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

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

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


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'pca_{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('Prostate Cancer — 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]['died_within_2_years'].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:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('2-Year Mortality (%)')
    ax.set_title('2-Year Mortality by Scenario')

    ax = axes[0, 1]
    stages = ['I', 'II', 'III', 'IV', 'unknown']
    for i, sc_name in enumerate(SCENARIOS):
        if sc_name in dfs:
            d = dfs[sc_name]
            vals = [(d['stage'] == s).mean()*100 for s in stages]
            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(stages, fontsize=9)
    ax.set_ylabel('Proportion (%)')
    ax.set_title('Stage at Diagnosis (late = poor outcome)')
    ax.legend(fontsize=7)

    ax = axes[1, 0]
    symptoms = df['presenting_symptom'].value_counts()
    ax.barh(range(len(symptoms)), symptoms.values, color='#3498db', alpha=0.7)
    ax.set_yticks(range(len(symptoms)))
    ax.set_yticklabels([s.replace('_', ' ').title() for s in symptoms.index], fontsize=8)
    ax.set_xlabel('Count')
    ax.set_title('Presenting Symptoms (LUTS & bone pain)')

    ax = axes[1, 1]
    tx = df['treatment_received'].value_counts()
    t_colors = ['#e74c3c', '#f39c12', '#3498db', '#9b59b6', '#2ecc71', '#e67e22', '#1abc9c']
    ax.pie(tx.values,
           labels=[s.replace('_', ' ').title() for s in tx.index],
           autopct='%1.0f%%', colors=t_colors[:len(tx)],
           startangle=90, textprops={'fontsize': 7})
    ax.set_title('Treatment Received')

    ax = axes[2, 0]
    no_tx = [dfs[sc][dfs[sc]['treatment_received'] == 'none'].shape[0] / len(dfs[sc]) * 100
             for sc in SCENARIOS if sc in dfs]
    ax.bar(x, no_tx, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Tertiary', 'District', 'Rural'], fontsize=9)
    for i, v in enumerate(no_tx):
        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Rate (%)')
    ax.set_title('No Treatment Received')

    ax = axes[2, 1]
    psa = [dfs[sc]['psa_tested'].mean()*100 for sc in SCENARIOS if sc in dfs]
    bx = [dfs[sc]['biopsy_performed'].mean()*100 for sc in SCENARIOS if sc in dfs]
    w = 0.3
    ax.bar(x - w/2, psa, w, label='PSA Tested', color='#3498db', alpha=0.8)
    ax.bar(x + w/2, bx, w, label='Biopsy Done', color='#f39c12', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Tertiary', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Diagnostic Access')
    ax.legend(fontsize=8)

    ax = axes[3, 0]
    stage4 = df[df['stage'] == 'IV']
    early = df[df['stage'].isin(['I', 'II'])]
    cats = ['2yr Mort', 'Bone Mets', 'No Tx']
    if len(stage4) > 0 and len(early) > 0:
        v4 = [stage4['died_within_2_years'].mean()*100,
              stage4['bone_metastases'].mean()*100,
              (stage4['treatment_received'] == 'none').mean()*100]
        ve = [early['died_within_2_years'].mean()*100,
              early['bone_metastases'].mean()*100,
              (early['treatment_received'] == 'none').mean()*100]
        w = 0.3
        ax.bar(np.arange(3) - w/2, v4, w, label='Stage IV', color='#e74c3c', alpha=0.8)
        ax.bar(np.arange(3) + w/2, ve, w, label='Stage I-II', color='#2ecc71', alpha=0.8)
        ax.set_xticks(np.arange(3))
        ax.set_xticklabels(cats, fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Stage IV vs Early Stage')
    ax.legend(fontsize=8)

    ax = axes[3, 1]
    ref = [dfs[sc]['referral_needed'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ref_c = [dfs[sc]['referral_completed'].mean()*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x - w/2, ref, w, label='Referral Needed', color='#f39c12', alpha=0.8)
    ax.bar(x + w/2, ref_c, w, label='Referral Completed', color='#2ecc71', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Tertiary', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Referral Gap')
    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)