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+
+Contributing to POT
+===================
+
+
+First off, thank you for considering contributing to POT. 
+
+How to contribute
+-----------------
+
+The preferred workflow for contributing to POT is to fork the
+[main repository](https://github.com/rflamary/POT) on
+GitHub, clone, and develop on a branch. Steps:
+
+1. Fork the [project repository](https://github.com/rflamary/POT)
+   by clicking on the 'Fork' button near the top right of the page. This creates
+   a copy of the code under your GitHub user account. For more details on
+   how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/).
+
+2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk:
+
+   ```bash
+   $ git clone git@github.com:YourLogin/POT.git
+   $ cd POT
+   ```
+
+3. Create a ``feature`` branch to hold your development changes:
+
+   ```bash
+   $ git checkout -b my-feature
+   ```
+
+   Always use a ``feature`` branch. It's good practice to never work on the ``master`` branch!
+
+4. Develop the feature on your feature branch. Add changed files using ``git add`` and then ``git commit`` files:
+
+   ```bash
+   $ git add modified_files
+   $ git commit
+   ```
+
+   to record your changes in Git, then push the changes to your GitHub account with:
+
+   ```bash
+   $ git push -u origin my-feature
+   ```
+
+5. Follow [these instructions](https://help.github.com/articles/creating-a-pull-request-from-a-fork)
+to create a pull request from your fork. This will send an email to the committers.
+
+(If any of the above seems like magic to you, please look up the
+[Git documentation](https://git-scm.com/documentation) on the web, or ask a friend or another contributor for help.)
+
+Pull Request Checklist
+----------------------
+
+We recommended that your contribution complies with the
+following rules before you submit a pull request:
+
+-  Follow the PEP8 Guidelines.
+
+-  If your pull request addresses an issue, please use the pull request title
+   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
+   created.
+
+-  All public methods should have informative docstrings with sample
+   usage presented as doctests when appropriate.
+
+-  Please prefix the title of your pull request with `[MRG]` (Ready for
+   Merge), if the contribution is complete and ready for a detailed review.
+   Two core developers will review your code and change the prefix of the pull
+   request to `[MRG + 1]` and `[MRG + 2]` on approval, making it eligible
+   for merging. An incomplete contribution -- where you expect to do more work before
+   receiving a full review -- should be prefixed `[WIP]` (to indicate a work
+   in progress) and changed to `[MRG]` when it matures. WIPs may be useful
+   to: indicate you are working on something to avoid duplicated work,
+   request broad review of functionality or API, or seek collaborators.
+   WIPs often benefit from the inclusion of a
+   [task list](https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments)
+   in the PR description.
+
+
+-  When adding additional functionality, provide at least one
+   example script in the ``examples/`` folder. Have a look at other
+   examples for reference. Examples should demonstrate why the new
+   functionality is useful in practice and, if possible, compare it
+   to other methods available in scikit-learn.
+
+-  Documentation and high-coverage tests are necessary for enhancements to be
+   accepted. Bug-fixes or new features should be provided with 
+   [non-regression tests](https://en.wikipedia.org/wiki/Non-regression_testing).
+   These tests verify the correct behavior of the fix or feature. In this
+   manner, further modifications on the code base are granted to be consistent
+   with the desired behavior.
+   For the Bug-fixes case, at the time of the PR, this tests should fail for
+   the code base in master and pass for the PR code.
+
+-  At least one paragraph of narrative documentation with links to
+   references in the literature (with PDF links when possible) and
+   the example.
+
+You can also check for common programming errors with the following
+tools:
+
+
+-  No pyflakes warnings, check with:
+
+  ```bash
+  $ pip install pyflakes
+  $ pyflakes path/to/module.py
+  ```
+
+-  No PEP8 warnings, check with:
+
+  ```bash
+  $ pip install pep8
+  $ pep8 path/to/module.py
+  ```
+
+-  AutoPEP8 can help you fix some of the easy redundant errors:
+
+  ```bash
+  $ pip install autopep8
+  $ autopep8 path/to/pep8.py
+  ```
+
+Bonus points for contributions that include a performance analysis with
+a benchmark script and profiling output (please report on the mailing
+list or on the GitHub issue).
+
+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](https://github.com/rflamary/POT/issues?q=)
+   or [pull requests](https://github.com/rflamary/POT/pulls?q=).
+
+-  Please ensure all code snippets and error messages are formatted in
+   appropriate code blocks.
+   See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks).
+
+-  Please include your operating system type and version number, as well
+   as your Python, scikit-learn, numpy, and scipy versions. This information
+   can be found by running the following code snippet:
+
+  ```python
+  import platform; print(platform.platform())
+  import sys; print("Python", sys.version)
+  import numpy; print("NumPy", numpy.__version__)
+  import scipy; print("SciPy", scipy.__version__)
+  import ot; print("POT", ot.__version__)
+  ```
+
+-  Please be specific about what estimators and/or functions are involved
+   and the shape of the data, as appropriate; please include a
+   [reproducible](http://stackoverflow.com/help/mcve) code snippet
+   or link to a [gist](https://gist.github.com). If an exception is raised,
+   please provide the traceback.
+
+New contributor tips
+--------------------
+
+A great way to start contributing to scikit-learn is to pick an item
+from the list of [Easy issues](https://github.com/scikit-learn/scikit-learn/issues?labels=Easy)
+in the issue tracker. Resolving these issues allow you to start
+contributing to the project without much prior knowledge. Your
+assistance in this area will be greatly appreciated by the more
+experienced developers as it helps free up their time to concentrate on
+other issues.
+
+Documentation
+-------------
+
+We are glad to accept any sort of documentation: function docstrings,
+reStructuredText documents (like this one), tutorials, etc.
+reStructuredText documents live in the source code repository under the
+doc/ directory.
+
+You can edit the documentation using any text editor and then generate
+the HTML output by typing ``make html`` from the doc/ directory.
+Alternatively, ``make`` can be used to quickly generate the
+documentation without the example gallery. 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](http://sphinx.pocoo.org/),
+[matplotlib](http://matplotlib.org/), and
+[pillow](http://pillow.readthedocs.io/en/latest/).
+
+When you are writing documentation, it is important to keep a good
+compromise between mathematical and algorithmic details, and give
+intuition to the reader on what the algorithm does. It is best to always
+start with a small paragraph with a hand-waving explanation of what the
+method does to the data and a figure (coming from an example)
+illustrating it.
+
+
+This Contrubution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team.