renovate requested to merge renovate/numpy-1.x into development May 27, 2022

This MR contains the following updates:

Package	Type	Update	Change
numpy (source)	ironbank-pypi	minor	`1.19.1` -> `1.22.4`
numpy (source)		minor	`==1.19.1` -> `==1.22.4`

Release Notes

numpy/numpy

`v1.22.4`

Compare Source

NumPy 1.22.4 Release Notes

NumPy 1.22.4 is a maintenance release that fixes bugs discovered after the 1.22.3 release. In addition, the wheels for this release are built using the recently released Cython 0.29.30, which should fix the reported problems with debugging.

The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.15 rather than 10.6 that was used in previous NumPy release cycles.

Contributors

A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Alexander Shadchin
Bas van Beek
Charles Harris
Hood Chatham
Jarrod Millman
John-Mark Gurney +
Junyan Ou +
Mariusz Felisiak +
Ross Barnowski
Sebastian Berg
Serge Guelton
Stefan van der Walt

Pull requests merged

A total of 22 pull requests were merged for this release.

#21191: TYP, BUG: Fix np.lib.stride_tricks re-exported under the...
#21192: TST: Bump mypy from 0.931 to 0.940
#21243: MAINT: Explicitly re-export the types in numpy._typing
#21245: MAINT: Specify sphinx, numpydoc versions for CI doc builds
#21275: BUG: Fix typos
#21277: ENH, BLD: Fix math feature detection for wasm
#21350: MAINT: Fix failing simd and cygwin tests.
#21438: MAINT: Fix failing Python 3.8 32-bit Windows test.
#21444: BUG: add linux guard per #21386
#21445: BUG: Allow legacy dtypes to cast to datetime again
#21446: BUG: Make mmap handling safer in frombuffer
#21447: BUG: Stop using PyBytesObject.ob_shash deprecated in Python 3.11.
#21448: ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS
#21472: BUG: Ensure compile errors are raised correclty
#21473: BUG: Fix segmentation fault
#21474: MAINT: Update doc requirements
#21475: MAINT: Mark npy_memchr with no_sanitize("alignment") on clang
#21512: DOC: Proposal - make the doc landing page cards more similar...
#21525: MAINT: Update Cython version to 0.29.30.
#21536: BUG: Fix GCC error during build configuration
#21541: REL: Prepare for the NumPy 1.22.4 release.
#21547: MAINT: Skip tests that fail on PyPy.

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`v1.22.3`

Compare Source

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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`v1.22.2`

Compare Source

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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`v1.22.1`

Compare Source

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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`v1.22.0`

Compare Source

NumPy 1.22.0 Release Notes

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

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

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

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

Expired deprecations

Deprecated numeric style dtype strings have been removed

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

(gh-19539)

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

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

(gh-19615)

Deprecations

Use delimiter rather than delimitor as kwarg in mrecords

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

(gh-19921)

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

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

(gh-20000)

The `np.MachAr` class has been deprecated

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

(gh-20201)

Compatibility notes

Distutils forces strict floating point model on clang

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

(gh-19479)

Removed floor division support for complex types

Floor division of complex types will now result in a TypeError

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

(gh-19135)

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

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

(gh-19356)

Python 3.7 is no longer supported

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

(gh-19665)

str/repr of complex dtypes now include space after punctuation

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

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

(gh-19687)

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

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

(gh-20049)

Change in generation of random 32 bit floating point variates

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

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

(gh-20314)

C API changes

Masked inner-loops cannot be customized anymore

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

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

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

(gh-19259)

New Features

NEP 49 configurable allocators

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

(gh-17582)

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

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

(gh-18585)

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

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

(gh-18884)

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

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

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

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

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-19062)

Add NEP 47-compatible dlpack support

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

(gh-19083)

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

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

(gh-19211)

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

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

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

(gh-19355)

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

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

(gh-19459)

Preliminary support for `windows/arm64` target

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

(gh-19513)

Added support for LoongArch

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

(gh-19527)

A `.clang-format` file has been added

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

(gh-19754)

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

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

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

(gh-19803)

Symbolic parser for Fortran dimension specifications

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

(gh-19805)

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

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

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

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

(gh-19879)

Improvements

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

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

(gh-17530)

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

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

(gh-18536)

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

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

(gh-19151)

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

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

(gh-19680)

Add new methods for `quantile` and `percentile`

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

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

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

(gh-19857)

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

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

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

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

(gh-20027)

Annotating the main Numpy namespace

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

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

(gh-20217)

Vectorize umath module using AVX-512

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

(gh-19478)

OpenBLAS v0.3.18

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

(gh-20058)

Checksums

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SHA256

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`v1.21.6`

Compare Source

NumPy 1.21.6 Release Notes

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

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

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

Checksums

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SHA256

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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

Checksums

MD5

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8b27b622f58caeeb7f14472651d655e3  numpy-1.21.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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f55c7ecfd35769fb3f6a408c0c123372  numpy-1.21.5-cp37-cp37m-win_amd64.whl
843e3431ba4b56d3fc36b7c4cb6fc10c  numpy-1.21.5-cp38-cp38-macosx_10_9_universal2.whl
4721e71bdc5697d310cd3a6b6cd60741  numpy-1.21.5-cp38-cp38-macosx_10_9_x86_64.whl
2169fb8ed40046e1e33d187fc85b91bb  numpy-1.21.5-cp38-cp38-macosx_11_0_arm64.whl
52de43977749109509ee708a142a7d97  numpy-1.21.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
703c0f54c5ede8cc0c648ef66cafac47  numpy-1.21.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
50432f9cf1d5b2278ceb7a96890353ed  numpy-1.21.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0c4c5336136e045d02c60ba8115eb6a2  numpy-1.21.5-cp38-cp38-win32.whl
c2e0744164f8255be70725ef42bc3f5b  numpy-1.21.5-cp38-cp38-win_amd64.whl
b16dd7103117d051cb6c3b6c4434f7d2  numpy-1.21.5-cp39-cp39-macosx_10_9_universal2.whl
220dd07273aeb0b2ca8f0e4f543e43c3  numpy-1.21.5-cp39-cp39-macosx_10_9_x86_64.whl
1dd09ad75eff93b274f650871e0b9287  numpy-1.21.5-cp39-cp39-macosx_11_0_arm64.whl
6801263f51d3b13420b59ff84c716869  numpy-1.21.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
035bde3955ae2f62ada65084d71a7421  numpy-1.21.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
09f202576cbd0ed6121cff10cdea831a  numpy-1.21.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c6a44c90c2d5124fea6cedbbf575e252  numpy-1.21.5-cp39-cp39-win32.whl
bbc11e31406a9fc48c18a41259bc8866  numpy-1.21.5-cp39-cp39-win_amd64.whl
5be2b6f6cf6fb3a3d98231e891260624  numpy-1.21.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
8bc9ff24bac9bf4268372cefea8f0b6b  numpy-1.21.5.tar.gz
88b5438ded7992fa2e6a810d43cd32a1  numpy-1.21.5.zip

