods=3, freq="h", tz="US/Pacific") + df = DataFrame(list(range(len(dti))), index=dti) + result = df.at_time(time(4, tzinfo=pytz.timezone("US/Eastern"))) + expected = df.iloc[1:2] + tm.assert_frame_equal(result, expected) + + def test_at_time_raises(self, frame_or_series): + # GH#20725 + obj = DataFrame([[1, 2, 3], [4, 5, 6]]) + obj = tm.get_obj(obj, frame_or_series) + msg = "Index must be DatetimeIndex" + with pytest.raises(TypeError, match=msg): # index is not a DatetimeIndex + obj.at_time("00:00") + + @pytest.mark.parametrize("axis", ["index", "columns", 0, 1]) + def test_at_time_axis(self, axis): + # issue 8839 + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + ts = DataFrame(np.random.default_rng(2).standard_normal((len(rng), len(rng)))) + ts.index, ts.columns = rng, rng + + indices = rng[(rng.hour == 9) & (rng.minute == 30) & (rng.second == 0)] + + if axis in ["index", 0]: + expected = ts.loc[indices, :] + elif axis in ["columns", 1]: + expected = ts.loc[:, indices] + + result = ts.at_time("9:30", axis=axis) + + # Without clearing freq, result has freq 1440T and expected 5T + result.index = result.index._with_freq(None) + expected.index = expected.index._with_freq(None) + tm.assert_frame_equal(result, expected) + + def test_at_time_datetimeindex(self): + index = date_range("2012-01-01", "2012-01-05", freq="30min") + df = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 5)), index=index + ) + akey = time(12, 0, 0) + ainds = [24, 72, 120, 168] + + result = df.at_time(akey) + expected = df.loc[akey] + expected2 = df.iloc[ainds] + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result, expected2) + assert len(result) == 4 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_clip.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..f783a388d75179e9b68e373da65218dfffb04b55 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_clip.py @@ -0,0 +1,199 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestDataFrameClip: + def test_clip(self, float_frame): + median = float_frame.median().median() + original = float_frame.copy() + + double = float_frame.clip(upper=median, lower=median) + assert not (double.values != median).any() + + # Verify that float_frame was not changed inplace + assert (float_frame.values == original.values).all() + + def test_inplace_clip(self, float_frame): + # GH#15388 + median = float_frame.median().median() + frame_copy = float_frame.copy() + + return_value = frame_copy.clip(upper=median, lower=median, inplace=True) + assert return_value is None + assert not (frame_copy.values != median).any() + + def test_dataframe_clip(self): + # GH#2747 + df = DataFrame(np.random.default_rng(2).standard_normal((1000, 2))) + + for lb, ub in [(-1, 1), (1, -1)]: + clipped_df = df.clip(lb, ub) + + lb, ub = min(lb, ub), max(ub, lb) + lb_mask = df.values <= lb + ub_mask = df.values >= ub + mask = ~lb_mask & ~ub_mask + assert (clipped_df.values[lb_mask] == lb).all() + assert (clipped_df.values[ub_mask] == ub).all() + assert (clipped_df.values[mask] == df.values[mask]).all() + + def test_clip_mixed_numeric(self): + # clip on mixed integer or floats + # GH#24162, clipping now preserves numeric types per column + df = DataFrame({"A": [1, 2, 3], "B": [1.0, np.nan, 3.0]}) + result = df.clip(1, 2) + expected = DataFrame({"A": [1, 2, 2], "B": [1.0, np.nan, 2.0]}) + tm.assert_frame_equal(result, expected) + + df = DataFrame([[1, 2, 3.4], [3, 4, 5.6]], columns=["foo", "bar", "baz"]) + expected = df.dtypes + result = df.clip(upper=3).dtypes + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("inplace", [True, False]) + def test_clip_against_series(self, inplace): + # GH#6966 + + df = DataFrame(np.random.default_rng(2).standard_normal((1000, 2))) + lb = Series(np.random.default_rng(2).standard_normal(1000)) + ub = lb + 1 + + original = df.copy() + clipped_df = df.clip(lb, ub, axis=0, inplace=inplace) + + if inplace: + clipped_df = df + + for i in range(2): + lb_mask = original.iloc[:, i] <= lb + ub_mask = original.iloc[:, i] >= ub + mask = ~lb_mask & ~ub_mask + + result = clipped_df.loc[lb_mask, i] + tm.assert_series_equal(result, lb[lb_mask], check_names=False) + assert result.name == i + + result = clipped_df.loc[ub_mask, i] + tm.assert_series_equal(result, ub[ub_mask], check_names=False) + assert result.name == i + + tm.assert_series_equal(clipped_df.loc[mask, i], df.loc[mask, i]) + + @pytest.mark.parametrize("inplace", [True, False]) + @pytest.mark.parametrize("lower", [[2, 3, 4], np.asarray([2, 3, 4])]) + @pytest.mark.parametrize( + "axis,res", + [ + (0, [[2.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 7.0, 7.0]]), + (1, [[2.0, 3.0, 4.0], [4.0, 5.0, 6.0], [5.0, 6.0, 7.0]]), + ], + ) + def test_clip_against_list_like(self, inplace, lower, axis, res): + # GH#15390 + arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]) + + original = DataFrame( + arr, columns=["one", "two", "three"], index=["a", "b", "c"] + ) + + result = original.clip(lower=lower, upper=[5, 6, 7], axis=axis, inplace=inplace) + + expected = DataFrame(res, columns=original.columns, index=original.index) + if inplace: + result = original + tm.assert_frame_equal(result, expected, check_exact=True) + + @pytest.mark.parametrize("axis", [0, 1, None]) + def test_clip_against_frame(self, axis): + df = DataFrame(np.random.default_rng(2).standard_normal((1000, 2))) + lb = DataFrame(np.random.default_rng(2).standard_normal((1000, 2))) + ub = lb + 1 + + clipped_df = df.clip(lb, ub, axis=axis) + + lb_mask = df <= lb + ub_mask = df >= ub + mask = ~lb_mask & ~ub_mask + + tm.assert_frame_equal(clipped_df[lb_mask], lb[lb_mask]) + tm.assert_frame_equal(clipped_df[ub_mask], ub[ub_mask]) + tm.assert_frame_equal(clipped_df[mask], df[mask]) + + def test_clip_against_unordered_columns(self): + # GH#20911 + df1 = DataFrame( + np.random.default_rng(2).standard_normal((1000, 4)), + columns=["A", "B", "C", "D"], + ) + df2 = DataFrame( + np.random.default_rng(2).standard_normal((1000, 4)), + columns=["D", "A", "B", "C"], + ) + df3 = DataFrame(df2.values - 1, columns=["B", "D", "C", "A"]) + result_upper = df1.clip(lower=0, upper=df2) + expected_upper = df1.clip(lower=0, upper=df2[df1.columns]) + result_lower = df1.clip(lower=df3, upper=3) + expected_lower = df1.clip(lower=df3[df1.columns], upper=3) + result_lower_upper = df1.clip(lower=df3, upper=df2) + expected_lower_upper = df1.clip(lower=df3[df1.columns], upper=df2[df1.columns]) + tm.assert_frame_equal(result_upper, expected_upper) + tm.assert_frame_equal(result_lower, expected_lower) + tm.assert_frame_equal(result_lower_upper, expected_lower_upper) + + def test_clip_with_na_args(self, float_frame): + """Should process np.nan argument as None""" + # GH#17276 + tm.assert_frame_equal(float_frame.clip(np.nan), float_frame) + tm.assert_frame_equal(float_frame.clip(upper=np.nan, lower=np.nan), float_frame) + + # GH#19992 and adjusted in GH#40420 + df = DataFrame({"col_0": [1, 2, 3], "col_1": [4, 5, 6], "col_2": [7, 8, 9]}) + + msg = "Downcasting behavior in Series and DataFrame methods 'where'" + # TODO: avoid this warning here? seems like we should never be upcasting + # in the first place? + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.clip(lower=[4, 5, np.nan], axis=0) + expected = DataFrame( + {"col_0": [4, 5, 3], "col_1": [4, 5, 6], "col_2": [7, 8, 9]} + ) + tm.assert_frame_equal(result, expected) + + result = df.clip(lower=[4, 5, np.nan], axis=1) + expected = DataFrame( + {"col_0": [4, 4, 4], "col_1": [5, 5, 6], "col_2": [7, 8, 9]} + ) + tm.assert_frame_equal(result, expected) + + # GH#40420 + data = {"col_0": [9, -3, 0, -1, 5], "col_1": [-2, -7, 6, 8, -5]} + df = DataFrame(data) + t = Series([2, -4, np.nan, 6, 3]) + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.clip(lower=t, axis=0) + expected = DataFrame({"col_0": [9, -3, 0, 6, 5], "col_1": [2, -4, 6, 8, 3]}) + tm.assert_frame_equal(result, expected) + + def test_clip_int_data_with_float_bound(self): + # GH51472 + df = DataFrame({"a": [1, 2, 3]}) + result = df.clip(lower=1.5) + expected = DataFrame({"a": [1.5, 2.0, 3.0]}) + tm.assert_frame_equal(result, expected) + + def test_clip_with_list_bound(self): + # GH#54817 + df = DataFrame([1, 5]) + expected = DataFrame([3, 5]) + result = df.clip([3]) + tm.assert_frame_equal(result, expected) + + expected = DataFrame([1, 3]) + result = df.clip(upper=[3]) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine.py new file mode 100644 index 0000000000000000000000000000000000000000..bc6a67e4e1f320dbb71220d33ba03eaf788bcb4b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine.py @@ -0,0 +1,47 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +class TestCombine: + @pytest.mark.parametrize( + "data", + [ + pd.date_range("2000", periods=4), + pd.date_range("2000", periods=4, tz="US/Central"), + pd.period_range("2000", periods=4), + pd.timedelta_range(0, periods=4), + ], + ) + def test_combine_datetlike_udf(self, data): + # GH#23079 + df = pd.DataFrame({"A": data}) + other = df.copy() + df.iloc[1, 0] = None + + def combiner(a, b): + return b + + result = df.combine(other, combiner) + tm.assert_frame_equal(result, other) + + def test_combine_generic(self, float_frame): + df1 = float_frame + df2 = float_frame.loc[float_frame.index[:-5], ["A", "B", "C"]] + + combined = df1.combine(df2, np.add) + combined2 = df2.combine(df1, np.add) + assert combined["D"].isna().all() + assert combined2["D"].isna().all() + + chunk = combined.loc[combined.index[:-5], ["A", "B", "C"]] + chunk2 = combined2.loc[combined2.index[:-5], ["A", "B", "C"]] + + exp = ( + float_frame.loc[float_frame.index[:-5], ["A", "B", "C"]].reindex_like(chunk) + * 2 + ) + tm.assert_frame_equal(chunk, exp) + tm.assert_frame_equal(chunk2, exp) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine_first.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine_first.py new file mode 100644 index 0000000000000000000000000000000000000000..8aeab5dacd8b4aeb96ef5b91ea0de34c485eab2c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_combine_first.py @@ -0,0 +1,556 @@ +from datetime import datetime + +import numpy as np +import pytest + +from pandas.core.dtypes.cast import find_common_type +from pandas.core.dtypes.common import is_dtype_equal + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, +) +import pandas._testing as tm + + +class TestDataFrameCombineFirst: + def test_combine_first_mixed(self): + a = Series(["a", "b"], index=range(2)) + b = Series(range(2), index=range(2)) + f = DataFrame({"A": a, "B": b}) + + a = Series(["a", "b"], index=range(5, 7)) + b = Series(range(2), index=range(5, 7)) + g = DataFrame({"A": a, "B": b}) + + exp = DataFrame({"A": list("abab"), "B": [0, 1, 0, 1]}, index=[0, 1, 5, 6]) + combined = f.combine_first(g) + tm.assert_frame_equal(combined, exp) + + def test_combine_first(self, float_frame, using_infer_string): + # disjoint + head, tail = float_frame[:5], float_frame[5:] + + combined = head.combine_first(tail) + reordered_frame = float_frame.reindex(combined.index) + tm.assert_frame_equal(combined, reordered_frame) + tm.assert_index_equal(combined.columns, float_frame.columns) + tm.assert_series_equal(combined["A"], reordered_frame["A"]) + + # same index + fcopy = float_frame.copy() + fcopy["A"] = 1 + del fcopy["C"] + + fcopy2 = float_frame.copy() + fcopy2["B"] = 0 + del fcopy2["D"] + + combined = fcopy.combine_first(fcopy2) + + assert (combined["A"] == 1).all() + tm.assert_series_equal(combined["B"], fcopy["B"]) + tm.assert_series_equal(combined["C"], fcopy2["C"]) + tm.assert_series_equal(combined["D"], fcopy["D"]) + + # overlap + head, tail = reordered_frame[:10].copy(), reordered_frame + head["A"] = 1 + + combined = head.combine_first(tail) + assert (combined["A"][:10] == 1).all() + + # reverse overlap + tail.iloc[:10, tail.columns.get_loc("A")] = 0 + combined = tail.combine_first(head) + assert (combined["A"][:10] == 0).all() + + # no overlap + f = float_frame[:10] + g = float_frame[10:] + combined = f.combine_first(g) + tm.assert_series_equal(combined["A"].reindex(f.index), f["A"]) + tm.assert_series_equal(combined["A"].reindex(g.index), g["A"]) + + # corner cases + warning = FutureWarning if using_infer_string else None + with tm.assert_produces_warning(warning, match="empty entries"): + comb = float_frame.combine_first(DataFrame()) + tm.assert_frame_equal(comb, float_frame) + + comb = DataFrame().combine_first(float_frame) + tm.assert_frame_equal(comb, float_frame.sort_index()) + + comb = float_frame.combine_first(DataFrame(index=["faz", "boo"])) + assert "faz" in comb.index + + # #2525 + df = DataFrame({"a": [1]}, index=[datetime(2012, 1, 1)]) + df2 = DataFrame(columns=["b"]) + result = df.combine_first(df2) + assert "b" in result + + def test_combine_first_mixed_bug(self): + idx = Index(["a", "b", "c", "e"]) + ser1 = Series([5.0, -9.0, 4.0, 100.0], index=idx) + ser2 = Series(["a", "b", "c", "e"], index=idx) + ser3 = Series([12, 4, 5, 97], index=idx) + + frame1 = DataFrame({"col0": ser1, "col2": ser2, "col3": ser3}) + + idx = Index(["a", "b", "c", "f"]) + ser1 = Series([5.0, -9.0, 4.0, 100.0], index=idx) + ser2 = Series(["a", "b", "c", "f"], index=idx) + ser3 = Series([12, 4, 5, 97], index=idx) + + frame2 = DataFrame({"col1": ser1, "col2": ser2, "col5": ser3}) + + combined = frame1.combine_first(frame2) + assert len(combined.columns) == 5 + + def test_combine_first_same_as_in_update(self): + # gh 3016 (same as in update) + df = DataFrame( + [[1.0, 2.0, False, True], [4.0, 5.0, True, False]], + columns=["A", "B", "bool1", "bool2"], + ) + + other = DataFrame([[45, 45]], index=[0], columns=["A", "B"]) + result = df.combine_first(other) + tm.assert_frame_equal(result, df) + + df.loc[0, "A"] = np.nan + result = df.combine_first(other) + df.loc[0, "A"] = 45 + tm.assert_frame_equal(result, df) + + def test_combine_first_doc_example(self): + # doc example + df1 = DataFrame( + {"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]} + ) + + df2 = DataFrame( + { + "A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0], + "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0], + } + ) + + result = df1.combine_first(df2) + expected = DataFrame({"A": [1, 2, 3, 5, 3, 7.0], "B": [np.nan, 2, 3, 4, 6, 8]}) + tm.assert_frame_equal(result, expected) + + def test_combine_first_return_obj_type_with_bools(self): + # GH3552 + + df1 = DataFrame( + [[np.nan, 3.0, True], [-4.6, np.nan, True], [np.nan, 7.0, False]] + ) + df2 = DataFrame([[-42.6, np.nan, True], [-5.0, 1.6, False]], index=[1, 2]) + + expected = Series([True, True, False], name=2, dtype=bool) + + result_12 = df1.combine_first(df2)[2] + tm.assert_series_equal(result_12, expected) + + result_21 = df2.combine_first(df1)[2] + tm.assert_series_equal(result_21, expected) + + @pytest.mark.parametrize( + "data1, data2, data_expected", + ( + ( + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + [pd.NaT, pd.NaT, pd.NaT], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + ), + ( + [pd.NaT, pd.NaT, pd.NaT], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + ), + ( + [datetime(2000, 1, 2), pd.NaT, pd.NaT], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + [datetime(2000, 1, 2), datetime(2000, 1, 2), datetime(2000, 1, 3)], + ), + ( + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + [datetime(2000, 1, 2), pd.NaT, pd.NaT], + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + ), + ), + ) + def test_combine_first_convert_datatime_correctly( + self, data1, data2, data_expected + ): + # GH 3593 + + df1, df2 = DataFrame({"a": data1}), DataFrame({"a": data2}) + result = df1.combine_first(df2) + expected = DataFrame({"a": data_expected}) + tm.assert_frame_equal(result, expected) + + def test_combine_first_align_nan(self): + # GH 7509 (not fixed) + dfa = DataFrame([[pd.Timestamp("2011-01-01"), 2]], columns=["a", "b"]) + dfb = DataFrame([[4], [5]], columns=["b"]) + assert dfa["a"].dtype == "datetime64[ns]" + assert dfa["b"].dtype == "int64" + + res = dfa.combine_first(dfb) + exp = DataFrame( + {"a": [pd.Timestamp("2011-01-01"), pd.NaT], "b": [2, 5]}, + columns=["a", "b"], + ) + tm.assert_frame_equal(res, exp) + assert res["a"].dtype == "datetime64[ns]" + # TODO: this must be int64 + assert res["b"].dtype == "int64" + + res = dfa.iloc[:0].combine_first(dfb) + exp = DataFrame({"a": [np.nan, np.nan], "b": [4, 5]}, columns=["a", "b"]) + tm.assert_frame_equal(res, exp) + # TODO: this must be datetime64 + assert res["a"].dtype == "float64" + # TODO: this must be int64 + assert res["b"].dtype == "int64" + + def test_combine_first_timezone(self, unit): + # see gh-7630 + data1 = pd.to_datetime("20100101 01:01").tz_localize("UTC").as_unit(unit) + df1 = DataFrame( + columns=["UTCdatetime", "abc"], + data=data1, + index=pd.date_range("20140627", periods=1), + ) + data2 = pd.to_datetime("20121212 12:12").tz_localize("UTC").as_unit(unit) + df2 = DataFrame( + columns=["UTCdatetime", "xyz"], + data=data2, + index=pd.date_range("20140628", periods=1), + ) + res = df2[["UTCdatetime"]].combine_first(df1) + exp = DataFrame( + { + "UTCdatetime": [ + pd.Timestamp("2010-01-01 01:01", tz="UTC"), + pd.Timestamp("2012-12-12 12:12", tz="UTC"), + ], + "abc": [pd.Timestamp("2010-01-01 01:01:00", tz="UTC"), pd.NaT], + }, + columns=["UTCdatetime", "abc"], + index=pd.date_range("20140627", periods=2, freq="D"), + dtype=f"datetime64[{unit}, UTC]", + ) + assert res["UTCdatetime"].dtype == f"datetime64[{unit}, UTC]" + assert res["abc"].dtype == f"datetime64[{unit}, UTC]" + + tm.assert_frame_equal(res, exp) + + def test_combine_first_timezone2(self, unit): + # see gh-10567 + dts1 = pd.date_range("2015-01-01", "2015-01-05", tz="UTC", unit=unit) + df1 = DataFrame({"DATE": dts1}) + dts2 = pd.date_range("2015-01-03", "2015-01-05", tz="UTC", unit=unit) + df2 = DataFrame({"DATE": dts2}) + + res = df1.combine_first(df2) + tm.assert_frame_equal(res, df1) + assert res["DATE"].dtype == f"datetime64[{unit}, UTC]" + + def test_combine_first_timezone3(self, unit): + dts1 = pd.DatetimeIndex( + ["2011-01-01", "NaT", "2011-01-03", "2011-01-04"], tz="US/Eastern" + ).as_unit(unit) + df1 = DataFrame({"DATE": dts1}, index=[1, 3, 5, 7]) + dts2 = pd.DatetimeIndex( + ["2012-01-01", "2012-01-02", "2012-01-03"], tz="US/Eastern" + ).as_unit(unit) + df2 = DataFrame({"DATE": dts2}, index=[2, 4, 5]) + + res = df1.combine_first(df2) + exp_dts = pd.DatetimeIndex( + [ + "2011-01-01", + "2012-01-01", + "NaT", + "2012-01-02", + "2011-01-03", + "2011-01-04", + ], + tz="US/Eastern", + ).as_unit(unit) + exp = DataFrame({"DATE": exp_dts}, index=[1, 2, 3, 4, 5, 7]) + tm.assert_frame_equal(res, exp) + + # FIXME: parametrizing over unit breaks on non-nano + def test_combine_first_timezone4(self): + # different tz + dts1 = pd.date_range("2015-01-01", "2015-01-05", tz="US/Eastern") + df1 = DataFrame({"DATE": dts1}) + dts2 = pd.date_range("2015-01-03", "2015-01-05") + df2 = DataFrame({"DATE": dts2}) + + # if df1 doesn't have NaN, keep its dtype + res = df1.combine_first(df2) + tm.assert_frame_equal(res, df1) + assert res["DATE"].dtype == "datetime64[ns, US/Eastern]" + + def test_combine_first_timezone5(self, unit): + dts1 = pd.date_range("2015-01-01", "2015-01-02", tz="US/Eastern", unit=unit) + df1 = DataFrame({"DATE": dts1}) + dts2 = pd.date_range("2015-01-01", "2015-01-03", unit=unit) + df2 = DataFrame({"DATE": dts2}) + + res = df1.combine_first(df2) + exp_dts = [ + pd.Timestamp("2015-01-01", tz="US/Eastern"), + pd.Timestamp("2015-01-02", tz="US/Eastern"), + pd.Timestamp("2015-01-03"), + ] + exp = DataFrame({"DATE": exp_dts}) + tm.assert_frame_equal(res, exp) + assert res["DATE"].dtype == "object" + + def test_combine_first_timedelta(self): + data1 = pd.TimedeltaIndex(["1 day", "NaT", "3 day", "4day"]) + df1 = DataFrame({"TD": data1}, index=[1, 3, 5, 7]) + data2 = pd.TimedeltaIndex(["10 day", "11 day", "12 day"]) + df2 = DataFrame({"TD": data2}, index=[2, 4, 5]) + + res = df1.combine_first(df2) + exp_dts = pd.TimedeltaIndex( + ["1 day", "10 day", "NaT", "11 day", "3 day", "4 day"] + ) + exp = DataFrame({"TD": exp_dts}, index=[1, 2, 3, 4, 5, 7]) + tm.assert_frame_equal(res, exp) + assert res["TD"].dtype == "timedelta64[ns]" + + def test_combine_first_period(self): + data1 = pd.PeriodIndex(["2011-01", "NaT", "2011-03", "2011-04"], freq="M") + df1 = DataFrame({"P": data1}, index=[1, 3, 5, 7]) + data2 = pd.PeriodIndex(["2012-01-01", "2012-02", "2012-03"], freq="M") + df2 = DataFrame({"P": data2}, index=[2, 4, 5]) + + res = df1.combine_first(df2) + exp_dts = pd.PeriodIndex( + ["2011-01", "2012-01", "NaT", "2012-02", "2011-03", "2011-04"], freq="M" + ) + exp = DataFrame({"P": exp_dts}, index=[1, 2, 3, 4, 5, 7]) + tm.assert_frame_equal(res, exp) + assert res["P"].dtype == data1.dtype + + # different freq + dts2 = pd.PeriodIndex(["2012-01-01", "2012-01-02", "2012-01-03"], freq="D") + df2 = DataFrame({"P": dts2}, index=[2, 4, 5]) + + res = df1.combine_first(df2) + exp_dts = [ + pd.Period("2011-01", freq="M"), + pd.Period("2012-01-01", freq="D"), + pd.NaT, + pd.Period("2012-01-02", freq="D"), + pd.Period("2011-03", freq="M"), + pd.Period("2011-04", freq="M"), + ] + exp = DataFrame({"P": exp_dts}, index=[1, 2, 3, 4, 5, 7]) + tm.assert_frame_equal(res, exp) + assert res["P"].dtype == "object" + + def test_combine_first_int(self): + # GH14687 - integer series that do no align exactly + + df1 = DataFrame({"a": [0, 1, 3, 5]}, dtype="int64") + df2 = DataFrame({"a": [1, 4]}, dtype="int64") + + result_12 = df1.combine_first(df2) + expected_12 = DataFrame({"a": [0, 1, 3, 5]}) + tm.assert_frame_equal(result_12, expected_12) + + result_21 = df2.combine_first(df1) + expected_21 = DataFrame({"a": [1, 4, 3, 5]}) + tm.assert_frame_equal(result_21, expected_21) + + @pytest.mark.parametrize("val", [1, 1.0]) + def test_combine_first_with_asymmetric_other(self, val): + # see gh-20699 + df1 = DataFrame({"isNum": [val]}) + df2 = DataFrame({"isBool": [True]}) + + res = df1.combine_first(df2) + exp = DataFrame({"isBool": [True], "isNum": [val]}) + + tm.assert_frame_equal(res, exp) + + def test_combine_first_string_dtype_only_na(self, nullable_string_dtype): + # GH: 37519 + df = DataFrame( + {"a": ["962", "85"], "b": [pd.NA] * 2}, dtype=nullable_string_dtype + ) + df2 = DataFrame({"a": ["85"], "b": [pd.NA]}, dtype=nullable_string_dtype) + df.set_index(["a", "b"], inplace=True) + df2.set_index(["a", "b"], inplace=True) + result = df.combine_first(df2) + expected = DataFrame( + {"a": ["962", "85"], "b": [pd.NA] * 2}, dtype=nullable_string_dtype + ).set_index(["a", "b"]) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "scalar1, scalar2", + [ + (datetime(2020, 1, 1), datetime(2020, 1, 2)), + (pd.Period("2020-01-01", "D"), pd.Period("2020-01-02", "D")), + (pd.Timedelta("89 days"), pd.Timedelta("60 min")), + (pd.Interval(left=0, right=1), pd.Interval(left=2, right=3, closed="left")), + ], +) +def test_combine_first_timestamp_bug(scalar1, scalar2, nulls_fixture): + # GH28481 + na_value = nulls_fixture + + frame = DataFrame([[na_value, na_value]], columns=["a", "b"]) + other = DataFrame([[scalar1, scalar2]], columns=["b", "c"]) + + common_dtype = find_common_type([frame.dtypes["b"], other.dtypes["b"]]) + + if is_dtype_equal(common_dtype, "object") or frame.dtypes["b"] == other.dtypes["b"]: + val = scalar1 + else: + val = na_value + + result = frame.combine_first(other) + + expected = DataFrame([[na_value, val, scalar2]], columns=["a", "b", "c"]) + + expected["b"] = expected["b"].astype(common_dtype) + + tm.assert_frame_equal(result, expected) + + +def test_combine_first_timestamp_bug_NaT(): + # GH28481 + frame = DataFrame([[pd.NaT, pd.NaT]], columns=["a", "b"]) + other = DataFrame( + [[datetime(2020, 1, 1), datetime(2020, 1, 2)]], columns=["b", "c"] + ) + + result = frame.combine_first(other) + expected = DataFrame( + [[pd.NaT, datetime(2020, 1, 1), datetime(2020, 1, 2)]], columns=["a", "b", "c"] + ) + + tm.assert_frame_equal(result, expected) + + +def test_combine_first_with_nan_multiindex(): + # gh-36562 + + mi1 = MultiIndex.from_arrays( + [["b", "b", "c", "a", "b", np.nan], [1, 2, 3, 4, 5, 6]], names=["a", "b"] + ) + df = DataFrame({"c": [1, 1, 1, 1, 1, 1]}, index=mi1) + mi2 = MultiIndex.from_arrays( + [["a", "b", "c", "a", "b", "d"], [1, 1, 1, 1, 1, 1]], names=["a", "b"] + ) + s = Series([1, 2, 3, 4, 5, 6], index=mi2) + res = df.combine_first(DataFrame({"d": s})) + mi_expected = MultiIndex.from_arrays( + [ + ["a", "a", "a", "b", "b", "b", "b", "c", "c", "d", np.nan], + [1, 1, 4, 1, 1, 2, 5, 1, 3, 1, 6], + ], + names=["a", "b"], + ) + expected = DataFrame( + { + "c": [np.nan, np.nan, 1, 1, 1, 1, 1, np.nan, 1, np.nan, 1], + "d": [1.0, 4.0, np.nan, 2.0, 5.0, np.nan, np.nan, 3.0, np.nan, 6.0, np.nan], + }, + index=mi_expected, + ) + tm.assert_frame_equal(res, expected) + + +def test_combine_preserve_dtypes(): + # GH7509 + a_column = Series(["a", "b"], index=range(2)) + b_column = Series(range(2), index=range(2)) + df1 = DataFrame({"A": a_column, "B": b_column}) + + c_column = Series(["a", "b"], index=range(5, 7)) + b_column = Series(range(-1, 1), index=range(5, 7)) + df2 = DataFrame({"B": b_column, "C": c_column}) + + expected = DataFrame( + { + "A": ["a", "b", np.nan, np.nan], + "B": [0, 1, -1, 0], + "C": [np.nan, np.nan, "a", "b"], + }, + index=[0, 1, 5, 6], + ) + combined = df1.combine_first(df2) + tm.assert_frame_equal(combined, expected) + + +def test_combine_first_duplicates_rows_for_nan_index_values(): + # GH39881 + df1 = DataFrame( + {"x": [9, 10, 11]}, + index=MultiIndex.from_arrays([[1, 2, 3], [np.nan, 5, 6]], names=["a", "b"]), + ) + + df2 = DataFrame( + {"y": [12, 13, 14]}, + index=MultiIndex.from_arrays([[1, 2, 4], [np.nan, 5, 7]], names=["a", "b"]), + ) + + expected = DataFrame( + { + "x": [9.0, 10.0, 11.0, np.nan], + "y": [12.0, 13.0, np.nan, 14.0], + }, + index=MultiIndex.from_arrays( + [[1, 2, 3, 4], [np.nan, 5, 6, 7]], names=["a", "b"] + ), + ) + combined = df1.combine_first(df2) + tm.assert_frame_equal(combined, expected) + + +def test_combine_first_int64_not_cast_to_float64(): + # GH 28613 + df_1 = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + df_2 = DataFrame({"A": [1, 20, 30], "B": [40, 50, 60], "C": [12, 34, 65]}) + result = df_1.combine_first(df_2) + expected = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [12, 34, 65]}) + tm.assert_frame_equal(result, expected) + + +def test_midx_losing_dtype(): + # GH#49830 + midx = MultiIndex.from_arrays([[0, 0], [np.nan, np.nan]]) + midx2 = MultiIndex.from_arrays([[1, 1], [np.nan, np.nan]]) + df1 = DataFrame({"a": [None, 4]}, index=midx) + df2 = DataFrame({"a": [3, 3]}, index=midx2) + result = df1.combine_first(df2) + expected_midx = MultiIndex.from_arrays( + [[0, 0, 1, 1], [np.nan, np.nan, np.nan, np.nan]] + ) + expected = DataFrame({"a": [np.nan, 4, 3, 3]}, index=expected_midx) + tm.assert_frame_equal(result, expected) + + +def test_combine_first_empty_columns(): + left = DataFrame(columns=["a", "b"]) + right = DataFrame(columns=["a", "c"]) + result = left.combine_first(right) + expected = DataFrame(columns=["a", "b", "c"]) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_compare.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_compare.py new file mode 100644 index 0000000000000000000000000000000000000000..a4d0a7068a3a650beb11529065d0b62ab702143b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_compare.py @@ -0,0 +1,305 @@ +import numpy as np +import pytest + +from pandas.compat.numpy import np_version_gte1p25 + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"]) +def test_compare_axis(align_axis): + # GH#30429 + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + columns=["col1", "col2", "col3"], + ) + df2 = df.copy() + df2.loc[0, "col1"] = "c" + df2.loc[2, "col3"] = 4.0 + + result = df.compare(df2, align_axis=align_axis) + + if align_axis in (1, "columns"): + indices = pd.Index([0, 2]) + columns = pd.MultiIndex.from_product([["col1", "col3"], ["self", "other"]]) + expected = pd.DataFrame( + [["a", "c", np.nan, np.nan], [np.nan, np.nan, 3.0, 4.0]], + index=indices, + columns=columns, + ) + else: + indices = pd.MultiIndex.from_product([[0, 2], ["self", "other"]]) + columns = pd.Index(["col1", "col3"]) + expected = pd.DataFrame( + [["a", np.nan], ["c", np.nan], [np.nan, 3.0], [np.nan, 4.0]], + index=indices, + columns=columns, + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "keep_shape, keep_equal", + [ + (True, False), + (False, True), + (True, True), + # False, False case is already covered in test_compare_axis + ], +) +def test_compare_various_formats(keep_shape, keep_equal): + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + columns=["col1", "col2", "col3"], + ) + df2 = df.copy() + df2.loc[0, "col1"] = "c" + df2.loc[2, "col3"] = 4.0 + + result = df.compare(df2, keep_shape=keep_shape, keep_equal=keep_equal) + + if keep_shape: + indices = pd.Index([0, 1, 2]) + columns = pd.MultiIndex.from_product( + [["col1", "col2", "col3"], ["self", "other"]] + ) + if keep_equal: + expected = pd.DataFrame( + [ + ["a", "c", 1.0, 1.0, 1.0, 1.0], + ["b", "b", 2.0, 2.0, 2.0, 2.0], + ["c", "c", np.nan, np.nan, 3.0, 4.0], + ], + index=indices, + columns=columns, + ) + else: + expected = pd.DataFrame( + [ + ["a", "c", np.nan, np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan, np.nan, 3.0, 4.0], + ], + index=indices, + columns=columns, + ) + else: + indices = pd.Index([0, 2]) + columns = pd.MultiIndex.from_product([["col1", "col3"], ["self", "other"]]) + expected = pd.DataFrame( + [["a", "c", 1.0, 1.0], ["c", "c", 3.0, 4.0]], index=indices, columns=columns + ) + tm.assert_frame_equal(result, expected) + + +def test_compare_with_equal_nulls(): + # We want to make sure two NaNs are considered the same + # and dropped where applicable + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + columns=["col1", "col2", "col3"], + ) + df2 = df.copy() + df2.loc[0, "col1"] = "c" + + result = df.compare(df2) + indices = pd.Index([0]) + columns = pd.MultiIndex.from_product([["col1"], ["self", "other"]]) + expected = pd.DataFrame([["a", "c"]], index=indices, columns=columns) + tm.assert_frame_equal(result, expected) + + +def test_compare_with_non_equal_nulls(): + # We want to make sure the relevant NaNs do not get dropped + # even if the entire row or column are NaNs + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + columns=["col1", "col2", "col3"], + ) + df2 = df.copy() + df2.loc[0, "col1"] = "c" + df2.loc[2, "col3"] = np.nan + + result = df.compare(df2) + + indices = pd.Index([0, 2]) + columns = pd.MultiIndex.from_product([["col1", "col3"], ["self", "other"]]) + expected = pd.DataFrame( + [["a", "c", np.nan, np.nan], [np.nan, np.nan, 3.0, np.nan]], + index=indices, + columns=columns, + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("align_axis", [0, 1]) +def test_compare_multi_index(align_axis): + df = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]} + ) + df.columns = pd.MultiIndex.from_arrays([["a", "a", "b"], ["col1", "col2", "col3"]]) + df.index = pd.MultiIndex.from_arrays([["x", "x", "y"], [0, 1, 2]]) + + df2 = df.copy() + df2.iloc[0, 0] = "c" + df2.iloc[2, 2] = 4.0 + + result = df.compare(df2, align_axis=align_axis) + + if align_axis == 0: + indices = pd.MultiIndex.from_arrays( + [["x", "x", "y", "y"], [0, 0, 2, 2], ["self", "other", "self", "other"]] + ) + columns = pd.MultiIndex.from_arrays([["a", "b"], ["col1", "col3"]]) + data = [["a", np.nan], ["c", np.nan], [np.nan, 3.0], [np.nan, 4.0]] + else: + indices = pd.MultiIndex.from_arrays([["x", "y"], [0, 2]]) + columns = pd.MultiIndex.from_arrays( + [ + ["a", "a", "b", "b"], + ["col1", "col1", "col3", "col3"], + ["self", "other", "self", "other"], + ] + ) + data = [["a", "c", np.nan, np.nan], [np.nan, np.nan, 3.0, 4.0]] + + expected = pd.DataFrame(data=data, index=indices, columns=columns) + tm.assert_frame_equal(result, expected) + + +def test_compare_unaligned_objects(): + # test DataFrames with different indices + msg = ( + r"Can only compare identically-labeled \(both index and columns\) DataFrame " + "objects" + ) + with pytest.raises(ValueError, match=msg): + df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"]) + df2 = pd.DataFrame([1, 2, 3], index=["a", "b", "d"]) + df1.compare(df2) + + # test DataFrames with different shapes + msg = ( + r"Can only compare identically-labeled \(both index and columns\) DataFrame " + "objects" + ) + with pytest.raises(ValueError, match=msg): + df1 = pd.DataFrame(np.ones((3, 3))) + df2 = pd.DataFrame(np.zeros((2, 1))) + df1.compare(df2) + + +def test_compare_result_names(): + # GH 44354 + df1 = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + ) + df2 = pd.DataFrame( + { + "col1": ["c", "b", "c"], + "col2": [1.0, 2.0, np.nan], + "col3": [1.0, 2.0, np.nan], + }, + ) + result = df1.compare(df2, result_names=("left", "right")) + expected = pd.DataFrame( + { + ("col1", "left"): {0: "a", 2: np.nan}, + ("col1", "right"): {0: "c", 2: np.nan}, + ("col3", "left"): {0: np.nan, 2: 3.0}, + ("col3", "right"): {0: np.nan, 2: np.nan}, + } + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "result_names", + [ + [1, 2], + "HK", + {"2": 2, "3": 3}, + 3, + 3.0, + ], +) +def test_invalid_input_result_names(result_names): + # GH 44354 + df1 = pd.DataFrame( + {"col1": ["a", "b", "c"], "col2": [1.0, 2.0, np.nan], "col3": [1.0, 2.0, 3.0]}, + ) + df2 = pd.DataFrame( + { + "col1": ["c", "b", "c"], + "col2": [1.0, 2.0, np.nan], + "col3": [1.0, 2.0, np.nan], + }, + ) + with pytest.raises( + TypeError, + match=( + f"Passing 'result_names' as a {type(result_names)} is not " + "supported. Provide 'result_names' as a tuple instead." + ), + ): + df1.compare(df2, result_names=result_names) + + +@pytest.mark.parametrize( + "val1,val2", + [(4, pd.NA), (pd.NA, pd.NA), (pd.NA, 4)], +) +def test_compare_ea_and_np_dtype(val1, val2): + # GH 48966 + arr = [4.0, val1] + ser = pd.Series([1, val2], dtype="Int64") + + df1 = pd.DataFrame({"a": arr, "b": [1.0, 2]}) + df2 = pd.DataFrame({"a": ser, "b": [1.0, 2]}) + expected = pd.DataFrame( + { + ("a", "self"): arr, + ("a", "other"): ser, + ("b", "self"): np.nan, + ("b", "other"): np.nan, + } + ) + if val1 is pd.NA and val2 is pd.NA: + # GH#18463 TODO: is this really the desired behavior? + expected.loc[1, ("a", "self")] = np.nan + + if val1 is pd.NA and np_version_gte1p25: + # can't compare with numpy array if it contains pd.NA + with pytest.raises(TypeError, match="boolean value of NA is ambiguous"): + result = df1.compare(df2, keep_shape=True) + else: + result = df1.compare(df2, keep_shape=True) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "df1_val,df2_val,diff_self,diff_other", + [ + (4, 3, 4, 3), + (4, 4, pd.NA, pd.NA), + (4, pd.NA, 4, pd.NA), + (pd.NA, pd.NA, pd.NA, pd.NA), + ], +) +def test_compare_nullable_int64_dtype(df1_val, df2_val, diff_self, diff_other): + # GH 48966 + df1 = pd.DataFrame({"a": pd.Series([df1_val, pd.NA], dtype="Int64"), "b": [1.0, 2]}) + df2 = df1.copy() + df2.loc[0, "a"] = df2_val + + expected = pd.DataFrame( + { + ("a", "self"): pd.Series([diff_self, pd.NA], dtype="Int64"), + ("a", "other"): pd.Series([diff_other, pd.NA], dtype="Int64"), + ("b", "self"): np.nan, + ("b", "other"): np.nan, + } + ) + result = df1.compare(df2, keep_shape=True) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_convert_dtypes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_convert_dtypes.py new file mode 100644 index 0000000000000000000000000000000000000000..e7f6e5d625d3ece20131a5a719bf4f545b21a19b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_convert_dtypes.py @@ -0,0 +1,198 @@ +import datetime + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +class TestConvertDtypes: + @pytest.mark.parametrize( + "convert_integer, expected", [(False, np.dtype("int32")), (True, "Int32")] + ) + def test_convert_dtypes(self, convert_integer, expected, string_storage): + # Specific types are tested in tests/series/test_dtypes.py + # Just check that it works for DataFrame here + df = pd.DataFrame( + { + "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")), + "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")), + } + ) + with pd.option_context("string_storage", string_storage): + result = df.convert_dtypes(True, True, convert_integer, False) + expected = pd.DataFrame( + { + "a": pd.Series([1, 2, 3], dtype=expected), + "b": pd.Series(["x", "y", "z"], dtype=f"string[{string_storage}]"), + } + ) + tm.assert_frame_equal(result, expected) + + def test_convert_empty(self): + # Empty DataFrame can pass convert_dtypes, see GH#40393 + empty_df = pd.DataFrame() + tm.assert_frame_equal(empty_df, empty_df.convert_dtypes()) + + def test_convert_dtypes_retain_column_names(self): + # GH#41435 + df = pd.DataFrame({"a": [1, 2], "b": [3, 4]}) + df.columns.name = "cols" + + result = df.convert_dtypes() + tm.assert_index_equal(result.columns, df.columns) + assert result.columns.name == "cols" + + def test_pyarrow_dtype_backend(self): + pa = pytest.importorskip("pyarrow") + df = pd.DataFrame( + { + "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")), + "b": pd.Series(["x", "y", None], dtype=np.dtype("O")), + "c": pd.Series([True, False, None], dtype=np.dtype("O")), + "d": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")), + "e": pd.Series(pd.date_range("2022", periods=3)), + "f": pd.Series(pd.date_range("2022", periods=3, tz="UTC").as_unit("s")), + "g": pd.Series(pd.timedelta_range("1D", periods=3)), + } + ) + result = df.convert_dtypes(dtype_backend="pyarrow") + expected = pd.DataFrame( + { + "a": pd.arrays.ArrowExtensionArray( + pa.array([1, 2, 3], type=pa.int32()) + ), + "b": pd.arrays.ArrowExtensionArray(pa.array(["x", "y", None])), + "c": pd.arrays.ArrowExtensionArray(pa.array([True, False, None])), + "d": pd.arrays.ArrowExtensionArray(pa.array([None, 100.5, 200.0])), + "e": pd.arrays.ArrowExtensionArray( + pa.array( + [ + datetime.datetime(2022, 1, 1), + datetime.datetime(2022, 1, 2), + datetime.datetime(2022, 1, 3), + ], + type=pa.timestamp(unit="ns"), + ) + ), + "f": pd.arrays.ArrowExtensionArray( + pa.array( + [ + datetime.datetime(2022, 1, 1), + datetime.datetime(2022, 1, 2), + datetime.datetime(2022, 1, 3), + ], + type=pa.timestamp(unit="s", tz="UTC"), + ) + ), + "g": pd.arrays.ArrowExtensionArray( + pa.array( + [ + datetime.timedelta(1), + datetime.timedelta(2), + datetime.timedelta(3), + ], + type=pa.duration("ns"), + ) + ), + } + ) + tm.assert_frame_equal(result, expected) + + def test_pyarrow_dtype_backend_already_pyarrow(self): + pytest.importorskip("pyarrow") + expected = pd.DataFrame([1, 2, 3], dtype="int64[pyarrow]") + result = expected.convert_dtypes(dtype_backend="pyarrow") + tm.assert_frame_equal(result, expected) + + def test_pyarrow_dtype_backend_from_pandas_nullable(self): + pa = pytest.importorskip("pyarrow") + df = pd.DataFrame( + { + "a": pd.Series([1, 2, None], dtype="Int32"), + "b": pd.Series(["x", "y", None], dtype="string[python]"), + "c": pd.Series([True, False, None], dtype="boolean"), + "d": pd.Series([None, 100.5, 200], dtype="Float64"), + } + ) + result = df.convert_dtypes(dtype_backend="pyarrow") + expected = pd.DataFrame( + { + "a": pd.arrays.ArrowExtensionArray( + pa.array([1, 2, None], type=pa.int32()) + ), + "b": pd.arrays.ArrowExtensionArray(pa.array(["x", "y", None])), + "c": pd.arrays.ArrowExtensionArray(pa.array([True, False, None])), + "d": pd.arrays.ArrowExtensionArray(pa.array([None, 100.5, 200.0])), + } + ) + tm.assert_frame_equal(result, expected) + + def test_pyarrow_dtype_empty_object(self): + # GH 50970 + pytest.importorskip("pyarrow") + expected = pd.DataFrame(columns=[0]) + result = expected.convert_dtypes(dtype_backend="pyarrow") + tm.assert_frame_equal(result, expected) + + def test_pyarrow_engine_lines_false(self): + # GH 48893 + df = pd.DataFrame({"a": [1, 2, 3]}) + msg = ( + "dtype_backend numpy is invalid, only 'numpy_nullable' and " + "'pyarrow' are allowed." + ) + with pytest.raises(ValueError, match=msg): + df.convert_dtypes(dtype_backend="numpy") + + def test_pyarrow_backend_no_conversion(self): + # GH#52872 + pytest.importorskip("pyarrow") + df = pd.DataFrame({"a": [1, 2], "b": 1.5, "c": True, "d": "x"}) + expected = df.copy() + result = df.convert_dtypes( + convert_floating=False, + convert_integer=False, + convert_boolean=False, + convert_string=False, + dtype_backend="pyarrow", + ) + tm.assert_frame_equal(result, expected) + + def test_convert_dtypes_pyarrow_to_np_nullable(self): + # GH 53648 + pytest.importorskip("pyarrow") + ser = pd.DataFrame(range(2), dtype="int32[pyarrow]") + result = ser.convert_dtypes(dtype_backend="numpy_nullable") + expected = pd.DataFrame(range(2), dtype="Int32") + tm.assert_frame_equal(result, expected) + + def test_convert_dtypes_pyarrow_timestamp(self): + # GH 54191 + pytest.importorskip("pyarrow") + ser = pd.Series(pd.date_range("2020-01-01", "2020-01-02", freq="1min")) + expected = ser.astype("timestamp[ms][pyarrow]") + result = expected.convert_dtypes(dtype_backend="pyarrow") + tm.assert_series_equal(result, expected) + + def test_convert_dtypes_avoid_block_splitting(self): + # GH#55341 + df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": "a"}) + result = df.convert_dtypes(convert_integer=False) + expected = pd.DataFrame( + { + "a": [1, 2, 3], + "b": [4, 5, 6], + "c": pd.Series(["a"] * 3, dtype="string[python]"), + } + ) + tm.assert_frame_equal(result, expected) + assert result._mgr.nblocks == 2 + + def test_convert_dtypes_from_arrow(self): + # GH#56581 + df = pd.DataFrame([["a", datetime.time(18, 12)]], columns=["a", "b"]) + result = df.convert_dtypes() + expected = df.astype({"a": "string[python]"}) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_copy.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_copy.py new file mode 100644 index 0000000000000000000000000000000000000000..e7901ed36310668dc21b96d44fed0686de368b1f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/methods/test_copy.py @@ -0,0 +1,64 @@ +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import DataFrame +import pandas._testing as tm + + +class TestCopy: + @pytest.mark.parametrize("attr", ["index", "columns"]) + def test_copy_index_name_checking(self, float_frame, attr): + # don't want to be able to modify the index stored elsewhere after + # making a copy + ind = getattr(float_frame, attr) + ind.name = None + cp = float_frame.copy() + getattr(cp, attr).name = "foo" + assert getattr(float_frame, attr).name is None + + @td.skip_copy_on_write_invalid_test + def test_copy_cache(self): + # GH#31784 _item_cache not cleared on copy causes incorrect reads after updates + df = DataFrame({"a": [1]}) + + df["x"] = [0] + df["a"] + + df.copy() + + df["a"].values[0] = -1 + + tm.assert_frame_equal(df, DataFrame({"a": [-1], "x": [0]})) + + df["y"] = [0] + + assert df["a"].values[0] == -1 + tm.assert_frame_equal(df, DataFrame({"a": [-1], "x": [0], "y": [0]})) + + def test_copy(self, float_frame, float_string_frame): + cop = float_frame.copy() + cop["E"] = cop["A"] + assert "E" not in float_frame + + # copy objects + copy = float_string_frame.copy() + assert copy._mgr is not float_string_frame._mgr + + @td.skip_array_manager_invalid_test + def test_copy_consolidates(self): + # GH#42477 + df = DataFrame( + { + "a": np.random.default_rng(2).integers(0, 100, size=55), + "b": np.random.default_rng(2).integers(0, 100, size=55), + } + ) + + for i in range(10): + df.loc[:, f"n_{i}"] = np.random.default_rng(2).integers(0, 100, size=55) + + assert len(df._mgr.blocks) == 11 + result = df.copy() + assert len(result._mgr.blocks) == 1 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_alter_axes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_alter_axes.py new file mode 100644 index 0000000000000000000000000000000000000000..c68171ab254c7c8582a206a8e9b44b3845c47efc --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_alter_axes.py @@ -0,0 +1,30 @@ +from datetime import datetime + +import pytz + +from pandas import DataFrame +import pandas._testing as tm + + +class TestDataFrameAlterAxes: + # Tests for setting index/columns attributes directly (i.e. __setattr__) + + def test_set_axis_setattr_index(self): + # GH 6785 + # set the index manually + + df = DataFrame([{"ts": datetime(2014, 4, 1, tzinfo=pytz.utc), "foo": 1}]) + expected = df.set_index("ts") + df.index = df["ts"] + df.pop("ts") + tm.assert_frame_equal(df, expected) + + # Renaming + + def test_assign_columns(self, float_frame): + float_frame["hi"] = "there" + + df = float_frame.copy() + df.columns = ["foo", "bar", "baz", "quux", "foo2"] + tm.assert_series_equal(float_frame["C"], df["baz"], check_names=False) + tm.assert_series_equal(float_frame["hi"], df["foo2"], check_names=False) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_api.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..6c6944f806a2ae77b4a826684a9474769cd18e30 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_api.py @@ -0,0 +1,395 @@ +from copy import deepcopy +import inspect +import pydoc + +import numpy as np +import pytest + +from pandas._config import using_string_dtype +from pandas._config.config import option_context + +from pandas.compat import HAS_PYARROW + +import pandas as pd +from pandas import ( + DataFrame, + Series, + date_range, + timedelta_range, +) +import pandas._testing as tm + + +class TestDataFrameMisc: + def test_getitem_pop_assign_name(self, float_frame): + s = float_frame["A"] + assert s.name == "A" + + s = float_frame.pop("A") + assert s.name == "A" + + s = float_frame.loc[:, "B"] + assert s.name == "B" + + s2 = s.loc[:] + assert s2.name == "B" + + def test_get_axis(self, float_frame): + f = float_frame + assert f._get_axis_number(0) == 0 + assert f._get_axis_number(1) == 1 + assert f._get_axis_number("index") == 0 + assert f._get_axis_number("rows") == 0 + assert f._get_axis_number("columns") == 1 + + assert f._get_axis_name(0) == "index" + assert f._get_axis_name(1) == "columns" + assert f._get_axis_name("index") == "index" + assert f._get_axis_name("rows") == "index" + assert f._get_axis_name("columns") == "columns" + + assert f._get_axis(0) is f.index + assert f._get_axis(1) is f.columns + + with pytest.raises(ValueError, match="No axis named"): + f._get_axis_number(2) + + with pytest.raises(ValueError, match="No axis.*foo"): + f._get_axis_name("foo") + + with pytest.raises(ValueError, match="No axis.*None"): + f._get_axis_name(None) + + with pytest.raises(ValueError, match="No axis named"): + f._get_axis_number(None) + + def test_column_contains_raises(self, float_frame): + with pytest.raises(TypeError, match="unhashable type: 'Index'"): + float_frame.columns in float_frame + + def test_tab_completion(self): + # DataFrame whose columns are identifiers shall have them in __dir__. + df = DataFrame([list("abcd"), list("efgh")], columns=list("ABCD")) + for key in list("ABCD"): + assert key in dir(df) + assert isinstance(df.__getitem__("A"), Series) + + # DataFrame whose first-level columns are identifiers shall have + # them in __dir__. + df = DataFrame( + [list("abcd"), list("efgh")], + columns=pd.MultiIndex.from_tuples(list(zip("ABCD", "EFGH"))), + ) + for key in list("ABCD"): + assert key in dir(df) + for key in list("EFGH"): + assert key not in dir(df) + assert isinstance(df.__getitem__("A"), DataFrame) + + def test_display_max_dir_items(self): + # display.max_dir_items increaes the number of columns that are in __dir__. + columns = ["a" + str(i) for i in range(420)] + values = [range(420), range(420)] + df = DataFrame(values, columns=columns) + + # The default value for display.max_dir_items is 100 + assert "a99" in dir(df) + assert "a100" not in dir(df) + + with option_context("display.max_dir_items", 300): + df = DataFrame(values, columns=columns) + assert "a299" in dir(df) + assert "a300" not in dir(df) + + with option_context("display.max_dir_items", None): + df = DataFrame(values, columns=columns) + assert "a419" in dir(df) + + def test_not_hashable(self): + empty_frame = DataFrame() + + df = DataFrame([1]) + msg = "unhashable type: 'DataFrame'" + with pytest.raises(TypeError, match=msg): + hash(df) + with pytest.raises(TypeError, match=msg): + hash(empty_frame) + + @pytest.mark.xfail( + using_string_dtype() and HAS_PYARROW, reason="surrogates not allowed" + ) + def test_column_name_contains_unicode_surrogate(self): + # GH 25509 + colname = "\ud83d" + df = DataFrame({colname: []}) + # this should not crash + assert colname not in dir(df) + assert df.columns[0] == colname + + def test_new_empty_index(self): + df1 = DataFrame(np.random.default_rng(2).standard_normal((0, 3))) + df2 = DataFrame(np.random.default_rng(2).standard_normal((0, 3))) + df1.index.name = "foo" + assert df2.index.name is None + + def test_get_agg_axis(self, float_frame): + cols = float_frame._get_agg_axis(0) + assert cols is float_frame.columns + + idx = float_frame._get_agg_axis(1) + assert idx is float_frame.index + + msg = r"Axis must be 0 or 1 \(got 2\)" + with pytest.raises(ValueError, match=msg): + float_frame._get_agg_axis(2) + + def test_empty(self, float_frame, float_string_frame): + empty_frame = DataFrame() + assert empty_frame.empty + + assert not float_frame.empty + assert not float_string_frame.empty + + # corner case + df = DataFrame({"A": [1.0, 2.0, 3.0], "B": ["a", "b", "c"]}, index=np.arange(3)) + del df["A"] + assert not df.empty + + def test_len(self, float_frame): + assert len(float_frame) == len(float_frame.index) + + # single block corner case + arr = float_frame[["A", "B"]].values + expected = float_frame.reindex(columns=["A", "B"]).values + tm.assert_almost_equal(arr, expected) + + def test_axis_aliases(self, float_frame): + f = float_frame + + # reg name + expected = f.sum(axis=0) + result = f.sum(axis="index") + tm.assert_series_equal(result, expected) + + expected = f.sum(axis=1) + result = f.sum(axis="columns") + tm.assert_series_equal(result, expected) + + def test_class_axis(self): + # GH 18147 + # no exception and no empty docstring + assert pydoc.getdoc(DataFrame.index) + assert pydoc.getdoc(DataFrame.columns) + + def test_series_put_names(self, float_string_frame): + series = float_string_frame._series + for k, v in series.items(): + assert v.name == k + + def test_empty_nonzero(self): + df = DataFrame([1, 2, 3]) + assert not df.empty + df = DataFrame(index=[1], columns=[1]) + assert not df.empty + df = DataFrame(index=["a", "b"], columns=["c", "d"]).dropna() + assert df.empty + assert df.T.empty + + @pytest.mark.parametrize( + "df", + [ + DataFrame(), + DataFrame(index=[1]), + DataFrame(columns=[1]), + DataFrame({1: []}), + ], + ) + def test_empty_like(self, df): + assert df.empty + assert df.T.empty + + def test_with_datetimelikes(self): + df = DataFrame( + { + "A": date_range("20130101", periods=10), + "B": timedelta_range("1 day", periods=10), + } + ) + t = df.T + + result = t.dtypes.value_counts() + expected = Series({np.dtype("object"): 10}, name="count") + tm.assert_series_equal(result, expected) + + def test_deepcopy(self, float_frame): + cp = deepcopy(float_frame) + cp.loc[0, "A"] = 10 + assert not float_frame.equals(cp) + + def test_inplace_return_self(self): + # GH 1893 + + data = DataFrame( + {"a": ["foo", "bar", "baz", "qux"], "b": [0, 0, 1, 1], "c": [1, 2, 3, 4]} + ) + + def _check_f(base, f): + result = f(base) + assert result is None + + # -----DataFrame----- + + # set_index + f = lambda x: x.set_index("a", inplace=True) + _check_f(data.copy(), f) + + # reset_index + f = lambda x: x.reset_index(inplace=True) + _check_f(data.set_index("a"), f) + + # drop_duplicates + f = lambda x: x.drop_duplicates(inplace=True) + _check_f(data.copy(), f) + + # sort + f = lambda x: x.sort_values("b", inplace=True) + _check_f(data.copy(), f) + + # sort_index + f = lambda x: x.sort_index(inplace=True) + _check_f(data.copy(), f) + + # fillna + f = lambda x: x.fillna(0, inplace=True) + _check_f(data.copy(), f) + + # replace + f = lambda x: x.replace(1, 0, inplace=True) + _check_f(data.copy(), f) + + # rename + f = lambda x: x.rename({1: "foo"}, inplace=True) + _check_f(data.copy(), f) + + # -----Series----- + d = data.copy()["c"] + + # reset_index + f = lambda x: x.reset_index(inplace=True, drop=True) + _check_f(data.set_index("a")["c"], f) + + # fillna + f = lambda x: x.fillna(0, inplace=True) + _check_f(d.copy(), f) + + # replace + f = lambda x: x.replace(1, 0, inplace=True) + _check_f(d.copy(), f) + + # rename + f = lambda x: x.rename({1: "foo"}, inplace=True) + _check_f(d.copy(), f) + + def test_tab_complete_warning(self, ip, frame_or_series): + # GH 16409 + pytest.importorskip("IPython", minversion="6.0.0") + from IPython.core.completer import provisionalcompleter + + if frame_or_series is DataFrame: + code = "from pandas import DataFrame; obj = DataFrame()" + else: + code = "from pandas import Series; obj = Series(dtype=object)" + + ip.run_cell(code) + # GH 31324 newer jedi version raises Deprecation warning; + # appears resolved 2021-02-02 + with tm.assert_produces_warning(None, raise_on_extra_warnings=False): + with provisionalcompleter("ignore"): + list(ip.Completer.completions("obj.", 1)) + + def test_attrs(self): + df = DataFrame({"A": [2, 3]}) + assert df.attrs == {} + df.attrs["version"] = 1 + + result = df.rename(columns=str) + assert result.attrs == {"version": 1} + + def test_attrs_deepcopy(self): + df = DataFrame({"A": [2, 3]}) + assert df.attrs == {} + df.attrs["tags"] = {"spam", "ham"} + + result = df.rename(columns=str) + assert result.attrs == df.attrs + assert result.attrs["tags"] is not df.attrs["tags"] + + @pytest.mark.parametrize("allows_duplicate_labels", [True, False, None]) + def test_set_flags( + self, + allows_duplicate_labels, + frame_or_series, + using_copy_on_write, + warn_copy_on_write, + ): + obj = DataFrame({"A": [1, 2]}) + key = (0, 0) + if frame_or_series is Series: + obj = obj["A"] + key = 0 + + result = obj.set_flags(allows_duplicate_labels=allows_duplicate_labels) + + if allows_duplicate_labels is None: + # We don't update when it's not provided + assert result.flags.allows_duplicate_labels is True + else: + assert result.flags.allows_duplicate_labels is allows_duplicate_labels + + # We made a copy + assert obj is not result + + # We didn't mutate obj + assert obj.flags.allows_duplicate_labels is True + + # But we didn't copy data + if frame_or_series is Series: + assert np.may_share_memory(obj.values, result.values) + else: + assert np.may_share_memory(obj["A"].values, result["A"].values) + + with tm.assert_cow_warning(warn_copy_on_write): + result.iloc[key] = 0 + if using_copy_on_write: + assert obj.iloc[key] == 1 + else: + assert obj.iloc[key] == 0 + # set back to 1 for test below + with tm.assert_cow_warning(warn_copy_on_write): + result.iloc[key] = 1 + + # Now we do copy. + result = obj.set_flags( + copy=True, allows_duplicate_labels=allows_duplicate_labels + ) + result.iloc[key] = 10 + assert obj.iloc[key] == 1 + + def test_constructor_expanddim(self): + # GH#33628 accessing _constructor_expanddim should not raise NotImplementedError + # GH38782 pandas has no container higher than DataFrame (two-dim), so + # DataFrame._constructor_expand_dim, doesn't make sense, so is removed. + df = DataFrame() + + msg = "'DataFrame' object has no attribute '_constructor_expanddim'" + with pytest.raises(AttributeError, match=msg): + df._constructor_expanddim(np.arange(27).reshape(3, 3, 3)) + + def test_inspect_getmembers(self): + # GH38740 + df = DataFrame() + msg = "DataFrame._data is deprecated" + with tm.assert_produces_warning( + DeprecationWarning, match=msg, check_stacklevel=False + ): + inspect.getmembers(df) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_arithmetic.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..195126f1c53822f103d2c558ebb2843feac45a30 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_arithmetic.py @@ -0,0 +1,2145 @@ +from collections import deque +from datetime import ( + datetime, + timezone, +) +from enum import Enum +import functools +import operator +import re + +import numpy as np +import pytest + +from pandas.compat import HAS_PYARROW +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, +) +import pandas._testing as tm +from pandas.core.computation import expressions as expr +from pandas.tests.frame.common import ( + _check_mixed_float, + _check_mixed_int, +) + + +@pytest.fixture +def simple_frame(): + """ + Fixture for simple 3x3 DataFrame + + Columns are ['one', 'two', 'three'], index is ['a', 'b', 'c']. + + one two three + a 1.0 2.0 3.0 + b 4.0 5.0 6.0 + c 7.0 8.0 9.0 + """ + arr = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]) + + return DataFrame(arr, columns=["one", "two", "three"], index=["a", "b", "c"]) + + +@pytest.fixture(autouse=True, params=[0, 100], ids=["numexpr", "python"]) +def switch_numexpr_min_elements(request, monkeypatch): + with monkeypatch.context() as m: + m.setattr(expr, "_MIN_ELEMENTS", request.param) + yield request.param + + +class DummyElement: + def __init__(self, value, dtype) -> None: + self.value = value + self.dtype = np.dtype(dtype) + + def __array__(self, dtype=None, copy=None): + return np.array(self.value, dtype=self.dtype) + + def __str__(self) -> str: + return f"DummyElement({self.value}, {self.dtype})" + + def __repr__(self) -> str: + return str(self) + + def astype(self, dtype, copy=False): + self.dtype = dtype + return self + + def view(self, dtype): + return type(self)(self.value.view(dtype), dtype) + + def any(self, axis=None): + return bool(self.value) + + +# ------------------------------------------------------------------- +# Comparisons + + +class TestFrameComparisons: + # Specifically _not_ flex-comparisons + + def test_comparison_with_categorical_dtype(self): + # GH#12564 + + df = DataFrame({"A": ["foo", "bar", "baz"]}) + exp = DataFrame({"A": [True, False, False]}) + + res = df == "foo" + tm.assert_frame_equal(res, exp) + + # casting to categorical shouldn't affect the result + df["A"] = df["A"].astype("category") + + res = df == "foo" + tm.assert_frame_equal(res, exp) + + def test_frame_in_list(self): + # GH#12689 this should raise at the DataFrame level, not blocks + df = DataFrame( + np.random.default_rng(2).standard_normal((6, 4)), columns=list("ABCD") + ) + msg = "The truth value of a DataFrame is ambiguous" + with pytest.raises(ValueError, match=msg): + df in [None] + + @pytest.mark.parametrize( + "arg, arg2", + [ + [ + { + "a": np.random.default_rng(2).integers(10, size=10), + "b": pd.date_range("20010101", periods=10), + }, + { + "a": np.random.default_rng(2).integers(10, size=10), + "b": np.random.default_rng(2).integers(10, size=10), + }, + ], + [ + { + "a": np.random.default_rng(2).integers(10, size=10), + "b": np.random.default_rng(2).integers(10, size=10), + }, + { + "a": np.random.default_rng(2).integers(10, size=10), + "b": pd.date_range("20010101", periods=10), + }, + ], + [ + { + "a": pd.date_range("20010101", periods=10), + "b": pd.date_range("20010101", periods=10), + }, + { + "a": np.random.default_rng(2).integers(10, size=10), + "b": np.random.default_rng(2).integers(10, size=10), + }, + ], + [ + { + "a": np.random.default_rng(2).integers(10, size=10), + "b": pd.date_range("20010101", periods=10), + }, + { + "a": pd.date_range("20010101", periods=10), + "b": pd.date_range("20010101", periods=10), + }, + ], + ], + ) + def test_comparison_invalid(self, arg, arg2): + # GH4968 + # invalid date/int comparisons + x = DataFrame(arg) + y = DataFrame(arg2) + # we expect the result to match Series comparisons for + # == and !=, inequalities should raise + result = x == y + expected = DataFrame( + {col: x[col] == y[col] for col in x.columns}, + index=x.index, + columns=x.columns, + ) + tm.assert_frame_equal(result, expected) + + result = x != y + expected = DataFrame( + {col: x[col] != y[col] for col in x.columns}, + index=x.index, + columns=x.columns, + ) + tm.assert_frame_equal(result, expected) + + msgs = [ + r"Invalid comparison between dtype=datetime64\[ns\] and ndarray", + "invalid type promotion", + ( + # npdev 1.20.0 + r"The DTypes and " + r" do not have a common DType." + ), + ] + msg = "|".join(msgs) + with pytest.raises(TypeError, match=msg): + x >= y + with pytest.raises(TypeError, match=msg): + x > y + with pytest.raises(TypeError, match=msg): + x < y + with pytest.raises(TypeError, match=msg): + x <= y + + @pytest.mark.parametrize( + "left, right", + [ + ("gt", "lt"), + ("lt", "gt"), + ("ge", "le"), + ("le", "ge"), + ("eq", "eq"), + ("ne", "ne"), + ], + ) + def test_timestamp_compare(self, left, right): + # make sure we can compare Timestamps on the right AND left hand side + # GH#4982 + df = DataFrame( + { + "dates1": pd.date_range("20010101", periods=10), + "dates2": pd.date_range("20010102", periods=10), + "intcol": np.random.default_rng(2).integers(1000000000, size=10), + "floatcol": np.random.default_rng(2).standard_normal(10), + "stringcol": [chr(100 + i) for i in range(10)], + } + ) + df.loc[np.random.default_rng(2).random(len(df)) > 0.5, "dates2"] = pd.NaT + left_f = getattr(operator, left) + right_f = getattr(operator, right) + + # no nats + if left in ["eq", "ne"]: + expected = left_f(df, pd.Timestamp("20010109")) + result = right_f(pd.Timestamp("20010109"), df) + tm.assert_frame_equal(result, expected) + else: + msg = ( + "'(<|>)=?' not supported between " + "instances of 'numpy.ndarray' and 'Timestamp'" + ) + with pytest.raises(TypeError, match=msg): + left_f(df, pd.Timestamp("20010109")) + with pytest.raises(TypeError, match=msg): + right_f(pd.Timestamp("20010109"), df) + # nats + if left in ["eq", "ne"]: + expected = left_f(df, pd.Timestamp("nat")) + result = right_f(pd.Timestamp("nat"), df) + tm.assert_frame_equal(result, expected) + else: + msg = ( + "'(<|>)=?' not supported between " + "instances of 'numpy.ndarray' and 'NaTType'" + ) + with pytest.raises(TypeError, match=msg): + left_f(df, pd.Timestamp("nat")) + with pytest.raises(TypeError, match=msg): + right_f(pd.Timestamp("nat"), df) + + def test_mixed_comparison(self): + # GH#13128, GH#22163 != datetime64 vs non-dt64 should be False, + # not raise TypeError + # (this appears to be fixed before GH#22163, not sure when) + df = DataFrame([["1989-08-01", 1], ["1989-08-01", 2]]) + other = DataFrame([["a", "b"], ["c", "d"]]) + + result = df == other + assert not result.any().any() + + result = df != other + assert result.all().all() + + def test_df_boolean_comparison_error(self): + # GH#4576, GH#22880 + # comparing DataFrame against list/tuple with len(obj) matching + # len(df.columns) is supported as of GH#22800 + df = DataFrame(np.arange(6).reshape((3, 2))) + + expected = DataFrame([[False, False], [True, False], [False, False]]) + + result = df == (2, 2) + tm.assert_frame_equal(result, expected) + + result = df == [2, 2] + tm.assert_frame_equal(result, expected) + + def test_df_float_none_comparison(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((8, 3)), + index=range(8), + columns=["A", "B", "C"], + ) + + result = df.__eq__(None) + assert not result.any().any() + + def test_df_string_comparison(self): + df = DataFrame([{"a": 1, "b": "foo"}, {"a": 2, "b": "bar"}]) + mask_a = df.a > 1 + tm.assert_frame_equal(df[mask_a], df.loc[1:1, :]) + tm.assert_frame_equal(df[-mask_a], df.loc[0:0, :]) + + mask_b = df.b == "foo" + tm.assert_frame_equal(df[mask_b], df.loc[0:0, :]) + tm.assert_frame_equal(df[-mask_b], df.loc[1:1, :]) + + +class TestFrameFlexComparisons: + # TODO: test_bool_flex_frame needs a better name + @pytest.mark.parametrize("op", ["eq", "ne", "gt", "lt", "ge", "le"]) + def test_bool_flex_frame(self, op): + data = np.random.default_rng(2).standard_normal((5, 3)) + other_data = np.random.default_rng(2).standard_normal((5, 3)) + df = DataFrame(data) + other = DataFrame(other_data) + ndim_5 = np.ones(df.shape + (1, 3)) + + # DataFrame + assert df.eq(df).values.all() + assert not df.ne(df).values.any() + f = getattr(df, op) + o = getattr(operator, op) + # No NAs + tm.assert_frame_equal(f(other), o(df, other)) + # Unaligned + part_o = other.loc[3:, 1:].copy() + rs = f(part_o) + xp = o(df, part_o.reindex(index=df.index, columns=df.columns)) + tm.assert_frame_equal(rs, xp) + # ndarray + tm.assert_frame_equal(f(other.values), o(df, other.values)) + # scalar + tm.assert_frame_equal(f(0), o(df, 0)) + # NAs + msg = "Unable to coerce to Series/DataFrame" + tm.assert_frame_equal(f(np.nan), o(df, np.nan)) + with pytest.raises(ValueError, match=msg): + f(ndim_5) + + @pytest.mark.parametrize("box", [np.array, Series]) + def test_bool_flex_series(self, box): + # Series + # list/tuple + data = np.random.default_rng(2).standard_normal((5, 3)) + df = DataFrame(data) + idx_ser = box(np.random.default_rng(2).standard_normal(5)) + col_ser = box(np.random.default_rng(2).standard_normal(3)) + + idx_eq = df.eq(idx_ser, axis=0) + col_eq = df.eq(col_ser) + idx_ne = df.ne(idx_ser, axis=0) + col_ne = df.ne(col_ser) + tm.assert_frame_equal(col_eq, df == Series(col_ser)) + tm.assert_frame_equal(col_eq, -col_ne) + tm.assert_frame_equal(idx_eq, -idx_ne) + tm.assert_frame_equal(idx_eq, df.T.eq(idx_ser).T) + tm.assert_frame_equal(col_eq, df.eq(list(col_ser))) + tm.assert_frame_equal(idx_eq, df.eq(Series(idx_ser), axis=0)) + tm.assert_frame_equal(idx_eq, df.eq(list(idx_ser), axis=0)) + + idx_gt = df.gt(idx_ser, axis=0) + col_gt = df.gt(col_ser) + idx_le = df.le(idx_ser, axis=0) + col_le = df.le(col_ser) + + tm.assert_frame_equal(col_gt, df > Series(col_ser)) + tm.assert_frame_equal(col_gt, -col_le) + tm.assert_frame_equal(idx_gt, -idx_le) + tm.assert_frame_equal(idx_gt, df.T.gt(idx_ser).T) + + idx_ge = df.ge(idx_ser, axis=0) + col_ge = df.ge(col_ser) + idx_lt = df.lt(idx_ser, axis=0) + col_lt = df.lt(col_ser) + tm.assert_frame_equal(col_ge, df >= Series(col_ser)) + tm.assert_frame_equal(col_ge, -col_lt) + tm.assert_frame_equal(idx_ge, -idx_lt) + tm.assert_frame_equal(idx_ge, df.T.ge(idx_ser).T) + + idx_ser = Series(np.random.default_rng(2).standard_normal(5)) + col_ser = Series(np.random.default_rng(2).standard_normal(3)) + + def test_bool_flex_frame_na(self): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + # NA + df.loc[0, 0] = np.nan + rs = df.eq(df) + assert not rs.loc[0, 0] + rs = df.ne(df) + assert rs.loc[0, 0] + rs = df.gt(df) + assert not rs.loc[0, 0] + rs = df.lt(df) + assert not rs.loc[0, 0] + rs = df.ge(df) + assert not rs.loc[0, 0] + rs = df.le(df) + assert not rs.loc[0, 0] + + def test_bool_flex_frame_complex_dtype(self): + # complex + arr = np.array([np.nan, 1, 6, np.nan]) + arr2 = np.array([2j, np.nan, 7, None]) + df = DataFrame({"a": arr}) + df2 = DataFrame({"a": arr2}) + + msg = "|".join( + [ + "'>' not supported between instances of '.*' and 'complex'", + r"unorderable types: .*complex\(\)", # PY35 + ] + ) + with pytest.raises(TypeError, match=msg): + # inequalities are not well-defined for complex numbers + df.gt(df2) + with pytest.raises(TypeError, match=msg): + # regression test that we get the same behavior for Series + df["a"].gt(df2["a"]) + with pytest.raises(TypeError, match=msg): + # Check that we match numpy behavior here + df.values > df2.values + + rs = df.ne(df2) + assert rs.values.all() + + arr3 = np.array([2j, np.nan, None]) + df3 = DataFrame({"a": arr3}) + + with pytest.raises(TypeError, match=msg): + # inequalities are not well-defined for complex numbers + df3.gt(2j) + with pytest.raises(TypeError, match=msg): + # regression test that we get the same behavior for Series + df3["a"].gt(2j) + with pytest.raises(TypeError, match=msg): + # Check that we match numpy behavior here + df3.values > 2j + + def test_bool_flex_frame_object_dtype(self): + # corner, dtype=object + df1 = DataFrame({"col": ["foo", np.nan, "bar"]}, dtype=object) + df2 = DataFrame({"col": ["foo", datetime.now(), "bar"]}, dtype=object) + result = df1.ne(df2) + exp = DataFrame({"col": [False, True, False]}) + tm.assert_frame_equal(result, exp) + + def test_flex_comparison_nat(self): + # GH 15697, GH 22163 df.eq(pd.NaT) should behave like df == pd.NaT, + # and _definitely_ not be NaN + df = DataFrame([pd.NaT]) + + result = df == pd.NaT + # result.iloc[0, 0] is a np.bool_ object + assert result.iloc[0, 0].item() is False + + result = df.eq(pd.NaT) + assert result.iloc[0, 0].item() is False + + result = df != pd.NaT + assert result.iloc[0, 0].item() is True + + result = df.ne(pd.NaT) + assert result.iloc[0, 0].item() is True + + @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"]) + def test_df_flex_cmp_constant_return_types(self, opname): + # GH 15077, non-empty DataFrame + df = DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]}) + const = 2 + + result = getattr(df, opname)(const).dtypes.value_counts() + tm.assert_series_equal( + result, Series([2], index=[np.dtype(bool)], name="count") + ) + + @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"]) + def test_df_flex_cmp_constant_return_types_empty(self, opname): + # GH 15077 empty DataFrame + df = DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]}) + const = 2 + + empty = df.iloc[:0] + result = getattr(empty, opname)(const).dtypes.value_counts() + tm.assert_series_equal( + result, Series([2], index=[np.dtype(bool)], name="count") + ) + + def test_df_flex_cmp_ea_dtype_with_ndarray_series(self): + ii = pd.IntervalIndex.from_breaks([1, 2, 3]) + df = DataFrame({"A": ii, "B": ii}) + + ser = Series([0, 0]) + res = df.eq(ser, axis=0) + + expected = DataFrame({"A": [False, False], "B": [False, False]}) + tm.assert_frame_equal(res, expected) + + ser2 = Series([1, 2], index=["A", "B"]) + res2 = df.eq(ser2, axis=1) + tm.assert_frame_equal(res2, expected) + + +# ------------------------------------------------------------------- +# Arithmetic + + +class TestFrameFlexArithmetic: + def test_floordiv_axis0(self): + # make sure we df.floordiv(ser, axis=0) matches column-wise result + arr = np.arange(3) + ser = Series(arr) + df = DataFrame({"A": ser, "B": ser}) + + result = df.floordiv(ser, axis=0) + + expected = DataFrame({col: df[col] // ser for col in df.columns}) + + tm.assert_frame_equal(result, expected) + + result2 = df.floordiv(ser.values, axis=0) + tm.assert_frame_equal(result2, expected) + + def test_df_add_td64_columnwise(self): + # GH 22534 Check that column-wise addition broadcasts correctly + dti = pd.date_range("2016-01-01", periods=10) + tdi = pd.timedelta_range("1", periods=10) + tser = Series(tdi) + df = DataFrame({0: dti, 1: tdi}) + + result = df.add(tser, axis=0) + expected = DataFrame({0: dti + tdi, 1: tdi + tdi}) + tm.assert_frame_equal(result, expected) + + def test_df_add_flex_filled_mixed_dtypes(self): + # GH 19611 + dti = pd.date_range("2016-01-01", periods=3) + ser = Series(["1 Day", "NaT", "2 Days"], dtype="timedelta64[ns]") + df = DataFrame({"A": dti, "B": ser}) + other = DataFrame({"A": ser, "B": ser}) + fill = pd.Timedelta(days=1).to_timedelta64() + result = df.add(other, fill_value=fill) + + expected = DataFrame( + { + "A": Series( + ["2016-01-02", "2016-01-03", "2016-01-05"], dtype="datetime64[ns]" + ), + "B": ser * 2, + } + ) + tm.assert_frame_equal(result, expected) + + def test_arith_flex_frame( + self, all_arithmetic_operators, float_frame, mixed_float_frame + ): + # one instance of parametrized fixture + op = all_arithmetic_operators + + def f(x, y): + # r-versions not in operator-stdlib; get op without "r" and invert + if op.startswith("__r"): + return getattr(operator, op.replace("__r", "__"))(y, x) + return getattr(operator, op)(x, y) + + result = getattr(float_frame, op)(2 * float_frame) + expected = f(float_frame, 2 * float_frame) + tm.assert_frame_equal(result, expected) + + # vs mix float + result = getattr(mixed_float_frame, op)(2 * mixed_float_frame) + expected = f(mixed_float_frame, 2 * mixed_float_frame) + tm.assert_frame_equal(result, expected) + _check_mixed_float(result, dtype={"C": None}) + + @pytest.mark.parametrize("op", ["__add__", "__sub__", "__mul__"]) + def test_arith_flex_frame_mixed( + self, + op, + int_frame, + mixed_int_frame, + mixed_float_frame, + switch_numexpr_min_elements, + ): + f = getattr(operator, op) + + # vs mix int + result = getattr(mixed_int_frame, op)(2 + mixed_int_frame) + expected = f(mixed_int_frame, 2 + mixed_int_frame) + + # no overflow in the uint + dtype = None + if op in ["__sub__"]: + dtype = {"B": "uint64", "C": None} + elif op in ["__add__", "__mul__"]: + dtype = {"C": None} + if expr.USE_NUMEXPR and switch_numexpr_min_elements == 0: + # when using numexpr, the casting rules are slightly different: + # in the `2 + mixed_int_frame` operation, int32 column becomes + # and int64 column (not preserving dtype in operation with Python + # scalar), and then the int32/int64 combo results in int64 result + dtype["A"] = (2 + mixed_int_frame)["A"].dtype + tm.assert_frame_equal(result, expected) + _check_mixed_int(result, dtype=dtype) + + # vs mix float + result = getattr(mixed_float_frame, op)(2 * mixed_float_frame) + expected = f(mixed_float_frame, 2 * mixed_float_frame) + tm.assert_frame_equal(result, expected) + _check_mixed_float(result, dtype={"C": None}) + + # vs plain int + result = getattr(int_frame, op)(2 * int_frame) + expected = f(int_frame, 2 * int_frame) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dim", range(3, 6)) + def test_arith_flex_frame_raise(self, all_arithmetic_operators, float_frame, dim): + # one instance of parametrized fixture + op = all_arithmetic_operators + + # Check that arrays with dim >= 3 raise + arr = np.ones((1,) * dim) + msg = "Unable to coerce to Series/DataFrame" + with pytest.raises(ValueError, match=msg): + getattr(float_frame, op)(arr) + + def test_arith_flex_frame_corner(self, float_frame): + const_add = float_frame.add(1) + tm.assert_frame_equal(const_add, float_frame + 1) + + # corner cases + result = float_frame.add(float_frame[:0]) + expected = float_frame.sort_index() * np.nan + tm.assert_frame_equal(result, expected) + + result = float_frame[:0].add(float_frame) + expected = float_frame.sort_index() * np.nan + tm.assert_frame_equal(result, expected) + + with pytest.raises(NotImplementedError, match="fill_value"): + float_frame.add(float_frame.iloc[0], fill_value=3) + + with pytest.raises(NotImplementedError, match="fill_value"): + float_frame.add(float_frame.iloc[0], axis="index", fill_value=3) + + @pytest.mark.parametrize("op", ["add", "sub", "mul", "mod"]) + def test_arith_flex_series_ops(self, simple_frame, op): + # after arithmetic refactor, add truediv here + df = simple_frame + + row = df.xs("a") + col = df["two"] + f = getattr(df, op) + op = getattr(operator, op) + tm.assert_frame_equal(f(row), op(df, row)) + tm.assert_frame_equal(f(col, axis=0), op(df.T, col).T) + + def test_arith_flex_series(self, simple_frame): + df = simple_frame + + row = df.xs("a") + col = df["two"] + # special case for some reason + tm.assert_frame_equal(df.add(row, axis=None), df + row) + + # cases which will be refactored after big arithmetic refactor + tm.assert_frame_equal(df.div(row), df / row) + tm.assert_frame_equal(df.div(col, axis=0), (df.T / col).T) + + @pytest.mark.parametrize("dtype", ["int64", "float64"]) + def test_arith_flex_series_broadcasting(self, dtype): + # broadcasting issue in GH 7325 + df = DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype=dtype) + expected = DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]]) + result = df.div(df[0], axis="index") + tm.assert_frame_equal(result, expected) + + def test_arith_flex_zero_len_raises(self): + # GH 19522 passing fill_value to frame flex arith methods should + # raise even in the zero-length special cases + ser_len0 = Series([], dtype=object) + df_len0 = DataFrame(columns=["A", "B"]) + df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]) + + with pytest.raises(NotImplementedError, match="fill_value"): + df.add(ser_len0, fill_value="E") + + with pytest.raises(NotImplementedError, match="fill_value"): + df_len0.sub(df["A"], axis=None, fill_value=3) + + def test_flex_add_scalar_fill_value(self): + # GH#12723 + dat = np.array([0, 1, np.nan, 3, 4, 5], dtype="float") + df = DataFrame({"foo": dat}, index=range(6)) + + exp = df.fillna(0).add(2) + res = df.add(2, fill_value=0) + tm.assert_frame_equal(res, exp) + + def test_sub_alignment_with_duplicate_index(self): + # GH#5185 dup aligning operations should work + df1 = DataFrame([1, 2, 3, 4, 5], index=[1, 2, 1, 2, 3]) + df2 = DataFrame([1, 2, 3], index=[1, 2, 3]) + expected = DataFrame([0, 2, 0, 2, 2], index=[1, 1, 2, 2, 3]) + result = df1.sub(df2) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("op", ["__add__", "__mul__", "__sub__", "__truediv__"]) + def test_arithmetic_with_duplicate_columns(self, op): + # operations + df = DataFrame({"A": np.arange(10), "B": np.random.default_rng(2).random(10)}) + expected = getattr(df, op)(df) + expected.columns = ["A", "A"] + df.columns = ["A", "A"] + result = getattr(df, op)(df) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("level", [0, None]) + def test_broadcast_multiindex(self, level): + # GH34388 + df1 = DataFrame({"A": [0, 1, 2], "B": [1, 2, 3]}) + df1.columns = df1.columns.set_names("L1") + + df2 = DataFrame({("A", "C"): [0, 0, 0], ("A", "D"): [0, 0, 0]}) + df2.columns = df2.columns.set_names(["L1", "L2"]) + + result = df1.add(df2, level=level) + expected = DataFrame({("A", "C"): [0, 1, 2], ("A", "D"): [0, 1, 2]}) + expected.columns = expected.columns.set_names(["L1", "L2"]) + + tm.assert_frame_equal(result, expected) + + def test_frame_multiindex_operations(self): + # GH 43321 + df = DataFrame( + {2010: [1, 2, 3], 2020: [3, 4, 5]}, + index=MultiIndex.from_product( + [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"] + ), + ) + + series = Series( + [0.4], + index=MultiIndex.from_product([["b"], ["a"]], names=["mod", "scen"]), + ) + + expected = DataFrame( + {2010: [1.4, 2.4, 3.4], 2020: [3.4, 4.4, 5.4]}, + index=MultiIndex.from_product( + [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"] + ), + ) + result = df.add(series, axis=0) + + tm.assert_frame_equal(result, expected) + + def test_frame_multiindex_operations_series_index_to_frame_index(self): + # GH 43321 + df = DataFrame( + {2010: [1], 2020: [3]}, + index=MultiIndex.from_product([["a"], ["b"]], names=["scen", "mod"]), + ) + + series = Series( + [10.0, 20.0, 30.0], + index=MultiIndex.from_product( + [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"] + ), + ) + + expected = DataFrame( + {2010: [11.0, 21, 31.0], 2020: [13.0, 23.0, 33.0]}, + index=MultiIndex.from_product( + [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"] + ), + ) + result = df.add(series, axis=0) + + tm.assert_frame_equal(result, expected) + + def test_frame_multiindex_operations_no_align(self): + df = DataFrame( + {2010: [1, 2, 3], 2020: [3, 4, 5]}, + index=MultiIndex.from_product( + [["a"], ["b"], [0, 1, 2]], names=["scen", "mod", "id"] + ), + ) + + series = Series( + [0.4], + index=MultiIndex.from_product([["c"], ["a"]], names=["mod", "scen"]), + ) + + expected = DataFrame( + {2010: np.nan, 2020: np.nan}, + index=MultiIndex.from_tuples( + [ + ("a", "b", 0), + ("a", "b", 1), + ("a", "b", 2), + ("a", "c", np.nan), + ], + names=["scen", "mod", "id"], + ), + ) + result = df.add(series, axis=0) + + tm.assert_frame_equal(result, expected) + + def test_frame_multiindex_operations_part_align(self): + df = DataFrame( + {2010: [1, 2, 3], 2020: [3, 4, 5]}, + index=MultiIndex.from_tuples( + [ + ("a", "b", 0), + ("a", "b", 1), + ("a", "c", 2), + ], + names=["scen", "mod", "id"], + ), + ) + + series = Series( + [0.4], + index=MultiIndex.from_product([["b"], ["a"]], names=["mod", "scen"]), + ) + + expected = DataFrame( + {2010: [1.4, 2.4, np.nan], 2020: [3.4, 4.4, np.nan]}, + index=MultiIndex.from_tuples( + [ + ("a", "b", 0), + ("a", "b", 1), + ("a", "c", 2), + ], + names=["scen", "mod", "id"], + ), + ) + result = df.add(series, axis=0) + + tm.assert_frame_equal(result, expected) + + +class TestFrameArithmetic: + def test_td64_op_nat_casting(self): + # Make sure we don't accidentally treat timedelta64(NaT) as datetime64 + # when calling dispatch_to_series in DataFrame arithmetic + ser = Series(["NaT", "NaT"], dtype="timedelta64[ns]") + df = DataFrame([[1, 2], [3, 4]]) + + result = df * ser + expected = DataFrame({0: ser, 1: ser}) + tm.assert_frame_equal(result, expected) + + def test_df_add_2d_array_rowlike_broadcasts(self): + # GH#23000 + arr = np.arange(6).reshape(3, 2) + df = DataFrame(arr, columns=[True, False], index=["A", "B", "C"]) + + rowlike = arr[[1], :] # shape --> (1, ncols) + assert rowlike.shape == (1, df.shape[1]) + + expected = DataFrame( + [[2, 4], [4, 6], [6, 8]], + columns=df.columns, + index=df.index, + # specify dtype explicitly to avoid failing + # on 32bit builds + dtype=arr.dtype, + ) + result = df + rowlike + tm.assert_frame_equal(result, expected) + result = rowlike + df + tm.assert_frame_equal(result, expected) + + def test_df_add_2d_array_collike_broadcasts(self): + # GH#23000 + arr = np.arange(6).reshape(3, 2) + df = DataFrame(arr, columns=[True, False], index=["A", "B", "C"]) + + collike = arr[:, [1]] # shape --> (nrows, 1) + assert collike.shape == (df.shape[0], 1) + + expected = DataFrame( + [[1, 2], [5, 6], [9, 10]], + columns=df.columns, + index=df.index, + # specify dtype explicitly to avoid failing + # on 32bit builds + dtype=arr.dtype, + ) + result = df + collike + tm.assert_frame_equal(result, expected) + result = collike + df + tm.assert_frame_equal(result, expected) + + def test_df_arith_2d_array_rowlike_broadcasts( + self, request, all_arithmetic_operators, using_array_manager + ): + # GH#23000 + opname = all_arithmetic_operators + + if using_array_manager and opname in ("__rmod__", "__rfloordiv__"): + # TODO(ArrayManager) decide on dtypes + td.mark_array_manager_not_yet_implemented(request) + + arr = np.arange(6).reshape(3, 2) + df = DataFrame(arr, columns=[True, False], index=["A", "B", "C"]) + + rowlike = arr[[1], :] # shape --> (1, ncols) + assert rowlike.shape == (1, df.shape[1]) + + exvals = [ + getattr(df.loc["A"], opname)(rowlike.squeeze()), + getattr(df.loc["B"], opname)(rowlike.squeeze()), + getattr(df.loc["C"], opname)(rowlike.squeeze()), + ] + + expected = DataFrame(exvals, columns=df.columns, index=df.index) + + result = getattr(df, opname)(rowlike) + tm.assert_frame_equal(result, expected) + + def test_df_arith_2d_array_collike_broadcasts( + self, request, all_arithmetic_operators, using_array_manager + ): + # GH#23000 + opname = all_arithmetic_operators + + if using_array_manager and opname in ("__rmod__", "__rfloordiv__"): + # TODO(ArrayManager) decide on dtypes + td.mark_array_manager_not_yet_implemented(request) + + arr = np.arange(6).reshape(3, 2) + df = DataFrame(arr, columns=[True, False], index=["A", "B", "C"]) + + collike = arr[:, [1]] # shape --> (nrows, 1) + assert collike.shape == (df.shape[0], 1) + + exvals = { + True: getattr(df[True], opname)(collike.squeeze()), + False: getattr(df[False], opname)(collike.squeeze()), + } + + dtype = None + if opname in ["__rmod__", "__rfloordiv__"]: + # Series ops may return mixed int/float dtypes in cases where + # DataFrame op will return all-float. So we upcast `expected` + dtype = np.common_type(*(x.values for x in exvals.values())) + + expected = DataFrame(exvals, columns=df.columns, index=df.index, dtype=dtype) + + result = getattr(df, opname)(collike) + tm.assert_frame_equal(result, expected) + + def test_df_bool_mul_int(self): + # GH 22047, GH 22163 multiplication by 1 should result in int dtype, + # not object dtype + df = DataFrame([[False, True], [False, False]]) + result = df * 1 + + # On appveyor this comes back as np.int32 instead of np.int64, + # so we check dtype.kind instead of just dtype + kinds = result.dtypes.apply(lambda x: x.kind) + assert (kinds == "i").all() + + result = 1 * df + kinds = result.dtypes.apply(lambda x: x.kind) + assert (kinds == "i").all() + + def test_arith_mixed(self): + left = DataFrame({"A": ["a", "b", "c"], "B": [1, 2, 3]}) + + result = left + left + expected = DataFrame({"A": ["aa", "bb", "cc"], "B": [2, 4, 6]}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("col", ["A", "B"]) + def test_arith_getitem_commute(self, all_arithmetic_functions, col): + df = DataFrame({"A": [1.1, 3.3], "B": [2.5, -3.9]}) + result = all_arithmetic_functions(df, 1)[col] + expected = all_arithmetic_functions(df[col], 1) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "values", [[1, 2], (1, 2), np.array([1, 2]), range(1, 3), deque([1, 2])] + ) + def test_arith_alignment_non_pandas_object(self, values): + # GH#17901 + df = DataFrame({"A": [1, 1], "B": [1, 1]}) + expected = DataFrame({"A": [2, 2], "B": [3, 3]}) + result = df + values + tm.assert_frame_equal(result, expected) + + def test_arith_non_pandas_object(self): + df = DataFrame( + np.arange(1, 10, dtype="f8").reshape(3, 3), + columns=["one", "two", "three"], + index=["a", "b", "c"], + ) + + val1 = df.xs("a").values + added = DataFrame(df.values + val1, index=df.index, columns=df.columns) + tm.assert_frame_equal(df + val1, added) + + added = DataFrame((df.values.T + val1).T, index=df.index, columns=df.columns) + tm.assert_frame_equal(df.add(val1, axis=0), added) + + val2 = list(df["two"]) + + added = DataFrame(df.values + val2, index=df.index, columns=df.columns) + tm.assert_frame_equal(df + val2, added) + + added = DataFrame((df.values.T + val2).T, index=df.index, columns=df.columns) + tm.assert_frame_equal(df.add(val2, axis="index"), added) + + val3 = np.random.default_rng(2).random(df.shape) + added = DataFrame(df.values + val3, index=df.index, columns=df.columns) + tm.assert_frame_equal(df.add(val3), added) + + def test_operations_with_interval_categories_index(self, all_arithmetic_operators): + # GH#27415 + op = all_arithmetic_operators + ind = pd.CategoricalIndex(pd.interval_range(start=0.0, end=2.0)) + data = [1, 2] + df = DataFrame([data], columns=ind) + num = 10 + result = getattr(df, op)(num) + expected = DataFrame([[getattr(n, op)(num) for n in data]], columns=ind) + tm.assert_frame_equal(result, expected) + + def test_frame_with_frame_reindex(self): + # GH#31623 + df = DataFrame( + { + "foo": [pd.Timestamp("2019"), pd.Timestamp("2020")], + "bar": [pd.Timestamp("2018"), pd.Timestamp("2021")], + }, + columns=["foo", "bar"], + dtype="M8[ns]", + ) + df2 = df[["foo"]] + + result = df - df2 + + expected = DataFrame( + {"foo": [pd.Timedelta(0), pd.Timedelta(0)], "bar": [np.nan, np.nan]}, + columns=["bar", "foo"], + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "value, dtype", + [ + (1, "i8"), + (1.0, "f8"), + (2**63, "f8"), + (1j, "complex128"), + (2**63, "complex128"), + (True, "bool"), + (np.timedelta64(20, "ns"), "]=?' not supported between instances of 'str' and 'int'", + "Invalid comparison between dtype=str and int", + ] + ) + with pytest.raises(TypeError, match=msg): + f(df, 0) + + def test_comparison_protected_from_errstate(self): + missing_df = DataFrame( + np.ones((10, 4), dtype=np.float64), + columns=Index(list("ABCD"), dtype=object), + ) + missing_df.loc[missing_df.index[0], "A"] = np.nan + with np.errstate(invalid="ignore"): + expected = missing_df.values < 0 + with np.errstate(invalid="raise"): + result = (missing_df < 0).values + tm.assert_numpy_array_equal(result, expected) + + def test_boolean_comparison(self): + # GH 4576 + # boolean comparisons with a tuple/list give unexpected results + df = DataFrame(np.arange(6).reshape((3, 2))) + b = np.array([2, 2]) + b_r = np.atleast_2d([2, 2]) + b_c = b_r.T + lst = [2, 2, 2] + tup = tuple(lst) + + # gt + expected = DataFrame([[False, False], [False, True], [True, True]]) + result = df > b + tm.assert_frame_equal(result, expected) + + result = df.values > b + tm.assert_numpy_array_equal(result, expected.values) + + msg1d = "Unable to coerce to Series, length must be 2: given 3" + msg2d = "Unable to coerce to DataFrame, shape must be" + msg2db = "operands could not be broadcast together with shapes" + with pytest.raises(ValueError, match=msg1d): + # wrong shape + df > lst + + with pytest.raises(ValueError, match=msg1d): + # wrong shape + df > tup + + # broadcasts like ndarray (GH#23000) + result = df > b_r + tm.assert_frame_equal(result, expected) + + result = df.values > b_r + tm.assert_numpy_array_equal(result, expected.values) + + with pytest.raises(ValueError, match=msg2d): + df > b_c + + with pytest.raises(ValueError, match=msg2db): + df.values > b_c + + # == + expected = DataFrame([[False, False], [True, False], [False, False]]) + result = df == b + tm.assert_frame_equal(result, expected) + + with pytest.raises(ValueError, match=msg1d): + df == lst + + with pytest.raises(ValueError, match=msg1d): + df == tup + + # broadcasts like ndarray (GH#23000) + result = df == b_r + tm.assert_frame_equal(result, expected) + + result = df.values == b_r + tm.assert_numpy_array_equal(result, expected.values) + + with pytest.raises(ValueError, match=msg2d): + df == b_c + + assert df.values.shape != b_c.shape + + # with alignment + df = DataFrame( + np.arange(6).reshape((3, 2)), columns=list("AB"), index=list("abc") + ) + expected.index = df.index + expected.columns = df.columns + + with pytest.raises(ValueError, match=msg1d): + df == lst + + with pytest.raises(ValueError, match=msg1d): + df == tup + + def test_inplace_ops_alignment(self): + # inplace ops / ops alignment + # GH 8511 + + columns = list("abcdefg") + X_orig = DataFrame( + np.arange(10 * len(columns)).reshape(-1, len(columns)), + columns=columns, + index=range(10), + ) + Z = 100 * X_orig.iloc[:, 1:-1].copy() + block1 = list("bedcf") + subs = list("bcdef") + + # add + X = X_orig.copy() + result1 = (X[block1] + Z).reindex(columns=subs) + + X[block1] += Z + result2 = X.reindex(columns=subs) + + X = X_orig.copy() + result3 = (X[block1] + Z[block1]).reindex(columns=subs) + + X[block1] += Z[block1] + result4 = X.reindex(columns=subs) + + tm.assert_frame_equal(result1, result2) + tm.assert_frame_equal(result1, result3) + tm.assert_frame_equal(result1, result4) + + # sub + X = X_orig.copy() + result1 = (X[block1] - Z).reindex(columns=subs) + + X[block1] -= Z + result2 = X.reindex(columns=subs) + + X = X_orig.copy() + result3 = (X[block1] - Z[block1]).reindex(columns=subs) + + X[block1] -= Z[block1] + result4 = X.reindex(columns=subs) + + tm.assert_frame_equal(result1, result2) + tm.assert_frame_equal(result1, result3) + tm.assert_frame_equal(result1, result4) + + def test_inplace_ops_identity(self): + # GH 5104 + # make sure that we are actually changing the object + s_orig = Series([1, 2, 3]) + df_orig = DataFrame( + np.random.default_rng(2).integers(0, 5, size=10).reshape(-1, 5) + ) + + # no dtype change + s = s_orig.copy() + s2 = s + s += 1 + tm.assert_series_equal(s, s2) + tm.assert_series_equal(s_orig + 1, s) + assert s is s2 + assert s._mgr is s2._mgr + + df = df_orig.copy() + df2 = df + df += 1 + tm.assert_frame_equal(df, df2) + tm.assert_frame_equal(df_orig + 1, df) + assert df is df2 + assert df._mgr is df2._mgr + + # dtype change + s = s_orig.copy() + s2 = s + s += 1.5 + tm.assert_series_equal(s, s2) + tm.assert_series_equal(s_orig + 1.5, s) + + df = df_orig.copy() + df2 = df + df += 1.5 + tm.assert_frame_equal(df, df2) + tm.assert_frame_equal(df_orig + 1.5, df) + assert df is df2 + assert df._mgr is df2._mgr + + # mixed dtype + arr = np.random.default_rng(2).integers(0, 10, size=5) + df_orig = DataFrame({"A": arr.copy(), "B": "foo"}) + df = df_orig.copy() + df2 = df + df["A"] += 1 + expected = DataFrame({"A": arr.copy() + 1, "B": "foo"}) + tm.assert_frame_equal(df, expected) + tm.assert_frame_equal(df2, expected) + assert df._mgr is df2._mgr + + df = df_orig.copy() + df2 = df + df["A"] += 1.5 + expected = DataFrame({"A": arr.copy() + 1.5, "B": "foo"}) + tm.assert_frame_equal(df, expected) + tm.assert_frame_equal(df2, expected) + assert df._mgr is df2._mgr + + @pytest.mark.parametrize( + "op", + [ + "add", + "and", + pytest.param( + "div", + marks=pytest.mark.xfail( + raises=AttributeError, reason="__idiv__ not implemented" + ), + ), + "floordiv", + "mod", + "mul", + "or", + "pow", + "sub", + "truediv", + "xor", + ], + ) + def test_inplace_ops_identity2(self, op): + df = DataFrame({"a": [1.0, 2.0, 3.0], "b": [1, 2, 3]}) + + operand = 2 + if op in ("and", "or", "xor"): + # cannot use floats for boolean ops + df["a"] = [True, False, True] + + df_copy = df.copy() + iop = f"__i{op}__" + op = f"__{op}__" + + # no id change and value is correct + getattr(df, iop)(operand) + expected = getattr(df_copy, op)(operand) + tm.assert_frame_equal(df, expected) + expected = id(df) + assert id(df) == expected + + @pytest.mark.parametrize( + "val", + [ + [1, 2, 3], + (1, 2, 3), + np.array([1, 2, 3], dtype=np.int64), + range(1, 4), + ], + ) + def test_alignment_non_pandas(self, val): + index = ["A", "B", "C"] + columns = ["X", "Y", "Z"] + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + index=index, + columns=columns, + ) + + align = DataFrame._align_for_op + + expected = DataFrame({"X": val, "Y": val, "Z": val}, index=df.index) + tm.assert_frame_equal(align(df, val, axis=0)[1], expected) + + expected = DataFrame( + {"X": [1, 1, 1], "Y": [2, 2, 2], "Z": [3, 3, 3]}, index=df.index + ) + tm.assert_frame_equal(align(df, val, axis=1)[1], expected) + + @pytest.mark.parametrize("val", [[1, 2], (1, 2), np.array([1, 2]), range(1, 3)]) + def test_alignment_non_pandas_length_mismatch(self, val): + index = ["A", "B", "C"] + columns = ["X", "Y", "Z"] + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + index=index, + columns=columns, + ) + + align = DataFrame._align_for_op + # length mismatch + msg = "Unable to coerce to Series, length must be 3: given 2" + with pytest.raises(ValueError, match=msg): + align(df, val, axis=0) + + with pytest.raises(ValueError, match=msg): + align(df, val, axis=1) + + def test_alignment_non_pandas_index_columns(self): + index = ["A", "B", "C"] + columns = ["X", "Y", "Z"] + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + index=index, + columns=columns, + ) + + align = DataFrame._align_for_op + val = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) + tm.assert_frame_equal( + align(df, val, axis=0)[1], + DataFrame(val, index=df.index, columns=df.columns), + ) + tm.assert_frame_equal( + align(df, val, axis=1)[1], + DataFrame(val, index=df.index, columns=df.columns), + ) + + # shape mismatch + msg = "Unable to coerce to DataFrame, shape must be" + val = np.array([[1, 2, 3], [4, 5, 6]]) + with pytest.raises(ValueError, match=msg): + align(df, val, axis=0) + + with pytest.raises(ValueError, match=msg): + align(df, val, axis=1) + + val = np.zeros((3, 3, 3)) + msg = re.escape( + "Unable to coerce to Series/DataFrame, dimension must be <= 2: (3, 3, 3)" + ) + with pytest.raises(ValueError, match=msg): + align(df, val, axis=0) + with pytest.raises(ValueError, match=msg): + align(df, val, axis=1) + + def test_no_warning(self, all_arithmetic_operators): + df = DataFrame({"A": [0.0, 0.0], "B": [0.0, None]}) + b = df["B"] + with tm.assert_produces_warning(None): + getattr(df, all_arithmetic_operators)(b) + + def test_dunder_methods_binary(self, all_arithmetic_operators): + # GH#??? frame.__foo__ should only accept one argument + df = DataFrame({"A": [0.0, 0.0], "B": [0.0, None]}) + b = df["B"] + with pytest.raises(TypeError, match="takes 2 positional arguments"): + getattr(df, all_arithmetic_operators)(b, 0) + + def test_align_int_fill_bug(self): + # GH#910 + X = np.arange(10 * 10, dtype="float64").reshape(10, 10) + Y = np.ones((10, 1), dtype=int) + + df1 = DataFrame(X) + df1["0.X"] = Y.squeeze() + + df2 = df1.astype(float) + + result = df1 - df1.mean() + expected = df2 - df2.mean() + tm.assert_frame_equal(result, expected) + + +def test_pow_with_realignment(): + # GH#32685 pow has special semantics for operating with null values + left = DataFrame({"A": [0, 1, 2]}) + right = DataFrame(index=[0, 1, 2]) + + result = left**right + expected = DataFrame({"A": [np.nan, 1.0, np.nan]}) + tm.assert_frame_equal(result, expected) + + +def test_dataframe_series_extension_dtypes(): + # https://github.com/pandas-dev/pandas/issues/34311 + df = DataFrame( + np.random.default_rng(2).integers(0, 100, (10, 3)), columns=["a", "b", "c"] + ) + ser = Series([1, 2, 3], index=["a", "b", "c"]) + + expected = df.to_numpy("int64") + ser.to_numpy("int64").reshape(-1, 3) + expected = DataFrame(expected, columns=df.columns, dtype="Int64") + + df_ea = df.astype("Int64") + result = df_ea + ser + tm.assert_frame_equal(result, expected) + result = df_ea + ser.astype("Int64") + tm.assert_frame_equal(result, expected) + + +def test_dataframe_blockwise_slicelike(): + # GH#34367 + arr = np.random.default_rng(2).integers(0, 1000, (100, 10)) + df1 = DataFrame(arr) + # Explicit cast to float to avoid implicit cast when setting nan + df2 = df1.copy().astype({1: "float", 3: "float", 7: "float"}) + df2.iloc[0, [1, 3, 7]] = np.nan + + # Explicit cast to float to avoid implicit cast when setting nan + df3 = df1.copy().astype({5: "float"}) + df3.iloc[0, [5]] = np.nan + + # Explicit cast to float to avoid implicit cast when setting nan + df4 = df1.copy().astype({2: "float", 3: "float", 4: "float"}) + df4.iloc[0, np.arange(2, 5)] = np.nan + # Explicit cast to float to avoid implicit cast when setting nan + df5 = df1.copy().astype({4: "float", 5: "float", 6: "float"}) + df5.iloc[0, np.arange(4, 7)] = np.nan + + for left, right in [(df1, df2), (df2, df3), (df4, df5)]: + res = left + right + + expected = DataFrame({i: left[i] + right[i] for i in left.columns}) + tm.assert_frame_equal(res, expected) + + +@pytest.mark.parametrize( + "df, col_dtype", + [ + (DataFrame([[1.0, 2.0], [4.0, 5.0]], columns=list("ab")), "float64"), + ( + DataFrame([[1.0, "b"], [4.0, "b"]], columns=list("ab")).astype( + {"b": object} + ), + "object", + ), + ], +) +def test_dataframe_operation_with_non_numeric_types(df, col_dtype): + # GH #22663 + expected = DataFrame([[0.0, np.nan], [3.0, np.nan]], columns=list("ab")) + expected = expected.astype({"b": col_dtype}) + result = df + Series([-1.0], index=list("a")) + tm.assert_frame_equal(result, expected) + + +def test_arith_reindex_with_duplicates(): + # https://github.com/pandas-dev/pandas/issues/35194 + df1 = DataFrame(data=[[0]], columns=["second"]) + df2 = DataFrame(data=[[0, 0, 0]], columns=["first", "second", "second"]) + result = df1 + df2 + expected = DataFrame([[np.nan, 0, 0]], columns=["first", "second", "second"]) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("to_add", [[Series([1, 1])], [Series([1, 1]), Series([1, 1])]]) +def test_arith_list_of_arraylike_raise(to_add): + # GH 36702. Raise when trying to add list of array-like to DataFrame + df = DataFrame({"x": [1, 2], "y": [1, 2]}) + + msg = f"Unable to coerce list of {type(to_add[0])} to Series/DataFrame" + with pytest.raises(ValueError, match=msg): + df + to_add + with pytest.raises(ValueError, match=msg): + to_add + df + + +def test_inplace_arithmetic_series_update(using_copy_on_write, warn_copy_on_write): + # https://github.com/pandas-dev/pandas/issues/36373 + df = DataFrame({"A": [1, 2, 3]}) + df_orig = df.copy() + series = df["A"] + vals = series._values + + with tm.assert_cow_warning(warn_copy_on_write): + series += 1 + if using_copy_on_write: + assert series._values is not vals + tm.assert_frame_equal(df, df_orig) + else: + assert series._values is vals + + expected = DataFrame({"A": [2, 3, 4]}) + tm.assert_frame_equal(df, expected) + + +def test_arithmetic_multiindex_align(): + """ + Regression test for: https://github.com/pandas-dev/pandas/issues/33765 + """ + df1 = DataFrame( + [[1]], + index=["a"], + columns=MultiIndex.from_product([[0], [1]], names=["a", "b"]), + ) + df2 = DataFrame([[1]], index=["a"], columns=Index([0], name="a")) + expected = DataFrame( + [[0]], + index=["a"], + columns=MultiIndex.from_product([[0], [1]], names=["a", "b"]), + ) + result = df1 - df2 + tm.assert_frame_equal(result, expected) + + +def test_bool_frame_mult_float(): + # GH 18549 + df = DataFrame(True, list("ab"), list("cd")) + result = df * 1.0 + expected = DataFrame(np.ones((2, 2)), list("ab"), list("cd")) + tm.assert_frame_equal(result, expected) + + +def test_frame_sub_nullable_int(any_int_ea_dtype): + # GH 32822 + series1 = Series([1, 2, None], dtype=any_int_ea_dtype) + series2 = Series([1, 2, 3], dtype=any_int_ea_dtype) + expected = DataFrame([0, 0, None], dtype=any_int_ea_dtype) + result = series1.to_frame() - series2.to_frame() + tm.assert_frame_equal(result, expected) + + +@pytest.mark.filterwarnings( + "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning" +) +def test_frame_op_subclass_nonclass_constructor(): + # GH#43201 subclass._constructor is a function, not the subclass itself + + class SubclassedSeries(Series): + @property + def _constructor(self): + return SubclassedSeries + + @property + def _constructor_expanddim(self): + return SubclassedDataFrame + + class SubclassedDataFrame(DataFrame): + _metadata = ["my_extra_data"] + + def __init__(self, my_extra_data, *args, **kwargs) -> None: + self.my_extra_data = my_extra_data + super().__init__(*args, **kwargs) + + @property + def _constructor(self): + return functools.partial(type(self), self.my_extra_data) + + @property + def _constructor_sliced(self): + return SubclassedSeries + + sdf = SubclassedDataFrame("some_data", {"A": [1, 2, 3], "B": [4, 5, 6]}) + result = sdf * 2 + expected = SubclassedDataFrame("some_data", {"A": [2, 4, 6], "B": [8, 10, 12]}) + tm.assert_frame_equal(result, expected) + + result = sdf + sdf + tm.assert_frame_equal(result, expected) + + +def test_enum_column_equality(): + Cols = Enum("Cols", "col1 col2") + + q1 = DataFrame({Cols.col1: [1, 2, 3]}) + q2 = DataFrame({Cols.col1: [1, 2, 3]}) + + result = q1[Cols.col1] == q2[Cols.col1] + expected = Series([True, True, True], name=Cols.col1) + + tm.assert_series_equal(result, expected) + + +def test_mixed_col_index_dtype(using_infer_string): + # GH 47382 + df1 = DataFrame(columns=list("abc"), data=1.0, index=[0]) + df2 = DataFrame(columns=list("abc"), data=0.0, index=[0]) + df1.columns = df2.columns.astype("string") + result = df1 + df2 + expected = DataFrame(columns=list("abc"), data=1.0, index=[0]) + if using_infer_string: + # df2.columns.dtype will be "str" instead of object, + # so the aligned result will be "string", not object + if HAS_PYARROW: + dtype = "string[pyarrow]" + else: + dtype = "string" + expected.columns = expected.columns.astype(dtype) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_arrow_interface.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_arrow_interface.py new file mode 100644 index 0000000000000000000000000000000000000000..b36b6b5ffe0cc3f843243a09cb08a2b74d6b728f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_arrow_interface.py @@ -0,0 +1,47 @@ +import ctypes + +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd + +pa = pytest.importorskip("pyarrow") + + +@td.skip_if_no("pyarrow", min_version="14.0") +def test_dataframe_arrow_interface(using_infer_string): + df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}) + + capsule = df.__arrow_c_stream__() + assert ( + ctypes.pythonapi.PyCapsule_IsValid( + ctypes.py_object(capsule), b"arrow_array_stream" + ) + == 1 + ) + + table = pa.table(df) + string_type = pa.large_string() if using_infer_string else pa.string() + expected = pa.table({"a": [1, 2, 3], "b": pa.array(["a", "b", "c"], string_type)}) + assert table.equals(expected) + + schema = pa.schema([("a", pa.int8()), ("b", pa.string())]) + table = pa.table(df, schema=schema) + expected = expected.cast(schema) + assert table.equals(expected) + + +@td.skip_if_no("pyarrow", min_version="15.0") +def test_dataframe_to_arrow(using_infer_string): + df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]}) + + table = pa.RecordBatchReader.from_stream(df).read_all() + string_type = pa.large_string() if using_infer_string else pa.string() + expected = pa.table({"a": [1, 2, 3], "b": pa.array(["a", "b", "c"], string_type)}) + assert table.equals(expected) + + schema = pa.schema([("a", pa.int8()), ("b", pa.string())]) + table = pa.RecordBatchReader.from_stream(df, schema=schema).read_all() + expected = expected.cast(schema) + assert table.equals(expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_block_internals.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_block_internals.py new file mode 100644 index 0000000000000000000000000000000000000000..b2fcba50de0972c1d101da1c2180c7516d507e0d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_block_internals.py @@ -0,0 +1,449 @@ +from datetime import ( + datetime, + timedelta, +) +import itertools + +import numpy as np +import pytest + +from pandas.errors import PerformanceWarning +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + Series, + Timestamp, + date_range, + option_context, +) +import pandas._testing as tm +from pandas.core.internals.blocks import NumpyBlock + +# Segregated collection of methods that require the BlockManager internal data +# structure + + +# TODO(ArrayManager) check which of those tests need to be rewritten to test the +# equivalent for ArrayManager +pytestmark = td.skip_array_manager_invalid_test + + +class TestDataFrameBlockInternals: + def test_setitem_invalidates_datetime_index_freq(self): + # GH#24096 altering a datetime64tz column inplace invalidates the + # `freq` attribute on the underlying DatetimeIndex + + dti = date_range("20130101", periods=3, tz="US/Eastern") + ts = dti[1] + + df = DataFrame({"B": dti}) + assert df["B"]._values.freq is None + + df.iloc[1, 0] = pd.NaT + assert df["B"]._values.freq is None + + # check that the DatetimeIndex was not altered in place + assert dti.freq == "D" + assert dti[1] == ts + + def test_cast_internals(self, float_frame): + msg = "Passing a BlockManager to DataFrame" + with tm.assert_produces_warning( + DeprecationWarning, match=msg, check_stacklevel=False + ): + casted = DataFrame(float_frame._mgr, dtype=int) + expected = DataFrame(float_frame._series, dtype=int) + tm.assert_frame_equal(casted, expected) + + with tm.assert_produces_warning( + DeprecationWarning, match=msg, check_stacklevel=False + ): + casted = DataFrame(float_frame._mgr, dtype=np.int32) + expected = DataFrame(float_frame._series, dtype=np.int32) + tm.assert_frame_equal(casted, expected) + + def test_consolidate(self, float_frame): + float_frame["E"] = 7.0 + consolidated = float_frame._consolidate() + assert len(consolidated._mgr.blocks) == 1 + + # Ensure copy, do I want this? + recons = consolidated._consolidate() + assert recons is not consolidated + tm.assert_frame_equal(recons, consolidated) + + float_frame["F"] = 8.0 + assert len(float_frame._mgr.blocks) == 3 + + return_value = float_frame._consolidate_inplace() + assert return_value is None + assert len(float_frame._mgr.blocks) == 1 + + def test_consolidate_inplace(self, float_frame): + # triggers in-place consolidation + for letter in range(ord("A"), ord("Z")): + float_frame[chr(letter)] = chr(letter) + + def test_modify_values(self, float_frame, using_copy_on_write): + if using_copy_on_write: + with pytest.raises(ValueError, match="read-only"): + float_frame.values[5] = 5 + assert (float_frame.values[5] != 5).all() + return + + float_frame.values[5] = 5 + assert (float_frame.values[5] == 5).all() + + # unconsolidated + float_frame["E"] = 7.0 + col = float_frame["E"] + float_frame.values[6] = 6 + # as of 2.0 .values does not consolidate, so subsequent calls to .values + # does not share data + assert not (float_frame.values[6] == 6).all() + + assert (col == 7).all() + + def test_boolean_set_uncons(self, float_frame): + float_frame["E"] = 7.0 + + expected = float_frame.values.copy() + expected[expected > 1] = 2 + + float_frame[float_frame > 1] = 2 + tm.assert_almost_equal(expected, float_frame.values) + + def test_constructor_with_convert(self): + # this is actually mostly a test of lib.maybe_convert_objects + # #2845 + df = DataFrame({"A": [2**63 - 1]}) + result = df["A"] + expected = Series(np.asarray([2**63 - 1], np.int64), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [2**63]}) + result = df["A"] + expected = Series(np.asarray([2**63], np.uint64), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [datetime(2005, 1, 1), True]}) + result = df["A"] + expected = Series( + np.asarray([datetime(2005, 1, 1), True], np.object_), name="A" + ) + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [None, 1]}) + result = df["A"] + expected = Series(np.asarray([np.nan, 1], np.float64), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [1.0, 2]}) + result = df["A"] + expected = Series(np.asarray([1.0, 2], np.float64), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [1.0 + 2.0j, 3]}) + result = df["A"] + expected = Series(np.asarray([1.0 + 2.0j, 3], np.complex128), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [1.0 + 2.0j, 3.0]}) + result = df["A"] + expected = Series(np.asarray([1.0 + 2.0j, 3.0], np.complex128), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [1.0 + 2.0j, True]}) + result = df["A"] + expected = Series(np.asarray([1.0 + 2.0j, True], np.object_), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [1.0, None]}) + result = df["A"] + expected = Series(np.asarray([1.0, np.nan], np.float64), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [1.0 + 2.0j, None]}) + result = df["A"] + expected = Series(np.asarray([1.0 + 2.0j, np.nan], np.complex128), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [2.0, 1, True, None]}) + result = df["A"] + expected = Series(np.asarray([2.0, 1, True, None], np.object_), name="A") + tm.assert_series_equal(result, expected) + + df = DataFrame({"A": [2.0, 1, datetime(2006, 1, 1), None]}) + result = df["A"] + expected = Series( + np.asarray([2.0, 1, datetime(2006, 1, 1), None], np.object_), name="A" + ) + tm.assert_series_equal(result, expected) + + def test_construction_with_mixed(self, float_string_frame, using_infer_string): + # mixed-type frames + float_string_frame["datetime"] = datetime.now() + float_string_frame["timedelta"] = timedelta(days=1, seconds=1) + assert float_string_frame["datetime"].dtype == "M8[us]" + assert float_string_frame["timedelta"].dtype == "m8[us]" + result = float_string_frame.dtypes + expected = Series( + [np.dtype("float64")] * 4 + + [ + np.dtype("object") + if not using_infer_string + else pd.StringDtype(na_value=np.nan), + np.dtype("datetime64[us]"), + np.dtype("timedelta64[us]"), + ], + index=list("ABCD") + ["foo", "datetime", "timedelta"], + ) + tm.assert_series_equal(result, expected) + + def test_construction_with_conversions(self): + # convert from a numpy array of non-ns timedelta64; as of 2.0 this does + # *not* convert + arr = np.array([1, 2, 3], dtype="timedelta64[s]") + df = DataFrame({"A": arr}) + expected = DataFrame( + {"A": pd.timedelta_range("00:00:01", periods=3, freq="s")}, index=range(3) + ) + tm.assert_numpy_array_equal(df["A"].to_numpy(), arr) + + expected = DataFrame( + { + "dt1": Timestamp("20130101"), + "dt2": date_range("20130101", periods=3).astype("M8[s]"), + # 'dt3' : date_range('20130101 00:00:01',periods=3,freq='s'), + # FIXME: don't leave commented-out + }, + index=range(3), + ) + assert expected.dtypes["dt1"] == "M8[s]" + assert expected.dtypes["dt2"] == "M8[s]" + + dt1 = np.datetime64("2013-01-01") + dt2 = np.array( + ["2013-01-01", "2013-01-02", "2013-01-03"], dtype="datetime64[D]" + ) + df = DataFrame({"dt1": dt1, "dt2": dt2}) + + # df['dt3'] = np.array(['2013-01-01 00:00:01','2013-01-01 + # 00:00:02','2013-01-01 00:00:03'],dtype='datetime64[s]') + # FIXME: don't leave commented-out + + tm.assert_frame_equal(df, expected) + + def test_constructor_compound_dtypes(self): + # GH 5191 + # compound dtypes should raise not-implementederror + + def f(dtype): + data = list(itertools.repeat((datetime(2001, 1, 1), "aa", 20), 9)) + return DataFrame(data=data, columns=["A", "B", "C"], dtype=dtype) + + msg = "compound dtypes are not implemented in the DataFrame constructor" + with pytest.raises(NotImplementedError, match=msg): + f([("A", "datetime64[h]"), ("B", "str"), ("C", "int32")]) + + # pre-2.0 these used to work (though results may be unexpected) + with pytest.raises(TypeError, match="argument must be"): + f("int64") + with pytest.raises(TypeError, match="argument must be"): + f("float64") + + # 10822 + msg = "^Unknown datetime string format, unable to parse: aa, at position 0$" + with pytest.raises(ValueError, match=msg): + f("M8[ns]") + + def test_pickle(self, float_string_frame, timezone_frame): + empty_frame = DataFrame() + + unpickled = tm.round_trip_pickle(float_string_frame) + tm.assert_frame_equal(float_string_frame, unpickled) + + # buglet + float_string_frame._mgr.ndim + + # empty + unpickled = tm.round_trip_pickle(empty_frame) + repr(unpickled) + + # tz frame + unpickled = tm.round_trip_pickle(timezone_frame) + tm.assert_frame_equal(timezone_frame, unpickled) + + def test_consolidate_datetime64(self): + # numpy vstack bug + + df = DataFrame( + { + "starting": pd.to_datetime( + [ + "2012-06-21 00:00", + "2012-06-23 07:00", + "2012-06-23 16:30", + "2012-06-25 08:00", + "2012-06-26 12:00", + ] + ), + "ending": pd.to_datetime( + [ + "2012-06-23 07:00", + "2012-06-23 16:30", + "2012-06-25 08:00", + "2012-06-26 12:00", + "2012-06-27 08:00", + ] + ), + "measure": [77, 65, 77, 0, 77], + } + ) + + ser_starting = df.starting + ser_starting.index = ser_starting.values + ser_starting = ser_starting.tz_localize("US/Eastern") + ser_starting = ser_starting.tz_convert("UTC") + ser_starting.index.name = "starting" + + ser_ending = df.ending + ser_ending.index = ser_ending.values + ser_ending = ser_ending.tz_localize("US/Eastern") + ser_ending = ser_ending.tz_convert("UTC") + ser_ending.index.name = "ending" + + df.starting = ser_starting.index + df.ending = ser_ending.index + + tm.assert_index_equal(pd.DatetimeIndex(df.starting), ser_starting.index) + tm.assert_index_equal(pd.DatetimeIndex(df.ending), ser_ending.index) + + def test_is_mixed_type(self, float_frame, float_string_frame): + assert not float_frame._is_mixed_type + assert float_string_frame._is_mixed_type + + def test_stale_cached_series_bug_473(self, using_copy_on_write, warn_copy_on_write): + # this is chained, but ok + with option_context("chained_assignment", None): + Y = DataFrame( + np.random.default_rng(2).random((4, 4)), + index=("a", "b", "c", "d"), + columns=("e", "f", "g", "h"), + ) + repr(Y) + Y["e"] = Y["e"].astype("object") + with tm.raises_chained_assignment_error(): + Y["g"]["c"] = np.nan + repr(Y) + Y.sum() + Y["g"].sum() + if using_copy_on_write: + assert not pd.isna(Y["g"]["c"]) + else: + assert pd.isna(Y["g"]["c"]) + + @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning") + def test_strange_column_corruption_issue(self, using_copy_on_write): + # TODO(wesm): Unclear how exactly this is related to internal matters + df = DataFrame(index=[0, 1]) + df[0] = np.nan + wasCol = {} + + with tm.assert_produces_warning( + PerformanceWarning, raise_on_extra_warnings=False + ): + for i, dt in enumerate(df.index): + for col in range(100, 200): + if col not in wasCol: + wasCol[col] = 1 + df[col] = np.nan + if using_copy_on_write: + df.loc[dt, col] = i + else: + df[col][dt] = i + + myid = 100 + + first = len(df.loc[pd.isna(df[myid]), [myid]]) + second = len(df.loc[pd.isna(df[myid]), [myid]]) + assert first == second == 0 + + def test_constructor_no_pandas_array(self): + # Ensure that NumpyExtensionArray isn't allowed inside Series + # See https://github.com/pandas-dev/pandas/issues/23995 for more. + arr = Series([1, 2, 3]).array + result = DataFrame({"A": arr}) + expected = DataFrame({"A": [1, 2, 3]}) + tm.assert_frame_equal(result, expected) + assert isinstance(result._mgr.blocks[0], NumpyBlock) + assert result._mgr.blocks[0].is_numeric + + def test_add_column_with_pandas_array(self): + # GH 26390 + df = DataFrame({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "d"]}) + df["c"] = pd.arrays.NumpyExtensionArray(np.array([1, 2, None, 3], dtype=object)) + df2 = DataFrame( + { + "a": [1, 2, 3, 4], + "b": ["a", "b", "c", "d"], + "c": pd.arrays.NumpyExtensionArray( + np.array([1, 2, None, 3], dtype=object) + ), + } + ) + assert type(df["c"]._mgr.blocks[0]) == NumpyBlock + assert df["c"]._mgr.blocks[0].is_object + assert type(df2["c"]._mgr.blocks[0]) == NumpyBlock + assert df2["c"]._mgr.blocks[0].is_object + tm.assert_frame_equal(df, df2) + + +def test_update_inplace_sets_valid_block_values(using_copy_on_write): + # https://github.com/pandas-dev/pandas/issues/33457 + df = DataFrame({"a": Series([1, 2, None], dtype="category")}) + + # inplace update of a single column + if using_copy_on_write: + with tm.raises_chained_assignment_error(): + df["a"].fillna(1, inplace=True) + else: + with tm.assert_produces_warning(FutureWarning, match="inplace method"): + df["a"].fillna(1, inplace=True) + + # check we haven't put a Series into any block.values + assert isinstance(df._mgr.blocks[0].values, Categorical) + + if not using_copy_on_write: + # smoketest for OP bug from GH#35731 + assert df.isnull().sum().sum() == 0 + + +def test_nonconsolidated_item_cache_take(): + # https://github.com/pandas-dev/pandas/issues/35521 + + # create non-consolidated dataframe with object dtype columns + df = DataFrame( + { + "col1": Series(["a"], dtype=object), + } + ) + df["col2"] = Series([0], dtype=object) + assert not df._mgr.is_consolidated() + + # access column (item cache) + df["col1"] == "A" + # take operation + # (regression was that this consolidated but didn't reset item cache, + # resulting in an invalid cache and the .at operation not working properly) + df[df["col2"] == 0] + + # now setting value should update actual dataframe + df.at[0, "col1"] = "A" + + expected = DataFrame({"col1": ["A"], "col2": [0]}, dtype=object) + tm.assert_frame_equal(df, expected) + assert df.at[0, "col1"] == "A" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_constructors.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..efc40536d56beb94b5a3f72f36267bd15cc72702 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_constructors.py @@ -0,0 +1,3387 @@ +import array +from collections import ( + OrderedDict, + abc, + defaultdict, + namedtuple, +) +from collections.abc import Iterator +from dataclasses import make_dataclass +from datetime import ( + date, + datetime, + timedelta, +) +import functools +import re + +import numpy as np +from numpy import ma +from numpy.ma import mrecords +import pytest +import pytz + +from pandas._libs import lib +from pandas.compat.numpy import np_version_gt2 +from pandas.errors import IntCastingNaNError +import pandas.util._test_decorators as td + +from pandas.core.dtypes.common import is_integer_dtype +from pandas.core.dtypes.dtypes import ( + DatetimeTZDtype, + IntervalDtype, + NumpyEADtype, + PeriodDtype, +) + +import pandas as pd +from pandas import ( + Categorical, + CategoricalIndex, + DataFrame, + DatetimeIndex, + Index, + Interval, + MultiIndex, + Period, + RangeIndex, + Series, + Timedelta, + Timestamp, + cut, + date_range, + isna, +) +import pandas._testing as tm +from pandas.arrays import ( + DatetimeArray, + IntervalArray, + PeriodArray, + SparseArray, + TimedeltaArray, +) + +MIXED_FLOAT_DTYPES = ["float16", "float32", "float64"] +MIXED_INT_DTYPES = [ + "uint8", + "uint16", + "uint32", + "uint64", + "int8", + "int16", + "int32", + "int64", +] + + +class TestDataFrameConstructors: + def test_constructor_from_ndarray_with_str_dtype(self): + # If we don't ravel/reshape around ensure_str_array, we end up + # with an array of strings each of which is e.g. "[0 1 2]" + arr = np.arange(12).reshape(4, 3) + df = DataFrame(arr, dtype=str) + expected = DataFrame(arr.astype(str), dtype="str") + tm.assert_frame_equal(df, expected) + + def test_constructor_from_2d_datetimearray(self, using_array_manager): + dti = date_range("2016-01-01", periods=6, tz="US/Pacific") + dta = dti._data.reshape(3, 2) + + df = DataFrame(dta) + expected = DataFrame({0: dta[:, 0], 1: dta[:, 1]}) + tm.assert_frame_equal(df, expected) + if not using_array_manager: + # GH#44724 big performance hit if we de-consolidate + assert len(df._mgr.blocks) == 1 + + def test_constructor_dict_with_tzaware_scalar(self): + # GH#42505 + dt = Timestamp("2019-11-03 01:00:00-0700").tz_convert("America/Los_Angeles") + dt = dt.as_unit("ns") + + df = DataFrame({"dt": dt}, index=[0]) + expected = DataFrame({"dt": [dt]}) + tm.assert_frame_equal(df, expected) + + # Non-homogeneous + df = DataFrame({"dt": dt, "value": [1]}) + expected = DataFrame({"dt": [dt], "value": [1]}) + tm.assert_frame_equal(df, expected) + + def test_construct_ndarray_with_nas_and_int_dtype(self): + # GH#26919 match Series by not casting np.nan to meaningless int + arr = np.array([[1, np.nan], [2, 3]]) + msg = r"Cannot convert non-finite values \(NA or inf\) to integer" + with pytest.raises(IntCastingNaNError, match=msg): + DataFrame(arr, dtype="i8") + + # check this matches Series behavior + with pytest.raises(IntCastingNaNError, match=msg): + Series(arr[0], dtype="i8", name=0) + + def test_construct_from_list_of_datetimes(self): + df = DataFrame([datetime.now(), datetime.now()]) + assert df[0].dtype == np.dtype("M8[ns]") + + def test_constructor_from_tzaware_datetimeindex(self): + # don't cast a DatetimeIndex WITH a tz, leave as object + # GH#6032 + naive = DatetimeIndex(["2013-1-1 13:00", "2013-1-2 14:00"], name="B") + idx = naive.tz_localize("US/Pacific") + + expected = Series(np.array(idx.tolist(), dtype="object"), name="B") + assert expected.dtype == idx.dtype + + # convert index to series + result = Series(idx) + tm.assert_series_equal(result, expected) + + def test_columns_with_leading_underscore_work_with_to_dict(self): + col_underscore = "_b" + df = DataFrame({"a": [1, 2], col_underscore: [3, 4]}) + d = df.to_dict(orient="records") + + ref_d = [{"a": 1, col_underscore: 3}, {"a": 2, col_underscore: 4}] + + assert ref_d == d + + def test_columns_with_leading_number_and_underscore_work_with_to_dict(self): + col_with_num = "1_b" + df = DataFrame({"a": [1, 2], col_with_num: [3, 4]}) + d = df.to_dict(orient="records") + + ref_d = [{"a": 1, col_with_num: 3}, {"a": 2, col_with_num: 4}] + + assert ref_d == d + + def test_array_of_dt64_nat_with_td64dtype_raises(self, frame_or_series): + # GH#39462 + nat = np.datetime64("NaT", "ns") + arr = np.array([nat], dtype=object) + if frame_or_series is DataFrame: + arr = arr.reshape(1, 1) + + msg = "Invalid type for timedelta scalar: " + with pytest.raises(TypeError, match=msg): + frame_or_series(arr, dtype="m8[ns]") + + @pytest.mark.parametrize("kind", ["m", "M"]) + def test_datetimelike_values_with_object_dtype(self, kind, frame_or_series): + # with dtype=object, we should cast dt64 values to Timestamps, not pydatetimes + if kind == "M": + dtype = "M8[ns]" + scalar_type = Timestamp + else: + dtype = "m8[ns]" + scalar_type = Timedelta + + arr = np.arange(6, dtype="i8").view(dtype).reshape(3, 2) + if frame_or_series is Series: + arr = arr[:, 0] + + obj = frame_or_series(arr, dtype=object) + assert obj._mgr.arrays[0].dtype == object + assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type) + + # go through a different path in internals.construction + obj = frame_or_series(frame_or_series(arr), dtype=object) + assert obj._mgr.arrays[0].dtype == object + assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type) + + obj = frame_or_series(frame_or_series(arr), dtype=NumpyEADtype(object)) + assert obj._mgr.arrays[0].dtype == object + assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type) + + if frame_or_series is DataFrame: + # other paths through internals.construction + sers = [Series(x) for x in arr] + obj = frame_or_series(sers, dtype=object) + assert obj._mgr.arrays[0].dtype == object + assert isinstance(obj._mgr.arrays[0].ravel()[0], scalar_type) + + def test_series_with_name_not_matching_column(self): + # GH#9232 + x = Series(range(5), name=1) + y = Series(range(5), name=0) + + result = DataFrame(x, columns=[0]) + expected = DataFrame([], columns=[0]) + tm.assert_frame_equal(result, expected) + + result = DataFrame(y, columns=[1]) + expected = DataFrame([], columns=[1]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "constructor", + [ + lambda: DataFrame(), + lambda: DataFrame(None), + lambda: DataFrame(()), + lambda: DataFrame([]), + lambda: DataFrame(_ for _ in []), + lambda: DataFrame(range(0)), + lambda: DataFrame(data=None), + lambda: DataFrame(data=()), + lambda: DataFrame(data=[]), + lambda: DataFrame(data=(_ for _ in [])), + lambda: DataFrame(data=range(0)), + ], + ) + def test_empty_constructor(self, constructor): + expected = DataFrame() + result = constructor() + assert len(result.index) == 0 + assert len(result.columns) == 0 + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "constructor", + [ + lambda: DataFrame({}), + lambda: DataFrame(data={}), + ], + ) + def test_empty_constructor_object_index(self, constructor): + expected = DataFrame(index=RangeIndex(0), columns=RangeIndex(0)) + result = constructor() + assert len(result.index) == 0 + assert len(result.columns) == 0 + tm.assert_frame_equal(result, expected, check_index_type=True) + + @pytest.mark.parametrize( + "emptylike,expected_index,expected_columns", + [ + ([[]], RangeIndex(1), RangeIndex(0)), + ([[], []], RangeIndex(2), RangeIndex(0)), + ([(_ for _ in [])], RangeIndex(1), RangeIndex(0)), + ], + ) + def test_emptylike_constructor(self, emptylike, expected_index, expected_columns): + expected = DataFrame(index=expected_index, columns=expected_columns) + result = DataFrame(emptylike) + tm.assert_frame_equal(result, expected) + + def test_constructor_mixed(self, float_string_frame, using_infer_string): + dtype = "str" if using_infer_string else np.object_ + assert float_string_frame["foo"].dtype == dtype + + def test_constructor_cast_failure(self): + # as of 2.0, we raise if we can't respect "dtype", previously we + # silently ignored + msg = "could not convert string to float" + with pytest.raises(ValueError, match=msg): + DataFrame({"a": ["a", "b", "c"]}, dtype=np.float64) + + # GH 3010, constructing with odd arrays + df = DataFrame(np.ones((4, 2))) + + # this is ok + df["foo"] = np.ones((4, 2)).tolist() + + # this is not ok + msg = "Expected a 1D array, got an array with shape \\(4, 2\\)" + with pytest.raises(ValueError, match=msg): + df["test"] = np.ones((4, 2)) + + # this is ok + df["foo2"] = np.ones((4, 2)).tolist() + + def test_constructor_dtype_copy(self): + orig_df = DataFrame({"col1": [1.0], "col2": [2.0], "col3": [3.0]}) + + new_df = DataFrame(orig_df, dtype=float, copy=True) + + new_df["col1"] = 200.0 + assert orig_df["col1"][0] == 1.0 + + def test_constructor_dtype_nocast_view_dataframe( + self, using_copy_on_write, warn_copy_on_write + ): + df = DataFrame([[1, 2]]) + should_be_view = DataFrame(df, dtype=df[0].dtype) + if using_copy_on_write: + should_be_view.iloc[0, 0] = 99 + assert df.values[0, 0] == 1 + else: + with tm.assert_cow_warning(warn_copy_on_write): + should_be_view.iloc[0, 0] = 99 + assert df.values[0, 0] == 99 + + def test_constructor_dtype_nocast_view_2d_array( + self, using_array_manager, using_copy_on_write, warn_copy_on_write + ): + df = DataFrame([[1, 2], [3, 4]], dtype="int64") + if not using_array_manager and not using_copy_on_write: + should_be_view = DataFrame(df.values, dtype=df[0].dtype) + # TODO(CoW-warn) this should warn + # with tm.assert_cow_warning(warn_copy_on_write): + should_be_view.iloc[0, 0] = 97 + assert df.values[0, 0] == 97 + else: + # INFO(ArrayManager) DataFrame(ndarray) doesn't necessarily preserve + # a view on the array to ensure contiguous 1D arrays + df2 = DataFrame(df.values, dtype=df[0].dtype) + assert df2._mgr.arrays[0].flags.c_contiguous + + @td.skip_array_manager_invalid_test + def test_1d_object_array_does_not_copy(self, using_infer_string): + # https://github.com/pandas-dev/pandas/issues/39272 + arr = np.array(["a", "b"], dtype="object") + df = DataFrame(arr, copy=False) + if using_infer_string: + if df[0].dtype.storage == "pyarrow": + # object dtype strings are converted to arrow memory, + # no numpy arrays to compare + pass + else: + assert np.shares_memory(df[0].to_numpy(), arr) + else: + assert np.shares_memory(df.values, arr) + + df = DataFrame(arr, dtype=object, copy=False) + assert np.shares_memory(df.values, arr) + + @td.skip_array_manager_invalid_test + def test_2d_object_array_does_not_copy(self, using_infer_string): + # https://github.com/pandas-dev/pandas/issues/39272 + arr = np.array([["a", "b"], ["c", "d"]], dtype="object") + df = DataFrame(arr, copy=False) + if using_infer_string: + if df[0].dtype.storage == "pyarrow": + # object dtype strings are converted to arrow memory, + # no numpy arrays to compare + pass + else: + assert np.shares_memory(df[0].to_numpy(), arr) + else: + assert np.shares_memory(df.values, arr) + + df = DataFrame(arr, dtype=object, copy=False) + assert np.shares_memory(df.values, arr) + + def test_constructor_dtype_list_data(self): + df = DataFrame([[1, "2"], [None, "a"]], dtype=object) + assert df.loc[1, 0] is None + assert df.loc[0, 1] == "2" + + def test_constructor_list_of_2d_raises(self): + # https://github.com/pandas-dev/pandas/issues/32289 + a = DataFrame() + b = np.empty((0, 0)) + with pytest.raises(ValueError, match=r"shape=\(1, 0, 0\)"): + DataFrame([a]) + + with pytest.raises(ValueError, match=r"shape=\(1, 0, 0\)"): + DataFrame([b]) + + a = DataFrame({"A": [1, 2]}) + with pytest.raises(ValueError, match=r"shape=\(2, 2, 1\)"): + DataFrame([a, a]) + + @pytest.mark.parametrize( + "typ, ad", + [ + # mixed floating and integer coexist in the same frame + ["float", {}], + # add lots of types + ["float", {"A": 1, "B": "foo", "C": "bar"}], + # GH 622 + ["int", {}], + ], + ) + def test_constructor_mixed_dtypes(self, typ, ad): + if typ == "int": + dtypes = MIXED_INT_DTYPES + arrays = [ + np.array(np.random.default_rng(2).random(10), dtype=d) for d in dtypes + ] + elif typ == "float": + dtypes = MIXED_FLOAT_DTYPES + arrays = [ + np.array(np.random.default_rng(2).integers(10, size=10), dtype=d) + for d in dtypes + ] + + for d, a in zip(dtypes, arrays): + assert a.dtype == d + ad.update(dict(zip(dtypes, arrays))) + df = DataFrame(ad) + + dtypes = MIXED_FLOAT_DTYPES + MIXED_INT_DTYPES + for d in dtypes: + if d in df: + assert df.dtypes[d] == d + + def test_constructor_complex_dtypes(self): + # GH10952 + a = np.random.default_rng(2).random(10).astype(np.complex64) + b = np.random.default_rng(2).random(10).astype(np.complex128) + + df = DataFrame({"a": a, "b": b}) + assert a.dtype == df.a.dtype + assert b.dtype == df.b.dtype + + def test_constructor_dtype_str_na_values(self, string_dtype): + # https://github.com/pandas-dev/pandas/issues/21083 + df = DataFrame({"A": ["x", None]}, dtype=string_dtype) + result = df.isna() + expected = DataFrame({"A": [False, True]}) + tm.assert_frame_equal(result, expected) + assert df.iloc[1, 0] is None + + df = DataFrame({"A": ["x", np.nan]}, dtype=string_dtype) + assert np.isnan(df.iloc[1, 0]) + + def test_constructor_rec(self, float_frame): + rec = float_frame.to_records(index=False) + rec.dtype.names = list(rec.dtype.names)[::-1] + + index = float_frame.index + + df = DataFrame(rec) + tm.assert_index_equal(df.columns, Index(rec.dtype.names)) + + df2 = DataFrame(rec, index=index) + tm.assert_index_equal(df2.columns, Index(rec.dtype.names)) + tm.assert_index_equal(df2.index, index) + + # case with columns != the ones we would infer from the data + rng = np.arange(len(rec))[::-1] + df3 = DataFrame(rec, index=rng, columns=["C", "B"]) + expected = DataFrame(rec, index=rng).reindex(columns=["C", "B"]) + tm.assert_frame_equal(df3, expected) + + def test_constructor_bool(self): + df = DataFrame({0: np.ones(10, dtype=bool), 1: np.zeros(10, dtype=bool)}) + assert df.values.dtype == np.bool_ + + def test_constructor_overflow_int64(self): + # see gh-14881 + values = np.array([2**64 - i for i in range(1, 10)], dtype=np.uint64) + + result = DataFrame({"a": values}) + assert result["a"].dtype == np.uint64 + + # see gh-2355 + data_scores = [ + (6311132704823138710, 273), + (2685045978526272070, 23), + (8921811264899370420, 45), + (17019687244989530680, 270), + (9930107427299601010, 273), + ] + dtype = [("uid", "u8"), ("score", "u8")] + data = np.zeros((len(data_scores),), dtype=dtype) + data[:] = data_scores + df_crawls = DataFrame(data) + assert df_crawls["uid"].dtype == np.uint64 + + @pytest.mark.parametrize( + "values", + [ + np.array([2**64], dtype=object), + np.array([2**65]), + [2**64 + 1], + np.array([-(2**63) - 4], dtype=object), + np.array([-(2**64) - 1]), + [-(2**65) - 2], + ], + ) + def test_constructor_int_overflow(self, values): + # see gh-18584 + value = values[0] + result = DataFrame(values) + + assert result[0].dtype == object + assert result[0][0] == value + + @pytest.mark.parametrize( + "values", + [ + np.array([1], dtype=np.uint16), + np.array([1], dtype=np.uint32), + np.array([1], dtype=np.uint64), + [np.uint16(1)], + [np.uint32(1)], + [np.uint64(1)], + ], + ) + def test_constructor_numpy_uints(self, values): + # GH#47294 + value = values[0] + result = DataFrame(values) + + assert result[0].dtype == value.dtype + assert result[0][0] == value + + def test_constructor_ordereddict(self): + nitems = 100 + nums = list(range(nitems)) + np.random.default_rng(2).shuffle(nums) + expected = [f"A{i:d}" for i in nums] + df = DataFrame(OrderedDict(zip(expected, [[0]] * nitems))) + assert expected == list(df.columns) + + def test_constructor_dict(self): + datetime_series = Series( + np.arange(30, dtype=np.float64), index=date_range("2020-01-01", periods=30) + ) + # test expects index shifted by 5 + datetime_series_short = datetime_series[5:] + + frame = DataFrame({"col1": datetime_series, "col2": datetime_series_short}) + + # col2 is padded with NaN + assert len(datetime_series) == 30 + assert len(datetime_series_short) == 25 + + tm.assert_series_equal(frame["col1"], datetime_series.rename("col1")) + + exp = Series( + np.concatenate([[np.nan] * 5, datetime_series_short.values]), + index=datetime_series.index, + name="col2", + ) + tm.assert_series_equal(exp, frame["col2"]) + + frame = DataFrame( + {"col1": datetime_series, "col2": datetime_series_short}, + columns=["col2", "col3", "col4"], + ) + + assert len(frame) == len(datetime_series_short) + assert "col1" not in frame + assert isna(frame["col3"]).all() + + # Corner cases + assert len(DataFrame()) == 0 + + # mix dict and array, wrong size - no spec for which error should raise + # first + msg = "Mixing dicts with non-Series may lead to ambiguous ordering." + with pytest.raises(ValueError, match=msg): + DataFrame({"A": {"a": "a", "b": "b"}, "B": ["a", "b", "c"]}) + + def test_constructor_dict_length1(self): + # Length-one dict micro-optimization + frame = DataFrame({"A": {"1": 1, "2": 2}}) + tm.assert_index_equal(frame.index, Index(["1", "2"])) + + def test_constructor_dict_with_index(self): + # empty dict plus index + idx = Index([0, 1, 2]) + frame = DataFrame({}, index=idx) + assert frame.index is idx + + def test_constructor_dict_with_index_and_columns(self): + # empty dict with index and columns + idx = Index([0, 1, 2]) + frame = DataFrame({}, index=idx, columns=idx) + assert frame.index is idx + assert frame.columns is idx + assert len(frame._series) == 3 + + def test_constructor_dict_of_empty_lists(self): + # with dict of empty list and Series + frame = DataFrame({"A": [], "B": []}, columns=["A", "B"]) + tm.assert_index_equal(frame.index, RangeIndex(0), exact=True) + + def test_constructor_dict_with_none(self): + # GH 14381 + # Dict with None value + frame_none = DataFrame({"a": None}, index=[0]) + frame_none_list = DataFrame({"a": [None]}, index=[0]) + assert frame_none._get_value(0, "a") is None + assert frame_none_list._get_value(0, "a") is None + tm.assert_frame_equal(frame_none, frame_none_list) + + def test_constructor_dict_errors(self): + # GH10856 + # dict with scalar values should raise error, even if columns passed + msg = "If using all scalar values, you must pass an index" + with pytest.raises(ValueError, match=msg): + DataFrame({"a": 0.7}) + + with pytest.raises(ValueError, match=msg): + DataFrame({"a": 0.7}, columns=["a"]) + + @pytest.mark.parametrize("scalar", [2, np.nan, None, "D"]) + def test_constructor_invalid_items_unused(self, scalar): + # No error if invalid (scalar) value is in fact not used: + result = DataFrame({"a": scalar}, columns=["b"]) + expected = DataFrame(columns=["b"]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("value", [2, np.nan, None, float("nan")]) + def test_constructor_dict_nan_key(self, value): + # GH 18455 + cols = [1, value, 3] + idx = ["a", value] + values = [[0, 3], [1, 4], [2, 5]] + data = {cols[c]: Series(values[c], index=idx) for c in range(3)} + result = DataFrame(data).sort_values(1).sort_values("a", axis=1) + expected = DataFrame( + np.arange(6, dtype="int64").reshape(2, 3), index=idx, columns=cols + ) + tm.assert_frame_equal(result, expected) + + result = DataFrame(data, index=idx).sort_values("a", axis=1) + tm.assert_frame_equal(result, expected) + + result = DataFrame(data, index=idx, columns=cols) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("value", [np.nan, None, float("nan")]) + def test_constructor_dict_nan_tuple_key(self, value): + # GH 18455 + cols = Index([(11, 21), (value, 22), (13, value)]) + idx = Index([("a", value), (value, 2)]) + values = [[0, 3], [1, 4], [2, 5]] + data = {cols[c]: Series(values[c], index=idx) for c in range(3)} + result = DataFrame(data).sort_values((11, 21)).sort_values(("a", value), axis=1) + expected = DataFrame( + np.arange(6, dtype="int64").reshape(2, 3), index=idx, columns=cols + ) + tm.assert_frame_equal(result, expected) + + result = DataFrame(data, index=idx).sort_values(("a", value), axis=1) + tm.assert_frame_equal(result, expected) + + result = DataFrame(data, index=idx, columns=cols) + tm.assert_frame_equal(result, expected) + + def test_constructor_dict_order_insertion(self): + datetime_series = Series( + np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10) + ) + datetime_series_short = datetime_series[:5] + + # GH19018 + # initialization ordering: by insertion order if python>= 3.6 + d = {"b": datetime_series_short, "a": datetime_series} + frame = DataFrame(data=d) + expected = DataFrame(data=d, columns=list("ba")) + tm.assert_frame_equal(frame, expected) + + def test_constructor_dict_nan_key_and_columns(self): + # GH 16894 + result = DataFrame({np.nan: [1, 2], 2: [2, 3]}, columns=[np.nan, 2]) + expected = DataFrame([[1, 2], [2, 3]], columns=[np.nan, 2]) + tm.assert_frame_equal(result, expected) + + def test_constructor_multi_index(self): + # GH 4078 + # construction error with mi and all-nan frame + tuples = [(2, 3), (3, 3), (3, 3)] + mi = MultiIndex.from_tuples(tuples) + df = DataFrame(index=mi, columns=mi) + assert isna(df).values.ravel().all() + + tuples = [(3, 3), (2, 3), (3, 3)] + mi = MultiIndex.from_tuples(tuples) + df = DataFrame(index=mi, columns=mi) + assert isna(df).values.ravel().all() + + def test_constructor_2d_index(self): + # GH 25416 + # handling of 2d index in construction + df = DataFrame([[1]], columns=[[1]], index=[1, 2]) + expected = DataFrame( + [1, 1], + index=Index([1, 2], dtype="int64"), + columns=MultiIndex(levels=[[1]], codes=[[0]]), + ) + tm.assert_frame_equal(df, expected) + + df = DataFrame([[1]], columns=[[1]], index=[[1, 2]]) + expected = DataFrame( + [1, 1], + index=MultiIndex(levels=[[1, 2]], codes=[[0, 1]]), + columns=MultiIndex(levels=[[1]], codes=[[0]]), + ) + tm.assert_frame_equal(df, expected) + + def test_constructor_error_msgs(self): + msg = "Empty data passed with indices specified." + # passing an empty array with columns specified. + with pytest.raises(ValueError, match=msg): + DataFrame(np.empty(0), index=[1]) + + msg = "Mixing dicts with non-Series may lead to ambiguous ordering." + # mix dict and array, wrong size + with pytest.raises(ValueError, match=msg): + DataFrame({"A": {"a": "a", "b": "b"}, "B": ["a", "b", "c"]}) + + # wrong size ndarray, GH 3105 + msg = r"Shape of passed values is \(4, 3\), indices imply \(3, 3\)" + with pytest.raises(ValueError, match=msg): + DataFrame( + np.arange(12).reshape((4, 3)), + columns=["foo", "bar", "baz"], + index=date_range("2000-01-01", periods=3), + ) + + arr = np.array([[4, 5, 6]]) + msg = r"Shape of passed values is \(1, 3\), indices imply \(1, 4\)" + with pytest.raises(ValueError, match=msg): + DataFrame(index=[0], columns=range(4), data=arr) + + arr = np.array([4, 5, 6]) + msg = r"Shape of passed values is \(3, 1\), indices imply \(1, 4\)" + with pytest.raises(ValueError, match=msg): + DataFrame(index=[0], columns=range(4), data=arr) + + # higher dim raise exception + with pytest.raises(ValueError, match="Must pass 2-d input"): + DataFrame(np.zeros((3, 3, 3)), columns=["A", "B", "C"], index=[1]) + + # wrong size axis labels + msg = r"Shape of passed values is \(2, 3\), indices imply \(1, 3\)" + with pytest.raises(ValueError, match=msg): + DataFrame( + np.random.default_rng(2).random((2, 3)), + columns=["A", "B", "C"], + index=[1], + ) + + msg = r"Shape of passed values is \(2, 3\), indices imply \(2, 2\)" + with pytest.raises(ValueError, match=msg): + DataFrame( + np.random.default_rng(2).random((2, 3)), + columns=["A", "B"], + index=[1, 2], + ) + + # gh-26429 + msg = "2 columns passed, passed data had 10 columns" + with pytest.raises(ValueError, match=msg): + DataFrame((range(10), range(10, 20)), columns=("ones", "twos")) + + msg = "If using all scalar values, you must pass an index" + with pytest.raises(ValueError, match=msg): + DataFrame({"a": False, "b": True}) + + def test_constructor_subclass_dict(self, dict_subclass): + # Test for passing dict subclass to constructor + data = { + "col1": dict_subclass((x, 10.0 * x) for x in range(10)), + "col2": dict_subclass((x, 20.0 * x) for x in range(10)), + } + df = DataFrame(data) + refdf = DataFrame({col: dict(val.items()) for col, val in data.items()}) + tm.assert_frame_equal(refdf, df) + + data = dict_subclass(data.items()) + df = DataFrame(data) + tm.assert_frame_equal(refdf, df) + + def test_constructor_defaultdict(self, float_frame): + # try with defaultdict + data = {} + float_frame.loc[: float_frame.index[10], "B"] = np.nan + + for k, v in float_frame.items(): + dct = defaultdict(dict) + dct.update(v.to_dict()) + data[k] = dct + frame = DataFrame(data) + expected = frame.reindex(index=float_frame.index) + tm.assert_frame_equal(float_frame, expected) + + def test_constructor_dict_block(self): + expected = np.array([[4.0, 3.0, 2.0, 1.0]]) + df = DataFrame( + {"d": [4.0], "c": [3.0], "b": [2.0], "a": [1.0]}, + columns=["d", "c", "b", "a"], + ) + tm.assert_numpy_array_equal(df.values, expected) + + def test_constructor_dict_cast(self, using_infer_string): + # cast float tests + test_data = {"A": {"1": 1, "2": 2}, "B": {"1": "1", "2": "2", "3": "3"}} + frame = DataFrame(test_data, dtype=float) + assert len(frame) == 3 + assert frame["B"].dtype == np.float64 + assert frame["A"].dtype == np.float64 + + frame = DataFrame(test_data) + assert len(frame) == 3 + assert frame["B"].dtype == np.object_ if not using_infer_string else "str" + assert frame["A"].dtype == np.float64 + + def test_constructor_dict_cast2(self): + # can't cast to float + test_data = { + "A": dict(zip(range(20), [f"word_{i}" for i in range(20)])), + "B": dict(zip(range(15), np.random.default_rng(2).standard_normal(15))), + } + with pytest.raises(ValueError, match="could not convert string"): + DataFrame(test_data, dtype=float) + + def test_constructor_dict_dont_upcast(self): + d = {"Col1": {"Row1": "A String", "Row2": np.nan}} + df = DataFrame(d) + assert isinstance(df["Col1"]["Row2"], float) + + def test_constructor_dict_dont_upcast2(self): + dm = DataFrame([[1, 2], ["a", "b"]], index=[1, 2], columns=[1, 2]) + assert isinstance(dm[1][1], int) + + def test_constructor_dict_of_tuples(self): + # GH #1491 + data = {"a": (1, 2, 3), "b": (4, 5, 6)} + + result = DataFrame(data) + expected = DataFrame({k: list(v) for k, v in data.items()}) + tm.assert_frame_equal(result, expected, check_dtype=False) + + def test_constructor_dict_of_ranges(self): + # GH 26356 + data = {"a": range(3), "b": range(3, 6)} + + result = DataFrame(data) + expected = DataFrame({"a": [0, 1, 2], "b": [3, 4, 5]}) + tm.assert_frame_equal(result, expected) + + def test_constructor_dict_of_iterators(self): + # GH 26349 + data = {"a": iter(range(3)), "b": reversed(range(3))} + + result = DataFrame(data) + expected = DataFrame({"a": [0, 1, 2], "b": [2, 1, 0]}) + tm.assert_frame_equal(result, expected) + + def test_constructor_dict_of_generators(self): + # GH 26349 + data = {"a": (i for i in (range(3))), "b": (i for i in reversed(range(3)))} + result = DataFrame(data) + expected = DataFrame({"a": [0, 1, 2], "b": [2, 1, 0]}) + tm.assert_frame_equal(result, expected) + + def test_constructor_dict_multiindex(self): + d = { + ("a", "a"): {("i", "i"): 0, ("i", "j"): 1, ("j", "i"): 2}, + ("b", "a"): {("i", "i"): 6, ("i", "j"): 5, ("j", "i"): 4}, + ("b", "c"): {("i", "i"): 7, ("i", "j"): 8, ("j", "i"): 9}, + } + _d = sorted(d.items()) + df = DataFrame(d) + expected = DataFrame( + [x[1] for x in _d], index=MultiIndex.from_tuples([x[0] for x in _d]) + ).T + expected.index = MultiIndex.from_tuples(expected.index) + tm.assert_frame_equal( + df, + expected, + ) + + d["z"] = {"y": 123.0, ("i", "i"): 111, ("i", "j"): 111, ("j", "i"): 111} + _d.insert(0, ("z", d["z"])) + expected = DataFrame( + [x[1] for x in _d], index=Index([x[0] for x in _d], tupleize_cols=False) + ).T + expected.index = Index(expected.index, tupleize_cols=False) + df = DataFrame(d) + df = df.reindex(columns=expected.columns, index=expected.index) + tm.assert_frame_equal(df, expected) + + def test_constructor_dict_datetime64_index(self): + # GH 10160 + dates_as_str = ["1984-02-19", "1988-11-06", "1989-12-03", "1990-03-15"] + + def create_data(constructor): + return {i: {constructor(s): 2 * i} for i, s in enumerate(dates_as_str)} + + data_datetime64 = create_data(np.datetime64) + data_datetime = create_data(lambda x: datetime.strptime(x, "%Y-%m-%d")) + data_Timestamp = create_data(Timestamp) + + expected = DataFrame( + [ + {0: 0, 1: None, 2: None, 3: None}, + {0: None, 1: 2, 2: None, 3: None}, + {0: None, 1: None, 2: 4, 3: None}, + {0: None, 1: None, 2: None, 3: 6}, + ], + index=[Timestamp(dt) for dt in dates_as_str], + ) + + result_datetime64 = DataFrame(data_datetime64) + result_datetime = DataFrame(data_datetime) + result_Timestamp = DataFrame(data_Timestamp) + tm.assert_frame_equal(result_datetime64, expected) + tm.assert_frame_equal(result_datetime, expected) + tm.assert_frame_equal(result_Timestamp, expected) + + @pytest.mark.parametrize( + "klass,name", + [ + (lambda x: np.timedelta64(x, "D"), "timedelta64"), + (lambda x: timedelta(days=x), "pytimedelta"), + (lambda x: Timedelta(x, "D"), "Timedelta[ns]"), + (lambda x: Timedelta(x, "D").as_unit("s"), "Timedelta[s]"), + ], + ) + def test_constructor_dict_timedelta64_index(self, klass, name): + # GH 10160 + td_as_int = [1, 2, 3, 4] + + data = {i: {klass(s): 2 * i} for i, s in enumerate(td_as_int)} + + expected = DataFrame( + [ + {0: 0, 1: None, 2: None, 3: None}, + {0: None, 1: 2, 2: None, 3: None}, + {0: None, 1: None, 2: 4, 3: None}, + {0: None, 1: None, 2: None, 3: 6}, + ], + index=[Timedelta(td, "D") for td in td_as_int], + ) + + result = DataFrame(data) + + tm.assert_frame_equal(result, expected) + + def test_constructor_period_dict(self): + # PeriodIndex + a = pd.PeriodIndex(["2012-01", "NaT", "2012-04"], freq="M") + b = pd.PeriodIndex(["2012-02-01", "2012-03-01", "NaT"], freq="D") + df = DataFrame({"a": a, "b": b}) + assert df["a"].dtype == a.dtype + assert df["b"].dtype == b.dtype + + # list of periods + df = DataFrame({"a": a.astype(object).tolist(), "b": b.astype(object).tolist()}) + assert df["a"].dtype == a.dtype + assert df["b"].dtype == b.dtype + + def test_constructor_dict_extension_scalar(self, ea_scalar_and_dtype): + ea_scalar, ea_dtype = ea_scalar_and_dtype + df = DataFrame({"a": ea_scalar}, index=[0]) + assert df["a"].dtype == ea_dtype + + expected = DataFrame(index=[0], columns=["a"], data=ea_scalar) + + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "data,dtype", + [ + (Period("2020-01"), PeriodDtype("M")), + (Interval(left=0, right=5), IntervalDtype("int64", "right")), + ( + Timestamp("2011-01-01", tz="US/Eastern"), + DatetimeTZDtype(unit="s", tz="US/Eastern"), + ), + ], + ) + def test_constructor_extension_scalar_data(self, data, dtype): + # GH 34832 + df = DataFrame(index=[0, 1], columns=["a", "b"], data=data) + + assert df["a"].dtype == dtype + assert df["b"].dtype == dtype + + arr = pd.array([data] * 2, dtype=dtype) + expected = DataFrame({"a": arr, "b": arr}) + + tm.assert_frame_equal(df, expected) + + def test_nested_dict_frame_constructor(self): + rng = pd.period_range("1/1/2000", periods=5) + df = DataFrame(np.random.default_rng(2).standard_normal((10, 5)), columns=rng) + + data = {} + for col in df.columns: + for row in df.index: + data.setdefault(col, {})[row] = df._get_value(row, col) + + result = DataFrame(data, columns=rng) + tm.assert_frame_equal(result, df) + + data = {} + for col in df.columns: + for row in df.index: + data.setdefault(row, {})[col] = df._get_value(row, col) + + result = DataFrame(data, index=rng).T + tm.assert_frame_equal(result, df) + + def _check_basic_constructor(self, empty): + # mat: 2d matrix with shape (3, 2) to input. empty - makes sized + # objects + mat = empty((2, 3), dtype=float) + # 2-D input + frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2]) + + assert len(frame.index) == 2 + assert len(frame.columns) == 3 + + # 1-D input + frame = DataFrame(empty((3,)), columns=["A"], index=[1, 2, 3]) + assert len(frame.index) == 3 + assert len(frame.columns) == 1 + + if empty is not np.ones: + msg = r"Cannot convert non-finite values \(NA or inf\) to integer" + with pytest.raises(IntCastingNaNError, match=msg): + DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64) + return + else: + frame = DataFrame( + mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64 + ) + assert frame.values.dtype == np.int64 + + # wrong size axis labels + msg = r"Shape of passed values is \(2, 3\), indices imply \(1, 3\)" + with pytest.raises(ValueError, match=msg): + DataFrame(mat, columns=["A", "B", "C"], index=[1]) + msg = r"Shape of passed values is \(2, 3\), indices imply \(2, 2\)" + with pytest.raises(ValueError, match=msg): + DataFrame(mat, columns=["A", "B"], index=[1, 2]) + + # higher dim raise exception + with pytest.raises(ValueError, match="Must pass 2-d input"): + DataFrame(empty((3, 3, 3)), columns=["A", "B", "C"], index=[1]) + + # automatic labeling + frame = DataFrame(mat) + tm.assert_index_equal(frame.index, Index(range(2)), exact=True) + tm.assert_index_equal(frame.columns, Index(range(3)), exact=True) + + frame = DataFrame(mat, index=[1, 2]) + tm.assert_index_equal(frame.columns, Index(range(3)), exact=True) + + frame = DataFrame(mat, columns=["A", "B", "C"]) + tm.assert_index_equal(frame.index, Index(range(2)), exact=True) + + # 0-length axis + frame = DataFrame(empty((0, 3))) + assert len(frame.index) == 0 + + frame = DataFrame(empty((3, 0))) + assert len(frame.columns) == 0 + + def test_constructor_ndarray(self): + self._check_basic_constructor(np.ones) + + frame = DataFrame(["foo", "bar"], index=[0, 1], columns=["A"]) + assert len(frame) == 2 + + def test_constructor_maskedarray(self): + self._check_basic_constructor(ma.masked_all) + + # Check non-masked values + mat = ma.masked_all((2, 3), dtype=float) + mat[0, 0] = 1.0 + mat[1, 2] = 2.0 + frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2]) + assert 1.0 == frame["A"][1] + assert 2.0 == frame["C"][2] + + # what is this even checking?? + mat = ma.masked_all((2, 3), dtype=float) + frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2]) + assert np.all(~np.asarray(frame == frame)) + + @pytest.mark.filterwarnings( + "ignore:elementwise comparison failed:DeprecationWarning" + ) + def test_constructor_maskedarray_nonfloat(self): + # masked int promoted to float + mat = ma.masked_all((2, 3), dtype=int) + # 2-D input + frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2]) + + assert len(frame.index) == 2 + assert len(frame.columns) == 3 + assert np.all(~np.asarray(frame == frame)) + + # cast type + frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.float64) + assert frame.values.dtype == np.float64 + + # Check non-masked values + mat2 = ma.copy(mat) + mat2[0, 0] = 1 + mat2[1, 2] = 2 + frame = DataFrame(mat2, columns=["A", "B", "C"], index=[1, 2]) + assert 1 == frame["A"][1] + assert 2 == frame["C"][2] + + # masked np.datetime64 stays (use NaT as null) + mat = ma.masked_all((2, 3), dtype="M8[ns]") + # 2-D input + frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2]) + + assert len(frame.index) == 2 + assert len(frame.columns) == 3 + assert isna(frame).values.all() + + # cast type + msg = r"datetime64\[ns\] values and dtype=int64 is not supported" + with pytest.raises(TypeError, match=msg): + DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=np.int64) + + # Check non-masked values + mat2 = ma.copy(mat) + mat2[0, 0] = 1 + mat2[1, 2] = 2 + frame = DataFrame(mat2, columns=["A", "B", "C"], index=[1, 2]) + assert 1 == frame["A"].astype("i8")[1] + assert 2 == frame["C"].astype("i8")[2] + + # masked bool promoted to object + mat = ma.masked_all((2, 3), dtype=bool) + # 2-D input + frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2]) + + assert len(frame.index) == 2 + assert len(frame.columns) == 3 + assert np.all(~np.asarray(frame == frame)) + + # cast type + frame = DataFrame(mat, columns=["A", "B", "C"], index=[1, 2], dtype=object) + assert frame.values.dtype == object + + # Check non-masked values + mat2 = ma.copy(mat) + mat2[0, 0] = True + mat2[1, 2] = False + frame = DataFrame(mat2, columns=["A", "B", "C"], index=[1, 2]) + assert frame["A"][1] is True + assert frame["C"][2] is False + + def test_constructor_maskedarray_hardened(self): + # Check numpy masked arrays with hard masks -- from GH24574 + mat_hard = ma.masked_all((2, 2), dtype=float).harden_mask() + result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2]) + expected = DataFrame( + {"A": [np.nan, np.nan], "B": [np.nan, np.nan]}, + columns=["A", "B"], + index=[1, 2], + dtype=float, + ) + tm.assert_frame_equal(result, expected) + # Check case where mask is hard but no data are masked + mat_hard = ma.ones((2, 2), dtype=float).harden_mask() + result = DataFrame(mat_hard, columns=["A", "B"], index=[1, 2]) + expected = DataFrame( + {"A": [1.0, 1.0], "B": [1.0, 1.0]}, + columns=["A", "B"], + index=[1, 2], + dtype=float, + ) + tm.assert_frame_equal(result, expected) + + def test_constructor_maskedrecarray_dtype(self): + # Ensure constructor honors dtype + data = np.ma.array( + np.ma.zeros(5, dtype=[("date", " None: + self._lst = lst + + def __getitem__(self, n): + return self._lst.