"""Validate synthetic rehabilitation workforce & training dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "urban_training_institution": "rehab_wf_urban.csv",
    "district_service_delivery": "rehab_wf_district.csv",
    "rural_task_shifted": "rehab_wf_rural.csv",
}


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()

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

    wf_cols = ["position_filled", "vacancy", "task_shifted", "burnout"]
    wf = df.groupby("scenario")[wf_cols].mean() * 100
    wf.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Workforce Status (%)")
    axes[1].legend(fontsize=6)

    comp_cols = ["assessment_competent", "treatment_competent", "assistive_device_competent"]
    comp = df.groupby("scenario")[comp_cols].mean() * 100
    comp.plot(kind="bar", ax=axes[2])
    axes[2].set_title("Competencies (%)")
    axes[2].legend(fontsize=6)

    train_cols = ["cpd_received", "supervision_regular", "regulation_registered"]
    train = df.groupby("scenario")[train_cols].mean() * 100
    train.plot(kind="bar", ax=axes[3])
    axes[3].set_title("Training & Regulation (%)")
    axes[3].legend(fontsize=7)

    infra_cols = ["equipment_available", "dedicated_space"]
    infra = df.groupby("scenario")[infra_cols].mean() * 100
    infra.plot(kind="bar", ax=axes[4])
    axes[4].set_title("Infrastructure (%)")
    axes[4].legend(fontsize=7)

    ret_cols = ["emigration_intent", "rural_willingness"]
    ret = df.groupby("scenario")[ret_cols].mean() * 100
    ret.plot(kind="bar", ax=axes[5])
    axes[5].set_title("Retention Factors (%)")
    axes[5].legend(fontsize=7)

    qual = df.groupby(["scenario", "qualification"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    qual.unstack().plot(kind="bar", stacked=True, ax=axes[6])
    axes[6].set_title("Qualification Level")
    axes[6].legend(fontsize=6)

    pol_cols = ["rehab_in_health_policy", "budget_for_rehab"]
    pol = df.groupby("scenario")[pol_cols].mean() * 100
    pol.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Policy & Budget (%)")
    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()