Update numpy to 1.24.0 #830

pyup-bot · 2022-12-18T22:11:49Z

This PR updates numpy from 1.18.4 to 1.24.0.

Changelog

1.24

The NumPy 1.24.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, and
clarify the documentation. There are also a large number of new and
expired deprecations due to changes in promotion and cleanups. This
might be called a deprecation release. Highlights are

-   Many new deprecations, check them out.
-   Many expired deprecations,
-   New F2PY features and fixes.
-   New \&quot;dtype\&quot; and \&quot;casting\&quot; keywords for stacking functions.

See below for the details,

Deprecations

Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose

The `numpy.fastCopyAndTranspose` function has been deprecated. Use the
corresponding copy and transpose methods directly:

 arr.T.copy()

The underlying C function `PyArray_CopyAndTranspose` has also been
deprecated from the NumPy C-API.

([gh-22313](https://github.com/numpy/numpy/pull/22313))

Conversion of out-of-bound Python integers

Attempting a conversion from a Python integer to a NumPy value will now
always check whether the result can be represented by NumPy. This means
the following examples will fail in the future and give a
`DeprecationWarning` now:

 np.uint8(-1)
 np.array([3000], dtype=np.int8)

Many of these did succeed before. Such code was mainly useful for
unsigned integers with negative values such as `np.uint8(-1)` giving
`np.iinfo(np.uint8).max`.

Note that conversion between NumPy integers is unaffected, so that
`np.array(-1).astype(np.uint8)` continues to work and use C integer
overflow logic.

([gh-22393](https://github.com/numpy/numpy/pull/22393))

Deprecate `msort`

The `numpy.msort` function is deprecated. Use `np.sort(a, axis=0)`
instead.

([gh-22456](https://github.com/numpy/numpy/pull/22456))

`np.str0` and similar are now deprecated

The scalar type aliases ending in a 0 bit size: `np.object0`, `np.str0`,
`np.bytes0`, `np.void0`, `np.int0`, `np.uint0` as well as `np.bool8` are
now deprecated and will eventually be removed.

([gh-22607](https://github.com/numpy/numpy/pull/22607))

Expired deprecations

-   The `normed` keyword argument has been removed from
 [np.histogram]{.title-ref}, [np.histogram2d]{.title-ref}, and
 [np.histogramdd]{.title-ref}. Use `density` instead. If `normed` was
 passed by position, `density` is now used.

 ([gh-21645](https://github.com/numpy/numpy/pull/21645))

-   Ragged array creation will now always raise a `ValueError` unless
 `dtype=object` is passed. This includes very deeply nested
 sequences.

 ([gh-22004](https://github.com/numpy/numpy/pull/22004))

-   Support for Visual Studio 2015 and earlier has been removed.

-   Support for the Windows Interix POSIX interop layer has been
 removed.

 ([gh-22139](https://github.com/numpy/numpy/pull/22139))

-   Support for cygwin \&lt; 3.3 has been removed.

 ([gh-22159](https://github.com/numpy/numpy/pull/22159))

-   The mini() method of `np.ma.MaskedArray` has been removed. Use
 either `np.ma.MaskedArray.min()` or `np.ma.minimum.reduce()`.

-   The single-argument form of `np.ma.minimum` and `np.ma.maximum` has
 been removed. Use `np.ma.minimum.reduce()` or
 `np.ma.maximum.reduce()` instead.

 ([gh-22228](https://github.com/numpy/numpy/pull/22228))

-   Passing dtype instances other than the canonical (mainly native
 byte-order) ones to `dtype=` or `signature=` in ufuncs will now
 raise a `TypeError`. We recommend passing the strings `&quot;int8&quot;` or
 scalar types `np.int8` since the byte-order, datetime/timedelta
 unit, etc. are never enforced. (Initially deprecated in NumPy 1.21.)

 ([gh-22540](https://github.com/numpy/numpy/pull/22540))

-   The `dtype=` argument to comparison ufuncs is now applied correctly.
 That means that only `bool` and `object` are valid values and
 `dtype=object` is enforced.

