Contributing to datadivr
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
You can contribute in many ways:
Types of Contributions
Report Bugs
Report bugs at https://github.com/menchelab/datadivr/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 a fix for 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
datadivr could always use more documentation, whether as part of the official 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 https://github.com/menchelab/datadivr/issues.
If you are proposing a new 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 datadivr
for local development.
Please note this documentation assumes you already have uv
and Git
installed and ready to go.
1. Fork the datadivr
repo on GitHub.
2. Clone your fork locally:
3. Now we need to install the environment. Navigate into the directory
Then, install and activate the environment with:
4. Install pre-commit to run linters/formatters at commit time:
5. Create a branch for local development:
6. Alternatively you can use vscode devcontainer feature which only requires you to have vscode and docker desktop installed, when you open the project in vscode install the extension and click the reopen in container button
Now you can make your changes locally.
6. Don't forget to add test cases for your added functionality to the tests
directory.
7. When you're done making changes, check that your changes pass the formatting tests.
Now, validate that all unit tests are passing:
9. Before raising a pull request you should also run tox. This will run the tests across different versions of Python:
10. 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
When writing commit messages, please follow the Conventional Commits specification. This helps maintain a standardized commit history and enables automated tooling. The basic format is:
Common types include:
feat
: A new featurefix
: A bug fixdocs
: Documentation changesstyle
: Code style changes (formatting, etc.)refactor
: Code changes that neither fix bugs nor add featurestest
: Adding or modifying testschore
: Changes to build process or auxiliary tools
Example commit messages:
feat(api): add new data validation endpoint
fix: correct memory leak in processing loop
docs: update installation instructions
11. Submit a pull request through the GitHub website.
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, the docs should be updated. Put your new functionality into a function with a docstring.