Contribution guidelines¶
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 diagnostic 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, for example by starting a discussion on our discussions page.
If you have a bug or other issue to report, please open an issue on the issues tab on the ESMValTool github repository.
If you would like to contribute a new diagnostic and recipe or a 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. This is also a good way to get help with the implementation.
Getting started¶
See Install from source for instructions on how to set up a development installation.
Running tests¶
Go to the directory where the repository is cloned and run
pytest. Tests will also be run automatically by
CircleCI.
These automated checks are run automatically when you add new commits to your pull request. They appear at the bottom of the pull request. Click on Details for more information
Code style¶
To increase the readability and maintainability or the ESMValTool 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, R, NCL, and yaml files, most of 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!
Pre-commit¶
To run pre-commit on your code, go to the ESMValTool directory
(cd ESMValTool) 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 your_script.py).
To make it only check some specific files, use
pre-commit run --files your_script.py
or
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
or
git commit -n
or uninstall the pre-commit hook
pre-commit uninstall
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 using isort and
yapf -i some_file.py
to add/remove whitespace as required by the standard using yapf,
docformatter -i your_script.py
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 ESMValTool, and run prospector
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 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.
NCL¶
Because there is no standard best practices document for NCL, we use
PEP8 for NCL code as
well, with some minor adjustments to accommodate for differences in the
languages. The most important difference is that for NCL code the
indentation should be 2 spaces instead of 4. Use the command
nclcodestyle /path/to/file.ncl to check if your code follows the
style guide.
R¶
Best practices for R code are described in The tidyverse style guide. We check adherence to this style guide by using lintr on CircleCI. Please use styler to automatically format your code according to this style guide. In the future we would also like to make use of goodpractice to assess the quality of R code.
YAML¶
Please use yamllint to check that your YAML files do not contain
mistakes.
Documentation¶
What should be documented¶
Any code documentation that is visible on docs.esmvaltool.org should be well written and adhere to the standards for documentation for the respective language. Recipes should have a page in the Recipes section. This is also the place to document recipe options for the diagnostic scripts used in those recipes. When adding a new recipe, please start from the template and do not forget to add your recipe to the index. Note that there is no need to write extensive documentation for functions that are not visible in the online documentation. However, a short description in the docstring helps other contributors to understand what a function is intended to do and and what its capabilities are. For short functions, a one-line docstring is usually sufficient, but more complex functions might require slightly more extensive 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¶
New development should preferably be done in the
ESMValTool
GitHub repository.
However, for scientists requiring confidentiality, private repositories are
available, see Moving work from the private to the public repository for more information.
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, Codacy to analyse your code, and readthedocs to build the documentation. 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 by clicking on Details. 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. Preferably Codacy code quality checks pass, however
a few remaining hard to solve Codacy issues are still acceptable.
If you suspect Codacy may be wrong, please ask by commenting.
The documentation can be seen by clicking on Details. Make sure the documentation is nicely formatted, and (if necessary) add the link to the top of the pull request.
Make sure your pull request has a descriptive title that can be used in the changelog.
Diagnostic script contributions¶
A pull request with diagnostic code should preferably not introduce new Codacy issues. However, we understand that there is a limit to how much time can be spend on polishing code, so up to 10 new (non-trivial) issues is still an acceptable amount.
List of authors¶
If you make a (significant) contribution to ESMValTool, 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