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

tools/setup_env.sh -h

We distinguish pinned installations based on exported (reproducible) environments and free installations where the installation is based on top-level dependencies listed in requirements/requirements.yml. If you start developing, you might want to do an unpinned installation and export the environment:

tools/setup_env.sh -u -e -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.

Hint: If you require GPU support, make sure to run the script on a compute node.

Hint: Use the flag -m to speed up the installation using mamba. Of course you will have to install mamba first (we recommend to install mamba into your base environment conda install -c conda-forge mamba. If you install mamba in another (maybe dedicated) environment, environments installed with mamba will be located in <miniconda_root_dir>/envs/mamba/envs, which is not very practical.

The package itself is installed with pip. For development, install in editable mode:

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:

conda activate <package_env_name>
pytest

If the tests pass, you are good to go. If not, contact the package administrator Simon Adamov. 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.

Generally, the source code of your library is located in src/<library_name>. The blueprint will generate some example code in utils.py and cli.py. cli.py thereby serves as an entry point for functionalities you want to execute from the command line, it is based on the Click library. If you do not need interactions with the command line, you should remove cli.py. Moreover, of course there exist other options for command line interfaces, a good overview may be found here (https://realpython.com/comparing-python-command-line-parsing-libraries-argparse-docopt-click/), we recommend however to use click. The provided example code should provide some guidance on how the individual source code files interact within the library. In addition to the example code in src/<library_name>, there are examples for unit tests in tests/<library_name>/, which can be triggered with pytest from the command line. Once you implemented a feature (and of course you also implemented a meaningful test ;-)), you are likely willing to commit it. First, go to the root directory of your package and run pytest.

conda activate <package_env_name>
cd <package-root-dir>
pytest

If you use the tools provided by the blueprint as is, pre-commit will not be triggered locally but only if you push to the main branch (or push to a PR to the main branch). If you consider it useful, you can set up pre-commit to run locally before every commit by initializing it once. In the root directory of your package, type:

pre-commit install

If you run pre-commit without installing it before (line above), it will fail and the only way to recover it, is to do a forced reinstallation (conda install --force-reinstall pre-commit). You can also just run pre-commit selectively, whenever you want by typing (pre-commit run --all-files). Note that mypy and pylint take a bit of time, so it is really up to you, if you want to use pre-commit locally or not. In any case, after running pytest, you can commit and the linters will run at the latest on the GitHub actions server, when you push your changes to the main branch. Note that pytest is currently not invoked by pre-commit, so it will not run automatically. Automated testing can be set up with GitHub Actions or be implemented in a Jenkins pipeline (template for a plan available in jenkins/. See the next section for more details.

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 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).

.github/workflows/pre-commit.yml contains a hook that will trigger the creation of your environment (unpinned) on the GitHub actions server and then run 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.

A jenkinsfile is available in the jenkins/ folder. It can be used for a multibranch jenkins project, which builds both commits on branches and PRs. 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.

The train_gnn.py script has the following features:

• Loads data from a Zarr archive using Dask
• Splits data into training and testing sets
• Defines a GNN model using GCNConv layers
• Trains the GNN model using the mean squared error loss function and the Adam optimizer

The following dependencies are required to use this package:

• dask
• torch
• torch_geometric
• zarr

To use this package, simply run the train_gnn.py script from the command line with the path to the Zarr archive as the only argument. For example:

python train_gnn.py data/data_combined.zarr

Note that this script assumes that the data in the Zarr archive is stored in a specific format. The data should be stored as a 4D array with dimensions (samples, channels, height, width), where the channels dimension contains the features for each node in the graph. The script also assumes that the labels for each sample are stored in the features of the nodes, and that the first channels_in features correspond to the input features and the remaining features correspond to the labels.

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