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

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

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


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'respiratory_{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('Asthma & COPD — Validation Report',
                 fontsize=16, fontweight='bold', y=0.98)
    df = dfs.get('district_hospital', list(dfs.values())[0])
    colors = ['#2ecc71', '#f39c12', '#e74c3c']

    ax = axes[0, 0]
    x = np.arange(len(SCENARIOS))
    mort = [dfs[sc]['died'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, mort, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Urban Clinic', 'District', 'Rural'], fontsize=9)
    for i, v in enumerate(mort):
        ax.text(i, v + 0.05, f'{v:.2f}%', ha='center', fontsize=10)
    ax.set_ylabel('Mortality (%)')
    ax.set_title('Mortality by Scenario')

    ax = axes[0, 1]
    diag = df['diagnosis'].value_counts()
    d_colors = ['#3498db', '#e74c3c', '#f39c12', '#9b59b6']
    ax.pie(diag.values,
           labels=[s.replace('_', ' ').title() for s in diag.index],
           autopct='%1.0f%%', colors=d_colors[:len(diag)],
           startangle=90, textprops={'fontsize': 9})
    ax.set_title('Diagnosis Distribution (asthma ~50%)')

    ax = axes[1, 0]
    risks = ['biomass_fuel_cooking', 'tobacco_smoking', 'occupational_dust',
             'outdoor_air_pollution', 'tb_history', 'family_history_asthma']
    r_labels = ['Biomass', 'Tobacco', 'Dust', 'Outdoor AP', 'TB Hx', 'Family Hx']
    vals = [df[r].mean()*100 for r in risks]
    ax.barh(range(6), vals, color='#3498db', alpha=0.7)
    ax.set_yticks(range(6))
    ax.set_yticklabels(r_labels, fontsize=9)
    for i, v in enumerate(vals):
        ax.text(v + 0.5, i, f'{v:.0f}%', va='center', fontsize=9)
    ax.set_xlabel('Prevalence (%)')
    ax.set_title('Risk Factors (biomass = #1)')

    ax = axes[1, 1]
    inhaler = [dfs[sc]['inhaler_prescribed'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, inhaler, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Urban Clinic', 'District', 'Rural'], fontsize=9)
    for i, v in enumerate(inhaler):
        ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Inhaler Access (0% rural)')

    ax = axes[2, 0]
    sev = df['severity'].value_counts()
    s_order = ['mild', 'moderate', 'severe']
    vals = [sev.get(s, 0) for s in s_order]
    ax.bar(range(3), vals, color=['#2ecc71', '#f39c12', '#e74c3c'], alpha=0.8)
    ax.set_xticks(range(3))
    ax.set_xticklabels(['Mild', 'Moderate', 'Severe'], fontsize=9)
    ax.set_ylabel('Count')
    ax.set_title('Severity Distribution')

    ax = axes[2, 1]
    spir = [dfs[sc]['spirometry_done'].mean()*100 for sc in SCENARIOS if sc in dfs]
    clin = [dfs[sc]['clinical_diagnosis_only'].mean()*100 for sc in SCENARIOS if sc in dfs]
    w = 0.3
    ax.bar(x - w/2, spir, w, label='Spirometry', color='#3498db', alpha=0.8)
    ax.bar(x + w/2, clin, w, label='Clinical Only', color='#e74c3c', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Urban Clinic', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Diagnostic Approach (spirometry rare)')
    ax.legend(fontsize=8)

    ax = axes[3, 0]
    users = df[df['inhaler_prescribed'] == 1]
    if len(users) > 0:
        tech = users['inhaler_technique_correct'].mean() * 100
        ax.bar(['Correct', 'Incorrect'], [tech, 100 - tech],
               color=['#2ecc71', '#e74c3c'], alpha=0.8)
        ax.text(0, tech + 1, f'{tech:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Inhaler Technique (~35% correct)')

    ax = axes[3, 1]
    exac = [dfs[sc]['exacerbations_past_year'].mean() for sc in SCENARIOS if sc in dfs]
    hosp = [dfs[sc]['hospitalised_exacerbation'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax2 = ax.twinx()
    ax.bar(x - 0.15, exac, 0.3, color='#f39c12', alpha=0.8, label='Mean Exacerbations')
    ax2.bar(x + 0.15, hosp, 0.3, color='#e74c3c', alpha=0.8, label='Hospitalised (%)')
    ax.set_xticks(x)
    ax.set_xticklabels(['Urban Clinic', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Mean Exacerbations/yr')
    ax2.set_ylabel('Hospitalised (%)')
    ax.set_title('Exacerbations & Hospitalisations')

    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)