#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Preterm/KMC Dataset."""

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

SCENARIOS = ['nicu', 'special_care_nursery', 'postnatal_ward']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'preterm_{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('Preterm Birth & Kangaroo Mother Care — Validation Report',
                 fontsize=16, fontweight='bold', y=0.98)
    df = dfs.get('special_care_nursery', list(dfs.values())[0])
    colors = ['#2ecc71', '#f39c12', '#e74c3c']

    ax = axes[0, 0]
    x = np.arange(len(SCENARIOS))
    mort = [dfs[sc]['neonatal_death'].mean() * 100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, mort, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['NICU', 'SCN', 'Postnatal'], fontsize=9)
    for i, v in enumerate(mort):
        ax.text(i, v + 0.5, f'{v:.1f}%', ha='center', fontsize=10)
    ax.set_ylabel('Neonatal Mortality (%)')
    ax.set_title('Neonatal Mortality (preterm = leading U5 cause)')

    ax = axes[0, 1]
    kmc = df[df['kmc_initiated'] == 1]
    no_kmc = df[df['kmc_initiated'] == 0]
    if len(kmc) > 0 and len(no_kmc) > 0:
        m_k = kmc['neonatal_death'].mean() * 100
        m_n = no_kmc['neonatal_death'].mean() * 100
        ax.bar(['KMC', 'No KMC'], [m_k, m_n], color=['#2ecc71', '#e74c3c'], alpha=0.8)
        for i, v in enumerate([m_k, m_n]):
            ax.text(i, v + 0.5, f'{v:.1f}%', ha='center', fontsize=10)
    ax.set_ylabel('Mortality (%)')
    ax.set_title('KMC Effect on Mortality (reduces 25-40%)')

    ax = axes[1, 0]
    ga_bins = [24, 28, 32, 34, 37]
    ga_labels = ['24-27', '28-31', '32-33', '34-36']
    df_copy = df.copy()
    df_copy['ga_bin'] = pd.cut(df_copy['gestational_age_weeks'], bins=ga_bins, labels=ga_labels, right=False)
    ga_mort = df_copy.groupby('ga_bin', observed=True)['neonatal_death'].mean() * 100
    ax.bar(range(len(ga_mort)), ga_mort.values,
           color=['#e74c3c', '#f39c12', '#3498db', '#2ecc71'], alpha=0.8)
    ax.set_xticks(range(len(ga_mort)))
    ax.set_xticklabels(ga_mort.index, fontsize=9)
    for i, v in enumerate(ga_mort.values):
        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=9)
    ax.set_ylabel('Mortality (%)')
    ax.set_title('Mortality by Gestational Age')

    ax = axes[1, 1]
    cascade = ['KMC', 'KMC <24h', 'EBF', 'CPAP', 'Surfactant', 'Steroids']
    for i, sc_name in enumerate(SCENARIOS):
        if sc_name in dfs:
            d = dfs[sc_name]
            vals = [d['kmc_initiated'].mean()*100, d['kmc_within_24h'].mean()*100,
                    d['exclusive_breastfeeding'].mean()*100, d['cpap_given'].mean()*100,
                    d['surfactant_given'].mean()*100, d['antenatal_steroids'].mean()*100]
            ax.plot(range(6), vals, 'o-', label=sc_name.replace('_', ' ').title()[:10],
                    color=colors[i], linewidth=2, markersize=6)
    ax.set_xticks(range(6))
    ax.set_xticklabels(cascade, fontsize=7, rotation=20)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Intervention Cascade')
    ax.legend(fontsize=7)

    ax = axes[2, 0]
    complications = ['rds', 'neonatal_sepsis', 'jaundice', 'hypothermia', 'apnoea', 'nec', 'ivh']
    c_labels = ['RDS', 'Sepsis', 'Jaundice', 'Hypothermia', 'Apnoea', 'NEC', 'IVH']
    vals = [df[c].mean() * 100 for c in complications]
    ax.barh(range(7), vals, color='#e74c3c', alpha=0.7)
    ax.set_yticks(range(7))
    ax.set_yticklabels(c_labels, fontsize=8)
    for i, v in enumerate(vals):
        ax.text(v + 0.3, i, f'{v:.1f}%', va='center', fontsize=8)
    ax.set_xlabel('Rate (%)')
    ax.set_title('Complications')

    ax = axes[2, 1]
    ax.hist(df['birth_weight_g'], bins=30, color='#3498db', alpha=0.7, edgecolor='white')
    ax.axvline(1500, color='red', linestyle='--', linewidth=2, label='VLBW <1500g')
    ax.axvline(1000, color='darkred', linestyle='--', linewidth=2, label='ELBW <1000g')
    ax.set_xlabel('Birth Weight (g)')
    ax.set_title('Birth Weight Distribution')
    ax.legend(fontsize=8)

    ax = axes[3, 0]
    kmc_hrs = df[df['kmc_initiated'] == 1]
    if len(kmc_hrs) > 0:
        ax.hist(kmc_hrs['kmc_hours_per_day'], bins=15, color='#2ecc71', alpha=0.7, edgecolor='white')
        ax.axvline(8, color='red', linestyle='--', linewidth=2, label='WHO: 8+ hrs/day')
    ax.set_xlabel('KMC Hours per Day')
    ax.set_title('KMC Duration (WHO: 8+ hrs/day)')
    ax.legend(fontsize=8)

    ax = axes[3, 1]
    steroids = df[df['antenatal_steroids'] == 1]
    no_steroids = df[df['antenatal_steroids'] == 0]
    cats = ['RDS', 'Mortality']
    if len(steroids) > 0 and len(no_steroids) > 0:
        vs = [steroids['rds'].mean()*100, steroids['neonatal_death'].mean()*100]
        vn = [no_steroids['rds'].mean()*100, no_steroids['neonatal_death'].mean()*100]
        w = 0.3
        ax.bar(np.arange(2) - w/2, vn, w, label='No Steroids', color='#e74c3c', alpha=0.8)
        ax.bar(np.arange(2) + w/2, vs, w, label='Steroids', color='#2ecc71', alpha=0.8)
        ax.set_xticks(np.arange(2))
        ax.set_xticklabels(cats, fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Antenatal Steroids Effect')
    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)