import argparse
from pathlib import Path

import matplotlib.colors as mcolors
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import numpy as np
from hilbertcurve.hilbertcurve import HilbertCurve

from check import plot_preds

# Extract XKCD colors
colors = list(mcolors.XKCD_COLORS.keys())
rgb_values = [mcolors.to_rgb(mcolors.XKCD_COLORS[color]) for color in colors]

# Parse command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument("-s", "--sort-by", type=str, default="hsv", help="kind of sorting")
parser.add_argument("--seed", type=int, default=21, help="seed for UMAP")
parser.add_argument("--dpi", type=int, default=300, help="dpi for saving")
parser.add_argument("--size", type=float, default=6.0, help="size of figure")
parser.add_argument(
    "--fontsize",
    type=float,
    default=0,
    help="fontsize of annotation (default: 0 = None)",
)
parser.add_argument(
    "--radius", type=float, default=1 / 2, help="inner radius of circle"
)
args = parser.parse_args()
KIND = args.sort_by
SEED = args.seed
DPI = args.dpi
SIZE = args.size
FONTSIZE = args.fontsize
INNER_RADIUS = args.radius
DIR = "/teamspace/studios/colors/umap"


prefix = ""
if KIND == "umap":
    prefix = f"{SEED:04d}_"
FDIR = f"{DIR}/{KIND}"
Path(FDIR).mkdir(exist_ok=True, parents=True)
fname = f"{FDIR}/{prefix}sorted_colors_circle.png"



if KIND in ("lex", "alpha", "abc"):
    preds = np.array(colors)

elif KIND == "umap":
    # from umap import UMAP
    from cuml import UMAP

    # Use UMAP to create a 1D representation
    reducer = UMAP(
        n_components=1,
        n_neighbors=250,
        min_dist=1e-2,
        metric="euclidean",
        random_state=SEED,
        negative_sample_rate=2,
    )
    embedding = reducer.fit_transform(np.array(rgb_values))

    # Sort colors by the 1D representation
    preds = embedding[:, 0]
    del reducer, embedding

elif KIND in ("cielab", "lab", "ciede2000"):
    from skimage.color import deltaE_ciede2000, rgb2lab

    # CIELAB
    # Convert RGB values to CIELAB
    lab_values = rgb2lab([rgb_values])

    # Reference color for sorting (can be the first color or any other reference point)
    reference_color = lab_values[0]

    # Compute CIEDE2000 distances of all colors to the reference color
    distances = [deltaE_ciede2000(reference_color, color) for color in lab_values]

    # Sort colors by their CIEDE2000 distance to the reference color
    # preds = distances).flatten()  # awful
    lab_values_flat = lab_values.reshape(-1, 3)
    # Sort colors based on the L* value in the CIELAB space
    # 0 corresponds to the L* channel
    preds = lab_values_flat[:, 0]

elif KIND == "hsv":
    from matplotlib.colors import rgb_to_hsv

    # Convert RGB values to HSV
    hsv_values = np.array([rgb_to_hsv(np.array(rgb)) for rgb in rgb_values])

    # Sort colors based on the hue value
    # 0 corresponds to the hue component
    preds = hsv_values[:, 0]
else:
    raise ValueError(f"Unknown kind: {KIND}")

sorted_indices = np.argsort(preds)

# Save the sorted indices to disk
# if (KIND == "umap") or (KIND != "umap"):
PDIR = f"scripts/{KIND}"
Path(PDIR).mkdir(parents=True, exist_ok=True)
file_path = f"{PDIR}/{SEED:06d}.npy"
np.save(file_path, preds.ravel())
print(f"Predictions saved to {file_path}")

# Sort colors by the 1D representation
sorted_colors = [colors[i] for i in sorted_indices]

plot_preds(
    preds,
    np.array(rgb_values),
    fname,
    roll=False,
    dpi=DPI,
    inner_radius=INNER_RADIUS,
    figsize=(SIZE, SIZE),
    fsize=FONTSIZE,
    label=f"{KIND.upper()}",
)
print(f"saved {fname}")

HILBERT = False

if HILBERT:
    # Create Hilbert curve
    # We'll set the order such that the number of positions is greater than or equal to the number of colors
    hilbert_order = int(np.ceil(0.5 * np.log2(len(sorted_colors))))
    hilbert_curve = HilbertCurve(hilbert_order, 2)

    # Create an image for visualization
    image_size = 2**hilbert_order
    image = np.ones((image_size, image_size, 3))

    for i, color in enumerate(sorted_colors):
        # Convert linear index to Hilbert coordinates
        coords = hilbert_curve.point_from_distance(i)
        image[coords[1], coords[0]] = mcolors.to_rgb(color)

    # annotation in upper right
    # Display the image
    fig, ax = plt.subplots(1, 1, figsize=(SIZE, SIZE))
    ax.imshow(image)
    ax.annotate(
        f"{KIND.upper()}",
        (1.0, 1.0),
        ha="right",
        va="top",
        size=FONTSIZE,
        xycoords="axes fraction",
    )
    ax.axis("off")
    ax.set_aspect("equal")
    fig.tight_layout()
    fname = f"{DIR}/{prefix}{KIND}_sorted_colors_hilbert.png"
    fig.savefig(
        fname,
        dpi=DPI,
        transparent=True,
        # bbox_inches="tight",
        # pad_inches=0
    )
    print(f"Saved {fname}")