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Update numpy to 1.23.5 #817

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This PR updates numpy from 1.18.4 to 1.23.5.

Changelog

1.23.5

the 1.23.4 release and keeps the build infrastructure current. The
Python versions supported for this release are 3.8-3.11.

Contributors

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

-   \DWesl
-   Aayush Agrawal +
-   Adam Knapp +
-   Charles Harris
-   Navpreet Singh +
-   Sebastian Berg
-   Tania Allard

Pull requests merged

A total of 10 pull requests were merged for this release.

-   [22489](https://github.com/numpy/numpy/pull/22489): TST, MAINT: Replace most setup with setup_method (also teardown)
-   [22490](https://github.com/numpy/numpy/pull/22490): MAINT, CI: Switch to cygwin/cygwin-install-actionv2
-   [22494](https://github.com/numpy/numpy/pull/22494): TST: Make test_partial_iteration_cleanup robust but require leak\...
-   [22592](https://github.com/numpy/numpy/pull/22592): MAINT: Ensure graceful handling of large header sizes
-   [22593](https://github.com/numpy/numpy/pull/22593): TYP: Spelling alignment for array flag literal
-   [22594](https://github.com/numpy/numpy/pull/22594): BUG: Fix bounds checking for `random.logseries`
-   [22595](https://github.com/numpy/numpy/pull/22595): DEV: Update GH actions and Dockerfile for Gitpod
-   [22596](https://github.com/numpy/numpy/pull/22596): CI: Only fetch in actions/checkout
-   [22597](https://github.com/numpy/numpy/pull/22597): BUG: Decrement ref count in gentype_reduce if allocated memory\...
-   [22625](https://github.com/numpy/numpy/pull/22625): BUG: Histogramdd breaks on big arrays in Windows

Checksums

MD5

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1.23.4

the 1.23.3 release and keeps the build infrastructure current. The main
improvements are fixes for some annotation corner cases, a fix for a
long time `nested_iters` memory leak, and a fix of complex vector dot
for very large arrays. The Python versions supported for this release
are 3.8-3.11.

Note that the mypy version needs to be 0.981+ if you test using Python
3.10.7, otherwise the typing tests will fail.

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
-   Matthew Barber
-   Matti Picus
-   Ralf Gommers
-   Ross Barnowski
-   Sebastian Berg
-   Sicheng Zeng +

Pull requests merged

A total of 13 pull requests were merged for this release.

-   [22368](https://github.com/numpy/numpy/pull/22368): BUG: Add `__array_api_version__` to `numpy.array_api` namespace
-   [22370](https://github.com/numpy/numpy/pull/22370): MAINT: update sde toolkit to 9.0, fix download link
-   [22382](https://github.com/numpy/numpy/pull/22382): BLD: use macos-11 image on azure, macos-1015 is deprecated
-   [22383](https://github.com/numpy/numpy/pull/22383): MAINT: random: remove `get_info` from \"extending with Cython\"\...
-   [22384](https://github.com/numpy/numpy/pull/22384): BUG: Fix complex vector dot with more than NPY_CBLAS_CHUNK elements
-   [22387](https://github.com/numpy/numpy/pull/22387): REV: Loosen `lookfor`\'s import try/except again
-   [22388](https://github.com/numpy/numpy/pull/22388): TYP,ENH: Mark `numpy.typing` protocols as runtime checkable
-   [22389](https://github.com/numpy/numpy/pull/22389): TYP,MAINT: Change more overloads to play nice with pyright
-   [22390](https://github.com/numpy/numpy/pull/22390): TST,TYP: Bump mypy to 0.981
-   [22391](https://github.com/numpy/numpy/pull/22391): DOC: Update delimiter param description.
-   [22392](https://github.com/numpy/numpy/pull/22392): BUG: Memory leaks in numpy.nested_iters
-   [22413](https://github.com/numpy/numpy/pull/22413): REL: Prepare for the NumPy 1.23.4 release.
-   [22424](https://github.com/numpy/numpy/pull/22424): TST: Fix failing aarch64 wheel builds.

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1.23.3

the 1.23.2 release. There is no major theme for this release, the main
improvements are for some downstream builds and some annotation corner
cases. The Python versions supported for this release are 3.8-3.11.

Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the

1.23.2

the 1.23.1 release. Notable features are:

-   Typing changes needed for Python 3.11
-   Wheels for Python 3.11.0rc1

The Python versions supported for this release are 3.8-3.11.

