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/plot_distance_over_time.py

@@ -0,0 +1,50 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+from config import DATA_PROCESSED, FIGURES
+
+# Load data
+trained_distances = pd.read_csv(DATA_PROCESSED / 'trained_distances.csv')
+untrained_distances = pd.read_csv(DATA_PROCESSED / 'untrained_distances.csv')
+
+# Remove NaN distances
+trained_clean = trained_distances.dropna(subset=['distance'])
+untrained_clean = untrained_distances.dropna(subset=['distance'])
+
+# Create the plot
+plt.figure(figsize=(12, 6))
+
+# Sample 1000 points from each group to avoid overcrowding
+if len(trained_clean) > 1000:
+    trained_sample = trained_clean.sample(1000, random_state=42)
+else:
+    trained_sample = trained_clean
+
+if len(untrained_clean) > 1000:
+    untrained_sample = untrained_clean.sample(1000, random_state=42)
+else:
+    untrained_sample = untrained_clean
+
+plt.scatter(trained_sample['t'], trained_sample['distance'],
+           alpha=0.5, s=1, label='Trained', color='blue')
+plt.scatter(untrained_sample['t'], untrained_sample['distance'],
+           alpha=0.5, s=1, label='Untrained', color='red')
+
+plt.xlabel('Time')
+plt.ylabel('Distance')
+plt.title('Distance Between Flies Over Time')
+plt.legend()
+plt.grid(True, alpha=0.3)
+
+plt.tight_layout()
+plt.savefig(FIGURES / 'distance_over_time.png', dpi=300, bbox_inches='tight')
+plt.show()
+
+print("Trained flies:")
+print(f"  Mean distance: {trained_clean['distance'].mean():.2f}")
+print(f"  Std distance: {trained_clean['distance'].std():.2f}")
+
+print("\nUntrained flies:")
+print(f"  Mean distance: {untrained_clean['distance'].mean():.2f}")
+print(f"  Std distance: {untrained_clean['distance'].std():.2f}")