SHA256

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

`v1.21.4`

Compare Source

NumPy 1.21.4 Release Notes

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

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

Contributors

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

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

Pull requests merged

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

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

Checksums

MD5

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06019c1116b3e2791bd507f898257e7f  numpy-1.21.4.tar.gz
b3c4477a027d5b6fba5e1065064fd076  numpy-1.21.4.zip

SHA256

8890b3360f345e8360133bc078d2dacc2843b6ee6059b568781b15b97acbe39f  numpy-1.21.4-cp310-cp310-macosx_10_9_universal2.whl
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e89717274b41ebd568cd7943fc9418eeb49b1785b66031bc8a7f6300463c5898  numpy-1.21.4-cp310-cp310-macosx_11_0_arm64.whl
0b78ecfa070460104934e2caf51694ccd00f37d5e5dbe76f021b1b0b0d221823  numpy-1.21.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
615d4e328af7204c13ae3d4df7615a13ff60a49cb0d9106fde07f541207883ca  numpy-1.21.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
1403b4e2181fc72664737d848b60e65150f272fe5a1c1cbc16145ed43884065a  numpy-1.21.4-cp310-cp310-win_amd64.whl
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92aafa03da8658609f59f18722b88f0a73a249101169e28415b4fa148caf7e41  numpy-1.21.4-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
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2e4ed57f45f0aa38beca2a03b6532e70e548faf2debbeb3291cfc9b315d9be8f  numpy-1.21.4-cp38-cp38-macosx_10_9_x86_64.whl
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34f3456f530ae8b44231c63082c8899fe9c983fd9b108c997c4b1c8c2d435333  numpy-1.21.4-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
4c9c23158b87ed0e70d9a50c67e5c0b3f75bcf2581a8e34668d4e9d7474d76c6  numpy-1.21.4-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
e4799be6a2d7d3c33699a6f77201836ac975b2e1b98c2a07f66a38f499cb50ce  numpy-1.21.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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ad010846cdffe7ec27e3f933397f8a8d6c801a48634f419e3d075db27acf5880  numpy-1.21.4-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
c74c699b122918a6c4611285cc2cad4a3aafdb135c22a16ec483340ef97d573c  numpy-1.21.4-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
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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SHA256

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`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.

Checksums

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SHA256

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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)

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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.

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e55185e51b18d788e49fe8305fd73ef4470596b33fc2c1ceb304566b99c71a69  numpy-1.20.3.zip

`v1.20.2`

Compare Source

NumPy 1.20.2 Release Notes

NumPy 1,20.2 is a bugfix release containing several fixes merged to the main branch after the NumPy 1.20.1 release.

Contributors

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

Allan Haldane
Bas van Beek
Charles Harris
Christoph Gohlke
Mateusz Sokół +
Michael Lamparski
Sebastian Berg

Pull requests merged

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

#18382: MAINT: Update f2py from master.
#18459: BUG: diagflat could overflow on windows or 32-bit platforms
#18460: BUG: Fix refcount leak in f2py complex_double_from_pyobj.
#18461: BUG: Fix tiny memory leaks when like= overrides are used
#18462: BUG: Remove temporary change of descr/flags in VOID functions
#18469: BUG: Segfault in nditer buffer dealloc for Object arrays
#18485: BUG: Remove suspicious type casting
#18486: BUG: remove nonsensical comparison of pointer < 0
#18487: BUG: verify pointer against NULL before using it
#18488: BUG: check if PyArray_malloc succeeded
#18546: BUG: incorrect error fallthrough in nditer
#18559: CI: Backport CI fixes from main.
#18599: MAINT: Add annotations for __getitem__, __mul__ and...
#18611: BUG: NameError in numpy.distutils.fcompiler.compaq
#18612: BUG: Fixed where keyword for np.mean & np.var methods
#18617: CI: Update apt package list before Python install
#18636: MAINT: Ensure that re-exported sub-modules are properly annotated
#18638: BUG: Fix ma coercion list-of-ma-arrays if they do not cast to...
#18661: BUG: Fix small valgrind-found issues
#18671: BUG: Fix small issues found with pytest-leaks

Checksums

MD5

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SHA256

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878922bf5ad7550aa044aa9301d417e2d3ae50f0f577de92051d739ac6096cee  numpy-1.20.2.zip

`v1.20.1`

Compare Source

NumPy 1.20.1 Release Notes

NumPy 1.20.1 is a rapid bugfix release fixing several bugs and regressions reported after the 1.20.0 release.

Highlights

The distutils bug that caused problems with downstream projects is fixed.
The random.shuffle regression is fixed.

Contributors

A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Bas van Beek
Charles Harris
Nicholas McKibben +
Pearu Peterson
Ralf Gommers
Sebastian Berg
Tyler Reddy
@Aerysv +

Pull requests merged

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

#18306: MAINT: Add missing placeholder annotations
#18310: BUG: Fix typo in numpy.__init__.py
#18326: BUG: don't mutate list of fake libraries while iterating over...
#18327: MAINT: gracefully shuffle memoryviews
#18328: BUG: Use C linkage for random distributions
#18336: CI: fix when GitHub Actions builds trigger, and allow ci skips
#18337: BUG: Allow unmodified use of isclose, allclose, etc. with timedelta
#18345: BUG: Allow pickling all relevant DType types/classes
#18351: BUG: Fix missing signed_char dependency. Closes #18335.
#18352: DOC: Change license date 2020 -> 2021
#18353: CI: CircleCI seems to occasionally time out, increase the limit
#18354: BUG: Fix f2py bugs when wrapping F90 subroutines.
#18356: MAINT: crackfortran regex simplify
#18357: BUG: threads.h existence test requires GLIBC > 2.12.
#18359: REL: Prepare for the NumPy 1.20.1 release.

