"""Validate synthetic wildfire smoke & respiratory outcomes dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "savanna_fire_belt": "wildfire_savanna_fire_belt.csv",
    "forest_clearing_burn": "wildfire_forest_clearing_burn.csv",
    "urban_peri_urban_haze": "wildfire_urban_peri_urban_haze.csv",
}

COLORS = {
    "savanna_fire_belt": "#e6550d",
    "forest_clearing_burn": "#31a354",
    "urban_peri_urban_haze": "#756bb1",
}


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: PM2.5 total by scenario
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[0].hist(subset["pm25_total_ugm3"], bins=40, alpha=0.5, color=COLORS[s], label=s)
    axes[0].set_title("PM2.5 Total Distribution by Scenario")
    axes[0].set_xlabel("PM2.5 (µg/m³)")
    axes[0].legend(fontsize=7)

    # Panel 2: Seasonality - PM2.5 by month
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        monthly = subset.groupby("month")["pm25_total_ugm3"].mean()
        axes[1].plot(monthly.index, monthly.values, marker="o", color=COLORS[s], label=s)
    axes[1].set_title("Monthly PM2.5 Trend")
    axes[1].set_xlabel("Month")
    axes[1].set_ylabel("Mean PM2.5")
    axes[1].legend(fontsize=7)

    # Panel 3: Respiratory symptoms by scenario
    symptom_cols = ["cough", "wheeze", "dyspnoea", "eye_irritation"]
    symptom_rates = df.groupby("scenario")[symptom_cols].mean() * 100
    symptom_rates.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Respiratory Symptom Prevalence (%)")
    axes[2].set_ylabel("Percent")
    axes[2].legend(fontsize=7)

    # Panel 4: Exposure risk vs ARI
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[3].scatter(
            subset["exposure_risk_score"], subset["ari_episode"],
            s=6, alpha=0.1, color=COLORS[s], label=s,
        )
    axes[3].set_title("Exposure Risk Score vs ARI Episode")
    axes[3].set_xlabel("Exposure risk score")
    axes[3].set_ylabel("ARI episode")
    axes[3].legend(fontsize=7)

    # Panel 5: Vulnerability index distribution
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[4].hist(subset["vulnerability_index"], bins=20, alpha=0.5, color=COLORS[s], label=s)
    axes[4].set_title("Vulnerability Index Distribution")
    axes[4].set_xlabel("Vulnerability index")
    axes[4].legend(fontsize=7)

    # Panel 6: Health outcomes cascade
    cascade_cols = ["ari_episode", "er_visit", "hospitalised", "mortality"]
    cascade = df.groupby("scenario")[cascade_cols].mean() * 100
    cascade.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Health Outcomes Cascade (%)")
    axes[5].set_ylabel("Percent")
    axes[5].legend(fontsize=7)

    # Panel 7: AQI category distribution
    aqi_counts = df.groupby(["scenario", "aqi_category"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    aqi_counts.unstack().plot(kind="bar", stacked=True, ax=axes[6])
    axes[6].set_title("AQI Category Distribution")
    axes[6].set_ylabel("Share")
    axes[6].legend(fontsize=6)

    # Panel 8: Fire proximity vs PM2.5
    fire_season = df[df["in_fire_season"] == 1]
    for s in SCENARIO_FILES:
        subset = fire_season[fire_season["scenario"] == s]
        axes[7].scatter(
            subset["fire_proximity_km"], subset["pm25_smoke_ugm3"],
            s=6, alpha=0.15, color=COLORS[s], label=s,
        )
    axes[7].set_title("Fire Proximity vs Smoke PM2.5 (fire season)")
    axes[7].set_xlabel("Fire proximity (km)")
    axes[7].set_ylabel("PM2.5 smoke (µg/m³)")
    axes[7].legend(fontsize=7)

    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()