Contributions are very welcome

We greatly value contributions of any kind. Contributions could include, but are not limited to documentation improvements, bug reports, new or improved code, scientific and technical code reviews, infrastructure improvements, mailing list and chat participation, community help/building, education and outreach. We value the time you invest in contributing and strive to make the process as easy as possible. If you have suggestions for improving the process of contributing, please do not hesitate to propose them.

If you have a bug or other issue to report or just need help, please open an issue on the issues tab on the ESMValCore github repository.

If you would like to contribute a new preprocessor function, derived variable, fix for a dataset, or another new feature, please discuss your idea with the development team before getting started, to avoid double work and/or disappointment later. A good way to do this is to open an issue on GitHub.

To get started developing, follow the instructions below. For help with common new features, please have a look at Development.

Getting started

To install for development, follow the instructions in Installation.

Running tests

Go to the directory where the repository is cloned and run pytest. Optionally you can skip tests which require additional dependencies for supported diagnostic script languages by adding -m 'not installation' to the previous command. Tests will also be run automatically by CircleCI.

Sample data

If you need sample data to work with, this repository contains samples of real data for use with ESMValTool development, demonstration purposes and automated testing. The goal is to keep the repository size small (~ 100 MB), so it can be easily downloaded and distributed.

The data are installed as part of the developer dependencies, and used by some larger tests (i.e. in the multimodel tests)

The loading and preprocessing of the data can be somewhat time-consuming (~30 secs) and are cached by pytest to make the tests more performant. Clear the cache by using running pytest with the --cache-clear flag. To avoid running these tests using sample data, use pytest -m “not use_sample_data”. If you are adding new tests using sample data, please use the decorator @pytest.mark.use_sample_data.

Code style

To increase the readability and maintainability or the ESMValCore source code, we aim to adhere to best practices and coding standards. All pull requests are reviewed and tested by one or more members of the core development team. For code in all languages, it is highly recommended that you split your code up in functions that are short enough to view without scrolling.

We include checks for Python and yaml files, which are described in more detail in the sections below. This includes checks for invalid syntax and formatting errors. Pre-commit is a handy tool that can run all of these checks automatically. It knows knows which tool to run for each filetype, and therefore provides a simple way to check your code!


To run pre-commit on your code, go to the ESMValCore directory (cd ESMValCore) and run

pre-commit run

By default, pre-commit will only run on the files that have been changed, meaning those that have been staged in git (i.e. after git add

To make it only check some specific files, use

pre-commit run --files


pre-commit run --files your_script.R

Alternatively, you can configure pre-commit to run on the staged files before every commit (i.e. git commit), by installing it as a git hook using

pre-commit install

Pre-commit hooks are used to inspect the code that is about to be committed. The commit will be aborted if files are changed or if any issues are found that cannot be fixed automatically. Some issues cannot be fixed (easily), so to bypass the check, run

git commit --no-verify


git commit -n

or uninstall the pre-commit hook

pre-commit uninstall


The standard document on best practices for Python code is PEP8 and there is PEP257 for documentation. We make use of numpy style docstrings to document Python functions that are visible on readthedocs.

Most formatting issues in Python code can be fixed automatically by running the commands


to sort the imports in the standard way using isort and

yapf -i

to add/remove whitespace as required by the standard using yapf,

docformatter -i

to run docformatter which helps formatting the doc strings (such as line length, spaces).

To check if your code adheres to the standard, go to the directory where the repository is cloned, e.g. cd ESMValCore, and run prospector

prospector esmvaltool/diag_scripts/your_diagnostic/


python lint

to see the warnings about the code style of the entire project.

We use flake8 on CircleCI to automatically check that there are no formatting mistakes and Codacy for monitoring (Python) code quality. Running prospector locally will give you quicker and sometimes more accurate results.


Please use yamllint to check that your YAML files do not contain mistakes.

Any text file

A generic tool to check for common spelling mistakes is codespell.


What should be documented

Any code documentation that is visible on readthedocs should be well written and adhere to the standards for documentation for the respective language. Note that there is no need to write extensive documentation for functions that are not visible on readthedocs. However, adding a one line docstring describing what a function does is always a good idea. When making changes/introducing a new preprocessor function, also update the preprocessor documentation.

How to build the documentation locally

Go to the directory where the repository is cloned and run

python build_sphinx -Ea

Make sure that your newly added documentation builds without warnings or errors.

Branches, pull requests and code review

The default git branch is master. Use this branch to create a new feature branch from and make a pull request against. This page offers a good introduction to git branches, but it was written for BitBucket while we use GitHub, so replace the word BitBucket by GitHub whenever you read it.

