"""Validate synthetic spinal cord injury & paraplegia dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "urban_spinal_unit": "sci_urban.csv",
    "district_hospital": "sci_district.csv",
    "rural_community": "sci_rural.csv",
}

COLORS = {"urban_spinal_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_acute", "one_year_survival", "died"]
    surv = df.groupby("scenario")[surv_cols].mean() * 100
    surv.plot(kind="bar", ax=axes[0])
    axes[0].set_title("Survival Outcomes (%)")
    axes[0].legend(fontsize=7)

    cau = df.groupby(["scenario", "cause"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    cau.unstack().plot(kind="bar", stacked=True, ax=axes[1])
    axes[1].set_title("Cause of SCI")
    axes[1].legend(fontsize=5)

    comp_cols = ["pressure_ulcer", "uti", "respiratory_complication", "pain_chronic", "depression"]
    comp = df.groupby("scenario")[comp_cols].mean() * 100
    comp.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Secondary Complications (%)")
    axes[2].legend(fontsize=5)

    rehab_cols = ["rehab_received", "physiotherapy", "wheelchair_received", "wheelchair_appropriate"]
    rehab = df.groupby("scenario")[rehab_cols].mean() * 100
    rehab.plot(kind="bar", ax=axes[3])
    axes[3].set_title("Rehabilitation Access (%)")
    axes[3].legend(fontsize=6)

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

    out_cols = ["independent_mobility", "return_home", "community_reintegration", "employed_post"]
    out = df.groupby("scenario")[out_cols].mean() * 100
    out.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Functional Outcomes (%)")
    axes[5].legend(fontsize=6)

    comp2 = df.groupby(["scenario", "completeness"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    comp2.unstack().plot(kind="bar", stacked=True, ax=axes[6])
    axes[6].set_title("Complete vs Incomplete SCI")
    axes[6].legend(fontsize=7)

    for s in SCENARIO_FILES:
        subset = df[(df["scenario"] == s) & (df["survived_acute"] == 1)]
        if len(subset) > 0:
            axes[7].hist(subset["days_hospitalised"], bins=30, alpha=0.5, color=COLORS[s], label=s, range=(0, 120))
    axes[7].set_title("Days Hospitalised")
    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()