#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Blood Bank Supply Management Dataset."""

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

SCENARIOS = ['national_blood_centre', 'district_hospital_bb', 'rural_hospital_bb']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'blood_{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(
        'Blood Bank Supply Management — Validation Report\n'
        '(National Centre → District Hospital → Rural Hospital)',
        fontsize=15, fontweight='bold', y=0.99)
    colors = ['#2ecc71', '#f39c12', '#e74c3c']
    x = np.arange(len(SCENARIOS))
    labels = ['National Centre', 'District Hosp', 'Rural Hosp']

    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('Blood Product Availability'); ax.set_ylim(0,100)

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

    ax = axes[1, 0]
    tti = [dfs[sc]['tti_screened_pct'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, tti, color=colors, alpha=0.8)
    ax.axhline(y=100, color='blue', linestyle='--', alpha=0.5, label='WHO Target')
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(tti):
        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Screening (%)'); ax.set_title('TTI Screening Completeness')
    ax.legend(fontsize=8)

    ax = axes[1, 1]
    df = dfs.get('district_hospital_bb', list(dfs.values())[0])
    so_df = df[df['shortage_in_last_month']==1]
    if len(so_df)>0:
        causes = so_df['shortage_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 Shortage Causes (District)')

    ax = axes[2, 0]
    w = 0.3
    coll = [dfs[sc]['units_collected_month'].mean() for sc in SCENARIOS if sc in dfs]
    req = [dfs[sc]['units_requested_month'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x-w/2, req, w, label='Requested', color='#e74c3c', alpha=0.8)
    ax.bar(x+w/2, coll, w, label='Collected', color='#2ecc71', alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    ax.set_ylabel('Mean Units/Month'); ax.set_title('Blood Collection vs Demand')
    ax.legend(fontsize=8)

    ax = axes[2, 1]
    maternal = [dfs[sc]['maternal_deaths_no_blood'].mean() for sc in SCENARIOS if sc in dfs]
    ax.bar(x, maternal, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(maternal):
        ax.text(i, v+0.01, f'{v:.2f}', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Mean Deaths'); ax.set_title('Maternal Deaths Due to Blood Shortage')

    ax = axes[3, 0]
    cc = [dfs[sc]['cold_chain_maintained'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, cc, color=colors, alpha=0.8)
    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
    for i, v in enumerate(cc):
        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
    ax.set_ylabel('Rate (%)'); ax.set_title('Blood Cold Chain Maintained')

    ax = axes[3, 1]
    bg = df.groupby('blood_group')['available_on_survey_day'].mean().sort_values()
    ax.barh(range(len(bg)), bg.values*100, color='#9b59b6', alpha=0.7)
    ax.set_yticks(range(len(bg)))
    ax.set_yticklabels(bg.index, fontsize=8)
    ax.set_xlabel('Availability (%)'); ax.set_title('Availability by Blood Group (District)')

    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)