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chore(deps): update dependency numpy to v1.22.0 [security] #26

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@renovate renovate bot commented Aug 7, 2024

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This PR contains the following updates:

Package Change Age Adoption Passing Confidence
numpy (source, changelog) ==1.18.5 -> ==1.22.0 age adoption passing confidence

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Some dependencies could not be looked up. Check the Dependency Dashboard for more information.

GitHub Vulnerability Alerts

CVE-2021-33430

A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service.

NOTE: The vendor does not agree this is a vulnerability; In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.

CVE-2021-41496

Buffer overflow in the array_from_pyobj function of fortranobject.c in NumPy < 1.19, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values.

CVE-2021-41495

Null Pointer Dereference vulnerability exists in numpy.sort in NumPy &lt and 1.19 in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays.

CVE-2021-34141

Incomplete string comparison in the numpy.core component in NumPy1.9.x, which allows attackers to fail the APIs via constructing specific string objects.


Release Notes

numpy/numpy (numpy)

v1.22.0

Compare Source

NumPy 1.22.0 Release Notes

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

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

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

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

Expired deprecations

Deprecated numeric style dtype strings have been removed

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

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

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

(gh-19615)

Deprecations

Use delimiter rather than delimitor as kwarg in mrecords

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

(gh-19921)

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

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

(gh-20000)

The np.MachAr class has been deprecated

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

(gh-20201)

Compatibility notes

Distutils forces strict floating point model on clang

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

(gh-19479)

Removed floor division support for complex types

Floor division of complex types will now result in a TypeError

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

(gh-19135)

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

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

(gh-19356)

Python 3.7 is no longer supported

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

(gh-19665)

str/repr of complex dtypes now include space after punctuation

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

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

(gh-19687)

Corrected advance in PCG64DSXM and PCG64

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

(gh-20049)

Change in generation of random 32 bit floating point variates

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

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

(gh-20314)

C API changes

Masked inner-loops cannot be customized anymore

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

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

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

(gh-19259)

New Features

NEP 49 configurable allocators

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

(gh-17582)

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

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

(gh-18585)

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

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

(gh-18884)

Assign the platform-specific c_intp precision via a mypy plugin

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

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

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

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-19062)

Add NEP 47-compatible dlpack support

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

(gh-19083)

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

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

(gh-19211)

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

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

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

(gh-19355)

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

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

(gh-19459)

Preliminary support for windows/arm64 target

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

(gh-19513)

Added support for LoongArch

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

(gh-19527)

A .clang-format file has been added

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

(gh-19754)

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

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

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

(gh-19803)

Symbolic parser for Fortran dimension specifications

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

(gh-19805)

ndarray, dtype and number are now runtime-subscriptable

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

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

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

(gh-19879)

Improvements

ctypeslib.load_library can now take any path-like object

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

(gh-17530)

Add smallest_normal and smallest_subnormal attributes to finfo

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

(gh-18536)

numpy.linalg.qr accepts stacked matrices as inputs

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

(gh-19151)

numpy.fromregex now accepts os.PathLike implementations

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

(gh-19680)

Add new methods for quantile and percentile

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

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

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

(gh-19857)

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

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

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

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

(gh-20027)

Annotating the main Numpy namespace

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

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

(gh-20217)

Vectorize umath module using AVX-512

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

(gh-19478)

OpenBLAS v0.3.18

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

(gh-20058)

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v1.21.6

Compare Source

NumPy 1.21.6 Release Notes

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

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

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

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SHA256
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ecb55251139706669fdec2ff073c98ef8e9a84473e51e716211b41aa0f18e656  numpy-1.21.6.zip

v1.21.5

Compare Source

NumPy 1.21.5 Release Notes

NumPy 1.21.5 is a maintenance release that fixes a few bugs discovered
after the 1.21.4 release and does some maintenance to extend the 1.21.x
lifetime. The Python versions supported in this release are 3.7-3.10. If
you want to compile your own version using gcc-11, you will need to use
gcc-11.2+ to avoid problems.

Contributors

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

  • Bas van Beek
  • Charles Harris
  • Matti Picus
  • Rohit Goswami
  • Ross Barnowski
  • Sayed Adel
  • Sebastian Berg

Pull requests merged

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

  • #​20357: MAINT: Do not forward __(deep)copy__ calls of _GenericAlias...
  • #​20462: BUG: Fix float16 einsum fastpaths using wrong tempvar
  • #​20463: BUG, DIST: Print os error message when the executable not exist
  • #​20464: BLD: Verify the ability to compile C++ sources before initiating...
  • #​20465: BUG: Force npymath to respect npy_longdouble
  • #​20466: BUG: Fix failure to create aligned, empty structured dtype
  • #​20467: ENH: provide a convenience function to replace npy_load_module
  • #​20495: MAINT: update wheel to version that supports python3.10
  • #​20497: BUG: Clear errors correctly in F2PY conversions
  • #​20613: DEV: add a warningfilter to fix pytest workflow.
  • #​20618: MAINT: Help boost::python libraries at least not crash

Checksums

MD5
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SHA256
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v1.21.4

Compare Source

NumPy 1.21.4 Release Notes

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

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

Contributors

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

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

Pull requests merged

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

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

Checksums

MD5
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v1.21.3

Compare Source

NumPy 1.21.3 Release Notes

The NumPy 1.21.3 is a maintenance release the fixes a few bugs
discovered after 1.21.2. It also provides 64 bit Python 3.10.0 wheels.
Note a few oddities about Python 3.10:

  • There are no 32 bit wheels for Windows, Mac, or Linux.
  • The Mac Intel builds are only available in universal2 wheels.

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

Contributors

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

  • Aaron Meurer
  • Bas van Beek
  • Charles Harris
  • Developer-Ecosystem-Engineering +
  • Kevin Sheppard
  • Sebastian Berg
  • Warren Weckesser

Pull requests merged

A total of 8 pull requests were merged for this release.

  • #​19745: ENH: Add dtype-support to 3 `generic/ndarray methods
  • #​19955: BUG: Resolve Divide by Zero on Apple silicon + test failures...
  • #​19958: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases...
  • #​19994: BUG: np.tan(np.inf) test failure
  • #​20080: BUG: Correct incorrect advance in PCG with emulated int128
  • #​20081: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime...
  • #​20082: DOC: Ensure that we add documentation also as to the dict for...
  • #​20106: BUG: core: result_type(0, np.timedelta64(4)) would seg. fault.

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v1.21.2

Compare Source

NumPy 1.21.2 Release Notes

The NumPy 1.21.2 is maintenance release that fixes bugs discovered after
1.21.1. It also provides 64 bit manylinux Python 3.10.0rc1 wheels for
downstream testing. Note that Python 3.10 is not yet final. There is
also preliminary support for Windows on ARM64 builds, but there is no
OpenBLAS for that platform and no wheels are available.

The Python versions supported for this release are 3.7-3.9. The 1.21.x
series is compatible with Python 3.10.0rc1 and Python 3.10 will be
officially supported after it is released. T


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@renovate renovate bot enabled auto-merge (squash) August 7, 2024 21:34
@renovate renovate bot changed the title chore(deps): update dependency numpy to v1.22.0 [security] chore(deps): update dependency numpy to v1.22.0 [security] - autoclosed Aug 7, 2024
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