Add files using upload-large-folder tool

README.md

@@ -0,0 +1,152 @@
+
+==================================================
+FIF FILES ANALYSIS REPORT
+==================================================
+
+1. OVERALL STATISTICS
+------------------------------
+Total .fif files found: 13443
+Number of subjects: 27
+Number of directories: 27
+
+2. FILES PER SUBJECT
+------------------------------
+Subject 062000: 424 files
+Subject 103918: 525 files
+Subject 145098: 532 files
+Subject 162636: 519 files
+Subject 192176: 530 files
+Subject 222299: 532 files
+Subject 254362: 530 files
+Subject 265350: 519 files
+Subject 440514: 46 files
+Subject 479015: 511 files
+Subject 541414: 537 files
+Subject 580357: 514 files
+Subject 587748: 391 files
+Subject 598027: 526 files
+Subject 665252: 529 files
+Subject 687106: 524 files
+Subject 701527: 355 files
+Subject 712563: 518 files
+Subject 750667: 526 files
+Subject 778279: 526 files
+Subject 825255: 487 files
+Subject 859899: 506 files
+Subject 901666: 514 files
+Subject 919513: 518 files
+Subject 946416: 528 files
+Subject 979253: 752 files
+Subject Zacker: 524 files
+
+3. FILES PER DIRECTORY
+------------------------------
+../split_fifs/062000: 424 files
+../split_fifs/103918: 525 files
+../split_fifs/145098: 532 files
+../split_fifs/162636: 519 files
+../split_fifs/192176: 530 files
+../split_fifs/222299: 532 files
+../split_fifs/254362: 530 files
+../split_fifs/265350: 519 files
+../split_fifs/440514: 46 files
+../split_fifs/479015: 511 files
+../split_fifs/541414: 537 files
+../split_fifs/580357: 514 files
+../split_fifs/587748: 391 files
+../split_fifs/598027: 526 files
+../split_fifs/665252: 529 files
+../split_fifs/687106: 524 files
+../split_fifs/701527: 355 files
+../split_fifs/712563: 518 files
+../split_fifs/750667: 526 files
+../split_fifs/778279: 526 files
+../split_fifs/825255: 487 files
+../split_fifs/859899: 506 files
+../split_fifs/901666: 514 files
+../split_fifs/919513: 518 files
+../split_fifs/946416: 528 files
+../split_fifs/979253: 752 files
+../split_fifs/Zacker: 524 files
+
+4. FILES PER EMOTION
+------------------------------
+Emotion ID 0 neutral: 2971 files
+Emotion ID 1 excited: 634 files
+Emotion ID 2 happy: 842 files
+Emotion ID 3 relaxed: 868 files
+Emotion ID 4 sad: 824 files
+Emotion ID 5 horror: 854 files
+Emotion ID 6 angry: 887 files
+Emotion ID 7 disgusted: 666 files
+
+Utility files (rating/label/navon): 3813 files
+
+5. FILES PER VIDEO TYPE
+------------------------------
+ChampDeath: 160 files
+NBA: 389 files
+angrydogs: 528 files
+asmr: 217 files
+conjuring: 887 files
+kenMiles: 315 files
+label: 3334 files
+meditation: 93 files
+motivationalAuthor: 868 files
+navon: 41 files
+neutralVideo: 2971 files
+ratatChaseScene: 245 files
+rating: 438 files
+sadBaby1: 62 files
+saddogs: 632 files
+scooby: 842 files
+thepiano: 138 files
+trainspotting: 769 files
+waterfall: 514 files
+
+6. FILES PER EPOCH
+------------------------------
+Epoch 1: 954 files
+Epoch 2: 240 files
+Epoch 3: 822 files
+Epoch 4: 824 files
+Epoch 5: 158 files
+Epoch 6: 861 files
+Epoch 7: 924 files
+Epoch 8: 162 files
+Epoch 9: 797 files
+Epoch 10: 775 files
+Epoch 11: 131 files
+Epoch 12: 825 files
+Epoch 13: 798 files
+Epoch 14: 103 files
+Epoch 15: 826 files
+Epoch 16: 825 files
+Epoch 17: 293 files
+Epoch 18: 792 files
+Epoch 19: 713 files
+Epoch 20: 101 files
+Epoch 21: 759 files
+Epoch 22: 666 files
+Epoch 23: 94 files
+
+7. CSV FILE OUTPUTS
+------------------------------
+1. Full analysis file: fif_file_analysis.csv
+2. Emotion-only file: emotion_files.csv
+
+Columns:
+- file_path
+- subject_id
+- epoch
+- video_type
+- emotion_id
+- emotion
+
+8. ADDITIONAL STATISTICS
+------------------------------
+Average files per directory: 497.89
+Average files per subject: 497.89
+
+Emotion files: 9630 (71.6%)
+Utility files: 3813 (28.4%)

count.md

@@ -0,0 +1,37 @@
+
+Total number of .fif files: 13503
+
+Breakdown by directory:
+--------------------------------------------------
+062000: 424 files
+103918: 525 files
+145098: 532 files
+162636: 519 files
+192176: 530 files
+222299: 532 files
+254362: 530 files
+265350: 519 files
+440514: 46 files
+479015: 511 files
+541414: 537 files
+580357: 514 files
+587748: 391 files
+598027: 526 files
+665252: 529 files
+687106: 524 files
+701527: 415 files
+712563: 518 files
+750667: 526 files
+778279: 526 files
+825255: 487 files
+859899: 506 files
+901666: 514 files
+919513: 518 files
+946416: 528 files
+979253: 752 files
+Zacker: 524 files
+
+Summary:
+Number of directories with .fif files: 27
+Average files per directory: 500.11
+Directory with most files: 979253 (752 files)

count.py

@@ -0,0 +1,49 @@
+import os
+
+def count_fif_files(root_dir="split_fifs"):
+    """
+    Count total number of .fif files in directory and its subdirectories.
+    Also provides counts per subdirectory.
+    """
+    # Total count of .fif files
+    total_fif_count = 0
+    # Dictionary to store counts per subdirectory
+    subdir_counts = {}
+    
+    # Walk through all directories
+    for dirpath, dirnames, filenames in os.walk(root_dir):
+        # Count .fif files in current directory
+        fif_files = [f for f in filenames if f.endswith('.raw.fif')]
+        count = len(fif_files)
+        
+        if count > 0:
+            # Get relative path for cleaner output
+            rel_path = os.path.relpath(dirpath, root_dir)
+            if rel_path == '.':
+                rel_path = root_dir
+            subdir_counts[rel_path] = count
+            total_fif_count += count
+    
+    # Print results
+    print(f"\nTotal number of .fif files: {total_fif_count}")
+    print("\nBreakdown by directory:")
+    print("-" * 50)
+    
+    # Sort by directory name and print counts
+    for dir_name, count in sorted(subdir_counts.items()):
+        print(f"{dir_name}: {count} files")
+    
+    # Additional statistics
+    print("\nSummary:")
+    print(f"Number of directories with .fif files: {len(subdir_counts)}")
+    if subdir_counts:
+        avg_files = total_fif_count / len(subdir_counts)
+        print(f"Average files per directory: {avg_files:.2f}")
+        max_dir = max(subdir_counts.items(), key=lambda x: x[1])
+        print(f"Directory with most files: {max_dir[0]} ({max_dir[1]} files)")
+    
+    return total_fif_count, subdir_counts
+
+if __name__ == "__main__":
+    # Run the count
+    total, counts = count_fif_files()

make7.py

@@ -0,0 +1,217 @@
+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()