#!/usr/bin/env python3
"""Validation & Diagnostic Visualization for Snakebite Envenomation Dataset."""

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

SCENARIOS = ['referral_hospital', 'district_hospital', 'rural_health_centre']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'snakebite_{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('Snakebite Envenomation — Validation Report',
                 fontsize=16, fontweight='bold', y=0.98)
    df = dfs.get('district_hospital', list(dfs.values())[0])
    env = df[df['dry_bite'] == 0]

    # Panel 1: Snake species distribution
    ax = axes[0, 0]
    sp = df['snake_species'].value_counts().head(7)
    ax.barh(range(len(sp)), sp.values, color='#2ecc71', alpha=0.7)
    ax.set_yticks(range(len(sp)))
    ax.set_yticklabels([s.replace('_', ' ').title()[:20] for s in sp.index], fontsize=7)
    ax.set_xlabel('Count')
    ax.set_title('Snake Species Distribution (District)')

    # Panel 2: Envenomation syndrome
    ax = axes[0, 1]
    syn = env['envenomation_syndrome'].value_counts()
    syn_colors = ['#e74c3c', '#f39c12', '#9b59b6', '#3498db', '#e67e22']
    ax.pie(syn.values, labels=syn.index, autopct='%1.1f%%',
           colors=syn_colors[:len(syn)], startangle=90, textprops={'fontsize': 8})
    ax.set_title('Envenomation Syndrome (Envenomated)')

    # Panel 3: Antivenom & mortality across scenarios
    ax = axes[1, 0]
    x = np.arange(len(SCENARIOS))
    width = 0.3
    av = [dfs[sc]['antivenom_given'].mean() * 100 for sc in SCENARIOS if sc in dfs]
    mort = [(dfs[sc]['outcome'] == 'died').mean() * 100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x - width/2, av, width, label='Antivenom %', color='#2ecc71', alpha=0.8)
    ax.bar(x + width/2, mort, width, label='Mortality %', color='#e74c3c', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Referral', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Percentage (%)')
    ax.set_title('Antivenom Access vs Mortality')
    ax.legend(fontsize=8)

    # Panel 4: Time to facility
    ax = axes[1, 1]
    for i, sc in enumerate(SCENARIOS):
        if sc in dfs:
            d = dfs[sc]
            ax.hist(d['time_to_facility_hours'].clip(0, 48), bins=30,
                    alpha=0.5, label=sc.replace('_', ' ').title()[:15],
                    edgecolor='white')
    ax.set_xlabel('Time to Facility (hours)')
    ax.set_title('Time to Facility Distribution')
    ax.legend(fontsize=7)

    # Panel 5: Severity by outcome
    ax = axes[2, 0]
    sevs = ['mild', 'moderate', 'severe']
    sev_colors = ['#f1c40f', '#e67e22', '#e74c3c']
    died_rates = []
    for sev in sevs:
        sub = env[env['severity'] == sev]
        died_rates.append((sub['outcome'] == 'died').mean() * 100 if len(sub) > 0 else 0)
    ax.bar(range(3), died_rates, color=sev_colors)
    ax.set_xticks(range(3))
    ax.set_xticklabels(['Mild', 'Moderate', 'Severe'])
    for i, v in enumerate(died_rates):
        ax.text(i, v + 0.3, f'{v:.1f}%', ha='center', fontsize=10)
    ax.set_ylabel('Mortality (%)')
    ax.set_title('Mortality by Severity (District)')

    # Panel 6: Clinical features prevalence
    ax = axes[2, 1]
    features = ['local_swelling', 'coagulopathy', 'neurotoxicity',
                'local_necrosis', 'acute_kidney_injury', 'compartment_syndrome']
    labels = ['Swelling', 'Coagulopathy', 'Neurotoxicity',
              'Necrosis', 'AKI', 'Compartment\nSyndrome']
    rates = [env[f].mean() * 100 for f in features]
    feat_colors = ['#3498db', '#e74c3c', '#9b59b6', '#e67e22', '#f39c12', '#1abc9c']
    ax.barh(range(len(features)), rates, color=feat_colors, alpha=0.8)
    ax.set_yticks(range(len(features)))
    ax.set_yticklabels(labels, fontsize=8)
    ax.set_xlabel('Prevalence (%)')
    ax.set_title('Clinical Features (Envenomated)')

    # 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=20, alpha=0.6, color='#3498db',
            label=f'Male (n={len(males)})', edgecolor='white')
    ax.hist(females, bins=20, alpha=0.6, color='#e74c3c',
            label=f'Female (n={len(females)})', edgecolor='white')
    ax.set_xlabel('Age (years)')
    ax.set_title('Age-Sex Distribution')
    ax.legend(fontsize=9)

    # Panel 8: Bite location
    ax = axes[3, 1]
    loc = df['bite_location'].value_counts()
    ax.bar(range(len(loc)), loc.values, color='#3498db', alpha=0.7)
    ax.set_xticks(range(len(loc)))
    ax.set_xticklabels([l.replace('_', ' ').title() for l in loc.index],
                       fontsize=8, rotation=30)
    ax.set_ylabel('Count')
    ax.set_title('Bite Location')

    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)