Equivariant machine learning library for learning from electronic structures.
Project description
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e3psi
About
Equivariant machine learning library for learning from electronic structures
Development
First things first
You can develop on Windows, GNU/Linux or Mac OS X. You need:
- Python 3.6 and above and a python virtual environment.
- Git
Getting Started
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 install
pre-commit install --hook-type commit-msg
pre-commit run --all-files
Now you can start working on the code.
Tests
Simply run pytest
. For more detailed output, including test coverage:
pytest -vv --cov=. --cov-report term-missing
Contributing
If 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-commit
will be invoked to ensure that your contributions and commits follow defined conventions. Seepre-commit-config.yaml
for 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 merge
andgit revert
. Once you are done, please create a pull request.
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