#!/usr/bin/env python3
"""Audit: Location (Coordinate RV) dataset."""

import sys
sys.path.insert(0, str(__import__('pathlib').Path(__file__).resolve().parent))

from _common import *


def main():
    output_dir = setup_output_dir('location')
    print_section('LOCATION DATASET AUDIT')
    print(f'Audit started: {datetime.now()}')

    df = load_parquet('location')

    print_section('DATASET OVERVIEW')
    df.info()

    critical_fields = ['x_guess', 'y_guess', 'x_target', 'y_target', 'z_score', 'count']
    available = [f for f in critical_fields if f in df.columns]
    print('\nCRITICAL FIELDS SUMMARY')
    for field in available:
        if df[field].dtype in ['int64', 'float64']:
            print(f'\n{field}:')
            print(df[field].describe())

    yearly_counts, gaps = standard_temporal_analysis(df, 'Location Dataset', output_dir)

    # Coordinate range validation (0-299)
    print_section('COORDINATE RANGE VALIDATION')
    coord_cols = ['x_guess', 'y_guess', 'x_target', 'y_target']
    for col in coord_cols:
        if col in df.columns:
            valid = df[col].dropna()
            out_of_range = valid[(valid < 0) | (valid > 299)]
            print(f'{col}: min={valid.min()}, max={valid.max()}, out-of-range={len(out_of_range):,}')
            if len(out_of_range) == 0:
                print(f'  PASS: All {col} values in range 0-299')
            else:
                print(f'  FAIL: {len(out_of_range):,} values outside 0-299')

    # Z-score distribution
    print_section('Z-SCORE DISTRIBUTION')
    if 'z_score' in df.columns:
        z = df['z_score'].dropna()
        print(f'Mean z-score:   {z.mean():.4f} (expected ~0)')
        print(f'Std z-score:    {z.std():.4f} (expected ~1)')
        print(f'Min:            {z.min():.4f}')
        print(f'Max:            {z.max():.4f}')

        plt.figure(figsize=(14, 5))
        z.hist(bins=100, color='steelblue', edgecolor='white', density=True)
        x_range = np.linspace(z.min(), z.max(), 200)
        plt.plot(x_range, stats.norm.pdf(x_range, 0, 1), 'r-', linewidth=2, label='Standard Normal')
        plt.title('Z-Score Distribution vs Standard Normal', fontweight='bold')
        plt.xlabel('Z-Score')
        plt.ylabel('Density')
        plt.legend()
        plt.tight_layout()
        save_fig(output_dir, 'z_score_distribution')

    standard_missing_data_analysis(df, output_dir)
    standard_audit_summary('Location Dataset', df, extra_lines=[
        f'Temporal Gaps >= 7 days: {len(gaps)}',
    ])


if __name__ == '__main__':
    main()