"""Validate synthetic arsenic in groundwater & health dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "geogenic_sahel_hotspot": "arsenic_sahel_geogenic.csv",
    "mining_affected_area": "arsenic_mining_affected.csv",
    "rift_valley_volcanic": "arsenic_rift_volcanic.csv",
}

COLORS = {"geogenic_sahel_hotspot": "#e6550d", "mining_affected_area": "#756bb1", "rift_valley_volcanic": "#31a354"}


def load_data() -> pd.DataFrame:
    frames = []
    for scenario, filename in SCENARIO_FILES.items():
        df = pd.read_csv(Path("data") / filename)
        frames.append(df)
    return pd.concat(frames, ignore_index=True)


def plot_validation(df: pd.DataFrame, output_path: Path) -> None:
    fig, axes = plt.subplots(4, 2, figsize=(14, 16))
    axes = axes.flatten()

    # Panel 1: Water As distribution
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[0].hist(subset["water_arsenic_ugL"], bins=40, alpha=0.5, color=COLORS[s], label=s, range=(0, 200))
    axes[0].axvline(10, color="red", ls="--", lw=1, label="WHO 10 µg/L")
    axes[0].set_title("Water Arsenic Distribution (µg/L)")
    axes[0].legend(fontsize=6)

    # Panel 2: Exceedance rates
    exc_cols = ["above_who_10", "above_50"]
    exc = df.groupby("scenario")[exc_cols].mean() * 100
    exc.plot(kind="bar", ax=axes[1])
    axes[1].set_title("WHO Guideline Exceedance (%)")
    axes[1].legend([">10 µg/L", ">50 µg/L"], fontsize=7)

    # Panel 3: Skin effects
    skin_cols = ["melanosis", "keratosis", "skin_lesion"]
    skin = df.groupby("scenario")[skin_cols].mean() * 100
    skin.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Skin Effects (%)")
    axes[2].legend(fontsize=7)

    # Panel 4: Water As vs skin lesion (dose-response)
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[3].scatter(subset["water_arsenic_ugL"], subset["skin_lesion"],
                        s=4, alpha=0.05, color=COLORS[s], label=s)
    axes[3].set_title("Water As vs Skin Lesion")
    axes[3].set_xlabel("Water As (µg/L)")
    axes[3].legend(fontsize=7)

    # Panel 5: Cancer & chronic disease
    chronic_cols = ["any_cancer", "cardiovascular", "diabetes", "neuropathy"]
    chronic = df.groupby("scenario")[chronic_cols].mean() * 100
    chronic.plot(kind="bar", ax=axes[4])
    axes[4].set_title("Cancer & Chronic Disease (%)")
    axes[4].legend(fontsize=6)

    # Panel 6: Water source distribution
    ws = df.groupby(["scenario", "water_source"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    ws.unstack().plot(kind="bar", stacked=True, ax=axes[5])
    axes[5].set_title("Water Source Distribution")
    axes[5].legend(fontsize=6)

    # Panel 7: Testing & mitigation
    mit_cols = ["water_tested", "safe_alternative_available", "uses_safe_alternative"]
    mit = df.groupby("scenario")[mit_cols].mean() * 100
    mit.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Testing & Mitigation (%)")
    axes[6].legend(fontsize=7)

    # Panel 8: Urine As distribution
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[7].hist(subset["urine_arsenic_ugL"], bins=40, alpha=0.5, color=COLORS[s], label=s, range=(0, 200))
    axes[7].set_title("Urine Arsenic Biomarker (µg/L)")
    axes[7].legend(fontsize=7)

    plt.tight_layout()
    fig.savefig(output_path, dpi=200)
    plt.close(fig)


def main() -> None:
    df = load_data()
    plot_validation(df, Path("validation_report.png"))
    print("Saved validation_report.png")


if __name__ == "__main__":
    main()