"""Validate synthetic anti-TB medicine quality dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "dots_program_public": "tb_dots_public.csv",
    "mdr_tb_treatment": "tb_mdr_treatment.csv",
    "informal_private_market": "tb_informal_private.csv",
}

COLORS = {"dots_program_public": "#e6550d", "mdr_tb_treatment": "#756bb1", "informal_private_market": "#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["api_pct_label"], bins=50, alpha=0.5, color=COLORS[s], label=s, range=(0, 120))
    axes[0].axvline(85, color="red", ls="--", lw=1, label="85% threshold")
    axes[0].set_title("API Content (% of label)")
    axes[0].legend(fontsize=6)

    sf_cols = ["is_substandard_falsified", "is_falsified", "rifampicin_degraded"]
    sf = df.groupby("scenario")[sf_cols].mean() * 100
    sf.plot(kind="bar", ax=axes[1])
    axes[1].set_title("SF Prevalence & Rifampicin Degradation (%)")
    axes[1].legend(fontsize=6)

    drug = df.groupby(["scenario", "drug"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    drug.unstack().plot(kind="bar", stacked=True, ax=axes[2])
    axes[2].set_title("Drug Distribution")
    axes[2].legend(fontsize=4)

    out_cols = ["treatment_failure", "mdr_emergence", "death", "hepatotoxicity"]
    out = df.groupby("scenario")[out_cols].mean() * 100
    out.plot(kind="bar", ax=axes[3])
    axes[3].set_title("Treatment Outcomes (%)")
    axes[3].legend(fontsize=7)

    dots_cols = ["dots_observed", "treatment_completed", "lost_to_followup"]
    dots = df.groupby("scenario")[dots_cols].mean() * 100
    dots.plot(kind="bar", ax=axes[4])
    axes[4].set_title("DOTS & Adherence (%)")
    axes[4].legend(fontsize=7)

    mfg = df.groupby(["scenario", "manufacturer"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    mfg.unstack().plot(kind="bar", stacked=True, ax=axes[5])
    axes[5].set_title("Manufacturer Origin")
    axes[5].legend(fontsize=5)

    surv_cols = ["quality_tested", "genexpert_done", "reported_to_ntp"]
    surv = df.groupby("scenario")[surv_cols].mean() * 100
    surv.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Surveillance & Testing (%)")
    axes[6].legend(fontsize=7)

    clin_cols = ["hiv_coinfected", "gdf_procured", "storage_poor"]
    clin = df.groupby("scenario")[clin_cols].mean() * 100
    clin.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Clinical & Supply Context (%)")
    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()