Update dependency numpy to v1.22.0 [SECURITY]

[renovate]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
numpy (changelog)	==1.19.4 -> ==1.22.0

---

Warning

Some dependencies could not be looked up. Check the Dependency Dashboard for more information.

GitHub Vulnerability Alerts

CVE-2021-33430

A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service.

NOTE: The vendor does not agree this is a vulnerability; In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.

CVE-2021-34141

Incomplete string comparison in the numpy.core component in NumPy1.9.x, which allows attackers to fail the APIs via constructing specific string objects.

---

Release Notes

numpy/numpy (numpy)

v1.22.0

Compare Source

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors
spread over 609 pull requests. There have been many improvements,
highlights are:

• Annotations of the main namespace are essentially complete. Upstream
  is a moving target, so there will likely be further improvements,
  but the major work is done. This is probably the most user visible
  enhancement in this release.
• A preliminary version of the proposed Array-API is provided. This is
  a step in creating a standard collection of functions that can be
  used across application such as CuPy and JAX.
• NumPy now has a DLPack backend. DLPack provides a common interchange
  format for array (tensor) data.
• New methods for quantile, percentile, and related functions. The
  new methods provide a complete set of the methods commonly found in
  the literature.
• A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for
commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7
has been dropped. Note that 32 bit wheels are only provided for Python
3.8 and 3.9 on Windows, all other wheels are 64 bits on account of
Ubuntu, Fedora, and other Linux distributions dropping 32 bit support.
All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix
the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32",
and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that
users use pickle.loads instead. ndfromtxt and mafromtxt were both
deprecated in v1.17 - users should use numpy.genfromtxt instead with
the appropriate value for the usemask parameter.

(gh-19615)

Deprecations

Use delimiter rather than delimitor as kwarg in mrecords

The misspelled keyword argument delimitor of
numpy.ma.mrecords.fromtextfile() has been changed to delimiter,
using it will emit a deprecation warning.

(gh-19921)

Passing boolean kth values to (arg-)partition has been deprecated

numpy.partition and numpy.argpartition would previously accept
boolean values for the kth parameter, which would subsequently be
converted into integers. This behavior has now been deprecated.

(gh-20000)

The np.MachAr class has been deprecated

The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have
been deprecated. Users are encouraged to access the property if interest
directly from the corresponding numpy.finfo attribute.

(gh-20201)

Compatibility notes

Distutils forces strict floating point model on clang

NumPy now sets the -ftrapping-math option on clang to enforce correct
floating point error handling for universal functions. Clang defaults to
non-IEEE and C99 conform behaviour otherwise. This change (using the
equivalent but newer -ffp-exception-behavior=strict) was attempted in
NumPy 1.21, but was effectively never used.

(gh-19479)

Removed floor division support for complex types

Floor division of complex types will now result in a TypeError

>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...

(gh-19135)

numpy.vectorize functions now produce the same output class as the base function

When a function that respects numpy.ndarray subclasses is vectorized
using numpy.vectorize, the vectorized function will now be
subclass-safe also for cases that a signature is given (i.e., when
creating a gufunc): the output class will be the same as that returned
by the first call to the underlying function.

(gh-19356)

Python 3.7 is no longer supported

Python support has been dropped. This is rather strict, there are
changes that require Python >= 3.8.

(gh-19665)

str/repr of complex dtypes now include space after punctuation

The repr of
np.dtype({"names": ["a"], "formats": [int], "offsets": [2]}) is now
dtype({'names': ['a'], 'formats': ['<i8'], 'offsets': [2], 'itemsize': 10}),
whereas spaces where previously omitted after colons and between fields.

The old behavior can be restored via
np.set_printoptions(legacy="1.21").

(gh-19687)

Corrected advance in PCG64DSXM and PCG64

Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug
only affects results when the step was larger than $2^{64}$ on platforms
that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

(gh-20049)

Change in generation of random 32 bit floating point variates

There was bug in the generation of 32 bit floating point values from the
uniform distribution that would result in the least significant bit of
the random variate always being 0. This has been fixed.

