"""Render dataset-card statistics figures into assets/."""
import sys
from pathlib import Path

import matplotlib

matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from tqdm import tqdm

sys.path.insert(0, str(Path(__file__).resolve().parent))
from vivid_utils import STYLE, apply_style

ROOT = Path(__file__).resolve().parents[1]
ASSETS = ROOT / "assets"
S = STYLE


def new_fig():
    fig, ax = plt.subplots(figsize=S["figsize"], dpi=S["dpi"], facecolor="white")
    ax.set_facecolor("white")
    apply_style(ax)
    return fig, ax


def save(fig, name):
    fig.tight_layout()
    fig.savefig(ASSETS / name, dpi=S["dpi"], facecolor="white", bbox_inches="tight")
    plt.close(fig)
    print("wrote", ASSETS / name)


def plot_hist(series, xlabel, title, name, callout=None):
    fig, ax = new_fig()
    ax.hist(series, bins=50, color=S["accent"], edgecolor="white", linewidth=0.4)
    mean, med = series.mean(), series.median()
    ax.axvline(mean, color=S["text"], linewidth=1.2, linestyle="--")
    ax.axvline(med, color=S["secondary"], linewidth=1.2, linestyle=":")
    ax.text(0.98, 0.95, f"mean {mean:.0f}  ·  median {med:.0f}",
            transform=ax.transAxes, ha="right", va="top", fontsize=10, color=S["text"])
    if callout:
        ax.text(0.98, 0.86, callout, transform=ax.transAxes, ha="right", va="top",
                fontsize=11, fontweight="bold", color=S["accent"])
    ax.text(0.98, 0.78, "x-axis clipped at 99th percentile", transform=ax.transAxes,
            ha="right", va="top", fontsize=8, color=S["secondary"])
    ax.set_xlabel(xlabel)
    ax.set_ylabel("images")
    ax.set_title(title, fontsize=12, loc="left")
    ax.set_xlim(0, series.quantile(0.99))
    save(fig, name)


def main():
    ASSETS.mkdir(exist_ok=True)
    meta = pd.read_csv(ROOT / "data" / "metadata.csv")
    clusters = pd.read_csv(ROOT / "scripts" / "cache" / "clusters.csv")

    total_berries = meta.berry_count.sum()
    plot_hist(meta.berry_count, "berry keypoints per image",
              "Berries per image", "berries_per_image.png",
              callout=f"{total_berries:,} berries total")

    fig, ax = new_fig()
    counts = meta.cluster_count.value_counts().sort_index()
    ax.bar(counts.index, counts.values, color=S["accent"], edgecolor="white", linewidth=0.4)
    ax.set_xlabel("grape clusters per image")
    ax.set_ylabel("images")
    ax.set_title(f"Clusters per image  ·  {meta.cluster_count.sum():,} total",
                 fontsize=12, loc="left")
    save(fig, "clusters_per_image.png")

    fig, ax = new_fig()
    ax.scatter(meta.cluster_count, meta.berry_count, s=8, alpha=0.25,
               color=S["accent"], edgecolors="none")
    ax.set_xlabel("clusters per image")
    ax.set_ylabel("berries per image")
    ax.set_title("Berries vs clusters per image", fontsize=12, loc="left")
    save(fig, "berries_vs_clusters.png")

    fig, ax = new_fig()
    ax.hist(clusters.rel_area * 100, bins=60, color=S["accent"],
            edgecolor="white", linewidth=0.4)
    ax.set_xlabel("cluster bbox area (% of image area)")
    ax.set_ylabel("cluster instances")
    ax.set_title("Cluster size distribution", fontsize=12, loc="left")
    save(fig, "cluster_size_distribution.png")

    dims = meta.set_index("file_name")[["width", "height"]]
    xs, ys = [], []
    for p in tqdm(sorted((ROOT / "data" / "anns" / "points").glob("*.npy")), desc="points"):
        pts = np.load(p)
        if len(pts) == 0:
            continue
        matches = meta[meta.file_name.str.startswith(p.stem + ".")]
        if len(matches) != 1:
            sys.exit(f"ABORT: {len(matches)} metadata rows match points file {p.name}")
        w, h = dims.loc[matches.iloc[0].file_name]
        # points are stored as (x, y) pixel coordinates
        xs.append(pts[:, 0] / w)
        ys.append(pts[:, 1] / h)
    xs, ys = np.concatenate(xs), np.concatenate(ys)
    assert xs.max() <= 1.05 and ys.max() <= 1.05

    fig, ax = plt.subplots(figsize=(6, 6), dpi=S["dpi"], facecolor="white")
    hb = ax.hist2d(xs, ys, bins=100, cmap="magma")
    ax.invert_yaxis()
    ax.set_xlabel("normalized x", color=S["text"])
    ax.set_ylabel("normalized y", color=S["text"])
    ax.set_title(f"Spatial density of {len(xs):,} berry centroids",
                 fontsize=12, loc="left", color=S["text"])
    ax.tick_params(colors=S["text"], labelsize=9)
    fig.colorbar(hb[3], ax=ax, shrink=0.85)
    save(fig, "spatial_density.png")


if __name__ == "__main__":
    main()