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chore(deps): update all dependencies#253
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This PR contains the following updates:
==2.3.3->==2.4.1==2.3.1->==2.3.2==1.19.5->==1.22.1==1.21.4->==1.22.1Release Notes
googleapis/google-auth-library-python
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Features
Bug Fixes
Documentation
verify_oauth2_token(#928) (82bc5f0)2.3.3 (2021-11-01)
Bug Fixes
2.3.2 (2021-10-26)
Bug Fixes
2.3.1 (2021-10-21)
Bug Fixes
Documentation
GCE_METADATA_HOST(#890) (e2b3c98)googleapis/python-language
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numpy/numpy
v1.22.1Compare Source
NumPy 1.22.1 Release Notes
The NumPy 1.22.1 is maintenance release that fixes bugs discovered after
the 1.22.0 release. Notable fixes are:
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.
Pull requests merged
A total of 20 pull requests were merged for this release.
__array_namespace__to ModuleTypendindexto accept integer tuplesdateandtimedelta...ndarray.__array_finalize__array_api.argsort(descending=True)respects relative...array_apiChecksums
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NumPy 1.22.0 Release Notes
NumPy 1.22.0 is a big release featuring the work of 153 contributors
spread over 609 pull requests. There have been many improvements,
highlights are:
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 step in creating a standard collection of functions that can be
used across application such as CuPy and JAX.
format for array (tensor) data.
quantile,percentile, and related functions. Thenew methods provide a complete set of the methods commonly found in
the literature.
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 aTypeError.(gh-19539)
Expired deprecations for
loads,ndfromtxt, andmafromtxtin npyionumpy.loadswas deprecated in v1.15, with the recommendation thatusers use
pickle.loadsinstead.ndfromtxtandmafromtxtwere bothdeprecated in v1.17 - users should use
numpy.genfromtxtinstead withthe appropriate value for the
usemaskparameter.(gh-19615)
Deprecations
Use delimiter rather than delimitor as kwarg in mrecords
The misspelled keyword argument
delimitorofnumpy.ma.mrecords.fromtextfile()has been changed todelimiter,using it will emit a deprecation warning.
(gh-19921)
Passing boolean
kthvalues to (arg-)partition has been deprecatednumpy.partitionandnumpy.argpartitionwould previously acceptboolean values for the
kthparameter, which would subsequently beconverted into integers. This behavior has now been deprecated.
(gh-20000)
The
np.MachArclass has been deprecatedThe
numpy.MachArclass andfinfo.machar <numpy.finfo>attribute havebeen deprecated. Users are encouraged to access the property if interest
directly from the corresponding
numpy.finfoattribute.(gh-20201)
Compatibility notes
Distutils forces strict floating point model on clang
NumPy now sets the
-ftrapping-mathoption on clang to enforce correctfloating 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 inNumPy 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(gh-19135)
numpy.vectorizefunctions now produce the same output class as the base functionWhen a function that respects
numpy.ndarraysubclasses is vectorizedusing
numpy.vectorize, the vectorized function will now besubclass-safe also for cases that a signature is given (i.e., when
creating a
gufunc): the output class will be the same as that returnedby 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 nowdtype({'names': ['a'], 'formats': ['<i8'], 'offsets': [2], 'itemsize': 10}),whereas spaces where previously omitted after colons and between fields.
The old behavior can be restored via
np.set_printoptions(legacy="1.21").(gh-19687)
Corrected
advanceinPCG64DSXMandPCG64Fixed a bug in the$2^{64}$ on platforms
advancemethod ofPCG64DSXMandPCG64. The bugonly affects results when the step was larger than
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.Generatormethods
random,standard_normal,standard_exponential, andstandard_gamma, but only when the dtype is specified asnumpy.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-textrole="ref"} reference docs. Also add a
NUMPY_WARN_IF_NO_MEM_POLICYoverride 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. Theimplementation is experimental and will issue a UserWarning on import,
as the array API standard is still in
draft state.
numpy.array_apiis a conforming implementation of thearray 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_apitocheck that they are only using functionality that is guaranteed to be
present in standard conforming implementations.
(gh-18585)
Generate C/C++ API reference documentation from comments blocks is now possible
This feature depends on Doxygen in
the generation process and on
Breathe to integrate it
with Sphinx.
(gh-18884)
Assign the platform-specific
c_intpprecision via a mypy pluginThe mypy plugin, introduced in
numpy/numpy#17843, has
again been expanded: the plugin now is now responsible for setting the
platform-specific precision of
numpy.ctypeslib.c_intp, the latterbeing used as data type for various
numpy.ndarray.ctypesattributes.Without the plugin, aforementioned type will default to
ctypes.c_int64.To enable the plugin, one must add it to their mypy configuration
file:
(gh-19062)
Add NEP 47-compatible dlpack support
Add a
ndarray.__dlpack__()method which returns adlpackC structurewrapped in a
PyCapsule. Also add anp._from_dlpack(obj)function,where
objsupports__dlpack__(), and returns anndarray.(gh-19083)
keepdimsoptional argument added tonumpy.argmin,numpy.argmaxkeepdimsargument is added tonumpy.argmin,numpy.argmax. If setto
True, the axes which are reduced are left in the result asdimensions with size one. The resulting array has the same number of
dimensions and will broadcast with the input array.
