Contribution Guide

Contributions are highly welcomed and appreciated. Every little help counts, so do not hesitate! You can make a high impact on xskillscore just by using it and reporting issues.

The following sections cover some general guidelines regarding development in xskillscore for maintainers and contributors.

Nothing here is set in stone and can’t be changed. Feel free to suggest improvements or changes in the workflow.

Feature requests and feedback

We are eager to hear about your requests for new features and any suggestions about the API, infrastructure, and so on. Feel free to submit these as issues with the label “feature request.”

Please make sure to explain in detail how the feature should work and keep the scope as narrow as possible. This will make it easier to implement in small PRs.

Report bugs

Report bugs for xskillscore in the issue tracker with the label “bug”.

If you are reporting a bug, please include:

If you can write a demonstration test that currently fails but should passm that is a very useful commit to make as well, even if you cannot fix the bug itself.

Fix bugs

Look through the GitHub issues for bugs.

Talk to developers to find out how you can fix specific bugs.

Write documentation

xskillscore could always use more documentation. What could you add?

  • More complementary documentation. Have you perhaps found something unclear?

  • Docstrings.

  • Example notebooks of xskillscore being used in real analyses.

Our documentation is written in reStructuredText. You can follow our conventions in already written documents. Some helpful guides are located here and here.


Build the documentation locally with the following command:

$ conda env update -f ci/doc.yml
$ conda activate xskillscore-docs
$ cd docs
$ make html

The build documentation should be available in the docs/build/ folder.

To lint the reStructuredText files in the home folder run:

$ cd ..
$ doc8 *.rst

If you are adding new functions to the API, run sphinx-autogen -o api api.rst from the docs/source directory and add the functions to api.rst.

Preparing Pull Requests

  1. Fork the xskillscore GitHub repository. It’s fine to use xskillscore as your fork repository name because it will live under your user.

  2. Clone your fork locally using git, connect your repository to the upstream (main project), and create a branch:

    $ git clone
    $ cd xskillscore
    $ git remote add upstream
    # now, to fix a bug or add feature create your own branch off "main":
    $ git checkout -b your-bugfix-feature-branch-name main

    If you need some help with Git, follow this quick start guide:

  3. Install dependencies into a new conda environment:

    $ conda env update -f ci/dev.yml
    $ conda activate xskillscore-dev
  4. Make an editable install of xskillscore by running:

    $ pip install --no-deps -e .
  5. Run pre-commit:

    $ pre-commit run --all-files
 is a framework for managing and maintaining multi-language pre-commit hooks to ensure code-style and code formatting is consistent.

  6. Break your edits up into reasonably sized commits:

    $ git commit -a -m "<commit message>"
    $ git push -u
  7. Run all the tests

    Now running tests is as simple as issuing this command:

    $ pytest xskillscore

    You can also test the code in the docstrings by doing:

    $ pytest --doctest-modules xskillscore --ignore xskillscore/tests

Please stick to xarray’s testing recommendations.

  1. Running the performance test suite

Performance matters and it is worth considering whether your code has introduced performance regressions. xskillscore is starting to write a suite of benchmarking tests using asv to enable easy monitoring of the performance of critical xskillscore operations. These benchmarks are all found in the asv_bench directory.

If you need to run a benchmark, change your directory to asv_bench/ and run:

$ asv continuous -f 1.1 upstream/main HEAD

You can replace HEAD with the name of the branch you are working on, and report benchmarks that changed by more than 10%. The command uses conda by default for creating the benchmark environments.

Running the full benchmark suite can take up to half an hour and use up a few GBs of RAM. Usually it is sufficient to paste only a subset of the results into the pull request to show that the committed changes do not cause unexpected performance regressions. You can run specific benchmarks using the -b flag, which takes a regular expression. For example, this will only run tests from a asv_bench/benchmarks/ file:

$ asv continuous -f 1.1 upstream/main HEAD -b ^deterministic

If you want to only run a specific group of tests from a file, you can do it using . as a separator. For example:

$ asv continuous -f 1.1 upstream/main HEAD -b deterministic.Compute_small.time_xskillscore_metric_small

will only run the time_xskillscore_metric_small benchmark of class Compute_small defined in

  1. Create a new changelog entry in CHANGELOG.rst:

    • The entry should be entered as:

    <description> (:pr:`#<pull request number>`) `<author's names>`_

    where <description> is the description of the PR related to the change and <pull request number> is the pull request number and <author's names> are your first and last names.

    • Add yourself to list of authors at the end of CHANGELOG.rst file if not there yet, in alphabetical order.

  2. Add yourself to the contributors list via docs/source/contributors.rst.

  3. Finally, submit a pull request through the GitHub website using this data:

    head-fork: YOUR_GITHUB_USERNAME/xskillscore
    compare: your-branch-name
    base-fork: xarray-contrib/xskillscore
    base: main

Note that you can create the Pull Request while you’re working on this. The PR will update as you add more commits. xskillscore developers and contributors can then review your code and offer suggestions.