Update dependency numpy to v1.22.4
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
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
withno_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
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
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
andimag
... - #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
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
andtimedelta
... -
#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
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)
loads
, ndfromtxt
, and mafromtxt
in npyio
Expired deprecations for 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)
kth
values to (arg-)partition has been deprecated
Passing boolean 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)
np.MachAr
class has been deprecated
The 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)
advance
in PCG64DSXM
and PCG64
Corrected 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)
c_intp
precision via a mypy plugin
Assign the platform-specific 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)
ndim
and axis
attributes have been added to numpy.AxisError
The The ndim
and axis
parameters are now also stored as attributes
within each numpy.AxisError
instance.
(gh-19459)
windows/arm64
target
Preliminary support for 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)
.clang-format
file has been added
A 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)
smallest_normal
and smallest_subnormal
attributes to finfo
Add 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)
quantile
and percentile
Add new methods for 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)
nan<x>
functions
Missing parameters have been added to the A number of the nan<x>
functions previously lacked parameters that
were present in their <x>
-based counterpart, e.g. the where
parameter was present in numpy.mean
but absent from numpy.nanmean
.
The following parameters have now been added to the nan<x>
functions:
- nanmin:
initial
&where
- nanmax:
initial
&where
- nanargmin:
keepdims
&out
- nanargmax:
keepdims
&out
- nansum:
initial
&where
- nanprod:
initial
&where
- nanmean:
where
- nanvar:
where
- nanstd:
where
(gh-20027)
Annotating the main Numpy namespace
Starting from the 1.20 release, PEP 484 type annotations have been included for parts of the NumPy library; annotating the remaining functions being a work in progress. With the release of 1.22 this process has been completed for the main NumPy namespace, which is now fully annotated.
Besides the main namespace, a limited number of sub-packages contain
annotations as well. This includes, among others, numpy.testing
,
numpy.linalg
and numpy.random
(available since 1.21).
(gh-20217)
Vectorize umath module using AVX-512
By leveraging Intel Short Vector Math Library (SVML), 18 umath functions
(exp2
, log2
, log10
, expm1
, log1p
, cbrt
, sin
, cos
, tan
,
arcsin
, arccos
, arctan
, sinh
, cosh
, tanh
, arcsinh
,
arccosh
, arctanh
) are vectorized using AVX-512 instruction set for
both single and double precision implementations. This change is
currently enabled only for Linux users and on processors with AVX-512
instruction set. It provides an average speed up of 32x and 14x for
single and double precision functions respectively.
(gh-19478)
OpenBLAS v0.3.18
Update the OpenBLAS used in testing and in wheels to v0.3.18
(gh-20058)
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v1.21.6
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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v1.21.5
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 respectnpy_longdouble
- #20466: BUG: Fix failure to create aligned, empty structured dtype
-
#20467: ENH: provide a convenience function to replace
npy_load_module
- #20495: MAINT: update wheel to version that supports python3.10
- #20497: BUG: Clear errors correctly in F2PY conversions
- #20613: DEV: add a warningfilter to fix pytest workflow.
- #20618: MAINT: Help boost::python libraries at least not crash
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v1.21.4
NumPy 1.21.4 Release Notes
The NumPy 1.21.4 is a maintenance release that fixes a few bugs discovered after 1.21.3. The most important fix here is a fix for the NumPy header files to make them work for both x86_64 and M1 hardware when included in the Mac universal2 wheels. Previously, the header files only worked for M1 and this caused problems for folks building x86_64 extensions. This problem was not seen before Python 3.10 because there were thin wheels for x86_64 that had precedence. This release also provides thin x86_64 Mac wheels for Python 3.10.
The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11, you will need to use gcc-11.2+ to avoid problems.
Contributors
A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- Bas van Beek
- Charles Harris
- Isuru Fernando
- Matthew Brett
- Sayed Adel
- Sebastian Berg
- 傅立业(Chris Fu) +
Pull requests merged
A total of 9 pull requests were merged for this release.
-
#20278: BUG: Fix shadowed reference of
dtype
in type stub - #20293: BUG: Fix headers for universal2 builds
-
#20294: BUG:
VOID_nonzero
could sometimes mutate alignment flag - #20295: BUG: Do not use nonzero fastpath on unaligned arrays
- #20296: BUG: Distutils patch to allow for 2 as a minor version (!)
- #20297: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar
- #20298: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC...
- #20300: REL: Prepare for the NumPy 1.21.4 release.
-
#20302: TST: Fix a
Arrayterator
typing test failure
Checksums
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v1.21.3
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.
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v1.21.2
NumPy 1.21.2 Release Notes
The NumPy 1.21.2 is maintenance release that fixes bugs discovered after 1.21.1. It also provides 64 bit manylinux Python 3.10.0rc1 wheels for downstream testing. Note that Python 3.10 is not yet final. There is also preliminary support for Windows on ARM64 builds, but there is no OpenBLAS for that platform and no wheels are available.