__getitem__(n) + + def __len__(self) -> int: + return self._lst.__len__() + + lst_containers = [DummyContainer([1, "a"]), DummyContainer([2, "b"])] + columns = ["num", "str"] + result = DataFrame(lst_containers, columns=columns) + expected = DataFrame([[1, "a"], [2, "b"]], columns=columns) + tm.assert_frame_equal(result, expected, check_dtype=False) + + def test_constructor_stdlib_array(self): + # GH 4297 + # support Array + result = DataFrame({"A": array.array("i", range(10))}) + expected = DataFrame({"A": list(range(10))}) + tm.assert_frame_equal(result, expected, check_dtype=False) + + expected = DataFrame([list(range(10)), list(range(10))]) + result = DataFrame([array.array("i", range(10)), array.array("i", range(10))]) + tm.assert_frame_equal(result, expected, check_dtype=False) + + def test_constructor_range(self): + # GH26342 + result = DataFrame(range(10)) + expected = DataFrame(list(range(10))) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_ranges(self): + result = DataFrame([range(10), range(10)]) + expected = DataFrame([list(range(10)), list(range(10))]) + tm.assert_frame_equal(result, expected) + + def test_constructor_iterable(self): + # GH 21987 + class Iter: + def __iter__(self) -> Iterator: + for i in range(10): + yield [1, 2, 3] + + expected = DataFrame([[1, 2, 3]] * 10) + result = DataFrame(Iter()) + tm.assert_frame_equal(result, expected) + + def test_constructor_iterator(self): + result = DataFrame(iter(range(10))) + expected = DataFrame(list(range(10))) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_iterators(self): + result = DataFrame([iter(range(10)), iter(range(10))]) + expected = DataFrame([list(range(10)), list(range(10))]) + tm.assert_frame_equal(result, expected) + + def test_constructor_generator(self): + # related #2305 + + gen1 = (i for i in range(10)) + gen2 = (i for i in range(10)) + + expected = DataFrame([list(range(10)), list(range(10))]) + result = DataFrame([gen1, gen2]) + tm.assert_frame_equal(result, expected) + + gen = ([i, "a"] for i in range(10)) + result = DataFrame(gen) + expected = DataFrame({0: range(10), 1: "a"}) + tm.assert_frame_equal(result, expected, check_dtype=False) + + def test_constructor_list_of_dicts(self): + result = DataFrame([{}]) + expected = DataFrame(index=RangeIndex(1), columns=[]) + tm.assert_frame_equal(result, expected) + + def test_constructor_ordered_dict_nested_preserve_order(self): + # see gh-18166 + nested1 = OrderedDict([("b", 1), ("a", 2)]) + nested2 = OrderedDict([("b", 2), ("a", 5)]) + data = OrderedDict([("col2", nested1), ("col1", nested2)]) + result = DataFrame(data) + data = {"col2": [1, 2], "col1": [2, 5]} + expected = DataFrame(data=data, index=["b", "a"]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dict_type", [dict, OrderedDict]) + def test_constructor_ordered_dict_preserve_order(self, dict_type): + # see gh-13304 + expected = DataFrame([[2, 1]], columns=["b", "a"]) + + data = dict_type() + data["b"] = [2] + data["a"] = [1] + + result = DataFrame(data) + tm.assert_frame_equal(result, expected) + + data = dict_type() + data["b"] = 2 + data["a"] = 1 + + result = DataFrame([data]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dict_type", [dict, OrderedDict]) + def test_constructor_ordered_dict_conflicting_orders(self, dict_type): + # the first dict element sets the ordering for the DataFrame, + # even if there are conflicting orders from subsequent ones + row_one = dict_type() + row_one["b"] = 2 + row_one["a"] = 1 + + row_two = dict_type() + row_two["a"] = 1 + row_two["b"] = 2 + + row_three = {"b": 2, "a": 1} + + expected = DataFrame([[2, 1], [2, 1]], columns=["b", "a"]) + result = DataFrame([row_one, row_two]) + tm.assert_frame_equal(result, expected) + + expected = DataFrame([[2, 1], [2, 1], [2, 1]], columns=["b", "a"]) + result = DataFrame([row_one, row_two, row_three]) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_series_aligned_index(self): + series = [Series(i, index=["b", "a", "c"], name=str(i)) for i in range(3)] + result = DataFrame(series) + expected = DataFrame( + {"b": [0, 1, 2], "a": [0, 1, 2], "c": [0, 1, 2]}, + columns=["b", "a", "c"], + index=["0", "1", "2"], + ) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_derived_dicts(self): + class CustomDict(dict): + pass + + d = {"a": 1.5, "b": 3} + + data_custom = [CustomDict(d)] + data = [d] + + result_custom = DataFrame(data_custom) + result = DataFrame(data) + tm.assert_frame_equal(result, result_custom) + + def test_constructor_ragged(self): + data = { + "A": np.random.default_rng(2).standard_normal(10), + "B": np.random.default_rng(2).standard_normal(8), + } + with pytest.raises(ValueError, match="All arrays must be of the same length"): + DataFrame(data) + + def test_constructor_scalar(self): + idx = Index(range(3)) + df = DataFrame({"a": 0}, index=idx) + expected = DataFrame({"a": [0, 0, 0]}, index=idx) + tm.assert_frame_equal(df, expected, check_dtype=False) + + def test_constructor_Series_copy_bug(self, float_frame): + df = DataFrame(float_frame["A"], index=float_frame.index, columns=["A"]) + df.copy() + + def test_constructor_mixed_dict_and_Series(self): + data = {} + data["A"] = {"foo": 1, "bar": 2, "baz": 3} + data["B"] = Series([4, 3, 2, 1], index=["bar", "qux", "baz", "foo"]) + + result = DataFrame(data) + assert result.index.is_monotonic_increasing + + # ordering ambiguous, raise exception + with pytest.raises(ValueError, match="ambiguous ordering"): + DataFrame({"A": ["a", "b"], "B": {"a": "a", "b": "b"}}) + + # this is OK though + result = DataFrame({"A": ["a", "b"], "B": Series(["a", "b"], index=["a", "b"])}) + expected = DataFrame({"A": ["a", "b"], "B": ["a", "b"]}, index=["a", "b"]) + tm.assert_frame_equal(result, expected) + + def test_constructor_mixed_type_rows(self): + # Issue 25075 + data = [[1, 2], (3, 4)] + result = DataFrame(data) + expected = DataFrame([[1, 2], [3, 4]]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "tuples,lists", + [ + ((), []), + ((()), []), + (((), ()), [(), ()]), + (((), ()), [[], []]), + (([], []), [[], []]), + (([1], [2]), [[1], [2]]), # GH 32776 + (([1, 2, 3], [4, 5, 6]), [[1, 2, 3], [4, 5, 6]]), + ], + ) + def test_constructor_tuple(self, tuples, lists): + # GH 25691 + result = DataFrame(tuples) + expected = DataFrame(lists) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_tuples(self): + result = DataFrame({"A": [(1, 2), (3, 4)]}) + expected = DataFrame({"A": Series([(1, 2), (3, 4)])}) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_namedtuples(self): + # GH11181 + named_tuple = namedtuple("Pandas", list("ab")) + tuples = [named_tuple(1, 3), named_tuple(2, 4)] + expected = DataFrame({"a": [1, 2], "b": [3, 4]}) + result = DataFrame(tuples) + tm.assert_frame_equal(result, expected) + + # with columns + expected = DataFrame({"y": [1, 2], "z": [3, 4]}) + result = DataFrame(tuples, columns=["y", "z"]) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_dataclasses(self): + # GH21910 + Point = make_dataclass("Point", [("x", int), ("y", int)]) + + data = [Point(0, 3), Point(1, 3)] + expected = DataFrame({"x": [0, 1], "y": [3, 3]}) + result = DataFrame(data) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_dataclasses_with_varying_types(self): + # GH21910 + # varying types + Point = make_dataclass("Point", [("x", int), ("y", int)]) + HLine = make_dataclass("HLine", [("x0", int), ("x1", int), ("y", int)]) + + data = [Point(0, 3), HLine(1, 3, 3)] + + expected = DataFrame( + {"x": [0, np.nan], "y": [3, 3], "x0": [np.nan, 1], "x1": [np.nan, 3]} + ) + result = DataFrame(data) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_of_dataclasses_error_thrown(self): + # GH21910 + Point = make_dataclass("Point", [("x", int), ("y", int)]) + + # expect TypeError + msg = "asdict() should be called on dataclass instances" + with pytest.raises(TypeError, match=re.escape(msg)): + DataFrame([Point(0, 0), {"x": 1, "y": 0}]) + + def test_constructor_list_of_dict_order(self): + # GH10056 + data = [ + {"First": 1, "Second": 4, "Third": 7, "Fourth": 10}, + {"Second": 5, "First": 2, "Fourth": 11, "Third": 8}, + {"Second": 6, "First": 3, "Fourth": 12, "Third": 9, "YYY": 14, "XXX": 13}, + ] + expected = DataFrame( + { + "First": [1, 2, 3], + "Second": [4, 5, 6], + "Third": [7, 8, 9], + "Fourth": [10, 11, 12], + "YYY": [None, None, 14], + "XXX": [None, None, 13], + } + ) + result = DataFrame(data) + tm.assert_frame_equal(result, expected) + + def test_constructor_Series_named(self): + a = Series([1, 2, 3], index=["a", "b", "c"], name="x") + df = DataFrame(a) + assert df.columns[0] == "x" + tm.assert_index_equal(df.index, a.index) + + # ndarray like + arr = np.random.default_rng(2).standard_normal(10) + s = Series(arr, name="x") + df = DataFrame(s) + expected = DataFrame({"x": s}) + tm.assert_frame_equal(df, expected) + + s = Series(arr, index=range(3, 13)) + df = DataFrame(s) + expected = DataFrame({0: s}) + tm.assert_frame_equal(df, expected) + + msg = r"Shape of passed values is \(10, 1\), indices imply \(10, 2\)" + with pytest.raises(ValueError, match=msg): + DataFrame(s, columns=[1, 2]) + + # #2234 + a = Series([], name="x", dtype=object) + df = DataFrame(a) + assert df.columns[0] == "x" + + # series with name and w/o + s1 = Series(arr, name="x") + df = DataFrame([s1, arr]).T + expected = DataFrame({"x": s1, "Unnamed 0": arr}, columns=["x", "Unnamed 0"]) + tm.assert_frame_equal(df, expected) + + # this is a bit non-intuitive here; the series collapse down to arrays + df = DataFrame([arr, s1]).T + expected = DataFrame({1: s1, 0: arr}, columns=[0, 1]) + tm.assert_frame_equal(df, expected) + + def test_constructor_Series_named_and_columns(self): + # GH 9232 validation + + s0 = Series(range(5), name=0) + s1 = Series(range(5), name=1) + + # matching name and column gives standard frame + tm.assert_frame_equal(DataFrame(s0, columns=[0]), s0.to_frame()) + tm.assert_frame_equal(DataFrame(s1, columns=[1]), s1.to_frame()) + + # non-matching produces empty frame + assert DataFrame(s0, columns=[1]).empty + assert DataFrame(s1, columns=[0]).empty + + def test_constructor_Series_differently_indexed(self): + # name + s1 = Series([1, 2, 3], index=["a", "b", "c"], name="x") + + # no name + s2 = Series([1, 2, 3], index=["a", "b", "c"]) + + other_index = Index(["a", "b"]) + + df1 = DataFrame(s1, index=other_index) + exp1 = DataFrame(s1.reindex(other_index)) + assert df1.columns[0] == "x" + tm.assert_frame_equal(df1, exp1) + + df2 = DataFrame(s2, index=other_index) + exp2 = DataFrame(s2.reindex(other_index)) + assert df2.columns[0] == 0 + tm.assert_index_equal(df2.index, other_index) + tm.assert_frame_equal(df2, exp2) + + @pytest.mark.parametrize( + "name_in1,name_in2,name_in3,name_out", + [ + ("idx", "idx", "idx", "idx"), + ("idx", "idx", None, None), + ("idx", None, None, None), + ("idx1", "idx2", None, None), + ("idx1", "idx1", "idx2", None), + ("idx1", "idx2", "idx3", None), + (None, None, None, None), + ], + ) + def test_constructor_index_names(self, name_in1, name_in2, name_in3, name_out): + # GH13475 + indices = [ + Index(["a", "b", "c"], name=name_in1), + Index(["b", "c", "d"], name=name_in2), + Index(["c", "d", "e"], name=name_in3), + ] + series = { + c: Series([0, 1, 2], index=i) for i, c in zip(indices, ["x", "y", "z"]) + } + result = DataFrame(series) + + exp_ind = Index(["a", "b", "c", "d", "e"], name=name_out) + expected = DataFrame( + { + "x": [0, 1, 2, np.nan, np.nan], + "y": [np.nan, 0, 1, 2, np.nan], + "z": [np.nan, np.nan, 0, 1, 2], + }, + index=exp_ind, + ) + + tm.assert_frame_equal(result, expected) + + def test_constructor_manager_resize(self, float_frame): + index = list(float_frame.index[:5]) + columns = list(float_frame.columns[:3]) + + msg = "Passing a BlockManager to DataFrame" + with tm.assert_produces_warning( + DeprecationWarning, match=msg, check_stacklevel=False + ): + result = DataFrame(float_frame._mgr, index=index, columns=columns) + tm.assert_index_equal(result.index, Index(index)) + tm.assert_index_equal(result.columns, Index(columns)) + + def test_constructor_mix_series_nonseries(self, float_frame): + df = DataFrame( + {"A": float_frame["A"], "B": list(float_frame["B"])}, columns=["A", "B"] + ) + tm.assert_frame_equal(df, float_frame.loc[:, ["A", "B"]]) + + msg = "does not match index length" + with pytest.raises(ValueError, match=msg): + DataFrame({"A": float_frame["A"], "B": list(float_frame["B"])[:-2]}) + + def test_constructor_miscast_na_int_dtype(self): + msg = r"Cannot convert non-finite values \(NA or inf\) to integer" + + with pytest.raises(IntCastingNaNError, match=msg): + DataFrame([[np.nan, 1], [1, 0]], dtype=np.int64) + + def test_constructor_column_duplicates(self): + # it works! #2079 + df = DataFrame([[8, 5]], columns=["a", "a"]) + edf = DataFrame([[8, 5]]) + edf.columns = ["a", "a"] + + tm.assert_frame_equal(df, edf) + + idf = DataFrame.from_records([(8, 5)], columns=["a", "a"]) + + tm.assert_frame_equal(idf, edf) + + def test_constructor_empty_with_string_dtype(self, using_infer_string): + # GH 9428 + expected = DataFrame(index=[0, 1], columns=[0, 1], dtype=object) + expected_str = DataFrame( + index=[0, 1], columns=[0, 1], dtype=pd.StringDtype(na_value=np.nan) + ) + + df = DataFrame(index=[0, 1], columns=[0, 1], dtype=str) + if using_infer_string: + tm.assert_frame_equal(df, expected_str) + else: + tm.assert_frame_equal(df, expected) + df = DataFrame(index=[0, 1], columns=[0, 1], dtype=np.str_) + tm.assert_frame_equal(df, expected) + df = DataFrame(index=[0, 1], columns=[0, 1], dtype="U5") + tm.assert_frame_equal(df, expected) + + def test_constructor_empty_with_string_extension(self, nullable_string_dtype): + # GH 34915 + expected = DataFrame(columns=["c1"], dtype=nullable_string_dtype) + df = DataFrame(columns=["c1"], dtype=nullable_string_dtype) + tm.assert_frame_equal(df, expected) + + def test_constructor_single_value(self): + # expecting single value upcasting here + df = DataFrame(0.0, index=[1, 2, 3], columns=["a", "b", "c"]) + tm.assert_frame_equal( + df, DataFrame(np.zeros(df.shape).astype("float64"), df.index, df.columns) + ) + + df = DataFrame(0, index=[1, 2, 3], columns=["a", "b", "c"]) + tm.assert_frame_equal( + df, DataFrame(np.zeros(df.shape).astype("int64"), df.index, df.columns) + ) + + df = DataFrame("a", index=[1, 2], columns=["a", "c"]) + tm.assert_frame_equal( + df, + DataFrame( + np.array([["a", "a"], ["a", "a"]], dtype=object), + index=[1, 2], + columns=["a", "c"], + ), + ) + + msg = "DataFrame constructor not properly called!" + with pytest.raises(ValueError, match=msg): + DataFrame("a", [1, 2]) + with pytest.raises(ValueError, match=msg): + DataFrame("a", columns=["a", "c"]) + + msg = "incompatible data and dtype" + with pytest.raises(TypeError, match=msg): + DataFrame("a", [1, 2], ["a", "c"], float) + + def test_constructor_with_datetimes(self, using_infer_string): + intname = np.dtype(int).name + floatname = np.dtype(np.float64).name + objectname = np.dtype(np.object_).name + + # single item + df = DataFrame( + { + "A": 1, + "B": "foo", + "C": "bar", + "D": Timestamp("20010101"), + "E": datetime(2001, 1, 2, 0, 0), + }, + index=np.arange(10), + ) + result = df.dtypes + expected = Series( + [np.dtype("int64")] + + [ + np.dtype(objectname) + if not using_infer_string + else pd.StringDtype(na_value=np.nan) + ] + * 2 + + [np.dtype("M8[s]"), np.dtype("M8[us]")], + index=list("ABCDE"), + ) + tm.assert_series_equal(result, expected) + + # check with ndarray construction ndim==0 (e.g. we are passing a ndim 0 + # ndarray with a dtype specified) + df = DataFrame( + { + "a": 1.0, + "b": 2, + "c": "foo", + floatname: np.array(1.0, dtype=floatname), + intname: np.array(1, dtype=intname), + }, + index=np.arange(10), + ) + result = df.dtypes + expected = Series( + [np.dtype("float64")] + + [np.dtype("int64")] + + [ + np.dtype("object") + if not using_infer_string + else pd.StringDtype(na_value=np.nan) + ] + + [np.dtype("float64")] + + [np.dtype(intname)], + index=["a", "b", "c", floatname, intname], + ) + tm.assert_series_equal(result, expected) + + # check with ndarray construction ndim>0 + df = DataFrame( + { + "a": 1.0, + "b": 2, + "c": "foo", + floatname: np.array([1.0] * 10, dtype=floatname), + intname: np.array([1] * 10, dtype=intname), + }, + index=np.arange(10), + ) + result = df.dtypes + expected = Series( + [np.dtype("float64")] + + [np.dtype("int64")] + + [ + np.dtype("object") + if not using_infer_string + else pd.StringDtype(na_value=np.nan) + ] + + [np.dtype("float64")] + + [np.dtype(intname)], + index=["a", "b", "c", floatname, intname], + ) + tm.assert_series_equal(result, expected) + + def test_constructor_with_datetimes1(self): + # GH 2809 + ind = date_range(start="2000-01-01", freq="D", periods=10) + datetimes = [ts.to_pydatetime() for ts in ind] + datetime_s = Series(datetimes) + assert datetime_s.dtype == "M8[ns]" + + def test_constructor_with_datetimes2(self): + # GH 2810 + ind = date_range(start="2000-01-01", freq="D", periods=10) + datetimes = [ts.to_pydatetime() for ts in ind] + dates = [ts.date() for ts in ind] + df = DataFrame(datetimes, columns=["datetimes"]) + df["dates"] = dates + result = df.dtypes + expected = Series( + [np.dtype("datetime64[ns]"), np.dtype("object")], + index=["datetimes", "dates"], + ) + tm.assert_series_equal(result, expected) + + def test_constructor_with_datetimes3(self): + # GH 7594 + # don't coerce tz-aware + tz = pytz.timezone("US/Eastern") + dt = tz.localize(datetime(2012, 1, 1)) + + df = DataFrame({"End Date": dt}, index=[0]) + assert df.iat[0, 0] == dt + tm.assert_series_equal( + df.dtypes, Series({"End Date": "datetime64[us, US/Eastern]"}, dtype=object) + ) + + df = DataFrame([{"End Date": dt}]) + assert df.iat[0, 0] == dt + tm.assert_series_equal( + df.dtypes, Series({"End Date": "datetime64[ns, US/Eastern]"}, dtype=object) + ) + + def test_constructor_with_datetimes4(self): + # tz-aware (UTC and other tz's) + # GH 8411 + dr = date_range("20130101", periods=3) + df = DataFrame({"value": dr}) + assert df.iat[0, 0].tz is None + dr = date_range("20130101", periods=3, tz="UTC") + df = DataFrame({"value": dr}) + assert str(df.iat[0, 0].tz) == "UTC" + dr = date_range("20130101", periods=3, tz="US/Eastern") + df = DataFrame({"value": dr}) + assert str(df.iat[0, 0].tz) == "US/Eastern" + + def test_constructor_with_datetimes5(self): + # GH 7822 + # preserver an index with a tz on dict construction + i = date_range("1/1/2011", periods=5, freq="10s", tz="US/Eastern") + + expected = DataFrame({"a": i.to_series().reset_index(drop=True)}) + df = DataFrame() + df["a"] = i + tm.assert_frame_equal(df, expected) + + df = DataFrame({"a": i}) + tm.assert_frame_equal(df, expected) + + def test_constructor_with_datetimes6(self): + # multiples + i = date_range("1/1/2011", periods=5, freq="10s", tz="US/Eastern") + i_no_tz = date_range("1/1/2011", periods=5, freq="10s") + df = DataFrame({"a": i, "b": i_no_tz}) + expected = DataFrame({"a": i.to_series().reset_index(drop=True), "b": i_no_tz}) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "arr", + [ + np.array([None, None, None, None, datetime.now(), None]), + np.array([None, None, datetime.now(), None]), + [[np.datetime64("NaT")], [None]], + [[np.datetime64("NaT")], [pd.NaT]], + [[None], [np.datetime64("NaT")]], + [[None], [pd.NaT]], + [[pd.NaT], [np.datetime64("NaT")]], + [[pd.NaT], [None]], + ], + ) + def test_constructor_datetimes_with_nulls(self, arr): + # gh-15869, GH#11220 + result = DataFrame(arr).dtypes + expected = Series([np.dtype("datetime64[ns]")]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("order", ["K", "A", "C", "F"]) + @pytest.mark.parametrize( + "unit", + ["M", "D", "h", "m", "s", "ms", "us", "ns"], + ) + def test_constructor_datetimes_non_ns(self, order, unit): + dtype = f"datetime64[{unit}]" + na = np.array( + [ + ["2015-01-01", "2015-01-02", "2015-01-03"], + ["2017-01-01", "2017-01-02", "2017-02-03"], + ], + dtype=dtype, + order=order, + ) + df = DataFrame(na) + expected = DataFrame(na.astype("M8[ns]")) + if unit in ["M", "D", "h", "m"]: + with pytest.raises(TypeError, match="Cannot cast"): + expected.astype(dtype) + + # instead the constructor casts to the closest supported reso, i.e. "s" + expected = expected.astype("datetime64[s]") + else: + expected = expected.astype(dtype=dtype) + + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("order", ["K", "A", "C", "F"]) + @pytest.mark.parametrize( + "unit", + [ + "D", + "h", + "m", + "s", + "ms", + "us", + "ns", + ], + ) + def test_constructor_timedelta_non_ns(self, order, unit): + dtype = f"timedelta64[{unit}]" + na = np.array( + [ + [np.timedelta64(1, "D"), np.timedelta64(2, "D")], + [np.timedelta64(4, "D"), np.timedelta64(5, "D")], + ], + dtype=dtype, + order=order, + ) + df = DataFrame(na) + if unit in ["D", "h", "m"]: + # we get the nearest supported unit, i.e. "s" + exp_unit = "s" + else: + exp_unit = unit + exp_dtype = np.dtype(f"m8[{exp_unit}]") + expected = DataFrame( + [ + [Timedelta(1, "D"), Timedelta(2, "D")], + [Timedelta(4, "D"), Timedelta(5, "D")], + ], + dtype=exp_dtype, + ) + # TODO(2.0): ideally we should get the same 'expected' without passing + # dtype=exp_dtype. + tm.assert_frame_equal(df, expected) + + def test_constructor_for_list_with_dtypes(self, using_infer_string): + # test list of lists/ndarrays + df = DataFrame([np.arange(5) for x in range(5)]) + result = df.dtypes + expected = Series([np.dtype("int")] * 5) + tm.assert_series_equal(result, expected) + + df = DataFrame([np.array(np.arange(5), dtype="int32") for x in range(5)]) + result = df.dtypes + expected = Series([np.dtype("int32")] * 5) + tm.assert_series_equal(result, expected) + + # overflow issue? (we always expected int64 upcasting here) + df = DataFrame({"a": [2**31, 2**31 + 1]}) + assert df.dtypes.iloc[0] == np.dtype("int64") + + # GH #2751 (construction with no index specified), make sure we cast to + # platform values + df = DataFrame([1, 2]) + assert df.dtypes.iloc[0] == np.dtype("int64") + + df = DataFrame([1.0, 2.0]) + assert df.dtypes.iloc[0] == np.dtype("float64") + + df = DataFrame({"a": [1, 2]}) + assert df.dtypes.iloc[0] == np.dtype("int64") + + df = DataFrame({"a": [1.0, 2.0]}) + assert df.dtypes.iloc[0] == np.dtype("float64") + + df = DataFrame({"a": 1}, index=range(3)) + assert df.dtypes.iloc[0] == np.dtype("int64") + + df = DataFrame({"a": 1.0}, index=range(3)) + assert df.dtypes.iloc[0] == np.dtype("float64") + + # with object list + df = DataFrame( + { + "a": [1, 2, 4, 7], + "b": [1.2, 2.3, 5.1, 6.3], + "c": list("abcd"), + "d": [datetime(2000, 1, 1) for i in range(4)], + "e": [1.0, 2, 4.0, 7], + } + ) + result = df.dtypes + expected = Series( + [ + np.dtype("int64"), + np.dtype("float64"), + np.dtype("object") + if not using_infer_string + else pd.StringDtype(na_value=np.nan), + np.dtype("datetime64[ns]"), + np.dtype("float64"), + ], + index=list("abcde"), + ) + tm.assert_series_equal(result, expected) + + def test_constructor_frame_copy(self, float_frame): + cop = DataFrame(float_frame, copy=True) + cop["A"] = 5 + assert (cop["A"] == 5).all() + assert not (float_frame["A"] == 5).all() + + def test_constructor_frame_shallow_copy(self, float_frame): + # constructing a DataFrame from DataFrame with copy=False should still + # give a "shallow" copy (share data, not attributes) + # https://github.com/pandas-dev/pandas/issues/49523 + orig = float_frame.copy() + cop = DataFrame(float_frame) + assert cop._mgr is not float_frame._mgr + # Overwriting index of copy doesn't change original + cop.index = np.arange(len(cop)) + tm.assert_frame_equal(float_frame, orig) + + def test_constructor_ndarray_copy( + self, float_frame, using_array_manager, using_copy_on_write + ): + if not using_array_manager: + arr = float_frame.values.copy() + df = DataFrame(arr) + + arr[5] = 5 + if using_copy_on_write: + assert not (df.values[5] == 5).all() + else: + assert (df.values[5] == 5).all() + + df = DataFrame(arr, copy=True) + arr[6] = 6 + assert not (df.values[6] == 6).all() + else: + arr = float_frame.values.copy() + # default: copy to ensure contiguous arrays + df = DataFrame(arr) + assert df._mgr.arrays[0].flags.c_contiguous + arr[0, 0] = 100 + assert df.iloc[0, 0] != 100 + + # manually specify copy=False + df = DataFrame(arr, copy=False) + assert not df._mgr.arrays[0].flags.c_contiguous + arr[0, 0] = 1000 + assert df.iloc[0, 0] == 1000 + + def test_constructor_series_copy(self, float_frame): + series = float_frame._series + + df = DataFrame({"A": series["A"]}, copy=True) + # TODO can be replaced with `df.loc[:, "A"] = 5` after deprecation about + # inplace mutation is enforced + df.loc[df.index[0] : df.index[-1], "A"] = 5 + + assert not (series["A"] == 5).all() + + @pytest.mark.parametrize( + "df", + [ + DataFrame([[1, 2, 3], [4, 5, 6]], index=[1, np.nan]), + DataFrame([[1, 2, 3], [4, 5, 6]], columns=[1.1, 2.2, np.nan]), + DataFrame([[0, 1, 2, 3], [4, 5, 6, 7]], columns=[np.nan, 1.1, 2.2, np.nan]), + DataFrame( + [[0.0, 1, 2, 3.0], [4, 5, 6, 7]], columns=[np.nan, 1.1, 2.2, np.nan] + ), + DataFrame([[0.0, 1, 2, 3.0], [4, 5, 6, 7]], columns=[np.nan, 1, 2, 2]), + ], + ) + def test_constructor_with_nas(self, df): + # GH 5016 + # na's in indices + # GH 21428 (non-unique columns) + + for i in range(len(df.columns)): + df.iloc[:, i] + + indexer = np.arange(len(df.columns))[isna(df.columns)] + + # No NaN found -> error + if len(indexer) == 0: + with pytest.raises(KeyError, match="^nan$"): + df.loc[:, np.nan] + # single nan should result in Series + elif len(indexer) == 1: + tm.assert_series_equal(df.iloc[:, indexer[0]], df.loc[:, np.nan]) + # multiple nans should result in DataFrame + else: + tm.assert_frame_equal(df.iloc[:, indexer], df.loc[:, np.nan]) + + def test_constructor_lists_to_object_dtype(self): + # from #1074 + d = DataFrame({"a": [np.nan, False]}) + assert d["a"].dtype == np.object_ + assert not d["a"][1] + + def test_constructor_ndarray_categorical_dtype(self): + cat = Categorical(["A", "B", "C"]) + arr = np.array(cat).reshape(-1, 1) + arr = np.broadcast_to(arr, (3, 4)) + + result = DataFrame(arr, dtype=cat.dtype) + + expected = DataFrame({0: cat, 1: cat, 2: cat, 3: cat}) + tm.assert_frame_equal(result, expected) + + def test_constructor_categorical(self): + # GH8626 + + # dict creation + df = DataFrame({"A": list("abc")}, dtype="category") + expected = Series(list("abc"), dtype="category", name="A") + tm.assert_series_equal(df["A"], expected) + + # to_frame + s = Series(list("abc"), dtype="category") + result = s.to_frame() + expected = Series(list("abc"), dtype="category", name=0) + tm.assert_series_equal(result[0], expected) + result = s.to_frame(name="foo") + expected = Series(list("abc"), dtype="category", name="foo") + tm.assert_series_equal(result["foo"], expected) + + # list-like creation + df = DataFrame(list("abc"), dtype="category") + expected = Series(list("abc"), dtype="category", name=0) + tm.assert_series_equal(df[0], expected) + + def test_construct_from_1item_list_of_categorical(self): + # pre-2.0 this behaved as DataFrame({0: cat}), in 2.0 we remove + # Categorical special case + # ndim != 1 + cat = Categorical(list("abc")) + df = DataFrame([cat]) + expected = DataFrame([cat.astype(object)]) + tm.assert_frame_equal(df, expected) + + def test_construct_from_list_of_categoricals(self): + # pre-2.0 this behaved as DataFrame({0: cat}), in 2.0 we remove + # Categorical special case + + df = DataFrame([Categorical(list("abc")), Categorical(list("abd"))]) + expected = DataFrame([["a", "b", "c"], ["a", "b", "d"]]) + tm.assert_frame_equal(df, expected) + + def test_from_nested_listlike_mixed_types(self): + # pre-2.0 this behaved as DataFrame({0: cat}), in 2.0 we remove + # Categorical special case + # mixed + df = DataFrame([Categorical(list("abc")), list("def")]) + expected = DataFrame([["a", "b", "c"], ["d", "e", "f"]]) + tm.assert_frame_equal(df, expected) + + def test_construct_from_listlikes_mismatched_lengths(self): + df = DataFrame([Categorical(list("abc")), Categorical(list("abdefg"))]) + expected = DataFrame([list("abc"), list("abdefg")]) + tm.assert_frame_equal(df, expected) + + def test_constructor_categorical_series(self): + items = [1, 2, 3, 1] + exp = Series(items).astype("category") + res = Series(items, dtype="category") + tm.assert_series_equal(res, exp) + + items = ["a", "b", "c", "a"] + exp = Series(items).astype("category") + res = Series(items, dtype="category") + tm.assert_series_equal(res, exp) + + # insert into frame with different index + # GH 8076 + index = date_range("20000101", periods=3) + expected = Series( + Categorical(values=[np.nan, np.nan, np.nan], categories=["a", "b", "c"]) + ) + expected.index = index + + expected = DataFrame({"x": expected}) + df = DataFrame({"x": Series(["a", "b", "c"], dtype="category")}, index=index) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "dtype", + tm.ALL_NUMERIC_DTYPES + + tm.DATETIME64_DTYPES + + tm.TIMEDELTA64_DTYPES + + tm.BOOL_DTYPES, + ) + def test_check_dtype_empty_numeric_column(self, dtype): + # GH24386: Ensure dtypes are set correctly for an empty DataFrame. + # Empty DataFrame is generated via dictionary data with non-overlapping columns. + data = DataFrame({"a": [1, 2]}, columns=["b"], dtype=dtype) + + assert data.b.dtype == dtype + + @pytest.mark.parametrize( + "dtype", tm.STRING_DTYPES + tm.BYTES_DTYPES + tm.OBJECT_DTYPES + ) + def test_check_dtype_empty_string_column(self, request, dtype, using_array_manager): + # GH24386: Ensure dtypes are set correctly for an empty DataFrame. + # Empty DataFrame is generated via dictionary data with non-overlapping columns. + data = DataFrame({"a": [1, 2]}, columns=["b"], dtype=dtype) + + if using_array_manager and dtype in tm.BYTES_DTYPES: + # TODO(ArrayManager) astype to bytes dtypes does not yet give object dtype + td.mark_array_manager_not_yet_implemented(request) + + assert data.b.dtype.name == "object" + + def test_to_frame_with_falsey_names(self): + # GH 16114 + result = Series(name=0, dtype=object).to_frame().dtypes + expected = Series({0: object}) + tm.assert_series_equal(result, expected) + + result = DataFrame(Series(name=0, dtype=object)).dtypes + tm.assert_series_equal(result, expected) + + @pytest.mark.arm_slow + @pytest.mark.parametrize("dtype", [None, "uint8", "category"]) + def test_constructor_range_dtype(self, dtype): + expected = DataFrame({"A": [0, 1, 2, 3, 4]}, dtype=dtype or "int64") + + # GH 26342 + result = DataFrame(range(5), columns=["A"], dtype=dtype) + tm.assert_frame_equal(result, expected) + + # GH 16804 + result = DataFrame({"A": range(5)}, dtype=dtype) + tm.assert_frame_equal(result, expected) + + def test_frame_from_list_subclass(self): + # GH21226 + class List(list): + pass + + expected = DataFrame([[1, 2, 3], [4, 5, 6]]) + result = DataFrame(List([List([1, 2, 3]), List([4, 5, 6])])) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "extension_arr", + [ + Categorical(list("aabbc")), + SparseArray([1, np.nan, np.nan, np.nan]), + IntervalArray([Interval(0, 1), Interval(1, 5)]), + PeriodArray(pd.period_range(start="1/1/2017", end="1/1/2018", freq="M")), + ], + ) + def test_constructor_with_extension_array(self, extension_arr): + # GH11363 + expected = DataFrame(Series(extension_arr)) + result = DataFrame(extension_arr) + tm.assert_frame_equal(result, expected) + + def test_datetime_date_tuple_columns_from_dict(self): + # GH 10863 + v = date.today() + tup = v, v + result = DataFrame({tup: Series(range(3), index=range(3))}, columns=[tup]) + expected = DataFrame([0, 1, 2], columns=Index(Series([tup]))) + tm.assert_frame_equal(result, expected) + + def test_construct_with_two_categoricalindex_series(self): + # GH 14600 + s1 = Series([39, 6, 4], index=CategoricalIndex(["female", "male", "unknown"])) + s2 = Series( + [2, 152, 2, 242, 150], + index=CategoricalIndex(["f", "female", "m", "male", "unknown"]), + ) + result = DataFrame([s1, s2]) + expected = DataFrame( + np.array([[39, 6, 4, np.nan, np.nan], [152.0, 242.0, 150.0, 2.0, 2.0]]), + columns=["female", "male", "unknown", "f", "m"], + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:invalid value encountered in cast:RuntimeWarning" + ) + def test_constructor_series_nonexact_categoricalindex(self): + # GH 42424 + ser = Series(range(100)) + ser1 = cut(ser, 10).value_counts().head(5) + ser2 = cut(ser, 10).value_counts().tail(5) + result = DataFrame({"1": ser1, "2": ser2}) + index = CategoricalIndex( + [ + Interval(-0.099, 9.9, closed="right"), + Interval(9.9, 19.8, closed="right"), + Interval(19.8, 29.7, closed="right"), + Interval(29.7, 39.6, closed="right"), + Interval(39.6, 49.5, closed="right"), + Interval(49.5, 59.4, closed="right"), + Interval(59.4, 69.3, closed="right"), + Interval(69.3, 79.2, closed="right"), + Interval(79.2, 89.1, closed="right"), + Interval(89.1, 99, closed="right"), + ], + ordered=True, + ) + expected = DataFrame( + {"1": [10] * 5 + [np.nan] * 5, "2": [np.nan] * 5 + [10] * 5}, index=index + ) + tm.assert_frame_equal(expected, result) + + def test_from_M8_structured(self): + dates = [(datetime(2012, 9, 9, 0, 0), datetime(2012, 9, 8, 15, 10))] + arr = np.array(dates, dtype=[("Date", "M8[us]"), ("Forecasting", "M8[us]")]) + df = DataFrame(arr) + + assert df["Date"][0] == dates[0][0] + assert df["Forecasting"][0] == dates[0][1] + + s = Series(arr["Date"]) + assert isinstance(s[0], Timestamp) + assert s[0] == dates[0][0] + + def test_from_datetime_subclass(self): + # GH21142 Verify whether Datetime subclasses are also of dtype datetime + class DatetimeSubclass(datetime): + pass + + data = DataFrame({"datetime": [DatetimeSubclass(2020, 1, 1, 1, 1)]}) + assert data.datetime.dtype == "datetime64[ns]" + + def test_with_mismatched_index_length_raises(self): + # GH#33437 + dti = date_range("2016-01-01", periods=3, tz="US/Pacific") + msg = "Shape of passed values|Passed arrays should have the same length" + with pytest.raises(ValueError, match=msg): + DataFrame(dti, index=range(4)) + + def test_frame_ctor_datetime64_column(self): + rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s") + dates = np.asarray(rng) + + df = DataFrame( + {"A": np.random.default_rng(2).standard_normal(len(rng)), "B": dates} + ) + assert np.issubdtype(df["B"].dtype, np.dtype("M8[ns]")) + + def test_dataframe_constructor_infer_multiindex(self): + index_lists = [["a", "a", "b", "b"], ["x", "y", "x", "y"]] + + multi = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), + index=[np.array(x) for x in index_lists], + ) + assert isinstance(multi.index, MultiIndex) + assert not isinstance(multi.columns, MultiIndex) + + multi = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), columns=index_lists + ) + assert isinstance(multi.columns, MultiIndex) + + @pytest.mark.parametrize( + "input_vals", + [ + ([1, 2]), + (["1", "2"]), + (list(date_range("1/1/2011", periods=2, freq="h"))), + (list(date_range("1/1/2011", periods=2, freq="h", tz="US/Eastern"))), + ([Interval(left=0, right=5)]), + ], + ) + def test_constructor_list_str(self, input_vals, string_dtype): + # GH#16605 + # Ensure that data elements are converted to strings when + # dtype is str, 'str', or 'U' + + result = DataFrame({"A": input_vals}, dtype=string_dtype) + expected = DataFrame({"A": input_vals}).astype({"A": string_dtype}) + tm.assert_frame_equal(result, expected) + + def test_constructor_list_str_na(self, string_dtype): + result = DataFrame({"A": [1.0, 2.0, None]}, dtype=string_dtype) + expected = DataFrame({"A": ["1.0", "2.0", None]}, dtype=object) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("copy", [False, True]) + def test_dict_nocopy( + self, + request, + copy, + any_numeric_ea_dtype, + any_numpy_dtype, + using_array_manager, + using_copy_on_write, + ): + if ( + using_array_manager + and not copy + and any_numpy_dtype not in tm.STRING_DTYPES + tm.BYTES_DTYPES + ): + # TODO(ArrayManager) properly honor copy keyword for dict input + td.mark_array_manager_not_yet_implemented(request) + + a = np.array([1, 2], dtype=any_numpy_dtype) + b = np.array([3, 4], dtype=any_numpy_dtype) + if b.dtype.kind in ["S", "U"]: + # These get cast, making the checks below more cumbersome + pytest.skip(f"{b.dtype} get cast, making the checks below more cumbersome") + + c = pd.array([1, 2], dtype=any_numeric_ea_dtype) + c_orig = c.copy() + df = DataFrame({"a": a, "b": b, "c": c}, copy=copy) + + def get_base(obj): + if isinstance(obj, np.ndarray): + return obj.base + elif isinstance(obj.dtype, np.dtype): + # i.e. DatetimeArray, TimedeltaArray + return obj._ndarray.base + else: + raise TypeError + + def check_views(c_only: bool = False): + # written to work for either BlockManager or ArrayManager + + # Check that the underlying data behind df["c"] is still `c` + # after setting with iloc. Since we don't know which entry in + # df._mgr.arrays corresponds to df["c"], we just check that exactly + # one of these arrays is `c`. GH#38939 + assert sum(x is c for x in df._mgr.arrays) == 1 + if c_only: + # If we ever stop consolidating in setitem_with_indexer, + # this will become unnecessary. + return + + assert ( + sum( + get_base(x) is a + for x in df._mgr.arrays + if isinstance(x.dtype, np.dtype) + ) + == 1 + ) + assert ( + sum( + get_base(x) is b + for x in df._mgr.arrays + if isinstance(x.dtype, np.dtype) + ) + == 1 + ) + + if not copy: + # constructor preserves views + check_views() + + # TODO: most of the rest of this test belongs in indexing tests + if lib.is_np_dtype(df.dtypes.iloc[0], "fciuO"): + warn = None + else: + warn = FutureWarning + with tm.assert_produces_warning(warn, match="incompatible dtype"): + df.iloc[0, 0] = 0 + df.iloc[0, 1] = 0 + if not copy: + check_views(True) + + # FIXME(GH#35417): until GH#35417, iloc.setitem into EA values does not preserve + # view, so we have to check in the other direction + df.iloc[:, 2] = pd.array([45, 46], dtype=c.dtype) + assert df.dtypes.iloc[2] == c.dtype + if not copy and not using_copy_on_write: + check_views(True) + + if copy: + if a.dtype.kind == "M": + assert a[0] == a.dtype.type(1, "ns") + assert b[0] == b.dtype.type(3, "ns") + else: + assert a[0] == a.dtype.type(1) + assert b[0] == b.dtype.type(3) + # FIXME(GH#35417): enable after GH#35417 + assert c[0] == c_orig[0] # i.e. df.iloc[0, 2]=45 did *not* update c + elif not using_copy_on_write: + # TODO: we can call check_views if we stop consolidating + # in setitem_with_indexer + assert c[0] == 45 # i.e. df.iloc[0, 2]=45 *did* update c + # TODO: we can check b[0] == 0 if we stop consolidating in + # setitem_with_indexer (except for datetimelike?) + + def test_construct_from_dict_ea_series(self): + # GH#53744 - default of copy=True should also apply for Series with + # extension dtype + ser = Series([1, 2, 3], dtype="Int64") + df = DataFrame({"a": ser}) + assert not np.shares_memory(ser.values._data, df["a"].values._data) + + def test_from_series_with_name_with_columns(self): + # GH 7893 + result = DataFrame(Series(1, name="foo"), columns=["bar"]) + expected = DataFrame(columns=["bar"]) + tm.assert_frame_equal(result, expected) + + def test_nested_list_columns(self): + # GH 14467 + result = DataFrame( + [[1, 2, 3], [4, 5, 6]], columns=[["A", "A", "A"], ["a", "b", "c"]] + ) + expected = DataFrame( + [[1, 2, 3], [4, 5, 6]], + columns=MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("A", "c")]), + ) + tm.assert_frame_equal(result, expected) + + def test_from_2d_object_array_of_periods_or_intervals(self): + # Period analogue to GH#26825 + pi = pd.period_range("2016-04-05", periods=3) + data = pi._data.astype(object).reshape(1, -1) + df = DataFrame(data) + assert df.shape == (1, 3) + assert (df.dtypes == pi.dtype).all() + assert (df == pi).all().all() + + ii = pd.IntervalIndex.from_breaks([3, 4, 5, 6]) + data2 = ii._data.astype(object).reshape(1, -1) + df2 = DataFrame(data2) + assert df2.shape == (1, 3) + assert (df2.dtypes == ii.dtype).all() + assert (df2 == ii).all().all() + + # mixed + data3 = np.r_[data, data2, data, data2].T + df3 = DataFrame(data3) + expected = DataFrame({0: pi, 1: ii, 2: pi, 3: ii}) + tm.assert_frame_equal(df3, expected) + + @pytest.mark.parametrize( + "col_a, col_b", + [ + ([[1], [2]], np.array([[1], [2]])), + (np.array([[1], [2]]), [[1], [2]]), + (np.array([[1], [2]]), np.array([[1], [2]])), + ], + ) + def test_error_from_2darray(self, col_a, col_b): + msg = "Per-column arrays must each be 1-dimensional" + with pytest.raises(ValueError, match=msg): + DataFrame({"a": col_a, "b": col_b}) + + def test_from_dict_with_missing_copy_false(self): + # GH#45369 filled columns should not be views of one another + df = DataFrame(index=[1, 2, 3], columns=["a", "b", "c"], copy=False) + assert not np.shares_memory(df["a"]._values, df["b"]._values) + + df.iloc[0, 0] = 0 + expected = DataFrame( + { + "a": [0, np.nan, np.nan], + "b": [np.nan, np.nan, np.nan], + "c": [np.nan, np.nan, np.nan], + }, + index=[1, 2, 3], + dtype=object, + ) + tm.assert_frame_equal(df, expected) + + def test_construction_empty_array_multi_column_raises(self): + # GH#46822 + msg = r"Shape of passed values is \(0, 1\), indices imply \(0, 2\)" + with pytest.raises(ValueError, match=msg): + DataFrame(data=np.array([]), columns=["a", "b"]) + + def test_construct_with_strings_and_none(self): + # GH#32218 + df = DataFrame(["1", "2", None], columns=["a"], dtype="str") + expected = DataFrame({"a": ["1", "2", None]}, dtype="str") + tm.assert_frame_equal(df, expected) + + def test_frame_string_inference(self): + # GH#54430 + dtype = pd.StringDtype(na_value=np.nan) + expected = DataFrame( + {"a": ["a", "b"]}, dtype=dtype, columns=Index(["a"], dtype=dtype) + ) + with pd.option_context("future.infer_string", True): + df = DataFrame({"a": ["a", "b"]}) + tm.assert_frame_equal(df, expected) + + expected = DataFrame( + {"a": ["a", "b"]}, + dtype=dtype, + columns=Index(["a"], dtype=dtype), + index=Index(["x", "y"], dtype=dtype), + ) + with pd.option_context("future.infer_string", True): + df = DataFrame({"a": ["a", "b"]}, index=["x", "y"]) + tm.assert_frame_equal(df, expected) + + expected = DataFrame( + {"a": ["a", 1]}, dtype="object", columns=Index(["a"], dtype=dtype) + ) + with pd.option_context("future.infer_string", True): + df = DataFrame({"a": ["a", 1]}) + tm.assert_frame_equal(df, expected) + + expected = DataFrame( + {"a": ["a", "b"]}, dtype="object", columns=Index(["a"], dtype=dtype) + ) + with pd.option_context("future.infer_string", True): + df = DataFrame({"a": ["a", "b"]}, dtype="object") + tm.assert_frame_equal(df, expected) + + def test_frame_string_inference_array_string_dtype(self): + # GH#54496 + dtype = pd.StringDtype(na_value=np.nan) + expected = DataFrame( + {"a": ["a", "b"]}, dtype=dtype, columns=Index(["a"], dtype=dtype) + ) + with pd.option_context("future.infer_string", True): + df = DataFrame({"a": np.array(["a", "b"])}) + tm.assert_frame_equal(df, expected) + + expected = DataFrame({0: ["a", "b"], 1: ["c", "d"]}, dtype=dtype) + with pd.option_context("future.infer_string", True): + df = DataFrame(np.array([["a", "c"], ["b", "d"]])) + tm.assert_frame_equal(df, expected) + + expected = DataFrame( + {"a": ["a", "b"], "b": ["c", "d"]}, + dtype=dtype, + columns=Index(["a", "b"], dtype=dtype), + ) + with pd.option_context("future.infer_string", True): + df = DataFrame(np.array([["a", "c"], ["b", "d"]]), columns=["a", "b"]) + tm.assert_frame_equal(df, expected) + + def test_frame_string_inference_block_dim(self): + # GH#55363 + with pd.option_context("future.infer_string", True): + df = DataFrame(np.array([["hello", "goodbye"], ["hello", "Hello"]])) + assert df._mgr.blocks[0].ndim == 2 + + def test_inference_on_pandas_objects(self): + # GH#56012 + idx = Index([Timestamp("2019-12-31")], dtype=object) + with tm.assert_produces_warning(FutureWarning, match="Dtype inference"): + result = DataFrame(idx, columns=["a"]) + assert result.dtypes.iloc[0] != np.object_ + result = DataFrame({"a": idx}) + assert result.dtypes.iloc[0] == np.object_ + + ser = Series([Timestamp("2019-12-31")], dtype=object) + + with tm.assert_produces_warning(FutureWarning, match="Dtype inference"): + result = DataFrame(ser, columns=["a"]) + assert result.dtypes.iloc[0] != np.object_ + result = DataFrame({"a": ser}) + assert result.dtypes.iloc[0] == np.object_ + + +class TestDataFrameConstructorIndexInference: + def test_frame_from_dict_of_series_overlapping_monthly_period_indexes(self): + rng1 = pd.period_range("1/1/1999", "1/1/2012", freq="M") + s1 = Series(np.random.default_rng(2).standard_normal(len(rng1)), rng1) + + rng2 = pd.period_range("1/1/1980", "12/1/2001", freq="M") + s2 = Series(np.random.default_rng(2).standard_normal(len(rng2)), rng2) + df = DataFrame({"s1": s1, "s2": s2}) + + exp = pd.period_range("1/1/1980", "1/1/2012", freq="M") + tm.assert_index_equal(df.index, exp) + + def test_frame_from_dict_with_mixed_tzaware_indexes(self): + # GH#44091 + dti = date_range("2016-01-01", periods=3) + + ser1 = Series(range(3), index=dti) + ser2 = Series(range(3), index=dti.tz_localize("UTC")) + ser3 = Series(range(3), index=dti.tz_localize("US/Central")) + ser4 = Series(range(3)) + + # no tz-naive, but we do have mixed tzs and a non-DTI + df1 = DataFrame({"A": ser2, "B": ser3, "C": ser4}) + exp_index = Index( + list(ser2.index) + list(ser3.index) + list(ser4.index), dtype=object + ) + tm.assert_index_equal(df1.index, exp_index) + + df2 = DataFrame({"A": ser2, "C": ser4, "B": ser3}) + exp_index3 = Index( + list(ser2.index) + list(ser4.index) + list(ser3.index), dtype=object + ) + tm.assert_index_equal(df2.index, exp_index3) + + df3 = DataFrame({"B": ser3, "A": ser2, "C": ser4}) + exp_index3 = Index( + list(ser3.index) + list(ser2.index) + list(ser4.index), dtype=object + ) + tm.assert_index_equal(df3.index, exp_index3) + + df4 = DataFrame({"C": ser4, "B": ser3, "A": ser2}) + exp_index4 = Index( + list(ser4.index) + list(ser3.index) + list(ser2.index), dtype=object + ) + tm.assert_index_equal(df4.index, exp_index4) + + # TODO: not clear if these raising is desired (no extant tests), + # but this is de facto behavior 2021-12-22 + msg = "Cannot join tz-naive with tz-aware DatetimeIndex" + with pytest.raises(TypeError, match=msg): + DataFrame({"A": ser2, "B": ser3, "C": ser4, "D": ser1}) + with pytest.raises(TypeError, match=msg): + DataFrame({"A": ser2, "B": ser3, "D": ser1}) + with pytest.raises(TypeError, match=msg): + DataFrame({"D": ser1, "A": ser2, "B": ser3}) + + @pytest.mark.parametrize( + "key_val, col_vals, col_type", + [ + ["3", ["3", "4"], "utf8"], + [3, [3, 4], "int8"], + ], + ) + def test_dict_data_arrow_column_expansion(self, key_val, col_vals, col_type): + # GH 53617 + pa = pytest.importorskip("pyarrow") + cols = pd.arrays.ArrowExtensionArray( + pa.array(col_vals, type=pa.dictionary(pa.int8(), getattr(pa, col_type)())) + ) + result = DataFrame({key_val: [1, 2]}, columns=cols) + expected = DataFrame([[1, np.nan], [2, np.nan]], columns=cols) + expected.isetitem(1, expected.iloc[:, 1].astype(object)) + tm.assert_frame_equal(result, expected) + + +class TestDataFrameConstructorWithDtypeCoercion: + def test_floating_values_integer_dtype(self): + # GH#40110 make DataFrame behavior with arraylike floating data and + # inty dtype match Series behavior + + arr = np.random.default_rng(2).standard_normal((10, 5)) + + # GH#49599 in 2.0 we raise instead of either + # a) silently ignoring dtype and returningfloat (the old Series behavior) or + # b) rounding (the old DataFrame behavior) + msg = "Trying to coerce float values to integers" + with pytest.raises(ValueError, match=msg): + DataFrame(arr, dtype="i8") + + df = DataFrame(arr.round(), dtype="i8") + assert (df.dtypes == "i8").all() + + # with NaNs, we go through a different path with a different warning + arr[0, 0] = np.nan + msg = r"Cannot convert non-finite values \(NA or inf\) to integer" + with pytest.raises(IntCastingNaNError, match=msg): + DataFrame(arr, dtype="i8") + with pytest.raises(IntCastingNaNError, match=msg): + Series(arr[0], dtype="i8") + # The future (raising) behavior matches what we would get via astype: + msg = r"Cannot convert non-finite values \(NA or inf\) to integer" + with pytest.raises(IntCastingNaNError, match=msg): + DataFrame(arr).astype("i8") + with pytest.raises(IntCastingNaNError, match=msg): + Series(arr[0]).astype("i8") + + +class TestDataFrameConstructorWithDatetimeTZ: + @pytest.mark.parametrize("tz", ["US/Eastern", "dateutil/US/Eastern"]) + def test_construction_preserves_tzaware_dtypes(self, tz): + # after GH#7822 + # these retain the timezones on dict construction + dr = date_range("2011/1/1", "2012/1/1", freq="W-FRI") + dr_tz = dr.tz_localize(tz) + df = DataFrame({"A": "foo", "B": dr_tz}, index=dr) + tz_expected = DatetimeTZDtype("ns", dr_tz.tzinfo) + assert df["B"].dtype == tz_expected + + # GH#2810 (with timezones) + datetimes_naive = [ts.to_pydatetime() for ts in dr] + datetimes_with_tz = [ts.to_pydatetime() for ts in dr_tz] + df = DataFrame({"dr": dr}) + df["dr_tz"] = dr_tz + df["datetimes_naive"] = datetimes_naive + df["datetimes_with_tz"] = datetimes_with_tz + result = df.dtypes + expected = Series( + [ + np.dtype("datetime64[ns]"), + DatetimeTZDtype(tz=tz), + np.dtype("datetime64[ns]"), + DatetimeTZDtype(tz=tz), + ], + index=["dr", "dr_tz", "datetimes_naive", "datetimes_with_tz"], + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("pydt", [True, False]) + def test_constructor_data_aware_dtype_naive(self, tz_aware_fixture, pydt): + # GH#25843, GH#41555, GH#33401 + tz = tz_aware_fixture + ts = Timestamp("2019", tz=tz) + if pydt: + ts = ts.to_pydatetime() + + msg = ( + "Cannot convert timezone-aware data to timezone-naive dtype. " + r"Use pd.Series\(values\).dt.tz_localize\(None\) instead." + ) + with pytest.raises(ValueError, match=msg): + DataFrame({0: [ts]}, dtype="datetime64[ns]") + + msg2 = "Cannot unbox tzaware Timestamp to tznaive dtype" + with pytest.raises(TypeError, match=msg2): + DataFrame({0: ts}, index=[0], dtype="datetime64[ns]") + + with pytest.raises(ValueError, match=msg): + DataFrame([ts], dtype="datetime64[ns]") + + with pytest.raises(ValueError, match=msg): + DataFrame(np.array([ts], dtype=object), dtype="datetime64[ns]") + + with pytest.raises(TypeError, match=msg2): + DataFrame(ts, index=[0], columns=[0], dtype="datetime64[ns]") + + with pytest.raises(ValueError, match=msg): + DataFrame([Series([ts])], dtype="datetime64[ns]") + + with pytest.raises(ValueError, match=msg): + DataFrame([[ts]], columns=[0], dtype="datetime64[ns]") + + def test_from_dict(self): + # 8260 + # support datetime64 with tz + + idx = Index(date_range("20130101", periods=3, tz="US/Eastern"), name="foo") + dr = date_range("20130110", periods=3) + + # construction + df = DataFrame({"A": idx, "B": dr}) + assert df["A"].dtype, "M8[ns, US/Eastern" + assert df["A"].name == "A" + tm.assert_series_equal(df["A"], Series(idx, name="A")) + tm.assert_series_equal(df["B"], Series(dr, name="B")) + + def test_from_index(self): + # from index + idx2 = date_range("20130101", periods=3, tz="US/Eastern", name="foo") + df2 = DataFrame(idx2) + tm.assert_series_equal(df2["foo"], Series(idx2, name="foo")) + df2 = DataFrame(Series(idx2)) + tm.assert_series_equal(df2["foo"], Series(idx2, name="foo")) + + idx2 = date_range("20130101", periods=3, tz="US/Eastern") + df2 = DataFrame(idx2) + tm.assert_series_equal(df2[0], Series(idx2, name=0)) + df2 = DataFrame(Series(idx2)) + tm.assert_series_equal(df2[0], Series(idx2, name=0)) + + def test_frame_dict_constructor_datetime64_1680(self): + dr = date_range("1/1/2012", periods=10) + s = Series(dr, index=dr) + + # it works! + DataFrame({"a": "foo", "b": s}, index=dr) + DataFrame({"a": "foo", "b": s.values}, index=dr) + + def test_frame_datetime64_mixed_index_ctor_1681(self): + dr = date_range("2011/1/1", "2012/1/1", freq="W-FRI") + ts = Series(dr) + + # it works! + d = DataFrame({"A": "foo", "B": ts}, index=dr) + assert d["B"].isna().all() + + def test_frame_timeseries_column(self): + # GH19157 + dr = date_range( + start="20130101T10:00:00", periods=3, freq="min", tz="US/Eastern" + ) + result = DataFrame(dr, columns=["timestamps"]) + expected = DataFrame( + { + "timestamps": [ + Timestamp("20130101T10:00:00", tz="US/Eastern"), + Timestamp("20130101T10:01:00", tz="US/Eastern"), + Timestamp("20130101T10:02:00", tz="US/Eastern"), + ] + } + ) + tm.assert_frame_equal(result, expected) + + def test_nested_dict_construction(self): + # GH22227 + columns = ["Nevada", "Ohio"] + pop = { + "Nevada": {2001: 2.4, 2002: 2.9}, + "Ohio": {2000: 1.5, 2001: 1.7, 2002: 3.6}, + } + result = DataFrame(pop, index=[2001, 2002, 2003], columns=columns) + expected = DataFrame( + [(2.4, 1.7), (2.9, 3.6), (np.nan, np.nan)], + columns=columns, + index=Index([2001, 2002, 2003]), + ) + tm.assert_frame_equal(result, expected) + + def test_from_tzaware_object_array(self): + # GH#26825 2D object array of tzaware timestamps should not raise + dti = date_range("2016-04-05 04:30", periods=3, tz="UTC") + data = dti._data.astype(object).reshape(1, -1) + df = DataFrame(data) + assert df.shape == (1, 3) + assert (df.dtypes == dti.dtype).all() + assert (df == dti).all().all() + + def test_from_tzaware_mixed_object_array(self): + # GH#26825 + arr = np.array( + [ + [ + Timestamp("2013-01-01 00:00:00"), + Timestamp("2013-01-02 00:00:00"), + Timestamp("2013-01-03 00:00:00"), + ], + [ + Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern"), + pd.NaT, + Timestamp("2013-01-03 00:00:00-0500", tz="US/Eastern"), + ], + [ + Timestamp("2013-01-01 00:00:00+0100", tz="CET"), + pd.NaT, + Timestamp("2013-01-03 00:00:00+0100", tz="CET"), + ], + ], + dtype=object, + ).T + res = DataFrame(arr, columns=["A", "B", "C"]) + + expected_dtypes = [ + "datetime64[ns]", + "datetime64[ns, US/Eastern]", + "datetime64[ns, CET]", + ] + assert (res.dtypes == expected_dtypes).all() + + def test_from_2d_ndarray_with_dtype(self): + # GH#12513 + array_dim2 = np.arange(10).reshape((5, 2)) + df = DataFrame(array_dim2, dtype="datetime64[ns, UTC]") + + expected = DataFrame(array_dim2).astype("datetime64[ns, UTC]") + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("typ", [set, frozenset]) + def test_construction_from_set_raises(self, typ): + # https://github.com/pandas-dev/pandas/issues/32582 + values = typ({1, 2, 3}) + msg = f"'{typ.__name__}' type is unordered" + with pytest.raises(TypeError, match=msg): + DataFrame({"a": values}) + + with pytest.raises(TypeError, match=msg): + Series(values) + + def test_construction_from_ndarray_datetimelike(self): + # ensure the underlying arrays are properly wrapped as EA when + # constructed from 2D ndarray + arr = np.arange(0, 12, dtype="datetime64[ns]").reshape(4, 3) + df = DataFrame(arr) + assert all(isinstance(arr, DatetimeArray) for arr in df._mgr.arrays) + + def test_construction_from_ndarray_with_eadtype_mismatched_columns(self): + arr = np.random.default_rng(2).standard_normal((10, 2)) + dtype = pd.array([2.0]).dtype + msg = r"len\(arrays\) must match len\(columns\)" + with pytest.raises(ValueError, match=msg): + DataFrame(arr, columns=["foo"], dtype=dtype) + + arr2 = pd.array([2.0, 3.0, 4.0]) + with pytest.raises(ValueError, match=msg): + DataFrame(arr2, columns=["foo", "bar"]) + + def test_columns_indexes_raise_on_sets(self): + # GH 47215 + data = [[1, 2, 3], [4, 5, 6]] + with pytest.raises(ValueError, match="index cannot be a set"): + DataFrame(data, index={"a", "b"}) + with pytest.raises(ValueError, match="columns cannot be a set"): + DataFrame(data, columns={"a", "b", "c"}) + + # TODO: make this not cast to object in pandas 3.0 + @pytest.mark.skipif( + not np_version_gt2, reason="StringDType only available in numpy 2 and above" + ) + @pytest.mark.parametrize( + "data", + [ + {"a": ["a", "b", "c"], "b": [1.0, 2.0, 3.0], "c": ["d", "e", "f"]}, + ], + ) + def test_np_string_array_object_cast(self, data): + from numpy.dtypes import StringDType + + data["a"] = np.array(data["a"], dtype=StringDType()) + res = DataFrame(data) + assert res["a"].dtype == np.object_ + assert (res["a"] == data["a"]).all() + + +def get1(obj): # TODO: make a helper in tm? + if isinstance(obj, Series): + return obj.iloc[0] + else: + return obj.iloc[0, 0] + + +class TestFromScalar: + @pytest.fixture(params=[list, dict, None]) + def box(self, request): + return request.param + + @pytest.fixture + def constructor(self, frame_or_series, box): + extra = {"index": range(2)} + if frame_or_series is DataFrame: + extra["columns"] = ["A"] + + if box is None: + return functools.partial(frame_or_series, **extra) + + elif box is dict: + if frame_or_series is Series: + return lambda x, **kwargs: frame_or_series( + {0: x, 1: x}, **extra, **kwargs + ) + else: + return lambda x, **kwargs: frame_or_series({"A": x}, **extra, **kwargs) + elif frame_or_series is Series: + return lambda x, **kwargs: frame_or_series([x, x], **extra, **kwargs) + else: + return lambda x, **kwargs: frame_or_series({"A": [x, x]}, **extra, **kwargs) + + @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"]) + def test_from_nat_scalar(self, dtype, constructor): + obj = constructor(pd.NaT, dtype=dtype) + assert np.all(obj.dtypes == dtype) + assert np.all(obj.isna()) + + def test_from_timedelta_scalar_preserves_nanos(self, constructor): + td = Timedelta(1) + + obj = constructor(td, dtype="m8[ns]") + assert get1(obj) == td + + def test_from_timestamp_scalar_preserves_nanos(self, constructor, fixed_now_ts): + ts = fixed_now_ts + Timedelta(1) + + obj = constructor(ts, dtype="M8[ns]") + assert get1(obj) == ts + + def test_from_timedelta64_scalar_object(self, constructor): + td = Timedelta(1) + td64 = td.to_timedelta64() + + obj = constructor(td64, dtype=object) + assert isinstance(get1(obj), np.timedelta64) + + @pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64]) + def test_from_scalar_datetimelike_mismatched(self, constructor, cls): + scalar = cls("NaT", "ns") + dtype = {np.datetime64: "m8[ns]", np.timedelta64: "M8[ns]"}[cls] + + if cls is np.datetime64: + msg1 = "Invalid type for timedelta scalar: " + else: + msg1 = " is not convertible to datetime" + msg = "|".join(["Cannot cast", msg1]) + + with pytest.raises(TypeError, match=msg): + constructor(scalar, dtype=dtype) + + scalar = cls(4, "ns") + with pytest.raises(TypeError, match=msg): + constructor(scalar, dtype=dtype) + + @pytest.mark.parametrize("cls", [datetime, np.datetime64]) + def test_from_out_of_bounds_ns_datetime( + self, constructor, cls, request, box, frame_or_series + ): + # scalar that won't fit in nanosecond dt64, but will fit in microsecond + if box is list or (frame_or_series is Series and box is dict): + mark = pytest.mark.xfail( + reason="Timestamp constructor has been updated to cast dt64 to " + "non-nano, but DatetimeArray._from_sequence has not", + strict=True, + ) + request.applymarker(mark) + + scalar = datetime(9999, 1, 1) + exp_dtype = "M8[us]" # pydatetime objects default to this reso + + if cls is np.datetime64: + scalar = np.datetime64(scalar, "D") + exp_dtype = "M8[s]" # closest reso to input + result = constructor(scalar) + + item = get1(result) + dtype = tm.get_dtype(result) + + assert type(item) is Timestamp + assert item.asm8.dtype == exp_dtype + assert dtype == exp_dtype + + @pytest.mark.skip_ubsan + def test_out_of_s_bounds_datetime64(self, constructor): + scalar = np.datetime64(np.iinfo(np.int64).max, "D") + result = constructor(scalar) + item = get1(result) + assert type(item) is np.datetime64 + dtype = tm.get_dtype(result) + assert dtype == object + + @pytest.mark.parametrize("cls", [timedelta, np.timedelta64]) + def test_from_out_of_bounds_ns_timedelta( + self, constructor, cls, request, box, frame_or_series + ): + # scalar that won't fit in nanosecond td64, but will fit in microsecond + if box is list or (frame_or_series is Series and box is dict): + mark = pytest.mark.xfail( + reason="TimedeltaArray constructor has been updated to cast td64 " + "to non-nano, but TimedeltaArray._from_sequence has not", + strict=True, + ) + request.applymarker(mark) + + scalar = datetime(9999, 1, 1) - datetime(1970, 1, 1) + exp_dtype = "m8[us]" # smallest reso that fits + if cls is np.timedelta64: + scalar = np.timedelta64(scalar, "D") + exp_dtype = "m8[s]" # closest reso to input + result = constructor(scalar) + + item = get1(result) + dtype = tm.get_dtype(result) + + assert type(item) is Timedelta + assert item.asm8.dtype == exp_dtype + assert dtype == exp_dtype + + @pytest.mark.skip_ubsan + @pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64]) + def test_out_of_s_bounds_timedelta64(self, constructor, cls): + scalar = cls(np.iinfo(np.int64).max, "D") + result = constructor(scalar) + item = get1(result) + assert type(item) is cls + dtype = tm.get_dtype(result) + assert dtype == object + + def test_tzaware_data_tznaive_dtype(self, constructor, box, frame_or_series): + tz = "US/Eastern" + ts = Timestamp("2019", tz=tz) + + if box is None or (frame_or_series is DataFrame and box is dict): + msg = "Cannot unbox tzaware Timestamp to tznaive dtype" + err = TypeError + else: + msg = ( + "Cannot convert timezone-aware data to timezone-naive dtype. " + r"Use pd.Series\(values\).dt.tz_localize\(None\) instead." + ) + err = ValueError + + with pytest.raises(err, match=msg): + constructor(ts, dtype="M8[ns]") + + +# TODO: better location for this test? +class TestAllowNonNano: + # Until 2.0, we do not preserve non-nano dt64/td64 when passed as ndarray, + # but do preserve it when passed as DTA/TDA + + @pytest.fixture(params=[True, False]) + def as_td(self, request): + return request.param + + @pytest.fixture + def arr(self, as_td): + values = np.arange(5).astype(np.int64).view("M8[s]") + if as_td: + values = values - values[0] + return TimedeltaArray._simple_new(values, dtype=values.dtype) + else: + return DatetimeArray._simple_new(values, dtype=values.dtype) + + def test_index_allow_non_nano(self, arr): + idx = Index(arr) + assert idx.dtype == arr.dtype + + def test_dti_tdi_allow_non_nano(self, arr, as_td): + if as_td: + idx = pd.TimedeltaIndex(arr) + else: + idx = DatetimeIndex(arr) + assert idx.dtype == arr.dtype + + def test_series_allow_non_nano(self, arr): + ser = Series(arr) + assert ser.dtype == arr.dtype + + def test_frame_allow_non_nano(self, arr): + df = DataFrame(arr) + assert df.dtypes[0] == arr.dtype + + def test_frame_from_dict_allow_non_nano(self, arr): + df = DataFrame({0: arr}) + assert df.dtypes[0] == arr.dtype diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_cumulative.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_cumulative.py new file mode 100644 index 0000000000000000000000000000000000000000..5bd9c426123159fcfcf6bf5289fd08a60dfd91b2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_cumulative.py @@ -0,0 +1,81 @@ +""" +Tests for DataFrame cumulative operations + +See also +-------- +tests.series.test_cumulative +""" + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +class TestDataFrameCumulativeOps: + # --------------------------------------------------------------------- + # Cumulative Operations - cumsum, cummax, ... + + def test_cumulative_ops_smoke(self): + # it works + df = DataFrame({"A": np.arange(20)}, index=np.arange(20)) + df.cummax() + df.cummin() + df.cumsum() + + dm = DataFrame(np.arange(20).reshape(4, 5), index=range(4), columns=range(5)) + # TODO(wesm): do something with this? + dm.cumsum() + + def test_cumprod_smoke(self, datetime_frame): + datetime_frame.iloc[5:10, 0] = np.nan + datetime_frame.iloc[10:15, 1] = np.nan + datetime_frame.iloc[15:, 2] = np.nan + + # ints + df = datetime_frame.fillna(0).astype(int) + df.cumprod(0) + df.cumprod(1) + + # ints32 + df = datetime_frame.fillna(0).astype(np.int32) + df.cumprod(0) + df.cumprod(1) + + @pytest.mark.parametrize("method", ["cumsum", "cumprod", "cummin", "cummax"]) + def test_cumulative_ops_match_series_apply(self, datetime_frame, method): + datetime_frame.iloc[5:10, 0] = np.nan + datetime_frame.iloc[10:15, 1] = np.nan + datetime_frame.iloc[15:, 2] = np.nan + + # axis = 0 + result = getattr(datetime_frame, method)() + expected = datetime_frame.apply(getattr(Series, method)) + tm.assert_frame_equal(result, expected) + + # axis = 1 + result = getattr(datetime_frame, method)(axis=1) + expected = datetime_frame.apply(getattr(Series, method), axis=1) + tm.assert_frame_equal(result, expected) + + # fix issue TODO: GH ref? + assert np.shape(result) == np.shape(datetime_frame) + + def test_cumsum_preserve_dtypes(self): + # GH#19296 dont incorrectly upcast to object + df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3.0], "C": [True, False, False]}) + + result = df.cumsum() + + expected = DataFrame( + { + "A": Series([1, 3, 6], dtype=np.int64), + "B": Series([1, 3, 6], dtype=np.float64), + "C": df["C"].cumsum(), + } + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_iteration.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_iteration.py new file mode 100644 index 0000000000000000000000000000000000000000..a1c23ff05f3e19aca490444216ec295453483e80 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_iteration.py @@ -0,0 +1,160 @@ +import datetime + +import numpy as np +import pytest + +from pandas.compat import ( + IS64, + is_platform_windows, +) + +from pandas import ( + Categorical, + DataFrame, + Series, + date_range, +) +import pandas._testing as tm + + +class TestIteration: + def test_keys(self, float_frame): + assert float_frame.keys() is float_frame.columns + + def test_iteritems(self): + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["a", "a", "b"]) + for k, v in df.items(): + assert isinstance(v, DataFrame._constructor_sliced) + + def test_items(self): + # GH#17213, GH#13918 + cols = ["a", "b", "c"] + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=cols) + for c, (k, v) in zip(cols, df.items()): + assert c == k + assert isinstance(v, Series) + assert (df[k] == v).all() + + def test_items_names(self, float_string_frame): + for k, v in float_string_frame.items(): + assert v.name == k + + def test_iter(self, float_frame): + assert list(float_frame) == list(float_frame.columns) + + def test_iterrows(self, float_frame, float_string_frame): + for k, v in float_frame.iterrows(): + exp = float_frame.loc[k] + tm.assert_series_equal(v, exp) + + for k, v in float_string_frame.iterrows(): + exp = float_string_frame.loc[k] + tm.assert_series_equal(v, exp) + + def test_iterrows_iso8601(self): + # GH#19671 + s = DataFrame( + { + "non_iso8601": ["M1701", "M1802", "M1903", "M2004"], + "iso8601": date_range("2000-01-01", periods=4, freq="ME"), + } + ) + for k, v in s.iterrows(): + exp = s.loc[k] + tm.assert_series_equal(v, exp) + + def test_iterrows_corner(self): + # GH#12222 + df = DataFrame( + { + "a": [datetime.datetime(2015, 1, 1)], + "b": [None], + "c": [None], + "d": [""], + "e": [[]], + "f": [set()], + "g": [{}], + } + ) + expected = Series( + [datetime.datetime(2015, 1, 1), None, None, "", [], set(), {}], + index=list("abcdefg"), + name=0, + dtype="object", + ) + _, result = next(df.iterrows()) + tm.assert_series_equal(result, expected) + + def test_itertuples(self, float_frame): + for i, tup in enumerate(float_frame.itertuples()): + ser = DataFrame._constructor_sliced(tup[1:]) + ser.name = tup[0] + expected = float_frame.iloc[i, :].reset_index(drop=True) + tm.assert_series_equal(ser, expected) + + def test_itertuples_index_false(self): + df = DataFrame( + {"floats": np.random.default_rng(2).standard_normal(5), "ints": range(5)}, + columns=["floats", "ints"], + ) + + for tup in df.itertuples(index=False): + assert isinstance(tup[1], int) + + def test_itertuples_duplicate_cols(self): + df = DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]}) + dfaa = df[["a", "a"]] + + assert list(dfaa.itertuples()) == [(0, 1, 1), (1, 2, 2), (2, 3, 3)] + + # repr with int on 32-bit/windows + if not (is_platform_windows() or not IS64): + assert ( + repr(list(df.itertuples(name=None))) + == "[(0, 1, 4), (1, 2, 5), (2, 3, 6)]" + ) + + def test_itertuples_tuple_name(self): + df = DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]}) + tup = next(df.itertuples(name="TestName")) + assert tup._fields == ("Index", "a", "b") + assert (tup.Index, tup.a, tup.b) == tup + assert type(tup).__name__ == "TestName" + + def test_itertuples_disallowed_col_labels(self): + df = DataFrame(data={"def": [1, 2, 3], "return": [4, 5, 6]}) + tup2 = next(df.itertuples(name="TestName")) + assert tup2 == (0, 1, 4) + assert tup2._fields == ("Index", "_1", "_2") + + @pytest.mark.parametrize("limit", [254, 255, 1024]) + @pytest.mark.parametrize("index", [True, False]) + def test_itertuples_py2_3_field_limit_namedtuple(self, limit, index): + # GH#28282 + df = DataFrame([{f"foo_{i}": f"bar_{i}" for i in range(limit)}]) + result = next(df.itertuples(index=index)) + assert isinstance(result, tuple) + assert hasattr(result, "_fields") + + def test_sequence_like_with_categorical(self): + # GH#7839 + # make sure can iterate + df = DataFrame( + {"id": [1, 2, 3, 4, 5, 6], "raw_grade": ["a", "b", "b", "a", "a", "e"]} + ) + df["grade"] = Categorical(df["raw_grade"]) + + # basic sequencing testing + result = list(df.grade.values) + expected = np.array(df.grade.values).tolist() + tm.assert_almost_equal(result, expected) + + # iteration + for t in df.itertuples(index=False): + str(t) + + for row, s in df.iterrows(): + str(s) + + for c, col in df.items(): + str(col) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_logical_ops.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_logical_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..f1163e994557f8469931c7b5c6833a84abac3a6d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_logical_ops.py @@ -0,0 +1,215 @@ +import operator +import re + +import numpy as np +import pytest + +from pandas import ( + CategoricalIndex, + DataFrame, + Interval, + Series, + isnull, +) +import pandas._testing as tm + + +class TestDataFrameLogicalOperators: + # &, |, ^ + + @pytest.mark.parametrize( + "left, right, op, expected", + [ + ( + [True, False, np.nan], + [True, False, True], + operator.and_, + [True, False, False], + ), + ( + [True, False, True], + [True, False, np.nan], + operator.and_, + [True, False, False], + ), + ( + [True, False, np.nan], + [True, False, True], + operator.or_, + [True, False, False], + ), + ( + [True, False, True], + [True, False, np.nan], + operator.or_, + [True, False, True], + ), + ], + ) + def test_logical_operators_nans(self, left, right, op, expected, frame_or_series): + # GH#13896 + result = op(frame_or_series(left), frame_or_series(right)) + expected = frame_or_series(expected) + + tm.assert_equal(result, expected) + + def test_logical_ops_empty_frame(self): + # GH#5808 + # empty frames, non-mixed dtype + df = DataFrame(index=[1]) + + result = df & df + tm.assert_frame_equal(result, df) + + result = df | df + tm.assert_frame_equal(result, df) + + df2 = DataFrame(index=[1, 2]) + result = df & df2 + tm.assert_frame_equal(result, df2) + + dfa = DataFrame(index=[1], columns=["A"]) + + result = dfa & dfa + expected = DataFrame(False, index=[1], columns=["A"]) + tm.assert_frame_equal(result, expected) + + def test_logical_ops_bool_frame(self): + # GH#5808 + df1a_bool = DataFrame(True, index=[1], columns=["A"]) + + result = df1a_bool & df1a_bool + tm.assert_frame_equal(result, df1a_bool) + + result = df1a_bool | df1a_bool + tm.assert_frame_equal(result, df1a_bool) + + def test_logical_ops_int_frame(self): + # GH#5808 + df1a_int = DataFrame(1, index=[1], columns=["A"]) + df1a_bool = DataFrame(True, index=[1], columns=["A"]) + + result = df1a_int | df1a_bool + tm.assert_frame_equal(result, df1a_bool) + + # Check that this matches Series behavior + res_ser = df1a_int["A"] | df1a_bool["A"] + tm.assert_series_equal(res_ser, df1a_bool["A"]) + + def test_logical_ops_invalid(self, using_infer_string): + # GH#5808 + + df1 = DataFrame(1.0, index=[1], columns=["A"]) + df2 = DataFrame(True, index=[1], columns=["A"]) + msg = re.escape("unsupported operand type(s) for |: 'float' and 'bool'") + with pytest.raises(TypeError, match=msg): + df1 | df2 + + df1 = DataFrame("foo", index=[1], columns=["A"]) + df2 = DataFrame(True, index=[1], columns=["A"]) + if using_infer_string and df1["A"].dtype.storage == "pyarrow": + msg = "operation 'or_' not supported for dtype 'str'" + else: + msg = re.escape("unsupported operand type(s) for |: 'str' and 'bool'") + with pytest.raises(TypeError, match=msg): + df1 | df2 + + def test_logical_operators(self): + def _check_bin_op(op): + result = op(df1, df2) + expected = DataFrame( + op(df1.values, df2.values), index=df1.index, columns=df1.columns + ) + assert result.values.dtype == np.bool_ + tm.assert_frame_equal(result, expected) + + def _check_unary_op(op): + result = op(df1) + expected = DataFrame(op(df1.values), index=df1.index, columns=df1.columns) + assert result.values.dtype == np.bool_ + tm.assert_frame_equal(result, expected) + + df1 = { + "a": {"a": True, "b": False, "c": False, "d": True, "e": True}, + "b": {"a": False, "b": True, "c": False, "d": False, "e": False}, + "c": {"a": False, "b": False, "c": True, "d": False, "e": False}, + "d": {"a": True, "b": False, "c": False, "d": True, "e": True}, + "e": {"a": True, "b": False, "c": False, "d": True, "e": True}, + } + + df2 = { + "a": {"a": True, "b": False, "c": True, "d": False, "e": False}, + "b": {"a": False, "b": True, "c": False, "d": False, "e": False}, + "c": {"a": True, "b": False, "c": True, "d": False, "e": False}, + "d": {"a": False, "b": False, "c": False, "d": True, "e": False}, + "e": {"a": False, "b": False, "c": False, "d": False, "e": True}, + } + + df1 = DataFrame(df1) + df2 = DataFrame(df2) + + _check_bin_op(operator.and_) + _check_bin_op(operator.or_) + _check_bin_op(operator.xor) + + _check_unary_op(operator.inv) # TODO: belongs elsewhere + + @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning") + def test_logical_with_nas(self): + d = DataFrame({"a": [np.nan, False], "b": [True, True]}) + + # GH4947 + # bool comparisons should return bool + result = d["a"] | d["b"] + expected = Series([False, True]) + tm.assert_series_equal(result, expected) + + # GH4604, automatic casting here + result = d["a"].fillna(False) | d["b"] + expected = Series([True, True]) + tm.assert_series_equal(result, expected) + + msg = "The 'downcast' keyword in fillna is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = d["a"].fillna(False, downcast=False) | d["b"] + expected = Series([True, True]) + tm.assert_series_equal(result, expected) + + def test_logical_ops_categorical_columns(self): + # GH#38367 + intervals = [Interval(1, 2), Interval(3, 4)] + data = DataFrame( + [[1, np.nan], [2, np.nan]], + columns=CategoricalIndex( + intervals, categories=intervals + [Interval(5, 6)] + ), + ) + mask = DataFrame( + [[False, False], [False, False]], columns=data.columns, dtype=bool + ) + result = mask | isnull(data) + expected = DataFrame( + [[False, True], [False, True]], + columns=CategoricalIndex( + intervals, categories=intervals + [Interval(5, 6)] + ), + ) + tm.assert_frame_equal(result, expected) + + def test_int_dtype_different_index_not_bool(self): + # GH 52500 + df1 = DataFrame([1, 2, 3], index=[10, 11, 23], columns=["a"]) + df2 = DataFrame([10, 20, 30], index=[11, 10, 23], columns=["a"]) + result = np.bitwise_xor(df1, df2) + expected = DataFrame([21, 8, 29], index=[10, 11, 23], columns=["a"]) + tm.assert_frame_equal(result, expected) + + result = df1 ^ df2 + tm.assert_frame_equal(result, expected) + + def test_different_dtypes_different_index_raises(self): + # GH 52538 + df1 = DataFrame([1, 2], index=["a", "b"]) + df2 = DataFrame([3, 4], index=["b", "c"]) + with pytest.raises(TypeError, match="unsupported operand type"): + df1 & df2 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_nonunique_indexes.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_nonunique_indexes.py new file mode 100644 index 0000000000000000000000000000000000000000..34f172e900ab7e16d66afe32aed3d5b87be301c3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_nonunique_indexes.py @@ -0,0 +1,337 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Series, + date_range, +) +import pandas._testing as tm + + +class TestDataFrameNonuniqueIndexes: + def test_setattr_columns_vs_construct_with_columns(self): + # assignment + # GH 3687 + arr = np.random.default_rng(2).standard_normal((3, 2)) + idx = list(range(2)) + df = DataFrame(arr, columns=["A", "A"]) + df.columns = idx + expected = DataFrame(arr, columns=idx) + tm.assert_frame_equal(df, expected) + + def test_setattr_columns_vs_construct_with_columns_datetimeindx(self): + idx = date_range("20130101", periods=4, freq="QE-NOV") + df = DataFrame( + [[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], columns=["a", "a", "a", "a"] + ) + df.columns = idx + expected = DataFrame([[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], columns=idx) + tm.assert_frame_equal(df, expected) + + def test_insert_with_duplicate_columns(self): + # insert + df = DataFrame( + [[1, 1, 1, 5], [1, 1, 2, 5], [2, 1, 3, 5]], + columns=["foo", "bar", "foo", "hello"], + ) + df["string"] = "bah" + expected = DataFrame( + [[1, 1, 1, 5, "bah"], [1, 1, 2, 5, "bah"], [2, 1, 3, 5, "bah"]], + columns=["foo", "bar", "foo", "hello", "string"], + ) + tm.assert_frame_equal(df, expected) + with pytest.raises(ValueError, match="Length of value"): + df.insert(0, "AnotherColumn", range(len(df.index) - 1)) + + # insert same dtype + df["foo2"] = 3 + expected = DataFrame( + [[1, 1, 1, 5, "bah", 3], [1, 1, 2, 5, "bah", 3], [2, 1, 3, 5, "bah", 3]], + columns=["foo", "bar", "foo", "hello", "string", "foo2"], + ) + tm.assert_frame_equal(df, expected) + + # set (non-dup) + df["foo2"] = 4 + expected = DataFrame( + [[1, 1, 1, 5, "bah", 4], [1, 1, 2, 5, "bah", 4], [2, 1, 3, 5, "bah", 4]], + columns=["foo", "bar", "foo", "hello", "string", "foo2"], + ) + tm.assert_frame_equal(df, expected) + df["foo2"] = 3 + + # delete (non dup) + del df["bar"] + expected = DataFrame( + [[1, 1, 5, "bah", 3], [1, 2, 5, "bah", 3], [2, 3, 5, "bah", 3]], + columns=["foo", "foo", "hello", "string", "foo2"], + ) + tm.assert_frame_equal(df, expected) + + # try to delete again (its not consolidated) + del df["hello"] + expected = DataFrame( + [[1, 1, "bah", 3], [1, 2, "bah", 3], [2, 3, "bah", 3]], + columns=["foo", "foo", "string", "foo2"], + ) + tm.assert_frame_equal(df, expected) + + # consolidate + df = df._consolidate() + expected = DataFrame( + [[1, 1, "bah", 3], [1, 2, "bah", 3], [2, 3, "bah", 3]], + columns=["foo", "foo", "string", "foo2"], + ) + tm.assert_frame_equal(df, expected) + + # insert + df.insert(2, "new_col", 5.0) + expected = DataFrame( + [[1, 1, 5.0, "bah", 3], [1, 2, 5.0, "bah", 3], [2, 3, 5.0, "bah", 3]], + columns=["foo", "foo", "new_col", "string", "foo2"], + ) + tm.assert_frame_equal(df, expected) + + # insert a dup + with pytest.raises(ValueError, match="cannot insert"): + df.insert(2, "new_col", 4.0) + + df.insert(2, "new_col", 4.0, allow_duplicates=True) + expected = DataFrame( + [ + [1, 1, 4.0, 5.0, "bah", 3], + [1, 2, 4.0, 5.0, "bah", 3], + [2, 3, 4.0, 5.0, "bah", 3], + ], + columns=["foo", "foo", "new_col", "new_col", "string", "foo2"], + ) + tm.assert_frame_equal(df, expected) + + # delete (dup) + del df["foo"] + expected = DataFrame( + [[4.0, 5.0, "bah", 3], [4.0, 5.0, "bah", 3], [4.0, 5.0, "bah", 3]], + columns=["new_col", "new_col", "string", "foo2"], + ) + tm.assert_frame_equal(df, expected) + + def test_dup_across_dtypes(self): + # dup across dtypes + df = DataFrame( + [[1, 1, 1.0, 5], [1, 1, 2.0, 5], [2, 1, 3.0, 5]], + columns=["foo", "bar", "foo", "hello"], + ) + + df["foo2"] = 7.0 + expected = DataFrame( + [[1, 1, 1.0, 5, 7.0], [1, 1, 2.0, 5, 7.0], [2, 1, 3.0, 5, 7.0]], + columns=["foo", "bar", "foo", "hello", "foo2"], + ) + tm.assert_frame_equal(df, expected) + + result = df["foo"] + expected = DataFrame([[1, 1.0], [1, 2.0], [2, 3.0]], columns=["foo", "foo"]) + tm.assert_frame_equal(result, expected) + + # multiple replacements + df["foo"] = "string" + expected = DataFrame( + [ + ["string", 1, "string", 5, 7.0], + ["string", 1, "string", 5, 7.0], + ["string", 1, "string", 5, 7.0], + ], + columns=["foo", "bar", "foo", "hello", "foo2"], + ) + tm.assert_frame_equal(df, expected) + + del df["foo"] + expected = DataFrame( + [[1, 5, 7.0], [1, 5, 7.0], [1, 5, 7.0]], columns=["bar", "hello", "foo2"] + ) + tm.assert_frame_equal(df, expected) + + def test_column_dups_indexes(self): + # check column dups with index equal and not equal to df's index + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + index=["a", "b", "c", "d", "e"], + columns=["A", "B", "A"], + ) + for index in [df.index, pd.Index(list("edcba"))]: + this_df = df.copy() + expected_ser = Series(index.values, index=this_df.index) + expected_df = DataFrame( + {"A": expected_ser, "B": this_df["B"]}, + columns=["A", "B", "A"], + ) + this_df["A"] = index + tm.assert_frame_equal(this_df, expected_df) + + def test_changing_dtypes_with_duplicate_columns(self): + # multiple assignments that change dtypes + # the location indexer is a slice + # GH 6120 + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=["that", "that"] + ) + expected = DataFrame(1.0, index=range(5), columns=["that", "that"]) + + df["that"] = 1.0 + tm.assert_frame_equal(df, expected) + + df = DataFrame( + np.random.default_rng(2).random((5, 2)), columns=["that", "that"] + ) + expected = DataFrame(1, index=range(5), columns=["that", "that"]) + + df["that"] = 1 + tm.assert_frame_equal(df, expected) + + def test_dup_columns_comparisons(self): + # equality + df1 = DataFrame([[1, 2], [2, np.nan], [3, 4], [4, 4]], columns=["A", "B"]) + df2 = DataFrame([[0, 1], [2, 4], [2, np.nan], [4, 5]], columns=["A", "A"]) + + # not-comparing like-labelled + msg = ( + r"Can only compare identically-labeled \(both index and columns\) " + "DataFrame objects" + ) + with pytest.raises(ValueError, match=msg): + df1 == df2 + + df1r = df1.reindex_like(df2) + result = df1r == df2 + expected = DataFrame( + [[False, True], [True, False], [False, False], [True, False]], + columns=["A", "A"], + ) + tm.assert_frame_equal(result, expected) + + def test_mixed_column_selection(self): + # mixed column selection + # GH 5639 + dfbool = DataFrame( + { + "one": Series([True, True, False], index=["a", "b", "c"]), + "two": Series([False, False, True, False], index=["a", "b", "c", "d"]), + "three": Series([False, True, True, True], index=["a", "b", "c", "d"]), + } + ) + expected = pd.concat([dfbool["one"], dfbool["three"], dfbool["one"]], axis=1) + result = dfbool[["one", "three", "one"]] + tm.assert_frame_equal(result, expected) + + def test_multi_axis_dups(self): + # multi-axis dups + # GH 6121 + df = DataFrame( + np.arange(25.0).reshape(5, 5), + index=["a", "b", "c", "d", "e"], + columns=["A", "B", "C", "D", "E"], + ) + z = df[["A", "C", "A"]].copy() + expected = z.loc[["a", "c", "a"]] + + df = DataFrame( + np.arange(25.0).reshape(5, 5), + index=["a", "b", "c", "d", "e"], + columns=["A", "B", "C", "D", "E"], + ) + z = df[["A", "C", "A"]] + result = z.loc[["a", "c", "a"]] + tm.assert_frame_equal(result, expected) + + def test_columns_with_dups(self): + # GH 3468 related + + # basic + df = DataFrame([[1, 2]], columns=["a", "a"]) + df.columns = ["a", "a.1"] + expected = DataFrame([[1, 2]], columns=["a", "a.1"]) + tm.assert_frame_equal(df, expected) + + df = DataFrame([[1, 2, 3]], columns=["b", "a", "a"]) + df.columns = ["b", "a", "a.1"] + expected = DataFrame([[1, 2, 3]], columns=["b", "a", "a.1"]) + tm.assert_frame_equal(df, expected) + + def test_columns_with_dup_index(self): + # with a dup index + df = DataFrame([[1, 2]], columns=["a", "a"]) + df.columns = ["b", "b"] + expected = DataFrame([[1, 2]], columns=["b", "b"]) + tm.assert_frame_equal(df, expected) + + def test_multi_dtype(self): + # multi-dtype + df = DataFrame( + [[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]], + columns=["a", "a", "b", "b", "d", "c", "c"], + ) + df.columns = list("ABCDEFG") + expected = DataFrame( + [[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]], columns=list("ABCDEFG") + ) + tm.assert_frame_equal(df, expected) + + def test_multi_dtype2(self): + df = DataFrame([[1, 2, "foo", "bar"]], columns=["a", "a", "a", "a"]) + df.columns = ["a", "a.1", "a.2", "a.3"] + expected = DataFrame([[1, 2, "foo", "bar"]], columns=["a", "a.1", "a.2", "a.3"]) + tm.assert_frame_equal(df, expected) + + def test_dups_across_blocks(self, using_array_manager): + # dups across blocks + df_float = DataFrame( + np.random.default_rng(2).standard_normal((10, 3)), dtype="float64" + ) + df_int = DataFrame( + np.random.default_rng(2).standard_normal((10, 3)).astype("int64") + ) + df_bool = DataFrame(True, index=df_float.index, columns=df_float.columns) + df_object = DataFrame("foo", index=df_float.index, columns=df_float.columns) + df_dt = DataFrame( + pd.Timestamp("20010101"), index=df_float.index, columns=df_float.columns + ) + df = pd.concat([df_float, df_int, df_bool, df_object, df_dt], axis=1) + + if not using_array_manager: + assert len(df._mgr.blknos) == len(df.columns) + assert len(df._mgr.blklocs) == len(df.columns) + + # testing iloc + for i in range(len(df.columns)): + df.iloc[:, i] + + def test_dup_columns_across_dtype(self): + # dup columns across dtype GH 2079/2194 + vals = [[1, -1, 2.0], [2, -2, 3.0]] + rs = DataFrame(vals, columns=["A", "A", "B"]) + xp = DataFrame(vals) + xp.columns = ["A", "A", "B"] + tm.assert_frame_equal(rs, xp) + + def test_set_value_by_index(self): + # See gh-12344 + warn = None + msg = "will attempt to set the values inplace" + + df = DataFrame(np.arange(9).reshape(3, 3).T) + df.columns = list("AAA") + expected = df.iloc[:, 2].copy() + + with tm.assert_produces_warning(warn, match=msg): + df.iloc[:, 0] = 3 + tm.assert_series_equal(df.iloc[:, 2], expected) + + df = DataFrame(np.arange(9).reshape(3, 3).T) + df.columns = [2, float(2), str(2)] + expected = df.iloc[:, 1].copy() + + with tm.assert_produces_warning(warn, match=msg): + df.iloc[:, 0] = 3 + tm.assert_series_equal(df.iloc[:, 1], expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_npfuncs.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_npfuncs.py new file mode 100644 index 0000000000000000000000000000000000000000..afb53bf2de93aa591ca9d7b99af185bc0c4083ee --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_npfuncs.py @@ -0,0 +1,89 @@ +""" +Tests for np.foo applied to DataFrame, not necessarily ufuncs. +""" +import numpy as np + +from pandas import ( + Categorical, + DataFrame, +) +import pandas._testing as tm + + +class TestAsArray: + def test_asarray_homogeneous(self): + df = DataFrame({"A": Categorical([1, 2]), "B": Categorical([1, 2])}) + result = np.asarray(df) + # may change from object in the future + expected = np.array([[1, 1], [2, 2]], dtype="object") + tm.assert_numpy_array_equal(result, expected) + + def test_np_sqrt(self, float_frame): + with np.errstate(all="ignore"): + result = np.sqrt(float_frame) + assert isinstance(result, type(float_frame)) + assert result.index.is_(float_frame.index) + assert result.columns.is_(float_frame.columns) + + tm.assert_frame_equal(result, float_frame.apply(np.sqrt)) + + def test_sum_deprecated_axis_behavior(self): + # GH#52042 deprecated behavior of df.sum(axis=None), which gets + # called when we do np.sum(df) + + arr = np.random.default_rng(2).standard_normal((4, 3)) + df = DataFrame(arr) + + msg = "The behavior of DataFrame.sum with axis=None is deprecated" + with tm.assert_produces_warning( + FutureWarning, match=msg, check_stacklevel=False + ): + res = np.sum(df) + + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = df.sum(axis=None) + tm.assert_series_equal(res, expected) + + def test_np_ravel(self): + # GH26247 + arr = np.array( + [ + [0.11197053, 0.44361564, -0.92589452], + [0.05883648, -0.00948922, -0.26469934], + ] + ) + + result = np.ravel([DataFrame(batch.reshape(1, 3)) for batch in arr]) + expected = np.array( + [ + 0.11197053, + 0.44361564, + -0.92589452, + 0.05883648, + -0.00948922, + -0.26469934, + ] + ) + tm.assert_numpy_array_equal(result, expected) + + result = np.ravel(DataFrame(arr[0].reshape(1, 3), columns=["x1", "x2", "x3"])) + expected = np.array([0.11197053, 0.44361564, -0.92589452]) + tm.assert_numpy_array_equal(result, expected) + + result = np.ravel( + [ + DataFrame(batch.reshape(1, 3), columns=["x1", "x2", "x3"]) + for batch in arr + ] + ) + expected = np.array( + [ + 0.11197053, + 0.44361564, + -0.92589452, + 0.05883648, + -0.00948922, + -0.26469934, + ] + ) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_query_eval.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_query_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..ffabf238a4884d3b7436c890da77e2700dc8bde4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_query_eval.py @@ -0,0 +1,1437 @@ +import operator + +import numpy as np +import pytest + +from pandas.errors import ( + NumExprClobberingError, + UndefinedVariableError, +) +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + date_range, +) +import pandas._testing as tm +from pandas.core.computation.check import NUMEXPR_INSTALLED + + +@pytest.fixture(params=["python", "pandas"], ids=lambda x: x) +def parser(request): + return request.param + + +@pytest.fixture( + params=["python", pytest.param("numexpr", marks=td.skip_if_no("numexpr"))], + ids=lambda x: x, +) +def engine(request): + return request.param + + +def skip_if_no_pandas_parser(parser): + if parser != "pandas": + pytest.skip(f"cannot evaluate with parser={parser}") + + +class TestCompat: + @pytest.fixture + def df(self): + return DataFrame({"A": [1, 2, 3]}) + + @pytest.fixture + def expected1(self, df): + return df[df.A > 0] + + @pytest.fixture + def expected2(self, df): + return df.A + 1 + + def test_query_default(self, df, expected1, expected2): + # GH 12749 + # this should always work, whether NUMEXPR_INSTALLED or not + result = df.query("A>0") + tm.assert_frame_equal(result, expected1) + result = df.eval("A+1") + tm.assert_series_equal(result, expected2, check_names=False) + + def test_query_None(self, df, expected1, expected2): + result = df.query("A>0", engine=None) + tm.assert_frame_equal(result, expected1) + result = df.eval("A+1", engine=None) + tm.assert_series_equal(result, expected2, check_names=False) + + def test_query_python(self, df, expected1, expected2): + result = df.query("A>0", engine="python") + tm.assert_frame_equal(result, expected1) + result = df.eval("A+1", engine="python") + tm.assert_series_equal(result, expected2, check_names=False) + + def test_query_numexpr(self, df, expected1, expected2): + if NUMEXPR_INSTALLED: + result = df.query("A>0", engine="numexpr") + tm.assert_frame_equal(result, expected1) + result = df.eval("A+1", engine="numexpr") + tm.assert_series_equal(result, expected2, check_names=False) + else: + msg = ( + r"'numexpr' is not installed or an unsupported version. " + r"Cannot use engine='numexpr' for query/eval if 'numexpr' is " + r"not installed" + ) + with pytest.raises(ImportError, match=msg): + df.query("A>0", engine="numexpr") + with pytest.raises(ImportError, match=msg): + df.eval("A+1", engine="numexpr") + + +class TestDataFrameEval: + # smaller hits python, larger hits numexpr + @pytest.mark.parametrize("n", [4, 4000]) + @pytest.mark.parametrize( + "op_str,op,rop", + [ + ("+", "__add__", "__radd__"), + ("-", "__sub__", "__rsub__"), + ("*", "__mul__", "__rmul__"), + ("/", "__truediv__", "__rtruediv__"), + ], + ) + def test_ops(self, op_str, op, rop, n): + # tst ops and reversed ops in evaluation + # GH7198 + + df = DataFrame(1, index=range(n), columns=list("abcd")) + df.iloc[0] = 2 + m = df.mean() + + base = DataFrame( # noqa: F841 + np.tile(m.values, n).reshape(n, -1), columns=list("abcd") + ) + + expected = eval(f"base {op_str} df") + + # ops as strings + result = eval(f"m {op_str} df") + tm.assert_frame_equal(result, expected) + + # these are commutative + if op in ["+", "*"]: + result = getattr(df, op)(m) + tm.assert_frame_equal(result, expected) + + # these are not + elif op in ["-", "/"]: + result = getattr(df, rop)(m) + tm.assert_frame_equal(result, expected) + + def test_dataframe_sub_numexpr_path(self): + # GH7192: Note we need a large number of rows to ensure this + # goes through the numexpr path + df = DataFrame({"A": np.random.default_rng(2).standard_normal(25000)}) + df.iloc[0:5] = np.nan + expected = 1 - np.isnan(df.iloc[0:25]) + result = (1 - np.isnan(df)).iloc[0:25] + tm.assert_frame_equal(result, expected) + + def test_query_non_str(self): + # GH 11485 + df = DataFrame({"A": [1, 2, 3], "B": ["a", "b", "b"]}) + + msg = "expr must be a string to be evaluated" + with pytest.raises(ValueError, match=msg): + df.query(lambda x: x.B == "b") + + with pytest.raises(ValueError, match=msg): + df.query(111) + + def test_query_empty_string(self): + # GH 13139 + df = DataFrame({"A": [1, 2, 3]}) + + msg = "expr cannot be an empty string" + with pytest.raises(ValueError, match=msg): + df.query("") + + def test_eval_resolvers_as_list(self): + # GH 14095 + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 2)), columns=list("ab") + ) + dict1 = {"a": 1} + dict2 = {"b": 2} + assert df.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"] + assert pd.eval("a + b", resolvers=[dict1, dict2]) == dict1["a"] + dict2["b"] + + def test_eval_resolvers_combined(self): + # GH 34966 + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 2)), columns=list("ab") + ) + dict1 = {"c": 2} + + # Both input and default index/column resolvers should be usable + result = df.eval("a + b * c", resolvers=[dict1]) + + expected = df["a"] + df["b"] * dict1["c"] + tm.assert_series_equal(result, expected) + + def test_eval_object_dtype_binop(self): + # GH#24883 + df = DataFrame({"a1": ["Y", "N"]}) + res = df.eval("c = ((a1 == 'Y') & True)") + expected = DataFrame({"a1": ["Y", "N"], "c": [True, False]}) + tm.assert_frame_equal(res, expected) + + def test_extension_array_eval(self, engine, parser, request): + # GH#58748 + if engine == "numexpr": + mark = pytest.mark.xfail( + reason="numexpr does not support extension array dtypes" + ) + request.applymarker(mark) + df = DataFrame({"a": pd.array([1, 2, 3]), "b": pd.array([4, 5, 6])}) + result = df.eval("a / b", engine=engine, parser=parser) + expected = Series(pd.array([0.25, 0.40, 0.50])) + tm.assert_series_equal(result, expected) + + def test_complex_eval(self, engine, parser): + # GH#21374 + df = DataFrame({"a": [1 + 2j], "b": [1 + 1j]}) + result = df.eval("a/b", engine=engine, parser=parser) + expected = Series([1.5 + 0.5j]) + tm.assert_series_equal(result, expected) + + +class TestDataFrameQueryWithMultiIndex: + def test_query_with_named_multiindex(self, parser, engine): + skip_if_no_pandas_parser(parser) + a = np.random.default_rng(2).choice(["red", "green"], size=10) + b = np.random.default_rng(2).choice(["eggs", "ham"], size=10) + index = MultiIndex.from_arrays([a, b], names=["color", "food"]) + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), index=index) + ind = Series( + df.index.get_level_values("color").values, index=index, name="color" + ) + + # equality + res1 = df.query('color == "red"', parser=parser, engine=engine) + res2 = df.query('"red" == color', parser=parser, engine=engine) + exp = df[ind == "red"] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # inequality + res1 = df.query('color != "red"', parser=parser, engine=engine) + res2 = df.query('"red" != color', parser=parser, engine=engine) + exp = df[ind != "red"] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # list equality (really just set membership) + res1 = df.query('color == ["red"]', parser=parser, engine=engine) + res2 = df.query('["red"] == color', parser=parser, engine=engine) + exp = df[ind.isin(["red"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + res1 = df.query('color != ["red"]', parser=parser, engine=engine) + res2 = df.query('["red"] != color', parser=parser, engine=engine) + exp = df[~ind.isin(["red"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # in/not in ops + res1 = df.query('["red"] in color', parser=parser, engine=engine) + res2 = df.query('"red" in color', parser=parser, engine=engine) + exp = df[ind.isin(["red"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + res1 = df.query('["red"] not in color', parser=parser, engine=engine) + res2 = df.query('"red" not in color', parser=parser, engine=engine) + exp = df[~ind.isin(["red"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + def test_query_with_unnamed_multiindex(self, parser, engine): + skip_if_no_pandas_parser(parser) + a = np.random.default_rng(2).choice(["red", "green"], size=10) + b = np.random.default_rng(2).choice(["eggs", "ham"], size=10) + index = MultiIndex.from_arrays([a, b]) + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), index=index) + ind = Series(df.index.get_level_values(0).values, index=index) + + res1 = df.query('ilevel_0 == "red"', parser=parser, engine=engine) + res2 = df.query('"red" == ilevel_0', parser=parser, engine=engine) + exp = df[ind == "red"] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # inequality + res1 = df.query('ilevel_0 != "red"', parser=parser, engine=engine) + res2 = df.query('"red" != ilevel_0', parser=parser, engine=engine) + exp = df[ind != "red"] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # list equality (really just set membership) + res1 = df.query('ilevel_0 == ["red"]', parser=parser, engine=engine) + res2 = df.query('["red"] == ilevel_0', parser=parser, engine=engine) + exp = df[ind.isin(["red"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + res1 = df.query('ilevel_0 != ["red"]', parser=parser, engine=engine) + res2 = df.query('["red"] != ilevel_0', parser=parser, engine=engine) + exp = df[~ind.isin(["red"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # in/not in ops + res1 = df.query('["red"] in ilevel_0', parser=parser, engine=engine) + res2 = df.query('"red" in ilevel_0', parser=parser, engine=engine) + exp = df[ind.isin(["red"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + res1 = df.query('["red"] not in ilevel_0', parser=parser, engine=engine) + res2 = df.query('"red" not in ilevel_0', parser=parser, engine=engine) + exp = df[~ind.isin(["red"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # ## LEVEL 1 + ind = Series(df.index.get_level_values(1).values, index=index) + res1 = df.query('ilevel_1 == "eggs"', parser=parser, engine=engine) + res2 = df.query('"eggs" == ilevel_1', parser=parser, engine=engine) + exp = df[ind == "eggs"] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # inequality + res1 = df.query('ilevel_1 != "eggs"', parser=parser, engine=engine) + res2 = df.query('"eggs" != ilevel_1', parser=parser, engine=engine) + exp = df[ind != "eggs"] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # list equality (really just set membership) + res1 = df.query('ilevel_1 == ["eggs"]', parser=parser, engine=engine) + res2 = df.query('["eggs"] == ilevel_1', parser=parser, engine=engine) + exp = df[ind.isin(["eggs"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + res1 = df.query('ilevel_1 != ["eggs"]', parser=parser, engine=engine) + res2 = df.query('["eggs"] != ilevel_1', parser=parser, engine=engine) + exp = df[~ind.isin(["eggs"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + # in/not in ops + res1 = df.query('["eggs"] in ilevel_1', parser=parser, engine=engine) + res2 = df.query('"eggs" in ilevel_1', parser=parser, engine=engine) + exp = df[ind.isin(["eggs"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + res1 = df.query('["eggs"] not in ilevel_1', parser=parser, engine=engine) + res2 = df.query('"eggs" not in ilevel_1', parser=parser, engine=engine) + exp = df[~ind.isin(["eggs"])] + tm.assert_frame_equal(res1, exp) + tm.assert_frame_equal(res2, exp) + + def test_query_with_partially_named_multiindex(self, parser, engine): + skip_if_no_pandas_parser(parser) + a = np.random.default_rng(2).choice(["red", "green"], size=10) + b = np.arange(10) + index = MultiIndex.from_arrays([a, b]) + index.names = [None, "rating"] + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2)), index=index) + res = df.query("rating == 1", parser=parser, engine=engine) + ind = Series( + df.index.get_level_values("rating").values, index=index, name="rating" + ) + exp = df[ind == 1] + tm.assert_frame_equal(res, exp) + + res = df.query("rating != 1", parser=parser, engine=engine) + ind = Series( + df.index.get_level_values("rating").values, index=index, name="rating" + ) + exp = df[ind != 1] + tm.assert_frame_equal(res, exp) + + res = df.query('ilevel_0 == "red"', parser=parser, engine=engine) + ind = Series(df.index.get_level_values(0).values, index=index) + exp = df[ind == "red"] + tm.assert_frame_equal(res, exp) + + res = df.query('ilevel_0 != "red"', parser=parser, engine=engine) + ind = Series(df.index.get_level_values(0).values, index=index) + exp = df[ind != "red"] + tm.assert_frame_equal(res, exp) + + def test_query_multiindex_get_index_resolvers(self): + df = DataFrame( + np.ones((10, 3)), + index=MultiIndex.from_arrays( + [range(10) for _ in range(2)], names=["spam", "eggs"] + ), + ) + resolvers = df._get_index_resolvers() + + def to_series(mi, level): + level_values = mi.get_level_values(level) + s = level_values.to_series() + s.index = mi + return s + + col_series = df.columns.to_series() + expected = { + "index": df.index, + "columns": col_series, + "spam": to_series(df.index, "spam"), + "eggs": to_series(df.index, "eggs"), + "clevel_0": col_series, + } + for k, v in resolvers.items(): + if isinstance(v, Index): + assert v.is_(expected[k]) + elif isinstance(v, Series): + tm.assert_series_equal(v, expected[k]) + else: + raise AssertionError("object must be a Series or Index") + + +@td.skip_if_no("numexpr") +class TestDataFrameQueryNumExprPandas: + @pytest.fixture + def engine(self): + return "numexpr" + + @pytest.fixture + def parser(self): + return "pandas" + + def test_date_query_with_attribute_access(self, engine, parser): + skip_if_no_pandas_parser(parser) + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + df["dates1"] = date_range("1/1/2012", periods=5) + df["dates2"] = date_range("1/1/2013", periods=5) + df["dates3"] = date_range("1/1/2014", periods=5) + res = df.query( + "@df.dates1 < 20130101 < @df.dates3", engine=engine, parser=parser + ) + expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_query_no_attribute_access(self, engine, parser): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + df["dates1"] = date_range("1/1/2012", periods=5) + df["dates2"] = date_range("1/1/2013", periods=5) + df["dates3"] = date_range("1/1/2014", periods=5) + res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser) + expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_query_with_NaT(self, engine, parser): + n = 10 + df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))) + df["dates1"] = date_range("1/1/2012", periods=n) + df["dates2"] = date_range("1/1/2013", periods=n) + df["dates3"] = date_range("1/1/2014", periods=n) + df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT + df.loc[np.random.default_rng(2).random(n) > 0.5, "dates3"] = pd.NaT + res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser) + expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_index_query(self, engine, parser): + n = 10 + df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))) + df["dates1"] = date_range("1/1/2012", periods=n) + df["dates3"] = date_range("1/1/2014", periods=n) + return_value = df.set_index("dates1", inplace=True, drop=True) + assert return_value is None + res = df.query("index < 20130101 < dates3", engine=engine, parser=parser) + expec = df[(df.index < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_index_query_with_NaT(self, engine, parser): + n = 10 + # Cast to object to avoid implicit cast when setting entry to pd.NaT below + df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))).astype( + {0: object} + ) + df["dates1"] = date_range("1/1/2012", periods=n) + df["dates3"] = date_range("1/1/2014", periods=n) + df.iloc[0, 0] = pd.NaT + return_value = df.set_index("dates1", inplace=True, drop=True) + assert return_value is None + res = df.query("index < 20130101 < dates3", engine=engine, parser=parser) + expec = df[(df.index < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_index_query_with_NaT_duplicates(self, engine, parser): + n = 10 + d = {} + d["dates1"] = date_range("1/1/2012", periods=n) + d["dates3"] = date_range("1/1/2014", periods=n) + df = DataFrame(d) + df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT + return_value = df.set_index("dates1", inplace=True, drop=True) + assert return_value is None + res = df.query("dates1 < 20130101 < dates3", engine=engine, parser=parser) + expec = df[(df.index.to_series() < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_query_with_non_date(self, engine, parser): + n = 10 + df = DataFrame( + {"dates": date_range("1/1/2012", periods=n), "nondate": np.arange(n)} + ) + + result = df.query("dates == nondate", parser=parser, engine=engine) + assert len(result) == 0 + + result = df.query("dates != nondate", parser=parser, engine=engine) + tm.assert_frame_equal(result, df) + + msg = r"Invalid comparison between dtype=datetime64\[ns\] and ndarray" + for op in ["<", ">", "<=", ">="]: + with pytest.raises(TypeError, match=msg): + df.query(f"dates {op} nondate", parser=parser, engine=engine) + + def test_query_syntax_error(self, engine, parser): + df = DataFrame({"i": range(10), "+": range(3, 13), "r": range(4, 14)}) + msg = "invalid syntax" + with pytest.raises(SyntaxError, match=msg): + df.query("i - +", engine=engine, parser=parser) + + def test_query_scope(self, engine, parser): + skip_if_no_pandas_parser(parser) + + df = DataFrame( + np.random.default_rng(2).standard_normal((20, 2)), columns=list("ab") + ) + + a, b = 1, 2 # noqa: F841 + res = df.query("a > b", engine=engine, parser=parser) + expected = df[df.a > df.b] + tm.assert_frame_equal(res, expected) + + res = df.query("@a > b", engine=engine, parser=parser) + expected = df[a > df.b] + tm.assert_frame_equal(res, expected) + + # no local variable c + with pytest.raises( + UndefinedVariableError, match="local variable 'c' is not defined" + ): + df.query("@a > b > @c", engine=engine, parser=parser) + + # no column named 'c' + with pytest.raises(UndefinedVariableError, match="name 'c' is not defined"): + df.query("@a > b > c", engine=engine, parser=parser) + + def test_query_doesnt_pickup_local(self, engine, parser): + n = m = 10 + df = DataFrame( + np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc") + ) + + # we don't pick up the local 'sin' + with pytest.raises(UndefinedVariableError, match="name 'sin' is not defined"): + df.query("sin > 5", engine=engine, parser=parser) + + def test_query_builtin(self, engine, parser): + n = m = 10 + df = DataFrame( + np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc") + ) + + df.index.name = "sin" + msg = "Variables in expression.+" + with pytest.raises(NumExprClobberingError, match=msg): + df.query("sin > 5", engine=engine, parser=parser) + + def test_query(self, engine, parser): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 3)), columns=["a", "b", "c"] + ) + + tm.assert_frame_equal( + df.query("a < b", engine=engine, parser=parser), df[df.a < df.b] + ) + tm.assert_frame_equal( + df.query("a + b > b * c", engine=engine, parser=parser), + df[df.a + df.b > df.b * df.c], + ) + + def test_query_index_with_name(self, engine, parser): + df = DataFrame( + np.random.default_rng(2).integers(10, size=(10, 3)), + index=Index(range(10), name="blob"), + columns=["a", "b", "c"], + ) + res = df.query("(blob < 5) & (a < b)", engine=engine, parser=parser) + expec = df[(df.index < 5) & (df.a < df.b)] + tm.assert_frame_equal(res, expec) + + res = df.query("blob < b", engine=engine, parser=parser) + expec = df[df.index < df.b] + + tm.assert_frame_equal(res, expec) + + def test_query_index_without_name(self, engine, parser): + df = DataFrame( + np.random.default_rng(2).integers(10, size=(10, 3)), + index=range(10), + columns=["a", "b", "c"], + ) + + # "index" should refer to the index + res = df.query("index < b", engine=engine, parser=parser) + expec = df[df.index < df.b] + tm.assert_frame_equal(res, expec) + + # test against a scalar + res = df.query("index < 5", engine=engine, parser=parser) + expec = df[df.index < 5] + tm.assert_frame_equal(res, expec) + + def test_nested_scope(self, engine, parser): + skip_if_no_pandas_parser(parser) + + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + expected = df[(df > 0) & (df2 > 0)] + + result = df.query("(@df > 0) & (@df2 > 0)", engine=engine, parser=parser) + tm.assert_frame_equal(result, expected) + + result = pd.eval("df[df > 0 and df2 > 0]", engine=engine, parser=parser) + tm.assert_frame_equal(result, expected) + + result = pd.eval( + "df[df > 0 and df2 > 0 and df[df > 0] > 0]", engine=engine, parser=parser + ) + expected = df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)] + tm.assert_frame_equal(result, expected) + + result = pd.eval("df[(df>0) & (df2>0)]", engine=engine, parser=parser) + expected = df.query("(@df>0) & (@df2>0)", engine=engine, parser=parser) + tm.assert_frame_equal(result, expected) + + def test_nested_raises_on_local_self_reference(self, engine, parser): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + + # can't reference ourself b/c we're a local so @ is necessary + with pytest.raises(UndefinedVariableError, match="name 'df' is not defined"): + df.query("df > 0", engine=engine, parser=parser) + + def test_local_syntax(self, engine, parser): + skip_if_no_pandas_parser(parser) + + df = DataFrame( + np.random.default_rng(2).standard_normal((100, 10)), + columns=list("abcdefghij"), + ) + b = 1 + expect = df[df.a < b] + result = df.query("a < @b", engine=engine, parser=parser) + tm.assert_frame_equal(result, expect) + + expect = df[df.a < df.b] + result = df.query("a < b", engine=engine, parser=parser) + tm.assert_frame_equal(result, expect) + + def test_chained_cmp_and_in(self, engine, parser): + skip_if_no_pandas_parser(parser) + cols = list("abc") + df = DataFrame( + np.random.default_rng(2).standard_normal((100, len(cols))), columns=cols + ) + res = df.query( + "a < b < c and a not in b not in c", engine=engine, parser=parser + ) + ind = (df.a < df.b) & (df.b < df.c) & ~df.b.isin(df.a) & ~df.c.isin(df.b) + expec = df[ind] + tm.assert_frame_equal(res, expec) + + def test_local_variable_with_in(self, engine, parser): + skip_if_no_pandas_parser(parser) + a = Series(np.random.default_rng(2).integers(3, size=15), name="a") + b = Series(np.random.default_rng(2).integers(10, size=15), name="b") + df = DataFrame({"a": a, "b": b}) + + expected = df.loc[(df.b - 1).isin(a)] + result = df.query("b - 1 in a", engine=engine, parser=parser) + tm.assert_frame_equal(expected, result) + + b = Series(np.random.default_rng(2).integers(10, size=15), name="b") + expected = df.loc[(b - 1).isin(a)] + result = df.query("@b - 1 in a", engine=engine, parser=parser) + tm.assert_frame_equal(expected, result) + + def test_at_inside_string(self, engine, parser): + skip_if_no_pandas_parser(parser) + c = 1 # noqa: F841 + df = DataFrame({"a": ["a", "a", "b", "b", "@c", "@c"]}) + result = df.query('a == "@c"', engine=engine, parser=parser) + expected = df[df.a == "@c"] + tm.assert_frame_equal(result, expected) + + def test_query_undefined_local(self): + engine, parser = self.engine, self.parser + skip_if_no_pandas_parser(parser) + + df = DataFrame(np.random.default_rng(2).random((10, 2)), columns=list("ab")) + with pytest.raises( + UndefinedVariableError, match="local variable 'c' is not defined" + ): + df.query("a == @c", engine=engine, parser=parser) + + def test_index_resolvers_come_after_columns_with_the_same_name( + self, engine, parser + ): + n = 1 # noqa: F841 + a = np.r_[20:101:20] + + df = DataFrame( + {"index": a, "b": np.random.default_rng(2).standard_normal(a.size)} + ) + df.index.name = "index" + result = df.query("index > 5", engine=engine, parser=parser) + expected = df[df["index"] > 5] + tm.assert_frame_equal(result, expected) + + df = DataFrame( + {"index": a, "b": np.random.default_rng(2).standard_normal(a.size)} + ) + result = df.query("ilevel_0 > 5", engine=engine, parser=parser) + expected = df.loc[df.index[df.index > 5]] + tm.assert_frame_equal(result, expected) + + df = DataFrame({"a": a, "b": np.random.default_rng(2).standard_normal(a.size)}) + df.index.name = "a" + result = df.query("a > 5", engine=engine, parser=parser) + expected = df[df.a > 5] + tm.assert_frame_equal(result, expected) + + result = df.query("index > 5", engine=engine, parser=parser) + expected = df.loc[df.index[df.index > 5]] + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("op, f", [["==", operator.eq], ["!=", operator.ne]]) + def test_inf(self, op, f, engine, parser): + n = 10 + df = DataFrame( + { + "a": np.random.default_rng(2).random(n), + "b": np.random.default_rng(2).random(n), + } + ) + df.loc[::2, 0] = np.inf + q = f"a {op} inf" + expected = df[f(df.a, np.inf)] + result = df.query(q, engine=engine, parser=parser) + tm.assert_frame_equal(result, expected) + + def test_check_tz_aware_index_query(self, tz_aware_fixture): + # https://github.com/pandas-dev/pandas/issues/29463 + tz = tz_aware_fixture + df_index = date_range( + start="2019-01-01", freq="1d", periods=10, tz=tz, name="time" + ) + expected = DataFrame(index=df_index) + df = DataFrame(index=df_index) + result = df.query('"2018-01-03 00:00:00+00" < time') + tm.assert_frame_equal(result, expected) + + expected = DataFrame(df_index) + result = df.reset_index().query('"2018-01-03 00:00:00+00" < time') + tm.assert_frame_equal(result, expected) + + def test_method_calls_in_query(self, engine, parser): + # https://github.com/pandas-dev/pandas/issues/22435 + n = 10 + df = DataFrame( + { + "a": 2 * np.random.default_rng(2).random(n), + "b": np.random.default_rng(2).random(n), + } + ) + expected = df[df["a"].astype("int") == 0] + result = df.query("a.astype('int') == 0", engine=engine, parser=parser) + tm.assert_frame_equal(result, expected) + + df = DataFrame( + { + "a": np.where( + np.random.default_rng(2).random(n) < 0.5, + np.nan, + np.random.default_rng(2).standard_normal(n), + ), + "b": np.random.default_rng(2).standard_normal(n), + } + ) + expected = df[df["a"].notnull()] + result = df.query("a.notnull()", engine=engine, parser=parser) + tm.assert_frame_equal(result, expected) + + +@td.skip_if_no("numexpr") +class TestDataFrameQueryNumExprPython(TestDataFrameQueryNumExprPandas): + @pytest.fixture + def engine(self): + return "numexpr" + + @pytest.fixture + def parser(self): + return "python" + + def test_date_query_no_attribute_access(self, engine, parser): + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + df["dates1"] = date_range("1/1/2012", periods=5) + df["dates2"] = date_range("1/1/2013", periods=5) + df["dates3"] = date_range("1/1/2014", periods=5) + res = df.query( + "(dates1 < 20130101) & (20130101 < dates3)", engine=engine, parser=parser + ) + expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_query_with_NaT(self, engine, parser): + n = 10 + df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))) + df["dates1"] = date_range("1/1/2012", periods=n) + df["dates2"] = date_range("1/1/2013", periods=n) + df["dates3"] = date_range("1/1/2014", periods=n) + df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT + df.loc[np.random.default_rng(2).random(n) > 0.5, "dates3"] = pd.NaT + res = df.query( + "(dates1 < 20130101) & (20130101 < dates3)", engine=engine, parser=parser + ) + expec = df[(df.dates1 < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_index_query(self, engine, parser): + n = 10 + df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))) + df["dates1"] = date_range("1/1/2012", periods=n) + df["dates3"] = date_range("1/1/2014", periods=n) + return_value = df.set_index("dates1", inplace=True, drop=True) + assert return_value is None + res = df.query( + "(index < 20130101) & (20130101 < dates3)", engine=engine, parser=parser + ) + expec = df[(df.index < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_index_query_with_NaT(self, engine, parser): + n = 10 + # Cast to object to avoid implicit cast when setting entry to pd.NaT below + df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))).astype( + {0: object} + ) + df["dates1"] = date_range("1/1/2012", periods=n) + df["dates3"] = date_range("1/1/2014", periods=n) + df.iloc[0, 0] = pd.NaT + return_value = df.set_index("dates1", inplace=True, drop=True) + assert return_value is None + res = df.query( + "(index < 20130101) & (20130101 < dates3)", engine=engine, parser=parser + ) + expec = df[(df.index < "20130101") & ("20130101" < df.dates3)] + tm.assert_frame_equal(res, expec) + + def test_date_index_query_with_NaT_duplicates(self, engine, parser): + n = 10 + df = DataFrame(np.random.default_rng(2).standard_normal((n, 3))) + df["dates1"] = date_range("1/1/2012", periods=n) + df["dates3"] = date_range("1/1/2014", periods=n) + df.loc[np.random.default_rng(2).random(n) > 0.5, "dates1"] = pd.NaT + return_value = df.set_index("dates1", inplace=True, drop=True) + assert return_value is None + msg = r"'BoolOp' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + df.query("index < 20130101 < dates3", engine=engine, parser=parser) + + def test_nested_scope(self, engine, parser): + # smoke test + x = 1 # noqa: F841 + result = pd.eval("x + 1", engine=engine, parser=parser) + assert result == 2 + + df = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + df2 = DataFrame(np.random.default_rng(2).standard_normal((5, 3))) + + # don't have the pandas parser + msg = r"The '@' prefix is only supported by the pandas parser" + with pytest.raises(SyntaxError, match=msg): + df.query("(@df>0) & (@df2>0)", engine=engine, parser=parser) + + with pytest.raises(UndefinedVariableError, match="name 'df' is not defined"): + df.query("(df>0) & (df2>0)", engine=engine, parser=parser) + + expected = df[(df > 0) & (df2 > 0)] + result = pd.eval("df[(df > 0) & (df2 > 0)]", engine=engine, parser=parser) + tm.assert_frame_equal(expected, result) + + expected = df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)] + result = pd.eval( + "df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]", engine=engine, parser=parser + ) + tm.assert_frame_equal(expected, result) + + def test_query_numexpr_with_min_and_max_columns(self): + df = DataFrame({"min": [1, 2, 3], "max": [4, 5, 6]}) + regex_to_match = ( + r"Variables in expression \"\(min\) == \(1\)\" " + r"overlap with builtins: \('min'\)" + ) + with pytest.raises(NumExprClobberingError, match=regex_to_match): + df.query("min == 1") + + regex_to_match = ( + r"Variables in expression \"\(max\) == \(1\)\" " + r"overlap with builtins: \('max'\)" + ) + with pytest.raises(NumExprClobberingError, match=regex_to_match): + df.query("max == 1") + + +class TestDataFrameQueryPythonPandas(TestDataFrameQueryNumExprPandas): + @pytest.fixture + def engine(self): + return "python" + + @pytest.fixture + def parser(self): + return "pandas" + + def test_query_builtin(self, engine, parser): + n = m = 10 + df = DataFrame( + np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc") + ) + + df.index.name = "sin" + expected = df[df.index > 5] + result = df.query("sin > 5", engine=engine, parser=parser) + tm.assert_frame_equal(expected, result) + + +class TestDataFrameQueryPythonPython(TestDataFrameQueryNumExprPython): + @pytest.fixture + def engine(self): + return "python" + + @pytest.fixture + def parser(self): + return "python" + + def test_query_builtin(self, engine, parser): + n = m = 10 + df = DataFrame( + np.random.default_rng(2).integers(m, size=(n, 3)), columns=list("abc") + ) + + df.index.name = "sin" + expected = df[df.index > 5] + result = df.query("sin > 5", engine=engine, parser=parser) + tm.assert_frame_equal(expected, result) + + +class TestDataFrameQueryStrings: + def test_str_query_method(self, parser, engine): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 1)), columns=["b"]) + df["strings"] = Series(list("aabbccddee")) + expect = df[df.strings == "a"] + + if parser != "pandas": + col = "strings" + lst = '"a"' + + lhs = [col] * 2 + [lst] * 2 + rhs = lhs[::-1] + + eq, ne = "==", "!=" + ops = 2 * ([eq] + [ne]) + msg = r"'(Not)?In' nodes are not implemented" + + for lhs, op, rhs in zip(lhs, ops, rhs): + ex = f"{lhs} {op} {rhs}" + with pytest.raises(NotImplementedError, match=msg): + df.query( + ex, + engine=engine, + parser=parser, + local_dict={"strings": df.strings}, + ) + else: + res = df.query('"a" == strings', engine=engine, parser=parser) + tm.assert_frame_equal(res, expect) + + res = df.query('strings == "a"', engine=engine, parser=parser) + tm.assert_frame_equal(res, expect) + tm.assert_frame_equal(res, df[df.strings.isin(["a"])]) + + expect = df[df.strings != "a"] + res = df.query('strings != "a"', engine=engine, parser=parser) + tm.assert_frame_equal(res, expect) + + res = df.query('"a" != strings', engine=engine, parser=parser) + tm.assert_frame_equal(res, expect) + tm.assert_frame_equal(res, df[~df.strings.isin(["a"])]) + + def test_str_list_query_method(self, parser, engine): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 1)), columns=["b"]) + df["strings"] = Series(list("aabbccddee")) + expect = df[df.strings.isin(["a", "b"])] + + if parser != "pandas": + col = "strings" + lst = '["a", "b"]' + + lhs = [col] * 2 + [lst] * 2 + rhs = lhs[::-1] + + eq, ne = "==", "!=" + ops = 2 * ([eq] + [ne]) + msg = r"'(Not)?In' nodes are not implemented" + + for lhs, op, rhs in zip(lhs, ops, rhs): + ex = f"{lhs} {op} {rhs}" + with pytest.raises(NotImplementedError, match=msg): + df.query(ex, engine=engine, parser=parser) + else: + res = df.query('strings == ["a", "b"]', engine=engine, parser=parser) + tm.assert_frame_equal(res, expect) + + res = df.query('["a", "b"] == strings', engine=engine, parser=parser) + tm.assert_frame_equal(res, expect) + + expect = df[~df.strings.isin(["a", "b"])] + + res = df.query('strings != ["a", "b"]', engine=engine, parser=parser) + tm.assert_frame_equal(res, expect) + + res = df.query('["a", "b"] != strings', engine=engine, parser=parser) + tm.assert_frame_equal(res, expect) + + def test_query_with_string_columns(self, parser, engine): + df = DataFrame( + { + "a": list("aaaabbbbcccc"), + "b": list("aabbccddeeff"), + "c": np.random.default_rng(2).integers(5, size=12), + "d": np.random.default_rng(2).integers(9, size=12), + } + ) + if parser == "pandas": + res = df.query("a in b", parser=parser, engine=engine) + expec = df[df.a.isin(df.b)] + tm.assert_frame_equal(res, expec) + + res = df.query("a in b and c < d", parser=parser, engine=engine) + expec = df[df.a.isin(df.b) & (df.c < df.d)] + tm.assert_frame_equal(res, expec) + else: + msg = r"'(Not)?In' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + df.query("a in b", parser=parser, engine=engine) + + msg = r"'BoolOp' nodes are not implemented" + with pytest.raises(NotImplementedError, match=msg): + df.query("a in b and c < d", parser=parser, engine=engine) + + def test_object_array_eq_ne(self, parser, engine): + df = DataFrame( + { + "a": list("aaaabbbbcccc"), + "b": list("aabbccddeeff"), + "c": np.random.default_rng(2).integers(5, size=12), + "d": np.random.default_rng(2).integers(9, size=12), + } + ) + res = df.query("a == b", parser=parser, engine=engine) + exp = df[df.a == df.b] + tm.assert_frame_equal(res, exp) + + res = df.query("a != b", parser=parser, engine=engine) + exp = df[df.a != df.b] + tm.assert_frame_equal(res, exp) + + def test_query_with_nested_strings(self, parser, engine): + skip_if_no_pandas_parser(parser) + events = [ + f"page {n} {act}" for n in range(1, 4) for act in ["load", "exit"] + ] * 2 + stamps1 = date_range("2014-01-01 0:00:01", freq="30s", periods=6) + stamps2 = date_range("2014-02-01 1:00:01", freq="30s", periods=6) + df = DataFrame( + { + "id": np.arange(1, 7).repeat(2), + "event": events, + "timestamp": stamps1.append(stamps2), + } + ) + + expected = df[df.event == '"page 1 load"'] + res = df.query("""'"page 1 load"' in event""", parser=parser, engine=engine) + tm.assert_frame_equal(expected, res) + + def test_query_with_nested_special_character(self, parser, engine): + skip_if_no_pandas_parser(parser) + df = DataFrame({"a": ["a", "b", "test & test"], "b": [1, 2, 3]}) + res = df.query('a == "test & test"', parser=parser, engine=engine) + expec = df[df.a == "test & test"] + tm.assert_frame_equal(res, expec) + + @pytest.mark.parametrize( + "op, func", + [ + ["<", operator.lt], + [">", operator.gt], + ["<=", operator.le], + [">=", operator.ge], + ], + ) + def test_query_lex_compare_strings(self, parser, engine, op, func): + a = Series(np.random.default_rng(2).choice(list("abcde"), 20)) + b = Series(np.arange(a.size)) + df = DataFrame({"X": a, "Y": b}) + + res = df.query(f'X {op} "d"', engine=engine, parser=parser) + expected = df[func(df.X, "d")] + tm.assert_frame_equal(res, expected) + + def test_query_single_element_booleans(self, parser, engine): + columns = "bid", "bidsize", "ask", "asksize" + data = np.random.default_rng(2).integers(2, size=(1, len(columns))).astype(bool) + df = DataFrame(data, columns=columns) + res = df.query("bid & ask", engine=engine, parser=parser) + expected = df[df.bid & df.ask] + tm.assert_frame_equal(res, expected) + + def test_query_string_scalar_variable(self, parser, engine): + skip_if_no_pandas_parser(parser) + df = DataFrame( + { + "Symbol": ["BUD US", "BUD US", "IBM US", "IBM US"], + "Price": [109.70, 109.72, 183.30, 183.35], + } + ) + e = df[df.Symbol == "BUD US"] + symb = "BUD US" # noqa: F841 + r = df.query("Symbol == @symb", parser=parser, engine=engine) + tm.assert_frame_equal(e, r) + + @pytest.mark.parametrize( + "in_list", + [ + [None, "asdf", "ghjk"], + ["asdf", None, "ghjk"], + ["asdf", "ghjk", None], + [None, None, "asdf"], + ["asdf", None, None], + [None, None, None], + ], + ) + def test_query_string_null_elements(self, in_list): + # GITHUB ISSUE #31516 + parser = "pandas" + engine = "python" + expected = {i: value for i, value in enumerate(in_list) if value == "asdf"} + + df_expected = DataFrame({"a": expected}, dtype="string") + df_expected.index = df_expected.index.astype("int64") + df = DataFrame({"a": in_list}, dtype="string") + res1 = df.query("a == 'asdf'", parser=parser, engine=engine) + res2 = df[df["a"] == "asdf"] + res3 = df.query("a <= 'asdf'", parser=parser, engine=engine) + tm.assert_frame_equal(res1, df_expected) + tm.assert_frame_equal(res1, res2) + tm.assert_frame_equal(res1, res3) + tm.assert_frame_equal(res2, res3) + + +class TestDataFrameEvalWithFrame: + @pytest.fixture + def frame(self): + return DataFrame( + np.random.default_rng(2).standard_normal((10, 3)), columns=list("abc") + ) + + def test_simple_expr(self, frame, parser, engine): + res = frame.eval("a + b", engine=engine, parser=parser) + expect = frame.a + frame.b + tm.assert_series_equal(res, expect) + + def test_bool_arith_expr(self, frame, parser, engine): + res = frame.eval("a[a < 1] + b", engine=engine, parser=parser) + expect = frame.a[frame.a < 1] + frame.b + tm.assert_series_equal(res, expect) + + @pytest.mark.parametrize("op", ["+", "-", "*", "/"]) + def test_invalid_type_for_operator_raises(self, parser, engine, op): + df = DataFrame({"a": [1, 2], "b": ["c", "d"]}) + msg = r"unsupported operand type\(s\) for .