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CONTRIBUTING.md

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Contributing code

This document is a 'getting started' summary for contributing code, documentation, testing, and filing issues.

How to contribute

The preferred way to contribute to Kymatio is to fork the main repository on GitHub:

  1. Fork the project repository: click on the 'Fork' button near the top of the page. This creates a copy of the code under your account on the GitHub server.

  2. Clone this copy to your local disk:

   $ git clone [email protected]:YourLogin/kymatio.git
   $ cd kymatio
  1. Remove any previously installed versions of Kymatio:
   $ pip uninstall kymatio

and install your local copy:

   $ pip install -e .
  1. Create a branch to hold your changes:
   $ git checkout -b my-feature

and start making changes. Never work in the master branch.

  1. Work on this copy on your computer using Git to do the version control. When you're done editing, do:
   $ git add modified_files
   $ git commit

to record your changes in git, then push them to GitHub with:

   $ git push -u origin my-feature

Finally, go to the web page of the your fork of the Kymatio repository, and click 'Pull request' to send your changes to the maintainers for review. This will send an email to the moderators.

For further details on using Git for version control, we recommend you look up its documentation.

Filing bugs

We use Github issues to track all bugs and feature requests. Feel free to open an issue if you have found a bug or wish to see a feature implemented.

It is recommended to check that your issue complies with the following rules before submitting:

  • Verify that your issue is not being currently addressed by other issues or pull requests.

  • Please ensure all code snippets and error messages are formatted in appropriate code blocks. See Creating and highlighting code blocks.

  • Please include your operating system type and version number, as well as your Python, NumPy, SciPy, PyTorch, and Kymatio versions. This information can be found by running the following code snippet:

import platform; print(platform.platform())
import sys; print("Python", sys.version)
import numpy; print("NumPy", numpy.__version__)
import scipy; print("SciPy", scipy.__version__)
import torch; print("PyTorch", torch.__version__)
import kymatio; print("Kymatio", kymatio.__version__)
  • If your issue is related to GPU acceleration, please copy-paste the output from the environment collection script of PyTorch. You can get the script and run it with: 
    wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py

Documentation

You can edit the documentation using any text editor and then generate the HTML output by typing make html from the docs/ directory. The resulting HTML files will be placed in _build/html/ and are viewable in a web browser. See the README file in the doc/ directory for more information.

For building the documentation, you will need sphinx, matplotlib, and numpydoc.

Acknowledgment

This document was adapted from scikit-learn.