| Package | Change | Age | Adoption | Passing | Confidence |
|---|---|---|---|---|---|
| numpy (source) | 1.21.0
-> 1.22.4
| | | | |
numpy/numpy
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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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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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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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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Compare Source
NumPy 1.22.0 Release Notes
NumPy 1.22.0 is a big release featuring the work of 153 contributors
spread over 609 pull requests. There have been many improvements,
highlights are:
- Annotations of the main namespace are essentially complete. Upstream
is a moving target, so there will likely be further improvements,
but the major work is done. This is probably the most user visible
enhancement in this release.
- A preliminary version of the proposed Array-API is provided. This is
a step in creating a standard collection of functions that can be
used across application such as CuPy and JAX.
- NumPy now has a DLPack backend. DLPack provides a common interchange
format for array (tensor) data.
- New methods for
quantile
, percentile
, and related functions. The
new methods provide a complete set of the methods commonly found in
the literature.
- A new configurable allocator for use by downstream projects.
These are in addition to the ongoing work to provide SIMD support for
commonly used functions, improvements to F2PY, and better documentation.
The Python versions supported in this release are 3.8-3.10, Python 3.7
has been dropped. Note that 32 bit wheels are only provided for Python
3.8 and 3.9 on Windows, all other wheels are 64 bits on account of
Ubuntu, Fedora, and other Linux distributions dropping 32 bit support.
All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix
the occasional problems encountered by folks using truly huge arrays.
Expired deprecations
Deprecated numeric style dtype strings have been removed
Using the strings "Bytes0"
, "Datetime64"
, "Str0"
, "Uint32"
,
and "Uint64"
as a dtype will now raise a TypeError
.
(gh-19539)
Expired deprecations for loads
, ndfromtxt
, and mafromtxt
in npyio
numpy.loads
was deprecated in v1.15, with the recommendation that
users use pickle.loads
instead. ndfromtxt
and mafromtxt
were both
deprecated in v1.17 - users should use numpy.genfromtxt
instead with
the appropriate value for the usemask
parameter.
(gh-19615)
Deprecations
Use delimiter rather than delimitor as kwarg in mrecords
The misspelled keyword argument delimitor
of
numpy.ma.mrecords.fromtextfile()
has been changed to delimiter
,
using it will emit a deprecation warning.
(gh-19921)
Passing boolean kth
values to (arg-)partition has been deprecated
numpy.partition
and numpy.argpartition
would previously accept
boolean values for the kth
parameter, which would subsequently be
converted into integers. This behavior has now been deprecated.
(gh-20000)
The np.MachAr
class has been deprecated
The numpy.MachAr
class and finfo.machar <numpy.finfo>
attribute have
been deprecated. Users are encouraged to access the property if interest
directly from the corresponding numpy.finfo
attribute.
(gh-20201)
Compatibility notes
Distutils forces strict floating point model on clang
NumPy now sets the -ftrapping-math
option on clang to enforce correct
floating point error handling for universal functions. Clang defaults to
non-IEEE and C99 conform behaviour otherwise. This change (using the
equivalent but newer -ffp-exception-behavior=strict
) was attempted in
NumPy 1.21, but was effectively never used.
(gh-19479)
Removed floor division support for complex types
Floor division of complex types will now result in a TypeError
>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...
(gh-19135)
numpy.vectorize
functions now produce the same output class as the base function
When a function that respects numpy.ndarray
subclasses is vectorized
using numpy.vectorize
, the vectorized function will now be
subclass-safe also for cases that a signature is given (i.e., when
creating a gufunc
): the output class will be the same as that returned
by the first call to the underlying function.
(gh-19356)
Python 3.7 is no longer supported
Python support has been dropped. This is rather strict, there are
changes that require Python >= 3.8.
(gh-19665)
str/repr of complex dtypes now include space after punctuation
The repr of
np.dtype({"names": ["a"], "formats": [int], "offsets": [2]})
is now
dtype({'names': ['a'], 'formats': ['<i8'], 'offsets': [2], 'itemsize': 10})
,
whereas spaces where previously omitted after colons and between fields.
The old behavior can be restored via
np.set_printoptions(legacy="1.21")
.
(gh-19687)
Corrected advance
in PCG64DSXM
and PCG64
Fixed a bug in the advance
method of PCG64DSXM
and PCG64
. The bug
only affects results when the step was larger than $2^{64}$ on platforms
that do not support 128-bit integers(e.g., Windows and 32-bit Linux).
