dev_guide.md

Contribute to CFL

Contributions to cfl are welcomed and encouraged! Here are some ways to contribute.

Submit a bug report or feature request

Please include:

• a short code snippet that reproduces the issue
• any error tracebacks
• operating system type and version number, python version number, and cfl version number

Contribute code, docs, and tests (more detailed instructions to come)

• fork the cfl repository
• clone your fork to your local machine
• install the development dependencies in requirements.yml
• add the upstream remote
• sync your main branch with the upstream main branch
• create a feature branch and make your changes on it
• run pytest to ensure all tests still pass
• commit and push
• open a pull request

Contribute Blocks

Did you develop your own conditional probability estimator or clusterer while performing your analysis? Please consider sharing it with others! Your Block should:

• algorithmically align with the type of Block it is (i.e. a new CauseClusterer Block should perform unsupervised clustering on the conditional probabilities estimated by any CondProbEstimator and return cluster labels over all samples)
• be placed in the corresponding directory
• pass all associated tests (instructions to come for how to test your Block)
• inherit the Block class or a child of the Block class Please follow the instructions under "Contribute code" to create a pull request.

Contribute examples

Have a cool dataset you've run cfl on? We'd love to see it!

• Put together a concise Jupyter Notebook with some background on your data and an annotated run of CFL, similar to the [El Niño example notebook] (https://cfl.readthedocs.io/en/latest/examples/el_nino_example.html).
• Include this notebook in the docs/source/user_examples/ directory.
• Follow the instructions under "Contribute code" to create a pull request.