"""Validate synthetic pharmacovigilance & adverse drug reaction dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "tertiary_hospital_pv": "pv_tertiary_hospital.csv",
    "community_primary_care": "pv_community_primary.csv",
    "mass_treatment_campaign": "pv_mass_treatment.csv",
}

COLORS = {"tertiary_hospital_pv": "#e6550d", "community_primary_care": "#756bb1", "mass_treatment_campaign": "#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()

    adr_cols = ["adr_occurred", "serious_adr", "hospitalisation_due_adr", "death_due_adr"]
    adr = df.groupby("scenario")[adr_cols].mean() * 100
    adr.plot(kind="bar", ax=axes[0])
    axes[0].set_title("ADR Occurrence & Severity (%)")
    axes[0].legend(fontsize=6)

    rep_cols = ["adr_reported", "reported_to_nmra", "vigiflow_submitted", "report_complete"]
    rep = df.groupby("scenario")[rep_cols].mean() * 100
    rep.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Reporting Cascade (%)")
    axes[1].legend(fontsize=6)

    adr_df = df[df["adr_occurred"] == 1]
    if len(adr_df) > 0:
        at = adr_df.groupby(["scenario", "adr_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
        at.unstack().plot(kind="bar", stacked=True, ax=axes[2])
    axes[2].set_title("ADR Type Distribution")
    axes[2].legend(fontsize=4)

    dc = df.groupby(["scenario", "drug_class"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    dc.unstack().plot(kind="bar", stacked=True, ax=axes[3])
    axes[3].set_title("Drug Class Distribution")
    axes[3].legend(fontsize=4)

    cap_cols = ["pv_focal_person", "adr_form_available", "pv_training_received", "knows_reporting_process"]
    cap = df.groupby("scenario")[cap_cols].mean() * 100
    cap.plot(kind="bar", ax=axes[4])
    axes[4].set_title("PV System Capacity (%)")
    axes[4].legend(fontsize=6)

    err_cols = ["medication_error"]
    err = df.groupby("scenario")[err_cols].mean() * 100
    err.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Medication Error Rate (%)")
    axes[5].legend(fontsize=7)

    if len(adr_df) > 0:
        sev = adr_df.groupby(["scenario", "adr_severity"]).size().groupby(level=0).apply(lambda s: s / s.sum())
        sev.unstack().plot(kind="bar", stacked=True, ax=axes[6])
    axes[6].set_title("ADR Severity Distribution")
    axes[6].legend(fontsize=7)

    rep_adr = df[df["adr_reported"] == 1]
    if len(rep_adr) > 0:
        rt = rep_adr.groupby(["scenario", "reporter_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
        rt.unstack().plot(kind="bar", stacked=True, ax=axes[7])
    axes[7].set_title("Reporter Type Distribution")
    axes[7].legend(fontsize=6)

    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()