This change affects the variates produced by the random.Generator
methods random, standard_normal, standard_exponential, and
standard_gamma, but only when the dtype is specified as
numpy.float32.

(gh-20314)

C API changes

Masked inner-loops cannot be customized anymore

The masked inner-loop selector is now never used. A warning will be
given in the unlikely event that it was customized.

We do not expect that any code uses this. If you do use it, you must
unset the selector on newer NumPy version. Please also contact the NumPy
developers, we do anticipate providing a new, more specific, mechanism.

The customization was part of a never-implemented feature to allow for
faster masked operations.

(gh-19259)

New Features

NEP 49 configurable allocators

As detailed in NEP 49, the
function used for allocation of the data segment of a ndarray can be
changed. The policy can be set globally or in a context. For more
information see the NEP and the data_memory{.interpreted-text
role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY
override to warn on dangerous use of transfering ownership by setting
NPY_ARRAY_OWNDATA.

(gh-17582)

Implementation of the NEP 47 (adopting the array API standard)

An initial implementation of NEP47, adoption
of the array API standard, has been added as numpy.array_api. The
implementation is experimental and will issue a UserWarning on import,
as the array API standard is still in
draft state. numpy.array_api is a conforming implementation of the
array API standard, which is also minimal, meaning that only those
functions and behaviors that are required by the standard are
implemented (see the NEP for more info). Libraries wishing to make use
of the array API standard are encouraged to use numpy.array_api to
check that they are only using functionality that is guaranteed to be
present in standard conforming implementations.

(gh-18585)

Generate C/C++ API reference documentation from comments blocks is now possible

This feature depends on Doxygen in
the generation process and on
Breathe to integrate it
with Sphinx.

(gh-18884)

Assign the platform-specific c_intp precision via a mypy plugin

The mypy plugin, introduced in
numpy/numpy#17843, has
again been expanded: the plugin now is now responsible for setting the
platform-specific precision of numpy.ctypeslib.c_intp, the latter
being used as data type for various numpy.ndarray.ctypes attributes.

Without the plugin, aforementioned type will default to
ctypes.c_int64.

To enable the plugin, one must add it to their mypy configuration
file:

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-19062)

Add NEP 47-compatible dlpack support

Add a ndarray.__dlpack__() method which returns a dlpack C structure
wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function,
where obj supports __dlpack__(), and returns an ndarray.

(gh-19083)

keepdims optional argument added to numpy.argmin, numpy.argmax

keepdims argument is added to numpy.argmin, numpy.argmax. If set
to True, the axes which are reduced are left in the result as
dimensions with size one. The resulting array has the same number of
dimensions and will broadcast with the input array.

(gh-19211)

bit_count to compute the number of 1-bits in an integer

Computes the number of 1-bits in the absolute value of the input. This
works on all the numpy integer types. Analogous to the builtin
int.bit_count or popcount in C++.

>>> np.uint32(1023).bit_count()
10
>>> np.int32(-127).bit_count()
7

(gh-19355)

The ndim and axis attributes have been added to numpy.AxisError

The ndim and axis parameters are now also stored as attributes
within each numpy.AxisError instance.

(gh-19459)

Preliminary support for windows/arm64 target

numpy added support for windows/arm64 target. Please note OpenBLAS
support is not yet available for windows/arm64 target.

(gh-19513)

Added support for LoongArch

LoongArch is a new instruction set, numpy compilation failure on
LoongArch architecture, so add the commit.

(gh-19527)

A .clang-format file has been added

Clang-format is a C/C++ code formatter, together with the added
.clang-format file, it produces code close enough to the NumPy
C_STYLE_GUIDE for general use. Clang-format version 12+ is required
due to the use of several new features, it is available in Fedora 34 and
Ubuntu Focal among other distributions.