 ([gh-22541](https://github.com/numpy/numpy/pull/22541))

-   The deprecation for the aliases `np.object`, `np.bool`, `np.float`,
 `np.complex`, `np.str`, and `np.int` is expired (introduces NumPy
 1.20). Some of these will now give a FutureWarning in addition to
 raising an error since they will be mapped to the NumPy scalars in
 the future.

 ([gh-22607](https://github.com/numpy/numpy/pull/22607))

Compatibility notes

`array.fill(scalar)` may behave slightly different

`numpy.ndarray.fill` may in some cases behave slightly different now due
to the fact that the logic is aligned with item assignment:

 arr = np.array([1])   with any dtype/value
 arr.fill(scalar)
  is now identical to:
 arr[0] = scalar

Previously casting may have produced slightly different answers when
using values that could not be represented in the target `dtype` or when
the target had `object` dtype.

([gh-20924](https://github.com/numpy/numpy/pull/20924))

Subarray to object cast now copies

Casting a dtype that includes a subarray to an object will now ensure a
copy of the subarray. Previously an unsafe view was returned:

 arr = np.ones(3, dtype=[(&quot;f&quot;, &quot;i&quot;, 3)])
 subarray_fields = arr.astype(object)[0]
 subarray = subarray_fields[0]   &quot;f&quot; field

 np.may_share_memory(subarray, arr)

Is now always false. While previously it was true for the specific cast.

([gh-21925](https://github.com/numpy/numpy/pull/21925))

Returned arrays respect uniqueness of dtype kwarg objects

When the `dtype` keyword argument is used with
:py`np.array()`{.interpreted-text role=&quot;func&quot;} or
:py`asarray()`{.interpreted-text role=&quot;func&quot;}, the dtype of the returned
array now always exactly matches the dtype provided by the caller.

In some cases this change means that a *view* rather than the input
array is returned. The following is an example for this on 64bit Linux
where `long` and `longlong` are the same precision but different
`dtypes`:

 &gt;&gt;&gt; arr = np.array([1, 2, 3], dtype=&quot;long&quot;)
 &gt;&gt;&gt; new_dtype = np.dtype(&quot;longlong&quot;)
 &gt;&gt;&gt; new = np.asarray(arr, dtype=new_dtype)
 &gt;&gt;&gt; new.dtype is new_dtype
 True
 &gt;&gt;&gt; new is arr
 False

Before the change, the `dtype` did not match because `new is arr` was
`True`.

([gh-21995](https://github.com/numpy/numpy/pull/21995))

DLPack export raises `BufferError`

When an array buffer cannot be exported via DLPack a `BufferError` is
now always raised where previously `TypeError` or `RuntimeError` was
raised. This allows falling back to the buffer protocol or
`__array_interface__` when DLPack was tried first.

([gh-22542](https://github.com/numpy/numpy/pull/22542))

NumPy builds are no longer tested on GCC-6

Ubuntu 18.04 is deprecated for GitHub actions and GCC-6 is not available
on Ubuntu 20.04, so builds using that compiler are no longer tested. We
still test builds using GCC-7 and GCC-8.

([gh-22598](https://github.com/numpy/numpy/pull/22598))

New Features

New attribute `symbol` added to polynomial classes

The polynomial classes in the `numpy.polynomial` package have a new
`symbol` attribute which is used to represent the indeterminate of the
polynomial. This can be used to change the value of the variable when
printing:

 &gt;&gt;&gt; P_y = np.polynomial.Polynomial([1, 0, -1], symbol=&quot;y&quot;)
 &gt;&gt;&gt; print(P_y)
 1.0 + 0.0·y¹ - 1.0·y²

Note that the polynomial classes only support 1D polynomials, so
operations that involve polynomials with different symbols are
disallowed when the result would be multivariate:

 &gt;&gt;&gt; P = np.polynomial.Polynomial([1, -1])   default symbol is &quot;x&quot;
 &gt;&gt;&gt; P_z = np.polynomial.Polynomial([1, 1], symbol=&quot;z&quot;)
 &gt;&gt;&gt; P * P_z
 Traceback (most recent call last)
    ...
 ValueError: Polynomial symbols differ

The symbol can be any valid Python identifier. The default is
`symbol=x`, consistent with existing behavior.

