Update dependency numpy to v1.21.0 [SECURITY]

[renovate]

This PR contains the following updates:

Package	Change	Age	Adoption	Passing	Confidence
numpy (source)	==1.19.2 -> ==1.21.0
numpy (source)	==1.9.2 -> ==1.21.0

GitHub Vulnerability Alerts

CVE-2021-33430

A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service.

---

Release Notes

numpy/numpy

v1.21.0

Compare Source

NumPy 1.21.0 Release Notes

The NumPy 1.21.0 release highlights are

• continued SIMD work covering more functions and platforms,
• initial work on the new dtype infrastructure and casting,
• universal2 wheels for Python 3.8 and Python 3.9 on Mac,
• improved documentation,
• improved annotations,
• new PCG64DXSM bitgenerator for random numbers.

In addition there are the usual large number of bug fixes and other
improvements.

The Python versions supported for this release are 3.7-3.9. Official
support for Python 3.10 will be added when it is released.

⚠️ Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

• Optimization level -O3 results in many wrong warnings when running the tests.
• On some hardware NumPy will hang in an infinite loop.

New functions

Add PCG64DXSM BitGenerator

Uses of the PCG64 BitGenerator in a massively-parallel context have
been shown to have statistical weaknesses that were not apparent at the
first release in numpy 1.17. Most users will never observe this weakness
and are safe to continue to use PCG64. We have introduced a new
PCG64DXSM BitGenerator that will eventually become the new default
BitGenerator implementation used by default_rng in future releases.
PCG64DXSM solves the statistical weakness while preserving the
performance and the features of PCG64.

See upgrading-pcg64 for more details.

(gh-18906)

Expired deprecations

• The shape argument numpy.unravel_index cannot be
  passed as dims keyword argument anymore. (Was deprecated in NumPy
  1.16.)

  (gh-17900)

• The function PyUFunc_GenericFunction has been disabled. It was
  deprecated in NumPy 1.19. Users should call the ufunc directly using
  the Python API.

  (gh-18697)

• The function PyUFunc_SetUsesArraysAsData has been disabled. It was
  deprecated in NumPy 1.19.

  (gh-18697)

• The class PolyBase has been removed (deprecated in numpy 1.9.0).
  Please use the abstract ABCPolyBase class instead.

  (gh-18963)

• The unused PolyError and PolyDomainError exceptions are removed.

  (gh-18963)

Deprecations

The .dtype attribute must return a dtype

A DeprecationWarning is now given if the .dtype attribute of an
object passed into np.dtype or as a dtype=obj argument is not a
dtype. NumPy will stop attempting to recursively coerce the result of
.dtype.

(gh-13578)

Inexact matches for numpy.convolve and numpy.correlate are deprecated

numpy.convolve and numpy.correlate now
emit a warning when there are case insensitive and/or inexact matches
found for mode argument in the functions. Pass full "same",
"valid", "full" strings instead of "s", "v", "f" for the
mode argument.

(gh-17492)

np.typeDict has been formally deprecated

np.typeDict is a deprecated alias for np.sctypeDict and has been so
for over 14 years
(6689502).
A deprecation warning will now be issued whenever getting np.typeDict.

(gh-17586)

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. A warning is now given when the exception is anything
but AttributeError. To silence the warning, the type raising the
exception has to be adapted to raise an AttributeError.

(gh-19001)

Four ndarray.ctypes methods have been deprecated

Four methods of the ndarray.ctypes object have been
deprecated, as they are (undocumentated) implementation artifacts of
their respective properties.

The methods in question are:

• _ctypes.get_data (use _ctypes.data instead)
• _ctypes.get_shape (use _ctypes.shape instead)
• _ctypes.get_strides (use _ctypes.strides instead)
• _ctypes.get_as_parameter (use _ctypes._as_parameter_ instead)

(gh-19031)

Expired deprecations

• The shape argument numpy.unravel_index] cannot be
  passed as dims keyword argument anymore. (Was deprecated in NumPy
  1.16.)

