Pin numpy to latest version 1.25.0

[pyup-bot]

This PR pins numpy to the latest release 1.25.0.

Changelog

1.25.0

The NumPy 1.25.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, and
clarify the documentation. There has also been work to prepare for the
future NumPy 2.0.0 release, resulting in a large number of new and
expired deprecation. Highlights are:

-   Support for MUSL, there are now MUSL wheels.
-   Support the Fujitsu C/C++ compiler.
-   Object arrays are now supported in einsum
-   Support for inplace matrix multiplication (`=`).

We will be releasing a NumPy 1.26 when Python 12 comes out. That is
needed because distutils has been dropped by Python 12 and we will be
switching to using meson for future builds. The next mainline release
will be NumPy 2.0.0. We plan that the 2.0 series will still support
downstream projects built against earlier versions of NumPy.

The Python versions supported in this release are 3.9-3.11.

Deprecations

-   `np.core.MachAr` is deprecated. It is private API. In names defined
 in `np.core` should generally be considered private.

 ([gh-22638](https://github.com/numpy/numpy/pull/22638))

-   `np.finfo(None)` is deprecated.

 ([gh-23011](https://github.com/numpy/numpy/pull/23011))

-   `np.round_` is deprecated. Use `np.round` instead.

 ([gh-23302](https://github.com/numpy/numpy/pull/23302))

-   `np.product` is deprecated. Use `np.prod` instead.

 ([gh-23314](https://github.com/numpy/numpy/pull/23314))

-   `np.cumproduct` is deprecated. Use `np.cumprod` instead.

 ([gh-23314](https://github.com/numpy/numpy/pull/23314))

-   `np.sometrue` is deprecated. Use `np.any` instead.

 ([gh-23314](https://github.com/numpy/numpy/pull/23314))

-   `np.alltrue` is deprecated. Use `np.all` instead.

 ([gh-23314](https://github.com/numpy/numpy/pull/23314))

-   Only ndim-0 arrays are treated as scalars. NumPy used to treat all
 arrays of size 1 (e.g., `np.array([3.14])`) as scalars. In the
 future, this will be limited to arrays of ndim 0 (e.g.,
 `np.array(3.14)`). The following expressions will report a
 deprecation warning:

  python
 a = np.array([3.14])
 float(a)   better: a[0] to get the numpy.float or a.item()

 b = np.array([[3.14]])
 c = numpy.random.rand(10)
 c[0] = b   better: c[0] = b[0, 0]
 

 ([gh-10615](https://github.com/numpy/numpy/pull/10615))

-   `numpy.find_common_type` is now deprecated and its use
 should be replaced with either `numpy.result_type` or
 `numpy.promote_types`. Most users leave the second
 `scalar_types` argument to `find_common_type` as `[]` in which case
 `np.result_type` and `np.promote_types` are both faster and more
 robust. When not using `scalar_types` the main difference is that
 the replacement intentionally converts non-native byte-order to
 native byte order. Further, `find_common_type` returns `object`
 dtype rather than failing promotion. This leads to differences when
 the inputs are not all numeric. Importantly, this also happens for
 e.g. timedelta/datetime for which NumPy promotion rules are
 currently sometimes surprising.

 When the `scalar_types` argument is not `[]` things are more
 complicated. In most cases, using `np.result_type` and passing the
 Python values `0`, `0.0`, or `0j` has the same result as using
 `int`, `float`, or `complex` in `scalar_types`.

 When `scalar_types` is constructed, `np.result_type` is the correct
 replacement and it may be passed scalar values like
 `np.float32(0.0)`. Passing values other than 0, may lead to
 value-inspecting behavior (which `np.find_common_type` never used
 and NEP 50 may change in the future). The main possible change in
 behavior in this case, is when the array types are signed integers
 and scalar types are unsigned.

 If you are unsure about how to replace a use of `scalar_types` or
 when non-numeric dtypes are likely, please do not hesitate to open a
 NumPy issue to ask for help.

 ([gh-22539](https://github.com/numpy/numpy/pull/22539))

Expired deprecations

-   `np.core.machar` and `np.finfo.machar` have been removed.

 ([gh-22638](https://github.com/numpy/numpy/pull/22638))

-   `+arr` will now raise an error when the dtype is not numeric (and
 positive is undefined).

