renovate requested to merge renovate/numpy-1.x into development Feb 24, 2022

This MR contains the following updates:

Package	Type	Update	Change
numpy (source)	ironbank-pypi	minor	`1.20.2` -> `1.22.2`

Release Notes

numpy/numpy

NumPy 1.22.2 Release Notes

The NumPy 1.22.2 is maintenance release that fixes bugs discovered after the 1.22.1 release. Notable fixes are:

Several build related fixes for downstream projects and other platforms.
Various Annotation fixes/additions.
Numpy wheels for Windows will use the 1.41 tool chain, fixing downstream link problems for projects using NumPy provided libraries on Windows.
Deal with CVE-2021-41495 complaint.

The Python versions supported for this release are 3.8-3.10.

Contributors

A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Andrew J. Hesford +
Bas van Beek
Brénainn Woodsend +
Charles Harris
Hood Chatham
Janus Heide +
Leo Singer
Matti Picus
Mukulika Pahari
Niyas Sait
Pearu Peterson
Ralf Gommers
Sebastian Berg
Serge Guelton

Pull requests merged

A total of 21 pull requests were merged for this release.

#20842: BLD: Add NPY_DISABLE_SVML env var to opt out of SVML
#20843: BUG: Fix build of third party extensions with Py_LIMITED_API
#20844: TYP: Fix pyright being unable to infer the real and imag...
#20845: BUG: Fix comparator function signatures
#20906: BUG: Avoid importing numpy.distutils on import numpy.testing
#20907: MAINT: remove outdated mingw32 fseek support
#20908: TYP: Relax the return type of np.vectorize
#20909: BUG: fix f2py's define for threading when building with Mingw
#20910: BUG: distutils: fix building mixed C/Fortran extensions
#20912: DOC,TST: Fix Pandas code example as per new release
#20935: TYP, MAINT: Add annotations for flatiter.__setitem__
#20936: MAINT, TYP: Added missing where typehints in fromnumeric.pyi
#20937: BUG: Fix build_ext interaction with non numpy extensions
#20938: BUG: Fix missing intrinsics for windows/arm64 target
#20945: REL: Prepare for the NumPy 1.22.2 release.
#20982: MAINT: f2py: don't generate code that triggers -Wsometimes-uninitialized.
#20983: BUG: Fix incorrect return type in reduce without initial value
#20984: ENH: review return values for PyArray_DescrNew
#20985: MAINT: be more tolerant of setuptools >= 60
#20986: BUG: Fix misplaced return.
#20992: MAINT: Further small return value validation fixes

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NumPy 1.22.1 Release Notes

The NumPy 1.22.1 is maintenance release that fixes bugs discovered after the 1.22.0 release. Notable fixes are:

Fix f2PY docstring problems (SciPy)
Fix reduction type problems (AstroPy)
Fix various typing bugs.

The Python versions supported for this release are 3.8-3.10.

Contributors

A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Arryan Singh
Bas van Beek
Charles Harris
Denis Laxalde
Isuru Fernando
Kevin Sheppard
Matthew Barber
Matti Picus
Melissa Weber Mendonça
Mukulika Pahari
Omid Rajaei +
Pearu Peterson
Ralf Gommers
Sebastian Berg

Pull requests merged

A total of 20 pull requests were merged for this release.

#20702: MAINT, DOC: Post 1.22.0 release fixes.
#20703: DOC, BUG: Use pngs instead of svgs.
#20704: DOC: Fixed the link on user-guide landing page
#20714: BUG: Restore vc141 support
#20724: BUG: Fix array dimensions solver for multidimensional arguments...
#20725: TYP: change type annotation for __array_namespace__ to ModuleType
#20726: TYP, MAINT: Allow ndindex to accept integer tuples
#20757: BUG: Relax dtype identity check in reductions
#20763: TYP: Allow time manipulation functions to accept date and timedelta...
#20768: TYP: Relax the type of ndarray.__array_finalize__
#20795: MAINT: Raise RuntimeError if setuptools version is too recent.
#20796: BUG, DOC: Fixes SciPy docs build warnings
#20797: DOC: fix OpenBLAS version in release note
#20798: PERF: Optimize array check for bounded 0,1 values
#20805: BUG: Fix that reduce-likes honor out always (and live in the...
#20806: BUG: array_api.argsort(descending=True) respects relative...
#20807: BUG: Allow integer inputs for pow-related functions in array_api
#20814: DOC: Refer to NumPy, not pandas, in main page
#20815: DOC: Update Copyright to 2022 [License]
#20819: BUG: Return correctly shaped inverse indices in array_api set...