+: '.+' and '.+'|Cannot" + + with pytest.raises(TypeError, match=msg): + df.eval(f"a {op} b", engine=engine, parser=parser) + + +class TestDataFrameQueryBacktickQuoting: + @pytest.fixture + def df(self): + """ + Yields a dataframe with strings that may or may not need escaping + by backticks. The last two columns cannot be escaped by backticks + and should raise a ValueError. + """ + yield DataFrame( + { + "A": [1, 2, 3], + "B B": [3, 2, 1], + "C C": [4, 5, 6], + "C C": [7, 4, 3], + "C_C": [8, 9, 10], + "D_D D": [11, 1, 101], + "E.E": [6, 3, 5], + "F-F": [8, 1, 10], + "1e1": [2, 4, 8], + "def": [10, 11, 2], + "A (x)": [4, 1, 3], + "B(x)": [1, 1, 5], + "B (x)": [2, 7, 4], + " &^ :!€$?(} > <++*'' ": [2, 5, 6], + "": [10, 11, 1], + " A": [4, 7, 9], + " ": [1, 2, 1], + "it's": [6, 3, 1], + "that's": [9, 1, 8], + "☺": [8, 7, 6], + "foo#bar": [2, 4, 5], + 1: [5, 7, 9], + } + ) + + def test_single_backtick_variable_query(self, df): + res = df.query("1 < `B B`") + expect = df[1 < df["B B"]] + tm.assert_frame_equal(res, expect) + + def test_two_backtick_variables_query(self, df): + res = df.query("1 < `B B` and 4 < `C C`") + expect = df[(1 < df["B B"]) & (4 < df["C C"])] + tm.assert_frame_equal(res, expect) + + def test_single_backtick_variable_expr(self, df): + res = df.eval("A + `B B`") + expect = df["A"] + df["B B"] + tm.assert_series_equal(res, expect) + + def test_two_backtick_variables_expr(self, df): + res = df.eval("`B B` + `C C`") + expect = df["B B"] + df["C C"] + tm.assert_series_equal(res, expect) + + def test_already_underscore_variable(self, df): + res = df.eval("`C_C` + A") + expect = df["C_C"] + df["A"] + tm.assert_series_equal(res, expect) + + def test_same_name_but_underscores(self, df): + res = df.eval("C_C + `C C`") + expect = df["C_C"] + df["C C"] + tm.assert_series_equal(res, expect) + + def test_mixed_underscores_and_spaces(self, df): + res = df.eval("A + `D_D D`") + expect = df["A"] + df["D_D D"] + tm.assert_series_equal(res, expect) + + def test_backtick_quote_name_with_no_spaces(self, df): + res = df.eval("A + `C_C`") + expect = df["A"] + df["C_C"] + tm.assert_series_equal(res, expect) + + def test_special_characters(self, df): + res = df.eval("`E.E` + `F-F` - A") + expect = df["E.E"] + df["F-F"] - df["A"] + tm.assert_series_equal(res, expect) + + def test_start_with_digit(self, df): + res = df.eval("A + `1e1`") + expect = df["A"] + df["1e1"] + tm.assert_series_equal(res, expect) + + def test_keyword(self, df): + res = df.eval("A + `def`") + expect = df["A"] + df["def"] + tm.assert_series_equal(res, expect) + + def test_unneeded_quoting(self, df): + res = df.query("`A` > 2") + expect = df[df["A"] > 2] + tm.assert_frame_equal(res, expect) + + def test_parenthesis(self, df): + res = df.query("`A (x)` > 2") + expect = df[df["A (x)"] > 2] + tm.assert_frame_equal(res, expect) + + def test_empty_string(self, df): + res = df.query("`` > 5") + expect = df[df[""] > 5] + tm.assert_frame_equal(res, expect) + + def test_multiple_spaces(self, df): + res = df.query("`C C` > 5") + expect = df[df["C C"] > 5] + tm.assert_frame_equal(res, expect) + + def test_start_with_spaces(self, df): + res = df.eval("` A` + ` `") + expect = df[" A"] + df[" "] + tm.assert_series_equal(res, expect) + + def test_lots_of_operators_string(self, df): + res = df.query("` &^ :!€$?(} > <++*'' ` > 4") + expect = df[df[" &^ :!€$?(} > <++*'' "] > 4] + tm.assert_frame_equal(res, expect) + + def test_missing_attribute(self, df): + message = "module 'pandas' has no attribute 'thing'" + with pytest.raises(AttributeError, match=message): + df.eval("@pd.thing") + + def test_failing_quote(self, df): + msg = r"(Could not convert ).*( to a valid Python identifier.)" + with pytest.raises(SyntaxError, match=msg): + df.query("`it's` > `that's`") + + def test_failing_character_outside_range(self, df): + msg = r"(Could not convert ).*( to a valid Python identifier.)" + with pytest.raises(SyntaxError, match=msg): + df.query("`☺` > 4") + + def test_failing_hashtag(self, df): + msg = "Failed to parse backticks" + with pytest.raises(SyntaxError, match=msg): + df.query("`foo#bar` > 4") + + def test_call_non_named_expression(self, df): + """ + Only attributes and variables ('named functions') can be called. + .__call__() is not an allowed attribute because that would allow + calling anything. + https://github.com/pandas-dev/pandas/pull/32460 + """ + + def func(*_): + return 1 + + funcs = [func] # noqa: F841 + + df.eval("@func()") + + with pytest.raises(TypeError, match="Only named functions are supported"): + df.eval("@funcs[0]()") + + with pytest.raises(TypeError, match="Only named functions are supported"): + df.eval("@funcs[0].__call__()") + + def test_ea_dtypes(self, any_numeric_ea_and_arrow_dtype): + # GH#29618 + df = DataFrame( + [[1, 2], [3, 4]], columns=["a", "b"], dtype=any_numeric_ea_and_arrow_dtype + ) + warning = RuntimeWarning if NUMEXPR_INSTALLED else None + with tm.assert_produces_warning(warning): + result = df.eval("c = b - a") + expected = DataFrame( + [[1, 2, 1], [3, 4, 1]], + columns=["a", "b", "c"], + dtype=any_numeric_ea_and_arrow_dtype, + ) + tm.assert_frame_equal(result, expected) + + def test_ea_dtypes_and_scalar(self): + # GH#29618 + df = DataFrame([[1, 2], [3, 4]], columns=["a", "b"], dtype="Float64") + warning = RuntimeWarning if NUMEXPR_INSTALLED else None + with tm.assert_produces_warning(warning): + result = df.eval("c = b - 1") + expected = DataFrame( + [[1, 2, 1], [3, 4, 3]], columns=["a", "b", "c"], dtype="Float64" + ) + tm.assert_frame_equal(result, expected) + + def test_ea_dtypes_and_scalar_operation(self, any_numeric_ea_and_arrow_dtype): + # GH#29618 + df = DataFrame( + [[1, 2], [3, 4]], columns=["a", "b"], dtype=any_numeric_ea_and_arrow_dtype + ) + result = df.eval("c = 2 - 1") + expected = DataFrame( + { + "a": Series([1, 3], dtype=any_numeric_ea_and_arrow_dtype), + "b": Series([2, 4], dtype=any_numeric_ea_and_arrow_dtype), + "c": Series([1, 1], dtype=result["c"].dtype), + } + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dtype", ["int64", "Int64", "int64[pyarrow]"]) + def test_query_ea_dtypes(self, dtype): + if dtype == "int64[pyarrow]": + pytest.importorskip("pyarrow") + # GH#50261 + df = DataFrame({"a": Series([1, 2], dtype=dtype)}) + ref = {2} # noqa: F841 + warning = RuntimeWarning if dtype == "Int64" and NUMEXPR_INSTALLED else None + with tm.assert_produces_warning(warning): + result = df.query("a in @ref") + expected = DataFrame({"a": Series([2], dtype=dtype, index=[1])}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("engine", ["python", "numexpr"]) + @pytest.mark.parametrize("dtype", ["int64", "Int64", "int64[pyarrow]"]) + def test_query_ea_equality_comparison(self, dtype, engine): + # GH#50261 + warning = RuntimeWarning if engine == "numexpr" else None + if engine == "numexpr" and not NUMEXPR_INSTALLED: + pytest.skip("numexpr not installed") + if dtype == "int64[pyarrow]": + pytest.importorskip("pyarrow") + df = DataFrame( + {"A": Series([1, 1, 2], dtype="Int64"), "B": Series([1, 2, 2], dtype=dtype)} + ) + with tm.assert_produces_warning(warning): + result = df.query("A == B", engine=engine) + expected = DataFrame( + { + "A": Series([1, 2], dtype="Int64", index=[0, 2]), + "B": Series([1, 2], dtype=dtype, index=[0, 2]), + } + ) + tm.assert_frame_equal(result, expected) + + def test_all_nat_in_object(self): + # GH#57068 + now = pd.Timestamp.now("UTC") # noqa: F841 + df = DataFrame({"a": pd.to_datetime([None, None], utc=True)}, dtype=object) + result = df.query("a > @now") + expected = DataFrame({"a": []}, dtype=object) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_reductions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_reductions.py new file mode 100644 index 0000000000000000000000000000000000000000..8b450cecfca00f0d82195d64a48a2f4e617c7660 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_reductions.py @@ -0,0 +1,2133 @@ +from datetime import timedelta +from decimal import Decimal +import re + +from dateutil.tz import tzlocal +import numpy as np +import pytest + +from pandas.compat import ( + IS64, + is_platform_windows, +) +from pandas.compat.numpy import np_version_gt2 +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + Categorical, + CategoricalDtype, + DataFrame, + DatetimeIndex, + Index, + PeriodIndex, + RangeIndex, + Series, + Timestamp, + date_range, + isna, + notna, + to_datetime, + to_timedelta, +) +import pandas._testing as tm +from pandas.core import ( + algorithms, + nanops, +) + +is_windows_np2_or_is32 = (is_platform_windows() and not np_version_gt2) or not IS64 +is_windows_or_is32 = is_platform_windows() or not IS64 + + +def make_skipna_wrapper(alternative, skipna_alternative=None): + """ + Create a function for calling on an array. + + Parameters + ---------- + alternative : function + The function to be called on the array with no NaNs. + Only used when 'skipna_alternative' is None. + skipna_alternative : function + The function to be called on the original array + + Returns + ------- + function + """ + if skipna_alternative: + + def skipna_wrapper(x): + return skipna_alternative(x.values) + + else: + + def skipna_wrapper(x): + nona = x.dropna() + if len(nona) == 0: + return np.nan + return alternative(nona) + + return skipna_wrapper + + +def assert_stat_op_calc( + opname, + alternative, + frame, + has_skipna=True, + check_dtype=True, + check_dates=False, + rtol=1e-5, + atol=1e-8, + skipna_alternative=None, +): + """ + Check that operator opname works as advertised on frame + + Parameters + ---------- + opname : str + Name of the operator to test on frame + alternative : function + Function that opname is tested against; i.e. "frame.opname()" should + equal "alternative(frame)". + frame : DataFrame + The object that the tests are executed on + has_skipna : bool, default True + Whether the method "opname" has the kwarg "skip_na" + check_dtype : bool, default True + Whether the dtypes of the result of "frame.opname()" and + "alternative(frame)" should be checked. + check_dates : bool, default false + Whether opname should be tested on a Datetime Series + rtol : float, default 1e-5 + Relative tolerance. + atol : float, default 1e-8 + Absolute tolerance. + skipna_alternative : function, default None + NaN-safe version of alternative + """ + f = getattr(frame, opname) + + if check_dates: + df = DataFrame({"b": date_range("1/1/2001", periods=2)}) + with tm.assert_produces_warning(None): + result = getattr(df, opname)() + assert isinstance(result, Series) + + df["a"] = range(len(df)) + with tm.assert_produces_warning(None): + result = getattr(df, opname)() + assert isinstance(result, Series) + assert len(result) + + if has_skipna: + + def wrapper(x): + return alternative(x.values) + + skipna_wrapper = make_skipna_wrapper(alternative, skipna_alternative) + result0 = f(axis=0, skipna=False) + result1 = f(axis=1, skipna=False) + tm.assert_series_equal( + result0, frame.apply(wrapper), check_dtype=check_dtype, rtol=rtol, atol=atol + ) + tm.assert_series_equal( + result1, + frame.apply(wrapper, axis=1), + rtol=rtol, + atol=atol, + ) + else: + skipna_wrapper = alternative + + result0 = f(axis=0) + result1 = f(axis=1) + tm.assert_series_equal( + result0, + frame.apply(skipna_wrapper), + check_dtype=check_dtype, + rtol=rtol, + atol=atol, + ) + + if opname in ["sum", "prod"]: + expected = frame.apply(skipna_wrapper, axis=1) + tm.assert_series_equal( + result1, expected, check_dtype=False, rtol=rtol, atol=atol + ) + + # check dtypes + if check_dtype: + lcd_dtype = frame.values.dtype + assert lcd_dtype == result0.dtype + assert lcd_dtype == result1.dtype + + # bad axis + with pytest.raises(ValueError, match="No axis named 2"): + f(axis=2) + + # all NA case + if has_skipna: + all_na = frame * np.nan + r0 = getattr(all_na, opname)(axis=0) + r1 = getattr(all_na, opname)(axis=1) + if opname in ["sum", "prod"]: + unit = 1 if opname == "prod" else 0 # result for empty sum/prod + expected = Series(unit, index=r0.index, dtype=r0.dtype) + tm.assert_series_equal(r0, expected) + expected = Series(unit, index=r1.index, dtype=r1.dtype) + tm.assert_series_equal(r1, expected) + + +@pytest.fixture +def bool_frame_with_na(): + """ + Fixture for DataFrame of booleans with index of unique strings + + Columns are ['A', 'B', 'C', 'D']; some entries are missing + """ + df = DataFrame( + np.concatenate( + [np.ones((15, 4), dtype=bool), np.zeros((15, 4), dtype=bool)], axis=0 + ), + index=Index([f"foo_{i}" for i in range(30)], dtype=object), + columns=Index(list("ABCD"), dtype=object), + dtype=object, + ) + # set some NAs + df.iloc[5:10] = np.nan + df.iloc[15:20, -2:] = np.nan + return df + + +@pytest.fixture +def float_frame_with_na(): + """ + Fixture for DataFrame of floats with index of unique strings + + Columns are ['A', 'B', 'C', 'D']; some entries are missing + """ + df = DataFrame( + np.random.default_rng(2).standard_normal((30, 4)), + index=Index([f"foo_{i}" for i in range(30)], dtype=object), + columns=Index(list("ABCD"), dtype=object), + ) + # set some NAs + df.iloc[5:10] = np.nan + df.iloc[15:20, -2:] = np.nan + return df + + +class TestDataFrameAnalytics: + # --------------------------------------------------------------------- + # Reductions + @pytest.mark.parametrize("axis", [0, 1]) + @pytest.mark.parametrize( + "opname", + [ + "count", + "sum", + "mean", + "product", + "median", + "min", + "max", + "nunique", + "var", + "std", + "sem", + pytest.param("skew", marks=td.skip_if_no("scipy")), + pytest.param("kurt", marks=td.skip_if_no("scipy")), + ], + ) + def test_stat_op_api_float_string_frame(self, float_string_frame, axis, opname): + if (opname in ("sum", "min", "max") and axis == 0) or opname in ( + "count", + "nunique", + ): + getattr(float_string_frame, opname)(axis=axis) + else: + if opname in ["var", "std", "sem", "skew", "kurt"]: + msg = "could not convert string to float: 'bar'" + elif opname == "product": + if axis == 1: + msg = "can't multiply sequence by non-int of type 'float'" + else: + msg = "can't multiply sequence by non-int of type 'str'" + elif opname == "sum": + msg = r"unsupported operand type\(s\) for \+: 'float' and 'str'" + elif opname == "mean": + if axis == 0: + # different message on different builds + msg = "|".join( + [ + r"Could not convert \['.*'\] to numeric", + "Could not convert string '(bar){30}' to numeric", + ] + ) + else: + msg = r"unsupported operand type\(s\) for \+: 'float' and 'str'" + elif opname in ["min", "max"]: + msg = "'[><]=' not supported between instances of 'float' and 'str'" + elif opname == "median": + msg = re.compile( + r"Cannot convert \[.*\] to numeric|does not support|Cannot perform", + flags=re.S, + ) + if not isinstance(msg, re.Pattern): + msg = msg + "|does not support|Cannot perform reduction" + with pytest.raises(TypeError, match=msg): + getattr(float_string_frame, opname)(axis=axis) + if opname != "nunique": + getattr(float_string_frame, opname)(axis=axis, numeric_only=True) + + @pytest.mark.parametrize("axis", [0, 1]) + @pytest.mark.parametrize( + "opname", + [ + "count", + "sum", + "mean", + "product", + "median", + "min", + "max", + "var", + "std", + "sem", + pytest.param("skew", marks=td.skip_if_no("scipy")), + pytest.param("kurt", marks=td.skip_if_no("scipy")), + ], + ) + def test_stat_op_api_float_frame(self, float_frame, axis, opname): + getattr(float_frame, opname)(axis=axis, numeric_only=False) + + def test_stat_op_calc(self, float_frame_with_na, mixed_float_frame): + def count(s): + return notna(s).sum() + + def nunique(s): + return len(algorithms.unique1d(s.dropna())) + + def var(x): + return np.var(x, ddof=1) + + def std(x): + return np.std(x, ddof=1) + + def sem(x): + return np.std(x, ddof=1) / np.sqrt(len(x)) + + assert_stat_op_calc( + "nunique", + nunique, + float_frame_with_na, + has_skipna=False, + check_dtype=False, + check_dates=True, + ) + + # GH#32571: rol needed for flaky CI builds + # mixed types (with upcasting happening) + assert_stat_op_calc( + "sum", + np.sum, + mixed_float_frame.astype("float32"), + check_dtype=False, + rtol=1e-3, + ) + + assert_stat_op_calc( + "sum", np.sum, float_frame_with_na, skipna_alternative=np.nansum + ) + assert_stat_op_calc("mean", np.mean, float_frame_with_na, check_dates=True) + assert_stat_op_calc( + "product", np.prod, float_frame_with_na, skipna_alternative=np.nanprod + ) + + assert_stat_op_calc("var", var, float_frame_with_na) + assert_stat_op_calc("std", std, float_frame_with_na) + assert_stat_op_calc("sem", sem, float_frame_with_na) + + assert_stat_op_calc( + "count", + count, + float_frame_with_na, + has_skipna=False, + check_dtype=False, + check_dates=True, + ) + + def test_stat_op_calc_skew_kurtosis(self, float_frame_with_na): + sp_stats = pytest.importorskip("scipy.stats") + + def skewness(x): + if len(x) < 3: + return np.nan + return sp_stats.skew(x, bias=False) + + def kurt(x): + if len(x) < 4: + return np.nan + return sp_stats.kurtosis(x, bias=False) + + assert_stat_op_calc("skew", skewness, float_frame_with_na) + assert_stat_op_calc("kurt", kurt, float_frame_with_na) + + def test_median(self, float_frame_with_na, int_frame): + def wrapper(x): + if isna(x).any(): + return np.nan + return np.median(x) + + assert_stat_op_calc("median", wrapper, float_frame_with_na, check_dates=True) + assert_stat_op_calc( + "median", wrapper, int_frame, check_dtype=False, check_dates=True + ) + + @pytest.mark.parametrize( + "method", ["sum", "mean", "prod", "var", "std", "skew", "min", "max"] + ) + @pytest.mark.parametrize( + "df", + [ + DataFrame( + { + "a": [ + -0.00049987540199591344, + -0.0016467257772919831, + 0.00067695870775883013, + ], + "b": [-0, -0, 0.0], + "c": [ + 0.00031111847529610595, + 0.0014902627951905339, + -0.00094099200035979691, + ], + }, + index=["foo", "bar", "baz"], + dtype="O", + ), + DataFrame({0: [np.nan, 2], 1: [np.nan, 3], 2: [np.nan, 4]}, dtype=object), + ], + ) + @pytest.mark.filterwarnings("ignore:Mismatched null-like values:FutureWarning") + def test_stat_operators_attempt_obj_array(self, method, df, axis): + # GH#676 + assert df.values.dtype == np.object_ + result = getattr(df, method)(axis=axis) + expected = getattr(df.astype("f8"), method)(axis=axis).astype(object) + if axis in [1, "columns"] and method in ["min", "max"]: + expected[expected.isna()] = None + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("op", ["mean", "std", "var", "skew", "kurt", "sem"]) + def test_mixed_ops(self, op): + # GH#16116 + df = DataFrame( + { + "int": [1, 2, 3, 4], + "float": [1.0, 2.0, 3.0, 4.0], + "str": ["a", "b", "c", "d"], + } + ) + msg = "|".join( + [ + "Could not convert", + "could not convert", + "can't multiply sequence by non-int", + "does not support", + "Cannot perform", + ] + ) + with pytest.raises(TypeError, match=msg): + getattr(df, op)() + + with pd.option_context("use_bottleneck", False): + with pytest.raises(TypeError, match=msg): + getattr(df, op)() + + def test_reduce_mixed_frame(self): + # GH 6806 + df = DataFrame( + { + "bool_data": [True, True, False, False, False], + "int_data": [10, 20, 30, 40, 50], + "string_data": ["a", "b", "c", "d", "e"], + } + ) + df.reindex(columns=["bool_data", "int_data", "string_data"]) + test = df.sum(axis=0) + tm.assert_numpy_array_equal( + test.values, np.array([2, 150, "abcde"], dtype=object) + ) + alt = df.T.sum(axis=1) + tm.assert_series_equal(test, alt) + + def test_nunique(self): + df = DataFrame({"A": [1, 1, 1], "B": [1, 2, 3], "C": [1, np.nan, 3]}) + tm.assert_series_equal(df.nunique(), Series({"A": 1, "B": 3, "C": 2})) + tm.assert_series_equal( + df.nunique(dropna=False), Series({"A": 1, "B": 3, "C": 3}) + ) + tm.assert_series_equal(df.nunique(axis=1), Series({0: 1, 1: 2, 2: 2})) + tm.assert_series_equal( + df.nunique(axis=1, dropna=False), Series({0: 1, 1: 3, 2: 2}) + ) + + @pytest.mark.parametrize("tz", [None, "UTC"]) + def test_mean_mixed_datetime_numeric(self, tz): + # https://github.com/pandas-dev/pandas/issues/24752 + df = DataFrame({"A": [1, 1], "B": [Timestamp("2000", tz=tz)] * 2}) + result = df.mean() + expected = Series([1.0, Timestamp("2000", tz=tz)], index=["A", "B"]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("tz", [None, "UTC"]) + def test_mean_includes_datetimes(self, tz): + # https://github.com/pandas-dev/pandas/issues/24752 + # Behavior in 0.24.0rc1 was buggy. + # As of 2.0 with numeric_only=None we do *not* drop datetime columns + df = DataFrame({"A": [Timestamp("2000", tz=tz)] * 2}) + result = df.mean() + + expected = Series([Timestamp("2000", tz=tz)], index=["A"]) + tm.assert_series_equal(result, expected) + + def test_mean_mixed_string_decimal(self): + # GH 11670 + # possible bug when calculating mean of DataFrame? + + d = [ + {"A": 2, "B": None, "C": Decimal("628.00")}, + {"A": 1, "B": None, "C": Decimal("383.00")}, + {"A": 3, "B": None, "C": Decimal("651.00")}, + {"A": 2, "B": None, "C": Decimal("575.00")}, + {"A": 4, "B": None, "C": Decimal("1114.00")}, + {"A": 1, "B": "TEST", "C": Decimal("241.00")}, + {"A": 2, "B": None, "C": Decimal("572.00")}, + {"A": 4, "B": None, "C": Decimal("609.00")}, + {"A": 3, "B": None, "C": Decimal("820.00")}, + {"A": 5, "B": None, "C": Decimal("1223.00")}, + ] + + df = DataFrame(d) + + with pytest.raises( + TypeError, match="unsupported operand type|does not support|Cannot perform" + ): + df.mean() + result = df[["A", "C"]].mean() + expected = Series([2.7, 681.6], index=["A", "C"], dtype=object) + tm.assert_series_equal(result, expected) + + def test_var_std(self, datetime_frame): + result = datetime_frame.std(ddof=4) + expected = datetime_frame.apply(lambda x: x.std(ddof=4)) + tm.assert_almost_equal(result, expected) + + result = datetime_frame.var(ddof=4) + expected = datetime_frame.apply(lambda x: x.var(ddof=4)) + tm.assert_almost_equal(result, expected) + + arr = np.repeat(np.random.default_rng(2).random((1, 1000)), 1000, 0) + result = nanops.nanvar(arr, axis=0) + assert not (result < 0).any() + + with pd.option_context("use_bottleneck", False): + result = nanops.nanvar(arr, axis=0) + assert not (result < 0).any() + + @pytest.mark.parametrize("meth", ["sem", "var", "std"]) + def test_numeric_only_flag(self, meth): + # GH 9201 + df1 = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + columns=["foo", "bar", "baz"], + ) + # Cast to object to avoid implicit cast when setting entry to "100" below + df1 = df1.astype({"foo": object}) + # set one entry to a number in str format + df1.loc[0, "foo"] = "100" + + df2 = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + columns=["foo", "bar", "baz"], + ) + # Cast to object to avoid implicit cast when setting entry to "a" below + df2 = df2.astype({"foo": object}) + # set one entry to a non-number str + df2.loc[0, "foo"] = "a" + + result = getattr(df1, meth)(axis=1, numeric_only=True) + expected = getattr(df1[["bar", "baz"]], meth)(axis=1) + tm.assert_series_equal(expected, result) + + result = getattr(df2, meth)(axis=1, numeric_only=True) + expected = getattr(df2[["bar", "baz"]], meth)(axis=1) + tm.assert_series_equal(expected, result) + + # df1 has all numbers, df2 has a letter inside + msg = r"unsupported operand type\(s\) for -: 'float' and 'str'" + with pytest.raises(TypeError, match=msg): + getattr(df1, meth)(axis=1, numeric_only=False) + msg = "could not convert string to float: 'a'" + with pytest.raises(TypeError, match=msg): + getattr(df2, meth)(axis=1, numeric_only=False) + + def test_sem(self, datetime_frame): + result = datetime_frame.sem(ddof=4) + expected = datetime_frame.apply(lambda x: x.std(ddof=4) / np.sqrt(len(x))) + tm.assert_almost_equal(result, expected) + + arr = np.repeat(np.random.default_rng(2).random((1, 1000)), 1000, 0) + result = nanops.nansem(arr, axis=0) + assert not (result < 0).any() + + with pd.option_context("use_bottleneck", False): + result = nanops.nansem(arr, axis=0) + assert not (result < 0).any() + + @pytest.mark.parametrize( + "dropna, expected", + [ + ( + True, + { + "A": [12], + "B": [10.0], + "C": [1.0], + "D": ["a"], + "E": Categorical(["a"], categories=["a"]), + "F": DatetimeIndex(["2000-01-02"], dtype="M8[ns]"), + "G": to_timedelta(["1 days"]), + }, + ), + ( + False, + { + "A": [12], + "B": [10.0], + "C": [np.nan], + "D": Series([np.nan], dtype="str"), + "E": Categorical([np.nan], categories=["a"]), + "F": DatetimeIndex([pd.NaT], dtype="M8[ns]"), + "G": to_timedelta([pd.NaT]), + }, + ), + ( + True, + { + "H": [8, 9, np.nan, np.nan], + "I": [8, 9, np.nan, np.nan], + "J": [1, np.nan, np.nan, np.nan], + "K": Categorical(["a", np.nan, np.nan, np.nan], categories=["a"]), + "L": DatetimeIndex( + ["2000-01-02", "NaT", "NaT", "NaT"], dtype="M8[ns]" + ), + "M": to_timedelta(["1 days", "nan", "nan", "nan"]), + "N": [0, 1, 2, 3], + }, + ), + ( + False, + { + "H": [8, 9, np.nan, np.nan], + "I": [8, 9, np.nan, np.nan], + "J": [1, np.nan, np.nan, np.nan], + "K": Categorical([np.nan, "a", np.nan, np.nan], categories=["a"]), + "L": DatetimeIndex( + ["NaT", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]" + ), + "M": to_timedelta(["nan", "1 days", "nan", "nan"]), + "N": [0, 1, 2, 3], + }, + ), + ], + ) + def test_mode_dropna(self, dropna, expected): + df = DataFrame( + { + "A": [12, 12, 19, 11], + "B": [10, 10, np.nan, 3], + "C": [1, np.nan, np.nan, np.nan], + "D": Series([np.nan, np.nan, "a", np.nan], dtype="str"), + "E": Categorical([np.nan, np.nan, "a", np.nan]), + "F": DatetimeIndex(["NaT", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"), + "G": to_timedelta(["1 days", "nan", "nan", "nan"]), + "H": [8, 8, 9, 9], + "I": [9, 9, 8, 8], + "J": [1, 1, np.nan, np.nan], + "K": Categorical(["a", np.nan, "a", np.nan]), + "L": DatetimeIndex( + ["2000-01-02", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]" + ), + "M": to_timedelta(["1 days", "nan", "1 days", "nan"]), + "N": np.arange(4, dtype="int64"), + } + ) + + result = df[sorted(expected.keys())].mode(dropna=dropna) + expected = DataFrame(expected) + tm.assert_frame_equal(result, expected) + + def test_mode_sort_with_na(self, using_infer_string): + df = DataFrame({"A": [np.nan, np.nan, "a", "a"]}) + expected = DataFrame({"A": ["a", np.nan]}) + result = df.mode(dropna=False) + tm.assert_frame_equal(result, expected) + + def test_mode_empty_df(self): + df = DataFrame([], columns=["a", "b"]) + result = df.mode() + expected = DataFrame([], columns=["a", "b"], index=Index([], dtype=np.int64)) + tm.assert_frame_equal(result, expected) + + def test_operators_timedelta64(self): + df = DataFrame( + { + "A": date_range("2012-1-1", periods=3, freq="D"), + "B": date_range("2012-1-2", periods=3, freq="D"), + "C": Timestamp("20120101") - timedelta(minutes=5, seconds=5), + } + ) + + diffs = DataFrame({"A": df["A"] - df["C"], "B": df["A"] - df["B"]}) + + # min + result = diffs.min() + assert result.iloc[0] == diffs.loc[0, "A"] + assert result.iloc[1] == diffs.loc[0, "B"] + + result = diffs.min(axis=1) + assert (result == diffs.loc[0, "B"]).all() + + # max + result = diffs.max() + assert result.iloc[0] == diffs.loc[2, "A"] + assert result.iloc[1] == diffs.loc[2, "B"] + + result = diffs.max(axis=1) + assert (result == diffs["A"]).all() + + # abs + result = diffs.abs() + result2 = abs(diffs) + expected = DataFrame({"A": df["A"] - df["C"], "B": df["B"] - df["A"]}) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result2, expected) + + # mixed frame + mixed = diffs.copy() + mixed["C"] = "foo" + mixed["D"] = 1 + mixed["E"] = 1.0 + mixed["F"] = Timestamp("20130101") + + # results in an object array + result = mixed.min() + expected = Series( + [ + pd.Timedelta(timedelta(seconds=5 * 60 + 5)), + pd.Timedelta(timedelta(days=-1)), + "foo", + 1, + 1.0, + Timestamp("20130101"), + ], + index=mixed.columns, + ) + tm.assert_series_equal(result, expected) + + # excludes non-numeric + result = mixed.min(axis=1, numeric_only=True) + expected = Series([1, 1, 1.0], index=[0, 1, 2]) + tm.assert_series_equal(result, expected) + + # works when only those columns are selected + result = mixed[["A", "B"]].min(1) + expected = Series([timedelta(days=-1)] * 3) + tm.assert_series_equal(result, expected) + + result = mixed[["A", "B"]].min() + expected = Series( + [timedelta(seconds=5 * 60 + 5), timedelta(days=-1)], index=["A", "B"] + ) + tm.assert_series_equal(result, expected) + + # GH 3106 + df = DataFrame( + { + "time": date_range("20130102", periods=5), + "time2": date_range("20130105", periods=5), + } + ) + df["off1"] = df["time2"] - df["time"] + assert df["off1"].dtype == "timedelta64[ns]" + + df["off2"] = df["time"] - df["time2"] + df._consolidate_inplace() + assert df["off1"].dtype == "timedelta64[ns]" + assert df["off2"].dtype == "timedelta64[ns]" + + def test_std_timedelta64_skipna_false(self): + # GH#37392 + tdi = pd.timedelta_range("1 Day", periods=10) + df = DataFrame({"A": tdi, "B": tdi}, copy=True) + df.iloc[-2, -1] = pd.NaT + + result = df.std(skipna=False) + expected = Series( + [df["A"].std(), pd.NaT], index=["A", "B"], dtype="timedelta64[ns]" + ) + tm.assert_series_equal(result, expected) + + result = df.std(axis=1, skipna=False) + expected = Series([pd.Timedelta(0)] * 8 + [pd.NaT, pd.Timedelta(0)]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "values", [["2022-01-01", "2022-01-02", pd.NaT, "2022-01-03"], 4 * [pd.NaT]] + ) + def test_std_datetime64_with_nat( + self, values, skipna, using_array_manager, request, unit + ): + # GH#51335 + if using_array_manager and ( + not skipna or all(value is pd.NaT for value in values) + ): + mark = pytest.mark.xfail( + reason="GH#51446: Incorrect type inference on NaT in reduction result" + ) + request.applymarker(mark) + dti = to_datetime(values).as_unit(unit) + df = DataFrame({"a": dti}) + result = df.std(skipna=skipna) + if not skipna or all(value is pd.NaT for value in values): + expected = Series({"a": pd.NaT}, dtype=f"timedelta64[{unit}]") + else: + # 86400000000000ns == 1 day + expected = Series({"a": 86400000000000}, dtype=f"timedelta64[{unit}]") + tm.assert_series_equal(result, expected) + + def test_sum_corner(self): + empty_frame = DataFrame() + + axis0 = empty_frame.sum(0) + axis1 = empty_frame.sum(1) + assert isinstance(axis0, Series) + assert isinstance(axis1, Series) + assert len(axis0) == 0 + assert len(axis1) == 0 + + @pytest.mark.parametrize( + "index", + [ + RangeIndex(0), + DatetimeIndex([]), + Index([], dtype=np.int64), + Index([], dtype=np.float64), + DatetimeIndex([], freq="ME"), + PeriodIndex([], freq="D"), + ], + ) + def test_axis_1_empty(self, all_reductions, index): + df = DataFrame(columns=["a"], index=index) + result = getattr(df, all_reductions)(axis=1) + if all_reductions in ("any", "all"): + expected_dtype = "bool" + elif all_reductions == "count": + expected_dtype = "int64" + else: + expected_dtype = "object" + expected = Series([], index=index, dtype=expected_dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("min_count", [0, 1]) + def test_axis_1_sum_na(self, string_dtype_no_object, skipna, min_count): + # https://github.com/pandas-dev/pandas/issues/60229 + dtype = string_dtype_no_object + df = DataFrame({"a": [pd.NA]}, dtype=dtype) + result = df.sum(axis=1, skipna=skipna, min_count=min_count) + value = "" if skipna and min_count == 0 else pd.NA + expected = Series([value], dtype=dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("method, unit", [("sum", 0), ("prod", 1)]) + @pytest.mark.parametrize("numeric_only", [None, True, False]) + def test_sum_prod_nanops(self, method, unit, numeric_only): + idx = ["a", "b", "c"] + df = DataFrame({"a": [unit, unit], "b": [unit, np.nan], "c": [np.nan, np.nan]}) + # The default + result = getattr(df, method)(numeric_only=numeric_only) + expected = Series([unit, unit, unit], index=idx, dtype="float64") + tm.assert_series_equal(result, expected) + + # min_count=1 + result = getattr(df, method)(numeric_only=numeric_only, min_count=1) + expected = Series([unit, unit, np.nan], index=idx) + tm.assert_series_equal(result, expected) + + # min_count=0 + result = getattr(df, method)(numeric_only=numeric_only, min_count=0) + expected = Series([unit, unit, unit], index=idx, dtype="float64") + tm.assert_series_equal(result, expected) + + result = getattr(df.iloc[1:], method)(numeric_only=numeric_only, min_count=1) + expected = Series([unit, np.nan, np.nan], index=idx) + tm.assert_series_equal(result, expected) + + # min_count > 1 + df = DataFrame({"A": [unit] * 10, "B": [unit] * 5 + [np.nan] * 5}) + result = getattr(df, method)(numeric_only=numeric_only, min_count=5) + expected = Series(result, index=["A", "B"]) + tm.assert_series_equal(result, expected) + + result = getattr(df, method)(numeric_only=numeric_only, min_count=6) + expected = Series(result, index=["A", "B"]) + tm.assert_series_equal(result, expected) + + def test_sum_nanops_timedelta(self): + # prod isn't defined on timedeltas + idx = ["a", "b", "c"] + df = DataFrame({"a": [0, 0], "b": [0, np.nan], "c": [np.nan, np.nan]}) + + df2 = df.apply(to_timedelta) + + # 0 by default + result = df2.sum() + expected = Series([0, 0, 0], dtype="m8[ns]", index=idx) + tm.assert_series_equal(result, expected) + + # min_count=0 + result = df2.sum(min_count=0) + tm.assert_series_equal(result, expected) + + # min_count=1 + result = df2.sum(min_count=1) + expected = Series([0, 0, np.nan], dtype="m8[ns]", index=idx) + tm.assert_series_equal(result, expected) + + def test_sum_nanops_min_count(self): + # https://github.com/pandas-dev/pandas/issues/39738 + df = DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]}) + result = df.sum(min_count=10) + expected = Series([np.nan, np.nan], index=["x", "y"]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("float_type", ["float16", "float32", "float64"]) + @pytest.mark.parametrize( + "kwargs, expected_result", + [ + ({"axis": 1, "min_count": 2}, [3.2, 5.3, np.nan]), + ({"axis": 1, "min_count": 3}, [np.nan, np.nan, np.nan]), + ({"axis": 1, "skipna": False}, [3.2, 5.3, np.nan]), + ], + ) + def test_sum_nanops_dtype_min_count(self, float_type, kwargs, expected_result): + # GH#46947 + df = DataFrame({"a": [1.0, 2.3, 4.4], "b": [2.2, 3, np.nan]}, dtype=float_type) + result = df.sum(**kwargs) + expected = Series(expected_result).astype(float_type) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("float_type", ["float16", "float32", "float64"]) + @pytest.mark.parametrize( + "kwargs, expected_result", + [ + ({"axis": 1, "min_count": 2}, [2.0, 4.0, np.nan]), + ({"axis": 1, "min_count": 3}, [np.nan, np.nan, np.nan]), + ({"axis": 1, "skipna": False}, [2.0, 4.0, np.nan]), + ], + ) + def test_prod_nanops_dtype_min_count(self, float_type, kwargs, expected_result): + # GH#46947 + df = DataFrame( + {"a": [1.0, 2.0, 4.4], "b": [2.0, 2.0, np.nan]}, dtype=float_type + ) + result = df.prod(**kwargs) + expected = Series(expected_result).astype(float_type) + tm.assert_series_equal(result, expected) + + def test_sum_object(self, float_frame): + values = float_frame.values.astype(int) + frame = DataFrame(values, index=float_frame.index, columns=float_frame.columns) + deltas = frame * timedelta(1) + deltas.sum() + + def test_sum_bool(self, float_frame): + # ensure this works, bug report + bools = np.isnan(float_frame) + bools.sum(1) + bools.sum(0) + + def test_sum_mixed_datetime(self): + # GH#30886 + df = DataFrame({"A": date_range("2000", periods=4), "B": [1, 2, 3, 4]}).reindex( + [2, 3, 4] + ) + with pytest.raises(TypeError, match="does not support reduction 'sum'"): + df.sum() + + def test_mean_corner(self, float_frame, float_string_frame): + # unit test when have object data + msg = "Could not convert|does not support|Cannot perform" + with pytest.raises(TypeError, match=msg): + float_string_frame.mean(axis=0) + + # xs sum mixed type, just want to know it works... + with pytest.raises(TypeError, match="unsupported operand type"): + float_string_frame.mean(axis=1) + + # take mean of boolean column + float_frame["bool"] = float_frame["A"] > 0 + means = float_frame.mean(0) + assert means["bool"] == float_frame["bool"].values.mean() + + def test_mean_datetimelike(self): + # GH#24757 check that datetimelike are excluded by default, handled + # correctly with numeric_only=True + # As of 2.0, datetimelike are *not* excluded with numeric_only=None + + df = DataFrame( + { + "A": np.arange(3), + "B": date_range("2016-01-01", periods=3), + "C": pd.timedelta_range("1D", periods=3), + "D": pd.period_range("2016", periods=3, freq="Y"), + } + ) + result = df.mean(numeric_only=True) + expected = Series({"A": 1.0}) + tm.assert_series_equal(result, expected) + + with pytest.raises(TypeError, match="mean is not implemented for PeriodArray"): + df.mean() + + def test_mean_datetimelike_numeric_only_false(self): + df = DataFrame( + { + "A": np.arange(3), + "B": date_range("2016-01-01", periods=3), + "C": pd.timedelta_range("1D", periods=3), + } + ) + + # datetime(tz) and timedelta work + result = df.mean(numeric_only=False) + expected = Series({"A": 1, "B": df.loc[1, "B"], "C": df.loc[1, "C"]}) + tm.assert_series_equal(result, expected) + + # mean of period is not allowed + df["D"] = pd.period_range("2016", periods=3, freq="Y") + + with pytest.raises(TypeError, match="mean is not implemented for Period"): + df.mean(numeric_only=False) + + def test_mean_extensionarray_numeric_only_true(self): + # https://github.com/pandas-dev/pandas/issues/33256 + arr = np.random.default_rng(2).integers(1000, size=(10, 5)) + df = DataFrame(arr, dtype="Int64") + result = df.mean(numeric_only=True) + expected = DataFrame(arr).mean().astype("Float64") + tm.assert_series_equal(result, expected) + + def test_stats_mixed_type(self, float_string_frame): + with pytest.raises(TypeError, match="could not convert"): + float_string_frame.std(1) + with pytest.raises(TypeError, match="could not convert"): + float_string_frame.var(1) + with pytest.raises(TypeError, match="unsupported operand type"): + float_string_frame.mean(1) + with pytest.raises(TypeError, match="could not convert"): + float_string_frame.skew(1) + + def test_sum_bools(self): + df = DataFrame(index=range(1), columns=range(10)) + bools = isna(df) + assert bools.sum(axis=1)[0] == 10 + + # ---------------------------------------------------------------------- + # Index of max / min + + @pytest.mark.parametrize("skipna", [True, False]) + @pytest.mark.parametrize("axis", [0, 1]) + def test_idxmin(self, float_frame, int_frame, skipna, axis): + frame = float_frame + frame.iloc[5:10] = np.nan + frame.iloc[15:20, -2:] = np.nan + for df in [frame, int_frame]: + warn = None + if skipna is False or axis == 1: + warn = None if df is int_frame else FutureWarning + msg = "The behavior of DataFrame.idxmin with all-NA values" + with tm.assert_produces_warning(warn, match=msg): + result = df.idxmin(axis=axis, skipna=skipna) + + msg2 = "The behavior of Series.idxmin" + with tm.assert_produces_warning(warn, match=msg2): + expected = df.apply(Series.idxmin, axis=axis, skipna=skipna) + expected = expected.astype(df.index.dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("axis", [0, 1]) + @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning") + def test_idxmin_empty(self, index, skipna, axis): + # GH53265 + if axis == 0: + frame = DataFrame(index=index) + else: + frame = DataFrame(columns=index) + + result = frame.idxmin(axis=axis, skipna=skipna) + expected = Series(dtype=index.dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("numeric_only", [True, False]) + def test_idxmin_numeric_only(self, numeric_only): + df = DataFrame({"a": [2, 3, 1], "b": [2, 1, 1], "c": list("xyx")}) + result = df.idxmin(numeric_only=numeric_only) + if numeric_only: + expected = Series([2, 1], index=["a", "b"]) + else: + expected = Series([2, 1, 0], index=["a", "b", "c"]) + tm.assert_series_equal(result, expected) + + def test_idxmin_axis_2(self, float_frame): + frame = float_frame + msg = "No axis named 2 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + frame.idxmin(axis=2) + + @pytest.mark.parametrize("axis", [0, 1]) + def test_idxmax(self, float_frame, int_frame, skipna, axis): + frame = float_frame + frame.iloc[5:10] = np.nan + frame.iloc[15:20, -2:] = np.nan + for df in [frame, int_frame]: + warn = None + if skipna is False or axis == 1: + warn = None if df is int_frame else FutureWarning + msg = "The behavior of DataFrame.idxmax with all-NA values" + with tm.assert_produces_warning(warn, match=msg): + result = df.idxmax(axis=axis, skipna=skipna) + + msg2 = "The behavior of Series.idxmax" + with tm.assert_produces_warning(warn, match=msg2): + expected = df.apply(Series.idxmax, axis=axis, skipna=skipna) + expected = expected.astype(df.index.dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("axis", [0, 1]) + @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning") + def test_idxmax_empty(self, index, skipna, axis): + # GH53265 + if axis == 0: + frame = DataFrame(index=index) + else: + frame = DataFrame(columns=index) + + result = frame.idxmax(axis=axis, skipna=skipna) + expected = Series(dtype=index.dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("numeric_only", [True, False]) + def test_idxmax_numeric_only(self, numeric_only): + df = DataFrame({"a": [2, 3, 1], "b": [2, 1, 1], "c": list("xyx")}) + result = df.idxmax(numeric_only=numeric_only) + if numeric_only: + expected = Series([1, 0], index=["a", "b"]) + else: + expected = Series([1, 0, 1], index=["a", "b", "c"]) + tm.assert_series_equal(result, expected) + + def test_idxmax_arrow_types(self): + # GH#55368 + pytest.importorskip("pyarrow") + + df = DataFrame({"a": [2, 3, 1], "b": [2, 1, 1]}, dtype="int64[pyarrow]") + result = df.idxmax() + expected = Series([1, 0], index=["a", "b"]) + tm.assert_series_equal(result, expected) + + result = df.idxmin() + expected = Series([2, 1], index=["a", "b"]) + tm.assert_series_equal(result, expected) + + df = DataFrame({"a": ["b", "c", "a"]}, dtype="string[pyarrow]") + result = df.idxmax(numeric_only=False) + expected = Series([1], index=["a"]) + tm.assert_series_equal(result, expected) + + result = df.idxmin(numeric_only=False) + expected = Series([2], index=["a"]) + tm.assert_series_equal(result, expected) + + def test_idxmax_axis_2(self, float_frame): + frame = float_frame + msg = "No axis named 2 for object type DataFrame" + with pytest.raises(ValueError, match=msg): + frame.idxmax(axis=2) + + def test_idxmax_mixed_dtype(self): + # don't cast to object, which would raise in nanops + dti = date_range("2016-01-01", periods=3) + + # Copying dti is needed for ArrayManager otherwise when we set + # df.loc[0, 3] = pd.NaT below it edits dti + df = DataFrame({1: [0, 2, 1], 2: range(3)[::-1], 3: dti.copy(deep=True)}) + + result = df.idxmax() + expected = Series([1, 0, 2], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + result = df.idxmin() + expected = Series([0, 2, 0], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + # with NaTs + df.loc[0, 3] = pd.NaT + result = df.idxmax() + expected = Series([1, 0, 2], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + result = df.idxmin() + expected = Series([0, 2, 1], index=[1, 2, 3]) + tm.assert_series_equal(result, expected) + + # with multi-column dt64 block + df[4] = dti[::-1] + df._consolidate_inplace() + + result = df.idxmax() + expected = Series([1, 0, 2, 0], index=[1, 2, 3, 4]) + tm.assert_series_equal(result, expected) + + result = df.idxmin() + expected = Series([0, 2, 1, 2], index=[1, 2, 3, 4]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "op, expected_value", + [("idxmax", [0, 4]), ("idxmin", [0, 5])], + ) + def test_idxmax_idxmin_convert_dtypes(self, op, expected_value): + # GH 40346 + df = DataFrame( + { + "ID": [100, 100, 100, 200, 200, 200], + "value": [0, 0, 0, 1, 2, 0], + }, + dtype="Int64", + ) + df = df.groupby("ID") + + result = getattr(df, op)() + expected = DataFrame( + {"value": expected_value}, + index=Index([100, 200], name="ID", dtype="Int64"), + ) + tm.assert_frame_equal(result, expected) + + def test_idxmax_dt64_multicolumn_axis1(self): + dti = date_range("2016-01-01", periods=3) + df = DataFrame({3: dti, 4: dti[::-1]}, copy=True) + df.iloc[0, 0] = pd.NaT + + df._consolidate_inplace() + + result = df.idxmax(axis=1) + expected = Series([4, 3, 3]) + tm.assert_series_equal(result, expected) + + result = df.idxmin(axis=1) + expected = Series([4, 3, 4]) + tm.assert_series_equal(result, expected) + + # ---------------------------------------------------------------------- + # Logical reductions + + @pytest.mark.parametrize("opname", ["any", "all"]) + @pytest.mark.parametrize("axis", [0, 1]) + @pytest.mark.parametrize("bool_only", [False, True]) + def test_any_all_mixed_float(self, opname, axis, bool_only, float_string_frame): + # make sure op works on mixed-type frame + mixed = float_string_frame + mixed["_bool_"] = np.random.default_rng(2).standard_normal(len(mixed)) > 0.5 + + getattr(mixed, opname)(axis=axis, bool_only=bool_only) + + @pytest.mark.parametrize("opname", ["any", "all"]) + @pytest.mark.parametrize("axis", [0, 1]) + def test_any_all_bool_with_na(self, opname, axis, bool_frame_with_na): + getattr(bool_frame_with_na, opname)(axis=axis, bool_only=False) + + @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning") + @pytest.mark.parametrize("opname", ["any", "all"]) + def test_any_all_bool_frame(self, opname, bool_frame_with_na): + # GH#12863: numpy gives back non-boolean data for object type + # so fill NaNs to compare with pandas behavior + frame = bool_frame_with_na.fillna(True) + alternative = getattr(np, opname) + f = getattr(frame, opname) + + def skipna_wrapper(x): + nona = x.dropna().values + return alternative(nona) + + def wrapper(x): + return alternative(x.values) + + result0 = f(axis=0, skipna=False) + result1 = f(axis=1, skipna=False) + + tm.assert_series_equal(result0, frame.apply(wrapper)) + tm.assert_series_equal(result1, frame.apply(wrapper, axis=1)) + + result0 = f(axis=0) + result1 = f(axis=1) + + tm.assert_series_equal(result0, frame.apply(skipna_wrapper)) + tm.assert_series_equal( + result1, frame.apply(skipna_wrapper, axis=1), check_dtype=False + ) + + # bad axis + with pytest.raises(ValueError, match="No axis named 2"): + f(axis=2) + + # all NA case + all_na = frame * np.nan + r0 = getattr(all_na, opname)(axis=0) + r1 = getattr(all_na, opname)(axis=1) + if opname == "any": + assert not r0.any() + assert not r1.any() + else: + assert r0.all() + assert r1.all() + + def test_any_all_extra(self): + df = DataFrame( + { + "A": [True, False, False], + "B": [True, True, False], + "C": [True, True, True], + }, + index=["a", "b", "c"], + ) + result = df[["A", "B"]].any(axis=1) + expected = Series([True, True, False], index=["a", "b", "c"]) + tm.assert_series_equal(result, expected) + + result = df[["A", "B"]].any(axis=1, bool_only=True) + tm.assert_series_equal(result, expected) + + result = df.all(1) + expected = Series([True, False, False], index=["a", "b", "c"]) + tm.assert_series_equal(result, expected) + + result = df.all(1, bool_only=True) + tm.assert_series_equal(result, expected) + + # Axis is None + result = df.all(axis=None).item() + assert result is False + + result = df.any(axis=None).item() + assert result is True + + result = df[["C"]].all(axis=None).item() + assert result is True + + @pytest.mark.parametrize("axis", [0, 1]) + @pytest.mark.parametrize("bool_agg_func", ["any", "all"]) + @pytest.mark.parametrize("skipna", [True, False]) + def test_any_all_object_dtype(self, axis, bool_agg_func, skipna): + # GH#35450 + df = DataFrame( + data=[ + [1, np.nan, np.nan, True], + [np.nan, 2, np.nan, True], + [np.nan, np.nan, np.nan, True], + [np.nan, np.nan, "5", np.nan], + ] + ) + result = getattr(df, bool_agg_func)(axis=axis, skipna=skipna) + expected = Series([True, True, True, True]) + tm.assert_series_equal(result, expected) + + # GH#50947 deprecates this but it is not emitting a warning in some builds. + @pytest.mark.filterwarnings( + "ignore:'any' with datetime64 dtypes is deprecated.*:FutureWarning" + ) + def test_any_datetime(self): + # GH 23070 + float_data = [1, np.nan, 3, np.nan] + datetime_data = [ + Timestamp("1960-02-15"), + Timestamp("1960-02-16"), + pd.NaT, + pd.NaT, + ] + df = DataFrame({"A": float_data, "B": datetime_data}) + + result = df.any(axis=1) + + expected = Series([True, True, True, False]) + tm.assert_series_equal(result, expected) + + def test_any_all_bool_only(self): + # GH 25101 + df = DataFrame( + {"col1": [1, 2, 3], "col2": [4, 5, 6], "col3": [None, None, None]}, + columns=Index(["col1", "col2", "col3"], dtype=object), + ) + + result = df.all(bool_only=True) + expected = Series(dtype=np.bool_, index=[]) + tm.assert_series_equal(result, expected) + + df = DataFrame( + { + "col1": [1, 2, 3], + "col2": [4, 5, 6], + "col3": [None, None, None], + "col4": [False, False, True], + } + ) + + result = df.all(bool_only=True) + expected = Series({"col4": False}) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "func, data, expected", + [ + (np.any, {}, False), + (np.all, {}, True), + (np.any, {"A": []}, False), + (np.all, {"A": []}, True), + (np.any, {"A": [False, False]}, False), + (np.all, {"A": [False, False]}, False), + (np.any, {"A": [True, False]}, True), + (np.all, {"A": [True, False]}, False), + (np.any, {"A": [True, True]}, True), + (np.all, {"A": [True, True]}, True), + (np.any, {"A": [False], "B": [False]}, False), + (np.all, {"A": [False], "B": [False]}, False), + (np.any, {"A": [False, False], "B": [False, True]}, True), + (np.all, {"A": [False, False], "B": [False, True]}, False), + # other types + (np.all, {"A": Series([0.0, 1.0], dtype="float")}, False), + (np.any, {"A": Series([0.0, 1.0], dtype="float")}, True), + (np.all, {"A": Series([0, 1], dtype=int)}, False), + (np.any, {"A": Series([0, 1], dtype=int)}, True), + pytest.param(np.all, {"A": Series([0, 1], dtype="M8[ns]")}, False), + pytest.param(np.all, {"A": Series([0, 1], dtype="M8[ns, UTC]")}, False), + pytest.param(np.any, {"A": Series([0, 1], dtype="M8[ns]")}, True), + pytest.param(np.any, {"A": Series([0, 1], dtype="M8[ns, UTC]")}, True), + pytest.param(np.all, {"A": Series([1, 2], dtype="M8[ns]")}, True), + pytest.param(np.all, {"A": Series([1, 2], dtype="M8[ns, UTC]")}, True), + pytest.param(np.any, {"A": Series([1, 2], dtype="M8[ns]")}, True), + pytest.param(np.any, {"A": Series([1, 2], dtype="M8[ns, UTC]")}, True), + pytest.param(np.all, {"A": Series([0, 1], dtype="m8[ns]")}, False), + pytest.param(np.any, {"A": Series([0, 1], dtype="m8[ns]")}, True), + pytest.param(np.all, {"A": Series([1, 2], dtype="m8[ns]")}, True), + pytest.param(np.any, {"A": Series([1, 2], dtype="m8[ns]")}, True), + # np.all on Categorical raises, so the reduction drops the + # column, so all is being done on an empty Series, so is True + (np.all, {"A": Series([0, 1], dtype="category")}, True), + (np.any, {"A": Series([0, 1], dtype="category")}, False), + (np.all, {"A": Series([1, 2], dtype="category")}, True), + (np.any, {"A": Series([1, 2], dtype="category")}, False), + # Mix GH#21484 + pytest.param( + np.all, + { + "A": Series([10, 20], dtype="M8[ns]"), + "B": Series([10, 20], dtype="m8[ns]"), + }, + True, + ), + ], + ) + def test_any_all_np_func(self, func, data, expected): + # GH 19976 + data = DataFrame(data) + + if any(isinstance(x, CategoricalDtype) for x in data.dtypes): + with pytest.raises( + TypeError, match="dtype category does not support reduction" + ): + func(data) + + # method version + with pytest.raises( + TypeError, match="dtype category does not support reduction" + ): + getattr(DataFrame(data), func.__name__)(axis=None) + else: + msg = "'(any|all)' with datetime64 dtypes is deprecated" + if data.dtypes.apply(lambda x: x.kind == "M").any(): + warn = FutureWarning + else: + warn = None + + with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False): + # GH#34479 + result = func(data) + assert isinstance(result, np.bool_) + assert result.item() is expected + + # method version + with tm.assert_produces_warning(warn, match=msg): + # GH#34479 + result = getattr(DataFrame(data), func.__name__)(axis=None) + assert isinstance(result, np.bool_) + assert result.item() is expected + + def test_any_all_object(self): + # GH 19976 + result = np.all(DataFrame(columns=["a", "b"])).item() + assert result is True + + result = np.any(DataFrame(columns=["a", "b"])).item() + assert result is False + + def test_any_all_object_bool_only(self): + df = DataFrame({"A": ["foo", 2], "B": [True, False]}).astype(object) + df._consolidate_inplace() + df["C"] = Series([True, True]) + + # Categorical of bools is _not_ considered booly + df["D"] = df["C"].astype("category") + + # The underlying bug is in DataFrame._get_bool_data, so we check + # that while we're here + res = df._get_bool_data() + expected = df[["C"]] + tm.assert_frame_equal(res, expected) + + res = df.all(bool_only=True, axis=0) + expected = Series([True], index=["C"]) + tm.assert_series_equal(res, expected) + + # operating on a subset of columns should not produce a _larger_ Series + res = df[["B", "C"]].all(bool_only=True, axis=0) + tm.assert_series_equal(res, expected) + + assert df.all(bool_only=True, axis=None) + + res = df.any(bool_only=True, axis=0) + expected = Series([True], index=["C"]) + tm.assert_series_equal(res, expected) + + # operating on a subset of columns should not produce a _larger_ Series + res = df[["C"]].any(bool_only=True, axis=0) + tm.assert_series_equal(res, expected) + + assert df.any(bool_only=True, axis=None) + + # --------------------------------------------------------------------- + # Unsorted + + def test_series_broadcasting(self): + # smoke test for numpy warnings + # GH 16378, GH 16306 + df = DataFrame([1.0, 1.0, 1.0]) + df_nan = DataFrame({"A": [np.nan, 2.0, np.nan]}) + s = Series([1, 1, 1]) + s_nan = Series([np.nan, np.nan, 1]) + + with tm.assert_produces_warning(None): + df_nan.clip(lower=s, axis=0) + for op in ["lt", "le", "gt", "ge", "eq", "ne"]: + getattr(df, op)(s_nan, axis=0) + + +class TestDataFrameReductions: + def test_min_max_dt64_with_NaT(self): + # Both NaT and Timestamp are in DataFrame. + df = DataFrame({"foo": [pd.NaT, pd.NaT, Timestamp("2012-05-01")]}) + + res = df.min() + exp = Series([Timestamp("2012-05-01")], index=["foo"]) + tm.assert_series_equal(res, exp) + + res = df.max() + exp = Series([Timestamp("2012-05-01")], index=["foo"]) + tm.assert_series_equal(res, exp) + + # GH12941, only NaTs are in DataFrame. + df = DataFrame({"foo": [pd.NaT, pd.NaT]}) + + res = df.min() + exp = Series([pd.NaT], index=["foo"]) + tm.assert_series_equal(res, exp) + + res = df.max() + exp = Series([pd.NaT], index=["foo"]) + tm.assert_series_equal(res, exp) + + def test_min_max_dt64_with_NaT_skipna_false(self, request, tz_naive_fixture): + # GH#36907 + tz = tz_naive_fixture + if isinstance(tz, tzlocal) and is_platform_windows(): + pytest.skip( + "GH#37659 OSError raised within tzlocal bc Windows " + "chokes in times before 1970-01-01" + ) + + df = DataFrame( + { + "a": [ + Timestamp("2020-01-01 08:00:00", tz=tz), + Timestamp("1920-02-01 09:00:00", tz=tz), + ], + "b": [Timestamp("2020-02-01 08:00:00", tz=tz), pd.NaT], + } + ) + res = df.min(axis=1, skipna=False) + expected = Series([df.loc[0, "a"], pd.NaT]) + assert expected.dtype == df["a"].dtype + + tm.assert_series_equal(res, expected) + + res = df.max(axis=1, skipna=False) + expected = Series([df.loc[0, "b"], pd.NaT]) + assert expected.dtype == df["a"].dtype + + tm.assert_series_equal(res, expected) + + def test_min_max_dt64_api_consistency_with_NaT(self): + # Calling the following sum functions returned an error for dataframes but + # returned NaT for series. These tests check that the API is consistent in + # min/max calls on empty Series/DataFrames. See GH:33704 for more + # information + df = DataFrame({"x": to_datetime([])}) + expected_dt_series = Series(to_datetime([])) + # check axis 0 + assert (df.min(axis=0).x is pd.NaT) == (expected_dt_series.min() is pd.NaT) + assert (df.max(axis=0).x is pd.NaT) == (expected_dt_series.max() is pd.NaT) + + # check axis 1 + tm.assert_series_equal(df.min(axis=1), expected_dt_series) + tm.assert_series_equal(df.max(axis=1), expected_dt_series) + + def test_min_max_dt64_api_consistency_empty_df(self): + # check DataFrame/Series api consistency when calling min/max on an empty + # DataFrame/Series. + df = DataFrame({"x": []}) + expected_float_series = Series([], dtype=float) + # check axis 0 + assert np.isnan(df.min(axis=0).x) == np.isnan(expected_float_series.min()) + assert np.isnan(df.max(axis=0).x) == np.isnan(expected_float_series.max()) + # check axis 1 + tm.assert_series_equal(df.min(axis=1), expected_float_series) + tm.assert_series_equal(df.min(axis=1), expected_float_series) + + @pytest.mark.parametrize( + "initial", + ["2018-10-08 13:36:45+00:00", "2018-10-08 13:36:45+03:00"], # Non-UTC timezone + ) + @pytest.mark.parametrize("method", ["min", "max"]) + def test_preserve_timezone(self, initial: str, method): + # GH 28552 + initial_dt = to_datetime(initial) + expected = Series([initial_dt]) + df = DataFrame([expected]) + result = getattr(df, method)(axis=1) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("method", ["min", "max"]) + def test_minmax_tzaware_skipna_axis_1(self, method, skipna): + # GH#51242 + val = to_datetime("1900-01-01", utc=True) + df = DataFrame( + {"a": Series([pd.NaT, pd.NaT, val]), "b": Series([pd.NaT, val, val])} + ) + op = getattr(df, method) + result = op(axis=1, skipna=skipna) + if skipna: + expected = Series([pd.NaT, val, val]) + else: + expected = Series([pd.NaT, pd.NaT, val]) + tm.assert_series_equal(result, expected) + + def test_frame_any_with_timedelta(self): + # GH#17667 + df = DataFrame( + { + "a": Series([0, 0]), + "t": Series([to_timedelta(0, "s"), to_timedelta(1, "ms")]), + } + ) + + result = df.any(axis=0) + expected = Series(data=[False, True], index=["a", "t"]) + tm.assert_series_equal(result, expected) + + result = df.any(axis=1) + expected = Series(data=[False, True]) + tm.assert_series_equal(result, expected) + + def test_reductions_skipna_none_raises( + self, request, frame_or_series, all_reductions + ): + if all_reductions == "count": + request.applymarker( + pytest.mark.xfail(reason="Count does not accept skipna") + ) + obj = frame_or_series([1, 2, 3]) + msg = 'For argument "skipna" expected type bool, received type NoneType.' + with pytest.raises(ValueError, match=msg): + getattr(obj, all_reductions)(skipna=None) + + @td.skip_array_manager_invalid_test + def test_reduction_timestamp_smallest_unit(self): + # GH#52524 + df = DataFrame( + { + "a": Series([Timestamp("2019-12-31")], dtype="datetime64[s]"), + "b": Series( + [Timestamp("2019-12-31 00:00:00.123")], dtype="datetime64[ms]" + ), + } + ) + result = df.max() + expected = Series( + [Timestamp("2019-12-31"), Timestamp("2019-12-31 00:00:00.123")], + dtype="datetime64[ms]", + index=["a", "b"], + ) + tm.assert_series_equal(result, expected) + + @td.skip_array_manager_not_yet_implemented + def test_reduction_timedelta_smallest_unit(self): + # GH#52524 + df = DataFrame( + { + "a": Series([pd.Timedelta("1 days")], dtype="timedelta64[s]"), + "b": Series([pd.Timedelta("1 days")], dtype="timedelta64[ms]"), + } + ) + result = df.max() + expected = Series( + [pd.Timedelta("1 days"), pd.Timedelta("1 days")], + dtype="timedelta64[ms]", + index=["a", "b"], + ) + tm.assert_series_equal(result, expected) + + +class TestNuisanceColumns: + @pytest.mark.parametrize("method", ["any", "all"]) + def test_any_all_categorical_dtype_nuisance_column(self, method): + # GH#36076 DataFrame should match Series behavior + ser = Series([0, 1], dtype="category", name="A") + df = ser.to_frame() + + # Double-check the Series behavior is to raise + with pytest.raises(TypeError, match="does not support reduction"): + getattr(ser, method)() + + with pytest.raises(TypeError, match="does not support reduction"): + getattr(np, method)(ser) + + with pytest.raises(TypeError, match="does not support reduction"): + getattr(df, method)(bool_only=False) + + with pytest.raises(TypeError, match="does not support reduction"): + getattr(df, method)(bool_only=None) + + with pytest.raises(TypeError, match="does not support reduction"): + getattr(np, method)(df, axis=0) + + def test_median_categorical_dtype_nuisance_column(self): + # GH#21020 DataFrame.median should match Series.median + df = DataFrame({"A": Categorical([1, 2, 2, 2, 3])}) + ser = df["A"] + + # Double-check the Series behavior is to raise + with pytest.raises(TypeError, match="does not support reduction"): + ser.median() + + with pytest.raises(TypeError, match="does not support reduction"): + df.median(numeric_only=False) + + with pytest.raises(TypeError, match="does not support reduction"): + df.median() + + # same thing, but with an additional non-categorical column + df["B"] = df["A"].astype(int) + + with pytest.raises(TypeError, match="does not support reduction"): + df.median(numeric_only=False) + + with pytest.raises(TypeError, match="does not support reduction"): + df.median() + + # TODO: np.median(df, axis=0) gives np.array([2.0, 2.0]) instead + # of expected.values + + @pytest.mark.parametrize("method", ["min", "max"]) + def test_min_max_categorical_dtype_non_ordered_nuisance_column(self, method): + # GH#28949 DataFrame.min should behave like Series.min + cat = Categorical(["a", "b", "c", "b"], ordered=False) + ser = Series(cat) + df = ser.to_frame("A") + + # Double-check the Series behavior + with pytest.raises(TypeError, match="is not ordered for operation"): + getattr(ser, method)() + + with pytest.raises(TypeError, match="is not ordered for operation"): + getattr(np, method)(ser) + + with pytest.raises(TypeError, match="is not ordered for operation"): + getattr(df, method)(numeric_only=False) + + with pytest.raises(TypeError, match="is not ordered for operation"): + getattr(df, method)() + + with pytest.raises(TypeError, match="is not ordered for operation"): + getattr(np, method)(df, axis=0) + + # same thing, but with an additional non-categorical column + df["B"] = df["A"].astype(object) + with pytest.raises(TypeError, match="is not ordered for operation"): + getattr(df, method)() + + with pytest.raises(TypeError, match="is not ordered for operation"): + getattr(np, method)(df, axis=0) + + +class TestEmptyDataFrameReductions: + @pytest.mark.parametrize( + "opname, dtype, exp_value, exp_dtype", + [ + ("sum", np.int8, 0, np.int64), + ("prod", np.int8, 1, np.int_), + ("sum", np.int64, 0, np.int64), + ("prod", np.int64, 1, np.int64), + ("sum", np.uint8, 0, np.uint64), + ("prod", np.uint8, 1, np.uint), + ("sum", np.uint64, 0, np.uint64), + ("prod", np.uint64, 1, np.uint64), + ("sum", np.float32, 0, np.float32), + ("prod", np.float32, 1, np.float32), + ("sum", np.float64, 0, np.float64), + ], + ) + def test_df_empty_min_count_0(self, opname, dtype, exp_value, exp_dtype): + df = DataFrame({0: [], 1: []}, dtype=dtype) + result = getattr(df, opname)(min_count=0) + + expected = Series([exp_value, exp_value], dtype=exp_dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "opname, dtype, exp_dtype", + [ + ("sum", np.int8, np.float64), + ("prod", np.int8, np.float64), + ("sum", np.int64, np.float64), + ("prod", np.int64, np.float64), + ("sum", np.uint8, np.float64), + ("prod", np.uint8, np.float64), + ("sum", np.uint64, np.float64), + ("prod", np.uint64, np.float64), + ("sum", np.float32, np.float32), + ("prod", np.float32, np.float32), + ("sum", np.float64, np.float64), + ], + ) + def test_df_empty_min_count_1(self, opname, dtype, exp_dtype): + df = DataFrame({0: [], 1: []}, dtype=dtype) + result = getattr(df, opname)(min_count=1) + + expected = Series([np.nan, np.nan], dtype=exp_dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "opname, dtype, exp_value, exp_dtype", + [ + ("sum", "Int8", 0, ("Int32" if is_windows_np2_or_is32 else "Int64")), + ("prod", "Int8", 1, ("Int32" if is_windows_np2_or_is32 else "Int64")), + ("prod", "Int8", 1, ("Int32" if is_windows_np2_or_is32 else "Int64")), + ("sum", "Int64", 0, "Int64"), + ("prod", "Int64", 1, "Int64"), + ("sum", "UInt8", 0, ("UInt32" if is_windows_np2_or_is32 else "UInt64")), + ("prod", "UInt8", 1, ("UInt32" if is_windows_np2_or_is32 else "UInt64")), + ("sum", "UInt64", 0, "UInt64"), + ("prod", "UInt64", 1, "UInt64"), + ("sum", "Float32", 0, "Float32"), + ("prod", "Float32", 1, "Float32"), + ("sum", "Float64", 0, "Float64"), + ], + ) + def test_df_empty_nullable_min_count_0(self, opname, dtype, exp_value, exp_dtype): + df = DataFrame({0: [], 1: []}, dtype=dtype) + result = getattr(df, opname)(min_count=0) + + expected = Series([exp_value, exp_value], dtype=exp_dtype) + tm.assert_series_equal(result, expected) + + # TODO: why does min_count=1 impact the resulting Windows dtype + # differently than min_count=0? + @pytest.mark.parametrize( + "opname, dtype, exp_dtype", + [ + ("sum", "Int8", ("Int32" if is_windows_or_is32 else "Int64")), + ("prod", "Int8", ("Int32" if is_windows_or_is32 else "Int64")), + ("sum", "Int64", "Int64"), + ("prod", "Int64", "Int64"), + ("sum", "UInt8", ("UInt32" if is_windows_or_is32 else "UInt64")), + ("prod", "UInt8", ("UInt32" if is_windows_or_is32 else "UInt64")), + ("sum", "UInt64", "UInt64"), + ("prod", "UInt64", "UInt64"), + ("sum", "Float32", "Float32"), + ("prod", "Float32", "Float32"), + ("sum", "Float64", "Float64"), + ], + ) + def test_df_empty_nullable_min_count_1(self, opname, dtype, exp_dtype): + df = DataFrame({0: [], 1: []}, dtype=dtype) + result = getattr(df, opname)(min_count=1) + + expected = Series([pd.NA, pd.NA], dtype=exp_dtype) + tm.assert_series_equal(result, expected) + + +def test_sum_timedelta64_skipna_false(using_array_manager, request): + # GH#17235 + if using_array_manager: + mark = pytest.mark.xfail( + reason="Incorrect type inference on NaT in reduction result" + ) + request.applymarker(mark) + + arr = np.arange(8).astype(np.int64).view("m8[s]").reshape(4, 2) + arr[-1, -1] = "Nat" + + df = DataFrame(arr) + assert (df.dtypes == arr.dtype).all() + + result = df.sum(skipna=False) + expected = Series([pd.Timedelta(seconds=12), pd.NaT], dtype="m8[s]") + tm.assert_series_equal(result, expected) + + result = df.sum(axis=0, skipna=False) + tm.assert_series_equal(result, expected) + + result = df.sum(axis=1, skipna=False) + expected = Series( + [ + pd.Timedelta(seconds=1), + pd.Timedelta(seconds=5), + pd.Timedelta(seconds=9), + pd.NaT, + ], + dtype="m8[s]", + ) + tm.assert_series_equal(result, expected) + + +def test_mixed_frame_with_integer_sum(): + # https://github.com/pandas-dev/pandas/issues/34520 + df = DataFrame([["a", 1]], columns=list("ab")) + df = df.astype({"b": "Int64"}) + result = df.sum() + expected = Series(["a", 1], index=["a", "b"]) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("numeric_only", [True, False, None]) +@pytest.mark.parametrize("method", ["min", "max"]) +def test_minmax_extensionarray(method, numeric_only): + # https://github.com/pandas-dev/pandas/issues/32651 + int64_info = np.iinfo("int64") + ser = Series([int64_info.max, None, int64_info.min], dtype=pd.Int64Dtype()) + df = DataFrame({"Int64": ser}) + result = getattr(df, method)(numeric_only=numeric_only) + expected = Series( + [getattr(int64_info, method)], + dtype="Int64", + index=Index(["Int64"]), + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("ts_value", [Timestamp("2000-01-01"), pd.NaT]) +def test_frame_mixed_numeric_object_with_timestamp(ts_value): + # GH 13912 + df = DataFrame({"a": [1], "b": [1.1], "c": ["foo"], "d": [ts_value]}) + with pytest.raises( + TypeError, match="does not support (operation|reduction)|Cannot perform" + ): + df.sum() + + +def test_prod_sum_min_count_mixed_object(): + # https://github.com/pandas-dev/pandas/issues/41074 + df = DataFrame([1, "a", True]) + + result = df.prod(axis=0, min_count=1, numeric_only=False) + expected = Series(["a"], dtype=object) + tm.assert_series_equal(result, expected) + + msg = re.escape("unsupported operand type(s) for +: 'int' and 'str'") + with pytest.raises(TypeError, match=msg): + df.sum(axis=0, min_count=1, numeric_only=False) + + +@pytest.mark.parametrize("method", ["min", "max", "mean", "median", "skew", "kurt"]) +@pytest.mark.parametrize("numeric_only", [True, False]) +@pytest.mark.parametrize("dtype", ["float64", "Float64"]) +def test_reduction_axis_none_returns_scalar(method, numeric_only, dtype): + # GH#21597 As of 2.0, axis=None reduces over all axes. + + df = DataFrame(np.random.default_rng(2).standard_normal((4, 4)), dtype=dtype) + + result = getattr(df, method)(axis=None, numeric_only=numeric_only) + np_arr = df.to_numpy(dtype=np.float64) + if method in {"skew", "kurt"}: + comp_mod = pytest.importorskip("scipy.stats") + if method == "kurt": + method = "kurtosis" + expected = getattr(comp_mod, method)(np_arr, bias=False, axis=None) + tm.assert_almost_equal(result, expected) + else: + expected = getattr(np, method)(np_arr, axis=None) + assert result == expected + + +@pytest.mark.parametrize( + "kernel", + [ + "corr", + "corrwith", + "cov", + "idxmax", + "idxmin", + "kurt", + "max", + "mean", + "median", + "min", + "prod", + "quantile", + "sem", + "skew", + "std", + "sum", + "var", + ], +) +def test_fails_on_non_numeric(kernel): + # GH#46852 + df = DataFrame({"a": [1, 2, 3], "b": object}) + args = (df,) if kernel == "corrwith" else () + msg = "|".join( + [ + "not allowed for this dtype", + "argument must be a string or a number", + "not supported between instances of", + "unsupported operand type", + "argument must be a string or a real number", + ] + ) + if kernel == "median": + # slightly different message on different builds + msg1 = ( + r"Cannot convert \[\[ " + r"\]\] to numeric" + ) + msg2 = ( + r"Cannot convert \[ " + r"\] to numeric" + ) + msg = "|".join([msg1, msg2]) + with pytest.raises(TypeError, match=msg): + getattr(df, kernel)(*args) + + +@pytest.mark.parametrize( + "method", + [ + "all", + "any", + "count", + "idxmax", + "idxmin", + "kurt", + "kurtosis", + "max", + "mean", + "median", + "min", + "nunique", + "prod", + "product", + "sem", + "skew", + "std", + "sum", + "var", + ], +) +@pytest.mark.parametrize("min_count", [0, 2]) +def test_numeric_ea_axis_1(method, skipna, min_count, any_numeric_ea_dtype): + # GH 54341 + df = DataFrame( + { + "a": Series([0, 1, 2, 3], dtype=any_numeric_ea_dtype), + "b": Series([0, 1, pd.NA, 3], dtype=any_numeric_ea_dtype), + }, + ) + expected_df = DataFrame( + { + "a": [0.0, 1.0, 2.0, 3.0], + "b": [0.0, 1.0, np.nan, 3.0], + }, + ) + if method in ("count", "nunique"): + expected_dtype = "int64" + elif method in ("all", "any"): + expected_dtype = "boolean" + elif method in ( + "kurt", + "kurtosis", + "mean", + "median", + "sem", + "skew", + "std", + "var", + ) and not any_numeric_ea_dtype.startswith("Float"): + expected_dtype = "Float64" + else: + expected_dtype = any_numeric_ea_dtype + + kwargs = {} + if method not in ("count", "nunique", "quantile"): + kwargs["skipna"] = skipna + if method in ("prod", "product", "sum"): + kwargs["min_count"] = min_count + + warn = None + msg = None + if not skipna and method in ("idxmax", "idxmin"): + warn = FutureWarning + msg = f"The behavior of DataFrame.{method} with all-NA values" + with tm.assert_produces_warning(warn, match=msg): + result = getattr(df, method)(axis=1, **kwargs) + with tm.assert_produces_warning(warn, match=msg): + expected = getattr(expected_df, method)(axis=1, **kwargs) + if method not in ("idxmax", "idxmin"): + expected = expected.astype(expected_dtype) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_repr.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_repr.py new file mode 100644 index 0000000000000000000000000000000000000000..6184e791cab5d00f8a1057d142e4c63c3690615a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_repr.py @@ -0,0 +1,518 @@ +from datetime import ( + datetime, + timedelta, +) +from io import StringIO + +import numpy as np +import pytest + +from pandas import ( + NA, + Categorical, + CategoricalIndex, + DataFrame, + IntervalIndex, + MultiIndex, + NaT, + PeriodIndex, + Series, + Timestamp, + date_range, + option_context, + period_range, +) +import pandas._testing as tm + + +class TestDataFrameRepr: + def test_repr_should_return_str(self): + # https://docs.python.org/3/reference/datamodel.html#object.__repr__ + # "...The return value must be a string object." + + # (str on py2.x, str (unicode) on py3) + + data = [8, 5, 3, 5] + index1 = ["\u03c3", "\u03c4", "\u03c5", "\u03c6"] + cols = ["\u03c8"] + df = DataFrame(data, columns=cols, index=index1) + assert type(df.__repr__()) is str # noqa: E721 + + ser = df[cols[0]] + assert type(ser.__repr__()) is str # noqa: E721 + + def test_repr_bytes_61_lines(self): + # GH#12857 + lets = list("ACDEFGHIJKLMNOP") + words = np.random.default_rng(2).choice(lets, (1000, 50)) + df = DataFrame(words).astype("U1") + assert (df.dtypes == object).all() + + # smoke tests; at one point this raised with 61 but not 60 + repr(df) + repr(df.iloc[:60, :]) + repr(df.iloc[:61, :]) + + def test_repr_unicode_level_names(self, frame_or_series): + index = MultiIndex.from_tuples([(0, 0), (1, 1)], names=["\u0394", "i1"]) + + obj = DataFrame(np.random.default_rng(2).standard_normal((2, 4)), index=index) + obj = tm.get_obj(obj, frame_or_series) + repr(obj) + + def test_assign_index_sequences(self): + # GH#2200 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}).set_index( + ["a", "b"] + ) + index = list(df.index) + index[0] = ("faz", "boo") + df.index = index + repr(df) + + # this travels an improper code path + index[0] = ["faz", "boo"] + df.index = index + repr(df) + + def test_repr_with_mi_nat(self): + df = DataFrame({"X": [1, 2]}, index=[[NaT, Timestamp("20130101")], ["a", "b"]]) + result = repr(df) + expected = " X\nNaT a 1\n2013-01-01 b 2" + assert result == expected + + def test_repr_with_different_nulls(self): + # GH45263 + df = DataFrame([1, 2, 3, 4], [True, None, np.nan, NaT]) + result = repr(df) + expected = """ 0 +True 1 +None 2 +NaN 3 +NaT 4""" + assert result == expected + + def test_repr_with_different_nulls_cols(self): + # GH45263 + d = {np.nan: [1, 2], None: [3, 4], NaT: [6, 7], True: [8, 9]} + df = DataFrame(data=d) + result = repr(df) + expected = """ NaN None NaT True +0 1 3 6 8 +1 2 4 7 9""" + assert result == expected + + def test_multiindex_na_repr(self): + # only an issue with long columns + df3 = DataFrame( + { + "A" * 30: {("A", "A0006000", "nuit"): "A0006000"}, + "B" * 30: {("A", "A0006000", "nuit"): np.nan}, + "C" * 30: {("A", "A0006000", "nuit"): np.nan}, + "D" * 30: {("A", "A0006000", "nuit"): np.nan}, + "E" * 30: {("A", "A0006000", "nuit"): "A"}, + "F" * 30: {("A", "A0006000", "nuit"): np.nan}, + } + ) + + idf = df3.set_index(["A" * 30, "C" * 30]) + repr(idf) + + def test_repr_name_coincide(self): + index = MultiIndex.from_tuples( + [("a", 0, "foo"), ("b", 1, "bar")], names=["a", "b", "c"] + ) + + df = DataFrame({"value": [0, 1]}, index=index) + + lines = repr(df).split("\n") + assert lines[2].startswith("a 0 foo") + + def test_repr_to_string( + self, + multiindex_year_month_day_dataframe_random_data, + multiindex_dataframe_random_data, + ): + ymd = multiindex_year_month_day_dataframe_random_data + frame = multiindex_dataframe_random_data + + repr(frame) + repr(ymd) + repr(frame.T) + repr(ymd.T) + + buf = StringIO() + frame.to_string(buf=buf) + ymd.to_string(buf=buf) + frame.T.to_string(buf=buf) + ymd.T.to_string(buf=buf) + + def test_repr_empty(self): + # empty + repr(DataFrame()) + + # empty with index + frame = DataFrame(index=np.arange(1000)) + repr(frame) + + def test_repr_mixed(self, float_string_frame): + # mixed + repr(float_string_frame) + + @pytest.mark.slow + def test_repr_mixed_big(self): + # big mixed + biggie = DataFrame( + { + "A": np.random.default_rng(2).standard_normal(200), + "B": [str(i) for i in range(200)], + }, + index=range(200), + ) + biggie.loc[:20, "A"] = np.nan + biggie.loc[:20, "B"] = np.nan + + repr(biggie) + + def test_repr(self): + # columns but no index + no_index = DataFrame(columns=[0, 1, 3]) + repr(no_index) + + df = DataFrame(["a\n\r\tb"], columns=["a\n\r\td"], index=["a\n\r\tf"]) + assert "\t" not in repr(df) + assert "\r" not in repr(df) + assert "a\n" not in repr(df) + + def test_repr_dimensions(self): + df = DataFrame([[1, 2], [3, 4]]) + with option_context("display.show_dimensions", True): + assert "2 rows x 2 columns" in repr(df) + + with option_context("display.show_dimensions", False): + assert "2 rows x 2 columns" not in repr(df) + + with option_context("display.show_dimensions", "truncate"): + assert "2 rows x 2 columns" not in repr(df) + + @pytest.mark.slow + def test_repr_big(self): + # big one + biggie = DataFrame(np.zeros((200, 4)), columns=range(4), index=range(200)) + repr(biggie) + + def test_repr_unsortable(self): + # columns are not sortable + + unsortable = DataFrame( + { + "foo": [1] * 50, + datetime.today(): [1] * 50, + "bar": ["bar"] * 50, + datetime.today() + timedelta(1): ["bar"] * 50, + }, + index=np.arange(50), + ) + repr(unsortable) + + def test_repr_float_frame_options(self, float_frame): + repr(float_frame) + + with option_context("display.precision", 3): + repr(float_frame) + + with option_context("display.max_rows", 10, "display.max_columns", 2): + repr(float_frame) + + with option_context("display.max_rows", 1000, "display.max_columns", 1000): + repr(float_frame) + + def test_repr_unicode(self): + uval = "\u03c3\u03c3\u03c3\u03c3" + + df = DataFrame({"A": [uval, uval]}) + + result = repr(df) + ex_top = " A" + assert result.split("\n")[0].rstrip() == ex_top + + df = DataFrame({"A": [uval, uval]}) + result = repr(df) + assert result.split("\n")[0].rstrip() == ex_top + + def test_unicode_string_with_unicode(self): + df = DataFrame({"A": ["\u05d0"]}) + str(df) + + def test_repr_unicode_columns(self): + df = DataFrame({"\u05d0": [1, 2, 3], "\u05d1": [4, 5, 6], "c": [7, 8, 9]}) + repr(df.columns) # should not raise UnicodeDecodeError + + def test_str_to_bytes_raises(self): + # GH 26447 + df = DataFrame({"A": ["abc"]}) + msg = "^'str' object cannot be interpreted as an integer$" + with pytest.raises(TypeError, match=msg): + bytes(df) + + def test_very_wide_repr(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 20)), + columns=np.array(["a" * 10] * 20, dtype=object), + ) + repr(df) + + def test_repr_column_name_unicode_truncation_bug(self): + # #1906 + df = DataFrame( + { + "Id": [7117434], + "StringCol": ( + "Is it possible to modify drop plot code" + "so that the output graph is displayed " + "in iphone simulator, Is it possible to " + "modify drop plot code so that the " + "output graph is \xe2\x80\xa8displayed " + "in iphone simulator.Now we are adding " + "the CSV file externally. I want to Call " + "the File through the code.." + ), + } + ) + + with option_context("display.max_columns", 20): + assert "StringCol" in repr(df) + + def test_latex_repr(self): + pytest.importorskip("jinja2") + expected = r"""\begin{tabular}{llll} +\toprule + & 0 & 1 & 2 \\ +\midrule +0 & $\alpha$ & b & c \\ +1 & 1 & 2 & 3 \\ +\bottomrule +\end{tabular} +""" + with option_context( + "styler.format.escape", None, "styler.render.repr", "latex" + ): + df = DataFrame([[r"$\alpha$", "b", "c"], [1, 2, 3]]) + result = df._repr_latex_() + assert result == expected + + # GH 12182 + assert df._repr_latex_() is None + + def test_repr_with_datetimeindex(self): + df = DataFrame({"A": [1, 2, 3]}, index=date_range("2000", periods=3)) + result = repr(df) + expected = " A\n2000-01-01 1\n2000-01-02 2\n2000-01-03 3" + assert result == expected + + def test_repr_with_intervalindex(self): + # https://github.com/pandas-dev/pandas/pull/24134/files + df = DataFrame( + {"A": [1, 2, 3, 4]}, index=IntervalIndex.from_breaks([0, 1, 2, 3, 4]) + ) + result = repr(df) + expected = " A\n(0, 1] 1\n(1, 2] 2\n(2, 3] 3\n(3, 4] 4" + assert result == expected + + def test_repr_with_categorical_index(self): + df = DataFrame({"A": [1, 2, 3]}, index=CategoricalIndex(["a", "b", "c"])) + result = repr(df) + expected = " A\na 1\nb 2\nc 3" + assert result == expected + + def test_repr_categorical_dates_periods(self): + # normal DataFrame + dt = date_range("2011-01-01 09:00", freq="h", periods=5, tz="US/Eastern") + p = period_range("2011-01", freq="M", periods=5) + df = DataFrame({"dt": dt, "p": p}) + exp = """ dt p +0 2011-01-01 09:00:00-05:00 2011-01 +1 2011-01-01 10:00:00-05:00 2011-02 +2 2011-01-01 11:00:00-05:00 2011-03 +3 2011-01-01 12:00:00-05:00 2011-04 +4 2011-01-01 13:00:00-05:00 2011-05""" + + assert repr(df) == exp + + df2 = DataFrame({"dt": Categorical(dt), "p": Categorical(p)}) + assert repr(df2) == exp + + @pytest.mark.parametrize("arg", [np.datetime64, np.timedelta64]) + @pytest.mark.parametrize( + "box, expected", + [[Series, "0 NaT\ndtype: object"], [DataFrame, " 0\n0 NaT"]], + ) + def test_repr_np_nat_with_object(self, arg, box, expected): + # GH 25445 + result = repr(box([arg("NaT")], dtype=object)) + assert result == expected + + def test_frame_datetime64_pre1900_repr(self): + df = DataFrame({"year": date_range("1/1/1700", periods=50, freq="YE-DEC")}) + # it works! + repr(df) + + def test_frame_to_string_with_periodindex(self): + index = PeriodIndex(["2011-1", "2011-2", "2011-3"], freq="M") + frame = DataFrame(np.random.default_rng(2).standard_normal((3, 4)), index=index) + + # it works! + frame.to_string() + + def test_to_string_ea_na_in_multiindex(self): + # GH#47986 + df = DataFrame( + {"a": [1, 2]}, + index=MultiIndex.from_arrays([Series([NA, 1], dtype="Int64")]), + ) + + result = df.to_string() + expected = """ a + 1 +1 2""" + assert result == expected + + def test_datetime64tz_slice_non_truncate(self): + # GH 30263 + df = DataFrame({"x": date_range("2019", periods=10, tz="UTC")}) + expected = repr(df) + df = df.iloc[:, :5] + result = repr(df) + assert result == expected + + def test_to_records_no_typeerror_in_repr(self): + # GH 48526 + df = DataFrame([["a", "b"], ["c", "d"], ["e", "f"]], columns=["left", "right"]) + df["record"] = df[["left", "right"]].to_records() + expected = """ left right record +0 a b [0, a, b] +1 c d [1, c, d] +2 e f [2, e, f]""" + result = repr(df) + assert result == expected + + def test_to_records_with_na_record_value(self): + # GH 48526 + df = DataFrame( + [["a", np.nan], ["c", "d"], ["e", "f"]], columns=["left", "right"] + ) + df["record"] = df[["left", "right"]].to_records() + expected = """ left right record +0 a NaN [0, a, nan] +1 c d [1, c, d] +2 e f [2, e, f]""" + result = repr(df) + assert result == expected + + def test_to_records_with_na_record(self): + # GH 48526 + df = DataFrame( + [["a", "b"], [np.nan, np.nan], ["e", "f"]], columns=[np.nan, "right"] + ) + df["record"] = df[[np.nan, "right"]].to_records() + expected = """ NaN right record +0 a b [0, a, b] +1 NaN NaN [1, nan, nan] +2 e f [2, e, f]""" + result = repr(df) + assert result == expected + + def test_to_records_with_inf_as_na_record(self): + # GH 48526 + expected = """ NaN inf record +0 inf b [0, inf, b] +1 NaN NaN [1, nan, nan] +2 e f [2, e, f]""" + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with option_context("use_inf_as_na", True): + df = DataFrame( + [[np.inf, "b"], [np.nan, np.nan], ["e", "f"]], + columns=[np.nan, np.inf], + ) + df["record"] = df[[np.nan, np.inf]].to_records() + result = repr(df) + assert result == expected + + def test_to_records_with_inf_record(self): + # GH 48526 + expected = """ NaN inf record +0 inf b [0, inf, b] +1 NaN NaN [1, nan, nan] +2 e f [2, e, f]""" + msg = "use_inf_as_na option is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + with option_context("use_inf_as_na", False): + df = DataFrame( + [[np.inf, "b"], [np.nan, np.nan], ["e", "f"]], + columns=[np.nan, np.inf], + ) + df["record"] = df[[np.nan, np.inf]].to_records() + result = repr(df) + assert result == expected + + def test_masked_ea_with_formatter(self): + # GH#39336 + df = DataFrame( + { + "a": Series([0.123456789, 1.123456789], dtype="Float64"), + "b": Series([1, 2], dtype="Int64"), + } + ) + result = df.to_string(formatters=["{:.2f}".format, "{:.2f}".format]) + expected = """ a b +0 0.12 1.00 +1 1.12 2.00""" + assert result == expected + + def test_repr_ea_columns(self, any_string_dtype): + # GH#54797 + pytest.importorskip("pyarrow") + df = DataFrame({"long_column_name": [1, 2, 3], "col2": [4, 5, 6]}) + df.columns = df.columns.astype(any_string_dtype) + expected = """ long_column_name col2 +0 1 4 +1 2 5 +2 3 6""" + assert repr(df) == expected + + +@pytest.mark.parametrize( + "data,output", + [ + ([2, complex("nan"), 1], [" 2.0+0.0j", " NaN+0.0j", " 1.0+0.0j"]), + ([2, complex("nan"), -1], [" 2.0+0.0j", " NaN+0.0j", "-1.0+0.0j"]), + ([-2, complex("nan"), -1], ["-2.0+0.0j", " NaN+0.0j", "-1.0+0.0j"]), + ([-1.23j, complex("nan"), -1], ["-0.00-1.23j", " NaN+0.00j", "-1.00+0.00j"]), + ([1.23j, complex("nan"), 1.23], [" 0.00+1.23j", " NaN+0.00j", " 1.23+0.00j"]), + ( + [-1.23j, complex(np.nan, np.nan), 1], + ["-0.00-1.23j", " NaN+ NaNj", " 1.00+0.00j"], + ), + ( + [-1.23j, complex(1.2, np.nan), 1], + ["-0.00-1.23j", " 1.20+ NaNj", " 1.00+0.00j"], + ), + ( + [-1.23j, complex(np.nan, -1.2), 1], + ["-0.00-1.23j", " NaN-1.20j", " 1.00+0.00j"], + ), + ], +) +@pytest.mark.parametrize("as_frame", [True, False]) +def test_repr_with_complex_nans(data, output, as_frame): + # GH#53762, GH#53841 + obj = Series(np.array(data)) + if as_frame: + obj = obj.to_frame(name="val") + reprs = [f"{i} {val}" for i, val in enumerate(output)] + expected = f"{'val': >{len(reprs[0])}}\n" + "\n".join(reprs) + else: + reprs = [f"{i} {val}" for i, val in enumerate(output)] + expected = "\n".join(reprs) + "\ndtype: complex128" + assert str(obj) == expected, f"\n{str(obj)}\n\n{expected}" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_stack_unstack.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_stack_unstack.py new file mode 100644 index 0000000000000000000000000000000000000000..de470fcda18ed47417ff84b88c6b48bc888cec8f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_stack_unstack.py @@ -0,0 +1,2684 @@ +from datetime import datetime +import itertools +import re + +import numpy as np +import pytest + +from pandas._libs import lib +from pandas.errors import PerformanceWarning + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Period, + Series, + Timedelta, + date_range, +) +import pandas._testing as tm +from pandas.core.reshape import reshape as reshape_lib + + +@pytest.fixture(params=[True, False]) +def future_stack(request): + return request.param + + +class TestDataFrameReshape: + def test_stack_unstack(self, float_frame, future_stack): + df = float_frame.copy() + df[:] = np.arange(np.prod(df.shape)).reshape(df.shape) + + stacked = df.stack(future_stack=future_stack) + stacked_df = DataFrame({"foo": stacked, "bar": stacked}) + + unstacked = stacked.unstack() + unstacked_df = stacked_df.unstack() + + tm.assert_frame_equal(unstacked, df) + tm.assert_frame_equal(unstacked_df["bar"], df) + + unstacked_cols = stacked.unstack(0) + unstacked_cols_df = stacked_df.unstack(0) + tm.assert_frame_equal(unstacked_cols.T, df) + tm.assert_frame_equal(unstacked_cols_df["bar"].T, df) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_mixed_level(self, future_stack): + # GH 18310 + levels = [range(3), [3, "a", "b"], [1, 2]] + + # flat columns: + df = DataFrame(1, index=levels[0], columns=levels[1]) + result = df.stack(future_stack=future_stack) + expected = Series(1, index=MultiIndex.from_product(levels[:2])) + tm.assert_series_equal(result, expected) + + # MultiIndex columns: + df = DataFrame(1, index=levels[0], columns=MultiIndex.from_product(levels[1:])) + result = df.stack(1, future_stack=future_stack) + expected = DataFrame( + 1, index=MultiIndex.from_product([levels[0], levels[2]]), columns=levels[1] + ) + tm.assert_frame_equal(result, expected) + + # as above, but used labels in level are actually of homogeneous type + result = df[["a", "b"]].stack(1, future_stack=future_stack) + expected = expected[["a", "b"]] + tm.assert_frame_equal(result, expected) + + def test_unstack_not_consolidated(self, using_array_manager): + # Gh#34708 + df = DataFrame({"x": [1, 2, np.nan], "y": [3.0, 4, np.nan]}) + df2 = df[["x"]] + df2["y"] = df["y"] + if not using_array_manager: + assert len(df2._mgr.blocks) == 2 + + res = df2.unstack() + expected = df.unstack() + tm.assert_series_equal(res, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_unstack_fill(self, future_stack): + # GH #9746: fill_value keyword argument for Series + # and DataFrame unstack + + # From a series + data = Series([1, 2, 4, 5], dtype=np.int16) + data.index = MultiIndex.from_tuples( + [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")] + ) + + result = data.unstack(fill_value=-1) + expected = DataFrame( + {"a": [1, -1, 5], "b": [2, 4, -1]}, index=["x", "y", "z"], dtype=np.int16 + ) + tm.assert_frame_equal(result, expected) + + # From a series with incorrect data type for fill_value + result = data.unstack(fill_value=0.5) + expected = DataFrame( + {"a": [1, 0.5, 5], "b": [2, 4, 0.5]}, index=["x", "y", "z"], dtype=float + ) + tm.assert_frame_equal(result, expected) + + # GH #13971: fill_value when unstacking multiple levels: + df = DataFrame( + {"x": ["a", "a", "b"], "y": ["j", "k", "j"], "z": [0, 1, 2], "w": [0, 1, 2]} + ).set_index(["x", "y", "z"]) + unstacked = df.unstack(["x", "y"], fill_value=0) + key = ("w", "b", "j") + expected = unstacked[key] + result = Series([0, 0, 2], index=unstacked.index, name=key) + tm.assert_series_equal(result, expected) + + stacked = unstacked.stack(["x", "y"], future_stack=future_stack) + stacked.index = stacked.index.reorder_levels(df.index.names) + # Workaround for GH #17886 (unnecessarily casts to float): + stacked = stacked.astype(np.int64) + result = stacked.loc[df.index] + tm.assert_frame_equal(result, df) + + # From a series + s = df["w"] + result = s.unstack(["x", "y"], fill_value=0) + expected = unstacked["w"] + tm.assert_frame_equal(result, expected) + + def test_unstack_fill_frame(self): + # From a dataframe + rows = [[1, 2], [3, 4], [5, 6], [7, 8]] + df = DataFrame(rows, columns=list("AB"), dtype=np.int32) + df.index = MultiIndex.from_tuples( + [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")] + ) + + result = df.unstack(fill_value=-1) + + rows = [[1, 3, 2, 4], [-1, 5, -1, 6], [7, -1, 8, -1]] + expected = DataFrame(rows, index=list("xyz"), dtype=np.int32) + expected.columns = MultiIndex.from_tuples( + [("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")] + ) + tm.assert_frame_equal(result, expected) + + # From a mixed type dataframe + df["A"] = df["A"].astype(np.int16) + df["B"] = df["B"].astype(np.float64) + + result = df.unstack(fill_value=-1) + expected["A"] = expected["A"].astype(np.int16) + expected["B"] = expected["B"].astype(np.float64) + tm.assert_frame_equal(result, expected) + + # From a dataframe with incorrect data type for fill_value + result = df.unstack(fill_value=0.5) + + rows = [[1, 3, 2, 4], [0.5, 5, 0.5, 6], [7, 0.5, 8, 0.5]] + expected = DataFrame(rows, index=list("xyz"), dtype=float) + expected.columns = MultiIndex.from_tuples( + [("A", "a"), ("A", "b"), ("B", "a"), ("B", "b")] + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_fill_frame_datetime(self): + # Test unstacking with date times + dv = date_range("2012-01-01", periods=4).values + data = Series(dv) + data.index = MultiIndex.from_tuples( + [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")] + ) + + result = data.unstack() + expected = DataFrame( + {"a": [dv[0], pd.NaT, dv[3]], "b": [dv[1], dv[2], pd.NaT]}, + index=["x", "y", "z"], + ) + tm.assert_frame_equal(result, expected) + + result = data.unstack(fill_value=dv[0]) + expected = DataFrame( + {"a": [dv[0], dv[0], dv[3]], "b": [dv[1], dv[2], dv[0]]}, + index=["x", "y", "z"], + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_fill_frame_timedelta(self): + # Test unstacking with time deltas + td = [Timedelta(days=i) for i in range(4)] + data = Series(td) + data.index = MultiIndex.from_tuples( + [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")] + ) + + result = data.unstack() + expected = DataFrame( + {"a": [td[0], pd.NaT, td[3]], "b": [td[1], td[2], pd.NaT]}, + index=["x", "y", "z"], + ) + tm.assert_frame_equal(result, expected) + + result = data.unstack(fill_value=td[1]) + expected = DataFrame( + {"a": [td[0], td[1], td[3]], "b": [td[1], td[2], td[1]]}, + index=["x", "y", "z"], + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_fill_frame_period(self): + # Test unstacking with period + periods = [ + Period("2012-01"), + Period("2012-02"), + Period("2012-03"), + Period("2012-04"), + ] + data = Series(periods) + data.index = MultiIndex.from_tuples( + [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")] + ) + + result = data.unstack() + expected = DataFrame( + {"a": [periods[0], None, periods[3]], "b": [periods[1], periods[2], None]}, + index=["x", "y", "z"], + ) + tm.assert_frame_equal(result, expected) + + result = data.unstack(fill_value=periods[1]) + expected = DataFrame( + { + "a": [periods[0], periods[1], periods[3]], + "b": [periods[1], periods[2], periods[1]], + }, + index=["x", "y", "z"], + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_fill_frame_categorical(self): + # Test unstacking with categorical + data = Series(["a", "b", "c", "a"], dtype="category") + data.index = MultiIndex.from_tuples( + [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")] + ) + + # By default missing values will be NaN + result = data.unstack() + expected = DataFrame( + { + "a": pd.Categorical(list("axa"), categories=list("abc")), + "b": pd.Categorical(list("bcx"), categories=list("abc")), + }, + index=list("xyz"), + ) + tm.assert_frame_equal(result, expected) + + # Fill with non-category results in a ValueError + msg = r"Cannot setitem on a Categorical with a new category \(d\)" + with pytest.raises(TypeError, match=msg): + data.unstack(fill_value="d") + + # Fill with category value replaces missing values as expected + result = data.unstack(fill_value="c") + expected = DataFrame( + { + "a": pd.Categorical(list("aca"), categories=list("abc")), + "b": pd.Categorical(list("bcc"), categories=list("abc")), + }, + index=list("xyz"), + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_tuplename_in_multiindex(self): + # GH 19966 + idx = MultiIndex.from_product( + [["a", "b", "c"], [1, 2, 3]], names=[("A", "a"), ("B", "b")] + ) + df = DataFrame({"d": [1] * 9, "e": [2] * 9}, index=idx) + result = df.unstack(("A", "a")) + + expected = DataFrame( + [[1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2], [1, 1, 1, 2, 2, 2]], + columns=MultiIndex.from_tuples( + [ + ("d", "a"), + ("d", "b"), + ("d", "c"), + ("e", "a"), + ("e", "b"), + ("e", "c"), + ], + names=[None, ("A", "a")], + ), + index=Index([1, 2, 3], name=("B", "b")), + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "unstack_idx, expected_values, expected_index, expected_columns", + [ + ( + ("A", "a"), + [[1, 1, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2], [1, 1, 2, 2]], + MultiIndex.from_tuples( + [(1, 3), (1, 4), (2, 3), (2, 4)], names=["B", "C"] + ), + MultiIndex.from_tuples( + [("d", "a"), ("d", "b"), ("e", "a"), ("e", "b")], + names=[None, ("A", "a")], + ), + ), + ( + (("A", "a"), "B"), + [[1, 1, 1, 1, 2, 2, 2, 2], [1, 1, 1, 1, 2, 2, 2, 2]], + Index([3, 4], name="C"), + MultiIndex.from_tuples( + [ + ("d", "a", 1), + ("d", "a", 2), + ("d", "b", 1), + ("d", "b", 2), + ("e", "a", 1), + ("e", "a", 2), + ("e", "b", 1), + ("e", "b", 2), + ], + names=[None, ("A", "a"), "B"], + ), + ), + ], + ) + def test_unstack_mixed_type_name_in_multiindex( + self, unstack_idx, expected_values, expected_index, expected_columns + ): + # GH 19966 + idx = MultiIndex.from_product( + [["a", "b"], [1, 2], [3, 4]], names=[("A", "a"), "B", "C"] + ) + df = DataFrame({"d": [1] * 8, "e": [2] * 8}, index=idx) + result = df.unstack(unstack_idx) + + expected = DataFrame( + expected_values, columns=expected_columns, index=expected_index + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_preserve_dtypes(self): + # Checks fix for #11847 + df = DataFrame( + { + "state": ["IL", "MI", "NC"], + "index": ["a", "b", "c"], + "some_categories": Series(["a", "b", "c"]).astype("category"), + "A": np.random.default_rng(2).random(3), + "B": 1, + "C": "foo", + "D": pd.Timestamp("20010102"), + "E": Series([1.0, 50.0, 100.0]).astype("float32"), + "F": Series([3.0, 4.0, 5.0]).astype("float64"), + "G": False, + "H": Series([1, 200, 923442]).astype("int8"), + } + ) + + def unstack_and_compare(df, column_name): + unstacked1 = df.unstack([column_name]) + unstacked2 = df.unstack(column_name) + tm.assert_frame_equal(unstacked1, unstacked2) + + df1 = df.set_index(["state", "index"]) + unstack_and_compare(df1, "index") + + df1 = df.set_index(["state", "some_categories"]) + unstack_and_compare(df1, "some_categories") + + df1 = df.set_index(["F", "C"]) + unstack_and_compare(df1, "F") + + df1 = df.set_index(["G", "B", "state"]) + unstack_and_compare(df1, "B") + + df1 = df.set_index(["E", "A"]) + unstack_and_compare(df1, "E") + + df1 = df.set_index(["state", "index"]) + s = df1["A"] + unstack_and_compare(s, "index") + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_ints(self, future_stack): + columns = MultiIndex.from_tuples(list(itertools.product(range(3), repeat=3))) + df = DataFrame( + np.random.default_rng(2).standard_normal((30, 27)), columns=columns + ) + + tm.assert_frame_equal( + df.stack(level=[1, 2], future_stack=future_stack), + df.stack(level=1, future_stack=future_stack).stack( + level=1, future_stack=future_stack + ), + ) + tm.assert_frame_equal( + df.stack(level=[-2, -1], future_stack=future_stack), + df.stack(level=1, future_stack=future_stack).stack( + level=1, future_stack=future_stack + ), + ) + + df_named = df.copy() + return_value = df_named.columns.set_names(range(3), inplace=True) + assert return_value is None + + tm.assert_frame_equal( + df_named.stack(level=[1, 2], future_stack=future_stack), + df_named.stack(level=1, future_stack=future_stack).stack( + level=1, future_stack=future_stack + ), + ) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_mixed_levels(self, future_stack): + columns = MultiIndex.from_tuples( + [ + ("A", "cat", "long"), + ("B", "cat", "long"), + ("A", "dog", "short"), + ("B", "dog", "short"), + ], + names=["exp", "animal", "hair_length"], + ) + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), columns=columns + ) + + animal_hair_stacked = df.stack( + level=["animal", "hair_length"], future_stack=future_stack + ) + exp_hair_stacked = df.stack( + level=["exp", "hair_length"], future_stack=future_stack + ) + + # GH #8584: Need to check that stacking works when a number + # is passed that is both a level name and in the range of + # the level numbers + df2 = df.copy() + df2.columns.names = ["exp", "animal", 1] + tm.assert_frame_equal( + df2.stack(level=["animal", 1], future_stack=future_stack), + animal_hair_stacked, + check_names=False, + ) + tm.assert_frame_equal( + df2.stack(level=["exp", 1], future_stack=future_stack), + exp_hair_stacked, + check_names=False, + ) + + # When mixed types are passed and the ints are not level + # names, raise + msg = ( + "level should contain all level names or all level numbers, not " + "a mixture of the two" + ) + with pytest.raises(ValueError, match=msg): + df2.stack(level=["animal", 0], future_stack=future_stack) + + # GH #8584: Having 0 in the level names could raise a + # strange error about lexsort depth + df3 = df.copy() + df3.columns.names = ["exp", "animal", 0] + tm.assert_frame_equal( + df3.stack(level=["animal", 0], future_stack=future_stack), + animal_hair_stacked, + check_names=False, + ) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_int_level_names(self, future_stack): + columns = MultiIndex.from_tuples( + [ + ("A", "cat", "long"), + ("B", "cat", "long"), + ("A", "dog", "short"), + ("B", "dog", "short"), + ], + names=["exp", "animal", "hair_length"], + ) + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), columns=columns + ) + + exp_animal_stacked = df.stack( + level=["exp", "animal"], future_stack=future_stack + ) + animal_hair_stacked = df.stack( + level=["animal", "hair_length"], future_stack=future_stack + ) + exp_hair_stacked = df.stack( + level=["exp", "hair_length"], future_stack=future_stack + ) + + df2 = df.copy() + df2.columns.names = [0, 1, 2] + tm.assert_frame_equal( + df2.stack(level=[1, 2], future_stack=future_stack), + animal_hair_stacked, + check_names=False, + ) + tm.assert_frame_equal( + df2.stack(level=[0, 1], future_stack=future_stack), + exp_animal_stacked, + check_names=False, + ) + tm.assert_frame_equal( + df2.stack(level=[0, 2], future_stack=future_stack), + exp_hair_stacked, + check_names=False, + ) + + # Out-of-order int column names + df3 = df.copy() + df3.columns.names = [2, 0, 1] + tm.assert_frame_equal( + df3.stack(level=[0, 1], future_stack=future_stack), + animal_hair_stacked, + check_names=False, + ) + tm.assert_frame_equal( + df3.stack(level=[2, 0], future_stack=future_stack), + exp_animal_stacked, + check_names=False, + ) + tm.assert_frame_equal( + df3.stack(level=[2, 1], future_stack=future_stack), + exp_hair_stacked, + check_names=False, + ) + + def test_unstack_bool(self): + df = DataFrame( + [False, False], + index=MultiIndex.from_arrays([["a", "b"], ["c", "l"]]), + columns=["col"], + ) + rs = df.unstack() + xp = DataFrame( + np.array([[False, np.nan], [np.nan, False]], dtype=object), + index=["a", "b"], + columns=MultiIndex.from_arrays([["col", "col"], ["c", "l"]]), + ) + tm.assert_frame_equal(rs, xp) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_unstack_level_binding(self, future_stack): + # GH9856 + mi = MultiIndex( + levels=[["foo", "bar"], ["one", "two"], ["a", "b"]], + codes=[[0, 0, 1, 1], [0, 1, 0, 1], [1, 0, 1, 0]], + names=["first", "second", "third"], + ) + s = Series(0, index=mi) + result = s.unstack([1, 2]).stack(0, future_stack=future_stack) + + expected_mi = MultiIndex( + levels=[["foo", "bar"], ["one", "two"]], + codes=[[0, 0, 1, 1], [0, 1, 0, 1]], + names=["first", "second"], + ) + + expected = DataFrame( + np.array( + [[0, np.nan], [np.nan, 0], [0, np.nan], [np.nan, 0]], dtype=np.float64 + ), + index=expected_mi, + columns=Index(["b", "a"], name="third"), + ) + + tm.assert_frame_equal(result, expected) + + def test_unstack_to_series(self, float_frame): + # check reversibility + data = float_frame.unstack() + + assert isinstance(data, Series) + undo = data.unstack().T + tm.assert_frame_equal(undo, float_frame) + + # check NA handling + data = DataFrame({"x": [1, 2, np.nan], "y": [3.0, 4, np.nan]}) + data.index = Index(["a", "b", "c"]) + result = data.unstack() + + midx = MultiIndex( + levels=[["x", "y"], ["a", "b", "c"]], + codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], + ) + expected = Series([1, 2, np.nan, 3, 4, np.nan], index=midx) + + tm.assert_series_equal(result, expected) + + # check composability of unstack + old_data = data.copy() + for _ in range(4): + data = data.unstack() + tm.assert_frame_equal(old_data, data) + + def test_unstack_dtypes(self, using_infer_string): + # GH 2929 + rows = [[1, 1, 3, 4], [1, 2, 3, 4], [2, 1, 3, 4], [2, 2, 3, 4]] + + df = DataFrame(rows, columns=list("ABCD")) + result = df.dtypes + expected = Series([np.dtype("int64")] * 4, index=list("ABCD")) + tm.assert_series_equal(result, expected) + + # single dtype + df2 = df.set_index(["A", "B"]) + df3 = df2.unstack("B") + result = df3.dtypes + expected = Series( + [np.dtype("int64")] * 4, + index=MultiIndex.from_arrays( + [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B") + ), + ) + tm.assert_series_equal(result, expected) + + # mixed + df2 = df.set_index(["A", "B"]) + df2["C"] = 3.0 + df3 = df2.unstack("B") + result = df3.dtypes + expected = Series( + [np.dtype("float64")] * 2 + [np.dtype("int64")] * 2, + index=MultiIndex.from_arrays( + [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B") + ), + ) + tm.assert_series_equal(result, expected) + df2["D"] = "foo" + df3 = df2.unstack("B") + result = df3.dtypes + dtype = ( + pd.StringDtype(na_value=np.nan) + if using_infer_string + else np.dtype("object") + ) + expected = Series( + [np.dtype("float64")] * 2 + [dtype] * 2, + index=MultiIndex.from_arrays( + [["C", "C", "D", "D"], [1, 2, 1, 2]], names=(None, "B") + ), + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "c, d", + ( + (np.zeros(5), np.zeros(5)), + (np.arange(5, dtype="f8"), np.arange(5, 10, dtype="f8")), + ), + ) + def test_unstack_dtypes_mixed_date(self, c, d): + # GH7405 + df = DataFrame( + { + "A": ["a"] * 5, + "C": c, + "D": d, + "B": date_range("2012-01-01", periods=5), + } + ) + + right = df.iloc[:3].copy(deep=True) + + df = df.set_index(["A", "B"]) + df["D"] = df["D"].astype("int64") + + left = df.iloc[:3].unstack(0) + right = right.set_index(["A", "B"]).unstack(0) + right[("D", "a")] = right[("D", "a")].astype("int64") + + assert left.shape == (3, 2) + tm.assert_frame_equal(left, right) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_unstack_non_unique_index_names(self, future_stack): + idx = MultiIndex.from_tuples([("a", "b"), ("c", "d")], names=["c1", "c1"]) + df = DataFrame([1, 2], index=idx) + msg = "The name c1 occurs multiple times, use a level number" + with pytest.raises(ValueError, match=msg): + df.unstack("c1") + + with pytest.raises(ValueError, match=msg): + df.T.stack("c1", future_stack=future_stack) + + def test_unstack_unused_levels(self): + # GH 17845: unused codes in index make unstack() cast int to float + idx = MultiIndex.from_product([["a"], ["A", "B", "C", "D"]])[:-1] + df = DataFrame([[1, 0]] * 3, index=idx) + + result = df.unstack() + exp_col = MultiIndex.from_product([[0, 1], ["A", "B", "C"]]) + expected = DataFrame([[1, 1, 1, 0, 0, 0]], index=["a"], columns=exp_col) + tm.assert_frame_equal(result, expected) + assert (result.columns.levels[1] == idx.levels[1]).all() + + # Unused items on both levels + levels = [[0, 1, 7], [0, 1, 2, 3]] + codes = [[0, 0, 1, 1], [0, 2, 0, 2]] + idx = MultiIndex(levels, codes) + block = np.arange(4).reshape(2, 2) + df = DataFrame(np.concatenate([block, block + 4]), index=idx) + result = df.unstack() + expected = DataFrame( + np.concatenate([block * 2, block * 2 + 1], axis=1), columns=idx + ) + tm.assert_frame_equal(result, expected) + assert (result.columns.levels[1] == idx.levels[1]).all() + + @pytest.mark.parametrize( + "level, idces, col_level, idx_level", + ( + (0, [13, 16, 6, 9, 2, 5, 8, 11], [np.nan, "a", 2], [np.nan, 5, 1]), + (1, [8, 11, 1, 4, 12, 15, 13, 16], [np.nan, 5, 1], [np.nan, "a", 2]), + ), + ) + def test_unstack_unused_levels_mixed_with_nan( + self, level, idces, col_level, idx_level + ): + # With mixed dtype and NaN + levels = [["a", 2, "c"], [1, 3, 5, 7]] + codes = [[0, -1, 1, 1], [0, 2, -1, 2]] + idx = MultiIndex(levels, codes) + data = np.arange(8) + df = DataFrame(data.reshape(4, 2), index=idx) + + result = df.unstack(level=level) + exp_data = np.zeros(18) * np.nan + exp_data[idces] = data + cols = MultiIndex.from_product([[0, 1], col_level]) + expected = DataFrame(exp_data.reshape(3, 6), index=idx_level, columns=cols) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("cols", [["A", "C"], slice(None)]) + def test_unstack_unused_level(self, cols): + # GH 18562 : unused codes on the unstacked level + df = DataFrame([[2010, "a", "I"], [2011, "b", "II"]], columns=["A", "B", "C"]) + + ind = df.set_index(["A", "B", "C"], drop=False) + selection = ind.loc[(slice(None), slice(None), "I"), cols] + result = selection.unstack() + + expected = ind.iloc[[0]][cols] + expected.columns = MultiIndex.from_product( + [expected.columns, ["I"]], names=[None, "C"] + ) + expected.index = expected.index.droplevel("C") + tm.assert_frame_equal(result, expected) + + def test_unstack_long_index(self): + # PH 32624: Error when using a lot of indices to unstack. + # The error occurred only, if a lot of indices are used. + df = DataFrame( + [[1]], + columns=MultiIndex.from_tuples([[0]], names=["c1"]), + index=MultiIndex.from_tuples( + [[0, 0, 1, 0, 0, 0, 1]], + names=["i1", "i2", "i3", "i4", "i5", "i6", "i7"], + ), + ) + result = df.unstack(["i2", "i3", "i4", "i5", "i6", "i7"]) + expected = DataFrame( + [[1]], + columns=MultiIndex.from_tuples( + [[0, 0, 1, 0, 0, 0, 1]], + names=["c1", "i2", "i3", "i4", "i5", "i6", "i7"], + ), + index=Index([0], name="i1"), + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_multi_level_cols(self): + # PH 24729: Unstack a df with multi level columns + df = DataFrame( + [[0.0, 0.0], [0.0, 0.0]], + columns=MultiIndex.from_tuples( + [["B", "C"], ["B", "D"]], names=["c1", "c2"] + ), + index=MultiIndex.from_tuples( + [[10, 20, 30], [10, 20, 40]], names=["i1", "i2", "i3"] + ), + ) + assert df.unstack(["i2", "i1"]).columns.names[-2:] == ["i2", "i1"] + + def test_unstack_multi_level_rows_and_cols(self): + # PH 28306: Unstack df with multi level cols and rows + df = DataFrame( + [[1, 2], [3, 4], [-1, -2], [-3, -4]], + columns=MultiIndex.from_tuples([["a", "b", "c"], ["d", "e", "f"]]), + index=MultiIndex.from_tuples( + [ + ["m1", "P3", 222], + ["m1", "A5", 111], + ["m2", "P3", 222], + ["m2", "A5", 111], + ], + names=["i1", "i2", "i3"], + ), + ) + result = df.unstack(["i3", "i2"]) + expected = df.unstack(["i3"]).unstack(["i2"]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("idx", [("jim", "joe"), ("joe", "jim")]) + @pytest.mark.parametrize("lev", list(range(2))) + def test_unstack_nan_index1(self, idx, lev): + # GH7466 + def cast(val): + val_str = "" if val != val else val + return f"{val_str:1}" + + df = DataFrame( + { + "jim": ["a", "b", np.nan, "d"], + "joe": ["w", "x", "y", "z"], + "jolie": ["a.w", "b.x", " .y", "d.z"], + } + ) + + left = df.set_index(["jim", "joe"]).unstack()["jolie"] + right = df.set_index(["joe", "jim"]).unstack()["jolie"].T + tm.assert_frame_equal(left, right) + + mi = df.set_index(list(idx)) + udf = mi.unstack(level=lev) + assert udf.notna().values.sum() == len(df) + mk_list = lambda a: list(a) if isinstance(a, tuple) else [a] + rows, cols = udf["jolie"].notna().values.nonzero() + for i, j in zip(rows, cols): + left = sorted(udf["jolie"].iloc[i, j].split(".")) + right = mk_list(udf["jolie"].index[i]) + mk_list(udf["jolie"].columns[j]) + right = sorted(map(cast, right)) + assert left == right + + @pytest.mark.parametrize("idx", itertools.permutations(["1st", "2nd", "3rd"])) + @pytest.mark.parametrize("lev", list(range(3))) + @pytest.mark.parametrize("col", ["4th", "5th"]) + def test_unstack_nan_index_repeats(self, idx, lev, col): + def cast(val): + val_str = "" if val != val else val + return f"{val_str:1}" + + df = DataFrame( + { + "1st": ["d"] * 3 + + [np.nan] * 5 + + ["a"] * 2 + + ["c"] * 3 + + ["e"] * 2 + + ["b"] * 5, + "2nd": ["y"] * 2 + + ["w"] * 3 + + [np.nan] * 3 + + ["z"] * 4 + + [np.nan] * 3 + + ["x"] * 3 + + [np.nan] * 2, + "3rd": [ + 67, + 39, + 53, + 72, + 57, + 80, + 31, + 18, + 11, + 30, + 59, + 50, + 62, + 59, + 76, + 52, + 14, + 53, + 60, + 51, + ], + } + ) + + df["4th"], df["5th"] = ( + df.apply(lambda r: ".".join(map(cast, r)), axis=1), + df.apply(lambda r: ".".join(map(cast, r.iloc[::-1])), axis=1), + ) + + mi = df.set_index(list(idx)) + udf = mi.unstack(level=lev) + assert udf.notna().values.sum() == 2 * len(df) + mk_list = lambda a: list(a) if isinstance(a, tuple) else [a] + rows, cols = udf[col].notna().values.nonzero() + for i, j in zip(rows, cols): + left = sorted(udf[col].iloc[i, j].split(".")) + right = mk_list(udf[col].index[i]) + mk_list(udf[col].columns[j]) + right = sorted(map(cast, right)) + assert left == right + + def test_unstack_nan_index2(self): + # GH7403 + df = DataFrame({"A": list("aaaabbbb"), "B": range(8), "C": range(8)}) + # Explicit cast to avoid implicit cast when setting to np.nan + df = df.astype({"B": "float"}) + df.iloc[3, 1] = np.nan + left = df.set_index(["A", "B"]).unstack(0) + + vals = [ + [3, 0, 1, 2, np.nan, np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan, np.nan, 4, 5, 6, 7], + ] + vals = list(map(list, zip(*vals))) + idx = Index([np.nan, 0, 1, 2, 4, 5, 6, 7], name="B") + cols = MultiIndex( + levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"] + ) + + right = DataFrame(vals, columns=cols, index=idx) + tm.assert_frame_equal(left, right) + + df = DataFrame({"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)}) + # Explicit cast to avoid implicit cast when setting to np.nan + df = df.astype({"B": "float"}) + df.iloc[2, 1] = np.nan + left = df.set_index(["A", "B"]).unstack(0) + + vals = [[2, np.nan], [0, 4], [1, 5], [np.nan, 6], [3, 7]] + cols = MultiIndex( + levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"] + ) + idx = Index([np.nan, 0, 1, 2, 3], name="B") + right = DataFrame(vals, columns=cols, index=idx) + tm.assert_frame_equal(left, right) + + df = DataFrame({"A": list("aaaabbbb"), "B": list(range(4)) * 2, "C": range(8)}) + # Explicit cast to avoid implicit cast when setting to np.nan + df = df.astype({"B": "float"}) + df.iloc[3, 1] = np.nan + left = df.set_index(["A", "B"]).unstack(0) + + vals = [[3, np.nan], [0, 4], [1, 5], [2, 6], [np.nan, 7]] + cols = MultiIndex( + levels=[["C"], ["a", "b"]], codes=[[0, 0], [0, 1]], names=[None, "A"] + ) + idx = Index([np.nan, 0, 1, 2, 3], name="B") + right = DataFrame(vals, columns=cols, index=idx) + tm.assert_frame_equal(left, right) + + def test_unstack_nan_index3(self, using_array_manager): + # GH7401 + df = DataFrame( + { + "A": list("aaaaabbbbb"), + "B": (date_range("2012-01-01", periods=5).tolist() * 2), + "C": np.arange(10), + } + ) + + df.iloc[3, 1] = np.nan + left = df.set_index(["A", "B"]).unstack() + + vals = np.array([[3, 0, 1, 2, np.nan, 4], [np.nan, 5, 6, 7, 8, 9]]) + idx = Index(["a", "b"], name="A") + cols = MultiIndex( + levels=[["C"], date_range("2012-01-01", periods=5)], + codes=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]], + names=[None, "B"], + ) + + right = DataFrame(vals, columns=cols, index=idx) + if using_array_manager: + # INFO(ArrayManager) with ArrayManager preserve dtype where possible + cols = right.columns[[1, 2, 3, 5]] + right[cols] = right[cols].astype(df["C"].dtype) + tm.assert_frame_equal(left, right) + + def test_unstack_nan_index4(self): + # GH4862 + vals = [ + ["Hg", np.nan, np.nan, 680585148], + ["U", 0.0, np.nan, 680585148], + ["Pb", 7.07e-06, np.nan, 680585148], + ["Sn", 2.3614e-05, 0.0133, 680607017], + ["Ag", 0.0, 0.0133, 680607017], + ["Hg", -0.00015, 0.0133, 680607017], + ] + df = DataFrame( + vals, + columns=["agent", "change", "dosage", "s_id"], + index=[17263, 17264, 17265, 17266, 17267, 17268], + ) + + left = df.copy().set_index(["s_id", "dosage", "agent"]).unstack() + + vals = [ + [np.nan, np.nan, 7.07e-06, np.nan, 0.0], + [0.0, -0.00015, np.nan, 2.3614e-05, np.nan], + ] + + idx = MultiIndex( + levels=[[680585148, 680607017], [0.0133]], + codes=[[0, 1], [-1, 0]], + names=["s_id", "dosage"], + ) + + cols = MultiIndex( + levels=[["change"], ["Ag", "Hg", "Pb", "Sn", "U"]], + codes=[[0, 0, 0, 0, 0], [0, 1, 2, 3, 4]], + names=[None, "agent"], + ) + + right = DataFrame(vals, columns=cols, index=idx) + tm.assert_frame_equal(left, right) + + left = df.loc[17264:].copy().set_index(["s_id", "dosage", "agent"]) + tm.assert_frame_equal(left.unstack(), right) + + def test_unstack_nan_index5(self): + # GH9497 - multiple unstack with nulls + df = DataFrame( + { + "1st": [1, 2, 1, 2, 1, 2], + "2nd": date_range("2014-02-01", periods=6, freq="D"), + "jim": 100 + np.arange(6), + "joe": (np.random.default_rng(2).standard_normal(6) * 10).round(2), + } + ) + + df["3rd"] = df["2nd"] - pd.Timestamp("2014-02-02") + df.loc[1, "2nd"] = df.loc[3, "2nd"] = np.nan + df.loc[1, "3rd"] = df.loc[4, "3rd"] = np.nan + + left = df.set_index(["1st", "2nd", "3rd"]).unstack(["2nd", "3rd"]) + assert left.notna().values.sum() == 2 * len(df) + + for col in ["jim", "joe"]: + for _, r in df.iterrows(): + key = r["1st"], (col, r["2nd"], r["3rd"]) + assert r[col] == left.loc[key] + + def test_stack_datetime_column_multiIndex(self, future_stack): + # GH 8039 + t = datetime(2014, 1, 1) + df = DataFrame([1, 2, 3, 4], columns=MultiIndex.from_tuples([(t, "A", "B")])) + warn = None if future_stack else FutureWarning + msg = "The previous implementation of stack is deprecated" + with tm.assert_produces_warning(warn, match=msg): + result = df.stack(future_stack=future_stack) + + eidx = MultiIndex.from_product([(0, 1, 2, 3), ("B",)]) + ecols = MultiIndex.from_tuples([(t, "A")]) + expected = DataFrame([1, 2, 3, 4], index=eidx, columns=ecols) + tm.assert_frame_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + @pytest.mark.parametrize( + "multiindex_columns", + [ + [0, 1, 2, 3, 4], + [0, 1, 2, 3], + [0, 1, 2, 4], + [0, 1, 2], + [1, 2, 3], + [2, 3, 4], + [0, 1], + [0, 2], + [0, 3], + [0], + [2], + [4], + [4, 3, 2, 1, 0], + [3, 2, 1, 0], + [4, 2, 1, 0], + [2, 1, 0], + [3, 2, 1], + [4, 3, 2], + [1, 0], + [2, 0], + [3, 0], + ], + ) + @pytest.mark.parametrize("level", (-1, 0, 1, [0, 1], [1, 0])) + def test_stack_partial_multiIndex(self, multiindex_columns, level, future_stack): + # GH 8844 + dropna = False if not future_stack else lib.no_default + full_multiindex = MultiIndex.from_tuples( + [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")], + names=["Upper", "Lower"], + ) + multiindex = full_multiindex[multiindex_columns] + df = DataFrame( + np.arange(3 * len(multiindex)).reshape(3, len(multiindex)), + columns=multiindex, + ) + result = df.stack(level=level, dropna=dropna, future_stack=future_stack) + + if isinstance(level, int) and not future_stack: + # Stacking a single level should not make any all-NaN rows, + # so df.stack(level=level, dropna=False) should be the same + # as df.stack(level=level, dropna=True). + expected = df.stack(level=level, dropna=True, future_stack=future_stack) + if isinstance(expected, Series): + tm.assert_series_equal(result, expected) + else: + tm.assert_frame_equal(result, expected) + + df.columns = MultiIndex.from_tuples( + df.columns.to_numpy(), names=df.columns.names + ) + expected = df.stack(level=level, dropna=dropna, future_stack=future_stack) + if isinstance(expected, Series): + tm.assert_series_equal(result, expected) + else: + tm.assert_frame_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_full_multiIndex(self, future_stack): + # GH 8844 + full_multiindex = MultiIndex.from_tuples( + [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")], + names=["Upper", "Lower"], + ) + df = DataFrame(np.arange(6).reshape(2, 3), columns=full_multiindex[[0, 1, 3]]) + dropna = False if not future_stack else lib.no_default + result = df.stack(dropna=dropna, future_stack=future_stack) + expected = DataFrame( + [[0, 2], [1, np.nan], [3, 5], [4, np.nan]], + index=MultiIndex( + levels=[[0, 1], ["u", "x", "y", "z"]], + codes=[[0, 0, 1, 1], [1, 3, 1, 3]], + names=[None, "Lower"], + ), + columns=Index(["B", "C"], name="Upper"), + ) + expected["B"] = expected["B"].astype(df.dtypes.iloc[0]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("ordered", [False, True]) + def test_stack_preserve_categorical_dtype(self, ordered, future_stack): + # GH13854 + cidx = pd.CategoricalIndex(list("yxz"), categories=list("xyz"), ordered=ordered) + df = DataFrame([[10, 11, 12]], columns=cidx) + result = df.stack(future_stack=future_stack) + + # `MultiIndex.from_product` preserves categorical dtype - + # it's tested elsewhere. + midx = MultiIndex.from_product([df.index, cidx]) + expected = Series([10, 11, 12], index=midx) + + tm.assert_series_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + @pytest.mark.parametrize("ordered", [False, True]) + @pytest.mark.parametrize( + "labels,data", + [ + (list("xyz"), [10, 11, 12, 13, 14, 15]), + (list("zyx"), [14, 15, 12, 13, 10, 11]), + ], + ) + def test_stack_multi_preserve_categorical_dtype( + self, ordered, labels, data, future_stack + ): + # GH-36991 + cidx = pd.CategoricalIndex(labels, categories=sorted(labels), ordered=ordered) + cidx2 = pd.CategoricalIndex(["u", "v"], ordered=ordered) + midx = MultiIndex.from_product([cidx, cidx2]) + df = DataFrame([sorted(data)], columns=midx) + result = df.stack([0, 1], future_stack=future_stack) + + labels = labels if future_stack else sorted(labels) + s_cidx = pd.CategoricalIndex(labels, ordered=ordered) + expected_data = sorted(data) if future_stack else data + expected = Series( + expected_data, index=MultiIndex.from_product([[0], s_cidx, cidx2]) + ) + + tm.assert_series_equal(result, expected) + + def test_stack_preserve_categorical_dtype_values(self, future_stack): + # GH-23077 + cat = pd.Categorical(["a", "a", "b", "c"]) + df = DataFrame({"A": cat, "B": cat}) + result = df.stack(future_stack=future_stack) + index = MultiIndex.from_product([[0, 1, 2, 3], ["A", "B"]]) + expected = Series( + pd.Categorical(["a", "a", "a", "a", "b", "b", "c", "c"]), index=index + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + @pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning") + @pytest.mark.parametrize( + "index, columns", + [ + ([0, 0, 1, 1], MultiIndex.from_product([[1, 2], ["a", "b"]])), + ([0, 0, 2, 3], MultiIndex.from_product([[1, 2], ["a", "b"]])), + ([0, 1, 2, 3], MultiIndex.from_product([[1, 2], ["a", "b"]])), + ], + ) + def test_stack_multi_columns_non_unique_index(self, index, columns, future_stack): + # GH-28301 + + df = DataFrame(index=index, columns=columns).fillna(1) + stacked = df.stack(future_stack=future_stack) + new_index = MultiIndex.from_tuples(stacked.index.to_numpy()) + expected = DataFrame( + stacked.to_numpy(), index=new_index, columns=stacked.columns + ) + tm.assert_frame_equal(stacked, expected) + stacked_codes = np.asarray(stacked.index.codes) + expected_codes = np.asarray(new_index.codes) + tm.assert_numpy_array_equal(stacked_codes, expected_codes) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + @pytest.mark.parametrize( + "vals1, vals2, dtype1, dtype2, expected_dtype", + [ + ([1, 2], [3.0, 4.0], "Int64", "Float64", "Float64"), + ([1, 2], ["foo", "bar"], "Int64", "string", "object"), + ], + ) + def test_stack_multi_columns_mixed_extension_types( + self, vals1, vals2, dtype1, dtype2, expected_dtype, future_stack + ): + # GH45740 + df = DataFrame( + { + ("A", 1): Series(vals1, dtype=dtype1), + ("A", 2): Series(vals2, dtype=dtype2), + } + ) + result = df.stack(future_stack=future_stack) + expected = ( + df.astype(object).stack(future_stack=future_stack).astype(expected_dtype) + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("level", [0, 1]) + def test_unstack_mixed_extension_types(self, level): + index = MultiIndex.from_tuples([("A", 0), ("A", 1), ("B", 1)], names=["a", "b"]) + df = DataFrame( + { + "A": pd.array([0, 1, None], dtype="Int64"), + "B": pd.Categorical(["a", "a", "b"]), + }, + index=index, + ) + + result = df.unstack(level=level) + expected = df.astype(object).unstack(level=level) + if level == 0: + expected[("A", "B")] = expected[("A", "B")].fillna(pd.NA) + else: + expected[("A", 0)] = expected[("A", 0)].fillna(pd.NA) + + expected_dtypes = Series( + [df.A.dtype] * 2 + [df.B.dtype] * 2, index=result.columns + ) + tm.assert_series_equal(result.dtypes, expected_dtypes) + tm.assert_frame_equal(result.astype(object), expected) + + @pytest.mark.parametrize("level", [0, "baz"]) + def test_unstack_swaplevel_sortlevel(self, level): + # GH 20994 + mi = MultiIndex.from_product([[0], ["d", "c"]], names=["bar", "baz"]) + df = DataFrame([[0, 2], [1, 3]], index=mi, columns=["B", "A"]) + df.columns.name = "foo" + + expected = DataFrame( + [[3, 1, 2, 0]], + columns=MultiIndex.from_tuples( + [("c", "A"), ("c", "B"), ("d", "A"), ("d", "B")], names=["baz", "foo"] + ), + ) + expected.index.name = "bar" + + result = df.unstack().swaplevel(axis=1).sort_index(axis=1, level=level) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("dtype", ["float64", "Float64"]) +def test_unstack_sort_false(frame_or_series, dtype): + # GH 15105 + index = MultiIndex.from_tuples( + [("two", "z", "b"), ("two", "y", "a"), ("one", "z", "b"), ("one", "y", "a")] + ) + obj = frame_or_series(np.arange(1.0, 5.0), index=index, dtype=dtype) + result = obj.unstack(level=-1, sort=False) + + if frame_or_series is DataFrame: + expected_columns = MultiIndex.from_tuples([(0, "b"), (0, "a")]) + else: + expected_columns = ["b", "a"] + expected = DataFrame( + [[1.0, np.nan], [np.nan, 2.0], [3.0, np.nan], [np.nan, 4.0]], + columns=expected_columns, + index=MultiIndex.from_tuples( + [("two", "z"), ("two", "y"), ("one", "z"), ("one", "y")] + ), + dtype=dtype, + ) + tm.assert_frame_equal(result, expected) + + result = obj.unstack(level=[1, 2], sort=False) + + if frame_or_series is DataFrame: + expected_columns = MultiIndex.from_tuples([(0, "z", "b"), (0, "y", "a")]) + else: + expected_columns = MultiIndex.from_tuples([("z", "b"), ("y", "a")]) + expected = DataFrame( + [[1.0, 2.0], [3.0, 4.0]], + index=["two", "one"], + columns=expected_columns, + dtype=dtype, + ) + tm.assert_frame_equal(result, expected) + + +def test_unstack_fill_frame_object(): + # GH12815 Test unstacking with object. + data = Series(["a", "b", "c", "a"], dtype="object") + data.index = MultiIndex.from_tuples( + [("x", "a"), ("x", "b"), ("y", "b"), ("z", "a")] + ) + + # By default missing values will be NaN + result = data.unstack() + expected = DataFrame( + {"a": ["a", np.nan, "a"], "b": ["b", "c", np.nan]}, + index=list("xyz"), + dtype=object, + ) + tm.assert_frame_equal(result, expected) + + # Fill with any value replaces missing values as expected + result = data.unstack(fill_value="d") + expected = DataFrame( + {"a": ["a", "d", "a"], "b": ["b", "c", "d"]}, index=list("xyz"), dtype=object + ) + tm.assert_frame_equal(result, expected) + + +def test_unstack_timezone_aware_values(): + # GH 18338 + df = DataFrame( + { + "timestamp": [pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC")], + "a": ["a"], + "b": ["b"], + "c": ["c"], + }, + columns=["timestamp", "a", "b", "c"], + ) + result = df.set_index(["a", "b"]).unstack() + expected = DataFrame( + [[pd.Timestamp("2017-08-27 01:00:00.709949+0000", tz="UTC"), "c"]], + index=Index(["a"], name="a"), + columns=MultiIndex( + levels=[["timestamp", "c"], ["b"]], + codes=[[0, 1], [0, 0]], + names=[None, "b"], + ), + ) + tm.assert_frame_equal(result, expected) + + +def test_stack_timezone_aware_values(future_stack): + # GH 19420 + ts = date_range(freq="D", start="20180101", end="20180103", tz="America/New_York") + df = DataFrame({"A": ts}, index=["a", "b", "c"]) + result = df.stack(future_stack=future_stack) + expected = Series( + ts, + index=MultiIndex(levels=[["a", "b", "c"], ["A"]], codes=[[0, 1, 2], [0, 0, 0]]), + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated") +@pytest.mark.parametrize("dropna", [True, False, lib.no_default]) +def test_stack_empty_frame(dropna, future_stack): + # GH 36113 + levels = [np.array([], dtype=np.int64), np.array([], dtype=np.int64)] + expected = Series(dtype=np.float64, index=MultiIndex(levels=levels, codes=[[], []])) + if future_stack and dropna is not lib.no_default: + with pytest.raises(ValueError, match="dropna must be unspecified"): + DataFrame(dtype=np.float64).stack(dropna=dropna, future_stack=future_stack) + else: + result = DataFrame(dtype=np.float64).stack( + dropna=dropna, future_stack=future_stack + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated") +@pytest.mark.parametrize("dropna", [True, False, lib.no_default]) +@pytest.mark.parametrize("fill_value", [None, 0]) +def test_stack_unstack_empty_frame(dropna, fill_value, future_stack): + # GH 36113 + if future_stack and dropna is not lib.no_default: + with pytest.raises(ValueError, match="dropna must be unspecified"): + DataFrame(dtype=np.int64).stack( + dropna=dropna, future_stack=future_stack + ).unstack(fill_value=fill_value) + else: + result = ( + DataFrame(dtype=np.int64) + .stack(dropna=dropna, future_stack=future_stack) + .unstack(fill_value=fill_value) + ) + expected = DataFrame(dtype=np.int64) + tm.assert_frame_equal(result, expected) + + +def test_unstack_single_index_series(): + # GH 36113 + msg = r"index must be a MultiIndex to unstack.*" + with pytest.raises(ValueError, match=msg): + Series(dtype=np.int64).unstack() + + +def test_unstacking_multi_index_df(): + # see gh-30740 + df = DataFrame( + { + "name": ["Alice", "Bob"], + "score": [9.5, 8], + "employed": [False, True], + "kids": [0, 0], + "gender": ["female", "male"], + } + ) + df = df.set_index(["name", "employed", "kids", "gender"]) + df = df.unstack(["gender"], fill_value=0) + expected = df.unstack("employed", fill_value=0).unstack("kids", fill_value=0) + result = df.unstack(["employed", "kids"], fill_value=0) + expected = DataFrame( + [[9.5, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 8.0]], + index=Index(["Alice", "Bob"], name="name"), + columns=MultiIndex.from_tuples( + [ + ("score", "female", False, 0), + ("score", "female", True, 0), + ("score", "male", False, 0), + ("score", "male", True, 0), + ], + names=[None, "gender", "employed", "kids"], + ), + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated") +def test_stack_positional_level_duplicate_column_names(future_stack): + # https://github.com/pandas-dev/pandas/issues/36353 + columns = MultiIndex.from_product([("x", "y"), ("y", "z")], names=["a", "a"]) + df = DataFrame([[1, 1, 1, 1]], columns=columns) + result = df.stack(0, future_stack=future_stack) + + new_columns = Index(["y", "z"], name="a") + new_index = MultiIndex.from_tuples([(0, "x"), (0, "y")], names=[None, "a"]) + expected = DataFrame([[1, 1], [1, 1]], index=new_index, columns=new_columns) + + tm.assert_frame_equal(result, expected) + + +def test_unstack_non_slice_like_blocks(using_array_manager): + # Case where the mgr_locs of a DataFrame's underlying blocks are not slice-like + + mi = MultiIndex.from_product([range(5), ["A", "B", "C"]]) + df = DataFrame( + { + 0: np.random.default_rng(2).standard_normal(15), + 1: np.random.default_rng(2).standard_normal(15).astype(np.int64), + 2: np.random.default_rng(2).standard_normal(15), + 3: np.random.default_rng(2).standard_normal(15), + }, + index=mi, + ) + if not using_array_manager: + assert any(not x.mgr_locs.is_slice_like for x in df._mgr.blocks) + + res = df.unstack() + + expected = pd.concat([df[n].unstack() for n in range(4)], keys=range(4), axis=1) + tm.assert_frame_equal(res, expected) + + +@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated") +def test_stack_sort_false(future_stack): + # GH 15105 + data = [[1, 2, 3.0, 4.0], [2, 3, 4.0, 5.0], [3, 4, np.nan, np.nan]] + df = DataFrame( + data, + columns=MultiIndex( + levels=[["B", "A"], ["x", "y"]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]] + ), + ) + kwargs = {} if future_stack else {"sort": False} + result = df.stack(level=0, future_stack=future_stack, **kwargs) + if future_stack: + expected = DataFrame( + { + "x": [1.0, 3.0, 2.0, 4.0, 3.0, np.nan], + "y": [2.0, 4.0, 3.0, 5.0, 4.0, np.nan], + }, + index=MultiIndex.from_arrays( + [[0, 0, 1, 1, 2, 2], ["B", "A", "B", "A", "B", "A"]] + ), + ) + else: + expected = DataFrame( + {"x": [1.0, 3.0, 2.0, 4.0, 3.0], "y": [2.0, 4.0, 3.0, 5.0, 4.0]}, + index=MultiIndex.from_arrays([[0, 0, 1, 1, 2], ["B", "A", "B", "A", "B"]]), + ) + tm.assert_frame_equal(result, expected) + + # Codes sorted in this call + df = DataFrame( + data, + columns=MultiIndex.from_arrays([["B", "B", "A", "A"], ["x", "y", "x", "y"]]), + ) + kwargs = {} if future_stack else {"sort": False} + result = df.stack(level=0, future_stack=future_stack, **kwargs) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.filterwarnings("ignore:The previous implementation of stack is deprecated") +def test_stack_sort_false_multi_level(future_stack): + # GH 15105 + idx = MultiIndex.from_tuples([("weight", "kg"), ("height", "m")]) + df = DataFrame([[1.0, 2.0], [3.0, 4.0]], index=["cat", "dog"], columns=idx) + kwargs = {} if future_stack else {"sort": False} + result = df.stack([0, 1], future_stack=future_stack, **kwargs) + expected_index = MultiIndex.from_tuples( + [ + ("cat", "weight", "kg"), + ("cat", "height", "m"), + ("dog", "weight", "kg"), + ("dog", "height", "m"), + ] + ) + expected = Series([1.0, 2.0, 3.0, 4.0], index=expected_index) + tm.assert_series_equal(result, expected) + + +class TestStackUnstackMultiLevel: + def test_unstack(self, multiindex_year_month_day_dataframe_random_data): + # just check that it works for now + ymd = multiindex_year_month_day_dataframe_random_data + + unstacked = ymd.unstack() + unstacked.unstack() + + # test that ints work + ymd.astype(int).unstack() + + # test that int32 work + ymd.astype(np.int32).unstack() + + @pytest.mark.parametrize( + "result_rows,result_columns,index_product,expected_row", + [ + ( + [[1, 1, None, None, 30.0, None], [2, 2, None, None, 30.0, None]], + ["ix1", "ix2", "col1", "col2", "col3", "col4"], + 2, + [None, None, 30.0, None], + ), + ( + [[1, 1, None, None, 30.0], [2, 2, None, None, 30.0]], + ["ix1", "ix2", "col1", "col2", "col3"], + 2, + [None, None, 30.0], + ), + ( + [[1, 1, None, None, 30.0], [2, None, None, None, 30.0]], + ["ix1", "ix2", "col1", "col2", "col3"], + None, + [None, None, 30.0], + ), + ], + ) + def test_unstack_partial( + self, result_rows, result_columns, index_product, expected_row + ): + # check for regressions on this issue: + # https://github.com/pandas-dev/pandas/issues/19351 + # make sure DataFrame.unstack() works when its run on a subset of the DataFrame + # and the Index levels contain values that are not present in the subset + result = DataFrame(result_rows, columns=result_columns).set_index( + ["ix1", "ix2"] + ) + result = result.iloc[1:2].unstack("ix2") + expected = DataFrame( + [expected_row], + columns=MultiIndex.from_product( + [result_columns[2:], [index_product]], names=[None, "ix2"] + ), + index=Index([2], name="ix1"), + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_multiple_no_empty_columns(self): + index = MultiIndex.from_tuples( + [(0, "foo", 0), (0, "bar", 0), (1, "baz", 1), (1, "qux", 1)] + ) + + s = Series(np.random.default_rng(2).standard_normal(4), index=index) + + unstacked = s.unstack([1, 2]) + expected = unstacked.dropna(axis=1, how="all") + tm.assert_frame_equal(unstacked, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack(self, multiindex_year_month_day_dataframe_random_data, future_stack): + ymd = multiindex_year_month_day_dataframe_random_data + + # regular roundtrip + unstacked = ymd.unstack() + restacked = unstacked.stack(future_stack=future_stack) + if future_stack: + # NA values in unstacked persist to restacked in version 3 + restacked = restacked.dropna(how="all") + tm.assert_frame_equal(restacked, ymd) + + unlexsorted = ymd.sort_index(level=2) + + unstacked = unlexsorted.unstack(2) + restacked = unstacked.stack(future_stack=future_stack) + if future_stack: + # NA values in unstacked persist to restacked in version 3 + restacked = restacked.dropna(how="all") + tm.assert_frame_equal(restacked.sort_index(level=0), ymd) + + unlexsorted = unlexsorted[::-1] + unstacked = unlexsorted.unstack(1) + restacked = unstacked.stack(future_stack=future_stack).swaplevel(1, 2) + if future_stack: + # NA values in unstacked persist to restacked in version 3 + restacked = restacked.dropna(how="all") + tm.assert_frame_equal(restacked.sort_index(level=0), ymd) + + unlexsorted = unlexsorted.swaplevel(0, 1) + unstacked = unlexsorted.unstack(0).swaplevel(0, 1, axis=1) + restacked = unstacked.stack(0, future_stack=future_stack).swaplevel(1, 2) + if future_stack: + # NA values in unstacked persist to restacked in version 3 + restacked = restacked.dropna(how="all") + tm.assert_frame_equal(restacked.sort_index(level=0), ymd) + + # columns unsorted + unstacked = ymd.unstack() + restacked = unstacked.stack(future_stack=future_stack) + if future_stack: + # NA values in unstacked persist to restacked in version 3 + restacked = restacked.dropna(how="all") + tm.assert_frame_equal(restacked, ymd) + + # more than 2 levels in the columns + unstacked = ymd.unstack(1).unstack(1) + + result = unstacked.stack(1, future_stack=future_stack) + expected = ymd.unstack() + tm.assert_frame_equal(result, expected) + + result = unstacked.stack(2, future_stack=future_stack) + expected = ymd.unstack(1) + tm.assert_frame_equal(result, expected) + + result = unstacked.stack(0, future_stack=future_stack) + expected = ymd.stack(future_stack=future_stack).unstack(1).unstack(1) + tm.assert_frame_equal(result, expected) + + # not all levels present in each echelon + unstacked = ymd.unstack(2).loc[:, ::3] + stacked = unstacked.stack(future_stack=future_stack).stack( + future_stack=future_stack + ) + ymd_stacked = ymd.stack(future_stack=future_stack) + if future_stack: + # NA values in unstacked persist to restacked in version 3 + stacked = stacked.dropna(how="all") + ymd_stacked = ymd_stacked.dropna(how="all") + tm.assert_series_equal(stacked, ymd_stacked.reindex(stacked.index)) + + # stack with negative number + result = ymd.unstack(0).stack(-2, future_stack=future_stack) + expected = ymd.unstack(0).stack(0, future_stack=future_stack) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize( + "idx, columns, exp_idx", + [ + [ + list("abab"), + ["1st", "2nd", "1st"], + MultiIndex( + levels=[["a", "b"], ["1st", "2nd"]], + codes=[np.tile(np.arange(2).repeat(3), 2), np.tile([0, 1, 0], 4)], + ), + ], + [ + MultiIndex.from_tuples((("a", 2), ("b", 1), ("a", 1), ("b", 2))), + ["1st", "2nd", "1st"], + MultiIndex( + levels=[["a", "b"], [1, 2], ["1st", "2nd"]], + codes=[ + np.tile(np.arange(2).repeat(3), 2), + np.repeat([1, 0, 1], [3, 6, 3]), + np.tile([0, 1, 0], 4), + ], + ), + ], + ], + ) + def test_stack_duplicate_index(self, idx, columns, exp_idx, future_stack): + # GH10417 + df = DataFrame( + np.arange(12).reshape(4, 3), + index=idx, + columns=columns, + ) + if future_stack: + msg = "Columns with duplicate values are not supported in stack" + with pytest.raises(ValueError, match=msg): + df.stack(future_stack=future_stack) + else: + result = df.stack(future_stack=future_stack) + expected = Series(np.arange(12), index=exp_idx) + tm.assert_series_equal(result, expected) + assert result.index.is_unique is False + li, ri = result.index, expected.index + tm.assert_index_equal(li, ri) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_unstack_odd_failure(self, future_stack): + mi = MultiIndex.from_arrays( + [ + ["Fri"] * 4 + ["Sat"] * 2 + ["Sun"] * 2 + ["Thu"] * 3, + ["Dinner"] * 2 + ["Lunch"] * 2 + ["Dinner"] * 5 + ["Lunch"] * 2, + ["No", "Yes"] * 4 + ["No", "No", "Yes"], + ], + names=["day", "time", "smoker"], + ) + df = DataFrame( + { + "sum": np.arange(11, dtype="float64"), + "len": np.arange(11, dtype="float64"), + }, + index=mi, + ) + # it works, #2100 + result = df.unstack(2) + + recons = result.stack(future_stack=future_stack) + if future_stack: + # NA values in unstacked persist to restacked in version 3 + recons = recons.dropna(how="all") + tm.assert_frame_equal(recons, df) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_mixed_dtype(self, multiindex_dataframe_random_data, future_stack): + frame = multiindex_dataframe_random_data + + df = frame.T + df["foo", "four"] = "foo" + df = df.sort_index(level=1, axis=1) + + stacked = df.stack(future_stack=future_stack) + result = df["foo"].stack(future_stack=future_stack).sort_index() + tm.assert_series_equal(stacked["foo"], result, check_names=False) + assert result.name is None + assert stacked["bar"].dtype == np.float64 + + def test_unstack_bug(self, future_stack): + df = DataFrame( + { + "state": ["naive", "naive", "naive", "active", "active", "active"], + "exp": ["a", "b", "b", "b", "a", "a"], + "barcode": [1, 2, 3, 4, 1, 3], + "v": ["hi", "hi", "bye", "bye", "bye", "peace"], + "extra": np.arange(6.0), + } + ) + + msg = "DataFrameGroupBy.apply operated on the grouping columns" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = df.groupby(["state", "exp", "barcode", "v"]).apply(len) + + unstacked = result.unstack() + restacked = unstacked.stack(future_stack=future_stack) + tm.assert_series_equal(restacked, result.reindex(restacked.index).astype(float)) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_unstack_preserve_names( + self, multiindex_dataframe_random_data, future_stack + ): + frame = multiindex_dataframe_random_data + + unstacked = frame.unstack() + assert unstacked.index.name == "first" + assert unstacked.columns.names == ["exp", "second"] + + restacked = unstacked.stack(future_stack=future_stack) + assert restacked.index.names == frame.index.names + + @pytest.mark.parametrize("method", ["stack", "unstack"]) + def test_stack_unstack_wrong_level_name( + self, method, multiindex_dataframe_random_data, future_stack + ): + # GH 18303 - wrong level name should raise + frame = multiindex_dataframe_random_data + + # A DataFrame with flat axes: + df = frame.loc["foo"] + + kwargs = {"future_stack": future_stack} if method == "stack" else {} + with pytest.raises(KeyError, match="does not match index name"): + getattr(df, method)("mistake", **kwargs) + + if method == "unstack": + # Same on a Series: + s = df.iloc[:, 0] + with pytest.raises(KeyError, match="does not match index name"): + getattr(s, method)("mistake", **kwargs) + + def test_unstack_level_name(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + + result = frame.unstack("second") + expected = frame.unstack(level=1) + tm.assert_frame_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_level_name(self, multiindex_dataframe_random_data, future_stack): + frame = multiindex_dataframe_random_data + + unstacked = frame.unstack("second") + result = unstacked.stack("exp", future_stack=future_stack) + expected = frame.unstack().stack(0, future_stack=future_stack) + tm.assert_frame_equal(result, expected) + + result = frame.stack("exp", future_stack=future_stack) + expected = frame.stack(future_stack=future_stack) + tm.assert_series_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_unstack_multiple( + self, multiindex_year_month_day_dataframe_random_data, future_stack + ): + ymd = multiindex_year_month_day_dataframe_random_data + + unstacked = ymd.unstack(["year", "month"]) + expected = ymd.unstack("year").unstack("month") + tm.assert_frame_equal(unstacked, expected) + assert unstacked.columns.names == expected.columns.names + + # series + s = ymd["A"] + s_unstacked = s.unstack(["year", "month"]) + tm.assert_frame_equal(s_unstacked, expected["A"]) + + restacked = unstacked.stack(["year", "month"], future_stack=future_stack) + if future_stack: + # NA values in unstacked persist to restacked in version 3 + restacked = restacked.dropna(how="all") + restacked = restacked.swaplevel(0, 1).swaplevel(1, 2) + restacked = restacked.sort_index(level=0) + + tm.assert_frame_equal(restacked, ymd) + assert restacked.index.names == ymd.index.names + + # GH #451 + unstacked = ymd.unstack([1, 2]) + expected = ymd.unstack(1).unstack(1).dropna(axis=1, how="all") + tm.assert_frame_equal(unstacked, expected) + + unstacked = ymd.unstack([2, 1]) + expected = ymd.unstack(2).unstack(1).dropna(axis=1, how="all") + tm.assert_frame_equal(unstacked, expected.loc[:, unstacked.columns]) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_names_and_numbers( + self, multiindex_year_month_day_dataframe_random_data, future_stack + ): + ymd = multiindex_year_month_day_dataframe_random_data + + unstacked = ymd.unstack(["year", "month"]) + + # Can't use mixture of names and numbers to stack + with pytest.raises(ValueError, match="level should contain"): + unstacked.stack([0, "month"], future_stack=future_stack) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_multiple_out_of_bounds( + self, multiindex_year_month_day_dataframe_random_data, future_stack + ): + # nlevels == 3 + ymd = multiindex_year_month_day_dataframe_random_data + + unstacked = ymd.unstack(["year", "month"]) + + with pytest.raises(IndexError, match="Too many levels"): + unstacked.stack([2, 3], future_stack=future_stack) + with pytest.raises(IndexError, match="not a valid level number"): + unstacked.stack([-4, -3], future_stack=future_stack) + + def test_unstack_period_series(self): + # GH4342 + idx1 = pd.PeriodIndex( + ["2013-01", "2013-01", "2013-02", "2013-02", "2013-03", "2013-03"], + freq="M", + name="period", + ) + idx2 = Index(["A", "B"] * 3, name="str") + value = [1, 2, 3, 4, 5, 6] + + idx = MultiIndex.from_arrays([idx1, idx2]) + s = Series(value, index=idx) + + result1 = s.unstack() + result2 = s.unstack(level=1) + result3 = s.unstack(level=0) + + e_idx = pd.PeriodIndex( + ["2013-01", "2013-02", "2013-03"], freq="M", name="period" + ) + expected = DataFrame( + {"A": [1, 3, 5], "B": [2, 4, 6]}, index=e_idx, columns=["A", "B"] + ) + expected.columns.name = "str" + + tm.assert_frame_equal(result1, expected) + tm.assert_frame_equal(result2, expected) + tm.assert_frame_equal(result3, expected.T) + + idx1 = pd.PeriodIndex( + ["2013-01", "2013-01", "2013-02", "2013-02", "2013-03", "2013-03"], + freq="M", + name="period1", + ) + + idx2 = pd.PeriodIndex( + ["2013-12", "2013-11", "2013-10", "2013-09", "2013-08", "2013-07"], + freq="M", + name="period2", + ) + idx = MultiIndex.from_arrays([idx1, idx2]) + s = Series(value, index=idx) + + result1 = s.unstack() + result2 = s.unstack(level=1) + result3 = s.unstack(level=0) + + e_idx = pd.PeriodIndex( + ["2013-01", "2013-02", "2013-03"], freq="M", name="period1" + ) + e_cols = pd.PeriodIndex( + ["2013-07", "2013-08", "2013-09", "2013-10", "2013-11", "2013-12"], + freq="M", + name="period2", + ) + expected = DataFrame( + [ + [np.nan, np.nan, np.nan, np.nan, 2, 1], + [np.nan, np.nan, 4, 3, np.nan, np.nan], + [6, 5, np.nan, np.nan, np.nan, np.nan], + ], + index=e_idx, + columns=e_cols, + ) + + tm.assert_frame_equal(result1, expected) + tm.assert_frame_equal(result2, expected) + tm.assert_frame_equal(result3, expected.T) + + def test_unstack_period_frame(self): + # GH4342 + idx1 = pd.PeriodIndex( + ["2014-01", "2014-02", "2014-02", "2014-02", "2014-01", "2014-01"], + freq="M", + name="period1", + ) + idx2 = pd.PeriodIndex( + ["2013-12", "2013-12", "2014-02", "2013-10", "2013-10", "2014-02"], + freq="M", + name="period2", + ) + value = {"A": [1, 2, 3, 4, 5, 6], "B": [6, 5, 4, 3, 2, 1]} + idx = MultiIndex.from_arrays([idx1, idx2]) + df = DataFrame(value, index=idx) + + result1 = df.unstack() + result2 = df.unstack(level=1) + result3 = df.unstack(level=0) + + e_1 = pd.PeriodIndex(["2014-01", "2014-02"], freq="M", name="period1") + e_2 = pd.PeriodIndex( + ["2013-10", "2013-12", "2014-02", "2013-10", "2013-12", "2014-02"], + freq="M", + name="period2", + ) + e_cols = MultiIndex.from_arrays(["A A A B B B".split(), e_2]) + expected = DataFrame( + [[5, 1, 6, 2, 6, 1], [4, 2, 3, 3, 5, 4]], index=e_1, columns=e_cols + ) + + tm.assert_frame_equal(result1, expected) + tm.assert_frame_equal(result2, expected) + + e_1 = pd.PeriodIndex( + ["2014-01", "2014-02", "2014-01", "2014-02"], freq="M", name="period1" + ) + e_2 = pd.PeriodIndex( + ["2013-10", "2013-12", "2014-02"], freq="M", name="period2" + ) + e_cols = MultiIndex.from_arrays(["A A B B".split(), e_1]) + expected = DataFrame( + [[5, 4, 2, 3], [1, 2, 6, 5], [6, 3, 1, 4]], index=e_2, columns=e_cols + ) + + tm.assert_frame_equal(result3, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_multiple_bug(self, future_stack, using_infer_string): + # bug when some uniques are not present in the data GH#3170 + id_col = ([1] * 3) + ([2] * 3) + name = (["a"] * 3) + (["b"] * 3) + date = pd.to_datetime(["2013-01-03", "2013-01-04", "2013-01-05"] * 2) + var1 = np.random.default_rng(2).integers(0, 100, 6) + df = DataFrame({"ID": id_col, "NAME": name, "DATE": date, "VAR1": var1}) + + multi = df.set_index(["DATE", "ID"]) + multi.columns.name = "Params" + unst = multi.unstack("ID") + msg = re.escape("agg function failed [how->mean,dtype->") + if using_infer_string: + msg = "dtype 'str' does not support operation 'mean'" + with pytest.raises(TypeError, match=msg): + unst.resample("W-THU").mean() + down = unst.resample("W-THU").mean(numeric_only=True) + rs = down.stack("ID", future_stack=future_stack) + xp = ( + unst.loc[:, ["VAR1"]] + .resample("W-THU") + .mean() + .stack("ID", future_stack=future_stack) + ) + xp.columns.name = "Params" + tm.assert_frame_equal(rs, xp) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_dropna(self, future_stack): + # GH#3997 + df = DataFrame({"A": ["a1", "a2"], "B": ["b1", "b2"], "C": [1, 1]}) + df = df.set_index(["A", "B"]) + + dropna = False if not future_stack else lib.no_default + stacked = df.unstack().stack(dropna=dropna, future_stack=future_stack) + assert len(stacked) > len(stacked.dropna()) + + if future_stack: + with pytest.raises(ValueError, match="dropna must be unspecified"): + df.unstack().stack(dropna=True, future_stack=future_stack) + else: + stacked = df.unstack().stack(dropna=True, future_stack=future_stack) + tm.assert_frame_equal(stacked, stacked.dropna()) + + def test_unstack_multiple_hierarchical(self, future_stack): + df = DataFrame( + index=[ + [0, 0, 0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 0, 0, 1, 1], + [0, 1, 0, 1, 0, 1, 0, 1], + ], + columns=[[0, 0, 1, 1], [0, 1, 0, 1]], + ) + + df.index.names = ["a", "b", "c"] + df.columns.names = ["d", "e"] + + # it works! + df.unstack(["b", "c"]) + + def test_unstack_sparse_keyspace(self): + # memory problems with naive impl GH#2278 + # Generate Long File & Test Pivot + NUM_ROWS = 1000 + + df = DataFrame( + { + "A": np.random.default_rng(2).integers(100, size=NUM_ROWS), + "B": np.random.default_rng(3).integers(300, size=NUM_ROWS), + "C": np.random.default_rng(4).integers(-7, 7, size=NUM_ROWS), + "D": np.random.default_rng(5).integers(-19, 19, size=NUM_ROWS), + "E": np.random.default_rng(6).integers(3000, size=NUM_ROWS), + "F": np.random.default_rng(7).standard_normal(NUM_ROWS), + } + ) + + idf = df.set_index(["A", "B", "C", "D", "E"]) + + # it works! is sufficient + idf.unstack("E") + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_unstack_unobserved_keys(self, future_stack): + # related to GH#2278 refactoring + levels = [[0, 1], [0, 1, 2, 3]] + codes = [[0, 0, 1, 1], [0, 2, 0, 2]] + + index = MultiIndex(levels, codes) + + df = DataFrame(np.random.default_rng(2).standard_normal((4, 2)), index=index) + + result = df.unstack() + assert len(result.columns) == 4 + + recons = result.stack(future_stack=future_stack) + tm.assert_frame_equal(recons, df) + + @pytest.mark.slow + def test_unstack_number_of_levels_larger_than_int32(self, monkeypatch): + # GH#20601 + # GH 26314: Change ValueError to PerformanceWarning + + class MockUnstacker(reshape_lib._Unstacker): + def __init__(self, *args, **kwargs) -> None: + # __init__ will raise the warning + super().