Changelog
### 1.24
```
The NumPy 1.24.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, and
clarify the documentation. There are also a large number of new and
expired deprecations due to changes in promotion and cleanups. This
might be called a deprecation release. Highlights are
- Many new deprecations, check them out.
- Many expired deprecations,
- New F2PY features and fixes.
- New \"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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f1f5fa912df64dd48ec55352b72f4b036ab7b3911e996703f436e17baca780f9 numpy-1.24.0rc1-cp38-cp38-win_amd64.whl
8d149b3c3062dc68e29bdb244edc30c5d80e2c654b5c27c32773bf7354452b48 numpy-1.24.0rc1-cp39-cp39-macosx_10_9_x86_64.whl
d177fbd4d22248640d73f07c3aac2cc1f79c412f61564452abd08606ee5e3713 numpy-1.24.0rc1-cp39-cp39-macosx_11_0_arm64.whl
05faa4ecb98d7bc593afc5b10c25f0e7dd65244b653756b083c605fbf60b9b67 numpy-1.24.0rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
06d8827c6fa511b61047376efc3a677d447193bf88e6bbde35b4e5223a4b58d6 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
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8b2692a511a3795f3af8af2cd7566a15 numpy-1.23.5.tar.gz
SHA256
9c88793f78fca17da0145455f0d7826bcb9f37da4764af27ac945488116efe63 numpy-1.23.5-cp310-cp310-macosx_10_9_x86_64.whl
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7903ba8ab592b82014713c491f6c5d3a1cde5b4a3bf116404e08f5b52f6daf43 numpy-1.23.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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af1da88f6bc3d2338ebbf0e22fe487821ea4d8e89053e25fa59d1d79786e7481 numpy-1.23.5-cp39-cp39-win32.whl
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abdde9f795cf292fb9651ed48185503a2ff29be87770c3b8e2a14b0cd7aa16f8 numpy-1.23.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
f9a909a8bae284d46bbfdefbdd4a262ba19d3bc9921b1e76126b1d21c3c34135 numpy-1.23.5-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
01dd17cbb340bf0fc23981e52e1d18a9d4050792e8fb8363cecbf066a84b827d numpy-1.23.5-pp38-pypy38_pp73-win_amd64.whl
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.
Checksums
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SHA256
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0fe563fc8ed9dc4474cbf70742673fc4391d70f4363f917599a7fa99f042d5a8 numpy-1.23.4-cp38-cp38-win_amd64.whl
c67b833dbccefe97cdd3f52798d430b9d3430396af7cdb2a0c32954c3ef73894 numpy-1.23.4-cp39-cp39-macosx_10_9_x86_64.whl
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f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef numpy-1.23.4-cp39-cp39-win32.whl
f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e numpy-1.23.4-cp39-cp39-win_amd64.whl
61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911 numpy-1.23.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810 numpy-1.23.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962 numpy-1.23.4-pp38-pypy38_pp73-win_amd64.whl
ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c numpy-1.23.4.tar.gz
```
### 1.23.3
```
the 1.23.2 release. There is no major theme for this release, the main
improvements are for some downstream builds and some annotation corner
cases. The Python versions supported for this release are 3.8-3.11.
Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the
```
### 1.23.2
```
the 1.23.1 release. Notable features are:
- Typing changes needed for Python 3.11
- Wheels for Python 3.11.0rc1
The Python versions supported for this release are 3.8-3.11.
Contributors
A total of 9 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.
- Alexander Grund +
- Bas van Beek
- Charles Harris
- Jon Cusick +
- Matti Picus
- Michael Osthege +
- Pal Barta +
- Ross Barnowski
- Sebastian Berg
Pull requests merged
A total of 15 pull requests were merged for this release.
- [22030](https://github.com/numpy/numpy/pull/22030): ENH: Add `__array_ufunc__` typing support to the `nin=1` ufuncs
- [22031](https://github.com/numpy/numpy/pull/22031): MAINT, TYP: Fix `np.angle` dtype-overloads
- [22032](https://github.com/numpy/numpy/pull/22032): MAINT: Do not let `_GenericAlias` wrap the underlying classes\'\...
- [22033](https://github.com/numpy/numpy/pull/22033): TYP,MAINT: Allow `einsum` subscripts to be passed via integer\...
- [22034](https://github.com/numpy/numpy/pull/22034): MAINT,TYP: Add object-overloads for the `np.generic` rich comparisons
- [22035](https://github.com/numpy/numpy/pull/22035): MAINT,TYP: Allow the `squeeze` and `transpose` method to\...