  (gh-17900)

• The function PyUFunc_GenericFunction has been disabled. It was
  deprecated in NumPy 1.19. Users should call the ufunc directly using
  the Python API.

  (gh-18697)

• The function PyUFunc_SetUsesArraysAsData has been disabled. It was
  deprecated in NumPy 1.19.

  (gh-18697)

Remove deprecated PolyBase and unused PolyError and PolyDomainError

The class PolyBase has been removed (deprecated in numpy 1.9.0).
Please use the abstract ABCPolyBase class instead.

Furthermore, the unused PolyError and PolyDomainError exceptions are
removed from the numpy.polynomial.

(gh-18963)

Compatibility notes

Error type changes in universal functions

The universal functions may now raise different errors on invalid input
in some cases. The main changes should be that a RuntimeError was
replaced with a more fitting TypeError. When multiple errors were
present in the same call, NumPy may now raise a different one.

(gh-15271)

__array_ufunc__ argument validation

NumPy will now partially validate arguments before calling
__array_ufunc__. Previously, it was possible to pass on invalid
arguments (such as a non-existing keyword argument) when dispatch was
known to occur.

(gh-15271)

__array_ufunc__ and additional positional arguments

Previously, all positionally passed arguments were checked for
__array_ufunc__ support. In the case of reduce, accumulate, and
reduceat all arguments may be passed by position. This means that when
they were passed by position, they could previously have been asked to
handle the ufunc call via __array_ufunc__. Since this depended on the
way the arguments were passed (by position or by keyword), NumPy will
now only dispatch on the input and output array. For example, NumPy will
never dispatch on the where array in a reduction such as
np.add.reduce.

(gh-15271)

Validate input values in Generator.uniform

Checked that high - low >= 0 in np.random.Generator.uniform. Raises
ValueError if low > high. Previously out-of-order inputs were
accepted and silently swapped, so that if low > high, the value
generated was high + (low - high) * random().

(gh-17921)

/usr/include removed from default include paths

The default include paths when building a package with numpy.distutils
no longer include /usr/include. This path is normally added by the
compiler, and hardcoding it can be problematic. In case this causes a
problem, please open an issue. A workaround is documented in PR 18658.

(gh-18658)

Changes to comparisons with dtype=...

When the dtype= (or signature) arguments to comparison ufuncs
(equal, less, etc.) is used, this will denote the desired output
dtype in the future. This means that:

np.equal(2, 3, dtype=object)

will give a FutureWarning that it will return an object array in the
future, which currently happens for:

np.equal(None, None, dtype=object)

due to the fact that np.array(None) is already an object array. (This
also happens for some other dtypes.)

Since comparisons normally only return boolean arrays, providing any
other dtype will always raise an error in the future and give a
DeprecationWarning now.

(gh-18718)

Changes to dtype and signature arguments in ufuncs

The universal function arguments dtype and signature which are also
valid for reduction such as np.add.reduce (which is the implementation
for np.sum) will now issue a warning when the dtype provided is not
a "basic" dtype.

NumPy almost always ignored metadata, byteorder or time units on these
inputs. NumPy will now always ignore it and raise an error if byteorder
or time unit changed. The following are the most important examples of
changes which will give the error. In some cases previously the
information stored was not ignored, in all of these an error is now
raised:

Previously ignored the byte-order (affect if non-native)

np.add(3, 5, dtype=">i32")

The biggest impact is for timedelta or datetimes:

arr = np.arange(10, dtype="m8[s]")

The examples always ignored the time unit "ns":

np.add(arr, arr, dtype="m8[ns]")
np.maximum.reduce(arr, dtype="m8[ns]")

The following previously did use "ns" (as opposed to arr.dtype)

np.add(3, 5, dtype="m8[ns]")  # Now return generic time units
np.maximum(arr, arr, dtype="m8[ns]")  # Now returns "s" (from `arr`)

The same applies for functions like np.sum which use these internally.
This change is necessary to achieve consistent handling within NumPy.

If you run into these, in most cases pass for example
dtype=np.timedelta64 which clearly denotes a general timedelta64
without any unit or byte-order defined. If you need to specify the
output dtype precisely, you may do so by either casting the inputs or
providing an output array using out=.

