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Contributing

General Guidelines

sparse is a community-driven project on GitHub. You can find our repository on GitHub. 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.

Filing Issues

If you find a bug or would like a new feature, you might want to consider filing a new issue on GitHub. 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 main branch 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.

Contributing Code

This project has a number of requirements for all code contributed.

  • We use pre-commit to 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 benchmarks.

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]

Pull requests

Please adhere to the following guidelines:

  1. Start your pull request title with a conventional commit tag. This helps us add your contribution to the right section of the changelog. We use "Type" from the Angular convention.
    TLDR:
    The PR title should start with any of these abbreviations: build, chore, ci, depr, docs, feat, fix, perf, refactor, release, test. Add a !at the end, if it is a breaking change. For example refactor!.
  2. This text will end up in the changelog.
  3. Please follow the instructions in the pull request form and submit.

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, with the pytest-cov extension to check code coverage and pytest-flake8 to 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.

Coverage

The pytest script automatically reports coverage, both on the terminal for missing line numbers, and in annotated HTML form in htmlcov/index.html.

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 Material for MkDocs to document this library. MkDocs, in turn, uses Markdown as its markup language for adding code.

We use mkdoctrings with the mkdocs-gen-files plugin to generate API references.

To build the documentation, you can run

mkdocs build
mkdocs serve

After this, you can see a version of the documentation on your local server.

Documentation for each pull requests is automatically built on Read the Docs. It is rebuilt with every new commit to your PR. There will be a link to preview it from your PR checks area on GitHub when ready.

Adding and Running Benchmarks

We use CodSpeed to run benchmarks. They are run in the CI environment when a pull request is opened. Then the results of the run are sent to CodSpeed servers to be analyzed. When the analysis is done, a report is generated and posted automatically as a comment to the PR. The report includes a link to CodSpeedcloud where you can see the all the results.

If you add benchmarks, they should be written as regular tests to be used with pytest, and use the fixture benchmark. Please see the CodSpeeddocumentation for more details.