"""Validate synthetic sea-level rise & coastal health infrastructure dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "west_africa_delta": "slr_west_africa_delta.csv",
    "east_africa_island": "slr_east_africa_island.csv",
    "southern_coastal_city": "slr_southern_coastal_city.csv",
}

COLORS = {
    "west_africa_delta": "#e6550d",
    "east_africa_island": "#3182bd",
    "southern_coastal_city": "#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: Flood risk by scenario
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[0].hist(subset["flood_risk"], bins=25, alpha=0.5, color=COLORS[s], label=s)
    axes[0].set_title("Flood Risk Distribution by Scenario")
    axes[0].set_xlabel("Flood risk")
    axes[0].legend(fontsize=7)

    # Panel 2: Health impacts
    health_cols = ["waterborne_disease", "malaria_case", "cholera_outbreak", "mental_health_impact"]
    rates = df.groupby("scenario")[health_cols].mean() * 100
    rates.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Health Impact Rates (%)")
    axes[1].set_ylabel("Percent")
    axes[1].legend(fontsize=7)

    # Panel 3: Facility damage & inaccessibility
    fac_cols = ["facility_at_risk", "facility_damaged", "facility_inaccessible"]
    fac_rates = df.groupby("scenario")[fac_cols].mean() * 100
    fac_rates.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Health Facility Risk (%)")
    axes[2].set_ylabel("Percent")
    axes[2].legend(fontsize=7)

    # Panel 4: Elevation vs flood risk
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[3].scatter(subset["elevation_m"], subset["flood_risk"], s=6, alpha=0.15, color=COLORS[s], label=s)
    axes[3].set_title("Elevation vs Flood Risk")
    axes[3].set_xlabel("Elevation (m)")
    axes[3].set_ylabel("Flood risk")
    axes[3].legend(fontsize=7)

    # Panel 5: Supply chain disruption
    sc_cols = ["supply_chain_disrupted", "essential_medicine_stockout"]
    sc_rates = df.groupby("scenario")[sc_cols].mean() * 100
    sc_rates.plot(kind="bar", ax=axes[4])
    axes[4].set_title("Supply Chain Disruption (%)")
    axes[4].set_ylabel("Percent")
    axes[4].legend(fontsize=7)

    # Panel 6: Health system resilience
    res_data = [df[df["scenario"] == s]["health_system_resilience"] for s in SCENARIO_FILES]
    bp = axes[5].boxplot(res_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[5].set_title("Health System Resilience Score")
    axes[5].set_ylabel("Score")

    # Panel 7: Cumulative SLR trend
    yearly = df.groupby(["scenario", "year"])["cumulative_slr_cm"].mean().reset_index()
    for s in SCENARIO_FILES:
        sub = yearly[yearly["scenario"] == s]
        axes[6].plot(sub["year"], sub["cumulative_slr_cm"], marker="o", color=COLORS[s], label=s)
    axes[6].set_title("Cumulative Sea-Level Rise Trend")
    axes[6].set_xlabel("Year")
    axes[6].set_ylabel("Cumulative SLR (cm)")
    axes[6].legend(fontsize=7)

    # Panel 8: Adaptation score
    adapt_data = [df[df["scenario"] == s]["adaptation_score"] for s in SCENARIO_FILES]
    bp2 = axes[7].boxplot(adapt_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[7].set_title("Adaptation Score by Scenario")
    axes[7].set_ylabel("Score")

    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()