 ([gh-22998](https://github.com/numpy/numpy/pull/22998))

-   A sequence must now be passed into the stacking family of functions
 (`stack`, `vstack`, `hstack`, `dstack` and `column_stack`).

 ([gh-23019](https://github.com/numpy/numpy/pull/23019))

-   `np.clip` now defaults to same-kind casting. Falling back to unsafe
 casting was deprecated in NumPy 1.17.

 ([gh-23403](https://github.com/numpy/numpy/pull/23403))

-   `np.clip` will now propagate `np.nan` values passed as `min` or
 `max`. Previously, a scalar NaN was usually ignored. This was
 deprecated in NumPy 1.17.

 ([gh-23403](https://github.com/numpy/numpy/pull/23403))

-   The `np.dual` submodule has been removed.

 ([gh-23480](https://github.com/numpy/numpy/pull/23480))

-   NumPy now always ignores sequence behavior for an array-like
 (defining one of the array protocols). (Deprecation started NumPy
 1.20)

 ([gh-23660](https://github.com/numpy/numpy/pull/23660))

-   The niche `FutureWarning` when casting to a subarray dtype in
 `astype` or the array creation functions such as `asarray` is now
 finalized. The behavior is now always the same as if the subarray
 dtype was wrapped into a single field (which was the workaround,
 previously). (FutureWarning since NumPy 1.20)

 ([gh-23666](https://github.com/numpy/numpy/pull/23666))

-   `==` and `!=` warnings have been finalized. The `==` and `!=`
 operators on arrays now always:

 -   raise errors that occur during comparisons such as when the
     arrays have incompatible shapes
     (`np.array([1, 2]) == np.array([1, 2, 3])`).

 -   return an array of all `True` or all `False` when values are
     fundamentally not comparable (e.g. have different dtypes). An
     example is `np.array(["a"]) == np.array([1])`.

     This mimics the Python behavior of returning `False` and `True`
     when comparing incompatible types like `"a" == 1` and
     `"a" != 1`. For a long time these gave `DeprecationWarning` or
     `FutureWarning`.

 ([gh-22707](https://github.com/numpy/numpy/pull/22707))

-   Nose support has been removed. NumPy switched to using pytest in
 2018 and nose has been unmaintained for many years. We have kept
 NumPy\'s nose support to avoid breaking downstream projects who
 might have been using it and not yet switched to pytest or some
 other testing framework. With the arrival of Python 3.12, unpatched
 nose will raise an error. It is time to move on.

 *Decorators removed*:

 -   raises
 -   slow
 -   setastest
 -   skipif
 -   knownfailif
 -   deprecated
 -   parametrize
 -   \_needs_refcount

 These are not to be confused with pytest versions with similar
 names, e.g., pytest.mark.slow, pytest.mark.skipif,
 pytest.mark.parametrize.

 *Functions removed*:

 -   Tester
 -   import_nose
 -   run_module_suite

 ([gh-23041](https://github.com/numpy/numpy/pull/23041))

-   The `numpy.testing.utils` shim has been removed. Importing from the
 `numpy.testing.utils` shim has been deprecated since 2019, the shim
 has now been removed. All imports should be made directly from
 `numpy.testing`.

 ([gh-23060](https://github.com/numpy/numpy/pull/23060))

-   The environment variable to disable dispatching has been removed.
 Support for the `NUMPY_EXPERIMENTAL_ARRAY_FUNCTION` environment
 variable has been removed. This variable disabled dispatching with
 `__array_function__`.

 ([gh-23376](https://github.com/numpy/numpy/pull/23376))

-   Support for `y=` as an alias of `out=` has been removed. The `fix`,
 `isposinf` and `isneginf` functions allowed using `y=` as a
 (deprecated) alias for `out=`. This is no longer supported.

 ([gh-23376](https://github.com/numpy/numpy/pull/23376))

Compatibility notes

-   The `busday_count` method now correctly handles cases where the
 `begindates` is later in time than the `enddates`. Previously, the
 `enddates` was included, even though the documentation states it is
 always excluded.

 ([gh-23229](https://github.com/numpy/numpy/pull/23229))

-   When comparing datetimes and timedelta using `np.equal` or
 `np.not_equal` numpy previously allowed the comparison with
 `casting="unsafe"`. This operation now fails. Forcing the output
 dtype using the `dtype` kwarg can make the operation succeed, but we
 do not recommend it.

