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

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

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


def load_scenarios(data_dir='data'):
    dfs = {}
    for sc in SCENARIOS:
        path = os.path.join(data_dir, f'hcw_{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('Healthcare Worker Workforce & Retention — 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))
    burnout = [dfs[sc][dfs[sc]['burnout_level'] == 'high'].shape[0] / len(dfs[sc]) * 100
               for sc in SCENARIOS if sc in dfs]
    ax.bar(x, burnout, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Urban Tertiary', 'District', 'Rural'], fontsize=9)
    for i, v in enumerate(burnout):
        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('High Burnout (%)')
    ax.set_title('Burnout by Scenario')

    ax = axes[0, 1]
    cadres = df['cadre'].value_counts()
    c_colors = ['#3498db', '#e74c3c', '#f39c12', '#9b59b6', '#2ecc71', '#e67e22', '#1abc9c']
    ax.pie(cadres.values,
           labels=[s.replace('_', ' ').title() for s in cadres.index],
           autopct='%1.0f%%', colors=c_colors[:len(cadres)],
           startangle=90, textprops={'fontsize': 8})
    ax.set_title('Cadre Distribution')

    ax = axes[1, 0]
    leave = [dfs[sc]['intention_to_leave'].mean()*100 for sc in SCENARIOS if sc in dfs]
    migrate = [dfs[sc]['intention_to_migrate'].mean()*100 for sc in SCENARIOS if sc in dfs]
    w = 0.3
    ax.bar(x - w/2, leave, w, label='Leave', color='#e74c3c', alpha=0.8)
    ax.bar(x + w/2, migrate, w, label='Migrate', color='#f39c12', alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Urban Tertiary', 'District', 'Rural'], fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Intention to Leave / Migrate')
    ax.legend(fontsize=8)

    ax = axes[1, 1]
    reasons = df[df['reason_to_leave'] != 'none']['reason_to_leave'].value_counts()
    if len(reasons) > 0:
        ax.barh(range(len(reasons)), reasons.values, color='#3498db', alpha=0.8)
        ax.set_yticks(range(len(reasons)))
        ax.set_yticklabels([s.replace('_', ' ').title() for s in reasons.index], fontsize=8)
    ax.set_xlabel('Count')
    ax.set_title('Reasons to Leave (salary #1)')

    ax = axes[2, 0]
    vacancy = [(1 - dfs[sc]['staffing_adequate'].mean())*100 for sc in SCENARIOS if sc in dfs]
    ax.bar(x, vacancy, color=colors, alpha=0.8)
    ax.set_xticks(x)
    ax.set_xticklabels(['Urban Tertiary', 'District', 'Rural'], fontsize=9)
    for i, v in enumerate(vacancy):
        ax.text(i, v + 0.5, f'{v:.0f}%', ha='center', fontsize=10)
    ax.set_ylabel('Vacancy Rate (%)')
    ax.set_title('Staffing Vacancy Rate')

    ax = axes[2, 1]
    sat = df['salary_satisfaction'].value_counts()
    s_order = ['satisfied', 'neutral', 'dissatisfied']
    vals = [sat.get(s, 0) for s in s_order]
    ax.bar(range(3), vals, color=['#2ecc71', '#f39c12', '#e74c3c'], alpha=0.8)
    ax.set_xticks(range(3))
    ax.set_xticklabels(['Satisfied', 'Neutral', 'Dissatisfied'], fontsize=9)
    ax.set_ylabel('Count')
    ax.set_title('Salary Satisfaction (~55% dissatisfied)')

    ax = axes[3, 0]
    dest = df[df['migration_destination'] != 'none']['migration_destination'].value_counts()
    if len(dest) > 0:
        ax.barh(range(len(dest)), dest.values,
                color=c_colors[:len(dest)], alpha=0.8)
        ax.set_yticks(range(len(dest)))
        ax.set_yticklabels([s.replace('_', ' ').title() for s in dest.index], fontsize=8)
    ax.set_xlabel('Count')
    ax.set_title('Migration Destinations')

    ax = axes[3, 1]
    safety = ['workplace_violence_past_year', 'needlestick_injury_past_year',
              'covid_infected', 'ppe_adequate']
    s_labels = ['Violence', 'Needlestick', 'COVID', 'PPE Adequate']
    vals = [df[s].mean()*100 for s in safety]
    ax.bar(range(4), vals, color=['#e74c3c', '#f39c12', '#9b59b6', '#2ecc71'], alpha=0.8)
    ax.set_xticks(range(4))
    ax.set_xticklabels(s_labels, fontsize=9)
    ax.set_ylabel('Rate (%)')
    ax.set_title('Workplace Safety')

    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)