(gh-19754)

is_integer is now available to numpy.floating and numpy.integer

Based on its counterpart in Python float and int, the numpy floating
point and integer types now support float.is_integer. Returns True
if the number is finite with integral value, and False otherwise.

>>> np.float32(-2.0).is_integer()
True
>>> np.float64(3.2).is_integer()
False
>>> np.int32(-2).is_integer()
True

(gh-19803)

Symbolic parser for Fortran dimension specifications

A new symbolic parser has been added to f2py in order to correctly parse
dimension specifications. The parser is the basis for future
improvements and provides compatibility with Draft Fortran 202x.

(gh-19805)

ndarray, dtype and number are now runtime-subscriptable

Mimicking PEP-585, the numpy.ndarray,
numpy.dtype and numpy.number classes are now subscriptable for
python 3.9 and later. Consequently, expressions that were previously
only allowed in .pyi stub files or with the help of
from __future__ import annotations are now also legal during runtime.

>>> import numpy as np
>>> from typing import Any

>>> np.ndarray[Any, np.dtype[np.float64]]
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]

(gh-19879)

Improvements

ctypeslib.load_library can now take any path-like object

All parameters in the can now take any
python:path-like object{.interpreted-text role="term"}. This includes
the likes of strings, bytes and objects implementing the
__fspath__<os.PathLike.__fspath__>{.interpreted-text role="meth"}
protocol.

(gh-17530)

Add smallest_normal and smallest_subnormal attributes to finfo

The attributes smallest_normal and smallest_subnormal are available
as an extension of finfo class for any floating-point data type. To
use these new attributes, write np.finfo(np.float64).smallest_normal
or np.finfo(np.float64).smallest_subnormal.

(gh-18536)

numpy.linalg.qr accepts stacked matrices as inputs

numpy.linalg.qr is able to produce results for stacked matrices as
inputs. Moreover, the implementation of QR decomposition has been
shifted to C from Python.

(gh-19151)

numpy.fromregex now accepts os.PathLike implementations

numpy.fromregex now accepts objects implementing the
__fspath__<os.PathLike> protocol, e.g. pathlib.Path.

(gh-19680)

Add new methods for quantile and percentile

quantile and percentile now have have a method= keyword argument
supporting 13 different methods. This replaces the interpolation=
keyword argument.

The methods are now aligned with nine methods which can be found in
scientific literature and the R language. The remaining methods are the
previous discontinuous variations of the default "linear" one.

Please see the documentation of numpy.percentile for more information.

(gh-19857)

Missing parameters have been added to the nan<x> functions

A number of the nan<x> functions previously lacked parameters that
were present in their <x>-based counterpart, e.g. the where
parameter was present in numpy.mean but absent from numpy.nanmean.

The following parameters have now been added to the nan<x> functions:

• nanmin: initial & where
• nanmax: initial & where
• nanargmin: keepdims & out
• nanargmax: keepdims & out
• nansum: initial & where
• nanprod: initial & where
• nanmean: where
• nanvar: where
• nanstd: where

(gh-20027)

Annotating the main Numpy namespace

Starting from the 1.20 release, PEP 484 type annotations have been
included for parts of the NumPy library; annotating the remaining
functions being a work in progress. With the release of 1.22 this
process has been completed for the main NumPy namespace, which is now
fully annotated.

Besides the main namespace, a limited number of sub-packages contain
annotations as well. This includes, among others, numpy.testing,
numpy.linalg and numpy.random (available since 1.21).

(gh-20217)

Vectorize umath module using AVX-512

By leveraging Intel Short Vector Math Library (SVML), 18 umath functions
(exp2, log2, log10, expm1, log1p, cbrt, sin, cos, tan,
arcsin, arccos, arctan, sinh, cosh, tanh, arcsinh,
arccosh, arctanh) are vectorized using AVX-512 instruction set for
both single and double precision implementations. This change is
currently enabled only for Linux users and on processors with AVX-512
instruction set. It provides an average speed up of 32x and 14x for
single and double precision functions respectively.