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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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`v1.21.5`

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

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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

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b3c4477a027d5b6fba5e1065064fd076  numpy-1.21.4.zip

SHA256

8890b3360f345e8360133bc078d2dacc2843b6ee6059b568781b15b97acbe39f  numpy-1.21.4-cp310-cp310-macosx_10_9_universal2.whl
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0b78ecfa070460104934e2caf51694ccd00f37d5e5dbe76f021b1b0b0d221823  numpy-1.21.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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81225e58ef5fce7f1d80399575576fc5febec79a8a2742e8ef86d7b03beef49f  numpy-1.21.4-cp37-cp37m-win32.whl
32fe5b12061f6446adcbb32cf4060a14741f9c21e15aaee59a207b6ce6423469  numpy-1.21.4-cp37-cp37m-win_amd64.whl
c449eb870616a7b62e097982c622d2577b3dbc800aaf8689254ec6e0197cbf1e  numpy-1.21.4-cp38-cp38-macosx_10_9_universal2.whl
2e4ed57f45f0aa38beca2a03b6532e70e548faf2debbeb3291cfc9b315d9be8f  numpy-1.21.4-cp38-cp38-macosx_10_9_x86_64.whl
1247ef28387b7bb7f21caf2dbe4767f4f4175df44d30604d42ad9bd701ebb31f  numpy-1.21.4-cp38-cp38-macosx_11_0_arm64.whl
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9864424631775b0c052f3bd98bc2712d131b3e2cd95d1c0c68b91709170890b0  numpy-1.21.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
b1e2312f5b8843a3e4e8224b2b48fe16119617b8fc0a54df8f50098721b5bed2  numpy-1.21.4-cp39-cp39-win32.whl
e3c3e990274444031482a31280bf48674441e0a5b55ddb168f3a6db3e0c38ec8  numpy-1.21.4-cp39-cp39-win_amd64.whl
a3deb31bc84f2b42584b8c4001c85d1934dbfb4030827110bc36bfd11509b7bf  numpy-1.21.4-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
5d412381aa489b8be82ac5c6a9e99c3eb3f754245ad3f90ab5c339d92f25fb47  numpy-1.21.4.tar.gz
e6c76a87633aa3fa16614b61ccedfae45b91df2767cf097aa9c933932a7ed1e0  numpy-1.21.4.zip

`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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b8e6b7165f105bde0b45cd9ae34bfe20  numpy-1.21.3.tar.gz
59d986f5ccf3edfb7d4d14949c6666ed  numpy-1.21.3.zip

SHA256

508b0b513fa1266875524ba8a9ecc27b02ad771fe1704a16314dc1a816a68737  numpy-1.21.3-cp310-cp310-macosx_10_9_universal2.whl
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d0bba24083c01ae43457514d875f10d9ce4c1125d55b1e2573277b2410f2d068  numpy-1.21.3.tar.gz
63571bb7897a584ca3249c86dd01c10bcb5fe4296e3568b2e9c1a55356b6410e  numpy-1.21.3.zip

`v1.21.2`

Compare Source

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 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Bas van Beek
Carl Johnsen +
Charles Harris
Gwyn Ciesla +
Matthieu Dartiailh
Matti Picus
Niyas Sait +
Ralf Gommers
Sayed Adel
Sebastian Berg

Pull requests merged

A total of 18 pull requests were merged for this release.

