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/statistical_tests.py

@@ -0,0 +1,90 @@
+import pandas as pd
+import numpy as np
+from scipy import stats
+
+from config import DATA_PROCESSED, DATA_METADATA
+
+# 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):
+    """Align distance data to barrier opening time.
+
+    Args:
+        df (pd.DataFrame): Distance data with machine_name and t columns.
+        opening_times (dict): Mapping of machine ID to opening time in ms.
+
+    Returns:
+        pd.DataFrame: Data with aligned_time column added.
+    """
+    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_aligned.loc[mask, 'aligned_time'] = df.loc[mask, 't'] - opening_time
+
+    df_aligned = df_aligned.dropna(subset=['aligned_time'])
+    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)
+
+# Remove NaN distances
+trained_clean = trained_aligned.dropna(subset=['distance'])
+untrained_clean = untrained_aligned.dropna(subset=['distance'])
+
+# Split into pre- and post-opening periods
+trained_pre = trained_clean[trained_clean['aligned_time'] < 0]['distance']
+trained_post = trained_clean[trained_clean['aligned_time'] > 0]['distance']
+untrained_pre = untrained_clean[untrained_clean['aligned_time'] < 0]['distance']
+untrained_post = untrained_clean[untrained_clean['aligned_time'] > 0]['distance']
+
+print("=== STATISTICAL TESTS ===")
+
+# Pre-opening period comparison
+t_stat_pre, p_val_pre = stats.ttest_ind(trained_pre, untrained_pre)
+cohens_d_pre = (trained_pre.mean() - untrained_pre.mean()) / np.sqrt(((len(trained_pre)-1)*trained_pre.var() + (len(untrained_pre)-1)*untrained_pre.var()) / (len(trained_pre) + len(untrained_pre) - 2))
+
+print(f"Pre-opening period:")
+print(f"  Trained mean: {trained_pre.mean():.2f}, Untrained mean: {untrained_pre.mean():.2f}")
+print(f"  T-statistic: {t_stat_pre:.4f}, P-value: {p_val_pre:.2e}")
+print(f"  Cohen's d: {cohens_d_pre:.4f}")
+
+# Post-opening period comparison
+t_stat_post, p_val_post = stats.ttest_ind(trained_post, untrained_post)
+cohens_d_post = (trained_post.mean() - untrained_post.mean()) / np.sqrt(((len(trained_post)-1)*trained_post.var() + (len(untrained_post)-1)*untrained_post.var()) / (len(trained_post) + len(untrained_post) - 2))
+
+print(f"\nPost-opening period:")
+print(f"  Trained mean: {trained_post.mean():.2f}, Untrained mean: {untrained_post.mean():.2f}")
+print(f"  T-statistic: {t_stat_post:.4f}, P-value: {p_val_post:.2e}")
+print(f"  Cohen's d: {cohens_d_post:.4f}")
+
+# Within-group comparisons (pre vs post)
+t_stat_trained, p_val_trained = stats.ttest_ind(trained_pre, trained_post)
+cohens_d_trained = (trained_post.mean() - trained_pre.mean()) / np.sqrt(((len(trained_post)-1)*trained_post.var() + (len(trained_pre)-1)*trained_pre.var()) / (len(trained_post) + len(trained_pre) - 2))
+
+t_stat_untrained, p_val_untrained = stats.ttest_ind(untrained_pre, untrained_post)
+cohens_d_untrained = (untrained_post.mean() - untrained_pre.mean()) / np.sqrt(((len(untrained_post)-1)*untrained_post.var() + (len(untrained_pre)-1)*untrained_pre.var()) / (len(untrained_post) + len(untrained_pre) - 2))
+
+print(f"\nWithin-group changes:")
+print(f"  Trained flies - Pre vs Post:")
+print(f"    Mean change: {trained_post.mean() - trained_pre.mean():.2f}")
+print(f"    T-statistic: {t_stat_trained:.4f}, P-value: {p_val_trained:.2e}")
+print(f"    Cohen's d: {cohens_d_trained:.4f}")
+
+print(f"  Untrained flies - Pre vs Post:")
+print(f"    Mean change: {untrained_post.mean() - untrained_pre.mean():.2f}")
+print(f"    T-statistic: {t_stat_untrained:.4f}, P-value: {p_val_untrained:.2e}")
+print(f"    Cohen's d: {cohens_d_untrained:.4f}")