(gh-19478)

OpenBLAS v0.3.18

Update the OpenBLAS used in testing and in wheels to v0.3.18

(gh-20058)

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v1.21.6

Compare Source

NumPy 1.21.6 Release Notes

NumPy 1.21.6 is a very small release that achieves two things:

• Backs out the mistaken backport of C++ code into 1.21.5.
• Provides a 32 bit Windows wheel for Python 3.10.

The provision of the 32 bit wheel is intended to make life easier for
oldest-supported-numpy.

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f7234e2ef837f5f6ddbde8db246fd05b  numpy-1.21.6-cp38-cp38-win32.whl
e1063e01fb44ea7a49adea0c33548217  numpy-1.21.6-cp38-cp38-win_amd64.whl
61c4caad729e3e0e688accbc1424ed45  numpy-1.21.6-cp39-cp39-macosx_10_9_universal2.whl
67488d8ccaeff798f2e314aae7c4c3d6  numpy-1.21.6-cp39-cp39-macosx_10_9_x86_64.whl
128c3713b5d1de45a0f522562bac5263  numpy-1.21.6-cp39-cp39-macosx_11_0_arm64.whl
50e79cd0610b4ed726b3bf08c3716dab  numpy-1.21.6-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
bd0c9e3c0e488faac61daf3227fb95af  numpy-1.21.6-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
aa5e9baf1dec16b15e481c23f8a23214  numpy-1.21.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a2405b0e5d3f775ad30177296a997092  numpy-1.21.6-cp39-cp39-win32.whl
f0d20eda8c78f957ea70c5527954303e  numpy-1.21.6-cp39-cp39-win_amd64.whl
9682abbcc38cccb7f56e48aacca7de23  numpy-1.21.6-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
6aa3c2e8ea2886bf593bd8e0a1425c64  numpy-1.21.6.tar.gz
04aea95dcb1d256d13a45df42173aa1e  numpy-1.21.6.zip

