v1.22.3

NumPy 1.22.3 Release Notes

NumPy 1.22.3 is a maintenance release that fixes bugs discovered after
the 1.22.2 release. The most noticeable fixes may be those for DLPack.
One that may cause some problems is disallowing strings as inputs to
logical ufuncs. It is still undecided how strings should be treated in
those functions and it was thought best to simply disallow them until a
decision was reached. That should not cause problems with older code.

The Python versions supported for this release are 3.8-3.10. Note that
the Mac wheels are now based on OS X 10.14 rather than 10.9 that was
used in previous NumPy release cycles. 10.14 is the oldest release
supported by Apple.

Contributors

A total of 9 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• @GalaxySnail +
• Alexandre de Siqueira
• Bas van Beek
• Charles Harris
• Melissa Weber Mendonça
• Ross Barnowski
• Sebastian Berg
• Tirth Patel
• Matthieu Darbois

Pull requests merged

A total of 10 pull requests were merged for this release.

• #21048: MAINT: Use "3.10" instead of "3.10-dev" on travis.
• #21106: TYP,MAINT: Explicitly allow sequences of array-likes in np.concatenate
• #21137: BLD,DOC: skip broken ipython 8.1.0
• #21138: BUG, ENH: np._from_dlpack: export correct device information
• #21139: BUG: Fix numba DUFuncs added loops getting picked up
• #21140: BUG: Fix unpickling an empty ndarray with a non-zero dimension...
• #21141: BUG: use ThreadPoolExecutor instead of ThreadPool
• #21142: API: Disallow strings in logical ufuncs
• #21143: MAINT, DOC: Fix SciPy intersphinx link
• #21148: BUG,ENH: np._from_dlpack: export arrays with any strided size-1...

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v1.22.2

NumPy 1.22.2 Release Notes

The NumPy 1.22.2 is maintenance release that fixes bugs discovered after
the 1.22.1 release. Notable fixes are:

• Several build related fixes for downstream projects and other
  platforms.
• Various Annotation fixes/additions.
• Numpy wheels for Windows will use the 1.41 tool chain, fixing
  downstream link problems for projects using NumPy provided libraries
  on Windows.
• Deal with CVE-2021-41495 complaint.

The Python versions supported for this release are 3.8-3.10.

Contributors

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

• Andrew J. Hesford +
• Bas van Beek
• Brénainn Woodsend +
• Charles Harris
• Hood Chatham
• Janus Heide +
• Leo Singer
• Matti Picus
• Mukulika Pahari
• Niyas Sait
• Pearu Peterson
• Ralf Gommers
• Sebastian Berg
• Serge Guelton

Pull requests merged

A total of 21 pull requests were merged for this release.

• #20842: BLD: Add NPY_DISABLE_SVML env var to opt out of SVML
• #20843: BUG: Fix build of third party extensions with Py_LIMITED_API
• #20844: TYP: Fix pyright being unable to infer the real and imag...
• #20845: BUG: Fix comparator function signatures
• #20906: BUG: Avoid importing numpy.distutils on import numpy.testing
• #20907: MAINT: remove outdated mingw32 fseek support
• #20908: TYP: Relax the return type of np.vectorize
• #20909: BUG: fix f2py's define for threading when building with Mingw
• #20910: BUG: distutils: fix building mixed C/Fortran extensions
• #20912: DOC,TST: Fix Pandas code example as per new release
• #20935: TYP, MAINT: Add annotations for flatiter.__setitem__
• #20936: MAINT, TYP: Added missing where typehints in fromnumeric.pyi
• #20937: BUG: Fix build_ext interaction with non numpy extensions
• #20938: BUG: Fix missing intrinsics for windows/arm64 target
• #20945: REL: Prepare for the NumPy 1.22.2 release.
• #20982: MAINT: f2py: don't generate code that triggers -Wsometimes-uninitialized.
• #20983: BUG: Fix incorrect return type in reduce without initial value
• #20984: ENH: review return values for PyArray_DescrNew
• #20985: MAINT: be more tolerant of setuptools >= 60
• #20986: BUG: Fix misplaced return.
• #20992: MAINT: Further small return value validation fixes

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v1.22.1

NumPy 1.22.1 Release Notes

The NumPy 1.22.1 is maintenance release that fixes bugs discovered after
the 1.22.0 release. Notable fixes are:

• Fix f2PY docstring problems (SciPy)
• Fix reduction type problems (AstroPy)
• Fix various typing bugs.

