Richards Standalone: Standalone solver for Richards' equation.

Start developing

Once you created or cloned this repository, make sure the installation is running properly. Install the package dependencies with the provided script setup_env.sh. Check available options with

setup_env.sh -h

We distinguish development installations which are editable and have additional dependencies on formatters and linters from productive installations which are non-editable and have no additional dependencies. Moreover we distinguish pinned installations based on exported (reproducible) environments and free installations where the installation is based on first level dependencies listed in requirements/requirements.yml and requirements/dev-requirements.yml respectively. If you start developing, you might want to do a free development installation and export the environments right away.

setup_env.sh -d -e -m -n <package_env_name>

Hint: If you are the package administrator, it is a good idea to understand what this script does, you can do everything manually with conda instructions.

The package itself is installed with pip:

conda activate <package_env_name>
pip install --editable .

Warning: Make sure you use the right pip, i.e. the one from the installed conda environment (which pip should point to something like path/to/miniconda/envs/<package_env_name>/bin/pip).

Once your package is installed, run the tests by typing

pytest

. If the tests pass, you are good to go. If not, contact the package administrator Daniel Regenass. Make sure to update your requirement files and export your environments after installation every time you add new imports while developing. Check the next section to find some guidance on the development process if you are new to Python and/ or APN.

Development tools

As this package was created with the APN Python blueprint, it comes with a stack of development tools, which are described in more detail on (https://meteoswiss-apn.github.io/mch-python-blueprint/). Here, we give a brief overview on what is implemented.

Testing and coding standards

Testing your code and compliance with the most important Python standards is a requirement for Python software written in APN. To make the life of package administrators easier, the most important checks are run automatically on GitHub actions. If your code goes into production, it must additionally be tested on CSCS machines, which is only possible with a Jenkins pipeline (GitHub actions is running on a GitHub server).

Pre-commit on GitHub actions

.github/workflows/pre-commit.yml contains a hook that will trigger the creation of your environment (unpinned, dev) on the GitHub actions server and then run pytest as well as various formatters and linters through pre-commit. This hook is only triggered upon pushes to the main branch (in general: don't do that) and in pull requests to the main branch.

Jenkins

Two jenkins plans are available in the jenkins/ folder. On the one hand jenkins/Jenkinsfile controls the nightly (weekly, monthly, ...) builds, on the other hand jenkins/JenkinsJobPR controls the pipeline invoked with the command launch jenkins in pull requests on GitHub. Your jenkins pipeline will not be set up automatically. If you need to run your tests on CSCS machines, contact DevOps to help you with the setup of the pipelines. Otherwise, you can ignore the jenkinsfiles and exclusively run your tests and checks on GitHub actions.

Features

• TODO

Credits

This package was created with copier and the MeteoSwiss-APN/mch-python-blueprint project template.