([gh-16154](https://github.com/numpy/numpy/pull/16154))

F2PY support for Fortran `character` strings

F2PY now supports wrapping Fortran functions with:

-   character (e.g. `character x`)
-   character array (e.g. `character, dimension(n) :: x`)
-   character string (e.g. `character(len=10) x`)
-   and character string array (e.g.
 `character(len=10), dimension(n, m) :: x`)

arguments, including passing Python unicode strings as Fortran character
string arguments.

([gh-19388](https://github.com/numpy/numpy/pull/19388))

New function `np.show_runtime`

A new function `numpy.show_runtime` has been added to display the
runtime information of the machine in addition to `numpy.show_config`
which displays the build-related information.

([gh-21468](https://github.com/numpy/numpy/pull/21468))

`strict` option for `testing.assert_array_equal`

The `strict` option is now available for `testing.assert_array_equal`.
Setting `strict=True` will disable the broadcasting behaviour for
scalars and ensure that input arrays have the same data type.

([gh-21595](https://github.com/numpy/numpy/pull/21595))

New parameter `equal_nan` added to `np.unique`

`np.unique` was changed in 1.21 to treat all `NaN` values as equal and
return a single `NaN`. Setting `equal_nan=False` will restore pre-1.21
behavior to treat `NaNs` as unique. Defaults to `True`.

([gh-21623](https://github.com/numpy/numpy/pull/21623))

`casting` and `dtype` keyword arguments for `numpy.stack`

The `casting` and `dtype` keyword arguments are now available for
`numpy.stack`. To use them, write
`np.stack(..., dtype=None, casting=&#x27;same_kind&#x27;)`.

`casting` and `dtype` keyword arguments for `numpy.vstack`

The `casting` and `dtype` keyword arguments are now available for
`numpy.vstack`. To use them, write
`np.vstack(..., dtype=None, casting=&#x27;same_kind&#x27;)`.

`casting` and `dtype` keyword arguments for `numpy.hstack`

The `casting` and `dtype` keyword arguments are now available for
`numpy.hstack`. To use them, write
`np.hstack(..., dtype=None, casting=&#x27;same_kind&#x27;)`.

([gh-21627](https://github.com/numpy/numpy/pull/21627))

The bit generator underlying the singleton RandomState can be changed

The singleton `RandomState` instance exposed in the `numpy.random`
module is initialized at startup with the `MT19937` bit generator. The
new function `set_bit_generator` allows the default bit generator to be
replaced with a user-provided bit generator. This function has been
introduced to provide a method allowing seamless integration of a
high-quality, modern bit generator in new code with existing code that
makes use of the singleton-provided random variate generating functions.
The companion function `get_bit_generator` returns the current bit
generator being used by the singleton `RandomState`. This is provided to
simplify restoring the original source of randomness if required.

The preferred method to generate reproducible random numbers is to use a
modern bit generator in an instance of `Generator`. The function
`default_rng` simplifies instantiation:

 &gt;&gt;&gt; rg = np.random.default_rng(3728973198)
 &gt;&gt;&gt; rg.random()

The same bit generator can then be shared with the singleton instance so
that calling functions in the `random` module will use the same bit
generator:

 &gt;&gt;&gt; orig_bit_gen = np.random.get_bit_generator()
 &gt;&gt;&gt; np.random.set_bit_generator(rg.bit_generator)
 &gt;&gt;&gt; np.random.normal()

The swap is permanent (until reversed) and so any call to functions in
the `random` module will use the new bit generator. The original can be
restored if required for code to run correctly:

 &gt;&gt;&gt; np.random.set_bit_generator(orig_bit_gen)

([gh-21976](https://github.com/numpy/numpy/pull/21976))

`np.void` now has a `dtype` argument

NumPy now allows constructing structured void scalars directly by
passing the `dtype` argument to `np.void`.