The Python versions supported for this release are 3.8-3.10.

Contributors

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

• Arryan Singh
• Bas van Beek
• Charles Harris
• Denis Laxalde
• Isuru Fernando
• Kevin Sheppard
• Matthew Barber
• Matti Picus
• Melissa Weber Mendonça
• Mukulika Pahari
• Omid Rajaei +
• Pearu Peterson
• Ralf Gommers
• Sebastian Berg

Pull requests merged

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

• #20702: MAINT, DOC: Post 1.22.0 release fixes.
• #20703: DOC, BUG: Use pngs instead of svgs.
• #20704: DOC: Fixed the link on user-guide landing page
• #20714: BUG: Restore vc141 support
• #20724: BUG: Fix array dimensions solver for multidimensional arguments...
• #20725: TYP: change type annotation for __array_namespace__ to ModuleType
• #20726: TYP, MAINT: Allow ndindex to accept integer tuples
• #20757: BUG: Relax dtype identity check in reductions
• #20763: TYP: Allow time manipulation functions to accept date and timedelta...
• #20768: TYP: Relax the type of ndarray.__array_finalize__
• #20795: MAINT: Raise RuntimeError if setuptools version is too recent.
• #20796: BUG, DOC: Fixes SciPy docs build warnings
• #20797: DOC: fix OpenBLAS version in release note
• #20798: PERF: Optimize array check for bounded 0,1 values
• #20805: BUG: Fix that reduce-likes honor out always (and live in the...
• #20806: BUG: array_api.argsort(descending=True) respects relative...
• #20807: BUG: Allow integer inputs for pow-related functions in array_api
• #20814: DOC: Refer to NumPy, not pandas, in main page
• #20815: DOC: Update Copyright to 2022 [License]
• #20819: BUG: Return correctly shaped inverse indices in array_api set...

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v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors
spread over 609 pull requests. There have been many improvements,
highlights are:

• Annotations of the main namespace are essentially complete. Upstream
  is a moving target, so there will likely be further improvements,
  but the major work is done. This is probably the most user visible
  enhancement in this release.
• A preliminary version of the proposed Array-API is provided. This is
  a step in creating a standard collection of functions that can be
  used across application such as CuPy and JAX.
• NumPy now has a DLPack backend. DLPack provides a common interchange
  format for array (tensor) data.
• New methods for quantile, percentile, and related functions. The
  new methods provide a complete set of the methods commonly found in
  the literature.
• A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for
commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7
has been dropped. Note that 32 bit wheels are only provided for Python
3.8 and 3.9 on Windows, all other wheels are 64 bits on account of
Ubuntu, Fedora, and other Linux distributions dropping 32 bit support.
All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix
the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32",
and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that
users use pickle.loads instead. ndfromtxt and mafromtxt were both
deprecated in v1.17 - users should use numpy.genfromtxt instead with
the appropriate value for the usemask parameter.

(gh-19615)

Deprecations

Use delimiter rather than delimitor as kwarg in mrecords

The misspelled keyword argument delimitor of
numpy.ma.mrecords.fromtextfile() has been changed to delimiter,
using it will emit a deprecation warning.

(gh-19921)

Passing boolean kth values to (arg-)partition has been deprecated

numpy.partition and numpy.argpartition would previously accept
boolean values for the kth parameter, which would subsequently be
converted into integers. This behavior has now been deprecated.

(gh-20000)

The np.MachAr class has been deprecated

The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have
been deprecated. Users are encouraged to access the property if interest
directly from the corresponding numpy.finfo attribute.

(gh-20201)

Compatibility notes

Distutils forces strict floating point model on clang

NumPy now sets the -ftrapping-math option on clang to enforce correct
floating point error handling for universal functions. Clang defaults to
non-IEEE and C99 conform behaviour otherwise. This change (using the
equivalent but newer -ffp-exception-behavior=strict) was attempted in
NumPy 1.21, but was effectively never used.

(gh-19479)

Removed floor division support for complex types

Floor division of complex types will now result in a TypeError

>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...

(gh-19135)

numpy.vectorize functions now produce the same output class as the base function

When a function that respects numpy.ndarray subclasses is vectorized
using numpy.vectorize, the vectorized function will now be
subclass-safe also for cases that a signature is given (i.e., when
creating a gufunc): the output class will be the same as that returned
by the first call to the underlying function.

(gh-19356)

Python 3.7 is no longer supported

Python support has been dropped. This is rather strict, there are
changes that require Python >= 3.8.

