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`](https://redirect.github.com/numpy/numpy/releases/tag/v1.22.0)
[Compare Source](https://redirect.github.com/numpy/numpy/compare/v1.21.6...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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/20000))
##### The `np.MachAr` class has been deprecated
The `numpy.MachAr` class and `finfo.machar ` attribute have
been deprecated. Users are encouraged to access the property if interest
directly from the corresponding `numpy.finfo` attribute.
([gh-20201](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/19479))
##### Removed floor division support for complex types
Floor division of complex types will now result in a `TypeError`
```{.python}
>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...
```
([gh-19135](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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': ['>> np.uint32(1023).bit_count()
10
>>> np.int32(-127).bit_count()
7
```
([gh-19355](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/19513))
##### Added support for LoongArch
LoongArch is a new instruction set, numpy compilation failure on
LoongArch architecture, so add the commit.
([gh-19527](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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.
```{.python}
>>> np.float32(-2.0).is_integer()
True
>>> np.float64(3.2).is_integer()
False
>>> np.int32(-2).is_integer()
True
```
([gh-19803](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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.
```{.python}
>>> import numpy as np
>>> from typing import Any
>>> np.ndarray[Any, np.dtype[np.float64]]
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
```
([gh-19879](https://redirect.github.com/numpy/numpy/pull/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__`{.interpreted-text role="meth"}
protocol.
([gh-17530](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/19151))
##### `numpy.fromregex` now accepts `os.PathLike` implementations
`numpy.fromregex` now accepts objects implementing the
`__fspath__` protocol, *e.g.* `pathlib.Path`.
([gh-19680](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/19857))
##### Missing parameters have been added to the `nan` functions
A number of the `nan` functions previously lacked parameters that
were present in their ``-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` 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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/19478))
##### OpenBLAS v0.3.18
Update the OpenBLAS used in testing and in wheels to v0.3.18
([gh-20058](https://redirect.github.com/numpy/numpy/pull/20058))
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### [`v1.21.6`](https://redirect.github.com/numpy/numpy/releases/tag/v1.21.6)
[Compare Source](https://redirect.github.com/numpy/numpy/compare/v1.21.5...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`](https://redirect.github.com/numpy/numpy/releases/tag/v1.21.5)
[Compare Source](https://redirect.github.com/numpy/numpy/compare/v1.21.4...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](https://redirect.github.com/numpy/numpy/pull/20357): MAINT: Do not forward `__(deep)copy__` calls of `_GenericAlias`...
- [#20462](https://redirect.github.com/numpy/numpy/pull/20462): BUG: Fix float16 einsum fastpaths using wrong tempvar
- [#20463](https://redirect.github.com/numpy/numpy/pull/20463): BUG, DIST: Print os error message when the executable not exist
- [#20464](https://redirect.github.com/numpy/numpy/pull/20464): BLD: Verify the ability to compile C++ sources before initiating...
- [#20465](https://redirect.github.com/numpy/numpy/pull/20465): BUG: Force `npymath` to respect `npy_longdouble`
- [#20466](https://redirect.github.com/numpy/numpy/pull/20466): BUG: Fix failure to create aligned, empty structured dtype
- [#20467](https://redirect.github.com/numpy/numpy/pull/20467): ENH: provide a convenience function to replace `npy_load_module`
- [#20495](https://redirect.github.com/numpy/numpy/pull/20495): MAINT: update wheel to version that supports python3.10
- [#20497](https://redirect.github.com/numpy/numpy/pull/20497): BUG: Clear errors correctly in F2PY conversions
- [#20613](https://redirect.github.com/numpy/numpy/pull/20613): DEV: add a warningfilter to fix pytest workflow.
- [#20618](https://redirect.github.com/numpy/numpy/pull/20618): MAINT: Help boost::python libraries at least not crash
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Configuration
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==1.21.4->==1.22.0GitHub Vulnerability Alerts
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`](https://redirect.github.com/numpy/numpy/releases/tag/v1.22.0) [Compare Source](https://redirect.github.com/numpy/numpy/compare/v1.21.6...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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/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](https://redirect.github.com/numpy/numpy/pull/20000)) ##### The `np.MachAr` class has been deprecated The `numpy.MachAr` class and `finfo.macharConfiguration
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