 ([gh-22707](https://github.com/numpy/numpy/pull/22707))

-   When loading data from a file handle using `np.load`, if the handle
 is at the end of file, as can happen when reading multiple arrays by
 calling `np.load` repeatedly, numpy previously raised `ValueError`
 if `allow_pickle=False`, and `OSError` if `allow_pickle=True`. Now
 it raises `EOFError` instead, in both cases.

 ([gh-23105](https://github.com/numpy/numpy/pull/23105))

`np.pad` with `mode=wrap` pads with strict multiples of original data

Code based on earlier version of `pad` that uses `mode="wrap"` will
return different results when the padding size is larger than initial
array.

`np.pad` with `mode=wrap` now always fills the space with strict
multiples of original data even if the padding size is larger than the
initial array.

([gh-22575](https://github.com/numpy/numpy/pull/22575))

Cython `long_t` and `ulong_t` removed

`long_t` and `ulong_t` were aliases for `longlong_t` and `ulonglong_t`
and confusing (a remainder from of Python 2). This change may lead to
the errors:

 'long_t' is not a type identifier
 'ulong_t' is not a type identifier

We recommend use of bit-sized types such as `cnp.int64_t` or the use of
`cnp.intp_t` which is 32 bits on 32 bit systems and 64 bits on 64 bit
systems (this is most compatible with indexing). If C `long` is desired,
use plain `long` or `npy_long`. `cnp.int_t` is also `long` (NumPy\'s
default integer). However, `long` is 32 bit on 64 bit windows and we may
wish to adjust this even in NumPy. (Please do not hesitate to contact
NumPy developers if you are curious about this.)

([gh-22637](https://github.com/numpy/numpy/pull/22637))

Changed error message and type for bad `axes` argument to `ufunc`

The error message and type when a wrong `axes` value is passed to
`ufunc(..., axes=[...])` has changed. The message is now more
indicative of the problem, and if the value is mismatched an
`AxisError` will be raised. A `TypeError` will still be raised for
invalidinput types.

([gh-22675](https://github.com/numpy/numpy/pull/22675))

Array-likes that define `__array_ufunc__` can now override ufuncs if used as `where`

If the `where` keyword argument of a `numpy.ufunc`{.interpreted-text
role="class"} is a subclass of `numpy.ndarray`{.interpreted-text
role="class"} or is a duck type that defines
`numpy.class.__array_ufunc__`{.interpreted-text role="func"} it can
override the behavior of the ufunc using the same mechanism as the input
and output arguments. Note that for this to work properly, the
`where.__array_ufunc__` implementation will have to unwrap the `where`
argument to pass it into the default implementation of the `ufunc` or,
for `numpy.ndarray`{.interpreted-text role="class"} subclasses before
using `super().__array_ufunc__`.

([gh-23240](https://github.com/numpy/numpy/pull/23240))

By default, the exported NumPy C API is now compatible with NumPy 1.19

Starting with NumPy 1.25 when including NumPy headers, NumPy now
defaults to exposing a backwards compatible API. This means that by
default binaries such as wheels build against NumPy 1.25 will also work
with NumPy 1.16 because it has the same API version as NumPy 1.19 which
is the oldest NumPy version compatible with Python 3.9.

You can customize this behavior using:

 define NPY_TARGET_VERSION NPY_1_22_API_VERSION

or the equivalent `-D` option to the compiler. For more details please
see `for-downstream-package-authors`{.interpreted-text role="ref"}. A
change should only be required in rare cases when a package relies on
newly added C-API.

([gh-23528](https://github.com/numpy/numpy/pull/23528))

New Features

`np.einsum` now accepts arrays with `object` dtype

The code path will call python operators on object dtype arrays, much
like `np.dot` and `np.matmul`.

