"""Validate synthetic indoor air pollution & clean cooking dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "traditional_biomass": "indoor_air_traditional_biomass.csv",
    "improved_stove_rollout": "indoor_air_improved_stove_rollout.csv",
    "clean_fuel_transition": "indoor_air_clean_fuel_transition.csv",
}

COLORS = {
    "traditional_biomass": "#a63603",
    "improved_stove_rollout": "#ef6548",
    "clean_fuel_transition": "#3182bd",
}


def load_data() -> pd.DataFrame:
    frames = []
    for scenario, filename in SCENARIO_FILES.items():
        df = pd.read_csv(Path("data") / filename)
        df["scenario"] = scenario
        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 by scenario
    pm25_data = [df[df["scenario"] == s]["pm25_kitchen_ugm3"] for s in SCENARIO_FILES]
    axes[0].boxplot(pm25_data, labels=SCENARIO_FILES.keys(), patch_artist=True)
    for patch, scenario in zip(axes[0].artists, SCENARIO_FILES.keys()):
        patch.set_facecolor(COLORS[scenario])
        patch.set_alpha(0.6)
    axes[0].set_title("Kitchen PM2.5 by scenario (µg/m³)")
    axes[0].set_ylabel("PM2.5")

    # Panel 2: CO by scenario
    co_data = [df[df["scenario"] == s]["co_ppm"] for s in SCENARIO_FILES]
    axes[1].boxplot(co_data, labels=SCENARIO_FILES.keys(), patch_artist=True)
    for patch, scenario in zip(axes[1].artists, SCENARIO_FILES.keys()):
        patch.set_facecolor(COLORS[scenario])
        patch.set_alpha(0.6)
    axes[1].set_title("CO exposure proxy by scenario (ppm)")
    axes[1].set_ylabel("CO ppm")

    # Panel 3: Exposure index vs pneumonia incidence
    for scenario in SCENARIO_FILES:
        subset = df[df["scenario"] == scenario]
        axes[2].scatter(
            subset["exposure_index"],
            subset["pneumonia_incidence_per100"],
            s=8,
            alpha=0.25,
            color=COLORS[scenario],
            label=scenario,
        )
    axes[2].set_title("Exposure index vs pneumonia incidence")
    axes[2].set_xlabel("Exposure index")
    axes[2].set_ylabel("Pneumonia incidence /100")
    axes[2].legend(fontsize=8, loc="upper left")

    # Panel 4: Clean cooking access vs PM2.5
    access_stats = (
        df.groupby(["scenario", "clean_cooking_access"])["pm25_kitchen_ugm3"]
        .mean()
        .unstack()
    )
    access_stats.plot(kind="bar", ax=axes[3], color=["#bdbdbd", "#2ca25f"])
    axes[3].set_title("Mean PM2.5 by clean cooking access")
    axes[3].set_ylabel("PM2.5 µg/m³")
    axes[3].set_xlabel("Scenario")
    axes[3].legend(["No clean access", "Clean access"], fontsize=8)

    # Panel 5: Fuel mix by scenario
    fuel_counts = (
        df.groupby(["scenario", "fuel_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    )
    fuel_counts.unstack().plot(kind="bar", stacked=True, ax=axes[4], legend=False)
    axes[4].set_title("Fuel mix by scenario")
    axes[4].set_ylabel("Share")

    # Panel 6: Ventilation vs PM2.5
    for scenario in SCENARIO_FILES:
        subset = df[df["scenario"] == scenario]
        axes[5].scatter(
            subset["ventilation_index"],
            subset["pm25_kitchen_ugm3"],
            s=8,
            alpha=0.25,
            color=COLORS[scenario],
        )
    axes[5].set_title("Ventilation index vs PM2.5")
    axes[5].set_xlabel("Ventilation index")
    axes[5].set_ylabel("PM2.5 µg/m³")

    # Panel 7: Health burden score by scenario
    hb_data = [df[df["scenario"] == s]["health_burden_score"] for s in SCENARIO_FILES]
    axes[6].boxplot(hb_data, labels=SCENARIO_FILES.keys(), patch_artist=True)
    for patch, scenario in zip(axes[6].artists, SCENARIO_FILES.keys()):
        patch.set_facecolor(COLORS[scenario])
        patch.set_alpha(0.6)
    axes[6].set_title("Composite health burden score")
    axes[6].set_ylabel("Score")

    # Panel 8: Fuel collection time
    fc_data = [df[df["scenario"] == s]["fuel_collection_hours"] for s in SCENARIO_FILES]
    axes[7].boxplot(fc_data, labels=SCENARIO_FILES.keys(), patch_artist=True)
    for patch, scenario in zip(axes[7].artists, SCENARIO_FILES.keys()):
        patch.set_facecolor(COLORS[scenario])
        patch.set_alpha(0.6)
    axes[7].set_title("Fuel collection time (hours/day)")
    axes[7].set_ylabel("Hours")

    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()