#!/usr/bin/env python3
"""Validation for eLearning & Digital Training 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, 'elearning_*.csv'))):
        name = os.path.basename(f).replace('.csv', '')[10:]
        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('eLearning & Digital Training — Validation Report', fontsize=14, fontweight='bold', y=0.98)
    colors = {'elearning_advanced': '#2ecc71', 'elearning_basic': '#f39c12', 'elearning_minimal': '#e74c3c'}
    labels = {'elearning_advanced': 'Advanced (SA/KE)', 'elearning_basic': 'Basic (KE/GH)', 'elearning_minimal': 'Minimal (DRC/SLE)'}
    scenarios = list(dfs.keys())

    ax = axes[0, 0]
    metrics = ['Access %', 'Completed %', 'Knowledge %', 'Certified %', 'Internet %']
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [d['elearning_access'].mean()*100, d['course_completed'].mean()*100, d['knowledge_improved'].mean()*100, d['certification_earned'].mean()*100, d['internet_adequate'].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]
    fmts = ['mobile_app','video','text_module','interactive','simulation','blended','whatsapp_group']
    for i, s in enumerate(scenarios):
        vals = [dfs[s]['learning_format'].value_counts(normalize=True).get(f,0)*100 for f in fmts]
        ax.bar(np.arange(len(fmts))+i*0.20, vals, 0.20, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(fmts))+0.20); ax.set_xticklabels([f.replace('_','\n') for f in fmts], fontsize=3); ax.set_ylabel('%'); ax.set_title('Panel 2: Learning Format'); ax.legend(fontsize=6)

    ax = axes[1, 0]
    cadres = ['nurse','chw','midwife','clinical_officer','lab_tech','doctor']
    for i, s in enumerate(scenarios):
        vals = [dfs[s]['cadre'].value_counts(normalize=True).get(c,0)*100 for c in cadres]
        ax.bar(np.arange(len(cadres))+i*0.20, vals, 0.20, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(cadres))+0.20); ax.set_xticklabels([c.replace('_','\n') for c in cadres], fontsize=5); ax.set_ylabel('%'); ax.set_title('Panel 3: Cadre'); ax.legend(fontsize=6)

    ax = axes[1, 1]
    barriers = ['Connectivity', 'Cost', 'Time', 'Digital Lit', 'Motivation']
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [d['barrier_connectivity'].mean()*100, d['barrier_cost'].mean()*100, d['barrier_time'].mean()*100, d['barrier_digital_literacy'].mean()*100, d['barrier_motivation'].mean()*100]
        ax.bar(np.arange(len(barriers))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(barriers))+0.25); ax.set_xticklabels(barriers, fontsize=6); ax.set_ylabel('%'); ax.set_title('Panel 4: Barriers'); ax.legend(fontsize=6)

    ax = axes[2, 0]
    topics = ['clinical_skills','infection_prevention','maternal_health','hiv_management','data_management']
    for i, s in enumerate(scenarios):
        vals = [dfs[s]['training_topic'].value_counts(normalize=True).get(t,0)*100 for t in topics]
        ax.bar(np.arange(len(topics))+i*0.20, vals, 0.20, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(topics))+0.20); ax.set_xticklabels([t.replace('_','\n') for t in topics], fontsize=4); ax.set_ylabel('%'); ax.set_title('Panel 5: Topics'); ax.legend(fontsize=6)

    ax = axes[2, 1]
    out = ['Started', 'Completed', 'Assessed', 'Certified', 'Practice\nChanged', 'Peer\nSharing']
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [d['course_started'].mean()*100, d['course_completed'].mean()*100, d['assessment_passed'].mean()*100, d['certification_earned'].mean()*100, d['practice_changed'].mean()*100, d['peer_sharing'].mean()*100]
        ax.bar(np.arange(len(out))+i*0.20, vals, 0.20, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(out))+0.20); ax.set_xticklabels(out, fontsize=5); ax.set_ylabel('%'); ax.set_title('Panel 6: Learning Outcomes'); ax.legend(fontsize=6)

    ax = axes[3, 0]
    started = all_df[all_df['course_started']==1]
    if len(started)>0:
        ax.hist(started['hours_studied'].clip(upper=30), bins=25, color='#3498db', alpha=0.7)
    ax.set_xlabel('Hours Studied'); ax.set_title('Panel 7: Study Hours (started)')

    ax = axes[3, 1]
    nc = ['elearning_access','course_completed','knowledge_improved','internet_adequate','personal_smartphone','barrier_connectivity']
    corr = all_df[nc].corr()
    im = ax.imshow(corr, cmap='RdBu_r', vmin=-1, vmax=1, aspect='auto')
    ax.set_xticks(range(len(nc))); ax.set_yticks(range(len(nc)))
    ax.set_xticklabels([c.replace('_','\n') for c in nc], fontsize=5, rotation=45, ha='right')
    ax.set_yticklabels([c.replace('_','\n') for c in nc], fontsize=5)
    ax.set_title('Panel 8: Correlations'); 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()