(gh-19665)

str/repr of complex dtypes now include space after punctuation

The repr of
np.dtype({"names": ["a"], "formats": [int], "offsets": [2]}) is now
dtype({'names': ['a'], 'formats': ['<i8'], 'offsets': [2], 'itemsize': 10}),
whereas spaces where previously omitted after colons and between fields.

The old behavior can be restored via
np.set_printoptions(legacy="1.21").

(gh-19687)

Corrected advance in PCG64DSXM and PCG64

Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug
only affects results when the step was larger than $2^{64}$ on platforms
that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

(gh-20049)

Change in generation of random 32 bit floating point variates

There was bug in the generation of 32 bit floating point values from the
uniform distribution that would result in the least significant bit of
the random variate always being 0. This has been fixed.

This change affects the variates produced by the random.Generator
methods random, standard_normal, standard_exponential, and
standard_gamma, but only when the dtype is specified as
numpy.float32.

(gh-20314)

C API changes

Masked inner-loops cannot be customized anymore

The masked inner-loop selector is now never used. A warning will be
given in the unlikely event that it was customized.

We do not expect that any code uses this. If you do use it, you must
unset the selector on newer NumPy version. Please also contact the NumPy
developers, we do anticipate providing a new, more specific, mechanism.

The customization was part of a never-implemented feature to allow for
faster masked operations.

(gh-19259)

New Features

NEP 49 configurable allocators

As detailed in NEP 49, the
function used for allocation of the data segment of a ndarray can be
changed. The policy can be set globally or in a context. For more
information see the NEP and the data_memory{.interpreted-text
role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY
override to warn on dangerous use of transfering ownership by setting
NPY_ARRAY_OWNDATA.

(gh-17582)

Implementation of the NEP 47 (adopting the array API standard)

An initial implementation of NEP47, adoption
of the array API standard, has been added as numpy.array_api. The
implementation is experimental and will issue a UserWarning on import,
as the array API standard is still in
draft state. numpy.array_api is a conforming implementation of the
array API standard, which is also minimal, meaning that only those
functions and behaviors that are required by the standard are
implemented (see the NEP for more info). Libraries wishing to make use
of the array API standard are encouraged to use numpy.array_api to
check that they are only using functionality that is guaranteed to be
present in standard conforming implementations.

(gh-18585)

Generate C/C++ API reference documentation from comments blocks is now possible

This feature depends on Doxygen in
the generation process and on
Breathe to integrate it
with Sphinx.

(gh-18884)

Assign the platform-specific c_intp precision via a mypy plugin

The mypy plugin, introduced in
numpy/numpy#17843, has
again been expanded: the plugin now is now responsible for setting the
platform-specific precision of numpy.ctypeslib.c_intp, the latter
being used as data type for various numpy.ndarray.ctypes attributes.

Without the plugin, aforementioned type will default to
ctypes.c_int64.

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

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-19062)

Add NEP 47-compatible dlpack support

Add a ndarray.__dlpack__() method which returns a dlpack C structure
wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function,
where obj supports __dlpack__(), and returns an ndarray.

(gh-19083)

keepdims optional argument added to numpy.argmin, numpy.argmax

keepdims argument is added to numpy.argmin, numpy.argmax. If set
to True, the axes which are reduced are left in the result as
dimensions with size one. The resulting array has the same number of
dimensions and will broadcast with the input array.

(gh-19211)

`bit_co...

v1.21.5

NumPy 1.21.5 Release Notes

NumPy 1.21.5 is a maintenance release that fixes a few bugs discovered
after the 1.21.4 release and does some maintenance to extend the 1.21.x
lifetime. The Python versions supported in this release are 3.7-3.10. If
you want to compile your own version using gcc-11, you will need to use
gcc-11.2+ to avoid problems.

Contributors

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

• Bas van Beek
• Charles Harris
• Matti Picus
• Rohit Goswami
• Ross Barnowski
• Sayed Adel
• Sebastian Berg

Pull requests merged

A total of 11 pull requests were merged for this release.

