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/calculate_distances.py

@@ -0,0 +1,118 @@
+import pandas as pd
+import numpy as np
+from scipy.spatial.distance import euclidean
+
+from config import DATA_PROCESSED
+
+
+def calculate_fly_distances(trained_file=None, untrained_file=None):
+    """Calculate distances between flies at each time point.
+
+    For each time point:
+    - If two flies are detected: calculate Cartesian distance between them
+    - If one fly is detected: set distance to 0 if area > average area, otherwise NaN
+
+    Args:
+        trained_file (Path): Path to trained ROI data CSV.
+        untrained_file (Path): Path to untrained ROI data CSV.
+
+    Returns:
+        tuple: (trained_distances, untrained_distances) DataFrames.
+    """
+    if trained_file is None:
+        trained_file = DATA_PROCESSED / 'trained_roi_data.csv'
+    if untrained_file is None:
+        untrained_file = DATA_PROCESSED / 'untrained_roi_data.csv'
+
+    trained_df = pd.read_csv(trained_file)
+    untrained_df = pd.read_csv(untrained_file)
+
+    trained_df['area'] = trained_df['w'] * trained_df['h']
+    untrained_df['area'] = untrained_df['w'] * untrained_df['h']
+
+    avg_area = np.mean([trained_df['area'].mean(), untrained_df['area'].mean()])
+    print(f"Average area across all data: {avg_area:.2f}")
+
+    trained_distances = process_distance_data(trained_df, avg_area)
+    untrained_distances = process_distance_data(untrained_df, avg_area)
+
+    return trained_distances, untrained_distances
+
+
+def process_distance_data(df, avg_area):
+    """Process a DataFrame to calculate distances between flies at each time point.
+
+    Args:
+        df (pd.DataFrame): Input tracking data.
+        avg_area (float): Average area threshold for single-fly detection.
+
+    Returns:
+        pd.DataFrame: Distance data with columns for machine, ROI, time, distance.
+    """
+    results = []
+
+    for (machine_name, roi), group in df.groupby(['machine_name', 'ROI']):
+        for t, time_group in group.groupby('t'):
+            time_group = time_group.sort_values('id').reset_index(drop=True)
+
+            if len(time_group) >= 2:
+                fly1 = time_group.iloc[0]
+                fly2 = time_group.iloc[1]
+                distance = euclidean([fly1['x'], fly1['y']], [fly2['x'], fly2['y']])
+
+                results.append({
+                    'machine_name': machine_name,
+                    'ROI': roi,
+                    't': t,
+                    'distance': distance,
+                    'n_flies': len(time_group),
+                    'area_fly1': fly1['area'],
+                    'area_fly2': fly2['area']
+                })
+            elif len(time_group) == 1:
+                fly = time_group.iloc[0]
+                area = fly['area']
+
+                if area > avg_area:
+                    distance = 0.0
+                else:
+                    distance = np.nan
+
+                results.append({
+                    'machine_name': machine_name,
+                    'ROI': roi,
+                    't': t,
+                    'distance': distance,
+                    'n_flies': 1,
+                    'area_fly1': area,
+                    'area_fly2': np.nan
+                })
+
+    return pd.DataFrame(results)
+
+
+def main():
+    """Run distance calculations and save results."""
+    trained_distances, untrained_distances = calculate_fly_distances()
+
+    print(f"Trained data distance summary:")
+    print(f"  Shape: {trained_distances.shape}")
+    print(f"  Distance stats:")
+    print(f"    Count: {trained_distances['distance'].count()}")
+    print(f"    Mean: {trained_distances['distance'].mean():.2f}")
+    print(f"    Std: {trained_distances['distance'].std():.2f}")
+
+    print(f"\nUntrained data distance summary:")
+    print(f"  Shape: {untrained_distances.shape}")
+    print(f"  Distance stats:")
+    print(f"    Count: {untrained_distances['distance'].count()}")
+    print(f"    Mean: {untrained_distances['distance'].mean():.2f}")
+    print(f"    Std: {untrained_distances['distance'].std():.2f}")
+
+    trained_distances.to_csv(DATA_PROCESSED / 'trained_distances.csv', index=False)
+    untrained_distances.to_csv(DATA_PROCESSED / 'untrained_distances.csv', index=False)
+    print("\nDistance data saved")
+
+
+if __name__ == "__main__":
+    main()