08 Jul 22:12

charris

609c539

v1.25.1

NumPy 1.25.1 Release Notes

NumPy 1.25.1 is a maintenance release that fixes bugs and regressions
discovered after the 1.25.0 release. The Python versions supported by
this release are 3.9-3.11.

Contributors

A total of 10 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Andrew Nelson
Charles Harris
Developer-Ecosystem-Engineering
Hood Chatham
Nathan Goldbaum
Rohit Goswami
Sebastian Berg
Tim Paine +
dependabot[bot]
matoro +

Pull requests merged

A total of 14 pull requests were merged for this release.

#23968: MAINT: prepare 1.25.x for further development
#24036: BLD: Port long double identification to C for meson
#24037: BUG: Fix reduction return NULL to be goto fail
#24038: BUG: Avoid undefined behavior in array.astype()
#24039: BUG: Ensure __array_ufunc__ works without any kwargs passed
#24117: MAINT: Pin urllib3 to avoid anaconda-client bug.
#24118: TST: Pin pydantic<2 in Pyodide workflow
#24119: MAINT: Bump pypa/cibuildwheel from 2.13.0 to 2.13.1
#24120: MAINT: Bump actions/checkout from 3.5.2 to 3.5.3
#24122: BUG: Multiply or Divides using SIMD without a full vector can...
#24127: MAINT: testing for IS_MUSL closes #24074
#24128: BUG: Only replace dtype temporarily if dimensions changed
#24129: MAINT: Bump actions/setup-node from 3.6.0 to 3.7.0
#24134: BUG: Fix private procedures in f2py modules

Checksums

MD5

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Assets 5

26 Jun 13:46

charris

v1.24.4

9315a90

v1.24.4

NumPy 1.24.4 Release Notes

NumPy 1.24.4 is a maintenance release that fixes a few bugs
discovered after the 1.24.3 release. It is the last planned
release in the 1.24.x cycle. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 4 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

Bas van Beek
Charles Harris
Sebastian Berg
Hongyang Peng +

Pull requests merged

A total of 6 pull requests were merged for this release.

#23720: MAINT, BLD: Pin rtools to version 4.0 for Windows builds.
#23739: BUG: fix the method for checking local files for 1.24.x
#23760: MAINT: Copy rtools installation from install-rtools.
#23761: BUG: Fix masked array ravel order for A (and somewhat K)
#23890: TYP,DOC: Annotate and document the metadata parameter of...
#23994: MAINT: Update rtools installation

Checksums

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Assets 5

17 Jun 15:21

charris

v1.25.0

db4f439

v1.25.0

NumPy 1.25.0 Release Notes

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

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

We will be releasing a NumPy 1.26 when Python 3.12 comes out. That is
needed because distutils has been dropped by Python 3.12 and we will be
switching to using meson for future builds. The next mainline release
will be NumPy 2.0.0. We plan that the 2.0 series will still support
downstream projects built against earlier versions of NumPy.

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

Deprecations

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

(gh-22638)
np.finfo(None) is deprecated.

(gh-23011)
np.round_ is deprecated. Use np.round instead.

(gh-23302)
np.product is deprecated. Use np.prod instead.

(gh-23314)
np.cumproduct is deprecated. Use np.cumprod instead.

(gh-23314)
np.sometrue is deprecated. Use np.any instead.

(gh-23314)
np.alltrue is deprecated. Use np.all instead.

(gh-23314)
Only ndim-0 arrays are treated as scalars. NumPy used to treat all
arrays of size 1 (e.g., np.array([3.14])) as scalars. In the
future, this will be limited to arrays of ndim 0 (e.g.,
np.array(3.14)). The following expressions will report a
deprecation warning:
```
a = np.array([3.14])
float(a)  # better: a[0] to get the numpy.float or a.item()

b = np.array([[3.14]])
c = numpy.random.rand(10)
c[0] = b  # better: c[0] = b[0, 0]
```
(gh-10615)
numpy.find_common_type is now deprecated and its use
should be replaced with either numpy.result_type or
numpy.promote_types. Most users leave the second
scalar_types argument to find_common_type as [] in which case
np.result_type and np.promote_types are both faster and more
robust. When not using scalar_types the main difference is that
the replacement intentionally converts non-native byte-order to
native byte order. Further, find_common_type returns object
dtype rather than failing promotion. This leads to differences when
the inputs are not all numeric. Importantly, this also happens for
e.g. timedelta/datetime for which NumPy promotion rules are
currently sometimes surprising.

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

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

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

(gh-22539)

Expired deprecations

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

(gh-22638)
+arr will now raise an error when the dtype is not numeric (and
positive is undefined).

(gh-22998)
A sequence must now be passed into the stacking family of functions
(stack, vstack, hstack, dstack and column_stack).