([gh-22316](https://github.com/numpy/numpy/pull/22316))

Improvements

F2PY Improvements

-   The generated extension modules don\&#x27;t use the deprecated NumPy-C
 API anymore
-   Improved `f2py` generated exception messages
-   Numerous bug and `flake8` warning fixes
-   various CPP macros that one can use within C-expressions of
 signature files are prefixed with `f2py_`. For example, one should
 use `f2py_len(x)` instead of `len(x)`
-   A new construct `character(f2py_len=...)` is introduced to support
 returning assumed length character strings (e.g. `character(len=*)`)
 from wrapper functions

A hook to support rewriting `f2py` internal data structures after
reading all its input files is introduced. This is required, for
instance, for BC of SciPy support where character arguments are treated
as character strings arguments in `C` expressions.

([gh-19388](https://github.com/numpy/numpy/pull/19388))

IBM zSystems Vector Extension Facility (SIMD)

Added support for SIMD extensions of zSystem (z13, z14, z15), through
the universal intrinsics interface. This support leads to performance
improvements for all SIMD kernels implemented using the universal
intrinsics, including the following operations: rint, floor, trunc,
ceil, sqrt, absolute, square, reciprocal, tanh, sin, cos, equal,
not_equal, greater, greater_equal, less, less_equal, maximum, minimum,
fmax, fmin, argmax, argmin, add, subtract, multiply, divide.

([gh-20913](https://github.com/numpy/numpy/pull/20913))

NumPy now gives floating point errors in casts

In most cases, NumPy previously did not give floating point warnings or
errors when these happened during casts. For examples, casts like:

 np.array([2e300]).astype(np.float32)   overflow for float32
 np.array([np.inf]).astype(np.int64)

Should now generally give floating point warnings. These warnings should
warn that floating point overflow occurred. For errors when converting
floating point values to integers users should expect invalid value
warnings.

Users can modify the behavior of these warnings using `np.errstate`.

Note that for float to int casts, the exact warnings that are given may
be platform dependent. For example:

 arr = np.full(100, value=1000, dtype=np.float64)
 arr.astype(np.int8)

May give a result equivalent to (the intermediate cast means no warning
is given):

 arr.astype(np.int64).astype(np.int8)

May return an undefined result, with a warning set:

 RuntimeWarning: invalid value encountered in cast

The precise behavior is subject to the C99 standard and its
implementation in both software and hardware.

([gh-21437](https://github.com/numpy/numpy/pull/21437))

F2PY supports the value attribute

The Fortran standard requires that variables declared with the `value`
attribute must be passed by value instead of reference. F2PY now
supports this use pattern correctly. So
`integer, intent(in), value :: x` in Fortran codes will have correct
wrappers generated.

([gh-21807](https://github.com/numpy/numpy/pull/21807))

Added pickle support for third-party BitGenerators

The pickle format for bit generators was extended to allow each bit
generator to supply its own constructor when during pickling. Previous
versions of NumPy only supported unpickling `Generator` instances
created with one of the core set of bit generators supplied with NumPy.
Attempting to unpickle a `Generator` that used a third-party bit
generators would fail since the constructor used during the unpickling
was only aware of the bit generators included in NumPy.

([gh-22014](https://github.com/numpy/numpy/pull/22014))

arange() now explicitly fails with dtype=str

Previously, the `np.arange(n, dtype=str)` function worked for `n=1` and
`n=2`, but would raise a non-specific exception message for other values
of `n`. Now, it raises a [TypeError]{.title-ref} informing that `arange`
does not support string dtypes:

 &gt;&gt;&gt; np.arange(2, dtype=str)
 Traceback (most recent call last)
    ...
 TypeError: arange() not supported for inputs with DType &lt;class &#x27;numpy.dtype[str_]&#x27;&gt;.

([gh-22055](https://github.com/numpy/numpy/pull/22055))

`numpy.typing` protocols are now runtime checkable

The protocols used in `numpy.typing.ArrayLike` and
`numpy.typing.DTypeLike` are now properly marked as runtime checkable,
making them easier to use for runtime type checkers.

([gh-22357](https://github.com/numpy/numpy/pull/22357))

Performance improvements and changes

Faster version of `np.isin` and `np.in1d` for integer arrays

`np.in1d` (used by `np.isin`) can now switch to a faster algorithm (up
to \&gt;10x faster) when it is passed two integer arrays. This is often
automatically used, but you can use `kind=&quot;sort&quot;` or `kind=&quot;table&quot;` to
force the old or new method, respectively.