__init__(*args, **kwargs) + raise Exception("Don't compute final result.") + + with monkeypatch.context() as m: + m.setattr(reshape_lib, "_Unstacker", MockUnstacker) + df = DataFrame( + np.zeros((2**16, 2)), + index=[np.arange(2**16), np.arange(2**16)], + ) + msg = "The following operation may generate" + with tm.assert_produces_warning(PerformanceWarning, match=msg): + with pytest.raises(Exception, match="Don't compute final result."): + df.unstack() + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + @pytest.mark.parametrize( + "levels", + itertools.chain.from_iterable( + itertools.product(itertools.permutations([0, 1, 2], width), repeat=2) + for width in [2, 3] + ), + ) + @pytest.mark.parametrize("stack_lev", range(2)) + @pytest.mark.parametrize("sort", [True, False]) + def test_stack_order_with_unsorted_levels( + self, levels, stack_lev, sort, future_stack + ): + # GH#16323 + # deep check for 1-row case + columns = MultiIndex(levels=levels, codes=[[0, 0, 1, 1], [0, 1, 0, 1]]) + df = DataFrame(columns=columns, data=[range(4)]) + kwargs = {} if future_stack else {"sort": sort} + df_stacked = df.stack(stack_lev, future_stack=future_stack, **kwargs) + for row in df.index: + for col in df.columns: + expected = df.loc[row, col] + result_row = row, col[stack_lev] + result_col = col[1 - stack_lev] + result = df_stacked.loc[result_row, result_col] + assert result == expected + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_order_with_unsorted_levels_multi_row(self, future_stack): + # GH#16323 + + # check multi-row case + mi = MultiIndex( + levels=[["A", "C", "B"], ["B", "A", "C"]], + codes=[np.repeat(range(3), 3), np.tile(range(3), 3)], + ) + df = DataFrame( + columns=mi, index=range(5), data=np.arange(5 * len(mi)).reshape(5, -1) + ) + assert all( + df.loc[row, col] + == df.stack(0, future_stack=future_stack).loc[(row, col[0]), col[1]] + for row in df.index + for col in df.columns + ) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_order_with_unsorted_levels_multi_row_2(self, future_stack): + # GH#53636 + levels = ((0, 1), (1, 0)) + stack_lev = 1 + columns = MultiIndex(levels=levels, codes=[[0, 0, 1, 1], [0, 1, 0, 1]]) + df = DataFrame(columns=columns, data=[range(4)], index=[1, 0, 2, 3]) + kwargs = {} if future_stack else {"sort": True} + result = df.stack(stack_lev, future_stack=future_stack, **kwargs) + expected_index = MultiIndex( + levels=[[0, 1, 2, 3], [0, 1]], + codes=[[1, 1, 0, 0, 2, 2, 3, 3], [1, 0, 1, 0, 1, 0, 1, 0]], + ) + expected = DataFrame( + { + 0: [0, 1, 0, 1, 0, 1, 0, 1], + 1: [2, 3, 2, 3, 2, 3, 2, 3], + }, + index=expected_index, + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_unstack_unordered_multiindex(self, future_stack): + # GH# 18265 + values = np.arange(5) + data = np.vstack( + [ + [f"b{x}" for x in values], # b0, b1, .. + [f"a{x}" for x in values], # a0, a1, .. + ] + ) + df = DataFrame(data.T, columns=["b", "a"]) + df.columns.name = "first" + second_level_dict = {"x": df} + multi_level_df = pd.concat(second_level_dict, axis=1) + multi_level_df.columns.names = ["second", "first"] + df = multi_level_df.reindex(sorted(multi_level_df.columns), axis=1) + result = df.stack(["first", "second"], future_stack=future_stack).unstack( + ["first", "second"] + ) + expected = DataFrame( + [["a0", "b0"], ["a1", "b1"], ["a2", "b2"], ["a3", "b3"], ["a4", "b4"]], + index=[0, 1, 2, 3, 4], + columns=MultiIndex.from_tuples( + [("a", "x"), ("b", "x")], names=["first", "second"] + ), + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_preserve_types( + self, multiindex_year_month_day_dataframe_random_data, using_infer_string + ): + # GH#403 + ymd = multiindex_year_month_day_dataframe_random_data + ymd["E"] = "foo" + ymd["F"] = 2 + + unstacked = ymd.unstack("month") + assert unstacked["A", 1].dtype == np.float64 + assert ( + unstacked["E", 1].dtype == np.object_ + if not using_infer_string + else "string" + ) + assert unstacked["F", 1].dtype == np.float64 + + def test_unstack_group_index_overflow(self, future_stack): + codes = np.tile(np.arange(500), 2) + level = np.arange(500) + + index = MultiIndex( + levels=[level] * 8 + [[0, 1]], + codes=[codes] * 8 + [np.arange(2).repeat(500)], + ) + + s = Series(np.arange(1000), index=index) + result = s.unstack() + assert result.shape == (500, 2) + + # test roundtrip + stacked = result.stack(future_stack=future_stack) + tm.assert_series_equal(s, stacked.reindex(s.index)) + + # put it at beginning + index = MultiIndex( + levels=[[0, 1]] + [level] * 8, + codes=[np.arange(2).repeat(500)] + [codes] * 8, + ) + + s = Series(np.arange(1000), index=index) + result = s.unstack(0) + assert result.shape == (500, 2) + + # put it in middle + index = MultiIndex( + levels=[level] * 4 + [[0, 1]] + [level] * 4, + codes=([codes] * 4 + [np.arange(2).repeat(500)] + [codes] * 4), + ) + + s = Series(np.arange(1000), index=index) + result = s.unstack(4) + assert result.shape == (500, 2) + + def test_unstack_with_missing_int_cast_to_float(self, using_array_manager): + # https://github.com/pandas-dev/pandas/issues/37115 + df = DataFrame( + { + "a": ["A", "A", "B"], + "b": ["ca", "cb", "cb"], + "v": [10] * 3, + } + ).set_index(["a", "b"]) + + # add another int column to get 2 blocks + df["is_"] = 1 + if not using_array_manager: + assert len(df._mgr.blocks) == 2 + + result = df.unstack("b") + result[("is_", "ca")] = result[("is_", "ca")].fillna(0) + + expected = DataFrame( + [[10.0, 10.0, 1.0, 1.0], [np.nan, 10.0, 0.0, 1.0]], + index=Index(["A", "B"], name="a"), + columns=MultiIndex.from_tuples( + [("v", "ca"), ("v", "cb"), ("is_", "ca"), ("is_", "cb")], + names=[None, "b"], + ), + ) + if using_array_manager: + # INFO(ArrayManager) with ArrayManager preserve dtype where possible + expected[("v", "cb")] = expected[("v", "cb")].astype("int64") + expected[("is_", "cb")] = expected[("is_", "cb")].astype("int64") + tm.assert_frame_equal(result, expected) + + def test_unstack_with_level_has_nan(self): + # GH 37510 + df1 = DataFrame( + { + "L1": [1, 2, 3, 4], + "L2": [3, 4, 1, 2], + "L3": [1, 1, 1, 1], + "x": [1, 2, 3, 4], + } + ) + df1 = df1.set_index(["L1", "L2", "L3"]) + new_levels = ["n1", "n2", "n3", None] + df1.index = df1.index.set_levels(levels=new_levels, level="L1") + df1.index = df1.index.set_levels(levels=new_levels, level="L2") + + result = df1.unstack("L3")[("x", 1)].sort_index().index + expected = MultiIndex( + levels=[["n1", "n2", "n3", None], ["n1", "n2", "n3", None]], + codes=[[0, 1, 2, 3], [2, 3, 0, 1]], + names=["L1", "L2"], + ) + + tm.assert_index_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_nan_in_multiindex_columns(self, future_stack): + # GH#39481 + df = DataFrame( + np.zeros([1, 5]), + columns=MultiIndex.from_tuples( + [ + (0, None, None), + (0, 2, 0), + (0, 2, 1), + (0, 3, 0), + (0, 3, 1), + ], + ), + ) + result = df.stack(2, future_stack=future_stack) + if future_stack: + index = MultiIndex(levels=[[0], [0.0, 1.0]], codes=[[0, 0, 0], [-1, 0, 1]]) + columns = MultiIndex(levels=[[0], [2, 3]], codes=[[0, 0, 0], [-1, 0, 1]]) + else: + index = Index([(0, None), (0, 0), (0, 1)]) + columns = Index([(0, None), (0, 2), (0, 3)]) + expected = DataFrame( + [[0.0, np.nan, np.nan], [np.nan, 0.0, 0.0], [np.nan, 0.0, 0.0]], + index=index, + columns=columns, + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_multi_level_stack_categorical(self, future_stack): + # GH 15239 + midx = MultiIndex.from_arrays( + [ + ["A"] * 2 + ["B"] * 2, + pd.Categorical(list("abab")), + pd.Categorical(list("ccdd")), + ] + ) + df = DataFrame(np.arange(8).reshape(2, 4), columns=midx) + result = df.stack([1, 2], future_stack=future_stack) + if future_stack: + expected = DataFrame( + [ + [0, np.nan], + [1, np.nan], + [np.nan, 2], + [np.nan, 3], + [4, np.nan], + [5, np.nan], + [np.nan, 6], + [np.nan, 7], + ], + columns=["A", "B"], + index=MultiIndex.from_arrays( + [ + [0] * 4 + [1] * 4, + pd.Categorical(list("abababab")), + pd.Categorical(list("ccddccdd")), + ] + ), + ) + else: + expected = DataFrame( + [ + [0, np.nan], + [np.nan, 2], + [1, np.nan], + [np.nan, 3], + [4, np.nan], + [np.nan, 6], + [5, np.nan], + [np.nan, 7], + ], + columns=["A", "B"], + index=MultiIndex.from_arrays( + [ + [0] * 4 + [1] * 4, + pd.Categorical(list("aabbaabb")), + pd.Categorical(list("cdcdcdcd")), + ] + ), + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_nan_level(self, future_stack): + # GH 9406 + df_nan = DataFrame( + np.arange(4).reshape(2, 2), + columns=MultiIndex.from_tuples( + [("A", np.nan), ("B", "b")], names=["Upper", "Lower"] + ), + index=Index([0, 1], name="Num"), + dtype=np.float64, + ) + result = df_nan.stack(future_stack=future_stack) + if future_stack: + index = MultiIndex( + levels=[[0, 1], [np.nan, "b"]], + codes=[[0, 0, 1, 1], [0, 1, 0, 1]], + names=["Num", "Lower"], + ) + else: + index = MultiIndex.from_tuples( + [(0, np.nan), (0, "b"), (1, np.nan), (1, "b")], names=["Num", "Lower"] + ) + expected = DataFrame( + [[0.0, np.nan], [np.nan, 1], [2.0, np.nan], [np.nan, 3.0]], + columns=Index(["A", "B"], name="Upper"), + index=index, + ) + tm.assert_frame_equal(result, expected) + + def test_unstack_categorical_columns(self): + # GH 14018 + idx = MultiIndex.from_product([["A"], [0, 1]]) + df = DataFrame({"cat": pd.Categorical(["a", "b"])}, index=idx) + result = df.unstack() + expected = DataFrame( + { + 0: pd.Categorical(["a"], categories=["a", "b"]), + 1: pd.Categorical(["b"], categories=["a", "b"]), + }, + index=["A"], + ) + expected.columns = MultiIndex.from_tuples([("cat", 0), ("cat", 1)]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_unsorted(self, future_stack): + # GH 16925 + PAE = ["ITA", "FRA"] + VAR = ["A1", "A2"] + TYP = ["CRT", "DBT", "NET"] + MI = MultiIndex.from_product([PAE, VAR, TYP], names=["PAE", "VAR", "TYP"]) + + V = list(range(len(MI))) + DF = DataFrame(data=V, index=MI, columns=["VALUE"]) + + DF = DF.unstack(["VAR", "TYP"]) + DF.columns = DF.columns.droplevel(0) + DF.loc[:, ("A0", "NET")] = 9999 + + result = DF.stack(["VAR", "TYP"], future_stack=future_stack).sort_index() + expected = ( + DF.sort_index(axis=1) + .stack(["VAR", "TYP"], future_stack=future_stack) + .sort_index() + ) + tm.assert_series_equal(result, expected) + + @pytest.mark.filterwarnings( + "ignore:The previous implementation of stack is deprecated" + ) + def test_stack_nullable_dtype(self, future_stack): + # GH#43561 + columns = MultiIndex.from_product( + [["54511", "54515"], ["r", "t_mean"]], names=["station", "element"] + ) + index = Index([1, 2, 3], name="time") + + arr = np.array([[50, 226, 10, 215], [10, 215, 9, 220], [305, 232, 111, 220]]) + df = DataFrame(arr, columns=columns, index=index, dtype=pd.Int64Dtype()) + + result = df.stack("station", future_stack=future_stack) + + expected = ( + df.astype(np.int64) + .stack("station", future_stack=future_stack) + .astype(pd.Int64Dtype()) + ) + tm.assert_frame_equal(result, expected) + + # non-homogeneous case + df[df.columns[0]] = df[df.columns[0]].astype(pd.Float64Dtype()) + result = df.stack("station", future_stack=future_stack) + + expected = DataFrame( + { + "r": pd.array( + [50.0, 10.0, 10.0, 9.0, 305.0, 111.0], dtype=pd.Float64Dtype() + ), + "t_mean": pd.array( + [226, 215, 215, 220, 232, 220], dtype=pd.Int64Dtype() + ), + }, + index=MultiIndex.from_product([index, columns.levels[0]]), + ) + expected.columns.name = "element" + tm.assert_frame_equal(result, expected) + + def test_unstack_mixed_level_names(self): + # GH#48763 + arrays = [["a", "a"], [1, 2], ["red", "blue"]] + idx = MultiIndex.from_arrays(arrays, names=("x", 0, "y")) + df = DataFrame({"m": [1, 2]}, index=idx) + result = df.unstack("x") + expected = DataFrame( + [[1], [2]], + columns=MultiIndex.from_tuples([("m", "a")], names=[None, "x"]), + index=MultiIndex.from_tuples([(1, "red"), (2, "blue")], names=[0, "y"]), + ) + tm.assert_frame_equal(result, expected) + + +def test_stack_tuple_columns(future_stack): + # GH#54948 - test stack when the input has a non-MultiIndex with tuples + df = DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=[("a", 1), ("a", 2), ("b", 1)] + ) + result = df.stack(future_stack=future_stack) + expected = Series( + [1, 2, 3, 4, 5, 6, 7, 8, 9], + index=MultiIndex( + levels=[[0, 1, 2], [("a", 1), ("a", 2), ("b", 1)]], + codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]], + ), + ) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "dtype, na_value", + [ + ("float64", np.nan), + ("Float64", np.nan), + ("Float64", pd.NA), + ("Int64", pd.NA), + ], +) +@pytest.mark.parametrize("test_multiindex", [True, False]) +def test_stack_preserves_na(dtype, na_value, test_multiindex): + # GH#56573 + if test_multiindex: + index = MultiIndex.from_arrays(2 * [Index([na_value], dtype=dtype)]) + else: + index = Index([na_value], dtype=dtype) + df = DataFrame({"a": [1]}, index=index) + result = df.stack(future_stack=True) + + if test_multiindex: + expected_index = MultiIndex.from_arrays( + [ + Index([na_value], dtype=dtype), + Index([na_value], dtype=dtype), + Index(["a"]), + ] + ) + else: + expected_index = MultiIndex.from_arrays( + [ + Index([na_value], dtype=dtype), + Index(["a"]), + ] + ) + expected = Series(1, index=expected_index) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_subclass.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_subclass.py new file mode 100644 index 0000000000000000000000000000000000000000..855b58229cbdb5819e83e3abe39a938bbb8658eb --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_subclass.py @@ -0,0 +1,825 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, +) +import pandas._testing as tm + +pytestmark = pytest.mark.filterwarnings( + "ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning" +) + + +@pytest.fixture() +def gpd_style_subclass_df(): + class SubclassedDataFrame(DataFrame): + @property + def _constructor(self): + return SubclassedDataFrame + + return SubclassedDataFrame({"a": [1, 2, 3]}) + + +class TestDataFrameSubclassing: + def test_no_warning_on_mgr(self): + # GH#57032 + df = tm.SubclassedDataFrame( + {"X": [1, 2, 3], "Y": [1, 2, 3]}, index=["a", "b", "c"] + ) + with tm.assert_produces_warning(None): + # df.isna() goes through _constructor_from_mgr, which we want to + # *not* pass a Manager do __init__ + df.isna() + df["X"].isna() + + def test_frame_subclassing_and_slicing(self): + # Subclass frame and ensure it returns the right class on slicing it + # In reference to PR 9632 + + class CustomSeries(Series): + @property + def _constructor(self): + return CustomSeries + + def custom_series_function(self): + return "OK" + + class CustomDataFrame(DataFrame): + """ + Subclasses pandas DF, fills DF with simulation results, adds some + custom plotting functions. + """ + + def __init__(self, *args, **kw) -> None: + super().__init__(*args, **kw) + + @property + def _constructor(self): + return CustomDataFrame + + _constructor_sliced = CustomSeries + + def custom_frame_function(self): + return "OK" + + data = {"col1": range(10), "col2": range(10)} + cdf = CustomDataFrame(data) + + # Did we get back our own DF class? + assert isinstance(cdf, CustomDataFrame) + + # Do we get back our own Series class after selecting a column? + cdf_series = cdf.col1 + assert isinstance(cdf_series, CustomSeries) + assert cdf_series.custom_series_function() == "OK" + + # Do we get back our own DF class after slicing row-wise? + cdf_rows = cdf[1:5] + assert isinstance(cdf_rows, CustomDataFrame) + assert cdf_rows.custom_frame_function() == "OK" + + # Make sure sliced part of multi-index frame is custom class + mcol = MultiIndex.from_tuples([("A", "A"), ("A", "B")]) + cdf_multi = CustomDataFrame([[0, 1], [2, 3]], columns=mcol) + assert isinstance(cdf_multi["A"], CustomDataFrame) + + mcol = MultiIndex.from_tuples([("A", ""), ("B", "")]) + cdf_multi2 = CustomDataFrame([[0, 1], [2, 3]], columns=mcol) + assert isinstance(cdf_multi2["A"], CustomSeries) + + def test_dataframe_metadata(self): + df = tm.SubclassedDataFrame( + {"X": [1, 2, 3], "Y": [1, 2, 3]}, index=["a", "b", "c"] + ) + df.testattr = "XXX" + + assert df.testattr == "XXX" + assert df[["X"]].testattr == "XXX" + assert df.loc[["a", "b"], :].testattr == "XXX" + assert df.iloc[[0, 1], :].testattr == "XXX" + + # see gh-9776 + assert df.iloc[0:1, :].testattr == "XXX" + + # see gh-10553 + unpickled = tm.round_trip_pickle(df) + tm.assert_frame_equal(df, unpickled) + assert df._metadata == unpickled._metadata + assert df.testattr == unpickled.testattr + + def test_indexing_sliced(self): + # GH 11559 + df = tm.SubclassedDataFrame( + {"X": [1, 2, 3], "Y": [4, 5, 6], "Z": [7, 8, 9]}, index=["a", "b", "c"] + ) + res = df.loc[:, "X"] + exp = tm.SubclassedSeries([1, 2, 3], index=list("abc"), name="X") + tm.assert_series_equal(res, exp) + assert isinstance(res, tm.SubclassedSeries) + + res = df.iloc[:, 1] + exp = tm.SubclassedSeries([4, 5, 6], index=list("abc"), name="Y") + tm.assert_series_equal(res, exp) + assert isinstance(res, tm.SubclassedSeries) + + res = df.loc[:, "Z"] + exp = tm.SubclassedSeries([7, 8, 9], index=list("abc"), name="Z") + tm.assert_series_equal(res, exp) + assert isinstance(res, tm.SubclassedSeries) + + res = df.loc["a", :] + exp = tm.SubclassedSeries([1, 4, 7], index=list("XYZ"), name="a") + tm.assert_series_equal(res, exp) + assert isinstance(res, tm.SubclassedSeries) + + res = df.iloc[1, :] + exp = tm.SubclassedSeries([2, 5, 8], index=list("XYZ"), name="b") + tm.assert_series_equal(res, exp) + assert isinstance(res, tm.SubclassedSeries) + + res = df.loc["c", :] + exp = tm.SubclassedSeries([3, 6, 9], index=list("XYZ"), name="c") + tm.assert_series_equal(res, exp) + assert isinstance(res, tm.SubclassedSeries) + + def test_subclass_attr_err_propagation(self): + # GH 11808 + class A(DataFrame): + @property + def nonexistence(self): + return self.i_dont_exist + + with pytest.raises(AttributeError, match=".*i_dont_exist.*"): + A().nonexistence + + def test_subclass_align(self): + # GH 12983 + df1 = tm.SubclassedDataFrame( + {"a": [1, 3, 5], "b": [1, 3, 5]}, index=list("ACE") + ) + df2 = tm.SubclassedDataFrame( + {"c": [1, 2, 4], "d": [1, 2, 4]}, index=list("ABD") + ) + + res1, res2 = df1.align(df2, axis=0) + exp1 = tm.SubclassedDataFrame( + {"a": [1, np.nan, 3, np.nan, 5], "b": [1, np.nan, 3, np.nan, 5]}, + index=list("ABCDE"), + ) + exp2 = tm.SubclassedDataFrame( + {"c": [1, 2, np.nan, 4, np.nan], "d": [1, 2, np.nan, 4, np.nan]}, + index=list("ABCDE"), + ) + assert isinstance(res1, tm.SubclassedDataFrame) + tm.assert_frame_equal(res1, exp1) + assert isinstance(res2, tm.SubclassedDataFrame) + tm.assert_frame_equal(res2, exp2) + + res1, res2 = df1.a.align(df2.c) + assert isinstance(res1, tm.SubclassedSeries) + tm.assert_series_equal(res1, exp1.a) + assert isinstance(res2, tm.SubclassedSeries) + tm.assert_series_equal(res2, exp2.c) + + def test_subclass_align_combinations(self): + # GH 12983 + df = tm.SubclassedDataFrame({"a": [1, 3, 5], "b": [1, 3, 5]}, index=list("ACE")) + s = tm.SubclassedSeries([1, 2, 4], index=list("ABD"), name="x") + + # frame + series + res1, res2 = df.align(s, axis=0) + exp1 = tm.SubclassedDataFrame( + {"a": [1, np.nan, 3, np.nan, 5], "b": [1, np.nan, 3, np.nan, 5]}, + index=list("ABCDE"), + ) + # name is lost when + exp2 = tm.SubclassedSeries( + [1, 2, np.nan, 4, np.nan], index=list("ABCDE"), name="x" + ) + + assert isinstance(res1, tm.SubclassedDataFrame) + tm.assert_frame_equal(res1, exp1) + assert isinstance(res2, tm.SubclassedSeries) + tm.assert_series_equal(res2, exp2) + + # series + frame + res1, res2 = s.align(df) + assert isinstance(res1, tm.SubclassedSeries) + tm.assert_series_equal(res1, exp2) + assert isinstance(res2, tm.SubclassedDataFrame) + tm.assert_frame_equal(res2, exp1) + + def test_subclass_iterrows(self): + # GH 13977 + df = tm.SubclassedDataFrame({"a": [1]}) + for i, row in df.iterrows(): + assert isinstance(row, tm.SubclassedSeries) + tm.assert_series_equal(row, df.loc[i]) + + def test_subclass_stack(self): + # GH 15564 + df = tm.SubclassedDataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], + index=["a", "b", "c"], + columns=["X", "Y", "Z"], + ) + + res = df.stack(future_stack=True) + exp = tm.SubclassedSeries( + [1, 2, 3, 4, 5, 6, 7, 8, 9], index=[list("aaabbbccc"), list("XYZXYZXYZ")] + ) + + tm.assert_series_equal(res, exp) + + def test_subclass_stack_multi(self): + # GH 15564 + df = tm.SubclassedDataFrame( + [[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]], + index=MultiIndex.from_tuples( + list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"] + ), + columns=MultiIndex.from_tuples( + list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"] + ), + ) + + exp = tm.SubclassedDataFrame( + [ + [10, 12], + [11, 13], + [20, 22], + [21, 23], + [30, 32], + [31, 33], + [40, 42], + [41, 43], + ], + index=MultiIndex.from_tuples( + list(zip(list("AAAABBBB"), list("ccddccdd"), list("yzyzyzyz"))), + names=["aaa", "ccc", "yyy"], + ), + columns=Index(["W", "X"], name="www"), + ) + + res = df.stack(future_stack=True) + tm.assert_frame_equal(res, exp) + + res = df.stack("yyy", future_stack=True) + tm.assert_frame_equal(res, exp) + + exp = tm.SubclassedDataFrame( + [ + [10, 11], + [12, 13], + [20, 21], + [22, 23], + [30, 31], + [32, 33], + [40, 41], + [42, 43], + ], + index=MultiIndex.from_tuples( + list(zip(list("AAAABBBB"), list("ccddccdd"), list("WXWXWXWX"))), + names=["aaa", "ccc", "www"], + ), + columns=Index(["y", "z"], name="yyy"), + ) + + res = df.stack("www", future_stack=True) + tm.assert_frame_equal(res, exp) + + def test_subclass_stack_multi_mixed(self): + # GH 15564 + df = tm.SubclassedDataFrame( + [ + [10, 11, 12.0, 13.0], + [20, 21, 22.0, 23.0], + [30, 31, 32.0, 33.0], + [40, 41, 42.0, 43.0], + ], + index=MultiIndex.from_tuples( + list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"] + ), + columns=MultiIndex.from_tuples( + list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"] + ), + ) + + exp = tm.SubclassedDataFrame( + [ + [10, 12.0], + [11, 13.0], + [20, 22.0], + [21, 23.0], + [30, 32.0], + [31, 33.0], + [40, 42.0], + [41, 43.0], + ], + index=MultiIndex.from_tuples( + list(zip(list("AAAABBBB"), list("ccddccdd"), list("yzyzyzyz"))), + names=["aaa", "ccc", "yyy"], + ), + columns=Index(["W", "X"], name="www"), + ) + + res = df.stack(future_stack=True) + tm.assert_frame_equal(res, exp) + + res = df.stack("yyy", future_stack=True) + tm.assert_frame_equal(res, exp) + + exp = tm.SubclassedDataFrame( + [ + [10.0, 11.0], + [12.0, 13.0], + [20.0, 21.0], + [22.0, 23.0], + [30.0, 31.0], + [32.0, 33.0], + [40.0, 41.0], + [42.0, 43.0], + ], + index=MultiIndex.from_tuples( + list(zip(list("AAAABBBB"), list("ccddccdd"), list("WXWXWXWX"))), + names=["aaa", "ccc", "www"], + ), + columns=Index(["y", "z"], name="yyy"), + ) + + res = df.stack("www", future_stack=True) + tm.assert_frame_equal(res, exp) + + def test_subclass_unstack(self): + # GH 15564 + df = tm.SubclassedDataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], + index=["a", "b", "c"], + columns=["X", "Y", "Z"], + ) + + res = df.unstack() + exp = tm.SubclassedSeries( + [1, 4, 7, 2, 5, 8, 3, 6, 9], index=[list("XXXYYYZZZ"), list("abcabcabc")] + ) + + tm.assert_series_equal(res, exp) + + def test_subclass_unstack_multi(self): + # GH 15564 + df = tm.SubclassedDataFrame( + [[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]], + index=MultiIndex.from_tuples( + list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"] + ), + columns=MultiIndex.from_tuples( + list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"] + ), + ) + + exp = tm.SubclassedDataFrame( + [[10, 20, 11, 21, 12, 22, 13, 23], [30, 40, 31, 41, 32, 42, 33, 43]], + index=Index(["A", "B"], name="aaa"), + columns=MultiIndex.from_tuples( + list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("cdcdcdcd"))), + names=["www", "yyy", "ccc"], + ), + ) + + res = df.unstack() + tm.assert_frame_equal(res, exp) + + res = df.unstack("ccc") + tm.assert_frame_equal(res, exp) + + exp = tm.SubclassedDataFrame( + [[10, 30, 11, 31, 12, 32, 13, 33], [20, 40, 21, 41, 22, 42, 23, 43]], + index=Index(["c", "d"], name="ccc"), + columns=MultiIndex.from_tuples( + list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("ABABABAB"))), + names=["www", "yyy", "aaa"], + ), + ) + + res = df.unstack("aaa") + tm.assert_frame_equal(res, exp) + + def test_subclass_unstack_multi_mixed(self): + # GH 15564 + df = tm.SubclassedDataFrame( + [ + [10, 11, 12.0, 13.0], + [20, 21, 22.0, 23.0], + [30, 31, 32.0, 33.0], + [40, 41, 42.0, 43.0], + ], + index=MultiIndex.from_tuples( + list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"] + ), + columns=MultiIndex.from_tuples( + list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"] + ), + ) + + exp = tm.SubclassedDataFrame( + [ + [10, 20, 11, 21, 12.0, 22.0, 13.0, 23.0], + [30, 40, 31, 41, 32.0, 42.0, 33.0, 43.0], + ], + index=Index(["A", "B"], name="aaa"), + columns=MultiIndex.from_tuples( + list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("cdcdcdcd"))), + names=["www", "yyy", "ccc"], + ), + ) + + res = df.unstack() + tm.assert_frame_equal(res, exp) + + res = df.unstack("ccc") + tm.assert_frame_equal(res, exp) + + exp = tm.SubclassedDataFrame( + [ + [10, 30, 11, 31, 12.0, 32.0, 13.0, 33.0], + [20, 40, 21, 41, 22.0, 42.0, 23.0, 43.0], + ], + index=Index(["c", "d"], name="ccc"), + columns=MultiIndex.from_tuples( + list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("ABABABAB"))), + names=["www", "yyy", "aaa"], + ), + ) + + res = df.unstack("aaa") + tm.assert_frame_equal(res, exp) + + def test_subclass_pivot(self): + # GH 15564 + df = tm.SubclassedDataFrame( + { + "index": ["A", "B", "C", "C", "B", "A"], + "columns": ["One", "One", "One", "Two", "Two", "Two"], + "values": [1.0, 2.0, 3.0, 3.0, 2.0, 1.0], + } + ) + + pivoted = df.pivot(index="index", columns="columns", values="values") + + expected = tm.SubclassedDataFrame( + { + "One": {"A": 1.0, "B": 2.0, "C": 3.0}, + "Two": {"A": 1.0, "B": 2.0, "C": 3.0}, + } + ) + + expected.index.name, expected.columns.name = "index", "columns" + + tm.assert_frame_equal(pivoted, expected) + + def test_subclassed_melt(self): + # GH 15564 + cheese = tm.SubclassedDataFrame( + { + "first": ["John", "Mary"], + "last": ["Doe", "Bo"], + "height": [5.5, 6.0], + "weight": [130, 150], + } + ) + + melted = pd.melt(cheese, id_vars=["first", "last"]) + + expected = tm.SubclassedDataFrame( + [ + ["John", "Doe", "height", 5.5], + ["Mary", "Bo", "height", 6.0], + ["John", "Doe", "weight", 130], + ["Mary", "Bo", "weight", 150], + ], + columns=["first", "last", "variable", "value"], + ) + + tm.assert_frame_equal(melted, expected) + + def test_subclassed_wide_to_long(self): + # GH 9762 + + x = np.random.default_rng(2).standard_normal(3) + df = tm.SubclassedDataFrame( + { + "A1970": {0: "a", 1: "b", 2: "c"}, + "A1980": {0: "d", 1: "e", 2: "f"}, + "B1970": {0: 2.5, 1: 1.2, 2: 0.7}, + "B1980": {0: 3.2, 1: 1.3, 2: 0.1}, + "X": dict(zip(range(3), x)), + } + ) + + df["id"] = df.index + exp_data = { + "X": x.tolist() + x.tolist(), + "A": ["a", "b", "c", "d", "e", "f"], + "B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1], + "year": [1970, 1970, 1970, 1980, 1980, 1980], + "id": [0, 1, 2, 0, 1, 2], + } + expected = tm.SubclassedDataFrame(exp_data) + expected = expected.set_index(["id", "year"])[["X", "A", "B"]] + long_frame = pd.wide_to_long(df, ["A", "B"], i="id", j="year") + + tm.assert_frame_equal(long_frame, expected) + + def test_subclassed_apply(self): + # GH 19822 + + def check_row_subclass(row): + assert isinstance(row, tm.SubclassedSeries) + + def stretch(row): + if row["variable"] == "height": + row["value"] += 0.5 + return row + + df = tm.SubclassedDataFrame( + [ + ["John", "Doe", "height", 5.5], + ["Mary", "Bo", "height", 6.0], + ["John", "Doe", "weight", 130], + ["Mary", "Bo", "weight", 150], + ], + columns=["first", "last", "variable", "value"], + ) + + df.apply(lambda x: check_row_subclass(x)) + df.apply(lambda x: check_row_subclass(x), axis=1) + + expected = tm.SubclassedDataFrame( + [ + ["John", "Doe", "height", 6.0], + ["Mary", "Bo", "height", 6.5], + ["John", "Doe", "weight", 130], + ["Mary", "Bo", "weight", 150], + ], + columns=["first", "last", "variable", "value"], + ) + + result = df.apply(lambda x: stretch(x), axis=1) + assert isinstance(result, tm.SubclassedDataFrame) + tm.assert_frame_equal(result, expected) + + expected = tm.SubclassedDataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]]) + + result = df.apply(lambda x: tm.SubclassedSeries([1, 2, 3]), axis=1) + assert isinstance(result, tm.SubclassedDataFrame) + tm.assert_frame_equal(result, expected) + + result = df.apply(lambda x: [1, 2, 3], axis=1, result_type="expand") + assert isinstance(result, tm.SubclassedDataFrame) + tm.assert_frame_equal(result, expected) + + expected = tm.SubclassedSeries([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]]) + + result = df.apply(lambda x: [1, 2, 3], axis=1) + assert not isinstance(result, tm.SubclassedDataFrame) + tm.assert_series_equal(result, expected) + + def test_subclassed_reductions(self, all_reductions): + # GH 25596 + + df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + result = getattr(df, all_reductions)() + assert isinstance(result, tm.SubclassedSeries) + + def test_subclassed_count(self): + df = tm.SubclassedDataFrame( + { + "Person": ["John", "Myla", "Lewis", "John", "Myla"], + "Age": [24.0, np.nan, 21.0, 33, 26], + "Single": [False, True, True, True, False], + } + ) + result = df.count() + assert isinstance(result, tm.SubclassedSeries) + + df = tm.SubclassedDataFrame({"A": [1, 0, 3], "B": [0, 5, 6], "C": [7, 8, 0]}) + result = df.count() + assert isinstance(result, tm.SubclassedSeries) + + df = tm.SubclassedDataFrame( + [[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]], + index=MultiIndex.from_tuples( + list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"] + ), + columns=MultiIndex.from_tuples( + list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"] + ), + ) + result = df.count() + assert isinstance(result, tm.SubclassedSeries) + + df = tm.SubclassedDataFrame() + result = df.count() + assert isinstance(result, tm.SubclassedSeries) + + def test_isin(self): + df = tm.SubclassedDataFrame( + {"num_legs": [2, 4], "num_wings": [2, 0]}, index=["falcon", "dog"] + ) + result = df.isin([0, 2]) + assert isinstance(result, tm.SubclassedDataFrame) + + def test_duplicated(self): + df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + result = df.duplicated() + assert isinstance(result, tm.SubclassedSeries) + + df = tm.SubclassedDataFrame() + result = df.duplicated() + assert isinstance(result, tm.SubclassedSeries) + + @pytest.mark.parametrize("idx_method", ["idxmax", "idxmin"]) + def test_idx(self, idx_method): + df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + result = getattr(df, idx_method)() + assert isinstance(result, tm.SubclassedSeries) + + def test_dot(self): + df = tm.SubclassedDataFrame([[0, 1, -2, -1], [1, 1, 1, 1]]) + s = tm.SubclassedSeries([1, 1, 2, 1]) + result = df.dot(s) + assert isinstance(result, tm.SubclassedSeries) + + df = tm.SubclassedDataFrame([[0, 1, -2, -1], [1, 1, 1, 1]]) + s = tm.SubclassedDataFrame([1, 1, 2, 1]) + result = df.dot(s) + assert isinstance(result, tm.SubclassedDataFrame) + + def test_memory_usage(self): + df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + result = df.memory_usage() + assert isinstance(result, tm.SubclassedSeries) + + result = df.memory_usage(index=False) + assert isinstance(result, tm.SubclassedSeries) + + def test_corrwith(self): + pytest.importorskip("scipy") + index = ["a", "b", "c", "d", "e"] + columns = ["one", "two", "three", "four"] + df1 = tm.SubclassedDataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + index=index, + columns=columns, + ) + df2 = tm.SubclassedDataFrame( + np.random.default_rng(2).standard_normal((4, 4)), + index=index[:4], + columns=columns, + ) + correls = df1.corrwith(df2, axis=1, drop=True, method="kendall") + + assert isinstance(correls, (tm.SubclassedSeries)) + + def test_asof(self): + N = 3 + rng = pd.date_range("1/1/1990", periods=N, freq="53s") + df = tm.SubclassedDataFrame( + { + "A": [np.nan, np.nan, np.nan], + "B": [np.nan, np.nan, np.nan], + "C": [np.nan, np.nan, np.nan], + }, + index=rng, + ) + + result = df.asof(rng[-2:]) + assert isinstance(result, tm.SubclassedDataFrame) + + result = df.asof(rng[-2]) + assert isinstance(result, tm.SubclassedSeries) + + result = df.asof("1989-12-31") + assert isinstance(result, tm.SubclassedSeries) + + def test_idxmin_preserves_subclass(self): + # GH 28330 + + df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + result = df.idxmin() + assert isinstance(result, tm.SubclassedSeries) + + def test_idxmax_preserves_subclass(self): + # GH 28330 + + df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + result = df.idxmax() + assert isinstance(result, tm.SubclassedSeries) + + def test_convert_dtypes_preserves_subclass(self, gpd_style_subclass_df): + # GH 43668 + df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + result = df.convert_dtypes() + assert isinstance(result, tm.SubclassedDataFrame) + + result = gpd_style_subclass_df.convert_dtypes() + assert isinstance(result, type(gpd_style_subclass_df)) + + def test_astype_preserves_subclass(self): + # GH#40810 + df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) + + result = df.astype({"A": np.int64, "B": np.int32, "C": np.float64}) + assert isinstance(result, tm.SubclassedDataFrame) + + def test_equals_subclass(self): + # https://github.com/pandas-dev/pandas/pull/34402 + # allow subclass in both directions + df1 = DataFrame({"a": [1, 2, 3]}) + df2 = tm.SubclassedDataFrame({"a": [1, 2, 3]}) + assert df1.equals(df2) + assert df2.equals(df1) + + def test_replace_list_method(self): + # https://github.com/pandas-dev/pandas/pull/46018 + df = tm.SubclassedDataFrame({"A": [0, 1, 2]}) + msg = "The 'method' keyword in SubclassedDataFrame.replace is deprecated" + with tm.assert_produces_warning( + FutureWarning, match=msg, raise_on_extra_warnings=False + ): + result = df.replace([1, 2], method="ffill") + expected = tm.SubclassedDataFrame({"A": [0, 0, 0]}) + assert isinstance(result, tm.SubclassedDataFrame) + tm.assert_frame_equal(result, expected) + + +class MySubclassWithMetadata(DataFrame): + _metadata = ["my_metadata"] + + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + + my_metadata = kwargs.pop("my_metadata", None) + if args and isinstance(args[0], MySubclassWithMetadata): + my_metadata = args[0].my_metadata # type: ignore[has-type] + self.my_metadata = my_metadata + + @property + def _constructor(self): + return MySubclassWithMetadata + + +def test_constructor_with_metadata(): + # https://github.com/pandas-dev/pandas/pull/54922 + # https://github.com/pandas-dev/pandas/issues/55120 + df = MySubclassWithMetadata( + np.random.default_rng(2).random((5, 3)), columns=["A", "B", "C"] + ) + subset = df[["A", "B"]] + assert isinstance(subset, MySubclassWithMetadata) + + +class SimpleDataFrameSubClass(DataFrame): + """A subclass of DataFrame that does not define a constructor.""" + + +class SimpleSeriesSubClass(Series): + """A subclass of Series that does not define a constructor.""" + + +class TestSubclassWithoutConstructor: + def test_copy_df(self): + expected = DataFrame({"a": [1, 2, 3]}) + result = SimpleDataFrameSubClass(expected).copy() + + assert ( + type(result) is DataFrame + ) # assert_frame_equal only checks isinstance(lhs, type(rhs)) + tm.assert_frame_equal(result, expected) + + def test_copy_series(self): + expected = Series([1, 2, 3]) + result = SimpleSeriesSubClass(expected).copy() + + tm.assert_series_equal(result, expected) + + def test_series_to_frame(self): + orig = Series([1, 2, 3]) + expected = orig.to_frame() + result = SimpleSeriesSubClass(orig).to_frame() + + assert ( + type(result) is DataFrame + ) # assert_frame_equal only checks isinstance(lhs, type(rhs)) + tm.assert_frame_equal(result, expected) + + def test_groupby(self): + df = SimpleDataFrameSubClass(DataFrame({"a": [1, 2, 3]})) + + for _, v in df.groupby("a"): + assert type(v) is DataFrame diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_ufunc.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_ufunc.py new file mode 100644 index 0000000000000000000000000000000000000000..88c62da2b0a735b103f7a6b03634aa185fc46d2c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_ufunc.py @@ -0,0 +1,311 @@ +from functools import partial +import re + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.api.types import is_extension_array_dtype + +dtypes = [ + "int64", + "Int64", + {"A": "int64", "B": "Int64"}, +] + + +@pytest.mark.parametrize("dtype", dtypes) +def test_unary_unary(dtype): + # unary input, unary output + values = np.array([[-1, -1], [1, 1]], dtype="int64") + df = pd.DataFrame(values, columns=["A", "B"], index=["a", "b"]).astype(dtype=dtype) + result = np.positive(df) + expected = pd.DataFrame( + np.positive(values), index=df.index, columns=df.columns + ).astype(dtype) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("dtype", dtypes) +def test_unary_binary(request, dtype): + # unary input, binary output + if is_extension_array_dtype(dtype) or isinstance(dtype, dict): + request.applymarker( + pytest.mark.xfail( + reason="Extension / mixed with multiple outputs not implemented." + ) + ) + + values = np.array([[-1, -1], [1, 1]], dtype="int64") + df = pd.DataFrame(values, columns=["A", "B"], index=["a", "b"]).astype(dtype=dtype) + result_pandas = np.modf(df) + assert isinstance(result_pandas, tuple) + assert len(result_pandas) == 2 + expected_numpy = np.modf(values) + + for result, b in zip(result_pandas, expected_numpy): + expected = pd.DataFrame(b, index=df.index, columns=df.columns) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("dtype", dtypes) +def test_binary_input_dispatch_binop(dtype): + # binop ufuncs are dispatched to our dunder methods. + values = np.array([[-1, -1], [1, 1]], dtype="int64") + df = pd.DataFrame(values, columns=["A", "B"], index=["a", "b"]).astype(dtype=dtype) + result = np.add(df, df) + expected = pd.DataFrame( + np.add(values, values), index=df.index, columns=df.columns + ).astype(dtype) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "func,arg,expected", + [ + (np.add, 1, [2, 3, 4, 5]), + ( + partial(np.add, where=[[False, True], [True, False]]), + np.array([[1, 1], [1, 1]]), + [0, 3, 4, 0], + ), + (np.power, np.array([[1, 1], [2, 2]]), [1, 2, 9, 16]), + (np.subtract, 2, [-1, 0, 1, 2]), + ( + partial(np.negative, where=np.array([[False, True], [True, False]])), + None, + [0, -2, -3, 0], + ), + ], +) +def test_ufunc_passes_args(func, arg, expected): + # GH#40662 + arr = np.array([[1, 2], [3, 4]]) + df = pd.DataFrame(arr) + result_inplace = np.zeros_like(arr) + # 1-argument ufunc + if arg is None: + result = func(df, out=result_inplace) + else: + result = func(df, arg, out=result_inplace) + + expected = np.array(expected).reshape(2, 2) + tm.assert_numpy_array_equal(result_inplace, expected) + + expected = pd.DataFrame(expected) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("dtype_a", dtypes) +@pytest.mark.parametrize("dtype_b", dtypes) +def test_binary_input_aligns_columns(request, dtype_a, dtype_b): + if ( + is_extension_array_dtype(dtype_a) + or isinstance(dtype_a, dict) + or is_extension_array_dtype(dtype_b) + or isinstance(dtype_b, dict) + ): + request.applymarker( + pytest.mark.xfail( + reason="Extension / mixed with multiple inputs not implemented." + ) + ) + + df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}).astype(dtype_a) + + if isinstance(dtype_a, dict) and isinstance(dtype_b, dict): + dtype_b = dtype_b.copy() + dtype_b["C"] = dtype_b.pop("B") + df2 = pd.DataFrame({"A": [1, 2], "C": [3, 4]}).astype(dtype_b) + # As of 2.0, align first before applying the ufunc + result = np.heaviside(df1, df2) + expected = np.heaviside( + np.array([[1, 3, np.nan], [2, 4, np.nan]]), + np.array([[1, np.nan, 3], [2, np.nan, 4]]), + ) + expected = pd.DataFrame(expected, index=[0, 1], columns=["A", "B", "C"]) + tm.assert_frame_equal(result, expected) + + result = np.heaviside(df1, df2.values) + expected = pd.DataFrame([[1.0, 1.0], [1.0, 1.0]], columns=["A", "B"]) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("dtype", dtypes) +def test_binary_input_aligns_index(request, dtype): + if is_extension_array_dtype(dtype) or isinstance(dtype, dict): + request.applymarker( + pytest.mark.xfail( + reason="Extension / mixed with multiple inputs not implemented." + ) + ) + df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}, index=["a", "b"]).astype(dtype) + df2 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}, index=["a", "c"]).astype(dtype) + result = np.heaviside(df1, df2) + expected = np.heaviside( + np.array([[1, 3], [3, 4], [np.nan, np.nan]]), + np.array([[1, 3], [np.nan, np.nan], [3, 4]]), + ) + # TODO(FloatArray): this will be Float64Dtype. + expected = pd.DataFrame(expected, index=["a", "b", "c"], columns=["A", "B"]) + tm.assert_frame_equal(result, expected) + + result = np.heaviside(df1, df2.values) + expected = pd.DataFrame( + [[1.0, 1.0], [1.0, 1.0]], columns=["A", "B"], index=["a", "b"] + ) + tm.assert_frame_equal(result, expected) + + +def test_binary_frame_series_raises(): + # We don't currently implement + df = pd.DataFrame({"A": [1, 2]}) + with pytest.raises(NotImplementedError, match="logaddexp"): + np.logaddexp(df, df["A"]) + + with pytest.raises(NotImplementedError, match="logaddexp"): + np.logaddexp(df["A"], df) + + +def test_unary_accumulate_axis(): + # https://github.com/pandas-dev/pandas/issues/39259 + df = pd.DataFrame({"a": [1, 3, 2, 4]}) + result = np.maximum.accumulate(df) + expected = pd.DataFrame({"a": [1, 3, 3, 4]}) + tm.assert_frame_equal(result, expected) + + df = pd.DataFrame({"a": [1, 3, 2, 4], "b": [0.1, 4.0, 3.0, 2.0]}) + result = np.maximum.accumulate(df) + # in theory could preserve int dtype for default axis=0 + expected = pd.DataFrame({"a": [1.0, 3.0, 3.0, 4.0], "b": [0.1, 4.0, 4.0, 4.0]}) + tm.assert_frame_equal(result, expected) + + result = np.maximum.accumulate(df, axis=0) + tm.assert_frame_equal(result, expected) + + result = np.maximum.accumulate(df, axis=1) + expected = pd.DataFrame({"a": [1.0, 3.0, 2.0, 4.0], "b": [1.0, 4.0, 3.0, 4.0]}) + tm.assert_frame_equal(result, expected) + + +def test_frame_outer_disallowed(): + df = pd.DataFrame({"A": [1, 2]}) + with pytest.raises(NotImplementedError, match=""): + # deprecation enforced in 2.0 + np.subtract.outer(df, df) + + +def test_alignment_deprecation_enforced(): + # Enforced in 2.0 + # https://github.com/pandas-dev/pandas/issues/39184 + df1 = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + df2 = pd.DataFrame({"b": [1, 2, 3], "c": [4, 5, 6]}) + s1 = pd.Series([1, 2], index=["a", "b"]) + s2 = pd.Series([1, 2], index=["b", "c"]) + + # binary dataframe / dataframe + expected = pd.DataFrame({"a": [2, 4, 6], "b": [8, 10, 12]}) + + with tm.assert_produces_warning(None): + # aligned -> no warning! + result = np.add(df1, df1) + tm.assert_frame_equal(result, expected) + + result = np.add(df1, df2.values) + tm.assert_frame_equal(result, expected) + + result = np.add(df1, df2) + expected = pd.DataFrame({"a": [np.nan] * 3, "b": [5, 7, 9], "c": [np.nan] * 3}) + tm.assert_frame_equal(result, expected) + + result = np.add(df1.values, df2) + expected = pd.DataFrame({"b": [2, 4, 6], "c": [8, 10, 12]}) + tm.assert_frame_equal(result, expected) + + # binary dataframe / series + expected = pd.DataFrame({"a": [2, 3, 4], "b": [6, 7, 8]}) + + with tm.assert_produces_warning(None): + # aligned -> no warning! + result = np.add(df1, s1) + tm.assert_frame_equal(result, expected) + + result = np.add(df1, s2.values) + tm.assert_frame_equal(result, expected) + + expected = pd.DataFrame( + {"a": [np.nan] * 3, "b": [5.0, 6.0, 7.0], "c": [np.nan] * 3} + ) + result = np.add(df1, s2) + tm.assert_frame_equal(result, expected) + + msg = "Cannot apply ufunc to mixed DataFrame and Series inputs." + with pytest.raises(NotImplementedError, match=msg): + np.add(s2, df1) + + +def test_alignment_deprecation_many_inputs_enforced(): + # Enforced in 2.0 + # https://github.com/pandas-dev/pandas/issues/39184 + # test that the deprecation also works with > 2 inputs -> using a numba + # written ufunc for this because numpy itself doesn't have such ufuncs + numba = pytest.importorskip("numba") + + @numba.vectorize([numba.float64(numba.float64, numba.float64, numba.float64)]) + def my_ufunc(x, y, z): + return x + y + z + + df1 = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + df2 = pd.DataFrame({"b": [1, 2, 3], "c": [4, 5, 6]}) + df3 = pd.DataFrame({"a": [1, 2, 3], "c": [4, 5, 6]}) + + result = my_ufunc(df1, df2, df3) + expected = pd.DataFrame(np.full((3, 3), np.nan), columns=["a", "b", "c"]) + tm.assert_frame_equal(result, expected) + + # all aligned -> no warning + with tm.assert_produces_warning(None): + result = my_ufunc(df1, df1, df1) + expected = pd.DataFrame([[3.0, 12.0], [6.0, 15.0], [9.0, 18.0]], columns=["a", "b"]) + tm.assert_frame_equal(result, expected) + + # mixed frame / arrays + msg = ( + r"operands could not be broadcast together with shapes \(3,3\) \(3,3\) \(3,2\)" + ) + with pytest.raises(ValueError, match=msg): + my_ufunc(df1, df2, df3.values) + + # single frame -> no warning + with tm.assert_produces_warning(None): + result = my_ufunc(df1, df2.values, df3.values) + tm.assert_frame_equal(result, expected) + + # takes indices of first frame + msg = ( + r"operands could not be broadcast together with shapes \(3,2\) \(3,3\) \(3,3\)" + ) + with pytest.raises(ValueError, match=msg): + my_ufunc(df1.values, df2, df3) + + +def test_array_ufuncs_for_many_arguments(): + # GH39853 + def add3(x, y, z): + return x + y + z + + ufunc = np.frompyfunc(add3, 3, 1) + df = pd.DataFrame([[1, 2], [3, 4]]) + + result = ufunc(df, df, 1) + expected = pd.DataFrame([[3, 5], [7, 9]], dtype=object) + tm.assert_frame_equal(result, expected) + + ser = pd.Series([1, 2]) + msg = ( + "Cannot apply ufunc " + "to mixed DataFrame and Series inputs." + ) + with pytest.raises(NotImplementedError, match=re.escape(msg)): + ufunc(df, df, ser) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_unary.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_unary.py new file mode 100644 index 0000000000000000000000000000000000000000..a48b5c51f9ca73495cfab4d9ace73b0cded221b4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_unary.py @@ -0,0 +1,195 @@ +from decimal import Decimal + +import numpy as np +import pytest + +from pandas.compat.numpy import np_version_gte1p25 + +import pandas as pd +import pandas._testing as tm + + +class TestDataFrameUnaryOperators: + # __pos__, __neg__, __invert__ + + @pytest.mark.parametrize( + "df,expected", + [ + (pd.DataFrame({"a": [-1, 1]}), pd.DataFrame({"a": [1, -1]})), + (pd.DataFrame({"a": [False, True]}), pd.DataFrame({"a": [True, False]})), + ( + pd.DataFrame({"a": pd.Series(pd.to_timedelta([-1, 1]))}), + pd.DataFrame({"a": pd.Series(pd.to_timedelta([1, -1]))}), + ), + ], + ) + def test_neg_numeric(self, df, expected): + tm.assert_frame_equal(-df, expected) + tm.assert_series_equal(-df["a"], expected["a"]) + + @pytest.mark.parametrize( + "df, expected", + [ + (np.array([1, 2], dtype=object), np.array([-1, -2], dtype=object)), + ([Decimal("1.0"), Decimal("2.0")], [Decimal("-1.0"), Decimal("-2.0")]), + ], + ) + def test_neg_object(self, df, expected): + # GH#21380 + df = pd.DataFrame({"a": df}) + expected = pd.DataFrame({"a": expected}) + tm.assert_frame_equal(-df, expected) + tm.assert_series_equal(-df["a"], expected["a"]) + + @pytest.mark.parametrize( + "df", + [ + pd.DataFrame({"a": ["a", "b"]}), + pd.DataFrame({"a": pd.to_datetime(["2017-01-22", "1970-01-01"])}), + ], + ) + def test_neg_raises(self, df, using_infer_string): + msg = ( + "bad operand type for unary -: 'str'|" + r"bad operand type for unary -: 'DatetimeArray'|" + "unary '-' not supported for dtype" + ) + with pytest.raises(TypeError, match=msg): + (-df) + with pytest.raises(TypeError, match=msg): + (-df["a"]) + + def test_invert(self, float_frame): + df = float_frame + + tm.assert_frame_equal(-(df < 0), ~(df < 0)) + + def test_invert_mixed(self): + shape = (10, 5) + df = pd.concat( + [ + pd.DataFrame(np.zeros(shape, dtype="bool")), + pd.DataFrame(np.zeros(shape, dtype=int)), + ], + axis=1, + ignore_index=True, + ) + result = ~df + expected = pd.concat( + [ + pd.DataFrame(np.ones(shape, dtype="bool")), + pd.DataFrame(-np.ones(shape, dtype=int)), + ], + axis=1, + ignore_index=True, + ) + tm.assert_frame_equal(result, expected) + + def test_invert_empty_not_input(self): + # GH#51032 + df = pd.DataFrame() + result = ~df + tm.assert_frame_equal(df, result) + assert df is not result + + @pytest.mark.parametrize( + "df", + [ + pd.DataFrame({"a": [-1, 1]}), + pd.DataFrame({"a": [False, True]}), + pd.DataFrame({"a": pd.Series(pd.to_timedelta([-1, 1]))}), + ], + ) + def test_pos_numeric(self, df): + # GH#16073 + tm.assert_frame_equal(+df, df) + tm.assert_series_equal(+df["a"], df["a"]) + + @pytest.mark.parametrize( + "df", + [ + pd.DataFrame({"a": np.array([-1, 2], dtype=object)}), + pd.DataFrame({"a": [Decimal("-1.0"), Decimal("2.0")]}), + ], + ) + def test_pos_object(self, df): + # GH#21380 + tm.assert_frame_equal(+df, df) + tm.assert_series_equal(+df["a"], df["a"]) + + @pytest.mark.parametrize( + "df", + [ + pytest.param( + pd.DataFrame({"a": ["a", "b"]}), + # filterwarnings removable once min numpy version is 1.25 + marks=[ + pytest.mark.filterwarnings("ignore:Applying:DeprecationWarning") + ], + ), + ], + ) + def test_pos_object_raises(self, df): + # GH#21380 + if np_version_gte1p25: + with pytest.raises( + TypeError, match=r"^bad operand type for unary \+: \'str\'$" + ): + tm.assert_frame_equal(+df, df) + else: + tm.assert_series_equal(+df["a"], df["a"]) + + @pytest.mark.parametrize( + "df", [pd.DataFrame({"a": pd.to_datetime(["2017-01-22", "1970-01-01"])})] + ) + def test_pos_raises(self, df): + msg = r"bad operand type for unary \+: 'DatetimeArray'" + with pytest.raises(TypeError, match=msg): + (+df) + with pytest.raises(TypeError, match=msg): + (+df["a"]) + + def test_unary_nullable(self): + df = pd.DataFrame( + { + "a": pd.array([1, -2, 3, pd.NA], dtype="Int64"), + "b": pd.array([4.0, -5.0, 6.0, pd.NA], dtype="Float32"), + "c": pd.array([True, False, False, pd.NA], dtype="boolean"), + # include numpy bool to make sure bool-vs-boolean behavior + # is consistent in non-NA locations + "d": np.array([True, False, False, True]), + } + ) + + result = +df + res_ufunc = np.positive(df) + expected = df + # TODO: assert that we have copies? + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(res_ufunc, expected) + + result = -df + res_ufunc = np.negative(df) + expected = pd.DataFrame( + { + "a": pd.array([-1, 2, -3, pd.NA], dtype="Int64"), + "b": pd.array([-4.0, 5.0, -6.0, pd.NA], dtype="Float32"), + "c": pd.array([False, True, True, pd.NA], dtype="boolean"), + "d": np.array([False, True, True, False]), + } + ) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(res_ufunc, expected) + + result = abs(df) + res_ufunc = np.abs(df) + expected = pd.DataFrame( + { + "a": pd.array([1, 2, 3, pd.NA], dtype="Int64"), + "b": pd.array([4.0, 5.0, 6.0, pd.NA], dtype="Float32"), + "c": pd.array([True, False, False, pd.NA], dtype="boolean"), + "d": np.array([True, False, False, True]), + } + ) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(res_ufunc, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_validate.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_validate.py new file mode 100644 index 0000000000000000000000000000000000000000..e99e0a686384883d570feef949597d08da7e8ff9 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/frame/test_validate.py @@ -0,0 +1,41 @@ +import pytest + +from pandas.core.frame import DataFrame + + +@pytest.fixture +def dataframe(): + return DataFrame({"a": [1, 2], "b": [3, 4]}) + + +class TestDataFrameValidate: + """Tests for error handling related to data types of method arguments.""" + + @pytest.mark.parametrize( + "func", + [ + "query", + "eval", + "set_index", + "reset_index", + "dropna", + "drop_duplicates", + "sort_values", + ], + ) + @pytest.mark.parametrize("inplace", [1, "True", [1, 2, 3], 5.0]) + def test_validate_bool_args(self, dataframe, func, inplace): + msg = 'For argument "inplace" expected type bool' + kwargs = {"inplace": inplace} + + if func == "query": + kwargs["expr"] = "a > b" + elif func == "eval": + kwargs["expr"] = "a + b" + elif func == "set_index": + kwargs["keys"] = ["a"] + elif func == "sort_values": + kwargs["by"] = ["a"] + + with pytest.raises(ValueError, match=msg): + getattr(dataframe, func)(**kwargs) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_constructors.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..dcf0165ead6c0edb2073ecd0c17cdd7da37daf78 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_constructors.py @@ -0,0 +1,78 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Index, + MultiIndex, + Series, +) +import pandas._testing as tm + + +class TestIndexConstructor: + # Tests for the Index constructor, specifically for cases that do + # not return a subclass + + @pytest.mark.parametrize("value", [1, np.int64(1)]) + def test_constructor_corner(self, value): + # corner case + msg = ( + r"Index\(\.\.\.\) must be called with a collection of some " + f"kind, {value} was passed" + ) + with pytest.raises(TypeError, match=msg): + Index(value) + + @pytest.mark.parametrize("index_vals", [[("A", 1), "B"], ["B", ("A", 1)]]) + def test_construction_list_mixed_tuples(self, index_vals): + # see gh-10697: if we are constructing from a mixed list of tuples, + # make sure that we are independent of the sorting order. + index = Index(index_vals) + assert isinstance(index, Index) + assert not isinstance(index, MultiIndex) + + def test_constructor_cast(self): + msg = "could not convert string to float" + with pytest.raises(ValueError, match=msg): + Index(["a", "b", "c"], dtype=float) + + @pytest.mark.parametrize("tuple_list", [[()], [(), ()]]) + def test_construct_empty_tuples(self, tuple_list): + # GH #45608 + result = Index(tuple_list) + expected = MultiIndex.from_tuples(tuple_list) + + tm.assert_index_equal(result, expected) + + def test_index_string_inference(self): + # GH#54430 + expected = Index(["a", "b"], dtype=pd.StringDtype(na_value=np.nan)) + with pd.option_context("future.infer_string", True): + ser = Index(["a", "b"]) + tm.assert_index_equal(ser, expected) + + expected = Index(["a", 1], dtype="object") + with pd.option_context("future.infer_string", True): + ser = Index(["a", 1]) + tm.assert_index_equal(ser, expected) + + def test_inference_on_pandas_objects(self): + # GH#56012 + idx = Index([pd.Timestamp("2019-12-31")], dtype=object) + with tm.assert_produces_warning(FutureWarning, match="Dtype inference"): + result = Index(idx) + assert result.dtype != np.object_ + + ser = Series([pd.Timestamp("2019-12-31")], dtype=object) + + with tm.assert_produces_warning(FutureWarning, match="Dtype inference"): + result = Index(ser) + assert result.dtype != np.object_ + + def test_constructor_not_read_only(self): + # GH#57130 + ser = Series([1, 2], dtype=object) + with pd.option_context("mode.copy_on_write", True): + idx = Index(ser) + assert idx._values.flags.writeable diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..2988fa7d1baa1e0bc0f6cc4b6dc32e5d12f332cf --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_indexing.py @@ -0,0 +1,104 @@ +import numpy as np +import pytest + +from pandas._libs import index as libindex + +import pandas as pd +from pandas import ( + Index, + NaT, +) +import pandas._testing as tm + + +class TestGetSliceBounds: + @pytest.mark.parametrize("side, expected", [("left", 4), ("right", 5)]) + def test_get_slice_bounds_within(self, side, expected): + index = Index(list("abcdef")) + result = index.get_slice_bound("e", side=side) + assert result == expected + + @pytest.mark.parametrize("side", ["left", "right"]) + @pytest.mark.parametrize( + "data, bound, expected", [(list("abcdef"), "x", 6), (list("bcdefg"), "a", 0)] + ) + def test_get_slice_bounds_outside(self, side, expected, data, bound): + index = Index(data) + result = index.get_slice_bound(bound, side=side) + assert result == expected + + def test_get_slice_bounds_invalid_side(self): + with pytest.raises(ValueError, match="Invalid value for side kwarg"): + Index([]).get_slice_bound("a", side="middle") + + +class TestGetIndexerNonUnique: + def test_get_indexer_non_unique_dtype_mismatch(self): + # GH#25459 + indexes, missing = Index(["A", "B"]).get_indexer_non_unique(Index([0])) + tm.assert_numpy_array_equal(np.array([-1], dtype=np.intp), indexes) + tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), missing) + + @pytest.mark.parametrize( + "idx_values,idx_non_unique", + [ + ([np.nan, 100, 200, 100], [np.nan, 100]), + ([np.nan, 100.0, 200.0, 100.0], [np.nan, 100.0]), + ], + ) + def test_get_indexer_non_unique_int_index(self, idx_values, idx_non_unique): + indexes, missing = Index(idx_values).get_indexer_non_unique(Index([np.nan])) + tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), indexes) + tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing) + + indexes, missing = Index(idx_values).get_indexer_non_unique( + Index(idx_non_unique) + ) + tm.assert_numpy_array_equal(np.array([0, 1, 3], dtype=np.intp), indexes) + tm.assert_numpy_array_equal(np.array([], dtype=np.intp), missing) + + +class TestGetLoc: + @pytest.mark.slow # to_flat_index takes a while + def test_get_loc_tuple_monotonic_above_size_cutoff(self, monkeypatch): + # Go through the libindex path for which using + # _bin_search vs ndarray.searchsorted makes a difference + + with monkeypatch.context(): + monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 100) + lev = list("ABCD") + dti = pd.date_range("2016-01-01", periods=10) + + mi = pd.MultiIndex.from_product([lev, range(5), dti]) + oidx = mi.to_flat_index() + + loc = len(oidx) // 2 + tup = oidx[loc] + + res = oidx.get_loc(tup) + assert res == loc + + def test_get_loc_nan_object_dtype_nonmonotonic_nonunique(self): + # case that goes through _maybe_get_bool_indexer + idx = Index(["foo", np.nan, None, "foo", 1.0, None], dtype=object) + + # we dont raise KeyError on nan + res = idx.get_loc(np.nan) + assert res == 1 + + # we only match on None, not on np.nan + res = idx.get_loc(None) + expected = np.array([False, False, True, False, False, True]) + tm.assert_numpy_array_equal(res, expected) + + # we don't match at all on mismatched NA + with pytest.raises(KeyError, match="NaT"): + idx.get_loc(NaT) + + +def test_getitem_boolean_ea_indexer(): + # GH#45806 + ser = pd.Series([True, False, pd.NA], dtype="boolean") + result = ser.index[ser] + expected = Index([0]) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_pickle.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_pickle.py new file mode 100644 index 0000000000000000000000000000000000000000..c670921decb78808fa54a35c45e3d2d15ab57a67 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_pickle.py @@ -0,0 +1,11 @@ +from pandas import Index +import pandas._testing as tm + + +def test_pickle_preserves_object_dtype(): + # GH#43188, GH#43155 don't infer numeric dtype + index = Index([1, 2, 3], dtype=object) + + result = tm.round_trip_pickle(index) + assert result.dtype == object + tm.assert_index_equal(index, result) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_setops.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_setops.py new file mode 100644 index 0000000000000000000000000000000000000000..3ef3f3ad4d3a20bd2e6303d781590396cbc00ae0 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_setops.py @@ -0,0 +1,266 @@ +from datetime import datetime + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Index, + Series, +) +import pandas._testing as tm +from pandas.core.algorithms import safe_sort + + +def equal_contents(arr1, arr2) -> bool: + """ + Checks if the set of unique elements of arr1 and arr2 are equivalent. + """ + return frozenset(arr1) == frozenset(arr2) + + +class TestIndexSetOps: + @pytest.mark.parametrize( + "method", ["union", "intersection", "difference", "symmetric_difference"] + ) + def test_setops_sort_validation(self, method): + idx1 = Index(["a", "b"]) + idx2 = Index(["b", "c"]) + + with pytest.raises(ValueError, match="The 'sort' keyword only takes"): + getattr(idx1, method)(idx2, sort=2) + + # sort=True is supported as of GH#?? + getattr(idx1, method)(idx2, sort=True) + + def test_setops_preserve_object_dtype(self): + idx = Index([1, 2, 3], dtype=object) + result = idx.intersection(idx[1:]) + expected = idx[1:] + tm.assert_index_equal(result, expected) + + # if other is not monotonic increasing, intersection goes through + # a different route + result = idx.intersection(idx[1:][::-1]) + tm.assert_index_equal(result, expected) + + result = idx._union(idx[1:], sort=None) + expected = idx + tm.assert_numpy_array_equal(result, expected.values) + + result = idx.union(idx[1:], sort=None) + tm.assert_index_equal(result, expected) + + # if other is not monotonic increasing, _union goes through + # a different route + result = idx._union(idx[1:][::-1], sort=None) + tm.assert_numpy_array_equal(result, expected.values) + + result = idx.union(idx[1:][::-1], sort=None) + tm.assert_index_equal(result, expected) + + def test_union_base(self): + index = Index([0, "a", 1, "b", 2, "c"]) + first = index[3:] + second = index[:5] + + result = first.union(second) + + expected = Index([0, 1, 2, "a", "b", "c"]) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("klass", [np.array, Series, list]) + def test_union_different_type_base(self, klass): + # GH 10149 + index = Index([0, "a", 1, "b", 2, "c"]) + first = index[3:] + second = index[:5] + + result = first.union(klass(second.values)) + + assert equal_contents(result, index) + + def test_union_sort_other_incomparable(self): + # https://github.com/pandas-dev/pandas/issues/24959 + idx = Index([1, pd.Timestamp("2000")]) + # default (sort=None) + with tm.assert_produces_warning(RuntimeWarning): + result = idx.union(idx[:1]) + + tm.assert_index_equal(result, idx) + + # sort=None + with tm.assert_produces_warning(RuntimeWarning): + result = idx.union(idx[:1], sort=None) + tm.assert_index_equal(result, idx) + + # sort=False + result = idx.union(idx[:1], sort=False) + tm.assert_index_equal(result, idx) + + def test_union_sort_other_incomparable_true(self): + idx = Index([1, pd.Timestamp("2000")]) + with pytest.raises(TypeError, match=".*"): + idx.union(idx[:1], sort=True) + + def test_intersection_equal_sort_true(self): + idx = Index(["c", "a", "b"]) + sorted_ = Index(["a", "b", "c"]) + tm.assert_index_equal(idx.intersection(idx, sort=True), sorted_) + + def test_intersection_base(self, sort): + # (same results for py2 and py3 but sortedness not tested elsewhere) + index = Index([0, "a", 1, "b", 2, "c"]) + first = index[:5] + second = index[:3] + + expected = Index([0, 1, "a"]) if sort is None else Index([0, "a", 1]) + result = first.intersection(second, sort=sort) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("klass", [np.array, Series, list]) + def test_intersection_different_type_base(self, klass, sort): + # GH 10149 + index = Index([0, "a", 1, "b", 2, "c"]) + first = index[:5] + second = index[:3] + + result = first.intersection(klass(second.values), sort=sort) + assert equal_contents(result, second) + + def test_intersection_nosort(self): + result = Index(["c", "b", "a"]).intersection(["b", "a"]) + expected = Index(["b", "a"]) + tm.assert_index_equal(result, expected) + + def test_intersection_equal_sort(self): + idx = Index(["c", "a", "b"]) + tm.assert_index_equal(idx.intersection(idx, sort=False), idx) + tm.assert_index_equal(idx.intersection(idx, sort=None), idx) + + def test_intersection_str_dates(self, sort): + dt_dates = [datetime(2012, 2, 9), datetime(2012, 2, 22)] + + i1 = Index(dt_dates, dtype=object) + i2 = Index(["aa"], dtype=object) + result = i2.intersection(i1, sort=sort) + + assert len(result) == 0 + + @pytest.mark.parametrize( + "index2,expected_arr", + [(Index(["B", "D"]), ["B"]), (Index(["B", "D", "A"]), ["A", "B"])], + ) + def test_intersection_non_monotonic_non_unique(self, index2, expected_arr, sort): + # non-monotonic non-unique + index1 = Index(["A", "B", "A", "C"]) + expected = Index(expected_arr) + result = index1.intersection(index2, sort=sort) + if sort is None: + expected = expected.sort_values() + tm.assert_index_equal(result, expected) + + def test_difference_base(self, sort): + # (same results for py2 and py3 but sortedness not tested elsewhere) + index = Index([0, "a", 1, "b", 2, "c"]) + first = index[:4] + second = index[3:] + + result = first.difference(second, sort) + expected = Index([0, "a", 1]) + if sort is None: + expected = Index(safe_sort(expected)) + tm.assert_index_equal(result, expected) + + def test_symmetric_difference(self): + # (same results for py2 and py3 but sortedness not tested elsewhere) + index = Index([0, "a", 1, "b", 2, "c"]) + first = index[:4] + second = index[3:] + + result = first.symmetric_difference(second) + expected = Index([0, 1, 2, "a", "c"]) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "method,expected,sort", + [ + ( + "intersection", + np.array( + [(1, "A"), (2, "A"), (1, "B"), (2, "B")], + dtype=[("num", int), ("let", "S1")], + ), + False, + ), + ( + "intersection", + np.array( + [(1, "A"), (1, "B"), (2, "A"), (2, "B")], + dtype=[("num", int), ("let", "S1")], + ), + None, + ), + ( + "union", + np.array( + [(1, "A"), (1, "B"), (1, "C"), (2, "A"), (2, "B"), (2, "C")], + dtype=[("num", int), ("let", "S1")], + ), + None, + ), + ], + ) + def test_tuple_union_bug(self, method, expected, sort): + index1 = Index( + np.array( + [(1, "A"), (2, "A"), (1, "B"), (2, "B")], + dtype=[("num", int), ("let", "S1")], + ) + ) + index2 = Index( + np.array( + [(1, "A"), (2, "A"), (1, "B"), (2, "B"), (1, "C"), (2, "C")], + dtype=[("num", int), ("let", "S1")], + ) + ) + + result = getattr(index1, method)(index2, sort=sort) + assert result.ndim == 1 + + expected = Index(expected) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("first_list", [["b", "a"], []]) + @pytest.mark.parametrize("second_list", [["a", "b"], []]) + @pytest.mark.parametrize( + "first_name, second_name, expected_name", + [("A", "B", None), (None, "B", None), ("A", None, None)], + ) + def test_union_name_preservation( + self, first_list, second_list, first_name, second_name, expected_name, sort + ): + first = Index(first_list, name=first_name) + second = Index(second_list, name=second_name) + union = first.union(second, sort=sort) + + vals = set(first_list).union(second_list) + + if sort is None and len(first_list) > 0 and len(second_list) > 0: + expected = Index(sorted(vals), name=expected_name) + tm.assert_index_equal(union, expected) + else: + expected = Index(vals, name=expected_name) + tm.assert_index_equal(union.sort_values(), expected.sort_values()) + + @pytest.mark.parametrize( + "diff_type, expected", + [["difference", [1, "B"]], ["symmetric_difference", [1, 2, "B", "C"]]], + ) + def test_difference_object_type(self, diff_type, expected): + # GH 13432 + idx1 = Index([0, 1, "A", "B"]) + idx2 = Index([0, 2, "A", "C"]) + result = getattr(idx1, diff_type)(idx2) + expected = Index(expected) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_where.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_where.py new file mode 100644 index 0000000000000000000000000000000000000000..0c8969735e14e2741bc029b499024af3ec378a92 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/base_class/test_where.py @@ -0,0 +1,13 @@ +import numpy as np + +from pandas import Index +import pandas._testing as tm + + +class TestWhere: + def test_where_intlike_str_doesnt_cast_ints(self): + idx = Index(range(3)) + mask = np.array([True, False, True]) + res = idx.where(mask, "2") + expected = Index([0, "2", 2]) + tm.assert_index_equal(res, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_interval_range.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_interval_range.py new file mode 100644 index 0000000000000000000000000000000000000000..e8de59f84bcc6d6cece2768f942b4599d3ce1a2d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_interval_range.py @@ -0,0 +1,369 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas.core.dtypes.common import is_integer + +from pandas import ( + DateOffset, + Interval, + IntervalIndex, + Timedelta, + Timestamp, + date_range, + interval_range, + timedelta_range, +) +import pandas._testing as tm + +from pandas.tseries.offsets import Day + + +@pytest.fixture(params=[None, "foo"]) +def name(request): + return request.param + + +class TestIntervalRange: + @pytest.mark.parametrize("freq, periods", [(1, 100), (2.5, 40), (5, 20), (25, 4)]) + def test_constructor_numeric(self, closed, name, freq, periods): + start, end = 0, 100 + breaks = np.arange(101, step=freq) + expected = IntervalIndex.from_breaks(breaks, name=name, closed=closed) + + # defined from start/end/freq + result = interval_range( + start=start, end=end, freq=freq, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + # defined from start/periods/freq + result = interval_range( + start=start, periods=periods, freq=freq, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + # defined from end/periods/freq + result = interval_range( + end=end, periods=periods, freq=freq, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + # GH 20976: linspace behavior defined from start/end/periods + result = interval_range( + start=start, end=end, periods=periods, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("tz", [None, "US/Eastern"]) + @pytest.mark.parametrize( + "freq, periods", [("D", 364), ("2D", 182), ("22D18h", 16), ("ME", 11)] + ) + def test_constructor_timestamp(self, closed, name, freq, periods, tz): + start, end = Timestamp("20180101", tz=tz), Timestamp("20181231", tz=tz) + breaks = date_range(start=start, end=end, freq=freq) + expected = IntervalIndex.from_breaks(breaks, name=name, closed=closed) + + # defined from start/end/freq + result = interval_range( + start=start, end=end, freq=freq, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + # defined from start/periods/freq + result = interval_range( + start=start, periods=periods, freq=freq, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + # defined from end/periods/freq + result = interval_range( + end=end, periods=periods, freq=freq, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + # GH 20976: linspace behavior defined from start/end/periods + if not breaks.freq.n == 1 and tz is None: + result = interval_range( + start=start, end=end, periods=periods, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "freq, periods", [("D", 100), ("2D12h", 40), ("5D", 20), ("25D", 4)] + ) + def test_constructor_timedelta(self, closed, name, freq, periods): + start, end = Timedelta("0 days"), Timedelta("100 days") + breaks = timedelta_range(start=start, end=end, freq=freq) + expected = IntervalIndex.from_breaks(breaks, name=name, closed=closed) + + # defined from start/end/freq + result = interval_range( + start=start, end=end, freq=freq, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + # defined from start/periods/freq + result = interval_range( + start=start, periods=periods, freq=freq, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + # defined from end/periods/freq + result = interval_range( + end=end, periods=periods, freq=freq, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + # GH 20976: linspace behavior defined from start/end/periods + result = interval_range( + start=start, end=end, periods=periods, name=name, closed=closed + ) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "start, end, freq, expected_endpoint", + [ + (0, 10, 3, 9), + (0, 10, 1.5, 9), + (0.5, 10, 3, 9.5), + (Timedelta("0D"), Timedelta("10D"), "2D4h", Timedelta("8D16h")), + ( + Timestamp("2018-01-01"), + Timestamp("2018-02-09"), + "MS", + Timestamp("2018-02-01"), + ), + ( + Timestamp("2018-01-01", tz="US/Eastern"), + Timestamp("2018-01-20", tz="US/Eastern"), + "5D12h", + Timestamp("2018-01-17 12:00:00", tz="US/Eastern"), + ), + ], + ) + def test_early_truncation(self, start, end, freq, expected_endpoint): + # index truncates early if freq causes end to be skipped + result = interval_range(start=start, end=end, freq=freq) + result_endpoint = result.right[-1] + assert result_endpoint == expected_endpoint + + @pytest.mark.parametrize( + "start, end, freq", + [(0.5, None, None), (None, 4.5, None), (0.5, None, 1.5), (None, 6.5, 1.5)], + ) + def test_no_invalid_float_truncation(self, start, end, freq): + # GH 21161 + if freq is None: + breaks = [0.5, 1.5, 2.5, 3.5, 4.5] + else: + breaks = [0.5, 2.0, 3.5, 5.0, 6.5] + expected = IntervalIndex.from_breaks(breaks) + + result = interval_range(start=start, end=end, periods=4, freq=freq) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "start, mid, end", + [ + ( + Timestamp("2018-03-10", tz="US/Eastern"), + Timestamp("2018-03-10 23:30:00", tz="US/Eastern"), + Timestamp("2018-03-12", tz="US/Eastern"), + ), + ( + Timestamp("2018-11-03", tz="US/Eastern"), + Timestamp("2018-11-04 00:30:00", tz="US/Eastern"), + Timestamp("2018-11-05", tz="US/Eastern"), + ), + ], + ) + def test_linspace_dst_transition(self, start, mid, end): + # GH 20976: linspace behavior defined from start/end/periods + # accounts for the hour gained/lost during DST transition + start = start.as_unit("ns") + mid = mid.as_unit("ns") + end = end.as_unit("ns") + result = interval_range(start=start, end=end, periods=2) + expected = IntervalIndex.from_breaks([start, mid, end]) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("freq", [2, 2.0]) + @pytest.mark.parametrize("end", [10, 10.0]) + @pytest.mark.parametrize("start", [0, 0.0]) + def test_float_subtype(self, start, end, freq): + # Has float subtype if any of start/end/freq are float, even if all + # resulting endpoints can safely be upcast to integers + + # defined from start/end/freq + index = interval_range(start=start, end=end, freq=freq) + result = index.dtype.subtype + expected = "int64" if is_integer(start + end + freq) else "float64" + assert result == expected + + # defined from start/periods/freq + index = interval_range(start=start, periods=5, freq=freq) + result = index.dtype.subtype + expected = "int64" if is_integer(start + freq) else "float64" + assert result == expected + + # defined from end/periods/freq + index = interval_range(end=end, periods=5, freq=freq) + result = index.dtype.subtype + expected = "int64" if is_integer(end + freq) else "float64" + assert result == expected + + # GH 20976: linspace behavior defined from start/end/periods + index = interval_range(start=start, end=end, periods=5) + result = index.dtype.subtype + expected = "int64" if is_integer(start + end) else "float64" + assert result == expected + + def test_interval_range_fractional_period(self): + # float value for periods + expected = interval_range(start=0, periods=10) + msg = "Non-integer 'periods' in pd.date_range, .* pd.interval_range" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = interval_range(start=0, periods=10.5) + tm.assert_index_equal(result, expected) + + def test_constructor_coverage(self): + # equivalent timestamp-like start/end + start, end = Timestamp("2017-01-01"), Timestamp("2017-01-15") + expected = interval_range(start=start, end=end) + + result = interval_range(start=start.to_pydatetime(), end=end.to_pydatetime()) + tm.assert_index_equal(result, expected) + + result = interval_range(start=start.asm8, end=end.asm8) + tm.assert_index_equal(result, expected) + + # equivalent freq with timestamp + equiv_freq = [ + "D", + Day(), + Timedelta(days=1), + timedelta(days=1), + DateOffset(days=1), + ] + for freq in equiv_freq: + result = interval_range(start=start, end=end, freq=freq) + tm.assert_index_equal(result, expected) + + # equivalent timedelta-like start/end + start, end = Timedelta(days=1), Timedelta(days=10) + expected = interval_range(start=start, end=end) + + result = interval_range(start=start.to_pytimedelta(), end=end.to_pytimedelta()) + tm.assert_index_equal(result, expected) + + result = interval_range(start=start.asm8, end=end.asm8) + tm.assert_index_equal(result, expected) + + # equivalent freq with timedelta + equiv_freq = ["D", Day(), Timedelta(days=1), timedelta(days=1)] + for freq in equiv_freq: + result = interval_range(start=start, end=end, freq=freq) + tm.assert_index_equal(result, expected) + + def test_errors(self): + # not enough params + msg = ( + "Of the four parameters: start, end, periods, and freq, " + "exactly three must be specified" + ) + + with pytest.raises(ValueError, match=msg): + interval_range(start=0) + + with pytest.raises(ValueError, match=msg): + interval_range(end=5) + + with pytest.raises(ValueError, match=msg): + interval_range(periods=2) + + with pytest.raises(ValueError, match=msg): + interval_range() + + # too many params + with pytest.raises(ValueError, match=msg): + interval_range(start=0, end=5, periods=6, freq=1.5) + + # mixed units + msg = "start, end, freq need to be type compatible" + with pytest.raises(TypeError, match=msg): + interval_range(start=0, end=Timestamp("20130101"), freq=2) + + with pytest.raises(TypeError, match=msg): + interval_range(start=0, end=Timedelta("1 day"), freq=2) + + with pytest.raises(TypeError, match=msg): + interval_range(start=0, end=10, freq="D") + + with pytest.raises(TypeError, match=msg): + interval_range(start=Timestamp("20130101"), end=10, freq="D") + + with pytest.raises(TypeError, match=msg): + interval_range( + start=Timestamp("20130101"), end=Timedelta("1 day"), freq="D" + ) + + with pytest.raises(TypeError, match=msg): + interval_range( + start=Timestamp("20130101"), end=Timestamp("20130110"), freq=2 + ) + + with pytest.raises(TypeError, match=msg): + interval_range(start=Timedelta("1 day"), end=10, freq="D") + + with pytest.raises(TypeError, match=msg): + interval_range( + start=Timedelta("1 day"), end=Timestamp("20130110"), freq="D" + ) + + with pytest.raises(TypeError, match=msg): + interval_range(start=Timedelta("1 day"), end=Timedelta("10 days"), freq=2) + + # invalid periods + msg = "periods must be a number, got foo" + with pytest.raises(TypeError, match=msg): + interval_range(start=0, periods="foo") + + # invalid start + msg = "start must be numeric or datetime-like, got foo" + with pytest.raises(ValueError, match=msg): + interval_range(start="foo", periods=10) + + # invalid end + msg = r"end must be numeric or datetime-like, got \(0, 1\]" + with pytest.raises(ValueError, match=msg): + interval_range(end=Interval(0, 1), periods=10) + + # invalid freq for datetime-like + msg = "freq must be numeric or convertible to DateOffset, got foo" + with pytest.raises(ValueError, match=msg): + interval_range(start=0, end=10, freq="foo") + + with pytest.raises(ValueError, match=msg): + interval_range(start=Timestamp("20130101"), periods=10, freq="foo") + + with pytest.raises(ValueError, match=msg): + interval_range(end=Timedelta("1 day"), periods=10, freq="foo") + + # mixed tz + start = Timestamp("2017-01-01", tz="US/Eastern") + end = Timestamp("2017-01-07", tz="US/Pacific") + msg = "Start and end cannot both be tz-aware with different timezones" + with pytest.raises(TypeError, match=msg): + interval_range(start=start, end=end) + + def test_float_freq(self): + # GH 54477 + result = interval_range(0, 1, freq=0.1) + expected = IntervalIndex.from_breaks([0 + 0.1 * n for n in range(11)]) + tm.assert_index_equal(result, expected) + + result = interval_range(0, 1, freq=0.6) + expected = IntervalIndex.from_breaks([0, 0.6]) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_interval_tree.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_interval_tree.py new file mode 100644 index 0000000000000000000000000000000000000000..78388e84fc6dc1af7dadd78b88a1155ed8cfd812 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/interval/test_interval_tree.py @@ -0,0 +1,208 @@ +from itertools import permutations + +import numpy as np +import pytest + +from pandas._libs.interval import IntervalTree +from pandas.compat import IS64 + +import pandas._testing as tm + + +def skipif_32bit(param): + """ + Skip parameters in a parametrize on 32bit systems. Specifically used + here to skip leaf_size parameters related to GH 23440. + """ + marks = pytest.mark.skipif(not IS64, reason="GH 23440: int type mismatch on 32bit") + return pytest.param(param, marks=marks) + + +@pytest.fixture(params=["int64", "float64", "uint64"]) +def dtype(request): + return request.param + + +@pytest.fixture(params=[skipif_32bit(1), skipif_32bit(2), 10]) +def leaf_size(request): + """ + Fixture to specify IntervalTree leaf_size parameter; to be used with the + tree fixture. + """ + return request.param + + +@pytest.fixture( + params=[ + np.arange(5, dtype="int64"), + np.arange(5, dtype="uint64"), + np.arange(5, dtype="float64"), + np.array([0, 1, 2, 3, 4, np.nan], dtype="float64"), + ] +) +def tree(request, leaf_size): + left = request.param + return IntervalTree(left, left + 2, leaf_size=leaf_size) + + +class TestIntervalTree: + def test_get_indexer(self, tree): + result = tree.get_indexer(np.array([1.0, 5.5, 6.5])) + expected = np.array([0, 4, -1], dtype="intp") + tm.assert_numpy_array_equal(result, expected) + + with pytest.raises( + KeyError, match="'indexer does not intersect a unique set of intervals'" + ): + tree.get_indexer(np.array([3.0])) + + @pytest.mark.parametrize( + "dtype, target_value, target_dtype", + [("int64", 2**63 + 1, "uint64"), ("uint64", -1, "int64")], + ) + def test_get_indexer_overflow(self, dtype, target_value, target_dtype): + left, right = np.array([0, 1], dtype=dtype), np.array([1, 2], dtype=dtype) + tree = IntervalTree(left, right) + + result = tree.get_indexer(np.array([target_value], dtype=target_dtype)) + expected = np.array([-1], dtype="intp") + tm.assert_numpy_array_equal(result, expected) + + def test_get_indexer_non_unique(self, tree): + indexer, missing = tree.get_indexer_non_unique(np.array([1.0, 2.0, 6.5])) + + result = indexer[:1] + expected = np.array([0], dtype="intp") + tm.assert_numpy_array_equal(result, expected) + + result = np.sort(indexer[1:3]) + expected = np.array([0, 1], dtype="intp") + tm.assert_numpy_array_equal(result, expected) + + result = np.sort(indexer[3:]) + expected = np.array([-1], dtype="intp") + tm.assert_numpy_array_equal(result, expected) + + result = missing + expected = np.array([2], dtype="intp") + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize( + "dtype, target_value, target_dtype", + [("int64", 2**63 + 1, "uint64"), ("uint64", -1, "int64")], + ) + def test_get_indexer_non_unique_overflow(self, dtype, target_value, target_dtype): + left, right = np.array([0, 2], dtype=dtype), np.array([1, 3], dtype=dtype) + tree = IntervalTree(left, right) + target = np.array([target_value], dtype=target_dtype) + + result_indexer, result_missing = tree.get_indexer_non_unique(target) + expected_indexer = np.array([-1], dtype="intp") + tm.assert_numpy_array_equal(result_indexer, expected_indexer) + + expected_missing = np.array([0], dtype="intp") + tm.assert_numpy_array_equal(result_missing, expected_missing) + + def test_duplicates(self, dtype): + left = np.array([0, 0, 0], dtype=dtype) + tree = IntervalTree(left, left + 1) + + with pytest.raises( + KeyError, match="'indexer does not intersect a unique set of intervals'" + ): + tree.get_indexer(np.array([0.5])) + + indexer, missing = tree.get_indexer_non_unique(np.array([0.5])) + result = np.sort(indexer) + expected = np.array([0, 1, 2], dtype="intp") + tm.assert_numpy_array_equal(result, expected) + + result = missing + expected = np.array([], dtype="intp") + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize( + "leaf_size", [skipif_32bit(1), skipif_32bit(10), skipif_32bit(100), 10000] + ) + def test_get_indexer_closed(self, closed, leaf_size): + x = np.arange(1000, dtype="float64") + found = x.astype("intp") + not_found = (-1 * np.ones(1000)).astype("intp") + + tree = IntervalTree(x, x + 0.5, closed=closed, leaf_size=leaf_size) + tm.assert_numpy_array_equal(found, tree.get_indexer(x + 0.25)) + + expected = found if tree.closed_left else not_found + tm.assert_numpy_array_equal(expected, tree.get_indexer(x + 0.0)) + + expected = found if tree.closed_right else not_found + tm.assert_numpy_array_equal(expected, tree.get_indexer(x + 0.5)) + + @pytest.mark.parametrize( + "left, right, expected", + [ + (np.array([0, 1, 4], dtype="int64"), np.array([2, 3, 5]), True), + (np.array([0, 1, 2], dtype="int64"), np.array([5, 4, 3]), True), + (np.array([0, 1, np.nan]), np.array([5, 4, np.nan]), True), + (np.array([0, 2, 4], dtype="int64"), np.array([1, 3, 5]), False), + (np.array([0, 2, np.nan]), np.array([1, 3, np.nan]), False), + ], + ) + @pytest.mark.parametrize("order", (list(x) for x in permutations(range(3)))) + def test_is_overlapping(self, closed, order, left, right, expected): + # GH 23309 + tree = IntervalTree(left[order], right[order], closed=closed) + result = tree.is_overlapping + assert result is expected + + @pytest.mark.parametrize("order", (list(x) for x in permutations(range(3)))) + def test_is_overlapping_endpoints(self, closed, order): + """shared endpoints are marked as overlapping""" + # GH 23309 + left, right = np.arange(3, dtype="int64"), np.arange(1, 4) + tree = IntervalTree(left[order], right[order], closed=closed) + result = tree.is_overlapping + expected = closed == "both" + assert result is expected + + @pytest.mark.parametrize( + "left, right", + [ + (np.array([], dtype="int64"), np.array([], dtype="int64")), + (np.array([0], dtype="int64"), np.array([1], dtype="int64")), + (np.array([np.nan]), np.array([np.nan])), + (np.array([np.nan] * 3), np.array([np.nan] * 3)), + ], + ) + def test_is_overlapping_trivial(self, closed, left, right): + # GH 23309 + tree = IntervalTree(left, right, closed=closed) + assert tree.is_overlapping is False + + @pytest.mark.skipif(not IS64, reason="GH 23440") + def test_construction_overflow(self): + # GH 25485 + left, right = np.arange(101, dtype="int64"), [np.iinfo(np.int64).max] * 101 + tree = IntervalTree(left, right) + + # pivot should be average of left/right medians + result = tree.root.pivot + expected = (50 + np.iinfo(np.int64).max) / 2 + assert result == expected + + @pytest.mark.parametrize( + "left, right, expected", + [ + ([-np.inf, 1.0], [1.0, 2.0], 0.0), + ([-np.inf, -2.0], [-2.0, -1.0], -2.0), + ([-2.0, -1.0], [-1.0, np.inf], 0.0), + ([1.0, 2.0], [2.0, np.inf], 2.0), + ], + ) + def test_inf_bound_infinite_recursion(self, left, right, expected): + # GH 46658 + + tree = IntervalTree(left * 101, right * 101) + + result = tree.root.pivot + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/conftest.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..15062aee56e3a1b91d1f6eb76a4f86e381e0ad44 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/conftest.py @@ -0,0 +1,27 @@ +import numpy as np +import pytest + +from pandas import ( + Index, + MultiIndex, +) + + +# Note: identical the "multi" entry in the top-level "index" fixture +@pytest.fixture +def idx(): + # a MultiIndex used to test the general functionality of the + # general functionality of this object + major_axis = Index(["foo", "bar", "baz", "qux"]) + minor_axis = Index(["one", "two"]) + + major_codes = np.array([0, 0, 1, 2, 3, 3]) + minor_codes = np.array([0, 1, 0, 1, 0, 1]) + index_names = ["first", "second"] + mi = MultiIndex( + levels=[major_axis, minor_axis], + codes=[major_codes, minor_codes], + names=index_names, + verify_integrity=False, + ) + return mi diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_analytics.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_analytics.py new file mode 100644 index 0000000000000000000000000000000000000000..87f1439db5fc87c3be08e3675df1dae0fdb5554d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_analytics.py @@ -0,0 +1,263 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Index, + MultiIndex, + date_range, + period_range, +) +import pandas._testing as tm + + +def test_infer_objects(idx): + with pytest.raises(NotImplementedError, match="to_frame"): + idx.infer_objects() + + +def test_shift(idx): + # GH8083 test the base class for shift + msg = ( + "This method is only implemented for DatetimeIndex, PeriodIndex and " + "TimedeltaIndex; Got type MultiIndex" + ) + with pytest.raises(NotImplementedError, match=msg): + idx.shift(1) + with pytest.raises(NotImplementedError, match=msg): + idx.shift(1, 2) + + +def test_groupby(idx): + groups = idx.groupby(np.array([1, 1, 1, 2, 2, 2])) + labels = idx.tolist() + exp = {1: labels[:3], 2: labels[3:]} + tm.assert_dict_equal(groups, exp) + + # GH5620 + groups = idx.groupby(idx) + exp = {key: [key] for key in idx} + tm.assert_dict_equal(groups, exp) + + +def test_truncate_multiindex(): + # GH 34564 for MultiIndex level names check + major_axis = Index(list(range(4))) + minor_axis = Index(list(range(2))) + + major_codes = np.array([0, 0, 1, 2, 3, 3]) + minor_codes = np.array([0, 1, 0, 1, 0, 1]) + + index = MultiIndex( + levels=[major_axis, minor_axis], + codes=[major_codes, minor_codes], + names=["L1", "L2"], + ) + + result = index.truncate(before=1) + assert "foo" not in result.levels[0] + assert 1 in result.levels[0] + assert index.names == result.names + + result = index.truncate(after=1) + assert 2 not in result.levels[0] + assert 1 in result.levels[0] + assert index.names == result.names + + result = index.truncate(before=1, after=2) + assert len(result.levels[0]) == 2 + assert index.names == result.names + + msg = "after < before" + with pytest.raises(ValueError, match=msg): + index.truncate(3, 1) + + +# TODO: reshape + + +def test_reorder_levels(idx): + # this blows up + with pytest.raises(IndexError, match="^Too many levels"): + idx.reorder_levels([2, 1, 0]) + + +def test_numpy_repeat(): + reps = 2 + numbers = [1, 2, 3] + names = np.array(["foo", "bar"]) + + m = MultiIndex.from_product([numbers, names], names=names) + expected = MultiIndex.from_product([numbers, names.repeat(reps)], names=names) + tm.assert_index_equal(np.repeat(m, reps), expected) + + msg = "the 'axis' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.repeat(m, reps, axis=1) + + +def test_append_mixed_dtypes(): + # GH 13660 + dti = date_range("2011-01-01", freq="ME", periods=3) + dti_tz = date_range("2011-01-01", freq="ME", periods=3, tz="US/Eastern") + pi = period_range("2011-01", freq="M", periods=3) + + mi = MultiIndex.from_arrays( + [[1, 2, 3], [1.1, np.nan, 3.3], ["a", "b", "c"], dti, dti_tz, pi] + ) + assert mi.nlevels == 6 + + res = mi.append(mi) + exp = MultiIndex.from_arrays( + [ + [1, 2, 3, 1, 2, 3], + [1.1, np.nan, 3.3, 1.1, np.nan, 3.3], + ["a", "b", "c", "a", "b", "c"], + dti.append(dti), + dti_tz.append(dti_tz), + pi.append(pi), + ] + ) + tm.assert_index_equal(res, exp) + + other = MultiIndex.from_arrays( + [ + ["x", "y", "z"], + ["x", "y", "z"], + ["x", "y", "z"], + ["x", "y", "z"], + ["x", "y", "z"], + ["x", "y", "z"], + ] + ) + + res = mi.append(other) + exp = MultiIndex.from_arrays( + [ + [1, 2, 3, "x", "y", "z"], + [1.1, np.nan, 3.3, "x", "y", "z"], + ["a", "b", "c", "x", "y", "z"], + dti.append(Index(["x", "y", "z"])), + dti_tz.append(Index(["x", "y", "z"])), + pi.append(Index(["x", "y", "z"])), + ] + ) + tm.assert_index_equal(res, exp) + + +def test_iter(idx): + result = list(idx) + expected = [ + ("foo", "one"), + ("foo", "two"), + ("bar", "one"), + ("baz", "two"), + ("qux", "one"), + ("qux", "two"), + ] + assert result == expected + + +def test_sub(idx): + first = idx + + # - now raises (previously was set op difference) + msg = "cannot perform __sub__ with this index type: MultiIndex" + with pytest.raises(TypeError, match=msg): + first - idx[-3:] + with pytest.raises(TypeError, match=msg): + idx[-3:] - first + with pytest.raises(TypeError, match=msg): + idx[-3:] - first.tolist() + msg = "cannot perform __rsub__ with this index type: MultiIndex" + with pytest.raises(TypeError, match=msg): + first.tolist() - idx[-3:] + + +def test_map(idx): + # callable + index = idx + + result = index.map(lambda x: x) + tm.assert_index_equal(result, index) + + +@pytest.mark.parametrize( + "mapper", + [ + lambda values, idx: {i: e for e, i in zip(values, idx)}, + lambda values, idx: pd.Series(values, idx), + ], +) +def test_map_dictlike(idx, mapper): + identity = mapper(idx.values, idx) + + # we don't infer to uint64 dtype for a dict + if idx.dtype == np.uint64 and isinstance(identity, dict): + expected = idx.astype("int64") + else: + expected = idx + + result = idx.map(identity) + tm.assert_index_equal(result, expected) + + # empty mappable + expected = Index([np.nan] * len(idx)) + result = idx.map(mapper(expected, idx)) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize( + "func", + [ + np.exp, + np.exp2, + np.expm1, + np.log, + np.log2, + np.log10, + np.log1p, + np.sqrt, + np.sin, + np.cos, + np.tan, + np.arcsin, + np.arccos, + np.arctan, + np.sinh, + np.cosh, + np.tanh, + np.arcsinh, + np.arccosh, + np.arctanh, + np.deg2rad, + np.rad2deg, + ], + ids=lambda func: func.__name__, +) +def test_numpy_ufuncs(idx, func): + # test ufuncs of numpy. see: + # https://numpy.org/doc/stable/reference/ufuncs.html + + expected_exception = TypeError + msg = ( + "loop of ufunc does not support argument 0 of type tuple which " + f"has no callable {func.__name__} method" + ) + with pytest.raises(expected_exception, match=msg): + func(idx) + + +@pytest.mark.parametrize( + "func", + [np.isfinite, np.isinf, np.isnan, np.signbit], + ids=lambda func: func.__name__, +) +def test_numpy_type_funcs(idx, func): + msg = ( + f"ufunc '{func.__name__}' not supported for the input types, and the inputs " + "could not be safely coerced to any supported types according to " + "the casting rule ''safe''" + ) + with pytest.raises(TypeError, match=msg): + func(idx) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_astype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..29908537fbe590328ac586e05f90f3cc24cab9ab --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_astype.py @@ -0,0 +1,30 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.dtypes import CategoricalDtype + +import pandas._testing as tm + + +def test_astype(idx): + expected = idx.copy() + actual = idx.astype("O") + tm.assert_copy(actual.levels, expected.levels) + tm.assert_copy(actual.codes, expected.codes) + assert actual.names == list(expected.names) + + with pytest.raises(TypeError, match="^Setting.*dtype.*object"): + idx.astype(np.dtype(int)) + + +@pytest.mark.parametrize("ordered", [True, False]) +def test_astype_category(idx, ordered): + # GH 18630 + msg = "> 1 ndim Categorical are not supported at this time" + with pytest.raises(NotImplementedError, match=msg): + idx.astype(CategoricalDtype(ordered=ordered)) + + if ordered is False: + # dtype='category' defaults to ordered=False, so only test once + with pytest.raises(NotImplementedError, match=msg): + idx.astype("category") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_compat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..27a8c6e9b715880a57e711e8eab457ae553a4867 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_compat.py @@ -0,0 +1,122 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import MultiIndex +import pandas._testing as tm + + +def test_numeric_compat(idx): + with pytest.raises(TypeError, match="cannot perform __mul__"): + idx * 1 + + with pytest.raises(TypeError, match="cannot perform __rmul__"): + 1 * idx + + div_err = "cannot perform __truediv__" + with pytest.raises(TypeError, match=div_err): + idx / 1 + + div_err = div_err.replace(" __", " __r") + with pytest.raises(TypeError, match=div_err): + 1 / idx + + with pytest.raises(TypeError, match="cannot perform __floordiv__"): + idx // 1 + + with pytest.raises(TypeError, match="cannot perform __rfloordiv__"): + 1 // idx + + +@pytest.mark.parametrize("method", ["all", "any", "__invert__"]) +def test_logical_compat(idx, method): + msg = f"cannot perform {method}" + + with pytest.raises(TypeError, match=msg): + getattr(idx, method)() + + +def test_inplace_mutation_resets_values(): + levels = [["a", "b", "c"], [4]] + levels2 = [[1, 2, 3], ["a"]] + codes = [[0, 1, 0, 2, 2, 0], [0, 0, 0, 0, 0, 0]] + + mi1 = MultiIndex(levels=levels, codes=codes) + mi2 = MultiIndex(levels=levels2, codes=codes) + + # instantiating MultiIndex should not access/cache _.values + assert "_values" not in mi1._cache + assert "_values" not in mi2._cache + + vals = mi1.values.copy() + vals2 = mi2.values.copy() + + # accessing .values should cache ._values + assert mi1._values is mi1._cache["_values"] + assert mi1.values is mi1._cache["_values"] + assert isinstance(mi1._cache["_values"], np.ndarray) + + # Make sure level setting works + new_vals = mi1.set_levels(levels2).values + tm.assert_almost_equal(vals2, new_vals) + + # Doesn't drop _values from _cache [implementation detail] + tm.assert_almost_equal(mi1._cache["_values"], vals) + + # ...and values is still same too + tm.assert_almost_equal(mi1.values, vals) + + # Make sure label setting works too + codes2 = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]] + exp_values = np.empty((6,), dtype=object) + exp_values[:] = [(1, "a")] * 6 + + # Must be 1d array of tuples + assert exp_values.shape == (6,) + + new_mi = mi2.set_codes(codes2) + assert "_values" not in new_mi._cache + new_values = new_mi.values + assert "_values" in new_mi._cache + + # Shouldn't change cache + tm.assert_almost_equal(mi2._cache["_values"], vals2) + + # Should have correct values + tm.assert_almost_equal(exp_values, new_values) + + +def test_boxable_categorical_values(): + cat = pd.Categorical(pd.date_range("2012-01-01", periods=3, freq="h")) + result = MultiIndex.from_product([["a", "b", "c"], cat]).values + expected = pd.Series( + [ + ("a", pd.Timestamp("2012-01-01 00:00:00")), + ("a", pd.Timestamp("2012-01-01 01:00:00")), + ("a", pd.Timestamp("2012-01-01 02:00:00")), + ("b", pd.Timestamp("2012-01-01 00:00:00")), + ("b", pd.Timestamp("2012-01-01 01:00:00")), + ("b", pd.Timestamp("2012-01-01 02:00:00")), + ("c", pd.Timestamp("2012-01-01 00:00:00")), + ("c", pd.Timestamp("2012-01-01 01:00:00")), + ("c", pd.Timestamp("2012-01-01 02:00:00")), + ] + ).values + tm.assert_numpy_array_equal(result, expected) + result = pd.DataFrame({"a": ["a", "b", "c"], "b": cat, "c": np.array(cat)}).values + expected = pd.DataFrame( + { + "a": ["a", "b", "c"], + "b": [ + pd.Timestamp("2012-01-01 00:00:00"), + pd.Timestamp("2012-01-01 01:00:00"), + pd.Timestamp("2012-01-01 02:00:00"), + ], + "c": [ + pd.Timestamp("2012-01-01 00:00:00"), + pd.Timestamp("2012-01-01 01:00:00"), + pd.Timestamp("2012-01-01 02:00:00"), + ], + } + ).values + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_constructors.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..b1180f2d7af145dd30592925bfd16a4c2484a88f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_constructors.py @@ -0,0 +1,860 @@ +from datetime import ( + date, + datetime, +) +import itertools + +import numpy as np +import pytest + +from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike + +import pandas as pd +from pandas import ( + Index, + MultiIndex, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + + +def test_constructor_single_level(): + result = MultiIndex( + levels=[["foo", "bar", "baz", "qux"]], codes=[[0, 1, 2, 3]], names=["first"] + ) + assert isinstance(result, MultiIndex) + expected = Index(["foo", "bar", "baz", "qux"], name="first") + tm.assert_index_equal(result.levels[0], expected) + assert result.names == ["first"] + + +def test_constructor_no_levels(): + msg = "non-zero number of levels/codes" + with pytest.raises(ValueError, match=msg): + MultiIndex(levels=[], codes=[]) + + msg = "Must pass both levels and codes" + with pytest.raises(TypeError, match=msg): + MultiIndex(levels=[]) + with pytest.raises(TypeError, match=msg): + MultiIndex(codes=[]) + + +def test_constructor_nonhashable_names(): + # GH 20527 + levels = [[1, 2], ["one", "two"]] + codes = [[0, 0, 1, 1], [0, 1, 0, 1]] + names = (["foo"], ["bar"]) + msg = r"MultiIndex\.name must be a hashable type" + with pytest.raises(TypeError, match=msg): + MultiIndex(levels=levels, codes=codes, names=names) + + # With .rename() + mi = MultiIndex( + levels=[[1, 2], ["one", "two"]], + codes=[[0, 0, 1, 1], [0, 1, 0, 1]], + names=("foo", "bar"), + ) + renamed = [["fooo"], ["barr"]] + with pytest.raises(TypeError, match=msg): + mi.rename(names=renamed) + + # With .set_names() + with pytest.raises(TypeError, match=msg): + mi.set_names(names=renamed) + + +def test_constructor_mismatched_codes_levels(idx): + codes = [np.array([1]), np.array([2]), np.array([3])] + levels = ["a"] + + msg = "Length of levels and codes must be the same" + with pytest.raises(ValueError, match=msg): + MultiIndex(levels=levels, codes=codes) + + length_error = ( + r"On level 0, code max \(3\) >= length of level \(1\)\. " + "NOTE: this index is in an inconsistent state" + ) + label_error = r"Unequal code lengths: \[4, 2\]" + code_value_error = r"On level 0, code value \(-2\) < -1" + + # important to check that it's looking at the right thing. + with pytest.raises(ValueError, match=length_error): + MultiIndex(levels=[["a"], ["b"]], codes=[[0, 1, 2, 3], [0, 3, 4, 1]]) + + with pytest.raises(ValueError, match=label_error): + MultiIndex(levels=[["a"], ["b"]], codes=[[0, 0, 0, 0], [0, 0]]) + + # external API + with pytest.raises(ValueError, match=length_error): + idx.copy().set_levels([["a"], ["b"]]) + + with pytest.raises(ValueError, match=label_error): + idx.copy().set_codes([[0, 0, 0, 0], [0, 0]]) + + # test set_codes with verify_integrity=False + # the setting should not raise any value error + idx.copy().set_codes(codes=[[0, 0, 0, 0], [0, 0]], verify_integrity=False) + + # code value smaller than -1 + with pytest.raises(ValueError, match=code_value_error): + MultiIndex(levels=[["a"], ["b"]], codes=[[0, -2], [0, 0]]) + + +def test_na_levels(): + # GH26408 + # test if codes are re-assigned value -1 for levels + # with missing values (NaN, NaT, None) + result = MultiIndex( + levels=[[np.nan, None, pd.NaT, 128, 2]], codes=[[0, -1, 1, 2, 3, 4]] + ) + expected = MultiIndex( + levels=[[np.nan, None, pd.NaT, 128, 2]], codes=[[-1, -1, -1, -1, 3, 4]] + ) + tm.assert_index_equal(result, expected) + + result = MultiIndex( + levels=[[np.nan, "s", pd.NaT, 128, None]], codes=[[0, -1, 1, 2, 3, 4]] + ) + expected = MultiIndex( + levels=[[np.nan, "s", pd.NaT, 128, None]], codes=[[-1, -1, 1, -1, 3, -1]] + ) + tm.assert_index_equal(result, expected) + + # verify set_levels and set_codes + result = MultiIndex( + levels=[[1, 2, 3, 4, 5]], codes=[[0, -1, 1, 2, 3, 4]] + ).set_levels([[np.nan, "s", pd.NaT, 128, None]]) + tm.assert_index_equal(result, expected) + + result = MultiIndex( + levels=[[np.nan, "s", pd.NaT, 128, None]], codes=[[1, 2, 2, 2, 2, 2]] + ).set_codes([[0, -1, 1, 2, 3, 4]]) + tm.assert_index_equal(result, expected) + + +def test_copy_in_constructor(): + levels = np.array(["a", "b", "c"]) + codes = np.array([1, 1, 2, 0, 0, 1, 1]) + val = codes[0] + mi = MultiIndex(levels=[levels, levels], codes=[codes, codes], copy=True) + assert mi.codes[0][0] == val + codes[0] = 15 + assert mi.codes[0][0] == val + val = levels[0] + levels[0] = "PANDA" + assert mi.levels[0][0] == val + + +# ---------------------------------------------------------------------------- +# from_arrays +# ---------------------------------------------------------------------------- +def test_from_arrays(idx): + arrays = [ + np.asarray(lev).take(level_codes) + for lev, level_codes in zip(idx.levels, idx.codes) + ] + + # list of arrays as input + result = MultiIndex.from_arrays(arrays, names=idx.names) + tm.assert_index_equal(result, idx) + + # infer correctly + result = MultiIndex.from_arrays([[pd.NaT, Timestamp("20130101")], ["a", "b"]]) + assert result.levels[0].equals(Index([Timestamp("20130101")])) + assert result.levels[1].equals(Index(["a", "b"])) + + +def test_from_arrays_iterator(idx): + # GH 18434 + arrays = [ + np.asarray(lev).take(level_codes) + for lev, level_codes in zip(idx.levels, idx.codes) + ] + + # iterator as input + result = MultiIndex.from_arrays(iter(arrays), names=idx.names) + tm.assert_index_equal(result, idx) + + # invalid iterator input + msg = "Input must be a list / sequence of array-likes." + with pytest.raises(TypeError, match=msg): + MultiIndex.from_arrays(0) + + +def test_from_arrays_tuples(idx): + arrays = tuple( + tuple(np.asarray(lev).take(level_codes)) + for lev, level_codes in zip(idx.levels, idx.codes) + ) + + # tuple of tuples as input + result = MultiIndex.from_arrays(arrays, names=idx.names) + tm.assert_index_equal(result, idx) + + +@pytest.mark.parametrize( + ("idx1", "idx2"), + [ + ( + pd.period_range("2011-01-01", freq="D", periods=3), + pd.period_range("2015-01-01", freq="h", periods=3), + ), + ( + date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern"), + date_range("2015-01-01 10:00", freq="h", periods=3, tz="Asia/Tokyo"), + ), + ( + pd.timedelta_range("1 days", freq="D", periods=3), + pd.timedelta_range("2 hours", freq="h", periods=3), + ), + ], +) +def test_from_arrays_index_series_period_datetimetz_and_timedelta(idx1, idx2): + result = MultiIndex.from_arrays([idx1, idx2]) + tm.assert_index_equal(result.get_level_values(0), idx1) + tm.assert_index_equal(result.get_level_values(1), idx2) + + result2 = MultiIndex.from_arrays([Series(idx1), Series(idx2)]) + tm.assert_index_equal(result2.get_level_values(0), idx1) + tm.assert_index_equal(result2.get_level_values(1), idx2) + + tm.assert_index_equal(result, result2) + + +def test_from_arrays_index_datetimelike_mixed(): + idx1 = date_range("2015-01-01 10:00", freq="D", periods=3, tz="US/Eastern") + idx2 = date_range("2015-01-01 10:00", freq="h", periods=3) + idx3 = pd.timedelta_range("1 days", freq="D", periods=3) + idx4 = pd.period_range("2011-01-01", freq="D", periods=3) + + result = MultiIndex.from_arrays([idx1, idx2, idx3, idx4]) + tm.assert_index_equal(result.get_level_values(0), idx1) + tm.assert_index_equal(result.get_level_values(1), idx2) + tm.assert_index_equal(result.get_level_values(2), idx3) + tm.assert_index_equal(result.get_level_values(3), idx4) + + result2 = MultiIndex.from_arrays( + [Series(idx1), Series(idx2), Series(idx3), Series(idx4)] + ) + tm.assert_index_equal(result2.get_level_values(0), idx1) + tm.assert_index_equal(result2.get_level_values(1), idx2) + tm.assert_index_equal(result2.get_level_values(2), idx3) + tm.assert_index_equal(result2.get_level_values(3), idx4) + + tm.assert_index_equal(result, result2) + + +def test_from_arrays_index_series_categorical(): + # GH13743 + idx1 = pd.CategoricalIndex(list("abcaab"), categories=list("bac"), ordered=False) + idx2 = pd.CategoricalIndex(list("abcaab"), categories=list("bac"), ordered=True) + + result = MultiIndex.from_arrays([idx1, idx2]) + tm.assert_index_equal(result.get_level_values(0), idx1) + tm.assert_index_equal(result.get_level_values(1), idx2) + + result2 = MultiIndex.from_arrays([Series(idx1), Series(idx2)]) + tm.assert_index_equal(result2.get_level_values(0), idx1) + tm.assert_index_equal(result2.get_level_values(1), idx2) + + result3 = MultiIndex.from_arrays([idx1.values, idx2.values]) + tm.assert_index_equal(result3.get_level_values(0), idx1) + tm.assert_index_equal(result3.get_level_values(1), idx2) + + +def test_from_arrays_empty(): + # 0 levels + msg = "Must pass non-zero number of levels/codes" + with pytest.raises(ValueError, match=msg): + MultiIndex.from_arrays(arrays=[]) + + # 1 level + result = MultiIndex.from_arrays(arrays=[[]], names=["A"]) + assert isinstance(result, MultiIndex) + expected = Index([], name="A") + tm.assert_index_equal(result.levels[0], expected) + assert result.names == ["A"] + + # N levels + for N in [2, 3]: + arrays = [[]] * N + names = list("ABC")[:N] + result = MultiIndex.from_arrays(arrays=arrays, names=names) + expected = MultiIndex(levels=[[]] * N, codes=[[]] * N, names=names) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize( + "invalid_sequence_of_arrays", + [ + 1, + [1], + [1, 2], + [[1], 2], + [1, [2]], + "a", + ["a"], + ["a", "b"], + [["a"], "b"], + (1,), + (1, 2), + ([1], 2), + (1, [2]), + "a", + ("a",), + ("a", "b"), + (["a"], "b"), + [(1,), 2], + [1, (2,)], + [("a",), "b"], + ((1,), 2), + (1, (2,)), + (("a",), "b"), + ], +) +def test_from_arrays_invalid_input(invalid_sequence_of_arrays): + msg = "Input must be a list / sequence of array-likes" + with pytest.raises(TypeError, match=msg): + MultiIndex.from_arrays(arrays=invalid_sequence_of_arrays) + + +@pytest.mark.parametrize( + "idx1, idx2", [([1, 2, 3], ["a", "b"]), ([], ["a", "b"]), ([1, 2, 3], [])] +) +def test_from_arrays_different_lengths(idx1, idx2): + # see gh-13599 + msg = "^all arrays must be same length$" + with pytest.raises(ValueError, match=msg): + MultiIndex.from_arrays([idx1, idx2]) + + +def test_from_arrays_respects_none_names(): + # GH27292 + a = Series([1, 2, 3], name="foo") + b = Series(["a", "b", "c"], name="bar") + + result = MultiIndex.from_arrays([a, b], names=None) + expected = MultiIndex( + levels=[[1, 2, 3], ["a", "b", "c"]], codes=[[0, 1, 2], [0, 1, 2]], names=None + ) + + tm.assert_index_equal(result, expected) + + +# ---------------------------------------------------------------------------- +# from_tuples +# ---------------------------------------------------------------------------- +def test_from_tuples(): + msg = "Cannot infer number of levels from empty list" + with pytest.raises(TypeError, match=msg): + MultiIndex.from_tuples([]) + + expected = MultiIndex( + levels=[[1, 3], [2, 4]], codes=[[0, 1], [0, 1]], names=["a", "b"] + ) + + # input tuples + result = MultiIndex.from_tuples(((1, 2), (3, 4)), names=["a", "b"]) + tm.assert_index_equal(result, expected) + + +def test_from_tuples_iterator(): + # GH 18434 + # input iterator for tuples + expected = MultiIndex( + levels=[[1, 3], [2, 4]], codes=[[0, 1], [0, 1]], names=["a", "b"] + ) + + result = MultiIndex.from_tuples(zip([1, 3], [2, 4]), names=["a", "b"]) + tm.assert_index_equal(result, expected) + + # input non-iterables + msg = "Input must be a list / sequence of tuple-likes." + with pytest.raises(TypeError, match=msg): + MultiIndex.from_tuples(0) + + +def test_from_tuples_empty(): + # GH 16777 + result = MultiIndex.from_tuples([], names=["a", "b"]) + expected = MultiIndex.from_arrays(arrays=[[], []], names=["a", "b"]) + tm.assert_index_equal(result, expected) + + +def test_from_tuples_index_values(idx): + result = MultiIndex.from_tuples(idx) + assert (result.values == idx.values).all() + + +def test_tuples_with_name_string(): + # GH 15110 and GH 14848 + + li = [(0, 0, 1), (0, 1, 0), (1, 0, 0)] + msg = "Names should be list-like for a MultiIndex" + with pytest.raises(ValueError, match=msg): + Index(li, name="abc") + with pytest.raises(ValueError, match=msg): + Index(li, name="a") + + +def test_from_tuples_with_tuple_label(): + # GH 15457 + expected = pd.DataFrame( + [[2, 1, 2], [4, (1, 2), 3]], columns=["a", "b", "c"] + ).set_index(["a", "b"]) + idx = MultiIndex.from_tuples([(2, 1), (4, (1, 2))], names=("a", "b")) + result = pd.DataFrame([2, 3], columns=["c"], index=idx) + tm.assert_frame_equal(expected, result) + + +# ---------------------------------------------------------------------------- +# from_product +# ---------------------------------------------------------------------------- +def test_from_product_empty_zero_levels(): + # 0 levels + msg = "Must pass non-zero number of levels/codes" + with pytest.raises(ValueError, match=msg): + MultiIndex.from_product([]) + + +def test_from_product_empty_one_level(): + result = MultiIndex.from_product([[]], names=["A"]) + expected = Index([], name="A") + tm.assert_index_equal(result.levels[0], expected) + assert result.names == ["A"] + + +@pytest.mark.parametrize( + "first, second", [([], []), (["foo", "bar", "baz"], []), ([], ["a", "b", "c"])] +) +def test_from_product_empty_two_levels(first, second): + names = ["A", "B"] + result = MultiIndex.from_product([first, second], names=names) + expected = MultiIndex(levels=[first, second], codes=[[], []], names=names) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("N", list(range(4))) +def test_from_product_empty_three_levels(N): + # GH12258 + names = ["A", "B", "C"] + lvl2 = list(range(N)) + result = MultiIndex.from_product([[], lvl2, []], names=names) + expected = MultiIndex(levels=[[], lvl2, []], codes=[[], [], []], names=names) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize( + "invalid_input", [1, [1], [1, 2], [[1], 2], "a", ["a"], ["a", "b"], [["a"], "b"]] +) +def test_from_product_invalid_input(invalid_input): + msg = r"Input must be a list / sequence of iterables|Input must be list-like" + with pytest.raises(TypeError, match=msg): + MultiIndex.from_product(iterables=invalid_input) + + +def test_from_product_datetimeindex(): + dt_index = date_range("2000-01-01", periods=2) + mi = MultiIndex.from_product([[1, 2], dt_index]) + etalon = construct_1d_object_array_from_listlike( + [ + (1, Timestamp("2000-01-01")), + (1, Timestamp("2000-01-02")), + (2, Timestamp("2000-01-01")), + (2, Timestamp("2000-01-02")), + ] + ) + tm.assert_numpy_array_equal(mi.values, etalon) + + +def test_from_product_rangeindex(): + # RangeIndex is preserved by factorize, so preserved in levels + rng = Index(range(5)) + other = ["a", "b"] + mi = MultiIndex.from_product([rng, other]) + tm.assert_index_equal(mi._levels[0], rng, exact=True) + + +@pytest.mark.parametrize("ordered", [False, True]) +@pytest.mark.parametrize("f", [lambda x: x, lambda x: Series(x), lambda x: x.values]) +def test_from_product_index_series_categorical(ordered, f): + # GH13743 + first = ["foo", "bar"] + + idx = pd.CategoricalIndex(list("abcaab"), categories=list("bac"), ordered=ordered) + expected = pd.CategoricalIndex( + list("abcaab") + list("abcaab"), categories=list("bac"), ordered=ordered + ) + + result = MultiIndex.from_product([first, f(idx)]) + tm.assert_index_equal(result.get_level_values(1), expected) + + +def test_from_product(): + first = ["foo", "bar", "buz"] + second = ["a", "b", "c"] + names = ["first", "second"] + result = MultiIndex.from_product([first, second], names=names) + + tuples = [ + ("foo", "a"), + ("foo", "b"), + ("foo", "c"), + ("bar", "a"), + ("bar", "b"), + ("bar", "c"), + ("buz", "a"), + ("buz", "b"), + ("buz", "c"), + ] + expected = MultiIndex.from_tuples(tuples, names=names) + + tm.assert_index_equal(result, expected) + + +def test_from_product_iterator(): + # GH 18434 + first = ["foo", "bar", "buz"] + second = ["a", "b", "c"] + names = ["first", "second"] + tuples = [ + ("foo", "a"), + ("foo", "b"), + ("foo", "c"), + ("bar", "a"), + ("bar", "b"), + ("bar", "c"), + ("buz", "a"), + ("buz", "b"), + ("buz", "c"), + ] + expected = MultiIndex.from_tuples(tuples, names=names) + + # iterator as input + result = MultiIndex.from_product(iter([first, second]), names=names) + tm.assert_index_equal(result, expected) + + # Invalid non-iterable input + msg = "Input must be a list / sequence of iterables." + with pytest.raises(TypeError, match=msg): + MultiIndex.from_product(0) + + +@pytest.mark.parametrize( + "a, b, expected_names", + [ + ( + Series([1, 2, 3], name="foo"), + Series(["a", "b"], name="bar"), + ["foo", "bar"], + ), + (Series([1, 2, 3], name="foo"), ["a", "b"], ["foo", None]), + ([1, 2, 3], ["a", "b"], None), + ], +) +def test_from_product_infer_names(a, b, expected_names): + # GH27292 + result = MultiIndex.from_product([a, b]) + expected = MultiIndex( + levels=[[1, 2, 3], ["a", "b"]], + codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]], + names=expected_names, + ) + tm.assert_index_equal(result, expected) + + +def test_from_product_respects_none_names(): + # GH27292 + a = Series([1, 2, 3], name="foo") + b = Series(["a", "b"], name="bar") + + result = MultiIndex.from_product([a, b], names=None) + expected = MultiIndex( + levels=[[1, 2, 3], ["a", "b"]], + codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]], + names=None, + ) + tm.assert_index_equal(result, expected) + + +def test_from_product_readonly(): + # GH#15286 passing read-only array to from_product + a = np.array(range(3)) + b = ["a", "b"] + expected = MultiIndex.from_product([a, b]) + + a.setflags(write=False) + result = MultiIndex.from_product([a, b]) + tm.assert_index_equal(result, expected) + + +def test_create_index_existing_name(idx): + # GH11193, when an existing index is passed, and a new name is not + # specified, the new index should inherit the previous object name + index = idx + index.names = ["foo", "bar"] + result = Index(index) + expected = Index( + Index( + [ + ("foo", "one"), + ("foo", "two"), + ("bar", "one"), + ("baz", "two"), + ("qux", "one"), + ("qux", "two"), + ], + dtype="object", + ) + ) + tm.assert_index_equal(result, expected) + + result = Index(index, name="A") + expected = Index( + Index( + [ + ("foo", "one"), + ("foo", "two"), + ("bar", "one"), + ("baz", "two"), + ("qux", "one"), + ("qux", "two"), + ], + dtype="object", + ), + name="A", + ) + tm.assert_index_equal(result, expected) + + +# ---------------------------------------------------------------------------- +# from_frame +# ---------------------------------------------------------------------------- +def test_from_frame(): + # GH 22420 + df = pd.DataFrame( + [["a", "a"], ["a", "b"], ["b", "a"], ["b", "b"]], columns=["L1", "L2"] + ) + expected = MultiIndex.from_tuples( + [("a", "a"), ("a", "b"), ("b", "a"), ("b", "b")], names=["L1", "L2"] + ) + result = MultiIndex.from_frame(df) + tm.assert_index_equal(expected, result) + + +def test_from_frame_missing_values_multiIndex(): + # GH 39984 + pa = pytest.importorskip("pyarrow") + + df = pd.DataFrame( + { + "a": Series([1, 2, None], dtype="Int64"), + "b": pd.Float64Dtype().__from_arrow__(pa.array([0.2, np.nan, None])), + } + ) + multi_indexed = MultiIndex.from_frame(df) + expected = MultiIndex.from_arrays( + [ + Series([1, 2, None]).astype("Int64"), + pd.Float64Dtype().__from_arrow__(pa.array([0.2, np.nan, None])), + ], + names=["a", "b"], + ) + tm.assert_index_equal(multi_indexed, expected) + + +@pytest.mark.parametrize( + "non_frame", + [ + Series([1, 2, 3, 4]), + [1, 2, 3, 4], + [[1, 2], [3, 4], [5, 6]], + Index([1, 2, 3, 4]), + np.array([[1, 2], [3, 4], [5, 6]]), + 27, + ], +) +def test_from_frame_error(non_frame): + # GH 22420 + with pytest.raises(TypeError, match="Input must be a DataFrame"): + MultiIndex.from_frame(non_frame) + + +def test_from_frame_dtype_fidelity(): + # GH 22420 + df = pd.DataFrame( + { + "dates": date_range("19910905", periods=6, tz="US/Eastern"), + "a": [1, 1, 1, 2, 2, 2], + "b": pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True), + "c": ["x", "x", "y", "z", "x", "y"], + } + ) + original_dtypes = df.dtypes.to_dict() + + expected_mi = MultiIndex.from_arrays( + [ + date_range("19910905", periods=6, tz="US/Eastern"), + [1, 1, 1, 2, 2, 2], + pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True), + ["x", "x", "y", "z", "x", "y"], + ], + names=["dates", "a", "b", "c"], + ) + mi = MultiIndex.from_frame(df) + mi_dtypes = {name: mi.levels[i].dtype for i, name in enumerate(mi.names)} + + tm.assert_index_equal(expected_mi, mi) + assert original_dtypes == mi_dtypes + + +@pytest.mark.parametrize( + "names_in,names_out", [(None, [("L1", "x"), ("L2", "y")]), (["x", "y"], ["x", "y"])] +) +def test_from_frame_valid_names(names_in, names_out): + # GH 22420 + df = pd.DataFrame( + [["a", "a"], ["a", "b"], ["b", "a"], ["b", "b"]], + columns=MultiIndex.from_tuples([("L1", "x"), ("L2", "y")]), + ) + mi = MultiIndex.from_frame(df, names=names_in) + assert mi.names == names_out + + +@pytest.mark.parametrize( + "names,expected_error_msg", + [ + ("bad_input", "Names should be list-like for a MultiIndex"), + (["a", "b", "c"], "Length of names must match number of levels in MultiIndex"), + ], +) +def test_from_frame_invalid_names(names, expected_error_msg): + # GH 22420 + df = pd.DataFrame( + [["a", "a"], ["a", "b"], ["b", "a"], ["b", "b"]], + columns=MultiIndex.from_tuples([("L1", "x"), ("L2", "y")]), + ) + with pytest.raises(ValueError, match=expected_error_msg): + MultiIndex.from_frame(df, names=names) + + +def test_index_equal_empty_iterable(): + # #16844 + a = MultiIndex(levels=[[], []], codes=[[], []], names=["a", "b"]) + b = MultiIndex.from_arrays(arrays=[[], []], names=["a", "b"]) + tm.assert_index_equal(a, b) + + +def test_raise_invalid_sortorder(): + # Test that the MultiIndex constructor raise when a incorrect sortorder is given + # GH#28518 + + levels = [[0, 1], [0, 1, 2]] + + # Correct sortorder + MultiIndex( + levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], sortorder=2 + ) + + with pytest.raises(ValueError, match=r".* sortorder 2 with lexsort_depth 1.*"): + MultiIndex( + levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]], sortorder=2 + ) + + with pytest.raises(ValueError, match=r".* sortorder 1 with lexsort_depth 0.*"): + MultiIndex( + levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]], sortorder=1 + ) + + +def test_datetimeindex(): + idx1 = pd.DatetimeIndex( + ["2013-04-01 9:00", "2013-04-02 9:00", "2013-04-03 9:00"] * 2, tz="Asia/Tokyo" + ) + idx2 = date_range("2010/01/01", periods=6, freq="ME", tz="US/Eastern") + idx = MultiIndex.from_arrays([idx1, idx2]) + + expected1 = pd.DatetimeIndex( + ["2013-04-01 9:00", "2013-04-02 9:00", "2013-04-03 9:00"], tz="Asia/Tokyo" + ) + + tm.assert_index_equal(idx.levels[0], expected1) + tm.assert_index_equal(idx.levels[1], idx2) + + # from datetime combos + # GH 7888 + date1 = np.datetime64("today") + date2 = datetime.today() + date3 = Timestamp.today() + + for d1, d2 in itertools.product([date1, date2, date3], [date1, date2, date3]): + index = MultiIndex.from_product([[d1], [d2]]) + assert isinstance(index.levels[0], pd.DatetimeIndex) + assert isinstance(index.levels[1], pd.DatetimeIndex) + + # but NOT date objects, matching Index behavior + date4 = date.today() + index = MultiIndex.from_product([[date4], [date2]]) + assert not isinstance(index.levels[0], pd.DatetimeIndex) + assert isinstance(index.levels[1], pd.DatetimeIndex) + + +def test_constructor_with_tz(): + index = pd.DatetimeIndex( + ["2013/01/01 09:00", "2013/01/02 09:00"], name="dt1", tz="US/Pacific" + ) + columns = pd.DatetimeIndex( + ["2014/01/01 09:00", "2014/01/02 09:00"], name="dt2", tz="Asia/Tokyo" + ) + + result = MultiIndex.from_arrays([index, columns]) + + assert result.names == ["dt1", "dt2"] + tm.assert_index_equal(result.levels[0], index) + tm.assert_index_equal(result.levels[1], columns) + + result = MultiIndex.from_arrays([Series(index), Series(columns)]) + + assert result.names == ["dt1", "dt2"] + tm.assert_index_equal(result.levels[0], index) + tm.assert_index_equal(result.levels[1], columns) + + +def test_multiindex_inference_consistency(): + # check that inference behavior matches the base class + + v = date.today() + + arr = [v, v] + + idx = Index(arr) + assert idx.dtype == object + + mi = MultiIndex.from_arrays([arr]) + lev = mi.levels[0] + assert lev.dtype == object + + mi = MultiIndex.from_product([arr]) + lev = mi.levels[0] + assert lev.dtype == object + + mi = MultiIndex.from_tuples([(x,) for x in arr]) + lev = mi.levels[0] + assert lev.dtype == object + + +def test_dtype_representation(using_infer_string): + # GH#46900 + pmidx = MultiIndex.from_arrays([[1], ["a"]], names=[("a", "b"), ("c", "d")]) + result = pmidx.dtypes + exp = "object" if not using_infer_string else pd.StringDtype(na_value=np.nan) + expected = Series( + ["int64", exp], + index=MultiIndex.from_tuples([("a", "b"), ("c", "d")]), + dtype=object, + ) + tm.assert_series_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_conversion.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_conversion.py new file mode 100644 index 0000000000000000000000000000000000000000..d62bd5438a1e39e2a371b731f7b74c48cd0cc3e3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_conversion.py @@ -0,0 +1,201 @@ +import numpy as np +import pytest + +from pandas.compat.numpy import np_version_gt2 + +import pandas as pd +from pandas import ( + DataFrame, + MultiIndex, +) +import pandas._testing as tm + + +def test_to_numpy(idx): + result = idx.to_numpy() + exp = idx.values + tm.assert_numpy_array_equal(result, exp) + + +def test_array_interface(idx): + # https://github.com/pandas-dev/pandas/pull/60046 + result = np.asarray(idx) + expected = np.empty((6,), dtype=object) + expected[:] = [ + ("foo", "one"), + ("foo", "two"), + ("bar", "one"), + ("baz", "two"), + ("qux", "one"), + ("qux", "two"), + ] + tm.assert_numpy_array_equal(result, expected) + + # it always gives a copy by default, but the values are cached, so results + # are still sharing memory + result_copy1 = np.asarray(idx) + result_copy2 = np.asarray(idx) + assert np.may_share_memory(result_copy1, result_copy2) + + # with explicit copy=True, then it is an actual copy + result_copy1 = np.array(idx, copy=True) + result_copy2 = np.array(idx, copy=True) + assert not np.may_share_memory(result_copy1, result_copy2) + + if not np_version_gt2: + # copy=False semantics are only supported in NumPy>=2. + return + + # for MultiIndex, copy=False is never allowed + msg = "Starting with NumPy 2.0, the behavior of the 'copy' keyword has changed" + with tm.assert_produces_warning(FutureWarning, match=msg): + np.array(idx, copy=False) + + +def test_to_frame(): + tuples = [(1, "one"), (1, "two"), (2, "one"), (2, "two")] + + index = MultiIndex.from_tuples(tuples) + result = index.to_frame(index=False) + expected = DataFrame(tuples) + tm.assert_frame_equal(result, expected) + + result = index.to_frame() + expected.index = index + tm.assert_frame_equal(result, expected) + + tuples = [(1, "one"), (1, "two"), (2, "one"), (2, "two")] + index = MultiIndex.from_tuples(tuples, names=["first", "second"]) + result = index.to_frame(index=False) + expected = DataFrame(tuples) + expected.columns = ["first", "second"] + tm.assert_frame_equal(result, expected) + + result = index.to_frame() + expected.index = index + tm.assert_frame_equal(result, expected) + + # See GH-22580 + index = MultiIndex.from_tuples(tuples) + result = index.to_frame(index=False, name=["first", "second"]) + expected = DataFrame(tuples) + expected.columns = ["first", "second"] + tm.assert_frame_equal(result, expected) + + result = index.to_frame(name=["first", "second"]) + expected.index = index + expected.columns = ["first", "second"] + tm.assert_frame_equal(result, expected) + + msg = "'name' must be a list / sequence of column names." + with pytest.raises(TypeError, match=msg): + index.to_frame(name="first") + + msg = "'name' should have same length as number of levels on index." + with pytest.raises(ValueError, match=msg): + index.to_frame(name=["first"]) + + # Tests for datetime index + index = MultiIndex.from_product([range(5), pd.date_range("20130101", periods=3)]) + result = index.to_frame(index=False) + expected = DataFrame( + { + 0: np.repeat(np.arange(5, dtype="int64"), 3), + 1: np.tile(pd.date_range("20130101", periods=3), 5), + } + ) + tm.assert_frame_equal(result, expected) + + result = index.to_frame() + expected.index = index + tm.assert_frame_equal(result, expected) + + # See GH-22580 + result = index.to_frame(index=False, name=["first", "second"]) + expected = DataFrame( + { + "first": np.repeat(np.arange(5, dtype="int64"), 3), + "second": np.tile(pd.date_range("20130101", periods=3), 5), + } + ) + tm.assert_frame_equal(result, expected) + + result = index.to_frame(name=["first", "second"]) + expected.index = index + tm.assert_frame_equal(result, expected) + + +def test_to_frame_dtype_fidelity(): + # GH 22420 + mi = MultiIndex.from_arrays( + [ + pd.date_range("19910905", periods=6, tz="US/Eastern"), + [1, 1, 1, 2, 2, 2], + pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True), + ["x", "x", "y", "z", "x", "y"], + ], + names=["dates", "a", "b", "c"], + ) + original_dtypes = {name: mi.levels[i].dtype for i, name in enumerate(mi.names)} + + expected_df = DataFrame( + { + "dates": pd.date_range("19910905", periods=6, tz="US/Eastern"), + "a": [1, 1, 1, 2, 2, 2], + "b": pd.Categorical(["a", "a", "b", "b", "c", "c"], ordered=True), + "c": ["x", "x", "y", "z", "x", "y"], + } + ) + df = mi.to_frame(index=False) + df_dtypes = df.dtypes.to_dict() + + tm.assert_frame_equal(df, expected_df) + assert original_dtypes == df_dtypes + + +def test_to_frame_resulting_column_order(): + # GH 22420 + expected = ["z", 0, "a"] + mi = MultiIndex.from_arrays( + [["a", "b", "c"], ["x", "y", "z"], ["q", "w", "e"]], names=expected + ) + result = mi.to_frame().columns.tolist() + assert result == expected + + +def test_to_frame_duplicate_labels(): + # GH 45245 + data = [(1, 2), (3, 4)] + names = ["a", "a"] + index = MultiIndex.from_tuples(data, names=names) + with pytest.raises(ValueError, match="Cannot create duplicate column labels"): + index.to_frame() + + result = index.to_frame(allow_duplicates=True) + expected = DataFrame(data, index=index, columns=names) + tm.assert_frame_equal(result, expected) + + names = [None, 0] + index = MultiIndex.from_tuples(data, names=names) + with pytest.raises(ValueError, match="Cannot create duplicate column labels"): + index.to_frame() + + result = index.to_frame(allow_duplicates=True) + expected = DataFrame(data, index=index, columns=[0, 0]) + tm.assert_frame_equal(result, expected) + + +def test_to_flat_index(idx): + expected = pd.Index( + ( + ("foo", "one"), + ("foo", "two"), + ("bar", "one"), + ("baz", "two"), + ("qux", "one"), + ("qux", "two"), + ), + tupleize_cols=False, + ) + result = idx.to_flat_index() + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_copy.