| Commit message (Collapse) | Author | Age | Files | Lines |
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numpy.testing.assert_array_less's docstring
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These were introduced many years ago when ufuncs were buggy and
could return NotImplemented sometimes. This has been fixed for
many years, though.
I suspect the errstate for `isfinite` is not required so removed it.
It was a 12+ year old work-around for warnings that really shouldn't
happen to begin with. (The commit mentions `np.isinf(np.inf)` giving
a warning, which doesn't make sense, I think.)
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MAINT: remove redundant open() modes and io.open() alias
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ENH: add support for fujitsu C/C++ compiler and SSL2 to numpy.
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[ci skip]
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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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assert_array_equal (#23015)
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[skip actions] [skip travis] [skip azp]
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This PR replaces the import of platform and a call to platform.python_implementation() with a check using sys.implementation.name to improve the numpy import time. The improvement is about 5-10 ms (system dependent).
Also see #22061
Co-authored-by: Matti Picus <matti.picus@gmail.com>
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ENH: Ensure that assertion of unsigned dtypes does not return results
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that wrapped around.
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API: Introduce optional (and partial) NEP 50 weak scalar logic
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Unfortunately, this shows that the rational tests are still broken a bit.
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This ensures that the precision is not downcast, which could make
a small value zero (for float16 mostly).
This lets tests pass that check whether `np.float16(0)` is almost
equal to 0, which otherwise fail (because `float16(0.00000001)`
will evaluate to 0 exactly.
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Even the new promotion has to use the min-scalar logic to avoid
picking up a float16 loop for `np.int8(3) * 3.`.
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Fixes #9542
Co-authored-by: Bas van Beek <43369155+BvB93@users.noreply.github.com>
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i.e. `numpy.typing` and `numpy._typing`
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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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TYP,ENH: Improve typing with the help of `ParamSpec`
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BUG: Avoid importing numpy.distutils on import numpy.testing (#20769)
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Move the offending imports into the functions that use them so that
numpy.distutils is only loaded if those functions are required.
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`load_module` is deprecated since python 3.4 and will be removed in python 3.12.
Use `exec_module` instead. Provide a convenience function in `distutils.misc_utils`
instead of `npy_load_module` from `compat.py3k`.
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Fixes gh-17467. Adds a public struct to hold memory manipulation routines PyDataMem_Handler and two new API functions PyDataMem_SetHandler to replace the current routines with the new ones, and PyDataMem_GetHandlerName to get the string name of the current routines (either globally or for a specific ndarray object). This also changes the size of the ndarray object to hold the PyDataMem_Handler active when it was created so subsequent actions on its data memory will remain consistent.
Tests and documentation are included. Along the way, I found some places in the code where the current policy is inconsistent (all data memory handling should have gone through npy_*_cache not PyDataMem_*) so even if this is rejected it might improve the cache handling.
The PyDataMem_Handler has fields to override memcpy, these are currently not implemented: memcpy in the code base is untouched. I think this PR is invasive enough as-is, if desired memcpy can be handled in a follow-up PR.
* ENH: add and use global configurable memory routines
* ENH: add tests and a way to compile c-extensions from tests
* fix allocation/free exposed by tests
* DOC: document the new APIs (and some old ones too)
* BUG: return void from FREE, also some cleanup
* MAINT: changes from review
* fixes from linter
* setting ndarray->descr on 0d or scalars mess with FREE
* make scalar allocation more consistent wrt np_alloc_cache
* change formatting for sphinx
* remove memcpy variants
* update to match NEP 49
* ENH: add a python-level get_handler_name
* ENH: add core.multiarray.get_handler_name
* Allow closure-like definition of the data mem routines
* Fix incompatible pointer warnings
* Note PyDataMemAllocator and PyMemAllocatorEx differentiation
Co-authored-by: Matti Picus <matti.picus@gmail.com>
* Redefine default allocator handling
* Always allocate new arrays using the current_handler
* Search for the mem_handler name of the data owner
* Sub-comparisons don't need a local mem_handler
* Make the default_handler a valid PyDataMem_Handler
* Fix PyDataMem_SetHandler description (NEP discussion)
* Pass the allocators by reference
* Implement allocator context-locality
* Fix documentation, make PyDataMem_GetHandler return const
* remove import of setuptools==49.1.3, doesn't work on python3.10
* Fix refcount leaks
* fix function signatures in test
* Return early on PyDataMem_GetHandler error (VOID_compare)
* Add context/thread-locality tests, allow testing custom policies
* ENH: add and use global configurable memory routines
* ENH: add tests and a way to compile c-extensions from tests
* fix allocation/free exposed by tests
* DOC: document the new APIs (and some old ones too)
* BUG: return void from FREE, also some cleanup
* MAINT: changes from review
* fixes from linter
* setting ndarray->descr on 0d or scalars mess with FREE
* make scalar allocation more consistent wrt np_alloc_cache
* change formatting for sphinx
* remove memcpy variants
* update to match NEP 49
* ENH: add a python-level get_handler_name
* ENH: add core.multiarray.get_handler_name
* Allow closure-like definition of the data mem routines
* Fix incompatible pointer warnings
* Note PyDataMemAllocator and PyMemAllocatorEx differentiation
Co-authored-by: Matti Picus <matti.picus@gmail.com>
* Redefine default allocator handling
* Always allocate new arrays using the current_handler
* Search for the mem_handler name of the data owner
* Sub-comparisons don't need a local mem_handler
* Make the default_handler a valid PyDataMem_Handler
* Fix PyDataMem_SetHandler description (NEP discussion)
* Pass the allocators by reference
* remove import of setuptools==49.1.3, doesn't work on python3.10
* fix function signatures in test
* try to fix cygwin extension building
* YAPF mem_policy test
* Less empty lines, more comments (tests)
* Apply suggestions from code review (set an exception and)
Co-authored-by: Matti Picus <matti.picus@gmail.com>
* skip test on cygwin
* update API hash for changed signature
* TST: add gc.collect to make sure cycles are broken
* Implement thread-locality for PyPy
Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>
* Update numpy/core/tests/test_mem_policy.py
Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>
* fixes from review
* update circleci config
* fix test
* make the connection between OWNDATA and having a allocator handle more explicit
* improve docstring, fix flake8 for tests
* update PyDataMem_GetHandler() from review
* Implement allocator lifetime management
* update NEP and add best-effort handling of error in PyDataMem_UserFREE
* ENH: fix and test for blindly taking ownership of data
* Update doc/neps/nep-0049.rst
Co-authored-by: Elias Koromilas <elias.koromilas@gmail.com>
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