Developing
Developer Environment
To develop, we suggest using Python virtual environments
together with pip
.
Once the virtual environment is activated and you have SSH keys setup with GitHub, clone the
repo from GitHub
git clone git@github.com:scikit-hep/pyhf
and install all necessary packages for development
python -m pip install --upgrade --editable .[complete]
Then setup the Git pre-commit hooks by running
pre-commit install
inside of the virtual environment. pre-commit.ci keeps the pre-commit hooks updated through time, so pre-commit will automatically update itself when you run it locally after the hooks were updated.
Testing
Writing tests
Data Files
A function-scoped fixture called datadir
exists for a given test module
which will automatically copy files from the associated test modules data
directory into a temporary directory for the given test execution. That is, for
example, if a test was defined in test_schema.py
, then data files located
in test_schema/
will be copied to a temporary directory whose path is made
available by the datadir
fixture. Therefore, one can do:
def test_patchset(datadir):
data_file = open(datadir.join("test.txt"), encoding="utf-8")
...
which will load the copy of text.txt
in the temporary directory. This also
works for parameterizations as this will effectively sandbox the file
modifications made.
Running with pytest
To run the test suite in full, from the top level of the repository run
pytest
More practically for most local testing you will not want to test the benchmarks, contrib module, or notebooks, and so instead to test the core codebase a developer can run
pytest --ignore tests/benchmarks/ --ignore tests/contrib --ignore tests/test_notebooks.py
Contrib module matplotlib image tests
To run the visualization tests for the contrib
module with the pytest-mpl
pytest
plugin run
pytest tests/contrib --mpl --mpl-baseline-path tests/contrib/baseline --mpl-generate-summary html
Doctest
pyhf
’s configuration of pytest
will automatically run doctest
on all the
modules when the full test suite is run.
To run doctest
on an individual module or file just run pytest
on its path.
For example, to run doctest
on the JAX backend run
pytest src/pyhf/tensor/jax_backend.py
Publishing
Publishing to TestPyPI and PyPI is automated through the PyPA’s PyPI publish
GitHub Action
and the pyhf
bump version GitHub Actions workflow.
Release Checklist
As part of the release process a checklist is required to be completed to make sure steps aren’t missed. There is a GitHub Issue template for this that the maintainer in charge of the release should step through and update if needed.
Deployment
The push of a tag to the repository will trigger a build of a sdist and wheel, and then the deployment of them to TestPyPI.
TestPyPI
pyhf
tests packaging and distribution by publishing to TestPyPI in advance of
releases.
Installation of the latest test release from TestPyPI can be tested
by first installing pyhf
normally, to ensure all dependencies are installed
from PyPI, and then upgrading pyhf
to a test release from TestPyPI
python -m pip install pyhf
python -m pip install --upgrade --extra-index-url https://test.pypi.org/simple/ --pre pyhf
Note
This adds TestPyPI as an additional package index to search
when installing.
PyPI will still be the default package index pip
will attempt to install
from for all dependencies, but if a package has a release on TestPyPI that
is a more recent release then the package will be installed from TestPyPI instead.
Note that dev releases are considered pre-releases, so 0.1.2
is a “newer”
release than 0.1.2.dev3
.
PyPI
Once the TestPyPI deployment has been examined, installed, and tested locally by the maintainers final deployment to PyPI can be done by creating a GitHub Release:
From the
pyhf
GitHub releases page select the “Draft a new release” button.Select the release tag that was just pushed, and set the release title to be the tag (e.g.
v1.2.3
).Use the “Auto-generate release notes” button to generate a skeleton of the release notes and then augment them with the preprepared release notes the release maintainer has written.
Select “This is a pre-release” if the release is a release candidate.
Select “Create a discussion for this release” if the release is a stable release.
Select “Publish release”.
Once the release has been published to GitHub, the publishing workflow will build a sdist and wheel, and then deploy them to PyPI.
Context Files and Archive Metadata
The .zenodo.json
and codemeta.json
files have the version number
automatically updated through tbump
, though their additional metadata
should be checked periodically by the dev team (probably every release).
The codemeta.json
file can be generated automatically from a PyPI install
of pyhf
using codemetapy
codemetapy --no-extras pyhf > codemeta.json
though the author
metadata will still need to be checked and revised by hand.
The .zenodo.json
is currently generated by hand, so it is worth using
codemeta.json
as a guide to edit it.