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‎CONTRIBUTING.md‎

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Contributing to POT
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===================
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First off, thank you for considering contributing to POT.
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How to contribute
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-----------------
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The preferred workflow for contributing to POT is to fork the
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[main repository](https://github.com/rflamary/POT) on
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GitHub, clone, and develop on a branch. Steps:
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1. Fork the [project repository](https://github.com/rflamary/POT)
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by clicking on the 'Fork' button near the top right of the page. This creates
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a copy of the code under your GitHub user account. For more details on
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how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/).
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2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk:
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```bash
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$ git clone git@github.com:YourLogin/POT.git
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$ cd POT
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```
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3. Create a ``feature`` branch to hold your development changes:
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```bash
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$ git checkout -b my-feature
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```
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Always use a ``feature`` branch. It's good practice to never work on the ``master`` branch!
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4. Develop the feature on your feature branch. Add changed files using ``git add`` and then ``git commit`` files:
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```bash
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$ git add modified_files
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$ git commit
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```
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to record your changes in Git, then push the changes to your GitHub account with:
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```bash
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$ git push -u origin my-feature
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```
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5. Follow [these instructions](https://help.github.com/articles/creating-a-pull-request-from-a-fork)
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to create a pull request from your fork. This will send an email to the committers.
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(If any of the above seems like magic to you, please look up the
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[Git documentation](https://git-scm.com/documentation) on the web, or ask a friend or another contributor for help.)
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Pull Request Checklist
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----------------------
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We recommended that your contribution complies with the
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following rules before you submit a pull request:
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- Follow the PEP8 Guidelines.
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- If your pull request addresses an issue, please use the pull request title
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to describe the issue and mention the issue number in the pull request description. This will make sure a link back to the original issue is
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created.
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- All public methods should have informative docstrings with sample
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usage presented as doctests when appropriate.
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- Please prefix the title of your pull request with `[MRG]` (Ready for
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Merge), if the contribution is complete and ready for a detailed review.
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Two core developers will review your code and change the prefix of the pull
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request to `[MRG + 1]` and `[MRG + 2]` on approval, making it eligible
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for merging. An incomplete contribution -- where you expect to do more work before
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receiving a full review -- should be prefixed `[WIP]` (to indicate a work
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in progress) and changed to `[MRG]` when it matures. WIPs may be useful
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to: indicate you are working on something to avoid duplicated work,
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request broad review of functionality or API, or seek collaborators.
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WIPs often benefit from the inclusion of a
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[task list](https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments)
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in the PR description.
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- When adding additional functionality, provide at least one
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example script in the ``examples/`` folder. Have a look at other
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examples for reference. Examples should demonstrate why the new
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functionality is useful in practice and, if possible, compare it
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to other methods available in scikit-learn.
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- Documentation and high-coverage tests are necessary for enhancements to be
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accepted. Bug-fixes or new features should be provided with
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[non-regression tests](https://en.wikipedia.org/wiki/Non-regression_testing).
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These tests verify the correct behavior of the fix or feature. In this
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manner, further modifications on the code base are granted to be consistent
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with the desired behavior.
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For the Bug-fixes case, at the time of the PR, this tests should fail for
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the code base in master and pass for the PR code.
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- At least one paragraph of narrative documentation with links to
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references in the literature (with PDF links when possible) and
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the example.
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You can also check for common programming errors with the following
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tools:
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- No pyflakes warnings, check with:
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```bash
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$ pip install pyflakes
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$ pyflakes path/to/module.py
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```
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- No PEP8 warnings, check with:
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```bash
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$ pip install pep8
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$ pep8 path/to/module.py
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```
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- AutoPEP8 can help you fix some of the easy redundant errors:
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```bash
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$ pip install autopep8
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$ autopep8 path/to/pep8.py
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```
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Bonus points for contributions that include a performance analysis with
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a benchmark script and profiling output (please report on the mailing
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list or on the GitHub issue).
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Filing bugs
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-----------
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We use Github issues to track all bugs and feature requests; feel free to
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open an issue if you have found a bug or wish to see a feature implemented.
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It is recommended to check that your issue complies with the
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following rules before submitting:
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- Verify that your issue is not being currently addressed by other
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[issues](https://github.com/rflamary/POT/issues?q=)
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or [pull requests](https://github.com/rflamary/POT/pulls?q=).
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- Please ensure all code snippets and error messages are formatted in
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appropriate code blocks.
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See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks).
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- Please include your operating system type and version number, as well
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as your Python, scikit-learn, numpy, and scipy versions. This information
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can be found by running the following code snippet:
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```python
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import platform; print(platform.platform())
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import sys; print("Python", sys.version)
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import numpy; print("NumPy", numpy.__version__)
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import scipy; print("SciPy", scipy.__version__)
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import ot; print("POT", ot.__version__)
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```
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- Please be specific about what estimators and/or functions are involved
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and the shape of the data, as appropriate; please include a
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[reproducible](http://stackoverflow.com/help/mcve) code snippet
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or link to a [gist](https://gist.github.com). If an exception is raised,
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please provide the traceback.
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New contributor tips
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--------------------
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A great way to start contributing to scikit-learn is to pick an item
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from the list of [Easy issues](https://github.com/scikit-learn/scikit-learn/issues?labels=Easy)
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in the issue tracker. Resolving these issues allow you to start
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contributing to the project without much prior knowledge. Your
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assistance in this area will be greatly appreciated by the more
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experienced developers as it helps free up their time to concentrate on
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other issues.
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Documentation
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-------------
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We are glad to accept any sort of documentation: function docstrings,
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reStructuredText documents (like this one), tutorials, etc.
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reStructuredText documents live in the source code repository under the
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doc/ directory.
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You can edit the documentation using any text editor and then generate
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the HTML output by typing ``make html`` from the doc/ directory.
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Alternatively, ``make`` can be used to quickly generate the
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documentation without the example gallery. The resulting HTML files will
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be placed in ``_build/html/`` and are viewable in a web browser. See the
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``README`` file in the ``doc/`` directory for more information.
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For building the documentation, you will need
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[sphinx](http://sphinx.pocoo.org/),
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[matplotlib](http://matplotlib.org/), and
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[pillow](http://pillow.readthedocs.io/en/latest/).
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When you are writing documentation, it is important to keep a good
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compromise between mathematical and algorithmic details, and give
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intuition to the reader on what the algorithm does. It is best to always
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start with a small paragraph with a hand-waving explanation of what the
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method does to the data and a figure (coming from an example)
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illustrating it.
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This Contrubution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team.

‎README.md‎

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* [Rémi Flamary](http://remi.flamary.com/)
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* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/)
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* [Alexandre Gramfort](http://alexandre.gramfort.net/)
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* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/)
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* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation)
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* [Léo Gautheron](https://github.com/aje) (GPU implementation)
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* [Antoine Rolet](https://arolet.github.io/) ( Mex file for EMD )
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* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda)
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## Contributions and code of conduct
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Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md).
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## References
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[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM.

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