Checksums

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SHA256

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9c94cab5054bad82a70b2e77741271790304651d584e2cdfe2041488e753863b  numpy-1.20.1-cp39-cp39-win_amd64.whl
9eb551d122fadca7774b97db8a112b77231dcccda8e91a5bc99e79890797175e  numpy-1.20.1-pp37-pypy37_pp73-manylinux2010_x86_64.whl
9bf51d69ebb4ca9239e55bedc2185fe2c0ec222da0adee7ece4125414676846d  numpy-1.20.1.tar.gz
3bc63486a870294683980d76ec1e3efc786295ae00128f9ea38e2c6e74d5a60a  numpy-1.20.1.zip

`v1.20.0`

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NumPy 1.20.0 Release Notes

This NumPy release is the largest so made to date, some 684 MRs contributed by 184 people have been merged. See the list of highlights below for more details. The Python versions supported for this release are 3.7-3.9, support for Python 3.6 has been dropped. Highlights are

Annotations for NumPy functions. This work is ongoing and improvements can be expected pending feedback from users.
Wider use of SIMD to increase execution speed of ufuncs. Much work has been done in introducing universal functions that will ease use of modern features across different hardware platforms. This work is ongoing.
Preliminary work in changing the dtype and casting implementations in order to provide an easier path to extending dtypes. This work is ongoing but enough has been done to allow experimentation and feedback.
Extensive documentation improvements comprising some 185 MR merges. This work is ongoing and part of the larger project to improve NumPy's online presence and usefulness to new users.
Further cleanups related to removing Python 2.7. This improves code readability and removes technical debt.
Preliminary support for the upcoming Cython 3.0.

New functions

The random.Generator class has a new `permuted` function.

The new function differs from shuffle and permutation in that the subarrays indexed by an axis are permuted rather than the axis being treated as a separate 1-D array for every combination of the other indexes. For example, it is now possible to permute the rows or columns of a 2-D array.

(gh-15121)

`sliding_window_view` provides a sliding window view for numpy arrays

numpy.lib.stride\_tricks.sliding\_window\_view constructs views on numpy arrays that offer a sliding or moving window access to the array. This allows for the simple implementation of certain algorithms, such as running means.

(gh-17394)

[numpy.broadcast_shapes]{.title-ref} is a new user-facing function

numpy.broadcast\_shapes gets the resulting shape from broadcasting the given shape tuples against each other.

>>> np.broadcast_shapes((1, 2), (3, 1))
(3, 2)

>>> np.broadcast_shapes(2, (3, 1))
(3, 2)

>>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
(5, 6, 7)

(gh-17535)

Deprecations

Using the aliases of builtin types like `np.int` is deprecated

For a long time, np.int has been an alias of the builtin int. This is repeatedly a cause of confusion for newcomers, and existed mainly for historic reasons.

These aliases have been deprecated. The table below shows the full list of deprecated aliases, along with their exact meaning. Replacing uses of items in the first column with the contents of the second column will work identically and silence the deprecation warning.

The third column lists alternative NumPy names which may occasionally be preferential. See also basics.types{.interpreted-text role="ref"} for additional details.

Deprecated name	Identical to	NumPy scalar type names
`numpy.bool`	`bool`	`numpy.bool\_`
`numpy.int`	`int`	`numpy.int\_` (default), `numpy.int64`, or `numpy.int32`
`numpy.float`	`float`	`numpy.float64`, `numpy.float\_`, `numpy.double` (equivalent)
`numpy.complex`	`complex`	`numpy.complex128`, `numpy.complex\_`, `numpy.cdouble` (equivalent)
`numpy.object`	`object`	`numpy.object\_`
`numpy.str`	`str`	`numpy.str\_`
`numpy.long`	`int`	`numpy.int\_` (C `long`), `numpy.longlong` (largest integer type)
`numpy.unicode`	`str`	`numpy.unicode\_`

To give a clear guideline for the vast majority of cases, for the types bool, object, str (and unicode) using the plain version is shorter and clear, and generally a good replacement. For float and complex you can use float64 and complex128 if you wish to be more explicit about the precision.

For np.int a direct replacement with np.int_ or int is also good and will not change behavior, but the precision will continue to depend on the computer and operating system. If you want to be more explicit and review the current use, you have the following alternatives:

np.int64 or np.int32 to specify the precision exactly. This ensures that results cannot depend on the computer or operating system.
np.int_ or int (the default), but be aware that it depends on the computer and operating system.
The C types: np.cint (int), np.int_ (long), np.longlong.
np.intp which is 32bit on 32bit machines 64bit on 64bit machines. This can be the best type to use for indexing.

When used with np.dtype(...) or dtype=... changing it to the NumPy name as mentioned above will have no effect on the output. If used as a scalar with:

np.float(123)

changing it can subtly change the result. In this case, the Python version float(123) or int(12.) is normally preferable, although the NumPy version may be useful for consistency with NumPy arrays (for example, NumPy behaves differently for things like division by zero).

(gh-14882)

Passing `shape=None` to functions with a non-optional shape argument is deprecated

Previously, this was an alias for passing shape=(). This deprecation is emitted by PyArray\_IntpConverter in the C API. If your API is intended to support passing None, then you should check for None prior to invoking the converter, so as to be able to distinguish None and ().

(gh-15886)

Indexing errors will be reported even when index result is empty

In the future, NumPy will raise an IndexError when an integer array index contains out of bound values even if a non-indexed dimension is of length 0. This will now emit a DeprecationWarning. This can happen when the array is previously empty, or an empty slice is involved:

arr1 = np.zeros((5, 0))
arr1[[20]]
arr2 = np.zeros((5, 5))
arr2[[20], :0]

Previously the non-empty index [20] was not checked for correctness. It will now be checked causing a deprecation warning which will be turned into an error. This also applies to assignments.

(gh-15900)

Inexact matches for `mode` and `searchside` are deprecated

Inexact and case insensitive matches for mode and searchside were valid inputs earlier and will give a DeprecationWarning now. For example, below are some example usages which are now deprecated and will give a DeprecationWarning:

import numpy as np
arr = np.array([[3, 6, 6], [4, 5, 1]])

mode: inexact match

np.ravel_multi_index(arr, (7, 6), mode="clap")  # should be "clip"

searchside: inexact match

np.searchsorted(arr[0], 4, side='random')  # should be "right"

(gh-16056)

Deprecation of [numpy.dual]{.title-ref}

The module numpy.dual is deprecated. Instead of importing functions from numpy.dual, the functions should be imported directly from NumPy or SciPy.