(gh-20049)
Change in generation of random 32 bit floating point variates
There was bug in the generation of 32 bit floating point values from the
uniform distribution that would result in the least significant bit of
the random variate always being 0. This has been fixed.
This change affects the variates produced by the random.Generator
methods random
, standard_normal
, standard_exponential
, and
standard_gamma
, but only when the dtype is specified as
numpy.float32
.
(gh-20314)
C API changes
Masked inner-loops cannot be customized anymore
The masked inner-loop selector is now never used. A warning will be
given in the unlikely event that it was customized.
We do not expect that any code uses this. If you do use it, you must
unset the selector on newer NumPy version. Please also contact the NumPy
developers, we do anticipate providing a new, more specific, mechanism.
The customization was part of a never-implemented feature to allow for
faster masked operations.
(gh-19259)
New Features
NEP 49 configurable allocators
As detailed in NEP 49, the
function used for allocation of the data segment of a ndarray can be
changed. The policy can be set globally or in a context. For more
information see the NEP and the data_memory
{.interpreted-text
role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY
override to warn on dangerous use of transfering ownership by setting
NPY_ARRAY_OWNDATA
.
(gh-17582)
Implementation of the NEP 47 (adopting the array API standard)
An initial implementation of NEP47, adoption
of the array API standard, has been added as numpy.array_api
. The
implementation is experimental and will issue a UserWarning on import,
as the array API standard is still in
draft state. numpy.array_api
is a conforming implementation of the
array API standard, which is also minimal, meaning that only those
functions and behaviors that are required by the standard are
implemented (see the NEP for more info). Libraries wishing to make use
of the array API standard are encouraged to use numpy.array_api
to
check that they are only using functionality that is guaranteed to be
present in standard conforming implementations.
(gh-18585)
Generate C/C++ API reference documentation from comments blocks is now possible
This feature depends on Doxygen in
the generation process and on
Breathe to integrate it
with Sphinx.
(gh-18884)
Assign the platform-specific c_intp
precision via a mypy plugin
The mypy plugin, introduced in
numpy/numpy#17843, has
again been expanded: the plugin now is now responsible for setting the
platform-specific precision of numpy.ctypeslib.c_intp
, the latter
being used as data type for various numpy.ndarray.ctypes
attributes.
Without the plugin, aforementioned type will default to
ctypes.c_int64
.
To enable the plugin, one must add it to their mypy configuration
file:
[mypy]
plugins = numpy.typing.mypy_plugin
(gh-19062)
Add NEP 47-compatible dlpack support
Add a ndarray.__dlpack__()
method which returns a dlpack
C structure
wrapped in a PyCapsule
. Also add a np._from_dlpack(obj)
function,
where obj
supports __dlpack__()
, and returns an ndarray
.
(gh-19083)
keepdims
optional argument added to numpy.argmin
, numpy.argmax
keepdims
argument is added to numpy.argmin
, numpy.argmax
. If set
to True
, the axes which are reduced are left in the result as
dimensions with size one. The resulting array has the same number of
dimensions and will broadcast with the input array.
(gh-19211)
bit_count
to compute the number of 1-bits in an integer
Computes the number of 1-bits in the absolute value of the input. This
works on all the numpy integer types. Analogous to the builtin
int.bit_count
or popcount
in C++.
>>> np.uint32(1023).bit_count()
10
>>> np.int32(-127).bit_count()
7
(gh-19355)
The ndim
and axis
attributes have been added to numpy.AxisError
The ndim
and axis
parameters are now also stored as attributes
within each numpy.AxisError
instance.
(gh-19459)
Preliminary support for windows/arm64
target
numpy
added support for windows/arm64 target. Please note OpenBLAS
support is not yet available for windows/arm64 target.
(gh-19513)
Added support for LoongArch
LoongArch is a new instruction set, numpy compilation failure on
LoongArch architecture, so add the commit.
(gh-19527)
A .clang-format
file has been added
Clang-format is a C/C++ code formatter, together with the added
.clang-format
file, it produces code close enough to the NumPy
C_STYLE_GUIDE for general use. Clang-format version 12+ is required
due to the use of several new features, it is available in Fedora 34 and
Ubuntu Focal among other distributions.
(gh-19754)
is_integer
is now available to numpy.floating
and numpy.integer
Based on its counterpart in Python float
and int
, the numpy floating
point and integer types now support float.is_integer
. Returns True
if the number is finite with integral value, and False
otherwise.