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_copy.py new file mode 100644 index 0000000000000000000000000000000000000000..2e09a580f9528bc8197d55c6a7533098e0129fa2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_copy.py @@ -0,0 +1,96 @@ +from copy import ( + copy, + deepcopy, +) + +import pytest + +from pandas import MultiIndex +import pandas._testing as tm + + +def assert_multiindex_copied(copy, original): + # Levels should be (at least, shallow copied) + tm.assert_copy(copy.levels, original.levels) + tm.assert_almost_equal(copy.codes, original.codes) + + # Labels doesn't matter which way copied + tm.assert_almost_equal(copy.codes, original.codes) + assert copy.codes is not original.codes + + # Names doesn't matter which way copied + assert copy.names == original.names + assert copy.names is not original.names + + # Sort order should be copied + assert copy.sortorder == original.sortorder + + +def test_copy(idx): + i_copy = idx.copy() + + assert_multiindex_copied(i_copy, idx) + + +def test_shallow_copy(idx): + i_copy = idx._view() + + assert_multiindex_copied(i_copy, idx) + + +def test_view(idx): + i_view = idx.view() + assert_multiindex_copied(i_view, idx) + + +@pytest.mark.parametrize("func", [copy, deepcopy]) +def test_copy_and_deepcopy(func): + idx = MultiIndex( + levels=[["foo", "bar"], ["fizz", "buzz"]], + codes=[[0, 0, 0, 1], [0, 0, 1, 1]], + names=["first", "second"], + ) + idx_copy = func(idx) + assert idx_copy is not idx + assert idx_copy.equals(idx) + + +@pytest.mark.parametrize("deep", [True, False]) +def test_copy_method(deep): + idx = MultiIndex( + levels=[["foo", "bar"], ["fizz", "buzz"]], + codes=[[0, 0, 0, 1], [0, 0, 1, 1]], + names=["first", "second"], + ) + idx_copy = idx.copy(deep=deep) + assert idx_copy.equals(idx) + + +@pytest.mark.parametrize("deep", [True, False]) +@pytest.mark.parametrize( + "kwarg, value", + [ + ("names", ["third", "fourth"]), + ], +) +def test_copy_method_kwargs(deep, kwarg, value): + # gh-12309: Check that the "name" argument as well other kwargs are honored + idx = MultiIndex( + levels=[["foo", "bar"], ["fizz", "buzz"]], + codes=[[0, 0, 0, 1], [0, 0, 1, 1]], + names=["first", "second"], + ) + idx_copy = idx.copy(**{kwarg: value, "deep": deep}) + assert getattr(idx_copy, kwarg) == value + + +def test_copy_deep_false_retains_id(): + # GH#47878 + idx = MultiIndex( + levels=[["foo", "bar"], ["fizz", "buzz"]], + codes=[[0, 0, 0, 1], [0, 0, 1, 1]], + names=["first", "second"], + ) + + res = idx.copy(deep=False) + assert res._id is idx._id diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_drop.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_drop.py new file mode 100644 index 0000000000000000000000000000000000000000..99c8ebb1e57b22059d5a545a79de7b8348d73b14 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_drop.py @@ -0,0 +1,190 @@ +import numpy as np +import pytest + +from pandas.errors import PerformanceWarning + +import pandas as pd +from pandas import ( + Index, + MultiIndex, +) +import pandas._testing as tm + + +def test_drop(idx): + dropped = idx.drop([("foo", "two"), ("qux", "one")]) + + index = MultiIndex.from_tuples([("foo", "two"), ("qux", "one")]) + dropped2 = idx.drop(index) + + expected = idx[[0, 2, 3, 5]] + tm.assert_index_equal(dropped, expected) + tm.assert_index_equal(dropped2, expected) + + dropped = idx.drop(["bar"]) + expected = idx[[0, 1, 3, 4, 5]] + tm.assert_index_equal(dropped, expected) + + dropped = idx.drop("foo") + expected = idx[[2, 3, 4, 5]] + tm.assert_index_equal(dropped, expected) + + index = MultiIndex.from_tuples([("bar", "two")]) + with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"): + idx.drop([("bar", "two")]) + with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"): + idx.drop(index) + with pytest.raises(KeyError, match=r"^'two'$"): + idx.drop(["foo", "two"]) + + # partially correct argument + mixed_index = MultiIndex.from_tuples([("qux", "one"), ("bar", "two")]) + with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"): + idx.drop(mixed_index) + + # error='ignore' + dropped = idx.drop(index, errors="ignore") + expected = idx[[0, 1, 2, 3, 4, 5]] + tm.assert_index_equal(dropped, expected) + + dropped = idx.drop(mixed_index, errors="ignore") + expected = idx[[0, 1, 2, 3, 5]] + tm.assert_index_equal(dropped, expected) + + dropped = idx.drop(["foo", "two"], errors="ignore") + expected = idx[[2, 3, 4, 5]] + tm.assert_index_equal(dropped, expected) + + # mixed partial / full drop + dropped = idx.drop(["foo", ("qux", "one")]) + expected = idx[[2, 3, 5]] + tm.assert_index_equal(dropped, expected) + + # mixed partial / full drop / error='ignore' + mixed_index = ["foo", ("qux", "one"), "two"] + with pytest.raises(KeyError, match=r"^'two'$"): + idx.drop(mixed_index) + dropped = idx.drop(mixed_index, errors="ignore") + expected = idx[[2, 3, 5]] + tm.assert_index_equal(dropped, expected) + + +def test_droplevel_with_names(idx): + index = idx[idx.get_loc("foo")] + dropped = index.droplevel(0) + assert dropped.name == "second" + + index = MultiIndex( + levels=[Index(range(4)), Index(range(4)), Index(range(4))], + codes=[ + np.array([0, 0, 1, 2, 2, 2, 3, 3]), + np.array([0, 1, 0, 0, 0, 1, 0, 1]), + np.array([1, 0, 1, 1, 0, 0, 1, 0]), + ], + names=["one", "two", "three"], + ) + dropped = index.droplevel(0) + assert dropped.names == ("two", "three") + + dropped = index.droplevel("two") + expected = index.droplevel(1) + assert dropped.equals(expected) + + +def test_droplevel_list(): + index = MultiIndex( + levels=[Index(range(4)), Index(range(4)), Index(range(4))], + codes=[ + np.array([0, 0, 1, 2, 2, 2, 3, 3]), + np.array([0, 1, 0, 0, 0, 1, 0, 1]), + np.array([1, 0, 1, 1, 0, 0, 1, 0]), + ], + names=["one", "two", "three"], + ) + + dropped = index[:2].droplevel(["three", "one"]) + expected = index[:2].droplevel(2).droplevel(0) + assert dropped.equals(expected) + + dropped = index[:2].droplevel([]) + expected = index[:2] + assert dropped.equals(expected) + + msg = ( + "Cannot remove 3 levels from an index with 3 levels: " + "at least one level must be left" + ) + with pytest.raises(ValueError, match=msg): + index[:2].droplevel(["one", "two", "three"]) + + with pytest.raises(KeyError, match="'Level four not found'"): + index[:2].droplevel(["one", "four"]) + + +def test_drop_not_lexsorted(): + # GH 12078 + + # define the lexsorted version of the multi-index + tuples = [("a", ""), ("b1", "c1"), ("b2", "c2")] + lexsorted_mi = MultiIndex.from_tuples(tuples, names=["b", "c"]) + assert lexsorted_mi._is_lexsorted() + + # and the not-lexsorted version + df = pd.DataFrame( + columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]] + ) + df = df.pivot_table(index="a", columns=["b", "c"], values="d") + df = df.reset_index() + not_lexsorted_mi = df.columns + assert not not_lexsorted_mi._is_lexsorted() + + # compare the results + tm.assert_index_equal(lexsorted_mi, not_lexsorted_mi) + with tm.assert_produces_warning(PerformanceWarning): + tm.assert_index_equal(lexsorted_mi.drop("a"), not_lexsorted_mi.drop("a")) + + +def test_drop_with_nan_in_index(nulls_fixture): + # GH#18853 + mi = MultiIndex.from_tuples([("blah", nulls_fixture)], names=["name", "date"]) + msg = r"labels \[Timestamp\('2001-01-01 00:00:00'\)\] not found in level" + with pytest.raises(KeyError, match=msg): + mi.drop(pd.Timestamp("2001"), level="date") + + +@pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning") +def test_drop_with_non_monotonic_duplicates(): + # GH#33494 + mi = MultiIndex.from_tuples([(1, 2), (2, 3), (1, 2)]) + result = mi.drop((1, 2)) + expected = MultiIndex.from_tuples([(2, 3)]) + tm.assert_index_equal(result, expected) + + +def test_single_level_drop_partially_missing_elements(): + # GH 37820 + + mi = MultiIndex.from_tuples([(1, 2), (2, 2), (3, 2)]) + msg = r"labels \[4\] not found in level" + with pytest.raises(KeyError, match=msg): + mi.drop(4, level=0) + with pytest.raises(KeyError, match=msg): + mi.drop([1, 4], level=0) + msg = r"labels \[nan\] not found in level" + with pytest.raises(KeyError, match=msg): + mi.drop([np.nan], level=0) + with pytest.raises(KeyError, match=msg): + mi.drop([np.nan, 1, 2, 3], level=0) + + mi = MultiIndex.from_tuples([(np.nan, 1), (1, 2)]) + msg = r"labels \['a'\] not found in level" + with pytest.raises(KeyError, match=msg): + mi.drop([np.nan, 1, "a"], level=0) + + +def test_droplevel_multiindex_one_level(): + # GH#37208 + index = MultiIndex.from_tuples([(2,)], names=("b",)) + result = index.droplevel([]) + expected = Index([2], name="b") + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_duplicates.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_duplicates.py new file mode 100644 index 0000000000000000000000000000000000000000..6c6d9022b1af31e905b5ec739753af77a52f438b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_duplicates.py @@ -0,0 +1,363 @@ +from itertools import product + +import numpy as np +import pytest + +from pandas._libs import ( + hashtable, + index as libindex, +) + +from pandas import ( + NA, + DatetimeIndex, + Index, + MultiIndex, + Series, +) +import pandas._testing as tm + + +@pytest.fixture +def idx_dup(): + # compare tests/indexes/multi/conftest.py + major_axis = Index(["foo", "bar", "baz", "qux"]) + minor_axis = Index(["one", "two"]) + + major_codes = np.array([0, 0, 1, 0, 1, 1]) + minor_codes = np.array([0, 1, 0, 1, 0, 1]) + index_names = ["first", "second"] + mi = MultiIndex( + levels=[major_axis, minor_axis], + codes=[major_codes, minor_codes], + names=index_names, + verify_integrity=False, + ) + return mi + + +@pytest.mark.parametrize("names", [None, ["first", "second"]]) +def test_unique(names): + mi = MultiIndex.from_arrays([[1, 2, 1, 2], [1, 1, 1, 2]], names=names) + + res = mi.unique() + exp = MultiIndex.from_arrays([[1, 2, 2], [1, 1, 2]], names=mi.names) + tm.assert_index_equal(res, exp) + + mi = MultiIndex.from_arrays([list("aaaa"), list("abab")], names=names) + res = mi.unique() + exp = MultiIndex.from_arrays([list("aa"), list("ab")], names=mi.names) + tm.assert_index_equal(res, exp) + + mi = MultiIndex.from_arrays([list("aaaa"), list("aaaa")], names=names) + res = mi.unique() + exp = MultiIndex.from_arrays([["a"], ["a"]], names=mi.names) + tm.assert_index_equal(res, exp) + + # GH #20568 - empty MI + mi = MultiIndex.from_arrays([[], []], names=names) + res = mi.unique() + tm.assert_index_equal(mi, res) + + +def test_unique_datetimelike(): + idx1 = DatetimeIndex( + ["2015-01-01", "2015-01-01", "2015-01-01", "2015-01-01", "NaT", "NaT"] + ) + idx2 = DatetimeIndex( + ["2015-01-01", "2015-01-01", "2015-01-02", "2015-01-02", "NaT", "2015-01-01"], + tz="Asia/Tokyo", + ) + result = MultiIndex.from_arrays([idx1, idx2]).unique() + + eidx1 = DatetimeIndex(["2015-01-01", "2015-01-01", "NaT", "NaT"]) + eidx2 = DatetimeIndex( + ["2015-01-01", "2015-01-02", "NaT", "2015-01-01"], tz="Asia/Tokyo" + ) + exp = MultiIndex.from_arrays([eidx1, eidx2]) + tm.assert_index_equal(result, exp) + + +@pytest.mark.parametrize("level", [0, "first", 1, "second"]) +def test_unique_level(idx, level): + # GH #17896 - with level= argument + result = idx.unique(level=level) + expected = idx.get_level_values(level).unique() + tm.assert_index_equal(result, expected) + + # With already unique level + mi = MultiIndex.from_arrays([[1, 3, 2, 4], [1, 3, 2, 5]], names=["first", "second"]) + result = mi.unique(level=level) + expected = mi.get_level_values(level) + tm.assert_index_equal(result, expected) + + # With empty MI + mi = MultiIndex.from_arrays([[], []], names=["first", "second"]) + result = mi.unique(level=level) + expected = mi.get_level_values(level) + tm.assert_index_equal(result, expected) + + +def test_duplicate_multiindex_codes(): + # GH 17464 + # Make sure that a MultiIndex with duplicate levels throws a ValueError + msg = r"Level values must be unique: \[[A', ]+\] on level 0" + with pytest.raises(ValueError, match=msg): + mi = MultiIndex([["A"] * 10, range(10)], [[0] * 10, range(10)]) + + # And that using set_levels with duplicate levels fails + mi = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]]) + msg = r"Level values must be unique: \[[AB', ]+\] on level 0" + with pytest.raises(ValueError, match=msg): + mi.set_levels([["A", "B", "A", "A", "B"], [2, 1, 3, -2, 5]]) + + +@pytest.mark.parametrize("names", [["a", "b", "a"], [1, 1, 2], [1, "a", 1]]) +def test_duplicate_level_names(names): + # GH18872, GH19029 + mi = MultiIndex.from_product([[0, 1]] * 3, names=names) + assert mi.names == names + + # With .rename() + mi = MultiIndex.from_product([[0, 1]] * 3) + mi = mi.rename(names) + assert mi.names == names + + # With .rename(., level=) + mi.rename(names[1], level=1, inplace=True) + mi = mi.rename([names[0], names[2]], level=[0, 2]) + assert mi.names == names + + +def test_duplicate_meta_data(): + # GH 10115 + mi = MultiIndex( + levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]] + ) + + for idx in [ + mi, + mi.set_names([None, None]), + mi.set_names([None, "Num"]), + mi.set_names(["Upper", "Num"]), + ]: + assert idx.has_duplicates + assert idx.drop_duplicates().names == idx.names + + +def test_has_duplicates(idx, idx_dup): + # see fixtures + assert idx.is_unique is True + assert idx.has_duplicates is False + assert idx_dup.is_unique is False + assert idx_dup.has_duplicates is True + + mi = MultiIndex( + levels=[[0, 1], [0, 1, 2]], codes=[[0, 0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 0, 1, 2]] + ) + assert mi.is_unique is False + assert mi.has_duplicates is True + + # single instance of NaN + mi_nan = MultiIndex( + levels=[["a", "b"], [0, 1]], codes=[[-1, 0, 0, 1, 1], [-1, 0, 1, 0, 1]] + ) + assert mi_nan.is_unique is True + assert mi_nan.has_duplicates is False + + # multiple instances of NaN + mi_nan_dup = MultiIndex( + levels=[["a", "b"], [0, 1]], codes=[[-1, -1, 0, 0, 1, 1], [-1, -1, 0, 1, 0, 1]] + ) + assert mi_nan_dup.is_unique is False + assert mi_nan_dup.has_duplicates is True + + +def test_has_duplicates_from_tuples(): + # GH 9075 + t = [ + ("x", "out", "z", 5, "y", "in", "z", 169), + ("x", "out", "z", 7, "y", "in", "z", 119), + ("x", "out", "z", 9, "y", "in", "z", 135), + ("x", "out", "z", 13, "y", "in", "z", 145), + ("x", "out", "z", 14, "y", "in", "z", 158), + ("x", "out", "z", 16, "y", "in", "z", 122), + ("x", "out", "z", 17, "y", "in", "z", 160), + ("x", "out", "z", 18, "y", "in", "z", 180), + ("x", "out", "z", 20, "y", "in", "z", 143), + ("x", "out", "z", 21, "y", "in", "z", 128), + ("x", "out", "z", 22, "y", "in", "z", 129), + ("x", "out", "z", 25, "y", "in", "z", 111), + ("x", "out", "z", 28, "y", "in", "z", 114), + ("x", "out", "z", 29, "y", "in", "z", 121), + ("x", "out", "z", 31, "y", "in", "z", 126), + ("x", "out", "z", 32, "y", "in", "z", 155), + ("x", "out", "z", 33, "y", "in", "z", 123), + ("x", "out", "z", 12, "y", "in", "z", 144), + ] + + mi = MultiIndex.from_tuples(t) + assert not mi.has_duplicates + + +@pytest.mark.parametrize("nlevels", [4, 8]) +@pytest.mark.parametrize("with_nulls", [True, False]) +def test_has_duplicates_overflow(nlevels, with_nulls): + # handle int64 overflow if possible + # no overflow with 4 + # overflow possible with 8 + codes = np.tile(np.arange(500), 2) + level = np.arange(500) + + if with_nulls: # inject some null values + codes[500] = -1 # common nan value + codes = [codes.copy() for i in range(nlevels)] + for i in range(nlevels): + codes[i][500 + i - nlevels // 2] = -1 + + codes += [np.array([-1, 1]).repeat(500)] + else: + codes = [codes] * nlevels + [np.arange(2).repeat(500)] + + levels = [level] * nlevels + [[0, 1]] + + # no dups + mi = MultiIndex(levels=levels, codes=codes) + assert not mi.has_duplicates + + # with a dup + if with_nulls: + + def f(a): + return np.insert(a, 1000, a[0]) + + codes = list(map(f, codes)) + mi = MultiIndex(levels=levels, codes=codes) + else: + values = mi.values.tolist() + mi = MultiIndex.from_tuples(values + [values[0]]) + + assert mi.has_duplicates + + +@pytest.mark.parametrize( + "keep, expected", + [ + ("first", np.array([False, False, False, True, True, False])), + ("last", np.array([False, True, True, False, False, False])), + (False, np.array([False, True, True, True, True, False])), + ], +) +def test_duplicated(idx_dup, keep, expected): + result = idx_dup.duplicated(keep=keep) + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.arm_slow +def test_duplicated_hashtable_impl(keep, monkeypatch): + # GH 9125 + n, k = 6, 10 + levels = [np.arange(n), [str(i) for i in range(n)], 1000 + np.arange(n)] + codes = [np.random.default_rng(2).choice(n, k * n) for _ in levels] + with monkeypatch.context() as m: + m.setattr(libindex, "_SIZE_CUTOFF", 50) + mi = MultiIndex(levels=levels, codes=codes) + + result = mi.duplicated(keep=keep) + expected = hashtable.duplicated(mi.values, keep=keep) + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("val", [101, 102]) +def test_duplicated_with_nan(val): + # GH5873 + mi = MultiIndex.from_arrays([[101, val], [3.5, np.nan]]) + assert not mi.has_duplicates + + tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(2, dtype="bool")) + + +@pytest.mark.parametrize("n", range(1, 6)) +@pytest.mark.parametrize("m", range(1, 5)) +def test_duplicated_with_nan_multi_shape(n, m): + # GH5873 + # all possible unique combinations, including nan + codes = product(range(-1, n), range(-1, m)) + mi = MultiIndex( + levels=[list("abcde")[:n], list("WXYZ")[:m]], + codes=np.random.default_rng(2).permutation(list(codes)).T, + ) + assert len(mi) == (n + 1) * (m + 1) + assert not mi.has_duplicates + + tm.assert_numpy_array_equal(mi.duplicated(), np.zeros(len(mi), dtype="bool")) + + +def test_duplicated_drop_duplicates(): + # GH#4060 + idx = MultiIndex.from_arrays(([1, 2, 3, 1, 2, 3], [1, 1, 1, 1, 2, 2])) + + expected = np.array([False, False, False, True, False, False], dtype=bool) + duplicated = idx.duplicated() + tm.assert_numpy_array_equal(duplicated, expected) + assert duplicated.dtype == bool + expected = MultiIndex.from_arrays(([1, 2, 3, 2, 3], [1, 1, 1, 2, 2])) + tm.assert_index_equal(idx.drop_duplicates(), expected) + + expected = np.array([True, False, False, False, False, False]) + duplicated = idx.duplicated(keep="last") + tm.assert_numpy_array_equal(duplicated, expected) + assert duplicated.dtype == bool + expected = MultiIndex.from_arrays(([2, 3, 1, 2, 3], [1, 1, 1, 2, 2])) + tm.assert_index_equal(idx.drop_duplicates(keep="last"), expected) + + expected = np.array([True, False, False, True, False, False]) + duplicated = idx.duplicated(keep=False) + tm.assert_numpy_array_equal(duplicated, expected) + assert duplicated.dtype == bool + expected = MultiIndex.from_arrays(([2, 3, 2, 3], [1, 1, 2, 2])) + tm.assert_index_equal(idx.drop_duplicates(keep=False), expected) + + +@pytest.mark.parametrize( + "dtype", + [ + np.complex64, + np.complex128, + ], +) +def test_duplicated_series_complex_numbers(dtype): + # GH 17927 + expected = Series( + [False, False, False, True, False, False, False, True, False, True], + dtype=bool, + ) + result = Series( + [ + np.nan + np.nan * 1j, + 0, + 1j, + 1j, + 1, + 1 + 1j, + 1 + 2j, + 1 + 1j, + np.nan, + np.nan + np.nan * 1j, + ], + dtype=dtype, + ).duplicated() + tm.assert_series_equal(result, expected) + + +def test_midx_unique_ea_dtype(): + # GH#48335 + vals_a = Series([1, 2, NA, NA], dtype="Int64") + vals_b = np.array([1, 2, 3, 3]) + midx = MultiIndex.from_arrays([vals_a, vals_b], names=["a", "b"]) + result = midx.unique() + + exp_vals_a = Series([1, 2, NA], dtype="Int64") + exp_vals_b = np.array([1, 2, 3]) + expected = MultiIndex.from_arrays([exp_vals_a, exp_vals_b], names=["a", "b"]) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_equivalence.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_equivalence.py new file mode 100644 index 0000000000000000000000000000000000000000..9babbd5b8d56d64d704978758efb81f8d730274f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_equivalence.py @@ -0,0 +1,284 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.common import is_any_real_numeric_dtype + +import pandas as pd +from pandas import ( + Index, + MultiIndex, + Series, +) +import pandas._testing as tm + + +def test_equals(idx): + assert idx.equals(idx) + assert idx.equals(idx.copy()) + assert idx.equals(idx.astype(object)) + assert idx.equals(idx.to_flat_index()) + assert idx.equals(idx.to_flat_index().astype("category")) + + assert not idx.equals(list(idx)) + assert not idx.equals(np.array(idx)) + + same_values = Index(idx, dtype=object) + assert idx.equals(same_values) + assert same_values.equals(idx) + + if idx.nlevels == 1: + # do not test MultiIndex + assert not idx.equals(Series(idx)) + + +def test_equals_op(idx): + # GH9947, GH10637 + index_a = idx + + n = len(index_a) + index_b = index_a[0:-1] + index_c = index_a[0:-1].append(index_a[-2:-1]) + index_d = index_a[0:1] + with pytest.raises(ValueError, match="Lengths must match"): + index_a == index_b + expected1 = np.array([True] * n) + expected2 = np.array([True] * (n - 1) + [False]) + tm.assert_numpy_array_equal(index_a == index_a, expected1) + tm.assert_numpy_array_equal(index_a == index_c, expected2) + + # test comparisons with numpy arrays + array_a = np.array(index_a) + array_b = np.array(index_a[0:-1]) + array_c = np.array(index_a[0:-1].append(index_a[-2:-1])) + array_d = np.array(index_a[0:1]) + with pytest.raises(ValueError, match="Lengths must match"): + index_a == array_b + tm.assert_numpy_array_equal(index_a == array_a, expected1) + tm.assert_numpy_array_equal(index_a == array_c, expected2) + + # test comparisons with Series + series_a = Series(array_a) + series_b = Series(array_b) + series_c = Series(array_c) + series_d = Series(array_d) + with pytest.raises(ValueError, match="Lengths must match"): + index_a == series_b + + tm.assert_numpy_array_equal(index_a == series_a, expected1) + tm.assert_numpy_array_equal(index_a == series_c, expected2) + + # cases where length is 1 for one of them + with pytest.raises(ValueError, match="Lengths must match"): + index_a == index_d + with pytest.raises(ValueError, match="Lengths must match"): + index_a == series_d + with pytest.raises(ValueError, match="Lengths must match"): + index_a == array_d + msg = "Can only compare identically-labeled Series objects" + with pytest.raises(ValueError, match=msg): + series_a == series_d + with pytest.raises(ValueError, match="Lengths must match"): + series_a == array_d + + # comparing with a scalar should broadcast; note that we are excluding + # MultiIndex because in this case each item in the index is a tuple of + # length 2, and therefore is considered an array of length 2 in the + # comparison instead of a scalar + if not isinstance(index_a, MultiIndex): + expected3 = np.array([False] * (len(index_a) - 2) + [True, False]) + # assuming the 2nd to last item is unique in the data + item = index_a[-2] + tm.assert_numpy_array_equal(index_a == item, expected3) + tm.assert_series_equal(series_a == item, Series(expected3)) + + +def test_compare_tuple(): + # GH#21517 + mi = MultiIndex.from_product([[1, 2]] * 2) + + all_false = np.array([False, False, False, False]) + + result = mi == mi[0] + expected = np.array([True, False, False, False]) + tm.assert_numpy_array_equal(result, expected) + + result = mi != mi[0] + tm.assert_numpy_array_equal(result, ~expected) + + result = mi < mi[0] + tm.assert_numpy_array_equal(result, all_false) + + result = mi <= mi[0] + tm.assert_numpy_array_equal(result, expected) + + result = mi > mi[0] + tm.assert_numpy_array_equal(result, ~expected) + + result = mi >= mi[0] + tm.assert_numpy_array_equal(result, ~all_false) + + +def test_compare_tuple_strs(): + # GH#34180 + + mi = MultiIndex.from_tuples([("a", "b"), ("b", "c"), ("c", "a")]) + + result = mi == ("c", "a") + expected = np.array([False, False, True]) + tm.assert_numpy_array_equal(result, expected) + + result = mi == ("c",) + expected = np.array([False, False, False]) + tm.assert_numpy_array_equal(result, expected) + + +def test_equals_multi(idx): + assert idx.equals(idx) + assert not idx.equals(idx.values) + assert idx.equals(Index(idx.values)) + + assert idx.equal_levels(idx) + assert not idx.equals(idx[:-1]) + assert not idx.equals(idx[-1]) + + # different number of levels + index = MultiIndex( + levels=[Index(list(range(4))), Index(list(range(4))), Index(list(range(4)))], + codes=[ + np.array([0, 0, 1, 2, 2, 2, 3, 3]), + np.array([0, 1, 0, 0, 0, 1, 0, 1]), + np.array([1, 0, 1, 1, 0, 0, 1, 0]), + ], + ) + + index2 = MultiIndex(levels=index.levels[:-1], codes=index.codes[:-1]) + assert not index.equals(index2) + assert not index.equal_levels(index2) + + # levels are different + major_axis = Index(list(range(4))) + minor_axis = Index(list(range(2))) + + major_codes = np.array([0, 0, 1, 2, 2, 3]) + minor_codes = np.array([0, 1, 0, 0, 1, 0]) + + index = MultiIndex( + levels=[major_axis, minor_axis], codes=[major_codes, minor_codes] + ) + assert not idx.equals(index) + assert not idx.equal_levels(index) + + # some of the labels are different + major_axis = Index(["foo", "bar", "baz", "qux"]) + minor_axis = Index(["one", "two"]) + + major_codes = np.array([0, 0, 2, 2, 3, 3]) + minor_codes = np.array([0, 1, 0, 1, 0, 1]) + + index = MultiIndex( + levels=[major_axis, minor_axis], codes=[major_codes, minor_codes] + ) + assert not idx.equals(index) + + +def test_identical(idx): + mi = idx.copy() + mi2 = idx.copy() + assert mi.identical(mi2) + + mi = mi.set_names(["new1", "new2"]) + assert mi.equals(mi2) + assert not mi.identical(mi2) + + mi2 = mi2.set_names(["new1", "new2"]) + assert mi.identical(mi2) + + mi4 = Index(mi.tolist(), tupleize_cols=False) + assert not mi.identical(mi4) + assert mi.equals(mi4) + + +def test_equals_operator(idx): + # GH9785 + assert (idx == idx).all() + + +def test_equals_missing_values(): + # make sure take is not using -1 + i = MultiIndex.from_tuples([(0, pd.NaT), (0, pd.Timestamp("20130101"))]) + result = i[0:1].equals(i[0]) + assert not result + result = i[1:2].equals(i[1]) + assert not result + + +def test_equals_missing_values_differently_sorted(): + # GH#38439 + mi1 = MultiIndex.from_tuples([(81.0, np.nan), (np.nan, np.nan)]) + mi2 = MultiIndex.from_tuples([(np.nan, np.nan), (81.0, np.nan)]) + assert not mi1.equals(mi2) + + mi2 = MultiIndex.from_tuples([(81.0, np.nan), (np.nan, np.nan)]) + assert mi1.equals(mi2) + + +def test_is_(): + mi = MultiIndex.from_tuples(zip(range(10), range(10))) + assert mi.is_(mi) + assert mi.is_(mi.view()) + assert mi.is_(mi.view().view().view().view()) + mi2 = mi.view() + # names are metadata, they don't change id + mi2.names = ["A", "B"] + assert mi2.is_(mi) + assert mi.is_(mi2) + + assert not mi.is_(mi.set_names(["C", "D"])) + # levels are inherent properties, they change identity + mi3 = mi2.set_levels([list(range(10)), list(range(10))]) + assert not mi3.is_(mi2) + # shouldn't change + assert mi2.is_(mi) + mi4 = mi3.view() + + # GH 17464 - Remove duplicate MultiIndex levels + mi4 = mi4.set_levels([list(range(10)), list(range(10))]) + assert not mi4.is_(mi3) + mi5 = mi.view() + mi5 = mi5.set_levels(mi5.levels) + assert not mi5.is_(mi) + + +def test_is_all_dates(idx): + assert not idx._is_all_dates + + +def test_is_numeric(idx): + # MultiIndex is never numeric + assert not is_any_real_numeric_dtype(idx) + + +def test_multiindex_compare(): + # GH 21149 + # Ensure comparison operations for MultiIndex with nlevels == 1 + # behave consistently with those for MultiIndex with nlevels > 1 + + midx = MultiIndex.from_product([[0, 1]]) + + # Equality self-test: MultiIndex object vs self + expected = Series([True, True]) + result = Series(midx == midx) + tm.assert_series_equal(result, expected) + + # Greater than comparison: MultiIndex object vs self + expected = Series([False, False]) + result = Series(midx > midx) + tm.assert_series_equal(result, expected) + + +def test_equals_ea_int_regular_int(): + # GH#46026 + mi1 = MultiIndex.from_arrays([Index([1, 2], dtype="Int64"), [3, 4]]) + mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]]) + assert not mi1.equals(mi2) + assert not mi2.equals(mi1) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_formats.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..52ff3109128f24f43d9a12527d08770b463459a5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_formats.py @@ -0,0 +1,249 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Index, + MultiIndex, +) +import pandas._testing as tm + + +def test_format(idx): + msg = "MultiIndex.format is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + idx.format() + idx[:0].format() + + +def test_format_integer_names(): + index = MultiIndex( + levels=[[0, 1], [0, 1]], codes=[[0, 0, 1, 1], [0, 1, 0, 1]], names=[0, 1] + ) + msg = "MultiIndex.format is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + index.format(names=True) + + +def test_format_sparse_config(idx): + # GH1538 + msg = "MultiIndex.format is deprecated" + with pd.option_context("display.multi_sparse", False): + with tm.assert_produces_warning(FutureWarning, match=msg): + result = idx.format() + assert result[1] == "foo two" + + +def test_format_sparse_display(): + index = MultiIndex( + levels=[[0, 1], [0, 1], [0, 1], [0]], + codes=[ + [0, 0, 0, 1, 1, 1], + [0, 0, 1, 0, 0, 1], + [0, 1, 0, 0, 1, 0], + [0, 0, 0, 0, 0, 0], + ], + ) + msg = "MultiIndex.format is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + result = index.format() + assert result[3] == "1 0 0 0" + + +def test_repr_with_unicode_data(): + with pd.option_context("display.encoding", "UTF-8"): + d = {"a": ["\u05d0", 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]} + index = pd.DataFrame(d).set_index(["a", "b"]).index + assert "\\" not in repr(index) # we don't want unicode-escaped + + +def test_repr_roundtrip_raises(): + mi = MultiIndex.from_product([list("ab"), range(3)], names=["first", "second"]) + msg = "Must pass both levels and codes" + with pytest.raises(TypeError, match=msg): + eval(repr(mi)) + + +def test_unicode_string_with_unicode(): + d = {"a": ["\u05d0", 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]} + idx = pd.DataFrame(d).set_index(["a", "b"]).index + str(idx) + + +def test_repr_max_seq_item_setting(idx): + # GH10182 + idx = idx.repeat(50) + with pd.option_context("display.max_seq_items", None): + repr(idx) + assert "..." not in str(idx) + + +class TestRepr: + def test_unicode_repr_issues(self): + levels = [Index(["a/\u03c3", "b/\u03c3", "c/\u03c3"]), Index([0, 1])] + codes = [np.arange(3).repeat(2), np.tile(np.arange(2), 3)] + index = MultiIndex(levels=levels, codes=codes) + + repr(index.levels) + repr(index.get_level_values(1)) + + def test_repr_max_seq_items_equal_to_n(self, idx): + # display.max_seq_items == n + with pd.option_context("display.max_seq_items", 6): + result = idx.__repr__() + expected = """\ +MultiIndex([('foo', 'one'), + ('foo', 'two'), + ('bar', 'one'), + ('baz', 'two'), + ('qux', 'one'), + ('qux', 'two')], + names=['first', 'second'])""" + assert result == expected + + def test_repr(self, idx): + result = idx[:1].__repr__() + expected = """\ +MultiIndex([('foo', 'one')], + names=['first', 'second'])""" + assert result == expected + + result = idx.__repr__() + expected = """\ +MultiIndex([('foo', 'one'), + ('foo', 'two'), + ('bar', 'one'), + ('baz', 'two'), + ('qux', 'one'), + ('qux', 'two')], + names=['first', 'second'])""" + assert result == expected + + with pd.option_context("display.max_seq_items", 5): + result = idx.__repr__() + expected = """\ +MultiIndex([('foo', 'one'), + ('foo', 'two'), + ... + ('qux', 'one'), + ('qux', 'two')], + names=['first', 'second'], length=6)""" + assert result == expected + + # display.max_seq_items == 1 + with pd.option_context("display.max_seq_items", 1): + result = idx.__repr__() + expected = """\ +MultiIndex([... + ('qux', 'two')], + names=['first', ...], length=6)""" + assert result == expected + + def test_rjust(self): + n = 1000 + ci = pd.CategoricalIndex(list("a" * n) + (["abc"] * n)) + dti = pd.date_range("2000-01-01", freq="s", periods=n * 2) + mi = MultiIndex.from_arrays([ci, ci.codes + 9, dti], names=["a", "b", "dti"]) + result = mi[:1].__repr__() + expected = """\ +MultiIndex([('a', 9, '2000-01-01 00:00:00')], + names=['a', 'b', 'dti'])""" + assert result == expected + + result = mi[::500].__repr__() + expected = """\ +MultiIndex([( 'a', 9, '2000-01-01 00:00:00'), + ( 'a', 9, '2000-01-01 00:08:20'), + ('abc', 10, '2000-01-01 00:16:40'), + ('abc', 10, '2000-01-01 00:25:00')], + names=['a', 'b', 'dti'])""" + assert result == expected + + result = mi.__repr__() + expected = """\ +MultiIndex([( 'a', 9, '2000-01-01 00:00:00'), + ( 'a', 9, '2000-01-01 00:00:01'), + ( 'a', 9, '2000-01-01 00:00:02'), + ( 'a', 9, '2000-01-01 00:00:03'), + ( 'a', 9, '2000-01-01 00:00:04'), + ( 'a', 9, '2000-01-01 00:00:05'), + ( 'a', 9, '2000-01-01 00:00:06'), + ( 'a', 9, '2000-01-01 00:00:07'), + ( 'a', 9, '2000-01-01 00:00:08'), + ( 'a', 9, '2000-01-01 00:00:09'), + ... + ('abc', 10, '2000-01-01 00:33:10'), + ('abc', 10, '2000-01-01 00:33:11'), + ('abc', 10, '2000-01-01 00:33:12'), + ('abc', 10, '2000-01-01 00:33:13'), + ('abc', 10, '2000-01-01 00:33:14'), + ('abc', 10, '2000-01-01 00:33:15'), + ('abc', 10, '2000-01-01 00:33:16'), + ('abc', 10, '2000-01-01 00:33:17'), + ('abc', 10, '2000-01-01 00:33:18'), + ('abc', 10, '2000-01-01 00:33:19')], + names=['a', 'b', 'dti'], length=2000)""" + assert result == expected + + def test_tuple_width(self): + n = 1000 + ci = pd.CategoricalIndex(list("a" * n) + (["abc"] * n)) + dti = pd.date_range("2000-01-01", freq="s", periods=n * 2) + levels = [ci, ci.codes + 9, dti, dti, dti] + names = ["a", "b", "dti_1", "dti_2", "dti_3"] + mi = MultiIndex.from_arrays(levels, names=names) + result = mi[:1].__repr__() + expected = """MultiIndex([('a', 9, '2000-01-01 00:00:00', '2000-01-01 00:00:00', ...)], + names=['a', 'b', 'dti_1', 'dti_2', 'dti_3'])""" # noqa: E501 + assert result == expected + + result = mi[:10].__repr__() + expected = """\ +MultiIndex([('a', 9, '2000-01-01 00:00:00', '2000-01-01 00:00:00', ...), + ('a', 9, '2000-01-01 00:00:01', '2000-01-01 00:00:01', ...), + ('a', 9, '2000-01-01 00:00:02', '2000-01-01 00:00:02', ...), + ('a', 9, '2000-01-01 00:00:03', '2000-01-01 00:00:03', ...), + ('a', 9, '2000-01-01 00:00:04', '2000-01-01 00:00:04', ...), + ('a', 9, '2000-01-01 00:00:05', '2000-01-01 00:00:05', ...), + ('a', 9, '2000-01-01 00:00:06', '2000-01-01 00:00:06', ...), + ('a', 9, '2000-01-01 00:00:07', '2000-01-01 00:00:07', ...), + ('a', 9, '2000-01-01 00:00:08', '2000-01-01 00:00:08', ...), + ('a', 9, '2000-01-01 00:00:09', '2000-01-01 00:00:09', ...)], + names=['a', 'b', 'dti_1', 'dti_2', 'dti_3'])""" + assert result == expected + + result = mi.__repr__() + expected = """\ +MultiIndex([( 'a', 9, '2000-01-01 00:00:00', '2000-01-01 00:00:00', ...), + ( 'a', 9, '2000-01-01 00:00:01', '2000-01-01 00:00:01', ...), + ( 'a', 9, '2000-01-01 00:00:02', '2000-01-01 00:00:02', ...), + ( 'a', 9, '2000-01-01 00:00:03', '2000-01-01 00:00:03', ...), + ( 'a', 9, '2000-01-01 00:00:04', '2000-01-01 00:00:04', ...), + ( 'a', 9, '2000-01-01 00:00:05', '2000-01-01 00:00:05', ...), + ( 'a', 9, '2000-01-01 00:00:06', '2000-01-01 00:00:06', ...), + ( 'a', 9, '2000-01-01 00:00:07', '2000-01-01 00:00:07', ...), + ( 'a', 9, '2000-01-01 00:00:08', '2000-01-01 00:00:08', ...), + ( 'a', 9, '2000-01-01 00:00:09', '2000-01-01 00:00:09', ...), + ... + ('abc', 10, '2000-01-01 00:33:10', '2000-01-01 00:33:10', ...), + ('abc', 10, '2000-01-01 00:33:11', '2000-01-01 00:33:11', ...), + ('abc', 10, '2000-01-01 00:33:12', '2000-01-01 00:33:12', ...), + ('abc', 10, '2000-01-01 00:33:13', '2000-01-01 00:33:13', ...), + ('abc', 10, '2000-01-01 00:33:14', '2000-01-01 00:33:14', ...), + ('abc', 10, '2000-01-01 00:33:15', '2000-01-01 00:33:15', ...), + ('abc', 10, '2000-01-01 00:33:16', '2000-01-01 00:33:16', ...), + ('abc', 10, '2000-01-01 00:33:17', '2000-01-01 00:33:17', ...), + ('abc', 10, '2000-01-01 00:33:18', '2000-01-01 00:33:18', ...), + ('abc', 10, '2000-01-01 00:33:19', '2000-01-01 00:33:19', ...)], + names=['a', 'b', 'dti_1', 'dti_2', 'dti_3'], length=2000)""" + assert result == expected + + def test_multiindex_long_element(self): + # Non-regression test towards GH#52960 + data = MultiIndex.from_tuples([("c" * 62,)]) + + expected = ( + "MultiIndex([('cccccccccccccccccccccccccccccccccccccccc" + "cccccccccccccccccccccc',)],\n )" + ) + assert str(data) == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_level_values.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_level_values.py new file mode 100644 index 0000000000000000000000000000000000000000..28c77e78924cbc35feed4ae838b81f6be38478b5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_level_values.py @@ -0,0 +1,124 @@ +import numpy as np + +import pandas as pd +from pandas import ( + CategoricalIndex, + Index, + MultiIndex, + Timestamp, + date_range, +) +import pandas._testing as tm + + +class TestGetLevelValues: + def test_get_level_values_box_datetime64(self): + dates = date_range("1/1/2000", periods=4) + levels = [dates, [0, 1]] + codes = [[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]] + + index = MultiIndex(levels=levels, codes=codes) + + assert isinstance(index.get_level_values(0)[0], Timestamp) + + +def test_get_level_values(idx): + result = idx.get_level_values(0) + expected = Index(["foo", "foo", "bar", "baz", "qux", "qux"], name="first") + tm.assert_index_equal(result, expected) + assert result.name == "first" + + result = idx.get_level_values("first") + expected = idx.get_level_values(0) + tm.assert_index_equal(result, expected) + + # GH 10460 + index = MultiIndex( + levels=[CategoricalIndex(["A", "B"]), CategoricalIndex([1, 2, 3])], + codes=[np.array([0, 0, 0, 1, 1, 1]), np.array([0, 1, 2, 0, 1, 2])], + ) + + exp = CategoricalIndex(["A", "A", "A", "B", "B", "B"]) + tm.assert_index_equal(index.get_level_values(0), exp) + exp = CategoricalIndex([1, 2, 3, 1, 2, 3]) + tm.assert_index_equal(index.get_level_values(1), exp) + + +def test_get_level_values_all_na(): + # GH#17924 when level entirely consists of nan + arrays = [[np.nan, np.nan, np.nan], ["a", np.nan, 1]] + index = MultiIndex.from_arrays(arrays) + result = index.get_level_values(0) + expected = Index([np.nan, np.nan, np.nan], dtype=np.float64) + tm.assert_index_equal(result, expected) + + result = index.get_level_values(1) + expected = Index(["a", np.nan, 1], dtype=object) + tm.assert_index_equal(result, expected) + + +def test_get_level_values_int_with_na(): + # GH#17924 + arrays = [["a", "b", "b"], [1, np.nan, 2]] + index = MultiIndex.from_arrays(arrays) + result = index.get_level_values(1) + expected = Index([1, np.nan, 2]) + tm.assert_index_equal(result, expected) + + arrays = [["a", "b", "b"], [np.nan, np.nan, 2]] + index = MultiIndex.from_arrays(arrays) + result = index.get_level_values(1) + expected = Index([np.nan, np.nan, 2]) + tm.assert_index_equal(result, expected) + + +def test_get_level_values_na(): + arrays = [[np.nan, np.nan, np.nan], ["a", np.nan, 1]] + index = MultiIndex.from_arrays(arrays) + result = index.get_level_values(0) + expected = Index([np.nan, np.nan, np.nan]) + tm.assert_index_equal(result, expected) + + result = index.get_level_values(1) + expected = Index(["a", np.nan, 1]) + tm.assert_index_equal(result, expected) + + arrays = [["a", "b", "b"], pd.DatetimeIndex([0, 1, pd.NaT])] + index = MultiIndex.from_arrays(arrays) + result = index.get_level_values(1) + expected = pd.DatetimeIndex([0, 1, pd.NaT]) + tm.assert_index_equal(result, expected) + + arrays = [[], []] + index = MultiIndex.from_arrays(arrays) + result = index.get_level_values(0) + expected = Index([], dtype=object) + tm.assert_index_equal(result, expected) + + +def test_get_level_values_when_periods(): + # GH33131. See also discussion in GH32669. + # This test can probably be removed when PeriodIndex._engine is removed. + from pandas import ( + Period, + PeriodIndex, + ) + + idx = MultiIndex.from_arrays( + [PeriodIndex([Period("2019Q1"), Period("2019Q2")], name="b")] + ) + idx2 = MultiIndex.from_arrays( + [idx._get_level_values(level) for level in range(idx.nlevels)] + ) + assert all(x.is_monotonic_increasing for x in idx2.levels) + + +def test_values_loses_freq_of_underlying_index(): + # GH#49054 + idx = pd.DatetimeIndex(date_range("20200101", periods=3, freq="BME")) + expected = idx.copy(deep=True) + idx2 = Index([1, 2, 3]) + midx = MultiIndex(levels=[idx, idx2], codes=[[0, 1, 2], [0, 1, 2]]) + midx.values + assert idx.freq is not None + tm.assert_index_equal(idx, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_set.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_set.py new file mode 100644 index 0000000000000000000000000000000000000000..17ca87648733025f8d9a4fd897e07660c38ac132 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_get_set.py @@ -0,0 +1,384 @@ +import numpy as np +import pytest + +from pandas.compat import PY311 + +from pandas.core.dtypes.dtypes import DatetimeTZDtype + +import pandas as pd +from pandas import ( + CategoricalIndex, + MultiIndex, +) +import pandas._testing as tm + + +def assert_matching(actual, expected, check_dtype=False): + # avoid specifying internal representation + # as much as possible + assert len(actual) == len(expected) + for act, exp in zip(actual, expected): + act = np.asarray(act) + exp = np.asarray(exp) + tm.assert_numpy_array_equal(act, exp, check_dtype=check_dtype) + + +def test_get_level_number_integer(idx): + idx.names = [1, 0] + assert idx._get_level_number(1) == 0 + assert idx._get_level_number(0) == 1 + msg = "Too many levels: Index has only 2 levels, not 3" + with pytest.raises(IndexError, match=msg): + idx._get_level_number(2) + with pytest.raises(KeyError, match="Level fourth not found"): + idx._get_level_number("fourth") + + +def test_get_dtypes(using_infer_string): + # Test MultiIndex.dtypes (# Gh37062) + idx_multitype = MultiIndex.from_product( + [[1, 2, 3], ["a", "b", "c"], pd.date_range("20200101", periods=2, tz="UTC")], + names=["int", "string", "dt"], + ) + + exp = "object" if not using_infer_string else pd.StringDtype(na_value=np.nan) + expected = pd.Series( + { + "int": np.dtype("int64"), + "string": exp, + "dt": DatetimeTZDtype(tz="utc"), + } + ) + tm.assert_series_equal(expected, idx_multitype.dtypes) + + +def test_get_dtypes_no_level_name(using_infer_string): + # Test MultiIndex.dtypes (# GH38580 ) + idx_multitype = MultiIndex.from_product( + [ + [1, 2, 3], + ["a", "b", "c"], + pd.date_range("20200101", periods=2, tz="UTC"), + ], + ) + exp = "object" if not using_infer_string else pd.StringDtype(na_value=np.nan) + expected = pd.Series( + { + "level_0": np.dtype("int64"), + "level_1": exp, + "level_2": DatetimeTZDtype(tz="utc"), + } + ) + tm.assert_series_equal(expected, idx_multitype.dtypes) + + +def test_get_dtypes_duplicate_level_names(using_infer_string): + # Test MultiIndex.dtypes with non-unique level names (# GH45174) + result = MultiIndex.from_product( + [ + [1, 2, 3], + ["a", "b", "c"], + pd.date_range("20200101", periods=2, tz="UTC"), + ], + names=["A", "A", "A"], + ).dtypes + exp = "object" if not using_infer_string else pd.StringDtype(na_value=np.nan) + expected = pd.Series( + [np.dtype("int64"), exp, DatetimeTZDtype(tz="utc")], + index=["A", "A", "A"], + ) + tm.assert_series_equal(result, expected) + + +def test_get_level_number_out_of_bounds(multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + + with pytest.raises(IndexError, match="Too many levels"): + frame.index._get_level_number(2) + with pytest.raises(IndexError, match="not a valid level number"): + frame.index._get_level_number(-3) + + +def test_set_name_methods(idx): + # so long as these are synonyms, we don't need to test set_names + index_names = ["first", "second"] + assert idx.rename == idx.set_names + new_names = [name + "SUFFIX" for name in index_names] + ind = idx.set_names(new_names) + assert idx.names == index_names + assert ind.names == new_names + msg = "Length of names must match number of levels in MultiIndex" + with pytest.raises(ValueError, match=msg): + ind.set_names(new_names + new_names) + new_names2 = [name + "SUFFIX2" for name in new_names] + res = ind.set_names(new_names2, inplace=True) + assert res is None + assert ind.names == new_names2 + + # set names for specific level (# GH7792) + ind = idx.set_names(new_names[0], level=0) + assert idx.names == index_names + assert ind.names == [new_names[0], index_names[1]] + + res = ind.set_names(new_names2[0], level=0, inplace=True) + assert res is None + assert ind.names == [new_names2[0], index_names[1]] + + # set names for multiple levels + ind = idx.set_names(new_names, level=[0, 1]) + assert idx.names == index_names + assert ind.names == new_names + + res = ind.set_names(new_names2, level=[0, 1], inplace=True) + assert res is None + assert ind.names == new_names2 + + +def test_set_levels_codes_directly(idx): + # setting levels/codes directly raises AttributeError + + levels = idx.levels + new_levels = [[lev + "a" for lev in level] for level in levels] + + codes = idx.codes + major_codes, minor_codes = codes + major_codes = [(x + 1) % 3 for x in major_codes] + minor_codes = [(x + 1) % 1 for x in minor_codes] + new_codes = [major_codes, minor_codes] + + msg = "Can't set attribute" + with pytest.raises(AttributeError, match=msg): + idx.levels = new_levels + + msg = ( + "property 'codes' of 'MultiIndex' object has no setter" + if PY311 + else "can't set attribute" + ) + with pytest.raises(AttributeError, match=msg): + idx.codes = new_codes + + +def test_set_levels(idx): + # side note - you probably wouldn't want to use levels and codes + # directly like this - but it is possible. + levels = idx.levels + new_levels = [[lev + "a" for lev in level] for level in levels] + + # level changing [w/o mutation] + ind2 = idx.set_levels(new_levels) + assert_matching(ind2.levels, new_levels) + assert_matching(idx.levels, levels) + + # level changing specific level [w/o mutation] + ind2 = idx.set_levels(new_levels[0], level=0) + assert_matching(ind2.levels, [new_levels[0], levels[1]]) + assert_matching(idx.levels, levels) + + ind2 = idx.set_levels(new_levels[1], level=1) + assert_matching(ind2.levels, [levels[0], new_levels[1]]) + assert_matching(idx.levels, levels) + + # level changing multiple levels [w/o mutation] + ind2 = idx.set_levels(new_levels, level=[0, 1]) + assert_matching(ind2.levels, new_levels) + assert_matching(idx.levels, levels) + + # illegal level changing should not change levels + # GH 13754 + original_index = idx.copy() + with pytest.raises(ValueError, match="^On"): + idx.set_levels(["c"], level=0) + assert_matching(idx.levels, original_index.levels, check_dtype=True) + + with pytest.raises(ValueError, match="^On"): + idx.set_codes([0, 1, 2, 3, 4, 5], level=0) + assert_matching(idx.codes, original_index.codes, check_dtype=True) + + with pytest.raises(TypeError, match="^Levels"): + idx.set_levels("c", level=0) + assert_matching(idx.levels, original_index.levels, check_dtype=True) + + with pytest.raises(TypeError, match="^Codes"): + idx.set_codes(1, level=0) + assert_matching(idx.codes, original_index.codes, check_dtype=True) + + +def test_set_codes(idx): + # side note - you probably wouldn't want to use levels and codes + # directly like this - but it is possible. + codes = idx.codes + major_codes, minor_codes = codes + major_codes = [(x + 1) % 3 for x in major_codes] + minor_codes = [(x + 1) % 1 for x in minor_codes] + new_codes = [major_codes, minor_codes] + + # changing codes w/o mutation + ind2 = idx.set_codes(new_codes) + assert_matching(ind2.codes, new_codes) + assert_matching(idx.codes, codes) + + # codes changing specific level w/o mutation + ind2 = idx.set_codes(new_codes[0], level=0) + assert_matching(ind2.codes, [new_codes[0], codes[1]]) + assert_matching(idx.codes, codes) + + ind2 = idx.set_codes(new_codes[1], level=1) + assert_matching(ind2.codes, [codes[0], new_codes[1]]) + assert_matching(idx.codes, codes) + + # codes changing multiple levels w/o mutation + ind2 = idx.set_codes(new_codes, level=[0, 1]) + assert_matching(ind2.codes, new_codes) + assert_matching(idx.codes, codes) + + # label changing for levels of different magnitude of categories + ind = MultiIndex.from_tuples([(0, i) for i in range(130)]) + new_codes = range(129, -1, -1) + expected = MultiIndex.from_tuples([(0, i) for i in new_codes]) + + # [w/o mutation] + result = ind.set_codes(codes=new_codes, level=1) + assert result.equals(expected) + + +def test_set_levels_codes_names_bad_input(idx): + levels, codes = idx.levels, idx.codes + names = idx.names + + with pytest.raises(ValueError, match="Length of levels"): + idx.set_levels([levels[0]]) + + with pytest.raises(ValueError, match="Length of codes"): + idx.set_codes([codes[0]]) + + with pytest.raises(ValueError, match="Length of names"): + idx.set_names([names[0]]) + + # shouldn't scalar data error, instead should demand list-like + with pytest.raises(TypeError, match="list of lists-like"): + idx.set_levels(levels[0]) + + # shouldn't scalar data error, instead should demand list-like + with pytest.raises(TypeError, match="list of lists-like"): + idx.set_codes(codes[0]) + + # shouldn't scalar data error, instead should demand list-like + with pytest.raises(TypeError, match="list-like"): + idx.set_names(names[0]) + + # should have equal lengths + with pytest.raises(TypeError, match="list of lists-like"): + idx.set_levels(levels[0], level=[0, 1]) + + with pytest.raises(TypeError, match="list-like"): + idx.set_levels(levels, level=0) + + # should have equal lengths + with pytest.raises(TypeError, match="list of lists-like"): + idx.set_codes(codes[0], level=[0, 1]) + + with pytest.raises(TypeError, match="list-like"): + idx.set_codes(codes, level=0) + + # should have equal lengths + with pytest.raises(ValueError, match="Length of names"): + idx.set_names(names[0], level=[0, 1]) + + with pytest.raises(TypeError, match="Names must be a"): + idx.set_names(names, level=0) + + +@pytest.mark.parametrize("inplace", [True, False]) +def test_set_names_with_nlevel_1(inplace): + # GH 21149 + # Ensure that .set_names for MultiIndex with + # nlevels == 1 does not raise any errors + expected = MultiIndex(levels=[[0, 1]], codes=[[0, 1]], names=["first"]) + m = MultiIndex.from_product([[0, 1]]) + result = m.set_names("first", level=0, inplace=inplace) + + if inplace: + result = m + + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("ordered", [True, False]) +def test_set_levels_categorical(ordered): + # GH13854 + index = MultiIndex.from_arrays([list("xyzx"), [0, 1, 2, 3]]) + + cidx = CategoricalIndex(list("bac"), ordered=ordered) + result = index.set_levels(cidx, level=0) + expected = MultiIndex(levels=[cidx, [0, 1, 2, 3]], codes=index.codes) + tm.assert_index_equal(result, expected) + + result_lvl = result.get_level_values(0) + expected_lvl = CategoricalIndex( + list("bacb"), categories=cidx.categories, ordered=cidx.ordered + ) + tm.assert_index_equal(result_lvl, expected_lvl) + + +def test_set_value_keeps_names(): + # motivating example from #3742 + lev1 = ["hans", "hans", "hans", "grethe", "grethe", "grethe"] + lev2 = ["1", "2", "3"] * 2 + idx = MultiIndex.from_arrays([lev1, lev2], names=["Name", "Number"]) + df = pd.DataFrame( + np.random.default_rng(2).standard_normal((6, 4)), + columns=["one", "two", "three", "four"], + index=idx, + ) + df = df.sort_index() + assert df._is_copy is None + assert df.index.names == ("Name", "Number") + df.at[("grethe", "4"), "one"] = 99.34 + assert df._is_copy is None + assert df.index.names == ("Name", "Number") + + +def test_set_levels_with_iterable(): + # GH23273 + sizes = [1, 2, 3] + colors = ["black"] * 3 + index = MultiIndex.from_arrays([sizes, colors], names=["size", "color"]) + + result = index.set_levels(map(int, ["3", "2", "1"]), level="size") + + expected_sizes = [3, 2, 1] + expected = MultiIndex.from_arrays([expected_sizes, colors], names=["size", "color"]) + tm.assert_index_equal(result, expected) + + +def test_set_empty_level(): + # GH#48636 + midx = MultiIndex.from_arrays([[]], names=["A"]) + result = midx.set_levels(pd.DatetimeIndex([]), level=0) + expected = MultiIndex.from_arrays([pd.DatetimeIndex([])], names=["A"]) + tm.assert_index_equal(result, expected) + + +def test_set_levels_pos_args_removal(): + # https://github.com/pandas-dev/pandas/issues/41485 + idx = MultiIndex.from_tuples( + [ + (1, "one"), + (3, "one"), + ], + names=["foo", "bar"], + ) + with pytest.raises(TypeError, match="positional arguments"): + idx.set_levels(["a", "b", "c"], 0) + + with pytest.raises(TypeError, match="positional arguments"): + idx.set_codes([[0, 1], [1, 0]], 0) + + +def test_set_levels_categorical_keep_dtype(): + # GH#52125 + midx = MultiIndex.from_arrays([[5, 6]]) + result = midx.set_levels(levels=pd.Categorical([1, 2]), level=0) + expected = MultiIndex.from_arrays([pd.Categorical([1, 2])]) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..5e2d3c23da6452a4155af2674b7ce4a6dd7d2680 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_indexing.py @@ -0,0 +1,1001 @@ +from datetime import timedelta +import re + +import numpy as np +import pytest + +from pandas._libs import index as libindex +from pandas.errors import ( + InvalidIndexError, + PerformanceWarning, +) + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + Index, + MultiIndex, + date_range, +) +import pandas._testing as tm + + +class TestSliceLocs: + def test_slice_locs_partial(self, idx): + sorted_idx, _ = idx.sortlevel(0) + + result = sorted_idx.slice_locs(("foo", "two"), ("qux", "one")) + assert result == (1, 5) + + result = sorted_idx.slice_locs(None, ("qux", "one")) + assert result == (0, 5) + + result = sorted_idx.slice_locs(("foo", "two"), None) + assert result == (1, len(sorted_idx)) + + result = sorted_idx.slice_locs("bar", "baz") + assert result == (2, 4) + + def test_slice_locs(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((50, 4)), + columns=Index(list("ABCD"), dtype=object), + index=date_range("2000-01-01", periods=50, freq="B"), + ) + stacked = df.stack(future_stack=True) + idx = stacked.index + + slob = slice(*idx.slice_locs(df.index[5], df.index[15])) + sliced = stacked[slob] + expected = df[5:16].stack(future_stack=True) + tm.assert_almost_equal(sliced.values, expected.values) + + slob = slice( + *idx.slice_locs( + df.index[5] + timedelta(seconds=30), + df.index[15] - timedelta(seconds=30), + ) + ) + sliced = stacked[slob] + expected = df[6:15].stack(future_stack=True) + tm.assert_almost_equal(sliced.values, expected.values) + + def test_slice_locs_with_type_mismatch(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + columns=Index(list("ABCD"), dtype=object), + index=date_range("2000-01-01", periods=10, freq="B"), + ) + stacked = df.stack(future_stack=True) + idx = stacked.index + with pytest.raises(TypeError, match="^Level type mismatch"): + idx.slice_locs((1, 3)) + with pytest.raises(TypeError, match="^Level type mismatch"): + idx.slice_locs(df.index[5] + timedelta(seconds=30), (5, 2)) + df = DataFrame( + np.ones((5, 5)), + index=Index([f"i-{i}" for i in range(5)], name="a"), + columns=Index([f"i-{i}" for i in range(5)], name="a"), + ) + stacked = df.stack(future_stack=True) + idx = stacked.index + with pytest.raises(TypeError, match="^Level type mismatch"): + idx.slice_locs(timedelta(seconds=30)) + # TODO: Try creating a UnicodeDecodeError in exception message + with pytest.raises(TypeError, match="^Level type mismatch"): + idx.slice_locs(df.index[1], (16, "a")) + + def test_slice_locs_not_sorted(self): + index = MultiIndex( + levels=[Index(np.arange(4)), Index(np.arange(4)), Index(np.arange(4))], + codes=[ + np.array([0, 0, 1, 2, 2, 2, 3, 3]), + np.array([0, 1, 0, 0, 0, 1, 0, 1]), + np.array([1, 0, 1, 1, 0, 0, 1, 0]), + ], + ) + msg = "[Kk]ey length.*greater than MultiIndex lexsort depth" + with pytest.raises(KeyError, match=msg): + index.slice_locs((1, 0, 1), (2, 1, 0)) + + # works + sorted_index, _ = index.sortlevel(0) + # should there be a test case here??? + sorted_index.slice_locs((1, 0, 1), (2, 1, 0)) + + def test_slice_locs_not_contained(self): + # some searchsorted action + + index = MultiIndex( + levels=[[0, 2, 4, 6], [0, 2, 4]], + codes=[[0, 0, 0, 1, 1, 2, 3, 3, 3], [0, 1, 2, 1, 2, 2, 0, 1, 2]], + ) + + result = index.slice_locs((1, 0), (5, 2)) + assert result == (3, 6) + + result = index.slice_locs(1, 5) + assert result == (3, 6) + + result = index.slice_locs((2, 2), (5, 2)) + assert result == (3, 6) + + result = index.slice_locs(2, 5) + assert result == (3, 6) + + result = index.slice_locs((1, 0), (6, 3)) + assert result == (3, 8) + + result = index.slice_locs(-1, 10) + assert result == (0, len(index)) + + @pytest.mark.parametrize( + "index_arr,expected,start_idx,end_idx", + [ + ([[np.nan, "a", "b"], ["c", "d", "e"]], (0, 3), np.nan, None), + ([[np.nan, "a", "b"], ["c", "d", "e"]], (0, 3), np.nan, "b"), + ([[np.nan, "a", "b"], ["c", "d", "e"]], (0, 3), np.nan, ("b", "e")), + ([["a", "b", "c"], ["d", np.nan, "e"]], (1, 3), ("b", np.nan), None), + ([["a", "b", "c"], ["d", np.nan, "e"]], (1, 3), ("b", np.nan), "c"), + ([["a", "b", "c"], ["d", np.nan, "e"]], (1, 3), ("b", np.nan), ("c", "e")), + ], + ) + def test_slice_locs_with_missing_value( + self, index_arr, expected, start_idx, end_idx + ): + # issue 19132 + idx = MultiIndex.from_arrays(index_arr) + result = idx.slice_locs(start=start_idx, end=end_idx) + assert result == expected + + +class TestPutmask: + def test_putmask_with_wrong_mask(self, idx): + # GH18368 + + msg = "putmask: mask and data must be the same size" + with pytest.raises(ValueError, match=msg): + idx.putmask(np.ones(len(idx) + 1, np.bool_), 1) + + with pytest.raises(ValueError, match=msg): + idx.putmask(np.ones(len(idx) - 1, np.bool_), 1) + + with pytest.raises(ValueError, match=msg): + idx.putmask("foo", 1) + + def test_putmask_multiindex_other(self): + # GH#43212 `value` is also a MultiIndex + + left = MultiIndex.from_tuples([(np.nan, 6), (np.nan, 6), ("a", 4)]) + right = MultiIndex.from_tuples([("a", 1), ("a", 1), ("d", 1)]) + mask = np.array([True, True, False]) + + result = left.putmask(mask, right) + + expected = MultiIndex.from_tuples([right[0], right[1], left[2]]) + tm.assert_index_equal(result, expected) + + def test_putmask_keep_dtype(self, any_numeric_ea_dtype): + # GH#49830 + midx = MultiIndex.from_arrays( + [pd.Series([1, 2, 3], dtype=any_numeric_ea_dtype), [10, 11, 12]] + ) + midx2 = MultiIndex.from_arrays( + [pd.Series([5, 6, 7], dtype=any_numeric_ea_dtype), [-1, -2, -3]] + ) + result = midx.putmask([True, False, False], midx2) + expected = MultiIndex.from_arrays( + [pd.Series([5, 2, 3], dtype=any_numeric_ea_dtype), [-1, 11, 12]] + ) + tm.assert_index_equal(result, expected) + + def test_putmask_keep_dtype_shorter_value(self, any_numeric_ea_dtype): + # GH#49830 + midx = MultiIndex.from_arrays( + [pd.Series([1, 2, 3], dtype=any_numeric_ea_dtype), [10, 11, 12]] + ) + midx2 = MultiIndex.from_arrays( + [pd.Series([5], dtype=any_numeric_ea_dtype), [-1]] + ) + result = midx.putmask([True, False, False], midx2) + expected = MultiIndex.from_arrays( + [pd.Series([5, 2, 3], dtype=any_numeric_ea_dtype), [-1, 11, 12]] + ) + tm.assert_index_equal(result, expected) + + +class TestGetIndexer: + def test_get_indexer(self): + major_axis = Index(np.arange(4)) + minor_axis = Index(np.arange(2)) + + major_codes = np.array([0, 0, 1, 2, 2, 3, 3], dtype=np.intp) + minor_codes = np.array([0, 1, 0, 0, 1, 0, 1], dtype=np.intp) + + index = MultiIndex( + levels=[major_axis, minor_axis], codes=[major_codes, minor_codes] + ) + idx1 = index[:5] + idx2 = index[[1, 3, 5]] + + r1 = idx1.get_indexer(idx2) + tm.assert_almost_equal(r1, np.array([1, 3, -1], dtype=np.intp)) + + r1 = idx2.get_indexer(idx1, method="pad") + e1 = np.array([-1, 0, 0, 1, 1], dtype=np.intp) + tm.assert_almost_equal(r1, e1) + + r2 = idx2.get_indexer(idx1[::-1], method="pad") + tm.assert_almost_equal(r2, e1[::-1]) + + rffill1 = idx2.get_indexer(idx1, method="ffill") + tm.assert_almost_equal(r1, rffill1) + + r1 = idx2.get_indexer(idx1, method="backfill") + e1 = np.array([0, 0, 1, 1, 2], dtype=np.intp) + tm.assert_almost_equal(r1, e1) + + r2 = idx2.get_indexer(idx1[::-1], method="backfill") + tm.assert_almost_equal(r2, e1[::-1]) + + rbfill1 = idx2.get_indexer(idx1, method="bfill") + tm.assert_almost_equal(r1, rbfill1) + + # pass non-MultiIndex + r1 = idx1.get_indexer(idx2.values) + rexp1 = idx1.get_indexer(idx2) + tm.assert_almost_equal(r1, rexp1) + + r1 = idx1.get_indexer([1, 2, 3]) + assert (r1 == [-1, -1, -1]).all() + + # create index with duplicates + idx1 = Index(list(range(10)) + list(range(10))) + idx2 = Index(list(range(20))) + + msg = "Reindexing only valid with uniquely valued Index objects" + with pytest.raises(InvalidIndexError, match=msg): + idx1.get_indexer(idx2) + + def test_get_indexer_nearest(self): + midx = MultiIndex.from_tuples([("a", 1), ("b", 2)]) + msg = ( + "method='nearest' not implemented yet for MultiIndex; " + "see GitHub issue 9365" + ) + with pytest.raises(NotImplementedError, match=msg): + midx.get_indexer(["a"], method="nearest") + msg = "tolerance not implemented yet for MultiIndex" + with pytest.raises(NotImplementedError, match=msg): + midx.get_indexer(["a"], method="pad", tolerance=2) + + def test_get_indexer_categorical_time(self): + # https://github.com/pandas-dev/pandas/issues/21390 + midx = MultiIndex.from_product( + [ + Categorical(["a", "b", "c"]), + Categorical(date_range("2012-01-01", periods=3, freq="h")), + ] + ) + result = midx.get_indexer(midx) + tm.assert_numpy_array_equal(result, np.arange(9, dtype=np.intp)) + + @pytest.mark.parametrize( + "index_arr,labels,expected", + [ + ( + [[1, np.nan, 2], [3, 4, 5]], + [1, np.nan, 2], + np.array([-1, -1, -1], dtype=np.intp), + ), + ([[1, np.nan, 2], [3, 4, 5]], [(np.nan, 4)], np.array([1], dtype=np.intp)), + ([[1, 2, 3], [np.nan, 4, 5]], [(1, np.nan)], np.array([0], dtype=np.intp)), + ( + [[1, 2, 3], [np.nan, 4, 5]], + [np.nan, 4, 5], + np.array([-1, -1, -1], dtype=np.intp), + ), + ], + ) + def test_get_indexer_with_missing_value(self, index_arr, labels, expected): + # issue 19132 + idx = MultiIndex.from_arrays(index_arr) + result = idx.get_indexer(labels) + tm.assert_numpy_array_equal(result, expected) + + def test_get_indexer_methods(self): + # https://github.com/pandas-dev/pandas/issues/29896 + # test getting an indexer for another index with different methods + # confirms that getting an indexer without a filling method, getting an + # indexer and backfilling, and getting an indexer and padding all behave + # correctly in the case where all of the target values fall in between + # several levels in the MultiIndex into which they are getting an indexer + # + # visually, the MultiIndexes used in this test are: + # mult_idx_1: + # 0: -1 0 + # 1: 2 + # 2: 3 + # 3: 4 + # 4: 0 0 + # 5: 2 + # 6: 3 + # 7: 4 + # 8: 1 0 + # 9: 2 + # 10: 3 + # 11: 4 + # + # mult_idx_2: + # 0: 0 1 + # 1: 3 + # 2: 4 + mult_idx_1 = MultiIndex.from_product([[-1, 0, 1], [0, 2, 3, 4]]) + mult_idx_2 = MultiIndex.from_product([[0], [1, 3, 4]]) + + indexer = mult_idx_1.get_indexer(mult_idx_2) + expected = np.array([-1, 6, 7], dtype=indexer.dtype) + tm.assert_almost_equal(expected, indexer) + + backfill_indexer = mult_idx_1.get_indexer(mult_idx_2, method="backfill") + expected = np.array([5, 6, 7], dtype=backfill_indexer.dtype) + tm.assert_almost_equal(expected, backfill_indexer) + + # ensure the legacy "bfill" option functions identically to "backfill" + backfill_indexer = mult_idx_1.get_indexer(mult_idx_2, method="bfill") + expected = np.array([5, 6, 7], dtype=backfill_indexer.dtype) + tm.assert_almost_equal(expected, backfill_indexer) + + pad_indexer = mult_idx_1.get_indexer(mult_idx_2, method="pad") + expected = np.array([4, 6, 7], dtype=pad_indexer.dtype) + tm.assert_almost_equal(expected, pad_indexer) + + # ensure the legacy "ffill" option functions identically to "pad" + pad_indexer = mult_idx_1.get_indexer(mult_idx_2, method="ffill") + expected = np.array([4, 6, 7], dtype=pad_indexer.dtype) + tm.assert_almost_equal(expected, pad_indexer) + + @pytest.mark.parametrize("method", ["pad", "ffill", "backfill", "bfill", "nearest"]) + def test_get_indexer_methods_raise_for_non_monotonic(self, method): + # 53452 + mi = MultiIndex.from_arrays([[0, 4, 2], [0, 4, 2]]) + if method == "nearest": + err = NotImplementedError + msg = "not implemented yet for MultiIndex" + else: + err = ValueError + msg = "index must be monotonic increasing or decreasing" + with pytest.raises(err, match=msg): + mi.get_indexer([(1, 1)], method=method) + + def test_get_indexer_three_or_more_levels(self): + # https://github.com/pandas-dev/pandas/issues/29896 + # tests get_indexer() on MultiIndexes with 3+ levels + # visually, these are + # mult_idx_1: + # 0: 1 2 5 + # 1: 7 + # 2: 4 5 + # 3: 7 + # 4: 6 5 + # 5: 7 + # 6: 3 2 5 + # 7: 7 + # 8: 4 5 + # 9: 7 + # 10: 6 5 + # 11: 7 + # + # mult_idx_2: + # 0: 1 1 8 + # 1: 1 5 9 + # 2: 1 6 7 + # 3: 2 1 6 + # 4: 2 7 6 + # 5: 2 7 8 + # 6: 3 6 8 + mult_idx_1 = MultiIndex.from_product([[1, 3], [2, 4, 6], [5, 7]]) + mult_idx_2 = MultiIndex.from_tuples( + [ + (1, 1, 8), + (1, 5, 9), + (1, 6, 7), + (2, 1, 6), + (2, 7, 7), + (2, 7, 8), + (3, 6, 8), + ] + ) + # sanity check + assert mult_idx_1.is_monotonic_increasing + assert mult_idx_1.is_unique + assert mult_idx_2.is_monotonic_increasing + assert mult_idx_2.is_unique + + # show the relationships between the two + assert mult_idx_2[0] < mult_idx_1[0] + assert mult_idx_1[3] < mult_idx_2[1] < mult_idx_1[4] + assert mult_idx_1[5] == mult_idx_2[2] + assert mult_idx_1[5] < mult_idx_2[3] < mult_idx_1[6] + assert mult_idx_1[5] < mult_idx_2[4] < mult_idx_1[6] + assert mult_idx_1[5] < mult_idx_2[5] < mult_idx_1[6] + assert mult_idx_1[-1] < mult_idx_2[6] + + indexer_no_fill = mult_idx_1.get_indexer(mult_idx_2) + expected = np.array([-1, -1, 5, -1, -1, -1, -1], dtype=indexer_no_fill.dtype) + tm.assert_almost_equal(expected, indexer_no_fill) + + # test with backfilling + indexer_backfilled = mult_idx_1.get_indexer(mult_idx_2, method="backfill") + expected = np.array([0, 4, 5, 6, 6, 6, -1], dtype=indexer_backfilled.dtype) + tm.assert_almost_equal(expected, indexer_backfilled) + + # now, the same thing, but forward-filled (aka "padded") + indexer_padded = mult_idx_1.get_indexer(mult_idx_2, method="pad") + expected = np.array([-1, 3, 5, 5, 5, 5, 11], dtype=indexer_padded.dtype) + tm.assert_almost_equal(expected, indexer_padded) + + # now, do the indexing in the other direction + assert mult_idx_2[0] < mult_idx_1[0] < mult_idx_2[1] + assert mult_idx_2[0] < mult_idx_1[1] < mult_idx_2[1] + assert mult_idx_2[0] < mult_idx_1[2] < mult_idx_2[1] + assert mult_idx_2[0] < mult_idx_1[3] < mult_idx_2[1] + assert mult_idx_2[1] < mult_idx_1[4] < mult_idx_2[2] + assert mult_idx_2[2] == mult_idx_1[5] + assert mult_idx_2[5] < mult_idx_1[6] < mult_idx_2[6] + assert mult_idx_2[5] < mult_idx_1[7] < mult_idx_2[6] + assert mult_idx_2[5] < mult_idx_1[8] < mult_idx_2[6] + assert mult_idx_2[5] < mult_idx_1[9] < mult_idx_2[6] + assert mult_idx_2[5] < mult_idx_1[10] < mult_idx_2[6] + assert mult_idx_2[5] < mult_idx_1[11] < mult_idx_2[6] + + indexer = mult_idx_2.get_indexer(mult_idx_1) + expected = np.array( + [-1, -1, -1, -1, -1, 2, -1, -1, -1, -1, -1, -1], dtype=indexer.dtype + ) + tm.assert_almost_equal(expected, indexer) + + backfill_indexer = mult_idx_2.get_indexer(mult_idx_1, method="bfill") + expected = np.array( + [1, 1, 1, 1, 2, 2, 6, 6, 6, 6, 6, 6], dtype=backfill_indexer.dtype + ) + tm.assert_almost_equal(expected, backfill_indexer) + + pad_indexer = mult_idx_2.get_indexer(mult_idx_1, method="pad") + expected = np.array( + [0, 0, 0, 0, 1, 2, 5, 5, 5, 5, 5, 5], dtype=pad_indexer.dtype + ) + tm.assert_almost_equal(expected, pad_indexer) + + def test_get_indexer_crossing_levels(self): + # https://github.com/pandas-dev/pandas/issues/29896 + # tests a corner case with get_indexer() with MultiIndexes where, when we + # need to "carry" across levels, proper tuple ordering is respected + # + # the MultiIndexes used in this test, visually, are: + # mult_idx_1: + # 0: 1 1 1 1 + # 1: 2 + # 2: 2 1 + # 3: 2 + # 4: 1 2 1 1 + # 5: 2 + # 6: 2 1 + # 7: 2 + # 8: 2 1 1 1 + # 9: 2 + # 10: 2 1 + # 11: 2 + # 12: 2 2 1 1 + # 13: 2 + # 14: 2 1 + # 15: 2 + # + # mult_idx_2: + # 0: 1 3 2 2 + # 1: 2 3 2 2 + mult_idx_1 = MultiIndex.from_product([[1, 2]] * 4) + mult_idx_2 = MultiIndex.from_tuples([(1, 3, 2, 2), (2, 3, 2, 2)]) + + # show the tuple orderings, which get_indexer() should respect + assert mult_idx_1[7] < mult_idx_2[0] < mult_idx_1[8] + assert mult_idx_1[-1] < mult_idx_2[1] + + indexer = mult_idx_1.get_indexer(mult_idx_2) + expected = np.array([-1, -1], dtype=indexer.dtype) + tm.assert_almost_equal(expected, indexer) + + backfill_indexer = mult_idx_1.get_indexer(mult_idx_2, method="bfill") + expected = np.array([8, -1], dtype=backfill_indexer.dtype) + tm.assert_almost_equal(expected, backfill_indexer) + + pad_indexer = mult_idx_1.get_indexer(mult_idx_2, method="ffill") + expected = np.array([7, 15], dtype=pad_indexer.dtype) + tm.assert_almost_equal(expected, pad_indexer) + + def test_get_indexer_kwarg_validation(self): + # GH#41918 + mi = MultiIndex.from_product([range(3), ["A", "B"]]) + + msg = "limit argument only valid if doing pad, backfill or nearest" + with pytest.raises(ValueError, match=msg): + mi.get_indexer(mi[:-1], limit=4) + + msg = "tolerance argument only valid if doing pad, backfill or nearest" + with pytest.raises(ValueError, match=msg): + mi.get_indexer(mi[:-1], tolerance="piano") + + def test_get_indexer_nan(self): + # GH#37222 + idx1 = MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"]) + idx2 = MultiIndex.from_product([["A"], [np.nan, 2.0]], names=["id1", "id2"]) + expected = np.array([-1, 1]) + result = idx2.get_indexer(idx1) + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + result = idx1.get_indexer(idx2) + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + +def test_getitem(idx): + # scalar + assert idx[2] == ("bar", "one") + + # slice + result = idx[2:5] + expected = idx[[2, 3, 4]] + assert result.equals(expected) + + # boolean + result = idx[[True, False, True, False, True, True]] + result2 = idx[np.array([True, False, True, False, True, True])] + expected = idx[[0, 2, 4, 5]] + assert result.equals(expected) + assert result2.equals(expected) + + +def test_getitem_group_select(idx): + sorted_idx, _ = idx.sortlevel(0) + assert sorted_idx.get_loc("baz") == slice(3, 4) + assert sorted_idx.get_loc("foo") == slice(0, 2) + + +@pytest.mark.parametrize("ind1", [[True] * 5, Index([True] * 5)]) +@pytest.mark.parametrize( + "ind2", + [[True, False, True, False, False], Index([True, False, True, False, False])], +) +def test_getitem_bool_index_all(ind1, ind2): + # GH#22533 + idx = MultiIndex.from_tuples([(10, 1), (20, 2), (30, 3), (40, 4), (50, 5)]) + tm.assert_index_equal(idx[ind1], idx) + + expected = MultiIndex.from_tuples([(10, 1), (30, 3)]) + tm.assert_index_equal(idx[ind2], expected) + + +@pytest.mark.parametrize("ind1", [[True], Index([True])]) +@pytest.mark.parametrize("ind2", [[False], Index([False])]) +def test_getitem_bool_index_single(ind1, ind2): + # GH#22533 + idx = MultiIndex.from_tuples([(10, 1)]) + tm.assert_index_equal(idx[ind1], idx) + + expected = MultiIndex( + levels=[np.array([], dtype=np.int64), np.array([], dtype=np.int64)], + codes=[[], []], + ) + tm.assert_index_equal(idx[ind2], expected) + + +class TestGetLoc: + def test_get_loc(self, idx): + assert idx.get_loc(("foo", "two")) == 1 + assert idx.get_loc(("baz", "two")) == 3 + with pytest.raises(KeyError, match=r"^\('bar', 'two'\)$"): + idx.get_loc(("bar", "two")) + with pytest.raises(KeyError, match=r"^'quux'$"): + idx.get_loc("quux") + + # 3 levels + index = MultiIndex( + levels=[Index(np.arange(4)), Index(np.arange(4)), Index(np.arange(4))], + codes=[ + np.array([0, 0, 1, 2, 2, 2, 3, 3]), + np.array([0, 1, 0, 0, 0, 1, 0, 1]), + np.array([1, 0, 1, 1, 0, 0, 1, 0]), + ], + ) + with pytest.raises(KeyError, match=r"^\(1, 1\)$"): + index.get_loc((1, 1)) + assert index.get_loc((2, 0)) == slice(3, 5) + + def test_get_loc_duplicates(self): + index = Index([2, 2, 2, 2]) + result = index.get_loc(2) + expected = slice(0, 4) + assert result == expected + + index = Index(["c", "a", "a", "b", "b"]) + rs = index.get_loc("c") + xp = 0 + assert rs == xp + + with pytest.raises(KeyError, match="2"): + index.get_loc(2) + + def test_get_loc_level(self): + index = MultiIndex( + levels=[Index(np.arange(4)), Index(np.arange(4)), Index(np.arange(4))], + codes=[ + np.array([0, 0, 1, 2, 2, 2, 3, 3]), + np.array([0, 1, 0, 0, 0, 1, 0, 1]), + np.array([1, 0, 1, 1, 0, 0, 1, 0]), + ], + ) + loc, new_index = index.get_loc_level((0, 1)) + expected = slice(1, 2) + exp_index = index[expected].droplevel(0).droplevel(0) + assert loc == expected + assert new_index.equals(exp_index) + + loc, new_index = index.get_loc_level((0, 1, 0)) + expected = 1 + assert loc == expected + assert new_index is None + + with pytest.raises(KeyError, match=r"^\(2, 2\)$"): + index.get_loc_level((2, 2)) + # GH 22221: unused label + with pytest.raises(KeyError, match=r"^2$"): + index.drop(2).get_loc_level(2) + # Unused label on unsorted level: + with pytest.raises(KeyError, match=r"^2$"): + index.drop(1, level=2).get_loc_level(2, level=2) + + index = MultiIndex( + levels=[[2000], list(range(4))], + codes=[np.array([0, 0, 0, 0]), np.array([0, 1, 2, 3])], + ) + result, new_index = index.get_loc_level((2000, slice(None, None))) + expected = slice(None, None) + assert result == expected + assert new_index.equals(index.droplevel(0)) + + @pytest.mark.parametrize("dtype1", [int, float, bool, str]) + @pytest.mark.parametrize("dtype2", [int, float, bool, str]) + def test_get_loc_multiple_dtypes(self, dtype1, dtype2): + # GH 18520 + levels = [np.array([0, 1]).astype(dtype1), np.array([0, 1]).astype(dtype2)] + idx = MultiIndex.from_product(levels) + assert idx.get_loc(idx[2]) == 2 + + @pytest.mark.parametrize("level", [0, 1]) + @pytest.mark.parametrize("dtypes", [[int, float], [float, int]]) + def test_get_loc_implicit_cast(self, level, dtypes): + # GH 18818, GH 15994 : as flat index, cast int to float and vice-versa + levels = [["a", "b"], ["c", "d"]] + key = ["b", "d"] + lev_dtype, key_dtype = dtypes + levels[level] = np.array([0, 1], dtype=lev_dtype) + key[level] = key_dtype(1) + idx = MultiIndex.from_product(levels) + assert idx.get_loc(tuple(key)) == 3 + + @pytest.mark.parametrize("dtype", [bool, object]) + def test_get_loc_cast_bool(self, dtype): + # GH 19086 : int is casted to bool, but not vice-versa (for object dtype) + # With bool dtype, we don't cast in either direction. + levels = [Index([False, True], dtype=dtype), np.arange(2, dtype="int64")] + idx = MultiIndex.from_product(levels) + + if dtype is bool: + with pytest.raises(KeyError, match=r"^\(0, 1\)$"): + assert idx.get_loc((0, 1)) == 1 + with pytest.raises(KeyError, match=r"^\(1, 0\)$"): + assert idx.get_loc((1, 0)) == 2 + else: + # We use python object comparisons, which treat 0 == False and 1 == True + assert idx.get_loc((0, 1)) == 1 + assert idx.get_loc((1, 0)) == 2 + + with pytest.raises(KeyError, match=r"^\(False, True\)$"): + idx.get_loc((False, True)) + with pytest.raises(KeyError, match=r"^\(True, False\)$"): + idx.get_loc((True, False)) + + @pytest.mark.parametrize("level", [0, 1]) + def test_get_loc_nan(self, level, nulls_fixture): + # GH 18485 : NaN in MultiIndex + levels = [["a", "b"], ["c", "d"]] + key = ["b", "d"] + levels[level] = np.array([0, nulls_fixture], dtype=type(nulls_fixture)) + key[level] = nulls_fixture + idx = MultiIndex.from_product(levels) + assert idx.get_loc(tuple(key)) == 3 + + def test_get_loc_missing_nan(self): + # GH 8569 + idx = MultiIndex.from_arrays([[1.0, 2.0], [3.0, 4.0]]) + assert isinstance(idx.get_loc(1), slice) + with pytest.raises(KeyError, match=r"^3$"): + idx.get_loc(3) + with pytest.raises(KeyError, match=r"^nan$"): + idx.get_loc(np.nan) + with pytest.raises(InvalidIndexError, match=r"\[nan\]"): + # listlike/non-hashable raises TypeError + idx.get_loc([np.nan]) + + def test_get_loc_with_values_including_missing_values(self): + # issue 19132 + idx = MultiIndex.from_product([[np.nan, 1]] * 2) + expected = slice(0, 2, None) + assert idx.get_loc(np.nan) == expected + + idx = MultiIndex.from_arrays([[np.nan, 1, 2, np.nan]]) + expected = np.array([True, False, False, True]) + tm.assert_numpy_array_equal(idx.get_loc(np.nan), expected) + + idx = MultiIndex.from_product([[np.nan, 1]] * 3) + expected = slice(2, 4, None) + assert idx.get_loc((np.nan, 1)) == expected + + def test_get_loc_duplicates2(self): + # TODO: de-duplicate with test_get_loc_duplicates above? + index = MultiIndex( + levels=[["D", "B", "C"], [0, 26, 27, 37, 57, 67, 75, 82]], + codes=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]], + names=["tag", "day"], + ) + + assert index.get_loc("D") == slice(0, 3) + + def test_get_loc_past_lexsort_depth(self): + # GH#30053 + idx = MultiIndex( + levels=[["a"], [0, 7], [1]], + codes=[[0, 0], [1, 0], [0, 0]], + names=["x", "y", "z"], + sortorder=0, + ) + key = ("a", 7) + + with tm.assert_produces_warning(PerformanceWarning): + # PerformanceWarning: indexing past lexsort depth may impact performance + result = idx.get_loc(key) + + assert result == slice(0, 1, None) + + def test_multiindex_get_loc_list_raises(self): + # GH#35878 + idx = MultiIndex.from_tuples([("a", 1), ("b", 2)]) + msg = r"\[\]" + with pytest.raises(InvalidIndexError, match=msg): + idx.get_loc([]) + + def test_get_loc_nested_tuple_raises_keyerror(self): + # raise KeyError, not TypeError + mi = MultiIndex.from_product([range(3), range(4), range(5), range(6)]) + key = ((2, 3, 4), "foo") + + with pytest.raises(KeyError, match=re.escape(str(key))): + mi.get_loc(key) + + +class TestWhere: + def test_where(self): + i = MultiIndex.from_tuples([("A", 1), ("A", 2)]) + + msg = r"\.where is not supported for MultiIndex operations" + with pytest.raises(NotImplementedError, match=msg): + i.where(True) + + def test_where_array_like(self, listlike_box): + mi = MultiIndex.from_tuples([("A", 1), ("A", 2)]) + cond = [False, True] + msg = r"\.where is not supported for MultiIndex operations" + with pytest.raises(NotImplementedError, match=msg): + mi.where(listlike_box(cond)) + + +class TestContains: + def test_contains_top_level(self): + midx = MultiIndex.from_product([["A", "B"], [1, 2]]) + assert "A" in midx + assert "A" not in midx._engine + + def test_contains_with_nat(self): + # MI with a NaT + mi = MultiIndex( + levels=[["C"], date_range("2012-01-01", periods=5)], + codes=[[0, 0, 0, 0, 0, 0], [-1, 0, 1, 2, 3, 4]], + names=[None, "B"], + ) + assert ("C", pd.Timestamp("2012-01-01")) in mi + for val in mi.values: + assert val in mi + + def test_contains(self, idx): + assert ("foo", "two") in idx + assert ("bar", "two") not in idx + assert None not in idx + + def test_contains_with_missing_value(self): + # GH#19132 + idx = MultiIndex.from_arrays([[1, np.nan, 2]]) + assert np.nan in idx + + idx = MultiIndex.from_arrays([[1, 2], [np.nan, 3]]) + assert np.nan not in idx + assert (1, np.nan) in idx + + def test_multiindex_contains_dropped(self): + # GH#19027 + # test that dropped MultiIndex levels are not in the MultiIndex + # despite continuing to be in the MultiIndex's levels + idx = MultiIndex.from_product([[1, 2], [3, 4]]) + assert 2 in idx + idx = idx.drop(2) + + # drop implementation keeps 2 in the levels + assert 2 in idx.levels[0] + # but it should no longer be in the index itself + assert 2 not in idx + + # also applies to strings + idx = MultiIndex.from_product([["a", "b"], ["c", "d"]]) + assert "a" in idx + idx = idx.drop("a") + assert "a" in idx.levels[0] + assert "a" not in idx + + def test_contains_td64_level(self): + # GH#24570 + tx = pd.timedelta_range("09:30:00", "16:00:00", freq="30 min") + idx = MultiIndex.from_arrays([tx, np.arange(len(tx))]) + assert tx[0] in idx + assert "element_not_exit" not in idx + assert "0 day 09:30:00" in idx + + def test_large_mi_contains(self, monkeypatch): + # GH#10645 + with monkeypatch.context(): + monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 10) + result = MultiIndex.from_arrays([range(10), range(10)]) + assert (10, 0) not in result + + +def test_timestamp_multiindex_indexer(): + # https://github.com/pandas-dev/pandas/issues/26944 + idx = MultiIndex.from_product( + [ + date_range("2019-01-01T00:15:33", periods=100, freq="h", name="date"), + ["x"], + [3], + ] + ) + df = DataFrame({"foo": np.arange(len(idx))}, idx) + result = df.loc[pd.IndexSlice["2019-1-2":, "x", :], "foo"] + qidx = MultiIndex.from_product( + [ + date_range( + start="2019-01-02T00:15:33", + end="2019-01-05T03:15:33", + freq="h", + name="date", + ), + ["x"], + [3], + ] + ) + should_be = pd.Series(data=np.arange(24, len(qidx) + 24), index=qidx, name="foo") + tm.assert_series_equal(result, should_be) + + +@pytest.mark.parametrize( + "index_arr,expected,target,algo", + [ + ([[np.nan, "a", "b"], ["c", "d", "e"]], 0, np.nan, "left"), + ([[np.nan, "a", "b"], ["c", "d", "e"]], 1, (np.nan, "c"), "right"), + ([["a", "b", "c"], ["d", np.nan, "d"]], 1, ("b", np.nan), "left"), + ], +) +def test_get_slice_bound_with_missing_value(index_arr, expected, target, algo): + # issue 19132 + idx = MultiIndex.from_arrays(index_arr) + result = idx.get_slice_bound(target, side=algo) + assert result == expected + + +@pytest.mark.parametrize( + "index_arr,expected,start_idx,end_idx", + [ + ([[np.nan, 1, 2], [3, 4, 5]], slice(0, 2, None), np.nan, 1), + ([[np.nan, 1, 2], [3, 4, 5]], slice(0, 3, None), np.nan, (2, 5)), + ([[1, 2, 3], [4, np.nan, 5]], slice(1, 3, None), (2, np.nan), 3), + ([[1, 2, 3], [4, np.nan, 5]], slice(1, 3, None), (2, np.nan), (3, 5)), + ], +) +def test_slice_indexer_with_missing_value(index_arr, expected, start_idx, end_idx): + # issue 19132 + idx = MultiIndex.from_arrays(index_arr) + result = idx.slice_indexer(start=start_idx, end=end_idx) + assert result == expected + + +def test_pyint_engine(): + # GH#18519 : when combinations of codes cannot be represented in 64 + # bits, the index underlying the MultiIndex engine works with Python + # integers, rather than uint64. + N = 5 + keys = [ + tuple(arr) + for arr in [ + [0] * 10 * N, + [1] * 10 * N, + [2] * 10 * N, + [np.nan] * N + [2] * 9 * N, + [0] * N + [2] * 9 * N, + [np.nan] * N + [2] * 8 * N + [0] * N, + ] + ] + # Each level contains 4 elements (including NaN), so it is represented + # in 2 bits, for a total of 2*N*10 = 100 > 64 bits. If we were using a + # 64 bit engine and truncating the first levels, the fourth and fifth + # keys would collide; if truncating the last levels, the fifth and + # sixth; if rotating bits rather than shifting, the third and fifth. + + for idx, key_value in enumerate(keys): + index = MultiIndex.from_tuples(keys) + assert index.get_loc(key_value) == idx + + expected = np.arange(idx + 1, dtype=np.intp) + result = index.get_indexer([keys[i] for i in expected]) + tm.assert_numpy_array_equal(result, expected) + + # With missing key: + idces = range(len(keys)) + expected = np.array([-1] + list(idces), dtype=np.intp) + missing = tuple([0, 1] * 5 * N) + result = index.get_indexer([missing] + [keys[i] for i in idces]) + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize( + "keys,expected", + [ + ((slice(None), [5, 4]), [1, 0]), + ((slice(None), [4, 5]), [0, 1]), + (([True, False, True], [4, 6]), [0, 2]), + (([True, False, True], [6, 4]), [0, 2]), + ((2, [4, 5]), [0, 1]), + ((2, [5, 4]), [1, 0]), + (([2], [4, 5]), [0, 1]), + (([2], [5, 4]), [1, 0]), + ], +) +def test_get_locs_reordering(keys, expected): + # GH48384 + idx = MultiIndex.from_arrays( + [ + [2, 2, 1], + [4, 5, 6], + ] + ) + result = idx.get_locs(keys) + expected = np.array(expected, dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + +def test_get_indexer_for_multiindex_with_nans(nulls_fixture): + # GH37222 + idx1 = MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"]) + idx2 = MultiIndex.from_product([["A"], [nulls_fixture, 2.0]], names=["id1", "id2"]) + + result = idx2.get_indexer(idx1) + expected = np.array([-1, 1], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + result = idx1.get_indexer(idx2) + expected = np.array([-1, 1], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_integrity.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_integrity.py new file mode 100644 index 0000000000000000000000000000000000000000..d956747cbc859f40b69e52ea78c85ebce31f3427 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_integrity.py @@ -0,0 +1,289 @@ +import re + +import numpy as np +import pytest + +from pandas._libs import index as libindex + +from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike + +import pandas as pd +from pandas import ( + Index, + IntervalIndex, + MultiIndex, + RangeIndex, +) +import pandas._testing as tm + + +def test_labels_dtypes(): + # GH 8456 + i = MultiIndex.from_tuples([("A", 1), ("A", 2)]) + assert i.codes[0].dtype == "int8" + assert i.codes[1].dtype == "int8" + + i = MultiIndex.from_product([["a"], range(40)]) + assert i.codes[1].dtype == "int8" + i = MultiIndex.from_product([["a"], range(400)]) + assert i.codes[1].dtype == "int16" + i = MultiIndex.from_product([["a"], range(40000)]) + assert i.codes[1].dtype == "int32" + + i = MultiIndex.from_product([["a"], range(1000)]) + assert (i.codes[0] >= 0).all() + assert (i.codes[1] >= 0).all() + + +def test_values_boxed(): + tuples = [ + (1, pd.Timestamp("2000-01-01")), + (2, pd.NaT), + (3, pd.Timestamp("2000-01-03")), + (1, pd.Timestamp("2000-01-04")), + (2, pd.Timestamp("2000-01-02")), + (3, pd.Timestamp("2000-01-03")), + ] + result = MultiIndex.from_tuples(tuples) + expected = construct_1d_object_array_from_listlike(tuples) + tm.assert_numpy_array_equal(result.values, expected) + # Check that code branches for boxed values produce identical results + tm.assert_numpy_array_equal(result.values[:4], result[:4].values) + + +def test_values_multiindex_datetimeindex(): + # Test to ensure we hit the boxing / nobox part of MI.values + ints = np.arange(10**18, 10**18 + 5) + naive = pd.DatetimeIndex(ints) + + aware = pd.DatetimeIndex(ints, tz="US/Central") + + idx = MultiIndex.from_arrays([naive, aware]) + result = idx.values + + outer = pd.DatetimeIndex([x[0] for x in result]) + tm.assert_index_equal(outer, naive) + + inner = pd.DatetimeIndex([x[1] for x in result]) + tm.assert_index_equal(inner, aware) + + # n_lev > n_lab + result = idx[:2].values + + outer = pd.DatetimeIndex([x[0] for x in result]) + tm.assert_index_equal(outer, naive[:2]) + + inner = pd.DatetimeIndex([x[1] for x in result]) + tm.assert_index_equal(inner, aware[:2]) + + +def test_values_multiindex_periodindex(): + # Test to ensure we hit the boxing / nobox part of MI.values + ints = np.arange(2007, 2012) + pidx = pd.PeriodIndex(ints, freq="D") + + idx = MultiIndex.from_arrays([ints, pidx]) + result = idx.values + + outer = Index([x[0] for x in result]) + tm.assert_index_equal(outer, Index(ints, dtype=np.int64)) + + inner = pd.PeriodIndex([x[1] for x in result]) + tm.assert_index_equal(inner, pidx) + + # n_lev > n_lab + result = idx[:2].values + + outer = Index([x[0] for x in result]) + tm.assert_index_equal(outer, Index(ints[:2], dtype=np.int64)) + + inner = pd.PeriodIndex([x[1] for x in result]) + tm.assert_index_equal(inner, pidx[:2]) + + +def test_consistency(): + # need to construct an overflow + major_axis = list(range(70000)) + minor_axis = list(range(10)) + + major_codes = np.arange(70000) + minor_codes = np.repeat(range(10), 7000) + + # the fact that is works means it's consistent + index = MultiIndex( + levels=[major_axis, minor_axis], codes=[major_codes, minor_codes] + ) + + # inconsistent + major_codes = np.array([0, 0, 1, 1, 1, 2, 2, 3, 3]) + minor_codes = np.array([0, 1, 0, 1, 1, 0, 1, 0, 1]) + index = MultiIndex( + levels=[major_axis, minor_axis], codes=[major_codes, minor_codes] + ) + + assert index.is_unique is False + + +@pytest.mark.slow +def test_hash_collisions(monkeypatch): + # non-smoke test that we don't get hash collisions + size_cutoff = 50 + with monkeypatch.context() as m: + m.setattr(libindex, "_SIZE_CUTOFF", size_cutoff) + index = MultiIndex.from_product( + [np.arange(8), np.arange(8)], names=["one", "two"] + ) + result = index.get_indexer(index.values) + tm.assert_numpy_array_equal(result, np.arange(len(index), dtype="intp")) + + for i in [0, 1, len(index) - 2, len(index) - 1]: + result = index.get_loc(index[i]) + assert result == i + + +def test_dims(): + pass + + +def test_take_invalid_kwargs(): + vals = [["A", "B"], [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]] + idx = MultiIndex.from_product(vals, names=["str", "dt"]) + indices = [1, 2] + + msg = r"take\(\) got an unexpected keyword argument 'foo'" + with pytest.raises(TypeError, match=msg): + idx.take(indices, foo=2) + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + idx.take(indices, out=indices) + + msg = "the 'mode' parameter is not supported" + with pytest.raises(ValueError, match=msg): + idx.take(indices, mode="clip") + + +def test_isna_behavior(idx): + # should not segfault GH5123 + # NOTE: if MI representation changes, may make sense to allow + # isna(MI) + msg = "isna is not defined for MultiIndex" + with pytest.raises(NotImplementedError, match=msg): + pd.isna(idx) + + +def test_large_multiindex_error(monkeypatch): + # GH12527 + size_cutoff = 50 + with monkeypatch.context() as m: + m.setattr(libindex, "_SIZE_CUTOFF", size_cutoff) + df_below_cutoff = pd.DataFrame( + 1, + index=MultiIndex.from_product([[1, 2], range(size_cutoff - 1)]), + columns=["dest"], + ) + with pytest.raises(KeyError, match=r"^\(-1, 0\)$"): + df_below_cutoff.loc[(-1, 0), "dest"] + with pytest.raises(KeyError, match=r"^\(3, 0\)$"): + df_below_cutoff.loc[(3, 0), "dest"] + df_above_cutoff = pd.DataFrame( + 1, + index=MultiIndex.from_product([[1, 2], range(size_cutoff + 1)]), + columns=["dest"], + ) + with pytest.raises(KeyError, match=r"^\(-1, 0\)$"): + df_above_cutoff.loc[(-1, 0), "dest"] + with pytest.raises(KeyError, match=r"^\(3, 0\)$"): + df_above_cutoff.loc[(3, 0), "dest"] + + +def test_mi_hashtable_populated_attribute_error(monkeypatch): + # GH 18165 + monkeypatch.setattr(libindex, "_SIZE_CUTOFF", 50) + r = range(50) + df = pd.DataFrame({"a": r, "b": r}, index=MultiIndex.from_arrays([r, r])) + + msg = "'Series' object has no attribute 'foo'" + with pytest.raises(AttributeError, match=msg): + df["a"].foo() + + +def test_can_hold_identifiers(idx): + key = idx[0] + assert idx._can_hold_identifiers_and_holds_name(key) is True + + +def test_metadata_immutable(idx): + levels, codes = idx.levels, idx.codes + # shouldn't be able to set at either the top level or base level + mutable_regex = re.compile("does not support mutable operations") + with pytest.raises(TypeError, match=mutable_regex): + levels[0] = levels[0] + with pytest.raises(TypeError, match=mutable_regex): + levels[0][0] = levels[0][0] + # ditto for labels + with pytest.raises(TypeError, match=mutable_regex): + codes[0] = codes[0] + with pytest.raises(ValueError, match="assignment destination is read-only"): + codes[0][0] = codes[0][0] + # and for names + names = idx.names + with pytest.raises(TypeError, match=mutable_regex): + names[0] = names[0] + + +def test_level_setting_resets_attributes(): + ind = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]]) + assert ind.is_monotonic_increasing + ind = ind.set_levels([["A", "B"], [1, 3, 2]]) + # if this fails, probably didn't reset the cache correctly. + assert not ind.is_monotonic_increasing + + +def test_rangeindex_fallback_coercion_bug(): + # GH 12893 + df1 = pd.DataFrame(np.arange(100).reshape((10, 10))) + df2 = pd.DataFrame(np.arange(100).reshape((10, 10))) + df = pd.concat( + {"df1": df1.stack(future_stack=True), "df2": df2.stack(future_stack=True)}, + axis=1, + ) + df.index.names = ["fizz", "buzz"] + + expected = pd.DataFrame( + {"df2": np.arange(100), "df1": np.arange(100)}, + index=MultiIndex.from_product([range(10), range(10)], names=["fizz", "buzz"]), + ) + tm.assert_frame_equal(df, expected, check_like=True) + + result = df.index.get_level_values("fizz") + expected = Index(np.arange(10, dtype=np.int64), name="fizz").repeat(10) + tm.assert_index_equal(result, expected) + + result = df.index.get_level_values("buzz") + expected = Index(np.tile(np.arange(10, dtype=np.int64), 10), name="buzz") + tm.assert_index_equal(result, expected) + + +def test_memory_usage(idx): + result = idx.memory_usage() + if len(idx): + idx.get_loc(idx[0]) + result2 = idx.memory_usage() + result3 = idx.memory_usage(deep=True) + + # RangeIndex, IntervalIndex + # don't have engines + if not isinstance(idx, (RangeIndex, IntervalIndex)): + assert result2 > result + + if idx.inferred_type == "object": + assert result3 > result2 + + else: + # we report 0 for no-length + assert result == 0 + + +def test_nlevels(idx): + assert idx.nlevels == 2 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_isin.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_isin.py new file mode 100644 index 0000000000000000000000000000000000000000..68fdf25359f1bbada24f6a2403d5a04331bee84c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_isin.py @@ -0,0 +1,103 @@ +import numpy as np +import pytest + +from pandas import MultiIndex +import pandas._testing as tm + + +def test_isin_nan(): + idx = MultiIndex.from_arrays([["foo", "bar"], [1.0, np.nan]]) + tm.assert_numpy_array_equal(idx.isin([("bar", np.nan)]), np.array([False, True])) + tm.assert_numpy_array_equal( + idx.isin([("bar", float("nan"))]), np.array([False, True]) + ) + + +def test_isin_missing(nulls_fixture): + # GH48905 + mi1 = MultiIndex.from_tuples([(1, nulls_fixture)]) + mi2 = MultiIndex.from_tuples([(1, 1), (1, 2)]) + result = mi2.isin(mi1) + expected = np.array([False, False]) + tm.assert_numpy_array_equal(result, expected) + + +def test_isin(): + values = [("foo", 2), ("bar", 3), ("quux", 4)] + + idx = MultiIndex.from_arrays([["qux", "baz", "foo", "bar"], np.arange(4)]) + result = idx.isin(values) + expected = np.array([False, False, True, True]) + tm.assert_numpy_array_equal(result, expected) + + # empty, return dtype bool + idx = MultiIndex.from_arrays([[], []]) + result = idx.isin(values) + assert len(result) == 0 + assert result.dtype == np.bool_ + + +def test_isin_level_kwarg(): + idx = MultiIndex.from_arrays([["qux", "baz", "foo", "bar"], np.arange(4)]) + + vals_0 = ["foo", "bar", "quux"] + vals_1 = [2, 3, 10] + + expected = np.array([False, False, True, True]) + tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level=0)) + tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level=-2)) + + tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level=1)) + tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level=-1)) + + msg = "Too many levels: Index has only 2 levels, not 6" + with pytest.raises(IndexError, match=msg): + idx.isin(vals_0, level=5) + msg = "Too many levels: Index has only 2 levels, -5 is not a valid level number" + with pytest.raises(IndexError, match=msg): + idx.isin(vals_0, level=-5) + + with pytest.raises(KeyError, match=r"'Level 1\.0 not found'"): + idx.isin(vals_0, level=1.0) + with pytest.raises(KeyError, match=r"'Level -1\.0 not found'"): + idx.isin(vals_1, level=-1.0) + with pytest.raises(KeyError, match="'Level A not found'"): + idx.isin(vals_1, level="A") + + idx.names = ["A", "B"] + tm.assert_numpy_array_equal(expected, idx.isin(vals_0, level="A")) + tm.assert_numpy_array_equal(expected, idx.isin(vals_1, level="B")) + + with pytest.raises(KeyError, match="'Level C not found'"): + idx.isin(vals_1, level="C") + + +@pytest.mark.parametrize( + "labels,expected,level", + [ + ([("b", np.nan)], np.array([False, False, True]), None), + ([np.nan, "a"], np.array([True, True, False]), 0), + (["d", np.nan], np.array([False, True, True]), 1), + ], +) +def test_isin_multi_index_with_missing_value(labels, expected, level): + # GH 19132 + midx = MultiIndex.from_arrays([[np.nan, "a", "b"], ["c", "d", np.nan]]) + result = midx.isin(labels, level=level) + tm.assert_numpy_array_equal(result, expected) + + +def test_isin_empty(): + # GH#51599 + midx = MultiIndex.from_arrays([[1, 2], [3, 4]]) + result = midx.isin([]) + expected = np.array([False, False]) + tm.assert_numpy_array_equal(result, expected) + + +def test_isin_generator(): + # GH#52568 + midx = MultiIndex.from_tuples([(1, 2)]) + result = midx.isin(x for x in [(1, 2)]) + expected = np.array([True]) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_join.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_join.py new file mode 100644 index 0000000000000000000000000000000000000000..edd0feaaa1159ff8340af772d27f2a7af09ceb87 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_join.py @@ -0,0 +1,268 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + Interval, + MultiIndex, + Series, + StringDtype, +) +import pandas._testing as tm + + +@pytest.mark.parametrize( + "other", [Index(["three", "one", "two"]), Index(["one"]), Index(["one", "three"])] +) +def test_join_level(idx, other, join_type): + join_index, lidx, ridx = other.join( + idx, how=join_type, level="second", return_indexers=True + ) + + exp_level = other.join(idx.levels[1], how=join_type) + assert join_index.levels[0].equals(idx.levels[0]) + assert join_index.levels[1].equals(exp_level) + + # pare down levels + mask = np.array([x[1] in exp_level for x in idx], dtype=bool) + exp_values = idx.values[mask] + tm.assert_numpy_array_equal(join_index.values, exp_values) + + if join_type in ("outer", "inner"): + join_index2, ridx2, lidx2 = idx.join( + other, how=join_type, level="second", return_indexers=True + ) + + assert join_index.equals(join_index2) + tm.assert_numpy_array_equal(lidx, lidx2) + tm.assert_numpy_array_equal(ridx, ridx2) + tm.assert_numpy_array_equal(join_index2.values, exp_values) + + +def test_join_level_corner_case(idx): + # some corner cases + index = Index(["three", "one", "two"]) + result = index.join(idx, level="second") + assert isinstance(result, MultiIndex) + + with pytest.raises(TypeError, match="Join.*MultiIndex.*ambiguous"): + idx.join(idx, level=1) + + +def test_join_self(idx, join_type): + result = idx.join(idx, how=join_type) + expected = idx + if join_type == "outer": + expected = expected.sort_values() + tm.assert_index_equal(result, expected) + + +def test_join_multi(): + # GH 10665 + midx = MultiIndex.from_product([np.arange(4), np.arange(4)], names=["a", "b"]) + idx = Index([1, 2, 5], name="b") + + # inner + jidx, lidx, ridx = midx.join(idx, how="inner", return_indexers=True) + exp_idx = MultiIndex.from_product([np.arange(4), [1, 2]], names=["a", "b"]) + exp_lidx = np.array([1, 2, 5, 6, 9, 10, 13, 14], dtype=np.intp) + exp_ridx = np.array([0, 1, 0, 1, 0, 1, 0, 1], dtype=np.intp) + tm.assert_index_equal(jidx, exp_idx) + tm.assert_numpy_array_equal(lidx, exp_lidx) + tm.assert_numpy_array_equal(ridx, exp_ridx) + # flip + jidx, ridx, lidx = idx.join(midx, how="inner", return_indexers=True) + tm.assert_index_equal(jidx, exp_idx) + tm.assert_numpy_array_equal(lidx, exp_lidx) + tm.assert_numpy_array_equal(ridx, exp_ridx) + + # keep MultiIndex + jidx, lidx, ridx = midx.join(idx, how="left", return_indexers=True) + exp_ridx = np.array( + [-1, 0, 1, -1, -1, 0, 1, -1, -1, 0, 1, -1, -1, 0, 1, -1], dtype=np.intp + ) + tm.assert_index_equal(jidx, midx) + assert lidx is None + tm.assert_numpy_array_equal(ridx, exp_ridx) + # flip + jidx, ridx, lidx = idx.join(midx, how="right", return_indexers=True) + tm.assert_index_equal(jidx, midx) + assert lidx is None + tm.assert_numpy_array_equal(ridx, exp_ridx) + + +def test_join_multi_wrong_order(): + # GH 25760 + # GH 28956 + + midx1 = MultiIndex.from_product([[1, 2], [3, 4]], names=["a", "b"]) + midx2 = MultiIndex.from_product([[1, 2], [3, 4]], names=["b", "a"]) + + join_idx, lidx, ridx = midx1.join(midx2, return_indexers=True) + + exp_ridx = np.array([-1, -1, -1, -1], dtype=np.intp) + + tm.assert_index_equal(midx1, join_idx) + assert lidx is None + tm.assert_numpy_array_equal(ridx, exp_ridx) + + +def test_join_multi_return_indexers(): + # GH 34074 + + midx1 = MultiIndex.from_product([[1, 2], [3, 4], [5, 6]], names=["a", "b", "c"]) + midx2 = MultiIndex.from_product([[1, 2], [3, 4]], names=["a", "b"]) + + result = midx1.join(midx2, return_indexers=False) + tm.assert_index_equal(result, midx1) + + +def test_join_overlapping_interval_level(): + # GH 44096 + idx_1 = MultiIndex.from_tuples( + [ + (1, Interval(0.0, 1.0)), + (1, Interval(1.0, 2.0)), + (1, Interval(2.0, 5.0)), + (2, Interval(0.0, 1.0)), + (2, Interval(1.0, 3.0)), # interval limit is here at 3.0, not at 2.0 + (2, Interval(3.0, 5.0)), + ], + names=["num", "interval"], + ) + + idx_2 = MultiIndex.from_tuples( + [ + (1, Interval(2.0, 5.0)), + (1, Interval(0.0, 1.0)), + (1, Interval(1.0, 2.0)), + (2, Interval(3.0, 5.0)), + (2, Interval(0.0, 1.0)), + (2, Interval(1.0, 3.0)), + ], + names=["num", "interval"], + ) + + expected = MultiIndex.from_tuples( + [ + (1, Interval(0.0, 1.0)), + (1, Interval(1.0, 2.0)), + (1, Interval(2.0, 5.0)), + (2, Interval(0.0, 1.0)), + (2, Interval(1.0, 3.0)), + (2, Interval(3.0, 5.0)), + ], + names=["num", "interval"], + ) + result = idx_1.join(idx_2, how="outer") + + tm.assert_index_equal(result, expected) + + +def test_join_midx_ea(): + # GH#49277 + midx = MultiIndex.from_arrays( + [Series([1, 1, 3], dtype="Int64"), Series([1, 2, 3], dtype="Int64")], + names=["a", "b"], + ) + midx2 = MultiIndex.from_arrays( + [Series([1], dtype="Int64"), Series([3], dtype="Int64")], names=["a", "c"] + ) + result = midx.join(midx2, how="inner") + expected = MultiIndex.from_arrays( + [ + Series([1, 1], dtype="Int64"), + Series([1, 2], dtype="Int64"), + Series([3, 3], dtype="Int64"), + ], + names=["a", "b", "c"], + ) + tm.assert_index_equal(result, expected) + + +def test_join_midx_string(): + # GH#49277 + midx = MultiIndex.from_arrays( + [ + Series(["a", "a", "c"], dtype=StringDtype()), + Series(["a", "b", "c"], dtype=StringDtype()), + ], + names=["a", "b"], + ) + midx2 = MultiIndex.from_arrays( + [Series(["a"], dtype=StringDtype()), Series(["c"], dtype=StringDtype())], + names=["a", "c"], + ) + result = midx.join(midx2, how="inner") + expected = MultiIndex.from_arrays( + [ + Series(["a", "a"], dtype=StringDtype()), + Series(["a", "b"], dtype=StringDtype()), + Series(["c", "c"], dtype=StringDtype()), + ], + names=["a", "b", "c"], + ) + tm.assert_index_equal(result, expected) + + +def test_join_multi_with_nan(): + # GH29252 + df1 = DataFrame( + data={"col1": [1.1, 1.2]}, + index=MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"]), + ) + df2 = DataFrame( + data={"col2": [2.1, 2.2]}, + index=MultiIndex.from_product([["A"], [np.nan, 2.0]], names=["id1", "id2"]), + ) + result = df1.join(df2) + expected = DataFrame( + data={"col1": [1.1, 1.2], "col2": [np.nan, 2.2]}, + index=MultiIndex.from_product([["A"], [1.0, 2.0]], names=["id1", "id2"]), + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("val", [0, 5]) +def test_join_dtypes(any_numeric_ea_dtype, val): + # GH#49830 + midx = MultiIndex.from_arrays([Series([1, 2], dtype=any_numeric_ea_dtype), [3, 4]]) + midx2 = MultiIndex.from_arrays( + [Series([1, val, val], dtype=any_numeric_ea_dtype), [3, 4, 4]] + ) + result = midx.join(midx2, how="outer") + expected = MultiIndex.from_arrays( + [Series([val, val, 1, 2], dtype=any_numeric_ea_dtype), [4, 4, 3, 4]] + ).sort_values() + tm.assert_index_equal(result, expected) + + +def test_join_dtypes_all_nan(any_numeric_ea_dtype): + # GH#49830 + midx = MultiIndex.from_arrays( + [Series([1, 2], dtype=any_numeric_ea_dtype), [np.nan, np.nan]] + ) + midx2 = MultiIndex.from_arrays( + [Series([1, 0, 0], dtype=any_numeric_ea_dtype), [np.nan, np.nan, np.nan]] + ) + result = midx.join(midx2, how="outer") + expected = MultiIndex.from_arrays( + [ + Series([0, 0, 1, 2], dtype=any_numeric_ea_dtype), + [np.nan, np.nan, np.nan, np.nan], + ] + ) + tm.assert_index_equal(result, expected) + + +def test_join_index_levels(): + # GH#53093 + midx = midx = MultiIndex.from_tuples([("a", "2019-02-01"), ("a", "2019-02-01")]) + midx2 = MultiIndex.from_tuples([("a", "2019-01-31")]) + result = midx.join(midx2, how="outer") + expected = MultiIndex.from_tuples( + [("a", "2019-01-31"), ("a", "2019-02-01"), ("a", "2019-02-01")] + ) + tm.assert_index_equal(result.levels[1], expected.levels[1]) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_lexsort.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_lexsort.py new file mode 100644 index 0000000000000000000000000000000000000000..fc16a4197a3a4daf65de6f58d85d13883d535d41 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_lexsort.py @@ -0,0 +1,46 @@ +from pandas import MultiIndex + + +class TestIsLexsorted: + def test_is_lexsorted(self): + levels = [[0, 1], [0, 1, 2]] + + index = MultiIndex( + levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]] + ) + assert index._is_lexsorted() + + index = MultiIndex( + levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]] + ) + assert not index._is_lexsorted() + + index = MultiIndex( + levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]] + ) + assert not index._is_lexsorted() + assert index._lexsort_depth == 0 + + +class TestLexsortDepth: + def test_lexsort_depth(self): + # Test that lexsort_depth return the correct sortorder + # when it was given to the MultiIndex const. + # GH#28518 + + levels = [[0, 1], [0, 1, 2]] + + index = MultiIndex( + levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]], sortorder=2 + ) + assert index._lexsort_depth == 2 + + index = MultiIndex( + levels=levels, codes=[[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 2, 1]], sortorder=1 + ) + assert index._lexsort_depth == 1 + + index = MultiIndex( + levels=levels, codes=[[0, 0, 1, 0, 1, 1], [0, 1, 0, 2, 2, 1]], sortorder=0 + ) + assert index._lexsort_depth == 0 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_missing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_missing.py new file mode 100644 index 0000000000000000000000000000000000000000..14ffc42fb4b59074c3c830a83ff6bdc36bdf099e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_missing.py @@ -0,0 +1,111 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import MultiIndex +import pandas._testing as tm + + +def test_fillna(idx): + # GH 11343 + msg = "isna is not defined for MultiIndex" + with pytest.raises(NotImplementedError, match=msg): + idx.fillna(idx[0]) + + +def test_dropna(): + # GH 6194 + idx = MultiIndex.from_arrays( + [ + [1, np.nan, 3, np.nan, 5], + [1, 2, np.nan, np.nan, 5], + ["a", "b", "c", np.nan, "e"], + ] + ) + + exp = MultiIndex.from_arrays([[1, 5], [1, 5], ["a", "e"]]) + tm.assert_index_equal(idx.dropna(), exp) + tm.assert_index_equal(idx.dropna(how="any"), exp) + + exp = MultiIndex.from_arrays( + [[1, np.nan, 3, 5], [1, 2, np.nan, 5], ["a", "b", "c", "e"]] + ) + tm.assert_index_equal(idx.dropna(how="all"), exp) + + msg = "invalid how option: xxx" + with pytest.raises(ValueError, match=msg): + idx.dropna(how="xxx") + + # GH26408 + # test if missing values are dropped for multiindex constructed + # from codes and values + idx = MultiIndex( + levels=[[np.nan, None, pd.NaT, "128", 2], [np.nan, None, pd.NaT, "128", 2]], + codes=[[0, -1, 1, 2, 3, 4], [0, -1, 3, 3, 3, 4]], + ) + expected = MultiIndex.from_arrays([["128", 2], ["128", 2]]) + tm.assert_index_equal(idx.dropna(), expected) + tm.assert_index_equal(idx.dropna(how="any"), expected) + + expected = MultiIndex.from_arrays( + [[np.nan, np.nan, "128", 2], ["128", "128", "128", 2]] + ) + tm.assert_index_equal(idx.dropna(how="all"), expected) + + +def test_nulls(idx): + # this is really a smoke test for the methods + # as these are adequately tested for function elsewhere + + msg = "isna is not defined for MultiIndex" + with pytest.raises(NotImplementedError, match=msg): + idx.isna() + + +@pytest.mark.xfail(reason="isna is not defined for MultiIndex") +def test_hasnans_isnans(idx): + # GH 11343, added tests for hasnans / isnans + index = idx.copy() + + # cases in indices doesn't include NaN + expected = np.array([False] * len(index), dtype=bool) + tm.assert_numpy_array_equal(index._isnan, expected) + assert index.hasnans is False + + index = idx.copy() + values = index.values + values[1] = np.nan + + index = type(idx)(values) + + expected = np.array([False] * len(index), dtype=bool) + expected[1] = True + tm.assert_numpy_array_equal(index._isnan, expected) + assert index.hasnans is True + + +def test_nan_stays_float(): + # GH 7031 + idx0 = MultiIndex(levels=[["A", "B"], []], codes=[[1, 0], [-1, -1]], names=[0, 1]) + idx1 = MultiIndex(levels=[["C"], ["D"]], codes=[[0], [0]], names=[0, 1]) + idxm = idx0.join(idx1, how="outer") + assert pd.isna(idx0.get_level_values(1)).all() + # the following failed in 0.14.1 + assert pd.isna(idxm.get_level_values(1)[:-1]).all() + + df0 = pd.DataFrame([[1, 2]], index=idx0) + df1 = pd.DataFrame([[3, 4]], index=idx1) + dfm = df0 - df1 + assert pd.isna(df0.index.get_level_values(1)).all() + # the following failed in 0.14.1 + assert pd.isna(dfm.index.get_level_values(1)[:-1]).all() + + +def test_tuples_have_na(): + index = MultiIndex( + levels=[[1, 0], [0, 1, 2, 3]], + codes=[[1, 1, 1, 1, -1, 0, 0, 0], [0, 1, 2, 3, 0, 1, 2, 3]], + ) + + assert pd.isna(index[4][0]) + assert pd.isna(index.values[4][0]) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_monotonic.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_monotonic.py new file mode 100644 index 0000000000000000000000000000000000000000..2b0b3f7cb36d72abedc538eda9e6a85eb45067e2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_monotonic.py @@ -0,0 +1,188 @@ +import numpy as np +import pytest + +from pandas import ( + Index, + MultiIndex, +) + + +def test_is_monotonic_increasing_lexsorted(lexsorted_two_level_string_multiindex): + # string ordering + mi = lexsorted_two_level_string_multiindex + assert mi.is_monotonic_increasing is False + assert Index(mi.values).is_monotonic_increasing is False + assert mi._is_strictly_monotonic_increasing is False + assert Index(mi.values)._is_strictly_monotonic_increasing is False + + +def test_is_monotonic_increasing(): + i = MultiIndex.from_product([np.arange(10), np.arange(10)], names=["one", "two"]) + assert i.is_monotonic_increasing is True + assert i._is_strictly_monotonic_increasing is True + assert Index(i.values).is_monotonic_increasing is True + assert i._is_strictly_monotonic_increasing is True + + i = MultiIndex.from_product( + [np.arange(10, 0, -1), np.arange(10)], names=["one", "two"] + ) + assert i.is_monotonic_increasing is False + assert i._is_strictly_monotonic_increasing is False + assert Index(i.values).is_monotonic_increasing is False + assert Index(i.values)._is_strictly_monotonic_increasing is False + + i = MultiIndex.from_product( + [np.arange(10), np.arange(10, 0, -1)], names=["one", "two"] + ) + assert i.is_monotonic_increasing is False + assert i._is_strictly_monotonic_increasing is False + assert Index(i.values).is_monotonic_increasing is False + assert Index(i.values)._is_strictly_monotonic_increasing is False + + i = MultiIndex.from_product([[1.0, np.nan, 2.0], ["a", "b", "c"]]) + assert i.is_monotonic_increasing is False + assert i._is_strictly_monotonic_increasing is False + assert Index(i.values).is_monotonic_increasing is False + assert Index(i.values)._is_strictly_monotonic_increasing is False + + i = MultiIndex( + levels=[["bar", "baz", "foo", "qux"], ["mom", "next", "zenith"]], + codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=["first", "second"], + ) + assert i.is_monotonic_increasing is True + assert Index(i.values).is_monotonic_increasing is True + assert i._is_strictly_monotonic_increasing is True + assert Index(i.values)._is_strictly_monotonic_increasing is True + + # mixed levels, hits the TypeError + i = MultiIndex( + levels=[ + [1, 2, 3, 4], + [ + "gb00b03mlx29", + "lu0197800237", + "nl0000289783", + "nl0000289965", + "nl0000301109", + ], + ], + codes=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]], + names=["household_id", "asset_id"], + ) + + assert i.is_monotonic_increasing is False + assert i._is_strictly_monotonic_increasing is False + + # empty + i = MultiIndex.from_arrays([[], []]) + assert i.is_monotonic_increasing is True + assert Index(i.values).is_monotonic_increasing is True + assert i._is_strictly_monotonic_increasing is True + assert Index(i.values)._is_strictly_monotonic_increasing is True + + +def test_is_monotonic_decreasing(): + i = MultiIndex.from_product( + [np.arange(9, -1, -1), np.arange(9, -1, -1)], names=["one", "two"] + ) + assert i.is_monotonic_decreasing is True + assert i._is_strictly_monotonic_decreasing is True + assert Index(i.values).is_monotonic_decreasing is True + assert i._is_strictly_monotonic_decreasing is True + + i = MultiIndex.from_product( + [np.arange(10), np.arange(10, 0, -1)], names=["one", "two"] + ) + assert i.is_monotonic_decreasing is False + assert i._is_strictly_monotonic_decreasing is False + assert Index(i.values).is_monotonic_decreasing is False + assert Index(i.values)._is_strictly_monotonic_decreasing is False + + i = MultiIndex.from_product( + [np.arange(10, 0, -1), np.arange(10)], names=["one", "two"] + ) + assert i.is_monotonic_decreasing is False + assert i._is_strictly_monotonic_decreasing is False + assert Index(i.values).is_monotonic_decreasing is False + assert Index(i.values)._is_strictly_monotonic_decreasing is False + + i = MultiIndex.from_product([[2.0, np.nan, 1.0], ["c", "b", "a"]]) + assert i.is_monotonic_decreasing is False + assert i._is_strictly_monotonic_decreasing is False + assert Index(i.values).is_monotonic_decreasing is False + assert Index(i.values)._is_strictly_monotonic_decreasing is False + + # string ordering + i = MultiIndex( + levels=[["qux", "foo", "baz", "bar"], ["three", "two", "one"]], + codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=["first", "second"], + ) + assert i.is_monotonic_decreasing is False + assert Index(i.values).is_monotonic_decreasing is False + assert i._is_strictly_monotonic_decreasing is False + assert Index(i.values)._is_strictly_monotonic_decreasing is False + + i = MultiIndex( + levels=[["qux", "foo", "baz", "bar"], ["zenith", "next", "mom"]], + codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], + names=["first", "second"], + ) + assert i.is_monotonic_decreasing is True + assert Index(i.values).is_monotonic_decreasing is True + assert i._is_strictly_monotonic_decreasing is True + assert Index(i.values)._is_strictly_monotonic_decreasing is True + + # mixed levels, hits the TypeError + i = MultiIndex( + levels=[ + [4, 3, 2, 1], + [ + "nl0000301109", + "nl0000289965", + "nl0000289783", + "lu0197800237", + "gb00b03mlx29", + ], + ], + codes=[[0, 1, 1, 2, 2, 2, 3], [4, 2, 0, 0, 1, 3, -1]], + names=["household_id", "asset_id"], + ) + + assert i.is_monotonic_decreasing is False + assert i._is_strictly_monotonic_decreasing is False + + # empty + i = MultiIndex.from_arrays([[], []]) + assert i.is_monotonic_decreasing is True + assert Index(i.values).is_monotonic_decreasing is True + assert i._is_strictly_monotonic_decreasing is True + assert Index(i.values)._is_strictly_monotonic_decreasing is True + + +def test_is_strictly_monotonic_increasing(): + idx = MultiIndex( + levels=[["bar", "baz"], ["mom", "next"]], codes=[[0, 0, 1, 1], [0, 0, 0, 1]] + ) + assert idx.is_monotonic_increasing is True + assert idx._is_strictly_monotonic_increasing is False + + +def test_is_strictly_monotonic_decreasing(): + idx = MultiIndex( + levels=[["baz", "bar"], ["next", "mom"]], codes=[[0, 0, 1, 1], [0, 0, 0, 1]] + ) + assert idx.is_monotonic_decreasing is True + assert idx._is_strictly_monotonic_decreasing is False + + +@pytest.mark.parametrize("attr", ["is_monotonic_increasing", "is_monotonic_decreasing"]) +@pytest.mark.parametrize( + "values", + [[(np.nan,), (1,), (2,)], [(1,), (np.nan,), (2,)], [(1,), (2,), (np.nan,)]], +) +def test_is_monotonic_with_nans(values, attr): + # GH: 37220 + idx = MultiIndex.from_tuples(values, names=["test"]) + assert getattr(idx, attr) is False diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_partial_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_partial_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..64cc1fa621b3195727cbfb3e62a8b6a6acf4dfaf --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_partial_indexing.py @@ -0,0 +1,148 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + IndexSlice, + MultiIndex, + date_range, +) +import pandas._testing as tm + + +@pytest.fixture +def df(): + # c1 + # 2016-01-01 00:00:00 a 0 + # b 1 + # c 2 + # 2016-01-01 12:00:00 a 3 + # b 4 + # c 5 + # 2016-01-02 00:00:00 a 6 + # b 7 + # c 8 + # 2016-01-02 12:00:00 a 9 + # b 10 + # c 11 + # 2016-01-03 00:00:00 a 12 + # b 13 + # c 14 + dr = date_range("2016-01-01", "2016-01-03", freq="12h") + abc = ["a", "b", "c"] + mi = MultiIndex.from_product([dr, abc]) + frame = DataFrame({"c1": range(15)}, index=mi) + return frame + + +def test_partial_string_matching_single_index(df): + # partial string matching on a single index + for df_swap in [df.swaplevel(), df.swaplevel(0), df.swaplevel(0, 1)]: + df_swap = df_swap.sort_index() + just_a = df_swap.loc["a"] + result = just_a.loc["2016-01-01"] + expected = df.loc[IndexSlice[:, "a"], :].iloc[0:2] + expected.index = expected.index.droplevel(1) + tm.assert_frame_equal(result, expected) + + +def test_get_loc_partial_timestamp_multiindex(df): + mi = df.index + key = ("2016-01-01", "a") + loc = mi.get_loc(key) + + expected = np.zeros(len(mi), dtype=bool) + expected[[0, 3]] = True + tm.assert_numpy_array_equal(loc, expected) + + key2 = ("2016-01-02", "a") + loc2 = mi.get_loc(key2) + expected2 = np.zeros(len(mi), dtype=bool) + expected2[[6, 9]] = True + tm.assert_numpy_array_equal(loc2, expected2) + + key3 = ("2016-01", "a") + loc3 = mi.get_loc(key3) + expected3 = np.zeros(len(mi), dtype=bool) + expected3[mi.get_level_values(1).get_loc("a")] = True + tm.assert_numpy_array_equal(loc3, expected3) + + key4 = ("2016", "a") + loc4 = mi.get_loc(key4) + expected4 = expected3 + tm.assert_numpy_array_equal(loc4, expected4) + + # non-monotonic + taker = np.arange(len(mi), dtype=np.intp) + taker[::2] = taker[::-2] + mi2 = mi.take(taker) + loc5 = mi2.get_loc(key) + expected5 = np.zeros(len(mi2), dtype=bool) + expected5[[3, 14]] = True + tm.assert_numpy_array_equal(loc5, expected5) + + +def test_partial_string_timestamp_multiindex(df): + # GH10331 + df_swap = df.swaplevel(0, 1).sort_index() + SLC = IndexSlice + + # indexing with IndexSlice + result = df.loc[SLC["2016-01-01":"2016-02-01", :], :] + expected = df + tm.assert_frame_equal(result, expected) + + # match on secondary index + result = df_swap.loc[SLC[:, "2016-01-01":"2016-01-01"], :] + expected = df_swap.iloc[[0, 1, 5, 6, 10, 11]] + tm.assert_frame_equal(result, expected) + + # partial string match on year only + result = df.loc["2016"] + expected = df + tm.assert_frame_equal(result, expected) + + # partial string match on date + result = df.loc["2016-01-01"] + expected = df.iloc[0:6] + tm.assert_frame_equal(result, expected) + + # partial string match on date and hour, from middle + result = df.loc["2016-01-02 12"] + # hourly resolution, same as index.levels[0], so we are _not_ slicing on + # that level, so that level gets dropped + expected = df.iloc[9:12].droplevel(0) + tm.assert_frame_equal(result, expected) + + # partial string match on secondary index + result = df_swap.loc[SLC[:, "2016-01-02"], :] + expected = df_swap.iloc[[2, 3, 7, 8, 12, 13]] + tm.assert_frame_equal(result, expected) + + # tuple selector with partial string match on date + # "2016-01-01" has daily resolution, so _is_ a slice on the first level. + result = df.loc[("2016-01-01", "a"), :] + expected = df.iloc[[0, 3]] + expected = df.iloc[[0, 3]].droplevel(1) + tm.assert_frame_equal(result, expected) + + # Slicing date on first level should break (of course) bc the DTI is the + # second level on df_swap + with pytest.raises(KeyError, match="'2016-01-01'"): + df_swap.loc["2016-01-01"] + + +def test_partial_string_timestamp_multiindex_str_key_raises(df): + # Even though this syntax works on a single index, this is somewhat + # ambiguous and we don't want to extend this behavior forward to work + # in multi-indexes. This would amount to selecting a scalar from a + # column. + with pytest.raises(KeyError, match="'2016-01-01'"): + df["2016-01-01"] + + +def test_partial_string_timestamp_multiindex_daily_resolution(df): + # GH12685 (partial string with daily resolution or below) + result = df.loc[IndexSlice["2013-03":"2013-03", :], :] + expected = df.iloc[118:180] + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_pickle.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_pickle.py new file mode 100644 index 0000000000000000000000000000000000000000..1d8b72140442159fa0b8c608022d167bddd95db4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_pickle.py @@ -0,0 +1,10 @@ +import pytest + +from pandas import MultiIndex + + +def test_pickle_compat_construction(): + # this is testing for pickle compat + # need an object to create with + with pytest.raises(TypeError, match="Must pass both levels and codes"): + MultiIndex() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reindex.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reindex.py new file mode 100644 index 0000000000000000000000000000000000000000..d1b4fe8b98760a0b776c5d81d471a7745e8407de --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reindex.py @@ -0,0 +1,174 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Index, + MultiIndex, +) +import pandas._testing as tm + + +def test_reindex(idx): + result, indexer = idx.reindex(list(idx[:4])) + assert isinstance(result, MultiIndex) + assert result.names == ["first", "second"] + assert [level.name for level in result.levels] == ["first", "second"] + + result, indexer = idx.reindex(list(idx)) + assert isinstance(result, MultiIndex) + assert indexer is None + assert result.names == ["first", "second"] + assert [level.name for level in result.levels] == ["first", "second"] + + +def test_reindex_level(idx): + index = Index(["one"]) + + target, indexer = idx.reindex(index, level="second") + target2, indexer2 = index.reindex(idx, level="second") + + exp_index = idx.join(index, level="second", how="right") + exp_index2 = idx.join(index, level="second", how="left") + + assert target.equals(exp_index) + exp_indexer = np.array([0, 2, 4]) + tm.assert_numpy_array_equal(indexer, exp_indexer, check_dtype=False) + + assert target2.equals(exp_index2) + exp_indexer2 = np.array([0, -1, 0, -1, 0, -1]) + tm.assert_numpy_array_equal(indexer2, exp_indexer2, check_dtype=False) + + with pytest.raises(TypeError, match="Fill method not supported"): + idx.reindex(idx, method="pad", level="second") + + +def test_reindex_preserves_names_when_target_is_list_or_ndarray(idx): + # GH6552 + idx = idx.copy() + target = idx.copy() + idx.names = target.names = [None, None] + + other_dtype = MultiIndex.from_product([[1, 2], [3, 4]]) + + # list & ndarray cases + assert idx.reindex([])[0].names == [None, None] + assert idx.reindex(np.array([]))[0].names == [None, None] + assert idx.reindex(target.tolist())[0].names == [None, None] + assert idx.reindex(target.values)[0].names == [None, None] + assert idx.reindex(other_dtype.tolist())[0].names == [None, None] + assert idx.reindex(other_dtype.values)[0].names == [None, None] + + idx.names = ["foo", "bar"] + assert idx.reindex([])[0].names == ["foo", "bar"] + assert idx.reindex(np.array([]))[0].names == ["foo", "bar"] + assert idx.reindex(target.tolist())[0].names == ["foo", "bar"] + assert idx.reindex(target.values)[0].names == ["foo", "bar"] + assert idx.reindex(other_dtype.tolist())[0].names == ["foo", "bar"] + assert idx.reindex(other_dtype.values)[0].names == ["foo", "bar"] + + +def test_reindex_lvl_preserves_names_when_target_is_list_or_array(): + # GH7774 + idx = MultiIndex.from_product([[0, 1], ["a", "b"]], names=["foo", "bar"]) + assert idx.reindex([], level=0)[0].names == ["foo", "bar"] + assert idx.reindex([], level=1)[0].names == ["foo", "bar"] + + +def test_reindex_lvl_preserves_type_if_target_is_empty_list_or_array( + using_infer_string, +): + # GH7774 + idx = MultiIndex.from_product([[0, 1], ["a", "b"]]) + assert idx.reindex([], level=0)[0].levels[0].dtype.type == np.int64 + exp = np.object_ if not using_infer_string else str + assert idx.reindex([], level=1)[0].levels[1].dtype.type == exp + + # case with EA levels + cat = pd.Categorical(["foo", "bar"]) + dti = pd.date_range("2016-01-01", periods=2, tz="US/Pacific") + mi = MultiIndex.from_product([cat, dti]) + assert mi.reindex([], level=0)[0].levels[0].dtype == cat.dtype + assert mi.reindex([], level=1)[0].levels[1].dtype == dti.dtype + + +def test_reindex_base(idx): + expected = np.arange(idx.size, dtype=np.intp) + + actual = idx.get_indexer(idx) + tm.assert_numpy_array_equal(expected, actual) + + with pytest.raises(ValueError, match="Invalid fill method"): + idx.get_indexer(idx, method="invalid") + + +def test_reindex_non_unique(): + idx = MultiIndex.from_tuples([(0, 0), (1, 1), (1, 1), (2, 2)]) + a = pd.Series(np.arange(4), index=idx) + new_idx = MultiIndex.from_tuples([(0, 0), (1, 1), (2, 2)]) + + msg = "cannot handle a non-unique multi-index!" + with pytest.raises(ValueError, match=msg): + a.reindex(new_idx) + + +@pytest.mark.parametrize("values", [[["a"], ["x"]], [[], []]]) +def test_reindex_empty_with_level(values): + # GH41170 + idx = MultiIndex.from_arrays(values) + result, result_indexer = idx.reindex(np.array(["b"]), level=0) + expected = MultiIndex(levels=[["b"], values[1]], codes=[[], []]) + expected_indexer = np.array([], dtype=result_indexer.dtype) + tm.assert_index_equal(result, expected) + tm.assert_numpy_array_equal(result_indexer, expected_indexer) + + +def test_reindex_not_all_tuples(): + keys = [("i", "i"), ("i", "j"), ("j", "i"), "j"] + mi = MultiIndex.from_tuples(keys[:-1]) + idx = Index(keys) + res, indexer = mi.reindex(idx) + + tm.assert_index_equal(res, idx) + expected = np.array([0, 1, 2, -1], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + +def test_reindex_limit_arg_with_multiindex(): + # GH21247 + + idx = MultiIndex.from_tuples([(3, "A"), (4, "A"), (4, "B")]) + + df = pd.Series([0.02, 0.01, 0.012], index=idx) + + new_idx = MultiIndex.from_tuples( + [ + (3, "A"), + (3, "B"), + (4, "A"), + (4, "B"), + (4, "C"), + (5, "B"), + (5, "C"), + (6, "B"), + (6, "C"), + ] + ) + + with pytest.raises( + ValueError, + match="limit argument only valid if doing pad, backfill or nearest reindexing", + ): + df.reindex(new_idx, fill_value=0, limit=1) + + +def test_reindex_with_none_in_nested_multiindex(): + # GH42883 + index = MultiIndex.from_tuples([(("a", None), 1), (("b", None), 2)]) + index2 = MultiIndex.from_tuples([(("b", None), 2), (("a", None), 1)]) + df1_dtype = pd.DataFrame([1, 2], index=index) + df2_dtype = pd.DataFrame([2, 1], index=index2) + + result = df1_dtype.reindex_like(df2_dtype) + expected = df2_dtype + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reshape.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reshape.py new file mode 100644 index 0000000000000000000000000000000000000000..06dbb33aadf97a54e4bb283d3aed8fe1169164b3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_reshape.py @@ -0,0 +1,224 @@ +from datetime import datetime + +import numpy as np +import pytest +import pytz + +import pandas as pd +from pandas import ( + Index, + MultiIndex, +) +import pandas._testing as tm + + +def test_insert(idx): + # key contained in all levels + new_index = idx.insert(0, ("bar", "two")) + assert new_index.equal_levels(idx) + assert new_index[0] == ("bar", "two") + + # key not contained in all levels + new_index = idx.insert(0, ("abc", "three")) + + exp0 = Index(list(idx.levels[0]) + ["abc"], name="first") + tm.assert_index_equal(new_index.levels[0], exp0) + assert new_index.names == ["first", "second"] + + exp1 = Index(list(idx.levels[1]) + ["three"], name="second") + tm.assert_index_equal(new_index.levels[1], exp1) + assert new_index[0] == ("abc", "three") + + # key wrong length + msg = "Item must have length equal to number of levels" + with pytest.raises(ValueError, match=msg): + idx.insert(0, ("foo2",)) + + left = pd.DataFrame([["a", "b", 0], ["b", "d", 1]], columns=["1st", "2nd", "3rd"]) + left.set_index(["1st", "2nd"], inplace=True) + ts = left["3rd"].copy(deep=True) + + left.loc[("b", "x"), "3rd"] = 2 + left.loc[("b", "a"), "3rd"] = -1 + left.loc[("b", "b"), "3rd"] = 3 + left.loc[("a", "x"), "3rd"] = 4 + left.loc[("a", "w"), "3rd"] = 5 + left.loc[("a", "a"), "3rd"] = 6 + + ts.loc[("b", "x")] = 2 + ts.loc["b", "a"] = -1 + ts.loc[("b", "b")] = 3 + ts.loc["a", "x"] = 4 + ts.loc[("a", "w")] = 5 + ts.loc["a", "a"] = 6 + + right = pd.DataFrame( + [ + ["a", "b", 0], + ["b", "d", 1], + ["b", "x", 2], + ["b", "a", -1], + ["b", "b", 3], + ["a", "x", 4], + ["a", "w", 5], + ["a", "a", 6], + ], + columns=["1st", "2nd", "3rd"], + ) + right.set_index(["1st", "2nd"], inplace=True) + # FIXME data types changes to float because + # of intermediate nan insertion; + tm.assert_frame_equal(left, right, check_dtype=False) + tm.assert_series_equal(ts, right["3rd"]) + + +def test_insert2(): + # GH9250 + idx = ( + [("test1", i) for i in range(5)] + + [("test2", i) for i in range(6)] + + [("test", 17), ("test", 18)] + ) + + left = pd.Series(np.linspace(0, 10, 11), MultiIndex.from_tuples(idx[:-2])) + + left.loc[("test", 17)] = 11 + left.loc[("test", 18)] = 12 + + right = pd.Series(np.linspace(0, 12, 13), MultiIndex.from_tuples(idx)) + + tm.assert_series_equal(left, right) + + +def test_append(idx): + result = idx[:3].append(idx[3:]) + assert result.equals(idx) + + foos = [idx[:1], idx[1:3], idx[3:]] + result = foos[0].append(foos[1:]) + assert result.equals(idx) + + # empty + result = idx.append([]) + assert result.equals(idx) + + +def test_append_index(): + idx1 = Index([1.1, 1.2, 1.3]) + idx2 = pd.date_range("2011-01-01", freq="D", periods=3, tz="Asia/Tokyo") + idx3 = Index(["A", "B", "C"]) + + midx_lv2 = MultiIndex.from_arrays([idx1, idx2]) + midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3]) + + result = idx1.append(midx_lv2) + + # see gh-7112 + tz = pytz.timezone("Asia/Tokyo") + expected_tuples = [ + (1.1, tz.localize(datetime(2011, 1, 1))), + (1.2, tz.localize(datetime(2011, 1, 2))), + (1.3, tz.localize(datetime(2011, 1, 3))), + ] + expected = Index([1.1, 1.2, 1.3] + expected_tuples) + tm.assert_index_equal(result, expected) + + result = midx_lv2.append(idx1) + expected = Index(expected_tuples + [1.1, 1.2, 1.3]) + tm.assert_index_equal(result, expected) + + result = midx_lv2.append(midx_lv2) + expected = MultiIndex.from_arrays([idx1.append(idx1), idx2.append(idx2)]) + tm.assert_index_equal(result, expected) + + result = midx_lv2.append(midx_lv3) + tm.assert_index_equal(result, expected) + + result = midx_lv3.append(midx_lv2) + expected = Index._simple_new( + np.array( + [ + (1.1, tz.localize(datetime(2011, 1, 1)), "A"), + (1.2, tz.localize(datetime(2011, 1, 2)), "B"), + (1.3, tz.localize(datetime(2011, 1, 3)), "C"), + ] + + expected_tuples, + dtype=object, + ), + None, + ) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("name, exp", [("b", "b"), ("c", None)]) +def test_append_names_match(name, exp): + # GH#48288 + midx = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"]) + midx2 = MultiIndex.from_arrays([[3], [5]], names=["a", name]) + result = midx.append(midx2) + expected = MultiIndex.from_arrays([[1, 2, 3], [3, 4, 5]], names=["a", exp]) + tm.assert_index_equal(result, expected) + + +def test_append_names_dont_match(): + # GH#48288 + midx = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"]) + midx2 = MultiIndex.from_arrays([[3], [5]], names=["x", "y"]) + result = midx.append(midx2) + expected = MultiIndex.from_arrays([[1, 2, 3], [3, 4, 5]], names=None) + tm.assert_index_equal(result, expected) + + +def test_append_overlapping_interval_levels(): + # GH 54934 + ivl1 = pd.IntervalIndex.from_breaks([0.0, 1.0, 2.0]) + ivl2 = pd.IntervalIndex.from_breaks([0.5, 1.5, 2.5]) + mi1 = MultiIndex.from_product([ivl1, ivl1]) + mi2 = MultiIndex.from_product([ivl2, ivl2]) + result = mi1.append(mi2) + expected = MultiIndex.from_tuples( + [ + (pd.Interval(0.0, 1.0), pd.Interval(0.0, 1.0)), + (pd.Interval(0.0, 1.0), pd.Interval(1.0, 2.0)), + (pd.Interval(1.0, 2.0), pd.Interval(0.0, 1.0)), + (pd.Interval(1.0, 2.0), pd.Interval(1.0, 2.0)), + (pd.Interval(0.5, 1.5), pd.Interval(0.5, 1.5)), + (pd.Interval(0.5, 1.5), pd.Interval(1.5, 2.5)), + (pd.Interval(1.5, 2.5), pd.Interval(0.5, 1.5)), + (pd.Interval(1.5, 2.5), pd.Interval(1.5, 2.5)), + ] + ) + tm.assert_index_equal(result, expected) + + +def test_repeat(): + reps = 2 + numbers = [1, 2, 3] + names = np.array(["foo", "bar"]) + + m = MultiIndex.from_product([numbers, names], names=names) + expected = MultiIndex.from_product([numbers, names.repeat(reps)], names=names) + tm.assert_index_equal(m.repeat(reps), expected) + + +def test_insert_base(idx): + result = idx[1:4] + + # test 0th element + assert idx[0:4].equals(result.insert(0, idx[0])) + + +def test_delete_base(idx): + expected = idx[1:] + result = idx.delete(0) + assert result.equals(expected) + assert result.name == expected.name + + expected = idx[:-1] + result = idx.delete(-1) + assert result.equals(expected) + assert result.name == expected.name + + msg = "index 6 is out of bounds for axis 0 with size 6" + with pytest.raises(IndexError, match=msg): + idx.delete(len(idx)) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_setops.