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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NumPy 1.21.2 Release Notes

The NumPy 1.21.2 is maintenance release that fixes bugs discovered after 1.21.1. It also provides 64 bit manylinux Python 3.10.0rc1 wheels for downstream testing. Note that Python 3.10 is not yet final. There is also preliminary support for Windows on ARM64 builds, but there is no OpenBLAS for that platform and no wheels are available.

The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with Python 3.10.0rc1 and Python 3.10 will be officially supported after it is released. The previous problems with gcc-11.1 have been fixed by gcc-11.2, check your version if you are using gcc-11.

Contributors

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

• Bas van Beek
• Carl Johnsen +
• Charles Harris
• Gwyn Ciesla +
• Matthieu Dartiailh
• Matti Picus
• Niyas Sait +
• Ralf Gommers
• Sayed Adel
• Sebastian Berg

Pull requests merged

A total of 18 pull requests were merged for this release.

• #19497: MAINT: set Python version for 1.21.x to <3.11
• #19533: BUG: Fix an issue wherein importing numpy.typing could raise
• #19646: MAINT: Update Cython version for Python 3.10.
• #19648: TST: Bump the python 3.10 test version from beta4 to rc1
• #19651: TST: avoid distutils.sysconfig in runtests.py
• #19652: MAINT: add missing dunder method to nditer type hints
• #19656: BLD, SIMD: Fix testing extra checks when -Werror isn't applicable...
• #19657: BUG: Remove logical object ufuncs with bool output
• #19658: MAINT: Include .coveragerc in source distributions to support...
• #19659: BUG: Fix bad write in masked iterator output copy paths
• #19660: ENH: Add support for windows on arm targets
• #19661: BUG: add base to templated arguments for platlib
• #19662: BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated
• #19666: MAINT: Add Python 3.10 to supported versions.
• #19668: TST,BUG: Sanitize path-separators when running runtest.py
• #19671: BLD: load extra flags when checking for libflame
• #19676: BLD: update circleCI docker image
• #19677: REL: Prepare for 1.21.2 release.

Checksums

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SHA256

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NumPy 1.21.1 Release Notes

The NumPy 1.21.1 is maintenance release that fixes bugs discovered after the 1.21.0 release and updates OpenBLAS to v0.3.17 to deal with problems on arm64.

The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with development Python 3.10. Python 3.10 will be officially supported after it is released.

:warning: There are unresolved problems compiling NumPy 1.20.0 with gcc-11.1.

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

Contributors

A total of 11 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• Bas van Beek
• Charles Harris
• Ganesh Kathiresan
• Gregory R. Lee
• Hugo Defois +
• Kevin Sheppard
• Matti Picus
• Ralf Gommers
• Sayed Adel
• Sebastian Berg
• Thomas J. Fan

Pull requests merged

A total of 26 pull requests were merged for this release.

• #19311: REV,BUG: Replace NotImplemented with typing.Any
• #19324: MAINT: Fixed the return-dtype of ndarray.real and imag
• #19330: MAINT: Replace "dtype[Any]" with dtype in the definiton of...
• #19342: DOC: Fix some docstrings that crash pdf generation.
• #19343: MAINT: bump scipy-mathjax
• #19347: BUG: Fix arr.flat.index for large arrays and big-endian machines
• #19348: ENH: add numpy.f2py.get_include function
• #19349: BUG: Fix reference count leak in ufunc dtype handling
• #19350: MAINT: Annotate missing attributes of np.number subclasses
• #19351: BUG: Fix cast safety and comparisons for zero sized voids
• #19352: BUG: Correct Cython declaration in random
• #19353: BUG: protect against accessing base attribute of a NULL subarray
• #19365: BUG, SIMD: Fix detecting AVX512 features on Darwin
• #19366: MAINT: remove print()'s in distutils template handling
• #19390: ENH: SIMD architectures to show_config
• #19391: BUG: Do not raise deprecation warning for all nans in unique...
• #19392: BUG: Fix NULL special case in object-to-any cast code
• #19430: MAINT: Use arm64-graviton2 for testing on travis
• #19495: BUILD: update OpenBLAS to v0.3.17
• #19496: MAINT: Avoid unicode characters in division SIMD code comments
• #19499: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11
• #19500: BUG: fix a numpy.npiter leak in npyiter_multi_index_set
• #19501: TST: Fix a GenericAlias test failure for python 3.9.0
• #19502: MAINT: Start testing with Python 3.10.0b3.
• #19503: MAINT: Add missing dtype overloads for object- and ctypes-based...
• #19510: REL: Prepare for NumPy 1.21.1 release.