• #20357: MAINT: Do not forward __(deep)copy__ calls of _GenericAlias...
• #20462: BUG: Fix float16 einsum fastpaths using wrong tempvar
• #20463: BUG, DIST: Print os error message when the executable not exist
• #20464: BLD: Verify the ability to compile C++ sources before initiating...
• #20465: BUG: Force npymath to respect npy_longdouble
• #20466: BUG: Fix failure to create aligned, empty structured dtype
• #20467: ENH: provide a convenience function to replace npy_load_module
• #20495: MAINT: update wheel to version that supports python3.10
• #20497: BUG: Clear errors correctly in F2PY conversions
• #20613: DEV: add a warningfilter to fix pytest workflow.
• #20618: MAINT: Help boost::python libraries at least not crash

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v1.22.0rc3

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 152 contributers
spread over 602 pull requests. There have been many improvements,
highlights are:

• Annotations of the main namespace are essentially complete. Upstream
  is a moving target, so there will likely be further improvements,
  but the major work is done. This is probably the most user visible
  enhancement in this release.
• A preliminary version of the proposed Array-API is provided. This is
  a step in creating a standard collection of functions that can be
  used across application such as CuPy and JAX.
• NumPy now has a DLPack backend. DLPack provides a common interchange
  format for array (tensor) data.
• New methods for quantile, percentile, and related functions. The
  new methods provide a complete set of the methods commonly found in
  the literature.
• A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for
commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7
has been dropped. Note that 32 bit wheels are only provided for Python
3.8 and 3.9 on Windows, all other wheels are 64 bits on account of
Ubuntu, Fedora, and other Linux distributions dropping 32 bit support.
All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix
the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32",
and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that
users use pickle.loads instead. ndfromtxt and mafromtxt were both
deprecated in v1.17 - users should use numpy.genfromtxt instead with
the appropriate value for the usemask parameter.

(gh-19615)

Deprecations

Use delimiter rather than delimitor as kwarg in mrecords

The misspelled keyword argument delimitor of
numpy.ma.mrecords.fromtextfile() has been changed to delimiter,
using it will emit a deprecation warning.

(gh-19921)

Passing boolean kth values to (arg-)partition has been deprecated

numpy.partition and numpy.argpartition would previously accept
boolean values for the kth parameter, which would subsequently be
converted into integers. This behavior has now been deprecated.

(gh-20000)

The np.MachAr class has been deprecated

The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have
been deprecated. Users are encouraged to access the property if interest
directly from the corresponding numpy.finfo attribute.

(gh-20201)

Compatibility notes

Distutils forces strict floating point model on clang

NumPy now sets the -ftrapping-math option on clang to enforce correct
floating point error handling for universal functions. Clang defaults to
non-IEEE and C99 conform behaviour otherwise. This change (using the
equivalent but newer -ffp-exception-behavior=strict) was attempted in
NumPy 1.21, but was effectively never used.

(gh-19479)

Removed floor division support for complex types

Floor division of complex types will now result in a TypeError

>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...

(gh-19135)

numpy.vectorize functions now produce the same output class as the base function

When a function that respects numpy.ndarray subclasses is vectorized
using numpy.vectorize, the vectorized function will now be
subclass-safe also for cases that a signature is given (i.e., when
creating a gufunc): the output class will be the same as that returned
by the first call to the underlying function.

(gh-19356)

Python 3.7 is no longer supported

Python support has been dropped. This is rather strict, there are
changes that require Python >= 3.8.

(gh-19665)

str/repr of complex dtypes now include space after punctuation

The repr of
np.dtype({"names": ["a"], "formats": [int], "offsets": [2]}) is now
dtype({'names': ['a'], 'formats': ['<i8'], 'offsets': [2], 'itemsize': 10}),
whereas spaces where previously omitted after colons and between fields.

The old behavior can be restored via
np.set_printoptions(legacy="1.21").

(gh-19687)

Corrected advance in PCG64DSXM and PCG64

Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug
only affects results when the step was larger than $2^{64}$ on platforms
that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

(gh-20049)

Change in generation of random 32 bit floating point variates

There was bug in the generation of 32 bit floating point values from the
uniform distribution that would result in the least significant bit of
the random variate always being 0. This has been fixed.

This change affects the variates produced by the random.Generator
methods random, standard_normal, standard_exponential, and
standard_gamma, but only when the dtype is specified as
numpy.float32.

(gh-20314)

C API changes

Masked inner-loops cannot be customized anymore

The masked inner-loop selector is now never used. A warning will be
given in the unlikely event that it was customized.

We do not expect that any code uses this. If you do use it, you must
unset the selector on newer NumPy version. Please also contact the NumPy
developers, we do anticipate providing a new, more specific, mechanism.

The customization was part of a never-implemented feature to allow for
faster masked operations.