- [22036](https://github.com/numpy/numpy/pull/22036): BUG: Fix subarray to object cast ownership details
- [22037](https://github.com/numpy/numpy/pull/22037): BUG: Use `Popen` to silently invoke f77 -v
- [22038](https://github.com/numpy/numpy/pull/22038): BUG: Avoid errors on NULL during deepcopy
- [22039](https://github.com/numpy/numpy/pull/22039): DOC: Add versionchanged for converter callable behavior.
- [22057](https://github.com/numpy/numpy/pull/22057): MAINT: Quiet the anaconda uploads.
- [22078](https://github.com/numpy/numpy/pull/22078): ENH: reorder includes for testing on top of system installations\...
- [22106](https://github.com/numpy/numpy/pull/22106): TST: fix test_linear_interpolation_formula_symmetric
- [22107](https://github.com/numpy/numpy/pull/22107): BUG: Fix skip condition for test_loss_of_precision\[complex256\]
- [22115](https://github.com/numpy/numpy/pull/22115): BLD: Build python3.11.0rc1 wheels.
Checksums
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SHA256
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b78d00e48261fbbd04aa0d7427cf78d18401ee0abd89c7559bbf422e5b1c7d01 numpy-1.23.2.tar.gz
```
### 1.23.1
```
The NumPy 1.23.1 is a maintenance release that fixes bugs discovered
after the 1.23.0 release. Notable fixes are:
- Fix searchsorted for float16 NaNs
- Fix compilation on Apple M1
- Fix KeyError in crackfortran operator support (Slycot)
The Python version supported for this release are 3.8-3.10.
Contributors
A total of 7 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.
- Charles Harris
- Matthias Koeppe +
- Pranab Das +
- Rohit Goswami
- Sebastian Berg
- Serge Guelton
- Srimukh Sripada +
Pull requests merged
A total of 8 pull requests were merged for this release.
- [21866](https://github.com/numpy/numpy/pull/21866): BUG: Fix discovered MachAr (still used within valgrind)
- [21867](https://github.com/numpy/numpy/pull/21867): BUG: Handle NaNs correctly for float16 during sorting
- [21868](https://github.com/numpy/numpy/pull/21868): BUG: Use `keepdims` during normalization in `np.average` and\...
- [21869](https://github.com/numpy/numpy/pull/21869): DOC: mention changes to `max_rows` behaviour in `np.loadtxt`
- [21870](https://github.com/numpy/numpy/pull/21870): BUG: Reject non integer array-likes with size 1 in delete
- [21949](https://github.com/numpy/numpy/pull/21949): BLD: Make can_link_svml return False for 32bit builds on x86_64
- [21951](https://github.com/numpy/numpy/pull/21951): BUG: Reorder extern \"C\" to only apply to function declarations\...
- [21952](https://github.com/numpy/numpy/pull/21952): BUG: Fix KeyError in crackfortran operator support
Checksums
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SHA256
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d748ef349bfef2e1194b59da37ed5a29c19ea8d7e6342019921ba2ba4fd8b624 numpy-1.23.1.tar.gz
```
### 1.23.0
```
The NumPy 1.23.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, clarify
the documentation, and expire old deprecations. The highlights are:
- Implementation of `loadtxt` in C, greatly improving its performance.
- Exposing DLPack at the Python level for easy data exchange.
- Changes to the promotion and comparisons of structured dtypes.
- Improvements to f2py.
See below for the details,
New functions
- A masked array specialization of `ndenumerate` is now available as
`numpy.ma.ndenumerate`. It provides an alternative to
`numpy.ndenumerate` and skips masked values by default.
([gh-20020](https://github.com/numpy/numpy/pull/20020))
- `numpy.from_dlpack` has been added to allow easy exchange of data
using the DLPack protocol. It accepts Python objects that implement
the `__dlpack__` and `__dlpack_device__` methods and returns a
ndarray object which is generally the view of the data of the input
object.
([gh-21145](https://github.com/numpy/numpy/pull/21145))
Deprecations
- Setting `__array_finalize__` to `None` is deprecated. It must now be
a method and may wish to call `super().__array_finalize__(obj)`
after checking for `None` or if the NumPy version is sufficiently
new.
([gh-20766](https://github.com/numpy/numpy/pull/20766))
- Using `axis=32` (`axis=np.MAXDIMS`) in many cases had the same
meaning as `axis=None`. This is deprecated and `axis=None` must be
used instead.
([gh-20920](https://github.com/numpy/numpy/pull/20920))
- The hook function `PyDataMem_SetEventHook` has been deprecated and
the demonstration of its use in tool/allocation_tracking has been
removed. The ability to track allocations is now built-in to python
via `tracemalloc`.