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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: 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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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.

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

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. ^1^

^1^ 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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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,
• improved documentation,
• improved annotations,
• the 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.

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{.interpreted-text role="ref"} for more details.

(gh-18906)

Expired deprecations

• The shape argument of 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

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)

Future Changes

Promotion of strings with numbers and bools will be deprecated

Any promotion of numbers and strings is deprecated and will give a FutureWarning the main affected functionalities are:

• numpy.promote_types and numpy.result_type which will raise an error in this case in the future.
• numpy.concatenate will raise an error when concatenating a string and numeric array. You can use dtype="S" to explicitly request a string result.
• numpy.array and related functions will start returning object arrays because these functions use object as a fallback when no common dtype can be found. However, it may happen that future releases of NumPy will generally error in these cases.

This will mainly affect code such as:

np.asarray(['string', 0])

and:

np.concatenate((['string'], [0]))

in both cases adding dtype="U" or dtype="S" will give the previous (string) result, while dtype=object will ensure an array with object dtype is returned.

Comparisons, universal functions, and casting are not affected by this.

(gh-18116)

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>{.interpreted-text role="ref"} 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:

[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

(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. ^1^

^1^ 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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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.

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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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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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SHA256

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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.

Checksums

MD5

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SHA256

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89e5336f2bec0c726ac7e7cdae181b325a9c0ee24e604704ed830d241c5e47ff  numpy-1.20.1-cp39-cp39-manylinux2010_i686.whl
032be656d89bbf786d743fee11d01ef318b0781281241997558fa7950028dd29  numpy-1.20.1-cp39-cp39-manylinux2010_x86_64.whl
66b467adfcf628f66ea4ac6430ded0614f5cc06ba530d09571ea404789064adc  numpy-1.20.1-cp39-cp39-manylinux2014_aarch64.whl
12e4ba5c6420917571f1a5becc9338abbde71dd811ce40b37ba62dec7b39af6d  numpy-1.20.1-cp39-cp39-win32.whl
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9eb551d122fadca7774b97db8a112b77231dcccda8e91a5bc99e79890797175e  numpy-1.20.1-pp37-pypy37_pp73-manylinux2010_x86_64.whl
9bf51d69ebb4ca9239e55bedc2185fe2c0ec222da0adee7ece4125414676846d  numpy-1.20.1.tar.gz
3bc63486a870294683980d76ec1e3efc786295ae00128f9ea38e2c6e74d5a60a  numpy-1.20.1.zip

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 which will be turned into an error. This also applies to assignments.

(gh-15900)

Inexact matches for mode and searchside are deprecated

Inexact and case insensitive matches for mode and searchside were valid inputs earlier and will give a DeprecationWarning now. For example, below are some example usages which are now deprecated and will give a DeprecationWarning:

import numpy as np
arr = np.array([[3, 6, 6], [4, 5, 1]])
# mode: inexact match
np.ravel_multi_index(arr, (7, 6), mode="clap")  # should be "clip"
# searchside: inexact match
np.searchsorted(arr[0], 4, side='random')  # should be "right"

(gh-16056)

Deprecation of [numpy.dual]{.title-ref}

The module numpy.dual is deprecated. Instead of importing functions from numpy.dual, the functions should be imported directly from NumPy or SciPy.

(gh-16156)

outer and ufunc.outer deprecated for matrix

np.matrix use with \~numpy.outer or generic ufunc outer calls such as numpy.add.outer. Previously, matrix was converted to an array here. This will not be done in the future requiring a manual conversion to arrays.

(gh-16232)

Further Numeric Style types Deprecated

The remaining numeric-style type codes Bytes0, Str0, Uint32, Uint64, and Datetime64 have been deprecated. The lower-case variants should be used instead. For bytes and string "S" and "U" are further alternatives.