Contributors

A total of 9 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Alexander Grund +
-   Bas van Beek
-   Charles Harris
-   Jon Cusick +
-   Matti Picus
-   Michael Osthege +
-   Pal Barta +
-   Ross Barnowski
-   Sebastian Berg

Pull requests merged

A total of 15 pull requests were merged for this release.

-   [22030](https://github.com/numpy/numpy/pull/22030): ENH: Add `__array_ufunc__` typing support to the `nin=1` ufuncs
-   [22031](https://github.com/numpy/numpy/pull/22031): MAINT, TYP: Fix `np.angle` dtype-overloads
-   [22032](https://github.com/numpy/numpy/pull/22032): MAINT: Do not let `_GenericAlias` wrap the underlying classes\'\...
-   [22033](https://github.com/numpy/numpy/pull/22033): TYP,MAINT: Allow `einsum` subscripts to be passed via integer\...
-   [22034](https://github.com/numpy/numpy/pull/22034): MAINT,TYP: Add object-overloads for the `np.generic` rich comparisons
-   [22035](https://github.com/numpy/numpy/pull/22035): MAINT,TYP: Allow the `squeeze` and `transpose` method to\...
-   [22036](https://github.com/numpy/numpy/pull/22036): BUG: Fix subarray to object cast ownership details
-   [22037](https://github.com/numpy/numpy/pull/22037): BUG: Use `Popen` to silently invoke f77 -v
-   [22038](https://github.com/numpy/numpy/pull/22038): BUG: Avoid errors on NULL during deepcopy
-   [22039](https://github.com/numpy/numpy/pull/22039): DOC: Add versionchanged for converter callable behavior.
-   [22057](https://github.com/numpy/numpy/pull/22057): MAINT: Quiet the anaconda uploads.
-   [22078](https://github.com/numpy/numpy/pull/22078): ENH: reorder includes for testing on top of system installations\...
-   [22106](https://github.com/numpy/numpy/pull/22106): TST: fix test_linear_interpolation_formula_symmetric
-   [22107](https://github.com/numpy/numpy/pull/22107): BUG: Fix skip condition for test_loss_of_precision\[complex256\]
-   [22115](https://github.com/numpy/numpy/pull/22115): BLD: Build python3.11.0rc1 wheels.

Checksums

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SHA256

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1.23.1

The NumPy 1.23.1 is a maintenance release that fixes bugs discovered
after the 1.23.0 release. Notable fixes are:

-   Fix searchsorted for float16 NaNs
-   Fix compilation on Apple M1
-   Fix KeyError in crackfortran operator support (Slycot)

The Python version supported for this release are 3.8-3.10.

Contributors

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

-   Charles Harris
-   Matthias Koeppe +
-   Pranab Das +
-   Rohit Goswami
-   Sebastian Berg
-   Serge Guelton
-   Srimukh Sripada +

Pull requests merged

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

-   [21866](https://github.com/numpy/numpy/pull/21866): BUG: Fix discovered MachAr (still used within valgrind)
-   [21867](https://github.com/numpy/numpy/pull/21867): BUG: Handle NaNs correctly for float16 during sorting
-   [21868](https://github.com/numpy/numpy/pull/21868): BUG: Use `keepdims` during normalization in `np.average` and\...
-   [21869](https://github.com/numpy/numpy/pull/21869): DOC: mention changes to `max_rows` behaviour in `np.loadtxt`
-   [21870](https://github.com/numpy/numpy/pull/21870): BUG: Reject non integer array-likes with size 1 in delete
-   [21949](https://github.com/numpy/numpy/pull/21949): BLD: Make can_link_svml return False for 32bit builds on x86_64
-   [21951](https://github.com/numpy/numpy/pull/21951): BUG: Reorder extern \"C\" to only apply to function declarations\...
-   [21952](https://github.com/numpy/numpy/pull/21952): BUG: Fix KeyError in crackfortran operator support

Checksums

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SHA256

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1.23.0

The NumPy 1.23.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, clarify
the documentation, and expire old deprecations. The highlights are:

-   Implementation of `loadtxt` in C, greatly improving its performance.
-   Exposing DLPack at the Python level for easy data exchange.
-   Changes to the promotion and comparisons of structured dtypes.
-   Improvements to f2py.