(gh-23019)
np.clip now defaults to same-kind casting. Falling back to unsafe
casting was deprecated in NumPy 1.17.

(gh-23403)
np.clip will now propagate np.nan values passed as min or
max. Previously, a scalar NaN was usually ignored. This was
deprecated in NumPy 1.17.

(gh-23403)
The np.dual submodule has been removed.

(gh-23480)
NumPy now always ignores sequence behavior for an array-like
(defining one of the array protocols). (Deprecation started NumPy
1.20)

(gh-23660)
The niche FutureWarning when casting to a subarray dtype in
astype or the array creation functions such as asarray is now
finalized. The behavior is now always the same as if the subarray
dtype was wrapped into a single field (which was the workaround,
previously). (FutureWarning since NumPy 1.20)

(gh-23666)
== and != warnings have been finalized. The == and !=
operators on arrays now always:
- raise errors that occur during comparisons such as when the
  arrays have incompatible shapes
  (np.array([1, 2]) == np.array([1, 2, 3])).
- return an array of all True or all False when values are
  fundamentally not comparable (e.g. have different dtypes). An
  example is np.array(["a"]) == np.array([1]).
  
  This mimics the Python behavior of returning False and True
  when comparing incompatible types like "a" == 1 and
  "a" != 1. For a long time these gave DeprecationWarning or
  FutureWarning.
(gh-22707)
Nose support has been removed. NumPy switched to using pytest in
2018 and nose has been unmaintained for many years. We have kept
NumPy's nose support to avoid breaking downstream projects who
might have been using it and not yet switched to pytest or some
other testing framework. With the arrival of Python 3.12, unpatched
nose will raise an error. It is time to move on.

Decorators removed:
- raises
- slow
- setastest
- skipif
- knownfailif
- deprecated
- parametrize
- _needs_refcount
These are not to be confused with pytest versions with similar
names, e.g., pytest.mark.slow, pytest.mark.skipif,
pytest.mark.parametrize.

Functions removed:
- Tester
- import_nose
- run_module_suite
(gh-23041)
The numpy.testing.utils shim has been removed. Importing from the
numpy.testing.utils shim has been deprecated since 2019, the shim
has now been removed. All imports should be made directly from
numpy.testing.

(gh-23060)
The environment variable to disable dispatching has been removed.
Support for the NUMPY_EXPERIMENTAL_ARRAY_FUNCTION environment
variable has been removed. This variable disabled dispatching with
__array_function__.

(gh-23376)
Support for y= as an alias of out= has been removed. The fix,
isposinf and isneginf functions allowed using y= as a
(deprecated) alias for out=. This is no longer supported.

(gh-23376)

Compatibility notes

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

(gh-23229)
When comparing datetimes and timedelta using np.equal or
np.not_equal numpy previously allowed the comparison with
casting="unsafe". This operation now fails. Forcing the output
dtype using the dtype kwarg can make the operation succeed, but we
do not recommend it.

(gh-22707)
When loading data from a file handle using np.load, if the handle
is at the end of file, as can happen when reading multiple arrays by
calling np.load repeatedly, numpy previously raised ValueError
if allow_pickle=False, and OSError if allow_pickle=True. Now
it raises EOFError instead, in both cases.

(gh-23105)

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

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

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

(gh-22575)

Cython `long_t` and `ulong_t` removed

long_t and ulong_t wer...

Assets 5

22 Apr 21:58

charris

v1.24.3

14bb214

v1.24.3

NumPy 1.24.3 Release Notes

NumPy 1.24.3 is a maintenance release that fixes bugs and regressions
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: BUG: fix for f2py string scalars (#23194)
#23207: BUG: datetime64/timedelta64 comparisons return NotImplemented
#23208: MAINT: Pin matplotlib to version 3.6.3 for refguide checks
#23221: DOC: Fix matplotlib error in documentation
#23226: CI: Ensure submodules are initialized in gitpod.
#23341: TYP: Replace duplicate reduce in ufunc type signature with reduceat.
#23342: TYP: Remove duplicate CLIP/WRAP/RAISE in __init__.pyi.
#23343: TYP: Mark d argument to fftfreq and rfftfreq as optional...
#23344: TYP: Add type annotations for comparison operators to MaskedArray.
#23345: TYP: Remove some stray type-check-only imports of msort
#23370: BUG: Ensure like is only stripped for like= dispatched functions
#23543: BUG: fix loading and storing big arrays on s390x
#23544: MAINT: Bump larsoner/circleci-artifacts-redirector-action
#23634: BUG: Ignore invalid and overflow warnings in masked setitem
#23635: BUG: Fix masked array raveling when order="A" or order="K"
#23636: MAINT: Update conftest for newer hypothesis versions
#23637: BUG: Fix bug in parsing F77 style string arrays.