(gh-16554)

The ndincr method of ndindex is deprecated

The documentation has warned against using this function since NumPy 1.8. Use next(it) instead of it.ndincr().

(gh-17233)

ArrayLike objects which do not define __len__ and __getitem__

Objects which define one of the protocols __array__, __array_interface__, or __array_struct__ but are not sequences (usually defined by having a __len__ and __getitem__) will behave differently during array-coercion in the future.

When nested inside sequences, such as np.array([array_like]), these were handled as a single Python object rather than an array. In the future they will behave identically to:

np.array([np.array(array_like)])

This change should only have an effect if np.array(array_like) is not 0-D. The solution to this warning may depend on the object:

• Some array-likes may expect the new behaviour, and users can ignore the warning. The object can choose to expose the sequence protocol to opt-in to the new behaviour.
• For example, shapely will allow conversion to an array-like using line.coords rather than np.asarray(line). Users may work around the warning, or use the new convention when it becomes available.

Unfortunately, using the new behaviour can only be achieved by calling np.array(array_like).

If you wish to ensure that the old behaviour remains unchanged, please create an object array and then fill it explicitly, for example:

arr = np.empty(3, dtype=object)
arr[:] = [array_like1, array_like2, array_like3]

This will ensure NumPy knows to not enter the array-like and use it as a object instead.

(gh-17973)

Future Changes

Arrays cannot be using subarray dtypes

Array creation and casting using np.array(arr, dtype) and arr.astype(dtype) will use different logic when dtype is a subarray dtype such as np.dtype("(2)i,").

For such a dtype the following behaviour is true:

res = np.array(arr, dtype)

res.dtype is not dtype
res.dtype is dtype.base
res.shape == arr.shape + dtype.shape

But res is filled using the logic:

res = np.empty(arr.shape + dtype.shape, dtype=dtype.base)
res[...] = arr

which uses incorrect broadcasting (and often leads to an error). In the future, this will instead cast each element individually, leading to the same result as:

res = np.array(arr, dtype=np.dtype(["f", dtype]))["f"]

Which can normally be used to opt-in to the new behaviour.

This change does not affect np.array(list, dtype="(2)i,") unless the list itself includes at least one array. In particular, the behaviour is unchanged for a list of tuples.

(gh-17596)

Expired deprecations

• The deprecation of numeric style type-codes np.dtype("Complex64") (with upper case spelling), is expired. "Complex64" corresponded to "complex128" and "Complex32" corresponded to "complex64".

• The deprecation of np.sctypeNA and np.typeNA is expired. Both have been removed from the public API. Use np.typeDict instead.

  (gh-16554)

• The 14-year deprecation of np.ctypeslib.ctypes_load_library is expired. Use ~numpy.ctypeslib.load_library{.interpreted-text role="func"} instead, which is identical.

  (gh-17116)

Financial functions removed

In accordance with NEP 32, the financial functions are removed from NumPy 1.20. The functions that have been removed are fv, ipmt, irr, mirr, nper, npv, pmt, ppmt, pv, and rate. These functions are available in the numpy_financial library.

(gh-17067)

Compatibility notes

isinstance(dtype, np.dtype) and not type(dtype) is not np.dtype

NumPy dtypes are not direct instances of np.dtype anymore. Code that may have used type(dtype) is np.dtype will always return False and must be updated to use the correct version isinstance(dtype, np.dtype).

This change also affects the C-side macro PyArray_DescrCheck if compiled against a NumPy older than 1.16.6. If code uses this macro and wishes to compile against an older version of NumPy, it must replace the macro (see also C API changes section).

Same kind casting in concatenate with axis=None

When [~numpy.concatenate]{.title-ref} is called with axis=None, the flattened arrays were cast with unsafe. Any other axis choice uses "same kind". That different default has been deprecated and "same kind" casting will be used instead. The new casting keyword argument can be used to retain the old behaviour.