SHA256

8737609c3bbdd48e380d463134a35ffad3b22dc56295eff6f79fd85bd0eeeb25  numpy-1.21.6-cp310-cp310-macosx_10_9_universal2.whl
fdffbfb6832cd0b300995a2b08b8f6fa9f6e856d562800fea9182316d99c4e8e  numpy-1.21.6-cp310-cp310-macosx_10_9_x86_64.whl
3820724272f9913b597ccd13a467cc492a0da6b05df26ea09e78b171a0bb9da6  numpy-1.21.6-cp310-cp310-macosx_11_0_arm64.whl
f17e562de9edf691a42ddb1eb4a5541c20dd3f9e65b09ded2beb0799c0cf29bb  numpy-1.21.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5f30427731561ce75d7048ac254dbe47a2ba576229250fb60f0fb74db96501a1  numpy-1.21.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d4bf4d43077db55589ffc9009c0ba0a94fa4908b9586d6ccce2e0b164c86303c  numpy-1.21.6-cp310-cp310-win32.whl
d136337ae3cc69aa5e447e78d8e1514be8c3ec9b54264e680cf0b4bd9011574f  numpy-1.21.6-cp310-cp310-win_amd64.whl
6aaf96c7f8cebc220cdfc03f1d5a31952f027dda050e5a703a0d1c396075e3e7  numpy-1.21.6-cp37-cp37m-macosx_10_9_x86_64.whl
67c261d6c0a9981820c3a149d255a76918278a6b03b6a036800359aba1256d46  numpy-1.21.6-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
a6be4cb0ef3b8c9250c19cc122267263093eee7edd4e3fa75395dfda8c17a8e2  numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
7c4068a8c44014b2d55f3c3f574c376b2494ca9cc73d2f1bd692382b6dffe3db  numpy-1.21.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7c7e5fa88d9ff656e067876e4736379cc962d185d5cd808014a8a928d529ef4e  numpy-1.21.6-cp37-cp37m-win32.whl
bcb238c9c96c00d3085b264e5c1a1207672577b93fa666c3b14a45240b14123a  numpy-1.21.6-cp37-cp37m-win_amd64.whl
82691fda7c3f77c90e62da69ae60b5ac08e87e775b09813559f8901a88266552  numpy-1.21.6-cp38-cp38-macosx_10_9_universal2.whl
643843bcc1c50526b3a71cd2ee561cf0d8773f062c8cbaf9ffac9fdf573f83ab  numpy-1.21.6-cp38-cp38-macosx_10_9_x86_64.whl
357768c2e4451ac241465157a3e929b265dfac85d9214074985b1786244f2ef3  numpy-1.21.6-cp38-cp38-macosx_11_0_arm64.whl
9f411b2c3f3d76bba0865b35a425157c5dcf54937f82bbeb3d3c180789dd66a6  numpy-1.21.6-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
4aa48afdce4660b0076a00d80afa54e8a97cd49f457d68a4342d188a09451c1a  numpy-1.21.6-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
d6a96eef20f639e6a97d23e57dd0c1b1069a7b4fd7027482a4c5c451cd7732f4  numpy-1.21.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5c3c8def4230e1b959671eb959083661b4a0d2e9af93ee339c7dada6759a9470  numpy-1.21.6-cp38-cp38-win32.whl
bf2ec4b75d0e9356edea834d1de42b31fe11f726a81dfb2c2112bc1eaa508fcf  numpy-1.21.6-cp38-cp38-win_amd64.whl
4391bd07606be175aafd267ef9bea87cf1b8210c787666ce82073b05f202add1  numpy-1.21.6-cp39-cp39-macosx_10_9_universal2.whl
67f21981ba2f9d7ba9ade60c9e8cbaa8cf8e9ae51673934480e45cf55e953673  numpy-1.21.6-cp39-cp39-macosx_10_9_x86_64.whl
ee5ec40fdd06d62fe5d4084bef4fd50fd4bb6bfd2bf519365f569dc470163ab0  numpy-1.21.6-cp39-cp39-macosx_11_0_arm64.whl
1dbe1c91269f880e364526649a52eff93ac30035507ae980d2fed33aaee633ac  numpy-1.21.6-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
d9caa9d5e682102453d96a0ee10c7241b72859b01a941a397fd965f23b3e016b  numpy-1.21.6-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
58459d3bad03343ac4b1b42ed14d571b8743dc80ccbf27444f266729df1d6f5b  numpy-1.21.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7f5ae4f304257569ef3b948810816bc87c9146e8c446053539947eedeaa32786  numpy-1.21.6-cp39-cp39-win32.whl
e31f0bb5928b793169b87e3d1e070f2342b22d5245c755e2b81caa29756246c3  numpy-1.21.6-cp39-cp39-win_amd64.whl
dd1c8f6bd65d07d3810b90d02eba7997e32abbdf1277a481d698969e921a3be0  numpy-1.21.6-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
d4efc6491a1cdc00f9eca9bf2c1aa13671776f6941c7321ddf75b45c862f0c2c  numpy-1.21.6.tar.gz
ecb55251139706669fdec2ff073c98ef8e9a84473e51e716211b41aa0f18e656  numpy-1.21.6.zip

v1.21.5

Compare Source

NumPy 1.21.5 Release Notes

NumPy 1.21.5 is a maintenance release that fixes a few bugs discovered
after the 1.21.4 release and does some maintenance to extend the 1.21.x
lifetime. The Python versions supported in this release are 3.7-3.10. If
you want to compile your own version using gcc-11, you will need to use
gcc-11.2+ to avoid problems.