([gh-20394](https://github.com/numpy/numpy/pull/20394))
- `numpy.distutils` has been deprecated, as a result of `distutils`
itself being deprecated. It will not be present in NumPy for
Python >= 3.12, and will be removed completely 2 years after the
release of Python 3.12 For more details, see
`distutils-status-migration`{.interpreted-text role="ref"}.
([gh-20875](https://github.com/numpy/numpy/pull/20875))
Expired deprecations
- The `NpzFile.iteritems()` and `NpzFile.iterkeys()` methods have been
removed as part of the continued removal of Python 2 compatibility.
This concludes the deprecation from 1.15.
([gh-16830](https://github.com/numpy/numpy/pull/16830))
- The `alen` and `asscalar` functions have been removed.
([gh-20414](https://github.com/numpy/numpy/pull/20414))
- The `UPDATEIFCOPY` array flag has been removed together with the
enum `NPY_ARRAY_UPDATEIFCOPY`. The associated (and deprecated)
`PyArray_XDECREF_ERR` was also removed. These were all deprecated in
1.14. They are replaced by `WRITEBACKIFCOPY`, that requires calling
`PyArray_ResoveWritebackIfCopy` before the array is deallocated.
([gh-20589](https://github.com/numpy/numpy/pull/20589))
- Exceptions will be raised during array-like creation. When an object
raised an exception during access of the special attributes
`__array__` or `__array_interface__`, this exception was usually
ignored. This behaviour was deprecated in 1.21, and the exception
will now be raised.
([gh-20835](https://github.com/numpy/numpy/pull/20835))
- Multidimensional indexing with non-tuple values is not allowed.
Previously, code such as `arr[ind]` where `ind = [[0, 1], [0, 1]]`
produced a `FutureWarning` and was interpreted as a multidimensional
index (i.e., `arr[tuple(ind)]`). Now this example is treated like an
array index over a single dimension (`arr[array(ind)]`).
Multidimensional indexing with anything but a tuple was deprecated
in NumPy 1.15.
([gh-21029](https://github.com/numpy/numpy/pull/21029))
- Changing to a dtype of different size in F-contiguous arrays is no
longer permitted. Deprecated since Numpy 1.11.0. See below for an
extended explanation of the effects of this change.
([gh-20722](https://github.com/numpy/numpy/pull/20722))
New Features
crackfortran has support for operator and assignment overloading
`crackfortran` parser now understands operator and assignment
definitions in a module. They are added in the `body` list of the module
which contains a new key `implementedby` listing the names of the
subroutines or functions implementing the operator or assignment.
([gh-15006](https://github.com/numpy/numpy/pull/15006))
f2py supports reading access type attributes from derived type statements
As a result, one does not need to use `public` or `private` statements
to specify derived type access properties.
([gh-15844](https://github.com/numpy/numpy/pull/15844))
New parameter `ndmin` added to `genfromtxt`
This parameter behaves the same as `ndmin` from `numpy.loadtxt`.
([gh-20500](https://github.com/numpy/numpy/pull/20500))
`np.loadtxt` now supports quote character and single converter function
`numpy.loadtxt` now supports an additional `quotechar` keyword argument
which is not set by default. Using `quotechar='"'` will read quoted
fields as used by the Excel CSV dialect.
Further, it is now possible to pass a single callable rather than a
dictionary for the `converters` argument.
([gh-20580](https://github.com/numpy/numpy/pull/20580))
Changing to dtype of a different size now requires contiguity of only the last axis
Previously, viewing an array with a dtype of a different item size
required that the entire array be C-contiguous. This limitation would
unnecessarily force the user to make contiguous copies of non-contiguous
arrays before being able to change the dtype.
This change affects not only `ndarray.view`, but other construction
mechanisms, including the discouraged direct assignment to
`ndarray.dtype`.
This change expires the deprecation regarding the viewing of
F-contiguous arrays, described elsewhere in the release notes.
([gh-20722](https://github.com/numpy/numpy/pull/20722))
Deterministic output files for F2PY
For F77 inputs, `f2py` will generate `modname-f2pywrappers.f`
unconditionally, though these may be empty. For free-form inputs,
`modname-f2pywrappers.f`, `modname-f2pywrappers2.f90` will both be
generated unconditionally, and may be empty. This allows writing generic
output rules in `cmake` or `meson` and other build systems. Older
behavior can be restored by passing `--skip-empty-wrappers` to `f2py`.
`f2py-meson`{.interpreted-text role="ref"} details usage.
([gh-21187](https://github.com/numpy/numpy/pull/21187))
`keepdims` parameter for `average`
The parameter `keepdims` was added to the functions `numpy.average` and
`numpy.ma.average`. The parameter has the same meaning as it does in
reduction functions such as `numpy.sum` or `numpy.mean`.
