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Equivariant machine learning library for learning from electronic structures
You can develop on Windows, GNU/Linux or Mac OS X. You need:
- Python 3.6 and above and a python virtual environment.
- Git
First, fork this repository, then fire up your command prompt and ...
- Clone the forked repository
- Navigate to the cloned project directory:
cd e3psi - activate your python virtual environment and
pip install -r requirements.txt. pre-commit installpre-commit install --hook-type commit-msgpre-commit run --all-files
Now you can start working on the code.
Simply run pytest. For more detailed output, including test coverage:
pytest -vv --cov=. --cov-report term-missingIf you would like to contribute to the project:
- if you're making code contributions, please try and write some tests to accompany your code, and ensure that the tests pass.
- commit your changes via
cz commit. Follow the prompts. When you're done,pre-commitwill be invoked to ensure that your contributions and commits follow defined conventions. Seepre-commit-config.yamlfor more details. - your commit messages should follow the conventions described here. Write your commit message in the imperative: "Fix bug" and not "Fixed bug" or "Fixes bug." This convention matches up with commit messages generated by commands like
git mergeandgit revert. Once you are done, please create a pull request.