"""Validate synthetic radon & indoor radiation exposure dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "granite_geology_rural": "radon_granite_rural.csv",
    "urban_residential": "radon_urban_residential.csv",
    "occupational_underground": "radon_occupational.csv",
}

COLORS = {"granite_geology_rural": "#e6550d", "urban_residential": "#756bb1", "occupational_underground": "#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()

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[0].hist(subset["radon_bqm3"], bins=40, alpha=0.5, color=COLORS[s], label=s, range=(0, 800))
    axes[0].axvline(100, color="red", ls="--", lw=1, label="WHO 100 Bq/m³")
    axes[0].axvline(300, color="orange", ls="--", lw=1, label="Action 300 Bq/m³")
    axes[0].set_title("Indoor Radon Distribution (Bq/m³)")
    axes[0].legend(fontsize=6)

    exc_cols = ["above_who_100", "above_action_300"]
    exc = df.groupby("scenario")[exc_cols].mean() * 100
    exc.plot(kind="bar", ax=axes[1])
    axes[1].set_title("WHO & Action Level Exceedance (%)")
    axes[1].legend(fontsize=7)

    health_cols = ["lung_cancer", "chronic_cough", "dyspnoea"]
    health = df.groupby("scenario")[health_cols].mean() * 100
    health.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Health Outcomes (%)")
    axes[2].legend(fontsize=7)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[3].scatter(subset["radon_bqm3"], subset["lung_cancer"],
                        s=4, alpha=0.05, color=COLORS[s], label=s)
    axes[3].set_title("Radon vs Lung Cancer")
    axes[3].legend(fontsize=7)

    bld = df.groupby(["scenario", "building_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    bld.unstack().plot(kind="bar", stacked=True, ax=axes[4])
    axes[4].set_title("Building Type Distribution")
    axes[4].legend(fontsize=5)

    flr = df.groupby(["scenario", "floor_level"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    flr.unstack().plot(kind="bar", stacked=True, ax=axes[5])
    axes[5].set_title("Floor Level Distribution")
    axes[5].legend(fontsize=7)

    risk_cols = ["ventilation_poor", "cracks_in_floor", "smoking", "uranium_geology"]
    risk = df.groupby("scenario")[risk_cols].mean() * 100
    risk.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Risk Factors (%)")
    axes[6].legend(fontsize=6)

    mit_cols = ["radon_measured", "aware_of_radon", "mitigation_installed", "ventilation_improved"]
    mit = df.groupby("scenario")[mit_cols].mean() * 100
    mit.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Measurement & Mitigation (%)")
    axes[7].legend(fontsize=6)

    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()