#19497: MAINT: set Python version for 1.21.x to <3.11
#19533: BUG: Fix an issue wherein importing numpy.typing could raise
#19646: MAINT: Update Cython version for Python 3.10.
#19648: TST: Bump the python 3.10 test version from beta4 to rc1
#19651: TST: avoid distutils.sysconfig in runtests.py
#19652: MAINT: add missing dunder method to nditer type hints
#19656: BLD, SIMD: Fix testing extra checks when -Werror isn't applicable...
#19657: BUG: Remove logical object ufuncs with bool output
#19658: MAINT: Include .coveragerc in source distributions to support...
#19659: BUG: Fix bad write in masked iterator output copy paths
#19660: ENH: Add support for windows on arm targets
#19661: BUG: add base to templated arguments for platlib
#19662: BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated
#19666: MAINT: Add Python 3.10 to supported versions.
#19668: TST,BUG: Sanitize path-separators when running runtest.py
#19671: BLD: load extra flags when checking for libflame
#19676: BLD: update circleCI docker image
#19677: REL: Prepare for 1.21.2 release.

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423216d8afc5923b15df86037c6053bf030d15cc9e3224206ef868c2d63dd6dc  numpy-1.21.2.zip

`v1.21.1`

Compare Source

NumPy 1.21.1 Release Notes

The NumPy 1.21.1 is maintenance release that fixes bugs discovered after the 1.21.0 release and updates OpenBLAS to v0.3.17 to deal with problems on arm64.

The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with development Python 3.10. Python 3.10 will be officially supported after it is released.

⚠ There are unresolved problems compiling NumPy 1.20.0 with gcc-11.1.

Optimization level -O3 results in many incorrect warnings when running the tests.
On some hardware NumPY will hang in an infinite loop.

Contributors

A total of 11 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Bas van Beek
Charles Harris
Ganesh Kathiresan
Gregory R. Lee
Hugo Defois +
Kevin Sheppard
Matti Picus
Ralf Gommers
Sayed Adel
Sebastian Berg
Thomas J. Fan

Pull requests merged

A total of 26 pull requests were merged for this release.

#19311: REV,BUG: Replace NotImplemented with typing.Any
#19324: MAINT: Fixed the return-dtype of ndarray.real and imag
#19330: MAINT: Replace "dtype[Any]" with dtype in the definiton of...
#19342: DOC: Fix some docstrings that crash pdf generation.
#19343: MAINT: bump scipy-mathjax
#19347: BUG: Fix arr.flat.index for large arrays and big-endian machines
#19348: ENH: add numpy.f2py.get_include function
#19349: BUG: Fix reference count leak in ufunc dtype handling
#19350: MAINT: Annotate missing attributes of np.number subclasses
#19351: BUG: Fix cast safety and comparisons for zero sized voids
#19352: BUG: Correct Cython declaration in random
#19353: BUG: protect against accessing base attribute of a NULL subarray
#19365: BUG, SIMD: Fix detecting AVX512 features on Darwin
#19366: MAINT: remove print()'s in distutils template handling
#19390: ENH: SIMD architectures to show_config
#19391: BUG: Do not raise deprecation warning for all nans in unique...
#19392: BUG: Fix NULL special case in object-to-any cast code
#19430: MAINT: Use arm64-graviton2 for testing on travis
#19495: BUILD: update OpenBLAS to v0.3.17
#19496: MAINT: Avoid unicode characters in division SIMD code comments
#19499: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11
#19500: BUG: fix a numpy.npiter leak in npyiter_multi_index_set
#19501: TST: Fix a GenericAlias test failure for python 3.9.0
#19502: MAINT: Start testing with Python 3.10.0b3.
#19503: MAINT: Add missing dtype overloads for object- and ctypes-based...
#19510: REL: Prepare for NumPy 1.21.1 release.

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`v1.21.0`

Compare Source

NumPy 1.21.0 Release Notes

The NumPy 1.21.0 release highlights are

continued SIMD work covering more functions and platforms,
initial work on the new dtype infrastructure and casting,
universal2 wheels for Python 3.8 and Python 3.9 on Mac,
improved documentation,
improved annotations,
new PCG64DXSM bitgenerator for random numbers.

