Contributing Guidelines

We’d like this to be a community driven project, so all kinds of input are welcome!

There are numerous way you could contribute:

  • Report bugs by submitting issues

  • Request features by submitting issues

  • Write examples and improve documentation

  • Contribute code: bug fixes, new features

This document is loosely based on the Contributing to xarray guide. It’s worth reading, it covers many of the subjects below in greater detail.

Reporting bugs

You can report bugs on the Issues pages. Please include a self-contained Python snippet that reproduces the problem. In the majority of cases, a Minimal, Complete, and Verifiable Example (MCVE) or Minimum Working Example (MWE) is the best way to communicate the problem and provide insight. Have a look at this stackoverflow article for an in-depth description.

Contributing Code

Version control

We use Git for version control. Git is excellent software, but it might take some time to wrap your head around it. There are many excellent resources online. Have a look at the extensive manual online, a shorter handbook, searchable GitHub help, a cheatsheet, or try this interactive tutorial.

Code style

We use Black for automatic code formatting. Like Black, we are uncompromising about formatting. Continuous Integration will fail if running black . from within the repository root folder would make any formatting changes.

Integration black into your workflow is easy. Find the instructions here. If you’re using VisualStudioCode (which we heartily recommend), consider enabling the Format On Save option – it’ll save a lot of hassle.

Automated testing

If you add functionality or fix a bug, always add a test. For a new feature, you’re testing anyway to see if it works… you might as well clean it up and include it in the test suite! In case of a bug, it means our test coverage is insufficient. Apart from fixing the bug, also include a test that addresses the bug so it can’t happen again in the future.

We use pytest to do automated testing. You can run the test suite locally by simply calling pytest in the project directory. pytest will pick up on all tests and run them automatically. Check the pytest documentation, and have a look at the test suite to figure out how it works.

Code review

Create a branch, and send a merge or pull request. Your code doesn’t have to be perfect! We’ll have a look, and we will probably suggest some modifications or ask for some clarifications.

How to release a new version

To follow these steps, you need to be one of the maintainers for imod on both PyPI and conda-forge.

  1. Update the Changelog.

  2. Tag in Gitlab UI. Old tags are here. Old releases are here. To make a tag show up under releases, fill in the release notes in the UI. Since we keep changes in the Changelog only, just put See in both the Message and Release notes box. The tag name should be vx.y.z, where x, y and z are version numbers according to Semantic Versioning.

  3. Locally, git fetch --tags and git pull, verify you are on the commit you want to release, and that it is clean.

  4. Remove the build and dist folders if present.

  5. Create a wheel under dist/ with python bdist_wheel

  6. Create a source distribution under dist/ with python sdist

  7. Upload the files from step 5 and 6 to PyPI with twine upload dist/*

  8. For conda-forge, a PR will be created automatically. If the requirements are up to date in meta.yaml then you can merge it. Otherwise you have to edit them and push this to the bot’s branch.

Debugging Continuous Integration

Continuous Integration runs on an image with a specific operating system, and Python installation. Due to system idiosyncrasies, CI failing might not reproduce locally. If an issue requires more than trial-and-error changes, Docker is likely the easiest way to debug.

On windows, install Docker:

Pull the CI image (at the time of writing), and run it interactively:

docker pull continuumio/miniconda3:latest
docker run -it continuumio/miniconda3

This should land you in the docker image. Next, we reproduce the CI setup steps. Some changes are required, such as installing git and cloning the repository, which happens automatically within CI.

apt-get update -q -y
apt-get install -y build-essential
conda update -n base conda
conda install git
cd /usr/src
git clone
cd imod-python
conda env create -f environment.yml
source activate imod
pip install -e .
curl -O -L
gunzip mf6.gz
chmod +x mf6
mv mf6 /opt/conda/envs/imod/bin

At this point, everything should be ready to run the tests on the Docker image.