"""Validate synthetic urban heat island & cardiovascular risk dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "megacity_tropical": "uhi_megacity_tropical.csv",
    "secondary_city_arid": "uhi_secondary_city_arid.csv",
    "highland_urbanizing": "uhi_highland_urbanizing.csv",
}

COLORS = {"megacity_tropical": "#e6550d", "secondary_city_arid": "#d95f02", "highland_urbanizing": "#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["experienced_temp_c"], bins=30, alpha=0.5, color=COLORS[s], label=s)
    axes[0].set_title("Experienced Temperature Distribution")
    axes[0].set_xlabel("Temperature (°C)")
    axes[0].legend(fontsize=7)

    cv_cols = ["cvd_event", "stroke", "arrhythmia", "heat_syncope"]
    rates = df.groupby("scenario")[cv_cols].mean() * 100
    rates.plot(kind="bar", ax=axes[1])
    axes[1].set_title("CV Event Rates (%)")
    axes[1].legend(fontsize=7)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[2].scatter(subset["heat_risk_score"], subset["any_cv_outcome"], s=6, alpha=0.1, color=COLORS[s], label=s)
    axes[2].set_title("Heat Risk Score vs CV Outcome")
    axes[2].legend(fontsize=7)

    uhi_data = [df[df["scenario"] == s]["uhi_intensity_c"] for s in SCENARIO_FILES]
    bp = axes[3].boxplot(uhi_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[3].set_title("UHI Intensity by Scenario")
    axes[3].set_ylabel("°C above ambient")

    risk_cols = ["hypertension", "diabetes", "obesity", "pre_existing_cvd"]
    risk = df.groupby("scenario")[risk_cols].mean() * 100
    risk.plot(kind="bar", ax=axes[4])
    axes[4].set_title("CVD Risk Factor Prevalence (%)")
    axes[4].legend(fontsize=7)

    cool_cols = ["ac_access", "fan_access", "green_space_access"]
    cool = df.groupby("scenario")[["ac_access", "fan_access"]].mean() * 100
    cool["green_space"] = df.groupby("scenario")["green_space_access"].mean() * 100
    cool.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Cooling Access (%)")
    axes[5].legend(fontsize=7)

    bp_data = [df[df["scenario"] == s]["systolic_bp_mmhg"] for s in SCENARIO_FILES]
    bp2 = axes[6].boxplot(bp_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[6].set_title("Systolic BP Distribution")
    axes[6].set_ylabel("mmHg")

    cascade_cols = ["any_cv_outcome", "er_visit", "hospitalised", "mortality"]
    cascade = df.groupby("scenario")[cascade_cols].mean() * 100
    cascade.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Health Outcomes Cascade (%)")
    axes[7].legend(fontsize=7)

    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()