devel/py-pytest-cases: Update to 3.10.1 Changes: https://github.com/smarie/python-pytest-cases/releases https://smarie.github.io/python-pytest-cases/changelog/4d518ceef7
Port details
devel/py-pytest-cases 3.10.1
Separate test code from test cases in pytest
- Maintainer
- sunpoet@FreeBSD.org
- Packages
- latest: 3.10.1
- Upstream
- up to date
- Last update
- to 3.10.1 · 4d518ceef7
- Quarterly
- 2026Q4: 3.10.1
- WWW
- https://smarie.github.io/python-pytest-cases/
- Links
- cgit · VuXML
Unaddressed and VuXML only (0)
No unaddressed CVE and nothing reported by
pkg audit for the packaged version 3.10.1.Fixed (0)
No VuXML entries for this package.
Problem reports (1) · open in Bugzilla, most recently changed first
| PR | Summary | Status | Changed |
|---|---|---|---|
| 299583 | maintainer-feedback? devel/py-pytest-cases: Fix with pytest 9.1 (IdMaker TypeError on import) | New | 2026-10-19 |
Commits (8)
devel/py-pytest-cases: Update to 3.9.1 Changes: https://github.com/smarie/python-pytest-cases/releases https://smarie.github.io/python-pytest-cases/changelog/7a8374b0bb
devel/py-pytest-cases: Add py-pytest-cases 3.8.6 Did you ever think that most of your test functions were actually the same test code, but with different data inputs and expected results/exceptions? - pytest-cases leverages pytest and its great @pytest.mark.parametrize decorator, so that you can separate your test cases from your test functions. - In addition, pytest-cases provides several useful goodies to empower pytest. In particular it improves the fixture mechanism to support "fixture unions". This is a major change in the internal pytest engine, unlocking many possibilities such as using fixture references as parameter values in a test function. See here. pytest-cases is fully compliant with pytest-harvest so you can easily monitor the execution times and created artifacts. With it, it becomes very easy to create a complete data science benchmark, for example comparing various models on various datasets.02595040cb
devel/py-pytest-cases: Remove expired port 2025-06-01 devel/py-pytest-cases: Depends on expired devel/py-makefun8129e9a158
devel/py-pytest-cases: Update to 3.8.6 Changes: https://github.com/smarie/python-pytest-cases/releases06c465ab76
all: expire (indirect) consumers of devel/py-makefun on 2025-06-0199723c254e
devel/py-setuptools-scm: update to 8.0.4
Directory and PORTNAME changed to match normalised name in release
tarball; consumers updated to match.
Although setuptools itself is specified as a run dependency in the
Python package metadata, it is currently left out here to prevent
environment pollution until at least PR 270510 is committed.
Additionally, this version of setuptools-scm requires setuptools>=61,
which means this version is only meant for USE_PYTHON=pep517 ports
as all USE_PYTHON=distutils ports will switch to devel/py-setuptools58
also after PR 270510. science/py-emmet-core is switched to
devel/py-setuptools_scm7 accordingly, as it specifies setuptools-scm<8.
devel/py-{flit-scm,hatch-vcs} have ${PY_SETUPTOOLS} added to
RUN_DEPENDS to compensate.
Further details: https://wiki.freebsd.org/Python/setuptools
Reported by: yuri
Co-authored by: matthew
Exp-run by: antoine (earlier iteration)
Approved by: yuri (science/py-emmet-core, previous iteration)
PR: 272134
Differential Revision: https://reviews.freebsd.org/D392885d4139f8bddevel/py-pytest-cases: Add py-pytest-cases 3.6.14 Did you ever think that most of your test functions were actually the same test code, but with different data inputs and expected results/exceptions? - pytest-cases leverages pytest and its great @pytest.mark.parametrize decorator, so that you can separate your test cases from your test functions. - In addition, pytest-cases provides several useful goodies to empower pytest. In particular it improves the fixture mechanism to support "fixture unions". This is a major change in the internal pytest engine, unlocking many possibilities such as using fixture references as parameter values in a test function. See here. pytest-cases is fully compliant with pytest-harvest so you can easily monitor the execution times and created artifacts. With it, it becomes very easy to create a complete data science benchmark, for example comparing various models on various datasets.62cbc4c293