#!/usr/bin/env python3
"""Validation for Leptospirosis Environmental Surveillance Dataset."""
import pandas as pd, numpy as np, matplotlib.pyplot as plt, os, glob

def load_scenarios(data_dir='data'):
    dfs = {}
    for f in sorted(glob.glob(os.path.join(data_dir, 'lepto_*.csv'))):
        name = os.path.basename(f).replace('.csv', '')[6:]
        dfs[name] = pd.read_csv(f)
    return dfs

def main():
    dfs = load_scenarios()
    if not dfs: return
    all_df = pd.concat([df.assign(scenario=n) for n, df in dfs.items()], ignore_index=True)
    fig, axes = plt.subplots(4, 2, figsize=(16, 20))
    fig.suptitle('Leptospirosis Environmental Surveillance — Validation Report', fontsize=14, fontweight='bold', y=0.98)
    colors = {'active_surveillance': '#2ecc71', 'moderate_awareness': '#f39c12', 'unrecognized_burden': '#e74c3c'}
    labels = {'active_surveillance': 'Active (TZ/KE)', 'moderate_awareness': 'Moderate (UG/MZ)', 'unrecognized_burden': 'Unrecognized (DRC/NG)'}
    scenarios = list(dfs.keys())

    ax = axes[0, 0]
    metrics = ['Confirmed %', 'Misdiag\nMalaria %', 'Treated %', 'Deaths']
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [d['confirmed_lepto'].mean()*100, (d['initial_diagnosis']=='malaria').mean()*100, d['treated_antibiotics'].mean()*100, d['died'].sum()/100]
        ax.bar(np.arange(len(metrics))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(metrics))+0.25); ax.set_xticklabels(metrics, fontsize=7); ax.set_ylabel('% / count/100'); ax.set_title('Panel 1: Key Metrics'); ax.legend(fontsize=7)

    ax = axes[0, 1]
    diags = ['leptospirosis','malaria','typhoid','unknown']
    for i, s in enumerate(scenarios):
        vals = [dfs[s]['initial_diagnosis'].value_counts(normalize=True).get(d,0)*100 for d in diags]
        ax.bar(np.arange(len(diags))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(diags))+0.25); ax.set_xticklabels(diags, fontsize=7); ax.set_ylabel('%'); ax.set_title('Panel 2: Initial Diagnosis'); ax.legend(fontsize=7)

    ax = axes[1, 0]
    exposures = ['flooding_exposure','rodent_exposure','animal_contact','contaminated_water']
    for i, s in enumerate(scenarios):
        vals = [dfs[s][e].mean()*100 for e in exposures]
        ax.bar(np.arange(len(exposures))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(exposures))+0.25); ax.set_xticklabels([e.replace('_','\n') for e in exposures], fontsize=5); ax.set_ylabel('%'); ax.set_title('Panel 3: Exposure Risk Factors'); ax.legend(fontsize=7)

    ax = axes[1, 1]
    sevs = ['mild','moderate','severe']
    for i, s in enumerate(scenarios):
        vals = [dfs[s]['severity'].value_counts(normalize=True).get(sv,0)*100 for sv in sevs]
        ax.bar(np.arange(len(sevs))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(sevs))+0.25); ax.set_xticklabels(sevs); ax.set_ylabel('%'); ax.set_title('Panel 4: Disease Severity'); ax.legend(fontsize=7)

    ax = axes[2, 0]
    for s in scenarios:
        ax.hist(dfs[s]['one_health_coordination'], bins=30, alpha=0.5, label=labels.get(s,s), color=colors[s], density=True)
    ax.set_xlabel('OH Score'); ax.set_title('Panel 5: One Health Coordination'); ax.legend(fontsize=7)

    ax = axes[2, 1]
    for s in scenarios:
        ax.hist(dfs[s]['water_sanitation_score'], bins=30, alpha=0.5, label=labels.get(s,s), color=colors[s], density=True)
    ax.set_xlabel('WASH Score'); ax.set_title('Panel 6: Water & Sanitation'); ax.legend(fontsize=7)

    ax = axes[3, 0]
    occs = ['rice_farmer','livestock_herder','fisher','urban_informal','child']
    for i, s in enumerate(scenarios):
        vals = [dfs[s]['occupation'].value_counts(normalize=True).get(o,0)*100 for o in occs]
        ax.bar(np.arange(len(occs))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(occs))+0.25); ax.set_xticklabels([o.replace('_','\n') for o in occs], fontsize=5); ax.set_ylabel('%'); ax.set_title('Panel 7: Occupation'); ax.legend(fontsize=7)

    ax = axes[3, 1]
    num_cols = ['confirmed_lepto','flooding_exposure','rodent_exposure','treated_antibiotics','one_health_coordination','died']
    corr = all_df[num_cols].corr()
    im = ax.imshow(corr, cmap='RdBu_r', vmin=-1, vmax=1, aspect='auto')
    ax.set_xticks(range(len(num_cols))); ax.set_yticks(range(len(num_cols)))
    ax.set_xticklabels([c.replace('_','\n') for c in num_cols], fontsize=5, rotation=45, ha='right')
    ax.set_yticklabels([c.replace('_','\n') for c in num_cols], fontsize=5)
    ax.set_title('Panel 8: Correlation Heatmap'); fig.colorbar(im, ax=ax, fraction=0.046)

    plt.tight_layout(rect=[0,0,1,0.96]); plt.savefig('validation_report.png', dpi=150, bbox_inches='tight'); plt.close()
    print("Saved validation_report.png")

if __name__ == '__main__':
    main()