Contributors

A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Bas van Beek
• Charles Harris
• Matti Picus
• Rohit Goswami
• Ross Barnowski
• Sayed Adel
• Sebastian Berg

Pull requests merged

A total of 11 pull requests were merged for this release.

• #​20357: MAINT: Do not forward __(deep)copy__ calls of _GenericAlias...
• #​20462: BUG: Fix float16 einsum fastpaths using wrong tempvar
• #​20463: BUG, DIST: Print os error message when the executable not exist
• #​20464: BLD: Verify the ability to compile C++ sources before initiating...
• #​20465: BUG: Force npymath to respect npy_longdouble
• #​20466: BUG: Fix failure to create aligned, empty structured dtype
• #​20467: ENH: provide a convenience function to replace npy_load_module
• #​20495: MAINT: update wheel to version that supports python3.10
• #​20497: BUG: Clear errors correctly in F2PY conversions
• #​20613: DEV: add a warningfilter to fix pytest workflow.
• #​20618: MAINT: Help boost::python libraries at least not crash

Checksums

MD5

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50e0526fa29110fb6033fa8285fba4e1  numpy-1.21.5-cp310-cp310-macosx_10_9_x86_64.whl
bdbb19e7656d66250aa67bd1c7924764  numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl
c5c982a07797c8963b8fec44aae6db09  numpy-1.21.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
8b27b622f58caeeb7f14472651d655e3  numpy-1.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e545f6f85f950f57606efcaeeac2e50a  numpy-1.21.5-cp310-cp310-win_amd64.whl
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0c4c5336136e045d02c60ba8115eb6a2  numpy-1.21.5-cp38-cp38-win32.whl
c2e0744164f8255be70725ef42bc3f5b  numpy-1.21.5-cp38-cp38-win_amd64.whl
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035bde3955ae2f62ada65084d71a7421  numpy-1.21.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
09f202576cbd0ed6121cff10cdea831a  numpy-1.21.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c6a44c90c2d5124fea6cedbbf575e252  numpy-1.21.5-cp39-cp39-win32.whl
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5be2b6f6cf6fb3a3d98231e891260624  numpy-1.21.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
8bc9ff24bac9bf4268372cefea8f0b6b  numpy-1.21.5.tar.gz
88b5438ded7992fa2e6a810d43cd32a1  numpy-1.21.5.zip