(gh-19259)

New Features

NEP 49 configurable allocators

As detailed in NEP 49, the
function used for allocation of the data segment of a ndarray can be
changed. The policy can be set globally or in a context. For more
information see the NEP and the data_memory{.interpreted-text
role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY
override to warn on dangerous use of transfering ownership by setting
NPY_ARRAY_OWNDATA.

(gh-17582)

Implementation of the NEP 47 (adopting the array API standard)

An initial implementation of NEP47, adoption
of the array API standard, has been added as numpy.array_api. The
implementation is experimental and will issue a UserWarning on import,
as the array API standard is still in
draft state. numpy.array_api is a conforming implementation of the
array API standard, which is also minimal, meaning that only those
functions and behaviors that are required by the standard are
implemented (see the NEP for more info). Libraries wishing to make use
of the array API standard are encouraged to use numpy.array_api to
check that they are only using functionality that is guaranteed to be
present in standard conforming implementations.

(gh-18585)

Generate C/C++ API reference documentation from comments blocks is now possible

This feature depends on Doxygen in
the generation process and on
Breathe to integrate it
with Sphinx.

(gh-18884)

Assign the platform-specific c_intp precision via a mypy plugin

The mypy plugin, introduced in
numpy/numpy#17843, has
again been expanded: the plugin now is now responsible for setting the
platform-specific precision of numpy.ctypeslib.c_intp, the latter
being used as data type for various numpy.ndarray.ctypes attributes.

Without the plugin, aforementioned type will default to
ctypes.c_int64.

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

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-19062)

Add NEP 47-compatible dlpack support

Add a ndarray.__dlpack__() method which returns a dlpack C structure
wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function,
where obj supports __dlpack__(), and returns an ndarray.

(gh-19083)

keepdims optional argument added to numpy.argmin, numpy.argmax

keepdims argument is added to numpy.argmin, numpy.argmax. If set
to True, the axes which are reduced are left in the result as
dimensions with size one. The resulting array has the same number of
dimensions and will broadcast with the input array.

(gh-19211)

`bit_co...

v1.22.0rc2

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 151 contributers
spread over 589 pull requests. There have been many improvements,
highlights are:

• Annotations of the main namespace are essentially complete. Upstream
  is a moving target, so there will likely be further improvements,
  but the major work is done. This is probably the most user visible
  enhancement in this release.
• A preliminary version of the proposed Array-API is provided. This is
  a step in creating a standard collection of functions that can be
  used across application such as CuPy and JAX.
• NumPy now has a DLPack backend. DLPack provides a common interchange
  format for array (tensor) data.
• New methods for quantile, percentile, and related functions. The
  new methods provide a complete set of the methods commonly found in
  the literature.
• A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for
commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7
has been dropped. Note that 32 bit wheels are only provided for Python
3.8 and 3.9 on Windows, all other wheels are 64 bits on account of
Ubuntu, Fedora, and other Linux distributions dropping 32 bit support.
All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix
the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32",
and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that
users use pickle.loads instead. ndfromtxt and mafromtxt were both
deprecated in v1.17 - users should use numpy.genfromtxt instead with
the appropriate value for the usemask parameter.

(gh-19615)

Deprecations

Use delimiter rather than delimitor as kwarg in mrecords

The misspelled keyword argument delimitor of
numpy.ma.mrecords.fromtextfile() has been changed to delimiter,
using it will emit a deprecation warning.

(gh-19921)

Passing boolean kth values to (arg-)partition has been deprecated

numpy.partition and numpy.argpartition would previously accept
boolean values for the kth parameter, which would subsequently be
converted into integers. This behavior has now been deprecated.

(gh-20000)

The np.MachAr class has been deprecated

The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have
been deprecated. Users are encouraged to access the property if interest
directly from the corresponding numpy.finfo attribute.

(gh-20201)

Compatibility notes

Distutils forces strict floating point model on clang

NumPy now sets the -ftrapping-math option on clang to enforce correct
floating point error handling for universal functions. Clang defaults to
non-IEEE and C99 conform behaviour otherwise. This change (using the
equivalent but newer -ffp-exception-behavior=strict) was attempted in
NumPy 1.21, but was effectively never used.

(gh-19479)

Removed floor division support for complex types

Floor division of complex types will now result in a TypeError

>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...

(gh-19135)

numpy.vectorize functions now produce the same output class as the base function

When a function that respects numpy.ndarray subclasses is vectorized
using numpy.vectorize, the vectorized function will now be
subclass-safe also for cases that a signature is given (i.e., when
creating a gufunc): the output class will be the same as that returned
by the first call to the underlying function.