In addition there are the usual large number of bug fixes and other improvements.

The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

⚠ Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

Optimization level -O3 results in many wrong warnings when running the tests.
On some hardware NumPy will hang in an infinite loop.

New functions

Add PCG64DXSM BitGenerator

Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

See upgrading-pcg64 for more details.

(gh-18906)

Expired deprecations

The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

(gh-17900)
The function PyUFunc_GenericFunction has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API.

(gh-18697)
The function PyUFunc_SetUsesArraysAsData has been disabled. It was deprecated in NumPy 1.19.

(gh-18697)
The class PolyBase has been removed (deprecated in numpy 1.9.0). Please use the abstract ABCPolyBase class instead.

(gh-18963)
The unused PolyError and PolyDomainError exceptions are removed.

(gh-18963)

Deprecations

The `.dtype` attribute must return a `dtype`

A DeprecationWarning is now given if the .dtype attribute of an object passed into np.dtype or as a dtype=obj argument is not a dtype. NumPy will stop attempting to recursively coerce the result of .dtype.

(gh-13578)

Inexact matches for `numpy.convolve` and `numpy.correlate` are deprecated

numpy.convolve and numpy.correlate now emit a warning when there are case insensitive and/or inexact matches found for mode argument in the functions. Pass full "same", "valid", "full" strings instead of "s", "v", "f" for the mode argument.

(gh-17492)

`np.typeDict` has been formally deprecated

np.typeDict is a deprecated alias for np.sctypeDict and has been so for over 14 years (6689502). A deprecation warning will now be issued whenever getting np.typeDict.

(gh-17586)

Exceptions will be raised during array-like creation

When an object raised an exception during access of the special attributes __array__ or __array_interface__, this exception was usually ignored. A warning is now given when the exception is anything but AttributeError. To silence the warning, the type raising the exception has to be adapted to raise an AttributeError.

(gh-19001)

Four `ndarray.ctypes` methods have been deprecated

Four methods of the ndarray.ctypes object have been deprecated, as they are (undocumentated) implementation artifacts of their respective properties.

The methods in question are:

_ctypes.get_data (use _ctypes.data instead)
_ctypes.get_shape (use _ctypes.shape instead)
_ctypes.get_strides (use _ctypes.strides instead)
_ctypes.get_as_parameter (use _ctypes._as_parameter_ instead)

(gh-19031)

Expired deprecations

The shape argument numpy.unravel_index] cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

(gh-17900)
The function PyUFunc_GenericFunction has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API.

(gh-18697)
The function PyUFunc_SetUsesArraysAsData has been disabled. It was deprecated in NumPy 1.19.

(gh-18697)

Remove deprecated `PolyBase` and unused `PolyError` and `PolyDomainError`

The class PolyBase has been removed (deprecated in numpy 1.9.0). Please use the abstract ABCPolyBase class instead.

Furthermore, the unused PolyError and PolyDomainError exceptions are removed from the numpy.polynomial.

(gh-18963)

Compatibility notes

Error type changes in universal functions

The universal functions may now raise different errors on invalid input in some cases. The main changes should be that a RuntimeError was replaced with a more fitting TypeError. When multiple errors were present in the same call, NumPy may now raise a different one.

(gh-15271)

`__array_ufunc__` argument validation

NumPy will now partially validate arguments before calling __array_ufunc__. Previously, it was possible to pass on invalid arguments (such as a non-existing keyword argument) when dispatch was known to occur.

(gh-15271)

`__array_ufunc__` and additional positional arguments

Previously, all positionally passed arguments were checked for __array_ufunc__ support. In the case of reduce, accumulate, and reduceat all arguments may be passed by position. This means that when they were passed by position, they could previously have been asked to handle the ufunc call via __array_ufunc__. Since this depended on the way the arguments were passed (by position or by keyword), NumPy will now only dispatch on the input and output array. For example, NumPy will never dispatch on the where array in a reduction such as np.add.reduce.

