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 Block
s
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 newCauseClusterer
Block
should perform unsupervised clustering on the conditional probabilities estimated by anyCondProbEstimator
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 theBlock
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.