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_setops.py new file mode 100644 index 0000000000000000000000000000000000000000..801a813955b41ed6f67f00996e2de371d20fded5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_setops.py @@ -0,0 +1,772 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + CategoricalIndex, + DataFrame, + Index, + IntervalIndex, + MultiIndex, + Series, +) +import pandas._testing as tm +from pandas.api.types import ( + is_float_dtype, + is_unsigned_integer_dtype, +) + + +@pytest.mark.parametrize("case", [0.5, "xxx"]) +@pytest.mark.parametrize( + "method", ["intersection", "union", "difference", "symmetric_difference"] +) +def test_set_ops_error_cases(idx, case, sort, method): + # non-iterable input + msg = "Input must be Index or array-like" + with pytest.raises(TypeError, match=msg): + getattr(idx, method)(case, sort=sort) + + +@pytest.mark.parametrize("klass", [MultiIndex, np.array, Series, list]) +def test_intersection_base(idx, sort, klass): + first = idx[2::-1] # first 3 elements reversed + second = idx[:5] + + if klass is not MultiIndex: + second = klass(second.values) + + intersect = first.intersection(second, sort=sort) + if sort is None: + expected = first.sort_values() + else: + expected = first + tm.assert_index_equal(intersect, expected) + + msg = "other must be a MultiIndex or a list of tuples" + with pytest.raises(TypeError, match=msg): + first.intersection([1, 2, 3], sort=sort) + + +@pytest.mark.arm_slow +@pytest.mark.parametrize("klass", [MultiIndex, np.array, Series, list]) +def test_union_base(idx, sort, klass): + first = idx[::-1] + second = idx[:5] + + if klass is not MultiIndex: + second = klass(second.values) + + union = first.union(second, sort=sort) + if sort is None: + expected = first.sort_values() + else: + expected = first + tm.assert_index_equal(union, expected) + + msg = "other must be a MultiIndex or a list of tuples" + with pytest.raises(TypeError, match=msg): + first.union([1, 2, 3], sort=sort) + + +def test_difference_base(idx, sort): + second = idx[4:] + answer = idx[:4] + result = idx.difference(second, sort=sort) + + if sort is None: + answer = answer.sort_values() + + assert result.equals(answer) + tm.assert_index_equal(result, answer) + + # GH 10149 + cases = [klass(second.values) for klass in [np.array, Series, list]] + for case in cases: + result = idx.difference(case, sort=sort) + tm.assert_index_equal(result, answer) + + msg = "other must be a MultiIndex or a list of tuples" + with pytest.raises(TypeError, match=msg): + idx.difference([1, 2, 3], sort=sort) + + +def test_symmetric_difference(idx, sort): + first = idx[1:] + second = idx[:-1] + answer = idx[[-1, 0]] + result = first.symmetric_difference(second, sort=sort) + + if sort is None: + answer = answer.sort_values() + + tm.assert_index_equal(result, answer) + + # GH 10149 + cases = [klass(second.values) for klass in [np.array, Series, list]] + for case in cases: + result = first.symmetric_difference(case, sort=sort) + tm.assert_index_equal(result, answer) + + msg = "other must be a MultiIndex or a list of tuples" + with pytest.raises(TypeError, match=msg): + first.symmetric_difference([1, 2, 3], sort=sort) + + +def test_multiindex_symmetric_difference(): + # GH 13490 + idx = MultiIndex.from_product([["a", "b"], ["A", "B"]], names=["a", "b"]) + result = idx.symmetric_difference(idx) + assert result.names == idx.names + + idx2 = idx.copy().rename(["A", "B"]) + result = idx.symmetric_difference(idx2) + assert result.names == [None, None] + + +def test_empty(idx): + # GH 15270 + assert not idx.empty + assert idx[:0].empty + + +def test_difference(idx, sort): + first = idx + result = first.difference(idx[-3:], sort=sort) + vals = idx[:-3].values + + if sort is None: + vals = sorted(vals) + + expected = MultiIndex.from_tuples(vals, sortorder=0, names=idx.names) + + assert isinstance(result, MultiIndex) + assert result.equals(expected) + assert result.names == idx.names + tm.assert_index_equal(result, expected) + + # empty difference: reflexive + result = idx.difference(idx, sort=sort) + expected = idx[:0] + assert result.equals(expected) + assert result.names == idx.names + + # empty difference: superset + result = idx[-3:].difference(idx, sort=sort) + expected = idx[:0] + assert result.equals(expected) + assert result.names == idx.names + + # empty difference: degenerate + result = idx[:0].difference(idx, sort=sort) + expected = idx[:0] + assert result.equals(expected) + assert result.names == idx.names + + # names not the same + chunklet = idx[-3:] + chunklet.names = ["foo", "baz"] + result = first.difference(chunklet, sort=sort) + assert result.names == (None, None) + + # empty, but non-equal + result = idx.difference(idx.sortlevel(1)[0], sort=sort) + assert len(result) == 0 + + # raise Exception called with non-MultiIndex + result = first.difference(first.values, sort=sort) + assert result.equals(first[:0]) + + # name from empty array + result = first.difference([], sort=sort) + assert first.equals(result) + assert first.names == result.names + + # name from non-empty array + result = first.difference([("foo", "one")], sort=sort) + expected = MultiIndex.from_tuples( + [("bar", "one"), ("baz", "two"), ("foo", "two"), ("qux", "one"), ("qux", "two")] + ) + expected.names = first.names + assert first.names == result.names + + msg = "other must be a MultiIndex or a list of tuples" + with pytest.raises(TypeError, match=msg): + first.difference([1, 2, 3, 4, 5], sort=sort) + + +def test_difference_sort_special(): + # GH-24959 + idx = MultiIndex.from_product([[1, 0], ["a", "b"]]) + # sort=None, the default + result = idx.difference([]) + tm.assert_index_equal(result, idx) + + +def test_difference_sort_special_true(): + idx = MultiIndex.from_product([[1, 0], ["a", "b"]]) + result = idx.difference([], sort=True) + expected = MultiIndex.from_product([[0, 1], ["a", "b"]]) + tm.assert_index_equal(result, expected) + + +def test_difference_sort_incomparable(): + # GH-24959 + idx = MultiIndex.from_product([[1, pd.Timestamp("2000"), 2], ["a", "b"]]) + + other = MultiIndex.from_product([[3, pd.Timestamp("2000"), 4], ["c", "d"]]) + # sort=None, the default + msg = "sort order is undefined for incomparable objects" + with tm.assert_produces_warning(RuntimeWarning, match=msg): + result = idx.difference(other) + tm.assert_index_equal(result, idx) + + # sort=False + result = idx.difference(other, sort=False) + tm.assert_index_equal(result, idx) + + +def test_difference_sort_incomparable_true(): + idx = MultiIndex.from_product([[1, pd.Timestamp("2000"), 2], ["a", "b"]]) + other = MultiIndex.from_product([[3, pd.Timestamp("2000"), 4], ["c", "d"]]) + + # TODO: this is raising in constructing a Categorical when calling + # algos.safe_sort. Should we catch and re-raise with a better message? + msg = "'values' is not ordered, please explicitly specify the categories order " + with pytest.raises(TypeError, match=msg): + idx.difference(other, sort=True) + + +def test_union(idx, sort): + piece1 = idx[:5][::-1] + piece2 = idx[3:] + + the_union = piece1.union(piece2, sort=sort) + + if sort in (None, False): + tm.assert_index_equal(the_union.sort_values(), idx.sort_values()) + else: + tm.assert_index_equal(the_union, idx) + + # corner case, pass self or empty thing: + the_union = idx.union(idx, sort=sort) + tm.assert_index_equal(the_union, idx) + + the_union = idx.union(idx[:0], sort=sort) + tm.assert_index_equal(the_union, idx) + + tuples = idx.values + result = idx[:4].union(tuples[4:], sort=sort) + if sort is None: + tm.assert_index_equal(result.sort_values(), idx.sort_values()) + else: + assert result.equals(idx) + + +def test_union_with_regular_index(idx, using_infer_string): + other = Index(["A", "B", "C"]) + + result = other.union(idx) + assert ("foo", "one") in result + assert "B" in result + + if using_infer_string: + with pytest.raises(NotImplementedError, match="Can only union"): + idx.union(other) + else: + msg = "The values in the array are unorderable" + with tm.assert_produces_warning(RuntimeWarning, match=msg): + result2 = idx.union(other) + # This is more consistent now, if sorting fails then we don't sort at all + # in the MultiIndex case. + assert not result.equals(result2) + + +def test_intersection(idx, sort): + piece1 = idx[:5][::-1] + piece2 = idx[3:] + + the_int = piece1.intersection(piece2, sort=sort) + + if sort in (None, True): + tm.assert_index_equal(the_int, idx[3:5]) + else: + tm.assert_index_equal(the_int.sort_values(), idx[3:5]) + + # corner case, pass self + the_int = idx.intersection(idx, sort=sort) + tm.assert_index_equal(the_int, idx) + + # empty intersection: disjoint + empty = idx[:2].intersection(idx[2:], sort=sort) + expected = idx[:0] + assert empty.equals(expected) + + tuples = idx.values + result = idx.intersection(tuples) + assert result.equals(idx) + + +@pytest.mark.parametrize( + "method", ["intersection", "union", "difference", "symmetric_difference"] +) +def test_setop_with_categorical(idx, sort, method): + other = idx.to_flat_index().astype("category") + res_names = [None] * idx.nlevels + + result = getattr(idx, method)(other, sort=sort) + expected = getattr(idx, method)(idx, sort=sort).rename(res_names) + tm.assert_index_equal(result, expected) + + result = getattr(idx, method)(other[:5], sort=sort) + expected = getattr(idx, method)(idx[:5], sort=sort).rename(res_names) + tm.assert_index_equal(result, expected) + + +def test_intersection_non_object(idx, sort): + other = Index(range(3), name="foo") + + result = idx.intersection(other, sort=sort) + expected = MultiIndex(levels=idx.levels, codes=[[]] * idx.nlevels, names=None) + tm.assert_index_equal(result, expected, exact=True) + + # if we pass a length-0 ndarray (i.e. no name, we retain our idx.name) + result = idx.intersection(np.asarray(other)[:0], sort=sort) + expected = MultiIndex(levels=idx.levels, codes=[[]] * idx.nlevels, names=idx.names) + tm.assert_index_equal(result, expected, exact=True) + + msg = "other must be a MultiIndex or a list of tuples" + with pytest.raises(TypeError, match=msg): + # With non-zero length non-index, we try and fail to convert to tuples + idx.intersection(np.asarray(other), sort=sort) + + +def test_intersect_equal_sort(): + # GH-24959 + idx = MultiIndex.from_product([[1, 0], ["a", "b"]]) + tm.assert_index_equal(idx.intersection(idx, sort=False), idx) + tm.assert_index_equal(idx.intersection(idx, sort=None), idx) + + +def test_intersect_equal_sort_true(): + idx = MultiIndex.from_product([[1, 0], ["a", "b"]]) + expected = MultiIndex.from_product([[0, 1], ["a", "b"]]) + result = idx.intersection(idx, sort=True) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("slice_", [slice(None), slice(0)]) +def test_union_sort_other_empty(slice_): + # https://github.com/pandas-dev/pandas/issues/24959 + idx = MultiIndex.from_product([[1, 0], ["a", "b"]]) + + # default, sort=None + other = idx[slice_] + tm.assert_index_equal(idx.union(other), idx) + tm.assert_index_equal(other.union(idx), idx) + + # sort=False + tm.assert_index_equal(idx.union(other, sort=False), idx) + + +def test_union_sort_other_empty_sort(): + idx = MultiIndex.from_product([[1, 0], ["a", "b"]]) + other = idx[:0] + result = idx.union(other, sort=True) + expected = MultiIndex.from_product([[0, 1], ["a", "b"]]) + tm.assert_index_equal(result, expected) + + +def test_union_sort_other_incomparable(): + # https://github.com/pandas-dev/pandas/issues/24959 + idx = MultiIndex.from_product([[1, pd.Timestamp("2000")], ["a", "b"]]) + + # default, sort=None + with tm.assert_produces_warning(RuntimeWarning): + result = idx.union(idx[:1]) + tm.assert_index_equal(result, idx) + + # sort=False + result = idx.union(idx[:1], sort=False) + tm.assert_index_equal(result, idx) + + +def test_union_sort_other_incomparable_sort(): + idx = MultiIndex.from_product([[1, pd.Timestamp("2000")], ["a", "b"]]) + msg = "'<' not supported between instances of 'Timestamp' and 'int'" + with pytest.raises(TypeError, match=msg): + idx.union(idx[:1], sort=True) + + +def test_union_non_object_dtype_raises(): + # GH#32646 raise NotImplementedError instead of less-informative error + mi = MultiIndex.from_product([["a", "b"], [1, 2]]) + + idx = mi.levels[1] + + msg = "Can only union MultiIndex with MultiIndex or Index of tuples" + with pytest.raises(NotImplementedError, match=msg): + mi.union(idx) + + +def test_union_empty_self_different_names(): + # GH#38423 + mi = MultiIndex.from_arrays([[]]) + mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"]) + result = mi.union(mi2) + expected = MultiIndex.from_arrays([[1, 2], [3, 4]]) + tm.assert_index_equal(result, expected) + + +def test_union_multiindex_empty_rangeindex(): + # GH#41234 + mi = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"]) + ri = pd.RangeIndex(0) + + result_left = mi.union(ri) + tm.assert_index_equal(mi, result_left, check_names=False) + + result_right = ri.union(mi) + tm.assert_index_equal(mi, result_right, check_names=False) + + +@pytest.mark.parametrize( + "method", ["union", "intersection", "difference", "symmetric_difference"] +) +def test_setops_sort_validation(method): + idx1 = MultiIndex.from_product([["a", "b"], [1, 2]]) + idx2 = MultiIndex.from_product([["b", "c"], [1, 2]]) + + with pytest.raises(ValueError, match="The 'sort' keyword only takes"): + getattr(idx1, method)(idx2, sort=2) + + # sort=True is supported as of GH#? + getattr(idx1, method)(idx2, sort=True) + + +@pytest.mark.parametrize("val", [pd.NA, 100]) +def test_difference_keep_ea_dtypes(any_numeric_ea_dtype, val): + # GH#48606 + midx = MultiIndex.from_arrays( + [Series([1, 2], dtype=any_numeric_ea_dtype), [2, 1]], names=["a", None] + ) + midx2 = MultiIndex.from_arrays( + [Series([1, 2, val], dtype=any_numeric_ea_dtype), [1, 1, 3]] + ) + result = midx.difference(midx2) + expected = MultiIndex.from_arrays([Series([1], dtype=any_numeric_ea_dtype), [2]]) + tm.assert_index_equal(result, expected) + + result = midx.difference(midx.sort_values(ascending=False)) + expected = MultiIndex.from_arrays( + [Series([], dtype=any_numeric_ea_dtype), Series([], dtype=np.int64)], + names=["a", None], + ) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("val", [pd.NA, 5]) +def test_symmetric_difference_keeping_ea_dtype(any_numeric_ea_dtype, val): + # GH#48607 + midx = MultiIndex.from_arrays( + [Series([1, 2], dtype=any_numeric_ea_dtype), [2, 1]], names=["a", None] + ) + midx2 = MultiIndex.from_arrays( + [Series([1, 2, val], dtype=any_numeric_ea_dtype), [1, 1, 3]] + ) + result = midx.symmetric_difference(midx2) + expected = MultiIndex.from_arrays( + [Series([1, 1, val], dtype=any_numeric_ea_dtype), [1, 2, 3]] + ) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize( + ("tuples", "exp_tuples"), + [ + ([("val1", "test1")], [("val1", "test1")]), + ([("val1", "test1"), ("val1", "test1")], [("val1", "test1")]), + ( + [("val2", "test2"), ("val1", "test1")], + [("val2", "test2"), ("val1", "test1")], + ), + ], +) +def test_intersect_with_duplicates(tuples, exp_tuples): + # GH#36915 + left = MultiIndex.from_tuples(tuples, names=["first", "second"]) + right = MultiIndex.from_tuples( + [("val1", "test1"), ("val1", "test1"), ("val2", "test2")], + names=["first", "second"], + ) + result = left.intersection(right) + expected = MultiIndex.from_tuples(exp_tuples, names=["first", "second"]) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize( + "data, names, expected", + [ + ((1,), None, [None, None]), + ((1,), ["a"], [None, None]), + ((1,), ["b"], [None, None]), + ((1, 2), ["c", "d"], [None, None]), + ((1, 2), ["b", "a"], [None, None]), + ((1, 2, 3), ["a", "b", "c"], [None, None]), + ((1, 2), ["a", "c"], ["a", None]), + ((1, 2), ["c", "b"], [None, "b"]), + ((1, 2), ["a", "b"], ["a", "b"]), + ((1, 2), [None, "b"], [None, "b"]), + ], +) +def test_maybe_match_names(data, names, expected): + # GH#38323 + mi = MultiIndex.from_tuples([], names=["a", "b"]) + mi2 = MultiIndex.from_tuples([data], names=names) + result = mi._maybe_match_names(mi2) + assert result == expected + + +def test_intersection_equal_different_names(): + # GH#30302 + mi1 = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["c", "b"]) + mi2 = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"]) + + result = mi1.intersection(mi2) + expected = MultiIndex.from_arrays([[1, 2], [3, 4]], names=[None, "b"]) + tm.assert_index_equal(result, expected) + + +def test_intersection_different_names(): + # GH#38323 + mi = MultiIndex.from_arrays([[1], [3]], names=["c", "b"]) + mi2 = MultiIndex.from_arrays([[1], [3]]) + result = mi.intersection(mi2) + tm.assert_index_equal(result, mi2) + + +def test_intersection_with_missing_values_on_both_sides(nulls_fixture): + # GH#38623 + mi1 = MultiIndex.from_arrays([[3, nulls_fixture, 4, nulls_fixture], [1, 2, 4, 2]]) + mi2 = MultiIndex.from_arrays([[3, nulls_fixture, 3], [1, 2, 4]]) + result = mi1.intersection(mi2) + expected = MultiIndex.from_arrays([[3, nulls_fixture], [1, 2]]) + tm.assert_index_equal(result, expected) + + +def test_union_with_missing_values_on_both_sides(nulls_fixture): + # GH#38623 + mi1 = MultiIndex.from_arrays([[1, nulls_fixture]]) + mi2 = MultiIndex.from_arrays([[1, nulls_fixture, 3]]) + result = mi1.union(mi2) + expected = MultiIndex.from_arrays([[1, 3, nulls_fixture]]) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("dtype", ["float64", "Float64"]) +@pytest.mark.parametrize("sort", [None, False]) +def test_union_nan_got_duplicated(dtype, sort): + # GH#38977, GH#49010 + mi1 = MultiIndex.from_arrays([pd.array([1.0, np.nan], dtype=dtype), [2, 3]]) + mi2 = MultiIndex.from_arrays([pd.array([1.0, np.nan, 3.0], dtype=dtype), [2, 3, 4]]) + result = mi1.union(mi2, sort=sort) + if sort is None: + expected = MultiIndex.from_arrays( + [pd.array([1.0, 3.0, np.nan], dtype=dtype), [2, 4, 3]] + ) + else: + expected = mi2 + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("val", [4, 1]) +def test_union_keep_ea_dtype(any_numeric_ea_dtype, val): + # GH#48505 + + arr1 = Series([val, 2], dtype=any_numeric_ea_dtype) + arr2 = Series([2, 1], dtype=any_numeric_ea_dtype) + midx = MultiIndex.from_arrays([arr1, [1, 2]], names=["a", None]) + midx2 = MultiIndex.from_arrays([arr2, [2, 1]]) + result = midx.union(midx2) + if val == 4: + expected = MultiIndex.from_arrays( + [Series([1, 2, 4], dtype=any_numeric_ea_dtype), [1, 2, 1]] + ) + else: + expected = MultiIndex.from_arrays( + [Series([1, 2], dtype=any_numeric_ea_dtype), [1, 2]] + ) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("dupe_val", [3, pd.NA]) +def test_union_with_duplicates_keep_ea_dtype(dupe_val, any_numeric_ea_dtype): + # GH48900 + mi1 = MultiIndex.from_arrays( + [ + Series([1, dupe_val, 2], dtype=any_numeric_ea_dtype), + Series([1, dupe_val, 2], dtype=any_numeric_ea_dtype), + ] + ) + mi2 = MultiIndex.from_arrays( + [ + Series([2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype), + Series([2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype), + ] + ) + result = mi1.union(mi2) + expected = MultiIndex.from_arrays( + [ + Series([1, 2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype), + Series([1, 2, dupe_val, dupe_val], dtype=any_numeric_ea_dtype), + ] + ) + tm.assert_index_equal(result, expected) + + +@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning") +def test_union_duplicates(index, request): + # GH#38977 + if index.empty or isinstance(index, (IntervalIndex, CategoricalIndex)): + pytest.skip(f"No duplicates in an empty {type(index).__name__}") + + values = index.unique().values.tolist() + mi1 = MultiIndex.from_arrays([values, [1] * len(values)]) + mi2 = MultiIndex.from_arrays([[values[0]] + values, [1] * (len(values) + 1)]) + result = mi2.union(mi1) + expected = mi2.sort_values() + tm.assert_index_equal(result, expected) + + if ( + is_unsigned_integer_dtype(mi2.levels[0]) + and (mi2.get_level_values(0) < 2**63).all() + ): + # GH#47294 - union uses lib.fast_zip, converting data to Python integers + # and loses type information. Result is then unsigned only when values are + # sufficiently large to require unsigned dtype. This happens only if other + # has dups or one of both have missing values + expected = expected.set_levels( + [expected.levels[0].astype(np.int64), expected.levels[1]] + ) + elif is_float_dtype(mi2.levels[0]): + # mi2 has duplicates witch is a different path than above, Fix that path + # to use correct float dtype? + expected = expected.set_levels( + [expected.levels[0].astype(float), expected.levels[1]] + ) + + result = mi1.union(mi2) + tm.assert_index_equal(result, expected) + + +def test_union_keep_dtype_precision(any_real_numeric_dtype): + # GH#48498 + arr1 = Series([4, 1, 1], dtype=any_real_numeric_dtype) + arr2 = Series([1, 4], dtype=any_real_numeric_dtype) + midx = MultiIndex.from_arrays([arr1, [2, 1, 1]], names=["a", None]) + midx2 = MultiIndex.from_arrays([arr2, [1, 2]], names=["a", None]) + + result = midx.union(midx2) + expected = MultiIndex.from_arrays( + ([Series([1, 1, 4], dtype=any_real_numeric_dtype), [1, 1, 2]]), + names=["a", None], + ) + tm.assert_index_equal(result, expected) + + +def test_union_keep_ea_dtype_with_na(any_numeric_ea_dtype): + # GH#48498 + arr1 = Series([4, pd.NA], dtype=any_numeric_ea_dtype) + arr2 = Series([1, pd.NA], dtype=any_numeric_ea_dtype) + midx = MultiIndex.from_arrays([arr1, [2, 1]], names=["a", None]) + midx2 = MultiIndex.from_arrays([arr2, [1, 2]]) + result = midx.union(midx2) + expected = MultiIndex.from_arrays( + [Series([1, 4, pd.NA, pd.NA], dtype=any_numeric_ea_dtype), [1, 2, 1, 2]] + ) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize( + "levels1, levels2, codes1, codes2, names", + [ + ( + [["a", "b", "c"], [0, ""]], + [["c", "d", "b"], [""]], + [[0, 1, 2], [1, 1, 1]], + [[0, 1, 2], [0, 0, 0]], + ["name1", "name2"], + ), + ], +) +def test_intersection_lexsort_depth(levels1, levels2, codes1, codes2, names): + # GH#25169 + mi1 = MultiIndex(levels=levels1, codes=codes1, names=names) + mi2 = MultiIndex(levels=levels2, codes=codes2, names=names) + mi_int = mi1.intersection(mi2) + assert mi_int._lexsort_depth == 2 + + +@pytest.mark.parametrize( + "a", + [pd.Categorical(["a", "b"], categories=["a", "b"]), ["a", "b"]], +) +@pytest.mark.parametrize( + "b", + [ + pd.Categorical(["a", "b"], categories=["b", "a"], ordered=True), + pd.Categorical(["a", "b"], categories=["b", "a"]), + ], +) +def test_intersection_with_non_lex_sorted_categories(a, b): + # GH#49974 + other = ["1", "2"] + + df1 = DataFrame({"x": a, "y": other}) + df2 = DataFrame({"x": b, "y": other}) + + expected = MultiIndex.from_arrays([a, other], names=["x", "y"]) + + res1 = MultiIndex.from_frame(df1).intersection( + MultiIndex.from_frame(df2.sort_values(["x", "y"])) + ) + res2 = MultiIndex.from_frame(df1).intersection(MultiIndex.from_frame(df2)) + res3 = MultiIndex.from_frame(df1.sort_values(["x", "y"])).intersection( + MultiIndex.from_frame(df2) + ) + res4 = MultiIndex.from_frame(df1.sort_values(["x", "y"])).intersection( + MultiIndex.from_frame(df2.sort_values(["x", "y"])) + ) + + tm.assert_index_equal(res1, expected) + tm.assert_index_equal(res2, expected) + tm.assert_index_equal(res3, expected) + tm.assert_index_equal(res4, expected) + + +@pytest.mark.parametrize("val", [pd.NA, 100]) +def test_intersection_keep_ea_dtypes(val, any_numeric_ea_dtype): + # GH#48604 + midx = MultiIndex.from_arrays( + [Series([1, 2], dtype=any_numeric_ea_dtype), [2, 1]], names=["a", None] + ) + midx2 = MultiIndex.from_arrays( + [Series([1, 2, val], dtype=any_numeric_ea_dtype), [1, 1, 3]] + ) + result = midx.intersection(midx2) + expected = MultiIndex.from_arrays([Series([2], dtype=any_numeric_ea_dtype), [1]]) + tm.assert_index_equal(result, expected) + + +def test_union_with_na_when_constructing_dataframe(): + # GH43222 + series1 = Series( + (1,), + index=MultiIndex.from_arrays( + [Series([None], dtype="str"), Series([None], dtype="str")] + ), + ) + series2 = Series((10, 20), index=MultiIndex.from_tuples(((None, None), ("a", "b")))) + result = DataFrame([series1, series2]) + expected = DataFrame({(np.nan, np.nan): [1.0, 10.0], ("a", "b"): [np.nan, 20.0]}) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_sorting.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_sorting.py new file mode 100644 index 0000000000000000000000000000000000000000..b4dcef71dcf50724c90599b03d1c1c5aa99b7916 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_sorting.py @@ -0,0 +1,349 @@ +import numpy as np +import pytest + +from pandas.errors import ( + PerformanceWarning, + UnsortedIndexError, +) + +from pandas import ( + CategoricalIndex, + DataFrame, + Index, + MultiIndex, + RangeIndex, + Series, + Timestamp, +) +import pandas._testing as tm +from pandas.core.indexes.frozen import FrozenList + + +def test_sortlevel(idx): + tuples = list(idx) + np.random.default_rng(2).shuffle(tuples) + + index = MultiIndex.from_tuples(tuples) + + sorted_idx, _ = index.sortlevel(0) + expected = MultiIndex.from_tuples(sorted(tuples)) + assert sorted_idx.equals(expected) + + sorted_idx, _ = index.sortlevel(0, ascending=False) + assert sorted_idx.equals(expected[::-1]) + + sorted_idx, _ = index.sortlevel(1) + by1 = sorted(tuples, key=lambda x: (x[1], x[0])) + expected = MultiIndex.from_tuples(by1) + assert sorted_idx.equals(expected) + + sorted_idx, _ = index.sortlevel(1, ascending=False) + assert sorted_idx.equals(expected[::-1]) + + +def test_sortlevel_not_sort_remaining(): + mi = MultiIndex.from_tuples([[1, 1, 3], [1, 1, 1]], names=list("ABC")) + sorted_idx, _ = mi.sortlevel("A", sort_remaining=False) + assert sorted_idx.equals(mi) + + +def test_sortlevel_deterministic(): + tuples = [ + ("bar", "one"), + ("foo", "two"), + ("qux", "two"), + ("foo", "one"), + ("baz", "two"), + ("qux", "one"), + ] + + index = MultiIndex.from_tuples(tuples) + + sorted_idx, _ = index.sortlevel(0) + expected = MultiIndex.from_tuples(sorted(tuples)) + assert sorted_idx.equals(expected) + + sorted_idx, _ = index.sortlevel(0, ascending=False) + assert sorted_idx.equals(expected[::-1]) + + sorted_idx, _ = index.sortlevel(1) + by1 = sorted(tuples, key=lambda x: (x[1], x[0])) + expected = MultiIndex.from_tuples(by1) + assert sorted_idx.equals(expected) + + sorted_idx, _ = index.sortlevel(1, ascending=False) + assert sorted_idx.equals(expected[::-1]) + + +def test_sortlevel_na_position(): + # GH#51612 + midx = MultiIndex.from_tuples([(1, np.nan), (1, 1)]) + result = midx.sortlevel(level=[0, 1], na_position="last")[0] + expected = MultiIndex.from_tuples([(1, 1), (1, np.nan)]) + tm.assert_index_equal(result, expected) + + +def test_numpy_argsort(idx): + result = np.argsort(idx) + expected = idx.argsort() + tm.assert_numpy_array_equal(result, expected) + + # these are the only two types that perform + # pandas compatibility input validation - the + # rest already perform separate (or no) such + # validation via their 'values' attribute as + # defined in pandas.core.indexes/base.py - they + # cannot be changed at the moment due to + # backwards compatibility concerns + if isinstance(type(idx), (CategoricalIndex, RangeIndex)): + msg = "the 'axis' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.argsort(idx, axis=1) + + msg = "the 'kind' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.argsort(idx, kind="mergesort") + + msg = "the 'order' parameter is not supported" + with pytest.raises(ValueError, match=msg): + np.argsort(idx, order=("a", "b")) + + +def test_unsortedindex(): + # GH 11897 + mi = MultiIndex.from_tuples( + [("z", "a"), ("x", "a"), ("y", "b"), ("x", "b"), ("y", "a"), ("z", "b")], + names=["one", "two"], + ) + df = DataFrame([[i, 10 * i] for i in range(6)], index=mi, columns=["one", "two"]) + + # GH 16734: not sorted, but no real slicing + result = df.loc(axis=0)["z", "a"] + expected = df.iloc[0] + tm.assert_series_equal(result, expected) + + msg = ( + "MultiIndex slicing requires the index to be lexsorted: " + r"slicing on levels \[1\], lexsort depth 0" + ) + with pytest.raises(UnsortedIndexError, match=msg): + df.loc(axis=0)["z", slice("a")] + df.sort_index(inplace=True) + assert len(df.loc(axis=0)["z", :]) == 2 + + with pytest.raises(KeyError, match="'q'"): + df.loc(axis=0)["q", :] + + +def test_unsortedindex_doc_examples(): + # https://pandas.pydata.org/pandas-docs/stable/advanced.html#sorting-a-multiindex + dfm = DataFrame( + { + "jim": [0, 0, 1, 1], + "joe": ["x", "x", "z", "y"], + "jolie": np.random.default_rng(2).random(4), + } + ) + + dfm = dfm.set_index(["jim", "joe"]) + with tm.assert_produces_warning(PerformanceWarning): + dfm.loc[(1, "z")] + + msg = r"Key length \(2\) was greater than MultiIndex lexsort depth \(1\)" + with pytest.raises(UnsortedIndexError, match=msg): + dfm.loc[(0, "y"):(1, "z")] + + assert not dfm.index._is_lexsorted() + assert dfm.index._lexsort_depth == 1 + + # sort it + dfm = dfm.sort_index() + dfm.loc[(1, "z")] + dfm.loc[(0, "y"):(1, "z")] + + assert dfm.index._is_lexsorted() + assert dfm.index._lexsort_depth == 2 + + +def test_reconstruct_sort(): + # starts off lexsorted & monotonic + mi = MultiIndex.from_arrays([["A", "A", "B", "B", "B"], [1, 2, 1, 2, 3]]) + assert mi.is_monotonic_increasing + recons = mi._sort_levels_monotonic() + assert recons.is_monotonic_increasing + assert mi is recons + + assert mi.equals(recons) + assert Index(mi.values).equals(Index(recons.values)) + + # cannot convert to lexsorted + mi = MultiIndex.from_tuples( + [("z", "a"), ("x", "a"), ("y", "b"), ("x", "b"), ("y", "a"), ("z", "b")], + names=["one", "two"], + ) + assert not mi.is_monotonic_increasing + recons = mi._sort_levels_monotonic() + assert not recons.is_monotonic_increasing + assert mi.equals(recons) + assert Index(mi.values).equals(Index(recons.values)) + + # cannot convert to lexsorted + mi = MultiIndex( + levels=[["b", "d", "a"], [1, 2, 3]], + codes=[[0, 1, 0, 2], [2, 0, 0, 1]], + names=["col1", "col2"], + ) + assert not mi.is_monotonic_increasing + recons = mi._sort_levels_monotonic() + assert not recons.is_monotonic_increasing + assert mi.equals(recons) + assert Index(mi.values).equals(Index(recons.values)) + + +def test_reconstruct_remove_unused(): + # xref to GH 2770 + df = DataFrame( + [["deleteMe", 1, 9], ["keepMe", 2, 9], ["keepMeToo", 3, 9]], + columns=["first", "second", "third"], + ) + df2 = df.set_index(["first", "second"], drop=False) + df2 = df2[df2["first"] != "deleteMe"] + + # removed levels are there + expected = MultiIndex( + levels=[["deleteMe", "keepMe", "keepMeToo"], [1, 2, 3]], + codes=[[1, 2], [1, 2]], + names=["first", "second"], + ) + result = df2.index + tm.assert_index_equal(result, expected) + + expected = MultiIndex( + levels=[["keepMe", "keepMeToo"], [2, 3]], + codes=[[0, 1], [0, 1]], + names=["first", "second"], + ) + result = df2.index.remove_unused_levels() + tm.assert_index_equal(result, expected) + + # idempotent + result2 = result.remove_unused_levels() + tm.assert_index_equal(result2, expected) + assert result2.is_(result) + + +@pytest.mark.parametrize( + "first_type,second_type", [("int64", "int64"), ("datetime64[D]", "str")] +) +def test_remove_unused_levels_large(first_type, second_type): + # GH16556 + + # because tests should be deterministic (and this test in particular + # checks that levels are removed, which is not the case for every + # random input): + rng = np.random.default_rng(10) # seed is arbitrary value that works + + size = 1 << 16 + df = DataFrame( + { + "first": rng.integers(0, 1 << 13, size).astype(first_type), + "second": rng.integers(0, 1 << 10, size).astype(second_type), + "third": rng.random(size), + } + ) + df = df.groupby(["first", "second"]).sum() + df = df[df.third < 0.1] + + result = df.index.remove_unused_levels() + assert len(result.levels[0]) < len(df.index.levels[0]) + assert len(result.levels[1]) < len(df.index.levels[1]) + assert result.equals(df.index) + + expected = df.reset_index().set_index(["first", "second"]).index + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("level0", [["a", "d", "b"], ["a", "d", "b", "unused"]]) +@pytest.mark.parametrize( + "level1", [["w", "x", "y", "z"], ["w", "x", "y", "z", "unused"]] +) +def test_remove_unused_nan(level0, level1): + # GH 18417 + mi = MultiIndex(levels=[level0, level1], codes=[[0, 2, -1, 1, -1], [0, 1, 2, 3, 2]]) + + result = mi.remove_unused_levels() + tm.assert_index_equal(result, mi) + for level in 0, 1: + assert "unused" not in result.levels[level] + + +def test_argsort(idx): + result = idx.argsort() + expected = idx.values.argsort() + tm.assert_numpy_array_equal(result, expected) + + +def test_remove_unused_levels_with_nan(): + # GH 37510 + idx = Index([(1, np.nan), (3, 4)]).rename(["id1", "id2"]) + idx = idx.set_levels(["a", np.nan], level="id1") + idx = idx.remove_unused_levels() + result = idx.levels + expected = FrozenList([["a", np.nan], [4]]) + assert str(result) == str(expected) + + +def test_sort_values_nan(): + # GH48495, GH48626 + midx = MultiIndex(levels=[["A", "B", "C"], ["D"]], codes=[[1, 0, 2], [-1, -1, 0]]) + result = midx.sort_values() + expected = MultiIndex( + levels=[["A", "B", "C"], ["D"]], codes=[[0, 1, 2], [-1, -1, 0]] + ) + tm.assert_index_equal(result, expected) + + +def test_sort_values_incomparable(): + # GH48495 + mi = MultiIndex.from_arrays( + [ + [1, Timestamp("2000-01-01")], + [3, 4], + ] + ) + match = "'<' not supported between instances of 'Timestamp' and 'int'" + with pytest.raises(TypeError, match=match): + mi.sort_values() + + +@pytest.mark.parametrize("na_position", ["first", "last"]) +@pytest.mark.parametrize("dtype", ["float64", "Int64", "Float64"]) +def test_sort_values_with_na_na_position(dtype, na_position): + # 51612 + arrays = [ + Series([1, 1, 2], dtype=dtype), + Series([1, None, 3], dtype=dtype), + ] + index = MultiIndex.from_arrays(arrays) + result = index.sort_values(na_position=na_position) + if na_position == "first": + arrays = [ + Series([1, 1, 2], dtype=dtype), + Series([None, 1, 3], dtype=dtype), + ] + else: + arrays = [ + Series([1, 1, 2], dtype=dtype), + Series([1, None, 3], dtype=dtype), + ] + expected = MultiIndex.from_arrays(arrays) + tm.assert_index_equal(result, expected) + + +def test_sort_unnecessary_warning(): + # GH#55386 + midx = MultiIndex.from_tuples([(1.5, 2), (3.5, 3), (0, 1)]) + midx = midx.set_levels([2.5, np.nan, 1], level=0) + result = midx.sort_values() + expected = MultiIndex.from_tuples([(1, 3), (2.5, 1), (np.nan, 2)]) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_take.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_take.py new file mode 100644 index 0000000000000000000000000000000000000000..543cba25c373b71b8c79c7fe0ea5ae2fb7f40b18 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/multi/test_take.py @@ -0,0 +1,78 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + + +def test_take(idx): + indexer = [4, 3, 0, 2] + result = idx.take(indexer) + expected = idx[indexer] + assert result.equals(expected) + + # GH 10791 + msg = "'MultiIndex' object has no attribute 'freq'" + with pytest.raises(AttributeError, match=msg): + idx.freq + + +def test_take_invalid_kwargs(idx): + indices = [1, 2] + + msg = r"take\(\) got an unexpected keyword argument 'foo'" + with pytest.raises(TypeError, match=msg): + idx.take(indices, foo=2) + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + idx.take(indices, out=indices) + + msg = "the 'mode' parameter is not supported" + with pytest.raises(ValueError, match=msg): + idx.take(indices, mode="clip") + + +def test_take_fill_value(): + # GH 12631 + vals = [["A", "B"], [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-02")]] + idx = pd.MultiIndex.from_product(vals, names=["str", "dt"]) + + result = idx.take(np.array([1, 0, -1])) + exp_vals = [ + ("A", pd.Timestamp("2011-01-02")), + ("A", pd.Timestamp("2011-01-01")), + ("B", pd.Timestamp("2011-01-02")), + ] + expected = pd.MultiIndex.from_tuples(exp_vals, names=["str", "dt"]) + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + exp_vals = [ + ("A", pd.Timestamp("2011-01-02")), + ("A", pd.Timestamp("2011-01-01")), + (np.nan, pd.NaT), + ] + expected = pd.MultiIndex.from_tuples(exp_vals, names=["str", "dt"]) + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) + exp_vals = [ + ("A", pd.Timestamp("2011-01-02")), + ("A", pd.Timestamp("2011-01-01")), + ("B", pd.Timestamp("2011-01-02")), + ] + expected = pd.MultiIndex.from_tuples(exp_vals, names=["str", "dt"]) + tm.assert_index_equal(result, expected) + + msg = "When allow_fill=True and fill_value is not None, all indices must be >= -1" + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + msg = "index -5 is out of bounds for( axis 0 with)? size 4" + with pytest.raises(IndexError, match=msg): + idx.take(np.array([1, -5])) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/numeric/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/numeric/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..cd28d519313ed36228040361dfbb2a8dccf77be5 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_indexing.py @@ -0,0 +1,611 @@ +import numpy as np +import pytest + +from pandas.errors import InvalidIndexError + +from pandas import ( + NA, + Index, + RangeIndex, + Series, + Timestamp, +) +import pandas._testing as tm +from pandas.core.arrays import ( + ArrowExtensionArray, + FloatingArray, +) + + +@pytest.fixture +def index_large(): + # large values used in Index[uint64] tests where no compat needed with Int64/Float64 + large = [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25] + return Index(large, dtype=np.uint64) + + +class TestGetLoc: + def test_get_loc(self): + index = Index([0, 1, 2]) + assert index.get_loc(1) == 1 + + def test_get_loc_raises_bad_label(self): + index = Index([0, 1, 2]) + with pytest.raises(InvalidIndexError, match=r"\[1, 2\]"): + index.get_loc([1, 2]) + + def test_get_loc_float64(self): + idx = Index([0.0, 1.0, 2.0], dtype=np.float64) + + with pytest.raises(KeyError, match="^'foo'$"): + idx.get_loc("foo") + with pytest.raises(KeyError, match=r"^1\.5$"): + idx.get_loc(1.5) + with pytest.raises(KeyError, match="^True$"): + idx.get_loc(True) + with pytest.raises(KeyError, match="^False$"): + idx.get_loc(False) + + def test_get_loc_na(self): + idx = Index([np.nan, 1, 2], dtype=np.float64) + assert idx.get_loc(1) == 1 + assert idx.get_loc(np.nan) == 0 + + idx = Index([np.nan, 1, np.nan], dtype=np.float64) + assert idx.get_loc(1) == 1 + + # representable by slice [0:2:2] + msg = "'Cannot get left slice bound for non-unique label: nan'" + with pytest.raises(KeyError, match=msg): + idx.slice_locs(np.nan) + # not representable by slice + idx = Index([np.nan, 1, np.nan, np.nan], dtype=np.float64) + assert idx.get_loc(1) == 1 + msg = "'Cannot get left slice bound for non-unique label: nan" + with pytest.raises(KeyError, match=msg): + idx.slice_locs(np.nan) + + def test_get_loc_missing_nan(self): + # GH#8569 + idx = Index([1, 2], dtype=np.float64) + assert idx.get_loc(1) == 0 + with pytest.raises(KeyError, match=r"^3$"): + idx.get_loc(3) + with pytest.raises(KeyError, match="^nan$"): + idx.get_loc(np.nan) + with pytest.raises(InvalidIndexError, match=r"\[nan\]"): + # listlike/non-hashable raises TypeError + idx.get_loc([np.nan]) + + @pytest.mark.parametrize("vals", [[1], [1.0], [Timestamp("2019-12-31")], ["test"]]) + def test_get_loc_float_index_nan_with_method(self, vals): + # GH#39382 + idx = Index(vals) + with pytest.raises(KeyError, match="nan"): + idx.get_loc(np.nan) + + @pytest.mark.parametrize("dtype", ["f8", "i8", "u8"]) + def test_get_loc_numericindex_none_raises(self, dtype): + # case that goes through searchsorted and key is non-comparable to values + arr = np.arange(10**7, dtype=dtype) + idx = Index(arr) + with pytest.raises(KeyError, match="None"): + idx.get_loc(None) + + def test_get_loc_overflows(self): + # unique but non-monotonic goes through IndexEngine.mapping.get_item + idx = Index([0, 2, 1]) + + val = np.iinfo(np.int64).max + 1 + + with pytest.raises(KeyError, match=str(val)): + idx.get_loc(val) + with pytest.raises(KeyError, match=str(val)): + idx._engine.get_loc(val) + + +class TestGetIndexer: + def test_get_indexer(self): + index1 = Index([1, 2, 3, 4, 5]) + index2 = Index([2, 4, 6]) + + r1 = index1.get_indexer(index2) + e1 = np.array([1, 3, -1], dtype=np.intp) + tm.assert_almost_equal(r1, e1) + + @pytest.mark.parametrize("reverse", [True, False]) + @pytest.mark.parametrize( + "expected,method", + [ + (np.array([-1, 0, 0, 1, 1], dtype=np.intp), "pad"), + (np.array([-1, 0, 0, 1, 1], dtype=np.intp), "ffill"), + (np.array([0, 0, 1, 1, 2], dtype=np.intp), "backfill"), + (np.array([0, 0, 1, 1, 2], dtype=np.intp), "bfill"), + ], + ) + def test_get_indexer_methods(self, reverse, expected, method): + index1 = Index([1, 2, 3, 4, 5]) + index2 = Index([2, 4, 6]) + + if reverse: + index1 = index1[::-1] + expected = expected[::-1] + + result = index2.get_indexer(index1, method=method) + tm.assert_almost_equal(result, expected) + + def test_get_indexer_invalid(self): + # GH10411 + index = Index(np.arange(10)) + + with pytest.raises(ValueError, match="tolerance argument"): + index.get_indexer([1, 0], tolerance=1) + + with pytest.raises(ValueError, match="limit argument"): + index.get_indexer([1, 0], limit=1) + + @pytest.mark.parametrize( + "method, tolerance, indexer, expected", + [ + ("pad", None, [0, 5, 9], [0, 5, 9]), + ("backfill", None, [0, 5, 9], [0, 5, 9]), + ("nearest", None, [0, 5, 9], [0, 5, 9]), + ("pad", 0, [0, 5, 9], [0, 5, 9]), + ("backfill", 0, [0, 5, 9], [0, 5, 9]), + ("nearest", 0, [0, 5, 9], [0, 5, 9]), + ("pad", None, [0.2, 1.8, 8.5], [0, 1, 8]), + ("backfill", None, [0.2, 1.8, 8.5], [1, 2, 9]), + ("nearest", None, [0.2, 1.8, 8.5], [0, 2, 9]), + ("pad", 1, [0.2, 1.8, 8.5], [0, 1, 8]), + ("backfill", 1, [0.2, 1.8, 8.5], [1, 2, 9]), + ("nearest", 1, [0.2, 1.8, 8.5], [0, 2, 9]), + ("pad", 0.2, [0.2, 1.8, 8.5], [0, -1, -1]), + ("backfill", 0.2, [0.2, 1.8, 8.5], [-1, 2, -1]), + ("nearest", 0.2, [0.2, 1.8, 8.5], [0, 2, -1]), + ], + ) + def test_get_indexer_nearest(self, method, tolerance, indexer, expected): + index = Index(np.arange(10)) + + actual = index.get_indexer(indexer, method=method, tolerance=tolerance) + tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp)) + + @pytest.mark.parametrize("listtype", [list, tuple, Series, np.array]) + @pytest.mark.parametrize( + "tolerance, expected", + list( + zip( + [[0.3, 0.3, 0.1], [0.2, 0.1, 0.1], [0.1, 0.5, 0.5]], + [[0, 2, -1], [0, -1, -1], [-1, 2, 9]], + ) + ), + ) + def test_get_indexer_nearest_listlike_tolerance( + self, tolerance, expected, listtype + ): + index = Index(np.arange(10)) + + actual = index.get_indexer( + [0.2, 1.8, 8.5], method="nearest", tolerance=listtype(tolerance) + ) + tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp)) + + def test_get_indexer_nearest_error(self): + index = Index(np.arange(10)) + with pytest.raises(ValueError, match="limit argument"): + index.get_indexer([1, 0], method="nearest", limit=1) + + with pytest.raises(ValueError, match="tolerance size must match"): + index.get_indexer([1, 0], method="nearest", tolerance=[1, 2, 3]) + + @pytest.mark.parametrize( + "method,expected", + [("pad", [8, 7, 0]), ("backfill", [9, 8, 1]), ("nearest", [9, 7, 0])], + ) + def test_get_indexer_nearest_decreasing(self, method, expected): + index = Index(np.arange(10))[::-1] + + actual = index.get_indexer([0, 5, 9], method=method) + tm.assert_numpy_array_equal(actual, np.array([9, 4, 0], dtype=np.intp)) + + actual = index.get_indexer([0.2, 1.8, 8.5], method=method) + tm.assert_numpy_array_equal(actual, np.array(expected, dtype=np.intp)) + + @pytest.mark.parametrize("idx_dtype", ["int64", "float64", "uint64", "range"]) + @pytest.mark.parametrize("method", ["get_indexer", "get_indexer_non_unique"]) + def test_get_indexer_numeric_index_boolean_target(self, method, idx_dtype): + # GH 16877 + + if idx_dtype == "range": + numeric_index = RangeIndex(4) + else: + numeric_index = Index(np.arange(4, dtype=idx_dtype)) + + other = Index([True, False, True]) + + result = getattr(numeric_index, method)(other) + expected = np.array([-1, -1, -1], dtype=np.intp) + if method == "get_indexer": + tm.assert_numpy_array_equal(result, expected) + else: + missing = np.arange(3, dtype=np.intp) + tm.assert_numpy_array_equal(result[0], expected) + tm.assert_numpy_array_equal(result[1], missing) + + @pytest.mark.parametrize("method", ["pad", "backfill", "nearest"]) + def test_get_indexer_with_method_numeric_vs_bool(self, method): + left = Index([1, 2, 3]) + right = Index([True, False]) + + with pytest.raises(TypeError, match="Cannot compare"): + left.get_indexer(right, method=method) + + with pytest.raises(TypeError, match="Cannot compare"): + right.get_indexer(left, method=method) + + def test_get_indexer_numeric_vs_bool(self): + left = Index([1, 2, 3]) + right = Index([True, False]) + + res = left.get_indexer(right) + expected = -1 * np.ones(len(right), dtype=np.intp) + tm.assert_numpy_array_equal(res, expected) + + res = right.get_indexer(left) + expected = -1 * np.ones(len(left), dtype=np.intp) + tm.assert_numpy_array_equal(res, expected) + + res = left.get_indexer_non_unique(right)[0] + expected = -1 * np.ones(len(right), dtype=np.intp) + tm.assert_numpy_array_equal(res, expected) + + res = right.get_indexer_non_unique(left)[0] + expected = -1 * np.ones(len(left), dtype=np.intp) + tm.assert_numpy_array_equal(res, expected) + + def test_get_indexer_float64(self): + idx = Index([0.0, 1.0, 2.0], dtype=np.float64) + tm.assert_numpy_array_equal( + idx.get_indexer(idx), np.array([0, 1, 2], dtype=np.intp) + ) + + target = [-0.1, 0.5, 1.1] + tm.assert_numpy_array_equal( + idx.get_indexer(target, "pad"), np.array([-1, 0, 1], dtype=np.intp) + ) + tm.assert_numpy_array_equal( + idx.get_indexer(target, "backfill"), np.array([0, 1, 2], dtype=np.intp) + ) + tm.assert_numpy_array_equal( + idx.get_indexer(target, "nearest"), np.array([0, 1, 1], dtype=np.intp) + ) + + def test_get_indexer_nan(self): + # GH#7820 + result = Index([1, 2, np.nan], dtype=np.float64).get_indexer([np.nan]) + expected = np.array([2], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + def test_get_indexer_int64(self): + index = Index(range(0, 20, 2), dtype=np.int64) + target = Index(np.arange(10), dtype=np.int64) + indexer = index.get_indexer(target) + expected = np.array([0, -1, 1, -1, 2, -1, 3, -1, 4, -1], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + target = Index(np.arange(10), dtype=np.int64) + indexer = index.get_indexer(target, method="pad") + expected = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + target = Index(np.arange(10), dtype=np.int64) + indexer = index.get_indexer(target, method="backfill") + expected = np.array([0, 1, 1, 2, 2, 3, 3, 4, 4, 5], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + def test_get_indexer_uint64(self, index_large): + target = Index(np.arange(10).astype("uint64") * 5 + 2**63) + indexer = index_large.get_indexer(target) + expected = np.array([0, -1, 1, 2, 3, 4, -1, -1, -1, -1], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + target = Index(np.arange(10).astype("uint64") * 5 + 2**63) + indexer = index_large.get_indexer(target, method="pad") + expected = np.array([0, 0, 1, 2, 3, 4, 4, 4, 4, 4], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + target = Index(np.arange(10).astype("uint64") * 5 + 2**63) + indexer = index_large.get_indexer(target, method="backfill") + expected = np.array([0, 1, 1, 2, 3, 4, -1, -1, -1, -1], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + @pytest.mark.parametrize("val, val2", [(4, 5), (4, 4), (4, NA), (NA, NA)]) + def test_get_loc_masked(self, val, val2, any_numeric_ea_and_arrow_dtype): + # GH#39133 + idx = Index([1, 2, 3, val, val2], dtype=any_numeric_ea_and_arrow_dtype) + result = idx.get_loc(2) + assert result == 1 + + with pytest.raises(KeyError, match="9"): + idx.get_loc(9) + + def test_get_loc_masked_na(self, any_numeric_ea_and_arrow_dtype): + # GH#39133 + idx = Index([1, 2, NA], dtype=any_numeric_ea_and_arrow_dtype) + result = idx.get_loc(NA) + assert result == 2 + + idx = Index([1, 2, NA, NA], dtype=any_numeric_ea_and_arrow_dtype) + result = idx.get_loc(NA) + tm.assert_numpy_array_equal(result, np.array([False, False, True, True])) + + idx = Index([1, 2, 3], dtype=any_numeric_ea_and_arrow_dtype) + with pytest.raises(KeyError, match="NA"): + idx.get_loc(NA) + + def test_get_loc_masked_na_and_nan(self): + # GH#39133 + idx = Index( + FloatingArray( + np.array([1, 2, 1, np.nan]), mask=np.array([False, False, True, False]) + ) + ) + result = idx.get_loc(NA) + assert result == 2 + result = idx.get_loc(np.nan) + assert result == 3 + + idx = Index( + FloatingArray(np.array([1, 2, 1.0]), mask=np.array([False, False, True])) + ) + result = idx.get_loc(NA) + assert result == 2 + with pytest.raises(KeyError, match="nan"): + idx.get_loc(np.nan) + + idx = Index( + FloatingArray( + np.array([1, 2, np.nan]), mask=np.array([False, False, False]) + ) + ) + result = idx.get_loc(np.nan) + assert result == 2 + with pytest.raises(KeyError, match="NA"): + idx.get_loc(NA) + + @pytest.mark.parametrize("val", [4, 2]) + def test_get_indexer_masked_na(self, any_numeric_ea_and_arrow_dtype, val): + # GH#39133 + idx = Index([1, 2, NA, 3, val], dtype=any_numeric_ea_and_arrow_dtype) + result = idx.get_indexer_for([1, NA, 5]) + expected = np.array([0, 2, -1]) + tm.assert_numpy_array_equal(result, expected, check_dtype=False) + + @pytest.mark.parametrize("dtype", ["boolean", "bool[pyarrow]"]) + def test_get_indexer_masked_na_boolean(self, dtype): + # GH#39133 + if dtype == "bool[pyarrow]": + pytest.importorskip("pyarrow") + idx = Index([True, False, NA], dtype=dtype) + result = idx.get_loc(False) + assert result == 1 + result = idx.get_loc(NA) + assert result == 2 + + def test_get_indexer_arrow_dictionary_target(self): + pa = pytest.importorskip("pyarrow") + target = Index( + ArrowExtensionArray( + pa.array([1, 2], type=pa.dictionary(pa.int8(), pa.int8())) + ) + ) + idx = Index([1]) + + result = idx.get_indexer(target) + expected = np.array([0, -1], dtype=np.int64) + tm.assert_numpy_array_equal(result, expected) + + result_1, result_2 = idx.get_indexer_non_unique(target) + expected_1, expected_2 = np.array([0, -1], dtype=np.int64), np.array( + [1], dtype=np.int64 + ) + tm.assert_numpy_array_equal(result_1, expected_1) + tm.assert_numpy_array_equal(result_2, expected_2) + + +class TestWhere: + @pytest.mark.parametrize( + "index", + [ + Index(np.arange(5, dtype="float64")), + Index(range(0, 20, 2), dtype=np.int64), + Index(np.arange(5, dtype="uint64")), + ], + ) + def test_where(self, listlike_box, index): + cond = [True] * len(index) + expected = index + result = index.where(listlike_box(cond)) + + cond = [False] + [True] * (len(index) - 1) + expected = Index([index._na_value] + index[1:].tolist(), dtype=np.float64) + result = index.where(listlike_box(cond)) + tm.assert_index_equal(result, expected) + + def test_where_uint64(self): + idx = Index([0, 6, 2], dtype=np.uint64) + mask = np.array([False, True, False]) + other = np.array([1], dtype=np.int64) + + expected = Index([1, 6, 1], dtype=np.uint64) + + result = idx.where(mask, other) + tm.assert_index_equal(result, expected) + + result = idx.putmask(~mask, other) + tm.assert_index_equal(result, expected) + + def test_where_infers_type_instead_of_trying_to_convert_string_to_float(self): + # GH 32413 + index = Index([1, np.nan]) + cond = index.notna() + other = Index(["a", "b"], dtype="string") + + expected = Index([1.0, "b"]) + result = index.where(cond, other) + + tm.assert_index_equal(result, expected) + + +class TestTake: + @pytest.mark.parametrize("idx_dtype", [np.float64, np.int64, np.uint64]) + def test_take_preserve_name(self, idx_dtype): + index = Index([1, 2, 3, 4], dtype=idx_dtype, name="foo") + taken = index.take([3, 0, 1]) + assert index.name == taken.name + + def test_take_fill_value_float64(self): + # GH 12631 + idx = Index([1.0, 2.0, 3.0], name="xxx", dtype=np.float64) + result = idx.take(np.array([1, 0, -1])) + expected = Index([2.0, 1.0, 3.0], dtype=np.float64, name="xxx") + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = Index([2.0, 1.0, np.nan], dtype=np.float64, name="xxx") + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) + expected = Index([2.0, 1.0, 3.0], dtype=np.float64, name="xxx") + tm.assert_index_equal(result, expected) + + msg = ( + "When allow_fill=True and fill_value is not None, " + "all indices must be >= -1" + ) + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + msg = "index -5 is out of bounds for (axis 0 with )?size 3" + with pytest.raises(IndexError, match=msg): + idx.take(np.array([1, -5])) + + @pytest.mark.parametrize("dtype", [np.int64, np.uint64]) + def test_take_fill_value_ints(self, dtype): + # see gh-12631 + idx = Index([1, 2, 3], dtype=dtype, name="xxx") + result = idx.take(np.array([1, 0, -1])) + expected = Index([2, 1, 3], dtype=dtype, name="xxx") + tm.assert_index_equal(result, expected) + + name = type(idx).__name__ + msg = f"Unable to fill values because {name} cannot contain NA" + + # fill_value=True + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -1]), fill_value=True) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) + expected = Index([2, 1, 3], dtype=dtype, name="xxx") + tm.assert_index_equal(result, expected) + + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + msg = "index -5 is out of bounds for (axis 0 with )?size 3" + with pytest.raises(IndexError, match=msg): + idx.take(np.array([1, -5])) + + +class TestContains: + @pytest.mark.parametrize("dtype", [np.float64, np.int64, np.uint64]) + def test_contains_none(self, dtype): + # GH#35788 should return False, not raise TypeError + index = Index([0, 1, 2, 3, 4], dtype=dtype) + assert None not in index + + def test_contains_float64_nans(self): + index = Index([1.0, 2.0, np.nan], dtype=np.float64) + assert np.nan in index + + def test_contains_float64_not_nans(self): + index = Index([1.0, 2.0, np.nan], dtype=np.float64) + assert 1.0 in index + + +class TestSliceLocs: + @pytest.mark.parametrize("dtype", [int, float]) + def test_slice_locs(self, dtype): + index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype)) + n = len(index) + + assert index.slice_locs(start=2) == (2, n) + assert index.slice_locs(start=3) == (3, n) + assert index.slice_locs(3, 8) == (3, 6) + assert index.slice_locs(5, 10) == (3, n) + assert index.slice_locs(end=8) == (0, 6) + assert index.slice_locs(end=9) == (0, 7) + + # reversed + index2 = index[::-1] + assert index2.slice_locs(8, 2) == (2, 6) + assert index2.slice_locs(7, 3) == (2, 5) + + @pytest.mark.parametrize("dtype", [int, float]) + def test_slice_locs_float_locs(self, dtype): + index = Index(np.array([0, 1, 2, 5, 6, 7, 9, 10], dtype=dtype)) + n = len(index) + assert index.slice_locs(5.0, 10.0) == (3, n) + assert index.slice_locs(4.5, 10.5) == (3, 8) + + index2 = index[::-1] + assert index2.slice_locs(8.5, 1.5) == (2, 6) + assert index2.slice_locs(10.5, -1) == (0, n) + + @pytest.mark.parametrize("dtype", [int, float]) + def test_slice_locs_dup_numeric(self, dtype): + index = Index(np.array([10, 12, 12, 14], dtype=dtype)) + assert index.slice_locs(12, 12) == (1, 3) + assert index.slice_locs(11, 13) == (1, 3) + + index2 = index[::-1] + assert index2.slice_locs(12, 12) == (1, 3) + assert index2.slice_locs(13, 11) == (1, 3) + + def test_slice_locs_na(self): + index = Index([np.nan, 1, 2]) + assert index.slice_locs(1) == (1, 3) + assert index.slice_locs(np.nan) == (0, 3) + + index = Index([0, np.nan, np.nan, 1, 2]) + assert index.slice_locs(np.nan) == (1, 5) + + def test_slice_locs_na_raises(self): + index = Index([np.nan, 1, 2]) + with pytest.raises(KeyError, match=""): + index.slice_locs(start=1.5) + + with pytest.raises(KeyError, match=""): + index.slice_locs(end=1.5) + + +class TestGetSliceBounds: + @pytest.mark.parametrize("side, expected", [("left", 4), ("right", 5)]) + def test_get_slice_bounds_within(self, side, expected): + index = Index(range(6)) + result = index.get_slice_bound(4, side=side) + assert result == expected + + @pytest.mark.parametrize("side", ["left", "right"]) + @pytest.mark.parametrize("bound, expected", [(-1, 0), (10, 6)]) + def test_get_slice_bounds_outside(self, side, expected, bound): + index = Index(range(6)) + result = index.get_slice_bound(bound, side=side) + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_setops.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_setops.py new file mode 100644 index 0000000000000000000000000000000000000000..376b51dd98bb1b1c7c6c8a67914bc72f6c6c588d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/numeric/test_setops.py @@ -0,0 +1,168 @@ +from datetime import ( + datetime, + timedelta, +) + +import numpy as np +import pytest + +import pandas._testing as tm +from pandas.core.indexes.api import ( + Index, + RangeIndex, +) + + +@pytest.fixture +def index_large(): + # large values used in TestUInt64Index where no compat needed with int64/float64 + large = [2**63, 2**63 + 10, 2**63 + 15, 2**63 + 20, 2**63 + 25] + return Index(large, dtype=np.uint64) + + +class TestSetOps: + @pytest.mark.parametrize("dtype", ["f8", "u8", "i8"]) + def test_union_non_numeric(self, dtype): + # corner case, non-numeric + index = Index(np.arange(5, dtype=dtype), dtype=dtype) + assert index.dtype == dtype + + other = Index([datetime.now() + timedelta(i) for i in range(4)], dtype=object) + result = index.union(other) + expected = Index(np.concatenate((index, other))) + tm.assert_index_equal(result, expected) + + result = other.union(index) + expected = Index(np.concatenate((other, index))) + tm.assert_index_equal(result, expected) + + def test_intersection(self): + index = Index(range(5), dtype=np.int64) + + other = Index([1, 2, 3, 4, 5]) + result = index.intersection(other) + expected = Index(np.sort(np.intersect1d(index.values, other.values))) + tm.assert_index_equal(result, expected) + + result = other.intersection(index) + expected = Index( + np.sort(np.asarray(np.intersect1d(index.values, other.values))) + ) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("dtype", ["int64", "uint64"]) + def test_int_float_union_dtype(self, dtype): + # https://github.com/pandas-dev/pandas/issues/26778 + # [u]int | float -> float + index = Index([0, 2, 3], dtype=dtype) + other = Index([0.5, 1.5], dtype=np.float64) + expected = Index([0.0, 0.5, 1.5, 2.0, 3.0], dtype=np.float64) + result = index.union(other) + tm.assert_index_equal(result, expected) + + result = other.union(index) + tm.assert_index_equal(result, expected) + + def test_range_float_union_dtype(self): + # https://github.com/pandas-dev/pandas/issues/26778 + index = RangeIndex(start=0, stop=3) + other = Index([0.5, 1.5], dtype=np.float64) + result = index.union(other) + expected = Index([0.0, 0.5, 1, 1.5, 2.0], dtype=np.float64) + tm.assert_index_equal(result, expected) + + result = other.union(index) + tm.assert_index_equal(result, expected) + + def test_range_uint64_union_dtype(self): + # https://github.com/pandas-dev/pandas/issues/26778 + index = RangeIndex(start=0, stop=3) + other = Index([0, 10], dtype=np.uint64) + result = index.union(other) + expected = Index([0, 1, 2, 10], dtype=object) + tm.assert_index_equal(result, expected) + + result = other.union(index) + tm.assert_index_equal(result, expected) + + def test_float64_index_difference(self): + # https://github.com/pandas-dev/pandas/issues/35217 + float_index = Index([1.0, 2, 3]) + string_index = Index(["1", "2", "3"]) + + result = float_index.difference(string_index) + tm.assert_index_equal(result, float_index) + + result = string_index.difference(float_index) + tm.assert_index_equal(result, string_index) + + def test_intersection_uint64_outside_int64_range(self, index_large): + other = Index([2**63, 2**63 + 5, 2**63 + 10, 2**63 + 15, 2**63 + 20]) + result = index_large.intersection(other) + expected = Index(np.sort(np.intersect1d(index_large.values, other.values))) + tm.assert_index_equal(result, expected) + + result = other.intersection(index_large) + expected = Index( + np.sort(np.asarray(np.intersect1d(index_large.values, other.values))) + ) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "index2,keeps_name", + [ + (Index([4, 7, 6, 5, 3], name="index"), True), + (Index([4, 7, 6, 5, 3], name="other"), False), + ], + ) + def test_intersection_monotonic(self, index2, keeps_name, sort): + index1 = Index([5, 3, 2, 4, 1], name="index") + expected = Index([5, 3, 4]) + + if keeps_name: + expected.name = "index" + + result = index1.intersection(index2, sort=sort) + if sort is None: + expected = expected.sort_values() + tm.assert_index_equal(result, expected) + + def test_symmetric_difference(self, sort): + # smoke + index1 = Index([5, 2, 3, 4], name="index1") + index2 = Index([2, 3, 4, 1]) + result = index1.symmetric_difference(index2, sort=sort) + expected = Index([5, 1]) + if sort is not None: + tm.assert_index_equal(result, expected) + else: + tm.assert_index_equal(result, expected.sort_values()) + assert result.name is None + if sort is None: + expected = expected.sort_values() + tm.assert_index_equal(result, expected) + + +class TestSetOpsSort: + @pytest.mark.parametrize("slice_", [slice(None), slice(0)]) + def test_union_sort_other_special(self, slice_): + # https://github.com/pandas-dev/pandas/issues/24959 + + idx = Index([1, 0, 2]) + # default, sort=None + other = idx[slice_] + tm.assert_index_equal(idx.union(other), idx) + tm.assert_index_equal(other.union(idx), idx) + + # sort=False + tm.assert_index_equal(idx.union(other, sort=False), idx) + + @pytest.mark.parametrize("slice_", [slice(None), slice(0)]) + def test_union_sort_special_true(self, slice_): + idx = Index([1, 0, 2]) + # default, sort=None + other = idx[slice_] + + result = idx.union(other, sort=True) + expected = Index([0, 1, 2]) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/ranges/test_setops.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/ranges/test_setops.py new file mode 100644 index 0000000000000000000000000000000000000000..d417b8b743dc589bdf9d6acf5bde396a129ece23 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/ranges/test_setops.py @@ -0,0 +1,493 @@ +from datetime import ( + datetime, + timedelta, +) + +from hypothesis import ( + assume, + given, + strategies as st, +) +import numpy as np +import pytest + +from pandas import ( + Index, + RangeIndex, +) +import pandas._testing as tm + + +class TestRangeIndexSetOps: + @pytest.mark.parametrize("dtype", [None, "int64", "uint64"]) + def test_intersection_mismatched_dtype(self, dtype): + # check that we cast to float, not object + index = RangeIndex(start=0, stop=20, step=2, name="foo") + index = Index(index, dtype=dtype) + + flt = index.astype(np.float64) + + # bc index.equals(flt), we go through fastpath and get RangeIndex back + result = index.intersection(flt) + tm.assert_index_equal(result, index, exact=True) + + result = flt.intersection(index) + tm.assert_index_equal(result, flt, exact=True) + + # neither empty, not-equals + result = index.intersection(flt[1:]) + tm.assert_index_equal(result, flt[1:], exact=True) + + result = flt[1:].intersection(index) + tm.assert_index_equal(result, flt[1:], exact=True) + + # empty other + result = index.intersection(flt[:0]) + tm.assert_index_equal(result, flt[:0], exact=True) + + result = flt[:0].intersection(index) + tm.assert_index_equal(result, flt[:0], exact=True) + + def test_intersection_empty(self, sort, names): + # name retention on empty intersections + index = RangeIndex(start=0, stop=20, step=2, name=names[0]) + + # empty other + result = index.intersection(index[:0].rename(names[1]), sort=sort) + tm.assert_index_equal(result, index[:0].rename(names[2]), exact=True) + + # empty self + result = index[:0].intersection(index.rename(names[1]), sort=sort) + tm.assert_index_equal(result, index[:0].rename(names[2]), exact=True) + + def test_intersection(self, sort): + # intersect with Index with dtype int64 + index = RangeIndex(start=0, stop=20, step=2) + other = Index(np.arange(1, 6)) + result = index.intersection(other, sort=sort) + expected = Index(np.sort(np.intersect1d(index.values, other.values))) + tm.assert_index_equal(result, expected) + + result = other.intersection(index, sort=sort) + expected = Index( + np.sort(np.asarray(np.intersect1d(index.values, other.values))) + ) + tm.assert_index_equal(result, expected) + + # intersect with increasing RangeIndex + other = RangeIndex(1, 6) + result = index.intersection(other, sort=sort) + expected = Index(np.sort(np.intersect1d(index.values, other.values))) + tm.assert_index_equal(result, expected, exact="equiv") + + # intersect with decreasing RangeIndex + other = RangeIndex(5, 0, -1) + result = index.intersection(other, sort=sort) + expected = Index(np.sort(np.intersect1d(index.values, other.values))) + tm.assert_index_equal(result, expected, exact="equiv") + + # reversed (GH 17296) + result = other.intersection(index, sort=sort) + tm.assert_index_equal(result, expected, exact="equiv") + + # GH 17296: intersect two decreasing RangeIndexes + first = RangeIndex(10, -2, -2) + other = RangeIndex(5, -4, -1) + expected = first.astype(int).intersection(other.astype(int), sort=sort) + result = first.intersection(other, sort=sort).astype(int) + tm.assert_index_equal(result, expected) + + # reversed + result = other.intersection(first, sort=sort).astype(int) + tm.assert_index_equal(result, expected) + + index = RangeIndex(5, name="foo") + + # intersect of non-overlapping indices + other = RangeIndex(5, 10, 1, name="foo") + result = index.intersection(other, sort=sort) + expected = RangeIndex(0, 0, 1, name="foo") + tm.assert_index_equal(result, expected) + + other = RangeIndex(-1, -5, -1) + result = index.intersection(other, sort=sort) + expected = RangeIndex(0, 0, 1) + tm.assert_index_equal(result, expected) + + # intersection of empty indices + other = RangeIndex(0, 0, 1) + result = index.intersection(other, sort=sort) + expected = RangeIndex(0, 0, 1) + tm.assert_index_equal(result, expected) + + result = other.intersection(index, sort=sort) + tm.assert_index_equal(result, expected) + + def test_intersection_non_overlapping_gcd(self, sort, names): + # intersection of non-overlapping values based on start value and gcd + index = RangeIndex(1, 10, 2, name=names[0]) + other = RangeIndex(0, 10, 4, name=names[1]) + result = index.intersection(other, sort=sort) + expected = RangeIndex(0, 0, 1, name=names[2]) + tm.assert_index_equal(result, expected) + + def test_union_noncomparable(self, sort): + # corner case, Index with non-int64 dtype + index = RangeIndex(start=0, stop=20, step=2) + other = Index([datetime.now() + timedelta(i) for i in range(4)], dtype=object) + result = index.union(other, sort=sort) + expected = Index(np.concatenate((index, other))) + tm.assert_index_equal(result, expected) + + result = other.union(index, sort=sort) + expected = Index(np.concatenate((other, index))) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "idx1, idx2, expected_sorted, expected_notsorted", + [ + ( + RangeIndex(0, 10, 1), + RangeIndex(0, 10, 1), + RangeIndex(0, 10, 1), + RangeIndex(0, 10, 1), + ), + ( + RangeIndex(0, 10, 1), + RangeIndex(5, 20, 1), + RangeIndex(0, 20, 1), + RangeIndex(0, 20, 1), + ), + ( + RangeIndex(0, 10, 1), + RangeIndex(10, 20, 1), + RangeIndex(0, 20, 1), + RangeIndex(0, 20, 1), + ), + ( + RangeIndex(0, -10, -1), + RangeIndex(0, -10, -1), + RangeIndex(0, -10, -1), + RangeIndex(0, -10, -1), + ), + ( + RangeIndex(0, -10, -1), + RangeIndex(-10, -20, -1), + RangeIndex(-19, 1, 1), + RangeIndex(0, -20, -1), + ), + ( + RangeIndex(0, 10, 2), + RangeIndex(1, 10, 2), + RangeIndex(0, 10, 1), + Index(list(range(0, 10, 2)) + list(range(1, 10, 2))), + ), + ( + RangeIndex(0, 11, 2), + RangeIndex(1, 12, 2), + RangeIndex(0, 12, 1), + Index(list(range(0, 11, 2)) + list(range(1, 12, 2))), + ), + ( + RangeIndex(0, 21, 4), + RangeIndex(-2, 24, 4), + RangeIndex(-2, 24, 2), + Index(list(range(0, 21, 4)) + list(range(-2, 24, 4))), + ), + ( + RangeIndex(0, -20, -2), + RangeIndex(-1, -21, -2), + RangeIndex(-19, 1, 1), + Index(list(range(0, -20, -2)) + list(range(-1, -21, -2))), + ), + ( + RangeIndex(0, 100, 5), + RangeIndex(0, 100, 20), + RangeIndex(0, 100, 5), + RangeIndex(0, 100, 5), + ), + ( + RangeIndex(0, -100, -5), + RangeIndex(5, -100, -20), + RangeIndex(-95, 10, 5), + Index(list(range(0, -100, -5)) + [5]), + ), + ( + RangeIndex(0, -11, -1), + RangeIndex(1, -12, -4), + RangeIndex(-11, 2, 1), + Index(list(range(0, -11, -1)) + [1, -11]), + ), + (RangeIndex(0), RangeIndex(0), RangeIndex(0), RangeIndex(0)), + ( + RangeIndex(0, -10, -2), + RangeIndex(0), + RangeIndex(0, -10, -2), + RangeIndex(0, -10, -2), + ), + ( + RangeIndex(0, 100, 2), + RangeIndex(100, 150, 200), + RangeIndex(0, 102, 2), + RangeIndex(0, 102, 2), + ), + ( + RangeIndex(0, -100, -2), + RangeIndex(-100, 50, 102), + RangeIndex(-100, 4, 2), + Index(list(range(0, -100, -2)) + [-100, 2]), + ), + ( + RangeIndex(0, -100, -1), + RangeIndex(0, -50, -3), + RangeIndex(-99, 1, 1), + RangeIndex(0, -100, -1), + ), + ( + RangeIndex(0, 1, 1), + RangeIndex(5, 6, 10), + RangeIndex(0, 6, 5), + RangeIndex(0, 10, 5), + ), + ( + RangeIndex(0, 10, 5), + RangeIndex(-5, -6, -20), + RangeIndex(-5, 10, 5), + Index([0, 5, -5]), + ), + ( + RangeIndex(0, 3, 1), + RangeIndex(4, 5, 1), + Index([0, 1, 2, 4]), + Index([0, 1, 2, 4]), + ), + ( + RangeIndex(0, 10, 1), + Index([], dtype=np.int64), + RangeIndex(0, 10, 1), + RangeIndex(0, 10, 1), + ), + ( + RangeIndex(0), + Index([1, 5, 6]), + Index([1, 5, 6]), + Index([1, 5, 6]), + ), + # GH 43885 + ( + RangeIndex(0, 10), + RangeIndex(0, 5), + RangeIndex(0, 10), + RangeIndex(0, 10), + ), + ], + ids=lambda x: repr(x) if isinstance(x, RangeIndex) else x, + ) + def test_union_sorted(self, idx1, idx2, expected_sorted, expected_notsorted): + res1 = idx1.union(idx2, sort=None) + tm.assert_index_equal(res1, expected_sorted, exact=True) + + res1 = idx1.union(idx2, sort=False) + tm.assert_index_equal(res1, expected_notsorted, exact=True) + + res2 = idx2.union(idx1, sort=None) + res3 = Index(idx1._values, name=idx1.name).union(idx2, sort=None) + tm.assert_index_equal(res2, expected_sorted, exact=True) + tm.assert_index_equal(res3, expected_sorted, exact="equiv") + + def test_union_same_step_misaligned(self): + # GH#44019 + left = RangeIndex(range(0, 20, 4)) + right = RangeIndex(range(1, 21, 4)) + + result = left.union(right) + expected = Index([0, 1, 4, 5, 8, 9, 12, 13, 16, 17]) + tm.assert_index_equal(result, expected, exact=True) + + def test_difference(self): + # GH#12034 Cases where we operate against another RangeIndex and may + # get back another RangeIndex + obj = RangeIndex.from_range(range(1, 10), name="foo") + + result = obj.difference(obj) + expected = RangeIndex.from_range(range(0), name="foo") + tm.assert_index_equal(result, expected, exact=True) + + result = obj.difference(expected.rename("bar")) + tm.assert_index_equal(result, obj.rename(None), exact=True) + + result = obj.difference(obj[:3]) + tm.assert_index_equal(result, obj[3:], exact=True) + + result = obj.difference(obj[-3:]) + tm.assert_index_equal(result, obj[:-3], exact=True) + + # Flipping the step of 'other' doesn't affect the result, but + # flipping the stepof 'self' does when sort=None + result = obj[::-1].difference(obj[-3:]) + tm.assert_index_equal(result, obj[:-3], exact=True) + + result = obj[::-1].difference(obj[-3:], sort=False) + tm.assert_index_equal(result, obj[:-3][::-1], exact=True) + + result = obj[::-1].difference(obj[-3:][::-1]) + tm.assert_index_equal(result, obj[:-3], exact=True) + + result = obj[::-1].difference(obj[-3:][::-1], sort=False) + tm.assert_index_equal(result, obj[:-3][::-1], exact=True) + + result = obj.difference(obj[2:6]) + expected = Index([1, 2, 7, 8, 9], name="foo") + tm.assert_index_equal(result, expected, exact=True) + + def test_difference_sort(self): + # GH#44085 ensure we respect the sort keyword + + idx = Index(range(4))[::-1] + other = Index(range(3, 4)) + + result = idx.difference(other) + expected = Index(range(3)) + tm.assert_index_equal(result, expected, exact=True) + + result = idx.difference(other, sort=False) + expected = expected[::-1] + tm.assert_index_equal(result, expected, exact=True) + + # case where the intersection is empty + other = range(10, 12) + result = idx.difference(other, sort=None) + expected = idx[::-1] + tm.assert_index_equal(result, expected, exact=True) + + def test_difference_mismatched_step(self): + obj = RangeIndex.from_range(range(1, 10), name="foo") + + result = obj.difference(obj[::2]) + expected = obj[1::2] + tm.assert_index_equal(result, expected, exact=True) + + result = obj[::-1].difference(obj[::2], sort=False) + tm.assert_index_equal(result, expected[::-1], exact=True) + + result = obj.difference(obj[1::2]) + expected = obj[::2] + tm.assert_index_equal(result, expected, exact=True) + + result = obj[::-1].difference(obj[1::2], sort=False) + tm.assert_index_equal(result, expected[::-1], exact=True) + + def test_difference_interior_overlap_endpoints_preserved(self): + left = RangeIndex(range(4)) + right = RangeIndex(range(1, 3)) + + result = left.difference(right) + expected = RangeIndex(0, 4, 3) + assert expected.tolist() == [0, 3] + tm.assert_index_equal(result, expected, exact=True) + + def test_difference_endpoints_overlap_interior_preserved(self): + left = RangeIndex(-8, 20, 7) + right = RangeIndex(13, -9, -3) + + result = left.difference(right) + expected = RangeIndex(-1, 13, 7) + assert expected.tolist() == [-1, 6] + tm.assert_index_equal(result, expected, exact=True) + + def test_difference_interior_non_preserving(self): + # case with intersection of length 1 but RangeIndex is not preserved + idx = Index(range(10)) + + other = idx[3:4] + result = idx.difference(other) + expected = Index([0, 1, 2, 4, 5, 6, 7, 8, 9]) + tm.assert_index_equal(result, expected, exact=True) + + # case with other.step / self.step > 2 + other = idx[::3] + result = idx.difference(other) + expected = Index([1, 2, 4, 5, 7, 8]) + tm.assert_index_equal(result, expected, exact=True) + + # cases with only reaching one end of left + obj = Index(range(20)) + other = obj[:10:2] + result = obj.difference(other) + expected = Index([1, 3, 5, 7, 9] + list(range(10, 20))) + tm.assert_index_equal(result, expected, exact=True) + + other = obj[1:11:2] + result = obj.difference(other) + expected = Index([0, 2, 4, 6, 8, 10] + list(range(11, 20))) + tm.assert_index_equal(result, expected, exact=True) + + def test_symmetric_difference(self): + # GH#12034 Cases where we operate against another RangeIndex and may + # get back another RangeIndex + left = RangeIndex.from_range(range(1, 10), name="foo") + + result = left.symmetric_difference(left) + expected = RangeIndex.from_range(range(0), name="foo") + tm.assert_index_equal(result, expected) + + result = left.symmetric_difference(expected.rename("bar")) + tm.assert_index_equal(result, left.rename(None)) + + result = left[:-2].symmetric_difference(left[2:]) + expected = Index([1, 2, 8, 9], name="foo") + tm.assert_index_equal(result, expected, exact=True) + + right = RangeIndex.from_range(range(10, 15)) + + result = left.symmetric_difference(right) + expected = RangeIndex.from_range(range(1, 15)) + tm.assert_index_equal(result, expected) + + result = left.symmetric_difference(right[1:]) + expected = Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14]) + tm.assert_index_equal(result, expected, exact=True) + + +def assert_range_or_not_is_rangelike(index): + """ + Check that we either have a RangeIndex or that this index *cannot* + be represented as a RangeIndex. + """ + if not isinstance(index, RangeIndex) and len(index) > 0: + diff = index[:-1] - index[1:] + assert not (diff == diff[0]).all() + + +@given( + st.integers(-20, 20), + st.integers(-20, 20), + st.integers(-20, 20), + st.integers(-20, 20), + st.integers(-20, 20), + st.integers(-20, 20), +) +def test_range_difference(start1, stop1, step1, start2, stop2, step2): + # test that + # a) we match Index[int64].difference and + # b) we return RangeIndex whenever it is possible to do so. + assume(step1 != 0) + assume(step2 != 0) + + left = RangeIndex(start1, stop1, step1) + right = RangeIndex(start2, stop2, step2) + + result = left.difference(right, sort=None) + assert_range_or_not_is_rangelike(result) + + left_int64 = Index(left.to_numpy()) + right_int64 = Index(right.to_numpy()) + + alt = left_int64.difference(right_int64, sort=None) + tm.assert_index_equal(result, alt, exact="equiv") + + result = left.difference(right, sort=False) + assert_range_or_not_is_rangelike(result) + + alt = left_int64.difference(right_int64, sort=False) + tm.assert_index_equal(result, alt, exact="equiv") diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/string/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/string/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..648ee47ddc34c1d4ae90bd986a283880743ac415 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/string/test_indexing.py @@ -0,0 +1,199 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import Index +import pandas._testing as tm + + +def _isnan(val): + try: + return val is not pd.NA and np.isnan(val) + except TypeError: + return False + + +def _equivalent_na(dtype, null): + if dtype.na_value is pd.NA and null is pd.NA: + return True + elif _isnan(dtype.na_value) and _isnan(null): + return True + else: + return False + + +class TestGetLoc: + def test_get_loc(self, any_string_dtype): + index = Index(["a", "b", "c"], dtype=any_string_dtype) + assert index.get_loc("b") == 1 + + def test_get_loc_raises(self, any_string_dtype): + index = Index(["a", "b", "c"], dtype=any_string_dtype) + with pytest.raises(KeyError, match="d"): + index.get_loc("d") + + def test_get_loc_invalid_value(self, any_string_dtype): + index = Index(["a", "b", "c"], dtype=any_string_dtype) + with pytest.raises(KeyError, match="1"): + index.get_loc(1) + + def test_get_loc_non_unique(self, any_string_dtype): + index = Index(["a", "b", "a"], dtype=any_string_dtype) + result = index.get_loc("a") + expected = np.array([True, False, True]) + tm.assert_numpy_array_equal(result, expected) + + def test_get_loc_non_missing(self, any_string_dtype, nulls_fixture): + index = Index(["a", "b", "c"], dtype=any_string_dtype) + with pytest.raises(KeyError): + index.get_loc(nulls_fixture) + + def test_get_loc_missing(self, any_string_dtype, nulls_fixture): + index = Index(["a", "b", nulls_fixture], dtype=any_string_dtype) + assert index.get_loc(nulls_fixture) == 2 + + +class TestGetIndexer: + @pytest.mark.parametrize( + "method,expected", + [ + ("pad", [-1, 0, 1, 1]), + ("backfill", [0, 0, 1, -1]), + ], + ) + def test_get_indexer_strings(self, any_string_dtype, method, expected): + expected = np.array(expected, dtype=np.intp) + index = Index(["b", "c"], dtype=any_string_dtype) + actual = index.get_indexer(["a", "b", "c", "d"], method=method) + + tm.assert_numpy_array_equal(actual, expected) + + def test_get_indexer_strings_raises(self, any_string_dtype): + index = Index(["b", "c"], dtype=any_string_dtype) + + msg = "|".join( + [ + "operation 'sub' not supported for dtype 'str", + r"unsupported operand type\(s\) for -: 'str' and 'str'", + ] + ) + with pytest.raises(TypeError, match=msg): + index.get_indexer(["a", "b", "c", "d"], method="nearest") + + with pytest.raises(TypeError, match=msg): + index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2) + + with pytest.raises(TypeError, match=msg): + index.get_indexer( + ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2] + ) + + @pytest.mark.parametrize("null", [None, np.nan, float("nan"), pd.NA]) + def test_get_indexer_missing(self, any_string_dtype, null, using_infer_string): + # NaT and Decimal("NaN") from null_fixture are not supported for string dtype + index = Index(["a", "b", null], dtype=any_string_dtype) + result = index.get_indexer(["a", null, "c"]) + if using_infer_string: + expected = np.array([0, 2, -1], dtype=np.intp) + elif any_string_dtype == "string" and not _equivalent_na( + any_string_dtype, null + ): + expected = np.array([0, -1, -1], dtype=np.intp) + else: + expected = np.array([0, 2, -1], dtype=np.intp) + + tm.assert_numpy_array_equal(result, expected) + + +class TestGetIndexerNonUnique: + @pytest.mark.parametrize("null", [None, np.nan, float("nan"), pd.NA]) + def test_get_indexer_non_unique_nas( + self, any_string_dtype, null, using_infer_string + ): + index = Index(["a", "b", null], dtype=any_string_dtype) + indexer, missing = index.get_indexer_non_unique(["a", null]) + + if using_infer_string: + expected_indexer = np.array([0, 2], dtype=np.intp) + expected_missing = np.array([], dtype=np.intp) + elif any_string_dtype == "string" and not _equivalent_na( + any_string_dtype, null + ): + expected_indexer = np.array([0, -1], dtype=np.intp) + expected_missing = np.array([1], dtype=np.intp) + else: + expected_indexer = np.array([0, 2], dtype=np.intp) + expected_missing = np.array([], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected_indexer) + tm.assert_numpy_array_equal(missing, expected_missing) + + # actually non-unique + index = Index(["a", null, "b", null], dtype=any_string_dtype) + indexer, missing = index.get_indexer_non_unique(["a", null]) + + if using_infer_string: + expected_indexer = np.array([0, 1, 3], dtype=np.intp) + elif any_string_dtype == "string" and not _equivalent_na( + any_string_dtype, null + ): + pass + else: + expected_indexer = np.array([0, 1, 3], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected_indexer) + tm.assert_numpy_array_equal(missing, expected_missing) + + +class TestSliceLocs: + @pytest.mark.parametrize( + "in_slice,expected", + [ + # error: Slice index must be an integer or None + (pd.IndexSlice[::-1], "yxdcb"), + (pd.IndexSlice["b":"y":-1], ""), # type: ignore[misc] + (pd.IndexSlice["b"::-1], "b"), # type: ignore[misc] + (pd.IndexSlice[:"b":-1], "yxdcb"), # type: ignore[misc] + (pd.IndexSlice[:"y":-1], "y"), # type: ignore[misc] + (pd.IndexSlice["y"::-1], "yxdcb"), # type: ignore[misc] + (pd.IndexSlice["y"::-4], "yb"), # type: ignore[misc] + # absent labels + (pd.IndexSlice[:"a":-1], "yxdcb"), # type: ignore[misc] + (pd.IndexSlice[:"a":-2], "ydb"), # type: ignore[misc] + (pd.IndexSlice["z"::-1], "yxdcb"), # type: ignore[misc] + (pd.IndexSlice["z"::-3], "yc"), # type: ignore[misc] + (pd.IndexSlice["m"::-1], "dcb"), # type: ignore[misc] + (pd.IndexSlice[:"m":-1], "yx"), # type: ignore[misc] + (pd.IndexSlice["a":"a":-1], ""), # type: ignore[misc] + (pd.IndexSlice["z":"z":-1], ""), # type: ignore[misc] + (pd.IndexSlice["m":"m":-1], ""), # type: ignore[misc] + ], + ) + def test_slice_locs_negative_step(self, in_slice, expected, any_string_dtype): + index = Index(list("bcdxy"), dtype=any_string_dtype) + + s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step) + result = index[s_start : s_stop : in_slice.step] + expected = Index(list(expected), dtype=any_string_dtype) + tm.assert_index_equal(result, expected) + + def test_slice_locs_negative_step_oob(self, any_string_dtype): + index = Index(list("bcdxy"), dtype=any_string_dtype) + + result = index[-10:5:1] + tm.assert_index_equal(result, index) + + result = index[4:-10:-1] + expected = Index(list("yxdcb"), dtype=any_string_dtype) + tm.assert_index_equal(result, expected) + + def test_slice_locs_dup(self, any_string_dtype): + index = Index(["a", "a", "b", "c", "d", "d"], dtype=any_string_dtype) + assert index.slice_locs("a", "d") == (0, 6) + assert index.slice_locs(end="d") == (0, 6) + assert index.slice_locs("a", "c") == (0, 4) + assert index.slice_locs("b", "d") == (2, 6) + + index2 = index[::-1] + assert index2.slice_locs("d", "a") == (0, 6) + assert index2.slice_locs(end="a") == (0, 6) + assert index2.slice_locs("d", "b") == (0, 4) + assert index2.slice_locs("c", "a") == (2, 6) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_astype.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_astype.py new file mode 100644 index 0000000000000000000000000000000000000000..5166cadae499e44a6dff420580c96043569b839b --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_astype.py @@ -0,0 +1,181 @@ +from datetime import timedelta + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Index, + NaT, + Timedelta, + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm +from pandas.core.arrays import TimedeltaArray + + +class TestTimedeltaIndex: + def test_astype_object(self): + idx = timedelta_range(start="1 days", periods=4, freq="D", name="idx") + expected_list = [ + Timedelta("1 days"), + Timedelta("2 days"), + Timedelta("3 days"), + Timedelta("4 days"), + ] + result = idx.astype(object) + expected = Index(expected_list, dtype=object, name="idx") + tm.assert_index_equal(result, expected) + assert idx.tolist() == expected_list + + def test_astype_object_with_nat(self): + idx = TimedeltaIndex( + [timedelta(days=1), timedelta(days=2), NaT, timedelta(days=4)], name="idx" + ) + expected_list = [ + Timedelta("1 days"), + Timedelta("2 days"), + NaT, + Timedelta("4 days"), + ] + result = idx.astype(object) + expected = Index(expected_list, dtype=object, name="idx") + tm.assert_index_equal(result, expected) + assert idx.tolist() == expected_list + + def test_astype(self, using_infer_string): + # GH 13149, GH 13209 + idx = TimedeltaIndex([1e14, "NaT", NaT, np.nan], name="idx") + + result = idx.astype(object) + expected = Index( + [Timedelta("1 days 03:46:40")] + [NaT] * 3, dtype=object, name="idx" + ) + tm.assert_index_equal(result, expected) + + result = idx.astype(np.int64) + expected = Index( + [100000000000000] + [-9223372036854775808] * 3, dtype=np.int64, name="idx" + ) + tm.assert_index_equal(result, expected) + + result = idx.astype(str) + if using_infer_string: + expected = Index( + [str(x) if x is not NaT else None for x in idx], name="idx", dtype="str" + ) + else: + expected = Index([str(x) for x in idx], name="idx", dtype=object) + tm.assert_index_equal(result, expected) + + rng = timedelta_range("1 days", periods=10) + result = rng.astype("i8") + tm.assert_index_equal(result, Index(rng.asi8)) + tm.assert_numpy_array_equal(rng.asi8, result.values) + + def test_astype_uint(self): + arr = timedelta_range("1h", periods=2) + + with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"): + arr.astype("uint64") + with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"): + arr.astype("uint32") + + def test_astype_timedelta64(self): + # GH 13149, GH 13209 + idx = TimedeltaIndex([1e14, "NaT", NaT, np.nan]) + + msg = ( + r"Cannot convert from timedelta64\[ns\] to timedelta64. " + "Supported resolutions are 's', 'ms', 'us', 'ns'" + ) + with pytest.raises(ValueError, match=msg): + idx.astype("timedelta64") + + result = idx.astype("timedelta64[ns]") + tm.assert_index_equal(result, idx) + assert result is not idx + + result = idx.astype("timedelta64[ns]", copy=False) + tm.assert_index_equal(result, idx) + assert result is idx + + def test_astype_to_td64d_raises(self, index_or_series): + # We don't support "D" reso + scalar = Timedelta(days=31) + td = index_or_series( + [scalar, scalar, scalar + timedelta(minutes=5, seconds=3), NaT], + dtype="m8[ns]", + ) + msg = ( + r"Cannot convert from timedelta64\[ns\] to timedelta64\[D\]. " + "Supported resolutions are 's', 'ms', 'us', 'ns'" + ) + with pytest.raises(ValueError, match=msg): + td.astype("timedelta64[D]") + + def test_astype_ms_to_s(self, index_or_series): + scalar = Timedelta(days=31) + td = index_or_series( + [scalar, scalar, scalar + timedelta(minutes=5, seconds=3), NaT], + dtype="m8[ns]", + ) + + exp_values = np.asarray(td).astype("m8[s]") + exp_tda = TimedeltaArray._simple_new(exp_values, dtype=exp_values.dtype) + expected = index_or_series(exp_tda) + assert expected.dtype == "m8[s]" + result = td.astype("timedelta64[s]") + tm.assert_equal(result, expected) + + def test_astype_freq_conversion(self): + # pre-2.0 td64 astype converted to float64. now for supported units + # (s, ms, us, ns) this converts to the requested dtype. + # This matches TDA and Series + tdi = timedelta_range("1 Day", periods=30) + + res = tdi.astype("m8[s]") + exp_values = np.asarray(tdi).astype("m8[s]") + exp_tda = TimedeltaArray._simple_new( + exp_values, dtype=exp_values.dtype, freq=tdi.freq + ) + expected = Index(exp_tda) + assert expected.dtype == "m8[s]" + tm.assert_index_equal(res, expected) + + # check this matches Series and TimedeltaArray + res = tdi._data.astype("m8[s]") + tm.assert_equal(res, expected._values) + + res = tdi.to_series().astype("m8[s]") + tm.assert_equal(res._values, expected._values._with_freq(None)) + + @pytest.mark.parametrize("dtype", [float, "datetime64", "datetime64[ns]"]) + def test_astype_raises(self, dtype): + # GH 13149, GH 13209 + idx = TimedeltaIndex([1e14, "NaT", NaT, np.nan]) + msg = "Cannot cast TimedeltaIndex to dtype" + with pytest.raises(TypeError, match=msg): + idx.astype(dtype) + + def test_astype_category(self): + obj = timedelta_range("1h", periods=2, freq="h") + + result = obj.astype("category") + expected = pd.CategoricalIndex([Timedelta("1h"), Timedelta("2h")]) + tm.assert_index_equal(result, expected) + + result = obj._data.astype("category") + expected = expected.values + tm.assert_categorical_equal(result, expected) + + def test_astype_array_fallback(self): + obj = timedelta_range("1h", periods=2) + result = obj.astype(bool) + expected = Index(np.array([True, True])) + tm.assert_index_equal(result, expected) + + result = obj._data.astype(bool) + expected = np.array([True, True]) + tm.assert_numpy_array_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py new file mode 100644 index 0000000000000000000000000000000000000000..24ab3888412d08b54543ed22910c67ce9bdf328f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py @@ -0,0 +1,40 @@ +import numpy as np + +from pandas import ( + TimedeltaIndex, + factorize, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexFactorize: + def test_factorize(self): + idx1 = TimedeltaIndex(["1 day", "1 day", "2 day", "2 day", "3 day", "3 day"]) + + exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp) + exp_idx = TimedeltaIndex(["1 day", "2 day", "3 day"]) + + arr, idx = idx1.factorize() + tm.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, exp_idx) + assert idx.freq == exp_idx.freq + + arr, idx = idx1.factorize(sort=True) + tm.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, exp_idx) + assert idx.freq == exp_idx.freq + + def test_factorize_preserves_freq(self): + # GH#38120 freq should be preserved + idx3 = timedelta_range("1 day", periods=4, freq="s") + exp_arr = np.array([0, 1, 2, 3], dtype=np.intp) + arr, idx = idx3.factorize() + tm.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, idx3) + assert idx.freq == idx3.freq + + arr, idx = factorize(idx3) + tm.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, idx3) + assert idx.freq == idx3.freq diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_fillna.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_fillna.py new file mode 100644 index 0000000000000000000000000000000000000000..40aa95d0a46058d2dc3fc5208ca39328d96b23fb --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_fillna.py @@ -0,0 +1,22 @@ +from pandas import ( + Index, + NaT, + Timedelta, + TimedeltaIndex, +) +import pandas._testing as tm + + +class TestFillNA: + def test_fillna_timedelta(self): + # GH#11343 + idx = TimedeltaIndex(["1 day", NaT, "3 day"]) + + exp = TimedeltaIndex(["1 day", "2 day", "3 day"]) + tm.assert_index_equal(idx.fillna(Timedelta("2 day")), exp) + + exp = TimedeltaIndex(["1 day", "3 hour", "3 day"]) + idx.fillna(Timedelta("3 hour")) + + exp = Index([Timedelta("1 day"), "x", Timedelta("3 day")], dtype=object) + tm.assert_index_equal(idx.fillna("x"), exp) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py new file mode 100644 index 0000000000000000000000000000000000000000..f8164102815f61ec61962524db2a2b3dd0ff6d55 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py @@ -0,0 +1,145 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas._libs import lib + +import pandas as pd +from pandas import ( + Index, + Timedelta, + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexInsert: + def test_insert(self): + idx = TimedeltaIndex(["4day", "1day", "2day"], name="idx") + + result = idx.insert(2, timedelta(days=5)) + exp = TimedeltaIndex(["4day", "1day", "5day", "2day"], name="idx") + tm.assert_index_equal(result, exp) + + # insertion of non-datetime should coerce to object index + result = idx.insert(1, "inserted") + expected = Index( + [Timedelta("4day"), "inserted", Timedelta("1day"), Timedelta("2day")], + name="idx", + ) + assert not isinstance(result, TimedeltaIndex) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + + idx = timedelta_range("1day 00:00:01", periods=3, freq="s", name="idx") + + # preserve freq + expected_0 = TimedeltaIndex( + ["1day", "1day 00:00:01", "1day 00:00:02", "1day 00:00:03"], + name="idx", + freq="s", + ) + expected_3 = TimedeltaIndex( + ["1day 00:00:01", "1day 00:00:02", "1day 00:00:03", "1day 00:00:04"], + name="idx", + freq="s", + ) + + # reset freq to None + expected_1_nofreq = TimedeltaIndex( + ["1day 00:00:01", "1day 00:00:01", "1day 00:00:02", "1day 00:00:03"], + name="idx", + freq=None, + ) + expected_3_nofreq = TimedeltaIndex( + ["1day 00:00:01", "1day 00:00:02", "1day 00:00:03", "1day 00:00:05"], + name="idx", + freq=None, + ) + + cases = [ + (0, Timedelta("1day"), expected_0), + (-3, Timedelta("1day"), expected_0), + (3, Timedelta("1day 00:00:04"), expected_3), + (1, Timedelta("1day 00:00:01"), expected_1_nofreq), + (3, Timedelta("1day 00:00:05"), expected_3_nofreq), + ] + + for n, d, expected in cases: + result = idx.insert(n, d) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + @pytest.mark.parametrize( + "null", [None, np.nan, np.timedelta64("NaT"), pd.NaT, pd.NA] + ) + def test_insert_nat(self, null): + # GH 18295 (test missing) + idx = timedelta_range("1day", "3day") + result = idx.insert(1, null) + expected = TimedeltaIndex(["1day", pd.NaT, "2day", "3day"]) + tm.assert_index_equal(result, expected) + + def test_insert_invalid_na(self): + idx = TimedeltaIndex(["4day", "1day", "2day"], name="idx") + + item = np.datetime64("NaT") + result = idx.insert(0, item) + + expected = Index([item] + list(idx), dtype=object, name="idx") + tm.assert_index_equal(result, expected) + + # Also works if we pass a different dt64nat object + item2 = np.datetime64("NaT") + result = idx.insert(0, item2) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "item", [0, np.int64(0), np.float64(0), np.array(0), np.datetime64(456, "us")] + ) + def test_insert_mismatched_types_raises(self, item): + # GH#33703 dont cast these to td64 + tdi = TimedeltaIndex(["4day", "1day", "2day"], name="idx") + + result = tdi.insert(1, item) + + expected = Index( + [tdi[0], lib.item_from_zerodim(item)] + list(tdi[1:]), + dtype=object, + name="idx", + ) + tm.assert_index_equal(result, expected) + + def test_insert_castable_str(self): + idx = timedelta_range("1day", "3day") + + result = idx.insert(0, "1 Day") + + expected = TimedeltaIndex([idx[0]] + list(idx)) + tm.assert_index_equal(result, expected) + + def test_insert_non_castable_str(self): + idx = timedelta_range("1day", "3day") + + result = idx.insert(0, "foo") + + expected = Index(["foo"] + list(idx), dtype=object) + tm.assert_index_equal(result, expected) + + def test_insert_empty(self): + # Corner case inserting with length zero doesn't raise IndexError + # GH#33573 for freq preservation + idx = timedelta_range("1 Day", periods=3) + td = idx[0] + + result = idx[:0].insert(0, td) + assert result.freq == "D" + + with pytest.raises(IndexError, match="loc must be an integer between"): + result = idx[:0].insert(1, td) + + with pytest.raises(IndexError, match="loc must be an integer between"): + result = idx[:0].insert(-1, td) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_repeat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_repeat.py new file mode 100644 index 0000000000000000000000000000000000000000..2a9b58d1bf322938e9344d0cbacfaa79674fcf0e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_repeat.py @@ -0,0 +1,34 @@ +import numpy as np + +from pandas import ( + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestRepeat: + def test_repeat(self): + index = timedelta_range("1 days", periods=2, freq="D") + exp = TimedeltaIndex(["1 days", "1 days", "2 days", "2 days"]) + for res in [index.repeat(2), np.repeat(index, 2)]: + tm.assert_index_equal(res, exp) + assert res.freq is None + + index = TimedeltaIndex(["1 days", "NaT", "3 days"]) + exp = TimedeltaIndex( + [ + "1 days", + "1 days", + "1 days", + "NaT", + "NaT", + "NaT", + "3 days", + "3 days", + "3 days", + ] + ) + for res in [index.repeat(3), np.repeat(index, 3)]: + tm.assert_index_equal(res, exp) + assert res.freq is None diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_shift.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_shift.py new file mode 100644 index 0000000000000000000000000000000000000000..a0986d1496881a2061ac8306d0a064f4393cd4e9 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/methods/test_shift.py @@ -0,0 +1,76 @@ +import pytest + +from pandas.errors import NullFrequencyError + +import pandas as pd +from pandas import TimedeltaIndex +import pandas._testing as tm + + +class TestTimedeltaIndexShift: + # ------------------------------------------------------------- + # TimedeltaIndex.shift is used by __add__/__sub__ + + def test_tdi_shift_empty(self): + # GH#9903 + idx = TimedeltaIndex([], name="xxx") + tm.assert_index_equal(idx.shift(0, freq="h"), idx) + tm.assert_index_equal(idx.shift(3, freq="h"), idx) + + def test_tdi_shift_hours(self): + # GH#9903 + idx = TimedeltaIndex(["5 hours", "6 hours", "9 hours"], name="xxx") + tm.assert_index_equal(idx.shift(0, freq="h"), idx) + exp = TimedeltaIndex(["8 hours", "9 hours", "12 hours"], name="xxx") + tm.assert_index_equal(idx.shift(3, freq="h"), exp) + exp = TimedeltaIndex(["2 hours", "3 hours", "6 hours"], name="xxx") + tm.assert_index_equal(idx.shift(-3, freq="h"), exp) + + def test_tdi_shift_minutes(self): + # GH#9903 + idx = TimedeltaIndex(["5 hours", "6 hours", "9 hours"], name="xxx") + tm.assert_index_equal(idx.shift(0, freq="min"), idx) + exp = TimedeltaIndex(["05:03:00", "06:03:00", "9:03:00"], name="xxx") + tm.assert_index_equal(idx.shift(3, freq="min"), exp) + exp = TimedeltaIndex(["04:57:00", "05:57:00", "8:57:00"], name="xxx") + tm.assert_index_equal(idx.shift(-3, freq="min"), exp) + + def test_tdi_shift_int(self): + # GH#8083 + tdi = pd.to_timedelta(range(5), unit="d") + trange = tdi._with_freq("infer") + pd.offsets.Hour(1) + result = trange.shift(1) + expected = TimedeltaIndex( + [ + "1 days 01:00:00", + "2 days 01:00:00", + "3 days 01:00:00", + "4 days 01:00:00", + "5 days 01:00:00", + ], + freq="D", + ) + tm.assert_index_equal(result, expected) + + def test_tdi_shift_nonstandard_freq(self): + # GH#8083 + tdi = pd.to_timedelta(range(5), unit="d") + trange = tdi._with_freq("infer") + pd.offsets.Hour(1) + result = trange.shift(3, freq="2D 1s") + expected = TimedeltaIndex( + [ + "6 days 01:00:03", + "7 days 01:00:03", + "8 days 01:00:03", + "9 days 01:00:03", + "10 days 01:00:03", + ], + freq="D", + ) + tm.assert_index_equal(result, expected) + + def test_shift_no_freq(self): + # GH#19147 + tdi = TimedeltaIndex(["1 days 01:00:00", "2 days 01:00:00"], freq=None) + with pytest.raises(NullFrequencyError, match="Cannot shift with no freq"): + tdi.shift(2) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_arithmetic.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..a431e10dc18ab15da0fd07f798d54b6dead26073 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_arithmetic.py @@ -0,0 +1,51 @@ +# Arithmetic tests for TimedeltaIndex are generally about the result's `freq` attribute. +# Other cases can be shared in tests.arithmetic.test_timedelta64 +import numpy as np + +from pandas import ( + NaT, + Timedelta, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexArithmetic: + def test_arithmetic_zero_freq(self): + # GH#51575 don't get a .freq with freq.n = 0 + tdi = timedelta_range(0, periods=100, freq="ns") + result = tdi / 2 + assert result.freq is None + expected = tdi[:50].repeat(2) + tm.assert_index_equal(result, expected) + + result2 = tdi // 2 + assert result2.freq is None + expected2 = expected + tm.assert_index_equal(result2, expected2) + + result3 = tdi * 0 + assert result3.freq is None + expected3 = tdi[:1].repeat(100) + tm.assert_index_equal(result3, expected3) + + def test_tdi_division(self, index_or_series): + # doc example + + scalar = Timedelta(days=31) + td = index_or_series( + [scalar, scalar, scalar + Timedelta(minutes=5, seconds=3), NaT], + dtype="m8[ns]", + ) + + result = td / np.timedelta64(1, "D") + expected = index_or_series( + [31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan] + ) + tm.assert_equal(result, expected) + + result = td / np.timedelta64(1, "s") + expected = index_or_series( + [31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan] + ) + tm.assert_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_constructors.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..0510700bb64d7a626761a67d72ecfa6ecfba9ac4 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_constructors.py @@ -0,0 +1,291 @@ +from datetime import timedelta + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Timedelta, + TimedeltaIndex, + timedelta_range, + to_timedelta, +) +import pandas._testing as tm +from pandas.core.arrays.timedeltas import TimedeltaArray + + +class TestTimedeltaIndex: + def test_closed_deprecated(self): + # GH#52628 + msg = "The 'closed' keyword" + with tm.assert_produces_warning(FutureWarning, match=msg): + TimedeltaIndex([], closed=True) + + def test_array_of_dt64_nat_raises(self): + # GH#39462 + nat = np.datetime64("NaT", "ns") + arr = np.array([nat], dtype=object) + + msg = "Invalid type for timedelta scalar" + with pytest.raises(TypeError, match=msg): + TimedeltaIndex(arr) + + with pytest.raises(TypeError, match=msg): + TimedeltaArray._from_sequence(arr, dtype="m8[ns]") + + with pytest.raises(TypeError, match=msg): + to_timedelta(arr) + + @pytest.mark.parametrize("unit", ["Y", "y", "M"]) + def test_unit_m_y_raises(self, unit): + msg = "Units 'M', 'Y', and 'y' are no longer supported" + depr_msg = "The 'unit' keyword in TimedeltaIndex construction is deprecated" + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + TimedeltaIndex([1, 3, 7], unit) + + def test_int64_nocopy(self): + # GH#23539 check that a copy isn't made when we pass int64 data + # and copy=False + arr = np.arange(10, dtype=np.int64) + tdi = TimedeltaIndex(arr, copy=False) + assert tdi._data._ndarray.base is arr + + def test_infer_from_tdi(self): + # GH#23539 + # fast-path for inferring a frequency if the passed data already + # has one + tdi = timedelta_range("1 second", periods=10**7, freq="1s") + + result = TimedeltaIndex(tdi, freq="infer") + assert result.freq == tdi.freq + + # check that inferred_freq was not called by checking that the + # value has not been cached + assert "inferred_freq" not in getattr(result, "_cache", {}) + + def test_infer_from_tdi_mismatch(self): + # GH#23539 + # fast-path for invalidating a frequency if the passed data already + # has one and it does not match the `freq` input + tdi = timedelta_range("1 second", periods=100, freq="1s") + + depr_msg = "TimedeltaArray.__init__ is deprecated" + msg = ( + "Inferred frequency .* from passed values does " + "not conform to passed frequency" + ) + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(tdi, freq="D") + + with pytest.raises(ValueError, match=msg): + # GH#23789 + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + TimedeltaArray(tdi, freq="D") + + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(tdi._data, freq="D") + + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + TimedeltaArray(tdi._data, freq="D") + + def test_dt64_data_invalid(self): + # GH#23539 + # passing tz-aware DatetimeIndex raises, naive or ndarray[datetime64] + # raise as of GH#29794 + dti = pd.date_range("2016-01-01", periods=3) + + msg = "cannot be converted to timedelta64" + with pytest.raises(TypeError, match=msg): + TimedeltaIndex(dti.tz_localize("Europe/Brussels")) + + with pytest.raises(TypeError, match=msg): + TimedeltaIndex(dti) + + with pytest.raises(TypeError, match=msg): + TimedeltaIndex(np.asarray(dti)) + + def test_float64_ns_rounded(self): + # GH#23539 without specifying a unit, floats are regarded as nanos, + # and fractional portions are truncated + tdi = TimedeltaIndex([2.3, 9.7]) + expected = TimedeltaIndex([2, 9]) + tm.assert_index_equal(tdi, expected) + + # integral floats are non-lossy + tdi = TimedeltaIndex([2.0, 9.0]) + expected = TimedeltaIndex([2, 9]) + tm.assert_index_equal(tdi, expected) + + # NaNs get converted to NaT + tdi = TimedeltaIndex([2.0, np.nan]) + expected = TimedeltaIndex([Timedelta(nanoseconds=2), pd.NaT]) + tm.assert_index_equal(tdi, expected) + + def test_float64_unit_conversion(self): + # GH#23539 + tdi = to_timedelta([1.5, 2.25], unit="D") + expected = TimedeltaIndex([Timedelta(days=1.5), Timedelta(days=2.25)]) + tm.assert_index_equal(tdi, expected) + + def test_construction_base_constructor(self): + arr = [Timedelta("1 days"), pd.NaT, Timedelta("3 days")] + tm.assert_index_equal(pd.Index(arr), TimedeltaIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), TimedeltaIndex(np.array(arr))) + + arr = [np.nan, pd.NaT, Timedelta("1 days")] + tm.assert_index_equal(pd.Index(arr), TimedeltaIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), TimedeltaIndex(np.array(arr))) + + @pytest.mark.filterwarnings( + "ignore:The 'unit' keyword in TimedeltaIndex construction:FutureWarning" + ) + def test_constructor(self): + expected = TimedeltaIndex( + [ + "1 days", + "1 days 00:00:05", + "2 days", + "2 days 00:00:02", + "0 days 00:00:03", + ] + ) + result = TimedeltaIndex( + [ + "1 days", + "1 days, 00:00:05", + np.timedelta64(2, "D"), + timedelta(days=2, seconds=2), + pd.offsets.Second(3), + ] + ) + tm.assert_index_equal(result, expected) + + expected = TimedeltaIndex( + ["0 days 00:00:00", "0 days 00:00:01", "0 days 00:00:02"] + ) + result = TimedeltaIndex(range(3), unit="s") + tm.assert_index_equal(result, expected) + expected = TimedeltaIndex( + ["0 days 00:00:00", "0 days 00:00:05", "0 days 00:00:09"] + ) + result = TimedeltaIndex([0, 5, 9], unit="s") + tm.assert_index_equal(result, expected) + expected = TimedeltaIndex( + ["0 days 00:00:00.400", "0 days 00:00:00.450", "0 days 00:00:01.200"] + ) + result = TimedeltaIndex([400, 450, 1200], unit="ms") + tm.assert_index_equal(result, expected) + + def test_constructor_iso(self): + # GH #21877 + expected = timedelta_range("1s", periods=9, freq="s") + durations = [f"P0DT0H0M{i}S" for i in range(1, 10)] + result = to_timedelta(durations) + tm.assert_index_equal(result, expected) + + def test_timedelta_range_fractional_period(self): + msg = "Non-integer 'periods' in pd.date_range, pd.timedelta_range" + with tm.assert_produces_warning(FutureWarning, match=msg): + rng = timedelta_range("1 days", periods=10.5) + exp = timedelta_range("1 days", periods=10) + tm.assert_index_equal(rng, exp) + + def test_constructor_coverage(self): + msg = "periods must be a number, got foo" + with pytest.raises(TypeError, match=msg): + timedelta_range(start="1 days", periods="foo", freq="D") + + msg = ( + r"TimedeltaIndex\(\.\.\.\) must be called with a collection of some kind, " + "'1 days' was passed" + ) + with pytest.raises(TypeError, match=msg): + TimedeltaIndex("1 days") + + # generator expression + gen = (timedelta(i) for i in range(10)) + result = TimedeltaIndex(gen) + expected = TimedeltaIndex([timedelta(i) for i in range(10)]) + tm.assert_index_equal(result, expected) + + # NumPy string array + strings = np.array(["1 days", "2 days", "3 days"]) + result = TimedeltaIndex(strings) + expected = to_timedelta([1, 2, 3], unit="d") + tm.assert_index_equal(result, expected) + + from_ints = TimedeltaIndex(expected.asi8) + tm.assert_index_equal(from_ints, expected) + + # non-conforming freq + msg = ( + "Inferred frequency None from passed values does not conform to " + "passed frequency D" + ) + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(["1 days", "2 days", "4 days"], freq="D") + + msg = ( + "Of the four parameters: start, end, periods, and freq, exactly " + "three must be specified" + ) + with pytest.raises(ValueError, match=msg): + timedelta_range(periods=10, freq="D") + + def test_constructor_name(self): + idx = timedelta_range(start="1 days", periods=1, freq="D", name="TEST") + assert idx.name == "TEST" + + # GH10025 + idx2 = TimedeltaIndex(idx, name="something else") + assert idx2.name == "something else" + + def test_constructor_no_precision_raises(self): + # GH-24753, GH-24739 + + msg = "with no precision is not allowed" + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(["2000"], dtype="timedelta64") + + msg = "The 'timedelta64' dtype has no unit. Please pass in" + with pytest.raises(ValueError, match=msg): + pd.Index(["2000"], dtype="timedelta64") + + def test_constructor_wrong_precision_raises(self): + msg = "Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns'" + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(["2000"], dtype="timedelta64[D]") + + # "timedelta64[us]" was unsupported pre-2.0, but now this works. + tdi = TimedeltaIndex(["2000"], dtype="timedelta64[us]") + assert tdi.dtype == "m8[us]" + + def test_explicit_none_freq(self): + # Explicitly passing freq=None is respected + tdi = timedelta_range(1, periods=5) + assert tdi.freq is not None + + result = TimedeltaIndex(tdi, freq=None) + assert result.freq is None + + result = TimedeltaIndex(tdi._data, freq=None) + assert result.freq is None + + msg = "TimedeltaArray.__init__ is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + tda = TimedeltaArray(tdi, freq=None) + assert tda.freq is None + + def test_from_categorical(self): + tdi = timedelta_range(1, periods=5) + + cat = pd.Categorical(tdi) + + result = TimedeltaIndex(cat) + tm.assert_index_equal(result, tdi) + + ci = pd.CategoricalIndex(tdi) + result = TimedeltaIndex(ci) + tm.assert_index_equal(result, tdi) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_delete.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_delete.py new file mode 100644 index 0000000000000000000000000000000000000000..6e6f54702ce1a09c0fccb0c44d0cd4a474c46a8c --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_delete.py @@ -0,0 +1,71 @@ +from pandas import ( + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexDelete: + def test_delete(self): + idx = timedelta_range(start="1 Days", periods=5, freq="D", name="idx") + + # preserve freq + expected_0 = timedelta_range(start="2 Days", periods=4, freq="D", name="idx") + expected_4 = timedelta_range(start="1 Days", periods=4, freq="D", name="idx") + + # reset freq to None + expected_1 = TimedeltaIndex( + ["1 day", "3 day", "4 day", "5 day"], freq=None, name="idx" + ) + + cases = { + 0: expected_0, + -5: expected_0, + -1: expected_4, + 4: expected_4, + 1: expected_1, + } + for n, expected in cases.items(): + result = idx.delete(n) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + with tm.external_error_raised((IndexError, ValueError)): + # either depending on numpy version + idx.delete(5) + + def test_delete_slice(self): + idx = timedelta_range(start="1 days", periods=10, freq="D", name="idx") + + # preserve freq + expected_0_2 = timedelta_range(start="4 days", periods=7, freq="D", name="idx") + expected_7_9 = timedelta_range(start="1 days", periods=7, freq="D", name="idx") + + # reset freq to None + expected_3_5 = TimedeltaIndex( + ["1 d", "2 d", "3 d", "7 d", "8 d", "9 d", "10d"], freq=None, name="idx" + ) + + cases = { + (0, 1, 2): expected_0_2, + (7, 8, 9): expected_7_9, + (3, 4, 5): expected_3_5, + } + for n, expected in cases.items(): + result = idx.delete(n) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + result = idx.delete(slice(n[0], n[-1] + 1)) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + def test_delete_doesnt_infer_freq(self): + # GH#30655 behavior matches DatetimeIndex + + tdi = TimedeltaIndex(["1 Day", "2 Days", None, "3 Days", "4 Days"]) + result = tdi.delete(2) + assert result.freq is None diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_formats.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..607336060cbbc2093e224e31614e26a2c03bd72f --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_formats.py @@ -0,0 +1,106 @@ +import pytest + +import pandas as pd +from pandas import ( + Series, + TimedeltaIndex, +) + + +class TestTimedeltaIndexRendering: + def test_repr_round_days_non_nano(self): + # GH#55405 + # we should get "1 days", not "1 days 00:00:00" with non-nano + tdi = TimedeltaIndex(["1 days"], freq="D").as_unit("s") + result = repr(tdi) + expected = "TimedeltaIndex(['1 days'], dtype='timedelta64[s]', freq='D')" + assert result == expected + + result2 = repr(Series(tdi)) + expected2 = "0 1 days\ndtype: timedelta64[s]" + assert result2 == expected2 + + @pytest.mark.parametrize("method", ["__repr__", "__str__"]) + def test_representation(self, method): + idx1 = TimedeltaIndex([], freq="D") + idx2 = TimedeltaIndex(["1 days"], freq="D") + idx3 = TimedeltaIndex(["1 days", "2 days"], freq="D") + idx4 = TimedeltaIndex(["1 days", "2 days", "3 days"], freq="D") + idx5 = TimedeltaIndex(["1 days 00:00:01", "2 days", "3 days"]) + + exp1 = "TimedeltaIndex([], dtype='timedelta64[ns]', freq='D')" + + exp2 = "TimedeltaIndex(['1 days'], dtype='timedelta64[ns]', freq='D')" + + exp3 = "TimedeltaIndex(['1 days', '2 days'], dtype='timedelta64[ns]', freq='D')" + + exp4 = ( + "TimedeltaIndex(['1 days', '2 days', '3 days'], " + "dtype='timedelta64[ns]', freq='D')" + ) + + exp5 = ( + "TimedeltaIndex(['1 days 00:00:01', '2 days 00:00:00', " + "'3 days 00:00:00'], dtype='timedelta64[ns]', freq=None)" + ) + + with pd.option_context("display.width", 300): + for idx, expected in zip( + [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5] + ): + result = getattr(idx, method)() + assert result == expected + + # TODO: this is a Series.__repr__ test + def test_representation_to_series(self): + idx1 = TimedeltaIndex([], freq="D") + idx2 = TimedeltaIndex(["1 days"], freq="D") + idx3 = TimedeltaIndex(["1 days", "2 days"], freq="D") + idx4 = TimedeltaIndex(["1 days", "2 days", "3 days"], freq="D") + idx5 = TimedeltaIndex(["1 days 00:00:01", "2 days", "3 days"]) + + exp1 = """Series([], dtype: timedelta64[ns])""" + + exp2 = "0 1 days\ndtype: timedelta64[ns]" + + exp3 = "0 1 days\n1 2 days\ndtype: timedelta64[ns]" + + exp4 = "0 1 days\n1 2 days\n2 3 days\ndtype: timedelta64[ns]" + + exp5 = ( + "0 1 days 00:00:01\n" + "1 2 days 00:00:00\n" + "2 3 days 00:00:00\n" + "dtype: timedelta64[ns]" + ) + + with pd.option_context("display.width", 300): + for idx, expected in zip( + [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5] + ): + result = repr(Series(idx)) + assert result == expected + + def test_summary(self): + # GH#9116 + idx1 = TimedeltaIndex([], freq="D") + idx2 = TimedeltaIndex(["1 days"], freq="D") + idx3 = TimedeltaIndex(["1 days", "2 days"], freq="D") + idx4 = TimedeltaIndex(["1 days", "2 days", "3 days"], freq="D") + idx5 = TimedeltaIndex(["1 days 00:00:01", "2 days", "3 days"]) + + exp1 = "TimedeltaIndex: 0 entries\nFreq: D" + + exp2 = "TimedeltaIndex: 1 entries, 1 days to 1 days\nFreq: D" + + exp3 = "TimedeltaIndex: 2 entries, 1 days to 2 days\nFreq: D" + + exp4 = "TimedeltaIndex: 3 entries, 1 days to 3 days\nFreq: D" + + exp5 = "TimedeltaIndex: 3 entries, 1 days 00:00:01 to 3 days 00:00:00" + + for idx, expected in zip( + [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5] + ): + result = idx._summary() + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py new file mode 100644 index 0000000000000000000000000000000000000000..1912c49d3000fcbef45dd081213778bfb387e38e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py @@ -0,0 +1,72 @@ +import pytest + +from pandas import TimedeltaIndex + +from pandas.tseries.offsets import ( + DateOffset, + Day, + Hour, + MonthEnd, +) + + +class TestFreq: + @pytest.mark.parametrize("values", [["0 days", "2 days", "4 days"], []]) + @pytest.mark.parametrize("freq", ["2D", Day(2), "48h", Hour(48)]) + def test_freq_setter(self, values, freq): + # GH#20678 + idx = TimedeltaIndex(values) + + # can set to an offset, converting from string if necessary + idx._data.freq = freq + assert idx.freq == freq + assert isinstance(idx.freq, DateOffset) + + # can reset to None + idx._data.freq = None + assert idx.freq is None + + def test_with_freq_empty_requires_tick(self): + idx = TimedeltaIndex([]) + + off = MonthEnd(1) + msg = "TimedeltaArray/Index freq must be a Tick" + with pytest.raises(TypeError, match=msg): + idx._with_freq(off) + with pytest.raises(TypeError, match=msg): + idx._data._with_freq(off) + + def test_freq_setter_errors(self): + # GH#20678 + idx = TimedeltaIndex(["0 days", "2 days", "4 days"]) + + # setting with an incompatible freq + msg = ( + "Inferred frequency 2D from passed values does not conform to " + "passed frequency 5D" + ) + with pytest.raises(ValueError, match=msg): + idx._data.freq = "5D" + + # setting with a non-fixed frequency + msg = r"<2 \* BusinessDays> is a non-fixed frequency" + with pytest.raises(ValueError, match=msg): + idx._data.freq = "2B" + + # setting with non-freq string + with pytest.raises(ValueError, match="Invalid frequency"): + idx._data.freq = "foo" + + def test_freq_view_safe(self): + # Setting the freq for one TimedeltaIndex shouldn't alter the freq + # for another that views the same data + + tdi = TimedeltaIndex(["0 days", "2 days", "4 days"], freq="2D") + tda = tdi._data + + tdi2 = TimedeltaIndex(tda)._with_freq(None) + assert tdi2.freq is None + + # Original was not altered + assert tdi.freq == "2D" + assert tda.freq == "2D" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_indexing.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..397f9d9e183319f6df9335fbae7f8cb7401d6ac1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_indexing.py @@ -0,0 +1,347 @@ +from datetime import datetime +import re + +import numpy as np +import pytest + +from pandas import ( + Index, + NaT, + Timedelta, + TimedeltaIndex, + Timestamp, + notna, + offsets, + timedelta_range, + to_timedelta, +) +import pandas._testing as tm + + +class TestGetItem: + def test_getitem_slice_keeps_name(self): + # GH#4226 + tdi = timedelta_range("1d", "5d", freq="h", name="timebucket") + assert tdi[1:].name == tdi.name + + def test_getitem(self): + idx1 = timedelta_range("1 day", "31 day", freq="D", name="idx") + + for idx in [idx1]: + result = idx[0] + assert result == Timedelta("1 day") + + result = idx[0:5] + expected = timedelta_range("1 day", "5 day", freq="D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx[0:10:2] + expected = timedelta_range("1 day", "9 day", freq="2D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx[-20:-5:3] + expected = timedelta_range("12 day", "24 day", freq="3D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx[4::-1] + expected = TimedeltaIndex( + ["5 day", "4 day", "3 day", "2 day", "1 day"], freq="-1D", name="idx" + ) + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + @pytest.mark.parametrize( + "key", + [ + Timestamp("1970-01-01"), + Timestamp("1970-01-02"), + datetime(1970, 1, 1), + Timestamp("1970-01-03").to_datetime64(), + # non-matching NA values + np.datetime64("NaT"), + ], + ) + def test_timestamp_invalid_key(self, key): + # GH#20464 + tdi = timedelta_range(0, periods=10) + with pytest.raises(KeyError, match=re.escape(repr(key))): + tdi.get_loc(key) + + +class TestGetLoc: + def test_get_loc_key_unit_mismatch(self): + idx = to_timedelta(["0 days", "1 days", "2 days"]) + key = idx[1].as_unit("ms") + loc = idx.get_loc(key) + assert loc == 1 + + def test_get_loc_key_unit_mismatch_not_castable(self): + tdi = to_timedelta(["0 days", "1 days", "2 days"]).astype("m8[s]") + assert tdi.dtype == "m8[s]" + key = tdi[0].as_unit("ns") + Timedelta(1) + + with pytest.raises(KeyError, match=r"Timedelta\('0 days 00:00:00.000000001'\)"): + tdi.get_loc(key) + + assert key not in tdi + + def test_get_loc(self): + idx = to_timedelta(["0 days", "1 days", "2 days"]) + + # GH 16909 + assert idx.get_loc(idx[1].to_timedelta64()) == 1 + + # GH 16896 + assert idx.get_loc("0 days") == 0 + + def test_get_loc_nat(self): + tidx = TimedeltaIndex(["1 days 01:00:00", "NaT", "2 days 01:00:00"]) + + assert tidx.get_loc(NaT) == 1 + assert tidx.get_loc(None) == 1 + assert tidx.get_loc(float("nan")) == 1 + assert tidx.get_loc(np.nan) == 1 + + +class TestGetIndexer: + def test_get_indexer(self): + idx = to_timedelta(["0 days", "1 days", "2 days"]) + tm.assert_numpy_array_equal( + idx.get_indexer(idx), np.array([0, 1, 2], dtype=np.intp) + ) + + target = to_timedelta(["-1 hour", "12 hours", "1 day 1 hour"]) + tm.assert_numpy_array_equal( + idx.get_indexer(target, "pad"), np.array([-1, 0, 1], dtype=np.intp) + ) + tm.assert_numpy_array_equal( + idx.get_indexer(target, "backfill"), np.array([0, 1, 2], dtype=np.intp) + ) + tm.assert_numpy_array_equal( + idx.get_indexer(target, "nearest"), np.array([0, 1, 1], dtype=np.intp) + ) + + res = idx.get_indexer(target, "nearest", tolerance=Timedelta("1 hour")) + tm.assert_numpy_array_equal(res, np.array([0, -1, 1], dtype=np.intp)) + + +class TestWhere: + def test_where_doesnt_retain_freq(self): + tdi = timedelta_range("1 day", periods=3, freq="D", name="idx") + cond = [True, True, False] + expected = TimedeltaIndex([tdi[0], tdi[1], tdi[0]], freq=None, name="idx") + + result = tdi.where(cond, tdi[::-1]) + tm.assert_index_equal(result, expected) + + def test_where_invalid_dtypes(self, fixed_now_ts): + tdi = timedelta_range("1 day", periods=3, freq="D", name="idx") + + tail = tdi[2:].tolist() + i2 = Index([NaT, NaT] + tail) + mask = notna(i2) + + expected = Index([NaT._value, NaT._value] + tail, dtype=object, name="idx") + assert isinstance(expected[0], int) + result = tdi.where(mask, i2.asi8) + tm.assert_index_equal(result, expected) + + ts = i2 + fixed_now_ts + expected = Index([ts[0], ts[1]] + tail, dtype=object, name="idx") + result = tdi.where(mask, ts) + tm.assert_index_equal(result, expected) + + per = (i2 + fixed_now_ts).to_period("D") + expected = Index([per[0], per[1]] + tail, dtype=object, name="idx") + result = tdi.where(mask, per) + tm.assert_index_equal(result, expected) + + ts = fixed_now_ts + expected = Index([ts, ts] + tail, dtype=object, name="idx") + result = tdi.where(mask, ts) + tm.assert_index_equal(result, expected) + + def test_where_mismatched_nat(self): + tdi = timedelta_range("1 day", periods=3, freq="D", name="idx") + cond = np.array([True, False, False]) + + dtnat = np.datetime64("NaT", "ns") + expected = Index([tdi[0], dtnat, dtnat], dtype=object, name="idx") + assert expected[2] is dtnat + result = tdi.where(cond, dtnat) + tm.assert_index_equal(result, expected) + + +class TestTake: + def test_take(self): + # GH 10295 + idx1 = timedelta_range("1 day", "31 day", freq="D", name="idx") + + for idx in [idx1]: + result = idx.take([0]) + assert result == Timedelta("1 day") + + result = idx.take([-1]) + assert result == Timedelta("31 day") + + result = idx.take([0, 1, 2]) + expected = timedelta_range("1 day", "3 day", freq="D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx.take([0, 2, 4]) + expected = timedelta_range("1 day", "5 day", freq="2D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx.take([7, 4, 1]) + expected = timedelta_range("8 day", "2 day", freq="-3D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx.take([3, 2, 5]) + expected = TimedeltaIndex(["4 day", "3 day", "6 day"], name="idx") + tm.assert_index_equal(result, expected) + assert result.freq is None + + result = idx.take([-3, 2, 5]) + expected = TimedeltaIndex(["29 day", "3 day", "6 day"], name="idx") + tm.assert_index_equal(result, expected) + assert result.freq is None + + def test_take_invalid_kwargs(self): + idx = timedelta_range("1 day", "31 day", freq="D", name="idx") + indices = [1, 6, 5, 9, 10, 13, 15, 3] + + msg = r"take\(\) got an unexpected keyword argument 'foo'" + with pytest.raises(TypeError, match=msg): + idx.take(indices, foo=2) + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + idx.take(indices, out=indices) + + msg = "the 'mode' parameter is not supported" + with pytest.raises(ValueError, match=msg): + idx.take(indices, mode="clip") + + def test_take_equiv_getitem(self): + tds = ["1day 02:00:00", "1 day 04:00:00", "1 day 10:00:00"] + idx = timedelta_range(start="1d", end="2d", freq="h", name="idx") + expected = TimedeltaIndex(tds, freq=None, name="idx") + + taken1 = idx.take([2, 4, 10]) + taken2 = idx[[2, 4, 10]] + + for taken in [taken1, taken2]: + tm.assert_index_equal(taken, expected) + assert isinstance(taken, TimedeltaIndex) + assert taken.freq is None + assert taken.name == expected.name + + def test_take_fill_value(self): + # GH 12631 + idx = TimedeltaIndex(["1 days", "2 days", "3 days"], name="xxx") + result = idx.take(np.array([1, 0, -1])) + expected = TimedeltaIndex(["2 days", "1 days", "3 days"], name="xxx") + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = TimedeltaIndex(["2 days", "1 days", "NaT"], name="xxx") + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) + expected = TimedeltaIndex(["2 days", "1 days", "3 days"], name="xxx") + tm.assert_index_equal(result, expected) + + msg = ( + "When allow_fill=True and fill_value is not None, " + "all indices must be >= -1" + ) + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + msg = "index -5 is out of bounds for (axis 0 with )?size 3" + with pytest.raises(IndexError, match=msg): + idx.take(np.array([1, -5])) + + +class TestMaybeCastSliceBound: + @pytest.fixture(params=["increasing", "decreasing", None]) + def monotonic(self, request): + return request.param + + @pytest.fixture + def tdi(self, monotonic): + tdi = timedelta_range("1 Day", periods=10) + if monotonic == "decreasing": + tdi = tdi[::-1] + elif monotonic is None: + taker = np.arange(10, dtype=np.intp) + np.random.default_rng(2).shuffle(taker) + tdi = tdi.take(taker) + return tdi + + def test_maybe_cast_slice_bound_invalid_str(self, tdi): + # test the low-level _maybe_cast_slice_bound and that we get the + # expected exception+message all the way up the stack + msg = ( + "cannot do slice indexing on TimedeltaIndex with these " + r"indexers \[foo\] of type str" + ) + with pytest.raises(TypeError, match=msg): + tdi._maybe_cast_slice_bound("foo", side="left") + with pytest.raises(TypeError, match=msg): + tdi.get_slice_bound("foo", side="left") + with pytest.raises(TypeError, match=msg): + tdi.slice_locs("foo", None, None) + + def test_slice_invalid_str_with_timedeltaindex( + self, tdi, frame_or_series, indexer_sl + ): + obj = frame_or_series(range(10), index=tdi) + + msg = ( + "cannot do slice indexing on TimedeltaIndex with these " + r"indexers \[foo\] of type str" + ) + with pytest.raises(TypeError, match=msg): + indexer_sl(obj)["foo":] + with pytest.raises(TypeError, match=msg): + indexer_sl(obj)["foo":-1] + with pytest.raises(TypeError, match=msg): + indexer_sl(obj)[:"foo"] + with pytest.raises(TypeError, match=msg): + indexer_sl(obj)[tdi[0] : "foo"] + + +class TestContains: + def test_contains_nonunique(self): + # GH#9512 + for vals in ( + [0, 1, 0], + [0, 0, -1], + [0, -1, -1], + ["00:01:00", "00:01:00", "00:02:00"], + ["00:01:00", "00:01:00", "00:00:01"], + ): + idx = TimedeltaIndex(vals) + assert idx[0] in idx + + def test_contains(self): + # Checking for any NaT-like objects + # GH#13603 + td = to_timedelta(range(5), unit="d") + offsets.Hour(1) + for v in [NaT, None, float("nan"), np.nan]: + assert v not in td + + td = to_timedelta([NaT]) + for v in [NaT, None, float("nan"), np.nan]: + assert v in td diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_join.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_join.py new file mode 100644 index 0000000000000000000000000000000000000000..cbd7a5de71b10a6004cd7a3f798fecd8e7631750 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_join.py @@ -0,0 +1,47 @@ +import numpy as np + +from pandas import ( + DataFrame, + Index, + Timedelta, + timedelta_range, +) +import pandas._testing as tm + + +class TestJoin: + def test_append_join_nondatetimeindex(self): + rng = timedelta_range("1 days", periods=10) + idx = Index(["a", "b", "c", "d"]) + + result = rng.append(idx) + assert isinstance(result[0], Timedelta) + + # it works + rng.join(idx, how="outer") + + def test_join_self(self, join_type): + index = timedelta_range("1 day", periods=10) + joined = index.join(index, how=join_type) + tm.assert_index_equal(index, joined) + + def test_does_not_convert_mixed_integer(self): + df = DataFrame(np.ones((5, 5)), columns=timedelta_range("1 day", periods=5)) + + cols = df.columns.join(df.index, how="outer") + joined = cols.join(df.columns) + assert cols.dtype == np.dtype("O") + assert cols.dtype == joined.dtype + tm.assert_index_equal(cols, joined) + + def test_join_preserves_freq(self): + # GH#32157 + tdi = timedelta_range("1 day", periods=10) + result = tdi[:5].join(tdi[5:], how="outer") + assert result.freq == tdi.freq + tm.assert_index_equal(result, tdi) + + result = tdi[:5].join(tdi[6:], how="outer") + assert result.freq is None + expected = tdi.delete(5) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_ops.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..f6013baf86edcd566a17cd3127467a7443ac475a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_ops.py @@ -0,0 +1,14 @@ +from pandas import ( + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexOps: + def test_infer_freq(self, freq_sample): + # GH#11018 + idx = timedelta_range("1", freq=freq_sample, periods=10) + result = TimedeltaIndex(idx.asi8, freq="infer") + tm.assert_index_equal(idx, result) + assert result.freq == freq_sample diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_pickle.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_pickle.py new file mode 100644 index 0000000000000000000000000000000000000000..befe709728bdd4e9fac3c626f4e33986d671c86d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_pickle.py @@ -0,0 +1,11 @@ +from pandas import timedelta_range +import pandas._testing as tm + + +class TestPickle: + def test_pickle_after_set_freq(self): + tdi = timedelta_range("1 day", periods=4, freq="s") + tdi = tdi._with_freq(None) + + res = tm.round_trip_pickle(tdi) + tm.assert_index_equal(res, tdi) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_scalar_compat.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_scalar_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..9f0552f8baa901addaae9b4ca0890f6edb272715 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_scalar_compat.py @@ -0,0 +1,142 @@ +""" +Tests for TimedeltaIndex methods behaving like their Timedelta counterparts +""" + +import numpy as np +import pytest + +from pandas._libs.tslibs.offsets import INVALID_FREQ_ERR_MSG + +from pandas import ( + Index, + Series, + Timedelta, + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestVectorizedTimedelta: + def test_tdi_total_seconds(self): + # GH#10939 + # test index + rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s") + expt = [ + 1 * 86400 + 10 * 3600 + 11 * 60 + 12 + 100123456.0 / 1e9, + 1 * 86400 + 10 * 3600 + 11 * 60 + 13 + 100123456.0 / 1e9, + ] + tm.assert_almost_equal(rng.total_seconds(), Index(expt)) + + # test Series + ser = Series(rng) + s_expt = Series(expt, index=[0, 1]) + tm.assert_series_equal(ser.dt.total_seconds(), s_expt) + + # with nat + ser[1] = np.nan + s_expt = Series( + [1 * 86400 + 10 * 3600 + 11 * 60 + 12 + 100123456.0 / 1e9, np.nan], + index=[0, 1], + ) + tm.assert_series_equal(ser.dt.total_seconds(), s_expt) + + def test_tdi_total_seconds_all_nat(self): + # with both nat + ser = Series([np.nan, np.nan], dtype="timedelta64[ns]") + result = ser.dt.total_seconds() + expected = Series([np.nan, np.nan]) + tm.assert_series_equal(result, expected) + + def test_tdi_round(self): + td = timedelta_range(start="16801 days", periods=5, freq="30Min") + elt = td[1] + + expected_rng = TimedeltaIndex( + [ + Timedelta("16801 days 00:00:00"), + Timedelta("16801 days 00:00:00"), + Timedelta("16801 days 01:00:00"), + Timedelta("16801 days 02:00:00"), + Timedelta("16801 days 02:00:00"), + ] + ) + expected_elt = expected_rng[1] + + tm.assert_index_equal(td.round(freq="h"), expected_rng) + assert elt.round(freq="h") == expected_elt + + msg = INVALID_FREQ_ERR_MSG + with pytest.raises(ValueError, match=msg): + td.round(freq="foo") + with pytest.raises(ValueError, match=msg): + elt.round(freq="foo") + + msg = " is a non-fixed frequency" + with pytest.raises(ValueError, match=msg): + td.round(freq="ME") + with pytest.raises(ValueError, match=msg): + elt.round(freq="ME") + + @pytest.mark.parametrize( + "freq,msg", + [ + ("YE", " is a non-fixed frequency"), + ("ME", " is a non-fixed frequency"), + ("foobar", "Invalid frequency: foobar"), + ], + ) + def test_tdi_round_invalid(self, freq, msg): + t1 = timedelta_range("1 days", periods=3, freq="1 min 2 s 3 us") + + with pytest.raises(ValueError, match=msg): + t1.round(freq) + with pytest.raises(ValueError, match=msg): + # Same test for TimedeltaArray + t1._data.round(freq) + + # TODO: de-duplicate with test_tdi_round + def test_round(self): + t1 = timedelta_range("1 days", periods=3, freq="1 min 2 s 3 us") + t2 = -1 * t1 + t1a = timedelta_range("1 days", periods=3, freq="1 min 2 s") + t1c = TimedeltaIndex(np.array([1, 1, 1], "m8[D]")).as_unit("ns") + + # note that negative times round DOWN! so don't give whole numbers + for freq, s1, s2 in [ + ("ns", t1, t2), + ("us", t1, t2), + ( + "ms", + t1a, + TimedeltaIndex( + ["-1 days +00:00:00", "-2 days +23:58:58", "-2 days +23:57:56"] + ), + ), + ( + "s", + t1a, + TimedeltaIndex( + ["-1 days +00:00:00", "-2 days +23:58:58", "-2 days +23:57:56"] + ), + ), + ("12min", t1c, TimedeltaIndex(["-1 days", "-1 days", "-1 days"])), + ("h", t1c, TimedeltaIndex(["-1 days", "-1 days", "-1 days"])), + ("d", t1c, -1 * t1c), + ]: + r1 = t1.round(freq) + tm.assert_index_equal(r1, s1) + r2 = t2.round(freq) + tm.assert_index_equal(r2, s2) + + def test_components(self): + rng = timedelta_range("1 days, 10:11:12", periods=2, freq="s") + rng.components + + # with nat + s = Series(rng) + s[1] = np.nan + + result = s.dt.components + assert not result.iloc[0].isna().all() + assert result.iloc[1].isna().all() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_searchsorted.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_searchsorted.py new file mode 100644 index 0000000000000000000000000000000000000000..710571ef383970097985f44e09ecba77fcf63f74 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_searchsorted.py @@ -0,0 +1,28 @@ +import numpy as np +import pytest + +from pandas import ( + TimedeltaIndex, + Timestamp, +) +import pandas._testing as tm + + +class TestSearchSorted: + def test_searchsorted_different_argument_classes(self, listlike_box): + idx = TimedeltaIndex(["1 day", "2 days", "3 days"]) + result = idx.searchsorted(listlike_box(idx)) + expected = np.arange(len(idx), dtype=result.dtype) + tm.assert_numpy_array_equal(result, expected) + + result = idx._data.searchsorted(listlike_box(idx)) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize( + "arg", [[1, 2], ["a", "b"], [Timestamp("2020-01-01", tz="Europe/London")] * 2] + ) + def test_searchsorted_invalid_argument_dtype(self, arg): + idx = TimedeltaIndex(["1 day", "2 days", "3 days"]) + msg = "value should be a 'Timedelta', 'NaT', or array of those. Got" + with pytest.raises(TypeError, match=msg): + idx.searchsorted(arg) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_setops.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_setops.py new file mode 100644 index 0000000000000000000000000000000000000000..fce10d9176d7438e63a5e46eede1bb96b41bb8bd --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_setops.py @@ -0,0 +1,254 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + Index, + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + +from pandas.tseries.offsets import Hour + + +class TestTimedeltaIndex: + def test_union(self): + i1 = timedelta_range("1day", periods=5) + i2 = timedelta_range("3day", periods=5) + result = i1.union(i2) + expected = timedelta_range("1day", periods=7) + tm.assert_index_equal(result, expected) + + i1 = Index(np.arange(0, 20, 2, dtype=np.int64)) + i2 = timedelta_range(start="1 day", periods=10, freq="D") + i1.union(i2) # Works + i2.union(i1) # Fails with "AttributeError: can't set attribute" + + def test_union_sort_false(self): + tdi = timedelta_range("1day", periods=5) + + left = tdi[3:] + right = tdi[:3] + + # Check that we are testing the desired code path + assert left._can_fast_union(right) + + result = left.union(right) + tm.assert_index_equal(result, tdi) + + result = left.union(right, sort=False) + expected = TimedeltaIndex(["4 Days", "5 Days", "1 Days", "2 Day", "3 Days"]) + tm.assert_index_equal(result, expected) + + def test_union_coverage(self): + idx = TimedeltaIndex(["3d", "1d", "2d"]) + ordered = TimedeltaIndex(idx.sort_values(), freq="infer") + result = ordered.union(idx) + tm.assert_index_equal(result, ordered) + + result = ordered[:0].union(ordered) + tm.assert_index_equal(result, ordered) + assert result.freq == ordered.freq + + def test_union_bug_1730(self): + rng_a = timedelta_range("1 day", periods=4, freq="3h") + rng_b = timedelta_range("1 day", periods=4, freq="4h") + + result = rng_a.union(rng_b) + exp = TimedeltaIndex(sorted(set(rng_a) | set(rng_b))) + tm.assert_index_equal(result, exp) + + def test_union_bug_1745(self): + left = TimedeltaIndex(["1 day 15:19:49.695000"]) + right = TimedeltaIndex( + ["2 day 13:04:21.322000", "1 day 15:27:24.873000", "1 day 15:31:05.350000"] + ) + + result = left.union(right) + exp = TimedeltaIndex(sorted(set(left) | set(right))) + tm.assert_index_equal(result, exp) + + def test_union_bug_4564(self): + left = timedelta_range("1 day", "30d") + right = left + pd.offsets.Minute(15) + + result = left.union(right) + exp = TimedeltaIndex(sorted(set(left) | set(right))) + tm.assert_index_equal(result, exp) + + def test_union_freq_infer(self): + # When taking the union of two TimedeltaIndexes, we infer + # a freq even if the arguments don't have freq. This matches + # DatetimeIndex behavior. + tdi = timedelta_range("1 Day", periods=5) + left = tdi[[0, 1, 3, 4]] + right = tdi[[2, 3, 1]] + + assert left.freq is None + assert right.freq is None + + result = left.union(right) + tm.assert_index_equal(result, tdi) + assert result.freq == "D" + + def test_intersection_bug_1708(self): + index_1 = timedelta_range("1 day", periods=4, freq="h") + index_2 = index_1 + pd.offsets.Hour(5) + + result = index_1.intersection(index_2) + assert len(result) == 0 + + index_1 = timedelta_range("1 day", periods=4, freq="h") + index_2 = index_1 + pd.offsets.Hour(1) + + result = index_1.intersection(index_2) + expected = timedelta_range("1 day 01:00:00", periods=3, freq="h") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + def test_intersection_equal(self, sort): + # GH 24471 Test intersection outcome given the sort keyword + # for equal indices intersection should return the original index + first = timedelta_range("1 day", periods=4, freq="h") + second = timedelta_range("1 day", periods=4, freq="h") + intersect = first.intersection(second, sort=sort) + if sort is None: + tm.assert_index_equal(intersect, second.sort_values()) + tm.assert_index_equal(intersect, second) + + # Corner cases + inter = first.intersection(first, sort=sort) + assert inter is first + + @pytest.mark.parametrize("period_1, period_2", [(0, 4), (4, 0)]) + def test_intersection_zero_length(self, period_1, period_2, sort): + # GH 24471 test for non overlap the intersection should be zero length + index_1 = timedelta_range("1 day", periods=period_1, freq="h") + index_2 = timedelta_range("1 day", periods=period_2, freq="h") + expected = timedelta_range("1 day", periods=0, freq="h") + result = index_1.intersection(index_2, sort=sort) + tm.assert_index_equal(result, expected) + + def test_zero_length_input_index(self, sort): + # GH 24966 test for 0-len intersections are copied + index_1 = timedelta_range("1 day", periods=0, freq="h") + index_2 = timedelta_range("1 day", periods=3, freq="h") + result = index_1.intersection(index_2, sort=sort) + assert index_1 is not result + assert index_2 is not result + tm.assert_copy(result, index_1) + + @pytest.mark.parametrize( + "rng, expected", + # if target has the same name, it is preserved + [ + ( + timedelta_range("1 day", periods=5, freq="h", name="idx"), + timedelta_range("1 day", periods=4, freq="h", name="idx"), + ), + # if target name is different, it will be reset + ( + timedelta_range("1 day", periods=5, freq="h", name="other"), + timedelta_range("1 day", periods=4, freq="h", name=None), + ), + # if no overlap exists return empty index + ( + timedelta_range("1 day", periods=10, freq="h", name="idx")[5:], + TimedeltaIndex([], freq="h", name="idx"), + ), + ], + ) + def test_intersection(self, rng, expected, sort): + # GH 4690 (with tz) + base = timedelta_range("1 day", periods=4, freq="h", name="idx") + result = base.intersection(rng, sort=sort) + if sort is None: + expected = expected.sort_values() + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + @pytest.mark.parametrize( + "rng, expected", + # part intersection works + [ + ( + TimedeltaIndex(["5 hour", "2 hour", "4 hour", "9 hour"], name="idx"), + TimedeltaIndex(["2 hour", "4 hour"], name="idx"), + ), + # reordered part intersection + ( + TimedeltaIndex(["2 hour", "5 hour", "5 hour", "1 hour"], name="other"), + TimedeltaIndex(["1 hour", "2 hour"], name=None), + ), + # reversed index + ( + TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx")[ + ::-1 + ], + TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx"), + ), + ], + ) + def test_intersection_non_monotonic(self, rng, expected, sort): + # 24471 non-monotonic + base = TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx") + result = base.intersection(rng, sort=sort) + if sort is None: + expected = expected.sort_values() + tm.assert_index_equal(result, expected) + assert result.name == expected.name + + # if reversed order, frequency is still the same + if all(base == rng[::-1]) and sort is None: + assert isinstance(result.freq, Hour) + else: + assert result.freq is None + + +class TestTimedeltaIndexDifference: + def test_difference_freq(self, sort): + # GH14323: Difference of TimedeltaIndex should not preserve frequency + + index = timedelta_range("0 days", "5 days", freq="D") + + other = timedelta_range("1 days", "4 days", freq="D") + expected = TimedeltaIndex(["0 days", "5 days"], freq=None) + idx_diff = index.difference(other, sort) + tm.assert_index_equal(idx_diff, expected) + tm.assert_attr_equal("freq", idx_diff, expected) + + # preserve frequency when the difference is a contiguous + # subset of the original range + other = timedelta_range("2 days", "5 days", freq="D") + idx_diff = index.difference(other, sort) + expected = TimedeltaIndex(["0 days", "1 days"], freq="D") + tm.assert_index_equal(idx_diff, expected) + tm.assert_attr_equal("freq", idx_diff, expected) + + def test_difference_sort(self, sort): + index = TimedeltaIndex( + ["5 days", "3 days", "2 days", "4 days", "1 days", "0 days"] + ) + + other = timedelta_range("1 days", "4 days", freq="D") + idx_diff = index.difference(other, sort) + + expected = TimedeltaIndex(["5 days", "0 days"], freq=None) + + if sort is None: + expected = expected.sort_values() + + tm.assert_index_equal(idx_diff, expected) + tm.assert_attr_equal("freq", idx_diff, expected) + + other = timedelta_range("2 days", "5 days", freq="D") + idx_diff = index.difference(other, sort) + expected = TimedeltaIndex(["1 days", "0 days"], freq=None) + + if sort is None: + expected = expected.sort_values() + + tm.assert_index_equal(idx_diff, expected) + tm.assert_attr_equal("freq", idx_diff, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py new file mode 100644 index 0000000000000000000000000000000000000000..3120066741ffa292dc1533056438ebf481cb1849 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py @@ -0,0 +1,61 @@ +import numpy as np +import pytest + +from pandas import ( + Index, + Series, + Timedelta, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndex: + def test_misc_coverage(self): + rng = timedelta_range("1 day", periods=5) + result = rng.groupby(rng.days) + assert isinstance(next(iter(result.values()))[0], Timedelta) + + def test_map(self): + # test_map_dictlike generally tests + + rng = timedelta_range("1 day", periods=10) + + f = lambda x: x.days + result = rng.map(f) + exp = Index([f(x) for x in rng], dtype=np.int64) + tm.assert_index_equal(result, exp) + + def test_fields(self): + rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s") + tm.assert_index_equal(rng.days, Index([1, 1], dtype=np.int64)) + tm.assert_index_equal( + rng.seconds, + Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype=np.int32), + ) + tm.assert_index_equal( + rng.microseconds, + Index([100 * 1000 + 123, 100 * 1000 + 123], dtype=np.int32), + ) + tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype=np.int32)) + + msg = "'TimedeltaIndex' object has no attribute '{}'" + with pytest.raises(AttributeError, match=msg.format("hours")): + rng.hours + with pytest.raises(AttributeError, match=msg.format("minutes")): + rng.minutes + with pytest.raises(AttributeError, match=msg.format("milliseconds")): + rng.milliseconds + + # with nat + s = Series(rng) + s[1] = np.nan + + tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=[0, 1])) + tm.assert_series_equal( + s.dt.seconds, Series([10 * 3600 + 11 * 60 + 12, np.nan], index=[0, 1]) + ) + + # preserve name (GH15589) + rng.name = "name" + assert rng.days.name == "name" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta_range.