([gh-18053](https://github.com/numpy/numpy/pull/18053))

Add support for inplace matrix multiplication

It is now possible to perform inplace matrix multiplication via the `=`
operator.

python
>>> import numpy as np

>>> a = np.arange(6).reshape(3, 2)
>>> print(a)
[[0 1]
[2 3]
[4 5]]

>>> b = np.ones((2, 2), dtype=int)
>>> a = b
>>> print(a)
[[1 1]
[5 5]
[9 9]]


([gh-21120](https://github.com/numpy/numpy/pull/21120))

Added `NPY_ENABLE_CPU_FEATURES` environment variable

Users may now choose to enable only a subset of the built CPU features
at runtime by specifying the `NPY_ENABLE_CPU_FEATURES`
environment variable. Note that these specified features must be outside
the baseline, since those are always assumed. Errors will be raised if
attempting to enable a feature that is either not supported by your CPU,
or that NumPy was not built with.

([gh-22137](https://github.com/numpy/numpy/pull/22137))

NumPy now has an `np.exceptions` namespace

NumPy now has a dedicated namespace making most exceptions and warnings
available. All of these remain available in the main namespace, although
some may be moved slowly in the future. The main reason for this is to
increase discoverability and add future exceptions.

([gh-22644](https://github.com/numpy/numpy/pull/22644))

`np.linalg` functions return NamedTuples

`np.linalg` functions that return tuples now return namedtuples. These
functions are `eig()`, `eigh()`, `qr()`, `slogdet()`, and `svd()`. The
return type is unchanged in instances where these functions return
non-tuples with certain keyword arguments (like
`svd(compute_uv=False)`).

([gh-22786](https://github.com/numpy/numpy/pull/22786))

String functions in `np.char` are compatible with NEP 42 custom dtypes

Custom dtypes that represent unicode strings or byte strings can now be
passed to the string functions in `np.char`.

([gh-22863](https://github.com/numpy/numpy/pull/22863))

String dtype instances can be created from the string abstract dtype classes

It is now possible to create a string dtype instance with a size without
using the string name of the dtype. For example,
`type(np.dtype('U'))(8)` will create a dtype that is equivalent to
`np.dtype('U8')`. This feature is most useful when writing generic code
dealing with string dtype classes.

([gh-22963](https://github.com/numpy/numpy/pull/22963))

Fujitsu C/C++ compiler is now supported

Support for Fujitsu compiler has been added. To build with Fujitsu
compiler, run:

> python setup.py build -c fujitsu

SSL2 is now supported

Support for SSL2 has been added. SSL2 is a library that provides
OpenBLAS compatible GEMM functions. To enable SSL2, it need to edit
site.cfg and build with Fujitsu compiler. See site.cfg.example.

([gh-22982](https://github.com/numpy/numpy/pull/22982))

Improvements

`NDArrayOperatorsMixin` specifies that it has no `__slots__`

The `NDArrayOperatorsMixin` class now specifies that it contains no
`__slots__`, ensuring that subclasses can now make use of this feature
in Python.

([gh-23113](https://github.com/numpy/numpy/pull/23113))

Fix power of complex zero

`np.power` now returns a different result for `0^{non-zero}` for complex
numbers. Note that the value is only defined when the real part of the
exponent is larger than zero. Previously, NaN was returned unless the
imaginary part was strictly zero. The return value is either `0+0j` or
`0-0j`.

([gh-18535](https://github.com/numpy/numpy/pull/18535))

New `DTypePromotionError`

NumPy now has a new `DTypePromotionError` which is used when two dtypes
cannot be promoted to a common one, for example:

 np.result_type("M8[s]", np.complex128)

raises this new exception.

([gh-22707](https://github.com/numpy/numpy/pull/22707))

`np.show_config` uses information from Meson

Build and system information now contains information from Meson.
`np.show_config` now has a new optional parameter `mode` to
help customize the output.

([gh-22769](https://github.com/numpy/numpy/pull/22769))

Fix `np.ma.diff` not preserving the mask when called with arguments prepend/append.

Calling `np.ma.diff` with arguments prepend and/or append now returns a
`MaskedArray` with the input mask preserved.

Previously, a `MaskedArray` without the mask was returned.

([gh-22776](https://github.com/numpy/numpy/pull/22776))

Corrected error handling for NumPy C-API in Cython

Many NumPy C functions defined for use in Cython were lacking the
correct error indicator like `except -1` or `except *`. These have now
been added.

([gh-22997](https://github.com/numpy/numpy/pull/22997))

Ability to directly spawn random number generators

`numpy.random.Generator.spawn` now allows to directly spawn new independent
child generators via the `numpy.random.SeedSequence.spawn` mechanism.
`numpy.random.BitGenerator.spawn` does the same for the underlying bit
generator.

