sparse is a community-driven project on GitHub. You can find our repository on GitHub <:ghuser:`pydata/sparse>`_. Feel free to open issues for new features or bugs, or open a pull request to fix a bug or add a new feature.
If you haven’t contributed to open-source before, we recommend you read this excellent guide by GitHub on how to contribute to open source. The guide is long, so you can gloss over things you’re familiar with.
If you’re not already familiar with it, we follow the fork and pull model on GitHub.
If you find a bug or would like a new feature, you might want to consider filing a new issue on GitHub <:ghuser:`pydata/sparse/issues>`_. Before you open a new issue, please make sure of the following:
This should go without saying, but make sure what you are requesting is within the scope of this project.
The bug/feature is still present/missing on the
masterbranch on GitHub.
A similar issue or pull request isn’t already open. If one already is, it’s better to contribute to the discussion there.
This project has a number of requirements for all code contributed.
flake8to automatically lint the code and maintain code style.
We use Numpy-style docstrings.
It’s ideal if user-facing API changes or new features have documentation added.
100% code coverage is recommended for all new code in any submitted PR. Doctests count toward coverage.
Performance optimizations should have benchmarks added in
Setting up Your Development Environment
The following bash script is all you need to set up your development environment, after forking and cloning the repository:
pip install -e .[all]
Running/Adding Unit Tests
It is best if all new functionality and/or bug fixes have unit tests added with each use-case.
We use pytest as our unit testing framework,
pytest-cov extension to check code coverage and
check code style. You don’t need to configure these extensions yourself. Once you’ve
configured your environment, you can just
cd to the root of your repository and run
pytest --pyargs sparse
This automatically checks code style and functionality, and prints code coverage, even though it doesn’t fail on low coverage.
Unit tests are automatically run on Travis CI for pull requests.
pytest script automatically reports coverage, both on the terminal for
missing line numbers, and in annotated HTML form in
Coverage is automatically checked on CodeCov for pull requests.
Adding/Building the Documentation
If a feature is stable and relatively finalized, it is time to add it to the documentation. If you are adding any private/public functions, it is best to add docstrings, to aid in reviewing code and also for the API reference.
We use Numpy style docstrings and Sphinx to document this library. Sphinx, in turn, uses reStructuredText as its markup language for adding code.
We use the Sphinx Autosummary extension
to generate API references. In particular, you may want do look at the
directory to see how these files look and where to add new functions, classes or modules.
For example, if you add a new function to the
sparse.COO class, you would open up
docs/generated/sparse.COO.rst, and add in the name of the function where appropriate.
To build the documentation, you can
cd into the
sphinx-build -W -b html . _build/html
After this, you can find an HTML version of the documentation in
Documentation for pull requests is automatically built on CircleCI and can be found in the build artifacts.
Adding and Running Benchmarks
We use Airspeed Velocity to run benchmarks. We have it set
up to use
conda, but you can edit the configuration locally if you so wish.