See below for the details,

New functions

-   A masked array specialization of `ndenumerate` is now available as
 `numpy.ma.ndenumerate`. It provides an alternative to
 `numpy.ndenumerate` and skips masked values by default.

 ([gh-20020](https://github.com/numpy/numpy/pull/20020))

-   `numpy.from_dlpack` has been added to allow easy exchange of data
 using the DLPack protocol. It accepts Python objects that implement
 the `__dlpack__` and `__dlpack_device__` methods and returns a
 ndarray object which is generally the view of the data of the input
 object.

 ([gh-21145](https://github.com/numpy/numpy/pull/21145))

Deprecations

-   Setting `__array_finalize__` to `None` is deprecated. It must now be
 a method and may wish to call `super().__array_finalize__(obj)`
 after checking for `None` or if the NumPy version is sufficiently
 new.

 ([gh-20766](https://github.com/numpy/numpy/pull/20766))

-   Using `axis=32` (`axis=np.MAXDIMS`) in many cases had the same
 meaning as `axis=None`. This is deprecated and `axis=None` must be
 used instead.

 ([gh-20920](https://github.com/numpy/numpy/pull/20920))

-   The hook function `PyDataMem_SetEventHook` has been deprecated and
 the demonstration of its use in tool/allocation_tracking has been
 removed. The ability to track allocations is now built-in to python
 via `tracemalloc`.

 ([gh-20394](https://github.com/numpy/numpy/pull/20394))

-   `numpy.distutils` has been deprecated, as a result of `distutils`
 itself being deprecated. It will not be present in NumPy for
 Python >= 3.12, and will be removed completely 2 years after the
 release of Python 3.12 For more details, see
 `distutils-status-migration`{.interpreted-text role="ref"}.

 ([gh-20875](https://github.com/numpy/numpy/pull/20875))

Expired deprecations

-   The `NpzFile.iteritems()` and `NpzFile.iterkeys()` methods have been
 removed as part of the continued removal of Python 2 compatibility.
 This concludes the deprecation from 1.15.

 ([gh-16830](https://github.com/numpy/numpy/pull/16830))

-   The `alen` and `asscalar` functions have been removed.

 ([gh-20414](https://github.com/numpy/numpy/pull/20414))

-   The `UPDATEIFCOPY` array flag has been removed together with the
 enum `NPY_ARRAY_UPDATEIFCOPY`. The associated (and deprecated)
 `PyArray_XDECREF_ERR` was also removed. These were all deprecated in
 1.14. They are replaced by `WRITEBACKIFCOPY`, that requires calling
 `PyArray_ResoveWritebackIfCopy` before the array is deallocated.

 ([gh-20589](https://github.com/numpy/numpy/pull/20589))

-   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. This behaviour was deprecated in 1.21, and the exception
 will now be raised.

 ([gh-20835](https://github.com/numpy/numpy/pull/20835))

-   Multidimensional indexing with non-tuple values is not allowed.
 Previously, code such as `arr[ind]` where `ind = [[0, 1], [0, 1]]`
 produced a `FutureWarning` and was interpreted as a multidimensional
 index (i.e., `arr[tuple(ind)]`). Now this example is treated like an
 array index over a single dimension (`arr[array(ind)]`).
 Multidimensional indexing with anything but a tuple was deprecated
 in NumPy 1.15.

 ([gh-21029](https://github.com/numpy/numpy/pull/21029))

-   Changing to a dtype of different size in F-contiguous arrays is no
 longer permitted. Deprecated since Numpy 1.11.0. See below for an
 extended explanation of the effects of this change.

 ([gh-20722](https://github.com/numpy/numpy/pull/20722))

New Features

crackfortran has support for operator and assignment overloading

`crackfortran` parser now understands operator and assignment
definitions in a module. They are added in the `body` list of the module
which contains a new key `implementedby` listing the names of the
subroutines or functions implementing the operator or assignment.

([gh-15006](https://github.com/numpy/numpy/pull/15006))

f2py supports reading access type attributes from derived type statements

As a result, one does not need to use `public` or `private` statements
to specify derived type access properties.

([gh-15844](https://github.com/numpy/numpy/pull/15844))

New parameter `ndmin` added to `genfromtxt`

This parameter behaves the same as `ndmin` from `numpy.loadtxt`.

([gh-20500](https://github.com/numpy/numpy/pull/20500))

`np.loadtxt` now supports quote character and single converter function

`numpy.loadtxt` now supports an additional `quotechar` keyword argument
which is not set by default. Using `quotechar='"'` will read quoted
fields as used by the Excel CSV dialect.