SHA256

301e408a052fdcda5cdcf03021ebafc3c6ea093021bf9d1aa47c54d48bdad166  numpy-1.21.5-cp310-cp310-macosx_10_9_universal2.whl
a7e8f6216f180f3fd4efb73de5d1eaefb5f5a1ee5b645c67333033e39440e63a  numpy-1.21.5-cp310-cp310-macosx_10_9_x86_64.whl
fc7a7d7b0ed72589fd8b8486b9b42a564f10b8762be8bd4d9df94b807af4a089  numpy-1.21.5-cp310-cp310-macosx_11_0_arm64.whl
58ca1d7c8aef6e996112d0ce873ac9dfa1eaf4a1196b4ff7ff73880a09923ba7  numpy-1.21.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
dc4b2fb01f1b4ddbe2453468ea0719f4dbb1f5caa712c8b21bb3dd1480cd30d9  numpy-1.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
cc1b30205d138d1005adb52087ff45708febbef0e420386f58664f984ef56954  numpy-1.21.5-cp310-cp310-win_amd64.whl
08de8472d9f7571f9d51b27b75e827f5296295fa78817032e84464be8bb905bc  numpy-1.21.5-cp37-cp37m-macosx_10_9_x86_64.whl
4fe6a006557b87b352c04596a6e3f12a57d6e5f401d804947bd3188e6b0e0e76  numpy-1.21.5-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
3d893b0871322eaa2f8c7072cdb552d8e2b27645b7875a70833c31e9274d4611  numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
341dddcfe3b7b6427a28a27baa59af5ad51baa59bfec3264f1ab287aa3b30b13  numpy-1.21.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
ca9c23848292c6fe0a19d212790e62f398fd9609aaa838859be8459bfbe558aa  numpy-1.21.5-cp37-cp37m-win32.whl
025b497014bc33fc23897859350f284323f32a2fff7654697f5a5fc2a19e9939  numpy-1.21.5-cp37-cp37m-win_amd64.whl
3a5098df115340fb17fc93867317a947e1dcd978c3888c5ddb118366095851f8  numpy-1.21.5-cp38-cp38-macosx_10_9_universal2.whl
311283acf880cfcc20369201bd75da907909afc4666966c7895cbed6f9d2c640  numpy-1.21.5-cp38-cp38-macosx_10_9_x86_64.whl
b545ebadaa2b878c8630e5bcdb97fc4096e779f335fc0f943547c1c91540c815  numpy-1.21.5-cp38-cp38-macosx_11_0_arm64.whl
c5562bcc1a9b61960fc8950ade44d00e3de28f891af0acc96307c73613d18f6e  numpy-1.21.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
eed2afaa97ec33b4411995be12f8bdb95c87984eaa28d76cf628970c8a2d689a  numpy-1.21.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
61bada43d494515d5b122f4532af226fdb5ee08fe5b5918b111279843dc6836a  numpy-1.21.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7b9d6b14fc9a4864b08d1ba57d732b248f0e482c7b2ff55c313137e3ed4d8449  numpy-1.21.5-cp38-cp38-win32.whl
dbce7adeb66b895c6aaa1fad796aaefc299ced597f6fbd9ceddb0dd735245354  numpy-1.21.5-cp38-cp38-win_amd64.whl
507c05c7a37b3683eb08a3ff993bd1ee1e6c752f77c2f275260533b265ecdb6c  numpy-1.21.5-cp39-cp39-macosx_10_9_universal2.whl
00c9fa73a6989895b8815d98300a20ac993c49ac36c8277e8ffeaa3631c0dbbb  numpy-1.21.5-cp39-cp39-macosx_10_9_x86_64.whl
69a5a8d71c308d7ef33ef72371c2388a90e3495dbb7993430e674006f94797d5  numpy-1.21.5-cp39-cp39-macosx_11_0_arm64.whl
2d8adfca843bc46ac199a4645233f13abf2011a0b2f4affc5c37cd552626f27b  numpy-1.21.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
c293d3c0321996cd8ffe84215ffe5d269fd9d1d12c6f4ffe2b597a7c30d3e593  numpy-1.21.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
3c978544be9e04ed12016dd295a74283773149b48f507d69b36f91aa90a643e5  numpy-1.21.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2a9add27d7fc0fdb572abc3b2486eb3b1395da71e0254c5552b2aad2a18b5441  numpy-1.21.5-cp39-cp39-win32.whl
1964db2d4a00348b7a60ee9d013c8cb0c566644a589eaa80995126eac3b99ced  numpy-1.21.5-cp39-cp39-win_amd64.whl
a7c4b701ca418cd39e28ec3b496e6388fe06de83f5f0cb74794fa31cfa384c02  numpy-1.21.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
1a7ee0ffb35dc7489aebe5185a483f4c43b0d2cf784c3c9940f975a7dde56506  numpy-1.21.5.tar.gz
6a5928bc6241264dce5ed509e66f33676fc97f464e7a919edc672fb5532221ee  numpy-1.21.5.zip

v1.21.4

Compare Source

NumPy 1.21.4 Release Notes

The NumPy 1.21.4 is a maintenance release that fixes a few bugs
discovered after 1.21.3. The most important fix here is a fix for the
NumPy header files to make them work for both x86_64 and M1 hardware
when included in the Mac universal2 wheels. Previously, the header files
only worked for M1 and this caused problems for folks building x86_64
extensions. This problem was not seen before Python 3.10 because there
were thin wheels for x86_64 that had precedence. This release also
provides thin x86_64 Mac wheels for Python 3.10.

