doc/source/release/1.22.0-notes.rst
.. currentmodule:: numpy
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:
quantile, percentile, and related functions. The new
methods provide a complete set of the methods commonly found in the
literature.NEP 43 <NEP43>. This also unlocks the ability to experiment with the
future DType API.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 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. Also 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.
Using the strings "Bytes0", "Datetime64", "Str0", "Uint32",
and "Uint64" as a dtype will now raise a TypeError.
(gh-19539 <https://github.com/numpy/numpy/pull/19539>__)
loads, ndfromtxt, and mafromtxt in npyionumpy.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 <https://github.com/numpy/numpy/pull/19615>__)
The misspelled keyword argument delimitor of
numpy.ma.mrecords.fromtextfile() has been changed to delimiter, using
it will emit a deprecation warning.
(gh-19921 <https://github.com/numpy/numpy/pull/19921>__)
kth values to (arg-)partition has been deprecatednumpy.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 <https://github.com/numpy/numpy/pull/20000>__)
np.MachAr class has been deprecatedThe 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 <https://github.com/numpy/numpy/pull/20201>__)
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 <https://github.com/numpy/numpy/pull/19479>__)
Floor division of complex types will now result in a TypeError
.. code-block:: python
>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...
(gh-19135 <https://github.com/numpy/numpy/pull/19135>__)
numpy.vectorize functions now produce the same output class as the base functionWhen 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 <https://github.com/numpy/numpy/pull/19356>__)
Python support has been dropped. This is rather strict, there are changes that require Python >= 3.8.
(gh-19665 <https://github.com/numpy/numpy/pull/19665>__)
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 <https://github.com/numpy/numpy/pull/19687>__)
advance in PCG64DSXM and PCG64Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug
only affects results when the step was larger than :math:2^{64} on platforms
that do not support 128-bit integers(e.g., Windows and 32-bit Linux).
(gh-20049 <https://github.com/numpy/numpy/pull/20049>__)
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 <https://github.com/numpy/numpy/pull/20314>__)
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 <https://github.com/numpy/numpy/pull/19259>__)
The new header experimental_public_dtype_api.h allows to experiment with
future API for improved universal function and especially user DType support.
At this time it is advisable to experiment using the development version
of NumPy since some changes are expected and new features will be unlocked.
(gh-19919 <https://github.com/numpy/numpy/pull/19919>__)
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 :ref:data_memory reference docs.
Also add a NUMPY_WARN_IF_NO_MEM_POLICY override to warn on dangerous use
of transferring ownership by setting NPY_ARRAY_OWNDATA.
.. _NEP 49: https://numpy.org/neps/nep-0049.html
(gh-17582 <https://github.com/numpy/numpy/pull/17582>__)
An initial implementation of NEP 47_ (adoption the array API standard) has
been added as numpy.array_api. The implementation is experimental and will
issue a UserWarning on import, as the array API standard <https://data-apis.org/array-api/latest/index.html>_ 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.
.. _NEP 47: https://numpy.org/neps/nep-0047-array-api-standard.html
(gh-18585 <https://github.com/numpy/numpy/pull/18585>__)
This feature depends on Doxygen_ in the generation process and on Breathe_ to integrate it with Sphinx.
.. _Doxygen: https://www.doxygen.nl/index.html
.. _Breathe: https://breathe.readthedocs.io/en/latest/
(gh-18884 <https://github.com/numpy/numpy/pull/18884>__)
c_intp precision via a mypy pluginThe 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_:
.. code-block:: ini
[mypy]
plugins = numpy.typing.mypy_plugin
.. _mypy: http://mypy-lang.org/
.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html
.. _numpy/numpy#17843: https://github.com/numpy/numpy/pull/17843
(gh-19062 <https://github.com/numpy/numpy/pull/19062>__)
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 <https://github.com/numpy/numpy/pull/19083>__)
keepdims optional argument added to numpy.argmin, numpy.argmaxkeepdims 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 <https://github.com/numpy/numpy/pull/19211>__)
bit_count to compute the number of 1-bits in an integerComputes 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++.
.. code-block:: python
>>> np.uint32(1023).bit_count()
10
>>> np.int32(-127).bit_count()
7
(gh-19355 <https://github.com/numpy/numpy/pull/19355>__)
ndim and axis attributes have been added to numpy.AxisErrorThe ndim and axis parameters are now also stored as attributes
within each numpy.AxisError instance.
(gh-19459 <https://github.com/numpy/numpy/pull/19459>__)
windows/arm64 targetnumpy added support for windows/arm64 target. Please note OpenBLAS
support is not yet available for windows/arm64 target.
(gh-19513 <https://github.com/numpy/numpy/pull/19513>__)
LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit.
(gh-19527 <https://github.com/numpy/numpy/pull/19527>__)
.clang-format file has been addedClang-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 <https://github.com/numpy/numpy/pull/19754>__)
is_integer is now available to numpy.floating and numpy.integerBased 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.
.. code-block:: python
>>> np.float32(-2.0).is_integer()
True
>>> np.float64(3.2).is_integer()
False
>>> np.int32(-2).is_integer()
True
(gh-19803 <https://github.com/numpy/numpy/pull/19803>__)
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 <https://github.com/numpy/numpy/pull/19805>__)
ndarray, dtype and number are now runtime-subscriptableMimicking :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.
.. code-block:: python
>>> 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 <https://github.com/numpy/numpy/pull/19879>__)
ctypeslib.load_library can now take any path-like objectAll parameters in the can now take any :term:python:path-like object.
This includes the likes of strings, bytes and objects implementing the
:meth:__fspath__<os.PathLike.__fspath__> protocol.
(gh-17530 <https://github.com/numpy/numpy/pull/17530>__)
smallest_normal and smallest_subnormal attributes to finfoThe 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 <https://github.com/numpy/numpy/pull/18536>__)
numpy.linalg.qr accepts stacked matrices as inputsnumpy.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 <https://github.com/numpy/numpy/pull/19151>__)
numpy.fromregex now accepts os.PathLike implementationsnumpy.fromregex now accepts objects implementing the __fspath__<os.PathLike>
protocol, e.g. pathlib.Path.
(gh-19680 <https://github.com/numpy/numpy/pull/19680>__)
quantile and percentilequantile 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 <https://github.com/numpy/numpy/pull/19857>__)
nan<x> functionsA 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:
initial & whereinitial & wherekeepdims & outkeepdims & outinitial & whereinitial & wherewherewherewhere(gh-20027 <https://github.com/numpy/numpy/pull/20027>__)
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 <https://github.com/numpy/numpy/pull/20217>__)
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 <https://github.com/numpy/numpy/pull/19478>__)
Update the OpenBLAS used in testing and in wheels to v0.3.18
(gh-20058 <https://github.com/numpy/numpy/pull/20058>__)