Further, it is now possible to pass a single callable rather than a
dictionary for the `converters` argument.

([gh-20580](https://github.com/numpy/numpy/pull/20580))

Changing to dtype of a different size now requires contiguity of only the last axis

Previously, viewing an array with a dtype of a different item size
required that the entire array be C-contiguous. This limitation would
unnecessarily force the user to make contiguous copies of non-contiguous
arrays before being able to change the dtype.

This change affects not only `ndarray.view`, but other construction
mechanisms, including the discouraged direct assignment to
`ndarray.dtype`.

This change expires the deprecation regarding the viewing of
F-contiguous arrays, described elsewhere in the release notes.

([gh-20722](https://github.com/numpy/numpy/pull/20722))

Deterministic output files for F2PY

For F77 inputs, `f2py` will generate `modname-f2pywrappers.f`
unconditionally, though these may be empty. For free-form inputs,
`modname-f2pywrappers.f`, `modname-f2pywrappers2.f90` will both be
generated unconditionally, and may be empty. This allows writing generic
output rules in `cmake` or `meson` and other build systems. Older
behavior can be restored by passing `--skip-empty-wrappers` to `f2py`.
`f2py-meson`{.interpreted-text role="ref"} details usage.

([gh-21187](https://github.com/numpy/numpy/pull/21187))

`keepdims` parameter for `average`

The parameter `keepdims` was added to the functions `numpy.average` and
`numpy.ma.average`. The parameter has the same meaning as it does in
reduction functions such as `numpy.sum` or `numpy.mean`.

([gh-21485](https://github.com/numpy/numpy/pull/21485))

Compatibility notes

1D `np.linalg.norm` preserves float input types, even for scalar results

Previously, this would promote to `float64` when the `ord` argument was
not one of the explicitly listed values, e.g. `ord=3`:

 >>> f32 = np.float32([1, 2])
 >>> np.linalg.norm(f32, 2).dtype
 dtype('float32')
 >>> np.linalg.norm(f32, 3)
 dtype('float64')   numpy 1.22
 dtype('float32')   numpy 1.23

This change affects only `float32` and `float16` vectors with `ord`
other than `-Inf`, `0`, `1`, `2`, and `Inf`.

([gh-17709](https://github.com/numpy/numpy/pull/17709))

Changes to structured (void) dtype promotion and comparisons

In general, NumPy now defines correct, but slightly limited, promotion
for structured dtypes by promoting the subtypes of each field instead of
raising an exception:

 >>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
 dtype([('f0', '<i4'), ('f1', '<f8')])

For promotion matching field names, order, and titles are enforced,
however padding is ignored. Promotion involving structured dtypes now
always ensures native byte-order for all fields (which may change the
result of `np.concatenate`) and ensures that the result will be
\"packed\", i.e. all fields are ordered contiguously and padding is
removed. See
`structured_dtype_comparison_and_promotion`{.interpreted-text
role="ref"} for further details.

The `repr` of aligned structures will now never print the long form
including `offsets` and `itemsize` unless the structure includes padding
not guaranteed by `align=True`.

In alignment with the above changes to the promotion logic, the casting
safety has been updated:

-   `"equiv"` enforces matching names and titles. The itemsize is
 allowed to differ due to padding.
-   `"safe"` allows mismatching field names and titles
-   The cast safety is limited by the cast safety of each included
 field.
-   The order of fields is used to decide cast safety of each individual
 field. Previously, the field names were used and only unsafe casts
 were possible when names mismatched.

The main important change here is that name mismatches are now
considered \"safe\" casts.

([gh-19226](https://github.com/numpy/numpy/pull/19226))

`NPY_RELAXED_STRIDES_CHECKING` has been removed

NumPy cannot be compiled with `NPY_RELAXED_STRIDES_CHECKING=0` anymore.
Relaxed strides have been the default for many years and the option was
initially introduced to allow a smoother transition.

([gh-20220](https://github.com/numpy/numpy/pull/20220))

`np.loadtxt` has recieved several changes

The row counting of `numpy.loadtxt` was fixed. `loadtxt` ignores fully
empty lines in the file, but counted them towards `max_rows`. When
`max_rows` is used and the file contains empty lines, these will now not
be counted. Previously, it was possible that the result contained fewer
than `max_rows` rows even though more data was available to be read. If
the old behaviour is required, `itertools.islice` may be used:

 import itertools
 lines = itertools.islice(open("file"), 0, max_rows)
 result = np.loadtxt(lines, ...)

