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This code originates from python 2.6, before there was an `as` clause in `except`.
This removes all callers of `numpy.distutils.compat.get_exception`.
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MAINT: refactor function out of test module
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MAINT: only add --std=c99 where needed
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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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MAINT: Cleaning up PY_MAJOR_VERSION/PY_VERSION_HEX
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On Python 3, int_asbuffer throws NotImplementedError and goes unused
internally.
Fixes #12425
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MAINT: Remove unnecessary 'from __future__ import ...' statements
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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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Dropping the support for python 2, the difference in module setup do not have to be accounted
for anymore. This removes the macros and ifdef's related to module setup code and python 2
support.
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BUG: lib: Fix handling of integer arrays by gradient.
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In numpy.gradient, convert integer array inputs to float64 to avoid
unwanted modular arithmetic.
Closes gh-15207.
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TST: improve assert message of assert_array_max_ulp
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It was not showing the max difference before, which makes it
hard to judge whether something is seriously wrong, or the test
precision simply needs to be bumped by a little.
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MAINT: unskip test on win32
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Make numpy.__config__.show() to print information about the fallback
lapack_lite library when it is used.
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Build the lapack fallback library (used when no LAPACK installed) with
64-bit integer size when building on a 64-bit platform.
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TST: add BLAS ILP64 run in Travis & Azure
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LAPACK lwork call does not require much memory, and can be used as a
smoketest to whether LAPACK really uses 64-bit integers.
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BUG: distutils: fix msvc+gfortran openblas handling corner case
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Ensure the openblas MSVC+gfortran temporary library names are unique
for the different openblas_* system_info classes.
If multiple openblas libraries (e.g. both a 32-bit and a 64-bit one) are
used in the same project, when compiling in the msvc+gfortran mode, this
previously resulted to only the last one being used.
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* Remove a few unused imports in several files.
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* Remove unused imports.
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* Remove conditional imports that handled Python 2.
* Remove unused imports.
* Partial PEP 8 clean up.
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* Remove the unused function _to_filehandle().
* Remove conditional imports that handled Python 2.
* Remove unused imports.
* Fix a few line lengths (PEP 8).
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This flag is only legal for C compilers
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* BUG: use tmp dir and check version for cython test
* TST, MAINT: skip on win32, fix formatting
* TST: fixes from review
* TST: fixes from review
* TST: fixes from review
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DOC: update cholesky docstring regarding input checking
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Fixes issue gh-12256 where
import numpy as np
class MyArray:
def __array__(self, result=None):
return np.array(['0'*70], dtype=object)
np.array(MyArray(), dtype=str)
# has dtype U64, not U70
The problem was the result of obj.__array__ was not used when filling out a flexible dtype "by-value".
I also renamed a test file, over time we should move the tests related to array protocol into it.
* BUG: test, fix flexible dtype conversion on class with __array__
* Update numpy/core/_add_newdocs.py
Thanks for the correction
Co-Authored-By: Eric Wieser <wieser.eric@gmail.com>
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
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Correct docstring
Correct exception message and standardize
Use correct type identifiers
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Add shuffle to signature
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DOC: Fix typos, via a Levenshtein-style corrector
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* DOC: Add links to scipy linalg fuctions and compare numpy.linalg vs scipy.linalg.
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* MAINT: only treat 0d case separately in randint, simplify some tests
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API: Use `ResultType` in `PyArray_ConvertToCommonType`
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This slightly modifies the behaviour of `np.choose` (practically a
bug fix) and the public function itself. The function is not used within
e.g. SciPy, so the small performance hit of this implementation
is probably insignificant.
The change should help clean up dtypes a bit, since the whole "scalar
cast" logic is brittle and should be deprecated/removed, and this is
probably one of the few places actually using it.
The choose change means that:
```
np.choose([0], (1000, np.array([1], dtype=np.uint8)))
```
will actually return a value of 1000 (the dtype not being uint8).
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