([gh-21485](https://github.com/numpy/numpy/pull/21485))
Compatibility notes
1D `np.linalg.norm` preserves float input types, even for scalar results
Previously, this would promote to `float64` when the `ord` argument was
not one of the explicitly listed values, e.g. `ord=3`:
>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2).dtype
dtype('float32')
>>> np.linalg.norm(f32, 3)
dtype('float64') numpy 1.22
dtype('float32') numpy 1.23
This change affects only `float32` and `float16` vectors with `ord`
other than `-Inf`, `0`, `1`, `2`, and `Inf`.
([gh-17709](https://github.com/numpy/numpy/pull/17709))
Changes to structured (void) dtype promotion and comparisons
In general, NumPy now defines correct, but slightly limited, promotion
for structured dtypes by promoting the subtypes of each field instead of
raising an exception:
>>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
dtype([('f0', '<i4'), ('f1', '<f8')])
For promotion matching field names, order, and titles are enforced,
however padding is ignored. Promotion involving structured dtypes now
always ensures native byte-order for all fields (which may change the
result of `np.concatenate`) and ensures that the result will be
\"packed\", i.e. all fields are ordered contiguously and padding is
removed. See
`structured_dtype_comparison_and_promotion`{.interpreted-text
role="ref"} for further details.
The `repr` of aligned structures will now never print the long form
including `offsets` and `itemsize` unless the structure includes padding
not guaranteed by `align=True`.
In alignment with the above changes to the promotion logic, the casting
safety has been updated:
- `"equiv"` enforces matching names and titles. The itemsize is
allowed to differ due to padding.
- `"safe"` allows mismatching field names and titles
- The cast safety is limited by the cast safety of each included
field.
- The order of fields is used to decide cast safety of each individual
field. Previously, the field names were used and only unsafe casts
were possible when names mismatched.
The main important change here is that name mismatches are now
considered \"safe\" casts.
([gh-19226](https://github.com/numpy/numpy/pull/19226))
`NPY_RELAXED_STRIDES_CHECKING` has been removed
NumPy cannot be compiled with `NPY_RELAXED_STRIDES_CHECKING=0` anymore.
Relaxed strides have been the default for many years and the option was
initially introduced to allow a smoother transition.
([gh-20220](https://github.com/numpy/numpy/pull/20220))
`np.loadtxt` has recieved several changes
The row counting of `numpy.loadtxt` was fixed. `loadtxt` ignores fully
empty lines in the file, but counted them towards `max_rows`. When
`max_rows` is used and the file contains empty lines, these will now not
be counted. Previously, it was possible that the result contained fewer
than `max_rows` rows even though more data was available to be read. If
This PR updates numpy from 1.18.4 to 1.24.0.
Changelog
### 1.24 ``` The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There are also a large number of new and expired deprecations due to changes in promotion and cleanups. This might be called a deprecation release. Highlights are - Many new deprecations, check them out. - Many expired deprecations, - New F2PY features and fixes. - New \"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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95de7dc7dc47a312f6feddd3da2500826defdccbc41608d0031276a24181a2c0 numpy-1.23.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef numpy-1.23.4-cp39-cp39-win32.whl f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e numpy-1.23.4-cp39-cp39-win_amd64.whl 61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911 numpy-1.23.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810 numpy-1.23.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962 numpy-1.23.4-pp38-pypy38_pp73-win_amd64.whl ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c numpy-1.23.4.tar.gz ``` ### 1.23.3 ``` the 1.23.2 release. There is no major theme for this release, the main improvements are for some downstream builds and some annotation corner cases. The Python versions supported for this release are 3.8-3.11. Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the ``` ### 1.23.2 ``` the 1.23.1 release. Notable features are: - Typing changes needed for Python 3.11 - Wheels for Python 3.11.0rc1 The Python versions supported for this release are 3.8-3.11. Contributors A total of 9 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Alexander Grund + - Bas van Beek - Charles Harris - Jon Cusick + - Matti Picus - Michael Osthege + - Pal Barta + - Ross Barnowski - Sebastian Berg Pull requests merged A total of 15 pull requests were merged for this release. - [22030](https://github.com/numpy/numpy/pull/22030): ENH: Add `__array_ufunc__` typing support to the `nin=1` ufuncs - [22031](https://github.com/numpy/numpy/pull/22031): MAINT, TYP: Fix `np.angle` dtype-overloads - [22032](https://github.com/numpy/numpy/pull/22032): MAINT: Do not let `_GenericAlias` wrap the underlying classes\'\... - [22033](https://github.com/numpy/numpy/pull/22033): TYP,MAINT: Allow `einsum` subscripts to be passed via integer\... - [22034](https://github.com/numpy/numpy/pull/22034): MAINT,TYP: Add object-overloads for the `np.generic` rich comparisons - [22035](https://github.com/numpy/numpy/pull/22035): MAINT,TYP: Allow the `squeeze` and `transpose` method to\... - [22036](https://github.com/numpy/numpy/pull/22036): BUG: Fix subarray to object cast ownership details - [22037](https://github.com/numpy/numpy/pull/22037): BUG: Use `Popen` to silently invoke f77 -v - [22038](https://github.com/numpy/numpy/pull/22038): BUG: Avoid errors on NULL during deepcopy - [22039](https://github.com/numpy/numpy/pull/22039): DOC: Add versionchanged for converter callable behavior. - [22057](https://github.com/numpy/numpy/pull/22057): MAINT: Quiet the anaconda uploads. - [22078](https://github.com/numpy/numpy/pull/22078): ENH: reorder includes for testing on top of system installations\... - [22106](https://github.com/numpy/numpy/pull/22106): TST: fix test_linear_interpolation_formula_symmetric - [22107](https://github.com/numpy/numpy/pull/22107): BUG: Fix skip condition for test_loss_of_precision\[complex256\] - [22115](https://github.com/numpy/numpy/pull/22115): BLD: Build python3.11.0rc1 wheels. 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c403c81bb8ffb1c993d0165a11493fd4bf1353d258f6997b3ee288b0a48fce77 numpy-1.23.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cf8c6aed12a935abf2e290860af8e77b26a042eb7f2582ff83dc7ed5f963340c numpy-1.23.2-cp39-cp39-win32.whl 5e28cd64624dc2354a349152599e55308eb6ca95a13ce6a7d5679ebff2962913 numpy-1.23.2-cp39-cp39-win_amd64.whl 806970e69106556d1dd200e26647e9bee5e2b3f1814f9da104a943e8d548ca38 numpy-1.23.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 2bd879d3ca4b6f39b7770829f73278b7c5e248c91d538aab1e506c628353e47f numpy-1.23.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl be6b350dfbc7f708d9d853663772a9310783ea58f6035eec649fb9c4371b5389 numpy-1.23.2-pp38-pypy38_pp73-win_amd64.whl b78d00e48261fbbd04aa0d7427cf78d18401ee0abd89c7559bbf422e5b1c7d01 numpy-1.23.2.tar.gz ``` ### 1.23.1 ``` The NumPy 1.23.1 is a maintenance release that fixes bugs discovered after the 1.23.0 release. Notable fixes are: - Fix searchsorted for float16 NaNs - Fix compilation on Apple M1 - Fix KeyError in crackfortran operator support (Slycot) The Python version supported for this release are 3.8-3.10. Contributors A total of 7 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Charles Harris - Matthias Koeppe + - Pranab Das + - Rohit Goswami - Sebastian Berg - Serge Guelton - Srimukh Sripada + Pull requests merged A total of 8 pull requests were merged for this release. - [21866](https://github.com/numpy/numpy/pull/21866): BUG: Fix discovered MachAr (still used within valgrind) - [21867](https://github.com/numpy/numpy/pull/21867): BUG: Handle NaNs correctly for float16 during sorting - [21868](https://github.com/numpy/numpy/pull/21868): BUG: Use `keepdims` during normalization in `np.average` and\... - [21869](https://github.com/numpy/numpy/pull/21869): DOC: mention changes to `max_rows` behaviour in `np.loadtxt` - [21870](https://github.com/numpy/numpy/pull/21870): BUG: Reject non integer array-likes with size 1 in delete - [21949](https://github.com/numpy/numpy/pull/21949): BLD: Make can_link_svml return False for 32bit builds on x86_64 - [21951](https://github.com/numpy/numpy/pull/21951): BUG: Reorder extern \"C\" to only apply to function declarations\... - [21952](https://github.com/numpy/numpy/pull/21952): BUG: Fix KeyError in crackfortran operator support Checksums MD5 79f0d8c114f282b834b49209d6955f98 numpy-1.23.1-cp310-cp310-macosx_10_9_x86_64.whl 42a89a88ef26b768e8933ce46b1cc2bd numpy-1.23.1-cp310-cp310-macosx_11_0_arm64.whl 1c1d68b3483eaf99b9a3583c8ac8bf47 numpy-1.23.