While generally much faster and improved, `numpy.loadtxt` may now fail
to converter certain strings to numbers that were previously
successfully read. The most important cases for this are:

-   Parsing floating point values such as `1.0` into integers will now
 fail
-   Parsing hexadecimal floats such as `0x3p3` will fail
-   An `_` was previously accepted as a thousands delimiter `100_000`.
 This will now result in an error.

If you experience these limitations, they can all be worked around by
passing appropriate `converters=`. NumPy now supports passing a single
converter to be used for all columns to make this more convenient. For
example, `converters=float.fromhex` can read hexadecimal float numbers
and `converters=int` will be able to read `100_000`.

Further, the error messages have been generally improved. However, this
means that error types may differ. In particularly, a `ValueError` is
now always raised when parsing of a single entry fails.

([gh-20580](https://github.com/numpy/numpy/pull/20580))

Improvements

`ndarray.__array_finalize__` is now callable

This means subclasses can now use `super().__array_finalize__(obj)`
without worrying whether `ndarray` is their superclass or not. The
actual call remains a no-op.

([gh-20766](https://github.com/numpy/numpy/pull/20766))

Add support for VSX4/Power10

With VSX4/Power10 enablement, the new instructions available in Power
ISA 3.1 can be used to accelerate some NumPy operations, e.g.,
floor_divide, modulo, etc.

([gh-20821](https://github.com/numpy/numpy/pull/20821))

`np.fromiter` now accepts objects and subarrays

The `numpy.fromiter` function now supports object and subarray dtypes.
Please see he function documentation for examples.

([gh-20993](https://github.com/numpy/numpy/pull/20993))

Math C library feature detection now uses correct signatures

Compiling is preceded by a detection phase to determine whether the
underlying libc supports certain math operations. Previously this code
did not respect the proper signatures. Fixing this enables compilation
for the `wasm-ld` backend (compilation for web assembly) and reduces the
number of warnings.

([gh-21154](https://github.com/numpy/numpy/pull/21154))

`np.kron` now maintains subclass information

`np.kron` maintains subclass information now such as masked arrays while
computing the Kronecker product of the inputs

python
>>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
>>> np.kron(x,x)
masked_array(
data=[[1, --, --, --],
     [--, 4, --, --],
     [--, --, 4, --],
     [--, --, --, 16]],
mask=[[False,  True,  True,  True],
     [ True, False,  True,  True],
     [ True,  True, False,  True],
     [ True,  True,  True, False]],
fill_value=999999)


:warning: Warning, `np.kron` output now follows `ufunc` ordering (`multiply`) to determine
the output class type

python
>>> class myarr(np.ndarray):
>>>    __array_priority__ = -1
>>> a = np.ones([2, 2])
>>> ma = myarray(a.shape, a.dtype, a.data)
>>> type(np.kron(a, ma)) == np.ndarray
False  Before it was True
>>> type(np.kron(a, ma)) == myarr
True


([gh-21262](https://github.com/numpy/numpy/pull/21262))

Performance improvements and changes

Faster `np.loadtxt`

`numpy.loadtxt` is now generally much faster than previously as most of
it is now implemented in C.

([gh-20580](https://github.com/numpy/numpy/pull/20580))

Faster reduction operators

Reduction operations like `numpy.sum`, `numpy.prod`, `numpy.add.reduce`,
`numpy.logical_and.reduce` on contiguous integer-based arrays are now
much faster.

([gh-21001](https://github.com/numpy/numpy/pull/21001))

Faster `np.where`

`numpy.where` is now much faster than previously on unpredictable/random
input data.

([gh-21130](https://github.com/numpy/numpy/pull/21130))

Faster operations on NumPy scalars

Many operations on NumPy scalars are now significantly faster, although
rare operations (e.g. with 0-D arrays rather than scalars) may be slower
in some cases. However, even with these improvements users who want the
best performance for their scalars, may want to convert a known NumPy
scalar into a Python one using `scalar.item()`.

([gh-21188](https://github.com/numpy/numpy/pull/21188))

Faster `np.kron`

`numpy.kron` is about 80% faster as the product is now computed using
broadcasting.