NumPy may choose to allow providing an exact output dtype here in the
future, which would be preceded by a FutureWarning.

(gh-18718)

Ufunc signature=... and dtype= generalization and casting

The behaviour for np.ufunc(1.0, 1.0, signature=...) or
np.ufunc(1.0, 1.0, dtype=...) can now yield different loops in 1.21
compared to 1.20 because of changes in promotion. When signature was
previously used, the casting check on inputs was relaxed, which could
lead to downcasting inputs unsafely especially if combined with
casting="unsafe".

Casting is now guaranteed to be safe. If a signature is only partially
provided, for example using signature=("float64", None, None), this
could lead to no loop being found (an error). In that case, it is
necessary to provide the complete signature to enforce casting the
inputs. If dtype="float64" is used or only outputs are set (e.g.
signature=(None, None, "float64") the is unchanged. We expect that
very few users are affected by this change.

Further, the meaning of dtype="float64" has been slightly modified and
now strictly enforces only the correct output (and not input) DTypes.
This means it is now always equivalent to:

signature=(None, None, "float64")

(If the ufunc has two inputs and one output). Since this could lead to
no loop being found in some cases, NumPy will normally also search for
the loop:

signature=("float64", "float64", "float64")

if the first search failed. In the future, this behaviour may be
customized to achieve the expected results for more complex ufuncs. (For
some universal functions such as np.ldexp inputs can have different
DTypes.)

(gh-18880)

Distutils forces strict floating point model on clang

NumPy distutils will now always add the -ffp-exception-behavior=strict
compiler flag when compiling with clang. Clang defaults to a non-strict
version, which allows the compiler to generate code that does not set
floating point warnings/errors correctly.

(gh-19049)

C API changes

Use of ufunc->type_resolver and "type tuple"

NumPy now normalizes the "type tuple" argument to the type resolver
functions before calling it. Note that in the use of this type resolver
is legacy behaviour and NumPy will not do so when possible. Calling
ufunc->type_resolver or PyUFunc_DefaultTypeResolver is strongly
discouraged and will now enforce a normalized type tuple if done. Note
that this does not affect providing a type resolver, which is expected
to keep working in most circumstances. If you have an unexpected
use-case for calling the type resolver, please inform the NumPy
developers so that a solution can be found.

(gh-18718)

New Features

Added a mypy plugin for handling platform-specific numpy.number precisions

A mypy plugin is now available for
automatically assigning the (platform-dependent) precisions of certain
numpy.number subclasses, including the likes of
numpy.int_, numpy.intp and
numpy.longlong. See the documentation on
scalar types <arrays.scalars.built-in>
for a comprehensive overview of the affected classes.

Note that while usage of the plugin is completely optional, without it
the precision of above-mentioned classes will be inferred as
typing.Any.

To enable the plugin, one must add it to their mypy [configuration file]
(https://mypy.readthedocs.io/en/stable/config_file.html):

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-17843)

Let the mypy plugin manage extended-precision numpy.number subclasses

The mypy plugin, introduced in
numpy/numpy#​17843, has
been expanded: the plugin now removes annotations for platform-specific
extended-precision types that are not available to the platform in
question. For example, it will remove numpy.float128
when not available.

Without the plugin all extended-precision types will, as far as mypy
is concerned, be available on all platforms.

To enable the plugin, one must add it to their mypy configuration
file:

[mypy]
plugins = numpy.typing.mypy_plugin
                                                                        cn

(gh-18322)

New min_digits argument for printing float values

A new min_digits argument has been added to the dragon4 float printing
functions numpy.format_float_positional and
numpy.format_float_scientific. This kwd guarantees
that at least the given number of digits will be printed when printing
in unique=True mode, even if the extra digits are unnecessary to
uniquely specify the value. It is the counterpart to the precision
argument which sets the maximum number of digits to be printed. When
unique=False in fixed precision mode, it has no effect and the precision
argument fixes the number of digits.

(gh-18629)

f2py now recognizes Fortran abstract interface blocks

numpy.f2py can now parse abstract interface blocks.