Additionally, `numpy.random.BitGenerator.seed_seq` now gives
direct access to the seed sequence used for initializing the bit
generator. This allows for example:

 seed = 0x2e09b90939db40c400f8f22dae617151
 rng = np.random.default_rng(seed)
 child_rng1, child_rng2 = rng.spawn(2)

  safely use rng, child_rng1, and child_rng2

Previously, this was hard to do without passing the `SeedSequence`
explicitly. Please see `numpy.random.SeedSequence` for more
information.

([gh-23195](https://github.com/numpy/numpy/pull/23195))

`numpy.logspace` now supports a non-scalar `base` argument

The `base` argument of `numpy.logspace` can now be array-like if it is
broadcastable against the `start` and `stop` arguments.

([gh-23275](https://github.com/numpy/numpy/pull/23275))

`np.ma.dot()` now supports for non-2d arrays

Previously `np.ma.dot()` only worked if `a` and `b` were both 2d. Now it
works for non-2d arrays as well as `np.dot()`.

([gh-23322](https://github.com/numpy/numpy/pull/23322))

Explicitly show keys of .npz file in repr

`NpzFile` shows keys of loaded .npz file when printed.

python
>>> npzfile = np.load('arr.npz')
>>> npzfile
NpzFile 'arr.npz' with keys arr_0, arr_1, arr_2, arr_3, arr_4...


([gh-23357](https://github.com/numpy/numpy/pull/23357))

NumPy now exposes DType classes in `np.dtypes`

The new `numpy.dtypes` module now exposes DType classes and will contain
future dtype related functionality. Most users should have no need to
use these classes directly.

([gh-23358](https://github.com/numpy/numpy/pull/23358))

Drop dtype metadata before saving in .npy or .npz files

Currently, a `*.npy` file containing a table with a dtype with metadata cannot
be read back. Now, `np.save` and `np.savez` drop metadata before saving.

([gh-23371](https://github.com/numpy/numpy/pull/23371))

`numpy.lib.recfunctions.structured_to_unstructured` returns views in more cases

`structured_to_unstructured` now returns a view, if the stride between
the fields is constant. Prior, padding between the fields or a reversed
field would lead to a copy. This change only applies to `ndarray`,
`memmap` and `recarray`. For all other array subclasses, the behavior
remains unchanged.

([gh-23652](https://github.com/numpy/numpy/pull/23652))

Signed and unsigned integers always compare correctly

When `uint64` and `int64` are mixed in NumPy, NumPy typically promotes
both to `float64`. This behavior may be argued about but is confusing
for comparisons `==`, `<=`, since the results returned can be incorrect
but the conversion is hidden since the result is a boolean. NumPy will
now return the correct results for these by avoiding the cast to float.

([gh-23713](https://github.com/numpy/numpy/pull/23713))

Performance improvements and changes

Faster `np.sort` on AVX-512 enabled processors

Quicksort for 16-bit and 64-bit dtypes gain up to 15x and 9x speed up on
processors that support AVX-512 instruction set.

Thanks to [Intel corporation](https://open.intel.com/) for sponsoring
this work.

([gh-22315](https://github.com/numpy/numpy/pull/22315))

`__array_function__` machinery is now much faster

The overhead of the majority of functions in NumPy is now smaller
especially when keyword arguments are used. This change significantly
speeds up many simple function calls.

([gh-23020](https://github.com/numpy/numpy/pull/23020))

`ufunc.at` can be much faster

Generic `ufunc.at` can be up to 9x faster. The conditions for this
speedup:

-   operands are aligned
-   no casting

If ufuncs with appropriate indexed loops on 1d arguments with the above
conditions, `ufunc.at` can be up to 60x faster (an additional 7x
speedup). Appropriate indexed loops have been added to `add`,
`subtract`, `multiply`, `floor_divide`, `maximum`, `minimum`, `fmax`,
and `fmin`.

The internal logic is similar to the logic used for regular ufuncs,
which also have fast paths.

Thanks to the [D. E. Shaw group](https://deshaw.com/) for sponsoring
this work.

([gh-23136](https://github.com/numpy/numpy/pull/23136))

Faster membership test on `NpzFile`

Membership test on `NpzFile` will no longer decompress the archive if it
is successful.

