"""Validate synthetic online pharmacy & unregistered medicines dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "social_media_marketplace": "online_social_media.csv",
    "dedicated_online_pharmacy": "online_dedicated.csv",
    "cross_border_ecommerce": "online_cross_border.csv",
}

COLORS = {"social_media_marketplace": "#e6550d", "dedicated_online_pharmacy": "#756bb1", "cross_border_ecommerce": "#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", "unregistered"]
    sf = df.groupby("scenario")[sf_cols].mean() * 100
    sf.plot(kind="bar", ax=axes[0])
    axes[0].set_title("SF & Unregistered Prevalence (%)")
    axes[0].legend(fontsize=7)

    plat = df.groupby(["scenario", "platform"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    plat.unstack().plot(kind="bar", stacked=True, ax=axes[1])
    axes[1].set_title("Platform Distribution")
    axes[1].legend(fontsize=5)

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

    use_cols = ["no_prescription", "self_medication", "pharmacist_involved", "verified_seller"]
    use = df.groupby("scenario")[use_cols].mean() * 100
    use.plot(kind="bar", ax=axes[3])
    axes[3].set_title("Purchase Patterns (%)")
    axes[3].legend(fontsize=6)

    out_cols = ["adr_occurred", "treatment_failure", "hospitalisation", "delayed_care"]
    out = df.groupby("scenario")[out_cols].mean() * 100
    out.plot(kind="bar", ax=axes[4])
    axes[4].set_title("Health Outcomes (%)")
    axes[4].legend(fontsize=7)

    reg_cols = ["nmra_approved", "manufacturer_known", "batch_traceable"]
    reg = df.groupby("scenario")[reg_cols].mean() * 100
    reg.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Traceability & Registration (%)")
    axes[5].legend(fontsize=7)

    enf_cols = ["reported_to_nmra", "consumer_aware_risk"]
    enf = df.groupby("scenario")[enf_cols].mean() * 100
    enf.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Reporting & Awareness (%)")
    axes[6].legend(fontsize=7)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[7].hist(subset["price_usd"], bins=30, alpha=0.5, color=COLORS[s], label=s, range=(0, 30))
    axes[7].set_title("Price Distribution (USD)")
    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()