([gh-21354](https://github.com/numpy/numpy/pull/21354))

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1.22.4

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](https://github.com/numpy/numpy/issues/21008).

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](https://github.com/numpy/numpy/pull/21191): TYP, BUG: Fix `np.lib.stride_tricks` re-exported under the\...
-   [21192](https://github.com/numpy/numpy/pull/21192): TST: Bump mypy from 0.931 to 0.940
-   [21243](https://github.com/numpy/numpy/pull/21243): MAINT: Explicitly re-export the types in `numpy._typing`
-   [21245](https://github.com/numpy/numpy/pull/21245): MAINT: Specify sphinx, numpydoc versions for CI doc builds
-   [21275](https://github.com/numpy/numpy/pull/21275): BUG: Fix typos
-   [21277](https://github.com/numpy/numpy/pull/21277): ENH, BLD: Fix math feature detection for wasm
-   [21350](https://github.com/numpy/numpy/pull/21350): MAINT: Fix failing simd and cygwin tests.
-   [21438](https://github.com/numpy/numpy/pull/21438): MAINT: Fix failing Python 3.8 32-bit Windows test.
-   [21444](https://github.com/numpy/numpy/pull/21444): BUG: add linux guard per #21386
-   [21445](https://github.com/numpy/numpy/pull/21445): BUG: Allow legacy dtypes to cast to datetime again
-   [21446](https://github.com/numpy/numpy/pull/21446): BUG: Make mmap handling safer in frombuffer
-   [21447](https://github.com/numpy/numpy/pull/21447): BUG: Stop using PyBytesObject.ob_shash deprecated in Python 3.11.
-   [21448](https://github.com/numpy/numpy/pull/21448): ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS
-   [21472](https://github.com/numpy/numpy/pull/21472): BUG: Ensure compile errors are raised correclty
-   [21473](https://github.com/numpy/numpy/pull/21473): BUG: Fix segmentation fault
-   [21474](https://github.com/numpy/numpy/pull/21474): MAINT: Update doc requirements
-   [21475](https://github.com/numpy/numpy/pull/21475): MAINT: Mark `npy_memchr` with `no_sanitize("alignment")` on clang
-   [21512](https://github.com/numpy/numpy/pull/21512): DOC: Proposal - make the doc landing page cards more similar\...
-   [21525](https://github.com/numpy/numpy/pull/21525): MAINT: Update Cython version to 0.29.30.
-   [21536](https://github.com/numpy/numpy/pull/21536): BUG: Fix GCC error during build configuration
-   [21541](https://github.com/numpy/numpy/pull/21541): REL: Prepare for the NumPy 1.22.4 release.
-   [21547](https://github.com/numpy/numpy/pull/21547): MAINT: Skip tests that fail on PyPy.

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1.22.3

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](https://github.com/numpy/numpy/pull/21048): MAINT: Use \"3.10\" instead of \"3.10-dev\" on travis.
-   [21106](https://github.com/numpy/numpy/pull/21106): TYP,MAINT: Explicitly allow sequences of array-likes in `np.concatenate`
-   [21137](https://github.com/numpy/numpy/pull/21137): BLD,DOC: skip broken ipython 8.1.0
-   [21138](https://github.com/numpy/numpy/pull/21138): BUG, ENH: np.\_from_dlpack: export correct device information
-   [21139](https://github.com/numpy/numpy/pull/21139): BUG: Fix numba DUFuncs added loops getting picked up
-   [21140](https://github.com/numpy/numpy/pull/21140): BUG: Fix unpickling an empty ndarray with a non-zero dimension\...
-   [21141](https://github.com/numpy/numpy/pull/21141): BUG: use ThreadPoolExecutor instead of ThreadPool
-   [21142](https://github.com/numpy/numpy/pull/21142): API: Disallow strings in logical ufuncs
-   [21143](https://github.com/numpy/numpy/pull/21143): MAINT, DOC: Fix SciPy intersphinx link
-   [21148](https://github.com/numpy/numpy/pull/21148): BUG,ENH: np.\_from_dlpack: export arrays with any strided size-1\...

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1.22.2

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
-   Mukuli

@pyup-bot pyup-bot mentioned this pull request Nov 20, 2022
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Closing this in favor of #830

@pyup-bot pyup-bot closed this Dec 18, 2022
@postrational postrational deleted the pyup-update-numpy-1.18.4-to-1.23.5 branch December 18, 2022 22:11
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