(gh-15271)

Validate input values in `Generator.uniform`

Checked that high - low >= 0 in np.random.Generator.uniform. Raises ValueError if low > high. Previously out-of-order inputs were accepted and silently swapped, so that if low > high, the value generated was high + (low - high) * random().

(gh-17921)

`/usr/include` removed from default include paths

The default include paths when building a package with numpy.distutils no longer include /usr/include. This path is normally added by the compiler, and hardcoding it can be problematic. In case this causes a problem, please open an issue. A workaround is documented in MR 18658.

(gh-18658)

Changes to comparisons with `dtype=...`

When the dtype= (or signature) arguments to comparison ufuncs (equal, less, etc.) is used, this will denote the desired output dtype in the future. This means that:

np.equal(2, 3, dtype=object)

will give a FutureWarning that it will return an object array in the future, which currently happens for:

np.equal(None, None, dtype=object)

due to the fact that np.array(None) is already an object array. (This also happens for some other dtypes.)

Since comparisons normally only return boolean arrays, providing any other dtype will always raise an error in the future and give a DeprecationWarning now.

(gh-18718)

Changes to `dtype` and `signature` arguments in ufuncs

The universal function arguments dtype and signature which are also valid for reduction such as np.add.reduce (which is the implementation for np.sum) will now issue a warning when the dtype provided is not a "basic" dtype.

NumPy almost always ignored metadata, byteorder or time units on these inputs. NumPy will now always ignore it and raise an error if byteorder or time unit changed. The following are the most important examples of changes which will give the error. In some cases previously the information stored was not ignored, in all of these an error is now raised:

Previously ignored the byte-order (affect if non-native)

np.add(3, 5, dtype=">i32")

The biggest impact is for timedelta or datetimes:

arr = np.arange(10, dtype="m8[s]")

The examples always ignored the time unit "ns":

np.add(arr, arr, dtype="m8[ns]")
np.maximum.reduce(arr, dtype="m8[ns]")

The following previously did use "ns" (as opposed to `arr.dtype`)

np.add(3, 5, dtype="m8[ns]")  # Now return generic time units
np.maximum(arr, arr, dtype="m8[ns]")  # Now returns "s" (from `arr`)

The same applies for functions like np.sum which use these internally. This change is necessary to achieve consistent handling within NumPy.

If you run into these, in most cases pass for example dtype=np.timedelta64 which clearly denotes a general timedelta64 without any unit or byte-order defined. If you need to specify the output dtype precisely, you may do so by either casting the inputs or providing an output array using out=.

NumPy may choose to allow providing an exact output dtype here in the future, which would be preceded by a FutureWarning.

(gh-18718)

Ufunc `signature=...` and `dtype=` generalization and `casting`

The behaviour for np.ufunc(1.0, 1.0, signature=...) or np.ufunc(1.0, 1.0, dtype=...) can now yield different loops in 1.21 compared to 1.20 because of changes in promotion. When signature was previously used, the casting check on inputs was relaxed, which could lead to downcasting inputs unsafely especially if combined with casting="unsafe".

Casting is now guaranteed to be safe. If a signature is only partially provided, for example using signature=("float64", None, None), this could lead to no loop being found (an error). In that case, it is necessary to provide the complete signature to enforce casting the inputs. If dtype="float64" is used or only outputs are set (e.g. signature=(None, None, "float64") the is unchanged. We expect that very few users are affected by this change.

Further, the meaning of dtype="float64" has been slightly modified and now strictly enforces only the correct output (and not input) DTypes. This means it is now always equivalent to:

signature=(None, None, "float64")

(If the ufunc has two inputs and one output). Since this could lead to no loop being found in some cases, NumPy will normally also search for the loop:

signature=("float64", "float64", "float64")

if the first search failed. In the future, this behaviour may be customized to achieve the expected results for more complex ufuncs. (For some universal functions such as np.ldexp inputs can have different DTypes.)

