Contributions are welcomed from anyone, including posting issues or submitting pull requests to the pynucastro github.


Creating an issue on github is a good way to request new features, file a bug report, or notify us of any difficulties that arise using pynucastro.

To request support using pynucastro, please create an issue on the pynucastro github and the developers will be happy to assist you.

If you are reporting a bug, please indicate any information necessary to reproduce the bug including your version of python.

Pull Requests

Any contributions that have the potential to change answers should be done via pull requests. A pull request should be generated from a feature branch in your fork of pynucastro and target the main branch.

You should run the test suite on your changes, if possible, before issuing the PR. See the section below for detailed instructions.

Once you have run the tests and submitted the PR, one of the pynucastro developers will review the PR and if needed, suggest modifications prior to merging the PR.

If there are a number of small commits making up the PR, we may wish to squash commits upon merge to have a clean history. Please ensure that your PR title and first post are descriptive, since these will be used for a squashed commit message.


We use pytest to do unit and regression tests. All commands should be run from the repository root.

  • To run all the tests:

    pytest -v --nbval
  • To run just the unit tests (this is faster, but may not catch all bugs):

    pytest -v
  • To run just the jupyter notebook regression tests:

    pytest -v --nbval -p no:python

    This executes each of the notebooks under examples/ and docs/source/ and compares against the stored outputs from each cell.

  • To check code coverage:

    pytest --cov=pynucastro --nbval

    The results can be inspected in more detail by running coverage html and opening the generated htmlcov/index.html in a web browser.

    Note that numba-accelerated routines (most notably the screening functions) do not support coverage reporting, and will always show up as missed.

  • To regenerate the reference network output files used in the unit tests:

    pytest -v -s --update-networks -k write_network

    This will be needed if any of the network generation code or C++ network templates are changed.

  • To re-run notebooks whose outputs have changed:

    docs/ <paths to notebooks>