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9d3e9f7f9b3dce6cf15209e4f25f346e numpy-1.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a9afb7c34b48d08fc50427ae6516b42d numpy-1.23.1-cp310-cp310-win32.whl a0e02823883bdfcec49309e108f65e13 numpy-1.23.1-cp310-cp310-win_amd64.whl f40cdf4ec7bb0cf31a90a4fa294323c2 numpy-1.23.1-cp38-cp38-macosx_10_9_x86_64.whl 80115a959f0fe30d6c401b2650a61c70 numpy-1.23.1-cp38-cp38-macosx_11_0_arm64.whl 1cf199b3a93960c4f269853a56a8d8eb numpy-1.23.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl aa6f0f192312c79cd770c2c395e9982a numpy-1.23.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d07bee0ea3142a96cb5e4e16aca273ca numpy-1.23.1-cp38-cp38-win32.whl 02d0734ae8ad5e18a40c6c6de18486a0 numpy-1.23.1-cp38-cp38-win_amd64.whl e1ca14acd7d83bc74bdf6ab0bb4bd195 numpy-1.23.1-cp39-cp39-macosx_10_9_x86_64.whl c9152c62b2f31e742e24bfdc97b28666 numpy-1.23.1-cp39-cp39-macosx_11_0_arm64.whl 05b0b37c92f7a7e7c01afac0a5322b40 numpy-1.23.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl d9810bb71a0ef9837e87ea5c44fcab5e numpy-1.23.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4255577f857e838f7a94e3a614ddc5eb numpy-1.23.1-cp39-cp39-win32.whl 787486e3cd87b98024ffe1c969c4db7a numpy-1.23.1-cp39-cp39-win_amd64.whl 5c7b2d1471b1b9ec6ff1cb3fe1f8ac14 numpy-1.23.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 40d5b2ff869707b0d97325ce44631135 numpy-1.23.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 44ce1e07927cc09415df9898857792da numpy-1.23.1-pp38-pypy38_pp73-win_amd64.whl 4f8636a9c1a77ca0fb923ba55378891f numpy-1.23.1.tar.gz SHA256 b15c3f1ed08df4980e02cc79ee058b788a3d0bef2fb3c9ca90bb8cbd5b8a3a04 numpy-1.23.1-cp310-cp310-macosx_10_9_x86_64.whl 9ce242162015b7e88092dccd0e854548c0926b75c7924a3495e02c6067aba1f5 numpy-1.23.1-cp310-cp310-macosx_11_0_arm64.whl e0d7447679ae9a7124385ccf0ea990bb85bb869cef217e2ea6c844b6a6855073 numpy-1.23.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3119daed207e9410eaf57dcf9591fdc68045f60483d94956bee0bfdcba790953 numpy-1.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3ab67966c8d45d55a2bdf40701536af6443763907086c0a6d1232688e27e5447 numpy-1.23.1-cp310-cp310-win32.whl 1865fdf51446839ca3fffaab172461f2b781163f6f395f1aed256b1ddc253622 numpy-1.23.1-cp310-cp310-win_amd64.whl aeba539285dcf0a1ba755945865ec61240ede5432df41d6e29fab305f4384db2 numpy-1.23.1-cp38-cp38-macosx_10_9_x86_64.whl 7e8229f3687cdadba2c4faef39204feb51ef7c1a9b669247d49a24f3e2e1617c numpy-1.23.1-cp38-cp38-macosx_11_0_arm64.whl 68b69f52e6545af010b76516f5daaef6173e73353e3295c5cb9f96c35d755641 numpy-1.23.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 1408c3527a74a0209c781ac82bde2182b0f0bf54dea6e6a363fe0cc4488a7ce7 numpy-1.23.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 47f10ab202fe4d8495ff484b5561c65dd59177949ca07975663f4494f7269e3e numpy-1.23.1-cp38-cp38-win32.whl 37e5ebebb0eb54c5b4a9b04e6f3018e16b8ef257d26c8945925ba8105008e645 numpy-1.23.1-cp38-cp38-win_amd64.whl 173f28921b15d341afadf6c3898a34f20a0569e4ad5435297ba262ee8941e77b numpy-1.23.1-cp39-cp39-macosx_10_9_x86_64.whl 876f60de09734fbcb4e27a97c9a286b51284df1326b1ac5f1bf0ad3678236b22 numpy-1.23.1-cp39-cp39-macosx_11_0_arm64.whl 35590b9c33c0f1c9732b3231bb6a72d1e4f77872390c47d50a615686ae7ed3fd numpy-1.23.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a35c4e64dfca659fe4d0f1421fc0f05b8ed1ca8c46fb73d9e5a7f175f85696bb numpy-1.23.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl c2f91f88230042a130ceb1b496932aa717dcbd665350beb821534c5c7e15881c numpy-1.23.1-cp39-cp39-win32.whl 37ece2bd095e9781a7156852e43d18044fd0d742934833335599c583618181b9 numpy-1.23.1-cp39-cp39-win_amd64.whl 8002574a6b46ac3b5739a003b5233376aeac5163e5dcd43dd7ad062f3e186129 numpy-1.23.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 5d732d17b8a9061540a10fda5bfeabca5785700ab5469a5e9b93aca5e2d3a5fb numpy-1.23.