import json
from pathlib import Path

import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import rasterio
from dbfread import DBF
from matplotlib.colors import Normalize
from osgeo import gdal
from rasterio.features import rasterize
from rasterio.transform import from_bounds


def process_tif(input_path: Path, move_list: list[Path] = None, nodata: float = None) -> list[Path]:
    """
    Process a single TIFF file by standardising and generating a PNG preview.

    Args:
        input_path (Path): Path to the TIFF file.
        move_list (list[Path], optional): List to append generated files to.
        nodata (float, optional): Overwriting nodata value.

    Returns:
        list[Path]: Updated list of processed files.
    """
    if move_list is None:
        move_list = []
    new_move_list = move_list[:]

    if input_path.suffix.lower() not in [".tif", ".tiff", ".asc"] or input_path.is_dir():
        print(f"⚠️ Skipped non-TIFF file: {input_path}")
        return new_move_list

    try:
        with rasterio.open(input_path) as src:
            data = src.read(1)  # preserve original dtype
            if nodata is None:
                nodata = src.nodata if src.nodata is not None else src.meta.get("nodata")

            # Save standardized compressed TIFF
            standardized_path = input_path.with_name(input_path.stem + ".standardized.tif")
            with rasterio.open(standardized_path, "w", **src.meta, compress="deflate") as dst:
                dst.write(data, 1)
            new_move_list.append(standardized_path)

            # Save PNG preview
            step = max(1, int(max(data.shape) / 1000))
            data_vis = data[::step, ::step]
            if nodata is not None:
                mask = np.isclose(data_vis, nodata)
                data_vis = np.where(mask, np.nan, data_vis)

            norm = Normalize(
                vmin=np.nanpercentile(data_vis, 2),
                vmax=np.nanpercentile(data_vis, 98)
            )
            scaled = np.uint8(np.clip(norm(data_vis) * 255, 0, 255))
            rgb = np.stack([scaled] * 3, axis=-1)
            rgb[np.isnan(data_vis)] = [255, 0, 255]

            png_path = input_path.with_suffix(".png")
            plt.imsave(png_path, rgb)
            new_move_list.append(png_path)

            print(f"✅ Processed {input_path.name}")

    except Exception as e:
        print(f"❌ Failed to process {input_path.name}: {e}")

    return new_move_list


def process_excel(input_path: Path, skip_rows=0, move_list: list[Path] = None) -> list[Path]:
    """
    Convert Excel file to CSV, skipping header rows automatically if requested.

    Args:
        input_path (Path): Path to the Excel file.
        skip_rows (int or 'auto'): Number of rows to skip before header, or 'auto' to detect.
        move_list (list[Path], optional): List to append output CSV to.

    Returns:
        list[Path]: Updated list of processed files.
    """
    if move_list is None:
        move_list = []
    new_move_list = move_list[:]

    if input_path.suffix.lower() not in [".xls", ".xlsx"]:
        print(f"⚠️ Skipped non-Excel file: {input_path.name}")
        return new_move_list

    try:
        # Auto-detect header if needed
        if skip_rows == "auto":
            peek = pd.read_excel(input_path, nrows=5, header=None)
            skip_rows = 0
            for _, row in peek.iterrows():
                if all(isinstance(cell, str) for cell in row if pd.notna(cell)):
                    break
                skip_rows += 1

        # Convert Excel to CSV
        df = pd.read_excel(input_path, skiprows=skip_rows)
        df.columns = df.columns.str.replace(r'[\r\n]+', ' ', regex=True).str.strip()
        csv_path = input_path.with_name(input_path.stem + ".csv")
        df.to_csv(csv_path, index=False)

        new_move_list.append(csv_path)
        print(f"✅ Converted Excel to CSV: {csv_path.name}")

    except Exception as e:
        print(f"❌ Failed to process {input_path.name}: {e}")

    return new_move_list


def process_dbf(input_path: Path, move_list: list[Path] = None) -> list[Path]:
    """
    Convert DBF file to CSV.

    Args:
        input_path (Path): Path to the DBF file.
        move_list (list[Path], optional): List to append output CSV to.

