File size: 5,551 Bytes
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Process full CardD dataset and export to Parquet.
Processes all 6000+ CardD 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.cardd_processor import CardDProcessor
def main():
parser = argparse.ArgumentParser(description='Process CardD 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("CardD 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 / 'cardD_cleaned.parquet'
print(f"Output file: {output_file}")
print()
# Create processor
processor = CardDProcessor(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 CardD files...")
print("This may take 30-60 minutes depending on system speed...")
print()
try:
df = processor.process()
elapsed = datetime.now() - start_time
print()
print("="*70)
print("✓ Processing Complete!")
print("="*70)
print()
# Show stats
stats = processor.get_stats()
print("Processing Statistics:")
print(f" Files processed: {stats['files_processed']:,}")
print(f" Files failed: {stats['files_failed']:,}")
print(f" Success rate: {stats['files_processed'] / len(all_files) * 100:.1f}%")
print()
print(f" Schema v1 files (pre 2006-06-22): {stats['schema_v1_files']:,}")
print(f" Schema v2 files (post 2006-06-22): {stats['schema_v2_files']:,}")
print()
print(f" Total rows: {len(df):,}")
print(f" Valid rows: {stats['rows_valid']:,}")
print()
# Data quality metrics
print("Data Quality:")
completeness = df.notna().sum() / len(df)
print(f" Completeness (avg): {completeness.mean():.1%}")
print(f" Missing timestamps: {df['timestamp'].isna().sum():,} ({df['timestamp'].isna().sum() / len(df) * 100:.1f}%)")
print()
# Date range
if 'timestamp' in df.columns:
valid_ts = df['timestamp'].dropna()
if not valid_ts.empty:
print(f"Date range: {valid_ts.min()} to {valid_ts.max()}")
print(f"Span: {(valid_ts.max() - valid_ts.min()).days} days")
print()
# User statistics
print("User Statistics:")
n_users = df['user_id'].nunique()
print(f" Unique users: {n_users:,}")
if n_users > 0:
print(f" Trials per user (mean): {len(df) / n_users:.0f}")
print()
# Performance stats
print(f"Processing time: {elapsed}")
print(f"Speed: {len(df) / elapsed.total_seconds():.0f} rows/second")
print()
# Export to Parquet
print("Exporting to Parquet...")
unlock_for_write(output_file)
df.to_parquet(
output_file,
engine='pyarrow',
compression='snappy',
index=False
)
seal_output(output_file)
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()
# Compression ratio
raw_size_estimate = len(df) * 200 # Rough estimate of ~200 bytes/row in CSV
compression_ratio = raw_size_estimate / output_file.stat().st_size
print(f" Compression ratio: {compression_ratio:.1f}x")
print()
# Show errata summary
errata_summary = processor.errata_logger.get_summary()
print("Error Summary:")
print(f" Total errors: {errata_summary['total_errors']:,}")
print(f" Files with errors: {errata_summary['files_with_errors']:,}")
if errata_summary['error_types']:
print(f" Error types:")
for error_type, count in errata_summary['error_types'].items():
print(f" - {error_type}: {count:,}")
print(f" Errata log: {errata_summary['log_file']}")
print()
print("="*70)
print("✓ CardD Dataset Processing Complete!")
print("="*70)
except KeyboardInterrupt:
print("\n\n✗ Processing interrupted by user")
sys.exit(1)
except Exception as e:
print(f"\n✗ Processing failed: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == '__main__':
main() |