(gh-18695)

BLAS and LAPACK configuration via environment variables

Autodetection of installed BLAS and LAPACK libraries can be bypassed by
using the NPY_BLAS_LIBS and NPY_LAPACK_LIBS environment variables.
Instead, the link flags in these environment variables will be used
directly, and the language is assumed to be F77. This is especially
useful in automated builds where the BLAS and LAPACK that are installed
are known exactly. A use case is replacing the actual implementation at
runtime via stub library links.

If NPY_CBLAS_LIBS is set (optional in addition to NPY_BLAS_LIBS),
this will be used as well, by defining HAVE_CBLAS and appending the
environment variable content to the link flags.

(gh-18737)

A runtime-subcriptable alias has been added for ndarray

numpy.typing.NDArray has been added, a runtime-subscriptable alias for
np.ndarray[Any, np.dtype[~Scalar]]. The new type alias can be used for
annotating arrays with a given dtype and unspecified shape.

NumPy does not support the annotating of array shapes as of 1.21,
this is expected to change in the future though (see
646{.interpreted-text role="pep"}).

Examples

>>> import numpy as np
>>> import numpy.typing as npt

>>> print(npt.NDArray)
numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]]

>>> print(npt.NDArray[np.float64])
numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]

>>> NDArrayInt = npt.NDArray[np.int_]
>>> a: NDArrayInt = np.arange(10)

>>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
...     return np.array(a)

(gh-18935)

Improvements

Arbitrary period option for numpy.unwrap

The size of the interval over which phases are unwrapped is no longer
restricted to 2 * pi. This is especially useful for unwrapping
degrees, but can also be used for other intervals.

>>> phase_deg = np.mod(np.linspace(0,720,19), 360) - 180
>>> phase_deg
array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
       -180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
       -180.])

>>> unwrap(phase_deg, period=360)
array([-180., -140., -100.,  -60.,  -20.,   20.,   60.,  100.,  140.,
        180.,  220.,  260.,  300.,  340.,  380.,  420.,  460.,  500.,
        540.])

(gh-16987)

np.unique now returns single NaN

When np.unique operated on an array with multiple NaN entries, its
return included a NaN for each entry that was NaN in the original
array. This is now improved such that the returned array contains just
one NaN as the last element.

Also for complex arrays all NaN values are considered equivalent (no
matter whether the NaN is in the real or imaginary part). As the
representant for the returned array the smallest one in the
lexicographical order is chosen - see np.sort for how the
lexicographical order is defined for complex arrays.

(gh-18070)

Generator.rayleigh and Generator.geometric performance improved

The performance of Rayleigh and geometric random variate generation in
Generator has improved. These are both transformation of exponential
random variables and the slow log-based inverse cdf transformation has
been replaced with the Ziggurat-based exponential variate generator.

This change breaks the stream of variates generated when variates from
either of these distributions are produced.

(gh-18666)

Placeholder annotations have been improved

All placeholder annotations, that were previously annotated as
typing.Any, have been improved. Where appropiate they have been
replaced with explicit function definitions, classes or other
miscellaneous objects.

(gh-18934)

Performance improvements

Improved performance in integer division of NumPy arrays

Integer division of NumPy arrays now uses
libdivide when the divisor is a constant. With
the usage of libdivide and other minor optimizations, there is a large
speedup. The // operator and np.floor_divide makes use of the new
changes.

(gh-17727)

Improve performance of np.save and np.load for small arrays

np.save is now a lot faster for small arrays.

np.load is also faster for small arrays, but only when serializing
with a version >= (3, 0).

Both are done by removing checks that are only relevant for Python 2,
while still maintaining compatibility with arrays which might have been
created by Python 2.

(gh-18657)

Changes

numpy.piecewise output class now matches the input class

When numpy.ndarray subclasses are used on input to
numpy.piecewise, they are passed on to the functions.
The output will now be of the same subclass as well.