(gh-16134)

NumPy Scalars are cast when assigned to arrays

When creating or assigning to arrays, in all relevant cases NumPy scalars will now be cast identically to NumPy arrays. In particular this changes the behaviour in some cases which previously raised an error:

np.array([np.float64(np.nan)], dtype=np.int64)

will succeed and return an undefined result (usually the smallest possible integer). This also affects assignments:

arr[0] = np.float64(np.nan)

At this time, NumPy retains the behaviour for:

np.array(np.float64(np.nan), dtype=np.int64)

The above changes do not affect Python scalars:

np.array([float("NaN")], dtype=np.int64)

remains unaffected (np.nan is a Python float, not a NumPy one). Unlike signed integers, unsigned integers do not retain this special case, since they always behaved more like casting. The following code stops raising an error:

np.array([np.float64(np.nan)], dtype=np.uint64)

To avoid backward compatibility issues, at this time assignment from datetime64 scalar to strings of too short length remains supported. This means that np.asarray(np.datetime64("2020-10-10"), dtype="S5") succeeds now, when it failed before. In the long term this may be deprecated or the unsafe cast may be allowed generally to make assignment of arrays and scalars behave consistently.

Array coercion changes when Strings and other types are mixed

When strings and other types are mixed, such as:

np.array(["string", np.float64(3.)], dtype="S")

The results will change, which may lead to string dtypes with longer strings in some cases. In particularly, if dtype="S" is not provided any numerical value will lead to a string results long enough to hold all possible numerical values. (e.g. "S32" for floats). Note that you should always provide dtype="S" when converting non-strings to strings.

If dtype="S" is provided the results will be largely identical to before, but NumPy scalars (not a Python float like 1.0), will still enforce a uniform string length:

np.array([np.float64(3.)], dtype="S")  # gives "S32"
np.array([3.0], dtype="S")  # gives "S3"

Previously the first version gave the same result as the second.

Array coercion restructure

Array coercion has been restructured. In general, this should not affect users. In extremely rare corner cases where array-likes are nested:

np.array([array_like1])

Things will now be more consistent with:

np.array([np.array(array_like1)])

This can subtly change output for some badly defined array-likes. One example for this are array-like objects which are not also sequences of matching shape. In NumPy 1.20, a warning will be given when an array-like is not also a sequence (but behaviour remains identical, see deprecations). If an array like is also a sequence (defines __getitem__ and __len__) NumPy will now only use the result given by __array__, __array_interface__, or __array_struct__. This will result in differences when the (nested) sequence describes a different shape.

(gh-16200)

Writing to the result of numpy.broadcast\_arrays will export readonly buffers

In NumPy 1.17 numpy.broadcast\_arrays started warning when the resulting array was written to. This warning was skipped when the array was used through the buffer interface (e.g. memoryview(arr)). The same thing will now occur for the two protocols __array_interface__, and __array_struct__ returning read-only buffers instead of giving a warning.

(gh-16350)

Numeric-style type names have been removed from type dictionaries

To stay in sync with the deprecation for np.dtype("Complex64") and other numeric-style (capital case) types. These were removed from np.sctypeDict and np.typeDict. You should use the lower case versions instead. Note that "Complex64" corresponds to "complex128" and "Complex32" corresponds to "complex64". The numpy style (new) versions, denote the full size and not the size of the real/imaginary part.

(gh-16554)

The operator.concat function now raises TypeError for array arguments

The previous behavior was to fall back to addition and add the two arrays, which was thought to be unexpected behavior for a concatenation function.

(gh-16570)

nickname attribute removed from ABCPolyBase

An abstract property nickname has been removed from ABCPolyBase as it was no longer used in the derived convenience classes. This may affect users who have derived classes from ABCPolyBase and overridden the methods for representation and display, e.g. __str__, __repr__, _repr_latex, etc.

(gh-16589)

float->timedelta and uint64->timedelta promotion will raise a TypeError

Float and timedelta promotion consistently raises a TypeError. np.promote_types("float32", "m8") aligns with np.promote_types("m8", "float32") now and both raise a TypeError. Previously, np.promote_types("float32", "m8") returned "m8" which was considered a bug.

Uint64 and timedelta promotion consistently raises a TypeError. np.promote_types("uint64", "m8") aligns with np.promote_types("m8", "uint64") now and both raise a TypeError. Previously, np.promote_types("uint64", "m8") returned "m8" which was considered a bug.

