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Contributing

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Report Bugs

Report bugs at github.com/Multiomics-Analytics-Group/acore/issues.

If you are reporting a bug, please include:

  • Your operating system name and version.
  • Any details about your local setup that might be helpful in troubleshooting.
  • Detailed steps to reproduce the bug.

Fix Bugs

Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.

Implement Features

Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.

Write Documentation

acore could always use more documentation, whether as part of the official acore docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback

The best way to send feedback is to file an issue at github.com/Multiomics-Analytics-Group/acore/issues.

If you are proposing a feature:

  • Explain in detail how it would work.
  • Keep the scope as narrow as possible, to make it easier to implement.
  • Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!

Ready to contribute? Here's how to set up acore for local development. Consider an advanced editor to help you with some of the common steps described below, e.g. VSCode.

  1. Fork the acore repo on GitHub.
  2. Clone your fork locally:
git clone https://github.com/Multiomics-Analytics-Group/acore.git
  1. Install your local copy into a virtual environment. Assuming you have Python available on your system, this can be done using venv. Alternatives are conda environments or uv to create and manage virtual environments.
cd acore/
python -m venv .env
source .env/bin/activate
pip install -e .[dev]

If you work on Windows, see the docs: https://docs.python.org/3/library/venv.html#how-venvs-work

  1. Create a branch for local development:
git checkout -b name-of-your-bugfix-or-feature
  1. When you're done making changes, check formatting and run tests locally:
black .
ruff check src
pytest .

Some ruff fixes can be applied automatically with --fix:

ruff check src --fix
  1. Commit your changes and push your branch to GitHub:
git add .
git commit -m "Your detailed description of your changes."
git push origin name-of-your-bugfix-or-feature
  1. Submit a pull request through the GitHub website.

General design principles in the library

  • Prefer a single DataFrame output type per module (subpackage). For example, the enrichment module should output a DataFrame that adheres to a single pandera schema defined under src/acore/types/enrichment_analysis.py. Thus there is a 'type' of enrichment analysis results.
  • User-facing functions should have clear names and good docstrings (e.g. run_analysis, run_enrichment_analysis, apply_normalization).
  • Docstrings may follow numpy, google, or classic Sphinx styles.

Pull Request Guidelines

Before you submit a pull request, check that it meets these guidelines:

  1. The pull request should include tests.
  2. If the pull request adds functionality, update the docs. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
  3. The pull request should pass the GitHub workflows.

See the PR template example: Add module PR template

Deploying

A reminder for maintainers on how to deploy:

  • Make sure all changes are committed (including an entry in HISTORY.rst).
  • Create a new GitHub release
  • The package will be deployed to PyPI if the tests pass.