(gh-16156)

`outer` and `ufunc.outer` deprecated for matrix

np.matrix use with \~numpy.outer or generic ufunc outer calls such as numpy.add.outer. Previously, matrix was converted to an array here. This will not be done in the future requiring a manual conversion to arrays.

(gh-16232)

Further Numeric Style types Deprecated

The remaining numeric-style type codes Bytes0, Str0, Uint32, Uint64, and Datetime64 have been deprecated. The lower-case variants should be used instead. For bytes and string "S" and "U" are further alternatives.

(gh-16554)

The `ndincr` method of `ndindex` is deprecated

The documentation has warned against using this function since NumPy 1.8. Use next(it) instead of it.ndincr().

(gh-17233)

ArrayLike objects which do not define `len` and `getitem`

Objects which define one of the protocols __array__, __array_interface__, or __array_struct__ but are not sequences (usually defined by having a __len__ and __getitem__) will behave differently during array-coercion in the future.

When nested inside sequences, such as np.array([array_like]), these were handled as a single Python object rather than an array. In the future they will behave identically to:

np.array([np.array(array_like)])

This change should only have an effect if np.array(array_like) is not 0-D. The solution to this warning may depend on the object:

Some array-likes may expect the new behaviour, and users can ignore the warning. The object can choose to expose the sequence protocol to opt-in to the new behaviour.
For example, shapely will allow conversion to an array-like using line.coords rather than np.asarray(line). Users may work around the warning, or use the new convention when it becomes available.

Unfortunately, using the new behaviour can only be achieved by calling np.array(array_like).

If you wish to ensure that the old behaviour remains unchanged, please create an object array and then fill it explicitly, for example:

arr = np.empty(3, dtype=object)
arr[:] = [array_like1, array_like2, array_like3]

This will ensure NumPy knows to not enter the array-like and use it as a object instead.

(gh-17973)

Future Changes

Arrays cannot be using subarray dtypes

Array creation and casting using np.array(arr, dtype) and arr.astype(dtype) will use different logic when dtype is a subarray dtype such as np.dtype("(2)i,").

For such a dtype the following behaviour is true:

res = np.array(arr, dtype)

res.dtype is not dtype
res.dtype is dtype.base
res.shape == arr.shape + dtype.shape

But res is filled using the logic:

res = np.empty(arr.shape + dtype.shape, dtype=dtype.base)
res[...] = arr

which uses incorrect broadcasting (and often leads to an error). In the future, this will instead cast each element individually, leading to the same result as:

res = np.array(arr, dtype=np.dtype(["f", dtype]))["f"]

Which can normally be used to opt-in to the new behaviour.

This change does not affect np.array(list, dtype="(2)i,") unless the list itself includes at least one array. In particular, the behaviour is unchanged for a list of tuples.

(gh-17596)

Expired deprecations

The deprecation of numeric style type-codes np.dtype("Complex64") (with upper case spelling), is expired. "Complex64" corresponded to "complex128" and "Complex32" corresponded to "complex64".
The deprecation of np.sctypeNA and np.typeNA is expired. Both have been removed from the public API. Use np.typeDict instead.

(gh-16554)
The 14-year deprecation of np.ctypeslib.ctypes_load_library is expired. Use ~numpy.ctypeslib.load_library{.interpreted-text role="func"} instead, which is identical.

(gh-17116)

Financial functions removed

In accordance with NEP 32, the financial functions are removed from NumPy 1.20. The functions that have been removed are fv, ipmt, irr, mirr, nper, npv, pmt, ppmt, pv, and rate. These functions are available in the numpy_financial library.

(gh-17067)

Compatibility notes

`isinstance(dtype, np.dtype)` and not `type(dtype) is not np.dtype`

NumPy dtypes are not direct instances of np.dtype anymore. Code that may have used type(dtype) is np.dtype will always return False and must be updated to use the correct version isinstance(dtype, np.dtype).

This change also affects the C-side macro PyArray_DescrCheck if compiled against a NumPy older than 1.16.6. If code uses this macro and wishes to compile against an older version of NumPy, it must replace the macro (see also C API changes section).

Same kind casting in concatenate with `axis=None`

When [~numpy.concatenate]{.title-ref} is called with axis=None, the flattened arrays were cast with unsafe. Any other axis choice uses "same kind". That different default has been deprecated and "same kind" casting will be used instead. The new casting keyword argument can be used to retain the old behaviour.

(gh-16134)

NumPy Scalars are cast when assigned to arrays

When creating or assigning to arrays, in all relevant cases NumPy scalars will now be cast identically to NumPy arrays. In particular this changes the behaviour in some cases which previously raised an error:

np.array([np.float64(np.nan)], dtype=np.int64)

will succeed and return an undefined result (usually the smallest possible integer). This also affects assignments:

arr[0] = np.float64(np.nan)

At this time, NumPy retains the behaviour for:

np.array(np.float64(np.nan), dtype=np.int64)

The above changes do not affect Python scalars:

np.array([float("NaN")], dtype=np.int64)

remains unaffected (np.nan is a Python float, not a NumPy one). Unlike signed integers, unsigned integers do not retain this special case, since they always behaved more like casting. The following code stops raising an error:

np.array([np.float64(np.nan)], dtype=np.uint64)

To avoid backward compatibility issues, at this time assignment from datetime64 scalar to strings of too short length remains supported. This means that np.asarray(np.datetime64("2020-10-10"), dtype="S5") succeeds now, when it failed before. In the long term this may be deprecated or the unsafe cast may be allowed generally to make assignment of arrays and scalars behave consistently.

Array coercion changes when Strings and other types are mixed

When strings and other types are mixed, such as:

np.array(["string", np.float64(3.)], dtype="S")

The results will change, which may lead to string dtypes with longer strings in some cases. In particularly, if dtype="S" is not provided any numerical value will lead to a string results long enough to hold all possible numerical values. (e.g. "S32" for floats). Note that you should always provide dtype="S" when converting non-strings to strings.

If dtype="S" is provided the results will be largely identical to before, but NumPy scalars (not a Python float like 1.0), will still enforce a uniform string length:

np.array([np.float64(3.)], dtype="S")  # gives "S32"
np.array([3.0], dtype="S")  # gives "S3"

Previously the first version gave the same result as the second.

