Contributing to kerchunk


Large parts of this document are based on the Xarray Contributing Guide , the Pandas Contributing Guide and the xbatcher Contributing Guide.


kerchunk is actively in development and breaking changes may be introduced at any point. In its current state, it is possible for experienced users to build kerchunk indices of datasets, which work today and will continue to work. End users of these reference sets will not be exposed to changes in kerchunk’s code and usually don’t even need to install kerchunk.

Bug reports and feature requests

Bug reports or feature requests to the kerchunk project can be submitted by opening up an Issue in the repository.

Contributing code

This project uses git for version control and github for issue tracking. If you need instructions on how to setup git, they can be found on GitHub.

Creating a Fork

Once you have your git credentials setup, the next step is to create a fork of the project to work off of. To fork the repo, navigate to the kerchunk repository on github and click the Fork button in the top right of the page. This will create a fork of the kerchunk project in your own repository.

Next, you will want to clone this forked version of the repository onto the machine you are working on. In your terminal/command prompt run:

git clone<yourusername>/kerchunk.git
cd kerchunk
git remote add upstream

This will create a directory from your fork of the repository on your local machine and connect it to the main repository.

Creating a development environment

To test your code changes, you will need to build kerchunk from source, which requires a Python environment.

Creating a Python Environment

To ensure that the python environment that you are using is the same one that everyone else is using, it is necessary to create a virtual environment. This will create an isolated development environment where you can install the kerchunk python dependencies.


If your conda build solving times are taking a long time, you can try mamba, which is a mirror of conda written in c++`.

First we’ll create and activate the build environment:

conda env create --name kerchunk --file ci/environment-py3<*>.yml
conda activate kerchunk

Now that you have the correct dependencies installed in your environment, you should be able to install your development version of kerchunk locally. In the projects home directory run:

pip install -e .

To test that the installation was successful run:

$ python  # start an interpreter
>>> import kerchunk
>>> kerchunk.__version__

To view your environments

conda info --envs

To return to your base environment

conda deactivate

See the full conda docs here.

Setting up pre-commit

We use pre-commit to manage code linting and style. To set up pre-commit after activating your conda environment, run:

pre-commit install

Now pre-commit will run whenever you create a git commit in the repository. You may need to edit files that pre-commit has issues with and re-add them to the commit.

Creating a branch

You want your main branch to reflect only production-ready code, so create a feature branch before making your changes. For example

git branch shiny-new-feature
git checkout shiny-new-feature

The above can be simplified to

git checkout -b shiny-new-feature

This changes your working directory to the shiny-new-feature branch. Keep any changes in this branch specific to one bug or feature so it is clear what the branch brings to kerchunk. You can have many “shiny-new-features” and switch in between them using the git checkout command.

To update this branch, you need to retrieve the changes from the main branch

git fetch upstream
git merge upstream/main

This will combine your commits with the latest kerchunk git main. If this leads to merge conflicts, you must resolve these before submitting your pull request. If you have uncommitted changes, you will need to git stash them prior to updating. This will effectively store your changes, which can be reapplied after updating.

Running the test suite

kerchunk uses the pytest framework for testing. You can run the test suite using:

pytest kerchunk

Ideally any new feature added should have test coverage.

Contributing documentation

Documentation improvements are appreciated. The documentation is contained within the docs directory of the project. It is written in ReStructured Text (.rst), which is similar to markdown, but features more functionality. These ReStructured text files are built into html using the python sphinx ( package.

You can create a virtual environment by running:

conda create --name kerchunk-docs python=3.8
conda activate kerchunk-docs
python -m pip install -r docs/requirements.txt

Once you make changes to the docs, you can build them with:

cd docs
make html

Contributing changes

Once you feel good about your changes you can see them by typing:

git status

If you have created a new file, it is not being tracked by git. Add it by typing:

git add path/to/

Now you can commit your changes in your local repository.

git commit -m "<commit message>"

When you want your changes to appear publicly on your GitHub page, push your commits to a branch off your fork.

git push origin shiny-new-feature

Here origin is the default name given to your remote repository on GitHub. You can see the remote repositories.

git remote -v

If you navigate to your branch on GitHub, you should see a banner to submit a pull request to the kerchunk repository.