(gh-19356)

Python 3.7 is no longer supported

Python support has been dropped. This is rather strict, there are
changes that require Python >= 3.8.

(gh-19665)

str/repr of complex dtypes now include space after punctuation

The repr of
np.dtype({"names": ["a"], "formats": [int], "offsets": [2]}) is now
dtype({'names': ['a'], 'formats': ['<i8'], 'offsets': [2], 'itemsize': 10}),
whereas spaces where previously omitted after colons and between fields.

The old behavior can be restored via
np.set_printoptions(legacy="1.21").

(gh-19687)

Corrected advance in PCG64DSXM and PCG64

Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug
only affects results when the step was larger than $2^{64}$ on platforms
that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

(gh-20049)

Change in generation of random 32 bit floating point variates

There was bug in the generation of 32 bit floating point values from the
uniform distribution that would result in the least significant bit of
the random variate always being 0. This has been fixed.

This change affects the variates produced by the random.Generator
methods random, standard_normal, standard_exponential, and
standard_gamma, but only when the dtype is specified as
numpy.float32.

(gh-20314)

C API changes

Masked inner-loops cannot be customized anymore

The masked inner-loop selector is now never used. A warning will be
given in the unlikely event that it was customized.

We do not expect that any code uses this. If you do use it, you must
unset the selector on newer NumPy version. Please also contact the NumPy
developers, we do anticipate providing a new, more specific, mechanism.

The customization was part of a never-implemented feature to allow for
faster masked operations.

(gh-19259)

New Features

NEP 49 configurable allocators

As detailed in NEP 49, the
function used for allocation of the data segment of a ndarray can be
changed. The policy can be set globally or in a context. For more
information see the NEP and the data_memory{.interpreted-text
role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY
override to warn on dangerous use of transfering ownership by setting
NPY_ARRAY_OWNDATA.

(gh-17582)

Implementation of the NEP 47 (adopting the array API standard)

An initial implementation of NEP47, adoption
of the array API standard, has been added as numpy.array_api. The
implementation is experimental and will issue a UserWarning on import,
as the array API standard is still in
draft state. numpy.array_api is a conforming implementation of the
array API standard, which is also minimal, meaning that only those
functions and behaviors that are required by the standard are
implemented (see the NEP for more info). Libraries wishing to make use
of the array API standard are encouraged to use numpy.array_api to
check that they are only using functionality that is guaranteed to be
present in standard conforming implementations.

(gh-18585)

Generate C/C++ API reference documentation from comments blocks is now possible

This feature depends on Doxygen in
the generation process and on
Breathe to integrate it
with Sphinx.

(gh-18884)

Assign the platform-specific c_intp precision via a mypy plugin

The mypy plugin, introduced in
numpy/numpy#17843, has
again been expanded: the plugin now is now responsible for setting the
platform-specific precision of numpy.ctypeslib.c_intp, the latter
being used as data type for various numpy.ndarray.ctypes attributes.

Without the plugin, aforementioned type will default to
ctypes.c_int64.

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

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-19062)

Add NEP 47-compatible dlpack support

Add a ndarray.__dlpack__() method which returns a dlpack C structure
wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function,
where obj supports __dlpack__(), and returns an ndarray.

(gh-19083)

keepdims optional argument added to numpy.argmin, numpy.argmax

keepdims argument is added to numpy.argmin, numpy.argmax. If set
to True, the axes which are reduced are left in the result as
dimensions with size one. The resulting array has the same number of
dimensions and will broadcast with the input array.

(gh-19211)

`bit_co...

v1.22.0rc1

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 150 contributers
spread over 575 pull requests. There have been many improvements,
highlights are:

• Annotations of the main namespace are essentially complete. Upstream
  is a moving target, so there will likely be further improvements,
  but the major work is done. This is probably the most user visible
  enhancement in this release.
• A preliminary version of the proposed Array-API is provided. This is
  a step in creating a standard collection of functions that can be
  used across application such as CuPy and JAX.
• NumPy now has a DLPack backend. DLPack provides a common interchange
  format for array (tensor) data.
• New methods for quantile, percentile, and related functions. The
  new methods provide a complete set of the methods commonly found in
  the literature.
• A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for
commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7
has been dropped. Note that 32 bit wheels are only provided for Python
3.8 and 3.9 on Windows, all other wheels are 64 bits on account of
Ubuntu, Fedora, and other Linux distributions dropping 32 bit support.
All 64 bit wheels are also linked with 64 bit OpenBLAS, which should fix
the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32",
and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that
users use pickle.loads instead. ndfromtxt and mafromtxt were both
deprecated in v1.17 - users should use numpy.genfromtxt instead with
the appropriate value for the usemask parameter.

