Initial commit: organized project structure for student handoff

Reorganized flat 41-file directory into structured layout with:
- scripts/ for Python analysis code with shared config.py
- notebooks/ for Jupyter analysis notebooks
- data/ split into raw/, metadata/, processed/
- docs/ with analysis summary, experimental design, and bimodal hypothesis tutorial
- tasks/ with todo checklist and lessons learned
- Comprehensive README, PLANNING.md, and .gitignore

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

scripts/load_roi_data.py

@@ -0,0 +1,90 @@
+import pandas as pd
+import sqlite3
+import re
+
+from config import DATA_RAW, DATA_METADATA, DATA_PROCESSED
+
+
+def load_roi_data():
+    """Load ROI data from SQLite databases and group by trained/untrained.
+
+    Returns:
+        tuple: (trained_df, untrained_df) DataFrames with tracking data.
+    """
+    metadata = pd.read_csv(DATA_METADATA / '2025_07_15_metadata_fixed.csv')
+    metadata['machine_name'] = metadata['machine_name'].astype(str)
+
+    trained_rois = metadata[metadata['group'] == 'trained']
+    untrained_rois = metadata[metadata['group'] == 'untrained']
+
+    db_files = list(DATA_RAW.glob('*_tracking.db'))
+
+    trained_df = pd.DataFrame()
+    untrained_df = pd.DataFrame()
+
+    for db_file in db_files:
+        print(f"Processing {db_file.name}")
+
+        pattern = r'_([0-9a-f]{32})__'
+        match = re.search(pattern, db_file.name)
+
+        if not match:
+            print(f"Could not extract UUID from {db_file.name}")
+            continue
+
+        uuid = match.group(1)
+        metadata_matches = metadata[metadata['path'].str.contains(uuid, na=False)]
+
+        if metadata_matches.empty:
+            print(f"No metadata matches found for UUID {uuid} from {db_file.name}")
+            continue
+
+        machine_id = metadata_matches.iloc[0]['machine_name']
+        print(f"Matched to machine ID: {machine_id}")
+
+        conn = sqlite3.connect(str(db_file))
+
+        machine_trained = trained_rois[trained_rois['machine_name'] == machine_id]
+        machine_untrained = untrained_rois[untrained_rois['machine_name'] == machine_id]
+
+        for _, row in machine_trained.iterrows():
+            roi = row['ROI']
+            try:
+                query = f"SELECT * FROM ROI_{roi}"
+                roi_data = pd.read_sql_query(query, conn)
+                roi_data['machine_name'] = machine_id
+                roi_data['ROI'] = roi
+                roi_data['group'] = 'trained'
+                trained_df = pd.concat([trained_df, roi_data], ignore_index=True)
+            except Exception as e:
+                print(f"Error loading ROI_{roi} from {db_file.name}: {e}")
+
+        for _, row in machine_untrained.iterrows():
+            roi = row['ROI']
+            try:
+                query = f"SELECT * FROM ROI_{roi}"
+                roi_data = pd.read_sql_query(query, conn)
+                roi_data['machine_name'] = machine_id
+                roi_data['ROI'] = roi
+                roi_data['group'] = 'untrained'
+                untrained_df = pd.concat([untrained_df, roi_data], ignore_index=True)
+            except Exception as e:
+                print(f"Error loading ROI_{roi} from {db_file.name}: {e}")
+
+        conn.close()
+
+    return trained_df, untrained_df
+
+
+if __name__ == "__main__":
+    trained_data, untrained_data = load_roi_data()
+    print(f"Trained data shape: {trained_data.shape}")
+    print(f"Untrained data shape: {untrained_data.shape}")
+    if not trained_data.empty:
+        print("Trained data columns:", trained_data.columns.tolist())
+    if not untrained_data.empty:
+        print("Untrained data columns:", untrained_data.columns.tolist())
+
+    trained_data.to_csv(DATA_PROCESSED / 'trained_roi_data.csv', index=False)
+    untrained_data.to_csv(DATA_PROCESSED / 'untrained_roi_data.csv', index=False)
+    print("Data saved to trained_roi_data.csv and untrained_roi_data.csv")