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

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

SCENARIOS = ['tertiary_hospital', 'district_hospital', 'rural_facility']


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'stroke_{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('Stroke & Cerebrovascular Disease — Validation Report',
                 fontsize=16, fontweight='bold', y=0.98)
    df = dfs.get('district_hospital', list(dfs.values())[0])
    colors = ['#2ecc71', '#f39c12', '#e74c3c']

    ax = axes[0, 0]
    x = np.arange(len(SCENARIOS))
    mort = [(dfs[sc]['outcome_30_day'] == 'died').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(['Tertiary', 'District', 'Rural'], fontsize=9)
    for i, v in enumerate(mort):
        ax.text(i, v + 0.5, f'{v:.1f}%', ha='center', fontsize=10)
    ax.set_ylabel('30-Day Mortality (%)')
    ax.set_title('30-Day Mortality (SSA pooled ~30%)')

    ax = axes[0, 1]
    types = ['ischaemic', 'intracerebral_haemorrhage', 'subarachnoid_haemorrhage']
    t_labels = ['Ischaemic', 'ICH', 'SAH']
    vals = [(df['stroke_type'] == t).mean() * 100 for t in types]
    t_colors = ['#3498db', '#e74c3c', '#f39c12']
    ax.pie(vals, labels=t_labels, autopct='%1.0f%%', colors=t_colors,
           startangle=90, textprops={'fontsize': 10})
    ax.set_title('Stroke Type (60% ischaemic, 32% ICH)')

    ax = axes[1, 0]
    risks = ['hypertension', 'diabetes', 'atrial_fibrillation', 'smoking',
             'alcohol_heavy', 'prior_stroke', 'hiv_positive']
    r_labels = ['HTN', 'DM', 'AF', 'Smoking', 'Alcohol', 'Prior Stroke', 'HIV']
    vals = [df[r].mean() * 100 for r in risks]
    ax.barh(range(7), vals, color='#e74c3c', alpha=0.7)
    ax.set_yticks(range(7))
    ax.set_yticklabels(r_labels, fontsize=8)
    for i, v in enumerate(vals):
        ax.text(v + 0.3, i, f'{v:.0f}%', va='center', fontsize=8)
    ax.set_xlabel('Prevalence (%)')
    ax.set_title('Risk Factors (HTN #1)')

    ax = axes[1, 1]
    for t, col, lab in [('ischaemic', '#3498db', 'Ischaemic'),
                         ('intracerebral_haemorrhage', '#e74c3c', 'ICH')]:
        sub = df[df['stroke_type'] == t]
        m = (sub['outcome_30_day'] == 'died').mean() * 100 if len(sub) > 0 else 0
        ax.bar(lab, m, color=col, alpha=0.8)
        ax.text(lab, m + 0.5, f'{m:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('30-Day Mortality (%)')
    ax.set_title('Mortality by Stroke Type (ICH > Ischaemic)')

    ax = axes[2, 0]
    for sc_name, col in zip(SCENARIOS, colors):
        if sc_name in dfs:
            d = dfs[sc_name]
            ax.hist(d['onset_to_arrival_hours'].clip(upper=72), bins=20,
                    alpha=0.4, color=col,
                    label=sc_name.replace('_', ' ').title()[:10], edgecolor='white')
    ax.set_xlabel('Hours from Onset to Arrival')
    ax.set_title('Presentation Delay')
    ax.legend(fontsize=7)

    ax = axes[2, 1]
    cascade = ['CT Scan', 'Antiplatelet', 'Thrombolysis', 'Statin', 'Physio']
    for i, sc_name in enumerate(SCENARIOS):
        if sc_name in dfs:
            d = dfs[sc_name]
            vals = [d['ct_performed'].mean()*100, d['antiplatelet'].mean()*100,
                    d['thrombolysis'].mean()*100, d['statin'].mean()*100,
                    d['physiotherapy_started'].mean()*100]
            ax.plot(range(5), vals, 'o-', label=sc_name.replace('_', ' ').title()[:10],
                    color=colors[i], linewidth=2, markersize=6)
    ax.set_xticks(range(5))
    ax.set_xticklabels(cascade, fontsize=8)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Treatment Cascade (thrombolysis near-zero SSA)')
    ax.legend(fontsize=7)

    ax = axes[3, 0]
    survived = df[df['outcome_30_day'] == 'survived']
    if len(survived) > 0:
        mrs = survived['mrs_discharge'].value_counts().sort_index()
        ax.bar(mrs.index.astype(str), mrs.values, color='#3498db', alpha=0.8)
    ax.set_xlabel('Modified Rankin Scale')
    ax.set_ylabel('Count')
    ax.set_title('Disability at Discharge (mRS)')

    ax = axes[3, 1]
    died = df[df['outcome_30_day'] == 'died']['age_years']
    surv = df[df['outcome_30_day'] == 'survived']['age_years']
    ax.hist(surv, bins=20, alpha=0.5, color='#2ecc71', label='Survived', edgecolor='white')
    ax.hist(died, bins=20, alpha=0.7, color='#e74c3c', label='Died', edgecolor='white')
    ax.set_xlabel('Age (years)')
    ax.set_title('Age Distribution (mean ~59, younger than HICs)')
    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)