ruff is faster and handle everything we had prior.
isort configuration done based on the indication from
https://github.com/astral-sh/ruff/issues/4670, previousely based on
reorder-python-import (#11896)
flake8-docstrings was a wrapper around pydocstyle (now archived) that
explicitly asks to use ruff in https://github.com/PyCQA/pydocstyle/pull/658.
flake8-typing-import is useful mainly for project that support python 3.7
and the one useful check will be implemented in https://github.com/astral-sh/ruff/issues/2302
We need to keep blacken-doc because ruff does not handle detection
of python code inside .md and .rst. The direct link to the repo is
now used to avoid a redirection.
Manual fixes:
- Lines that became too long
- % formatting that was not done automatically
- type: ignore that were moved around
- noqa of hard to fix issues (UP031 generally)
- fmt: off and fmt: on that is not really identical
between black and ruff
- autofix re-order in pre-commit from faster to slower
Co-authored-by: Ran Benita <ran@unusedvar.com>
Change our mypy configuration to disallow untyped defs by default, which ensures *new* files added to the code base are fully typed.
To avoid having to type-annotate everything now, add `# mypy: allow-untyped-defs` to files which are not fully type annotated yet.
As we fully type annotate those modules, we can then just remove that directive from the top.
Previously the error report would have all sections glued together:
- The assertion representation
- The error explanation
- The full diff
This makes it hard to see at a glance where which one starts and ends.
One of the representation (dataclasses, tuples, attrs) does display a
newlines at the start already.
Let's add a newlines before the error explanation and before the full
diff, so we get an easier to read report.
This has one disadvantage: we get one line less in the least verbose
mode, where the output gets truncated.
Dict comparsion in the ApproxMapping class did not check if values were None before attempting to subtract for max_abs_diff stat, which was throwing an TypeError instead of being handled by pytest error assertion. Check for None has been added before these calculations, so that None will properly show as Obtained/Expected in pytest assert message
Some of the top search-engine hits for pytest.approx use the function without actually comparing it to anything.
This PR will cause these tests to fail by implementing approx.__bool__() to raise an AssertionError that briefly explains how to correctly use approx.
* [pre-commit.ci] pre-commit autoupdate
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* manual fixes after configuration update
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Anthony Sottile <asottile@umich.edu>
* Update setup.py requires and classifiers
* Drop Python 2.7 and 3.4 from CI
* Update docs dropping 2.7 and 3.4 support
* Fix mock imports and remove tests related to pypi's mock module
* Add py27 and 34 support docs to the sidebar
* Remove usage of six from tmpdir
* Remove six.PY* code blocks
* Remove sys.version_info related code
* Cleanup compat
* Remove obsolete safe_str
* Remove obsolete __unicode__ methods
* Remove compat.PY35 and compat.PY36: not really needed anymore
* Remove unused UNICODE_TYPES
* Remove Jython specific code
* Remove some Python 2 references from docs
Related to #5275
approx() was updated in 9f3122fe to work better with numpy arrays,
however at the same time the requirements were tightened from
requiring an Iterable to requiring a Sequence - the former being
tested only on interface, while the latter requires subclassing or
registration with the abc.
Since the ApproxSequence only used __iter__ and __len__ this commit
reduces the requirement to only what's used, and allows unregistered
Sequence-like containers to be used.
Since numpy arrays qualify for the new criteria, reorder the checks so
that generic sequences are checked for after numpy arrays.
If the user pass as a expected value a numpy array created like
numpy.array(5); it will creates an array with one element without shape,
when used with approx it will raise an error
'TypeError: iteration over a 0-d array'