([gh-12065](https://github.com/numpy/numpy/pull/12065))

Faster comparison operators

The comparison functions (`numpy.equal`, `numpy.not_equal`,
`numpy.less`, `numpy.less_equal`, `numpy.greater` and
`numpy.greater_equal`) are now much faster as they are now vectorized
with universal intrinsics. For a CPU with SIMD extension AVX512BW, the
performance gain is up to 2.57x, 1.65x and 19.15x for integer, float and
boolean data types, respectively (with N=50000).

([gh-21483](https://github.com/numpy/numpy/pull/21483))

Changes

Better reporting of integer division overflow

Integer division overflow of scalars and arrays used to provide a
`RuntimeWarning` and the return value was undefined leading to crashes
at rare occasions:

 &gt;&gt;&gt; np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
 &lt;stdin&gt;:1: RuntimeWarning: divide by zero encountered in floor_divide
 array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)

Integer division overflow now returns the input dtype\&#x27;s minimum value
and raise the following `RuntimeWarning`:

 &gt;&gt;&gt; np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
 &lt;stdin&gt;:1: RuntimeWarning: overflow encountered in floor_divide
 array([-2147483648, -2147483648, -2147483648, -2147483648, -2147483648,
        -2147483648, -2147483648, -2147483648, -2147483648, -2147483648],
       dtype=int32)

([gh-21506](https://github.com/numpy/numpy/pull/21506))

`masked_invalid` now modifies the mask in-place

When used with `copy=False`, `numpy.ma.masked_invalid` now modifies the
input masked array in-place. This makes it behave identically to
`masked_where` and better matches the documentation.

([gh-22046](https://github.com/numpy/numpy/pull/22046))

`nditer`/`NpyIter` allows all allocating all operands

The NumPy iterator available through `np.nditer` in Python and as
`NpyIter` in C now supports allocating all arrays. The iterator shape
defaults to `()` in this case. The operands dtype must be provided,
since a \&quot;common dtype\&quot; cannot be inferred from the other inputs.

([gh-22457](https://github.com/numpy/numpy/pull/22457))

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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 \&quot;+\&quot; 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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SHA256

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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 \&quot;+\&quot; 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 \&quot;extending with Cython\&quot;\...
-   [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`\&#x27;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.

Checksums

MD5

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SHA256

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 ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c  numpy-1.23.4.tar.gz

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 \&quot;+\&quot; 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\&#x27;\...
-   [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

MD5

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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 \&quot;+\&quot; 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 \&quot;C\&quot; to only apply to function declarations\...
-   [21952](https://github.com/numpy/numpy/pull/21952): BUG: Fix KeyError in crackfortran operator support

Checksums

MD5

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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 &gt;= 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=&quot;ref&quot;}.

 ([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=&#x27;&quot;&#x27;` 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=&quot;ref&quot;} 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`:

 &gt;&gt;&gt; f32 = np.float32([1, 2])
 &gt;&gt;&gt; np.linalg.norm(f32, 2).dtype
 dtype(&#x27;float32&#x27;)
 &gt;&gt;&gt; np.linalg.norm(f32, 3)
 dtype(&#x27;float64&#x27;)   numpy 1.22
 dtype(&#x27;float32&#x27;)   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:

 &gt;&gt;&gt; np.result_type(np.dtype(&quot;i,i&quot;), np.dtype(&quot;i,d&quot;))
 dtype([(&#x27;f0&#x27;, &#x27;&lt;i4&#x27;), (&#x27;f1&#x27;, &#x27;&lt;f8&#x27;)])

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
\&quot;packed\&quot;, i.e. all fields are ordered contiguously and padding is
removed. See
`structured_dtype_comparison_and_promotion`{.interpreted-text
role=&quot;ref&quot;} 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:

-   `&quot;equiv&quot;` enforces matching names and titles. The itemsize is
 allowed to differ due to padding.
-   `&quot;safe&quot;` 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 \&quot;safe\&quot; 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

Update numpy from 1.18.4 to 1.24.0

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1.23.5

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1.23.3

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