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta_range.py new file mode 100644 index 0000000000000000000000000000000000000000..f22bdb7a90516a7162ebdb1cc2d8cbfd9531b9e7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/indexes/timedeltas/test_timedelta_range.py @@ -0,0 +1,173 @@ +import numpy as np +import pytest + +from pandas import ( + Timedelta, + TimedeltaIndex, + timedelta_range, + to_timedelta, +) +import pandas._testing as tm + +from pandas.tseries.offsets import ( + Day, + Second, +) + + +class TestTimedeltas: + def test_timedelta_range_unit(self): + # GH#49824 + tdi = timedelta_range("0 Days", periods=10, freq="100000D", unit="s") + exp_arr = (np.arange(10, dtype="i8") * 100_000).view("m8[D]").astype("m8[s]") + tm.assert_numpy_array_equal(tdi.to_numpy(), exp_arr) + + def test_timedelta_range(self): + expected = to_timedelta(np.arange(5), unit="D") + result = timedelta_range("0 days", periods=5, freq="D") + tm.assert_index_equal(result, expected) + + expected = to_timedelta(np.arange(11), unit="D") + result = timedelta_range("0 days", "10 days", freq="D") + tm.assert_index_equal(result, expected) + + expected = to_timedelta(np.arange(5), unit="D") + Second(2) + Day() + result = timedelta_range("1 days, 00:00:02", "5 days, 00:00:02", freq="D") + tm.assert_index_equal(result, expected) + + expected = to_timedelta([1, 3, 5, 7, 9], unit="D") + Second(2) + result = timedelta_range("1 days, 00:00:02", periods=5, freq="2D") + tm.assert_index_equal(result, expected) + + expected = to_timedelta(np.arange(50), unit="min") * 30 + result = timedelta_range("0 days", freq="30min", periods=50) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "depr_unit, unit", + [ + ("H", "hour"), + ("T", "minute"), + ("t", "minute"), + ("S", "second"), + ("L", "millisecond"), + ("l", "millisecond"), + ("U", "microsecond"), + ("u", "microsecond"), + ("N", "nanosecond"), + ("n", "nanosecond"), + ], + ) + def test_timedelta_units_H_T_S_L_U_N_deprecated(self, depr_unit, unit): + # GH#52536 + depr_msg = ( + f"'{depr_unit}' is deprecated and will be removed in a future version." + ) + + expected = to_timedelta(np.arange(5), unit=unit) + with tm.assert_produces_warning(FutureWarning, match=depr_msg): + result = to_timedelta(np.arange(5), unit=depr_unit) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "periods, freq", [(3, "2D"), (5, "D"), (6, "19h12min"), (7, "16h"), (9, "12h")] + ) + def test_linspace_behavior(self, periods, freq): + # GH 20976 + result = timedelta_range(start="0 days", end="4 days", periods=periods) + expected = timedelta_range(start="0 days", end="4 days", freq=freq) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("msg_freq, freq", [("H", "19H12min"), ("T", "19h12T")]) + def test_timedelta_range_H_T_deprecated(self, freq, msg_freq): + # GH#52536 + msg = f"'{msg_freq}' is deprecated and will be removed in a future version." + + result = timedelta_range(start="0 days", end="4 days", periods=6) + with tm.assert_produces_warning(FutureWarning, match=msg): + expected = timedelta_range(start="0 days", end="4 days", freq=freq) + tm.assert_index_equal(result, expected) + + def test_errors(self): + # not enough params + msg = ( + "Of the four parameters: start, end, periods, and freq, " + "exactly three must be specified" + ) + with pytest.raises(ValueError, match=msg): + timedelta_range(start="0 days") + + with pytest.raises(ValueError, match=msg): + timedelta_range(end="5 days") + + with pytest.raises(ValueError, match=msg): + timedelta_range(periods=2) + + with pytest.raises(ValueError, match=msg): + timedelta_range() + + # too many params + with pytest.raises(ValueError, match=msg): + timedelta_range(start="0 days", end="5 days", periods=10, freq="h") + + @pytest.mark.parametrize( + "start, end, freq, expected_periods", + [ + ("1D", "10D", "2D", (10 - 1) // 2 + 1), + ("2D", "30D", "3D", (30 - 2) // 3 + 1), + ("2s", "50s", "5s", (50 - 2) // 5 + 1), + # tests that worked before GH 33498: + ("4D", "16D", "3D", (16 - 4) // 3 + 1), + ("8D", "16D", "40s", (16 * 3600 * 24 - 8 * 3600 * 24) // 40 + 1), + ], + ) + def test_timedelta_range_freq_divide_end(self, start, end, freq, expected_periods): + # GH 33498 only the cases where `(end % freq) == 0` used to fail + res = timedelta_range(start=start, end=end, freq=freq) + assert Timedelta(start) == res[0] + assert Timedelta(end) >= res[-1] + assert len(res) == expected_periods + + def test_timedelta_range_infer_freq(self): + # https://github.com/pandas-dev/pandas/issues/35897 + result = timedelta_range("0s", "1s", periods=31) + assert result.freq is None + + @pytest.mark.parametrize( + "freq_depr, start, end, expected_values, expected_freq", + [ + ( + "3.5S", + "05:03:01", + "05:03:10", + ["0 days 05:03:01", "0 days 05:03:04.500000", "0 days 05:03:08"], + "3500ms", + ), + ( + "2.5T", + "5 hours", + "5 hours 8 minutes", + [ + "0 days 05:00:00", + "0 days 05:02:30", + "0 days 05:05:00", + "0 days 05:07:30", + ], + "150s", + ), + ], + ) + def test_timedelta_range_deprecated_freq( + self, freq_depr, start, end, expected_values, expected_freq + ): + # GH#52536 + msg = ( + f"'{freq_depr[-1]}' is deprecated and will be removed in a future version." + ) + + with tm.assert_produces_warning(FutureWarning, match=msg): + result = timedelta_range(start=start, end=end, freq=freq_depr) + expected = TimedeltaIndex( + expected_values, dtype="timedelta64[ns]", freq=expected_freq + ) + tm.assert_index_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/conftest.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..a5ddda9d66e7af4a418a65650f1f44ae35bec4a1 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/conftest.py @@ -0,0 +1,225 @@ +import shlex +import subprocess +import time +import uuid + +import pytest + +from pandas.compat import ( + is_ci_environment, + is_platform_arm, + is_platform_mac, + is_platform_windows, +) +import pandas.util._test_decorators as td + +import pandas.io.common as icom +from pandas.io.parsers import read_csv + + +@pytest.fixture +def compression_to_extension(): + return {value: key for key, value in icom.extension_to_compression.items()} + + +@pytest.fixture +def tips_file(datapath): + """Path to the tips dataset""" + return datapath("io", "data", "csv", "tips.csv") + + +@pytest.fixture +def jsonl_file(datapath): + """Path to a JSONL dataset""" + return datapath("io", "parser", "data", "items.jsonl") + + +@pytest.fixture +def salaries_table(datapath): + """DataFrame with the salaries dataset""" + return read_csv(datapath("io", "parser", "data", "salaries.csv"), sep="\t") + + +@pytest.fixture +def feather_file(datapath): + return datapath("io", "data", "feather", "feather-0_3_1.feather") + + +@pytest.fixture +def xml_file(datapath): + return datapath("io", "data", "xml", "books.xml") + + +@pytest.fixture +def s3_base(worker_id, monkeypatch): + """ + Fixture for mocking S3 interaction. + + Sets up moto server in separate process locally + Return url for motoserver/moto CI service + """ + pytest.importorskip("s3fs") + pytest.importorskip("boto3") + + # temporary workaround as moto fails for botocore >= 1.11 otherwise, + # see https://github.com/spulec/moto/issues/1924 & 1952 + monkeypatch.setenv("AWS_ACCESS_KEY_ID", "foobar_key") + monkeypatch.setenv("AWS_SECRET_ACCESS_KEY", "foobar_secret") + if is_ci_environment(): + if is_platform_arm() or is_platform_mac() or is_platform_windows(): + # NOT RUN on Windows/macOS, only Ubuntu + # - subprocess in CI can cause timeouts + # - GitHub Actions do not support + # container services for the above OSs + pytest.skip( + "S3 tests do not have a corresponding service on " + "Windows or macOS platforms" + ) + else: + # set in .github/workflows/unit-tests.yml + yield "http://localhost:5000" + else: + requests = pytest.importorskip("requests") + pytest.importorskip("moto") + pytest.importorskip("flask") # server mode needs flask too + + # Launching moto in server mode, i.e., as a separate process + # with an S3 endpoint on localhost + + worker_id = "5" if worker_id == "master" else worker_id.lstrip("gw") + endpoint_port = f"555{worker_id}" + endpoint_uri = f"http://127.0.0.1:{endpoint_port}/" + + # pipe to null to avoid logging in terminal + with subprocess.Popen( + shlex.split(f"moto_server s3 -p {endpoint_port}"), + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) as proc: + timeout = 5 + while timeout > 0: + try: + # OK to go once server is accepting connections + r = requests.get(endpoint_uri) + if r.ok: + break + except Exception: + pass + timeout -= 0.1 + time.sleep(0.1) + yield endpoint_uri + + proc.terminate() + + +@pytest.fixture +def s3so(s3_base): + return {"client_kwargs": {"endpoint_url": s3_base}} + + +@pytest.fixture +def s3_resource(s3_base): + import boto3 + + s3 = boto3.resource("s3", endpoint_url=s3_base) + return s3 + + +@pytest.fixture +def s3_public_bucket(s3_resource): + bucket = s3_resource.Bucket(f"pandas-test-{uuid.uuid4()}") + bucket.create() + yield bucket + bucket.objects.delete() + bucket.delete() + + +@pytest.fixture +def s3_public_bucket_with_data( + s3_public_bucket, tips_file, jsonl_file, feather_file, xml_file +): + """ + The following datasets + are loaded. + + - tips.csv + - tips.csv.gz + - tips.csv.bz2 + - items.jsonl + """ + test_s3_files = [ + ("tips#1.csv", tips_file), + ("tips.csv", tips_file), + ("tips.csv.gz", tips_file + ".gz"), + ("tips.csv.bz2", tips_file + ".bz2"), + ("items.jsonl", jsonl_file), + ("simple_dataset.feather", feather_file), + ("books.xml", xml_file), + ] + for s3_key, file_name in test_s3_files: + with open(file_name, "rb") as f: + s3_public_bucket.put_object(Key=s3_key, Body=f) + return s3_public_bucket + + +@pytest.fixture +def s3_private_bucket(s3_resource): + bucket = s3_resource.Bucket(f"cant_get_it-{uuid.uuid4()}") + bucket.create(ACL="private") + yield bucket + bucket.objects.delete() + bucket.delete() + + +@pytest.fixture +def s3_private_bucket_with_data( + s3_private_bucket, tips_file, jsonl_file, feather_file, xml_file +): + """ + The following datasets + are loaded. + + - tips.csv + - tips.csv.gz + - tips.csv.bz2 + - items.jsonl + """ + test_s3_files = [ + ("tips#1.csv", tips_file), + ("tips.csv", tips_file), + ("tips.csv.gz", tips_file + ".gz"), + ("tips.csv.bz2", tips_file + ".bz2"), + ("items.jsonl", jsonl_file), + ("simple_dataset.feather", feather_file), + ("books.xml", xml_file), + ] + for s3_key, file_name in test_s3_files: + with open(file_name, "rb") as f: + s3_private_bucket.put_object(Key=s3_key, Body=f) + return s3_private_bucket + + +_compression_formats_params = [ + (".no_compress", None), + ("", None), + (".gz", "gzip"), + (".GZ", "gzip"), + (".bz2", "bz2"), + (".BZ2", "bz2"), + (".zip", "zip"), + (".ZIP", "zip"), + (".xz", "xz"), + (".XZ", "xz"), + pytest.param((".zst", "zstd"), marks=td.skip_if_no("zstandard")), + pytest.param((".ZST", "zstd"), marks=td.skip_if_no("zstandard")), +] + + +@pytest.fixture(params=_compression_formats_params[1:]) +def compression_format(request): + return request.param + + +@pytest.fixture(params=_compression_formats_params) +def compression_ext(request): + return request.param[0] diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_odf.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_odf.py new file mode 100644 index 0000000000000000000000000000000000000000..b5bb9b27258d86cda6e44aeae17a4cdba4157a43 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_odf.py @@ -0,0 +1,77 @@ +import functools + +import numpy as np +import pytest + +from pandas.compat import is_platform_windows + +import pandas as pd +import pandas._testing as tm + +pytest.importorskip("odf") + +if is_platform_windows(): + pytestmark = pytest.mark.single_cpu + + +@pytest.fixture(autouse=True) +def cd_and_set_engine(monkeypatch, datapath): + func = functools.partial(pd.read_excel, engine="odf") + monkeypatch.setattr(pd, "read_excel", func) + monkeypatch.chdir(datapath("io", "data", "excel")) + + +def test_read_invalid_types_raises(): + # the invalid_value_type.ods required manually editing + # of the included content.xml file + with pytest.raises(ValueError, match="Unrecognized type awesome_new_type"): + pd.read_excel("invalid_value_type.ods") + + +def test_read_writer_table(): + # Also test reading tables from an text OpenDocument file + # (.odt) + index = pd.Index(["Row 1", "Row 2", "Row 3"], name="Header") + expected = pd.DataFrame( + [[1, np.nan, 7], [2, np.nan, 8], [3, np.nan, 9]], + index=index, + columns=["Column 1", "Unnamed: 2", "Column 3"], + ) + + result = pd.read_excel("writertable.odt", sheet_name="Table1", index_col=0) + + tm.assert_frame_equal(result, expected) + + +def test_read_newlines_between_xml_elements_table(): + # GH#45598 + expected = pd.DataFrame( + [[1.0, 4.0, 7], [np.nan, np.nan, 8], [3.0, 6.0, 9]], + columns=["Column 1", "Column 2", "Column 3"], + ) + + result = pd.read_excel("test_newlines.ods") + + tm.assert_frame_equal(result, expected) + + +def test_read_unempty_cells(): + expected = pd.DataFrame( + [1, np.nan, 3, np.nan, 5], + columns=["Column 1"], + ) + + result = pd.read_excel("test_unempty_cells.ods") + + tm.assert_frame_equal(result, expected) + + +def test_read_cell_annotation(): + expected = pd.DataFrame( + ["test", np.nan, "test 3"], + columns=["Column 1"], + ) + + result = pd.read_excel("test_cell_annotation.ods") + + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_odswriter.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_odswriter.py new file mode 100644 index 0000000000000000000000000000000000000000..1c728ad801bc139c1ca1cd2e902884a5a2c91ffc --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_odswriter.py @@ -0,0 +1,106 @@ +from datetime import ( + date, + datetime, +) +import re + +import pytest + +from pandas.compat import is_platform_windows + +import pandas as pd +import pandas._testing as tm + +from pandas.io.excel import ExcelWriter + +odf = pytest.importorskip("odf") + +if is_platform_windows(): + pytestmark = pytest.mark.single_cpu + + +@pytest.fixture +def ext(): + return ".ods" + + +def test_write_append_mode_raises(ext): + msg = "Append mode is not supported with odf!" + + with tm.ensure_clean(ext) as f: + with pytest.raises(ValueError, match=msg): + ExcelWriter(f, engine="odf", mode="a") + + +@pytest.mark.parametrize("engine_kwargs", [None, {"kwarg": 1}]) +def test_engine_kwargs(ext, engine_kwargs): + # GH 42286 + # GH 43445 + # test for error: OpenDocumentSpreadsheet does not accept any arguments + with tm.ensure_clean(ext) as f: + if engine_kwargs is not None: + error = re.escape( + "OpenDocumentSpreadsheet() got an unexpected keyword argument 'kwarg'" + ) + with pytest.raises( + TypeError, + match=error, + ): + ExcelWriter(f, engine="odf", engine_kwargs=engine_kwargs) + else: + with ExcelWriter(f, engine="odf", engine_kwargs=engine_kwargs) as _: + pass + + +def test_book_and_sheets_consistent(ext): + # GH#45687 - Ensure sheets is updated if user modifies book + with tm.ensure_clean(ext) as f: + with ExcelWriter(f) as writer: + assert writer.sheets == {} + table = odf.table.Table(name="test_name") + writer.book.spreadsheet.addElement(table) + assert writer.sheets == {"test_name": table} + + +@pytest.mark.parametrize( + ["value", "cell_value_type", "cell_value_attribute", "cell_value"], + argvalues=[ + (True, "boolean", "boolean-value", "true"), + ("test string", "string", "string-value", "test string"), + (1, "float", "value", "1"), + (1.5, "float", "value", "1.5"), + ( + datetime(2010, 10, 10, 10, 10, 10), + "date", + "date-value", + "2010-10-10T10:10:10", + ), + (date(2010, 10, 10), "date", "date-value", "2010-10-10"), + ], +) +def test_cell_value_type(ext, value, cell_value_type, cell_value_attribute, cell_value): + # GH#54994 ODS: cell attributes should follow specification + # http://docs.oasis-open.org/office/v1.2/os/OpenDocument-v1.2-os-part1.html#refTable13 + from odf.namespaces import OFFICENS + from odf.table import ( + TableCell, + TableRow, + ) + + table_cell_name = TableCell().qname + + with tm.ensure_clean(ext) as f: + pd.DataFrame([[value]]).to_excel(f, header=False, index=False) + + with pd.ExcelFile(f) as wb: + sheet = wb._reader.get_sheet_by_index(0) + sheet_rows = sheet.getElementsByType(TableRow) + sheet_cells = [ + x + for x in sheet_rows[0].childNodes + if hasattr(x, "qname") and x.qname == table_cell_name + ] + + cell = sheet_cells[0] + assert cell.attributes.get((OFFICENS, "value-type")) == cell_value_type + assert cell.attributes.get((OFFICENS, cell_value_attribute)) == cell_value diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_openpyxl.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_openpyxl.py new file mode 100644 index 0000000000000000000000000000000000000000..e53b5830ec6a4b315165f4896aed27bdaadfbda6 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_openpyxl.py @@ -0,0 +1,432 @@ +import contextlib +from pathlib import Path +import re + +import numpy as np +import pytest + +from pandas.compat import is_platform_windows + +import pandas as pd +from pandas import DataFrame +import pandas._testing as tm + +from pandas.io.excel import ( + ExcelWriter, + _OpenpyxlWriter, +) +from pandas.io.excel._openpyxl import OpenpyxlReader + +openpyxl = pytest.importorskip("openpyxl") + +if is_platform_windows(): + pytestmark = pytest.mark.single_cpu + + +@pytest.fixture +def ext(): + return ".xlsx" + + +def test_to_excel_styleconverter(): + from openpyxl import styles + + hstyle = { + "font": {"color": "00FF0000", "bold": True}, + "borders": {"top": "thin", "right": "thin", "bottom": "thin", "left": "thin"}, + "alignment": {"horizontal": "center", "vertical": "top"}, + "fill": {"patternType": "solid", "fgColor": {"rgb": "006666FF", "tint": 0.3}}, + "number_format": {"format_code": "0.00"}, + "protection": {"locked": True, "hidden": False}, + } + + font_color = styles.Color("00FF0000") + font = styles.Font(bold=True, color=font_color) + side = styles.Side(style=styles.borders.BORDER_THIN) + border = styles.Border(top=side, right=side, bottom=side, left=side) + alignment = styles.Alignment(horizontal="center", vertical="top") + fill_color = styles.Color(rgb="006666FF", tint=0.3) + fill = styles.PatternFill(patternType="solid", fgColor=fill_color) + + number_format = "0.00" + + protection = styles.Protection(locked=True, hidden=False) + + kw = _OpenpyxlWriter._convert_to_style_kwargs(hstyle) + assert kw["font"] == font + assert kw["border"] == border + assert kw["alignment"] == alignment + assert kw["fill"] == fill + assert kw["number_format"] == number_format + assert kw["protection"] == protection + + +def test_write_cells_merge_styled(ext): + from pandas.io.formats.excel import ExcelCell + + sheet_name = "merge_styled" + + sty_b1 = {"font": {"color": "00FF0000"}} + sty_a2 = {"font": {"color": "0000FF00"}} + + initial_cells = [ + ExcelCell(col=1, row=0, val=42, style=sty_b1), + ExcelCell(col=0, row=1, val=99, style=sty_a2), + ] + + sty_merged = {"font": {"color": "000000FF", "bold": True}} + sty_kwargs = _OpenpyxlWriter._convert_to_style_kwargs(sty_merged) + openpyxl_sty_merged = sty_kwargs["font"] + merge_cells = [ + ExcelCell( + col=0, row=0, val="pandas", mergestart=1, mergeend=1, style=sty_merged + ) + ] + + with tm.ensure_clean(ext) as path: + with _OpenpyxlWriter(path) as writer: + writer._write_cells(initial_cells, sheet_name=sheet_name) + writer._write_cells(merge_cells, sheet_name=sheet_name) + + wks = writer.sheets[sheet_name] + xcell_b1 = wks["B1"] + xcell_a2 = wks["A2"] + assert xcell_b1.font == openpyxl_sty_merged + assert xcell_a2.font == openpyxl_sty_merged + + +@pytest.mark.parametrize("iso_dates", [True, False]) +def test_engine_kwargs_write(ext, iso_dates): + # GH 42286 GH 43445 + engine_kwargs = {"iso_dates": iso_dates} + with tm.ensure_clean(ext) as f: + with ExcelWriter(f, engine="openpyxl", engine_kwargs=engine_kwargs) as writer: + assert writer.book.iso_dates == iso_dates + # ExcelWriter won't allow us to close without writing something + DataFrame().to_excel(writer) + + +def test_engine_kwargs_append_invalid(ext): + # GH 43445 + # test whether an invalid engine kwargs actually raises + with tm.ensure_clean(ext) as f: + DataFrame(["hello", "world"]).to_excel(f) + with pytest.raises( + TypeError, + match=re.escape( + "load_workbook() got an unexpected keyword argument 'apple_banana'" + ), + ): + with ExcelWriter( + f, engine="openpyxl", mode="a", engine_kwargs={"apple_banana": "fruit"} + ) as writer: + # ExcelWriter needs us to write something to close properly + DataFrame(["good"]).to_excel(writer, sheet_name="Sheet2") + + +@pytest.mark.parametrize("data_only, expected", [(True, 0), (False, "=1+1")]) +def test_engine_kwargs_append_data_only(ext, data_only, expected): + # GH 43445 + # tests whether the data_only engine_kwarg actually works well for + # openpyxl's load_workbook + with tm.ensure_clean(ext) as f: + DataFrame(["=1+1"]).to_excel(f) + with ExcelWriter( + f, engine="openpyxl", mode="a", engine_kwargs={"data_only": data_only} + ) as writer: + assert writer.sheets["Sheet1"]["B2"].value == expected + # ExcelWriter needs us to writer something to close properly? + DataFrame().to_excel(writer, sheet_name="Sheet2") + + # ensure that data_only also works for reading + # and that formulas/values roundtrip + assert ( + pd.read_excel( + f, + sheet_name="Sheet1", + engine="openpyxl", + engine_kwargs={"data_only": data_only}, + ).iloc[0, 1] + == expected + ) + + +@pytest.mark.parametrize("kwarg_name", ["read_only", "data_only"]) +@pytest.mark.parametrize("kwarg_value", [True, False]) +def test_engine_kwargs_append_reader(datapath, ext, kwarg_name, kwarg_value): + # GH 55027 + # test that `read_only` and `data_only` can be passed to + # `openpyxl.reader.excel.load_workbook` via `engine_kwargs` + filename = datapath("io", "data", "excel", "test1" + ext) + with contextlib.closing( + OpenpyxlReader(filename, engine_kwargs={kwarg_name: kwarg_value}) + ) as reader: + assert getattr(reader.book, kwarg_name) == kwarg_value + + +@pytest.mark.parametrize( + "mode,expected", [("w", ["baz"]), ("a", ["foo", "bar", "baz"])] +) +def test_write_append_mode(ext, mode, expected): + df = DataFrame([1], columns=["baz"]) + + with tm.ensure_clean(ext) as f: + wb = openpyxl.Workbook() + wb.worksheets[0].title = "foo" + wb.worksheets[0]["A1"].value = "foo" + wb.create_sheet("bar") + wb.worksheets[1]["A1"].value = "bar" + wb.save(f) + + with ExcelWriter(f, engine="openpyxl", mode=mode) as writer: + df.to_excel(writer, sheet_name="baz", index=False) + + with contextlib.closing(openpyxl.load_workbook(f)) as wb2: + result = [sheet.title for sheet in wb2.worksheets] + assert result == expected + + for index, cell_value in enumerate(expected): + assert wb2.worksheets[index]["A1"].value == cell_value + + +@pytest.mark.parametrize( + "if_sheet_exists,num_sheets,expected", + [ + ("new", 2, ["apple", "banana"]), + ("replace", 1, ["pear"]), + ("overlay", 1, ["pear", "banana"]), + ], +) +def test_if_sheet_exists_append_modes(ext, if_sheet_exists, num_sheets, expected): + # GH 40230 + df1 = DataFrame({"fruit": ["apple", "banana"]}) + df2 = DataFrame({"fruit": ["pear"]}) + + with tm.ensure_clean(ext) as f: + df1.to_excel(f, engine="openpyxl", sheet_name="foo", index=False) + with ExcelWriter( + f, engine="openpyxl", mode="a", if_sheet_exists=if_sheet_exists + ) as writer: + df2.to_excel(writer, sheet_name="foo", index=False) + + with contextlib.closing(openpyxl.load_workbook(f)) as wb: + assert len(wb.sheetnames) == num_sheets + assert wb.sheetnames[0] == "foo" + result = pd.read_excel(wb, "foo", engine="openpyxl") + assert list(result["fruit"]) == expected + if len(wb.sheetnames) == 2: + result = pd.read_excel(wb, wb.sheetnames[1], engine="openpyxl") + tm.assert_frame_equal(result, df2) + + +@pytest.mark.parametrize( + "startrow, startcol, greeting, goodbye", + [ + (0, 0, ["poop", "world"], ["goodbye", "people"]), + (0, 1, ["hello", "world"], ["poop", "people"]), + (1, 0, ["hello", "poop"], ["goodbye", "people"]), + (1, 1, ["hello", "world"], ["goodbye", "poop"]), + ], +) +def test_append_overlay_startrow_startcol(ext, startrow, startcol, greeting, goodbye): + df1 = DataFrame({"greeting": ["hello", "world"], "goodbye": ["goodbye", "people"]}) + df2 = DataFrame(["poop"]) + + with tm.ensure_clean(ext) as f: + df1.to_excel(f, engine="openpyxl", sheet_name="poo", index=False) + with ExcelWriter( + f, engine="openpyxl", mode="a", if_sheet_exists="overlay" + ) as writer: + # use startrow+1 because we don't have a header + df2.to_excel( + writer, + index=False, + header=False, + startrow=startrow + 1, + startcol=startcol, + sheet_name="poo", + ) + + result = pd.read_excel(f, sheet_name="poo", engine="openpyxl") + expected = DataFrame({"greeting": greeting, "goodbye": goodbye}) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "if_sheet_exists,msg", + [ + ( + "invalid", + "'invalid' is not valid for if_sheet_exists. Valid options " + "are 'error', 'new', 'replace' and 'overlay'.", + ), + ( + "error", + "Sheet 'foo' already exists and if_sheet_exists is set to 'error'.", + ), + ( + None, + "Sheet 'foo' already exists and if_sheet_exists is set to 'error'.", + ), + ], +) +def test_if_sheet_exists_raises(ext, if_sheet_exists, msg): + # GH 40230 + df = DataFrame({"fruit": ["pear"]}) + with tm.ensure_clean(ext) as f: + with pytest.raises(ValueError, match=re.escape(msg)): + df.to_excel(f, sheet_name="foo", engine="openpyxl") + with ExcelWriter( + f, engine="openpyxl", mode="a", if_sheet_exists=if_sheet_exists + ) as writer: + df.to_excel(writer, sheet_name="foo") + + +def test_to_excel_with_openpyxl_engine(ext): + # GH 29854 + with tm.ensure_clean(ext) as filename: + df1 = DataFrame({"A": np.linspace(1, 10, 10)}) + df2 = DataFrame({"B": np.linspace(1, 20, 10)}) + df = pd.concat([df1, df2], axis=1) + styled = df.style.map( + lambda val: f"color: {'red' if val < 0 else 'black'}" + ).highlight_max() + + styled.to_excel(filename, engine="openpyxl") + + +@pytest.mark.parametrize("read_only", [True, False]) +def test_read_workbook(datapath, ext, read_only): + # GH 39528 + filename = datapath("io", "data", "excel", "test1" + ext) + with contextlib.closing( + openpyxl.load_workbook(filename, read_only=read_only) + ) as wb: + result = pd.read_excel(wb, engine="openpyxl") + expected = pd.read_excel(filename) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "header, expected_data", + [ + ( + 0, + { + "Title": [np.nan, "A", 1, 2, 3], + "Unnamed: 1": [np.nan, "B", 4, 5, 6], + "Unnamed: 2": [np.nan, "C", 7, 8, 9], + }, + ), + (2, {"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}), + ], +) +@pytest.mark.parametrize( + "filename", ["dimension_missing", "dimension_small", "dimension_large"] +) +# When read_only is None, use read_excel instead of a workbook +@pytest.mark.parametrize("read_only", [True, False, None]) +def test_read_with_bad_dimension( + datapath, ext, header, expected_data, filename, read_only +): + # GH 38956, 39001 - no/incorrect dimension information + path = datapath("io", "data", "excel", f"{filename}{ext}") + if read_only is None: + result = pd.read_excel(path, header=header) + else: + with contextlib.closing( + openpyxl.load_workbook(path, read_only=read_only) + ) as wb: + result = pd.read_excel(wb, engine="openpyxl", header=header) + expected = DataFrame(expected_data) + tm.assert_frame_equal(result, expected) + + +def test_append_mode_file(ext): + # GH 39576 + df = DataFrame() + + with tm.ensure_clean(ext) as f: + df.to_excel(f, engine="openpyxl") + + with ExcelWriter( + f, mode="a", engine="openpyxl", if_sheet_exists="new" + ) as writer: + df.to_excel(writer) + + # make sure that zip files are not concatenated by making sure that + # "docProps/app.xml" only occurs twice in the file + data = Path(f).read_bytes() + first = data.find(b"docProps/app.xml") + second = data.find(b"docProps/app.xml", first + 1) + third = data.find(b"docProps/app.xml", second + 1) + assert second != -1 and third == -1 + + +# When read_only is None, use read_excel instead of a workbook +@pytest.mark.parametrize("read_only", [True, False, None]) +def test_read_with_empty_trailing_rows(datapath, ext, read_only): + # GH 39181 + path = datapath("io", "data", "excel", f"empty_trailing_rows{ext}") + if read_only is None: + result = pd.read_excel(path) + else: + with contextlib.closing( + openpyxl.load_workbook(path, read_only=read_only) + ) as wb: + result = pd.read_excel(wb, engine="openpyxl") + expected = DataFrame( + { + "Title": [np.nan, "A", 1, 2, 3], + "Unnamed: 1": [np.nan, "B", 4, 5, 6], + "Unnamed: 2": [np.nan, "C", 7, 8, 9], + } + ) + tm.assert_frame_equal(result, expected) + + +# When read_only is None, use read_excel instead of a workbook +@pytest.mark.parametrize("read_only", [True, False, None]) +def test_read_empty_with_blank_row(datapath, ext, read_only): + # GH 39547 - empty excel file with a row that has no data + path = datapath("io", "data", "excel", f"empty_with_blank_row{ext}") + if read_only is None: + result = pd.read_excel(path) + else: + with contextlib.closing( + openpyxl.load_workbook(path, read_only=read_only) + ) as wb: + result = pd.read_excel(wb, engine="openpyxl") + expected = DataFrame() + tm.assert_frame_equal(result, expected) + + +def test_book_and_sheets_consistent(ext): + # GH#45687 - Ensure sheets is updated if user modifies book + with tm.ensure_clean(ext) as f: + with ExcelWriter(f, engine="openpyxl") as writer: + assert writer.sheets == {} + sheet = writer.book.create_sheet("test_name", 0) + assert writer.sheets == {"test_name": sheet} + + +def test_ints_spelled_with_decimals(datapath, ext): + # GH 46988 - openpyxl returns this sheet with floats + path = datapath("io", "data", "excel", f"ints_spelled_with_decimals{ext}") + result = pd.read_excel(path) + expected = DataFrame(range(2, 12), columns=[1]) + tm.assert_frame_equal(result, expected) + + +def test_read_multiindex_header_no_index_names(datapath, ext): + # GH#47487 + path = datapath("io", "data", "excel", f"multiindex_no_index_names{ext}") + result = pd.read_excel(path, index_col=[0, 1, 2], header=[0, 1, 2]) + expected = DataFrame( + [[np.nan, "x", "x", "x"], ["x", np.nan, np.nan, np.nan]], + columns=pd.MultiIndex.from_tuples( + [("X", "Y", "A1"), ("X", "Y", "A2"), ("XX", "YY", "B1"), ("XX", "YY", "B2")] + ), + index=pd.MultiIndex.from_tuples([("A", "AA", "AAA"), ("A", "BB", "BBB")]), + ) + tm.assert_frame_equal(result, expected) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_readers.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_readers.py new file mode 100644 index 0000000000000000000000000000000000000000..c62144adbaecbdd445a4171896e9ca2905f13205 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_readers.py @@ -0,0 +1,1735 @@ +from __future__ import annotations + +from datetime import ( + datetime, + time, +) +from functools import partial +from io import BytesIO +import os +from pathlib import Path +import platform +import re +from urllib.error import URLError +from zipfile import BadZipFile + +import numpy as np +import pytest + +from pandas.compat import is_platform_windows +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + read_csv, +) +import pandas._testing as tm + +if is_platform_windows(): + pytestmark = pytest.mark.single_cpu + +read_ext_params = [".xls", ".xlsx", ".xlsm", ".xlsb", ".ods"] +engine_params = [ + # Add any engines to test here + # When defusedxml is installed it triggers deprecation warnings for + # xlrd and openpyxl, so catch those here + pytest.param( + "xlrd", + marks=[ + td.skip_if_no("xlrd"), + ], + ), + pytest.param( + "openpyxl", + marks=[ + td.skip_if_no("openpyxl"), + ], + ), + pytest.param( + None, + marks=[ + td.skip_if_no("xlrd"), + ], + ), + pytest.param("pyxlsb", marks=td.skip_if_no("pyxlsb")), + pytest.param("odf", marks=td.skip_if_no("odf")), + pytest.param("calamine", marks=td.skip_if_no("python_calamine")), +] + + +def _is_valid_engine_ext_pair(engine, read_ext: str) -> bool: + """ + Filter out invalid (engine, ext) pairs instead of skipping, as that + produces 500+ pytest.skips. + """ + engine = engine.values[0] + if engine == "openpyxl" and read_ext == ".xls": + return False + if engine == "odf" and read_ext != ".ods": + return False + if read_ext == ".ods" and engine not in {"odf", "calamine"}: + return False + if engine == "pyxlsb" and read_ext != ".xlsb": + return False + if read_ext == ".xlsb" and engine not in {"pyxlsb", "calamine"}: + return False + if engine == "xlrd" and read_ext != ".xls": + return False + return True + + +def _transfer_marks(engine, read_ext): + """ + engine gives us a pytest.param object with some marks, read_ext is just + a string. We need to generate a new pytest.param inheriting the marks. + """ + values = engine.values + (read_ext,) + new_param = pytest.param(values, marks=engine.marks) + return new_param + + +@pytest.fixture( + params=[ + _transfer_marks(eng, ext) + for eng in engine_params + for ext in read_ext_params + if _is_valid_engine_ext_pair(eng, ext) + ], + ids=str, +) +def engine_and_read_ext(request): + """ + Fixture for Excel reader engine and read_ext, only including valid pairs. + """ + return request.param + + +@pytest.fixture +def engine(engine_and_read_ext): + engine, read_ext = engine_and_read_ext + return engine + + +@pytest.fixture +def read_ext(engine_and_read_ext): + engine, read_ext = engine_and_read_ext + return read_ext + + +@pytest.fixture +def df_ref(datapath): + """ + Obtain the reference data from read_csv with the Python engine. + """ + filepath = datapath("io", "data", "csv", "test1.csv") + df_ref = read_csv(filepath, index_col=0, parse_dates=True, engine="python") + return df_ref + + +def get_exp_unit(read_ext: str, engine: str | None) -> str: + return "ns" + + +def adjust_expected(expected: DataFrame, read_ext: str, engine: str) -> None: + expected.index.name = None + unit = get_exp_unit(read_ext, engine) + # error: "Index" has no attribute "as_unit" + expected.index = expected.index.as_unit(unit) # type: ignore[attr-defined] + + +def xfail_datetimes_with_pyxlsb(engine, request): + if engine == "pyxlsb": + request.applymarker( + pytest.mark.xfail( + reason="Sheets containing datetimes not supported by pyxlsb" + ) + ) + + +class TestReaders: + @pytest.fixture(autouse=True) + def cd_and_set_engine(self, engine, datapath, monkeypatch): + """ + Change directory and set engine for read_excel calls. + """ + func = partial(pd.read_excel, engine=engine) + monkeypatch.chdir(datapath("io", "data", "excel")) + monkeypatch.setattr(pd, "read_excel", func) + + def test_engine_used(self, read_ext, engine, monkeypatch): + # GH 38884 + def parser(self, *args, **kwargs): + return self.engine + + monkeypatch.setattr(pd.ExcelFile, "parse", parser) + + expected_defaults = { + "xlsx": "openpyxl", + "xlsm": "openpyxl", + "xlsb": "pyxlsb", + "xls": "xlrd", + "ods": "odf", + } + + with open("test1" + read_ext, "rb") as f: + result = pd.read_excel(f) + + if engine is not None: + expected = engine + else: + expected = expected_defaults[read_ext[1:]] + assert result == expected + + def test_engine_kwargs(self, read_ext, engine): + # GH#52214 + expected_defaults = { + "xlsx": {"foo": "abcd"}, + "xlsm": {"foo": 123}, + "xlsb": {"foo": "True"}, + "xls": {"foo": True}, + "ods": {"foo": "abcd"}, + } + + if engine in {"xlrd", "pyxlsb"}: + msg = re.escape(r"open_workbook() got an unexpected keyword argument 'foo'") + elif engine == "odf": + msg = re.escape(r"load() got an unexpected keyword argument 'foo'") + else: + msg = re.escape(r"load_workbook() got an unexpected keyword argument 'foo'") + + if engine is not None: + with pytest.raises(TypeError, match=msg): + pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet1", + index_col=0, + engine_kwargs=expected_defaults[read_ext[1:]], + ) + + def test_usecols_int(self, read_ext): + # usecols as int + msg = "Passing an integer for `usecols`" + with pytest.raises(ValueError, match=msg): + pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=3 + ) + + # usecols as int + with pytest.raises(ValueError, match=msg): + pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols=3, + ) + + def test_usecols_list(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref[["B", "C"]] + adjust_expected(expected, read_ext, engine) + + df1 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=[0, 2, 3] + ) + df2 = pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols=[0, 2, 3], + ) + + # TODO add index to xls file) + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + def test_usecols_str(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref[["A", "B", "C"]] + adjust_expected(expected, read_ext, engine) + + df2 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A:D" + ) + df3 = pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols="A:D", + ) + + # TODO add index to xls, read xls ignores index name ? + tm.assert_frame_equal(df2, expected) + tm.assert_frame_equal(df3, expected) + + expected = df_ref[["B", "C"]] + adjust_expected(expected, read_ext, engine) + + df2 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,C,D" + ) + df3 = pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols="A,C,D", + ) + # TODO add index to xls file + tm.assert_frame_equal(df2, expected) + tm.assert_frame_equal(df3, expected) + + df2 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,C:D" + ) + df3 = pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols="A,C:D", + ) + tm.assert_frame_equal(df2, expected) + tm.assert_frame_equal(df3, expected) + + @pytest.mark.parametrize( + "usecols", [[0, 1, 3], [0, 3, 1], [1, 0, 3], [1, 3, 0], [3, 0, 1], [3, 1, 0]] + ) + def test_usecols_diff_positional_int_columns_order( + self, request, engine, read_ext, usecols, df_ref + ): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref[["A", "C"]] + adjust_expected(expected, read_ext, engine) + + result = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=usecols + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("usecols", [["B", "D"], ["D", "B"]]) + def test_usecols_diff_positional_str_columns_order(self, read_ext, usecols, df_ref): + expected = df_ref[["B", "D"]] + expected.index = range(len(expected)) + + result = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", usecols=usecols) + tm.assert_frame_equal(result, expected) + + def test_read_excel_without_slicing(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + result = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", index_col=0) + tm.assert_frame_equal(result, expected) + + def test_usecols_excel_range_str(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref[["C", "D"]] + adjust_expected(expected, read_ext, engine) + + result = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,D:E" + ) + tm.assert_frame_equal(result, expected) + + def test_usecols_excel_range_str_invalid(self, read_ext): + msg = "Invalid column name: E1" + + with pytest.raises(ValueError, match=msg): + pd.read_excel("test1" + read_ext, sheet_name="Sheet1", usecols="D:E1") + + def test_index_col_label_error(self, read_ext): + msg = "list indices must be integers.*, not str" + + with pytest.raises(TypeError, match=msg): + pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet1", + index_col=["A"], + usecols=["A", "C"], + ) + + def test_index_col_str(self, read_ext): + # see gh-52716 + result = pd.read_excel("test1" + read_ext, sheet_name="Sheet3", index_col="A") + expected = DataFrame( + columns=["B", "C", "D", "E", "F"], index=Index([], name="A") + ) + tm.assert_frame_equal(result, expected) + + def test_index_col_empty(self, read_ext): + # see gh-9208 + result = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet3", index_col=["A", "B", "C"] + ) + expected = DataFrame( + columns=["D", "E", "F"], + index=MultiIndex(levels=[[]] * 3, codes=[[]] * 3, names=["A", "B", "C"]), + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("index_col", [None, 2]) + def test_index_col_with_unnamed(self, read_ext, index_col): + # see gh-18792 + result = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet4", index_col=index_col + ) + expected = DataFrame( + [["i1", "a", "x"], ["i2", "b", "y"]], columns=["Unnamed: 0", "col1", "col2"] + ) + if index_col: + expected = expected.set_index(expected.columns[index_col]) + + tm.assert_frame_equal(result, expected) + + def test_usecols_pass_non_existent_column(self, read_ext): + msg = ( + "Usecols do not match columns, " + "columns expected but not found: " + r"\['E'\]" + ) + + with pytest.raises(ValueError, match=msg): + pd.read_excel("test1" + read_ext, usecols=["E"]) + + def test_usecols_wrong_type(self, read_ext): + msg = ( + "'usecols' must either be list-like of " + "all strings, all unicode, all integers or a callable." + ) + + with pytest.raises(ValueError, match=msg): + pd.read_excel("test1" + read_ext, usecols=["E1", 0]) + + def test_excel_stop_iterator(self, read_ext): + parsed = pd.read_excel("test2" + read_ext, sheet_name="Sheet1") + expected = DataFrame([["aaaa", "bbbbb"]], columns=["Test", "Test1"]) + tm.assert_frame_equal(parsed, expected) + + def test_excel_cell_error_na(self, request, engine, read_ext): + xfail_datetimes_with_pyxlsb(engine, request) + + # https://github.com/tafia/calamine/issues/355 + if engine == "calamine" and read_ext == ".ods": + request.applymarker( + pytest.mark.xfail(reason="Calamine can't extract error from ods files") + ) + + parsed = pd.read_excel("test3" + read_ext, sheet_name="Sheet1") + expected = DataFrame([[np.nan]], columns=["Test"]) + tm.assert_frame_equal(parsed, expected) + + def test_excel_table(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + df1 = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", index_col=0) + df2 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet2", skiprows=[1], index_col=0 + ) + # TODO add index to file + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + df3 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, skipfooter=1 + ) + tm.assert_frame_equal(df3, df1.iloc[:-1]) + + def test_reader_special_dtypes(self, request, engine, read_ext): + xfail_datetimes_with_pyxlsb(engine, request) + + unit = get_exp_unit(read_ext, engine) + expected = DataFrame.from_dict( + { + "IntCol": [1, 2, -3, 4, 0], + "FloatCol": [1.25, 2.25, 1.83, 1.92, 0.0000000005], + "BoolCol": [True, False, True, True, False], + "StrCol": [1, 2, 3, 4, 5], + "Str2Col": ["a", 3, "c", "d", "e"], + "DateCol": Index( + [ + datetime(2013, 10, 30), + datetime(2013, 10, 31), + datetime(1905, 1, 1), + datetime(2013, 12, 14), + datetime(2015, 3, 14), + ], + dtype=f"M8[{unit}]", + ), + }, + ) + basename = "test_types" + + # should read in correctly and infer types + actual = pd.read_excel(basename + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, expected) + + # if not coercing number, then int comes in as float + float_expected = expected.copy() + float_expected.loc[float_expected.index[1], "Str2Col"] = 3.0 + actual = pd.read_excel(basename + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, float_expected) + + # check setting Index (assuming xls and xlsx are the same here) + for icol, name in enumerate(expected.columns): + actual = pd.read_excel( + basename + read_ext, sheet_name="Sheet1", index_col=icol + ) + exp = expected.set_index(name) + tm.assert_frame_equal(actual, exp) + + expected["StrCol"] = expected["StrCol"].apply(str) + actual = pd.read_excel( + basename + read_ext, sheet_name="Sheet1", converters={"StrCol": str} + ) + tm.assert_frame_equal(actual, expected) + + # GH8212 - support for converters and missing values + def test_reader_converters(self, read_ext): + basename = "test_converters" + + expected = DataFrame.from_dict( + { + "IntCol": [1, 2, -3, -1000, 0], + "FloatCol": [12.5, np.nan, 18.3, 19.2, 0.000000005], + "BoolCol": ["Found", "Found", "Found", "Not found", "Found"], + "StrCol": ["1", np.nan, "3", "4", "5"], + } + ) + + converters = { + "IntCol": lambda x: int(x) if x != "" else -1000, + "FloatCol": lambda x: 10 * x if x else np.nan, + 2: lambda x: "Found" if x != "" else "Not found", + 3: lambda x: str(x) if x else "", + } + + # should read in correctly and set types of single cells (not array + # dtypes) + actual = pd.read_excel( + basename + read_ext, sheet_name="Sheet1", converters=converters + ) + tm.assert_frame_equal(actual, expected) + + def test_reader_dtype(self, read_ext): + # GH 8212 + basename = "testdtype" + actual = pd.read_excel(basename + read_ext) + + expected = DataFrame( + { + "a": [1, 2, 3, 4], + "b": [2.5, 3.5, 4.5, 5.5], + "c": [1, 2, 3, 4], + "d": [1.0, 2.0, np.nan, 4.0], + } + ) + + tm.assert_frame_equal(actual, expected) + + actual = pd.read_excel( + basename + read_ext, dtype={"a": "float64", "b": "float32", "c": str} + ) + + expected["a"] = expected["a"].astype("float64") + expected["b"] = expected["b"].astype("float32") + expected["c"] = Series(["001", "002", "003", "004"], dtype="str") + tm.assert_frame_equal(actual, expected) + + msg = "Unable to convert column d to type int64" + with pytest.raises(ValueError, match=msg): + pd.read_excel(basename + read_ext, dtype={"d": "int64"}) + + @pytest.mark.parametrize( + "dtype,expected", + [ + ( + None, + DataFrame( + { + "a": [1, 2, 3, 4], + "b": [2.5, 3.5, 4.5, 5.5], + "c": [1, 2, 3, 4], + "d": [1.0, 2.0, np.nan, 4.0], + } + ), + ), + ( + {"a": "float64", "b": "float32", "c": str, "d": str}, + DataFrame( + { + "a": Series([1, 2, 3, 4], dtype="float64"), + "b": Series([2.5, 3.5, 4.5, 5.5], dtype="float32"), + "c": Series(["001", "002", "003", "004"], dtype="str"), + "d": Series(["1", "2", np.nan, "4"], dtype="str"), + }, + ), + ), + ], + ) + def test_reader_dtype_str(self, read_ext, dtype, expected): + # see gh-20377 + basename = "testdtype" + + actual = pd.read_excel(basename + read_ext, dtype=dtype) + tm.assert_frame_equal(actual, expected) + + def test_dtype_backend(self, read_ext, dtype_backend, engine): + # GH#36712 + if read_ext in (".xlsb", ".xls"): + pytest.skip(f"No engine for filetype: '{read_ext}'") + + df = DataFrame( + { + "a": Series([1, 3], dtype="Int64"), + "b": Series([2.5, 4.5], dtype="Float64"), + "c": Series([True, False], dtype="boolean"), + "d": Series(["a", "b"], dtype="string"), + "e": Series([pd.NA, 6], dtype="Int64"), + "f": Series([pd.NA, 7.5], dtype="Float64"), + "g": Series([pd.NA, True], dtype="boolean"), + "h": Series([pd.NA, "a"], dtype="string"), + "i": Series([pd.Timestamp("2019-12-31")] * 2), + "j": Series([pd.NA, pd.NA], dtype="Int64"), + } + ) + with tm.ensure_clean(read_ext) as file_path: + df.to_excel(file_path, sheet_name="test", index=False) + result = pd.read_excel( + file_path, sheet_name="test", dtype_backend=dtype_backend + ) + if dtype_backend == "pyarrow": + import pyarrow as pa + + from pandas.arrays import ArrowExtensionArray + + expected = DataFrame( + { + col: ArrowExtensionArray(pa.array(df[col], from_pandas=True)) + for col in df.columns + } + ) + # pyarrow by default infers timestamp resolution as us, not ns + expected["i"] = ArrowExtensionArray( + expected["i"].array._pa_array.cast(pa.timestamp(unit="us")) + ) + # pyarrow supports a null type, so don't have to default to Int64 + expected["j"] = ArrowExtensionArray(pa.array([None, None])) + else: + expected = df + unit = get_exp_unit(read_ext, engine) + expected["i"] = expected["i"].astype(f"M8[{unit}]") + + tm.assert_frame_equal(result, expected) + + def test_dtype_backend_and_dtype(self, read_ext): + # GH#36712 + if read_ext in (".xlsb", ".xls"): + pytest.skip(f"No engine for filetype: '{read_ext}'") + + df = DataFrame({"a": [np.nan, 1.0], "b": [2.5, np.nan]}) + with tm.ensure_clean(read_ext) as file_path: + df.to_excel(file_path, sheet_name="test", index=False) + result = pd.read_excel( + file_path, + sheet_name="test", + dtype_backend="numpy_nullable", + dtype="float64", + ) + tm.assert_frame_equal(result, df) + + def test_dtype_backend_string(self, read_ext, string_storage): + # GH#36712 + if read_ext in (".xlsb", ".xls"): + pytest.skip(f"No engine for filetype: '{read_ext}'") + + with pd.option_context("mode.string_storage", string_storage): + df = DataFrame( + { + "a": np.array(["a", "b"], dtype=np.object_), + "b": np.array(["x", pd.NA], dtype=np.object_), + } + ) + + with tm.ensure_clean(read_ext) as file_path: + df.to_excel(file_path, sheet_name="test", index=False) + result = pd.read_excel( + file_path, sheet_name="test", dtype_backend="numpy_nullable" + ) + + expected = DataFrame( + { + "a": Series(["a", "b"], dtype=pd.StringDtype(string_storage)), + "b": Series(["x", None], dtype=pd.StringDtype(string_storage)), + } + ) + # the storage of the str columns' Index is also affected by the + # string_storage setting -> ignore that for checking the result + tm.assert_frame_equal(result, expected, check_column_type=False) + + @pytest.mark.parametrize("dtypes, exp_value", [({}, 1), ({"a.1": "int64"}, 1)]) + def test_dtype_mangle_dup_cols(self, read_ext, dtypes, exp_value): + # GH#35211 + basename = "df_mangle_dup_col_dtypes" + dtype_dict = {"a": object, **dtypes} + dtype_dict_copy = dtype_dict.copy() + # GH#42462 + result = pd.read_excel(basename + read_ext, dtype=dtype_dict) + expected = DataFrame( + { + "a": Series([1], dtype=object), + "a.1": Series([exp_value], dtype=object if not dtypes else None), + } + ) + assert dtype_dict == dtype_dict_copy, "dtype dict changed" + tm.assert_frame_equal(result, expected) + + def test_reader_spaces(self, read_ext): + # see gh-32207 + basename = "test_spaces" + + actual = pd.read_excel(basename + read_ext) + expected = DataFrame( + { + "testcol": [ + "this is great", + "4 spaces", + "1 trailing ", + " 1 leading", + "2 spaces multiple times", + ] + } + ) + tm.assert_frame_equal(actual, expected) + + # gh-36122, gh-35802 + @pytest.mark.parametrize( + "basename,expected", + [ + ("gh-35802", DataFrame({"COLUMN": ["Test (1)"]})), + ("gh-36122", DataFrame(columns=["got 2nd sa"])), + ], + ) + def test_read_excel_ods_nested_xml(self, engine, read_ext, basename, expected): + # see gh-35802 + if engine != "odf": + pytest.skip(f"Skipped for engine: {engine}") + + actual = pd.read_excel(basename + read_ext) + tm.assert_frame_equal(actual, expected) + + def test_reading_all_sheets(self, read_ext): + # Test reading all sheet names by setting sheet_name to None, + # Ensure a dict is returned. + # See PR #9450 + basename = "test_multisheet" + dfs = pd.read_excel(basename + read_ext, sheet_name=None) + # ensure this is not alphabetical to test order preservation + expected_keys = ["Charlie", "Alpha", "Beta"] + tm.assert_contains_all(expected_keys, dfs.keys()) + # Issue 9930 + # Ensure sheet order is preserved + assert expected_keys == list(dfs.keys()) + + def test_reading_multiple_specific_sheets(self, read_ext): + # Test reading specific sheet names by specifying a mixed list + # of integers and strings, and confirm that duplicated sheet + # references (positions/names) are removed properly. + # Ensure a dict is returned + # See PR #9450 + basename = "test_multisheet" + # Explicitly request duplicates. Only the set should be returned. + expected_keys = [2, "Charlie", "Charlie"] + dfs = pd.read_excel(basename + read_ext, sheet_name=expected_keys) + expected_keys = list(set(expected_keys)) + tm.assert_contains_all(expected_keys, dfs.keys()) + assert len(expected_keys) == len(dfs.keys()) + + def test_reading_all_sheets_with_blank(self, read_ext): + # Test reading all sheet names by setting sheet_name to None, + # In the case where some sheets are blank. + # Issue #11711 + basename = "blank_with_header" + dfs = pd.read_excel(basename + read_ext, sheet_name=None) + expected_keys = ["Sheet1", "Sheet2", "Sheet3"] + tm.assert_contains_all(expected_keys, dfs.keys()) + + # GH6403 + def test_read_excel_blank(self, read_ext): + actual = pd.read_excel("blank" + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, DataFrame()) + + def test_read_excel_blank_with_header(self, read_ext): + expected = DataFrame(columns=["col_1", "col_2"]) + actual = pd.read_excel("blank_with_header" + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, expected) + + def test_exception_message_includes_sheet_name(self, read_ext): + # GH 48706 + with pytest.raises(ValueError, match=r" \(sheet: Sheet1\)$"): + pd.read_excel("blank_with_header" + read_ext, header=[1], sheet_name=None) + with pytest.raises(ZeroDivisionError, match=r" \(sheet: Sheet1\)$"): + pd.read_excel("test1" + read_ext, usecols=lambda x: 1 / 0, sheet_name=None) + + @pytest.mark.filterwarnings("ignore:Cell A4 is marked:UserWarning:openpyxl") + def test_date_conversion_overflow(self, request, engine, read_ext): + # GH 10001 : pandas.ExcelFile ignore parse_dates=False + xfail_datetimes_with_pyxlsb(engine, request) + + expected = DataFrame( + [ + [pd.Timestamp("2016-03-12"), "Marc Johnson"], + [pd.Timestamp("2016-03-16"), "Jack Black"], + [1e20, "Timothy Brown"], + ], + columns=["DateColWithBigInt", "StringCol"], + ) + + if engine == "openpyxl": + request.applymarker( + pytest.mark.xfail(reason="Maybe not supported by openpyxl") + ) + + if engine is None and read_ext in (".xlsx", ".xlsm"): + # GH 35029 + request.applymarker( + pytest.mark.xfail(reason="Defaults to openpyxl, maybe not supported") + ) + + result = pd.read_excel("testdateoverflow" + read_ext) + tm.assert_frame_equal(result, expected) + + def test_sheet_name(self, request, read_ext, engine, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + filename = "test1" + sheet_name = "Sheet1" + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + df1 = pd.read_excel( + filename + read_ext, sheet_name=sheet_name, index_col=0 + ) # doc + df2 = pd.read_excel(filename + read_ext, index_col=0, sheet_name=sheet_name) + + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + def test_excel_read_buffer(self, read_ext): + pth = "test1" + read_ext + expected = pd.read_excel(pth, sheet_name="Sheet1", index_col=0) + with open(pth, "rb") as f: + actual = pd.read_excel(f, sheet_name="Sheet1", index_col=0) + tm.assert_frame_equal(expected, actual) + + def test_bad_engine_raises(self): + bad_engine = "foo" + with pytest.raises(ValueError, match="Unknown engine: foo"): + pd.read_excel("", engine=bad_engine) + + @pytest.mark.parametrize( + "sheet_name", + [3, [0, 3], [3, 0], "Sheet4", ["Sheet1", "Sheet4"], ["Sheet4", "Sheet1"]], + ) + def test_bad_sheetname_raises(self, read_ext, sheet_name): + # GH 39250 + msg = "Worksheet index 3 is invalid|Worksheet named 'Sheet4' not found" + with pytest.raises(ValueError, match=msg): + pd.read_excel("blank" + read_ext, sheet_name=sheet_name) + + def test_missing_file_raises(self, read_ext): + bad_file = f"foo{read_ext}" + # CI tests with other languages, translates to "No such file or directory" + match = "|".join( + [ + "(No such file or directory", + "没有那个文件或目录", + "File o directory non esistente)", + ] + ) + with pytest.raises(FileNotFoundError, match=match): + pd.read_excel(bad_file) + + def test_corrupt_bytes_raises(self, engine): + bad_stream = b"foo" + if engine is None: + error = ValueError + msg = ( + "Excel file format cannot be determined, you must " + "specify an engine manually." + ) + elif engine == "xlrd": + from xlrd import XLRDError + + error = XLRDError + msg = ( + "Unsupported format, or corrupt file: Expected BOF " + "record; found b'foo'" + ) + elif engine == "calamine": + from python_calamine import CalamineError + + error = CalamineError + msg = "Cannot detect file format" + else: + error = BadZipFile + msg = "File is not a zip file" + with pytest.raises(error, match=msg): + pd.read_excel(BytesIO(bad_stream)) + + @pytest.mark.network + @pytest.mark.single_cpu + def test_read_from_http_url(self, httpserver, read_ext): + with open("test1" + read_ext, "rb") as f: + httpserver.serve_content(content=f.read()) + url_table = pd.read_excel(httpserver.url) + local_table = pd.read_excel("test1" + read_ext) + tm.assert_frame_equal(url_table, local_table) + + @td.skip_if_not_us_locale + @pytest.mark.single_cpu + def test_read_from_s3_url(self, read_ext, s3_public_bucket, s3so): + # Bucket created in tests/io/conftest.py + with open("test1" + read_ext, "rb") as f: + s3_public_bucket.put_object(Key="test1" + read_ext, Body=f) + + url = f"s3://{s3_public_bucket.name}/test1" + read_ext + + url_table = pd.read_excel(url, storage_options=s3so) + local_table = pd.read_excel("test1" + read_ext) + tm.assert_frame_equal(url_table, local_table) + + @pytest.mark.single_cpu + def test_read_from_s3_object(self, read_ext, s3_public_bucket, s3so): + # GH 38788 + # Bucket created in tests/io/conftest.py + with open("test1" + read_ext, "rb") as f: + s3_public_bucket.put_object(Key="test1" + read_ext, Body=f) + + import s3fs + + s3 = s3fs.S3FileSystem(**s3so) + + with s3.open(f"s3://{s3_public_bucket.name}/test1" + read_ext) as f: + url_table = pd.read_excel(f) + + local_table = pd.read_excel("test1" + read_ext) + tm.assert_frame_equal(url_table, local_table) + + @pytest.mark.slow + def test_read_from_file_url(self, read_ext, datapath): + # FILE + localtable = os.path.join(datapath("io", "data", "excel"), "test1" + read_ext) + local_table = pd.read_excel(localtable) + + try: + url_table = pd.read_excel("file://localhost/" + localtable) + except URLError: + # fails on some systems + platform_info = " ".join(platform.uname()).strip() + pytest.skip(f"failing on {platform_info}") + + tm.assert_frame_equal(url_table, local_table) + + def test_read_from_pathlib_path(self, read_ext): + # GH12655 + str_path = "test1" + read_ext + expected = pd.read_excel(str_path, sheet_name="Sheet1", index_col=0) + + path_obj = Path("test1" + read_ext) + actual = pd.read_excel(path_obj, sheet_name="Sheet1", index_col=0) + + tm.assert_frame_equal(expected, actual) + + @td.skip_if_no("py.path") + def test_read_from_py_localpath(self, read_ext): + # GH12655 + from py.path import local as LocalPath + + str_path = os.path.join("test1" + read_ext) + expected = pd.read_excel(str_path, sheet_name="Sheet1", index_col=0) + + path_obj = LocalPath().join("test1" + read_ext) + actual = pd.read_excel(path_obj, sheet_name="Sheet1", index_col=0) + + tm.assert_frame_equal(expected, actual) + + def test_close_from_py_localpath(self, read_ext): + # GH31467 + str_path = os.path.join("test1" + read_ext) + with open(str_path, "rb") as f: + x = pd.read_excel(f, sheet_name="Sheet1", index_col=0) + del x + # should not throw an exception because the passed file was closed + f.read() + + def test_reader_seconds(self, request, engine, read_ext): + xfail_datetimes_with_pyxlsb(engine, request) + + # GH 55045 + if engine == "calamine" and read_ext == ".ods": + request.applymarker( + pytest.mark.xfail( + reason="ODS file contains bad datetime (seconds as text)" + ) + ) + + # Test reading times with and without milliseconds. GH5945. + expected = DataFrame.from_dict( + { + "Time": [ + time(1, 2, 3), + time(2, 45, 56, 100000), + time(4, 29, 49, 200000), + time(6, 13, 42, 300000), + time(7, 57, 35, 400000), + time(9, 41, 28, 500000), + time(11, 25, 21, 600000), + time(13, 9, 14, 700000), + time(14, 53, 7, 800000), + time(16, 37, 0, 900000), + time(18, 20, 54), + ] + } + ) + + actual = pd.read_excel("times_1900" + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, expected) + + actual = pd.read_excel("times_1904" + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, expected) + + def test_read_excel_multiindex(self, request, engine, read_ext): + # see gh-4679 + xfail_datetimes_with_pyxlsb(engine, request) + + unit = get_exp_unit(read_ext, engine) + + mi = MultiIndex.from_product([["foo", "bar"], ["a", "b"]]) + mi_file = "testmultiindex" + read_ext + + # "mi_column" sheet + expected = DataFrame( + [ + [1, 2.5, pd.Timestamp("2015-01-01"), True], + [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=mi, + ) + expected[mi[2]] = expected[mi[2]].astype(f"M8[{unit}]") + + actual = pd.read_excel( + mi_file, sheet_name="mi_column", header=[0, 1], index_col=0 + ) + tm.assert_frame_equal(actual, expected) + + # "mi_index" sheet + expected.index = mi + expected.columns = ["a", "b", "c", "d"] + + actual = pd.read_excel(mi_file, sheet_name="mi_index", index_col=[0, 1]) + tm.assert_frame_equal(actual, expected) + + # "both" sheet + expected.columns = mi + + actual = pd.read_excel( + mi_file, sheet_name="both", index_col=[0, 1], header=[0, 1] + ) + tm.assert_frame_equal(actual, expected) + + # "mi_index_name" sheet + expected.columns = ["a", "b", "c", "d"] + expected.index = mi.set_names(["ilvl1", "ilvl2"]) + + actual = pd.read_excel(mi_file, sheet_name="mi_index_name", index_col=[0, 1]) + tm.assert_frame_equal(actual, expected) + + # "mi_column_name" sheet + expected.index = list(range(4)) + expected.columns = mi.set_names(["c1", "c2"]) + actual = pd.read_excel( + mi_file, sheet_name="mi_column_name", header=[0, 1], index_col=0 + ) + tm.assert_frame_equal(actual, expected) + + # see gh-11317 + # "name_with_int" sheet + expected.columns = mi.set_levels([1, 2], level=1).set_names(["c1", "c2"]) + + actual = pd.read_excel( + mi_file, sheet_name="name_with_int", index_col=0, header=[0, 1] + ) + tm.assert_frame_equal(actual, expected) + + # "both_name" sheet + expected.columns = mi.set_names(["c1", "c2"]) + expected.index = mi.set_names(["ilvl1", "ilvl2"]) + + actual = pd.read_excel( + mi_file, sheet_name="both_name", index_col=[0, 1], header=[0, 1] + ) + tm.assert_frame_equal(actual, expected) + + # "both_skiprows" sheet + actual = pd.read_excel( + mi_file, + sheet_name="both_name_skiprows", + index_col=[0, 1], + header=[0, 1], + skiprows=2, + ) + tm.assert_frame_equal(actual, expected) + + @pytest.mark.parametrize( + "sheet_name,idx_lvl2", + [ + ("both_name_blank_after_mi_name", [np.nan, "b", "a", "b"]), + ("both_name_multiple_blanks", [np.nan] * 4), + ], + ) + def test_read_excel_multiindex_blank_after_name( + self, request, engine, read_ext, sheet_name, idx_lvl2 + ): + # GH34673 + xfail_datetimes_with_pyxlsb(engine, request) + + mi_file = "testmultiindex" + read_ext + mi = MultiIndex.from_product([["foo", "bar"], ["a", "b"]], names=["c1", "c2"]) + + unit = get_exp_unit(read_ext, engine) + + expected = DataFrame( + [ + [1, 2.5, pd.Timestamp("2015-01-01"), True], + [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=mi, + index=MultiIndex.from_arrays( + (["foo", "foo", "bar", "bar"], idx_lvl2), + names=["ilvl1", "ilvl2"], + ), + ) + expected[mi[2]] = expected[mi[2]].astype(f"M8[{unit}]") + result = pd.read_excel( + mi_file, + sheet_name=sheet_name, + index_col=[0, 1], + header=[0, 1], + ) + tm.assert_frame_equal(result, expected) + + def test_read_excel_multiindex_header_only(self, read_ext): + # see gh-11733. + # + # Don't try to parse a header name if there isn't one. + mi_file = "testmultiindex" + read_ext + result = pd.read_excel(mi_file, sheet_name="index_col_none", header=[0, 1]) + + exp_columns = MultiIndex.from_product([("A", "B"), ("key", "val")]) + expected = DataFrame([[1, 2, 3, 4]] * 2, columns=exp_columns) + tm.assert_frame_equal(result, expected) + + def test_excel_old_index_format(self, read_ext): + # see gh-4679 + filename = "test_index_name_pre17" + read_ext + + # We detect headers to determine if index names exist, so + # that "index" name in the "names" version of the data will + # now be interpreted as rows that include null data. + data = np.array( + [ + [np.nan, np.nan, np.nan, np.nan, np.nan], + ["R0C0", "R0C1", "R0C2", "R0C3", "R0C4"], + ["R1C0", "R1C1", "R1C2", "R1C3", "R1C4"], + ["R2C0", "R2C1", "R2C2", "R2C3", "R2C4"], + ["R3C0", "R3C1", "R3C2", "R3C3", "R3C4"], + ["R4C0", "R4C1", "R4C2", "R4C3", "R4C4"], + ], + dtype=object, + ) + columns = ["C_l0_g0", "C_l0_g1", "C_l0_g2", "C_l0_g3", "C_l0_g4"] + mi = MultiIndex( + levels=[ + ["R0", "R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], + ["R1", "R_l1_g0", "R_l1_g1", "R_l1_g2", "R_l1_g3", "R_l1_g4"], + ], + codes=[[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]], + names=[None, None], + ) + si = Index( + ["R0", "R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], name=None + ) + + expected = DataFrame(data, index=si, columns=columns) + + actual = pd.read_excel(filename, sheet_name="single_names", index_col=0) + tm.assert_frame_equal(actual, expected) + + expected.index = mi + + actual = pd.read_excel(filename, sheet_name="multi_names", index_col=[0, 1]) + tm.assert_frame_equal(actual, expected) + + # The analogous versions of the "names" version data + # where there are explicitly no names for the indices. + data = np.array( + [ + ["R0C0", "R0C1", "R0C2", "R0C3", "R0C4"], + ["R1C0", "R1C1", "R1C2", "R1C3", "R1C4"], + ["R2C0", "R2C1", "R2C2", "R2C3", "R2C4"], + ["R3C0", "R3C1", "R3C2", "R3C3", "R3C4"], + ["R4C0", "R4C1", "R4C2", "R4C3", "R4C4"], + ] + ) + columns = ["C_l0_g0", "C_l0_g1", "C_l0_g2", "C_l0_g3", "C_l0_g4"] + mi = MultiIndex( + levels=[ + ["R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], + ["R_l1_g0", "R_l1_g1", "R_l1_g2", "R_l1_g3", "R_l1_g4"], + ], + codes=[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], + names=[None, None], + ) + si = Index(["R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], name=None) + + expected = DataFrame(data, index=si, columns=columns) + + actual = pd.read_excel(filename, sheet_name="single_no_names", index_col=0) + tm.assert_frame_equal(actual, expected) + + expected.index = mi + + actual = pd.read_excel(filename, sheet_name="multi_no_names", index_col=[0, 1]) + tm.assert_frame_equal(actual, expected) + + def test_read_excel_bool_header_arg(self, read_ext): + # GH 6114 + msg = "Passing a bool to header is invalid" + for arg in [True, False]: + with pytest.raises(TypeError, match=msg): + pd.read_excel("test1" + read_ext, header=arg) + + def test_read_excel_skiprows(self, request, engine, read_ext): + # GH 4903 + xfail_datetimes_with_pyxlsb(engine, request) + + unit = get_exp_unit(read_ext, engine) + + actual = pd.read_excel( + "testskiprows" + read_ext, sheet_name="skiprows_list", skiprows=[0, 2] + ) + expected = DataFrame( + [ + [1, 2.5, pd.Timestamp("2015-01-01"), True], + [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=["a", "b", "c", "d"], + ) + expected["c"] = expected["c"].astype(f"M8[{unit}]") + tm.assert_frame_equal(actual, expected) + + actual = pd.read_excel( + "testskiprows" + read_ext, + sheet_name="skiprows_list", + skiprows=np.array([0, 2]), + ) + tm.assert_frame_equal(actual, expected) + + # GH36435 + actual = pd.read_excel( + "testskiprows" + read_ext, + sheet_name="skiprows_list", + skiprows=lambda x: x in [0, 2], + ) + tm.assert_frame_equal(actual, expected) + + actual = pd.read_excel( + "testskiprows" + read_ext, + sheet_name="skiprows_list", + skiprows=3, + names=["a", "b", "c", "d"], + ) + expected = DataFrame( + [ + # [1, 2.5, pd.Timestamp("2015-01-01"), True], + [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=["a", "b", "c", "d"], + ) + expected["c"] = expected["c"].astype(f"M8[{unit}]") + tm.assert_frame_equal(actual, expected) + + def test_read_excel_skiprows_callable_not_in(self, request, engine, read_ext): + # GH 4903 + xfail_datetimes_with_pyxlsb(engine, request) + unit = get_exp_unit(read_ext, engine) + + actual = pd.read_excel( + "testskiprows" + read_ext, + sheet_name="skiprows_list", + skiprows=lambda x: x not in [1, 3, 5], + ) + expected = DataFrame( + [ + [1, 2.5, pd.Timestamp("2015-01-01"), True], + # [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + # [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=["a", "b", "c", "d"], + ) + expected["c"] = expected["c"].astype(f"M8[{unit}]") + tm.assert_frame_equal(actual, expected) + + def test_read_excel_nrows(self, read_ext): + # GH 16645 + num_rows_to_pull = 5 + actual = pd.read_excel("test1" + read_ext, nrows=num_rows_to_pull) + expected = pd.read_excel("test1" + read_ext) + expected = expected[:num_rows_to_pull] + tm.assert_frame_equal(actual, expected) + + def test_read_excel_nrows_greater_than_nrows_in_file(self, read_ext): + # GH 16645 + expected = pd.read_excel("test1" + read_ext) + num_records_in_file = len(expected) + num_rows_to_pull = num_records_in_file + 10 + actual = pd.read_excel("test1" + read_ext, nrows=num_rows_to_pull) + tm.assert_frame_equal(actual, expected) + + def test_read_excel_nrows_non_integer_parameter(self, read_ext): + # GH 16645 + msg = "'nrows' must be an integer >=0" + with pytest.raises(ValueError, match=msg): + pd.read_excel("test1" + read_ext, nrows="5") + + @pytest.mark.parametrize( + "filename,sheet_name,header,index_col,skiprows", + [ + ("testmultiindex", "mi_column", [0, 1], 0, None), + ("testmultiindex", "mi_index", None, [0, 1], None), + ("testmultiindex", "both", [0, 1], [0, 1], None), + ("testmultiindex", "mi_column_name", [0, 1], 0, None), + ("testskiprows", "skiprows_list", None, None, [0, 2]), + ("testskiprows", "skiprows_list", None, None, lambda x: x in (0, 2)), + ], + ) + def test_read_excel_nrows_params( + self, read_ext, filename, sheet_name, header, index_col, skiprows + ): + """ + For various parameters, we should get the same result whether we + limit the rows during load (nrows=3) or after (df.iloc[:3]). + """ + # GH 46894 + expected = pd.read_excel( + filename + read_ext, + sheet_name=sheet_name, + header=header, + index_col=index_col, + skiprows=skiprows, + ).iloc[:3] + actual = pd.read_excel( + filename + read_ext, + sheet_name=sheet_name, + header=header, + index_col=index_col, + skiprows=skiprows, + nrows=3, + ) + tm.assert_frame_equal(actual, expected) + + def test_deprecated_kwargs(self, read_ext): + with pytest.raises(TypeError, match="but 3 positional arguments"): + pd.read_excel("test1" + read_ext, "Sheet1", 0) + + def test_no_header_with_list_index_col(self, read_ext): + # GH 31783 + file_name = "testmultiindex" + read_ext + data = [("B", "B"), ("key", "val"), (3, 4), (3, 4)] + idx = MultiIndex.from_tuples( + [("A", "A"), ("key", "val"), (1, 2), (1, 2)], names=(0, 1) + ) + expected = DataFrame(data, index=idx, columns=(2, 3)) + result = pd.read_excel( + file_name, sheet_name="index_col_none", index_col=[0, 1], header=None + ) + tm.assert_frame_equal(expected, result) + + def test_one_col_noskip_blank_line(self, read_ext): + # GH 39808 + file_name = "one_col_blank_line" + read_ext + data = [0.5, np.nan, 1, 2] + expected = DataFrame(data, columns=["numbers"]) + result = pd.read_excel(file_name) + tm.assert_frame_equal(result, expected) + + def test_multiheader_two_blank_lines(self, read_ext): + # GH 40442 + file_name = "testmultiindex" + read_ext + columns = MultiIndex.from_tuples([("a", "A"), ("b", "B")]) + data = [[np.nan, np.nan], [np.nan, np.nan], [1, 3], [2, 4]] + expected = DataFrame(data, columns=columns) + result = pd.read_excel( + file_name, sheet_name="mi_column_empty_rows", header=[0, 1] + ) + tm.assert_frame_equal(result, expected) + + def test_trailing_blanks(self, read_ext): + """ + Sheets can contain blank cells with no data. Some of our readers + were including those cells, creating many empty rows and columns + """ + file_name = "trailing_blanks" + read_ext + result = pd.read_excel(file_name) + assert result.shape == (3, 3) + + def test_ignore_chartsheets_by_str(self, request, engine, read_ext): + # GH 41448 + if read_ext == ".ods": + pytest.skip("chartsheets do not exist in the ODF format") + if engine == "pyxlsb": + request.applymarker( + pytest.mark.xfail( + reason="pyxlsb can't distinguish chartsheets from worksheets" + ) + ) + with pytest.raises(ValueError, match="Worksheet named 'Chart1' not found"): + pd.read_excel("chartsheet" + read_ext, sheet_name="Chart1") + + def test_ignore_chartsheets_by_int(self, request, engine, read_ext): + # GH 41448 + if read_ext == ".ods": + pytest.skip("chartsheets do not exist in the ODF format") + if engine == "pyxlsb": + request.applymarker( + pytest.mark.xfail( + reason="pyxlsb can't distinguish chartsheets from worksheets" + ) + ) + with pytest.raises( + ValueError, match="Worksheet index 1 is invalid, 1 worksheets found" + ): + pd.read_excel("chartsheet" + read_ext, sheet_name=1) + + def test_euro_decimal_format(self, read_ext): + # copied from read_csv + result = pd.read_excel("test_decimal" + read_ext, decimal=",", skiprows=1) + expected = DataFrame( + [ + [1, 1521.1541, 187101.9543, "ABC", "poi", 4.738797819], + [2, 121.12, 14897.76, "DEF", "uyt", 0.377320872], + [3, 878.158, 108013.434, "GHI", "rez", 2.735694704], + ], + columns=["Id", "Number1", "Number2", "Text1", "Text2", "Number3"], + ) + tm.assert_frame_equal(result, expected) + + +class TestExcelFileRead: + def test_deprecate_bytes_input(self, engine, read_ext): + # GH 53830 + msg = ( + "Passing bytes to 'read_excel' is deprecated and " + "will be removed in a future version. To read from a " + "byte string, wrap it in a `BytesIO` object." + ) + + with tm.assert_produces_warning( + FutureWarning, match=msg, raise_on_extra_warnings=False + ): + with open("test1" + read_ext, "rb") as f: + pd.read_excel(f.read(), engine=engine) + + @pytest.fixture(autouse=True) + def cd_and_set_engine(self, engine, datapath, monkeypatch): + """ + Change directory and set engine for ExcelFile objects. + """ + func = partial(pd.ExcelFile, engine=engine) + monkeypatch.chdir(datapath("io", "data", "excel")) + monkeypatch.setattr(pd, "ExcelFile", func) + + def test_engine_used(self, read_ext, engine): + expected_defaults = { + "xlsx": "openpyxl", + "xlsm": "openpyxl", + "xlsb": "pyxlsb", + "xls": "xlrd", + "ods": "odf", + } + + with pd.ExcelFile("test1" + read_ext) as excel: + result = excel.engine + + if engine is not None: + expected = engine + else: + expected = expected_defaults[read_ext[1:]] + assert result == expected + + def test_excel_passes_na(self, read_ext): + with pd.ExcelFile("test4" + read_ext) as excel: + parsed = pd.read_excel( + excel, sheet_name="Sheet1", keep_default_na=False, na_values=["apple"] + ) + expected = DataFrame( + [["NA"], [1], ["NA"], [np.nan], ["rabbit"]], columns=["Test"] + ) + tm.assert_frame_equal(parsed, expected) + + with pd.ExcelFile("test4" + read_ext) as excel: + parsed = pd.read_excel( + excel, sheet_name="Sheet1", keep_default_na=True, na_values=["apple"] + ) + expected = DataFrame( + [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]], columns=["Test"] + ) + tm.assert_frame_equal(parsed, expected) + + # 13967 + with pd.ExcelFile("test5" + read_ext) as excel: + parsed = pd.read_excel( + excel, sheet_name="Sheet1", keep_default_na=False, na_values=["apple"] + ) + expected = DataFrame( + [["1.#QNAN"], [1], ["nan"], [np.nan], ["rabbit"]], columns=["Test"] + ) + tm.assert_frame_equal(parsed, expected) + + with pd.ExcelFile("test5" + read_ext) as excel: + parsed = pd.read_excel( + excel, sheet_name="Sheet1", keep_default_na=True, na_values=["apple"] + ) + expected = DataFrame( + [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]], columns=["Test"] + ) + tm.assert_frame_equal(parsed, expected) + + @pytest.mark.parametrize("na_filter", [None, True, False]) + def test_excel_passes_na_filter(self, read_ext, na_filter): + # gh-25453 + kwargs = {} + + if na_filter is not None: + kwargs["na_filter"] = na_filter + + with pd.ExcelFile("test5" + read_ext) as excel: + parsed = pd.read_excel( + excel, + sheet_name="Sheet1", + keep_default_na=True, + na_values=["apple"], + **kwargs, + ) + + if na_filter is False: + expected = [["1.#QNAN"], [1], ["nan"], ["apple"], ["rabbit"]] + else: + expected = [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]] + + expected = DataFrame(expected, columns=["Test"]) + tm.assert_frame_equal(parsed, expected) + + def test_excel_table_sheet_by_index(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + with pd.ExcelFile("test1" + read_ext) as excel: + df1 = pd.read_excel(excel, sheet_name=0, index_col=0) + df2 = pd.read_excel(excel, sheet_name=1, skiprows=[1], index_col=0) + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + with pd.ExcelFile("test1" + read_ext) as excel: + df1 = excel.parse(0, index_col=0) + df2 = excel.parse(1, skiprows=[1], index_col=0) + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + with pd.ExcelFile("test1" + read_ext) as excel: + df3 = pd.read_excel(excel, sheet_name=0, index_col=0, skipfooter=1) + tm.assert_frame_equal(df3, df1.iloc[:-1]) + + with pd.ExcelFile("test1" + read_ext) as excel: + df3 = excel.parse(0, index_col=0, skipfooter=1) + + tm.assert_frame_equal(df3, df1.iloc[:-1]) + + def test_sheet_name(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + filename = "test1" + sheet_name = "Sheet1" + + with pd.ExcelFile(filename + read_ext) as excel: + df1_parse = excel.parse(sheet_name=sheet_name, index_col=0) # doc + + with pd.ExcelFile(filename + read_ext) as excel: + df2_parse = excel.parse(index_col=0, sheet_name=sheet_name) + + tm.assert_frame_equal(df1_parse, expected) + tm.assert_frame_equal(df2_parse, expected) + + @pytest.mark.parametrize( + "sheet_name", + [3, [0, 3], [3, 0], "Sheet4", ["Sheet1", "Sheet4"], ["Sheet4", "Sheet1"]], + ) + def test_bad_sheetname_raises(self, read_ext, sheet_name): + # GH 39250 + msg = "Worksheet index 3 is invalid|Worksheet named 'Sheet4' not found" + with pytest.raises(ValueError, match=msg): + with pd.ExcelFile("blank" + read_ext) as excel: + excel.parse(sheet_name=sheet_name) + + def test_excel_read_buffer(self, engine, read_ext): + pth = "test1" + read_ext + expected = pd.read_excel(pth, sheet_name="Sheet1", index_col=0, engine=engine) + + with open(pth, "rb") as f: + with pd.ExcelFile(f) as xls: + actual = pd.read_excel(xls, sheet_name="Sheet1", index_col=0) + + tm.assert_frame_equal(expected, actual) + + def test_reader_closes_file(self, engine, read_ext): + with open("test1" + read_ext, "rb") as f: + with pd.ExcelFile(f) as xlsx: + # parses okay + pd.read_excel(xlsx, sheet_name="Sheet1", index_col=0, engine=engine) + + assert f.closed + + def test_conflicting_excel_engines(self, read_ext): + # GH 26566 + msg = "Engine should not be specified when passing an ExcelFile" + + with pd.ExcelFile("test1" + read_ext) as xl: + with pytest.raises(ValueError, match=msg): + pd.read_excel(xl, engine="foo") + + def test_excel_read_binary(self, engine, read_ext): + # GH 15914 + expected = pd.read_excel("test1" + read_ext, engine=engine) + + with open("test1" + read_ext, "rb") as f: + data = f.read() + + actual = pd.read_excel(BytesIO(data), engine=engine) + tm.assert_frame_equal(expected, actual) + + def test_excel_read_binary_via_read_excel(self, read_ext, engine): + # GH 38424 + with open("test1" + read_ext, "rb") as f: + result = pd.read_excel(f, engine=engine) + expected = pd.read_excel("test1" + read_ext, engine=engine) + tm.assert_frame_equal(result, expected) + + def test_read_excel_header_index_out_of_range(self, engine): + # GH#43143 + with open("df_header_oob.xlsx", "rb") as f: + with pytest.raises(ValueError, match="exceeds maximum"): + pd.read_excel(f, header=[0, 1]) + + @pytest.mark.parametrize("filename", ["df_empty.xlsx", "df_equals.xlsx"]) + def test_header_with_index_col(self, filename): + # GH 33476 + idx = Index(["Z"], name="I2") + cols = MultiIndex.from_tuples([("A", "B"), ("A", "B.1")], names=["I11", "I12"]) + expected = DataFrame([[1, 3]], index=idx, columns=cols, dtype="int64") + result = pd.read_excel( + filename, sheet_name="Sheet1", index_col=0, header=[0, 1] + ) + tm.assert_frame_equal(expected, result) + + def test_read_datetime_multiindex(self, request, engine, read_ext): + # GH 34748 + xfail_datetimes_with_pyxlsb(engine, request) + + f = "test_datetime_mi" + read_ext + with pd.ExcelFile(f) as excel: + actual = pd.read_excel(excel, header=[0, 1], index_col=0, engine=engine) + + unit = get_exp_unit(read_ext, engine) + dti = pd.DatetimeIndex(["2020-02-29", "2020-03-01"], dtype=f"M8[{unit}]") + expected_column_index = MultiIndex.from_arrays( + [dti[:1], dti[1:]], + names=[ + dti[0].to_pydatetime(), + dti[1].to_pydatetime(), + ], + ) + expected = DataFrame([], index=[], columns=expected_column_index) + + tm.assert_frame_equal(expected, actual) + + def test_engine_invalid_option(self, read_ext): + # read_ext includes the '.' hence the weird formatting + with pytest.raises(ValueError, match="Value must be one of *"): + with pd.option_context(f"io.excel{read_ext}.reader", "abc"): + pass + + def test_ignore_chartsheets(self, request, engine, read_ext): + # GH 41448 + if read_ext == ".ods": + pytest.skip("chartsheets do not exist in the ODF format") + if engine == "pyxlsb": + request.applymarker( + pytest.mark.xfail( + reason="pyxlsb can't distinguish chartsheets from worksheets" + ) + ) + with pd.ExcelFile("chartsheet" + read_ext) as excel: + assert excel.sheet_names == ["Sheet1"] + + def test_corrupt_files_closed(self, engine, read_ext): + # GH41778 + errors = (BadZipFile,) + if engine is None: + pytest.skip(f"Invalid test for engine={engine}") + elif engine == "xlrd": + import xlrd + + errors = (BadZipFile, xlrd.biffh.XLRDError) + elif engine == "calamine": + from python_calamine import CalamineError + + errors = (CalamineError,) + + with tm.ensure_clean(f"corrupt{read_ext}") as file: + Path(file).write_text("corrupt", encoding="utf-8") + with tm.assert_produces_warning(False): + try: + pd.ExcelFile(file, engine=engine) + except errors: + pass diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_style.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_style.py new file mode 100644 index 0000000000000000000000000000000000000000..89615172688d7b56fbb070dbcd4750365d7d612d --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_style.py @@ -0,0 +1,298 @@ +import contextlib +import time + +import numpy as np +import pytest + +from pandas.compat import is_platform_windows +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + read_excel, +) +import pandas._testing as tm + +from pandas.io.excel import ExcelWriter +from pandas.io.formats.excel import ExcelFormatter + +pytest.importorskip("jinja2") +# jinja2 is currently required for Styler.__init__(). Technically Styler.to_excel +# could compute styles and render to excel without jinja2, since there is no +# 'template' file, but this needs the import error to delayed until render time. + +if is_platform_windows(): + pytestmark = pytest.mark.single_cpu + + +def assert_equal_cell_styles(cell1, cell2): + # TODO: should find a better way to check equality + assert cell1.alignment.__dict__ == cell2.alignment.__dict__ + assert cell1.border.__dict__ == cell2.border.__dict__ + assert cell1.fill.__dict__ == cell2.fill.__dict__ + assert cell1.font.__dict__ == cell2.font.__dict__ + assert cell1.number_format == cell2.number_format + assert cell1.protection.__dict__ == cell2.protection.__dict__ + + +@pytest.mark.parametrize( + "engine", + ["xlsxwriter", "openpyxl"], +) +def test_styler_to_excel_unstyled(engine): + # compare DataFrame.to_excel and Styler.to_excel when no styles applied + pytest.importorskip(engine) + df = DataFrame(np.random.default_rng(2).standard_normal((2, 2))) + with tm.ensure_clean(".xlsx") as path: + with ExcelWriter(path, engine=engine) as writer: + df.to_excel(writer, sheet_name="dataframe") + df.style.to_excel(writer, sheet_name="unstyled") + + openpyxl = pytest.importorskip("openpyxl") # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(path)) as wb: + for col1, col2 in zip(wb["dataframe"].columns, wb["unstyled"].columns): + assert len(col1) == len(col2) + for cell1, cell2 in zip(col1, col2): + assert cell1.value == cell2.value + assert_equal_cell_styles(cell1, cell2) + + +shared_style_params = [ + ( + "background-color: #111222", + ["fill", "fgColor", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ( + "color: #111222", + ["font", "color", "value"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("font-family: Arial;", ["font", "name"], "arial"), + ("font-weight: bold;", ["font", "b"], True), + ("font-style: italic;", ["font", "i"], True), + ("text-decoration: underline;", ["font", "u"], "single"), + ("number-format: $??,???.00;", ["number_format"], "$??,???.00"), + ("text-align: left;", ["alignment", "horizontal"], "left"), + ( + "vertical-align: bottom;", + ["alignment", "vertical"], + {"xlsxwriter": None, "openpyxl": "bottom"}, # xlsxwriter Fails + ), + ("vertical-align: middle;", ["alignment", "vertical"], "center"), + # Border widths + ("border-left: 2pt solid red", ["border", "left", "style"], "medium"), + ("border-left: 1pt dotted red", ["border", "left", "style"], "dotted"), + ("border-left: 2pt dotted red", ["border", "left", "style"], "mediumDashDotDot"), + ("border-left: 1pt dashed red", ["border", "left", "style"], "dashed"), + ("border-left: 2pt dashed red", ["border", "left", "style"], "mediumDashed"), + ("border-left: 1pt solid red", ["border", "left", "style"], "thin"), + ("border-left: 3pt solid red", ["border", "left", "style"], "thick"), + # Border expansion + ( + "border-left: 2pt solid #111222", + ["border", "left", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("border: 1pt solid red", ["border", "top", "style"], "thin"), + ( + "border: 1pt solid #111222", + ["border", "top", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("border: 1pt solid red", ["border", "right", "style"], "thin"), + ( + "border: 1pt solid #111222", + ["border", "right", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("border: 1pt solid red", ["border", "bottom", "style"], "thin"), + ( + "border: 1pt solid #111222", + ["border", "bottom", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("border: 1pt solid red", ["border", "left", "style"], "thin"), + ( + "border: 1pt solid #111222", + ["border", "left", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + # Border styles + ( + "border-left-style: hair; border-left-color: black", + ["border", "left", "style"], + "hair", + ), +] + + +@pytest.mark.parametrize( + "engine", + ["xlsxwriter", "openpyxl"], +) +@pytest.mark.parametrize("css, attrs, expected", shared_style_params) +def test_styler_to_excel_basic(engine, css, attrs, expected): + pytest.importorskip(engine) + df = DataFrame(np.random.default_rng(2).standard_normal((1, 1))) + styler = df.style.map(lambda x: css) + + with tm.ensure_clean(".xlsx") as path: + with ExcelWriter(path, engine=engine) as writer: + df.to_excel(writer, sheet_name="dataframe") + styler.to_excel(writer, sheet_name="styled") + + openpyxl = pytest.importorskip("openpyxl") # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(path)) as wb: + # test unstyled data cell does not have expected styles + # test styled cell has expected styles + u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2) + for attr in attrs: + u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr) + + if isinstance(expected, dict): + assert u_cell is None or u_cell != expected[engine] + assert s_cell == expected[engine] + else: + assert u_cell is None or u_cell != expected + assert s_cell == expected + + +@pytest.mark.parametrize( + "engine", + ["xlsxwriter", "openpyxl"], +) +@pytest.mark.parametrize("css, attrs, expected", shared_style_params) +def test_styler_to_excel_basic_indexes(engine, css, attrs, expected): + pytest.importorskip(engine) + df = DataFrame(np.random.default_rng(2).standard_normal((1, 1))) + + styler = df.style + styler.map_index(lambda x: css, axis=0) + styler.map_index(lambda x: css, axis=1) + + null_styler = df.style + null_styler.map(lambda x: "null: css;") + null_styler.map_index(lambda x: "null: css;", axis=0) + null_styler.map_index(lambda x: "null: css;", axis=1) + + with tm.ensure_clean(".xlsx") as path: + with ExcelWriter(path, engine=engine) as writer: + null_styler.to_excel(writer, sheet_name="null_styled") + styler.to_excel(writer, sheet_name="styled") + + openpyxl = pytest.importorskip("openpyxl") # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(path)) as wb: + # test null styled index cells does not have expected styles + # test styled cell has expected styles + ui_cell, si_cell = wb["null_styled"].cell(2, 1), wb["styled"].cell(2, 1) + uc_cell, sc_cell = wb["null_styled"].cell(1, 2), wb["styled"].cell(1, 2) + for attr in attrs: + ui_cell, si_cell = getattr(ui_cell, attr, None), getattr(si_cell, attr) + uc_cell, sc_cell = getattr(uc_cell, attr, None), getattr(sc_cell, attr) + + if isinstance(expected, dict): + assert ui_cell is None or ui_cell != expected[engine] + assert si_cell == expected[engine] + assert uc_cell is None or uc_cell != expected[engine] + assert sc_cell == expected[engine] + else: + assert ui_cell is None or ui_cell != expected + assert si_cell == expected + assert uc_cell is None or uc_cell != expected + assert sc_cell == expected + + +# From https://openpyxl.readthedocs.io/en/stable/api/openpyxl.styles.borders.html +# Note: Leaving behavior of "width"-type styles undefined; user should use border-width +# instead +excel_border_styles = [ + # "thin", + "dashed", + "mediumDashDot", + "dashDotDot", + "hair", + "dotted", + "mediumDashDotDot", + # "medium", + "double", + "dashDot", + "slantDashDot", + # "thick", + "mediumDashed", +] + + +@pytest.mark.parametrize( + "engine", + ["xlsxwriter", "openpyxl"], +) +@pytest.mark.parametrize("border_style", excel_border_styles) +def test_styler_to_excel_border_style(engine, border_style): + css = f"border-left: {border_style} black thin" + attrs = ["border", "left", "style"] + expected = border_style + + pytest.importorskip(engine) + df = DataFrame(np.random.default_rng(2).standard_normal((1, 1))) + styler = df.style.map(lambda x: css) + + with tm.ensure_clean(".xlsx") as path: + with ExcelWriter(path, engine=engine) as writer: + df.to_excel(writer, sheet_name="dataframe") + styler.to_excel(writer, sheet_name="styled") + + openpyxl = pytest.importorskip("openpyxl") # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(path)) as wb: + # test unstyled data cell does not have expected styles + # test styled cell has expected styles + u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2) + for attr in attrs: + u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr) + + if isinstance(expected, dict): + assert u_cell is None or u_cell != expected[engine] + assert s_cell == expected[engine] + else: + assert u_cell is None or u_cell != expected + assert s_cell == expected + + +def test_styler_custom_converter(): + openpyxl = pytest.importorskip("openpyxl") + + def custom_converter(css): + return {"font": {"color": {"rgb": "111222"}}} + + df = DataFrame(np.random.default_rng(2).standard_normal((1, 1))) + styler = df.style.map(lambda x: "color: #888999") + with tm.ensure_clean(".xlsx") as path: + with ExcelWriter(path, engine="openpyxl") as writer: + ExcelFormatter(styler, style_converter=custom_converter).write( + writer, sheet_name="custom" + ) + + with contextlib.closing(openpyxl.load_workbook(path)) as wb: + assert wb["custom"].cell(2, 2).font.color.value == "00111222" + + +@pytest.mark.single_cpu +@td.skip_if_not_us_locale +def test_styler_to_s3(s3_public_bucket, s3so): + # GH#46381 + + mock_bucket_name, target_file = s3_public_bucket.name, "test.xlsx" + df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]}) + styler = df.style.set_sticky(axis="index") + styler.to_excel(f"s3://{mock_bucket_name}/{target_file}", storage_options=s3so) + timeout = 5 + while True: + if target_file in (obj.key for obj in s3_public_bucket.objects.all()): + break + time.sleep(0.1) + timeout -= 0.1 + assert timeout > 0, "Timed out waiting for file to appear on moto" + result = read_excel( + f"s3://{mock_bucket_name}/{target_file}", index_col=0, storage_options=s3so + ) + tm.assert_frame_equal(result, df) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_writers.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_writers.py new file mode 100644 index 0000000000000000000000000000000000000000..d6e99de4f9d91a6da9b96c1601511d00ed5e36e2 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_writers.py @@ -0,0 +1,1514 @@ +from datetime import ( + date, + datetime, + timedelta, +) +from functools import partial +from io import BytesIO +import os +import re + +import numpy as np +import pytest + +from pandas.compat import is_platform_windows +from pandas.compat._constants import PY310 +from pandas.compat._optional import import_optional_dependency +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + date_range, + option_context, +) +import pandas._testing as tm + +from pandas.io.excel import ( + ExcelFile, + ExcelWriter, + _OpenpyxlWriter, + _XlsxWriter, + register_writer, +) +from pandas.io.excel._util import _writers + +if is_platform_windows(): + pytestmark = pytest.mark.single_cpu + + +def get_exp_unit(path: str) -> str: + return "ns" + + +@pytest.fixture +def frame(float_frame): + """ + Returns the first ten items in fixture "float_frame". + """ + return float_frame[:10] + + +@pytest.fixture(params=[True, False]) +def merge_cells(request): + return request.param + + +@pytest.fixture +def path(ext): + """ + Fixture to open file for use in each test case. + """ + with tm.ensure_clean(ext) as file_path: + yield file_path + + +@pytest.fixture +def set_engine(engine, ext): + """ + Fixture to set engine for use in each test case. + + Rather than requiring `engine=...` to be provided explicitly as an + argument in each test, this fixture sets a global option to dictate + which engine should be used to write Excel files. After executing + the test it rolls back said change to the global option. + """ + option_name = f"io.excel.{ext.strip('.')}.writer" + with option_context(option_name, engine): + yield + + +@pytest.mark.parametrize( + "ext", + [ + pytest.param(".xlsx", marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")]), + pytest.param(".xlsm", marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")]), + pytest.param( + ".xlsx", marks=[td.skip_if_no("xlsxwriter"), td.skip_if_no("xlrd")] + ), + pytest.param(".ods", marks=td.skip_if_no("odf")), + ], +) +class TestRoundTrip: + @pytest.mark.parametrize( + "header,expected", + [(None, DataFrame([np.nan] * 4)), (0, DataFrame({"Unnamed: 0": [np.nan] * 3}))], + ) + def test_read_one_empty_col_no_header(self, ext, header, expected): + # xref gh-12292 + filename = "no_header" + df = DataFrame([["", 1, 100], ["", 2, 200], ["", 3, 300], ["", 4, 400]]) + + with tm.ensure_clean(ext) as path: + df.to_excel(path, sheet_name=filename, index=False, header=False) + result = pd.read_excel( + path, sheet_name=filename, usecols=[0], header=header + ) + + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "header,expected", + [(None, DataFrame([0] + [np.nan] * 4)), (0, DataFrame([np.nan] * 4))], + ) + def test_read_one_empty_col_with_header(self, ext, header, expected): + filename = "with_header" + df = DataFrame([["", 1, 100], ["", 2, 200], ["", 3, 300], ["", 4, 400]]) + + with tm.ensure_clean(ext) as path: + df.to_excel(path, sheet_name="with_header", index=False, header=True) + result = pd.read_excel( + path, sheet_name=filename, usecols=[0], header=header + ) + + tm.assert_frame_equal(result, expected) + + def test_set_column_names_in_parameter(self, ext): + # GH 12870 : pass down column names associated with + # keyword argument names + refdf = DataFrame([[1, "foo"], [2, "bar"], [3, "baz"]], columns=["a", "b"]) + + with tm.ensure_clean(ext) as pth: + with ExcelWriter(pth) as writer: + refdf.to_excel( + writer, sheet_name="Data_no_head", header=False, index=False + ) + refdf.to_excel(writer, sheet_name="Data_with_head", index=False) + + refdf.columns = ["A", "B"] + + with ExcelFile(pth) as reader: + xlsdf_no_head = pd.read_excel( + reader, sheet_name="Data_no_head", header=None, names=["A", "B"] + ) + xlsdf_with_head = pd.read_excel( + reader, + sheet_name="Data_with_head", + index_col=None, + names=["A", "B"], + ) + + tm.assert_frame_equal(xlsdf_no_head, refdf) + tm.assert_frame_equal(xlsdf_with_head, refdf) + + def test_creating_and_reading_multiple_sheets(self, ext): + # see gh-9450 + # + # Test reading multiple sheets, from a runtime + # created Excel file with multiple sheets. + def tdf(col_sheet_name): + d, i = [11, 22, 33], [1, 2, 3] + return DataFrame(d, i, columns=[col_sheet_name]) + + sheets = ["AAA", "BBB", "CCC"] + + dfs = [tdf(s) for s in sheets] + dfs = dict(zip(sheets, dfs)) + + with tm.ensure_clean(ext) as pth: + with ExcelWriter(pth) as ew: + for sheetname, df in dfs.items(): + df.to_excel(ew, sheet_name=sheetname) + + dfs_returned = pd.read_excel(pth, sheet_name=sheets, index_col=0) + + for s in sheets: + tm.assert_frame_equal(dfs[s], dfs_returned[s]) + + def test_read_excel_multiindex_empty_level(self, ext): + # see gh-12453 + with tm.ensure_clean(ext) as path: + df = DataFrame( + { + ("One", "x"): {0: 1}, + ("Two", "X"): {0: 3}, + ("Two", "Y"): {0: 7}, + ("Zero", ""): {0: 0}, + } + ) + + expected = DataFrame( + { + ("One", "x"): {0: 1}, + ("Two", "X"): {0: 3}, + ("Two", "Y"): {0: 7}, + ("Zero", "Unnamed: 4_level_1"): {0: 0}, + } + ) + + df.to_excel(path) + actual = pd.read_excel(path, header=[0, 1], index_col=0) + tm.assert_frame_equal(actual, expected) + + df = DataFrame( + { + ("Beg", ""): {0: 0}, + ("Middle", "x"): {0: 1}, + ("Tail", "X"): {0: 3}, + ("Tail", "Y"): {0: 7}, + } + ) + + expected = DataFrame( + { + ("Beg", "Unnamed: 1_level_1"): {0: 0}, + ("Middle", "x"): {0: 1}, + ("Tail", "X"): {0: 3}, + ("Tail", "Y"): {0: 7}, + } + ) + + df.to_excel(path) + actual = pd.read_excel(path, header=[0, 1], index_col=0) + tm.assert_frame_equal(actual, expected) + + @pytest.mark.parametrize("c_idx_names", ["a", None]) + @pytest.mark.parametrize("r_idx_names", ["b", None]) + @pytest.mark.parametrize("c_idx_levels", [1, 3]) + @pytest.mark.parametrize("r_idx_levels", [1, 3]) + def test_excel_multindex_roundtrip( + self, ext, c_idx_names, r_idx_names, c_idx_levels, r_idx_levels, request + ): + # see gh-4679 + with tm.ensure_clean(ext) as pth: + # Empty name case current read in as + # unnamed levels, not Nones. + check_names = bool(r_idx_names) or r_idx_levels <= 1 + + if c_idx_levels == 1: + columns = Index(list("abcde")) + else: + columns = MultiIndex.from_arrays( + [range(5) for _ in range(c_idx_levels)], + names=[f"{c_idx_names}-{i}" for i in range(c_idx_levels)], + ) + if r_idx_levels == 1: + index = Index(list("ghijk")) + else: + index = MultiIndex.from_arrays( + [range(5) for _ in range(r_idx_levels)], + names=[f"{r_idx_names}-{i}" for i in range(r_idx_levels)], + ) + df = DataFrame( + 1.1 * np.ones((5, 5)), + columns=columns, + index=index, + ) + df.to_excel(pth) + + act = pd.read_excel( + pth, + index_col=list(range(r_idx_levels)), + header=list(range(c_idx_levels)), + ) + tm.assert_frame_equal(df, act, check_names=check_names) + + df.iloc[0, :] = np.nan + df.to_excel(pth) + + act = pd.read_excel( + pth, + index_col=list(range(r_idx_levels)), + header=list(range(c_idx_levels)), + ) + tm.assert_frame_equal(df, act, check_names=check_names) + + df.iloc[-1, :] = np.nan + df.to_excel(pth) + act = pd.read_excel( + pth, + index_col=list(range(r_idx_levels)), + header=list(range(c_idx_levels)), + ) + tm.assert_frame_equal(df, act, check_names=check_names) + + def test_read_excel_parse_dates(self, ext): + # see gh-11544, gh-12051 + df = DataFrame( + {"col": [1, 2, 3], "date_strings": date_range("2012-01-01", periods=3)} + ) + df2 = df.copy() + df2["date_strings"] = df2["date_strings"].dt.strftime("%m/%d/%Y") + + with tm.ensure_clean(ext) as pth: + df2.to_excel(pth) + + res = pd.read_excel(pth, index_col=0) + tm.assert_frame_equal(df2, res) + + res = pd.read_excel(pth, parse_dates=["date_strings"], index_col=0) + tm.assert_frame_equal(df, res) + + date_parser = lambda x: datetime.strptime(x, "%m/%d/%Y") + with tm.assert_produces_warning( + FutureWarning, + match="use 'date_format' instead", + raise_on_extra_warnings=False, + ): + res = pd.read_excel( + pth, + parse_dates=["date_strings"], + date_parser=date_parser, + index_col=0, + ) + tm.assert_frame_equal(df, res) + res = pd.read_excel( + pth, parse_dates=["date_strings"], date_format="%m/%d/%Y", index_col=0 + ) + tm.assert_frame_equal(df, res) + + def test_multiindex_interval_datetimes(self, ext): + # GH 30986 + midx = MultiIndex.from_arrays( + [ + range(4), + pd.interval_range( + start=pd.Timestamp("2020-01-01"), periods=4, freq="6ME" + ), + ] + ) + df = DataFrame(range(4), index=midx) + with tm.ensure_clean(ext) as pth: + df.to_excel(pth) + result = pd.read_excel(pth, index_col=[0, 1]) + expected = DataFrame( + range(4), + MultiIndex.from_arrays( + [ + range(4), + [ + "(2020-01-31 00:00:00, 2020-07-31 00:00:00]", + "(2020-07-31 00:00:00, 2021-01-31 00:00:00]", + "(2021-01-31 00:00:00, 2021-07-31 00:00:00]", + "(2021-07-31 00:00:00, 2022-01-31 00:00:00]", + ], + ] + ), + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "engine,ext", + [ + pytest.param( + "openpyxl", + ".xlsx", + marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")], + ), + pytest.param( + "openpyxl", + ".xlsm", + marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")], + ), + pytest.param( + "xlsxwriter", + ".xlsx", + marks=[td.skip_if_no("xlsxwriter"), td.skip_if_no("xlrd")], + ), + pytest.param("odf", ".ods", marks=td.skip_if_no("odf")), + ], +) +@pytest.mark.usefixtures("set_engine") +class TestExcelWriter: + def test_excel_sheet_size(self, path): + # GH 26080 + breaking_row_count = 2**20 + 1 + breaking_col_count = 2**14 + 1 + # purposely using two arrays to prevent memory issues while testing + row_arr = np.zeros(shape=(breaking_row_count, 1)) + col_arr = np.zeros(shape=(1, breaking_col_count)) + row_df = DataFrame(row_arr) + col_df = DataFrame(col_arr) + + msg = "sheet is too large" + with pytest.raises(ValueError, match=msg): + row_df.to_excel(path) + + with pytest.raises(ValueError, match=msg): + col_df.to_excel(path) + + def test_excel_sheet_by_name_raise(self, path): + gt = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) + gt.to_excel(path) + + with ExcelFile(path) as xl: + df = pd.read_excel(xl, sheet_name=0, index_col=0) + + tm.assert_frame_equal(gt, df) + + msg = "Worksheet named '0' not found" + with pytest.raises(ValueError, match=msg): + pd.read_excel(xl, "0") + + def test_excel_writer_context_manager(self, frame, path): + with ExcelWriter(path) as writer: + frame.to_excel(writer, sheet_name="Data1") + frame2 = frame.copy() + frame2.columns = frame.columns[::-1] + frame2.to_excel(writer, sheet_name="Data2") + + with ExcelFile(path) as reader: + found_df = pd.read_excel(reader, sheet_name="Data1", index_col=0) + found_df2 = pd.read_excel(reader, sheet_name="Data2", index_col=0) + + tm.assert_frame_equal(found_df, frame) + tm.assert_frame_equal(found_df2, frame2) + + def test_roundtrip(self, frame, path): + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + + frame.to_excel(path, sheet_name="test1") + frame.to_excel(path, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(path, sheet_name="test1", header=False) + frame.to_excel(path, sheet_name="test1", index=False) + + # test roundtrip + frame.to_excel(path, sheet_name="test1") + recons = pd.read_excel(path, sheet_name="test1", index_col=0) + tm.assert_frame_equal(frame, recons) + + frame.to_excel(path, sheet_name="test1", index=False) + recons = pd.read_excel(path, sheet_name="test1", index_col=None) + recons.index = frame.index + tm.assert_frame_equal(frame, recons) + + frame.to_excel(path, sheet_name="test1", na_rep="NA") + recons = pd.read_excel(path, sheet_name="test1", index_col=0, na_values=["NA"]) + tm.assert_frame_equal(frame, recons) + + # GH 3611 + frame.to_excel(path, sheet_name="test1", na_rep="88") + recons = pd.read_excel(path, sheet_name="test1", index_col=0, na_values=["88"]) + tm.assert_frame_equal(frame, recons) + + frame.to_excel(path, sheet_name="test1", na_rep="88") + recons = pd.read_excel( + path, sheet_name="test1", index_col=0, na_values=[88, 88.0] + ) + tm.assert_frame_equal(frame, recons) + + # GH 6573 + frame.to_excel(path, sheet_name="Sheet1") + recons = pd.read_excel(path, index_col=0) + tm.assert_frame_equal(frame, recons) + + frame.to_excel(path, sheet_name="0") + recons = pd.read_excel(path, index_col=0) + tm.assert_frame_equal(frame, recons) + + # GH 8825 Pandas Series should provide to_excel method + s = frame["A"] + s.to_excel(path) + recons = pd.read_excel(path, index_col=0) + tm.assert_frame_equal(s.to_frame(), recons) + + def test_mixed(self, frame, path): + mixed_frame = frame.copy() + mixed_frame["foo"] = "bar" + + mixed_frame.to_excel(path, sheet_name="test1") + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(mixed_frame, recons) + + def test_ts_frame(self, path): + unit = get_exp_unit(path) + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + + # freq doesn't round-trip + index = pd.DatetimeIndex(np.asarray(df.index), freq=None) + df.index = index + + expected = df[:] + expected.index = expected.index.as_unit(unit) + + df.to_excel(path, sheet_name="test1") + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(expected, recons) + + def test_basics_with_nan(self, frame, path): + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + frame.to_excel(path, sheet_name="test1") + frame.to_excel(path, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(path, sheet_name="test1", header=False) + frame.to_excel(path, sheet_name="test1", index=False) + + @pytest.mark.parametrize("np_type", [np.int8, np.int16, np.int32, np.int64]) + def test_int_types(self, np_type, path): + # Test np.int values read come back as int + # (rather than float which is Excel's format). + df = DataFrame( + np.random.default_rng(2).integers(-10, 10, size=(10, 2)), dtype=np_type + ) + df.to_excel(path, sheet_name="test1") + + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + + int_frame = df.astype(np.int64) + tm.assert_frame_equal(int_frame, recons) + + recons2 = pd.read_excel(path, sheet_name="test1", index_col=0) + tm.assert_frame_equal(int_frame, recons2) + + @pytest.mark.parametrize("np_type", [np.float16, np.float32, np.float64]) + def test_float_types(self, np_type, path): + # Test np.float values read come back as float. + df = DataFrame(np.random.default_rng(2).random(10), dtype=np_type) + df.to_excel(path, sheet_name="test1") + + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np_type + ) + + tm.assert_frame_equal(df, recons) + + def test_bool_types(self, path): + # Test np.bool_ values read come back as float. + df = DataFrame([1, 0, True, False], dtype=np.bool_) + df.to_excel(path, sheet_name="test1") + + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np.bool_ + ) + + tm.assert_frame_equal(df, recons) + + def test_inf_roundtrip(self, path): + df = DataFrame([(1, np.inf), (2, 3), (5, -np.inf)]) + df.to_excel(path, sheet_name="test1") + + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + + tm.assert_frame_equal(df, recons) + + def test_sheets(self, frame, path): + # freq doesn't round-trip + unit = get_exp_unit(path) + tsframe = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + index = pd.DatetimeIndex(np.asarray(tsframe.index), freq=None) + tsframe.index = index + + expected = tsframe[:] + expected.index = expected.index.as_unit(unit) + + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + + frame.to_excel(path, sheet_name="test1") + frame.to_excel(path, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(path, sheet_name="test1", header=False) + frame.to_excel(path, sheet_name="test1", index=False) + + # Test writing to separate sheets + with ExcelWriter(path) as writer: + frame.to_excel(writer, sheet_name="test1") + tsframe.to_excel(writer, sheet_name="test2") + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(frame, recons) + recons = pd.read_excel(reader, sheet_name="test2", index_col=0) + tm.assert_frame_equal(expected, recons) + assert 2 == len(reader.sheet_names) + assert "test1" == reader.sheet_names[0] + assert "test2" == reader.sheet_names[1] + + def test_colaliases(self, frame, path): + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + + frame.to_excel(path, sheet_name="test1") + frame.to_excel(path, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(path, sheet_name="test1", header=False) + frame.to_excel(path, sheet_name="test1", index=False) + + # column aliases + col_aliases = Index(["AA", "X", "Y", "Z"]) + frame.to_excel(path, sheet_name="test1", header=col_aliases) + with ExcelFile(path) as reader: + rs = pd.read_excel(reader, sheet_name="test1", index_col=0) + xp = frame.copy() + xp.columns = col_aliases + tm.assert_frame_equal(xp, rs) + + def test_roundtrip_indexlabels(self, merge_cells, frame, path): + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + + frame.to_excel(path, sheet_name="test1") + frame.to_excel(path, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(path, sheet_name="test1", header=False) + frame.to_excel(path, sheet_name="test1", index=False) + + # test index_label + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0 + df.to_excel( + path, sheet_name="test1", index_label=["test"], merge_cells=merge_cells + ) + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np.int64 + ) + df.index.names = ["test"] + assert df.index.names == recons.index.names + + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0 + df.to_excel( + path, + sheet_name="test1", + index_label=["test", "dummy", "dummy2"], + merge_cells=merge_cells, + ) + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np.int64 + ) + df.index.names = ["test"] + assert df.index.names == recons.index.names + + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0 + df.to_excel( + path, sheet_name="test1", index_label="test", merge_cells=merge_cells + ) + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np.int64 + ) + df.index.names = ["test"] + tm.assert_frame_equal(df, recons.astype(bool)) + + frame.to_excel( + path, + sheet_name="test1", + columns=["A", "B", "C", "D"], + index=False, + merge_cells=merge_cells, + ) + # take 'A' and 'B' as indexes (same row as cols 'C', 'D') + df = frame.copy() + df = df.set_index(["A", "B"]) + + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1]) + tm.assert_frame_equal(df, recons) + + def test_excel_roundtrip_indexname(self, merge_cells, path): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))) + df.index.name = "foo" + + df.to_excel(path, merge_cells=merge_cells) + + with ExcelFile(path) as xf: + result = pd.read_excel(xf, sheet_name=xf.sheet_names[0], index_col=0) + + tm.assert_frame_equal(result, df) + assert result.index.name == "foo" + + def test_excel_roundtrip_datetime(self, merge_cells, path): + # datetime.date, not sure what to test here exactly + unit = get_exp_unit(path) + + # freq does not round-trip + tsframe = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + index = pd.DatetimeIndex(np.asarray(tsframe.index), freq=None) + tsframe.index = index + + tsf = tsframe.copy() + + tsf.index = [x.date() for x in tsframe.index] + tsf.to_excel(path, sheet_name="test1", merge_cells=merge_cells) + + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + + expected = tsframe[:] + expected.index = expected.index.as_unit(unit) + tm.assert_frame_equal(expected, recons) + + def test_excel_date_datetime_format(self, ext, path): + # see gh-4133 + # + # Excel output format strings + unit = get_exp_unit(path) + + df = DataFrame( + [ + [date(2014, 1, 31), date(1999, 9, 24)], + [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)], + ], + index=["DATE", "DATETIME"], + columns=["X", "Y"], + ) + df_expected = DataFrame( + [ + [datetime(2014, 1, 31), datetime(1999, 9, 24)], + [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)], + ], + index=["DATE", "DATETIME"], + columns=["X", "Y"], + ) + df_expected = df_expected.astype(f"M8[{unit}]") + + with tm.ensure_clean(ext) as filename2: + with ExcelWriter(path) as writer1: + df.to_excel(writer1, sheet_name="test1") + + with ExcelWriter( + filename2, + date_format="DD.MM.YYYY", + datetime_format="DD.MM.YYYY HH-MM-SS", + ) as writer2: + df.to_excel(writer2, sheet_name="test1") + + with ExcelFile(path) as reader1: + rs1 = pd.read_excel(reader1, sheet_name="test1", index_col=0) + + with ExcelFile(filename2) as reader2: + rs2 = pd.read_excel(reader2, sheet_name="test1", index_col=0) + + tm.assert_frame_equal(rs1, rs2) + + # Since the reader returns a datetime object for dates, + # we need to use df_expected to check the result. + tm.assert_frame_equal(rs2, df_expected) + + @pytest.mark.filterwarnings( + "ignore:invalid value encountered in cast:RuntimeWarning" + ) + def test_to_excel_interval_no_labels(self, path, using_infer_string): + # see gh-19242 + # + # Test writing Interval without labels. + df = DataFrame( + np.random.default_rng(2).integers(-10, 10, size=(20, 1)), dtype=np.int64 + ) + expected = df.copy() + + df["new"] = pd.cut(df[0], 10) + expected["new"] = pd.cut(expected[0], 10).astype( + str if not using_infer_string else "str" + ) + + df.to_excel(path, sheet_name="test1") + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(expected, recons) + + def test_to_excel_interval_labels(self, path): + # see gh-19242 + # + # Test writing Interval with labels. + df = DataFrame( + np.random.default_rng(2).integers(-10, 10, size=(20, 1)), dtype=np.int64 + ) + expected = df.copy() + intervals = pd.cut( + df[0], 10, labels=["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"] + ) + df["new"] = intervals + expected["new"] = pd.Series(list(intervals)) + + df.to_excel(path, sheet_name="test1") + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(expected, recons) + + def test_to_excel_timedelta(self, path): + # see gh-19242, gh-9155 + # + # Test writing timedelta to xls. + df = DataFrame( + np.random.default_rng(2).integers(-10, 10, size=(20, 1)), + columns=["A"], + dtype=np.int64, + ) + expected = df.copy() + + df["new"] = df["A"].apply(lambda x: timedelta(seconds=x)) + expected["new"] = expected["A"].apply( + lambda x: timedelta(seconds=x).total_seconds() / 86400 + ) + + df.to_excel(path, sheet_name="test1") + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(expected, recons) + + def test_to_excel_periodindex(self, path): + # xp has a PeriodIndex + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + xp = df.resample("ME").mean().to_period("M") + + xp.to_excel(path, sheet_name="sht1") + + with ExcelFile(path) as reader: + rs = pd.read_excel(reader, sheet_name="sht1", index_col=0) + tm.assert_frame_equal(xp, rs.to_period("M")) + + def test_to_excel_multiindex(self, merge_cells, frame, path): + arrays = np.arange(len(frame.index) * 2, dtype=np.int64).reshape(2, -1) + new_index = MultiIndex.from_arrays(arrays, names=["first", "second"]) + frame.index = new_index + + frame.to_excel(path, sheet_name="test1", header=False) + frame.to_excel(path, sheet_name="test1", columns=["A", "B"]) + + # round trip + frame.to_excel(path, sheet_name="test1", merge_cells=merge_cells) + with ExcelFile(path) as reader: + df = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1]) + tm.assert_frame_equal(frame, df) + + # GH13511 + def test_to_excel_multiindex_nan_label(self, merge_cells, path): + df = DataFrame( + { + "A": [None, 2, 3], + "B": [10, 20, 30], + "C": np.random.default_rng(2).random(3), + } + ) + df = df.set_index(["A", "B"]) + + df.to_excel(path, merge_cells=merge_cells) + df1 = pd.read_excel(path, index_col=[0, 1]) + tm.assert_frame_equal(df, df1) + + # Test for Issue 11328. If column indices are integers, make + # sure they are handled correctly for either setting of + # merge_cells + def test_to_excel_multiindex_cols(self, merge_cells, frame, path): + arrays = np.arange(len(frame.index) * 2, dtype=np.int64).reshape(2, -1) + new_index = MultiIndex.from_arrays(arrays, names=["first", "second"]) + frame.index = new_index + + new_cols_index = MultiIndex.from_tuples([(40, 1), (40, 2), (50, 1), (50, 2)]) + frame.columns = new_cols_index + header = [0, 1] + if not merge_cells: + header = 0 + + # round trip + frame.to_excel(path, sheet_name="test1", merge_cells=merge_cells) + with ExcelFile(path) as reader: + df = pd.read_excel( + reader, sheet_name="test1", header=header, index_col=[0, 1] + ) + if not merge_cells: + fm = frame.columns._format_multi(sparsify=False, include_names=False) + frame.columns = [".".join(map(str, q)) for q in zip(*fm)] + tm.assert_frame_equal(frame, df) + + def test_to_excel_multiindex_dates(self, merge_cells, path): + # try multiindex with dates + unit = get_exp_unit(path) + tsframe = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + tsframe.index = MultiIndex.from_arrays( + [ + tsframe.index.as_unit(unit), + np.arange(len(tsframe.index), dtype=np.int64), + ], + names=["time", "foo"], + ) + + tsframe.to_excel(path, sheet_name="test1", merge_cells=merge_cells) + with ExcelFile(path) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1]) + + tm.assert_frame_equal(tsframe, recons) + assert recons.index.names == ("time", "foo") + + def test_to_excel_multiindex_no_write_index(self, path): + # Test writing and re-reading a MI without the index. GH 5616. + + # Initial non-MI frame. + frame1 = DataFrame({"a": [10, 20], "b": [30, 40], "c": [50, 60]}) + + # Add a MI. + frame2 = frame1.copy() + multi_index = MultiIndex.from_tuples([(70, 80), (90, 100)]) + frame2.index = multi_index + + # Write out to Excel without the index. + frame2.to_excel(path, sheet_name="test1", index=False) + + # Read it back in. + with ExcelFile(path) as reader: + frame3 = pd.read_excel(reader, sheet_name="test1") + + # Test that it is the same as the initial frame. + tm.assert_frame_equal(frame1, frame3) + + def test_to_excel_empty_multiindex(self, path): + # GH 19543. + expected = DataFrame([], columns=[0, 1, 2]) + + df = DataFrame([], index=MultiIndex.from_tuples([], names=[0, 1]), columns=[2]) + df.to_excel(path, sheet_name="test1") + + with ExcelFile(path) as reader: + result = pd.read_excel(reader, sheet_name="test1") + tm.assert_frame_equal( + result, expected, check_index_type=False, check_dtype=False + ) + + def test_to_excel_float_format(self, path): + df = DataFrame( + [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + df.to_excel(path, sheet_name="test1", float_format="%.2f") + + with ExcelFile(path) as reader: + result = pd.read_excel(reader, sheet_name="test1", index_col=0) + + expected = DataFrame( + [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + tm.assert_frame_equal(result, expected) + + def test_to_excel_output_encoding(self, ext): + # Avoid mixed inferred_type. + df = DataFrame( + [["\u0192", "\u0193", "\u0194"], ["\u0195", "\u0196", "\u0197"]], + index=["A\u0192", "B"], + columns=["X\u0193", "Y", "Z"], + ) + + with tm.ensure_clean("__tmp_to_excel_float_format__." + ext) as filename: + df.to_excel(filename, sheet_name="TestSheet") + result = pd.read_excel(filename, sheet_name="TestSheet", index_col=0) + tm.assert_frame_equal(result, df) + + def test_to_excel_unicode_filename(self, ext): + with tm.ensure_clean("\u0192u." + ext) as filename: + try: + with open(filename, "wb"): + pass + except UnicodeEncodeError: + pytest.skip("No unicode file names on this system") + + df = DataFrame( + [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + df.to_excel(filename, sheet_name="test1", float_format="%.2f") + + with ExcelFile(filename) as reader: + result = pd.read_excel(reader, sheet_name="test1", index_col=0) + + expected = DataFrame( + [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("use_headers", [True, False]) + @pytest.mark.parametrize("r_idx_nlevels", [1, 2, 3]) + @pytest.mark.parametrize("c_idx_nlevels", [1, 2, 3]) + def test_excel_010_hemstring( + self, merge_cells, c_idx_nlevels, r_idx_nlevels, use_headers, path + ): + def roundtrip(data, header=True, parser_hdr=0, index=True): + data.to_excel(path, header=header, merge_cells=merge_cells, index=index) + + with ExcelFile(path) as xf: + return pd.read_excel( + xf, sheet_name=xf.sheet_names[0], header=parser_hdr + ) + + # Basic test. + parser_header = 0 if use_headers else None + res = roundtrip(DataFrame([0]), use_headers, parser_header) + + assert res.shape == (1, 2) + assert res.iloc[0, 0] is not np.nan + + # More complex tests with multi-index. + nrows = 5 + ncols = 3 + + # ensure limited functionality in 0.10 + # override of gh-2370 until sorted out in 0.11 + + if c_idx_nlevels == 1: + columns = Index([f"a-{i}" for i in range(ncols)], dtype=object) + else: + columns = MultiIndex.from_arrays( + [range(ncols) for _ in range(c_idx_nlevels)], + names=[f"i-{i}" for i in range(c_idx_nlevels)], + ) + if r_idx_nlevels == 1: + index = Index([f"b-{i}" for i in range(nrows)], dtype=object) + else: + index = MultiIndex.from_arrays( + [range(nrows) for _ in range(r_idx_nlevels)], + names=[f"j-{i}" for i in range(r_idx_nlevels)], + ) + + df = DataFrame( + np.ones((nrows, ncols)), + columns=columns, + index=index, + ) + + # This if will be removed once multi-column Excel writing + # is implemented. For now fixing gh-9794. + if c_idx_nlevels > 1: + msg = ( + "Writing to Excel with MultiIndex columns and no index " + "\\('index'=False\\) is not yet implemented." + ) + with pytest.raises(NotImplementedError, match=msg): + roundtrip(df, use_headers, index=False) + else: + res = roundtrip(df, use_headers) + + if use_headers: + assert res.shape == (nrows, ncols + r_idx_nlevels) + else: + # First row taken as columns. + assert res.shape == (nrows - 1, ncols + r_idx_nlevels) + + # No NaNs. + for r in range(len(res.index)): + for c in range(len(res.columns)): + assert res.iloc[r, c] is not np.nan + + def test_duplicated_columns(self, path): + # see gh-5235 + df = DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3]], columns=["A", "B", "B"]) + df.to_excel(path, sheet_name="test1") + expected = DataFrame( + [[1, 2, 3], [1, 2, 3], [1, 2, 3]], columns=["A", "B", "B.1"] + ) + + # By default, we mangle. + result = pd.read_excel(path, sheet_name="test1", index_col=0) + tm.assert_frame_equal(result, expected) + + # see gh-11007, gh-10970 + df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "A", "B"]) + df.to_excel(path, sheet_name="test1") + + result = pd.read_excel(path, sheet_name="test1", index_col=0) + expected = DataFrame( + [[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "A.1", "B.1"] + ) + tm.assert_frame_equal(result, expected) + + # see gh-10982 + df.to_excel(path, sheet_name="test1", index=False, header=False) + result = pd.read_excel(path, sheet_name="test1", header=None) + + expected = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]]) + tm.assert_frame_equal(result, expected) + + def test_swapped_columns(self, path): + # Test for issue #5427. + write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2]}) + write_frame.to_excel(path, sheet_name="test1", columns=["B", "A"]) + + read_frame = pd.read_excel(path, sheet_name="test1", header=0) + + tm.assert_series_equal(write_frame["A"], read_frame["A"]) + tm.assert_series_equal(write_frame["B"], read_frame["B"]) + + def test_invalid_columns(self, path): + # see gh-10982 + write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2]}) + + with pytest.raises(KeyError, match="Not all names specified"): + write_frame.to_excel(path, sheet_name="test1", columns=["B", "C"]) + + with pytest.raises( + KeyError, match="'passes columns are not ALL present dataframe'" + ): + write_frame.to_excel(path, sheet_name="test1", columns=["C", "D"]) + + @pytest.mark.parametrize( + "to_excel_index,read_excel_index_col", + [ + (True, 0), # Include index in write to file + (False, None), # Dont include index in write to file + ], + ) + def test_write_subset_columns(self, path, to_excel_index, read_excel_index_col): + # GH 31677 + write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2], "C": [3, 3, 3]}) + write_frame.to_excel( + path, sheet_name="col_subset_bug", columns=["A", "B"], index=to_excel_index + ) + + expected = write_frame[["A", "B"]] + read_frame = pd.read_excel( + path, sheet_name="col_subset_bug", index_col=read_excel_index_col + ) + + tm.assert_frame_equal(expected, read_frame) + + def test_comment_arg(self, path): + # see gh-18735 + # + # Test the comment argument functionality to pd.read_excel. + + # Create file to read in. + df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]}) + df.to_excel(path, sheet_name="test_c") + + # Read file without comment arg. + result1 = pd.read_excel(path, sheet_name="test_c", index_col=0) + + result1.iloc[1, 0] = None + result1.iloc[1, 1] = None + result1.iloc[2, 1] = None + + result2 = pd.read_excel(path, sheet_name="test_c", comment="#", index_col=0) + tm.assert_frame_equal(result1, result2) + + def test_comment_default(self, path): + # Re issue #18735 + # Test the comment argument default to pd.read_excel + + # Create file to read in + df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]}) + df.to_excel(path, sheet_name="test_c") + + # Read file with default and explicit comment=None + result1 = pd.read_excel(path, sheet_name="test_c") + result2 = pd.read_excel(path, sheet_name="test_c", comment=None) + tm.assert_frame_equal(result1, result2) + + def test_comment_used(self, path): + # see gh-18735 + # + # Test the comment argument is working as expected when used. + + # Create file to read in. + df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]}) + df.to_excel(path, sheet_name="test_c") + + # Test read_frame_comment against manually produced expected output. + expected = DataFrame({"A": ["one", None, "one"], "B": ["two", None, None]}) + result = pd.read_excel(path, sheet_name="test_c", comment="#", index_col=0) + tm.assert_frame_equal(result, expected) + + def test_comment_empty_line(self, path): + # Re issue #18735 + # Test that pd.read_excel ignores commented lines at the end of file + + df = DataFrame({"a": ["1", "#2"], "b": ["2", "3"]}) + df.to_excel(path, index=False) + + # Test that all-comment lines at EoF are ignored + expected = DataFrame({"a": [1], "b": [2]}) + result = pd.read_excel(path, comment="#") + tm.assert_frame_equal(result, expected) + + def test_datetimes(self, path): + # Test writing and reading datetimes. For issue #9139. (xref #9185) + unit = get_exp_unit(path) + datetimes = [ + datetime(2013, 1, 13, 1, 2, 3), + datetime(2013, 1, 13, 2, 45, 56), + datetime(2013, 1, 13, 4, 29, 49), + datetime(2013, 1, 13, 6, 13, 42), + datetime(2013, 1, 13, 7, 57, 35), + datetime(2013, 1, 13, 9, 41, 28), + datetime(2013, 1, 13, 11, 25, 21), + datetime(2013, 1, 13, 13, 9, 14), + datetime(2013, 1, 13, 14, 53, 7), + datetime(2013, 1, 13, 16, 37, 0), + datetime(2013, 1, 13, 18, 20, 52), + ] + + write_frame = DataFrame({"A": datetimes}) + write_frame.to_excel(path, sheet_name="Sheet1") + read_frame = pd.read_excel(path, sheet_name="Sheet1", header=0) + + expected = write_frame.astype(f"M8[{unit}]") + tm.assert_series_equal(expected["A"], read_frame["A"]) + + def test_bytes_io(self, engine): + # see gh-7074 + with BytesIO() as bio: + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) + + # Pass engine explicitly, as there is no file path to infer from. + with ExcelWriter(bio, engine=engine) as writer: + df.to_excel(writer) + + bio.seek(0) + reread_df = pd.read_excel(bio, index_col=0) + tm.assert_frame_equal(df, reread_df) + + def test_engine_kwargs(self, engine, path): + # GH#52368 + df = DataFrame([{"A": 1, "B": 2}, {"A": 3, "B": 4}]) + + msgs = { + "odf": r"OpenDocumentSpreadsheet() got an unexpected keyword " + r"argument 'foo'", + "openpyxl": r"__init__() got an unexpected keyword argument 'foo'", + "xlsxwriter": r"__init__() got an unexpected keyword argument 'foo'", + } + + if PY310: + msgs[ + "openpyxl" + ] = "Workbook.__init__() got an unexpected keyword argument 'foo'" + msgs[ + "xlsxwriter" + ] = "Workbook.__init__() got an unexpected keyword argument 'foo'" + + # Handle change in error message for openpyxl (write and append mode) + if engine == "openpyxl" and not os.path.exists(path): + msgs[ + "openpyxl" + ] = r"load_workbook() got an unexpected keyword argument 'foo'" + + with pytest.raises(TypeError, match=re.escape(msgs[engine])): + df.to_excel( + path, + engine=engine, + engine_kwargs={"foo": "bar"}, + ) + + def test_write_lists_dict(self, path): + # see gh-8188. + df = DataFrame( + { + "mixed": ["a", ["b", "c"], {"d": "e", "f": 2}], + "numeric": [1, 2, 3.0], + "str": ["apple", "banana", "cherry"], + } + ) + df.to_excel(path, sheet_name="Sheet1") + read = pd.read_excel(path, sheet_name="Sheet1", header=0, index_col=0) + + expected = df.copy() + expected.mixed = expected.mixed.apply(str) + expected.numeric = expected.numeric.astype("int64") + + tm.assert_frame_equal(read, expected) + + def test_render_as_column_name(self, path): + # see gh-34331 + df = DataFrame({"render": [1, 2], "data": [3, 4]}) + df.to_excel(path, sheet_name="Sheet1") + read = pd.read_excel(path, "Sheet1", index_col=0) + expected = df + tm.assert_frame_equal(read, expected) + + def test_true_and_false_value_options(self, path): + # see gh-13347 + df = DataFrame([["foo", "bar"]], columns=["col1", "col2"], dtype=object) + with option_context("future.no_silent_downcasting", True): + expected = df.replace({"foo": True, "bar": False}).astype("bool") + + df.to_excel(path) + read_frame = pd.read_excel( + path, true_values=["foo"], false_values=["bar"], index_col=0 + ) + tm.assert_frame_equal(read_frame, expected) + + def test_freeze_panes(self, path): + # see gh-15160 + expected = DataFrame([[1, 2], [3, 4]], columns=["col1", "col2"]) + expected.to_excel(path, sheet_name="Sheet1", freeze_panes=(1, 1)) + + result = pd.read_excel(path, index_col=0) + tm.assert_frame_equal(result, expected) + + def test_path_path_lib(self, engine, ext): + df = DataFrame( + 1.1 * np.arange(120).reshape((30, 4)), + columns=Index(list("ABCD")), + index=Index([f"i-{i}" for i in range(30)]), + ) + writer = partial(df.to_excel, engine=engine) + + reader = partial(pd.read_excel, index_col=0) + result = tm.round_trip_pathlib(writer, reader, path=f"foo{ext}") + tm.assert_frame_equal(result, df) + + def test_path_local_path(self, engine, ext): + df = DataFrame( + 1.1 * np.arange(120).reshape((30, 4)), + columns=Index(list("ABCD")), + index=Index([f"i-{i}" for i in range(30)]), + ) + writer = partial(df.to_excel, engine=engine) + + reader = partial(pd.read_excel, index_col=0) + result = tm.round_trip_localpath(writer, reader, path=f"foo{ext}") + tm.assert_frame_equal(result, df) + + def test_merged_cell_custom_objects(self, path): + # see GH-27006 + mi = MultiIndex.from_tuples( + [ + (pd.Period("2018"), pd.Period("2018Q1")), + (pd.Period("2018"), pd.Period("2018Q2")), + ] + ) + expected = DataFrame(np.ones((2, 2), dtype="int64"), columns=mi) + expected.to_excel(path) + result = pd.read_excel(path, header=[0, 1], index_col=0) + # need to convert PeriodIndexes to standard Indexes for assert equal + expected.columns = expected.columns.set_levels( + [[str(i) for i in mi.levels[0]], [str(i) for i in mi.levels[1]]], + level=[0, 1], + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dtype", [None, object]) + def test_raise_when_saving_timezones(self, dtype, tz_aware_fixture, path): + # GH 27008, GH 7056 + tz = tz_aware_fixture + data = pd.Timestamp("2019", tz=tz) + df = DataFrame([data], dtype=dtype) + with pytest.raises(ValueError, match="Excel does not support"): + df.to_excel(path) + + data = data.to_pydatetime() + df = DataFrame([data], dtype=dtype) + with pytest.raises(ValueError, match="Excel does not support"): + df.to_excel(path) + + def test_excel_duplicate_columns_with_names(self, path): + # GH#39695 + df = DataFrame({"A": [0, 1], "B": [10, 11]}) + df.to_excel(path, columns=["A", "B", "A"], index=False) + + result = pd.read_excel(path) + expected = DataFrame([[0, 10, 0], [1, 11, 1]], columns=["A", "B", "A.1"]) + tm.assert_frame_equal(result, expected) + + def test_if_sheet_exists_raises(self, ext): + # GH 40230 + msg = "if_sheet_exists is only valid in append mode (mode='a')" + + with tm.ensure_clean(ext) as f: + with pytest.raises(ValueError, match=re.escape(msg)): + ExcelWriter(f, if_sheet_exists="replace") + + def test_excel_writer_empty_frame(self, engine, ext): + # GH#45793 + with tm.ensure_clean(ext) as path: + with ExcelWriter(path, engine=engine) as writer: + DataFrame().to_excel(writer) + result = pd.read_excel(path) + expected = DataFrame() + tm.assert_frame_equal(result, expected) + + def test_to_excel_empty_frame(self, engine, ext): + # GH#45793 + with tm.ensure_clean(ext) as path: + DataFrame().to_excel(path, engine=engine) + result = pd.read_excel(path) + expected = DataFrame() + tm.assert_frame_equal(result, expected) + + +class TestExcelWriterEngineTests: + @pytest.mark.parametrize( + "klass,ext", + [ + pytest.param(_XlsxWriter, ".xlsx", marks=td.skip_if_no("xlsxwriter")), + pytest.param(_OpenpyxlWriter, ".xlsx", marks=td.skip_if_no("openpyxl")), + ], + ) + def test_ExcelWriter_dispatch(self, klass, ext): + with tm.ensure_clean(ext) as path: + with ExcelWriter(path) as writer: + if ext == ".xlsx" and bool( + import_optional_dependency("xlsxwriter", errors="ignore") + ): + # xlsxwriter has preference over openpyxl if both installed + assert isinstance(writer, _XlsxWriter) + else: + assert isinstance(writer, klass) + + def test_ExcelWriter_dispatch_raises(self): + with pytest.raises(ValueError, match="No engine"): + ExcelWriter("nothing") + + def test_register_writer(self): + class DummyClass(ExcelWriter): + called_save = False + called_write_cells = False + called_sheets = False + _supported_extensions = ("xlsx", "xls") + _engine = "dummy" + + def book(self): + pass + + def _save(self): + type(self).called_save = True + + def _write_cells(self, *args, **kwargs): + type(self).called_write_cells = True + + @property + def sheets(self): + type(self).called_sheets = True + + @classmethod + def assert_called_and_reset(cls): + assert cls.called_save + assert cls.called_write_cells + assert not cls.called_sheets + cls.called_save = False + cls.called_write_cells = False + + register_writer(DummyClass) + + with option_context("io.excel.xlsx.writer", "dummy"): + path = "something.xlsx" + with tm.ensure_clean(path) as filepath: + with ExcelWriter(filepath) as writer: + assert isinstance(writer, DummyClass) + df = DataFrame( + ["a"], + columns=Index(["b"], name="foo"), + index=Index(["c"], name="bar"), + ) + df.to_excel(filepath) + DummyClass.assert_called_and_reset() + + with tm.ensure_clean("something.xls") as filepath: + df.to_excel(filepath, engine="dummy") + DummyClass.assert_called_and_reset() + + +@td.skip_if_no("xlrd") +@td.skip_if_no("openpyxl") +class TestFSPath: + def test_excelfile_fspath(self): + with tm.ensure_clean("foo.xlsx") as path: + df = DataFrame({"A": [1, 2]}) + df.to_excel(path) + with ExcelFile(path) as xl: + result = os.fspath(xl) + assert result == path + + def test_excelwriter_fspath(self): + with tm.ensure_clean("foo.xlsx") as path: + with ExcelWriter(path) as writer: + assert os.fspath(writer) == str(path) + + def test_to_excel_pos_args_deprecation(self): + # GH-54229 + df = DataFrame({"a": [1, 2, 3]}) + msg = ( + r"Starting with pandas version 3.0 all arguments of to_excel except " + r"for the argument 'excel_writer' will be keyword-only." + ) + with tm.assert_produces_warning(FutureWarning, match=msg): + buf = BytesIO() + writer = ExcelWriter(buf) + df.to_excel(writer, "Sheet_name_1") + + +@pytest.mark.parametrize("klass", _writers.values()) +def test_subclass_attr(klass): + # testing that subclasses of ExcelWriter don't have public attributes (issue 49602) + attrs_base = {name for name in dir(ExcelWriter) if not name.startswith("_")} + attrs_klass = {name for name in dir(klass) if not name.startswith("_")} + assert not attrs_base.symmetric_difference(attrs_klass) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlrd.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlrd.py new file mode 100644 index 0000000000000000000000000000000000000000..066393d91eeadcdc08873f4ffeedda0f689337fe --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlrd.py @@ -0,0 +1,76 @@ +import io + +import numpy as np +import pytest + +from pandas.compat import is_platform_windows + +import pandas as pd +import pandas._testing as tm + +from pandas.io.excel import ExcelFile +from pandas.io.excel._base import inspect_excel_format + +xlrd = pytest.importorskip("xlrd") + +if is_platform_windows(): + pytestmark = pytest.mark.single_cpu + + +@pytest.fixture(params=[".xls"]) +def read_ext_xlrd(request): + """ + Valid extensions for reading Excel files with xlrd. + + Similar to read_ext, but excludes .ods, .xlsb, and for xlrd>2 .xlsx, .xlsm + """ + return request.param + + +def test_read_xlrd_book(read_ext_xlrd, datapath): + engine = "xlrd" + sheet_name = "Sheet1" + pth = datapath("io", "data", "excel", "test1.xls") + with xlrd.open_workbook(pth) as book: + with ExcelFile(book, engine=engine) as xl: + result = pd.read_excel(xl, sheet_name=sheet_name, index_col=0) + + expected = pd.read_excel( + book, sheet_name=sheet_name, engine=engine, index_col=0 + ) + tm.assert_frame_equal(result, expected) + + +def test_read_xlsx_fails(datapath): + # GH 29375 + from xlrd.biffh import XLRDError + + path = datapath("io", "data", "excel", "test1.xlsx") + with pytest.raises(XLRDError, match="Excel xlsx file; not supported"): + pd.read_excel(path, engine="xlrd") + + +def test_nan_in_xls(datapath): + # GH 54564 + path = datapath("io", "data", "excel", "test6.xls") + + expected = pd.DataFrame({0: np.r_[0, 2].astype("int64"), 1: np.r_[1, np.nan]}) + + result = pd.read_excel(path, header=None) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "file_header", + [ + b"\x09\x00\x04\x00\x07\x00\x10\x00", + b"\x09\x02\x06\x00\x00\x00\x10\x00", + b"\x09\x04\x06\x00\x00\x00\x10\x00", + b"\xd0\xcf\x11\xe0\xa1\xb1\x1a\xe1", + ], +) +def test_read_old_xls_files(file_header): + # GH 41226 + f = io.BytesIO(file_header) + assert inspect_excel_format(f) == "xls" diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlsxwriter.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlsxwriter.py new file mode 100644 index 0000000000000000000000000000000000000000..529367761fc025e3e5d02bea85741c82f64c97ca --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/excel/test_xlsxwriter.py @@ -0,0 +1,86 @@ +import contextlib + +import pytest + +from pandas.compat import is_platform_windows + +from pandas import DataFrame +import pandas._testing as tm + +from pandas.io.excel import ExcelWriter + +xlsxwriter = pytest.importorskip("xlsxwriter") + +if is_platform_windows(): + pytestmark = pytest.mark.single_cpu + + +@pytest.fixture +def ext(): + return ".xlsx" + + +def test_column_format(ext): + # Test that column formats are applied to cells. Test for issue #9167. + # Applicable to xlsxwriter only. + openpyxl = pytest.importorskip("openpyxl") + + with tm.ensure_clean(ext) as path: + frame = DataFrame({"A": [123456, 123456], "B": [123456, 123456]}) + + with ExcelWriter(path) as writer: + frame.to_excel(writer) + + # Add a number format to col B and ensure it is applied to cells. + num_format = "#,##0" + write_workbook = writer.book + write_worksheet = write_workbook.worksheets()[0] + col_format = write_workbook.add_format({"num_format": num_format}) + write_worksheet.set_column("B:B", None, col_format) + + with contextlib.closing(openpyxl.load_workbook(path)) as read_workbook: + try: + read_worksheet = read_workbook["Sheet1"] + except TypeError: + # compat + read_worksheet = read_workbook.get_sheet_by_name(name="Sheet1") + + # Get the number format from the cell. + try: + cell = read_worksheet["B2"] + except TypeError: + # compat + cell = read_worksheet.cell("B2") + + try: + read_num_format = cell.number_format + except AttributeError: + read_num_format = cell.style.number_format._format_code + + assert read_num_format == num_format + + +def test_write_append_mode_raises(ext): + msg = "Append mode is not supported with xlsxwriter!" + + with tm.ensure_clean(ext) as f: + with pytest.raises(ValueError, match=msg): + ExcelWriter(f, engine="xlsxwriter", mode="a") + + +@pytest.mark.parametrize("nan_inf_to_errors", [True, False]) +def test_engine_kwargs(ext, nan_inf_to_errors): + # GH 42286 + engine_kwargs = {"options": {"nan_inf_to_errors": nan_inf_to_errors}} + with tm.ensure_clean(ext) as f: + with ExcelWriter(f, engine="xlsxwriter", engine_kwargs=engine_kwargs) as writer: + assert writer.book.nan_inf_to_errors == nan_inf_to_errors + + +def test_book_and_sheets_consistent(ext): + # GH#45687 - Ensure sheets is updated if user modifies book + with tm.ensure_clean(ext) as f: + with ExcelWriter(f, engine="xlsxwriter") as writer: + assert writer.sheets == {} + sheet = writer.book.add_worksheet("test_name") + assert writer.sheets == {"test_name": sheet} diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/__init__.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_bar.