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CONTRIBUTING.md

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Contributing to NumbaCS

First off, thank you for considering contributing to NumbaCS.

How to contribute

The preferred workflow for contributing to NumbaCS is to fork the main repository on GitHub, clone, and develop on a branch. Steps:

  1. Fork the project repository by clicking on the 'Fork' button near the top right of the page. This creates a copy of the code under your GitHub user account. For more details on how to fork a repository see this guide.

  2. Clone your fork of the NumbaCS repo from your GitHub account to your local disk:

    $ git clone git@github.com:YourLogin/numbacs.git
$ cd numbacs

  3. Create a feature branch to hold your development changes:

    $ git checkout -b my-feature

    Always use a feature branch. It's good practice to never work on the main branch!

  4. Develop the feature on your feature branch. Add changed files using git add and then git commit files:

    $ git add modified_files
$ git commit

    to record your changes in Git, then push the changes to your GitHub account with:

    $ git push -u origin my-feature

  5. Follow these instructions to create a pull request from your fork. This will send an email to the committers.

(If any of the above seems like magic to you, please look up the Git documentation on the web, or ask a friend or another contributor for help.)

Pull Request Checklist

We recommended that your contribution complies with the following rules before you submit a pull request:

  • If your pull request addresses an issue, please use the pull request title to describe the issue and mention the issue number in the pull request description. This will make sure a link back to the original issue is created.

  • All public functions/methods should have informative docstrings with sample usage presented as doctests when appropriate.

  • Please prefix the title of your pull request with [MRG] (Ready for Merge), if the contribution is complete and ready for a detailed review. A developer will review your code and change the prefix of the pull request to [MRG + 1] on approval, making it eligible for merging. An incomplete contribution -- where you expect to do more work before receiving a full review -- should be prefixed [WIP] (to indicate a work in progress) and changed to [MRG] when it matures. WIPs may be useful to: indicate you are working on something to avoid duplicated work, request broad review of functionality or API, or seek collaborators. WIPs often benefit from the inclusion of a task list in the PR description.

  • When adding additional functionality, provide at least one example script in the examples/ folder. Have a look at other examples for reference. Examples should demonstrate why the new functionality is useful in practice and, if possible, compare it to other methods available in NumbaCS.

  • Documentation and tests are necessary for enhancements to be accepted. Bug-fixes or new features should be provided with with passing tests using pytest. If you are unfamiliar with pytest, see the pytest documentation for details. These tests verify the correct behavior of the fix or feature. For the Bug-fixes case, at the time of the PR, tests should fail for the code base in main and pass for the PR code.

  • At least one paragraph of narrative documentation with links to references in the literature (with PDF links when possible) and the example.

Bonus points for contributions that include a performance analysis with a benchmark script and profiling output (please report on the GitHub issue).

Filing bugs

We use Github issues to track all bugs and feature requests; feel free to open an issue if you have found a bug or wish to see a feature implemented.

It is recommended to check that your issue complies with the following rules before submitting:

  • Verify that your issue is not being currently addressed by other issues or pull requests.

  • Please ensure all code snippets and error messages are formatted in appropriate code blocks. See Creating and highlighting code blocks.

  • Please include your operating system type and version number, as well as your Python, NumbaCS, Numba, NumPy, SciPy, numbalsoda, interpolation, and ContourPy versions. This information can be found by running the following code snippet:

import platform; print(platform.platform())
import sys; print("Python", sys.version)
import numbacs; print("NumbaCS", numbacs.__version__)
import numba; print("Numba", numba.__version__)
import numpy; print("NumPy", numpy.__version__)
import scipy; print("SciPy", scipy.__version__)
import contourpy; print("ContourPy", contourpy.__version__)

The rest (numbalsoda and interpolation) can be found by opening a terminal and running the following commands:

$ conda activate numbacs_env
$ conda list

where numbacs_env is the conda enviornment that NumbaCS is installed in.

  • Please be specific about what functions are involved and the shape of the data, as appropriate; please include a reproducible code snippet or link to a gist. If an exception is raised, please provide the traceback.

Documentation

We are glad to accept any sort of documentation: function docstrings, reStructuredText documents (like this one), tutorials, etc. reStructuredText documents live in the source code repository under the doc/ directory.

You can edit the documentation using any text editor and then generate the HTML output by typing make html from the docs/ directory. Alternatively, make can be used to quickly generate the documentation without the example gallery with make html-noplot. The resulting HTML files will be placed in docs/build/html/ and are viewable in a web browser.

For building the documentation, you will need sphinx, matplotlib, and pillow.

When you are writing documentation, it is important to keep a good compromise between mathematical and algorithmic details, and give intuition to the reader on what the algorithm does. It is best to always start with a small paragraph with a hand-waving explanation of what the method does to the data and a figure (coming from an example) illustrating it.

This Contribution guide is strongly inspired by the one of the POT team, which was inspired by the one of the scikit-learn team.