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CONTRIBUTING.md

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How to contribute

If you're interested in contributing to the behavenet package, please contact the project developer Matt Whiteway at m.whiteway ( at ) columbia.edu.

If you would like to add a new Pytorch model to the package, you can find more detailed information here.

Before submitting a pull request, please follow these steps:

Style

The behavenet package follows the PEP8 style guidelines, and allows for line lengths of up to 99 characters. To ensure that your code matches these guidelines, please flake your code using the provided configuration file. You will need to first install flake8 in the behavenet conda environment:

(behavenet) $: pip install flake8

Once all code, tests, and documentation are in place, you can run the flaker from from the project directory:

(behavenet) $: flake8

Documentation

Behavenet uses Sphinx and readthedocs to provide documentation to developers and users.

  • complete all docstrings in new functions using google's style (see source code for examples)
  • provide inline comments when necessary; the more the merrier
  • add a new user guide if necessary (docs/source/user_guide.[new_model].rst)
  • update data structure docs if adding to hdf5 (docs/source/data_structure.rst)
  • add new hyperparams to glossary (docs/source/glossary.rst)

To check the documentation, you can compile it on your local machine first. To do so you will need to first install sphinx in the behavenet conda environment:

(behavenet) $: pip install sphinx==3.2.0 sphinx_rtd_theme==0.4.3 sphinx-automodapi==0.12

To compile the documentation, from the behavenet project directory cd to behavenet/docs and run the make file:

(behavenet) $: cd docs
(behavenet) $: make html

Testing

Behavenet uses pytest to unit test the package; in addition, there is an integration script provided to ensure the interlocking pieces play nicely. Please write unit tests for all new (non-plotting) functions, and if you updated any existing functions please update the corresponding unit tests.

To run the unit tests, first install pytest in the behavenet conda environment:

(behavenet) $: pip install pytest

Then, from the project directory, run:

(behavenet) $: pytest

To run the integration script:

(behavenet) $: python tests/integration.py

Running the integration test will take approximately 1 minute with a GPU.