Skip to main content

Equivariant machine learning library for learning from electronic structures.

Project description

Table of Contents generated with DocToc

e3psi

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

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:

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.

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. 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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

e3psi-0.3.1.tar.gz (18.6 kB view details)

Uploaded Source

Built Distribution

e3psi-0.3.1-py3-none-any.whl (12.9 kB view details)

Uploaded Python 3

File details

Details for the file e3psi-0.3.1.tar.gz.

File metadata

  • Download URL: e3psi-0.3.1.tar.gz
  • Upload date:
  • Size: 18.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.28.1

File hashes

Hashes for e3psi-0.3.1.tar.gz
Algorithm Hash digest
SHA256 7ada985f335df8347bd0f4e42bfda6d146e4ba2aedf1551e0c9cf397092d67d9
MD5 6eab05882cbd55fac9bbbc38f09208a0
BLAKE2b-256 82c227fd3da45a7115ee71ce35745b27f0616bcbb301ede8dbf707bbe00ab8ba

See more details on using hashes here.

File details

Details for the file e3psi-0.3.1-py3-none-any.whl.

File metadata

  • Download URL: e3psi-0.3.1-py3-none-any.whl
  • Upload date:
  • Size: 12.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: python-requests/2.28.1

File hashes

Hashes for e3psi-0.3.1-py3-none-any.whl
Algorithm Hash digest
SHA256 d68bddf12d7e671bf457e0cb9c91ec6a8bd7e4f4bd724aea8db54863e4f76ad6
MD5 ee708296c335e596be9357dba3fd7a22
BLAKE2b-256 91526e5485d244698bbb0ef1935364686d0b5fb52e060c02fb76d132cf4c8f3f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page