([gh-23661](https://github.com/numpy/numpy/pull/23661))

Changes

`np.r_[]` and `np.c_[]` with certain scalar values

In rare cases, using mainly `np.r_` with scalars can lead to different
results. The main potential changes are highlighted by the following:

 >>> np.r_[np.arange(5, dtype=np.uint8), -1].dtype
 int16   rather than the default integer (int64 or int32)
 >>> np.r_[np.arange(5, dtype=np.int8), 255]
 array([  0,   1,   2,   3,   4, 255], dtype=int16)

Where the second example returned:

 array([ 0,  1,  2,  3,  4, -1], dtype=int8)

The first one is due to a signed integer scalar with an unsigned integer
array, while the second is due to `255` not fitting into `int8` and
NumPy currently inspecting values to make this work. (Note that the
second example is expected to change in the future due to
`NEP 50 <NEP50>`{.interpreted-text role="ref"}; it will then raise an
error.)

([gh-22539](https://github.com/numpy/numpy/pull/22539))

Most NumPy functions are wrapped into a C-callable

To speed up the `__array_function__` dispatching, most NumPy functions
are now wrapped into C-callables and are not proper Python functions or
C methods. They still look and feel the same as before (like a Python
function), and this should only improve performance and user experience
(cleaner tracebacks). However, please inform the NumPy developers if
this change confuses your program for some reason.

([gh-23020](https://github.com/numpy/numpy/pull/23020))

C++ standard library usage

NumPy builds now depend on the C++ standard library, because the
`numpy.core._multiarray_umath` extension is linked with the C++ linker.

([gh-23601](https://github.com/numpy/numpy/pull/23601))

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 15ade485b3919f6839af2d30222b2cdd  numpy-1.25.0rc1-cp39-cp39-win_amd64.whl
 47aaca6df8579400191feac209f87589  numpy-1.25.0rc1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
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 283924a7c0b1050e6b455d86e311c666  numpy-1.25.0rc1.tar.gz

SHA256

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 1365157813810cfda2be9518806bf32f6b5f56e5e501d8299e3b681d53e405e2  numpy-1.25.0rc1-cp310-cp310-musllinux_1_1_x86_64.whl
 8aad2f86d2036622af1e1eb9db94e26618f42a571e02583fa72d5b1983782bf8  numpy-1.25.0rc1-cp310-cp310-win32.whl
 a4a9f1eaa63b5e35e23e5465ed59746b0a680eb5b5da06f2d432f828d32b26c1  numpy-1.25.0rc1-cp310-cp310-win_amd64.whl
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 165b0fb4d5b6349eef7b909be2d61a673bc6e75e0eec43776eea3222385a9d11  numpy-1.25.0rc1-cp311-cp311-musllinux_1_1_x86_64.whl
 b1a22ae597ee1d0e2336044854b33965fd92e731efe3c2ab965826e02cca2a8c  numpy-1.25.0rc1-cp311-cp311-win32.whl
 48e33b46b7db13de75dd0c1c919b8b297b5d7a4dc50b181066977ee17bed7cc3  numpy-1.25.0rc1-cp311-cp311-win_amd64.whl
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 7261d100c9bf722057fd5b9cd5b48f2973b17792b41e689eeaf9b55843cd1afd  numpy-1.25.0rc1-cp39-cp39-win32.whl
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 224e8862a1cd357eede831b270b9e6c51d2cbc2bb5cc2e2b8d0c76d52cbd1edc  numpy-1.25.0rc1.tar.gz

1.24.3

discovered after the 1.24.2 release. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 12 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Aleksei Nikiforov +
-   Alexander Heger
-   Bas van Beek
-   Bob Eldering
-   Brock Mendel
-   Charles Harris
-   Kyle Sunden
-   Peter Hawkins
-   Rohit Goswami
-   Sebastian Berg
-   Warren Weckesser
-   dependabot\[bot\]

Pull requests merged

A total of 17 pull requests were merged for this release.

