"""Validate synthetic One Health climate surveillance dataset."""

from __future__ import annotations

from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd

SCENARIO_FILES = {
    "zoonotic_hotspot_forest": "onehealth_zoonotic_forest.csv",
    "pastoral_livestock_interface": "onehealth_pastoral_livestock.csv",
    "urban_periurban_market": "onehealth_urban_market.csv",
}

COLORS = {"zoonotic_hotspot_forest": "#31a354", "pastoral_livestock_interface": "#d95f02", "urban_periurban_market": "#756bb1"}


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()

    zoo_cols = ["zoonotic_spillover_event", "rvf_case", "ebola_signal", "lassa_case", "rabies_case"]
    rates = df.groupby("scenario")[zoo_cols].mean() * 100
    rates.plot(kind="bar", ax=axes[0])
    axes[0].set_title("Zoonotic Event Rates (%)")
    axes[0].legend(fontsize=6)

    surv_cols = ["surveillance_active", "event_detected", "lab_confirmed"]
    surv = df.groupby("scenario")[surv_cols].mean() * 100
    surv.plot(kind="bar", ax=axes[1])
    axes[1].set_title("Surveillance Cascade (%)")
    axes[1].legend(fontsize=7)

    res_counts = df.groupby(["scenario", "animal_reservoir"]).size().groupby(level=0).apply(lambda s: s / s.sum())
    res_counts.unstack().plot(kind="bar", stacked=True, ax=axes[2])
    axes[2].set_title("Animal Reservoir Distribution")
    axes[2].legend(fontsize=6)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[3].hist(subset["detection_delay_days"], bins=30, alpha=0.5, color=COLORS[s], label=s)
    axes[3].set_title("Detection Delay (days)")
    axes[3].legend(fontsize=7)

    oh_cols = ["one_health_platform", "joint_investigation", "response_initiated"]
    oh = df.groupby("scenario")[oh_cols].mean() * 100
    oh.plot(kind="bar", ax=axes[4])
    axes[4].set_title("One Health Response (%)")
    axes[4].legend(fontsize=7)

    amr_cols = ["amr_detected_livestock", "antibiotic_livestock_use"]
    amr = df.groupby("scenario")[amr_cols].mean() * 100
    amr.plot(kind="bar", ax=axes[5])
    axes[5].set_title("AMR & Antibiotic Use in Livestock (%)")
    axes[5].legend(fontsize=7)

    for s in SCENARIO_FILES:
        subset = df[df["scenario"] == s]
        axes[6].scatter(subset["deforestation_rate"], subset["any_zoonotic_event"], s=6, alpha=0.1, color=COLORS[s], label=s)
    axes[6].set_title("Deforestation vs Zoonotic Events")
    axes[6].legend(fontsize=7)

    cases = df.groupby("scenario")[["human_cases", "animal_cases", "human_deaths"]].sum()
    cases.plot(kind="bar", ax=axes[7])
    axes[7].set_title("Total Cases & Deaths by Scenario")
    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()