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Equivariant machine learning library for learning from electronic structures.

Project description

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python3 CircleCI Coverage Status Code style: black Commitizen friendly security: bandit DeepSource dependabot Requirements Status


Equivariant machine learning library for learning from electronic structures


First things first

You can develop on Windows, GNU/Linux or Mac OS X. You need:

Getting Started

First, fork this repository, then fire up your command prompt and ...

  1. Clone the forked repository
  2. Navigate to the cloned project directory: cd e3psi
  3. activate your python virtual environment and pip install -r requirements.txt.
  4. pre-commit install
  5. pre-commit install --hook-type commit-msg
  6. pre-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-missing


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. See pre-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 and git revert. Once you are done, please create a pull request.

Project details

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