scripts/process_cardS_full.py

"""
Process full CardS dataset and export to Parquet.

Processes all 7000+ CardS 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.cardS_processor import CardSProcessor


def main():
    parser = argparse.ArgumentParser(description='Process CardS 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("CardS 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 / 'cardS_cleaned.parquet'

    print(f"Output file: {output_file}")
    print()

    # Create processor
    processor = CardSProcessor(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 CardS files...")
    print("This may take 30-60 minutes depending on system speed...")
    print()

    try:
        df = processor.process()

        # Get statistics
        stats = processor.get_stats()

        print()
        print("="*70)
        print("✓ 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(f"  Step rows: {stats['rows_step']:,}")
        print(f"  Completion rows: {stats['rows_completion']:,}")
        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 (completion rows only)
                completion_df = df[df['row_type'] == 'completion']
                if len(completion_df) > 0:
                    trials_per_user = completion_df.groupby('user_id')['trial'].max().mean()
                    print(f"  Trials per user (mean): {trials_per_user:.0f}")
                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"✓ Exported to: {output_file}")
        print(f"  File size: {file_size_mb:.1f} MB")
        print()

        # Calculate compression ratio (estimate)
        if len(df) > 0:
            # Rough estimate: 200 bytes per row uncompressed
            est_uncompressed = (len(df) * 200) / (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/cardS_{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("✓ CardS Dataset Processing Complete!")
        print("="*70)

    except Exception as e:
        print(f"\n❌ Error during processing: {e}")
        import traceback
        traceback.print_exc()
        sys.exit(1)


if __name__ == '__main__':
    import pandas as pd
    main()