(gh-18110)

Enable Accelerate Framework

With the release of macOS 11.3, several different issues that numpy was
encountering when using Accelerate Framework's implementation of BLAS
and LAPACK should be resolved. This change enables the Accelerate
Framework as an option on macOS. If additional issues are found, please
file a bug report against Accelerate using the developer feedback
assistant tool (https://developer.apple.com/bug-reporting/). We intend
to address issues promptly and plan to continue supporting and updating
our BLAS and LAPACK libraries.

(gh-18874)

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cf680682ad0a3bef56dae200dbcbac2d57294a73e5b0f9864955e7dd7c2c2491  numpy-1.21.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d95d16204cd51ff1a1c8d5f9958ce90ae190be81d348b514f9be39f878b8044a  numpy-1.21.0-cp39-cp39-win32.whl
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3c40e6b860220ed862e8097b8f81c9af6d7405b723f4a7af24a267b46f90e461  numpy-1.21.0-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
b662c841b29848c04d9134f31dbaa7d4c8e673f45bb3a5f28d02f49c424d558a  numpy-1.21.0.tar.gz
e80fe25cba41c124d04c662f33f6364909b985f2eb5998aaa5ae4b9587242cce  numpy-1.21.0.zip

v1.20.3

Compare Source

NumPy 1.20.3 Release Notes

NumPy 1.20.3 is a bugfix release containing several fixes merged to the
main branch after the NumPy 1.20.2 release.

Contributors

A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Anne Archibald
• Bas van Beek
• Charles Harris
• Dong Keun Oh +
• Kamil Choudhury +
• Sayed Adel
• Sebastian Berg

Pull requests merged

A total of 15 pull requests were merged for this release.

• #​18763: BUG: Correct datetime64 missing type overload for datetime.date...
• #​18764: MAINT: Remove __all__ in favor of explicit re-exports
• #​18768: BLD: Strip extra newline when dumping gfortran version on MacOS
• #​18769: BUG: fix segfault in object/longdouble operations
• #​18794: MAINT: Use towncrier build explicitly
• #​18887: MAINT: Relax certain integer-type constraints
• #​18915: MAINT: Remove unsafe unions and ABCs from return-annotations
• #​18921: MAINT: Allow more recursion depth for scalar tests.
• #​18922: BUG: Initialize the full nditer buffer in case of error
• #​18923: BLD: remove unnecessary flag -faltivec on macOS
• #​18924: MAINT, CI: treats _SIMD module build warnings as errors through...
• #​18925: BUG: for MINGW, threads.h existence test requires GLIBC > 2.12
• #​18941: BUG: Make changelog recognize gh- as a PR number prefix.
• #​18948: REL, DOC: Prepare for the NumPy 1.20.3 release.
• #​18953: BUG: Fix failing mypy test in 1.20.x.

Checksums

MD5

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949d9114af9accc25ede1daa359c4227  numpy-1.20.3.zip

SHA256

70eb5808127284c4e5c9e836208e09d685a7978b6a216db85960b1a112eeace8  numpy-1.20.3-cp37-cp37m-macosx_10_9_x86_64.whl
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e55185e51b18d788e49fe8305fd73ef4470596b33fc2c1ceb304566b99c71a69  numpy-1.20.3.zip

v1.20.2

Compare Source

NumPy 1.20.2 Release Notes

NumPy 1,20.2 is a bugfix release containing several fixes merged to the
main branch after the NumPy 1.20.1 release.

Contributors

A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Allan Haldane
• Bas van Beek
• Charles Harris
• Christoph Gohlke
• Mateusz Sokół +
• Michael Lamparski
• Sebastian Berg

Pull requests merged

A total of 20 pull requests were merged for this release.