-   [23206](https://github.com/numpy/numpy/pull/23206): BUG: fix for f2py string scalars (#23194)
-   [23207](https://github.com/numpy/numpy/pull/23207): BUG: datetime64/timedelta64 comparisons return NotImplemented
-   [23208](https://github.com/numpy/numpy/pull/23208): MAINT: Pin matplotlib to version 3.6.3 for refguide checks
-   [23221](https://github.com/numpy/numpy/pull/23221): DOC: Fix matplotlib error in documentation
-   [23226](https://github.com/numpy/numpy/pull/23226): CI: Ensure submodules are initialized in gitpod.
-   [23341](https://github.com/numpy/numpy/pull/23341): TYP: Replace duplicate reduce in ufunc type signature with reduceat.
-   [23342](https://github.com/numpy/numpy/pull/23342): TYP: Remove duplicate CLIP/WRAP/RAISE in `__init__.pyi`.
-   [23343](https://github.com/numpy/numpy/pull/23343): TYP: Mark `d` argument to fftfreq and rfftfreq as optional\...
-   [23344](https://github.com/numpy/numpy/pull/23344): TYP: Add type annotations for comparison operators to MaskedArray.
-   [23345](https://github.com/numpy/numpy/pull/23345): TYP: Remove some stray type-check-only imports of `msort`
-   [23370](https://github.com/numpy/numpy/pull/23370): BUG: Ensure like is only stripped for `like=` dispatched functions
-   [23543](https://github.com/numpy/numpy/pull/23543): BUG: fix loading and storing big arrays on s390x
-   [23544](https://github.com/numpy/numpy/pull/23544): MAINT: Bump larsoner/circleci-artifacts-redirector-action
-   [23634](https://github.com/numpy/numpy/pull/23634): BUG: Ignore invalid and overflow warnings in masked setitem
-   [23635](https://github.com/numpy/numpy/pull/23635): BUG: Fix masked array raveling when `order="A"` or `order="K"`
-   [23636](https://github.com/numpy/numpy/pull/23636): MAINT: Update conftest for newer hypothesis versions
-   [23637](https://github.com/numpy/numpy/pull/23637): BUG: Fix bug in parsing F77 style string arrays.

Checksums

MD5

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SHA256

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 ab344f1bf21f140adab8e47fdbc7c35a477dc01408791f8ba00d018dd0bc5155  numpy-1.24.3.tar.gz

1.24.2

discovered after the 1.24.1 release. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 14 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Bas van Beek
-   Charles Harris
-   Khem Raj +
-   Mark Harfouche
-   Matti Picus
-   Panagiotis Zestanakis +
-   Peter Hawkins
-   Pradipta Ghosh
-   Ross Barnowski
-   Sayed Adel
-   Sebastian Berg
-   Syam Gadde +
-   dmbelov +
-   pkubaj +

Pull requests merged

A total of 17 pull requests were merged for this release.

-   [22965](https://github.com/numpy/numpy/pull/22965): MAINT: Update python 3.11-dev to 3.11.
-   [22966](https://github.com/numpy/numpy/pull/22966): DOC: Remove dangling deprecation warning
-   [22967](https://github.com/numpy/numpy/pull/22967): ENH: Detect CPU features on FreeBSD/powerpc64\*
-   [22968](https://github.com/numpy/numpy/pull/22968): BUG: np.loadtxt cannot load text file with quoted fields separated\...
-   [22969](https://github.com/numpy/numpy/pull/22969): TST: Add fixture to avoid issue with randomizing test order.
-   [22970](https://github.com/numpy/numpy/pull/22970): BUG: Fix fill violating read-only flag. (#22959)
-   [22971](https://github.com/numpy/numpy/pull/22971): MAINT: Add additional information to missing scalar AttributeError
-   [22972](https://github.com/numpy/numpy/pull/22972): MAINT: Move export for scipy arm64 helper into main module
-   [22976](https://github.com/numpy/numpy/pull/22976): BUG, SIMD: Fix spurious invalid exception for sin/cos on arm64/clang
-   [22989](https://github.com/numpy/numpy/pull/22989): BUG: Ensure correct loop order in sin, cos, and arctan2
-   [23030](https://github.com/numpy/numpy/pull/23030): DOC: Add version added information for the strict parameter in\...
-   [23031](https://github.com/numpy/numpy/pull/23031): BUG: use `_Alignof` rather than `offsetof()` on most compilers
-   [23147](https://github.com/numpy/numpy/pull/23147): BUG: Fix for npyv\_\_trunc_s32_f32 (VXE)
-   [23148](https://github.com/numpy/numpy/pull/23148): BUG: Fix integer / float scalar promotion
-   [23149](https://github.com/numpy/numpy/pull/23149): BUG: Add missing \<type_traits> header.
-   [23150](https://github.com/numpy/numpy/pull/23150): TYP, MAINT: Add a missing explicit `Any` parameter to the `npt.ArrayLike`\...
-   [23161](https://github.com/numpy/numpy/pull/23161): BLD: remove redundant definition of npy_nextafter \[wheel build\]