Array coercion restructure

Array coercion has been restructured. In general, this should not affect users. In extremely rare corner cases where array-likes are nested:

np.array([array_like1])

Things will now be more consistent with:

np.array([np.array(array_like1)])

This can subtly change output for some badly defined array-likes. One example for this are array-like objects which are not also sequences of matching shape. In NumPy 1.20, a warning will be given when an array-like is not also a sequence (but behaviour remains identical, see deprecations). If an array like is also a sequence (defines __getitem__ and __len__) NumPy will now only use the result given by __array__, __array_interface__, or __array_struct__. This will result in differences when the (nested) sequence describes a different shape.

(gh-16200)

Writing to the result of `numpy.broadcast\_arrays` will export readonly buffers

In NumPy 1.17 numpy.broadcast\_arrays started warning when the resulting array was written to. This warning was skipped when the array was used through the buffer interface (e.g. memoryview(arr)). The same thing will now occur for the two protocols __array_interface__, and __array_struct__ returning read-only buffers instead of giving a warning.

(gh-16350)

Numeric-style type names have been removed from type dictionaries

To stay in sync with the deprecation for np.dtype("Complex64") and other numeric-style (capital case) types. These were removed from np.sctypeDict and np.typeDict. You should use the lower case versions instead. Note that "Complex64" corresponds to "complex128" and "Complex32" corresponds to "complex64". The numpy style (new) versions, denote the full size and not the size of the real/imaginary part.

(gh-16554)

The `operator.concat` function now raises TypeError for array arguments

The previous behavior was to fall back to addition and add the two arrays, which was thought to be unexpected behavior for a concatenation function.

(gh-16570)

`nickname` attribute removed from ABCPolyBase

An abstract property nickname has been removed from ABCPolyBase as it was no longer used in the derived convenience classes. This may affect users who have derived classes from ABCPolyBase and overridden the methods for representation and display, e.g. __str__, __repr__, _repr_latex, etc.

(gh-16589)

`float->timedelta` and `uint64->timedelta` promotion will raise a TypeError

Float and timedelta promotion consistently raises a TypeError. np.promote_types("float32", "m8") aligns with np.promote_types("m8", "float32") now and both raise a TypeError. Previously, np.promote_types("float32", "m8") returned "m8" which was considered a bug.

Uint64 and timedelta promotion consistently raises a TypeError. np.promote_types("uint64", "m8") aligns with np.promote_types("m8", "uint64") now and both raise a TypeError. Previously, np.promote_types("uint64", "m8") returned "m8" which was considered a bug.

(gh-16592)

`numpy.genfromtxt` now correctly unpacks structured arrays

Previously, numpy.genfromtxt failed to unpack if it was called with unpack=True and a structured datatype was passed to the dtype argument (or dtype=None was passed and a structured datatype was inferred). For example:

>>> data = StringIO("21 58.0\n35 72.0")
>>> np.genfromtxt(data, dtype=None, unpack=True)
array([(21, 58.), (35, 72.)], dtype=[('f0', '<i8'), ('f1', '<f8')])

Structured arrays will now correctly unpack into a list of arrays, one for each column:

>>> np.genfromtxt(data, dtype=None, unpack=True)
[array([21, 35]), array([58., 72.])]

(gh-16650)

`mgrid`, `r_`, etc. consistently return correct outputs for non-default precision input

Previously, np.mgrid[np.float32(0.1):np.float32(0.35):np.float32(0.1),] and np.r_[0:10:np.complex64(3j)] failed to return meaningful output. This bug potentially affects [~numpy.mgrid]{.title-ref}, numpy.ogrid, numpy.r\_, and numpy.c\_ when an input with dtype other than the default float64 and complex128 and equivalent Python types were used. The methods have been fixed to handle varying precision correctly.

(gh-16815)

Boolean array indices with mismatching shapes now properly give `IndexError`

Previously, if a boolean array index matched the size of the indexed array but not the shape, it was incorrectly allowed in some cases. In other cases, it gave an error, but the error was incorrectly a ValueError with a message about broadcasting instead of the correct IndexError.

For example, the following used to incorrectly give ValueError: operands could not be broadcast together with shapes (2,2) (1,4):

np.empty((2, 2))[np.array([[True, False, False, False]])]

And the following used to incorrectly return array([], dtype=float64):

np.empty((2, 2))[np.array([[False, False, False, False]])]

Both now correctly give IndexError: boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 1.

(gh-17010)

Casting errors interrupt Iteration

When iterating while casting values, an error may stop the iteration earlier than before. In any case, a failed casting operation always returned undefined, partial results. Those may now be even more undefined and partial. For users of the NpyIter C-API such cast errors will now cause the [iternext()]{.title-ref} function to return 0 and thus abort iteration. Currently, there is no API to detect such an error directly. It is necessary to check PyErr_Occurred(), which may be problematic in combination with NpyIter_Reset. These issues always existed, but new API could be added if required by users.

(gh-17029)

f2py generated code may return unicode instead of byte strings

Some byte strings previously returned by f2py generated code may now be unicode strings. This results from the ongoing Python2 -> Python3 cleanup.

(gh-17068)

The first element of the `__array_interface__["data"]` tuple must be an integer

This has been the documented interface for many years, but there was still code that would accept a byte string representation of the pointer address. That code has been removed, passing the address as a byte string will now raise an error.

(gh-17241)

poly1d respects the dtype of all-zero argument

Previously, constructing an instance of poly1d with all-zero coefficients would cast the coefficients to np.float64. This affected the output dtype of methods which construct poly1d instances internally, such as np.polymul.

(gh-17577)

The numpy.i file for swig is Python 3 only.

Uses of Python 2.7 C-API functions have been updated to Python 3 only. Users who need the old version should take it from an older version of NumPy.

(gh-17580)

Void dtype discovery in `np.array`

In calls using np.array(..., dtype="V"), arr.astype("V"), and similar a TypeError will now be correctly raised unless all elements have the identical void length. An example for this is:

np.array([b"1", b"12"], dtype="V")

Which previously returned an array with dtype "V2" which cannot represent b"1" faithfully.

