Cupido: Drosophila Social Interaction Tracking

Behavioral analysis of trained vs untrained Drosophila melanogaster in a barrier-opening social interaction assay. Part of the Cupido project studying learned social behaviors.

Quick Start

# Clone the repository
git clone ssh://git@git.lab.gilest.ro:222/lab/cupido.git
cd cupido

# Create virtual environment
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Project data lives outside the repo at /mnt/data/projects/cupido/:
#   tracked/  → SQLite tracking DBs
#   targets/  → target-point JSONs
#   all_video_info_merged.{xlsx,tsv}  → metadata spreadsheet
# Generated CSVs land in data/processed/ (gitignored).

# Run the main analysis notebook
jupyter notebook notebooks/flies_analysis_simple.ipynb

Project Overview

The Experiment

Pairs of flies are placed in chambers (ROIs) separated by a physical barrier. After a configurable delay, the barrier is removed, allowing flies to interact. We track the distance between flies over time to compare social approach behavior between trained (socially experienced) and untrained (naive) groups.

• 3 ethoscope machines, 5 recording sessions, 6 ROIs each = 30 ROIs with data
• 18 trained ROIs, 18 untrained ROIs (6 from Machine 139 have no tracking data)
• See docs/experimental_design.md for full details

Current Findings

Aggregate analysis shows statistically significant but tiny differences:

• Post-opening distance: Cohen's d = 0.09 (96% distribution overlap)
• Max velocity (50-200s): Cohen's d = 0.14

These effect sizes are inflated by pseudoreplication (230K data points from 18 independent ROIs per group).

Next Direction: Bimodal Hypothesis

The key insight: not all "trained" flies may have actually learned. The trained group likely contains true learners (showing distinct behavior) and non-learners (indistinguishable from untrained). Testing this requires per-ROI analysis and bimodality testing.

Read docs/bimodal_hypothesis.md for the detailed analysis plan and code sketches.

Offline Tracking Pipeline (added Apr 2026)

For tracking new videos that have no auto-detectable targets, the pipeline is split in two stages so you can sit at the screen and click for an hour, then let the tracker grind through overnight.

# extra deps (set ETHOSCOPE_SRC env var if your ethoscope clone isn't at ~/Code/ethoscope_project/...)
pip install -r requirements-tracking.txt

# 1) build the inventory (xlsx ↔ /mnt/ethoscope_data/videos/)
python scripts/build_video_inventory.py

# 2) interactive: click TOP, CORNER, LEFT on each video (one frame per video)
python scripts/pick_targets.py             # process all not-yet-picked
python scripts/pick_targets.py --redo      # re-pick already-picked videos
# keys: r=reset  n=skip  f=jump frame  q/ESC=quit  ENTER=save

# 3) batch tracking (idempotent, can run in background)
python scripts/track_videos.py --jobs 4    # parallel
# output → /mnt/data/projects/cupido/tracked/*_tracking.db (SQLite)

See tasks/todo.md "Offline Tracking" section for the full plan, and data/metadata/video_inventory.csv for the list of videos to process.

Folder Structure

tracking/
├── README.md              # This file
├── PLANNING.md            # Architecture & conventions
├── requirements.txt       # Python dependencies
├── data/
│   ├── metadata/          # Experiment metadata CSVs (small, hand-curated)
│   ├── processed/         # Generated analysis CSVs (gitignored)
│   └── logs/              # Tracker logs (gitignored)
├── scripts/               # Python analysis scripts
│   ├── config.py          # Shared path constants
│   ├── load_roi_data.py   # Extract data from DBs
│   ├── calculate_distances.py
│   ├── analyze_distances.py
│   ├── statistical_tests.py
│   ├── ml_classification.py
│   └── plot_*.py          # Plotting scripts
├── notebooks/             # Jupyter notebooks
│   ├── flies_analysis_simple.ipynb  # Main analysis (use this one)
│   └── flies_analysis.ipynb         # Full pipeline from DB extraction
├── figures/               # Generated plots (gitignored)
├── docs/                  # Scientific documentation
│   ├── analysis_summary.md
│   ├── bimodal_hypothesis.md
│   └── experimental_design.md
└── tasks/
    ├── todo.md            # Task checklist
    └── lessons.md         # Pitfalls & patterns

Data Pipeline

SQLite DBs (/mnt/data/projects/cupido/tracked/) + merged TSV
    │
    ▼  scripts/load_roi_data.py
single DataFrame stamped with experimental metadata
    │
    ▼  notebooks/flies_analysis_simple.ipynb (steps 2–4)
Aligned distance CSVs (data/processed/*_distances_aligned.csv)
    │
    ├──▶ Plots (figures/)
    ├──▶ Statistical tests
    └──▶ Identity tracking → Velocity analysis

Key Files

File	Purpose
notebooks/flies_analysis_simple.ipynb	Start here - main analysis notebook
docs/bimodal_hypothesis.md	Read next - the new analysis direction
data/metadata/2025_07_15_metadata_fixed.csv	ROI-to-group mapping
data/metadata/2025_07_15_barrier_opening.csv	Barrier opening times per machine
scripts/config.py	Shared path constants for all scripts

Requirements

• Python 3.10+
• See requirements.txt for packages (numpy, pandas, matplotlib, seaborn, scipy, scikit-learn, jupyter)
• Large data files (~370MB CSVs + ~33MB DBs) must be obtained separately