| Commit message (Collapse) | Author | Age | Files | Lines |
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As discussed in
https://mail.python.org/archives/list/numpy-discussion@python.org/thread/UKZJACAP5FUG7KP2AQDPE4P5ADNWLOHZ/
This flag was always meant to be temporary, and cleaning it up is
long overdue.
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NumPy switched to using pytest in 2018 and nose has been unmaintained
for many years. We have kept NumPy's nose support to avoid breaking
downstream projects who might have been using it and not yet switched to
pytest or some other testing framework. With the arrival of Python 3.12,
unpatched nose will raise an error. It it time to move on.
Decorators removed
- raises
- slow
- setastest
- skipif
- knownfailif
- deprecated
- parametrize
- _needs_refcount
These are not to be confused with pytest versions with similar names,
e.g., pytest.mark.slow, pytest.mark.skipif, pytest.mark.parametrize.
Functions removed
- Tester
- import_nose
- run_module_suite
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In some cases, the replacement is clearly not what is intended,
in those (where setup was called explicitly), I mostly renamed
`setup` to `_setup`.
The `test_ccompile_opt` is a bit confusing, so left it right now
(this will probably fail)
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ENH: Ensure that assertion of unsigned dtypes does not return results
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Fixes #9542
Co-authored-by: Bas van Beek <43369155+BvB93@users.noreply.github.com>
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seberg/fix-void-cast-safety-promotion-and-comparison
API: Fix structured dtype cast-safety, promotion, and comparison
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This PR replaces the old gh-15509 implementing proper type promotion
for structured voids. It further fixes the casting safety to consider
casts with equivalent field number and matching order as "safe"
and if the names, titles, and offsets match as "equiv".
The change perculates into the void comparison, and since it fixes
the order, it removes the current FutureWarning there as well.
This addresses https://github.com/liberfa/pyerfa/issues/77
and replaces gh-15509 (the implementation has changed too much).
Fixes gh-15494 (and probably a few more)
Co-authored-by: Allan Haldane <allan.haldane@gmail.com>
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* TST: Test suppression of asset_array_equal RuntimeWarning See #18992
* ENH: Suppress over-/underflow warnings on asset_array_equal - Closes #18992
* MAINT: Resolve linting issues of prior commit
* MAINT: Simplified ignore and test case of asset_array_equal
* MAINT: Removed unused import in test_utils.py
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Disallowing timedelta64+float promotion (to timedelta64) in all cases
(previously it was assymetric and "half allowed") meant that isclose,
allclose, np.ma.allclose, and assert_arrays_almost_equal (which uses
isclose), would stop work for timedelta64. Hardcoding that timedelta64
is passed on unmodified retains the old behaviour.
It may make sense to deprecate or change this behaviour in the future,
but for the 1.20 release, the behaviour should be as much unmodified
as possible.
Closes gh-18286
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Move test_ignore_nan_ulperror to `numpy/testing/tests/test_utils.py
and extend to all floating types.
Closes 16600.
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* Cleanup unused imports (F401) of mostly standard Python modules,
or some internal but unlikely referenced modules
* Where internal imports are potentially used, mark with noqa
* Avoid redefinition of imports (F811)
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Inheriting from object was necessary for Python 2 compatibility to use
new-style classes. In Python 3, this is unnecessary as there are no
old-style classes.
Dropping the object is more idiomatic Python.
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As numpy is Python 3 only, these import statements are now unnecessary
and don't alter runtime behavior.
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* MAINT: only treat 0d case separately in randint, simplify some tests
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Fixed maximum relative error reporting in assert_allclose:
In cases where the two arrays have zeros at the same positions it will
no longer report nan as the max relative error
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The original message included "Mismatch: 33.3%". It's not obvious what this
percentage means. This commit changes the text to
"Mismatched elements: 1 / 3 (33.3%)".
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MAINT: revert __skip_array_function__ from NEP-18
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- Always enable __array_function__ overrides.
- Remove special cases for Python 2 compatibility.
- Document these changes in 1.17.0-notes.rst.
It will be good to see ASV numbers to understand the performance implications
of these changes. If need be, we can speed up NumPy functions internally by
using non-dispatched functions (with ``.__wrapped__``).
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Example behavior:
>>> x = np.array([1, 2, 3])
>>> y = np.array([1, 2, 3.0001])
>>> np.testing.assert_allclose(x, y)
AssertionError:
Not equal to tolerance rtol=1e-07, atol=0
Mismatch: 33.3%
Max absolute difference: 0.0001
Max relative difference: 3.33322223e-05
x: array([1, 2, 3])
y: array([1. , 2. , 3.0001])
Motivation: when writing numerical algorithms, I frequently find myself
experimenting to pick the right value of `atol` and `rtol` for
`np.testing.assert_allclose()`. If I make the tolerance too generous, I risk
missing regressions in accuracy, so I usually try to pick the smallest values
for which tests pass. This change immediately reveals appropriate values to
use for these parameters, so I don't need to guess and check.
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(#12448)
* Review F401,F841,F842 flake8 errors (unused variables, imports)
* Review comments
* More tests in test_installed_npymath_ini
* Review comments
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BUG: Fix misleading assert message in assert_almost_equal #12200
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Fixes #12200 by making a copy of the matrix before NaN's are excluded.
Add a test for it.
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* ENH: __array_function__ support for np.lib, part 2
xref GH12028
np.lib.npyio through np.lib.ufunclike
* Fix failures in numpy/core/tests/test_overrides.py
* CLN: handle depreaction in dispatchers for np.lib.ufunclike
* CLN: fewer dispatchers in lib.twodim_base
* CLN: fewer dispatchers in lib.shape_base
* CLN: more dispatcher consolidation
* BUG: fix test failure
* Use all method instead of function in assert_equal
* DOC: indicate n is array_like in scimath.logn
* MAINT: updates per review
* MAINT: more conservative changes in assert_array_equal
* MAINT: add back in comment
* MAINT: casting tweaks in assert_array_equal
* MAINT: fixes and tests for assert_array_equal on subclasses
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* previously, stochastic failures were possible
when running the NumPy test suite in parallel
because of an assumption made by assert_warn_len_equal()
* we now guard against this failure by gracefully
handling the testing contexts of both serial and
parallel execution of the module
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After the pytest migration, test classes no longer inherit
from unittest.TestCase and and the fail method does not
exist anymore.
In all these cases, we can use assert_raises and assert_raises_regex instead
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This brought to light two bugs in tests, which are fixed here, viz.,
that a sample ndarray subclass that tested propagation of an added
parameter was incomplete, in that in propagating the parameter in
__array_wrap__ it assumed it was there on self, but that assumption
could be broken when a view of self was taken (as is done by
x[~flagged] in the test routine), since there was no
__array_finalize__ defined.
The other subclass bug counted, incorrectly, on only needing to provide
one type of comparison, the __lt__ being explicitly tested. But flags
are compared with __eq__ and those flags will have the same subclass.
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The removal of nan and inf from arrays that are compared using
test routines like assert_array_equal treated the two arrays
separately, which for masked arrays meant that some elements would
not be removed when they should have been. This PR corrects this.
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The underlying problem is that ma.all() evaluates to masked,
which is falsy, and thus triggers test failures.
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It's not always possible to guarantee this, so also adds a test to verify that we don't hang
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This also means we can now test that our test is actually able to detect the type of failure we expect
Trying to give myself some tools to debug the failure at https://github.com/numpy/numpy/pull/10882/files#r180813166
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That function is nose specific and has not worked since `__init__` files
were added to the tests directories.
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