Contribution Guide

These guidelines have been largely adopted from the scikit-learn contribution guidelines.

How to contribute

The preferred workflow for contributing to sklearn-xarray is to fork the repository, clone, and develop on a branch. Steps:

Fork the project repository 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.
Clone your fork of the sklearn-xarray repo from your GitHub account to your local disk:
```
$ git clone git@github.com:YourLogin/sklearn-xarray.git
$ cd sklearn-xarray
```
Create a feature branch to hold your development changes:
```
$ git checkout -b my-feature
```
Always use a feature branch. It's good practice to never work on the master branch!
Develop the feature on your feature branch. Add changed files using git add and then git commit files:
```
$ git add modified_files
$ git commit
```
to record your changes in Git, then push the changes to your GitHub account with:
```
$ git push -u origin my-feature
```
Follow these instructions to create a pull request from your fork. This will send an email to the committers.

Pull Request Checklist

We recommended that your contribution complies with the following rules before you submit a pull request:

Follow the scikit-learn coding-guidelines.
Use, when applicable, the validation tools and scripts in the sklearn-xarray.utils submodule as well as sklearn.utils from scikit-learn.
Give your pull request a helpful title that summarises what your contribution does. In some cases Fix <ISSUE TITLE> is enough. Fix #<ISSUE NUMBER> is not enough.
Often pull requests resolve one or more other issues (or pull requests). If merging your pull request means that some other issues/PRs should be closed, you should use keywords to create link to them (e.g., Fixes #1234; multiple issues/PRs are allowed as long as each one is preceded by a keyword). Upon merging, those issues/PRs will automatically be closed by GitHub. If your pull request is simply related to some other issues/PRs, create a link to them without using the keywords (e.g., See also #1234).
All public methods should have informative docstrings with sample usage presented as doctests when appropriate.

If you add a new module, add a file doc/content/api/your_module.rst that looks as follows:

<Your module title>
===================

.. automodule:: sklearn_xarray.your_module
   :members:

Update doc/content/api.rst by adding a section for your module that looks like this:
```
<Your module title>
------------------

.. py:currentmodule:: sklearn_xarray.your_module

Module: :py:mod:`sklearn_xarray.your_module`

.. autosummary::
    :nosignatures:

    YourClass1
    YourClass2
    your_function_1
    your_function_2
```
and add api/your_module.rst to the toctree at the end of the file. If you add new classes or functions to an existing module you just have to add their names to the autosummary list like in the snippet above.
If you add new functionality you should demonstrate it by adding an example in the examples folder. Take a look at the source code of the existing examples as a syntax reference.
Documentation and high-coverage tests are necessary for enhancements to be accepted. Bug-fixes or new features should be provided with non-regression tests. 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, these tests should fail for the code base in master and pass for the PR code.

You can also check for common programming errors with the following tools:

Code with good unittest coverage (at least 80%), check with:

$ pip install nose coverage
$ nosetests --with-coverage path/to/tests_for_package

No pyflakes warnings, check with:

$ pip install pyflakes
$ pyflakes path/to/module.py

No PEP8 warnings, check with:

$ pip install pep8
$ pep8 path/to/module.py

AutoPEP8 can help you fix some of the easy redundant errors:
```
$ pip install autopep8
$ autopep8 path/to/pep8.py
```

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, scikit-learn, numpy, and scipy 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 sklearn; print("Scikit-Learn", sklearn.__version__)

Please be specific about what estimators and/or functions are involved and the shape of the data, as appropriate; please include a reproducible code snippet or link to a gist. If an exception is raised, please provide the traceback.

New contributor tips

A great way to start contributing to sklearn-xarray is to pick an item from the list of good first issues. Issues that might be a little more complicated to tackle are marked with help wanted.

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.

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

Provide feedback

Saved searches