(gh-17706)

C API changes

The `PyArray_DescrCheck` macro is modified

The PyArray_DescrCheck macro has been updated since NumPy 1.16.6 to be:

#define PyArray_DescrCheck(op) PyObject_TypeCheck(op, &PyArrayDescr_Type)

Starting with NumPy 1.20 code that is compiled against an earlier version will be API incompatible with NumPy 1.20. The fix is to either compile against 1.16.6 (if the NumPy 1.16 release is the oldest release you wish to support), or manually inline the macro by replacing it with the new definition:

PyObject_TypeCheck(op, &PyArrayDescr_Type)

which is compatible with all NumPy versions.

Size of `np.ndarray` and `np.void_` changed

The size of the PyArrayObject and PyVoidScalarObject structures have changed. The following header definition has been removed:

#define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields))

since the size must not be considered a compile time constant: it will change for different runtime versions of NumPy.

The most likely relevant use are potential subclasses written in C which will have to be recompiled and should be updated. Please see the documentation for :cPyArrayObject{.interpreted-text role="type"} for more details and contact the NumPy developers if you are affected by this change.

NumPy will attempt to give a graceful error but a program expecting a fixed structure size may have undefined behaviour and likely crash.

(gh-16938)

New Features

`where` keyword argument for `numpy.all` and `numpy.any` functions

The keyword argument where is added and allows to only consider specified elements or subaxes from an array in the Boolean evaluation of all and any. This new keyword is available to the functions all and any both via numpy directly or in the methods of numpy.ndarray.

Any broadcastable Boolean array or a scalar can be set as where. It defaults to True to evaluate the functions for all elements in an array if where is not set by the user. Examples are given in the documentation of the functions.

`where` keyword argument for `numpy` functions `mean`, `std`, `var`

The keyword argument where is added and allows to limit the scope in the calculation of mean, std and var to only a subset of elements. It is available both via numpy directly or in the methods of numpy.ndarray.

(gh-15852)

`norm=backward`, `forward` keyword options for `numpy.fft` functions

The keyword argument option norm=backward is added as an alias for None and acts as the default option; using it has the direct transforms unscaled and the inverse transforms scaled by 1/n.

Using the new keyword argument option norm=forward has the direct transforms scaled by 1/n and the inverse transforms unscaled (i.e. exactly opposite to the default option norm=backward).

(gh-16476)

NumPy is now typed

Type annotations have been added for large parts of NumPy. There is also a new [numpy.typing]{.title-ref} module that contains useful types for end-users. The currently available types are

ArrayLike: for objects that can be coerced to an array
DtypeLike: for objects that can be coerced to a dtype

(gh-16515)

`numpy.typing` is accessible at runtime

The types in numpy.typing can now be imported at runtime. Code like the following will now work:

from numpy.typing import ArrayLike
x: ArrayLike = [1, 2, 3, 4]

(gh-16558)

New `__f2py_numpy_version__` attribute for f2py generated modules.

Because f2py is released together with NumPy, __f2py_numpy_version__ provides a way to track the version f2py used to generate the module.

(gh-16594)

`mypy` tests can be run via runtests.py

Currently running mypy with the NumPy stubs configured requires either:

Installing NumPy
Adding the source directory to MYPYPATH and linking to the mypy.ini

Both options are somewhat inconvenient, so add a --mypy option to runtests that handles setting things up for you. This will also be useful in the future for any typing codegen since it will ensure the project is built before type checking.

(gh-17123)

Negation of user defined BLAS/LAPACK detection order

[~numpy.distutils]{.title-ref} allows negation of libraries when determining BLAS/LAPACK libraries. This may be used to remove an item from the library resolution phase, i.e. to disallow NetLIB libraries one could do:

NPY_BLAS_ORDER='^blas' NPY_LAPACK_ORDER='^lapack' python setup.py build

That will use any of the accelerated libraries instead.

(gh-17219)

Allow passing optimizations arguments to asv build

It is now possible to pass -j, --cpu-baseline, --cpu-dispatch and --disable-optimization flags to ASV build when the --bench-compare argument is used.

(gh-17284)

The NVIDIA HPC SDK nvfortran compiler is now supported

Support for the nvfortran compiler, a version of pgfortran, has been added.

(gh-17344)

`dtype` option for `cov` and `corrcoef`

The dtype option is now available for [numpy.cov]{.title-ref} and [numpy.corrcoef]{.title-ref}. It specifies which data-type the returned result should have. By default the functions still return a [numpy.float64]{.title-ref} result.

(gh-17456)

Improvements

Improved string representation for polynomials (`str`)

The string representation (__str__) of all six polynomial types in [numpy.polynomial]{.title-ref} has been updated to give the polynomial as a mathematical expression instead of an array of coefficients. Two package-wide formats for the polynomial expressions are available - one using Unicode characters for superscripts and subscripts, and another using only ASCII characters.

(gh-15666)

Remove the Accelerate library as a candidate LAPACK library

Apple no longer supports Accelerate. Remove it.

(gh-15759)

Object arrays containing multi-line objects have a more readable `repr`

If elements of an object array have a repr containing new lines, then the wrapped lines will be aligned by column. Notably, this improves the repr of nested arrays:

>>> np.array([np.eye(2), np.eye(3)], dtype=object)
array([array([[1., 0.],
              [0., 1.]]),
       array([[1., 0., 0.],
              [0., 1., 0.],
              [0., 0., 1.]])], dtype=object)

(gh-15997)

Concatenate supports providing an output dtype

Support was added to [~numpy.concatenate]{.title-ref} to provide an output dtype and casting using keyword arguments. The dtype argument cannot be provided in conjunction with the out one.

(gh-16134)

Thread safe f2py callback functions

Callback functions in f2py are now thread safe.

(gh-16519)

[numpy.core.records.fromfile]{.title-ref} now supports file-like objects

[numpy.rec.fromfile]{.title-ref} can now use file-like objects, for instance :pyio.BytesIO{.interpreted-text role="class"}

(gh-16675)

RPATH support on AIX added to distutils

This allows SciPy to be built on AIX.

(gh-16710)

Use f90 compiler specified by the command line args

The compiler command selection for Fortran Portland Group Compiler is changed in [numpy.distutils.fcompiler]{.title-ref}. This only affects the linking command. This forces the use of the executable provided by the command line option (if provided) instead of the pgfortran executable. If no executable is provided to the command line option it defaults to the pgf90 executable, wich is an alias for pgfortran according to the PGI documentation.

