#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for DS-TB Treatment Cascade Dataset."""

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

SCENARIOS = ['urban_dots_centre', 'district_hospital', 'rural_health_post']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'tb_{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('Drug-Sensitive TB Treatment Cascade — 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))
    success = [dfs[sc]['treatment_success'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, success, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Urban DOTS', 'District', 'Rural'], fontsize=9)
    for i, v in enumerate(success):
        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Treatment Success (%)')
    ax.set_title('Treatment Success (WHO target ≥85%)')

    ax = axes[0, 1]
    outcomes = df['treatment_outcome'].value_counts()
    o_colors = ['#2ecc71', '#3498db', '#e74c3c', '#f39c12', '#9b59b6', '#95a5a6']
    ax.pie(outcomes.values,
           labels=[s.replace('_', ' ').title() for s in outcomes.index],
           autopct='%1.0f%%', colors=o_colors[:len(outcomes)],
           startangle=90, textprops={'fontsize': 8})
    ax.set_title('Treatment Outcomes')

    ax = axes[1, 0]
    cascade = ['Tx Started', 'Intensive', 'Continuation', 'Success']
    for i, sc_name in enumerate(SCENARIOS):
        if sc_name in dfs:
            d = dfs[sc_name]
            vals = [d['treatment_started'].mean()*100,
                    d['intensive_phase_completed'].mean()*100,
                    d['continuation_phase_completed'].mean()*100,
                    d['treatment_success'].mean()*100]
            ax.plot(range(4), vals, 'o-', label=sc_name.replace('_', ' ').title()[:12],
                    color=colors[i], linewidth=2, markersize=6)
    ax.set_xticks(range(4))
    ax.set_xticklabels(cascade, fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Treatment Cascade (drop-off at each stage)')
    ax.legend(fontsize=7)

    ax = axes[1, 1]
    hiv_pos = df[df['hiv_positive'] == 1]
    hiv_neg = df[df['hiv_positive'] == 0]
    cats = ['Tx Success', 'Died', 'LTFU']
    if len(hiv_pos) > 0 and len(hiv_neg) > 0:
        vp = [hiv_pos['treatment_success'].mean()*100,
              hiv_pos['died_during_treatment'].mean()*100,
              (hiv_pos['treatment_outcome'] == 'lost_to_followup').mean()*100]
        vn = [hiv_neg['treatment_success'].mean()*100,
              hiv_neg['died_during_treatment'].mean()*100,
              (hiv_neg['treatment_outcome'] == 'lost_to_followup').mean()*100]
        w = 0.3
        ax.bar(np.arange(3) - w/2, vp, w, label='HIV+', color='#e74c3c', alpha=0.8)
        ax.bar(np.arange(3) + w/2, vn, w, label='HIV-', color='#2ecc71', alpha=0.8)
        ax.set_xticks(np.arange(3))
        ax.set_xticklabels(cats, fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('TB-HIV Coinfection Impact (~35%)')
    ax.legend(fontsize=8)

    ax = axes[2, 0]
    diag = df['diagnostic_method'].value_counts()
    ax.bar(range(len(diag)), diag.values,
           color=['#3498db', '#f39c12', '#e74c3c'][:len(diag)], alpha=0.8)
    ax.set_xticks(range(len(diag)))
    ax.set_xticklabels([s.replace('_', ' ').title() for s in diag.index], fontsize=8)
    ax.set_ylabel('Count')
    ax.set_title('Diagnostic Method (GeneXpert rollout)')

    ax = axes[2, 1]
    ax.hist(df['diagnosis_delay_days'], bins=30, color='#f39c12',
            alpha=0.7, edgecolor='white')
    ax.axvline(30, color='red', linestyle='--', linewidth=2, label='30 days')
    ax.set_xlabel('Delay (days)')
    ax.set_title('Diagnostic Delay')
    ax.legend(fontsize=8)

    ax = axes[3, 0]
    ltfu = [dfs[sc][dfs[sc]['treatment_outcome'] == 'lost_to_followup'].shape[0] /
            max(1, dfs[sc]['treatment_started'].sum()) * 100
            for sc in SCENARIOS if sc in dfs]
    died = [dfs[sc]['died_during_treatment'].mean()*100 for sc in SCENARIOS if sc in dfs]
    w = 0.3
    ax.bar(x - w/2, ltfu, w, label='LTFU', color='#f39c12', alpha=0.8)
    ax.bar(x + w/2, died, w, label='Died', color='#e74c3c', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Urban DOTS', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('LTFU & Mortality')
    ax.legend(fontsize=8)

    ax = axes[3, 1]
    dot_y = df[df['dot_received'] == 1]
    dot_n = df[df['dot_received'] == 0]
    if len(dot_y) > 0 and len(dot_n) > 0:
        ax.hist(dot_y['adherence_rate'], bins=20, alpha=0.6, label='DOT', color='#2ecc71')
        ax.hist(dot_n['adherence_rate'], bins=20, alpha=0.6, label='No DOT', color='#e74c3c')
    ax.set_xlabel('Adherence Rate')
    ax.set_title('Adherence: DOT vs Self-Administered')
    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)