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Get Started!

Ready to contribute? Here's how to set up MatPlotCheck for local development.

1. Fork the repository on GitHub

To create your own copy of the repository on GitHub, navigate to the earthlab/matplotcheck repository and click the Fork button in the top-right corner of the page.

2. Clone your fork locally

Use git clone to get a local copy of your MatPlotCheck repository on your local filesystem:

$ git clone [email protected]:your_name_here/matplotcheck.git
$ cd matplotcheck/

3. Set up your fork for local development

Create an environment

Using conda, there are two options.

1. The easiest option is to create an environment from the environment.yml file. Note that this will only allow you to test against one version of python locally, but this is the recommended option on Windows and MacOS:

$ conda env create -f environment.yml
$ conda activate matplotcheck-dev

Install the package

Once your matplotcheck-dev environment is activated, install MatPlotCheck in editable mode, along with the development requirements and pre-commit hooks:

$ pip install -e .
$ pip install -r dev-requirements.txt
$ pre-commit install

4. Create a branch for local development

Use the git checkout command to create your own branch, and pick a name that describes the changes that you are making:

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

Now you can make your changes locally.

5. Test the package

Ensure that the tests pass, and the documentation builds successfully:

$ pytest
$ make docs

Documentation Updates

Improving the documentation and testing for code already in MatPlotCheck is a great way to get started if you'd like to make a contribution. Please note that our documentation files are in ReStructuredText (.rst) format and format your pull request accordingly.

To build the documentation, use the command:

$ make docs

By default make docs will only rebuild the documentation if source files (e.g., .py or .rst files) have changed. To force a rebuild, use make -B docs. You can preview the generated documentation by opening docs/_build/html/index.html in a web browser.

MatPlotCheck uses doctest to test code in the documentation, which includes docstrings in MatPlotCheck's modules, and code chunks in the reStructuredText source files. This enables the actual output of code examples to be checked against expected output. When the output of an example is not always identical (e.g., the memory address of an object), use an ellipsis (...) to match any substring of the actual output, e.g.:

>>> print(list(range(20)))
[0, 1, ..., 18, 19]

MatPlotCheck also uses the Matplotlib plot directive in the documentation to generate figures. To include a figure in an example, prefix the example with .. plot::, e.g.,:

.. plot::

   >>> import matplotlib.pyplot as plt
   >>> plt.plot([1, 2, 3], [4, 5, 6])

Code style

  • MatPlotCheck currently only supports Python 3 (3.6+). Please test code locally in Python 3 when possible (all supported versions will be automatically tested on Travis CI).

  • MatPlotCheck uses a pre-commit hook that runs the black code autoformatter. Be sure to execute pre-commit install as described above, which will cause black to autoformat code prior to commits. If this step is skipped, black may cause build failures on Travis CI due to formatting issues.

  • Follow PEP 8 when possible. Some standards that we follow include:

    • The first word of a comment should be capitalized with a space following the # sign like this: # This is a comment here
    • Variable and function names should be all lowercase with words separated by _.
    • Class definitions should use camel case - example: ClassNameHere .

  • Imports should be grouped with standard library imports first, 3rd-party libraries next, and MatPlotCheck imports third following PEP 8 standards. Within each grouping, imports should be alphabetized. Always use absolute imports when possible, and explicit relative imports for local imports when necessary in tests.

Deploying

A reminder for the maintainers on how to deploy. Make sure all your changes are committed, then run:

$ bumpversion patch # possible: major / minor / patch

This will increment the version according to a major release (e.g., 0.1.0 to 1.0.0), a minor release (e.g., 0.1.0 to 0.2.0), or a patch (e.g., 0.1.0 to 0.1.1), following the guidelines for semantic versioning: https://semver.org/.

Bumpversion updates the version number throughout the package, and generates a git commit along with an associated git tag for the new version. For more on bumpversion, see: https://github.com/peritus/bumpversion

To deploy MatPlotCheck,

  1. First update the changelog with the new version being pushed and create a new unreleased section.

  2. Then, push the commit and the version tags:

    $ git push
$ git push --tags

Once the push has built on GitHub actions, you are ready to make a final release. To do that, go to GitHub, create a new release with the tag version you just pushed. In the release, you can mention the changes listed in the changelog (just copy and paste them). GitHub actions will then release to PyPi.