    Returns:
        list[Path]: Updated list of processed files.
    """
    if move_list is None:
        move_list = []
    new_move_list = move_list[:]

    if input_path.suffix.lower() != ".dbf":
        print(f"⚠️ Skipped non-DBF file: {input_path.name}")
        return new_move_list

    try:
        # Read DBF file
        df = pd.DataFrame(iter(DBF(input_path)))

        # Clean column names
        df.columns = df.columns.str.replace(r'[\r\n]+', ' ', regex=True).str.strip()

        # Save as CSV
        csv_path = input_path.with_name(input_path.stem + ".csv")
        df.to_csv(csv_path, index=False)

        new_move_list.append(csv_path)
        print(f"✅ Converted DBF to CSV: {csv_path.name}")

    except Exception as e:
        print(f"❌ Failed to process {input_path.name}: {e}")

    return new_move_list


def process_shp(input_path: Path, move_list: list[Path] = None,
                drop_geometry=False) -> list[Path]:
    """
    Process a single Shapefile by extracting the attribute table and saving a meta.

    Args:
        input_path (Path): Path to the Shapefile (.shp).
        move_list (list[Path], optional): List to append output files to.
        drop_geometry (bool, optional): Whether to drop geometry columns.

    Returns:
        list[Path]: Updated list of processed files.
    """
    if move_list is None:
        move_list = []
    new_move_list = move_list[:]

    if input_path.suffix.lower() != ".shp":
        print(f"⚠️ Skipped non-Shapefile: {input_path.name}")
        return new_move_list

    try:
        # Read the shapefile
        gdf = gpd.read_file(input_path)

        # Save attribute table (drop geometry)
        df = gdf.drop(columns=gdf.geometry.name, errors='ignore') if drop_geometry else gdf
        csv_path = input_path.with_name(input_path.stem + ".shp.csv")
        df.to_csv(csv_path, index=False)
        new_move_list.append(csv_path)

        # Save basic meta as JSON
        meta = {
            "n_features": len(gdf),
            "crs": str(gdf.crs),
            "bounds": gdf.total_bounds.tolist(),  # [minx, miny, maxx, maxy]
            "fields": list(df.columns)
        }
        json_path = input_path.with_name(input_path.stem + ".meta.json")
        with open(json_path, "w") as f:
            json.dump(meta, f, indent=2)
        new_move_list.append(json_path)

        print(f"✅ Processed Shapefile: {input_path.name}")

    except Exception as e:
        print(f"❌ Failed to process {input_path.name}: {e}")

    return new_move_list


def process_rdc_rst(rdc_path: Path, move_list: list[Path] = None, nodata: float = None) -> list[Path]:
    """
    Process an RDC/RST pair by standardising and generating a PNG preview.

    Args:
        rdc_path (Path): Path to the RDC file.
        move_list (list[Path], optional): List to append generated files to.
        nodata (float, optional): Overwriting nodata value.

    Returns:
        list[Path]: Updated list of processed files.
    """

    if move_list is None:
        move_list = []
    new_move_list = move_list[:]

    if rdc_path.suffix.lower() != ".rdc":
        print(f"⚠️ Skipped non-RDC file: {rdc_path}")
        return new_move_list

    # Parse RDC metadata
    try:
        with open(rdc_path, "r") as f:
            lines = f.readlines()
        meta_dict = {}
        for line in lines:
            if ":" in line:
                key, value = line.split(":", 1)
                meta_dict[key.strip().lower()] = value.strip()

        cols = int(meta_dict["columns"])
        rows = int(meta_dict["rows"])
        min_x = float(meta_dict["min. x"])
        max_y = float(meta_dict["max. y"])
        if nodata is None:
            nodata_str = meta_dict.get("flag value", "none")
            nodata = None if nodata_str.lower() == "none" else float(nodata_str)

        # Resolution handling
        try:
            res = float(meta_dict["resolution"])
        except ValueError:
            # Compute resolution from extents
            res_x = (float(meta_dict["max. x"]) - float(meta_dict["min. x"])) / cols
            res_y = (float(meta_dict["max. y"]) - float(meta_dict["min. y"])) / rows
            if not np.isclose(res_x, res_y):
                print(f"⚠️ WARNING: non-square pixel! res_x={res_x}, res_y={res_y}")
            res = res_x  # or res_y — assume square pixels

        # Define geotransform: (min_x, res, 0, max_y, 0, -res)
        geotransform = (min_x, res, 0, max_y, 0, -res)

    except Exception as e:
        print(f"❌ Failed to parse RDC: {rdc_path.name}: {e}")
        return new_move_list

