Contributing to pyradiomics
===========================

There are many ways to contribute to pyradiomics, with varying levels of effort.  Do try to
look through the documentation first if something is unclear, and let us know how we can
do better.

  * Ask a question on the [pyradiomics email list](https://groups.google.com/forum/#!forum/pyradiomics)
  * Submit a feature request or bug, or add to the discussion on the [pyradiomics issue tracker](https://github.com/Radiomics/pyradiomics/issues)
  * Submit a [Pull Request](https://github.com/Radiomics/pyradiomics/pulls) to improve pyradiomics or its documentation

We encourage a range of Pull Requests, from patches that include passing tests and
documentation, all the way down to half-baked ideas that launch discussions.

The PR Process, Circle CI, and Related Gotchas
----------------------------------------------

#### How to submit a PR ?

If you are new to pyradiomics development and you don't have push access to the pyradiomics
repository, here are the steps:

1. [Fork and clone](https://help.github.com/articles/fork-a-repo/) the repository.
3. Create a branch.
4. [Push](https://help.github.com/articles/pushing-to-a-remote/) the branch to your GitHub fork.
5. Create a [Pull Request](https://github.com/Radiomics/pyradiomics/pulls).

This corresponds to the `Fork & Pull Model` mentioned in the [GitHub flow](https://guides.github.com/introduction/flow/index.html)
guides.

If you have push access to pyradiomics repository, you could simply push your branch
into the main repository and create a [Pull Request](https://github.com/Radiomics/pyradiomics/pulls). This corresponds to the
`Shared Repository Model` and will facilitate other developers to checkout your
topic without having to [configure a remote](https://help.github.com/articles/configuring-a-remote-for-a-fork/).
It will also simplify the workflow when you are _co-developing_ a branch.

When submitting a PR, make sure to add a `Cc: @Radiomics/developers` comment to notify pyradiomics
developers of your awesome contributions. Based on the
comments posted by the reviewers, you may have to revisit your patches.

#### How to integrate a PR ?

Getting your contributions integrated is relatively straightforward, here
is the checklist:

* Your changes include an update of the documentation if necessary
  * Documentation on modules, classes and functions is contained in the respective docstrings
  * More global documentation is contained in the `docs` folder.
  * New modules need to be added to the auto-generated documentation. See 
    [here](http://pyradiomics.readthedocs.io/en/latest/developers.html#documentation) for more
    information on adding new modules to the documentation.
* Your changes are added in the [changelog](CHANGES.rst) in the _Next Release_  section.
* All tests pass
* Consensus is reached. This usually means that at least one reviewer reviewed and approved your
  changes or added a `LGTM` comment, which is an acronym for _Looks Good to Me_.

Next, there are two scenarios:
* You do NOT have push access: A pyradiomics core developer will integrate your PR.
* You have push access: Simply click on the "Merge pull request" button.

Then, click on the "Delete branch" button that appears afterward.

#### Automatic testing of pull requests

Every pull request is tested automatically using CircleCI, TravisCI and AppVeyor each time you push
a commit to it. The Github UI will restrict users from merging pull requests until
the builds have returned with a successful result indicating that all tests have
passed and there were no problems detected by the linter. These tests include the following
* flake8 to check adherence to the code style. See `.flake8` and `.editorconfig` for styles,
  exceptions to the PEP8 style, etc.
* If a feature class has a function `_calculateCMatrix`, identifying it as a C enhanced class,
  output from the C extension is compared to the output from full python calculation. A absolute
  difference of 1e-3 is allowed to account for machine precision errors.
* All implemented features and feature classes have docstrings at the class level and feature
  definition level.
* A baseline is available for all features extracted from the 5 included test cases and
  calculated features match this baseline to within 3% (allowing for machine precision errors)