"""Validate synthetic e-waste recycling & occupational health dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "mega_site_west_africa": "ewaste_mega_site.csv",
    "medium_site_urban": "ewaste_medium_urban.csv",
    "small_dispersed_sites": "ewaste_small_dispersed.csv",
}

COLORS = {"mega_site_west_africa": "#e6550d", "medium_site_urban": "#756bb1", "small_dispersed_sites": "#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()

    # Panel 1: Blood Pb distribution
    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[0].hist(subset["blood_pb_ugdL"], bins=40, alpha=0.5, color=COLORS[s], label=s)
    axes[0].set_title("Blood Lead Distribution (µg/dL)")
    axes[0].legend(fontsize=7)

    # Panel 2: Respiratory outcomes
    resp_cols = ["cough_chronic", "wheeze", "dyspnoea", "reduced_fev1"]
    resp = df.groupby("scenario")[resp_cols].mean() * 100
    resp.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Respiratory Outcomes (%)")
    axes[1].legend(fontsize=6)

    # Panel 3: Elevated metal biomarkers
    bio_cols = ["elevated_pb", "elevated_cd"]
    bio = df.groupby("scenario")[bio_cols].mean() * 100
    bio.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Elevated Biomarker Prevalence (%)")
    axes[2].legend(["Pb ≥10 µg/dL", "Cd ≥1.0 µg/L"], fontsize=7)

    # Panel 4: Role distribution
    role_dist = df.groupby(["scenario", "role"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    role_dist.unstack().plot(kind="bar", stacked=True, ax=axes[3])
    axes[3].set_title("Worker Role Distribution")
    axes[3].legend(fontsize=5)

    # Panel 5: Processing method
    workers = df[df["is_worker"] == 1]
    proc_dist = workers.groupby(["scenario", "processing_method"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    proc_dist.unstack().plot(kind="bar", stacked=True, ax=axes[4])
    axes[4].set_title("Processing Method (Workers)")
    axes[4].legend(fontsize=5)

    # Panel 6: PPE use & safety
    safety_cols = ["ppe_use", "gloves", "mask", "health_screening_access"]
    safety = df.groupby("scenario")[safety_cols].mean() * 100
    safety.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Safety & Screening (%)")
    axes[5].legend(fontsize=6)

    # Panel 7: Skin, burns, eye outcomes
    skin_cols = ["skin_rash", "burns_injury", "eye_irritation"]
    skin = df.groupby("scenario")[skin_cols].mean() * 100
    skin.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Dermal & Ocular Outcomes (%)")
    axes[6].legend(fontsize=7)

    # Panel 8: DNA damage & child developmental
    other_cols = ["dna_damage_risk", "child_developmental", "proteinuria"]
    other = df.groupby("scenario")[other_cols].mean() * 100
    other.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Other Health Outcomes (%)")
    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()