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
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.
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 for
xskillscore in the issue tracker
with the label “bug”.
If you are reporting a bug, please include:
Any details about your local setup that might be helpful in troubleshooting, specifically the Python interpreter version, installed libraries, and
Detailed steps to reproduce the bug, ideally a Minimal, Complete and Verifiable Example (http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports)
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.
Look through the GitHub issues for bugs.
Talk to developers to find out how you can fix specific bugs.
xskillscore could always use more documentation. What could you add?
More complementary documentation. Have you perhaps found something unclear?
Example notebooks of
xskillscorebeing used in real analyses.
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
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
Fork the xskillscore GitHub repository. It’s fine to use
xskillscoreas your fork repository name because it will live under your user.
Clone your fork locally using git, connect your repository to the upstream (main project), and create a branch:
$ git clone email@example.com:YOUR_GITHUB_USERNAME/xskillscore.git $ cd xskillscore $ git remote add upstream firstname.lastname@example.org:raybellwaves/xskillscore.git # now, to fix a bug or add feature create your own branch off "master": $ git checkout -b your-bugfix-feature-branch-name master
If you need some help with Git, follow this quick start guide: https://git.wiki.kernel.org/index.php/QuickStart
Install dependencies into a new conda environment:
$ conda env update -f ci/requirements-py36.yml $ conda activate xskillscore-dev
Make an editable install of xskillscore by running:
$ pip install --no-deps -e .
$ pre-commit run --all-files
https://pre-commit.com/ is a framework for managing and maintaining multi-language pre-commit hooks to ensure code-style and code formatting is consistent.
Break your edits up into reasonably sized commits:
$ git commit -a -m "<commit message>" $ git push -u
Run all the tests
Now running tests is as simple as issuing this command:
$ pytest xskillscore
Check that your contribution is covered by tests and therefore increases the overall test coverage:
$ coverage run --source xskillscore -m py.test $ coverage report $ coveralls
Please stick to xarray’s testing recommendations.
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
to enable easy monitoring of the performance of critical xskillscore operations.
These benchmarks are all found in the
If you need to run a benchmark, change your directory to
asv_bench/ and run:
$ asv continuous -f 1.1 upstream/master 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
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 continuous -f 1.1 upstream/master HEAD -b ^deterministic
If you want to only run a specific group of tests from a file, you can do it
. as a separator. For example:
$ asv continuous -f 1.1 upstream/master HEAD -b deterministic.Compute_small.time_xskillscore_metric_small
will only run the
time_xskillscore_metric_small benchmark of class
Create a new changelog entry in
The entry should be entered as:
:pr:`#<pull request number>`)
<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.rstfile if not there yet, in alphabetical order.
Add yourself to the contributors list via
Finally, submit a pull request through the GitHub website using this data:
head-fork: YOUR_GITHUB_USERNAME/xskillscore compare: your-branch-name base-fork: raybellwaves/xskillscore base: master
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.