    # Read RST with GDAL
    try:
        rst_path = rdc_path.with_suffix(".rst")
        ds = gdal.Open(str(rst_path))
        band = ds.GetRasterBand(1)
        data = band.ReadAsArray()

        # Apply nodata mask
        if nodata is not None:
            data = np.ma.masked_equal(data, nodata)

        # Save standardized compressed TIFF
        standardized_path = rdc_path.with_name(rdc_path.stem + ".standardized.tif")
        driver = gdal.GetDriverByName("GTiff")
        out_ds = driver.Create(
            str(standardized_path), cols, rows, 1, gdal.GDT_Float32, options=["COMPRESS=DEFLATE"]
        )
        out_ds.SetGeoTransform(geotransform)
        out_ds.SetProjection("")  # No CRS info in RDC, can leave empty or set if known
        out_band = out_ds.GetRasterBand(1)
        out_band.WriteArray(data)
        if nodata is not None:
            out_band.SetNoDataValue(nodata)
        out_ds.FlushCache()
        new_move_list.append(standardized_path)

        # Save PNG preview
        step = max(1, int(max(data.shape) / 1000))
        data_vis = data[::step, ::step]
        if nodata is not None:
            mask = np.isclose(data_vis, nodata)
            data_vis = np.where(mask, np.nan, data_vis)

        norm = Normalize(
            vmin=np.nanpercentile(data_vis, 2),
            vmax=np.nanpercentile(data_vis, 98)
        )
        scaled = np.uint8(np.clip(norm(data_vis) * 255, 0, 255))
        rgb = np.stack([scaled] * 3, axis=-1)
        rgb[np.isnan(data_vis)] = [255, 0, 255]

        png_path = rdc_path.with_suffix(".png")
        plt.imsave(png_path, rgb)
        new_move_list.append(png_path)

        print(f"✅ Processed RDC/RST pair: {rdc_path.name}")

    except Exception as e:
        print(f"❌ Failed to process RDC/RST pair: {rdc_path.name}: {e}")

    return new_move_list


def rasterize_shp(shp_path, columns, resolution_m=1000, out_dir=None, crs="EPSG:3035"):
    """
    Rasterize specified columns from a shapefile into GeoTIFF rasters.

    Parameters
    ----------
    shp_path : str or Path
        Path to the shapefile (.shp).
    columns : list of str
        List of column names in the shapefile to rasterize.
    resolution_m : float, optional
        Pixel size in meters (default = 1000, i.e. 1 km resolution).
    out_dir : str or Path, optional
        Output directory for GeoTIFFs. If None, same directory as shapefile.
    crs : str or int, optional
        Target coordinate reference system (default = 'EPSG:3035').

    Output
    ------
    Saves GeoTIFFs named '<shapefile_stem>__<column>.tif' in the output directory.
    """
    shp_path = Path(shp_path)
    out_dir = Path(out_dir) if out_dir else shp_path.parent
    out_dir.mkdir(parents=True, exist_ok=True)

    gdf = gpd.read_file(shp_path)
    gdf = gdf.to_crs(crs)

    missing_cols = [c for c in columns if c not in gdf.columns]
    if missing_cols:
        raise ValueError(f"Missing columns in shapefile: {missing_cols}")

    minx, miny, maxx, maxy = gdf.total_bounds
    width = int((maxx - minx) / resolution_m)
    height = int((maxy - miny) / resolution_m)
    transform = from_bounds(minx, miny, maxx, maxy, width, height)

    stem = shp_path.stem

    for col in columns:
        print(f"Rasterizing '{col}' ...")
        shapes = ((geom, val) for geom, val in zip(gdf.geometry, gdf[col]))

        out_path = out_dir / f"{stem}__{col}.tif"
        with rasterio.open(
                out_path,
                "w",
                driver="GTiff",
                height=height,
                width=width,
                count=1,
                dtype="float32",
                crs=gdf.crs,
                transform=transform,
                nodata=-9999,
        ) as dst:
            raster = rasterize(
                shapes=shapes,
                out_shape=(height, width),
                transform=transform,
                fill=-9999,
                dtype="float32",
            )
            dst.write(raster, 1)

        print(f"✅ Saved → {out_path}")

    print("🎉 Rasterization complete.")