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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:

Types of Contributions

Report Bugs

Report bugs at https://github.com/matplotlib/mpl-probscale/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

mpl-probscale could always use more documentation, whether as part of the official mpl-probscale 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/matplotlib/mpl-probscale/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 probscale for local development.

  1. Fork the probscale repo on GitHub.

  2. Clone your fork locally:

    $ git clone [email protected]:your_name_here/probscale.git

  3. Install your local copy into a conda environment. Assuming you have conda installed, this is how you set up your fork for local development:

    $ conda config --add channels conda-forge
$ conda create --name=probscale python=3.5 numpy matplotlib pytest pytest-cov pytest-pep8 pytest-mpl
$ cd probscale/
$ pip install -e .

  4. Create a branch for local development:

    $ git checkout -b name-of-your-bugfix-or-feature

    Now you can make your changes locally.

  5. When you're done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:

    $ python -m pytest --mpl --pep8 --cov

  6. Commit your changes and push your branch to GitHub:

    $ git add <files you want to stage>
$ git commit -m "Your detailed description of your changes."
$ git push origin name-of-your-bugfix-or-feature

  7. Submit a pull request through the GitHub website.

Matplotlib has good info on working with source code using git and GitHub.

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, and add the feature to the list in README.rst.
  3. The pull request should work for Python 3.4 and higher. Check https://travis-ci.org/matplotlib/mpl-probscale/pull_requests and make sure that the tests pass for all supported Python versions.

Tips

To run a subset of tests:

$ py.test tests.test_probscale

After this, hitting ctrl+b in either text editor will run the test suite.