#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Supply Chain Integrity & Track-and-Trace Dataset."""

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

SCENARIOS = ['serialized_track_trace', 'partial_visibility', 'opaque_uncontrolled']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'sct_{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(
        'Supply Chain Integrity & Track-and-Trace — Validation Report\n'
        '(Serialized → Partial Visibility → Opaque)',
        fontsize=15, fontweight='bold', y=0.99)
    colors = ['#2ecc71', '#f39c12', '#e74c3c']
    x = np.arange(len(SCENARIOS))
    labels = ['Serialized', 'Partial', 'Opaque']

    ax = axes[0, 0]
    sf = [dfs[sc]['sf_product_detected'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, sf, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(sf):
        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('SF Rate (%)'); ax.set_title('SF Product Detection Rate')

    ax = axes[0, 1]
    ser = [dfs[sc]['serialized'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, ser, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(ser):
        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)'); ax.set_title('Serialization Coverage')

    ax = axes[1, 0]
    div = [dfs[sc]['diversion_detected'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, div, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(div):
        ax.text(i, v+0.3, f'{v:.1f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)'); ax.set_title('Diversion Detected')

    ax = axes[1, 1]
    interm = [dfs[sc]['intermediary_count'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, interm, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(interm):
        ax.text(i, v+0.1, f'{v:.1f}', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Count'); ax.set_title('Average Intermediaries')

    ax = axes[2, 0]
    df = dfs.get('partial_visibility', list(dfs.values())[1])
    ep = df[df['sf_product_detected']==1]['sf_entry_point'].value_counts()
    if len(ep) > 0:
        ax.barh(range(len(ep)), ep.values, color='#e74c3c', alpha=0.7)
        ax.set_yticks(range(len(ep)))
        ax.set_yticklabels([s.replace('_', ' ').title() for s in ep.index], fontsize=7)
        ax.set_xlabel('Count')
    ax.set_title('SF Entry Points (Partial Visibility)')

    ax = axes[2, 1]
    temp = [dfs[sc]['temperature_monitored'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, temp, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(temp):
        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)'); ax.set_title('Temperature Monitoring')

    ax = axes[3, 0]
    transit = [dfs[sc]['transit_days'].median() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, transit, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(transit):
        ax.text(i, v+0.5, f'{v:.0f}d', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Days'); ax.set_title('Median Transit Days')

    ax = axes[3, 1]
    recall = [dfs[sc]['recall_initiated'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, recall, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(recall):
        ax.text(i, v+0.2, f'{v:.1f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)'); ax.set_title('Recall Initiated')

    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)