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_bar.py new file mode 100644 index 0000000000000000000000000000000000000000..d28c7c566d851f16f81cdc04f22e04ca8bde2c71 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_bar.py @@ -0,0 +1,359 @@ +import io + +import numpy as np +import pytest + +from pandas import ( + NA, + DataFrame, + read_csv, +) + +pytest.importorskip("jinja2") + + +def bar_grad(a=None, b=None, c=None, d=None): + """Used in multiple tests to simplify formatting of expected result""" + ret = [("width", "10em")] + if all(x is None for x in [a, b, c, d]): + return ret + return ret + [ + ( + "background", + f"linear-gradient(90deg,{','.join([x for x in [a, b, c, d] if x])})", + ) + ] + + +def no_bar(): + return bar_grad() + + +def bar_to(x, color="#d65f5f"): + return bar_grad(f" {color} {x:.1f}%", f" transparent {x:.1f}%") + + +def bar_from_to(x, y, color="#d65f5f"): + return bar_grad( + f" transparent {x:.1f}%", + f" {color} {x:.1f}%", + f" {color} {y:.1f}%", + f" transparent {y:.1f}%", + ) + + +@pytest.fixture +def df_pos(): + return DataFrame([[1], [2], [3]]) + + +@pytest.fixture +def df_neg(): + return DataFrame([[-1], [-2], [-3]]) + + +@pytest.fixture +def df_mix(): + return DataFrame([[-3], [1], [2]]) + + +@pytest.mark.parametrize( + "align, exp", + [ + ("left", [no_bar(), bar_to(50), bar_to(100)]), + ("right", [bar_to(100), bar_from_to(50, 100), no_bar()]), + ("mid", [bar_to(33.33), bar_to(66.66), bar_to(100)]), + ("zero", [bar_from_to(50, 66.7), bar_from_to(50, 83.3), bar_from_to(50, 100)]), + ("mean", [bar_to(50), no_bar(), bar_from_to(50, 100)]), + (2.0, [bar_to(50), no_bar(), bar_from_to(50, 100)]), + (np.median, [bar_to(50), no_bar(), bar_from_to(50, 100)]), + ], +) +def test_align_positive_cases(df_pos, align, exp): + # test different align cases for all positive values + result = df_pos.style.bar(align=align)._compute().ctx + expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]} + assert result == expected + + +@pytest.mark.parametrize( + "align, exp", + [ + ("left", [bar_to(100), bar_to(50), no_bar()]), + ("right", [no_bar(), bar_from_to(50, 100), bar_to(100)]), + ("mid", [bar_from_to(66.66, 100), bar_from_to(33.33, 100), bar_to(100)]), + ("zero", [bar_from_to(33.33, 50), bar_from_to(16.66, 50), bar_to(50)]), + ("mean", [bar_from_to(50, 100), no_bar(), bar_to(50)]), + (-2.0, [bar_from_to(50, 100), no_bar(), bar_to(50)]), + (np.median, [bar_from_to(50, 100), no_bar(), bar_to(50)]), + ], +) +def test_align_negative_cases(df_neg, align, exp): + # test different align cases for all negative values + result = df_neg.style.bar(align=align)._compute().ctx + expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]} + assert result == expected + + +@pytest.mark.parametrize( + "align, exp", + [ + ("left", [no_bar(), bar_to(80), bar_to(100)]), + ("right", [bar_to(100), bar_from_to(80, 100), no_bar()]), + ("mid", [bar_to(60), bar_from_to(60, 80), bar_from_to(60, 100)]), + ("zero", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]), + ("mean", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]), + (-0.0, [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]), + (np.nanmedian, [bar_to(50), no_bar(), bar_from_to(50, 62.5)]), + ], +) +@pytest.mark.parametrize("nans", [True, False]) +def test_align_mixed_cases(df_mix, align, exp, nans): + # test different align cases for mixed positive and negative values + # also test no impact of NaNs and no_bar + expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]} + if nans: + df_mix.loc[3, :] = np.nan + expected.update({(3, 0): no_bar()}) + result = df_mix.style.bar(align=align)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "align, exp", + [ + ( + "left", + { + "index": [[no_bar(), no_bar()], [bar_to(100), bar_to(100)]], + "columns": [[no_bar(), bar_to(100)], [no_bar(), bar_to(100)]], + "none": [[no_bar(), bar_to(33.33)], [bar_to(66.66), bar_to(100)]], + }, + ), + ( + "mid", + { + "index": [[bar_to(33.33), bar_to(50)], [bar_to(100), bar_to(100)]], + "columns": [[bar_to(50), bar_to(100)], [bar_to(75), bar_to(100)]], + "none": [[bar_to(25), bar_to(50)], [bar_to(75), bar_to(100)]], + }, + ), + ( + "zero", + { + "index": [ + [bar_from_to(50, 66.66), bar_from_to(50, 75)], + [bar_from_to(50, 100), bar_from_to(50, 100)], + ], + "columns": [ + [bar_from_to(50, 75), bar_from_to(50, 100)], + [bar_from_to(50, 87.5), bar_from_to(50, 100)], + ], + "none": [ + [bar_from_to(50, 62.5), bar_from_to(50, 75)], + [bar_from_to(50, 87.5), bar_from_to(50, 100)], + ], + }, + ), + ( + 2, + { + "index": [ + [bar_to(50), no_bar()], + [bar_from_to(50, 100), bar_from_to(50, 100)], + ], + "columns": [ + [bar_to(50), no_bar()], + [bar_from_to(50, 75), bar_from_to(50, 100)], + ], + "none": [ + [bar_from_to(25, 50), no_bar()], + [bar_from_to(50, 75), bar_from_to(50, 100)], + ], + }, + ), + ], +) +@pytest.mark.parametrize("axis", ["index", "columns", "none"]) +def test_align_axis(align, exp, axis): + # test all axis combinations with positive values and different aligns + data = DataFrame([[1, 2], [3, 4]]) + result = ( + data.style.bar(align=align, axis=None if axis == "none" else axis) + ._compute() + .ctx + ) + expected = { + (0, 0): exp[axis][0][0], + (0, 1): exp[axis][0][1], + (1, 0): exp[axis][1][0], + (1, 1): exp[axis][1][1], + } + assert result == expected + + +@pytest.mark.parametrize( + "values, vmin, vmax", + [ + ("positive", 1.5, 2.5), + ("negative", -2.5, -1.5), + ("mixed", -2.5, 1.5), + ], +) +@pytest.mark.parametrize("nullify", [None, "vmin", "vmax"]) # test min/max separately +@pytest.mark.parametrize("align", ["left", "right", "zero", "mid"]) +def test_vmin_vmax_clipping(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align): + # test that clipping occurs if any vmin > data_values or vmax < data_values + if align == "mid": # mid acts as left or right in each case + if values == "positive": + align = "left" + elif values == "negative": + align = "right" + df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values] + vmin = None if nullify == "vmin" else vmin + vmax = None if nullify == "vmax" else vmax + + clip_df = df.where(df <= (vmax if vmax else 999), other=vmax) + clip_df = clip_df.where(clip_df >= (vmin if vmin else -999), other=vmin) + + result = ( + df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"]) + ._compute() + .ctx + ) + expected = clip_df.style.bar(align=align, color=["red", "green"])._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "values, vmin, vmax", + [ + ("positive", 0.5, 4.5), + ("negative", -4.5, -0.5), + ("mixed", -4.5, 4.5), + ], +) +@pytest.mark.parametrize("nullify", [None, "vmin", "vmax"]) # test min/max separately +@pytest.mark.parametrize("align", ["left", "right", "zero", "mid"]) +def test_vmin_vmax_widening(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align): + # test that widening occurs if any vmax > data_values or vmin < data_values + if align == "mid": # mid acts as left or right in each case + if values == "positive": + align = "left" + elif values == "negative": + align = "right" + df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values] + vmin = None if nullify == "vmin" else vmin + vmax = None if nullify == "vmax" else vmax + + expand_df = df.copy() + expand_df.loc[3, :], expand_df.loc[4, :] = vmin, vmax + + result = ( + df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"]) + ._compute() + .ctx + ) + expected = expand_df.style.bar(align=align, color=["red", "green"])._compute().ctx + assert result.items() <= expected.items() + + +def test_numerics(): + # test data is pre-selected for numeric values + data = DataFrame([[1, "a"], [2, "b"]]) + result = data.style.bar()._compute().ctx + assert (0, 1) not in result + assert (1, 1) not in result + + +@pytest.mark.parametrize( + "align, exp", + [ + ("left", [no_bar(), bar_to(100, "green")]), + ("right", [bar_to(100, "red"), no_bar()]), + ("mid", [bar_to(25, "red"), bar_from_to(25, 100, "green")]), + ("zero", [bar_from_to(33.33, 50, "red"), bar_from_to(50, 100, "green")]), + ], +) +def test_colors_mixed(align, exp): + data = DataFrame([[-1], [3]]) + result = data.style.bar(align=align, color=["red", "green"])._compute().ctx + assert result == {(0, 0): exp[0], (1, 0): exp[1]} + + +def test_bar_align_height(): + # test when keyword height is used 'no-repeat center' and 'background-size' present + data = DataFrame([[1], [2]]) + result = data.style.bar(align="left", height=50)._compute().ctx + bg_s = "linear-gradient(90deg, #d65f5f 100.0%, transparent 100.0%) no-repeat center" + expected = { + (0, 0): [("width", "10em")], + (1, 0): [ + ("width", "10em"), + ("background", bg_s), + ("background-size", "100% 50.0%"), + ], + } + assert result == expected + + +def test_bar_value_error_raises(): + df = DataFrame({"A": [-100, -60, -30, -20]}) + + msg = "`align` should be in {'left', 'right', 'mid', 'mean', 'zero'} or" + with pytest.raises(ValueError, match=msg): + df.style.bar(align="poorly", color=["#d65f5f", "#5fba7d"]).to_html() + + msg = r"`width` must be a value in \[0, 100\]" + with pytest.raises(ValueError, match=msg): + df.style.bar(width=200).to_html() + + msg = r"`height` must be a value in \[0, 100\]" + with pytest.raises(ValueError, match=msg): + df.style.bar(height=200).to_html() + + +def test_bar_color_and_cmap_error_raises(): + df = DataFrame({"A": [1, 2, 3, 4]}) + msg = "`color` and `cmap` cannot both be given" + # Test that providing both color and cmap raises a ValueError + with pytest.raises(ValueError, match=msg): + df.style.bar(color="#d65f5f", cmap="viridis").to_html() + + +def test_bar_invalid_color_type_error_raises(): + df = DataFrame({"A": [1, 2, 3, 4]}) + msg = ( + r"`color` must be string or list or tuple of 2 strings," + r"\(eg: color=\['#d65f5f', '#5fba7d'\]\)" + ) + # Test that providing an invalid color type raises a ValueError + with pytest.raises(ValueError, match=msg): + df.style.bar(color=123).to_html() + + # Test that providing a color list with more than two elements raises a ValueError + with pytest.raises(ValueError, match=msg): + df.style.bar(color=["#d65f5f", "#5fba7d", "#abcdef"]).to_html() + + +def test_styler_bar_with_NA_values(): + df1 = DataFrame({"A": [1, 2, NA, 4]}) + df2 = DataFrame([[NA, NA], [NA, NA]]) + expected_substring = "style type=" + html_output1 = df1.style.bar(subset="A").to_html() + html_output2 = df2.style.bar(align="left", axis=None).to_html() + assert expected_substring in html_output1 + assert expected_substring in html_output2 + + +def test_style_bar_with_pyarrow_NA_values(): + pytest.importorskip("pyarrow") + data = """name,age,test1,test2,teacher + Adam,15,95.0,80,Ashby + Bob,16,81.0,82,Ashby + Dave,16,89.0,84,Jones + Fred,15,,88,Jones""" + df = read_csv(io.StringIO(data), dtype_backend="pyarrow") + expected_substring = "style type=" + html_output = df.style.bar(subset="test1").to_html() + assert expected_substring in html_output diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_exceptions.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..d52e3a37e7693dadce34f73fc03a0790c7a0b4d3 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_exceptions.py @@ -0,0 +1,44 @@ +import pytest + +jinja2 = pytest.importorskip("jinja2") + +from pandas import ( + DataFrame, + MultiIndex, +) + +from pandas.io.formats.style import Styler + + +@pytest.fixture +def df(): + return DataFrame( + data=[[0, -0.609], [1, -1.228]], + columns=["A", "B"], + index=["x", "y"], + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +def test_concat_bad_columns(styler): + msg = "`other.data` must have same columns as `Styler.data" + with pytest.raises(ValueError, match=msg): + styler.concat(DataFrame([[1, 2]]).style) + + +def test_concat_bad_type(styler): + msg = "`other` must be of type `Styler`" + with pytest.raises(TypeError, match=msg): + styler.concat(DataFrame([[1, 2]])) + + +def test_concat_bad_index_levels(styler, df): + df = df.copy() + df.index = MultiIndex.from_tuples([(0, 0), (1, 1)]) + msg = "number of index levels must be same in `other`" + with pytest.raises(ValueError, match=msg): + styler.concat(df.style) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_format.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_format.py new file mode 100644 index 0000000000000000000000000000000000000000..1c84816ead140b95f14df8dbeccc83b317ac239a --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_format.py @@ -0,0 +1,562 @@ +import numpy as np +import pytest + +from pandas import ( + NA, + DataFrame, + IndexSlice, + MultiIndex, + NaT, + Timestamp, + option_context, +) + +pytest.importorskip("jinja2") +from pandas.io.formats.style import Styler +from pandas.io.formats.style_render import _str_escape + + +@pytest.fixture +def df(): + return DataFrame( + data=[[0, -0.609], [1, -1.228]], + columns=["A", "B"], + index=["x", "y"], + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +@pytest.fixture +def df_multi(): + return DataFrame( + data=np.arange(16).reshape(4, 4), + columns=MultiIndex.from_product([["A", "B"], ["a", "b"]]), + index=MultiIndex.from_product([["X", "Y"], ["x", "y"]]), + ) + + +@pytest.fixture +def styler_multi(df_multi): + return Styler(df_multi, uuid_len=0) + + +def test_display_format(styler): + ctx = styler.format("{:0.1f}")._translate(True, True) + assert all(["display_value" in c for c in row] for row in ctx["body"]) + assert all([len(c["display_value"]) <= 3 for c in row[1:]] for row in ctx["body"]) + assert len(ctx["body"][0][1]["display_value"].lstrip("-")) <= 3 + + +@pytest.mark.parametrize("index", [True, False]) +@pytest.mark.parametrize("columns", [True, False]) +def test_display_format_index(styler, index, columns): + exp_index = ["x", "y"] + if index: + styler.format_index(lambda v: v.upper(), axis=0) # test callable + exp_index = ["X", "Y"] + + exp_columns = ["A", "B"] + if columns: + styler.format_index("*{}*", axis=1) # test string + exp_columns = ["*A*", "*B*"] + + ctx = styler._translate(True, True) + + for r, row in enumerate(ctx["body"]): + assert row[0]["display_value"] == exp_index[r] + + for c, col in enumerate(ctx["head"][1:]): + assert col["display_value"] == exp_columns[c] + + +def test_format_dict(styler): + ctx = styler.format({"A": "{:0.1f}", "B": "{0:.2%}"})._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "0.0" + assert ctx["body"][0][2]["display_value"] == "-60.90%" + + +def test_format_index_dict(styler): + ctx = styler.format_index({0: lambda v: v.upper()})._translate(True, True) + for i, val in enumerate(["X", "Y"]): + assert ctx["body"][i][0]["display_value"] == val + + +def test_format_string(styler): + ctx = styler.format("{:.2f}")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "0.00" + assert ctx["body"][0][2]["display_value"] == "-0.61" + assert ctx["body"][1][1]["display_value"] == "1.00" + assert ctx["body"][1][2]["display_value"] == "-1.23" + + +def test_format_callable(styler): + ctx = styler.format(lambda v: "neg" if v < 0 else "pos")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "pos" + assert ctx["body"][0][2]["display_value"] == "neg" + assert ctx["body"][1][1]["display_value"] == "pos" + assert ctx["body"][1][2]["display_value"] == "neg" + + +def test_format_with_na_rep(): + # GH 21527 28358 + df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"]) + + ctx = df.style.format(None, na_rep="-")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "-" + assert ctx["body"][0][2]["display_value"] == "-" + + ctx = df.style.format("{:.2%}", na_rep="-")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "-" + assert ctx["body"][0][2]["display_value"] == "-" + assert ctx["body"][1][1]["display_value"] == "110.00%" + assert ctx["body"][1][2]["display_value"] == "120.00%" + + ctx = df.style.format("{:.2%}", na_rep="-", subset=["B"])._translate(True, True) + assert ctx["body"][0][2]["display_value"] == "-" + assert ctx["body"][1][2]["display_value"] == "120.00%" + + +def test_format_index_with_na_rep(): + df = DataFrame([[1, 2, 3, 4, 5]], columns=["A", None, np.nan, NaT, NA]) + ctx = df.style.format_index(None, na_rep="--", axis=1)._translate(True, True) + assert ctx["head"][0][1]["display_value"] == "A" + for i in [2, 3, 4, 5]: + assert ctx["head"][0][i]["display_value"] == "--" + + +def test_format_non_numeric_na(): + # GH 21527 28358 + df = DataFrame( + { + "object": [None, np.nan, "foo"], + "datetime": [None, NaT, Timestamp("20120101")], + } + ) + ctx = df.style.format(None, na_rep="-")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "-" + assert ctx["body"][0][2]["display_value"] == "-" + assert ctx["body"][1][1]["display_value"] == "-" + assert ctx["body"][1][2]["display_value"] == "-" + + +@pytest.mark.parametrize( + "func, attr, kwargs", + [ + ("format", "_display_funcs", {}), + ("format_index", "_display_funcs_index", {"axis": 0}), + ("format_index", "_display_funcs_columns", {"axis": 1}), + ], +) +def test_format_clear(styler, func, attr, kwargs): + assert (0, 0) not in getattr(styler, attr) # using default + getattr(styler, func)("{:.2f}", **kwargs) + assert (0, 0) in getattr(styler, attr) # formatter is specified + getattr(styler, func)(**kwargs) + assert (0, 0) not in getattr(styler, attr) # formatter cleared to default + + +@pytest.mark.parametrize( + "escape, exp", + [ + ("html", "<>&"%$#_{}~^\\~ ^ \\ "), + ( + "latex", + '<>\\&"\\%\\$\\#\\_\\{\\}\\textasciitilde \\textasciicircum ' + "\\textbackslash \\textasciitilde \\space \\textasciicircum \\space " + "\\textbackslash \\space ", + ), + ], +) +def test_format_escape_html(escape, exp): + chars = '<>&"%$#_{}~^\\~ ^ \\ ' + df = DataFrame([[chars]]) + + s = Styler(df, uuid_len=0).format("&{0}&", escape=None) + expected = f'&{chars}&' + assert expected in s.to_html() + + # only the value should be escaped before passing to the formatter + s = Styler(df, uuid_len=0).format("&{0}&", escape=escape) + expected = f'&{exp}&' + assert expected in s.to_html() + + # also test format_index() + styler = Styler(DataFrame(columns=[chars]), uuid_len=0) + styler.format_index("&{0}&", escape=None, axis=1) + assert styler._translate(True, True)["head"][0][1]["display_value"] == f"&{chars}&" + styler.format_index("&{0}&", escape=escape, axis=1) + assert styler._translate(True, True)["head"][0][1]["display_value"] == f"&{exp}&" + + +@pytest.mark.parametrize( + "chars, expected", + [ + ( + r"$ \$&%#_{}~^\ $ &%#_{}~^\ $", + "".join( + [ + r"$ \$&%#_{}~^\ $ ", + r"\&\%\#\_\{\}\textasciitilde \textasciicircum ", + r"\textbackslash \space \$", + ] + ), + ), + ( + r"\( &%#_{}~^\ \) &%#_{}~^\ \(", + "".join( + [ + r"\( &%#_{}~^\ \) ", + r"\&\%\#\_\{\}\textasciitilde \textasciicircum ", + r"\textbackslash \space \textbackslash (", + ] + ), + ), + ( + r"$\&%#_{}^\$", + r"\$\textbackslash \&\%\#\_\{\}\textasciicircum \textbackslash \$", + ), + ( + r"$ \frac{1}{2} $ \( \frac{1}{2} \)", + "".join( + [ + r"$ \frac{1}{2} $", + r" \textbackslash ( \textbackslash frac\{1\}\{2\} \textbackslash )", + ] + ), + ), + ], +) +def test_format_escape_latex_math(chars, expected): + # GH 51903 + # latex-math escape works for each DataFrame cell separately. If we have + # a combination of dollar signs and brackets, the dollar sign would apply. + df = DataFrame([[chars]]) + s = df.style.format("{0}", escape="latex-math") + assert s._translate(True, True)["body"][0][1]["display_value"] == expected + + +def test_format_escape_na_rep(): + # tests the na_rep is not escaped + df = DataFrame([['<>&"', None]]) + s = Styler(df, uuid_len=0).format("X&{0}>X", escape="html", na_rep="&") + ex = 'X&<>&">X' + expected2 = '&' + assert ex in s.to_html() + assert expected2 in s.to_html() + + # also test for format_index() + df = DataFrame(columns=['<>&"', None]) + styler = Styler(df, uuid_len=0) + styler.format_index("X&{0}>X", escape="html", na_rep="&", axis=1) + ctx = styler._translate(True, True) + assert ctx["head"][0][1]["display_value"] == "X&<>&">X" + assert ctx["head"][0][2]["display_value"] == "&" + + +def test_format_escape_floats(styler): + # test given formatter for number format is not impacted by escape + s = styler.format("{:.1f}", escape="html") + for expected in [">0.0<", ">1.0<", ">-1.2<", ">-0.6<"]: + assert expected in s.to_html() + # tests precision of floats is not impacted by escape + s = styler.format(precision=1, escape="html") + for expected in [">0<", ">1<", ">-1.2<", ">-0.6<"]: + assert expected in s.to_html() + + +@pytest.mark.parametrize("formatter", [5, True, [2.0]]) +@pytest.mark.parametrize("func", ["format", "format_index"]) +def test_format_raises(styler, formatter, func): + with pytest.raises(TypeError, match="expected str or callable"): + getattr(styler, func)(formatter) + + +@pytest.mark.parametrize( + "precision, expected", + [ + (1, ["1.0", "2.0", "3.2", "4.6"]), + (2, ["1.00", "2.01", "3.21", "4.57"]), + (3, ["1.000", "2.009", "3.212", "4.566"]), + ], +) +def test_format_with_precision(precision, expected): + # Issue #13257 + df = DataFrame([[1.0, 2.0090, 3.2121, 4.566]], columns=[1.0, 2.0090, 3.2121, 4.566]) + styler = Styler(df) + styler.format(precision=precision) + styler.format_index(precision=precision, axis=1) + + ctx = styler._translate(True, True) + for col, exp in enumerate(expected): + assert ctx["body"][0][col + 1]["display_value"] == exp # format test + assert ctx["head"][0][col + 1]["display_value"] == exp # format_index test + + +@pytest.mark.parametrize("axis", [0, 1]) +@pytest.mark.parametrize( + "level, expected", + [ + (0, ["X", "X", "_", "_"]), # level int + ("zero", ["X", "X", "_", "_"]), # level name + (1, ["_", "_", "X", "X"]), # other level int + ("one", ["_", "_", "X", "X"]), # other level name + ([0, 1], ["X", "X", "X", "X"]), # both levels + ([0, "zero"], ["X", "X", "_", "_"]), # level int and name simultaneous + ([0, "one"], ["X", "X", "X", "X"]), # both levels as int and name + (["one", "zero"], ["X", "X", "X", "X"]), # both level names, reversed + ], +) +def test_format_index_level(axis, level, expected): + midx = MultiIndex.from_arrays([["_", "_"], ["_", "_"]], names=["zero", "one"]) + df = DataFrame([[1, 2], [3, 4]]) + if axis == 0: + df.index = midx + else: + df.columns = midx + + styler = df.style.format_index(lambda v: "X", level=level, axis=axis) + ctx = styler._translate(True, True) + + if axis == 0: # compare index + result = [ctx["body"][s][0]["display_value"] for s in range(2)] + result += [ctx["body"][s][1]["display_value"] for s in range(2)] + else: # compare columns + result = [ctx["head"][0][s + 1]["display_value"] for s in range(2)] + result += [ctx["head"][1][s + 1]["display_value"] for s in range(2)] + + assert expected == result + + +def test_format_subset(): + df = DataFrame([[0.1234, 0.1234], [1.1234, 1.1234]], columns=["a", "b"]) + ctx = df.style.format( + {"a": "{:0.1f}", "b": "{0:.2%}"}, subset=IndexSlice[0, :] + )._translate(True, True) + expected = "0.1" + raw_11 = "1.123400" + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][1][1]["display_value"] == raw_11 + assert ctx["body"][0][2]["display_value"] == "12.34%" + + ctx = df.style.format("{:0.1f}", subset=IndexSlice[0, :])._translate(True, True) + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][1][1]["display_value"] == raw_11 + + ctx = df.style.format("{:0.1f}", subset=IndexSlice["a"])._translate(True, True) + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][0][2]["display_value"] == "0.123400" + + ctx = df.style.format("{:0.1f}", subset=IndexSlice[0, "a"])._translate(True, True) + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][1][1]["display_value"] == raw_11 + + ctx = df.style.format("{:0.1f}", subset=IndexSlice[[0, 1], ["a"]])._translate( + True, True + ) + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][1][1]["display_value"] == "1.1" + assert ctx["body"][0][2]["display_value"] == "0.123400" + assert ctx["body"][1][2]["display_value"] == raw_11 + + +@pytest.mark.parametrize("formatter", [None, "{:,.1f}"]) +@pytest.mark.parametrize("decimal", [".", "*"]) +@pytest.mark.parametrize("precision", [None, 2]) +@pytest.mark.parametrize("func, col", [("format", 1), ("format_index", 0)]) +def test_format_thousands(formatter, decimal, precision, func, col): + styler = DataFrame([[1000000.123456789]], index=[1000000.123456789]).style + result = getattr(styler, func)( # testing float + thousands="_", formatter=formatter, decimal=decimal, precision=precision + )._translate(True, True) + assert "1_000_000" in result["body"][0][col]["display_value"] + + styler = DataFrame([[1000000]], index=[1000000]).style + result = getattr(styler, func)( # testing int + thousands="_", formatter=formatter, decimal=decimal, precision=precision + )._translate(True, True) + assert "1_000_000" in result["body"][0][col]["display_value"] + + styler = DataFrame([[1 + 1000000.123456789j]], index=[1 + 1000000.123456789j]).style + result = getattr(styler, func)( # testing complex + thousands="_", formatter=formatter, decimal=decimal, precision=precision + )._translate(True, True) + assert "1_000_000" in result["body"][0][col]["display_value"] + + +@pytest.mark.parametrize("formatter", [None, "{:,.4f}"]) +@pytest.mark.parametrize("thousands", [None, ",", "*"]) +@pytest.mark.parametrize("precision", [None, 4]) +@pytest.mark.parametrize("func, col", [("format", 1), ("format_index", 0)]) +def test_format_decimal(formatter, thousands, precision, func, col): + styler = DataFrame([[1000000.123456789]], index=[1000000.123456789]).style + result = getattr(styler, func)( # testing float + decimal="_", formatter=formatter, thousands=thousands, precision=precision + )._translate(True, True) + assert "000_123" in result["body"][0][col]["display_value"] + + styler = DataFrame([[1 + 1000000.123456789j]], index=[1 + 1000000.123456789j]).style + result = getattr(styler, func)( # testing complex + decimal="_", formatter=formatter, thousands=thousands, precision=precision + )._translate(True, True) + assert "000_123" in result["body"][0][col]["display_value"] + + +def test_str_escape_error(): + msg = "`escape` only permitted in {'html', 'latex', 'latex-math'}, got " + with pytest.raises(ValueError, match=msg): + _str_escape("text", "bad_escape") + + with pytest.raises(ValueError, match=msg): + _str_escape("text", []) + + _str_escape(2.00, "bad_escape") # OK since dtype is float + + +def test_long_int_formatting(): + df = DataFrame(data=[[1234567890123456789]], columns=["test"]) + styler = df.style + ctx = styler._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "1234567890123456789" + + styler = df.style.format(thousands="_") + ctx = styler._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "1_234_567_890_123_456_789" + + +def test_format_options(): + df = DataFrame({"int": [2000, 1], "float": [1.009, None], "str": ["&<", "&~"]}) + ctx = df.style._translate(True, True) + + # test option: na_rep + assert ctx["body"][1][2]["display_value"] == "nan" + with option_context("styler.format.na_rep", "MISSING"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][1][2]["display_value"] == "MISSING" + + # test option: decimal and precision + assert ctx["body"][0][2]["display_value"] == "1.009000" + with option_context("styler.format.decimal", "_"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][2]["display_value"] == "1_009000" + with option_context("styler.format.precision", 2): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][2]["display_value"] == "1.01" + + # test option: thousands + assert ctx["body"][0][1]["display_value"] == "2000" + with option_context("styler.format.thousands", "_"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][1]["display_value"] == "2_000" + + # test option: escape + assert ctx["body"][0][3]["display_value"] == "&<" + assert ctx["body"][1][3]["display_value"] == "&~" + with option_context("styler.format.escape", "html"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][3]["display_value"] == "&<" + with option_context("styler.format.escape", "latex"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][1][3]["display_value"] == "\\&\\textasciitilde " + with option_context("styler.format.escape", "latex-math"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][1][3]["display_value"] == "\\&\\textasciitilde " + + # test option: formatter + with option_context("styler.format.formatter", {"int": "{:,.2f}"}): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][1]["display_value"] == "2,000.00" + + +def test_precision_zero(df): + styler = Styler(df, precision=0) + ctx = styler._translate(True, True) + assert ctx["body"][0][2]["display_value"] == "-1" + assert ctx["body"][1][2]["display_value"] == "-1" + + +@pytest.mark.parametrize( + "formatter, exp", + [ + (lambda x: f"{x:.3f}", "9.000"), + ("{:.2f}", "9.00"), + ({0: "{:.1f}"}, "9.0"), + (None, "9"), + ], +) +def test_formatter_options_validator(formatter, exp): + df = DataFrame([[9]]) + with option_context("styler.format.formatter", formatter): + assert f" {exp} " in df.style.to_latex() + + +def test_formatter_options_raises(): + msg = "Value must be an instance of" + with pytest.raises(ValueError, match=msg): + with option_context("styler.format.formatter", ["bad", "type"]): + DataFrame().style.to_latex() + + +def test_1level_multiindex(): + # GH 43383 + midx = MultiIndex.from_product([[1, 2]], names=[""]) + df = DataFrame(-1, index=midx, columns=[0, 1]) + ctx = df.style._translate(True, True) + assert ctx["body"][0][0]["display_value"] == "1" + assert ctx["body"][0][0]["is_visible"] is True + assert ctx["body"][1][0]["display_value"] == "2" + assert ctx["body"][1][0]["is_visible"] is True + + +def test_boolean_format(): + # gh 46384: booleans do not collapse to integer representation on display + df = DataFrame([[True, False]]) + ctx = df.style._translate(True, True) + assert ctx["body"][0][1]["display_value"] is True + assert ctx["body"][0][2]["display_value"] is False + + +@pytest.mark.parametrize( + "hide, labels", + [ + (False, [1, 2]), + (True, [1, 2, 3, 4]), + ], +) +def test_relabel_raise_length(styler_multi, hide, labels): + if hide: + styler_multi.hide(axis=0, subset=[("X", "x"), ("Y", "y")]) + with pytest.raises(ValueError, match="``labels`` must be of length equal"): + styler_multi.relabel_index(labels=labels) + + +def test_relabel_index(styler_multi): + labels = [(1, 2), (3, 4)] + styler_multi.hide(axis=0, subset=[("X", "x"), ("Y", "y")]) + styler_multi.relabel_index(labels=labels) + ctx = styler_multi._translate(True, True) + assert {"value": "X", "display_value": 1}.items() <= ctx["body"][0][0].items() + assert {"value": "y", "display_value": 2}.items() <= ctx["body"][0][1].items() + assert {"value": "Y", "display_value": 3}.items() <= ctx["body"][1][0].items() + assert {"value": "x", "display_value": 4}.items() <= ctx["body"][1][1].items() + + +def test_relabel_columns(styler_multi): + labels = [(1, 2), (3, 4)] + styler_multi.hide(axis=1, subset=[("A", "a"), ("B", "b")]) + styler_multi.relabel_index(axis=1, labels=labels) + ctx = styler_multi._translate(True, True) + assert {"value": "A", "display_value": 1}.items() <= ctx["head"][0][3].items() + assert {"value": "B", "display_value": 3}.items() <= ctx["head"][0][4].items() + assert {"value": "b", "display_value": 2}.items() <= ctx["head"][1][3].items() + assert {"value": "a", "display_value": 4}.items() <= ctx["head"][1][4].items() + + +def test_relabel_roundtrip(styler): + styler.relabel_index(["{}", "{}"]) + ctx = styler._translate(True, True) + assert {"value": "x", "display_value": "x"}.items() <= ctx["body"][0][0].items() + assert {"value": "y", "display_value": "y"}.items() <= ctx["body"][1][0].items() diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_highlight.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_highlight.py new file mode 100644 index 0000000000000000000000000000000000000000..3d59719010ee03cc53373a1c96f5f8c5611d7681 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_highlight.py @@ -0,0 +1,218 @@ +import numpy as np +import pytest + +from pandas import ( + NA, + DataFrame, + IndexSlice, +) + +pytest.importorskip("jinja2") + +from pandas.io.formats.style import Styler + + +@pytest.fixture(params=[(None, "float64"), (NA, "Int64")]) +def df(request): + # GH 45804 + return DataFrame( + {"A": [0, np.nan, 10], "B": [1, request.param[0], 2]}, dtype=request.param[1] + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +def test_highlight_null(styler): + result = styler.highlight_null()._compute().ctx + expected = { + (1, 0): [("background-color", "red")], + (1, 1): [("background-color", "red")], + } + assert result == expected + + +def test_highlight_null_subset(styler): + # GH 31345 + result = ( + styler.highlight_null(color="red", subset=["A"]) + .highlight_null(color="green", subset=["B"]) + ._compute() + .ctx + ) + expected = { + (1, 0): [("background-color", "red")], + (1, 1): [("background-color", "green")], + } + assert result == expected + + +@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"]) +def test_highlight_minmax_basic(df, f): + expected = { + (0, 1): [("background-color", "red")], + # ignores NaN row, + (2, 0): [("background-color", "red")], + } + if f == "highlight_min": + df = -df + result = getattr(df.style, f)(axis=1, color="red")._compute().ctx + assert result == expected + + +@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"]) +@pytest.mark.parametrize( + "kwargs", + [ + {"axis": None, "color": "red"}, # test axis + {"axis": 0, "subset": ["A"], "color": "red"}, # test subset and ignores NaN + {"axis": None, "props": "background-color: red"}, # test props + ], +) +def test_highlight_minmax_ext(df, f, kwargs): + expected = {(2, 0): [("background-color", "red")]} + if f == "highlight_min": + df = -df + result = getattr(df.style, f)(**kwargs)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"]) +@pytest.mark.parametrize("axis", [None, 0, 1]) +def test_highlight_minmax_nulls(f, axis): + # GH 42750 + expected = { + (1, 0): [("background-color", "yellow")], + (1, 1): [("background-color", "yellow")], + } + if axis == 1: + expected.update({(2, 1): [("background-color", "yellow")]}) + + if f == "highlight_max": + df = DataFrame({"a": [NA, 1, None], "b": [np.nan, 1, -1]}) + else: + df = DataFrame({"a": [NA, -1, None], "b": [np.nan, -1, 1]}) + + result = getattr(df.style, f)(axis=axis)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "kwargs", + [ + {"left": 0, "right": 1}, # test basic range + {"left": 0, "right": 1, "props": "background-color: yellow"}, # test props + {"left": -100, "right": 100, "subset": IndexSlice[[0, 1], :]}, # test subset + {"left": 0, "subset": IndexSlice[[0, 1], :]}, # test no right + {"right": 1}, # test no left + {"left": [0, 0, 11], "axis": 0}, # test left as sequence + {"left": DataFrame({"A": [0, 0, 11], "B": [1, 1, 11]}), "axis": None}, # axis + {"left": 0, "right": [0, 1], "axis": 1}, # test sequence right + ], +) +def test_highlight_between(styler, kwargs): + expected = { + (0, 0): [("background-color", "yellow")], + (0, 1): [("background-color", "yellow")], + } + result = styler.highlight_between(**kwargs)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "arg, map, axis", + [ + ("left", [1, 2], 0), # 0 axis has 3 elements not 2 + ("left", [1, 2, 3], 1), # 1 axis has 2 elements not 3 + ("left", np.array([[1, 2], [1, 2]]), None), # df is (2,3) not (2,2) + ("right", [1, 2], 0), # same tests as above for 'right' not 'left' + ("right", [1, 2, 3], 1), # .. + ("right", np.array([[1, 2], [1, 2]]), None), # .. + ], +) +def test_highlight_between_raises(arg, styler, map, axis): + msg = f"supplied '{arg}' is not correct shape" + with pytest.raises(ValueError, match=msg): + styler.highlight_between(**{arg: map, "axis": axis})._compute() + + +def test_highlight_between_raises2(styler): + msg = "values can be 'both', 'left', 'right', or 'neither'" + with pytest.raises(ValueError, match=msg): + styler.highlight_between(inclusive="badstring")._compute() + + with pytest.raises(ValueError, match=msg): + styler.highlight_between(inclusive=1)._compute() + + +@pytest.mark.parametrize( + "inclusive, expected", + [ + ( + "both", + { + (0, 0): [("background-color", "yellow")], + (0, 1): [("background-color", "yellow")], + }, + ), + ("neither", {}), + ("left", {(0, 0): [("background-color", "yellow")]}), + ("right", {(0, 1): [("background-color", "yellow")]}), + ], +) +def test_highlight_between_inclusive(styler, inclusive, expected): + kwargs = {"left": 0, "right": 1, "subset": IndexSlice[[0, 1], :]} + result = styler.highlight_between(**kwargs, inclusive=inclusive)._compute() + assert result.ctx == expected + + +@pytest.mark.parametrize( + "kwargs", + [ + {"q_left": 0.5, "q_right": 1, "axis": 0}, # base case + {"q_left": 0.5, "q_right": 1, "axis": None}, # test axis + {"q_left": 0, "q_right": 1, "subset": IndexSlice[2, :]}, # test subset + {"q_left": 0.5, "axis": 0}, # test no high + {"q_right": 1, "subset": IndexSlice[2, :], "axis": 1}, # test no low + {"q_left": 0.5, "axis": 0, "props": "background-color: yellow"}, # tst prop + ], +) +def test_highlight_quantile(styler, kwargs): + expected = { + (2, 0): [("background-color", "yellow")], + (2, 1): [("background-color", "yellow")], + } + result = styler.highlight_quantile(**kwargs)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "f,kwargs", + [ + ("highlight_min", {"axis": 1, "subset": IndexSlice[1, :]}), + ("highlight_max", {"axis": 0, "subset": [0]}), + ("highlight_quantile", {"axis": None, "q_left": 0.6, "q_right": 0.8}), + ("highlight_between", {"subset": [0]}), + ], +) +@pytest.mark.parametrize( + "df", + [ + DataFrame([[0, 10], [20, 30]], dtype=int), + DataFrame([[0, 10], [20, 30]], dtype=float), + DataFrame([[0, 10], [20, 30]], dtype="datetime64[ns]"), + DataFrame([[0, 10], [20, 30]], dtype=str), + DataFrame([[0, 10], [20, 30]], dtype="timedelta64[ns]"), + ], +) +def test_all_highlight_dtypes(f, kwargs, df): + if f == "highlight_quantile" and isinstance(df.iloc[0, 0], (str)): + return None # quantile incompatible with str + if f == "highlight_between": + kwargs["left"] = df.iloc[1, 0] # set the range low for testing + + expected = {(1, 0): [("background-color", "yellow")]} + result = getattr(df.style, f)(**kwargs)._compute().ctx + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_html.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_html.py new file mode 100644 index 0000000000000000000000000000000000000000..1e345eb82ed3c31e7a5e0f89fa574aea84923dd7 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_html.py @@ -0,0 +1,1009 @@ +from textwrap import ( + dedent, + indent, +) + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + MultiIndex, + option_context, +) + +jinja2 = pytest.importorskip("jinja2") +from pandas.io.formats.style import Styler + + +@pytest.fixture +def env(): + loader = jinja2.PackageLoader("pandas", "io/formats/templates") + env = jinja2.Environment(loader=loader, trim_blocks=True) + return env + + +@pytest.fixture +def styler(): + return Styler(DataFrame([[2.61], [2.69]], index=["a", "b"], columns=["A"])) + + +@pytest.fixture +def styler_mi(): + midx = MultiIndex.from_product([["a", "b"], ["c", "d"]]) + return Styler(DataFrame(np.arange(16).reshape(4, 4), index=midx, columns=midx)) + + +@pytest.fixture +def tpl_style(env): + return env.get_template("html_style.tpl") + + +@pytest.fixture +def tpl_table(env): + return env.get_template("html_table.tpl") + + +def test_html_template_extends_options(): + # make sure if templates are edited tests are updated as are setup fixtures + # to understand the dependency + with open("pandas/io/formats/templates/html.tpl", encoding="utf-8") as file: + result = file.read() + assert "{% include html_style_tpl %}" in result + assert "{% include html_table_tpl %}" in result + + +def test_exclude_styles(styler): + result = styler.to_html(exclude_styles=True, doctype_html=True) + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + +
 A
a2.610000
b2.690000
+ + + """ + ) + assert result == expected + + +def test_w3_html_format(styler): + styler.set_uuid("").set_table_styles([{"selector": "th", "props": "att2:v2;"}]).map( + lambda x: "att1:v1;" + ).set_table_attributes('class="my-cls1" style="attr3:v3;"').set_td_classes( + DataFrame(["my-cls2"], index=["a"], columns=["A"]) + ).format( + "{:.1f}" + ).set_caption( + "A comprehensive test" + ) + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + +
A comprehensive test
 A
a2.6
b2.7
+ """ + ) + assert expected == styler.to_html() + + +def test_colspan_w3(): + # GH 36223 + df = DataFrame(data=[[1, 2]], columns=[["l0", "l0"], ["l1a", "l1b"]]) + styler = Styler(df, uuid="_", cell_ids=False) + assert 'l0' in styler.to_html() + + +def test_rowspan_w3(): + # GH 38533 + df = DataFrame(data=[[1, 2]], index=[["l0", "l0"], ["l1a", "l1b"]]) + styler = Styler(df, uuid="_", cell_ids=False) + assert 'l0' in styler.to_html() + + +def test_styles(styler): + styler.set_uuid("abc") + styler.set_table_styles([{"selector": "td", "props": "color: red;"}]) + result = styler.to_html(doctype_html=True) + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + + +
 A
a2.610000
b2.690000
+ + + """ + ) + assert result == expected + + +def test_doctype(styler): + result = styler.to_html(doctype_html=False) + assert "" not in result + assert "" not in result + assert "" not in result + assert "" not in result + + +def test_doctype_encoding(styler): + with option_context("styler.render.encoding", "ASCII"): + result = styler.to_html(doctype_html=True) + assert '' in result + result = styler.to_html(doctype_html=True, encoding="ANSI") + assert '' in result + + +def test_bold_headers_arg(styler): + result = styler.to_html(bold_headers=True) + assert "th {\n font-weight: bold;\n}" in result + result = styler.to_html() + assert "th {\n font-weight: bold;\n}" not in result + + +def test_caption_arg(styler): + result = styler.to_html(caption="foo bar") + assert "foo bar" in result + result = styler.to_html() + assert "foo bar" not in result + + +def test_block_names(tpl_style, tpl_table): + # catch accidental removal of a block + expected_style = { + "before_style", + "style", + "table_styles", + "before_cellstyle", + "cellstyle", + } + expected_table = { + "before_table", + "table", + "caption", + "thead", + "tbody", + "after_table", + "before_head_rows", + "head_tr", + "after_head_rows", + "before_rows", + "tr", + "after_rows", + } + result1 = set(tpl_style.blocks) + assert result1 == expected_style + + result2 = set(tpl_table.blocks) + assert result2 == expected_table + + +def test_from_custom_template_table(tmpdir): + p = tmpdir.mkdir("tpl").join("myhtml_table.tpl") + p.write( + dedent( + """\ + {% extends "html_table.tpl" %} + {% block table %} +

{{custom_title}}

+ {{ super() }} + {% endblock table %}""" + ) + ) + result = Styler.from_custom_template(str(tmpdir.join("tpl")), "myhtml_table.tpl") + assert issubclass(result, Styler) + assert result.env is not Styler.env + assert result.template_html_table is not Styler.template_html_table + styler = result(DataFrame({"A": [1, 2]})) + assert "

My Title

\n\n\n + {{ super() }} + {% endblock style %}""" + ) + ) + result = Styler.from_custom_template( + str(tmpdir.join("tpl")), html_style="myhtml_style.tpl" + ) + assert issubclass(result, Styler) + assert result.env is not Styler.env + assert result.template_html_style is not Styler.template_html_style + styler = result(DataFrame({"A": [1, 2]})) + assert '\n\nfull cap" in styler.to_html() + + +@pytest.mark.parametrize("index", [False, True]) +@pytest.mark.parametrize("columns", [False, True]) +@pytest.mark.parametrize("index_name", [True, False]) +def test_sticky_basic(styler, index, columns, index_name): + if index_name: + styler.index.name = "some text" + if index: + styler.set_sticky(axis=0) + if columns: + styler.set_sticky(axis=1) + + left_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " left: 0px;\n z-index: {1};\n}}" + ) + top_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " top: {1}px;\n z-index: {2};\n{3}}}" + ) + + res = styler.set_uuid("").to_html() + + # test index stickys over thead and tbody + assert (left_css.format("thead tr th:nth-child(1)", "3 !important") in res) is index + assert (left_css.format("tbody tr th:nth-child(1)", "1") in res) is index + + # test column stickys including if name row + assert ( + top_css.format("thead tr:nth-child(1) th", "0", "2", " height: 25px;\n") in res + ) is (columns and index_name) + assert ( + top_css.format("thead tr:nth-child(2) th", "25", "2", " height: 25px;\n") + in res + ) is (columns and index_name) + assert (top_css.format("thead tr:nth-child(1) th", "0", "2", "") in res) is ( + columns and not index_name + ) + + +@pytest.mark.parametrize("index", [False, True]) +@pytest.mark.parametrize("columns", [False, True]) +def test_sticky_mi(styler_mi, index, columns): + if index: + styler_mi.set_sticky(axis=0) + if columns: + styler_mi.set_sticky(axis=1) + + left_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " left: {1}px;\n min-width: 75px;\n max-width: 75px;\n z-index: {2};\n}}" + ) + top_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " top: {1}px;\n height: 25px;\n z-index: {2};\n}}" + ) + + res = styler_mi.set_uuid("").to_html() + + # test the index stickys for thead and tbody over both levels + assert ( + left_css.format("thead tr th:nth-child(1)", "0", "3 !important") in res + ) is index + assert (left_css.format("tbody tr th.level0", "0", "1") in res) is index + assert ( + left_css.format("thead tr th:nth-child(2)", "75", "3 !important") in res + ) is index + assert (left_css.format("tbody tr th.level1", "75", "1") in res) is index + + # test the column stickys for each level row + assert (top_css.format("thead tr:nth-child(1) th", "0", "2") in res) is columns + assert (top_css.format("thead tr:nth-child(2) th", "25", "2") in res) is columns + + +@pytest.mark.parametrize("index", [False, True]) +@pytest.mark.parametrize("columns", [False, True]) +@pytest.mark.parametrize("levels", [[1], ["one"], "one"]) +def test_sticky_levels(styler_mi, index, columns, levels): + styler_mi.index.names, styler_mi.columns.names = ["zero", "one"], ["zero", "one"] + if index: + styler_mi.set_sticky(axis=0, levels=levels) + if columns: + styler_mi.set_sticky(axis=1, levels=levels) + + left_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " left: {1}px;\n min-width: 75px;\n max-width: 75px;\n z-index: {2};\n}}" + ) + top_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " top: {1}px;\n height: 25px;\n z-index: {2};\n}}" + ) + + res = styler_mi.set_uuid("").to_html() + + # test no sticking of level0 + assert "#T_ thead tr th:nth-child(1)" not in res + assert "#T_ tbody tr th.level0" not in res + assert "#T_ thead tr:nth-child(1) th" not in res + + # test sticking level1 + assert ( + left_css.format("thead tr th:nth-child(2)", "0", "3 !important") in res + ) is index + assert (left_css.format("tbody tr th.level1", "0", "1") in res) is index + assert (top_css.format("thead tr:nth-child(2) th", "0", "2") in res) is columns + + +def test_sticky_raises(styler): + with pytest.raises(ValueError, match="No axis named bad for object type DataFrame"): + styler.set_sticky(axis="bad") + + +@pytest.mark.parametrize( + "sparse_index, sparse_columns", + [(True, True), (True, False), (False, True), (False, False)], +) +def test_sparse_options(sparse_index, sparse_columns): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=ridx, columns=cidx) + styler = df.style + + default_html = styler.to_html() # defaults under pd.options to (True , True) + + with option_context( + "styler.sparse.index", sparse_index, "styler.sparse.columns", sparse_columns + ): + html1 = styler.to_html() + assert (html1 == default_html) is (sparse_index and sparse_columns) + html2 = styler.to_html(sparse_index=sparse_index, sparse_columns=sparse_columns) + assert html1 == html2 + + +@pytest.mark.parametrize("index", [True, False]) +@pytest.mark.parametrize("columns", [True, False]) +def test_map_header_cell_ids(styler, index, columns): + # GH 41893 + func = lambda v: "attr: val;" + styler.uuid, styler.cell_ids = "", False + if index: + styler.map_index(func, axis="index") + if columns: + styler.map_index(func, axis="columns") + + result = styler.to_html() + + # test no data cell ids + assert '2.610000' in result + assert '2.690000' in result + + # test index header ids where needed and css styles + assert ( + 'a' in result + ) is index + assert ( + 'b' in result + ) is index + assert ("#T__level0_row0, #T__level0_row1 {\n attr: val;\n}" in result) is index + + # test column header ids where needed and css styles + assert ( + 'A' in result + ) is columns + assert ("#T__level0_col0 {\n attr: val;\n}" in result) is columns + + +@pytest.mark.parametrize("rows", [True, False]) +@pytest.mark.parametrize("cols", [True, False]) +def test_maximums(styler_mi, rows, cols): + result = styler_mi.to_html( + max_rows=2 if rows else None, + max_columns=2 if cols else None, + ) + + assert ">5" in result # [[0,1], [4,5]] always visible + assert (">8" in result) is not rows # first trimmed vertical element + assert (">2" in result) is not cols # first trimmed horizontal element + + +def test_replaced_css_class_names(): + css = { + "row_heading": "ROWHEAD", + # "col_heading": "COLHEAD", + "index_name": "IDXNAME", + # "col": "COL", + "row": "ROW", + # "col_trim": "COLTRIM", + "row_trim": "ROWTRIM", + "level": "LEVEL", + "data": "DATA", + "blank": "BLANK", + } + midx = MultiIndex.from_product([["a", "b"], ["c", "d"]]) + styler_mi = Styler( + DataFrame(np.arange(16).reshape(4, 4), index=midx, columns=midx), + uuid_len=0, + ).set_table_styles(css_class_names=css) + styler_mi.index.names = ["n1", "n2"] + styler_mi.hide(styler_mi.index[1:], axis=0) + styler_mi.hide(styler_mi.columns[1:], axis=1) + styler_mi.map_index(lambda v: "color: red;", axis=0) + styler_mi.map_index(lambda v: "color: green;", axis=1) + styler_mi.map(lambda v: "color: blue;") + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + + + + +
 n1a
 n2c
n1n2 
ac0
+ """ + ) + result = styler_mi.to_html() + assert result == expected + + +def test_include_css_style_rules_only_for_visible_cells(styler_mi): + # GH 43619 + result = ( + styler_mi.set_uuid("") + .map(lambda v: "color: blue;") + .hide(styler_mi.data.columns[1:], axis="columns") + .hide(styler_mi.data.index[1:], axis="index") + .to_html() + ) + expected_styles = dedent( + """\ + + """ + ) + assert expected_styles in result + + +def test_include_css_style_rules_only_for_visible_index_labels(styler_mi): + # GH 43619 + result = ( + styler_mi.set_uuid("") + .map_index(lambda v: "color: blue;", axis="index") + .hide(styler_mi.data.columns, axis="columns") + .hide(styler_mi.data.index[1:], axis="index") + .to_html() + ) + expected_styles = dedent( + """\ + + """ + ) + assert expected_styles in result + + +def test_include_css_style_rules_only_for_visible_column_labels(styler_mi): + # GH 43619 + result = ( + styler_mi.set_uuid("") + .map_index(lambda v: "color: blue;", axis="columns") + .hide(styler_mi.data.columns[1:], axis="columns") + .hide(styler_mi.data.index, axis="index") + .to_html() + ) + expected_styles = dedent( + """\ + + """ + ) + assert expected_styles in result + + +def test_hiding_index_columns_multiindex_alignment(): + # gh 43644 + midx = MultiIndex.from_product( + [["i0", "j0"], ["i1"], ["i2", "j2"]], names=["i-0", "i-1", "i-2"] + ) + cidx = MultiIndex.from_product( + [["c0"], ["c1", "d1"], ["c2", "d2"]], names=["c-0", "c-1", "c-2"] + ) + df = DataFrame(np.arange(16).reshape(4, 4), index=midx, columns=cidx) + styler = Styler(df, uuid_len=0) + styler.hide(level=1, axis=0).hide(level=0, axis=1) + styler.hide([("j0", "i1", "j2")], axis=0) + styler.hide([("c0", "d1", "d2")], axis=1) + result = styler.to_html() + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 c-1c1d1
 c-2c2d2c2
i-0i-2   
i0i2012
j2456
j0i28910
+ """ + ) + assert result == expected + + +def test_hiding_index_columns_multiindex_trimming(): + # gh 44272 + df = DataFrame(np.arange(64).reshape(8, 8)) + df.columns = MultiIndex.from_product([[0, 1, 2, 3], [0, 1]]) + df.index = MultiIndex.from_product([[0, 1, 2, 3], [0, 1]]) + df.index.names, df.columns.names = ["a", "b"], ["c", "d"] + styler = Styler(df, cell_ids=False, uuid_len=0) + styler.hide([(0, 0), (0, 1), (1, 0)], axis=1).hide([(0, 0), (0, 1), (1, 0)], axis=0) + with option_context("styler.render.max_rows", 4, "styler.render.max_columns", 4): + result = styler.to_html() + + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 c123
 d1010...
ab     
1127282930...
2035363738...
143444546...
3051525354...
.....................
+ """ + ) + + assert result == expected + + +@pytest.mark.parametrize("type", ["data", "index"]) +@pytest.mark.parametrize( + "text, exp, found", + [ + ("no link, just text", False, ""), + ("subdomain not www: sub.web.com", False, ""), + ("www subdomain: www.web.com other", True, "www.web.com"), + ("scheme full structure: http://www.web.com", True, "http://www.web.com"), + ("scheme no top-level: http://www.web", True, "http://www.web"), + ("no scheme, no top-level: www.web", False, "www.web"), + ("https scheme: https://www.web.com", True, "https://www.web.com"), + ("ftp scheme: ftp://www.web", True, "ftp://www.web"), + ("ftps scheme: ftps://www.web", True, "ftps://www.web"), + ("subdirectories: www.web.com/directory", True, "www.web.com/directory"), + ("Multiple domains: www.1.2.3.4", True, "www.1.2.3.4"), + ("with port: http://web.com:80", True, "http://web.com:80"), + ( + "full net_loc scheme: http://user:pass@web.com", + True, + "http://user:pass@web.com", + ), + ( + "with valid special chars: http://web.com/,.':;~!@#$*()[]", + True, + "http://web.com/,.':;~!@#$*()[]", + ), + ], +) +def test_rendered_links(type, text, exp, found): + if type == "data": + df = DataFrame([text]) + styler = df.style.format(hyperlinks="html") + else: + df = DataFrame([0], index=[text]) + styler = df.style.format_index(hyperlinks="html") + + rendered = f'{found}' + result = styler.to_html() + assert (rendered in result) is exp + assert (text in result) is not exp # test conversion done when expected and not + + +def test_multiple_rendered_links(): + links = ("www.a.b", "http://a.c", "https://a.d", "ftp://a.e") + # pylint: disable-next=consider-using-f-string + df = DataFrame(["text {} {} text {} {}".format(*links)]) + result = df.style.format(hyperlinks="html").to_html() + href = '{0}' + for link in links: + assert href.format(link) in result + assert href.format("text") not in result + + +def test_concat(styler): + other = styler.data.agg(["mean"]).style + styler.concat(other).set_uuid("X") + result = styler.to_html() + fp = "foot0_" + expected = dedent( + f"""\ + + b + 2.690000 + + + mean + 2.650000 + + + + """ + ) + assert expected in result + + +def test_concat_recursion(styler): + df = styler.data + styler1 = styler + styler2 = Styler(df.agg(["mean"]), precision=3) + styler3 = Styler(df.agg(["mean"]), precision=4) + styler1.concat(styler2.concat(styler3)).set_uuid("X") + result = styler.to_html() + # notice that the second concat (last of the output html), + # there are two `foot_` in the id and class + fp1 = "foot0_" + fp2 = "foot0_foot0_" + expected = dedent( + f"""\ + + b + 2.690000 + + + mean + 2.650 + + + mean + 2.6500 + + + + """ + ) + assert expected in result + + +def test_concat_chain(styler): + df = styler.data + styler1 = styler + styler2 = Styler(df.agg(["mean"]), precision=3) + styler3 = Styler(df.agg(["mean"]), precision=4) + styler1.concat(styler2).concat(styler3).set_uuid("X") + result = styler.to_html() + fp1 = "foot0_" + fp2 = "foot1_" + expected = dedent( + f"""\ + + b + 2.690000 + + + mean + 2.650 + + + mean + 2.6500 + + + + """ + ) + assert expected in result + + +def test_concat_combined(): + def html_lines(foot_prefix: str): + assert foot_prefix.endswith("_") or foot_prefix == "" + fp = foot_prefix + return indent( + dedent( + f"""\ + + a + 2.610000 + + + b + 2.690000 + + """ + ), + prefix=" " * 4, + ) + + df = DataFrame([[2.61], [2.69]], index=["a", "b"], columns=["A"]) + s1 = df.style.highlight_max(color="red") + s2 = df.style.highlight_max(color="green") + s3 = df.style.highlight_max(color="blue") + s4 = df.style.highlight_max(color="yellow") + + result = s1.concat(s2).concat(s3.concat(s4)).set_uuid("X").to_html() + expected_css = dedent( + """\ + + """ + ) + expected_table = ( + dedent( + """\ + + + + + + + + + """ + ) + + html_lines("") + + html_lines("foot0_") + + html_lines("foot1_") + + html_lines("foot1_foot0_") + + dedent( + """\ + +
 A
+ """ + ) + ) + assert expected_css + expected_table == result + + +def test_to_html_na_rep_non_scalar_data(datapath): + # GH47103 + df = DataFrame([{"a": 1, "b": [1, 2, 3], "c": np.nan}]) + result = df.style.format(na_rep="-").to_html(table_uuid="test") + expected = """\ + + + + + + + + + + + + + + + + + + +
 abc
01[1, 2, 3]-
+""" + assert result == expected diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_matplotlib.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_matplotlib.py new file mode 100644 index 0000000000000000000000000000000000000000..fb7a77f1ddb27db66a847fc1a1d87d14d95822aa --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_matplotlib.py @@ -0,0 +1,335 @@ +import gc + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + IndexSlice, + Series, +) + +pytest.importorskip("matplotlib") +pytest.importorskip("jinja2") + +import matplotlib as mpl + +from pandas.io.formats.style import Styler + + +@pytest.fixture(autouse=True) +def mpl_cleanup(): + # matplotlib/testing/decorators.py#L24 + # 1) Resets units registry + # 2) Resets rc_context + # 3) Closes all figures + mpl = pytest.importorskip("matplotlib") + mpl_units = pytest.importorskip("matplotlib.units") + plt = pytest.importorskip("matplotlib.pyplot") + orig_units_registry = mpl_units.registry.copy() + with mpl.rc_context(): + mpl.use("template") + yield + mpl_units.registry.clear() + mpl_units.registry.update(orig_units_registry) + plt.close("all") + # https://matplotlib.org/stable/users/prev_whats_new/whats_new_3.6.0.html#garbage-collection-is-no-longer-run-on-figure-close # noqa: E501 + gc.collect(1) + + +@pytest.fixture +def df(): + return DataFrame([[1, 2], [2, 4]], columns=["A", "B"]) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +@pytest.fixture +def df_blank(): + return DataFrame([[0, 0], [0, 0]], columns=["A", "B"], index=["X", "Y"]) + + +@pytest.fixture +def styler_blank(df_blank): + return Styler(df_blank, uuid_len=0) + + +@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"]) +def test_function_gradient(styler, f): + for c_map in [None, "YlOrRd"]: + result = getattr(styler, f)(cmap=c_map)._compute().ctx + assert all("#" in x[0][1] for x in result.values()) + assert result[(0, 0)] == result[(0, 1)] + assert result[(1, 0)] == result[(1, 1)] + + +@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"]) +def test_background_gradient_color(styler, f): + result = getattr(styler, f)(subset=IndexSlice[1, "A"])._compute().ctx + if f == "background_gradient": + assert result[(1, 0)] == [("background-color", "#fff7fb"), ("color", "#000000")] + elif f == "text_gradient": + assert result[(1, 0)] == [("color", "#fff7fb")] + + +@pytest.mark.parametrize( + "axis, expected", + [ + (0, ["low", "low", "high", "high"]), + (1, ["low", "high", "low", "high"]), + (None, ["low", "mid", "mid", "high"]), + ], +) +@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"]) +def test_background_gradient_axis(styler, axis, expected, f): + if f == "background_gradient": + colors = { + "low": [("background-color", "#f7fbff"), ("color", "#000000")], + "mid": [("background-color", "#abd0e6"), ("color", "#000000")], + "high": [("background-color", "#08306b"), ("color", "#f1f1f1")], + } + elif f == "text_gradient": + colors = { + "low": [("color", "#f7fbff")], + "mid": [("color", "#abd0e6")], + "high": [("color", "#08306b")], + } + result = getattr(styler, f)(cmap="Blues", axis=axis)._compute().ctx + for i, cell in enumerate([(0, 0), (0, 1), (1, 0), (1, 1)]): + assert result[cell] == colors[expected[i]] + + +@pytest.mark.parametrize( + "cmap, expected", + [ + ( + "PuBu", + { + (4, 5): [("background-color", "#86b0d3"), ("color", "#000000")], + (4, 6): [("background-color", "#83afd3"), ("color", "#f1f1f1")], + }, + ), + ( + "YlOrRd", + { + (4, 8): [("background-color", "#fd913e"), ("color", "#000000")], + (4, 9): [("background-color", "#fd8f3d"), ("color", "#f1f1f1")], + }, + ), + ( + None, + { + (7, 0): [("background-color", "#48c16e"), ("color", "#f1f1f1")], + (7, 1): [("background-color", "#4cc26c"), ("color", "#000000")], + }, + ), + ], +) +def test_text_color_threshold(cmap, expected): + # GH 39888 + df = DataFrame(np.arange(100).reshape(10, 10)) + result = df.style.background_gradient(cmap=cmap, axis=None)._compute().ctx + for k in expected.keys(): + assert result[k] == expected[k] + + +def test_background_gradient_vmin_vmax(): + # GH 12145 + df = DataFrame(range(5)) + ctx = df.style.background_gradient(vmin=1, vmax=3)._compute().ctx + assert ctx[(0, 0)] == ctx[(1, 0)] + assert ctx[(4, 0)] == ctx[(3, 0)] + + +def test_background_gradient_int64(): + # GH 28869 + df1 = Series(range(3)).to_frame() + df2 = Series(range(3), dtype="Int64").to_frame() + ctx1 = df1.style.background_gradient()._compute().ctx + ctx2 = df2.style.background_gradient()._compute().ctx + assert ctx2[(0, 0)] == ctx1[(0, 0)] + assert ctx2[(1, 0)] == ctx1[(1, 0)] + assert ctx2[(2, 0)] == ctx1[(2, 0)] + + +@pytest.mark.parametrize( + "axis, gmap, expected", + [ + ( + 0, + [1, 2], + { + (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")], + (1, 0): [("background-color", "#023858"), ("color", "#f1f1f1")], + (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")], + (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")], + }, + ), + ( + 1, + [1, 2], + { + (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")], + (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")], + (0, 1): [("background-color", "#023858"), ("color", "#f1f1f1")], + (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")], + }, + ), + ( + None, + np.array([[2, 1], [1, 2]]), + { + (0, 0): [("background-color", "#023858"), ("color", "#f1f1f1")], + (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")], + (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")], + (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")], + }, + ), + ], +) +def test_background_gradient_gmap_array(styler_blank, axis, gmap, expected): + # tests when gmap is given as a sequence and converted to ndarray + result = styler_blank.background_gradient(axis=axis, gmap=gmap)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "gmap, axis", [([1, 2, 3], 0), ([1, 2], 1), (np.array([[1, 2], [1, 2]]), None)] +) +def test_background_gradient_gmap_array_raises(gmap, axis): + # test when gmap as converted ndarray is bad shape + df = DataFrame([[0, 0, 0], [0, 0, 0]]) + msg = "supplied 'gmap' is not correct shape" + with pytest.raises(ValueError, match=msg): + df.style.background_gradient(gmap=gmap, axis=axis)._compute() + + +@pytest.mark.parametrize( + "gmap", + [ + DataFrame( # reverse the columns + [[2, 1], [1, 2]], columns=["B", "A"], index=["X", "Y"] + ), + DataFrame( # reverse the index + [[2, 1], [1, 2]], columns=["A", "B"], index=["Y", "X"] + ), + DataFrame( # reverse the index and columns + [[1, 2], [2, 1]], columns=["B", "A"], index=["Y", "X"] + ), + DataFrame( # add unnecessary columns + [[1, 2, 3], [2, 1, 3]], columns=["A", "B", "C"], index=["X", "Y"] + ), + DataFrame( # add unnecessary index + [[1, 2], [2, 1], [3, 3]], columns=["A", "B"], index=["X", "Y", "Z"] + ), + ], +) +@pytest.mark.parametrize( + "subset, exp_gmap", # exp_gmap is underlying map DataFrame should conform to + [ + (None, [[1, 2], [2, 1]]), + (["A"], [[1], [2]]), # slice only column "A" in data and gmap + (["B", "A"], [[2, 1], [1, 2]]), # reverse the columns in data + (IndexSlice["X", :], [[1, 2]]), # slice only index "X" in data and gmap + (IndexSlice[["Y", "X"], :], [[2, 1], [1, 2]]), # reverse the index in data + ], +) +def test_background_gradient_gmap_dataframe_align(styler_blank, gmap, subset, exp_gmap): + # test gmap given as DataFrame that it aligns to the data including subset + expected = styler_blank.background_gradient(axis=None, gmap=exp_gmap, subset=subset) + result = styler_blank.background_gradient(axis=None, gmap=gmap, subset=subset) + assert expected._compute().ctx == result._compute().ctx + + +@pytest.mark.parametrize( + "gmap, axis, exp_gmap", + [ + (Series([2, 1], index=["Y", "X"]), 0, [[1, 1], [2, 2]]), # revrse the index + (Series([2, 1], index=["B", "A"]), 1, [[1, 2], [1, 2]]), # revrse the cols + (Series([1, 2, 3], index=["X", "Y", "Z"]), 0, [[1, 1], [2, 2]]), # add idx + (Series([1, 2, 3], index=["A", "B", "C"]), 1, [[1, 2], [1, 2]]), # add col + ], +) +def test_background_gradient_gmap_series_align(styler_blank, gmap, axis, exp_gmap): + # test gmap given as Series that it aligns to the data including subset + expected = styler_blank.background_gradient(axis=None, gmap=exp_gmap)._compute() + result = styler_blank.background_gradient(axis=axis, gmap=gmap)._compute() + assert expected.ctx == result.ctx + + +@pytest.mark.parametrize( + "gmap, axis", + [ + (DataFrame([[1, 2], [2, 1]], columns=["A", "B"], index=["X", "Y"]), 1), + (DataFrame([[1, 2], [2, 1]], columns=["A", "B"], index=["X", "Y"]), 0), + ], +) +def test_background_gradient_gmap_wrong_dataframe(styler_blank, gmap, axis): + # test giving a gmap in DataFrame but with wrong axis + msg = "'gmap' is a DataFrame but underlying data for operations is a Series" + with pytest.raises(ValueError, match=msg): + styler_blank.background_gradient(gmap=gmap, axis=axis)._compute() + + +def test_background_gradient_gmap_wrong_series(styler_blank): + # test giving a gmap in Series form but with wrong axis + msg = "'gmap' is a Series but underlying data for operations is a DataFrame" + gmap = Series([1, 2], index=["X", "Y"]) + with pytest.raises(ValueError, match=msg): + styler_blank.background_gradient(gmap=gmap, axis=None)._compute() + + +def test_background_gradient_nullable_dtypes(): + # GH 50712 + df1 = DataFrame([[1], [0], [np.nan]], dtype=float) + df2 = DataFrame([[1], [0], [None]], dtype="Int64") + + ctx1 = df1.style.background_gradient()._compute().ctx + ctx2 = df2.style.background_gradient()._compute().ctx + assert ctx1 == ctx2 + + +@pytest.mark.parametrize( + "cmap", + ["PuBu", mpl.colormaps["PuBu"]], +) +def test_bar_colormap(cmap): + data = DataFrame([[1, 2], [3, 4]]) + ctx = data.style.bar(cmap=cmap, axis=None)._compute().ctx + pubu_colors = { + (0, 0): "#d0d1e6", + (1, 0): "#056faf", + (0, 1): "#73a9cf", + (1, 1): "#023858", + } + for k, v in pubu_colors.items(): + assert v in ctx[k][1][1] + + +def test_bar_color_raises(df): + msg = "`color` must be string or list or tuple of 2 strings" + with pytest.raises(ValueError, match=msg): + df.style.bar(color={"a", "b"}).to_html() + with pytest.raises(ValueError, match=msg): + df.style.bar(color=["a", "b", "c"]).to_html() + + msg = "`color` and `cmap` cannot both be given" + with pytest.raises(ValueError, match=msg): + df.style.bar(color="something", cmap="something else").to_html() + + +@pytest.mark.parametrize( + "plot_method", + ["scatter", "hexbin"], +) +def test_pass_colormap_instance(df, plot_method): + # https://github.com/pandas-dev/pandas/issues/49374 + cmap = mpl.colors.ListedColormap([[1, 1, 1], [0, 0, 0]]) + df["c"] = df.A + df.B + kwargs = {"x": "A", "y": "B", "c": "c", "colormap": cmap} + if plot_method == "hexbin": + kwargs["C"] = kwargs.pop("c") + getattr(df.plot, plot_method)(**kwargs) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_non_unique.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_non_unique.py new file mode 100644 index 0000000000000000000000000000000000000000..e4d31fe21f2c9cf3454a67f8c7443382f7f1c0ef --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_non_unique.py @@ -0,0 +1,140 @@ +from textwrap import dedent + +import pytest + +from pandas import ( + DataFrame, + IndexSlice, +) + +pytest.importorskip("jinja2") + +from pandas.io.formats.style import Styler + + +@pytest.fixture +def df(): + return DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], + index=["i", "j", "j"], + columns=["c", "d", "d"], + dtype=float, + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +def test_format_non_unique(df): + # GH 41269 + + # test dict + html = df.style.format({"d": "{:.1f}"}).to_html() + for val in ["1.000000<", "4.000000<", "7.000000<"]: + assert val in html + for val in ["2.0<", "3.0<", "5.0<", "6.0<", "8.0<", "9.0<"]: + assert val in html + + # test subset + html = df.style.format(precision=1, subset=IndexSlice["j", "d"]).to_html() + for val in ["1.000000<", "4.000000<", "7.000000<", "2.000000<", "3.000000<"]: + assert val in html + for val in ["5.0<", "6.0<", "8.0<", "9.0<"]: + assert val in html + + +@pytest.mark.parametrize("func", ["apply", "map"]) +def test_apply_map_non_unique_raises(df, func): + # GH 41269 + if func == "apply": + op = lambda s: ["color: red;"] * len(s) + else: + op = lambda v: "color: red;" + + with pytest.raises(KeyError, match="`Styler.apply` and `.map` are not"): + getattr(df.style, func)(op)._compute() + + +def test_table_styles_dict_non_unique_index(styler): + styles = styler.set_table_styles( + {"j": [{"selector": "td", "props": "a: v;"}]}, axis=1 + ).table_styles + assert styles == [ + {"selector": "td.row1", "props": [("a", "v")]}, + {"selector": "td.row2", "props": [("a", "v")]}, + ] + + +def test_table_styles_dict_non_unique_columns(styler): + styles = styler.set_table_styles( + {"d": [{"selector": "td", "props": "a: v;"}]}, axis=0 + ).table_styles + assert styles == [ + {"selector": "td.col1", "props": [("a", "v")]}, + {"selector": "td.col2", "props": [("a", "v")]}, + ] + + +def test_tooltips_non_unique_raises(styler): + # ttips has unique keys + ttips = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "b"]) + styler.set_tooltips(ttips=ttips) # OK + + # ttips has non-unique columns + ttips = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "c"], index=["a", "b"]) + with pytest.raises(KeyError, match="Tooltips render only if `ttips` has unique"): + styler.set_tooltips(ttips=ttips) + + # ttips has non-unique index + ttips = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "a"]) + with pytest.raises(KeyError, match="Tooltips render only if `ttips` has unique"): + styler.set_tooltips(ttips=ttips) + + +def test_set_td_classes_non_unique_raises(styler): + # classes has unique keys + classes = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "b"]) + styler.set_td_classes(classes=classes) # OK + + # classes has non-unique columns + classes = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "c"], index=["a", "b"]) + with pytest.raises(KeyError, match="Classes render only if `classes` has unique"): + styler.set_td_classes(classes=classes) + + # classes has non-unique index + classes = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "a"]) + with pytest.raises(KeyError, match="Classes render only if `classes` has unique"): + styler.set_td_classes(classes=classes) + + +def test_hide_columns_non_unique(styler): + ctx = styler.hide(["d"], axis="columns")._translate(True, True) + + assert ctx["head"][0][1]["display_value"] == "c" + assert ctx["head"][0][1]["is_visible"] is True + + assert ctx["head"][0][2]["display_value"] == "d" + assert ctx["head"][0][2]["is_visible"] is False + + assert ctx["head"][0][3]["display_value"] == "d" + assert ctx["head"][0][3]["is_visible"] is False + + assert ctx["body"][0][1]["is_visible"] is True + assert ctx["body"][0][2]["is_visible"] is False + assert ctx["body"][0][3]["is_visible"] is False + + +def test_latex_non_unique(styler): + result = styler.to_latex() + assert result == dedent( + """\ + \\begin{tabular}{lrrr} + & c & d & d \\\\ + i & 1.000000 & 2.000000 & 3.000000 \\\\ + j & 4.000000 & 5.000000 & 6.000000 \\\\ + j & 7.000000 & 8.000000 & 9.000000 \\\\ + \\end{tabular} + """ + ) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_style.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_style.py new file mode 100644 index 0000000000000000000000000000000000000000..6fa72bd48031cca999b81cccfcedafcd3abcd924 --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_style.py @@ -0,0 +1,1588 @@ +import contextlib +import copy +import re +from textwrap import dedent + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + IndexSlice, + MultiIndex, + Series, + option_context, +) +import pandas._testing as tm + +jinja2 = pytest.importorskip("jinja2") +from pandas.io.formats.style import ( # isort:skip + Styler, +) +from pandas.io.formats.style_render import ( + _get_level_lengths, + _get_trimming_maximums, + maybe_convert_css_to_tuples, + non_reducing_slice, +) + + +@pytest.fixture +def mi_df(): + return DataFrame( + [[1, 2], [3, 4]], + index=MultiIndex.from_product([["i0"], ["i1_a", "i1_b"]]), + columns=MultiIndex.from_product([["c0"], ["c1_a", "c1_b"]]), + dtype=int, + ) + + +@pytest.fixture +def mi_styler(mi_df): + return Styler(mi_df, uuid_len=0) + + +@pytest.fixture +def mi_styler_comp(mi_styler): + # comprehensively add features to mi_styler + mi_styler = mi_styler._copy(deepcopy=True) + mi_styler.css = {**mi_styler.css, "row": "ROW", "col": "COL"} + mi_styler.uuid_len = 5 + mi_styler.uuid = "abcde" + mi_styler.set_caption("capt") + mi_styler.set_table_styles([{"selector": "a", "props": "a:v;"}]) + mi_styler.hide(axis="columns") + mi_styler.hide([("c0", "c1_a")], axis="columns", names=True) + mi_styler.hide(axis="index") + mi_styler.hide([("i0", "i1_a")], axis="index", names=True) + mi_styler.set_table_attributes('class="box"') + other = mi_styler.data.agg(["mean"]) + other.index = MultiIndex.from_product([[""], other.index]) + mi_styler.concat(other.style) + mi_styler.format(na_rep="MISSING", precision=3) + mi_styler.format_index(precision=2, axis=0) + mi_styler.format_index(precision=4, axis=1) + mi_styler.highlight_max(axis=None) + mi_styler.map_index(lambda x: "color: white;", axis=0) + mi_styler.map_index(lambda x: "color: black;", axis=1) + mi_styler.set_td_classes( + DataFrame( + [["a", "b"], ["a", "c"]], index=mi_styler.index, columns=mi_styler.columns + ) + ) + mi_styler.set_tooltips( + DataFrame( + [["a2", "b2"], ["a2", "c2"]], + index=mi_styler.index, + columns=mi_styler.columns, + ) + ) + return mi_styler + + +@pytest.fixture +def blank_value(): + return " " + + +@pytest.fixture +def df(): + df = DataFrame({"A": [0, 1], "B": np.random.default_rng(2).standard_normal(2)}) + return df + + +@pytest.fixture +def styler(df): + df = DataFrame({"A": [0, 1], "B": np.random.default_rng(2).standard_normal(2)}) + return Styler(df) + + +@pytest.mark.parametrize( + "sparse_columns, exp_cols", + [ + ( + True, + [ + {"is_visible": True, "attributes": 'colspan="2"', "value": "c0"}, + {"is_visible": False, "attributes": "", "value": "c0"}, + ], + ), + ( + False, + [ + {"is_visible": True, "attributes": "", "value": "c0"}, + {"is_visible": True, "attributes": "", "value": "c0"}, + ], + ), + ], +) +def test_mi_styler_sparsify_columns(mi_styler, sparse_columns, exp_cols): + exp_l1_c0 = {"is_visible": True, "attributes": "", "display_value": "c1_a"} + exp_l1_c1 = {"is_visible": True, "attributes": "", "display_value": "c1_b"} + + ctx = mi_styler._translate(True, sparse_columns) + + assert exp_cols[0].items() <= ctx["head"][0][2].items() + assert exp_cols[1].items() <= ctx["head"][0][3].items() + assert exp_l1_c0.items() <= ctx["head"][1][2].items() + assert exp_l1_c1.items() <= ctx["head"][1][3].items() + + +@pytest.mark.parametrize( + "sparse_index, exp_rows", + [ + ( + True, + [ + {"is_visible": True, "attributes": 'rowspan="2"', "value": "i0"}, + {"is_visible": False, "attributes": "", "value": "i0"}, + ], + ), + ( + False, + [ + {"is_visible": True, "attributes": "", "value": "i0"}, + {"is_visible": True, "attributes": "", "value": "i0"}, + ], + ), + ], +) +def test_mi_styler_sparsify_index(mi_styler, sparse_index, exp_rows): + exp_l1_r0 = {"is_visible": True, "attributes": "", "display_value": "i1_a"} + exp_l1_r1 = {"is_visible": True, "attributes": "", "display_value": "i1_b"} + + ctx = mi_styler._translate(sparse_index, True) + + assert exp_rows[0].items() <= ctx["body"][0][0].items() + assert exp_rows[1].items() <= ctx["body"][1][0].items() + assert exp_l1_r0.items() <= ctx["body"][0][1].items() + assert exp_l1_r1.items() <= ctx["body"][1][1].items() + + +def test_mi_styler_sparsify_options(mi_styler): + with option_context("styler.sparse.index", False): + html1 = mi_styler.to_html() + with option_context("styler.sparse.index", True): + html2 = mi_styler.to_html() + + assert html1 != html2 + + with option_context("styler.sparse.columns", False): + html1 = mi_styler.to_html() + with option_context("styler.sparse.columns", True): + html2 = mi_styler.to_html() + + assert html1 != html2 + + +@pytest.mark.parametrize( + "rn, cn, max_els, max_rows, max_cols, exp_rn, exp_cn", + [ + (100, 100, 100, None, None, 12, 6), # reduce to (12, 6) < 100 elements + (1000, 3, 750, None, None, 250, 3), # dynamically reduce rows to 250, keep cols + (4, 1000, 500, None, None, 4, 125), # dynamically reduce cols to 125, keep rows + (1000, 3, 750, 10, None, 10, 3), # overwrite above dynamics with max_row + (4, 1000, 500, None, 5, 4, 5), # overwrite above dynamics with max_col + (100, 100, 700, 50, 50, 25, 25), # rows cols below given maxes so < 700 elmts + ], +) +def test_trimming_maximum(rn, cn, max_els, max_rows, max_cols, exp_rn, exp_cn): + rn, cn = _get_trimming_maximums( + rn, cn, max_els, max_rows, max_cols, scaling_factor=0.5 + ) + assert (rn, cn) == (exp_rn, exp_cn) + + +@pytest.mark.parametrize( + "option, val", + [ + ("styler.render.max_elements", 6), + ("styler.render.max_rows", 3), + ], +) +def test_render_trimming_rows(option, val): + # test auto and specific trimming of rows + df = DataFrame(np.arange(120).reshape(60, 2)) + with option_context(option, val): + ctx = df.style._translate(True, True) + assert len(ctx["head"][0]) == 3 # index + 2 data cols + assert len(ctx["body"]) == 4 # 3 data rows + trimming row + assert len(ctx["body"][0]) == 3 # index + 2 data cols + + +@pytest.mark.parametrize( + "option, val", + [ + ("styler.render.max_elements", 6), + ("styler.render.max_columns", 2), + ], +) +def test_render_trimming_cols(option, val): + # test auto and specific trimming of cols + df = DataFrame(np.arange(30).reshape(3, 10)) + with option_context(option, val): + ctx = df.style._translate(True, True) + assert len(ctx["head"][0]) == 4 # index + 2 data cols + trimming col + assert len(ctx["body"]) == 3 # 3 data rows + assert len(ctx["body"][0]) == 4 # index + 2 data cols + trimming col + + +def test_render_trimming_mi(): + midx = MultiIndex.from_product([[1, 2], [1, 2, 3]]) + df = DataFrame(np.arange(36).reshape(6, 6), columns=midx, index=midx) + with option_context("styler.render.max_elements", 4): + ctx = df.style._translate(True, True) + + assert len(ctx["body"][0]) == 5 # 2 indexes + 2 data cols + trimming row + assert {"attributes": 'rowspan="2"'}.items() <= ctx["body"][0][0].items() + assert {"class": "data row0 col_trim"}.items() <= ctx["body"][0][4].items() + assert {"class": "data row_trim col_trim"}.items() <= ctx["body"][2][4].items() + assert len(ctx["body"]) == 3 # 2 data rows + trimming row + + +def test_render_empty_mi(): + # GH 43305 + df = DataFrame(index=MultiIndex.from_product([["A"], [0, 1]], names=[None, "one"])) + expected = dedent( + """\ + > + + +   + one + + + """ + ) + assert expected in df.style.to_html() + + +@pytest.mark.parametrize("comprehensive", [True, False]) +@pytest.mark.parametrize("render", [True, False]) +@pytest.mark.parametrize("deepcopy", [True, False]) +def test_copy(comprehensive, render, deepcopy, mi_styler, mi_styler_comp): + styler = mi_styler_comp if comprehensive else mi_styler + styler.uuid_len = 5 + + s2 = copy.deepcopy(styler) if deepcopy else copy.copy(styler) # make copy and check + assert s2 is not styler + + if render: + styler.to_html() + + excl = [ + "cellstyle_map", # render time vars.. + "cellstyle_map_columns", + "cellstyle_map_index", + "template_latex", # render templates are class level + "template_html", + "template_html_style", + "template_html_table", + ] + if not deepcopy: # check memory locations are equal for all included attributes + for attr in [a for a in styler.__dict__ if (not callable(a) and a not in excl)]: + assert id(getattr(s2, attr)) == id(getattr(styler, attr)) + else: # check memory locations are different for nested or mutable vars + shallow = [ + "data", + "columns", + "index", + "uuid_len", + "uuid", + "caption", + "cell_ids", + "hide_index_", + "hide_columns_", + "hide_index_names", + "hide_column_names", + "table_attributes", + ] + for attr in shallow: + assert id(getattr(s2, attr)) == id(getattr(styler, attr)) + + for attr in [ + a + for a in styler.__dict__ + if (not callable(a) and a not in excl and a not in shallow) + ]: + if getattr(s2, attr) is None: + assert id(getattr(s2, attr)) == id(getattr(styler, attr)) + else: + assert id(getattr(s2, attr)) != id(getattr(styler, attr)) + + +@pytest.mark.parametrize("deepcopy", [True, False]) +def test_inherited_copy(mi_styler, deepcopy): + # Ensure that the inherited class is preserved when a Styler object is copied. + # GH 52728 + class CustomStyler(Styler): + pass + + custom_styler = CustomStyler(mi_styler.data) + custom_styler_copy = ( + copy.deepcopy(custom_styler) if deepcopy else copy.copy(custom_styler) + ) + assert isinstance(custom_styler_copy, CustomStyler) + + +def test_clear(mi_styler_comp): + # NOTE: if this test fails for new features then 'mi_styler_comp' should be updated + # to ensure proper testing of the 'copy', 'clear', 'export' methods with new feature + # GH 40675 + styler = mi_styler_comp + styler._compute() # execute applied methods + + clean_copy = Styler(styler.data, uuid=styler.uuid) + + excl = [ + "data", + "index", + "columns", + "uuid", + "uuid_len", # uuid is set to be the same on styler and clean_copy + "cell_ids", + "cellstyle_map", # execution time only + "cellstyle_map_columns", # execution time only + "cellstyle_map_index", # execution time only + "template_latex", # render templates are class level + "template_html", + "template_html_style", + "template_html_table", + ] + # tests vars are not same vals on obj and clean copy before clear (except for excl) + for attr in [a for a in styler.__dict__ if not (callable(a) or a in excl)]: + res = getattr(styler, attr) == getattr(clean_copy, attr) + if hasattr(res, "__iter__") and len(res) > 0: + assert not all(res) # some element in iterable differs + elif hasattr(res, "__iter__") and len(res) == 0: + pass # empty array + else: + assert not res # explicit var differs + + # test vars have same vales on obj and clean copy after clearing + styler.clear() + for attr in [a for a in styler.__dict__ if not callable(a)]: + res = getattr(styler, attr) == getattr(clean_copy, attr) + assert all(res) if hasattr(res, "__iter__") else res + + +def test_export(mi_styler_comp, mi_styler): + exp_attrs = [ + "_todo", + "hide_index_", + "hide_index_names", + "hide_columns_", + "hide_column_names", + "table_attributes", + "table_styles", + "css", + ] + for attr in exp_attrs: + check = getattr(mi_styler, attr) == getattr(mi_styler_comp, attr) + assert not ( + all(check) if (hasattr(check, "__iter__") and len(check) > 0) else check + ) + + export = mi_styler_comp.export() + used = mi_styler.use(export) + for attr in exp_attrs: + check = getattr(used, attr) == getattr(mi_styler_comp, attr) + assert all(check) if (hasattr(check, "__iter__") and len(check) > 0) else check + + used.to_html() + + +def test_hide_raises(mi_styler): + msg = "`subset` and `level` cannot be passed simultaneously" + with pytest.raises(ValueError, match=msg): + mi_styler.hide(axis="index", subset="something", level="something else") + + msg = "`level` must be of type `int`, `str` or list of such" + with pytest.raises(ValueError, match=msg): + mi_styler.hide(axis="index", level={"bad": 1, "type": 2}) + + +@pytest.mark.parametrize("level", [1, "one", [1], ["one"]]) +def test_hide_index_level(mi_styler, level): + mi_styler.index.names, mi_styler.columns.names = ["zero", "one"], ["zero", "one"] + ctx = mi_styler.hide(axis="index", level=level)._translate(False, True) + assert len(ctx["head"][0]) == 3 + assert len(ctx["head"][1]) == 3 + assert len(ctx["head"][2]) == 4 + assert ctx["head"][2][0]["is_visible"] + assert not ctx["head"][2][1]["is_visible"] + + assert ctx["body"][0][0]["is_visible"] + assert not ctx["body"][0][1]["is_visible"] + assert ctx["body"][1][0]["is_visible"] + assert not ctx["body"][1][1]["is_visible"] + + +@pytest.mark.parametrize("level", [1, "one", [1], ["one"]]) +@pytest.mark.parametrize("names", [True, False]) +def test_hide_columns_level(mi_styler, level, names): + mi_styler.columns.names = ["zero", "one"] + if names: + mi_styler.index.names = ["zero", "one"] + ctx = mi_styler.hide(axis="columns", level=level)._translate(True, False) + assert len(ctx["head"]) == (2 if names else 1) + + +@pytest.mark.parametrize("method", ["map", "apply"]) +@pytest.mark.parametrize("axis", ["index", "columns"]) +def test_apply_map_header(method, axis): + # GH 41893 + df = DataFrame({"A": [0, 0], "B": [1, 1]}, index=["C", "D"]) + func = { + "apply": lambda s: ["attr: val" if ("A" in v or "C" in v) else "" for v in s], + "map": lambda v: "attr: val" if ("A" in v or "C" in v) else "", + } + + # test execution added to todo + result = getattr(df.style, f"{method}_index")(func[method], axis=axis) + assert len(result._todo) == 1 + assert len(getattr(result, f"ctx_{axis}")) == 0 + + # test ctx object on compute + result._compute() + expected = { + (0, 0): [("attr", "val")], + } + assert getattr(result, f"ctx_{axis}") == expected + + +@pytest.mark.parametrize("method", ["apply", "map"]) +@pytest.mark.parametrize("axis", ["index", "columns"]) +def test_apply_map_header_mi(mi_styler, method, axis): + # GH 41893 + func = { + "apply": lambda s: ["attr: val;" if "b" in v else "" for v in s], + "map": lambda v: "attr: val" if "b" in v else "", + } + result = getattr(mi_styler, f"{method}_index")(func[method], axis=axis)._compute() + expected = {(1, 1): [("attr", "val")]} + assert getattr(result, f"ctx_{axis}") == expected + + +def test_apply_map_header_raises(mi_styler): + # GH 41893 + with pytest.raises(ValueError, match="No axis named bad for object type DataFrame"): + mi_styler.map_index(lambda v: "attr: val;", axis="bad")._compute() + + +class TestStyler: + def test_init_non_pandas(self): + msg = "``data`` must be a Series or DataFrame" + with pytest.raises(TypeError, match=msg): + Styler([1, 2, 3]) + + def test_init_series(self): + result = Styler(Series([1, 2])) + assert result.data.ndim == 2 + + def test_repr_html_ok(self, styler): + styler._repr_html_() + + def test_repr_html_mathjax(self, styler): + # gh-19824 / 41395 + assert "tex2jax_ignore" not in styler._repr_html_() + + with option_context("styler.html.mathjax", False): + assert "tex2jax_ignore" in styler._repr_html_() + + def test_update_ctx(self, styler): + styler._update_ctx(DataFrame({"A": ["color: red", "color: blue"]})) + expected = {(0, 0): [("color", "red")], (1, 0): [("color", "blue")]} + assert styler.ctx == expected + + def test_update_ctx_flatten_multi_and_trailing_semi(self, styler): + attrs = DataFrame({"A": ["color: red; foo: bar", "color:blue ; foo: baz;"]}) + styler._update_ctx(attrs) + expected = { + (0, 0): [("color", "red"), ("foo", "bar")], + (1, 0): [("color", "blue"), ("foo", "baz")], + } + assert styler.ctx == expected + + def test_render(self): + df = DataFrame({"A": [0, 1]}) + style = lambda x: Series(["color: red", "color: blue"], name=x.name) + s = Styler(df, uuid="AB").apply(style) + s.to_html() + # it worked? + + def test_multiple_render(self, df): + # GH 39396 + s = Styler(df, uuid_len=0).map(lambda x: "color: red;", subset=["A"]) + s.to_html() # do 2 renders to ensure css styles not duplicated + assert ( + '" in s.to_html() + ) + + def test_render_empty_dfs(self): + empty_df = DataFrame() + es = Styler(empty_df) + es.to_html() + # An index but no columns + DataFrame(columns=["a"]).style.to_html() + # A column but no index + DataFrame(index=["a"]).style.to_html() + # No IndexError raised? + + def test_render_double(self): + df = DataFrame({"A": [0, 1]}) + style = lambda x: Series( + ["color: red; border: 1px", "color: blue; border: 2px"], name=x.name + ) + s = Styler(df, uuid="AB").apply(style) + s.to_html() + # it worked? + + def test_set_properties(self): + df = DataFrame({"A": [0, 1]}) + result = df.style.set_properties(color="white", size="10px")._compute().ctx + # order is deterministic + v = [("color", "white"), ("size", "10px")] + expected = {(0, 0): v, (1, 0): v} + assert result.keys() == expected.keys() + for v1, v2 in zip(result.values(), expected.values()): + assert sorted(v1) == sorted(v2) + + def test_set_properties_subset(self): + df = DataFrame({"A": [0, 1]}) + result = ( + df.style.set_properties(subset=IndexSlice[0, "A"], color="white") + ._compute() + .ctx + ) + expected = {(0, 0): [("color", "white")]} + assert result == expected + + def test_empty_index_name_doesnt_display(self, blank_value): + # https://github.com/pandas-dev/pandas/pull/12090#issuecomment-180695902 + df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]}) + result = df.style._translate(True, True) + assert len(result["head"]) == 1 + expected = { + "class": "blank level0", + "type": "th", + "value": blank_value, + "is_visible": True, + "display_value": blank_value, + } + assert expected.items() <= result["head"][0][0].items() + + def test_index_name(self): + # https://github.com/pandas-dev/pandas/issues/11655 + df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]}) + result = df.set_index("A").style._translate(True, True) + expected = { + "class": "index_name level0", + "type": "th", + "value": "A", + "is_visible": True, + "display_value": "A", + } + assert expected.items() <= result["head"][1][0].items() + + def test_numeric_columns(self): + # https://github.com/pandas-dev/pandas/issues/12125 + # smoke test for _translate + df = DataFrame({0: [1, 2, 3]}) + df.style._translate(True, True) + + def test_apply_axis(self): + df = DataFrame({"A": [0, 0], "B": [1, 1]}) + f = lambda x: [f"val: {x.max()}" for v in x] + result = df.style.apply(f, axis=1) + assert len(result._todo) == 1 + assert len(result.ctx) == 0 + result._compute() + expected = { + (0, 0): [("val", "1")], + (0, 1): [("val", "1")], + (1, 0): [("val", "1")], + (1, 1): [("val", "1")], + } + assert result.ctx == expected + + result = df.style.apply(f, axis=0) + expected = { + (0, 0): [("val", "0")], + (0, 1): [("val", "1")], + (1, 0): [("val", "0")], + (1, 1): [("val", "1")], + } + result._compute() + assert result.ctx == expected + result = df.style.apply(f) # default + result._compute() + assert result.ctx == expected + + @pytest.mark.parametrize("axis", [0, 1]) + def test_apply_series_return(self, axis): + # GH 42014 + df = DataFrame([[1, 2], [3, 4]], index=["X", "Y"], columns=["X", "Y"]) + + # test Series return where len(Series) < df.index or df.columns but labels OK + func = lambda s: Series(["color: red;"], index=["Y"]) + result = df.style.apply(func, axis=axis)._compute().ctx + assert result[(1, 1)] == [("color", "red")] + assert result[(1 - axis, axis)] == [("color", "red")] + + # test Series return where labels align but different order + func = lambda s: Series(["color: red;", "color: blue;"], index=["Y", "X"]) + result = df.style.apply(func, axis=axis)._compute().ctx + assert result[(0, 0)] == [("color", "blue")] + assert result[(1, 1)] == [("color", "red")] + assert result[(1 - axis, axis)] == [("color", "red")] + assert result[(axis, 1 - axis)] == [("color", "blue")] + + @pytest.mark.parametrize("index", [False, True]) + @pytest.mark.parametrize("columns", [False, True]) + def test_apply_dataframe_return(self, index, columns): + # GH 42014 + df = DataFrame([[1, 2], [3, 4]], index=["X", "Y"], columns=["X", "Y"]) + idxs = ["X", "Y"] if index else ["Y"] + cols = ["X", "Y"] if columns else ["Y"] + df_styles = DataFrame("color: red;", index=idxs, columns=cols) + result = df.style.apply(lambda x: df_styles, axis=None)._compute().ctx + + assert result[(1, 1)] == [("color", "red")] # (Y,Y) styles always present + assert (result[(0, 1)] == [("color", "red")]) is index # (X,Y) only if index + assert (result[(1, 0)] == [("color", "red")]) is columns # (Y,X) only if cols + assert (result[(0, 0)] == [("color", "red")]) is (index and columns) # (X,X) + + @pytest.mark.parametrize( + "slice_", + [ + IndexSlice[:], + IndexSlice[:, ["A"]], + IndexSlice[[1], :], + IndexSlice[[1], ["A"]], + IndexSlice[:2, ["A", "B"]], + ], + ) + @pytest.mark.parametrize("axis", [0, 1]) + def test_apply_subset(self, slice_, axis, df): + def h(x, color="bar"): + return Series(f"color: {color}", index=x.index, name=x.name) + + result = df.style.apply(h, axis=axis, subset=slice_, color="baz")._compute().ctx + expected = { + (r, c): [("color", "baz")] + for r, row in enumerate(df.index) + for c, col in enumerate(df.columns) + if row in df.loc[slice_].index and col in df.loc[slice_].columns + } + assert result == expected + + @pytest.mark.parametrize( + "slice_", + [ + IndexSlice[:], + IndexSlice[:, ["A"]], + IndexSlice[[1], :], + IndexSlice[[1], ["A"]], + IndexSlice[:2, ["A", "B"]], + ], + ) + def test_map_subset(self, slice_, df): + result = df.style.map(lambda x: "color:baz;", subset=slice_)._compute().ctx + expected = { + (r, c): [("color", "baz")] + for r, row in enumerate(df.index) + for c, col in enumerate(df.columns) + if row in df.loc[slice_].index and col in df.loc[slice_].columns + } + assert result == expected + + @pytest.mark.parametrize( + "slice_", + [ + IndexSlice[:, IndexSlice["x", "A"]], + IndexSlice[:, IndexSlice[:, "A"]], + IndexSlice[:, IndexSlice[:, ["A", "C"]]], # missing col element + IndexSlice[IndexSlice["a", 1], :], + IndexSlice[IndexSlice[:, 1], :], + IndexSlice[IndexSlice[:, [1, 3]], :], # missing row element + IndexSlice[:, ("x", "A")], + IndexSlice[("a", 1), :], + ], + ) + def test_map_subset_multiindex(self, slice_): + # GH 19861 + # edited for GH 33562 + if ( + isinstance(slice_[-1], tuple) + and isinstance(slice_[-1][-1], list) + and "C" in slice_[-1][-1] + ): + ctx = pytest.raises(KeyError, match="C") + elif ( + isinstance(slice_[0], tuple) + and isinstance(slice_[0][1], list) + and 3 in slice_[0][1] + ): + ctx = pytest.raises(KeyError, match="3") + else: + ctx = contextlib.nullcontext() + + idx = MultiIndex.from_product([["a", "b"], [1, 2]]) + col = MultiIndex.from_product([["x", "y"], ["A", "B"]]) + df = DataFrame(np.random.default_rng(2).random((4, 4)), columns=col, index=idx) + + with ctx: + df.style.map(lambda x: "color: red;", subset=slice_).to_html() + + def test_map_subset_multiindex_code(self): + # https://github.com/pandas-dev/pandas/issues/25858 + # Checks styler.map works with multindex when codes are provided + codes = np.array([[0, 0, 1, 1], [0, 1, 0, 1]]) + columns = MultiIndex( + levels=[["a", "b"], ["%", "#"]], codes=codes, names=["", ""] + ) + df = DataFrame( + [[1, -1, 1, 1], [-1, 1, 1, 1]], index=["hello", "world"], columns=columns + ) + pct_subset = IndexSlice[:, IndexSlice[:, "%":"%"]] + + def color_negative_red(val): + color = "red" if val < 0 else "black" + return f"color: {color}" + + df.loc[pct_subset] + df.style.map(color_negative_red, subset=pct_subset) + + @pytest.mark.parametrize( + "stylefunc", ["background_gradient", "bar", "text_gradient"] + ) + def test_subset_for_boolean_cols(self, stylefunc): + # GH47838 + df = DataFrame( + [ + [1, 2], + [3, 4], + ], + columns=[False, True], + ) + styled = getattr(df.style, stylefunc)() + styled._compute() + assert set(styled.ctx) == {(0, 0), (0, 1), (1, 0), (1, 1)} + + def test_empty(self): + df = DataFrame({"A": [1, 0]}) + s = df.style + s.ctx = {(0, 0): [("color", "red")], (1, 0): [("", "")]} + + result = s._translate(True, True)["cellstyle"] + expected = [ + {"props": [("color", "red")], "selectors": ["row0_col0"]}, + {"props": [("", "")], "selectors": ["row1_col0"]}, + ] + assert result == expected + + def test_duplicate(self): + df = DataFrame({"A": [1, 0]}) + s = df.style + s.ctx = {(0, 0): [("color", "red")], (1, 0): [("color", "red")]} + + result = s._translate(True, True)["cellstyle"] + expected = [ + {"props": [("color", "red")], "selectors": ["row0_col0", "row1_col0"]} + ] + assert result == expected + + def test_init_with_na_rep(self): + # GH 21527 28358 + df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"]) + + ctx = Styler(df, na_rep="NA")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "NA" + assert ctx["body"][0][2]["display_value"] == "NA" + + def test_caption(self, df): + styler = Styler(df, caption="foo") + result = styler.to_html() + assert all(["caption" in result, "foo" in result]) + + styler = df.style + result = styler.set_caption("baz") + assert styler is result + assert styler.caption == "baz" + + def test_uuid(self, df): + styler = Styler(df, uuid="abc123") + result = styler.to_html() + assert "abc123" in result + + styler = df.style + result = styler.set_uuid("aaa") + assert result is styler + assert result.uuid == "aaa" + + def test_unique_id(self): + # See https://github.com/pandas-dev/pandas/issues/16780 + df = DataFrame({"a": [1, 3, 5, 6], "b": [2, 4, 12, 21]}) + result = df.style.to_html(uuid="test") + assert "test" in result + ids = re.findall('id="(.*?)"', result) + assert np.unique(ids).size == len(ids) + + def test_table_styles(self, df): + style = [{"selector": "th", "props": [("foo", "bar")]}] # default format + styler = Styler(df, table_styles=style) + result = " ".join(styler.to_html().split()) + assert "th { foo: bar; }" in result + + styler = df.style + result = styler.set_table_styles(style) + assert styler is result + assert styler.table_styles == style + + # GH 39563 + style = [{"selector": "th", "props": "foo:bar;"}] # css string format + styler = df.style.set_table_styles(style) + result = " ".join(styler.to_html().split()) + assert "th { foo: bar; }" in result + + def test_table_styles_multiple(self, df): + ctx = df.style.set_table_styles( + [ + {"selector": "th,td", "props": "color:red;"}, + {"selector": "tr", "props": "color:green;"}, + ] + )._translate(True, True)["table_styles"] + assert ctx == [ + {"selector": "th", "props": [("color", "red")]}, + {"selector": "td", "props": [("color", "red")]}, + {"selector": "tr", "props": [("color", "green")]}, + ] + + def test_table_styles_dict_multiple_selectors(self, df): + # GH 44011 + result = df.style.set_table_styles( + { + "B": [ + {"selector": "th,td", "props": [("border-left", "2px solid black")]} + ] + } + )._translate(True, True)["table_styles"] + + expected = [ + {"selector": "th.col1", "props": [("border-left", "2px solid black")]}, + {"selector": "td.col1", "props": [("border-left", "2px solid black")]}, + ] + + assert result == expected + + def test_maybe_convert_css_to_tuples(self): + expected = [("a", "b"), ("c", "d e")] + assert maybe_convert_css_to_tuples("a:b;c:d e;") == expected + assert maybe_convert_css_to_tuples("a: b ;c: d e ") == expected + expected = [] + assert maybe_convert_css_to_tuples("") == expected + + def test_maybe_convert_css_to_tuples_err(self): + msg = "Styles supplied as string must follow CSS rule formats" + with pytest.raises(ValueError, match=msg): + maybe_convert_css_to_tuples("err") + + def test_table_attributes(self, df): + attributes = 'class="foo" data-bar' + styler = Styler(df, table_attributes=attributes) + result = styler.to_html() + assert 'class="foo" data-bar' in result + + result = df.style.set_table_attributes(attributes).to_html() + assert 'class="foo" data-bar' in result + + def test_apply_none(self): + def f(x): + return DataFrame( + np.where(x == x.max(), "color: red", ""), + index=x.index, + columns=x.columns, + ) + + result = DataFrame([[1, 2], [3, 4]]).style.apply(f, axis=None)._compute().ctx + assert result[(1, 1)] == [("color", "red")] + + def test_trim(self, df): + result = df.style.to_html() # trim=True + assert result.count("#") == 0 + + result = df.style.highlight_max().to_html() + assert result.count("#") == len(df.columns) + + def test_export(self, df, styler): + f = lambda x: "color: red" if x > 0 else "color: blue" + g = lambda x, z: f"color: {z}" if x > 0 else f"color: {z}" + style1 = styler + style1.map(f).map(g, z="b").highlight_max()._compute() # = render + result = style1.export() + style2 = df.style + style2.use(result) + assert style1._todo == style2._todo + style2.to_html() + + def test_bad_apply_shape(self): + df = DataFrame([[1, 2], [3, 4]], index=["A", "B"], columns=["X", "Y"]) + + msg = "resulted in the apply method collapsing to a Series." + with pytest.raises(ValueError, match=msg): + df.style._apply(lambda x: "x") + + msg = "created invalid {} labels" + with pytest.raises(ValueError, match=msg.format("index")): + df.style._apply(lambda x: [""]) + + with pytest.raises(ValueError, match=msg.format("index")): + df.style._apply(lambda x: ["", "", "", ""]) + + with pytest.raises(ValueError, match=msg.format("index")): + df.style._apply(lambda x: Series(["a:v;", ""], index=["A", "C"]), axis=0) + + with pytest.raises(ValueError, match=msg.format("columns")): + df.style._apply(lambda x: ["", "", ""], axis=1) + + with pytest.raises(ValueError, match=msg.format("columns")): + df.style._apply(lambda x: Series(["a:v;", ""], index=["X", "Z"]), axis=1) + + msg = "returned ndarray with wrong shape" + with pytest.raises(ValueError, match=msg): + df.style._apply(lambda x: np.array([[""], [""]]), axis=None) + + def test_apply_bad_return(self): + def f(x): + return "" + + df = DataFrame([[1, 2], [3, 4]]) + msg = ( + "must return a DataFrame or ndarray when passed to `Styler.apply` " + "with axis=None" + ) + with pytest.raises(TypeError, match=msg): + df.style._apply(f, axis=None) + + @pytest.mark.parametrize("axis", ["index", "columns"]) + def test_apply_bad_labels(self, axis): + def f(x): + return DataFrame(**{axis: ["bad", "labels"]}) + + df = DataFrame([[1, 2], [3, 4]]) + msg = f"created invalid {axis} labels." + with pytest.raises(ValueError, match=msg): + df.style._apply(f, axis=None) + + def test_get_level_lengths(self): + index = MultiIndex.from_product([["a", "b"], [0, 1, 2]]) + expected = { + (0, 0): 3, + (0, 3): 3, + (1, 0): 1, + (1, 1): 1, + (1, 2): 1, + (1, 3): 1, + (1, 4): 1, + (1, 5): 1, + } + result = _get_level_lengths(index, sparsify=True, max_index=100) + tm.assert_dict_equal(result, expected) + + expected = { + (0, 0): 1, + (0, 1): 1, + (0, 2): 1, + (0, 3): 1, + (0, 4): 1, + (0, 5): 1, + (1, 0): 1, + (1, 1): 1, + (1, 2): 1, + (1, 3): 1, + (1, 4): 1, + (1, 5): 1, + } + result = _get_level_lengths(index, sparsify=False, max_index=100) + tm.assert_dict_equal(result, expected) + + def test_get_level_lengths_un_sorted(self): + index = MultiIndex.from_arrays([[1, 1, 2, 1], ["a", "b", "b", "d"]]) + expected = { + (0, 0): 2, + (0, 2): 1, + (0, 3): 1, + (1, 0): 1, + (1, 1): 1, + (1, 2): 1, + (1, 3): 1, + } + result = _get_level_lengths(index, sparsify=True, max_index=100) + tm.assert_dict_equal(result, expected) + + expected = { + (0, 0): 1, + (0, 1): 1, + (0, 2): 1, + (0, 3): 1, + (1, 0): 1, + (1, 1): 1, + (1, 2): 1, + (1, 3): 1, + } + result = _get_level_lengths(index, sparsify=False, max_index=100) + tm.assert_dict_equal(result, expected) + + def test_mi_sparse_index_names(self, blank_value): + # Test the class names and displayed value are correct on rendering MI names + df = DataFrame( + {"A": [1, 2]}, + index=MultiIndex.from_arrays( + [["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"] + ), + ) + result = df.style._translate(True, True) + head = result["head"][1] + expected = [ + { + "class": "index_name level0", + "display_value": "idx_level_0", + "is_visible": True, + }, + { + "class": "index_name level1", + "display_value": "idx_level_1", + "is_visible": True, + }, + { + "class": "blank col0", + "display_value": blank_value, + "is_visible": True, + }, + ] + for i, expected_dict in enumerate(expected): + assert expected_dict.items() <= head[i].items() + + def test_mi_sparse_column_names(self, blank_value): + df = DataFrame( + np.arange(16).reshape(4, 4), + index=MultiIndex.from_arrays( + [["a", "a", "b", "a"], [0, 1, 1, 2]], + names=["idx_level_0", "idx_level_1"], + ), + columns=MultiIndex.from_arrays( + [["C1", "C1", "C2", "C2"], [1, 0, 1, 0]], names=["colnam_0", "colnam_1"] + ), + ) + result = Styler(df, cell_ids=False)._translate(True, True) + + for level in [0, 1]: + head = result["head"][level] + expected = [ + { + "class": "blank", + "display_value": blank_value, + "is_visible": True, + }, + { + "class": f"index_name level{level}", + "display_value": f"colnam_{level}", + "is_visible": True, + }, + ] + for i, expected_dict in enumerate(expected): + assert expected_dict.items() <= head[i].items() + + def test_hide_column_headers(self, df, styler): + ctx = styler.hide(axis="columns")._translate(True, True) + assert len(ctx["head"]) == 0 # no header entries with an unnamed index + + df.index.name = "some_name" + ctx = df.style.hide(axis="columns")._translate(True, True) + assert len(ctx["head"]) == 1 + # index names still visible, changed in #42101, reverted in 43404 + + def test_hide_single_index(self, df): + # GH 14194 + # single unnamed index + ctx = df.style._translate(True, True) + assert ctx["body"][0][0]["is_visible"] + assert ctx["head"][0][0]["is_visible"] + ctx2 = df.style.hide(axis="index")._translate(True, True) + assert not ctx2["body"][0][0]["is_visible"] + assert not ctx2["head"][0][0]["is_visible"] + + # single named index + ctx3 = df.set_index("A").style._translate(True, True) + assert ctx3["body"][0][0]["is_visible"] + assert len(ctx3["head"]) == 2 # 2 header levels + assert ctx3["head"][0][0]["is_visible"] + + ctx4 = df.set_index("A").style.hide(axis="index")._translate(True, True) + assert not ctx4["body"][0][0]["is_visible"] + assert len(ctx4["head"]) == 1 # only 1 header levels + assert not ctx4["head"][0][0]["is_visible"] + + def test_hide_multiindex(self): + # GH 14194 + df = DataFrame( + {"A": [1, 2], "B": [1, 2]}, + index=MultiIndex.from_arrays( + [["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"] + ), + ) + ctx1 = df.style._translate(True, True) + # tests for 'a' and '0' + assert ctx1["body"][0][0]["is_visible"] + assert ctx1["body"][0][1]["is_visible"] + # check for blank header rows + assert len(ctx1["head"][0]) == 4 # two visible indexes and two data columns + + ctx2 = df.style.hide(axis="index")._translate(True, True) + # tests for 'a' and '0' + assert not ctx2["body"][0][0]["is_visible"] + assert not ctx2["body"][0][1]["is_visible"] + # check for blank header rows + assert len(ctx2["head"][0]) == 3 # one hidden (col name) and two data columns + assert not ctx2["head"][0][0]["is_visible"] + + def test_hide_columns_single_level(self, df): + # GH 14194 + # test hiding single column + ctx = df.style._translate(True, True) + assert ctx["head"][0][1]["is_visible"] + assert ctx["head"][0][1]["display_value"] == "A" + assert ctx["head"][0][2]["is_visible"] + assert ctx["head"][0][2]["display_value"] == "B" + assert ctx["body"][0][1]["is_visible"] # col A, row 1 + assert ctx["body"][1][2]["is_visible"] # col B, row 1 + + ctx = df.style.hide("A", axis="columns")._translate(True, True) + assert not ctx["head"][0][1]["is_visible"] + assert not ctx["body"][0][1]["is_visible"] # col A, row 1 + assert ctx["body"][1][2]["is_visible"] # col B, row 1 + + # test hiding multiple columns + ctx = df.style.hide(["A", "B"], axis="columns")._translate(True, True) + assert not ctx["head"][0][1]["is_visible"] + assert not ctx["head"][0][2]["is_visible"] + assert not ctx["body"][0][1]["is_visible"] # col A, row 1 + assert not ctx["body"][1][2]["is_visible"] # col B, row 1 + + def test_hide_columns_index_mult_levels(self): + # GH 14194 + # setup dataframe with multiple column levels and indices + i1 = MultiIndex.from_arrays( + [["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"] + ) + i2 = MultiIndex.from_arrays( + [["b", "b"], [0, 1]], names=["col_level_0", "col_level_1"] + ) + df = DataFrame([[1, 2], [3, 4]], index=i1, columns=i2) + ctx = df.style._translate(True, True) + # column headers + assert ctx["head"][0][2]["is_visible"] + assert ctx["head"][1][2]["is_visible"] + assert ctx["head"][1][3]["display_value"] == "1" + # indices + assert ctx["body"][0][0]["is_visible"] + # data + assert ctx["body"][1][2]["is_visible"] + assert ctx["body"][1][2]["display_value"] == "3" + assert ctx["body"][1][3]["is_visible"] + assert ctx["body"][1][3]["display_value"] == "4" + + # hide top column level, which hides both columns + ctx = df.style.hide("b", axis="columns")._translate(True, True) + assert not ctx["head"][0][2]["is_visible"] # b + assert not ctx["head"][1][2]["is_visible"] # 0 + assert not ctx["body"][1][2]["is_visible"] # 3 + assert ctx["body"][0][0]["is_visible"] # index + + # hide first column only + ctx = df.style.hide([("b", 0)], axis="columns")._translate(True, True) + assert not ctx["head"][0][2]["is_visible"] # b + assert ctx["head"][0][3]["is_visible"] # b + assert not ctx["head"][1][2]["is_visible"] # 0 + assert not ctx["body"][1][2]["is_visible"] # 3 + assert ctx["body"][1][3]["is_visible"] + assert ctx["body"][1][3]["display_value"] == "4" + + # hide second column and index + ctx = df.style.hide([("b", 1)], axis=1).hide(axis=0)._translate(True, True) + assert not ctx["body"][0][0]["is_visible"] # index + assert len(ctx["head"][0]) == 3 + assert ctx["head"][0][1]["is_visible"] # b + assert ctx["head"][1][1]["is_visible"] # 0 + assert not ctx["head"][1][2]["is_visible"] # 1 + assert not ctx["body"][1][3]["is_visible"] # 4 + assert ctx["body"][1][2]["is_visible"] + assert ctx["body"][1][2]["display_value"] == "3" + + # hide top row level, which hides both rows so body empty + ctx = df.style.hide("a", axis="index")._translate(True, True) + assert ctx["body"] == [] + + # hide first row only + ctx = df.style.hide(("a", 0), axis="index")._translate(True, True) + for i in [0, 1, 2, 3]: + assert "row1" in ctx["body"][0][i]["class"] # row0 not included in body + assert ctx["body"][0][i]["is_visible"] + + def test_pipe(self, df): + def set_caption_from_template(styler, a, b): + return styler.set_caption(f"Dataframe with a = {a} and b = {b}") + + styler = df.style.pipe(set_caption_from_template, "A", b="B") + assert "Dataframe with a = A and b = B" in styler.to_html() + + # Test with an argument that is a (callable, keyword_name) pair. + def f(a, b, styler): + return (a, b, styler) + + styler = df.style + result = styler.pipe((f, "styler"), a=1, b=2) + assert result == (1, 2, styler) + + def test_no_cell_ids(self): + # GH 35588 + # GH 35663 + df = DataFrame(data=[[0]]) + styler = Styler(df, uuid="_", cell_ids=False) + styler.to_html() + s = styler.to_html() # render twice to ensure ctx is not updated + assert s.find('') != -1 + + @pytest.mark.parametrize( + "classes", + [ + DataFrame( + data=[["", "test-class"], [np.nan, None]], + columns=["A", "B"], + index=["a", "b"], + ), + DataFrame(data=[["test-class"]], columns=["B"], index=["a"]), + DataFrame(data=[["test-class", "unused"]], columns=["B", "C"], index=["a"]), + ], + ) + def test_set_data_classes(self, classes): + # GH 36159 + df = DataFrame(data=[[0, 1], [2, 3]], columns=["A", "B"], index=["a", "b"]) + s = Styler(df, uuid_len=0, cell_ids=False).set_td_classes(classes).to_html() + assert '0' in s + assert '1' in s + assert '2' in s + assert '3' in s + # GH 39317 + s = Styler(df, uuid_len=0, cell_ids=True).set_td_classes(classes).to_html() + assert '0' in s + assert '1' in s + assert '2' in s + assert '3' in s + + def test_set_data_classes_reindex(self): + # GH 39317 + df = DataFrame( + data=[[0, 1, 2], [3, 4, 5], [6, 7, 8]], columns=[0, 1, 2], index=[0, 1, 2] + ) + classes = DataFrame( + data=[["mi", "ma"], ["mu", "mo"]], + columns=[0, 2], + index=[0, 2], + ) + s = Styler(df, uuid_len=0).set_td_classes(classes).to_html() + assert '0' in s + assert '2' in s + assert '4' in s + assert '6' in s + assert '8' in s + + def test_chaining_table_styles(self): + # GH 35607 + df = DataFrame(data=[[0, 1], [1, 2]], columns=["A", "B"]) + styler = df.style.set_table_styles( + [{"selector": "", "props": [("background-color", "yellow")]}] + ).set_table_styles( + [{"selector": ".col0", "props": [("background-color", "blue")]}], + overwrite=False, + ) + assert len(styler.table_styles) == 2 + + def test_column_and_row_styling(self): + # GH 35607 + df = DataFrame(data=[[0, 1], [1, 2]], columns=["A", "B"]) + s = Styler(df, uuid_len=0) + s = s.set_table_styles({"A": [{"selector": "", "props": [("color", "blue")]}]}) + assert "#T_ .col0 {\n color: blue;\n}" in s.to_html() + s = s.set_table_styles( + {0: [{"selector": "", "props": [("color", "blue")]}]}, axis=1 + ) + assert "#T_ .row0 {\n color: blue;\n}" in s.to_html() + + @pytest.mark.parametrize("len_", [1, 5, 32, 33, 100]) + def test_uuid_len(self, len_): + # GH 36345 + df = DataFrame(data=[["A"]]) + s = Styler(df, uuid_len=len_, cell_ids=False).to_html() + strt = s.find('id="T_') + end = s[strt + 6 :].find('"') + if len_ > 32: + assert end == 32 + else: + assert end == len_ + + @pytest.mark.parametrize("len_", [-2, "bad", None]) + def test_uuid_len_raises(self, len_): + # GH 36345 + df = DataFrame(data=[["A"]]) + msg = "``uuid_len`` must be an integer in range \\[0, 32\\]." + with pytest.raises(TypeError, match=msg): + Styler(df, uuid_len=len_, cell_ids=False).to_html() + + @pytest.mark.parametrize( + "slc", + [ + IndexSlice[:, :], + IndexSlice[:, 1], + IndexSlice[1, :], + IndexSlice[[1], [1]], + IndexSlice[1, [1]], + IndexSlice[[1], 1], + IndexSlice[1], + IndexSlice[1, 1], + slice(None, None, None), + [0, 1], + np.array([0, 1]), + Series([0, 1]), + ], + ) + def test_non_reducing_slice(self, slc): + df = DataFrame([[0, 1], [2, 3]]) + + tslice_ = non_reducing_slice(slc) + assert isinstance(df.loc[tslice_], DataFrame) + + @pytest.mark.parametrize("box", [list, Series, np.array]) + def test_list_slice(self, box): + # like dataframe getitem + subset = box(["A"]) + + df = DataFrame({"A": [1, 2], "B": [3, 4]}, index=["A", "B"]) + expected = IndexSlice[:, ["A"]] + + result = non_reducing_slice(subset) + tm.assert_frame_equal(df.loc[result], df.loc[expected]) + + def test_non_reducing_slice_on_multiindex(self): + # GH 19861 + dic = { + ("a", "d"): [1, 4], + ("a", "c"): [2, 3], + ("b", "c"): [3, 2], + ("b", "d"): [4, 1], + } + df = DataFrame(dic, index=[0, 1]) + idx = IndexSlice + slice_ = idx[:, idx["b", "d"]] + tslice_ = non_reducing_slice(slice_) + + result = df.loc[tslice_] + expected = DataFrame({("b", "d"): [4, 1]}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "slice_", + [ + IndexSlice[:, :], + # check cols + IndexSlice[:, IndexSlice[["a"]]], # inferred deeper need list + IndexSlice[:, IndexSlice[["a"], ["c"]]], # inferred deeper need list + IndexSlice[:, IndexSlice["a", "c", :]], + IndexSlice[:, IndexSlice["a", :, "e"]], + IndexSlice[:, IndexSlice[:, "c", "e"]], + IndexSlice[:, IndexSlice["a", ["c", "d"], :]], # check list + IndexSlice[:, IndexSlice["a", ["c", "d", "-"], :]], # don't allow missing + IndexSlice[:, IndexSlice["a", ["c", "d", "-"], "e"]], # no slice + # check rows + IndexSlice[IndexSlice[["U"]], :], # inferred deeper need list + IndexSlice[IndexSlice[["U"], ["W"]], :], # inferred deeper need list + IndexSlice[IndexSlice["U", "W", :], :], + IndexSlice[IndexSlice["U", :, "Y"], :], + IndexSlice[IndexSlice[:, "W", "Y"], :], + IndexSlice[IndexSlice[:, "W", ["Y", "Z"]], :], # check list + IndexSlice[IndexSlice[:, "W", ["Y", "Z", "-"]], :], # don't allow missing + IndexSlice[IndexSlice["U", "W", ["Y", "Z", "-"]], :], # no slice + # check simultaneous + IndexSlice[IndexSlice[:, "W", "Y"], IndexSlice["a", "c", :]], + ], + ) + def test_non_reducing_multi_slice_on_multiindex(self, slice_): + # GH 33562 + cols = MultiIndex.from_product([["a", "b"], ["c", "d"], ["e", "f"]]) + idxs = MultiIndex.from_product([["U", "V"], ["W", "X"], ["Y", "Z"]]) + df = DataFrame(np.arange(64).reshape(8, 8), columns=cols, index=idxs) + + for lvl in [0, 1]: + key = slice_[lvl] + if isinstance(key, tuple): + for subkey in key: + if isinstance(subkey, list) and "-" in subkey: + # not present in the index level, raises KeyError since 2.0 + with pytest.raises(KeyError, match="-"): + df.loc[slice_] + return + + expected = df.loc[slice_] + result = df.loc[non_reducing_slice(slice_)] + tm.assert_frame_equal(result, expected) + + +def test_hidden_index_names(mi_df): + mi_df.index.names = ["Lev0", "Lev1"] + mi_styler = mi_df.style + ctx = mi_styler._translate(True, True) + assert len(ctx["head"]) == 3 # 2 column index levels + 1 index names row + + mi_styler.hide(axis="index", names=True) + ctx = mi_styler._translate(True, True) + assert len(ctx["head"]) == 2 # index names row is unparsed + for i in range(4): + assert ctx["body"][0][i]["is_visible"] # 2 index levels + 2 data values visible + + mi_styler.hide(axis="index", level=1) + ctx = mi_styler._translate(True, True) + assert len(ctx["head"]) == 2 # index names row is still hidden + assert ctx["body"][0][0]["is_visible"] is True + assert ctx["body"][0][1]["is_visible"] is False + + +def test_hidden_column_names(mi_df): + mi_df.columns.names = ["Lev0", "Lev1"] + mi_styler = mi_df.style + ctx = mi_styler._translate(True, True) + assert ctx["head"][0][1]["display_value"] == "Lev0" + assert ctx["head"][1][1]["display_value"] == "Lev1" + + mi_styler.hide(names=True, axis="columns") + ctx = mi_styler._translate(True, True) + assert ctx["head"][0][1]["display_value"] == " " + assert ctx["head"][1][1]["display_value"] == " " + + mi_styler.hide(level=0, axis="columns") + ctx = mi_styler._translate(True, True) + assert len(ctx["head"]) == 1 # no index names and only one visible column headers + assert ctx["head"][0][1]["display_value"] == " " + + +@pytest.mark.parametrize("caption", [1, ("a", "b", "c"), (1, "s")]) +def test_caption_raises(mi_styler, caption): + msg = "`caption` must be either a string or 2-tuple of strings." + with pytest.raises(ValueError, match=msg): + mi_styler.set_caption(caption) + + +def test_hiding_headers_over_index_no_sparsify(): + # GH 43464 + midx = MultiIndex.from_product([[1, 2], ["a", "a", "b"]]) + df = DataFrame(9, index=midx, columns=[0]) + ctx = df.style._translate(False, False) + assert len(ctx["body"]) == 6 + ctx = df.style.hide((1, "a"), axis=0)._translate(False, False) + assert len(ctx["body"]) == 4 + assert "row2" in ctx["body"][0][0]["class"] + + +def test_hiding_headers_over_columns_no_sparsify(): + # GH 43464 + midx = MultiIndex.from_product([[1, 2], ["a", "a", "b"]]) + df = DataFrame(9, columns=midx, index=[0]) + ctx = df.style._translate(False, False) + for ix in [(0, 1), (0, 2), (1, 1), (1, 2)]: + assert ctx["head"][ix[0]][ix[1]]["is_visible"] is True + ctx = df.style.hide((1, "a"), axis="columns")._translate(False, False) + for ix in [(0, 1), (0, 2), (1, 1), (1, 2)]: + assert ctx["head"][ix[0]][ix[1]]["is_visible"] is False + + +def test_get_level_lengths_mi_hidden(): + # GH 43464 + index = MultiIndex.from_arrays([[1, 1, 1, 2, 2, 2], ["a", "a", "b", "a", "a", "b"]]) + expected = { + (0, 2): 1, + (0, 3): 1, + (0, 4): 1, + (0, 5): 1, + (1, 2): 1, + (1, 3): 1, + (1, 4): 1, + (1, 5): 1, + } + result = _get_level_lengths( + index, + sparsify=False, + max_index=100, + hidden_elements=[0, 1, 0, 1], # hidden element can repeat if duplicated index + ) + tm.assert_dict_equal(result, expected) + + +def test_row_trimming_hide_index(): + # gh 43703 + df = DataFrame([[1], [2], [3], [4], [5]]) + with option_context("styler.render.max_rows", 2): + ctx = df.style.hide([0, 1], axis="index")._translate(True, True) + assert len(ctx["body"]) == 3 + for r, val in enumerate(["3", "4", "..."]): + assert ctx["body"][r][1]["display_value"] == val + + +def test_row_trimming_hide_index_mi(): + # gh 44247 + df = DataFrame([[1], [2], [3], [4], [5]]) + df.index = MultiIndex.from_product([[0], [0, 1, 2, 3, 4]]) + with option_context("styler.render.max_rows", 2): + ctx = df.style.hide([(0, 0), (0, 1)], axis="index")._translate(True, True) + assert len(ctx["body"]) == 3 + + # level 0 index headers (sparsified) + assert {"value": 0, "attributes": 'rowspan="2"', "is_visible": True}.items() <= ctx[ + "body" + ][0][0].items() + assert {"value": 0, "attributes": "", "is_visible": False}.items() <= ctx["body"][ + 1 + ][0].items() + assert {"value": "...", "is_visible": True}.items() <= ctx["body"][2][0].items() + + for r, val in enumerate(["2", "3", "..."]): + assert ctx["body"][r][1]["display_value"] == val # level 1 index headers + for r, val in enumerate(["3", "4", "..."]): + assert ctx["body"][r][2]["display_value"] == val # data values + + +def test_col_trimming_hide_columns(): + # gh 44272 + df = DataFrame([[1, 2, 3, 4, 5]]) + with option_context("styler.render.max_columns", 2): + ctx = df.style.hide([0, 1], axis="columns")._translate(True, True) + + assert len(ctx["head"][0]) == 6 # blank, [0, 1 (hidden)], [2 ,3 (visible)], + trim + for c, vals in enumerate([(1, False), (2, True), (3, True), ("...", True)]): + assert ctx["head"][0][c + 2]["value"] == vals[0] + assert ctx["head"][0][c + 2]["is_visible"] == vals[1] + + assert len(ctx["body"][0]) == 6 # index + 2 hidden + 2 visible + trimming col + + +def test_no_empty_apply(mi_styler): + # 45313 + mi_styler.apply(lambda s: ["a:v;"] * 2, subset=[False, False]) + mi_styler._compute() + + +@pytest.mark.parametrize("format", ["html", "latex", "string"]) +def test_output_buffer(mi_styler, format): + # gh 47053 + with tm.ensure_clean(f"delete_me.{format}") as f: + getattr(mi_styler, f"to_{format}")(f) diff --git a/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_to_latex.py b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_to_latex.py new file mode 100644 index 0000000000000000000000000000000000000000..7f1443c3ee66be040f668f546682924207cfd31e --- /dev/null +++ b/Scripts_Climate_to_LAI/.venv/lib/python3.10/site-packages/pandas/tests/io/formats/style/test_to_latex.py @@ -0,0 +1,1090 @@ +from textwrap import dedent + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + MultiIndex, + Series, + option_context, +) + +pytest.importorskip("jinja2") +from pandas.io.formats.style import Styler +from pandas.io.formats.style_render import ( + _parse_latex_cell_styles, + _parse_latex_css_conversion, + _parse_latex_header_span, + _parse_latex_table_styles, + _parse_latex_table_wrapping, +) + + +@pytest.fixture +def df(): + return DataFrame( + {"A": [0, 1], "B": [-0.61, -1.22], "C": Series(["ab", "cd"], dtype=object)} + ) + + +@pytest.fixture +def df_ext(): + return DataFrame( + {"A": [0, 1, 2], "B": [-0.61, -1.22, -2.22], "C": ["ab", "cd", "de"]} + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0, precision=2) + + +def test_minimal_latex_tabular(styler): + expected = dedent( + """\ + \\begin{tabular}{lrrl} + & A & B & C \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + assert styler.to_latex() == expected + + +def test_tabular_hrules(styler): + expected = dedent( + """\ + \\begin{tabular}{lrrl} + \\toprule + & A & B & C \\\\ + \\midrule + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\bottomrule + \\end{tabular} + """ + ) + assert styler.to_latex(hrules=True) == expected + + +def test_tabular_custom_hrules(styler): + styler.set_table_styles( + [ + {"selector": "toprule", "props": ":hline"}, + {"selector": "bottomrule", "props": ":otherline"}, + ] + ) # no midrule + expected = dedent( + """\ + \\begin{tabular}{lrrl} + \\hline + & A & B & C \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\otherline + \\end{tabular} + """ + ) + assert styler.to_latex() == expected + + +def test_column_format(styler): + # default setting is already tested in `test_latex_minimal_tabular` + styler.set_table_styles([{"selector": "column_format", "props": ":cccc"}]) + + assert "\\begin{tabular}{rrrr}" in styler.to_latex(column_format="rrrr") + styler.set_table_styles([{"selector": "column_format", "props": ":r|r|cc"}]) + assert "\\begin{tabular}{r|r|cc}" in styler.to_latex() + + +def test_siunitx_cols(styler): + expected = dedent( + """\ + \\begin{tabular}{lSSl} + {} & {A} & {B} & {C} \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + assert styler.to_latex(siunitx=True) == expected + + +def test_position(styler): + assert "\\begin{table}[h!]" in styler.to_latex(position="h!") + assert "\\end{table}" in styler.to_latex(position="h!") + styler.set_table_styles([{"selector": "position", "props": ":b!"}]) + assert "\\begin{table}[b!]" in styler.to_latex() + assert "\\end{table}" in styler.to_latex() + + +@pytest.mark.parametrize("env", [None, "longtable"]) +def test_label(styler, env): + assert "\n\\label{text}" in styler.to_latex(label="text", environment=env) + styler.set_table_styles([{"selector": "label", "props": ":{more §text}"}]) + assert "\n\\label{more :text}" in styler.to_latex(environment=env) + + +def test_position_float_raises(styler): + msg = "`position_float` should be one of 'raggedright', 'raggedleft', 'centering'," + with pytest.raises(ValueError, match=msg): + styler.to_latex(position_float="bad_string") + + msg = "`position_float` cannot be used in 'longtable' `environment`" + with pytest.raises(ValueError, match=msg): + styler.to_latex(position_float="centering", environment="longtable") + + +@pytest.mark.parametrize("label", [(None, ""), ("text", "\\label{text}")]) +@pytest.mark.parametrize("position", [(None, ""), ("h!", "{table}[h!]")]) +@pytest.mark.parametrize("caption", [(None, ""), ("text", "\\caption{text}")]) +@pytest.mark.parametrize("column_format", [(None, ""), ("rcrl", "{tabular}{rcrl}")]) +@pytest.mark.parametrize("position_float", [(None, ""), ("centering", "\\centering")]) +def test_kwargs_combinations( + styler, label, position, caption, column_format, position_float +): + result = styler.to_latex( + label=label[0], + position=position[0], + caption=caption[0], + column_format=column_format[0], + position_float=position_float[0], + ) + assert label[1] in result + assert position[1] in result + assert caption[1] in result + assert column_format[1] in result + assert position_float[1] in result + + +def test_custom_table_styles(styler): + styler.set_table_styles( + [ + {"selector": "mycommand", "props": ":{myoptions}"}, + {"selector": "mycommand2", "props": ":{myoptions2}"}, + ] + ) + expected = dedent( + """\ + \\begin{table} + \\mycommand{myoptions} + \\mycommand2{myoptions2} + """ + ) + assert expected in styler.to_latex() + + +def test_cell_styling(styler): + styler.highlight_max(props="itshape:;Huge:--wrap;") + expected = dedent( + """\ + \\begin{tabular}{lrrl} + & A & B & C \\\\ + 0 & 0 & \\itshape {\\Huge -0.61} & ab \\\\ + 1 & \\itshape {\\Huge 1} & -1.22 & \\itshape {\\Huge cd} \\\\ + \\end{tabular} + """ + ) + assert expected == styler.to_latex() + + +def test_multiindex_columns(df): + cidx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df.columns = cidx + expected = dedent( + """\ + \\begin{tabular}{lrrl} + & \\multicolumn{2}{r}{A} & B \\\\ + & a & b & c \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + s = df.style.format(precision=2) + assert expected == s.to_latex() + + # non-sparse + expected = dedent( + """\ + \\begin{tabular}{lrrl} + & A & A & B \\\\ + & a & b & c \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + s = df.style.format(precision=2) + assert expected == s.to_latex(sparse_columns=False) + + +def test_multiindex_row(df_ext): + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index = ridx + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & A & B & C \\\\ + \\multirow[c]{2}{*}{A} & a & 0 & -0.61 & ab \\\\ + & b & 1 & -1.22 & cd \\\\ + B & c & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + styler = df_ext.style.format(precision=2) + result = styler.to_latex() + assert expected == result + + # non-sparse + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & A & B & C \\\\ + A & a & 0 & -0.61 & ab \\\\ + A & b & 1 & -1.22 & cd \\\\ + B & c & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + result = styler.to_latex(sparse_index=False) + assert expected == result + + +def test_multirow_naive(df_ext): + ridx = MultiIndex.from_tuples([("X", "x"), ("X", "y"), ("Y", "z")]) + df_ext.index = ridx + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & A & B & C \\\\ + X & x & 0 & -0.61 & ab \\\\ + & y & 1 & -1.22 & cd \\\\ + Y & z & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + styler = df_ext.style.format(precision=2) + result = styler.to_latex(multirow_align="naive") + assert expected == result + + +def test_multiindex_row_and_col(df_ext): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & \\multicolumn{2}{l}{Z} & Y \\\\ + & & a & b & c \\\\ + \\multirow[b]{2}{*}{A} & a & 0 & -0.61 & ab \\\\ + & b & 1 & -1.22 & cd \\\\ + B & c & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + styler = df_ext.style.format(precision=2) + result = styler.to_latex(multirow_align="b", multicol_align="l") + assert result == expected + + # non-sparse + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & Z & Z & Y \\\\ + & & a & b & c \\\\ + A & a & 0 & -0.61 & ab \\\\ + A & b & 1 & -1.22 & cd \\\\ + B & c & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + result = styler.to_latex(sparse_index=False, sparse_columns=False) + assert result == expected + + +@pytest.mark.parametrize( + "multicol_align, siunitx, header", + [ + ("naive-l", False, " & A & &"), + ("naive-r", False, " & & & A"), + ("naive-l", True, "{} & {A} & {} & {}"), + ("naive-r", True, "{} & {} & {} & {A}"), + ], +) +def test_multicol_naive(df, multicol_align, siunitx, header): + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("A", "c")]) + df.columns = ridx + level1 = " & a & b & c" if not siunitx else "{} & {a} & {b} & {c}" + col_format = "lrrl" if not siunitx else "lSSl" + expected = dedent( + f"""\ + \\begin{{tabular}}{{{col_format}}} + {header} \\\\ + {level1} \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{{tabular}} + """ + ) + styler = df.style.format(precision=2) + result = styler.to_latex(multicol_align=multicol_align, siunitx=siunitx) + assert expected == result + + +def test_multi_options(df_ext): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + styler = df_ext.style.format(precision=2) + + expected = dedent( + """\ + & & \\multicolumn{2}{r}{Z} & Y \\\\ + & & a & b & c \\\\ + \\multirow[c]{2}{*}{A} & a & 0 & -0.61 & ab \\\\ + """ + ) + result = styler.to_latex() + assert expected in result + + with option_context("styler.latex.multicol_align", "l"): + assert " & & \\multicolumn{2}{l}{Z} & Y \\\\" in styler.to_latex() + + with option_context("styler.latex.multirow_align", "b"): + assert "\\multirow[b]{2}{*}{A} & a & 0 & -0.61 & ab \\\\" in styler.to_latex() + + +def test_multiindex_columns_hidden(): + df = DataFrame([[1, 2, 3, 4]]) + df.columns = MultiIndex.from_tuples([("A", 1), ("A", 2), ("A", 3), ("B", 1)]) + s = df.style + assert "{tabular}{lrrrr}" in s.to_latex() + s.set_table_styles([]) # reset the position command + s.hide([("A", 2)], axis="columns") + assert "{tabular}{lrrr}" in s.to_latex() + + +@pytest.mark.parametrize( + "option, value", + [ + ("styler.sparse.index", True), + ("styler.sparse.index", False), + ("styler.sparse.columns", True), + ("styler.sparse.columns", False), + ], +) +def test_sparse_options(df_ext, option, value): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + styler = df_ext.style + + latex1 = styler.to_latex() + with option_context(option, value): + latex2 = styler.to_latex() + assert (latex1 == latex2) is value + + +def test_hidden_index(styler): + styler.hide(axis="index") + expected = dedent( + """\ + \\begin{tabular}{rrl} + A & B & C \\\\ + 0 & -0.61 & ab \\\\ + 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + assert styler.to_latex() == expected + + +@pytest.mark.parametrize("environment", ["table", "figure*", None]) +def test_comprehensive(df_ext, environment): + # test as many low level features simultaneously as possible + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + stlr = df_ext.style + stlr.set_caption("mycap") + stlr.set_table_styles( + [ + {"selector": "label", "props": ":{fig§item}"}, + {"selector": "position", "props": ":h!"}, + {"selector": "position_float", "props": ":centering"}, + {"selector": "column_format", "props": ":rlrlr"}, + {"selector": "toprule", "props": ":toprule"}, + {"selector": "midrule", "props": ":midrule"}, + {"selector": "bottomrule", "props": ":bottomrule"}, + {"selector": "rowcolors", "props": ":{3}{pink}{}"}, # custom command + ] + ) + stlr.highlight_max(axis=0, props="textbf:--rwrap;cellcolor:[rgb]{1,1,0.6}--rwrap") + stlr.highlight_max(axis=None, props="Huge:--wrap;", subset=[("Z", "a"), ("Z", "b")]) + + expected = ( + """\ +\\begin{table}[h!] +\\centering +\\caption{mycap} +\\label{fig:item} +\\rowcolors{3}{pink}{} +\\begin{tabular}{rlrlr} +\\toprule + & & \\multicolumn{2}{r}{Z} & Y \\\\ + & & a & b & c \\\\ +\\midrule +\\multirow[c]{2}{*}{A} & a & 0 & \\textbf{\\cellcolor[rgb]{1,1,0.6}{-0.61}} & ab \\\\ + & b & 1 & -1.22 & cd \\\\ +B & c & \\textbf{\\cellcolor[rgb]{1,1,0.6}{{\\Huge 2}}} & -2.22 & """ + """\ +\\textbf{\\cellcolor[rgb]{1,1,0.6}{de}} \\\\ +\\bottomrule +\\end{tabular} +\\end{table} +""" + ).replace("table", environment if environment else "table") + result = stlr.format(precision=2).to_latex(environment=environment) + assert result == expected + + +def test_environment_option(styler): + with option_context("styler.latex.environment", "bar-env"): + assert "\\begin{bar-env}" in styler.to_latex() + assert "\\begin{foo-env}" in styler.to_latex(environment="foo-env") + + +def test_parse_latex_table_styles(styler): + styler.set_table_styles( + [ + {"selector": "foo", "props": [("attr", "value")]}, + {"selector": "bar", "props": [("attr", "overwritten")]}, + {"selector": "bar", "props": [("attr", "baz"), ("attr2", "ignored")]}, + {"selector": "label", "props": [("", "{fig§item}")]}, + ] + ) + assert _parse_latex_table_styles(styler.table_styles, "bar") == "baz" + + # test '§' replaced by ':' [for CSS compatibility] + assert _parse_latex_table_styles(styler.table_styles, "label") == "{fig:item}" + + +def test_parse_latex_cell_styles_basic(): # test nesting + cell_style = [("itshape", "--rwrap"), ("cellcolor", "[rgb]{0,1,1}--rwrap")] + expected = "\\itshape{\\cellcolor[rgb]{0,1,1}{text}}" + assert _parse_latex_cell_styles(cell_style, "text") == expected + + +@pytest.mark.parametrize( + "wrap_arg, expected", + [ # test wrapping + ("", "\\ "), + ("--wrap", "{\\ }"), + ("--nowrap", "\\ "), + ("--lwrap", "{\\} "), + ("--dwrap", "{\\}{}"), + ("--rwrap", "\\{}"), + ], +) +def test_parse_latex_cell_styles_braces(wrap_arg, expected): + cell_style = [("", f"{wrap_arg}")] + assert _parse_latex_cell_styles(cell_style, "") == expected + + +def test_parse_latex_header_span(): + cell = {"attributes": 'colspan="3"', "display_value": "text", "cellstyle": []} + expected = "\\multicolumn{3}{Y}{text}" + assert _parse_latex_header_span(cell, "X", "Y") == expected + + cell = {"attributes": 'rowspan="5"', "display_value": "text", "cellstyle": []} + expected = "\\multirow[X]{5}{*}{text}" + assert _parse_latex_header_span(cell, "X", "Y") == expected + + cell = {"display_value": "text", "cellstyle": []} + assert _parse_latex_header_span(cell, "X", "Y") == "text" + + cell = {"display_value": "text", "cellstyle": [("bfseries", "--rwrap")]} + assert _parse_latex_header_span(cell, "X", "Y") == "\\bfseries{text}" + + +def test_parse_latex_table_wrapping(styler): + styler.set_table_styles( + [ + {"selector": "toprule", "props": ":value"}, + {"selector": "bottomrule", "props": ":value"}, + {"selector": "midrule", "props": ":value"}, + {"selector": "column_format", "props": ":value"}, + ] + ) + assert _parse_latex_table_wrapping(styler.table_styles, styler.caption) is False + assert _parse_latex_table_wrapping(styler.table_styles, "some caption") is True + styler.set_table_styles( + [ + {"selector": "not-ignored", "props": ":value"}, + ], + overwrite=False, + ) + assert _parse_latex_table_wrapping(styler.table_styles, None) is True + + +def test_short_caption(styler): + result = styler.to_latex(caption=("full cap", "short cap")) + assert "\\caption[short cap]{full cap}" in result + + +@pytest.mark.parametrize( + "css, expected", + [ + ([("color", "red")], [("color", "{red}")]), # test color and input format types + ( + [("color", "rgb(128, 128, 128 )")], + [("color", "[rgb]{0.502, 0.502, 0.502}")], + ), + ( + [("color", "rgb(128, 50%, 25% )")], + [("color", "[rgb]{0.502, 0.500, 0.250}")], + ), + ( + [("color", "rgba(128,128,128,1)")], + [("color", "[rgb]{0.502, 0.502, 0.502}")], + ), + ([("color", "#FF00FF")], [("color", "[HTML]{FF00FF}")]), + ([("color", "#F0F")], [("color", "[HTML]{FF00FF}")]), + ([("font-weight", "bold")], [("bfseries", "")]), # test font-weight and types + ([("font-weight", "bolder")], [("bfseries", "")]), + ([("font-weight", "normal")], []), + ([("background-color", "red")], [("cellcolor", "{red}--lwrap")]), + ( + [("background-color", "#FF00FF")], # test background-color command and wrap + [("cellcolor", "[HTML]{FF00FF}--lwrap")], + ), + ([("font-style", "italic")], [("itshape", "")]), # test font-style and types + ([("font-style", "oblique")], [("slshape", "")]), + ([("font-style", "normal")], []), + ([("color", "red /*--dwrap*/")], [("color", "{red}--dwrap")]), # css comments + ([("background-color", "red /* --dwrap */")], [("cellcolor", "{red}--dwrap")]), + ], +) +def test_parse_latex_css_conversion(css, expected): + result = _parse_latex_css_conversion(css) + assert result == expected + + +@pytest.mark.parametrize( + "env, inner_env", + [ + (None, "tabular"), + ("table", "tabular"), + ("longtable", "longtable"), + ], +) +@pytest.mark.parametrize( + "convert, exp", [(True, "bfseries"), (False, "font-weightbold")] +) +def test_parse_latex_css_convert_minimal(styler, env, inner_env, convert, exp): + # parameters ensure longtable template is also tested + styler.highlight_max(props="font-weight:bold;") + result = styler.to_latex(convert_css=convert, environment=env) + expected = dedent( + f"""\ + 0 & 0 & \\{exp} -0.61 & ab \\\\ + 1 & \\{exp} 1 & -1.22 & \\{exp} cd \\\\ + \\end{{{inner_env}}} + """ + ) + assert expected in result + + +def test_parse_latex_css_conversion_option(): + css = [("command", "option--latex--wrap")] + expected = [("command", "option--wrap")] + result = _parse_latex_css_conversion(css) + assert result == expected + + +def test_styler_object_after_render(styler): + # GH 42320 + pre_render = styler._copy(deepcopy=True) + styler.to_latex( + column_format="rllr", + position="h", + position_float="centering", + hrules=True, + label="my lab", + caption="my cap", + ) + + assert pre_render.table_styles == styler.table_styles + assert pre_render.caption == styler.caption + + +def test_longtable_comprehensive(styler): + result = styler.to_latex( + environment="longtable", hrules=True, label="fig:A", caption=("full", "short") + ) + expected = dedent( + """\ + \\begin{longtable}{lrrl} + \\caption[short]{full} \\label{fig:A} \\\\ + \\toprule + & A & B & C \\\\ + \\midrule + \\endfirsthead + \\caption[]{full} \\\\ + \\toprule + & A & B & C \\\\ + \\midrule + \\endhead + \\midrule + \\multicolumn{4}{r}{Continued on next page} \\\\ + \\midrule + \\endfoot + \\bottomrule + \\endlastfoot + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{longtable} + """ + ) + assert result == expected + + +def test_longtable_minimal(styler): + result = styler.to_latex(environment="longtable") + expected = dedent( + """\ + \\begin{longtable}{lrrl} + & A & B & C \\\\ + \\endfirsthead + & A & B & C \\\\ + \\endhead + \\multicolumn{4}{r}{Continued on next page} \\\\ + \\endfoot + \\endlastfoot + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{longtable} + """ + ) + assert result == expected + + +@pytest.mark.parametrize( + "sparse, exp, siunitx", + [ + (True, "{} & \\multicolumn{2}{r}{A} & {B}", True), + (False, "{} & {A} & {A} & {B}", True), + (True, " & \\multicolumn{2}{r}{A} & B", False), + (False, " & A & A & B", False), + ], +) +def test_longtable_multiindex_columns(df, sparse, exp, siunitx): + cidx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df.columns = cidx + with_si = "{} & {a} & {b} & {c} \\\\" + without_si = " & a & b & c \\\\" + expected = dedent( + f"""\ + \\begin{{longtable}}{{l{"SS" if siunitx else "rr"}l}} + {exp} \\\\ + {with_si if siunitx else without_si} + \\endfirsthead + {exp} \\\\ + {with_si if siunitx else without_si} + \\endhead + """ + ) + result = df.style.to_latex( + environment="longtable", sparse_columns=sparse, siunitx=siunitx + ) + assert expected in result + + +@pytest.mark.parametrize( + "caption, cap_exp", + [ + ("full", ("{full}", "")), + (("full", "short"), ("{full}", "[short]")), + ], +) +@pytest.mark.parametrize("label, lab_exp", [(None, ""), ("tab:A", " \\label{tab:A}")]) +def test_longtable_caption_label(styler, caption, cap_exp, label, lab_exp): + cap_exp1 = f"\\caption{cap_exp[1]}{cap_exp[0]}" + cap_exp2 = f"\\caption[]{cap_exp[0]}" + + expected = dedent( + f"""\ + {cap_exp1}{lab_exp} \\\\ + & A & B & C \\\\ + \\endfirsthead + {cap_exp2} \\\\ + """ + ) + assert expected in styler.to_latex( + environment="longtable", caption=caption, label=label + ) + + +@pytest.mark.parametrize("index", [True, False]) +@pytest.mark.parametrize( + "columns, siunitx", + [ + (True, True), + (True, False), + (False, False), + ], +) +def test_apply_map_header_render_mi(df_ext, index, columns, siunitx): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + styler = df_ext.style + + func = lambda v: "bfseries: --rwrap" if "A" in v or "Z" in v or "c" in v else None + + if index: + styler.map_index(func, axis="index") + if columns: + styler.map_index(func, axis="columns") + + result = styler.to_latex(siunitx=siunitx) + + expected_index = dedent( + """\ + \\multirow[c]{2}{*}{\\bfseries{A}} & a & 0 & -0.610000 & ab \\\\ + \\bfseries{} & b & 1 & -1.220000 & cd \\\\ + B & \\bfseries{c} & 2 & -2.220000 & de \\\\ + """ + ) + assert (expected_index in result) is index + + exp_cols_si = dedent( + """\ + {} & {} & \\multicolumn{2}{r}{\\bfseries{Z}} & {Y} \\\\ + {} & {} & {a} & {b} & {\\bfseries{c}} \\\\ + """ + ) + exp_cols_no_si = """\ + & & \\multicolumn{2}{r}{\\bfseries{Z}} & Y \\\\ + & & a & b & \\bfseries{c} \\\\ +""" + assert ((exp_cols_si if siunitx else exp_cols_no_si) in result) is columns + + +def test_repr_option(styler): + assert "