• #​18382: MAINT: Update f2py from master.
• #​18459: BUG: diagflat could overflow on windows or 32-bit platforms
• #​18460: BUG: Fix refcount leak in f2py complex_double_from_pyobj.
• #​18461: BUG: Fix tiny memory leaks when like= overrides are used
• #​18462: BUG: Remove temporary change of descr/flags in VOID functions
• #​18469: BUG: Segfault in nditer buffer dealloc for Object arrays
• #​18485: BUG: Remove suspicious type casting
• #​18486: BUG: remove nonsensical comparison of pointer < 0
• #​18487: BUG: verify pointer against NULL before using it
• #​18488: BUG: check if PyArray_malloc succeeded
• #​18546: BUG: incorrect error fallthrough in nditer
• #​18559: CI: Backport CI fixes from main.
• #​18599: MAINT: Add annotations for __getitem__, __mul__ and...
• #​18611: BUG: NameError in numpy.distutils.fcompiler.compaq
• #​18612: BUG: Fixed where keyword for np.mean & np.var methods
• #​18617: CI: Update apt package list before Python install
• #​18636: MAINT: Ensure that re-exported sub-modules are properly annotated
• #​18638: BUG: Fix ma coercion list-of-ma-arrays if they do not cast to...
• #​18661: BUG: Fix small valgrind-found issues
• #​18671: BUG: Fix small issues found with pytest-leaks

Checksums

MD5

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5e1b381630af4d18db0fedd56b6d8da2  numpy-1.20.2.zip

SHA256

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780ae5284cb770ade51d4b4a7dce4faa554eb1d88a56d0e8b9f35fca9b0270ff  numpy-1.20.2-cp39-cp39-win32.whl
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878922bf5ad7550aa044aa9301d417e2d3ae50f0f577de92051d739ac6096cee  numpy-1.20.2.zip

v1.20.1

Compare Source

NumPy 1.20.1 Release Notes

NumPy 1.20.1 is a rapid bugfix release fixing several bugs and
regressions reported after the 1.20.0 release.

Highlights

• The distutils bug that caused problems with downstream projects is
  fixed.
• The random.shuffle regression is fixed.

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
• Nicholas McKibben +
• Pearu Peterson
• Ralf Gommers
• Sebastian Berg
• Tyler Reddy
• @​Aerysv +

Pull requests merged

A total of 15 pull requests were merged for this release.

• #​18306: MAINT: Add missing placeholder annotations
• #​18310: BUG: Fix typo in numpy.__init__.py
• #​18326: BUG: don't mutate list of fake libraries while iterating over...
• #​18327: MAINT: gracefully shuffle memoryviews
• #​18328: BUG: Use C linkage for random distributions
• #​18336: CI: fix when GitHub Actions builds trigger, and allow ci skips
• #​18337: BUG: Allow unmodified use of isclose, allclose, etc. with timedelta
• #​18345: BUG: Allow pickling all relevant DType types/classes
• #​18351: BUG: Fix missing signed_char dependency. Closes #​18335.
• #​18352: DOC: Change license date 2020 -> 2021
• #​18353: CI: CircleCI seems to occasionally time out, increase the limit
• #​18354: BUG: Fix f2py bugs when wrapping F90 subroutines.
• #​18356: MAINT: crackfortran regex simplify
• #​18357: BUG: threads.h existence test requires GLIBC > 2.12.
• #​18359: REL: Prepare for the NumPy 1.20.1 release.

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v1.20.0

Compare Source

NumPy 1.20.0 Release Notes

This NumPy release is the largest so made to date, some 684 PRs
contributed by 184 people have been merged. See the list of highlights
below for more details. The Python versions supported for this release
are 3.7-3.9, support for Python 3.6 has been dropped. Highlights are

• Annotations for NumPy functions. This work is ongoing and
  improvements can be expected pending feedback from users.
• Wider use of SIMD to increase execution speed of ufuncs. Much work
  has been done in introducing universal functions that will ease use
  of modern features across different hardware platforms. This work is
  ongoing.
• Preliminary work in changing the dtype and casting implementations
  in order to provide an easier path to extending dtypes. This work is
  ongoing but enough has been done to allow experimentation and
  feedback.
• Extensive documentation improvements comprising some 185 PR merges.
  This work is ongoing and part of the larger project to improve
  NumPy's online presence and usefulness to new users.
• Further cleanups related to removing Python 2.7. This improves code
  readability and removes technical debt.
• Preliminary support for the upcoming Cython 3.0.

New functions

The random.Generator class has a new permuted function.

