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_avg_distance_aligned.py

@@ -0,0 +1,101 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+from config import DATA_PROCESSED, DATA_METADATA, FIGURES
+
+# Load data
+trained_distances = pd.read_csv(DATA_PROCESSED / 'trained_distances.csv')
+untrained_distances = pd.read_csv(DATA_PROCESSED / 'untrained_distances.csv')
+barrier_data = pd.read_csv(DATA_METADATA / '2025_07_15_barrier_opening.csv')
+
+# Convert opening_time to milliseconds and create a mapping
+barrier_data['opening_time_ms'] = barrier_data['opening_time'] * 1000
+opening_times = dict(zip(barrier_data['machine'], barrier_data['opening_time_ms']))
+
+
+def align_to_opening_time(df, opening_times, max_time=300000):
+    """Align distance data to barrier opening time.
+
+    Args:
+        df (pd.DataFrame): Distance data.
+        opening_times (dict): Machine to opening time mapping.
+        max_time (int): Maximum time in ms to include.
+
+    Returns:
+        pd.DataFrame: Aligned data filtered to +/-150s around opening.
+    """
+    df_aligned = df.copy()
+    df_aligned['aligned_time'] = np.nan
+
+    for machine in df['machine_name'].unique():
+        if machine in opening_times:
+            opening_time = opening_times[machine]
+            mask = (df['machine_name'] == machine) & (df['t'] <= max_time)
+            df_aligned.loc[mask, 'aligned_time'] = df.loc[mask, 't'] - opening_time
+
+    df_aligned = df_aligned.dropna(subset=['aligned_time'])
+    df_aligned = df_aligned[(df_aligned['aligned_time'] >= -150000) &
+                           (df_aligned['aligned_time'] <= 150000)]
+
+    return df_aligned
+
+
+# Align the data
+trained_aligned = align_to_opening_time(trained_distances, opening_times)
+untrained_aligned = align_to_opening_time(untrained_distances, opening_times)
+
+# Calculate average distance over aligned time
+trained_avg = trained_aligned.groupby('aligned_time')['distance'].mean()
+untrained_avg = untrained_aligned.groupby('aligned_time')['distance'].mean()
+
+# Apply smoothing
+window_size = 50
+trained_smooth = trained_avg.rolling(window=window_size, center=True).mean()
+untrained_smooth = untrained_avg.rolling(window=window_size, center=True).mean()
+
+# Create the plot
+plt.figure(figsize=(12, 6))
+
+plt.plot(trained_smooth.index/1000, trained_smooth.values,
+         label='Trained (smoothed)', color='blue', linewidth=2)
+plt.plot(untrained_smooth.index/1000, untrained_smooth.values,
+         label='Untrained (smoothed)', color='red', linewidth=2)
+
+plt.axvline(x=0, color='black', linestyle='--', alpha=0.7, label='Barrier Opening')
+
+plt.xlabel('Time (seconds relative to barrier opening)')
+plt.ylabel('Average Distance')
+plt.title('Average Distance Between Flies Aligned to Barrier Opening Time')
+plt.legend()
+plt.grid(True, alpha=0.3)
+plt.xlim(-150, 150)
+
+plt.tight_layout()
+plt.savefig(FIGURES / 'avg_distance_aligned_to_opening.png', dpi=300, bbox_inches='tight')
+plt.show()
+
+# Print statistics
+print("Trained flies (aligned to barrier opening):")
+print(f"  Data points: {len(trained_aligned)}")
+print(f"  Mean distance: {trained_aligned['distance'].mean():.2f}")
+print(f"  Std distance: {trained_aligned['distance'].std():.2f}")
+
+print("\nUntrained flies (aligned to barrier opening):")
+print(f"  Data points: {len(untrained_aligned)}")
+print(f"  Mean distance: {untrained_aligned['distance'].mean():.2f}")
+print(f"  Std distance: {untrained_aligned['distance'].std():.2f}")
+
+# Pre/post analysis
+trained_pre = trained_aligned[trained_aligned['aligned_time'] < 0]
+trained_post = trained_aligned[trained_aligned['aligned_time'] > 0]
+untrained_pre = untrained_aligned[untrained_aligned['aligned_time'] < 0]
+untrained_post = untrained_aligned[untrained_aligned['aligned_time'] > 0]
+
+print("\nPre-opening period (t < 0):")
+print(f"  Trained mean distance: {trained_pre['distance'].mean():.2f}")
+print(f"  Untrained mean distance: {untrained_pre['distance'].mean():.2f}")
+
+print("\nPost-opening period (t > 0):")
+print(f"  Trained mean distance: {trained_post['distance'].mean():.2f}")
+print(f"  Untrained mean distance: {untrained_post['distance'].mean():.2f}")