This is a early-development implementation of an AiiDAlab application for Quantum ESPRESSO workflow. The app allows the execution of a workflow with Quantum ESPRESSO that includes the selection of an input structure, its relaxation, and the bands structure calculation.
The app is currently in an early development stage!
The package uses pre-commit hooks to check the style consistency of all commits. To use those you need to first install the pre-commit package itself, e.g. with:
pip install .[dev]
and then install the pre-commit hooks with
pre-commit install
The pre-commit checks should now be automatically executed prior to each commit.
To run unit tests in the AiiDAlab container, you need to run pytest from within the aiida-core-services conda environment:
conda activate aiida-core-services
pytest -sv tests
To run the integration tests, you need to build the Docker image first:
cd docker/
docker buildx bake -f build.json -f docker-bake.hcl --set "*.platform=linux/amd64" --load
Then, you can run the integration tests with:
JUPYTER_TOKEN=max TAG=newly-baked pytest --driver Chrome tests_integration -svTo create a new release, clone the repository, install development dependencies with pip install '.[dev]', and then execute bumpver update.
This will:
- Create a tagged release with bumped version and push it to the repository.
- Trigger a GitHub actions workflow that creates a GitHub release.
Additional notes:
- Use the
--dryoption to preview the release change. - The release tag (e.g. a/b/rc) is determined from the last release.
Use the
--tagoption to switch the release tag.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957189. The project is part of BATTERY 2030+, the large-scale European research initiative for inventing the sustainable batteries of the future. Also supported by the MARVEL National Centre for Competency in Research funded by the Swiss National Science Foundation, the MARKETPLACE project funded by Horizon 2020 under the H2020-NMBP-25-2017 call (Grant No. 760173), as well as by the MaX European Centre of Excellence funded by the Horizon 2020 EINFRA-5 program, Grant No. 676598.