It is recommended that you open a draft pull request early, as this will cause CircleCI to run the unit tests and Codacy to analyse your code. It’s also easier to get help from other developers if your code is visible in a pull request.

You can view the results of the automatic checks below your pull request. If one of the tests shows a red cross instead of a green approval sign, please click the link and try to solve the issue. Note that this kind of automated checks make it easier to review code, but they are not flawless, so occasionally Codacy will report false positives.

Contributing to the ESMValCore package

Contributions to ESMValCore should

  • Preferably be covered by unit tests. Unit tests are mandatory for new preprocessor functions or modifications to existing functions. If you do not know how to start with writing unit tests, let us know in a comment on the pull request and a core development team member will try to help you get started.

  • Be accompanied by appropriate documentation.

  • Introduce no new issues on Codacy.

List of authors

If you make a contribution to ESMValCore and would like to be listed as an author, please add your name to the list of authors in CITATION.cff and regenerate the file .zenodo.json by running the command

pip install cffconvert
cffconvert --ignore-suspect-keys --outputformat zenodo --outfile .zenodo.json

How to make a release

The release manager makes the release, assisted by the release manager of the previous release, or if that person is not available, another previous release manager. Perform the steps listed below with two persons, to reduce the risk of error.

To make a new release of the package, follow these steps:

1. Check the tests on GitHub Actions and CircleCI

Check the nightly build on CircleCI and the GitHub Actions run. All tests should pass before making a release (branch).

2. Increase the version number

The version number is stored in esmvalcore/, package/meta.yaml, CITATION.cff. Make sure to update all files. Also update the release date in CITATION.cff. See for more information on choosing a version number. Make a pull request and get it merged into master.

3. Add release notes

Use the script esmvaltool/utils/ to create create a draft of the release notes. This script uses the titles and labels of merged pull requests since the previous release. Review the results, and if anything needs changing, change it on GitHub and re-run the script until the changelog looks acceptable. Copy the result to the file doc/changelog.rst. Make a pull request and get it merged into master.

4. Create a release branch

Create a branch off the master branch and push it to GitHub. Ask someone with administrative permissions to set up branch protection rules for it so only you and the person helping you with the release can push to it. Announce the name of the branch in an issue and ask the members of the ESMValTool development team to run their favourite recipe using this branch.

5. Cherry pick bugfixes into the release branch

If a bug is found and fixed (i.e. pull request merged into the master branch) during the period of testing, use the command git cherry-pick to include the commit for this bugfix into the release branch. When the testing period is over, make a pull request to update the release notes with the latest changes, get it merged into master and cherry-pick it into the release branch.

6. Make the release on GitHub

Do a final check that all tests on CircleCI and GitHub Actions completed successfully. Then click the releases tab and create the new release from the release branch (i.e. not from master).

7. Create and upload the Conda package

The package is automatically uploaded to the ESMValGroup conda channel by a GitHub action. If this has failed for some reason, build and upload the package manually by following the instructions below.

Follow these steps to create a new conda package:

  • Check out the tag corresponding to the release, e.g. git checkout tags/v2.1.0

  • Make sure your current working directory is clean by checking the output of git status and by running git clean -xdf to remove any files ignored by git.

  • Edit package/meta.yaml and uncomment the lines starting with git_rev and git_url, remove the line starting with path in the source section.

  • Activate the base environment conda activate base

  • Install the required packages: conda install -y conda-build conda-verify ripgrep anaconda-client

  • Run conda build package -c conda-forge -c esmvalgroup to build the conda package

  • If the build was successful, upload the package to the esmvalgroup conda channel, e.g. anaconda upload --user esmvalgroup /path/to/conda/conda-bld/noarch/esmvalcore-2.1.0-py_0.tar.bz2.

8. Create and upload the PyPI package

The package is automatically uploaded to the PyPI by a GitHub action. If has failed for some reason, build and upload the package manually by following the instructions below.

Follow these steps to create a new Python package:

  • Check out the tag corresponding to the release, e.g. git checkout tags/v2.1.0

  • Make sure your current working directory is clean by checking the output of git status and by running git clean -xdf to remove any files ignored by git.

  • Install the required packages: python3 -m pip install --upgrade pep517 twine

  • Build the package: python3 -m --source --binary --out-dir dist/ . This command should generate two files in the dist directory, e.g. ESMValCore-2.1.0-py3-none-any.whl and ESMValCore-2.1.0.tar.gz.

  • Upload the package: python3 -m twine upload dist/* You will be prompted for an API token if you have not set this up before, see here for more information.

You can read more about this in Packaging Python Projects.