File size: 6,560 Bytes
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Process full RV (Full Remote Viewing) dataset and export to Parquet.
Processes all RV 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.rv_processor import RVProcessor
def main():
parser = argparse.ArgumentParser(description='Process RV 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("RV (Full Remote Viewing) 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 / 'rv_cleaned.parquet'
print(f"Output file: {output_file}")
print()
# Create processor
processor = RVProcessor(config)
# Get file count
try:
all_files = processor.get_file_list()
print(f"Total files to process: {len(all_files)}")
print()
except Exception as e:
print(f"✗ Error getting file list: {e}")
print()
print("Expected directory: data/rv/")
print("Make sure the raw data is in the correct location.")
sys.exit(1)
# Process all files
print("Processing all RV files...")
print("This may take some time - large dataset with ~11,708 files...")
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" Total rows: {len(df):,}")
print(f" Valid rows: {stats['rows_valid']:,}")
print()
# Method distribution
print("Scoring Method Distribution:")
print(f" Original (method 0/10): {stats['method_original']:,}")
print(f" Match judges (method 3/13): {stats['method_match_judges']:,}")
print(f" Keywords only (method 9): {stats['method_keywords']:,}")
print()
# Data quality metrics
print("Data Quality:")
completeness = df.notna().sum() / len(df)
print(f" Completeness (avg): {completeness.mean():.1%}")
print(f" Missing start_time: {df['start_time'].isna().sum():,} ({df['start_time'].isna().sum() / len(df) * 100:.1f}%)")
print(f" Missing end_time: {df['end_time'].isna().sum():,} ({df['end_time'].isna().sum() / len(df) * 100:.1f}%)")
print()
# Date range
if 'start_time' in df.columns:
valid_ts = df['start_time'].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()
# Score distribution
if 'total_score' in df.columns:
print("Score Distribution:")
score_stats = df['total_score'].describe()
print(f" Mean: {score_stats['mean']:.2f}")
print(f" Median: {score_stats['50%']:.2f}")
print(f" Std Dev: {score_stats['std']:.2f}")
print(f" Min: {score_stats['min']:.2f}")
print(f" Max: {score_stats['max']:.2f}")
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) * 300 # Rough estimate of ~300 bytes/row in CSV (more columns)
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("✓ RV 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()
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