The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with Python 3.10.0rc1 and Python 3.10 will be officially supported after it is released. The previous problems with gcc-11.1 have been fixed by gcc-11.2, check your version if you are using gcc-11.
Contributors
A total of 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time.
- Bas van Beek
- Carl Johnsen +
- Charles Harris
- Gwyn Ciesla +
- Matthieu Dartiailh
- Matti Picus
- Niyas Sait +
- Ralf Gommers
- Sayed Adel
- Sebastian Berg
Pull requests merged
A total of 18 pull requests were merged for this release.
-
#19497: MAINT: set Python version for 1.21.x to
<3.11
-
#19533: BUG: Fix an issue wherein importing
numpy.typing
could raise - #19646: MAINT: Update Cython version for Python 3.10.
- #19648: TST: Bump the python 3.10 test version from beta4 to rc1
- #19651: TST: avoid distutils.sysconfig in runtests.py
- #19652: MAINT: add missing dunder method to nditer type hints
-
#19656: BLD, SIMD: Fix testing extra checks when
-Werror
isn't applicable... - #19657: BUG: Remove logical object ufuncs with bool output
- #19658: MAINT: Include .coveragerc in source distributions to support...
- #19659: BUG: Fix bad write in masked iterator output copy paths
- #19660: ENH: Add support for windows on arm targets
- #19661: BUG: add base to templated arguments for platlib
- #19662: BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated
- #19666: MAINT: Add Python 3.10 to supported versions.
-
#19668: TST,BUG: Sanitize path-separators when running
runtest.py
- #19671: BLD: load extra flags when checking for libflame
- #19676: BLD: update circleCI docker image
- #19677: REL: Prepare for 1.21.2 release.
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v1.21.1
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.
- 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
withtyping.Any
-
#19324: MAINT: Fixed the return-dtype of
ndarray.real
andimag
-
#19330: MAINT: Replace
"dtype[Any]"
withdtype
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
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.
- 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
argumentnumpy.unravel_index
cannot be passed asdims
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 abstractABCPolyBase
class instead.(gh-18963)
-
The unused
PolyError
andPolyDomainError
exceptions are removed.(gh-18963)
Deprecations
.dtype
attribute must return a dtype
The 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)
numpy.convolve
and numpy.correlate
are deprecated
Inexact matches for 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)
ndarray.ctypes
methods have been deprecated
Four 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
argumentnumpy.unravel_index
] cannot be passed asdims
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)
PolyBase
and unused PolyError
and PolyDomainError
Remove deprecated 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)
Generator.uniform
Validate input values in 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)
dtype=...
Changes to comparisons with 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)
dtype
and signature
arguments in ufuncs
Changes to 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]")
arr.dtype
)
The following previously did use "ns" (as opposed to 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)
signature=...
and dtype=
generalization and casting
Ufunc 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
ufunc->type_resolver
and "type tuple"
Use of 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
numpy.number
precisions
Added a mypy plugin for handling platform-specific 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)
numpy.number
subclasses
Let the mypy plugin manage extended-precision 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)
min_digits
argument for printing float values
New 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)
ndarray
A runtime-subcriptable alias has been added for 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
period
option for numpy.unwrap
Arbitrary 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)
np.save
and np.load
for small arrays
Improve performance of 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
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 fordatetime.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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v1.20.2
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 fornp.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
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v1.20.1
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.
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v1.20.0
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
permuted
function.
The random.Generator class has a new 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
np.int
is deprecated
Using the aliases of builtin types like 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
ornp.int32
to specify the precision exactly. This ensures that results cannot depend on the computer or operating system. -
np.int_
orint
(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)
shape=None
to functions with a non-optional shape argument is deprecated
Passing 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)
mode
and searchside
are deprecated
Inexact matches for 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)
ndincr
method of ndindex
is deprecated
The The documentation has warned against using this function since NumPy
1.8. Use next(it)
instead of it.ndincr()
.
(gh-17233)
__len__
and __getitem__
ArrayLike objects which do not define 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 usingline.coords
rather thannp.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
andnp.typeNA
is expired. Both have been removed from the public API. Usenp.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).
axis=None
Same kind casting in concatenate with 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)
numpy.broadcast\_arrays
will export readonly buffers
Writing to the result of 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)
operator.concat
function now raises TypeError for array arguments
The 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)
IndexError
Boolean array indices with mismatching shapes now properly give 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)
__array_interface__["data"]
tuple must be an integer
The first element of the 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)
np.array
Void dtype discovery in 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
PyArray_DescrCheck
macro is modified
The 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.
np.ndarray
and np.void_
changed
Size of 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
.
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.
(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)
__f2py_numpy_version__
attribute for f2py generated modules.
New 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
__str__
)
Improved string representation for polynomials (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)
repr
Object arrays containing multi-line objects have a more readable 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 ismin
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 ismax -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 calledNPY_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
divmod(1., 0.)
and related functions
Changed behavior of 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
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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v1.19.4
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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SHA256
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v1.19.3
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
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SHA256
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v1.19.2
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
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.
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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