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 55df0f7483b822855af67e38fb3a526e787adf189383b4934305565d71c4b148 numpy-1.23.1-pp38-pypy38_pp73-win_amd64.whl d748ef349bfef2e1194b59da37ed5a29c19ea8d7e6342019921ba2ba4fd8b624 numpy-1.23.1.tar.gz ``` ### 1.23.0 ``` The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are: - Implementation of `loadtxt` in C, greatly improving its performance. - Exposing DLPack at the Python level for easy data exchange. - Changes to the promotion and comparisons of structured dtypes. - Improvements to f2py. See below for the details, New functions - A masked array specialization of `ndenumerate` is now available as `numpy.ma.ndenumerate`. It provides an alternative to `numpy.ndenumerate` and skips masked values by default. ([gh-20020](https://github.com/numpy/numpy/pull/20020)) - `numpy.from_dlpack` has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the `__dlpack__` and `__dlpack_device__` methods and returns a ndarray object which is generally the view of the data of the input object. ([gh-21145](https://github.com/numpy/numpy/pull/21145)) Deprecations - Setting `__array_finalize__` to `None` is deprecated. It must now be a method and may wish to call `super().__array_finalize__(obj)` after checking for `None` or if the NumPy version is sufficiently new. ([gh-20766](https://github.com/numpy/numpy/pull/20766)) - Using `axis=32` (`axis=np.MAXDIMS`) in many cases had the same meaning as `axis=None`. This is deprecated and `axis=None` must be used instead. ([gh-20920](https://github.com/numpy/numpy/pull/20920)) - The hook function `PyDataMem_SetEventHook` has been deprecated and the demonstration of its use in tool/allocation_tracking has been removed. The ability to track allocations is now built-in to python via `tracemalloc`. ([gh-20394](https://github.com/numpy/numpy/pull/20394)) - `numpy.distutils` has been deprecated, as a result of `distutils` itself being deprecated. It will not be present in NumPy for Python >= 3.12, and will be removed completely 2 years after the release of Python 3.12 For more details, see `distutils-status-migration`{.interpreted-text role="ref"}. ([gh-20875](https://github.com/numpy/numpy/pull/20875)) Expired deprecations - The `NpzFile.iteritems()` and `NpzFile.iterkeys()` methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15. ([gh-16830](https://github.com/numpy/numpy/pull/16830)) - The `alen` and `asscalar` functions have been removed. ([gh-20414](https://github.com/numpy/numpy/pull/20414)) - The `UPDATEIFCOPY` array flag has been removed together with the enum `NPY_ARRAY_UPDATEIFCOPY`. The associated (and deprecated) `PyArray_XDECREF_ERR` was also removed. These were all deprecated in 1.14. They are replaced by `WRITEBACKIFCOPY`, that requires calling `PyArray_ResoveWritebackIfCopy` before the array is deallocated. ([gh-20589](https://github.com/numpy/numpy/pull/20589)) - Exceptions will be raised during array-like creation. When an object raised an exception during access of the special attributes `__array__` or `__array_interface__`, this exception was usually ignored. This behaviour was deprecated in 1.21, and the exception will now be raised. ([gh-20835](https://github.com/numpy/numpy/pull/20835)) - Multidimensional indexing with non-tuple values is not allowed. Previously, code such as `arr[ind]` where `ind = [[0, 1], [0, 1]]` produced a `FutureWarning` and was interpreted as a multidimensional index (i.e., `arr[tuple(ind)]`). Now this example is treated like an array index over a single dimension (`arr[array(ind)]`). Multidimensional indexing with anything but a tuple was deprecated in NumPy 1.15. ([gh-21029](https://github.com/numpy/numpy/pull/21029)) - Changing to a dtype of different size in F-contiguous arrays is no longer permitted. Deprecated since Numpy 1.11.0. See below for an extended explanation of the effects of this change. ([gh-20722](https://github.com/numpy/numpy/pull/20722)) New Features crackfortran has support for operator and assignment overloading `crackfortran` parser now understands operator and assignment definitions in a module. They are added in the `body` list of the module which contains a new key `implementedby` listing the names of the subroutines or functions implementing the operator or assignment. ([gh-15006](https://github.com/numpy/numpy/pull/15006)) f2py supports reading access type attributes from derived type statements As a result, one does not need to use `public` or `private` statements to specify derived type access properties. ([gh-15844](https://github.com/numpy/numpy/pull/15844)) New parameter `ndmin` added to `genfromtxt` This parameter behaves the same as `ndmin` from `numpy.loadtxt`. ([gh-20500](https://github.com/numpy/numpy/pull/20500)) `np.loadtxt` now supports quote character and single converter function `numpy.loadtxt` now supports an additional `quotechar` keyword argument which is not set by default. Using `quotechar='"'` will read quoted fields as used by the Excel CSV dialect. Further, it is now possible to pass a single callable rather than a dictionary for the `converters` argument. ([gh-20580](https://github.com/numpy/numpy/pull/20580)) Changing to dtype of a different size now requires contiguity of only the last axis Previously, viewing an array with a dtype of a different item size required that the entire array be C-contiguous. This limitation would unnecessarily force the user to make contiguous copies of non-contiguous arrays before being able to change the dtype. This change affects not only `ndarray.view`, but other construction mechanisms, including the discouraged direct assignment to `ndarray.dtype`. This change expires the deprecation regarding the viewing of F-contiguous arrays, described elsewhere in the release notes. ([gh-20722](https://github.com/numpy/numpy/pull/20722)) Deterministic output files for F2PY For F77 inputs, `f2py` will generate `modname-f2pywrappers.f` unconditionally, though these may be empty. For free-form inputs, `modname-f2pywrappers.f`, `modname-f2pywrappers2.f90` will both be generated unconditionally, and may be empty. This allows writing generic output rules in `cmake` or `meson` and other build systems. Older behavior can be restored by passing `--skip-empty-wrappers` to `f2py`. `f2py-meson`{.interpreted-text role="ref"} details usage. ([gh-21187](https://github.com/numpy/numpy/pull/21187)) `keepdims` parameter for `average` The parameter `keepdims` was added to the functions `numpy.average` and `numpy.ma.average`. The parameter has the same meaning as it does in reduction functions such as `numpy.sum` or `numpy.mean`. ([gh-21485](https://github.com/numpy/numpy/pull/21485)) Compatibility notes 1D `np.linalg.norm` preserves float input types, even for scalar results Previously, this would promote to `float64` when the `ord` argument was not one of the explicitly listed values, e.g. `ord=3`: >>> f32 = np.float32([1, 2]) >>> np.linalg.norm(f32, 2).dtype dtype('float32') >>> np.linalg.norm(f32, 3) dtype('float64') numpy 1.22 dtype('float32') numpy 1.23 This change affects only `float32` and `float16` vectors with `ord` other than `-Inf`, `0`, `1`, `2`, and `Inf`. ([gh-17709](https://github.com/numpy/numpy/pull/17709)) Changes to structured (void) dtype promotion and comparisons In general, NumPy now defines correct, but slightly limited, promotion for structured dtypes by promoting the subtypes of each field instead of raising an exception: >>> np.result_type(np.dtype("i,i"), np.dtype("i,d")) dtype([('f0', '<i4'), ('f1', '<f8')]) For promotion matching field names, order, and titles are enforced, however padding is ignored. Promotion involving structured dtypes now always ensures native byte-order for all fields (which may change the result of `np.concatenate`) and ensures that the result will be \"packed\", i.e. all fields are ordered contiguously and padding is removed. See `structured_dtype_comparison_and_promotion`{.interpreted-text role="ref"} for further details. The `repr` of aligned structures will now never print the long form including `offsets` and `itemsize` unless the structure includes padding not guaranteed by `align=True`. In alignment with the above changes to the promotion logic, the casting safety has been updated: - `"equiv"` enforces matching names and titles. The itemsize is allowed to differ due to padding. - `"safe"` allows mismatching field names and titles - The cast safety is limited by the cast safety of each included field. - The order of fields is used to decide cast safety of each individual field. Previously, the field names were used and only unsafe casts were possible when names mismatched. The main important change here is that name mismatches are now considered \"safe\" casts. ([gh-19226](https://github.com/numpy/numpy/pull/19226)) `NPY_RELAXED_STRIDES_CHECKING` has been removed NumPy cannot be compiled with `NPY_RELAXED_STRIDES_CHECKING=0` anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition. ([gh-20220](https://github.com/numpy/numpy/pull/20220)) `np.loadtxt` has recieved several changes The row counting of `numpy.loadtxt` was fixed. `loadtxt` ignores fully empty lines in the file, but counted them towards `max_rows`. When `max_rows` is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than `max_rows` rows even though more data was available to be read. If