Checksums

MD5

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SHA256

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1.24.1

discovered after the 1.24.0 release. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 12 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Andrew Nelson
-   Ben Greiner +
-   Charles Harris
-   Clément Robert
-   Matteo Raso
-   Matti Picus
-   Melissa Weber Mendonça
-   Miles Cranmer
-   Ralf Gommers
-   Rohit Goswami
-   Sayed Adel
-   Sebastian Berg

Pull requests merged

A total of 18 pull requests were merged for this release.

-   [22820](https://github.com/numpy/numpy/pull/22820): BLD: add workaround in setup.py for newer setuptools
-   [22830](https://github.com/numpy/numpy/pull/22830): BLD: CIRRUS_TAG redux
-   [22831](https://github.com/numpy/numpy/pull/22831): DOC: fix a couple typos in 1.23 notes
-   [22832](https://github.com/numpy/numpy/pull/22832): BUG: Fix refcounting errors found using pytest-leaks
-   [22834](https://github.com/numpy/numpy/pull/22834): BUG, SIMD: Fix invalid value encountered in several ufuncs
-   [22837](https://github.com/numpy/numpy/pull/22837): TST: ignore more np.distutils.log imports
-   [22839](https://github.com/numpy/numpy/pull/22839): BUG: Do not use getdata() in np.ma.masked_invalid
-   [22847](https://github.com/numpy/numpy/pull/22847): BUG: Ensure correct behavior for rows ending in delimiter in\...
-   [22848](https://github.com/numpy/numpy/pull/22848): BUG, SIMD: Fix the bitmask of the boolean comparison
-   [22857](https://github.com/numpy/numpy/pull/22857): BLD: Help raspian arm + clang 13 about \_\_builtin_mul_overflow
-   [22858](https://github.com/numpy/numpy/pull/22858): API: Ensure a full mask is returned for masked_invalid
-   [22866](https://github.com/numpy/numpy/pull/22866): BUG: Polynomials now copy properly (#22669)
-   [22867](https://github.com/numpy/numpy/pull/22867): BUG, SIMD: Fix memory overlap in ufunc comparison loops
-   [22868](https://github.com/numpy/numpy/pull/22868): BUG: Fortify string casts against floating point warnings
-   [22875](https://github.com/numpy/numpy/pull/22875): TST: Ignore nan-warnings in randomized out tests
-   [22883](https://github.com/numpy/numpy/pull/22883): MAINT: restore npymath implementations needed for freebsd
-   [22884](https://github.com/numpy/numpy/pull/22884): BUG: Fix integer overflow in in1d for mixed integer dtypes #22877
-   [22887](https://github.com/numpy/numpy/pull/22887): BUG: Use whole file for encoding checks with `charset_normalizer`.

Checksums

MD5

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SHA256

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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 \"dtype\" and \"casting\" 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 \< 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 `"int8"` 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=[("f", "i", 3)])
 subarray_fields = arr.astype(object)[0]
 subarray = subarray_fields[0]   "f" 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="func"} or
:py`asarray()`{.interpreted-text role="func"}, 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`:

 >>> arr = np.array([1, 2, 3], dtype="long")
 >>> new_dtype = np.dtype("longlong")
 >>> new = np.asarray(arr, dtype=new_dtype)
 >>> new.dtype is new_dtype
 True
 >>> 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:

 >>> P_y = np.polynomial.Polynomial([1, 0, -1], symbol="y")
 >>> 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:

 >>> P = np.polynomial.Polynomial([1, -1])   default symbol is "x"
 >>> P_z = np.polynomial.Polynomial([1, 1], symbol="z")
 >>> 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='same_kind')`.

`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='same_kind')`.

`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='same_kind')`.

([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