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>

tasks/todo.md

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+# Task List
+
+## Completed Work
+
+- [x] Extract ROI data from SQLite databases grouped by trained/untrained
+- [x] Calculate inter-fly distances at each time point
+- [x] Align data to barrier opening time (t=0)
+- [x] Plot average distance over time (entire experiment + 300s window)
+- [x] Track fly identities across frames (Hungarian algorithm)
+- [x] Calculate max velocity over 10-second moving windows
+- [x] Statistical tests (t-tests, Cohen's d) comparing groups
+- [x] ML classification attempt (Logistic Regression, Random Forest)
+- [x] Clustering analysis (K-means)
+- [x] Organize project structure for student handoff
+
+## Priority: Bimodal Hypothesis Analysis
+
+See `docs/bimodal_hypothesis.md` for detailed methodology.
+
+### Phase 1: Per-ROI Feature Extraction
+- [ ] Compute per-ROI summary statistics from aligned distance data
+  - Mean distance post-opening (0-300s)
+  - Median distance post-opening
+  - Fraction of time at distance < 50px ("close proximity")
+  - Mean max velocity post-opening
+- [ ] Create a summary DataFrame with N=18 trained + N=18 untrained rows
+- [ ] **Note**: Only 30 ROIs have data (Machine 139 missing = 6 ROIs lost)
+
+### Phase 2: Distribution Visualization
+- [ ] Plot histograms/KDE of per-ROI metrics for each group
+- [ ] Look for bimodality in trained group vs unimodality in untrained
+
+### Phase 3: Formal Bimodality Testing
+- [ ] Hartigan's dip test on trained per-ROI distributions
+- [ ] Fit Gaussian Mixture Models (1 vs 2 components) to trained data
+- [ ] Compare BIC scores to determine optimal number of components
+
+### Phase 4: Subgroup Identification
+- [ ] If bimodal: classify trained ROIs as "learner" vs "non-learner" using GMM posteriors
+- [ ] Compare learner subgroup vs untrained group (expect larger effect size)
+
+### Phase 5: Effect Size Re-estimation
+- [ ] Mann-Whitney U test (appropriate for small N)
+- [ ] Bootstrap confidence intervals for effect sizes
+- [ ] Account for session as random effect
+
+## Maintenance Items
+
+- [ ] Investigate missing Machine 139 data (has metadata but no tracking DB)
+- [ ] Add `diptest` to requirements.txt when starting bimodal analysis
+- [ ] Consider converting pixel distances to physical units (need calibration)
+- [ ] The second notebook (`flies_analysis.ipynb`) re-runs from DB extraction - consider deprecating
+
+## Discovered During Work
+
+(Add new items here as they come up during analysis)