(gh-18880)

Distutils forces strict floating point model on clang

NumPy distutils will now always add the -ffp-exception-behavior=strict compiler flag when compiling with clang. Clang defaults to a non-strict version, which allows the compiler to generate code that does not set floating point warnings/errors correctly.

(gh-19049)

C API changes

Use of `ufunc->type_resolver` and "type tuple"

NumPy now normalizes the "type tuple" argument to the type resolver functions before calling it. Note that in the use of this type resolver is legacy behaviour and NumPy will not do so when possible. Calling ufunc->type_resolver or PyUFunc_DefaultTypeResolver is strongly discouraged and will now enforce a normalized type tuple if done. Note that this does not affect providing a type resolver, which is expected to keep working in most circumstances. If you have an unexpected use-case for calling the type resolver, please inform the NumPy developers so that a solution can be found.

(gh-18718)

New Features

Added a mypy plugin for handling platform-specific `numpy.number` precisions

A mypy plugin is now available for automatically assigning the (platform-dependent) precisions of certain numpy.number subclasses, including the likes of numpy.int_, numpy.intp and numpy.longlong. See the documentation on scalar types <arrays.scalars.built-in> for a comprehensive overview of the affected classes.

Note that while usage of the plugin is completely optional, without it the precision of above-mentioned classes will be inferred as typing.Any.

To enable the plugin, one must add it to their mypy [configuration file] (https://mypy.readthedocs.io/en/stable/config_file.html):

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-17843)

Let the mypy plugin manage extended-precision `numpy.number` subclasses

The mypy plugin, introduced in numpy/numpy#17843, has been expanded: the plugin now removes annotations for platform-specific extended-precision types that are not available to the platform in question. For example, it will remove numpy.float128 when not available.

Without the plugin all extended-precision types will, as far as mypy is concerned, be available on all platforms.

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

[mypy]
plugins = numpy.typing.mypy_plugin
                                                                        cn

(gh-18322)

New `min_digits` argument for printing float values

A new min_digits argument has been added to the dragon4 float printing functions numpy.format_float_positional and numpy.format_float_scientific. This kwd guarantees that at least the given number of digits will be printed when printing in unique=True mode, even if the extra digits are unnecessary to uniquely specify the value. It is the counterpart to the precision argument which sets the maximum number of digits to be printed. When unique=False in fixed precision mode, it has no effect and the precision argument fixes the number of digits.

(gh-18629)

f2py now recognizes Fortran abstract interface blocks

numpy.f2py can now parse abstract interface blocks.

(gh-18695)

BLAS and LAPACK configuration via environment variables

Autodetection of installed BLAS and LAPACK libraries can be bypassed by using the NPY_BLAS_LIBS and NPY_LAPACK_LIBS environment variables. Instead, the link flags in these environment variables will be used directly, and the language is assumed to be F77. This is especially useful in automated builds where the BLAS and LAPACK that are installed are known exactly. A use case is replacing the actual implementation at runtime via stub library links.

If NPY_CBLAS_LIBS is set (optional in addition to NPY_BLAS_LIBS), this will be used as well, by defining HAVE_CBLAS and appending the environment variable content to the link flags.

(gh-18737)

A runtime-subcriptable alias has been added for `ndarray`

numpy.typing.NDArray has been added, a runtime-subscriptable alias for np.ndarray[Any, np.dtype[~Scalar]]. The new type alias can be used for annotating arrays with a given dtype and unspecified shape.

NumPy does not support the annotating of array shapes as of 1.21, this is expected to change in the future though (see 646{.interpreted-text role="pep"}).

Examples

>>> import numpy as np
>>> import numpy.typing as npt

>>> print(npt.NDArray)
numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]]

>>> print(npt.NDArray[np.float64])
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]

>>> NDArrayInt = npt.NDArray[np.int_]
>>> a: NDArrayInt = np.arange(10)

>>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
...     return np.array(a)

(gh-18935)

Improvements

Arbitrary `period` option for `numpy.unwrap`

The size of the interval over which phases are unwrapped is no longer restricted to 2 * pi. This is especially useful for unwrapping degrees, but can also be used for other intervals.

