How to contribute to Gammapy#

What is this?#

This page is an overview of how to make a change or addition to the Gammapy code, tests or documentation. It’s partly an introduction to the process, partly a guide to some technical aspects.

It is not a tutorial introduction explaining all the tools (git, GitHub, Sphinx, pytest) or code (Python, Numpy, Scipy, Astropy) in detail. In the Gammapy docs, we don’t have such a tutorial introduction written up, but we’re happy to point out other tutorials or help you get started at any skill level anytime if you ask.

Before attempting to make a contribution, you should use Gammapy a bit at least:

We’d like to note that there are many ways to contribute to the Gammapy project. For example if you mention it to a colleague or suggest it to a student, or if you use it and acknowledge Gammapy in a presentation, poster or publication, or if you report an issue on the mailing list, those are contributions we value. The rest of this page though is concerned only with the process and technical steps how to contribute a code or documentation change via a pull request against the Gammapy repository.

So let’s assume you’ve used Gammapy for a while, and now you’d like to fix or add something to the Gammapy code, tests or docs. Here are the steps and commands to do it …

Get in touch early#

Usually the first step, before doing any work, is to get in touch with the Gammapy developers!

Especially if you’re new to the project, and don’t have an overview of ongoing activities, there’s a risk that your work will be in vain if you don’t talk to us early. E.g. it could happen that someone else is currently working on similar functionality, or that you’ve found a code or documentation bug and are willing to fix it, but it then turns out that this was for some part of Gammapy that we wanted to re-write or remove soon anyway.

Also, it’s usually more fun if you get a mentor or reviewer early in the process, so that you have someone to bug with questions and issues that come up while executing the steps outlined below.

After you’ve done a few contributions to Gammapy and know about the status of ongoing work, the best way to proceed is to file an issue or pull request on GitHub at the stage where you want feedback or review. Sometimes you’re not sure how to best do something, and you start by discussing it on the mailing list or in a GitHub issue. Sometimes you know how you’d like to do it, and you just code or write it up and make a pull request when it’s basically finished.

In any case, please keep the following point also in mind …

Make small pull requests#

Contributions to Gammapy happen via pull requests on GitHub. We like them small.

So as we’ll explain in more detail below, the contribution cycle to Gammapy is roughly:

  1. Get the latest development version (master branch) of Gammapy

  2. Make fixes, changes and additions locally

  3. Make a pull request

  4. Someone else reviews the pull request, you iterate, others can chime in

  5. Someone else signs off on or merges your pull request

  6. You update to the latest master branch

Then you’re done, and can start using the new version, or start a new pull request with further developments. It is possible and common to work on things in parallel using git branches.

So how large should one pull request be?

Our experience in Gammapy (and others confirm, see e.g. here) is that smaller is better. Working on a pull request for an hour or maximum a day, and having a diff of a few to maximum a few 100 lines to review and discuss is pleasant.

A pull request that drags on for more than a few days, or that contains a diff or 1000 lines, is almost always painful and inefficient for the person making it, but even more so for the person reviewing it.

The worst case is if you start a pull request, put in a lot of hours, but then don’t have time to “finish” it, and it’s sitting there for a week or a month without getting merged. Then it’s either blocking others that want to work on the same part of the code or docs, or they do it, and then you have merged conflicts to resolve when you come back to it. And coming back to a large pull request after a long time always means a large investment of time for the reviewer, because they probably have to re-read the previous discussion, and look through the large diff again.

So pull requests that are small, e.g. one bug fix with the addition of one regression test, or one new function or class or file, or one documentation example, and that get reviewed and merged quickly (ideally the same day, certainly the same week), are best.

Get set up#

Warning

The rest of this page isn’t written yet. It’s almost identical to https://cta-observatory.github.io/ctapipe/getting_started/index.html so for now, see there. Also, we shouldn’t duplicate content from https://docs.astropy.org/en/latest/#developer-documentation but link there instead.

The first steps are basically identical to https://cta-observatory.github.io/ctapipe/getting_started/index.html (until step 4, excluding 5) and http://astropy.readthedocs.io/en/latest/development/workflow/get_devel_version.html (up to Create your own private workspace). The following is a quick summary of commands to set up an environment for Gammapy development:

# Fork the gammapy repository on GitHub, https://github.com/gammapy/gammapy
cd code # Go somewhere on your machine where you want to code
git clone https://github.com/[your-github-username]/gammapy.git
cd gammapy
conda env create -f environment-dev.yml

# To speed up the environment solving you can use mamba instead of conda
# mamba env create -f environment-dev.yml
conda activate gammapy-dev

# for conda versions <4.4.0 you may have to execute
# 'source activate gammapy-dev' instead
git remote add gammapy git@github.com:gammapy/gammapy.git
git remote rename origin [your-user-name]

Mamba is an alternative package manager that offers higher installation speed and more reliable environment solutions.

It is also common to stick with the name origin for your repository and to use upstream for the repository you forked from. In any case, you can use $ git remote -v to list all your configured remotes.

In case you are working with the development version environment and you want to update this environment with the content present in environment-dev.yml see below:

$ conda env update environment-dev.yml --prune

When developing Gammapy you never want to work on the master branch, but always on a dedicated feature branch.

git branch [branch-name]
git checkout [branch-name]

To activate your development version (branch) of Gammapy in your environment:

python -m pip install -e .

This build is necessary to compile the few Cython code (*.pyx). If you skip this step, some imports depending on Cython code will fail. If you want to remove the generated files run make clean.

For the development it is also convenient to have declared $GAMMAPY_DATA environment variable. You can download the Gammapy datasets with gammapy download datasets and then point your $GAMMAPY_DATA to the local path you have chosen.

# Download GAMMAPY_DATA
gammapy download datasets --out GAMMAPY_DATA
export GAMMAPY_DATA=$PWD/GAMMAPY_DATA

We adhere to the PEP8 coding style. To enforce this, setup the pre-commit hook:

pre-commit install

Running tests & building Documentation#

To run tests and build documentation we use tool tox. It is a virtual environment management tool which allows you to test Gammapy locally in mutltiple test environments with different versions of Python and our dependencies. It is also used to build the documentation and check the codestyle in a specific environment. The same setup based on tox is used in our CI build.

Once you have created and activated the gammapy-dev environment, made some modification to the code, you should run the tests:

tox -e test

This will execute the tests in the standard test` environment. If you would like to test with a different environment you can use:

tox -e py310-test-numpy121

Which will test the code with Python 3.10 and numpy 1.21. All available pre-defined environments can be listed using:

tox --listenvs

However for most contributions testing with the standard tox -e test command is sufficient. Additional arguments for pytest can be passed after --:

tox -e test -- -n auto

Of course you can always use pytest directly to run tests, e.g. to run tests in a specific sub-package:

pytest gammapy/maps

To build the documentation locally you can use:

tox -e build_docs

And use make docs-show to open a browser and preview the result.

The codestyle can be checked using the command:

tox -e codestyle

Which will run the tool flak8 to check for code style issues.