""" Process full Location dataset and export to Parquet. Processes all Location (Remote Viewing Coordinates) files and exports cleaned data. """ import sys import argparse from pathlib import Path from datetime import datetime sys.path.insert(0, str(Path(__file__).parent.parent)) from src.core.config import Config from src.core.output_lock import seal_output, unlock_for_write from src.processors.location_processor import LocationProcessor def main(): parser = argparse.ArgumentParser(description='Process Location dataset') parser.add_argument('--audit', action='store_true', help='Include source_file and source_row_number columns in output') parser.add_argument('--data-dir', help='Override raw data directory (default: data/)') args = parser.parse_args() print("="*70) print("Location Full Dataset Processing") print("="*70) print() start_time = datetime.now() # Load config config = Config('config/cleaning_config.yaml') # Override audit mode if --audit flag is provided if args.audit: config.set('processing.audit_mode', True) print("Audit mode: ENABLED (including source_file and source_row_number columns)") print() # Override data directory if --data-dir flag is provided if args.data_dir: config.set('directories.raw_data', args.data_dir) # Create output directory output_dir = config.output_dir / 'parquet' output_dir.mkdir(parents=True, exist_ok=True) output_file = output_dir / 'location_cleaned.parquet' print(f"Output file: {output_file}") print() # Create processor processor = LocationProcessor(config) # Get file count all_files = processor.get_file_list() print(f"Total files to process: {len(all_files)}") print() # Process all files print("Processing all Location files...") print() try: df = processor.process() # Get statistics stats = processor.get_stats() print() print("="*70) print("āœ“ Processing Complete!") print("="*70) print() print("Processing Statistics:") print(f" Files processed: {stats['files_processed']:,}") print(f" Files failed: {stats['files_failed']:,}") success_rate = (stats['files_processed'] / len(all_files)) * 100 print(f" Success rate: {success_rate:.1f}%") print() print(f" Total rows: {len(df):,}") print(f" Valid rows: {stats['rows_valid']:,}") print() # Data quality metrics if len(df) > 0: completeness = df.notna().sum() / len(df) avg_completeness = completeness.mean() * 100 # Date range if 'timestamp' in df.columns and df['timestamp'].notna().any(): date_col = pd.to_datetime(df['timestamp'], errors='coerce') min_date = date_col.min() max_date = date_col.max() date_span = (max_date - min_date).days print("Data Quality:") print(f" Completeness (avg): {avg_completeness:.1f}%") missing_ts = df['timestamp'].isna().sum() print(f" Missing timestamps: {missing_ts:,} ({missing_ts/len(df)*100:.1f}%)") print() print(f"Date range: {min_date} to {max_date}") print(f"Span: {date_span:,} days") print() # User statistics if 'user_id' in df.columns: unique_users = df['user_id'].nunique() print("User Statistics:") print(f" Unique users: {unique_users:,}") # Calculate trials per user if 'trial_number' in df.columns: trials_per_user = df.groupby('user_id')['trial_number'].max().mean() print(f" Trials per user (mean): {trials_per_user:.0f}") print() # Coordinate statistics if 'x_guess' in df.columns and 'x_target' in df.columns: print("Coordinate Statistics:") # Calculate distance (Euclidean) df['distance'] = ((df['x_guess'] - df['x_target'])**2 + (df['y_guess'] - df['y_target'])**2)**0.5 avg_distance = df['distance'].mean() print(f" Average distance from target: {avg_distance:.1f} pixels") # Z-score statistics if 'z_score' in df.columns: avg_z = df['z_score'].mean() print(f" Average z-score: {avg_z:.3f}") print() processing_time = datetime.now() - start_time print(f"Processing time: {processing_time}") if len(df) > 0: rows_per_sec = len(df) / processing_time.total_seconds() print(f"Speed: {rows_per_sec:.0f} rows/second") print() # Export to Parquet print("Exporting to Parquet...") unlock_for_write(output_file) df.to_parquet(output_file, compression='snappy', engine='pyarrow', index=False) seal_output(output_file) # Get file size file_size_mb = output_file.stat().st_size / (1024 * 1024) print(f"āœ“ Exported to: {output_file}") print(f" File size: {file_size_mb:.1f} MB") print() # Calculate compression ratio (estimate) if len(df) > 0: # Rough estimate: 150 bytes per row uncompressed est_uncompressed = (len(df) * 150) / (1024 * 1024) compression_ratio = est_uncompressed / file_size_mb print(f" Compression ratio: {compression_ratio:.1f}x") print() # Error summary errata_log = Path(f"logs/errata/location_{datetime.now().strftime('%Y%m%d_%H%M%S')}_errata.jsonl") if errata_log.exists(): print("Error Summary:") import json error_types = {} total_errors = 0 with open(errata_log, 'r') as f: for line in f: try: data = json.loads(line) if data.get('type') == 'error': error_type = data.get('error_type', 'unknown') error_types[error_type] = error_types.get(error_type, 0) + 1 total_errors += 1 except: pass print(f" Total errors: {total_errors}") files_with_errors = stats['files_failed'] print(f" Files with errors: {files_with_errors}") if error_types: print(f" Error types:") for error_type, count in sorted(error_types.items(), key=lambda x: x[1], reverse=True): print(f" - {error_type}: {count}") print(f" Errata log: {errata_log}") print() print("="*70) print("āœ“ Location Dataset Processing Complete!") print("="*70) except Exception as e: print(f"\nāŒ Error during processing: {e}") import traceback traceback.print_exc() sys.exit(1) if __name__ == '__main__': import pandas as pd main()