>>> phase_deg = np.mod(np.linspace(0,720,19), 360) - 180
>>> phase_deg
array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
       -180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
       -180.])

>>> unwrap(phase_deg, period=360)
array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
        180.,  220.,  260.,  300.,  340.,  380.,  420.,  460.,  500.,
        540.])

(gh-16987)

`np.unique` now returns single `NaN`

When np.unique operated on an array with multiple NaN entries, its return included a NaN for each entry that was NaN in the original array. This is now improved such that the returned array contains just one NaN as the last element.

Also for complex arrays all NaN values are considered equivalent (no matter whether the NaN is in the real or imaginary part). As the representant for the returned array the smallest one in the lexicographical order is chosen - see np.sort for how the lexicographical order is defined for complex arrays.

(gh-18070)

`Generator.rayleigh` and `Generator.geometric` performance improved

The performance of Rayleigh and geometric random variate generation in Generator has improved. These are both transformation of exponential random variables and the slow log-based inverse cdf transformation has been replaced with the Ziggurat-based exponential variate generator.

This change breaks the stream of variates generated when variates from either of these distributions are produced.

(gh-18666)

Placeholder annotations have been improved

All placeholder annotations, that were previously annotated as typing.Any, have been improved. Where appropiate they have been replaced with explicit function definitions, classes or other miscellaneous objects.

(gh-18934)

Performance improvements

Improved performance in integer division of NumPy arrays

Integer division of NumPy arrays now uses libdivide when the divisor is a constant. With the usage of libdivide and other minor optimizations, there is a large speedup. The // operator and np.floor_divide makes use of the new changes.

(gh-17727)

Improve performance of `np.save` and `np.load` for small arrays

np.save is now a lot faster for small arrays.

np.load is also faster for small arrays, but only when serializing with a version >= (3, 0).

Both are done by removing checks that are only relevant for Python 2, while still maintaining compatibility with arrays which might have been created by Python 2.

(gh-18657)

Changes

`numpy.piecewise` output class now matches the input class

When numpy.ndarray subclasses are used on input to numpy.piecewise, they are passed on to the functions. The output will now be of the same subclass as well.

(gh-18110)

Enable Accelerate Framework

With the release of macOS 11.3, several different issues that numpy was encountering when using Accelerate Framework's implementation of BLAS and LAPACK should be resolved. This change enables the Accelerate Framework as an option on macOS. If additional issues are found, please file a bug report against Accelerate using the developer feedback assistant tool (https://developer.apple.com/bug-reporting/). We intend to address issues promptly and plan to continue supporting and updating our BLAS and LAPACK libraries.

(gh-18874)

Checksums

MD5

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`v1.20.3`

Compare Source

NumPy 1.20.3 Release Notes

NumPy 1.20.3 is a bugfix release containing several fixes merged to the main branch after the NumPy 1.20.2 release.

Contributors

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

Anne Archibald
Bas van Beek
Charles Harris
Dong Keun Oh +
Kamil Choudhury +
Sayed Adel
Sebastian Berg

Pull requests merged

A total of 15 pull requests were merged for this release.

#18763: BUG: Correct datetime64 missing type overload for datetime.date...
#18764: MAINT: Remove __all__ in favor of explicit re-exports
#18768: BLD: Strip extra newline when dumping gfortran version on MacOS
#18769: BUG: fix segfault in object/longdouble operations
#18794: MAINT: Use towncrier build explicitly
#18887: MAINT: Relax certain integer-type constraints
#18915: MAINT: Remove unsafe unions and ABCs from return-annotations
#18921: MAINT: Allow more recursion depth for scalar tests.
#18922: BUG: Initialize the full nditer buffer in case of error
#18923: BLD: remove unnecessary flag -faltivec on macOS
#18924: MAINT, CI: treats _SIMD module build warnings as errors through...
#18925: BUG: for MINGW, threads.h existence test requires GLIBC > 2.12
#18941: BUG: Make changelog recognize gh- as a MR number prefix.
#18948: REL, DOC: Prepare for the NumPy 1.20.3 release.
#18953: BUG: Fix failing mypy test in 1.20.x.