(gh-16730)

Add NumPy declarations for Cython 3.0 and later

The pxd declarations for Cython 3.0 were improved to avoid using deprecated NumPy C-API features. Extension modules built with Cython 3.0+ that use NumPy can now set the C macro NPY_NO_DEMRECATED_API=NPY_1_7_API_VERSION to avoid C compiler warnings about deprecated API usage.

(gh-16986)

Make the window functions exactly symmetric

Make sure the window functions provided by NumPy are symmetric. There were previously small deviations from symmetry due to numerical precision that are now avoided by better arrangement of the computation.

(gh-17195)

Performance improvements and changes

Enable multi-platform SIMD compiler optimizations

A series of improvements for NumPy infrastructure to pave the way to NEP-38, that can be summarized as follow:

New Build Arguments
- --cpu-baseline to specify the minimal set of required optimizations, default value is min which provides the minimum CPU features that can safely run on a wide range of users platforms.
- --cpu-dispatch to specify the dispatched set of additional optimizations, default value is max -xop -fma4 which enables all CPU features, except for AMD legacy features.
- --disable-optimization to explicitly disable the whole new improvements, It also adds a new C compiler #definition called NPY_DISABLE_OPTIMIZATION which it can be used as guard for any SIMD code.
Advanced CPU dispatcher

A flexible cross-architecture CPU dispatcher built on the top of Python/Numpy distutils, support all common compilers with a wide range of CPU features.

The new dispatcher requires a special file extension *.dispatch.c to mark the dispatch-able C sources. These sources have the ability to be compiled multiple times so that each compilation process represents certain CPU features and provides different #definitions and flags that affect the code paths.
New auto-generated C header ``core/src/common/_cpu_dispatch.h``

This header is generated by the distutils module ccompiler_opt, and contains all the #definitions and headers of instruction sets, that had been configured through command arguments '--cpu-baseline' and '--cpu-dispatch'.
New C header ``core/src/common/npy_cpu_dispatch.h``

This header contains all utilities that required for the whole CPU dispatching process, it also can be considered as a bridge linking the new infrastructure work with NumPy CPU runtime detection.
Add new attributes to NumPy umath module(Python level)
- __cpu_baseline__ a list contains the minimal set of required optimizations that supported by the compiler and platform according to the specified values to command argument '--cpu-baseline'.
- __cpu_dispatch__ a list contains the dispatched set of additional optimizations that supported by the compiler and platform according to the specified values to command argument '--cpu-dispatch'.
Print the supported CPU features during the run of PytestTester

(gh-13516)

Changes

Changed behavior of `divmod(1., 0.)` and related functions

The changes also assure that different compiler versions have the same behavior for nan or inf usages in these operations. This was previously compiler dependent, we now force the invalid and divide by zero flags, making the results the same across compilers. For example, gcc-5, gcc-8, or gcc-9 now result in the same behavior. The changes are tabulated below:

Operator	Old Warning	New Warning	Old Result	New Result	Works on MacOS
np.divmod(1.0, 0.0)	Invalid	Invalid and Dividebyzero	nan, nan	inf, nan	Yes
np.fmod(1.0, 0.0)	Invalid	Invalid	nan	nan	No? Yes
np.floor_divide(1.0, 0.0)	Invalid	Dividebyzero	nan	inf	Yes
np.remainder(1.0, 0.0)	Invalid	Invalid	nan	nan	Yes

: Summary of New Behavior

(gh-16161)

`np.linspace` on integers now uses floor

When using a int dtype in [numpy.linspace]{.title-ref}, previously float values would be rounded towards zero. Now [numpy.floor]{.title-ref} is used instead, which rounds toward -inf. This changes the results for negative values. For example, the following would previously give:

>>> np.linspace(-3, 1, 8, dtype=int)
array([-3, -2, -1, -1,  0,  0,  0,  1])

and now results in:

>>> np.linspace(-3, 1, 8, dtype=int)
array([-3, -3, -2, -2, -1, -1,  0,  1])

The former result can still be obtained with:

>>> np.linspace(-3, 1, 8).astype(int)
array([-3, -2, -1, -1,  0,  0,  0,  1])

(gh-16841)

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`v1.19.5`

Compare Source

NumPy 1.19.5 Release Notes

NumPy 1.19.5 is a short bugfix release. Apart from fixing several bugs, the main improvement is the update to OpenBLAS 0.3.13 that works around the windows 2004 bug while not breaking execution on other platforms. This release supports Python 3.6-3.9 and is planned to be the last release in the 1.19.x cycle.

Contributors

A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Christoph Gohlke
Matti Picus
Raghuveer Devulapalli
Sebastian Berg
Simon Graham +
Veniamin Petrenko +
Bernie Gray +

Pull requests merged

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

#17756: BUG: Fix segfault due to out of bound pointer in floatstatus...
#17774: BUG: fix np.timedelta64('nat').__format__ throwing an exception
#17775: BUG: Fixed file handle leak in array_tofile.
#17786: BUG: Raise recursion error during dimension discovery
#17917: BUG: Fix subarray dtype used with too large count in fromfile
#17918: BUG: 'bool' object has no attribute 'ndim'
#17919: BUG: ensure _UFuncNoLoopError can be pickled
#17924: BLD: use BUFFERSIZE=20 in OpenBLAS
#18026: BLD: update to OpenBLAS 0.3.13
#18036: BUG: make a variable volatile to work around clang compiler bug
#18114: REL: Prepare for the NumPy 1.19.5 release.

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d1654047d75fb9d55cc3d46f312d5247eec5f4999039874d2f571bb8021d8f0b  numpy-1.19.5.tar.gz
a76f502430dd98d7546e1ea2250a7360c065a5fdea52b2dffe8ae7180909b6f4  numpy-1.19.5.zip

`v1.19.4`

Compare Source

NumPy 1.19.4 Release Notes

NumPy 1.19.4 is a quick release to revert the OpenBLAS library version. It was hoped that the 0.3.12 OpenBLAS version used in 1.19.3 would work around the Microsoft fmod bug, but problems in some docker environments turned up. Instead, 1.19.4 will use the older library and run a sanity check on import, raising an error if the problem is detected. Microsoft is aware of the problem and has promised a fix, users should upgrade when it becomes available.

This release supports Python 3.6-3.9

Contributors

A total of 1 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris

Pull requests merged

A total of 2 pull requests were merged for this release.

