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/data-exp-lab/yt_xarray/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

yt_xarray could always use more documentation, whether as part of the official yt_xarray 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/data-exp-lab/yt_xarray/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 yt_xarray for local development.

  1. Fork the yt_xarray repo on GitHub.

  2. Clone your fork locally:

    $ git clone git@github.com:your_name_here/yt_xarray.git

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

    $ mkvirtualenv yt_xarray
$ cd yt_xarray/
$ python -m pip install -e .
$ python -m pip install -r requirements_dev.txt

  4. (optional) Setup pre-commit:

    $ pre-commit install

  5. Create a branch for local development:

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

Now you can make your changes locally.

  1. When you’re done making changes, check that your changes pass the tests:

    $ pytest

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

    $ git add .
$ git commit -m "Your detailed description of your changes."

If you’ve installed pre-commit, then pre-commit will run your changes through some style checks. It will try to fix files if needed. If it finds errors, you will need to re-add those files (after fixing it if pre-commit could not do so automatically) and then commit again.

Now your branch is ready to push:

$ git push origin name-of-your-bugfix-or-feature

  1. 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.

  3. The pull request should pass all automated tests.

Tips

To run a subset of tests:

$ pytest tests.test_yt_xarray

Deploying

A reminder for the maintainers on how to deploy.

First, make sure that:

  • all your changes are committed (including an entry in HISTORY.md).

  • the version in yt_xarray/__init__.py matches the version you are releasing.

Then, double check your main branch mactches upstream (assuming that git remote -v lists git@github.com:data-exp-lab/yt_xarray.git as the upstream repo):

$ git fetch --all
$ git checkout main
$ git rebase upstream/main

And then create a tag for your new release. For example, if you are releasing version 1.1.2:

$ git tav v1.1.2

Now push that tag to the upstream repo:

$ git push upstream v1.1.2

This will trigger a number of automated Github actions that include an automated push to PyPI (if tests pass) and the creation of a draft release on Github. Wait for the push to PyPI to finish then go edit the draft release that should be visible at https://github.com/data-exp-lab/yt_xarray/releases and hit publish after editing release notes.