import os import pandas as pd import re from collections import defaultdict def analyze_fif_paths(root_dir="split_fifs"): """ Extract .fif file paths using regex and provide detailed analysis including emotion types. """ # Define emotion mapping with corrected IDs emotion_mapping = { # Neutral (ID: 0) 'neutralVideo': {'id': 0, 'emotion': 'neutral'}, # Excited (ID: 1) 'ratatChaseScene': {'id': 1, 'emotion': 'excited'}, 'Kenmiles': {'id': 1, 'emotion': 'excited'}, 'NBA': {'id': 1, 'emotion': 'excited'}, # Happy (ID: 2) 'scooby': {'id': 2, 'emotion': 'happy'}, # Happy (ID: 3) 'motivationalAuthor': {'id': 3, 'emotion': 'motivated'}, # Relaxed (ID: 4) 'waterfall': {'id': 4, 'emotion': 'relaxed'}, 'asmr': {'id': 4, 'emotion': 'relaxed'}, 'meditation': {'id': 4, 'emotion': 'relaxed'}, # Sad (ID: 5) 'saddogs': {'id': 5, 'emotion': 'sad'}, 'sadbaby': {'id': 5, 'emotion': 'sad'}, 'ChampDeath': {'id': 5, 'emotion': 'sad'}, 'sadBaby1': {'id': 5, 'emotion': 'sad'}, # Horror (ID: 6) 'conjuring': {'id': 6, 'emotion': 'horror'}, # Angry (ID: 7) 'angrydogs': {'id': 7, 'emotion': 'angry'}, 'thepiano': {'id': 7, 'emotion': 'angry'}, # Disgusted (ID: 8) 'trainspotting': {'id': 8, 'emotion': 'disgusted'}, # Utility files (no emotion) 'label': {'id': -1, 'emotion': 'utility'}, 'rating': {'id': -1, 'emotion': 'utility'}, 'navon': {'id': -1, 'emotion': 'utility'} } # Dictionary to store analysis data analysis = { 'total_files': 0, 'directory_counts': defaultdict(int), 'subject_counts': defaultdict(int), 'epoch_counts': defaultdict(int), 'video_type_counts': defaultdict(int), 'emotion_counts': defaultdict(int), 'emotion_id_counts': defaultdict(int) } # List to store file paths file_data = [] # Regex pattern for .fif files pattern = r'(\d+|Zacker)-mapped_epoch_(\d+)_(\w+)raw_interval_(\d+)\.raw\.fif$' # Walk through the directory for dirpath, dirnames, filenames in os.walk(root_dir): for filename in filenames: match = re.match(pattern, filename) if match: full_path = os.path.join(dirpath, filename) # Extract components from filename subject_id = match.group(1) epoch_num = match.group(2) video_type = match.group(3) # Get emotion data if video type is in mapping emotion_info = emotion_mapping.get(video_type, {'id': -1, 'emotion': 'unknown'}) emotion_id = emotion_info['id'] emotion = emotion_info['emotion'] # Update counts analysis['total_files'] += 1 analysis['directory_counts'][dirpath] += 1 analysis['subject_counts'][subject_id] += 1 analysis['epoch_counts'][epoch_num] += 1 analysis['video_type_counts'][video_type] += 1 if emotion != 'utility': # Only count actual emotions analysis['emotion_counts'][emotion] += 1 analysis['emotion_id_counts'][emotion_id] += 1 # Add to file data file_data.append({ 'file_path': full_path, 'subject_id': subject_id, 'epoch': int(epoch_num), 'video_type': video_type, 'emotion_id': emotion_id, 'emotion': emotion }) # Create DataFrame df = pd.DataFrame(file_data) # Sort the DataFrame df = df.sort_values(['subject_id', 'epoch', 'file_path']) # Save the full analysis to CSV output_file = 'fif_file_analysis4.csv' df.to_csv(output_file, index=False) # Create a filtered DataFrame with only emotion-related files emotion_df = df[df['emotion_id'] >= 0].copy() emotion_output_file = 'emotion_files.csv' emotion_df.to_csv(emotion_output_file, index=False) # Print the analysis print_analysis(analysis, df) return df, analysis def print_analysis(analysis, df): """Print detailed analysis of the .fif files.""" print("\n" + "="*50) print("FIF FILES ANALYSIS REPORT") print("="*50) # Overall Statistics print("\n1. OVERALL STATISTICS") print("-"*30) print(f"Total .fif files found: {analysis['total_files']}") print(f"Number of subjects: {len(analysis['subject_counts'])}") print(f"Number of directories: {len(analysis['directory_counts'])}") # Subject Breakdown print("\n2. FILES PER SUBJECT") print("-"*30) for subject, count in sorted(analysis['subject_counts'].items()): print(f"Subject {subject}: {count} files") # Directory Breakdown print("\n3. FILES PER DIRECTORY") print("-"*30) for directory, count in sorted(analysis['directory_counts'].items()): rel_path = os.path.relpath(directory, "splif_fifs") print(f"{rel_path}: {count} files") # Emotion Analysis print("\n4. FILES PER EMOTION") print("-"*30) # Define the exact order of emotions with their IDs ordered_emotions = [ (0, 'neutral'), (1, 'excited'), (2, 'happy'), (3, 'relaxed'), (4, 'sad'), (5, 'horror'), (6, 'angry'), (7, 'disgusted') ] for emotion_id, emotion_name in ordered_emotions: if emotion_id in analysis['emotion_id_counts']: count = analysis['emotion_id_counts'][emotion_id] print(f"Emotion ID {emotion_id} {emotion_name}: {count} files") # Print utility files separately utility_count = len(df[df['emotion'] == 'utility']) if utility_count > 0: print(f"\nUtility files (rating/label/navon): {utility_count} files") # Video Type Analysis print("\n5. FILES PER VIDEO TYPE") print("-"*30) for video_type, count in sorted(analysis['video_type_counts'].items()): print(f"{video_type}: {count} files") # Epoch Analysis print("\n6. FILES PER EPOCH") print("-"*30) for epoch, count in sorted(analysis['epoch_counts'].items(), key=lambda x: int(x[0])): print(f"Epoch {epoch}: {count} files") # CSV File Information print("\n7. CSV FILE OUTPUTS") print("-"*30) print("1. Full analysis file: fif_file_analysis.csv") print("2. Emotion-only file: emotion_files.csv") print("\nColumns:") for col in df.columns: print(f"- {col}") # Additional Statistics print("\n8. ADDITIONAL STATISTICS") print("-"*30) if analysis['total_files'] > 0: avg_files_per_dir = analysis['total_files'] / len(analysis['directory_counts']) avg_files_per_subject = analysis['total_files'] / len(analysis['subject_counts']) print(f"Average files per directory: {avg_files_per_dir:.2f}") print(f"Average files per subject: {avg_files_per_subject:.2f}") # Calculate percentages of emotion files emotion_files = sum(analysis['emotion_id_counts'].values()) utility_files = utility_count print(f"\nEmotion files: {emotion_files} ({(emotion_files/analysis['total_files']*100):.1f}%)") print(f"Utility files: {utility_files} ({(utility_files/analysis['total_files']*100):.1f}%)") if __name__ == "__main__": # Run analysis df, analysis = analyze_fif_paths()