#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Epilepsy Dataset."""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os

SCENARIOS = ['neurology_clinic', 'district_hospital', 'rural_health_centre']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'epilepsy_{sc}.csv')
        if os.path.exists(path):
            dfs[sc] = pd.read_csv(path)
    return dfs


def make_report(dfs, output='validation_report.png'):
    fig, axes = plt.subplots(4, 2, figsize=(16, 22))
    fig.suptitle('Epilepsy & Neurological Disorders — Validation Report',
                 fontsize=16, fontweight='bold', y=0.98)
    colors = ['#2ecc71', '#f39c12', '#e74c3c']
    x = np.arange(len(SCENARIOS))
    labels = ['Neuro Clinic', 'District', 'Rural']

    # Panel 1: Treatment gap by scenario
    ax = axes[0, 0]
    gap = [dfs[sc]['in_treatment_gap'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, gap, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(gap):
        ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Treatment Gap (25% → 55% → 80%)')

    # Panel 2: Seizure type distribution
    ax = axes[0, 1]
    df = dfs.get('district_hospital', list(dfs.values())[0])
    sz = df['seizure_type'].value_counts()
    ax.barh(range(len(sz)), sz.values, color='#3498db', alpha=0.7)
    ax.set_yticks(range(len(sz)))
    ax.set_yticklabels([s.replace('_', ' ').title() for s in sz.index], fontsize=7)
    ax.set_xlabel('Count')
    ax.set_title('Seizure Type Distribution')

    # Panel 3: AED prescribed by scenario
    ax = axes[1, 0]
    aed = [(dfs[sc]['aed_prescribed'] != 'none').mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, aed, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(aed):
        ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Rate (%)')
    ax.set_title('AED Prescribed')

    # Panel 4: Seizure-free 12m by scenario
    ax = axes[1, 1]
    sf = [dfs[sc]['seizure_free_12m'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, sf, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(sf):
        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Seizure-Free at 12 Months')

    # Panel 5: Stigma & psychosocial
    ax = axes[2, 0]
    impacts = ['stigma_experienced', 'conceals_diagnosis', 'depression_comorbid',
               'employment_discrimination', 'marriage_affected']
    i_labels = ['Stigma', 'Conceals Dx', 'Depression', 'Employment\nDiscrim.', 'Marriage\nAffected']
    vals = [df[i].mean()*100 for i in impacts]
    ax.barh(range(5), vals, color='#9b59b6', alpha=0.7)
    ax.set_yticks(range(5))
    ax.set_yticklabels(i_labels, fontsize=8)
    ax.set_xlabel('Prevalence (%)')
    ax.set_title('Stigma & Psychosocial Impact')

    # Panel 6: Traditional healer / faith healing
    ax = axes[2, 1]
    trad = [dfs[sc]['traditional_healer_consulted'].mean()*100 for sc in SCENARIOS if sc in dfs]
    faith = [dfs[sc]['faith_healing_sought'].mean()*100 for sc in SCENARIOS if sc in dfs]
    w = 0.3
    ax.bar(x - w/2, trad, w, label='Traditional Healer', color='#e67e22', alpha=0.8)
    ax.bar(x + w/2, faith, w, label='Faith Healing', color='#9b59b6', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Alternative Care Seeking (~40% / ~30%)')
    ax.legend(fontsize=8)

    # Panel 7: Etiology
    ax = axes[3, 0]
    etio = df['etiology'].value_counts()
    e_colors = ['#e74c3c', '#f39c12', '#3498db', '#9b59b6', '#2ecc71',
                '#e67e22', '#1abc9c', '#95a5a6']
    ax.pie(etio.values,
           labels=[s.replace('_', ' ').title() for s in etio.index],
           autopct='%1.0f%%', colors=e_colors[:len(etio)],
           startangle=90, textprops={'fontsize': 7})
    ax.set_title('Etiology Distribution')

    # Panel 8: AED stock-out by scenario
    ax = axes[3, 1]
    stockout = [dfs[sc]['aed_stock_out_experienced'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, stockout, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(stockout):
        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Rate (%)')
    ax.set_title('AED Stock-Out Experienced')

    plt.tight_layout(rect=[0, 0, 1, 0.97])
    plt.savefig(output, dpi=150, bbox_inches='tight')
    print(f'Saved validation report to {output}')
    plt.close()


if __name__ == '__main__':
    dfs = load_scenarios()
    if dfs:
        make_report(dfs)