Changelog
### 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.
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```
### 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 \[wheel build\]
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92011118955724465fb6853def593cf397b4a1367495e0b59a7e69d40c4eb71d numpy-1.24.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
f9006288bcf4895917d02583cf3411f98631275bc67cce355a7f39f8c14338fa numpy-1.24.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
150947adbdfeceec4e5926d956a06865c1c690f2fd902efede4ca6fe2e657c3f numpy-1.24.2-pp38-pypy38_pp73-win_amd64.whl
003a9f530e880cb2cd177cba1af7220b9aa42def9c4afc2a2fc3ee6be7eb2b22 numpy-1.24.2.tar.gz
```
### 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`.
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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 an instance of `Generator`. The function
`default_rng` simplifies instantiation:
>>> rg = np.random.default_rng(3728973198)
>>> 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:
>>> orig_bit_gen = np.random.get_bit_generator()
>>> np.random.set_bit_generator(rg.bit_generator)
>>> 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:
>>> 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\'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:
>>> np.arange(2, dtype=str)
Traceback (most recent call last)
...
TypeError: arange() not supported for inputs with DType <class 'numpy.dtype[str_]'>.
([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 \>10x faster) when it is passed two integer arrays. This is often
automatically used, but you can use `kind="sort"` or `kind="table"` 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:
>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>: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\'s minimum value
and raise the following `RuntimeWarning`:
>>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1)
<stdin>: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 \"common dtype\" 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 \"+\" 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
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1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a numpy-1.23.5.tar.gz
```
### 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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61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595
pyup-bot
commented
1 year ago
This PR updates numpy from 1.22.2 to 1.24.3.
Changelog
### 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. 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210461d87fb02a84ef243cac5e814aad2b7f4be953b32cb53327bb49fd77fbb4 numpy-1.24.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 784c6da1a07818491b0ffd63c6bbe5a33deaa0e25a20e1b3ea20cf0e43f8046c numpy-1.24.3-cp39-cp39-win32.whl d5036197ecae68d7f491fcdb4df90082b0d4960ca6599ba2659957aafced7c17 numpy-1.24.3-cp39-cp39-win_amd64.whl 352ee00c7f8387b44d19f4cada524586f07379c0d49270f87233983bc5087ca0 numpy-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 1a7d6acc2e7524c9955e5c903160aa4ea083736fde7e91276b0e5d98e6332812 numpy-1.24.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 35400e6a8d102fd07c71ed7dcadd9eb62ee9a6e84ec159bd48c28235bbb0f8e4 numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl 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 73fe0b507f56c0baf43171a76ad2003f numpy-1.24.2-cp310-cp310-macosx_10_9_x86_64.whl 2dbbe6f8a14e14978d24de9fcc8b49fe numpy-1.24.2-cp310-cp310-macosx_11_0_arm64.whl 9ddadbf9cac2742318d8b292cb9ca579 numpy-1.24.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 969f4f33baaff53dbbbaf1a146c43534 numpy-1.24.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6df575dff02feac835d22debb15d190e numpy-1.24.2-cp310-cp310-win32.whl 2f939228a8c33265f2a8a1fce349d6f1 numpy-1.24.2-cp310-cp310-win_amd64.whl c093e61421be01ffff435387839949f1 numpy-1.24.2-cp311-cp311-macosx_10_9_x86_64.whl 03d71e3d9a086b56837c461fd7c9188b numpy-1.24.2-cp311-cp311-macosx_11_0_arm64.whl c0dc33697d156e2b9a029095efeb1b10 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003a9f530e880cb2cd177cba1af7220b9aa42def9c4afc2a2fc3ee6be7eb2b22 numpy-1.24.2.tar.gz ``` ### 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`. 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ef85cf1f693c88c1fd229ccd1055570cb41cdf4875873b7728b6301f12cd05bf numpy-1.24.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 87a118968fba001b248aac90e502c0b13606721b1343cdaddbc6e552e8dfb56f numpy-1.24.1-cp39-cp39-win32.whl ddc7ab52b322eb1e40521eb422c4e0a20716c271a306860979d450decbb51b8e numpy-1.24.1-cp39-cp39-win_amd64.whl ed5fb71d79e771ec930566fae9c02626b939e37271ec285e9efaf1b5d4370e7d numpy-1.24.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl ad2925567f43643f51255220424c23d204024ed428afc5aad0f86f3ffc080086 numpy-1.24.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cfa1161c6ac8f92dea03d625c2d0c05e084668f4a06568b77a25a89111621566 numpy-1.24.1-pp38-pypy38_pp73-win_amd64.whl 2386da9a471cc00a1f47845e27d916d5ec5346ae9696e01a8a34760858fe9dd2 numpy-1.24.1.tar.gz ``` ### 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 an instance of `Generator`. The function `default_rng` simplifies instantiation: >>> rg = np.random.default_rng(3728973198) >>> 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: >>> orig_bit_gen = np.random.get_bit_generator() >>> np.random.set_bit_generator(rg.bit_generator) >>> 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: >>> 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\'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: >>> np.arange(2, dtype=str) Traceback (most recent call last) ... TypeError: arange() not supported for inputs with DType <class 'numpy.dtype[str_]'>. ([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 \>10x faster) when it is passed two integer arrays. This is often automatically used, but you can use `kind="sort"` or `kind="table"` 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: >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1) <stdin>: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\'s minimum value and raise the following `RuntimeWarning`: >>> np.array([np.iinfo(np.int32).min]*10, dtype=np.int32) // np.int32(-1) <stdin>: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 \"common dtype\" cannot be inferred from the other inputs. 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numpy-1.24.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 15605b92bf10b10e110a9c0f1c4ef6cd58246532c62a0c3d3188c05e69cdcdb6 numpy-1.24.0rc1-cp39-cp39-win32.whl 8046f5c23769791be8432a592b9881984e0e4abc7f552c7e5c349420a27323e7 numpy-1.24.0rc1-cp39-cp39-win_amd64.whl aa9c4a2f65d669e6559123154da944ad6bd7605cbba5cce81bf6794617870510 numpy-1.24.0rc1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl e44fd1bdfa50979ddec76318e21abc82ee3858e5f45dfc5153b6f660d9d29851 numpy-1.24.0rc1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1802199d70d9f8ac11eb63a1ef50d33915b78a84bacacaadb2896175005103d4 numpy-1.24.0rc1-pp38-pypy38_pp73-win_amd64.whl d601180710004799acb8f80e564b84e71490fac9d84e115e2f5b0f6709754f16 numpy-1.24.0rc1.tar.gz ``` ### 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 8a412b79d975199cefadb465279fd569 numpy-1.23.5-cp310-cp310-macosx_10_9_x86_64.whl 1b56e8e6a0516c78473657abf0710538 numpy-1.23.5-cp310-cp310-macosx_11_0_arm64.whl c787f4763c9a5876e86a17f1651ba458 numpy-1.23.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl db07645022e56747ba3f00c2d742232e numpy-1.23.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl c63a6fb7cc16a13aabc82ec57ac6bb4d numpy-1.23.5-cp310-cp310-win32.whl 3fea9247e1d812600015641941fa273f numpy-1.23.5-cp310-cp310-win_amd64.whl 4222cfb36e5ac9aec348c81b075e2c05 numpy-1.23.5-cp311-cp311-macosx_10_9_x86_64.whl 6c7102f185b310ac70a62c13d46f04e6 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1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a numpy-1.23.5.tar.gz ``` ### 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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