"""Validate synthetic climate & maternal-neonatal outcomes dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "heat_exposed_pregnancy": "maternal_heat_exposed.csv",
    "drought_food_insecure_pregnancy": "maternal_drought_food_insecure.csv",
    "flood_disrupted_services": "maternal_flood_disrupted.csv",
}

COLORS = {"heat_exposed_pregnancy": "#e6550d", "drought_food_insecure_pregnancy": "#d95f02", "flood_disrupted_services": "#3182bd"}


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()

    outcome_cols = ["preterm", "low_birth_weight", "stillbirth", "preeclampsia"]
    rates = df.groupby("scenario")[outcome_cols].mean() * 100
    rates.plot(kind="bar", ax=axes[0])
    axes[0].set_title("Adverse Pregnancy Outcomes (%)")
    axes[0].legend(fontsize=7)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[1].hist(subset["birth_weight_g"], bins=30, alpha=0.5, color=COLORS[s], label=s)
    axes[1].set_title("Birth Weight Distribution")
    axes[1].set_xlabel("Birth weight (g)")
    axes[1].legend(fontsize=7)

    svc_cols = ["anc_4plus", "skilled_birth_attendant", "facility_delivery", "facility_accessible"]
    svc = df.groupby("scenario")[svc_cols].mean() * 100
    svc.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Service Access (%)")
    axes[2].legend(fontsize=7)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[3].scatter(subset["heat_index"], subset["preterm"], s=6, alpha=0.1, color=COLORS[s], label=s)
    axes[3].set_title("Heat Index vs Preterm Birth")
    axes[3].set_xlabel("Heat index")
    axes[3].legend(fontsize=7)

    risk_cols = ["anaemia", "malaria_in_pregnancy", "food_insecure", "dehydration"]
    risk = df.groupby("scenario")[risk_cols].mean() * 100
    risk.plot(kind="bar", ax=axes[4])
    axes[4].set_title("Risk Factors (%)")
    axes[4].legend(fontsize=7)

    mort_cols = ["neonatal_death", "maternal_death"]
    mort = df.groupby("scenario")[mort_cols].mean() * 1000
    mort.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Mortality (per 1000)")
    axes[5].legend(fontsize=7)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[6].hist(subset["gestational_age_weeks"], bins=25, alpha=0.5, color=COLORS[s], label=s)
    axes[6].set_title("Gestational Age Distribution")
    axes[6].set_xlabel("Weeks")
    axes[6].legend(fontsize=7)

    neo_cols = ["neonatal_sepsis", "birth_asphyxia", "pph"]
    neo = df.groupby("scenario")[neo_cols].mean() * 100
    neo.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Neonatal/Maternal Complications (%)")
    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()