The Python versions supported in this release are 3.7-3.10. If you want
to compile your own version using gcc-11, you will need to use gcc-11.2+
to avoid problems.

Contributors

A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Bas van Beek
• Charles Harris
• Isuru Fernando
• Matthew Brett
• Sayed Adel
• Sebastian Berg
• 傅立业（Chris Fu） +

Pull requests merged

A total of 9 pull requests were merged for this release.

• #​20278: BUG: Fix shadowed reference of dtype in type stub
• #​20293: BUG: Fix headers for universal2 builds
• #​20294: BUG: VOID_nonzero could sometimes mutate alignment flag
• #​20295: BUG: Do not use nonzero fastpath on unaligned arrays
• #​20296: BUG: Distutils patch to allow for 2 as a minor version (!)
• #​20297: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar
• #​20298: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC...
• #​20300: REL: Prepare for the NumPy 1.21.4 release.
• #​20302: TST: Fix a Arrayterator typing test failure

Checksums

MD5

95486a3ed027c926fb3fc279db6d843e  numpy-1.21.4-cp310-cp310-macosx_10_9_universal2.whl
9f57fad74762f7665669af33583a3dc9  numpy-1.21.4-cp310-cp310-macosx_10_9_x86_64.whl
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SHA256

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v1.21.3

Compare Source

NumPy 1.21.3 Release Notes

The NumPy 1.21.3 is a maintenance release the fixes a few bugs
discovered after 1.21.2. It also provides 64 bit Python 3.10.0 wheels.
Note a few oddities about Python 3.10:

• There are no 32 bit wheels for Windows, Mac, or Linux.
• The Mac Intel builds are only available in universal2 wheels.

The Python versions supported in this release are 3.7-3.10. If you want
to compile your own version using gcc-11 you will need to use gcc-11.2+
to avoid problems.

Contributors

A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Aaron Meurer
• Bas van Beek
• Charles Harris
• Developer-Ecosystem-Engineering +
• Kevin Sheppard
• Sebastian Berg
• Warren Weckesser

Pull requests merged

A total of 8 pull requests were merged for this release.

• #​19745: ENH: Add dtype-support to 3 `generic/ndarray methods
• #​19955: BUG: Resolve Divide by Zero on Apple silicon + test failures...
• #​19958: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases...
• #​19994: BUG: np.tan(np.inf) test failure
• #​20080: BUG: Correct incorrect advance in PCG with emulated int128
• #​20081: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime...
• #​20082: DOC: Ensure that we add documentation also as to the dict for...
• #​20106: BUG: core: result_type(0, np.timedelta64(4)) would seg. fault.

Checksums

MD5

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SHA256

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98339aa9911853f131de11010f6dd94c8cec254d3d1f7261528c3b3e3219f139  numpy-1.21.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
d0bba24083c01ae43457514d875f10d9ce4c1125d55b1e2573277b2410f2d068  numpy-1.21.3.tar.gz
63571bb7897a584ca3249c86dd01c10bcb5fe4296e3568b2e9c1a55356b6410e  numpy-1.21.3.zip

v1.21.2

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NumPy 1.21.2 Release Notes

The NumPy 1.21.2 is maintenance release that fixes bugs discovered after
1.21.1. It also provides 64 bit manylinux Python 3.10.0rc1 wheels for
downstream testing. Note that Python 3.10 is not yet final. There is
also preliminary support for Windows on ARM64 builds, but there is no
OpenBLAS for that platform and no wheels are available.

The Python versions supported for this release are 3.7-3.9. The 1.21.x
series is compatible with Python 3.10.0rc1 and Python 3.10 will be
officially supported after it is released. The previous problems with
gcc-11.1 have been fixed by gcc-11.2, check your version if you are
using gcc-11.

Contributors

A total of

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