"""Validate synthetic stroke rehabilitation & recovery dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "urban_stroke_unit": "stroke_urban.csv",
    "district_hospital": "stroke_district.csv",
    "rural_community": "stroke_rural.csv",
}

COLORS = {"urban_stroke_unit": "#e6550d", "district_hospital": "#756bb1", "rural_community": "#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()

    surv_cols = ["survived", "one_year_mortality", "recurrent_stroke"]
    surv = df.groupby("scenario")[surv_cols].mean() * 100
    surv.plot(kind="bar", ax=axes[0])
    axes[0].set_title("Survival & Recurrence (%)")
    axes[0].legend(fontsize=7)

    dis_cols = ["hemiplegia", "aphasia", "dysphagia", "depression_post"]
    dis = df.groupby("scenario")[dis_cols].mean() * 100
    dis.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Post-Stroke Disability (%)")
    axes[1].legend(fontsize=7)

    rehab_cols = ["rehab_received", "physiotherapy", "speech_therapy", "early_mobilisation"]
    rehab = df.groupby("scenario")[rehab_cols].mean() * 100
    rehab.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Rehabilitation Access (%)")
    axes[2].legend(fontsize=6)

    out_cols = ["functional_recovery", "independent_adl", "walking_ability", "return_to_work"]
    out = df.groupby("scenario")[out_cols].mean() * 100
    out.plot(kind="bar", ax=axes[3])
    axes[3].set_title("Functional Outcomes (%)")
    axes[3].legend(fontsize=6)

    st = df.groupby(["scenario", "stroke_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    st.unstack().plot(kind="bar", stacked=True, ax=axes[4])
    axes[4].set_title("Stroke Type Distribution")
    axes[4].legend(fontsize=7)

    rf_cols = ["hypertension", "diabetes", "hiv_positive"]
    rf = df.groupby("scenario")[rf_cols].mean() * 100
    rf.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Risk Factors (%)")
    axes[5].legend(fontsize=7)

    prev_cols = ["antihypertensive", "bp_controlled"]
    prev = df.groupby("scenario")[prev_cols].mean() * 100
    prev.plot(kind="bar", ax=axes[6])
    axes[6].set_title("Secondary Prevention (%)")
    axes[6].legend(fontsize=7)

    diag_cols = ["ct_scan", "thrombolysis"]
    diag = df.groupby("scenario")[diag_cols].mean() * 100
    diag.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Acute Diagnostics & Treatment (%)")
    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()