#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Hearing Loss/Ear Disease Dataset."""

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

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


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'ear_{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('Hearing Loss & Ear Disease — 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]
    conds = df['ear_condition'].value_counts()
    c_colors = ['#e74c3c', '#f39c12', '#3498db', '#9b59b6', '#2ecc71',
                '#e67e22', '#1abc9c', '#95a5a6', '#c0392b']
    ax.barh(range(len(conds)), conds.values, color=c_colors[:len(conds)], alpha=0.8)
    ax.set_yticks(range(len(conds)))
    ax.set_yticklabels([s.replace('_', ' ').title() for s in conds.index], fontsize=7)
    ax.set_xlabel('Count')
    ax.set_title('Ear Conditions (CSOM #1)')

    ax = axes[0, 1]
    x = np.arange(len(SCENARIOS))
    audio = [dfs[sc]['audiometry_done'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ha = [dfs[sc]['hearing_aid_fitted'].mean()*100 for sc in SCENARIOS if sc in dfs]
    w = 0.3
    ax.bar(x - w/2, audio, w, label='Audiometry', color='#3498db', alpha=0.8)
    ax.bar(x + w/2, ha, w, label='Hearing Aid', color='#2ecc71', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['ENT Centre', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Audiometry & Hearing Aid Access')
    ax.legend(fontsize=8)

    ax = axes[1, 0]
    sev = df['hearing_loss_severity'].value_counts()
    s_order = ['normal', 'mild', 'moderate', 'severe', 'profound']
    vals = [sev.get(s, 0) for s in s_order]
    ax.bar(range(5), vals, color=['#2ecc71', '#f1c40f', '#f39c12', '#e74c3c', '#8e44ad'], alpha=0.8)
    ax.set_xticks(range(5))
    ax.set_xticklabels(s_order, fontsize=9)
    ax.set_ylabel('Count')
    ax.set_title('Hearing Loss Severity')

    ax = axes[1, 1]
    hl_type = df[df['hearing_loss_type'] != 'none']['hearing_loss_type'].value_counts()
    if len(hl_type) > 0:
        ax.pie(hl_type.values,
               labels=[s.title() for s in hl_type.index],
               autopct='%1.0f%%', colors=['#3498db', '#e74c3c', '#f39c12'],
               startangle=90, textprops={'fontsize': 10})
    ax.set_title('Hearing Loss Type')

    ax = axes[2, 0]
    children = df[df['child'] == 1]
    if len(children) > 0:
        impacts = ['speech_development_affected', 'school_performance_affected']
        i_labels = ['Speech Affected', 'School Affected']
        vals = [children[i].mean()*100 for i in impacts]
        ax.bar(range(2), vals, color=['#e74c3c', '#f39c12'], alpha=0.8)
        ax.set_xticks(range(2))
        ax.set_xticklabels(i_labels, fontsize=9)
        for i, v in enumerate(vals):
            ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Impact on Children')

    ax = axes[2, 1]
    risks = ['recurrent_ari', 'malnutrition', 'overcrowding',
             'noise_exposure', 'traditional_ear_drops']
    r_labels = ['Recurrent ARI', 'Malnutrition', 'Overcrowding',
                'Noise', 'Trad. Drops']
    vals = [df[r].mean()*100 for r in risks]
    ax.barh(range(5), vals, color='#3498db', alpha=0.7)
    ax.set_yticks(range(5))
    ax.set_yticklabels(r_labels, fontsize=9)
    ax.set_xlabel('Prevalence (%)')
    ax.set_title('Risk Factors')

    ax = axes[3, 0]
    tx = ['antibiotics_given', 'ear_drops_given', 'ear_syringing',
          'surgery_performed', 'hearing_aid_fitted']
    t_labels = ['Antibiotics', 'Ear Drops', 'Syringing', 'Surgery', 'Hearing Aid']
    for i, sc_name in enumerate(SCENARIOS):
        if sc_name in dfs:
            d = dfs[sc_name]
            vals = [d[t].mean()*100 for t in tx]
            ax.plot(range(5), vals, 'o-', label=sc_name.replace('_', ' ').title()[:10],
                    color=colors[i], linewidth=2, markersize=5)
    ax.set_xticks(range(5))
    ax.set_xticklabels(t_labels, fontsize=7, rotation=15)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Treatment Cascade by Scenario')
    ax.legend(fontsize=7)

    ax = axes[3, 1]
    ref = [dfs[sc]['referred_ent'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ref_c = [dfs[sc]['referral_completed'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x - w/2, ref, w, label='Referred ENT', color='#f39c12', alpha=0.8)
    ax.bar(x + w/2, ref_c, w, label='Referral Done', color='#2ecc71', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['ENT Centre', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('ENT Referral Gap (~20% completion)')
    ax.legend(fontsize=8)

    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)