Checksums

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Configuration

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Update dependency numpy to v1.22.2

Release Notes

NumPy 1.22.2 Release Notes

Contributors

Pull requests merged

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NumPy 1.22.1 Release Notes

Contributors

Pull requests merged

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NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Deprecations

Use delimiter rather than delimitor as kwarg in mrecords

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

The np.MachAr class has been deprecated

Compatibility notes

Distutils forces strict floating point model on clang

Removed floor division support for complex types

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

Python 3.7 is no longer supported

str/repr of complex dtypes now include space after punctuation

Corrected advance in PCG64DSXM and PCG64

Change in generation of random 32 bit floating point variates

C API changes

Masked inner-loops cannot be customized anymore

New Features

NEP 49 configurable allocators

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

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

Assign the platform-specific c_intp precision via a mypy plugin

Add NEP 47-compatible dlpack support

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

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

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

Preliminary support for windows/arm64 target

Added support for LoongArch

A .clang-format file has been added

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

Symbolic parser for Fortran dimension specifications

ndarray, dtype and number are now runtime-subscriptable

Improvements

ctypeslib.load_library can now take any path-like object

Add smallest_normal and smallest_subnormal attributes to finfo

numpy.linalg.qr accepts stacked matrices as inputs

numpy.fromregex now accepts os.PathLike implementations

Add new methods for quantile and percentile

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

Annotating the main Numpy namespace

Vectorize umath module using AVX-512

OpenBLAS v0.3.18

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NumPy 1.21.5 Release Notes

Contributors

Pull requests merged

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NumPy 1.21.4 Release Notes

Contributors

Pull requests merged

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NumPy 1.21.3 Release Notes

Contributors

Pull requests merged

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NumPy 1.21.2 Release Notes

Contributors

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio

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

The `np.MachAr` class has been deprecated

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

Corrected `advance` in `PCG64DSXM` and `PCG64`

Assign the platform-specific `c_intp` precision via a mypy plugin

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

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

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

Preliminary support for `windows/arm64` target

A `.clang-format` file has been added

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

`ndarray`, `dtype` and `number` are now runtime-subscriptable

`ctypeslib.load_library` can now take any path-like object

Add `smallest_normal` and `smallest_subnormal` attributes to `finfo`

`numpy.linalg.qr` accepts stacked matrices as inputs

`numpy.fromregex` now accepts `os.PathLike` implementations

Add new methods for `quantile` and `percentile`

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

The `.dtype` attribute must return a `dtype`

Inexact matches for `numpy.convolve` and `numpy.correlate` are deprecated

`np.typeDict` has been formally deprecated

Four `ndarray.ctypes` methods have been deprecated

Remove deprecated `PolyBase` and unused `PolyError` and `PolyDomainError`

`__array_ufunc__` argument validation

`__array_ufunc__` and additional positional arguments

Validate input values in `Generator.uniform`

`/usr/include` removed from default include paths

Changes to comparisons with `dtype=...`

Changes to `dtype` and `signature` arguments in ufuncs

The following previously did use "ns" (as opposed to `arr.dtype`)

Ufunc `signature=...` and `dtype=` generalization and `casting`

Use of `ufunc->type_resolver` and "type tuple"

Added a mypy plugin for handling platform-specific `numpy.number` precisions

Let the mypy plugin manage extended-precision `numpy.number` subclasses

New `min_digits` argument for printing float values

A runtime-subcriptable alias has been added for `ndarray`

Arbitrary `period` option for `numpy.unwrap`

`np.unique` now returns single `NaN`

`Generator.rayleigh` and `Generator.geometric` performance improved

Improve performance of `np.save` and `np.load` for small arrays

`numpy.piecewise` output class now matches the input class