"""Validate synthetic medicine quality testing laboratory dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "national_qc_laboratory": "qclab_national.csv",
    "field_screening_minilab": "qclab_field_minilab.csv",
    "referral_confirmatory": "qclab_referral.csv",
}

COLORS = {"national_qc_laboratory": "#e6550d", "field_screening_minilab": "#756bb1", "referral_confirmatory": "#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()

    acc_cols = ["iso_17025", "who_pq_lab", "quality_manual", "proficiency_testing"]
    acc = df.groupby("scenario")[acc_cols].mean() * 100
    acc.plot(kind="bar", ax=axes[0])
    axes[0].set_title("Accreditation & Quality Systems (%)")
    axes[0].legend(fontsize=6)

    eq_cols = ["equipment_functional", "calibrated_current", "maintenance_contract"]
    eq = df.groupby("scenario")[eq_cols].mean() * 100
    eq.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Equipment Status (%)")
    axes[1].legend(fontsize=7)

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

    perf_cols = ["test_completed", "result_accurate", "sf_detected", "reported_to_nmra"]
    perf = df.groupby("scenario")[perf_cols].mean() * 100
    perf.plot(kind="bar", ax=axes[3])
    axes[3].set_title("Testing Performance (%)")
    axes[3].legend(fontsize=6)

    res_cols = ["reagent_available", "reference_standard_available", "staff_qualified"]
    res = df.groupby("scenario")[res_cols].mean() * 100
    res.plot(kind="bar", ax=axes[4])
    axes[4].set_title("Resources & Staff (%)")
    axes[4].legend(fontsize=7)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[5].hist(subset["turnaround_days"], bins=30, alpha=0.5, color=COLORS[s], label=s, range=(0, 100))
    axes[5].set_title("Turnaround Time (days)")
    axes[5].legend(fontsize=7)

    med = df.groupby(["scenario", "medicine_tested"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    med.unstack().plot(kind="bar", stacked=True, ax=axes[6])
    axes[6].set_title("Medicine Tested Distribution")
    axes[6].legend(fontsize=4)

    bud_cols = ["budget_adequate", "donor_funded"]
    bud = df.groupby("scenario")[bud_cols].mean() * 100
    bud.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Budget & Funding (%)")
    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()