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 python setup.py test. Optionally you can skip tests which require additional dependencies for supported diagnostic script languages by adding --addopts '-m "not installation"' to the previous command. Tests will also be run automatically by CircleCI.

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.

Python¶

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

isort some_file.py

to sort the imports in the standard way and

yapf -i some_file.py

to add/remove whitespace as required by the standard.

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

prospector esmvaltool/diag_scripts/your_diagnostic/your_script.py

Run

python setup.py lint

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

We use pycodestyle 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.

YAML¶

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.

Documentation¶

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 setup.py 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 (significant) contribution to ESMValCore, 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¶

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

1. Check that the nightly build on CircleCI was successful¶

Check the nightly build on CircleCI. All tests should pass before making a release.

2. Make a pull request to increase the version number¶

The version number is stored in esmvalcore/_version.py, package/meta.yaml, CITATION.cff. Make sure to update all files. See https://semver.org for more information on choosing a version number.

3. Make the release on GitHub¶

Click the releases tab and draft the new release. Do not forget to tick the pre-release box for a beta release. Use the script esmvalcore/utils/draft_release_notes.py to create a draft version of the release notes and edit those.

4. Create and upload the Conda package¶

Follow these steps to create a new conda package:

Check out the tag corresponding to the release, e.g. git checkout v2.0.0b6
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
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-2.0.0b6-py_0.tar.bz2.