#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for HIV/ART Treatment Cascade Dataset."""

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

SCENARIOS = ['high_performing', 'moderate_performing', 'low_performing']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'hiv_art_{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('HIV/ART Treatment Cascade — Validation Report',
                 fontsize=16, fontweight='bold', y=0.98)
    df = dfs.get('moderate_performing', list(dfs.values())[0])

    # Panel 1: Cascade stages across scenarios
    ax = axes[0, 0]
    stages = ['undiagnosed', 'diagnosed_not_on_ART', 'on_ART']
    colors = ['#e74c3c', '#f39c12', '#2ecc71']
    x = np.arange(len(SCENARIOS))
    width = 0.25
    for j, stage in enumerate(stages):
        vals = []
        for sc in SCENARIOS:
            if sc in dfs:
                vals.append((dfs[sc]['cascade_stage'] == stage).mean() * 100)
        ax.bar(x + j * width, vals, width, label=stage.replace('_', ' '),
               color=colors[j], alpha=0.8)
    ax.set_xticks(x + width)
    ax.set_xticklabels(['High', 'Moderate', 'Low'], fontsize=9)
    ax.set_ylabel('Percentage (%)')
    ax.set_title('Treatment Cascade Stages Across Scenarios')
    ax.legend(fontsize=7)

    # Panel 2: Viral load distribution (on ART)
    ax = axes[0, 1]
    on_art = df[df['cascade_stage'] == 'on_ART']
    vl_log = np.log10(on_art['viral_load_copies_ml'].clip(1))
    ax.hist(vl_log, bins=50, color='#3498db', alpha=0.7, edgecolor='white')
    ax.axvline(3, color='red', ls='--', lw=1.5, label='1000 copies/mL (WHO threshold)')
    ax.set_xlabel('log₁₀ Viral Load (copies/mL)')
    ax.set_ylabel('Count')
    ax.set_title('Viral Load Distribution — On ART')
    ax.legend(fontsize=8)

    # Panel 3: CD4 current by cascade stage
    ax = axes[1, 0]
    data_cd4 = []
    labels_cd4 = []
    for stage in stages:
        sub = df[df['cascade_stage'] == stage]['cd4_current']
        if len(sub) > 0:
            data_cd4.append(sub.values)
            labels_cd4.append(stage.replace('_', '\n'))
    bp = ax.boxplot(data_cd4, tick_labels=labels_cd4, patch_artist=True)
    for patch, c in zip(bp['boxes'], colors):
        patch.set_facecolor(c)
        patch.set_alpha(0.6)
    ax.set_ylabel('CD4 (cells/µL)')
    ax.set_title('Current CD4 by Cascade Stage')

    # Panel 4: ART regimen distribution
    ax = axes[1, 1]
    reg_counts = on_art['art_regimen'].value_counts()
    reg_colors = ['#2ecc71', '#3498db', '#f39c12', '#e74c3c']
    ax.barh(range(len(reg_counts)), reg_counts.values, color=reg_colors[:len(reg_counts)])
    ax.set_yticks(range(len(reg_counts)))
    ax.set_yticklabels(reg_counts.index, fontsize=8)
    ax.set_xlabel('Count')
    ax.set_title('ART Regimen Distribution')

    # Panel 5: VL suppression by adherence
    ax = axes[2, 0]
    adh_cats = ['good', 'moderate', 'poor']
    adh_colors = ['#2ecc71', '#f39c12', '#e74c3c']
    supp_rates = []
    for cat in adh_cats:
        sub = on_art[on_art['adherence_category'] == cat]
        if len(sub) > 0:
            supp_rates.append(sub['vl_suppressed'].mean() * 100)
        else:
            supp_rates.append(0)
    ax.bar(range(3), supp_rates, color=adh_colors)
    ax.set_xticks(range(3))
    ax.set_xticklabels(['Good', 'Moderate', 'Poor'])
    for i, v in enumerate(supp_rates):
        ax.text(i, v + 1, f'{v:.1f}%', ha='center', fontsize=10)
    ax.set_ylabel('VL Suppression (%)')
    ax.set_title('Viral Suppression by Adherence Category')

    # Panel 6: Cross-scenario key metrics
    ax = axes[2, 1]
    metrics = ['Deaths %', 'LTFU %', 'Pop VL Supp %']
    x = np.arange(len(metrics))
    width = 0.25
    sc_colors = ['#2ecc71', '#f39c12', '#e74c3c']
    for i, sc in enumerate(SCENARIOS):
        if sc in dfs:
            d = dfs[sc]
            vals = [
                (d['outcome'] == 'died').mean() * 100,
                (d['outcome'] == 'ltfu').mean() * 100,
                d['vl_suppressed'].mean() * 100,
            ]
            ax.bar(x + i * width, vals, width, label=sc.replace('_', ' ').title(),
                   color=sc_colors[i], alpha=0.8)
    ax.set_xticks(x + width)
    ax.set_xticklabels(metrics, fontsize=9)
    ax.set_ylabel('Percentage (%)')
    ax.set_title('Key Metrics Across Scenarios')
    ax.legend(fontsize=7)

    # Panel 7: Age-sex distribution
    ax = axes[3, 0]
    males = df[df['sex'] == 'M']['age_years']
    females = df[df['sex'] == 'F']['age_years']
    ax.hist(males, bins=25, alpha=0.6, color='#3498db', label=f'Male (n={len(males)})',
            edgecolor='white')
    ax.hist(females, bins=25, alpha=0.6, color='#e74c3c', label=f'Female (n={len(females)})',
            edgecolor='white')
    ax.set_xlabel('Age (years)')
    ax.set_title('Age Distribution by Sex')
    ax.legend(fontsize=9)

    # Panel 8: WHO clinical stage distribution
    ax = axes[3, 1]
    who_counts = df['who_clinical_stage'].value_counts().sort_index()
    who_colors = ['#2ecc71', '#f39c12', '#e67e22', '#e74c3c']
    ax.bar(who_counts.index, who_counts.values, color=who_colors[:len(who_counts)])
    ax.set_xlabel('WHO Clinical Stage')
    ax.set_ylabel('Count')
    ax.set_title('WHO Clinical Stage Distribution')

    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)