Demo

Demo#

A functionality demo of the full camap analysis pipeline on the small dataset bundled with the repository under tests/assets/regression_2d/. The data is a 2D arena recording cut down to 10 units and 5000 neural frames, with the shuffle count lowered to 100. It is a minimal functionality check for the install, not a full experiment: with this little data, no unit is expected to pass the place-cell criteria.

Setup#

The demo data ships in the repository, so clone it and install from the checkout (see Installation for oasis-deconv):

git clone https://github.com/miniscope/CaMAP.git
cd CaMAP
pip install .
pip install --no-binary oasis-deconv oasis-deconv

Run#

From the repository root:

camap analysis \
  -c tests/assets/regression_2d/analysis_config.yaml \
  -d tests/assets/regression_2d/data_paths.yaml \
  -o output/demo_arena.camap -y

-y skips the interactive “Proceed with analyze_units?” prompt.

Expected output#

The console shows the pipeline steps, a 10-unit progress bar, and ends with:

Done. 10 units analyzed.
Total: 10 | SI pass: 0 (0%) | Stability pass: 0 (0%) | Both: 0 (0%) | Neither: 10
Full analysis saved to output/demo_arena.camap

Zero place cells is the expected result for this subset. NumPy RuntimeWarning: invalid value encountered in divide lines may appear during the stability shuffles; they come from constant-valued map halves in the tiny dataset and can be ignored here.

output/demo_arena.camap/ contains:

  • unit_results/scalars.csv, unit_results/stability_scalars.csv (csv, per-unit spatial information, p-values, stability)

  • unit_results/events.parquet (parquet, per-unit events), unit_results/*.npz (npz, rate maps and shuffle arrays)

  • figures/*.pdf (pdf, nine summary figures: arena_calibration, behavior_preview, diagnostics, footprints, occupancy, preprocess_steps, stability_splits, summary_scatter, timestamp_diagnostics)

  • canonical.parquet, trajectory*.parquet (parquet, per-frame position, speed, deconvolved activity)

  • deconv.npz, spatial.npz (npz, deconvolution and occupancy arrays), traces.nc, footprints.nc (netCDF, raw traces and footprints)

  • config.yaml, data_config.yaml (yaml, copies of the input configs), metadata.json (json, bundle schema and camap version)

The same fixture is checked against a saved reference bundle by tests/test_regression_2d.py, so a successful run plus a passing test suite confirms the outputs match.

Run time#

Below a few minutes on a normal desktop (about 20 s measured on a laptop, single worker).

Running the tests#

The test suite runs the same pipeline on the bundled 2D arena and 1D maze fixtures and compares against saved references:

pip install ".[tests]"
pytest tests/

Expect all tests to pass in below a few minutes (about 15 s measured on a laptop).