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.

Checksums

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NumPy 1.22.3 Release Notes

NumPy 1.22.3 is a maintenance release that fixes bugs discovered after the 1.22.2 release. The most noticeable fixes may be those for DLPack. One that may cause some problems is disallowing strings as inputs to logical ufuncs. It is still undecided how strings should be treated in those functions and it was thought best to simply disallow them until a decision was reached. That should not cause problems with older code.

The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.14 rather than 10.9 that was used in previous NumPy release cycles. 10.14 is the oldest release supported by Apple.

Contributors

A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• @GalaxySnail +
• Alexandre de Siqueira
• Bas van Beek
• Charles Harris
• Melissa Weber Mendonça
• Ross Barnowski
• Sebastian Berg
• Tirth Patel
• Matthieu Darbois

Pull requests merged

A total of 10 pull requests were merged for this release.

• #21048: MAINT: Use "3.10" instead of "3.10-dev" on travis.
• #21106: TYP,MAINT: Explicitly allow sequences of array-likes in np.concatenate
• #21137: BLD,DOC: skip broken ipython 8.1.0
• #21138: BUG, ENH: np._from_dlpack: export correct device information
• #21139: BUG: Fix numba DUFuncs added loops getting picked up
• #21140: BUG: Fix unpickling an empty ndarray with a non-zero dimension...
• #21141: BUG: use ThreadPoolExecutor instead of ThreadPool
• #21142: API: Disallow strings in logical ufuncs
• #21143: MAINT, DOC: Fix SciPy intersphinx link
• #21148: BUG,ENH: np._from_dlpack: export arrays with any strided size-1...

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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)

Checksums

MD5

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SHA256

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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

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NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 152 contributers spread over 602 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 v0.3.17

Update the OpenBLAS used in testing and in wheels to v0.3.17

(gh-19462)

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NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 151 contributers spread over 589 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 v0.3.17

Update the OpenBLAS used in testing and in wheels to v0.3.17

(gh-19462)

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NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 150 contributers spread over 575 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 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 NEP 47 (adoption 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 585{.interpreted-text role="pep"}, 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 v0.3.17

Update the OpenBLAS used in testing and in wheels to v0.3.17

(gh-19462)

Checksums

MD5

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SHA256

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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

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SHA256

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