#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for HIV Test Kit & ARV Supply Dataset."""

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

SCENARIOS = ['pepfar_supported_urban', 'district_hospital_art', 'rural_health_centre_art']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'hiv_supply_{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, 24))
    fig.suptitle(
        'HIV Test Kit & ARV Supply — Validation Report\n'
        '(PEPFAR Urban → District Hospital → Rural HC)',
        fontsize=15, fontweight='bold', y=0.99)
    colors = ['#2ecc71', '#f39c12', '#e74c3c']
    x = np.arange(len(SCENARIOS))
    labels = ['PEPFAR Urban', 'District Hosp', 'Rural HC']

    # Panel 1: Availability on survey day
    ax = axes[0, 0]
    avail = [dfs[sc]['available_on_survey_day'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, avail, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(avail):
        ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Availability (%)')
    ax.set_title('HIV Commodity Availability on Survey Day')
    ax.set_ylim(0, 100)

    # Panel 2: Availability by commodity category (district)
    ax = axes[0, 1]
    df = dfs.get('district_hospital_art', list(dfs.values())[0])
    cat_avail = df.groupby('commodity_category')['available_on_survey_day'].mean().sort_values()
    ax.barh(range(len(cat_avail)), cat_avail.values * 100, color='#3498db', alpha=0.7)
    ax.set_yticks(range(len(cat_avail)))
    ax.set_yticklabels([s.replace('_', ' ').title() for s in cat_avail.index], fontsize=7)
    ax.set_xlabel('Availability (%)')
    ax.set_title('Availability by Commodity Category (District)')

    # Panel 3: Stockout in last 6 months
    ax = axes[1, 0]
    so = [dfs[sc]['stocked_out_in_last_6m'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, so, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(so):
        ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)')
    ax.set_title('Stocked Out ≥1x in Last 6 Months')

    # Panel 4: Mean stockout duration
    ax = axes[1, 1]
    dur = []
    for sc in SCENARIOS:
        if sc in dfs:
            subset = dfs[sc][dfs[sc]['stocked_out_in_last_6m'] == 1]
            dur.append(subset['stockout_days_last_6m'].mean() if len(subset) > 0 else 0)
    ax.bar(x, dur, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(dur):
        ax.text(i, v + 0.5, f'{v:.0f}d', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Days')
    ax.set_title('Mean Stockout Duration (among stocked-out)')

    # Panel 5: ART interruption & testing not done
    ax = axes[2, 0]
    art_int = [dfs[sc]['art_interruption_due_to_stockout'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, art_int, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(art_int):
        ax.text(i, v + 0.3, f'{v:.1f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)')
    ax.set_title('ART Interruption Due to Stockout')

    # Panel 6: Stockout causes (district)
    ax = axes[2, 1]
    so_df = df[df['stocked_out_in_last_6m'] == 1]
    if len(so_df) > 0:
        causes = so_df['stockout_cause'].value_counts().head(8)
        ax.barh(range(len(causes)), causes.values, color='#e74c3c', alpha=0.7)
        ax.set_yticks(range(len(causes)))
        ax.set_yticklabels([s.replace('_', ' ').title() for s in causes.index], fontsize=7)
        ax.set_xlabel('Count')
    ax.set_title('Top Stockout Causes (District)')

    # Panel 7: Order fill rate
    ax = axes[3, 0]
    ofr = [dfs[sc]['order_fill_rate_pct'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, ofr, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(ofr):
        ax.text(i, v + 1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Fill Rate (%)')
    ax.set_title('Order Fill Rate')

    # Panel 8: Months of stock on hand
    ax = axes[3, 1]
    mos = [dfs[sc]['months_of_stock_on_hand'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, mos, color=colors, alpha=0.8)
    ax.axhline(y=2, color='blue', linestyle='--', alpha=0.5, label='Min 2 months')
    ax.set_xticks(x)
    ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(mos):
        ax.text(i, v + 0.05, f'{v:.1f}m', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Months')
    ax.set_title('Months of Stock on Hand')
    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)