"""Validate synthetic water scarcity & waterborne disease dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "arid_groundwater_depletion": "water_arid_groundwater.csv",
    "seasonal_scarcity": "water_seasonal_scarcity.csv",
    "urban_water_crisis": "water_urban_crisis.csv",
}

COLORS = {
    "arid_groundwater_depletion": "#d95f02",
    "seasonal_scarcity": "#1b9e77",
    "urban_water_crisis": "#7570b3",
}


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 stress index
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[0].hist(subset["water_stress_index"], bins=25, alpha=0.5, color=COLORS[s], label=s)
    axes[0].set_title("Water Stress Index Distribution")
    axes[0].set_xlabel("Water stress index")
    axes[0].legend(fontsize=7)

    # Panel 2: Disease prevalence
    disease_cols = ["diarrhoea", "cholera", "typhoid", "trachoma"]
    prev = df.groupby("scenario")[disease_cols].mean() * 100
    prev.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Waterborne Disease Prevalence (%)")
    axes[1].set_ylabel("Percent")
    axes[1].legend(fontsize=7)

    # Panel 3: WASH score by scenario
    ws_data = [df[df["scenario"] == s]["wash_score"] for s in SCENARIO_FILES]
    bp = axes[2].boxplot(ws_data, tick_labels=SCENARIO_FILES.keys(), patch_artist=True)
    for patch, s in zip(bp["boxes"], SCENARIO_FILES.keys()):
        patch.set_facecolor(COLORS[s])
        patch.set_alpha(0.6)
    axes[2].set_title("WASH Score by Scenario")
    axes[2].set_ylabel("Score")

    # Panel 4: Water stress vs diarrhoea
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[3].scatter(subset["water_stress_index"], subset["diarrhoea"], s=6, alpha=0.1, color=COLORS[s], label=s)
    axes[3].set_title("Water Stress vs Diarrhoea")
    axes[3].set_xlabel("Water stress index")
    axes[3].set_ylabel("Diarrhoea")
    axes[3].legend(fontsize=7)

    # Panel 5: Water source distribution
    src_counts = df.groupby(["scenario", "water_source"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    src_counts.unstack().plot(kind="bar", stacked=True, ax=axes[4])
    axes[4].set_title("Water Source Distribution")
    axes[4].set_ylabel("Share")
    axes[4].legend(fontsize=6)

    # Panel 6: Litres per capita per day
    lpd_data = [df[df["scenario"] == s]["litres_per_capita_day"] for s in SCENARIO_FILES]
    bp2 = axes[5].boxplot(lpd_data, tick_labels=SCENARIO_FILES.keys(), patch_artist=True)
    for patch, s in zip(bp2["boxes"], SCENARIO_FILES.keys()):
        patch.set_facecolor(COLORS[s])
        patch.set_alpha(0.6)
    axes[5].set_title("Litres per Capita per Day")
    axes[5].set_ylabel("Litres")

    # Panel 7: Health outcomes cascade
    cascade_cols = ["any_waterborne_disease", "dehydration", "hospitalised", "mortality"]
    cascade = df.groupby("scenario")[cascade_cols].mean() * 100
    cascade.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Health Outcomes Cascade (%)")
    axes[6].set_ylabel("Percent")
    axes[6].legend(fontsize=7)

    # Panel 8: Collection time
    ct_data = [df[df["scenario"] == s]["water_collection_minutes"] for s in SCENARIO_FILES]
    bp3 = axes[7].boxplot(ct_data, tick_labels=SCENARIO_FILES.keys(), patch_artist=True)
    for patch, s in zip(bp3["boxes"], SCENARIO_FILES.keys()):
        patch.set_facecolor(COLORS[s])
        patch.set_alpha(0.6)
    axes[7].set_title("Water Collection Time (minutes)")
    axes[7].set_ylabel("Minutes")

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


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


if __name__ == "__main__":
    main()