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

You can contribute in many ways to any of the packages that are included in HyRiver project. The workflow is the same for all packages. In this page, a contribution workflow for PyGridMET is explained. You can easily adapt it to other packages by replacing pygridmet with the name of the package that you want to contribute to.

Types of Contributions#

Report Bugs#

Report bugs at hyriver/pygridmet#issues.

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#

Other than new features that you might have in mind, you can look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.

Write Documentation#

PyGridMET could always use more documentation, whether as part of the official PyGridMET 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 hyriver/pygridmet#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 pygridmet for local development.

  1. Fork the PyGridMET repo through the GitHub website.

  2. Clone your fork locally and add the main pygridmet as the upstream remote:

$ git clone
$ git remote add upstream
  1. Install your local copy into a virtualenv. Assuming you have mamba installed, this is how you can set up your fork for local development:

$ cd pygridmet/
$ mamba env create -f ci/requirements/environment-dev.yml
$ mamba activate pygridmet-dev
$ python -m pip install . --no-deps
  1. Create a branch for local development:

$ git checkout -b bugfix-or-feature/name-of-your-bugfix-or-feature
$ git push
  1. Now you can make your changes locally, make sure to add a description of the changes to HISTORY.rst file and add extra tests, if applicable, to tests folder. Also, make sure to give yourself credit by adding your name at the end of the item(s) that you add in the history like this By `Taher Chegini <>`_. Then, fetch the latest updates from the remote and resolve any merge conflicts:

$ git fetch upstream
$ git merge upstream/name-of-your-branch
  1. Then create a new environment for linting and another for testing:

 $ mamba create -n py11 python=3.11 nox tomli pre-commit codespell gdal
 $ mamba activate py11
 $ nox -s pre-commit
 $ nox -s type-check

 $ mamba create -n py38 python=3.8 nox tomli pre-commit codespell gdal
 $ mamba activate py38
 $ nox -s tests

Note that if Python 3.11 is already installed on your system, you can
skip creating the ``py11`` environment and just use your system's Python 3.11
to run the linting and type-checking tests, like this:
$ mamba create -n py38 python=3.8 nox tomli pre-commit codespell gdal
$ mamba activate py38
$ nox
  1. If you are making breaking changes make sure to reflect them in the documentation, README.rst, and tests if necessary.

  2. Commit your changes and push your branch to GitHub. Start the commit message with ENH:, BUG:, DOC: to indicate whether the commit is a new feature, documentation related, or a bug fix. For example:

$ git add .
$ git commit -m "ENH: A detailed description of your changes."
$ git push origin name-of-your-branch
  1. Submit a pull request through the GitHub website.


To run a subset of tests:

$ nox -s tests -- -n=1 -k "test_name1 or test_name2"


A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.rst). Then run:

$ git tag -a vX.X.X -m "vX.X.X"
$ git push --follow-tags

where X.X.X is the version number following the semantic versioning spec i.e., MAJOR.MINOR.PATCH. Then release the tag from Github and Github Actions will deploy it to PyPi.