"""Validate synthetic pediatric medicine quality & dosing dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "public_paediatric_care": "paed_public_care.csv",
    "community_otc_children": "paed_community_otc.csv",
    "neonatal_icu_specialist": "paed_neonatal_icu.csv",
}

COLORS = {"public_paediatric_care": "#e6550d", "community_otc_children": "#756bb1", "neonatal_icu_specialist": "#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()

    sf_cols = ["is_substandard_falsified", "is_falsified", "deg_contaminated"]
    sf = df.groupby("scenario")[sf_cols].mean() * 100
    sf.plot(kind="bar", ax=axes[0])
    axes[0].set_title("SF & DEG Contamination (%)")
    axes[0].legend(fontsize=7)

    dose_cols = ["dosing_error", "weight_based_dosing", "measuring_device_used"]
    dose = df.groupby("scenario")[dose_cols].mean() * 100
    dose.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Dosing Practices (%)")
    axes[1].legend(fontsize=7)

    form = df.groupby(["scenario", "formulation"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    form.unstack().plot(kind="bar", stacked=True, ax=axes[2])
    axes[2].set_title("Formulation Distribution")
    axes[2].legend(fontsize=5)

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

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

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[5].hist(subset["api_pct_label"], bins=50, alpha=0.5, color=COLORS[s], label=s, range=(0, 120))
    axes[5].axvline(85, color="red", ls="--", lw=1, label="85% threshold")
    axes[5].set_title("API Content (% of label)")
    axes[5].legend(fontsize=6)

    avail_cols = ["child_formulation_used", "adult_formulation_adapted"]
    avail = df.groupby("scenario")[avail_cols].mean() * 100
    avail.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Formulation Availability (%)")
    axes[6].legend(fontsize=7)

    err_df = df[df["dosing_error"] == 1]
    if len(err_df) > 0:
        et = err_df.groupby(["scenario", "error_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
        et.unstack().plot(kind="bar", stacked=True, ax=axes[7])
    axes[7].set_title("Dosing Error Types")
    axes[7].legend(fontsize=5)

    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()