(gh-19615)

Deprecations

Use delimiter rather than delimitor as kwarg in mrecords

The misspelled keyword argument delimitor of
numpy.ma.mrecords.fromtextfile() has been changed to delimiter,
using it will emit a deprecation warning.

(gh-19921)

Passing boolean kth values to (arg-)partition has been deprecated

numpy.partition and numpy.argpartition would previously accept
boolean values for the kth parameter, which would subsequently be
converted into integers. This behavior has now been deprecated.

(gh-20000)

The np.MachAr class has been deprecated

The numpy.MachAr class and finfo.machar <numpy.finfo> attribute have
been deprecated. Users are encouraged to access the property if interest
directly from the corresponding numpy.finfo attribute.

(gh-20201)

Compatibility notes

Distutils forces strict floating point model on clang

NumPy now sets the -ftrapping-math option on clang to enforce correct
floating point error handling for universal functions. Clang defaults to
non-IEEE and C99 conform behaviour otherwise. This change (using the
equivalent but newer -ffp-exception-behavior=strict) was attempted in
NumPy 1.21, but was effectively never used.

(gh-19479)

Removed floor division support for complex types

Floor division of complex types will now result in a TypeError

>>> a = np.arange(10) + 1j* np.arange(10)
>>> a // 1
TypeError: ufunc 'floor_divide' not supported for the input types...

(gh-19135)

numpy.vectorize functions now produce the same output class as the base function

When a function that respects numpy.ndarray subclasses is vectorized
using numpy.vectorize, the vectorized function will now be
subclass-safe also for cases that a signature is given (i.e., when
creating a gufunc): the output class will be the same as that returned
by the first call to the underlying function.

(gh-19356)

Python 3.7 is no longer supported

Python support has been dropped. This is rather strict, there are
changes that require Python >= 3.8.

(gh-19665)

str/repr of complex dtypes now include space after punctuation

The repr of
np.dtype({"names": ["a"], "formats": [int], "offsets": [2]}) is now
dtype({'names': ['a'], 'formats': ['<i8'], 'offsets': [2], 'itemsize': 10}),
whereas spaces where previously omitted after colons and between fields.

The old behavior can be restored via
np.set_printoptions(legacy="1.21").

(gh-19687)

Corrected advance in PCG64DSXM and PCG64

Fixed a bug in the advance method of PCG64DSXM and PCG64. The bug
only affects results when the step was larger than $2^{64}$ on platforms
that do not support 128-bit integers(e.g., Windows and 32-bit Linux).

(gh-20049)

Change in generation of random 32 bit floating point variates

There was bug in the generation of 32 bit floating point values from the
uniform distribution that would result in the least significant bit of
the random variate always being 0. This has been fixed.

This change affects the variates produced by the random.Generator
methods random, standard_normal, standard_exponential, and
standard_gamma, but only when the dtype is specified as
numpy.float32.

(gh-20314)

C API changes

Masked inner-loops cannot be customized anymore

The masked inner-loop selector is now never used. A warning will be
given in the unlikely event that it was customized.

We do not expect that any code uses this. If you do use it, you must
unset the selector on newer NumPy version. Please also contact the NumPy
developers, we do anticipate providing a new, more specific, mechanism.

The customization was part of a never-implemented feature to allow for
faster masked operations.

(gh-19259)

New Features

NEP 49 configurable allocators

As detailed in NEP 49, the
function used for allocation of the data segment of a ndarray can be
changed. The policy can be set globally or in a context. For more
information see the NEP and the data_memory{.interpreted-text
role="ref"} reference docs. Also add a NUMPY_WARN_IF_NO_MEM_POLICY
override to warn on dangerous use of transfering ownership by setting
NPY_ARRAY_OWNDATA.

(gh-17582)

Implementation of the NEP 47 (adopting the array API standard)

An initial implementation of NEP
47 (adoption
the array API standard) has been added as numpy.array_api. The
implementation is experimental and will issue a UserWarning on import,
as the array API
standard is still in
draft state. numpy.array_api is a conforming implementation of the
array API standard, which is also minimal, meaning that only those
functions and behaviors that are required by the standard are
implemented (see the NEP for more info). Libraries wishing to make use
of the array API standard are encouraged to use numpy.array_api to
check that they are only using functionality that is guaranteed to be
present in standard conforming implementations.