Checksums

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SHA256

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Assets 5

05 Feb 20:16

charris

v1.24.2

85f38ab

v1.24.2

NumPy 1.24.2 Release Notes

NumPy 1.24.2 is a maintenance release that fixes bugs and regressions
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: MAINT: Update python 3.11-dev to 3.11.
#22966: DOC: Remove dangling deprecation warning
#22967: ENH: Detect CPU features on FreeBSD/powerpc64*
#22968: BUG: np.loadtxt cannot load text file with quoted fields separated...
#22969: TST: Add fixture to avoid issue with randomizing test order.
#22970: BUG: Fix fill violating read-only flag. (#22959)
#22971: MAINT: Add additional information to missing scalar AttributeError
#22972: MAINT: Move export for scipy arm64 helper into main module
#22976: BUG, SIMD: Fix spurious invalid exception for sin/cos on arm64/clang
#22989: BUG: Ensure correct loop order in sin, cos, and arctan2
#23030: DOC: Add version added information for the strict parameter in...
#23031: BUG: use _Alignof rather than offsetof() on most compilers
#23147: BUG: Fix for npyv__trunc_s32_f32 (VXE)
#23148: BUG: Fix integer / float scalar promotion
#23149: BUG: Add missing <type_traits> header.
#23150: TYP, MAINT: Add a missing explicit Any parameter to the npt.ArrayLike...
#23161: BLD: remove redundant definition of npy_nextafter [wheel build]

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SHA256

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Assets 5

26 Dec 13:56

charris

v1.24.1

a28f4f2

v1.24.1

NumPy 1.24.1 Release Notes

NumPy 1.24.1 is a maintenance release that fixes bugs and regressions
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: BLD: add workaround in setup.py for newer setuptools
#22830: BLD: CIRRUS_TAG redux
#22831: DOC: fix a couple typos in 1.23 notes
#22832: BUG: Fix refcounting errors found using pytest-leaks
#22834: BUG, SIMD: Fix invalid value encountered in several ufuncs
#22837: TST: ignore more np.distutils.log imports
#22839: BUG: Do not use getdata() in np.ma.masked_invalid
#22847: BUG: Ensure correct behavior for rows ending in delimiter in...
#22848: BUG, SIMD: Fix the bitmask of the boolean comparison
#22857: BLD: Help raspian arm + clang 13 about __builtin_mul_overflow
#22858: API: Ensure a full mask is returned for masked_invalid
#22866: BUG: Polynomials now copy properly (#22669)
#22867: BUG, SIMD: Fix memory overlap in ufunc comparison loops
#22868: BUG: Fortify string casts against floating point warnings
#22875: TST: Ignore nan-warnings in randomized out tests
#22883: MAINT: restore npymath implementations needed for freebsd
#22884: BUG: Fix integer overflow in in1d for mixed integer dtypes #22877
#22887: BUG: Use whole file for encoding checks with charset_normalizer.

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Assets 5

18 Dec 20:43

charris

v1.24.0

8cec820

v1.24.0

NumPy 1.24 Release Notes

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,

This release supports Python versions 3.8-3.11.

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)

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. For negative values, it will also work to view the
array: np.array(-1, dtype=np.int8).view(np.uint8). In some cases,
using np.iinfo(np.uint8).max or val % 2**8 may also work well.

In rare cases input data may mix both negative values and very large
unsigned values (i.e. -1 and 2**63). There it is unfortunately
necessary to use % on the Python value or use signed or unsigned
conversion depending on whether negative values are expected.

(gh-22385)

Deprecate `msort`

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

(gh-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)

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

(gh-22004)
Support for Visual Studio 2015 and earlier has been removed.
Support for the Windows Interix POSIX interop layer has been
removed.

(gh-22139)
Support for Cygwin < 3.3 has been removed.

(gh-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)
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)
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)
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)

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)

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)

Returned arrays respect uniqueness of dtype kwarg objects

When the dtype keyword argument is used with
:pynp.array(){.interpreted-text role="func"} or
:pyasarray(){.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)

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)

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)

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)

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)

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)

`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)

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)

`casting` ...

Assets 5

04 Dec 13:56

charris

v1.24.0rc2

754e59d

v1.24.0rc2 Pre-release

Pre-release

NumPy 1.24 Release Notes

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)

Conversion of out-of-bound Python integers

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)

Deprecate `msort`

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

(gh-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)

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

(gh-22004)
Support for Visual Studio 2015 and earlier has been removed.
Support for the Windows Interix POSIX interop layer has been
removed.

(gh-22139)
Support for cygwin < 3.3 has been removed.

(gh-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)
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)
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)
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)

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)

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)

Returned arrays respect uniqueness of dtype kwarg objects

>>> 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)

DLPack export raises `BufferError`

(gh-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)

New Features

New attribute `symbol` added to polynomial classes

>>> 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)

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)

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)

`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)

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)

`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 a...