(gh-19211)
bit_countto compute the number of 1-bits in an integerComputes the number of 1-bits in the absolute value of the input. This
works on all the numpy integer types. Analogous to the builtin
int.bit_countorpopcountin C++.(gh-19355)
The
ndimandaxisattributes have been added tonumpy.AxisErrorThe
ndimandaxisparameters are now also stored as attributeswithin each
numpy.AxisErrorinstance.(gh-19459)
Preliminary support for
windows/arm64targetnumpyadded support for windows/arm64 target. Please noteOpenBLASsupport is not yet available for windows/arm64 target.
(gh-19513)
Added support for LoongArch
LoongArch is a new instruction set, numpy compilation failure on
LoongArch architecture, so add the commit.
(gh-19527)
A
.clang-formatfile has been addedClang-format is a C/C++ code formatter, together with the added
.clang-formatfile, it produces code close enough to the NumPyC_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_integeris now available tonumpy.floatingandnumpy.integerBased on its counterpart in Python
floatandint, the numpy floatingpoint and integer types now support
float.is_integer. ReturnsTrueif the number is finite with integral value, and
Falseotherwise.(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,dtypeandnumberare now runtime-subscriptableMimicking PEP-585, the
numpy.ndarray,numpy.dtypeandnumpy.numberclasses are now subscriptable forpython 3.9 and later. Consequently, expressions that were previously
only allowed in .pyi stub files or with the help of
from __future__ import annotationsare now also legal during runtime.(gh-19879)
Improvements
ctypeslib.load_librarycan now take any path-like objectAll parameters in the can now take any
python:path-like object{.interpreted-text role="term"}. This includesthe likes of strings, bytes and objects implementing the
__fspath__<os.PathLike.__fspath__>{.interpreted-text role="meth"}protocol.
(gh-17530)
Add
smallest_normalandsmallest_subnormalattributes tofinfoThe attributes
smallest_normalandsmallest_subnormalare availableas an extension of
finfoclass for any floating-point data type. Touse these new attributes, write
np.finfo(np.float64).smallest_normalor
np.finfo(np.float64).smallest_subnormal.(gh-18536)
numpy.linalg.qraccepts stacked matrices as inputsnumpy.linalg.qris able to produce results for stacked matrices asinputs. Moreover, the implementation of QR decomposition has been
shifted to C from Python.
(gh-19151)
numpy.fromregexnow acceptsos.PathLikeimplementationsnumpy.fromregexnow accepts objects implementing the__fspath__<os.PathLike>protocol, e.g.pathlib.Path.(gh-19680)
Add new methods for
quantileandpercentilequantileandpercentilenow have have amethod=keyword argumentsupporting 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.percentilefor more information.(gh-19857)
Missing parameters have been added to the
nan<x>functionsA number of the
nan<x>functions previously lacked parameters thatwere present in their
<x>-based counterpart, e.g. thewhereparameter was present in
numpy.meanbut absent fromnumpy.nanmean.The following parameters have now been added to the
nan<x>functions:initial&whereinitial&wherekeepdims&outkeepdims&outinitial&whereinitial&wherewherewherewhere(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.linalgandnumpy.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 forboth 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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v1.21.5Compare Source
NumPy 1.21.5 Release Notes
NumPy 1.21.5 is a maintenance release that fixes a few bugs discovered
after the 1.21.4 release and does some maintenance to extend the 1.21.x
lifetime. The Python versions supported in this release are 3.7-3.10. If
you want to compile your own version using gcc-11, you will need to use
gcc-11.2+ to avoid problems.
Contributors
A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 11 pull requests were merged for this release.
__(deep)copy__calls of_GenericAlias...npymathto respectnpy_longdoublenpy_load_moduleChecksums
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NumPy 1.21.4 Release Notes
The NumPy 1.21.4 is a maintenance release that fixes a few bugs
discovered after 1.21.3. The most important fix here is a fix for the
NumPy header files to make them work for both x86_64 and M1 hardware
when included in the Mac universal2 wheels. Previously, the header files
only worked for M1 and this caused problems for folks building x86_64
extensions. This problem was not seen before Python 3.10 because there
were thin wheels for x86_64 that had precedence. This release also
provides thin x86_64 Mac wheels for Python 3.10.
The Python versions supported in this release are 3.7-3.10. If you want
to compile your own version using gcc-11, you will need to use gcc-11.2+
to avoid problems.
Contributors
A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 9 pull requests were merged for this release.
dtypein type stubVOID_nonzerocould sometimes mutate alignment flagArrayteratortyping test failureChecksums
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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:
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.
Pull requests merged
A total of 8 pull requests were merged for this release.
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