This page gives an overview of the technical infrastructure we have set up to develop and maintain Gammapy. If you just want to make contribution to the Gammapy code or documentation, you don’t need to know about most of the things mentioned on this page. But for Gammapy maintainers it’s helpful to have a reference that explains what we have and how things work.
This section explains the content of the main repository for Gammapy:
Package and docs#
The two main folders of interest for developers are the
gammapy folder and
docs folder. In
gammapy you find the Gammapy package, i.e. all code,
but also tests are included there in sub-folders called
folder contains the documentation pages mostly in restructured text (RST) format. The
Sphinx documentation generator is used to convert those RST files to the HTML
docs/tutorials folder contains tutorials that are part of the user
documentation for Gammapy in the form of Jupyter notebooks. There may be also notebooks in
other parts of the documentation. Except those specifically declared (see Skip notebooks from being executed), all
the notebooks present in the documentation are executed during the doc building process. They are all
converted to the Sphinx-formatted HTML files, where clean output stripped
.ipynb notebooks files
.py scripts versions are generated and placed in the
We perform automated set-up testing for all notebooks (just check that they run
and don’t raise an exception) during the CI process (see below). It is also possible to
perform tests locally on notebooks with the
gammapy jupyter command. This
command provides functionalities for testing, code formatting, stripping output cells
and execution. See
gammapy jupyter -h for more info on this.
gammapy download command allows downloading notebooks published in the documentation
as well as the related datasets needed to execute them. The set of notebooks is versioned
for each stable release as tar bundles published within the versioned documentation in the
The same happens for conda working environments of stable releases, whose yaml files are published
in the gammapy-web repository. The datasets are not
versioned, and they are placed in the gammapy-data
Makefile contain code to build and install Gammapy, as
well as to run the tests and build the documentation, see How to contribute to Gammapy.
environment-dev.yml file contains the conda environment specification
that allows one to quickly set up a conda environment for Gammapy development,
see Get set up.
We also have some Cython code in Gammapy, at the time of this writing less than 10% in this file:
There are two more folders in the
examples folder contains the following:
Python scripts that could be used as example scripts tutorials in Scripts.
Python scripts needed by the sphinx-gallery extension to produce collections of examples use cases.
The Python scripts needed by sphinx-gallery extension are placed in folders declared in the
sphinx_gallery_conf variable in
dev folder is a place for Gammapy developers to put stuff that is useful for maintenance,
such as i.e. a helper script to produce a list of contributors.
The file in
github/workflows/ci.yml is the configuration file for the continuous
integration (CI) we use with GitHub actions.
Finally, there are some folders that are generated and filled by various build steps:
buildcontains the Gammapy package if you run
python setup.py build. If you run
python setup.py install, first the build is run and files placed there, and after that files are copied from the
buildfolder to your
docs/_buildcontains the generated documentation, especially
docs/_build/htmlthe HTML version.
.coverageis where the test coverage report is stored.
vis a folder Pytest uses for caching information about failing tests across test runs. This is what makes it possible to execute tests e.g. with the
--lfoption and just run the tests that “last failed”.
distcontains the Gammapy distribution if you run
python setup.py sdist
You may find here the datasets needed to execute the notebooks, perform the CI tests, build the documentation and check tutorials.
For Gammapy we have a second repository for most of the example data files and a few other things:
Old example data#
datasets/tests folders contain example datasets that were
used by the Gammapy documentation and tests. Note that here is a lot of old cruft,
because Gammapy was developed since 2013 in parallel with the development of data
formats for gamma-ray astronomy (see below).
Many old files in those folders can just be deleted; in some cases where documentation or tests access the old files, they should be changed to access newer files or generate test datasets from scratch. Doing this “cleanup” and improvement of curated example datasets will be an ongoing task in Gammapy for the coming years, that has to proceed in parallel with code, test and documentation improvements.
figuresfolder contains images that we show in the documentation (or in presentations or publications), for cases where the analysis and image takes a while to compute (i.e. something we don’t want to do all the time during the Gammapy documentation build). In each case, there should be a Python script to generate the image.
checksfolders contain Python scripts and notebooks with, well, experiments and checks by Gammapy developers. Some are still work in progress and of interest, most could probably be deleted.
logofolder contains the Gammapy logo and banner in a few different variants.
presentationsfolders contain a few Gammapy posters and presentations, for cases where the poster or presentation isn’t available somewhere else on the web. It’s hugely incomplete and probably not very useful as-is, and we should discuss if this is useful at all, and if yes, how we want to maintain it.
Performance benchmarks for Gammapy:
Data from tutorials sometimes accesses files here:
Information from meetings is here:
In addition, we have Binder set up to allow users to try Gammapy in the browser.
https://gammapy.org/ is a small landing page for the Gammapy project. The page shown there is a static webpage served via GitHub pages.
To update it, edit the HTML and CSS files in the
gammapy-webpage GitHub repository
and then make a pull request against the default branch for that repo, called
Once it’s merged, the webpage at https://gammapy.org/ usually updates within less than a minute.
https://docs.gammapy.org/ contains most of the documentation for Gammapy, including information about Gammapy, the changelog, tutorials, …
The dev version of the docs is built and updated with an automated GitHub action for every
pull request merged in the
gammapy GitHub code repository. All the docs are versioned,
and each version of the docs is placed in its dedicated version-labelled folder. It is recommended
to build the docs locally before each release to identify and fix possible Sphinx warnings from
badly formatted RST files or failing Python scripts used to display figures.
We have set up https://mybinder.org/ for each released version of Gammapy, which allows users to execute the notebooks present in the versioned docs within the web browser, without having to install software or download data to their local machine. This can be useful for people to get started, and for tutorials. Every HTML-fixed version of the notebooks that you can find in the Tutorials section has a link to Binder that allows you to execute the tutorial in the myBinder cloud infrastructure.
myBinder provides versioned virtual environments coupled with every release.
The myBinder docker image is created using the
binder.py files placed
in the master branch of the gammapy-webpage GitHub repository.
The Dockerfile makes the Docker image used by Binder running some linux commands to install base-packages
and copy the notebooks and datasets needed. It executes
binder.py to conda
install Gammapy dependencies listed in the environment YAML published within the versioned
We are running various builds as GitHub actions workflows for CI.
Code coverage: https://coveralls.io/r/gammapy/gammapy
To run all tests and measure coverage, type the command
$ make test-cov
At this time, making a Gammapy release is a sequence of steps to execute in the command line and on some webpages, that is fully documented in this checklist: How to make a Gammapy release. It is difficult to automate this procedure more, but it is already pretty straightforward and quick to do. If all goes well, making a release takes about 1 hour of human time and one or two days of real time, with the building of the conda binary packages being the slowest step, something we wait for before announcing a new release to users (because many use conda and will try to update as soon as they get the announcement email).