#!/usr/bin/env python3
"""Validation for Stroke Emergency Care 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, 'stroke_*.csv'))):
        name = os.path.basename(f).replace('.csv', '')[7:]
        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('Stroke Emergency Care — Validation Report', fontsize=14, fontweight='bold', y=0.98)
    colors = {'stroke_unit_available': '#2ecc71', 'basic_stroke_care': '#f39c12', 'no_stroke_services': '#e74c3c'}
    labels = {'stroke_unit_available': 'Stroke Unit (SA/KE)', 'basic_stroke_care': 'Basic (KE/GH/TZ)', 'no_stroke_services': 'None (DRC/SLE)'}
    scenarios = list(dfs.keys())

    ax = axes[0, 0]
    metrics = ['CT Done %', 'Thrombolysis %', 'Stroke Unit %', 'Mortality %']
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [d['ct_scan_done'].mean()*100, d['thrombolysis_given'].mean()*100, d['stroke_unit_admitted'].mean()*100, d['died_in_hospital'].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=7); ax.set_ylabel('%'); ax.set_title('Panel 1: Key Metrics'); ax.legend(fontsize=7)

    ax = axes[0, 1]
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [(d['stroke_type']=='ischemic').mean()*100, (d['stroke_type']=='hemorrhagic').mean()*100]
        ax.bar(np.arange(2)+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(2)+0.25); ax.set_xticklabels(['Ischemic','Hemorrhagic']); ax.set_ylabel('%'); ax.set_title('Panel 2: Stroke Type'); ax.legend(fontsize=7)

    ax = axes[1, 0]
    for s in scenarios:
        ax.hist(dfs[s]['onset_to_arrival_hrs'].clip(upper=72), bins=30, alpha=0.5, label=labels.get(s,s), color=colors[s], density=True)
    ax.axvline(4.5, color='black', linestyle='--', alpha=0.5, label='4.5hr window')
    ax.set_xlabel('Onset to Arrival (hours)'); ax.set_title('Panel 3: Presentation Delay'); ax.legend(fontsize=7)

    ax = axes[1, 1]
    for s in scenarios:
        ax.hist(dfs[s]['nihss_score'].clip(upper=35), bins=25, alpha=0.5, label=labels.get(s,s), color=colors[s], density=True)
    ax.set_xlabel('NIHSS Score'); ax.set_title('Panel 4: Stroke Severity'); ax.legend(fontsize=7)

    ax = axes[2, 0]
    tx = ['Aspirin', 'Anti-HTN', 'Statin', 'DVT\nProphyl', 'Dysphagia\nScreen', 'Neuro\nConsult']
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [d['aspirin_given'].mean()*100, d['antihypertensive_given'].mean()*100, d['statin_given'].mean()*100, d['dvt_prophylaxis'].mean()*100, d['dysphagia_screening'].mean()*100, d['neurologist_consulted'].mean()*100]
        ax.bar(np.arange(len(tx))+i*0.20, vals, 0.20, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(tx))+0.20); ax.set_xticklabels(tx, fontsize=5); ax.set_ylabel('%'); ax.set_title('Panel 5: Treatments'); ax.legend(fontsize=6)

    ax = axes[2, 1]
    comp = ['Aspiration\nPneumonia', 'DVT/PE', 'Seizure', 'Raised\nICP']
    for i, s in enumerate(scenarios):
        d = dfs[s]; vals = [d['aspiration_pneumonia'].mean()*100, d['dvt_pe'].mean()*100, d['seizure'].mean()*100, d['raised_icp'].mean()*100]
        ax.bar(np.arange(len(comp))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(comp))+0.25); ax.set_xticklabels(comp, fontsize=6); ax.set_ylabel('%'); ax.set_title('Panel 6: Complications'); ax.legend(fontsize=7)

    ax = axes[3, 0]
    mrs_vals = [0,1,2,3,4,5]
    for i, s in enumerate(scenarios):
        alive = dfs[s][dfs[s]['died_in_hospital']==0]
        if len(alive) > 0:
            vals = [alive['mrs_discharge'].value_counts(normalize=True).get(m,0)*100 for m in mrs_vals]
            ax.bar(np.arange(len(mrs_vals))+i*0.25, vals, 0.25, label=labels.get(s,s), color=colors[s], alpha=0.8)
    ax.set_xticks(np.arange(len(mrs_vals))+0.25); ax.set_xticklabels([f'mRS {m}' for m in mrs_vals], fontsize=6); ax.set_ylabel('%'); ax.set_title('Panel 7: Discharge mRS (survivors)'); ax.legend(fontsize=7)

    ax = axes[3, 1]
    num_cols = ['ct_scan_done','thrombolysis_given','stroke_unit_admitted','nihss_score','aspiration_pneumonia','died_in_hospital']
    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()