The new function differs from shuffle and permutation in that the
subarrays indexed by an axis are permuted rather than the axis being
treated as a separate 1-D array for every combination of the other
indexes. For example, it is now possible to permute the rows or columns
of a 2-D array.

(gh-15121)

sliding_window_view provides a sliding window view for numpy arrays

numpy.lib.stride\_tricks.sliding\_window\_view constructs
views on numpy arrays that offer a sliding or moving window access to
the array. This allows for the simple implementation of certain
algorithms, such as running means.

(gh-17394)

[numpy.broadcast_shapes]{.title-ref} is a new user-facing function

numpy.broadcast\_shapes gets the resulting shape from
broadcasting the given shape tuples against each other.

>>> np.broadcast_shapes((1, 2), (3, 1))
(3, 2)

>>> np.broadcast_shapes(2, (3, 1))
(3, 2)

>>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
(5, 6, 7)

(gh-17535)

Deprecations

Using the aliases of builtin types like np.int is deprecated

For a long time, np.int has been an alias of the builtin int. This
is repeatedly a cause of confusion for newcomers, and existed mainly for
historic reasons.

These aliases have been deprecated. The table below shows the full list
of deprecated aliases, along with their exact meaning. Replacing uses of
items in the first column with the contents of the second column will
work identically and silence the deprecation warning.

The third column lists alternative NumPy names which may occasionally be
preferential. See also basics.types{.interpreted-text role="ref"} for
additional details.

Deprecated name	Identical to	NumPy scalar type names
numpy.bool	bool	numpy.bool\_
numpy.int	int	numpy.int\_ (default), numpy.int64, or numpy.int32
numpy.float	float	numpy.float64, numpy.float\_, numpy.double (equivalent)
numpy.complex	complex	numpy.complex128, numpy.complex\_, numpy.cdouble (equivalent)
numpy.object	object	numpy.object\_
numpy.str	str	numpy.str\_
numpy.long	int	numpy.int\_ (C long), numpy.longlong (largest integer type)
numpy.unicode	str	numpy.unicode\_

To give a clear guideline for the vast majority of cases, for the types
bool, object, str (and unicode) using the plain version is
shorter and clear, and generally a good replacement. For float and
complex you can use float64 and complex128 if you wish to be more
explicit about the precision.

For np.int a direct replacement with np.int_ or int is also good
and will not change behavior, but the precision will continue to depend
on the computer and operating system. If you want to be more explicit
and review the current use, you have the following alternatives:

• np.int64 or np.int32 to specify the precision exactly. This
  ensures that results cannot depend on the computer or operating
  system.
• np.int_ or int (the default), but be aware that it depends on
  the computer and operating system.
• The C types: np.cint (int), np.int_ (long), np.longlong.
• np.intp which is 32bit on 32bit machines 64bit on 64bit machines.
  This can be the best type to use for indexing.

When used with np.dtype(...) or dtype=... changing it to the NumPy
name as mentioned above will have no effect on the output. If used as a
scalar with:

np.float(123)

changing it can subtly change the result. In this case, the Python
version float(123) or int(12.) is normally preferable, although the
NumPy version may be useful for consistency with NumPy arrays (for
example, NumPy behaves differently for things like division by zero).

(gh-14882)

Passing shape=None to functions with a non-optional shape argument is deprecated

Previously, this was an alias for passing shape=(). This deprecation
is emitted by PyArray\_IntpConverter in the C API. If your
API is intended to support passing None, then you should check for
None prior to invoking the converter, so as to be able to distinguish
None and ().

(gh-15886)

Indexing errors will be reported even when index result is empty

In the future, NumPy will raise an IndexError when an integer array
index contains out of bound values even if a non-indexed dimension is of
length 0. This will now emit a DeprecationWarning. This can happen when
the array is previously empty, or an empty slice is involved:

arr1 = np.zeros((5, 0))
arr1[[20]]
arr2 = np.zeros((5, 5))
arr2[[20], :0]

Previously the non-empty index [20] was not checked for correctness.
It will now be checked causing a deprecation warning whic

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