>>> np.float32(-2.0).is_integer()
True
>>> np.float64(3.2).is_integer()
False
>>> np.int32(-2).is_integer()
True
(gh-19803)
Symbolic parser for Fortran dimension specifications
A new symbolic parser has been added to f2py in order to correctly parse
dimension specifications. The parser is the basis for future
improvements and provides compatibility with Draft Fortran 202x.
(gh-19805)
ndarray
, dtype
and number
are now runtime-subscriptable
Mimicking PEP-585, the numpy.ndarray
,
numpy.dtype
and numpy.number
classes are now subscriptable for
python 3.9 and later. Consequently, expressions that were previously
only allowed in .pyi stub files or with the help of
from __future__ import annotations
are now also legal during runtime.
>>> import numpy as np
>>> from typing import Any
>>> np.ndarray[Any, np.dtype[np.float64]]
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
(gh-19879)
Improvements
ctypeslib.load_library
can now take any path-like object
All parameters in the can now take any
python:path-like object
{.interpreted-text role="term"}. This includes
the likes of strings, bytes and objects implementing the
__fspath__<os.PathLike.__fspath__>
{.interpreted-text role="meth"}
protocol.
(gh-17530)
Add smallest_normal
and smallest_subnormal
attributes to finfo
The attributes smallest_normal
and smallest_subnormal
are available
as an extension of finfo
class for any floating-point data type. To
use these new attributes, write np.finfo(np.float64).smallest_normal
or np.finfo(np.float64).smallest_subnormal
.
(gh-18536)
numpy.linalg.qr
accepts stacked matrices as inputs
numpy.linalg.qr
is able to produce results for stacked matrices as
inputs. Moreover, the implementation of QR decomposition has been
shifted to C from Python.
(gh-19151)
numpy.fromregex
now accepts os.PathLike
implementations
numpy.fromregex
now accepts objects implementing the
__fspath__<os.PathLike>
protocol, e.g. pathlib.Path
.
(gh-19680)
Add new methods for quantile
and percentile
quantile
and percentile
now have have a method=
keyword argument
supporting 13 different methods. This replaces the interpolation=
keyword argument.
The methods are now aligned with nine methods which can be found in
scientific literature and the R language. The remaining methods are the
previous discontinuous variations of the default "linear" one.
Please see the documentation of numpy.percentile
for more information.
(gh-19857)
Missing parameters have been added to the nan<x>
functions
A number of the nan<x>
functions previously lacked parameters that
were present in their <x>
-based counterpart, e.g. the where
parameter was present in numpy.mean
but absent from numpy.nanmean
.
The following parameters have now been added to the nan<x>
functions:
- nanmin:
initial
& where
- nanmax:
initial
& where
- nanargmin:
keepdims
& out
- nanargmax:
keepdims
& out
- nansum:
initial
& where
- nanprod:
initial
& where
- nanmean:
where
- nanvar:
where
- nanstd:
where
(gh-20027)
Annotating the main Numpy namespace
Starting from the 1.20 release, PEP 484 type annotations have been
included for parts of the NumPy library; annotating the remaining
functions being a work in progress. With the release of 1.22 this
process has been completed for the main NumPy namespace, which is now
fully annotated.
Besides the main namespace, a limited number of sub-packages contain
annotations as well. This includes, among others, numpy.testing
,
numpy.linalg
and numpy.random
(available since 1.21).
(gh-20217)
Vectorize umath module using AVX-512
By leveraging Intel Short Vector Math Library (SVML), 18 umath functions
(exp2
, log2
, log10
, expm1
, log1p
, cbrt
, sin
, cos
, tan
,
arcsin
, arccos
, arctan
, sinh
, cosh
, tanh
, arcsinh
,
arccosh
, arctanh
) are vectorized using AVX-512 instruction set for
both single and double precision implementations. This change is
currently enabled only for Linux users and on processors with AVX-512
instruction set. It provides an average speed up of 32x and 14x for
single and double precision functions respectively.
(gh-19478)
OpenBLAS v0.3.18
Update the OpenBLAS used in testing and in wheels to v0.3.18
(gh-20058)
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Compare Source
NumPy 1.21.6 Release Notes
NumPy 1.21.6 is a very small release that achieves two things:
- Backs out the mistaken backport of C++ code into 1.21.5.
- Provides a 32 bit Windows wheel for Python 3.10.
The provision of the 32 bit wheel is intended to make life easier for
oldest-supported-numpy.
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