Assets 5

24 Nov 13:17

charris

v1.24.0rc1

4f0a3eb

v1.24.0rc1 Pre-release

Pre-release

NumPy 1.24 Release Notes

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)

Conversion of out-of-bound Python integers

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)

Deprecate `msort`

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

(gh-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)

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

(gh-22004)
Support for Visual Studio 2015 and earlier has been removed.
Support for the Windows Interix POSIX interop layer has been
removed.

(gh-22139)
Support for cygwin < 3.3 has been removed.

(gh-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)
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)
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)
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)

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)

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)

Returned arrays respect uniqueness of dtype kwarg objects

>>> 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)

DLPack export raises `BufferError`

(gh-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)

New Features

New attribute `symbol` added to polynomial classes

>>> 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)

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)

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)

`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)

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)

`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 a...

Assets 5

20 Nov 01:40

charris

v1.23.5

de82cd9

v1.23.5

NumPy 1.23.5 Release Notes

NumPy 1.23.5 is a maintenance release that fixes bugs discovered after
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: TST, MAINT: Replace most setup with setup_method (also teardown)
#22490: MAINT, CI: Switch to cygwin/cygwin-install-action@v2
#22494: TST: Make test_partial_iteration_cleanup robust but require leak...
#22592: MAINT: Ensure graceful handling of large header sizes
#22593: TYP: Spelling alignment for array flag literal
#22594: BUG: Fix bounds checking for random.logseries
#22595: DEV: Update GH actions and Dockerfile for Gitpod
#22596: CI: Only fetch in actions/checkout
#22597: BUG: Decrement ref count in gentype_reduce if allocated memory...
#22625: BUG: Histogramdd breaks on big arrays in Windows

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01dd17cbb340bf0fc23981e52e1d18a9d4050792e8fb8363cecbf066a84b827d  numpy-1.23.5-pp38-pypy38_pp73-win_amd64.whl
1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a  numpy-1.23.5.tar.gz

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Assets 5

Releases: numpy/numpy

v1.25.1

NumPy 1.25.1 Release Notes

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Pull requests merged

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v1.24.4

NumPy 1.24.4 Release Notes

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Pull requests merged

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v1.25.0

NumPy 1.25.0 Release Notes

Deprecations

Expired deprecations

Compatibility notes

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

Cython long_t and ulong_t removed

v1.24.3

NumPy 1.24.3 Release Notes

Contributors

Pull requests merged

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v1.24.2

NumPy 1.24.2 Release Notes

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Pull requests merged

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v1.24.1

NumPy 1.24.1 Release Notes

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Pull requests merged

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v1.24.0

NumPy 1.24 Release Notes

Deprecations

Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose

Conversion of out-of-bound Python integers

Deprecate msort

np.str0 and similar are now deprecated

Expired deprecations

Compatibility notes

array.fill(scalar) may behave slightly different

Subarray to object cast now copies

Returned arrays respect uniqueness of dtype kwarg objects

DLPack export raises BufferError

NumPy builds are no longer tested on GCC-6

New Features

New attribute symbol added to polynomial classes

F2PY support for Fortran character strings

New function np.show_runtime

strict option for testing.assert_array_equal

New parameter equal_nan added to np.unique

casting ...

v1.24.0rc2

NumPy 1.24 Release Notes

Deprecations

Deprecate fastCopyAndTranspose and PyArray_CopyAndTranspose

Conversion of out-of-bound Python integers

Deprecate msort

np.str0 and similar are now deprecated

Expired deprecations

Compatibility notes

array.fill(scalar) may behave slightly different

Subarray to object cast now copies

Returned arrays respect uniqueness of dtype kwarg objects

DLPack export raises BufferError

NumPy builds are no longer tested on GCC-6

New Features

New attribute symbol added to polynomial classes

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

Cython `long_t` and `ulong_t` removed

Deprecate `msort`

`np.str0` and similar are now deprecated

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

DLPack export raises `BufferError`

New attribute `symbol` added to polynomial classes

F2PY support for Fortran `character` strings

New function `np.show_runtime`

`strict` option for `testing.assert_array_equal`

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

`casting` ...

Deprecate `msort`

`np.str0` and similar are now deprecated

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

DLPack export raises `BufferError`

New attribute `symbol` added to polynomial classes

F2PY support for Fortran `character` strings

New function `np.show_runtime`

`strict` option for `testing.assert_array_equal`

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

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

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

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

Deprecate `msort`

`np.str0` and similar are now deprecated

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

DLPack export raises `BufferError`

New attribute `symbol` added to polynomial classes

F2PY support for Fortran `character` strings

New function `np.show_runtime`

`strict` option for `testing.assert_array_equal`

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

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

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

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