#17679: MAINT: Add check for Windows 10 version 2004 bug.
#17680: REV: Revert OpenBLAS to 1.19.2 version for 1.19.4

Checksums

MD5

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d40f6fcf611ab40eed4ff90606e05307  numpy-1.19.4.zip

SHA256

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448ebb1b3bf64c0267d6b09a7cba26b5ae61b6d2dbabff7c91b660c7eccf2bdb  numpy-1.19.4-cp36-cp36m-win_amd64.whl
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fe45becb4c2f72a0907c1d0246ea6449fe7a9e2293bb0e11c4e9a32bb0930a15  numpy-1.19.4-cp37-cp37m-manylinux2010_x86_64.whl
6d7593a705d662be5bfe24111af14763016765f43cb6923ed86223f965f52387  numpy-1.19.4-cp37-cp37m-manylinux2014_aarch64.whl
6ae6c680f3ebf1cf7ad1d7748868b39d9f900836df774c453c11c5440bc15b36  numpy-1.19.4-cp37-cp37m-win32.whl
9eeb7d1d04b117ac0d38719915ae169aa6b61fca227b0b7d198d43728f0c879c  numpy-1.19.4-cp37-cp37m-win_amd64.whl
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1aeef46a13e51931c0b1cf8ae1168b4a55ecd282e6688fdb0a948cc5a1d5afb9  numpy-1.19.4-cp38-cp38-manylinux2010_x86_64.whl
08308c38e44cc926bdfce99498b21eec1f848d24c302519e64203a8da99a97db  numpy-1.19.4-cp38-cp38-manylinux2014_aarch64.whl
5734bdc0342aba9dfc6f04920988140fb41234db42381cf7ccba64169f9fe7ac  numpy-1.19.4-cp38-cp38-win32.whl
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50e86c076611212ca62e5a59f518edafe0c0730f7d9195fec718da1a5c2bb1fc  numpy-1.19.4-cp39-cp39-manylinux2010_i686.whl
f0d3929fe88ee1c155129ecd82f981b8856c5d97bcb0d5f23e9b4242e79d1de3  numpy-1.19.4-cp39-cp39-manylinux2010_x86_64.whl
c42c4b73121caf0ed6cd795512c9c09c52a7287b04d105d112068c1736d7c753  numpy-1.19.4-cp39-cp39-manylinux2014_aarch64.whl
8cac8790a6b1ddf88640a9267ee67b1aee7a57dfa2d2dd33999d080bc8ee3a0f  numpy-1.19.4-cp39-cp39-win32.whl
4377e10b874e653fe96985c05feed2225c912e328c8a26541f7fc600fb9c637b  numpy-1.19.4-cp39-cp39-win_amd64.whl
2a2740aa9733d2e5b2dfb33639d98a64c3b0f24765fed86b0fd2aec07f6a0a08  numpy-1.19.4-pp36-pypy36_pp73-manylinux2010_x86_64.whl
fe836a685d6838dbb3f603caef01183ea98e88febf4ce956a2ea484a75378413  numpy-1.19.4.tar.gz
141ec3a3300ab89c7f2b0775289954d193cc8edb621ea05f99db9cb181530512  numpy-1.19.4.zip

`v1.19.3`

Compare Source

NumPy 1.19.3 Release Notes

NumPy 1.19.3 is a small maintenace release with two major improvements:

Python 3.9 binary wheels on all supported platforms.
OpenBLAS fixes for Windows 10 version 2004 fmod bug.

This release supports Python 3.6-3.9 and is linked with OpenBLAS 3.7 to avoid some of the fmod problems on Windows 10 version 2004. Microsoft is aware of the problem and users should upgrade when the fix becomes available, the fix here is limited in scope.

Contributors

A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Chris Brown +
Daniel Vanzo +
E. Madison Bray +
Hugo van Kemenade +
Ralf Gommers
Sebastian Berg
@danbeibei +

Pull requests merged

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

#17298: BLD: set upper versions for build dependencies
#17336: BUG: Set deprecated fields to null in PyArray_InitArrFuncs
#17446: ENH: Warn on unsupported Python 3.10+
#17450: MAINT: Update test_requirements.txt.
#17522: ENH: Support for the NVIDIA HPC SDK nvfortran compiler
#17568: BUG: Cygwin Workaround for #14787 on affected platforms
#17647: BUG: Fix memory leak of buffer-info cache due to relaxed strides
#17652: MAINT: Backport openblas_support from master.
#17653: TST: Add Python 3.9 to the CI testing on Windows, Mac.
#17660: TST: Simplify source path names in test_extending.

Checksums

MD5

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7f014f9964987b59083c8dc4d158d45a  numpy-1.19.3.zip

SHA256

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`v1.19.2`

Compare Source

NumPy 1.19.2 Release Notes

NumPy 1.19.2 fixes several bugs, prepares for the upcoming Cython 3.x release. and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros.

This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be used when building with Python 3.9 for testing purposes.

There is a known problem with Windows 10 version=2004 and OpenBLAS svd that we are trying to debug. If you are running that Windows version you should use a NumPy version that links to the MKL library, earlier Windows versions are fine.

Improvements

Add NumPy declarations for Cython 3.0 and later

Contributors

A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Charles Harris
Matti Picus
Pauli Virtanen
Philippe Ombredanne +
Sebastian Berg
Stefan Behnel +
Stephan Loyd +
Zac Hatfield-Dodds

Pull requests merged

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

#16959: TST: Change aarch64 to arm64 in travis.yml.
#16998: MAINT: Configure hypothesis in np.test() for determinism,...
#17000: BLD: pin setuptools < 49.2.0
#17015: ENH: Add NumPy declarations to be used by Cython 3.0+
#17125: BUG: Remove non-threadsafe sigint handling from fft calculation
#17243: BUG: core: fix ilp64 blas dot/vdot/... for strides > int32 max
#17244: DOC: Use SPDX license expressions with correct license
#17245: DOC: Fix the link to the quick-start in the old API functions
#17272: BUG: fix pickling of arrays larger than 2GiB

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This MR has been generated by Renovate Bot.

Edited May 28, 2022 by renovate

Update dependency numpy to v1.22.4

Release Notes

NumPy 1.22.4 Release Notes

Contributors

Pull requests merged

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NumPy 1.22.3 Release Notes

Contributors

Pull requests merged

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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 1.21.6 Release Notes

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NumPy 1.21.5 Release Notes

Contributors

Pull requests merged

Checksums

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

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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