#!/usr/bin/env python3
"""Validation for HIV Viral Load & CD4 Testing 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, 'hiv_vl_cd4_*.csv'))):
        name = os.path.basename(f).replace('.csv', '')[11:]
        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('HIV Viral Load & CD4 Testing — Validation Report', fontsize=14, fontweight='bold', y=0.98)
    colors = {'vl_accessible': '#2ecc71', 'vl_limited': '#f39c12', 'no_vl_access': '#e74c3c'}
    labels = {'vl_accessible': 'VL Access (SA/BW)', 'vl_limited': 'Limited (KE/GH/TZ)', 'no_vl_access': 'None (DRC/SLE)'}
    scenarios = list(dfs.keys())

    ax = axes[0, 0]
    metrics = ['VL Ordered %', 'CD4 Ordered %', 'Result\nReturned %', 'EAC %', 'Stockout %']
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [d['vl_ordered'].mean()*100, d['cd4_ordered'].mean()*100, d['result_returned'].mean()*100, d['eac_provided'].mean()*100, d['reagent_stockout'].mean()*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=6); ax.set_ylabel('%'); ax.set_title('Panel 1: Key Metrics'); ax.legend(fontsize=6)

    ax = axes[0, 1]
    for s in scenarios:
        vl = dfs[s][dfs[s]['vl_ordered']==1]['vl_result'].dropna()
        if len(vl) > 0: ax.hist(np.log10(vl.clip(lower=1)), bins=25, alpha=0.5, label=labels.get(s,s), color=colors[s], density=True)
    ax.axvline(3, color='black', linestyle='--', alpha=0.5, label='1000 cp/mL')
    ax.set_xlabel('Log10 VL (copies/mL)'); ax.set_title('Panel 2: Viral Load Distribution'); ax.legend(fontsize=7)

    ax = axes[1, 0]
    for s in scenarios:
        cd4 = dfs[s][dfs[s]['cd4_ordered']==1]['cd4_result'].dropna()
        if len(cd4) > 0: ax.hist(cd4.clip(upper=1000), bins=25, alpha=0.5, label=labels.get(s,s), color=colors[s], density=True)
    ax.axvline(200, color='black', linestyle='--', alpha=0.5, label='200 cells')
    ax.set_xlabel('CD4 (cells/uL)'); ax.set_title('Panel 3: CD4 Distribution'); ax.legend(fontsize=7)

    ax = axes[1, 1]
    for s in scenarios:
        vl_tat = dfs[s][dfs[s]['vl_ordered']==1]['vl_tat_days'].dropna()
        if len(vl_tat) > 0: ax.hist(vl_tat.clip(upper=60), bins=25, alpha=0.5, label=labels.get(s,s), color=colors[s], density=True)
    ax.set_xlabel('VL TAT (days)'); ax.set_title('Panel 4: VL Turnaround Time'); ax.legend(fontsize=7)

    ax = axes[2, 0]
    plats = ['conventional_central','poc_vl','dbs_referred','not_done']
    for i, s in enumerate(scenarios):
        vals = [dfs[s]['vl_platform'].value_counts(normalize=True).get(p,0)*100 for p in plats]
        ax.bar(np.arange(len(plats))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(plats))+0.25); ax.set_xticklabels([p.replace('_','\n') for p in plats], fontsize=5); ax.set_ylabel('%'); ax.set_title('Panel 5: VL Platform'); ax.legend(fontsize=6)

    ax = axes[2, 1]
    acts = ['EAC', 'Regimen\nSwitch', 'OI\nProphylaxis', 'Transport\nIssue', 'Sample\nRejected']
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [d['eac_provided'].mean()*100, d['regimen_switch'].mean()*100, d['oi_prophylaxis'].mean()*100, d['specimen_transport_issue'].mean()*100, d['sample_rejected'].mean()*100]
        ax.bar(np.arange(len(acts))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(acts))+0.25); ax.set_xticklabels(acts, fontsize=5); ax.set_ylabel('%'); ax.set_title('Panel 6: Clinical Actions & Issues'); ax.legend(fontsize=6)

    ax = axes[3, 0]
    regs = ['TLD','TLE','AZT_based','PI_based','none']
    for i, s in enumerate(scenarios):
        vals = [dfs[s]['art_regimen'].value_counts(normalize=True).get(r,0)*100 for r in regs]
        ax.bar(np.arange(len(regs))+i*0.20, vals, 0.20, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(regs))+0.20); ax.set_xticklabels(regs, fontsize=6); ax.set_ylabel('%'); ax.set_title('Panel 7: ART Regimen'); ax.legend(fontsize=6)

    ax = axes[3, 1]
    num_cols = ['vl_ordered','cd4_ordered','result_returned','reagent_stockout','eac_provided','on_art']
    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()