(gh-18585)

Generate C/C++ API reference documentation from comments blocks is now possible

This feature depends on Doxygen in
the generation process and on
Breathe to integrate it
with Sphinx.

(gh-18884)

Assign the platform-specific c_intp precision via a mypy plugin

The mypy plugin, introduced in
numpy/numpy#17843, has
again been expanded: the plugin now is now responsible for setting the
platform-specific precision of numpy.ctypeslib.c_intp, the latter
being used as data type for various numpy.ndarray.ctypes attributes.

Without the plugin, aforementioned type will default to
ctypes.c_int64.

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

[mypy]
plugins = numpy.typing.mypy_plugin

(gh-19062)

Add NEP 47-compatible dlpack support

Add a ndarray.__dlpack__() method which returns a dlpack C structure
wrapped in a PyCapsule. Also add a np._from_dlpack(obj) function,
where obj supports __dlpack__(), and returns an ndarray.

(gh-19083)

keepdims optional argument added to numpy.argmin, numpy.argmax

keepdims argument is added to numpy.argmin, numpy.argmax. If set
to True, the axes which are reduced are left in the result as
dimensions with size one. The resulting array has the same number of
dimensions and will broadcast with the input array.

(gh-19211)

bit_count to ...

v1.21.4

NumPy 1.21.4 Release Notes

The NumPy 1.21.4 is a maintenance release that fixes a few bugs
discovered after 1.21.3. The most important fix here is a fix for the
NumPy header files to make them work for both x86_64 and M1 hardware
when included in the Mac universal2 wheels. Previously, the header files
only worked for M1 and this caused problems for folks building x86_64
extensions. This problem was not seen before Python 3.10 because there
were thin wheels for x86_64 that had precedence. This release also
provides thin x86_64 Mac wheels for Python 3.10.

The Python versions supported in this release are 3.7-3.10. If you want
to compile your own version using gcc-11, you will need to use gcc-11.2+
to avoid problems.

Contributors

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

• Bas van Beek
• Charles Harris
• Isuru Fernando
• Matthew Brett
• Sayed Adel
• Sebastian Berg
• 傅立业（Chris Fu） +

Pull requests merged

A total of 9 pull requests were merged for this release.

• #20278: BUG: Fix shadowed reference of dtype in type stub
• #20293: BUG: Fix headers for universal2 builds
• #20294: BUG: VOID_nonzero could sometimes mutate alignment flag
• #20295: BUG: Do not use nonzero fastpath on unaligned arrays
• #20296: BUG: Distutils patch to allow for 2 as a minor version (!)
• #20297: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar
• #20298: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC...
• #20300: REL: Prepare for the NumPy 1.21.4 release.
• #20302: TST: Fix a Arrayterator typing test failure

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SHA256

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v1.21.3

NumPy 1.21.3 Release Notes

The NumPy 1.21.3 is a maintenance release the fixes a few bugs
discovered after 1.21.2. It also provides 64 bit Python 3.10.0 wheels.
Note a few oddities about Python 3.10:

• There are no 32 bit wheels for Windows, Mac, or Linux.
• The Mac Intel builds are only available in universal2 wheels.

The Python versions supported in this release are 3.7-3.10. If you want
to compile your own version using gcc-11 you will need to use gcc-11.2+
to avoid problems.

Contributors

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

• Aaron Meurer
• Bas van Beek
• Charles Harris
• Developer-Ecosystem-Engineering +
• Kevin Sheppard
• Sebastian Berg
• Warren Weckesser

Pull requests merged

A total of 8 pull requests were merged for this release.

• #19745: ENH: Add dtype-support to 3 `generic/ndarray methods
• #19955: BUG: Resolve Divide by Zero on Apple silicon + test failures...
• #19958: MAINT: Mark type-check-only ufunc subclasses as ufunc aliases...
• #19994: BUG: np.tan(np.inf) test failure
• #20080: BUG: Correct incorrect advance in PCG with emulated int128
• #20081: BUG: Fix NaT handling in the PyArray_CompareFunc for datetime...
• #20082: DOC: Ensure that we add documentation also as to the dict for...
• #20106: BUG: core: result_type(0, np.timedelta64(4)) would seg. fault.

Checksums

MD5

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SHA256

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