(gh-16592)

numpy.genfromtxt now correctly unpacks structured arrays

Previously, numpy.genfromtxt failed to unpack if it was called with unpack=True and a structured datatype was passed to the dtype argument (or dtype=None was passed and a structured datatype was inferred). For example:

>>> data = StringIO("21 58.0\n35 72.0")
>>> np.genfromtxt(data, dtype=None, unpack=True)
array([(21, 58.), (35, 72.)], dtype=[('f0', '<i8'), ('f1', '<f8')])

Structured arrays will now correctly unpack into a list of arrays, one for each column:

>>> np.genfromtxt(data, dtype=None, unpack=True)
[array([21, 35]), array([58., 72.])]

(gh-16650)

mgrid, r_, etc. consistently return correct outputs for non-default precision input

Previously, np.mgrid[np.float32(0.1):np.float32(0.35):np.float32(0.1),] and np.r_[0:10:np.complex64(3j)] failed to return meaningful output. This bug potentially affects [~numpy.mgrid]{.title-ref}, numpy.ogrid, numpy.r\_, and numpy.c\_ when an input with dtype other than the default float64 and complex128 and equivalent Python types were used. The methods have been fixed to handle varying precision correctly.

(gh-16815)

Boolean array indices with mismatching shapes now properly give IndexError

Previously, if a boolean array index matched the size of the indexed array but not the shape, it was incorrectly allowed in some cases. In other cases, it gave an error, but the error was incorrectly a ValueError with a message about broadcasting instead of the correct IndexError.

For example, the following used to incorrectly give ValueError: operands could not be broadcast together with shapes (2,2) (1,4):

np.empty((2, 2))[np.array([[True, False, False, False]])]

And the following used to incorrectly return array([], dtype=float64):

np.empty((2, 2))[np.array([[False, False, False, False]])]

Both now correctly give IndexError: boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 1.

(gh-17010)

Casting errors interrupt Iteration

When iterating while casting values, an error may stop the iteration earlier than before. In any case, a failed casting operation always returned undefined, partial results. Those may now be even more undefined and partial. For users of the NpyIter C-API such cast errors will now cause the [iternext()]{.title-ref} function to return 0 and thus abort iteration. Currently, there is no API to detect such an error directly. It is necessary to check PyErr_Occurred(), which may be problematic in combination with NpyIter_Reset. These issues always existed, but new API could be added if required by users.

(gh-17029)

f2py generated code may return unicode instead of byte strings

Some byte strings previously returned by f2py generated code may now be unicode strings. This results from the ongoing Python2 -> Python3 cleanup.

(gh-17068)

The first element of the __array_interface__["data"] tuple must be an integer

This has been the documented interface for many years, but there was still code that would accept a byte string representation of the pointer address. That code has been removed, passing the address as a byte string will now raise an error.

(gh-17241)

poly1d respects the dtype of all-zero argument

Previously, constructing an instance of poly1d with all-zero coefficients would cast the coefficients to np.float64. This affected the output dtype of methods which construct poly1d instances internally, such as np.polymul.

(gh-17577)

The numpy.i file for swig is Python 3 only.

Uses of Python 2.7 C-API functions have been updated to Python 3 only. Users who need the old version should take it from an older version of NumPy.

(gh-17580)

Void dtype discovery in np.array

In calls using np.array(..., dtype="V"), arr.astype("V"), and similar a TypeError will now be correctly raised unless all elements have the identical void length. An example for this is:

np.array([b"1", b"12"], dtype="V")

Which previously returned an array with dtype "V2" which cannot represent b"1" faithfully.

(gh-17706)

C API changes

The PyArray_DescrCheck macro is modified

The PyArray_DescrCheck macro has been updated since NumPy 1.16.6 to be:

#define PyArray_DescrCheck(op) PyObject_TypeCheck(op, &PyArrayDescr_Type)

Starting with NumPy 1.20 code that is compiled against an earlier version will be API incompatible with NumPy 1.20. The fix is to either compile against 1.16.6 (if the NumPy 1.16 release is the oldest release you wish to support), or manually inline the macro by replacing it with the new definition:

PyObject_TypeCheck(op, &PyArrayDescr_Type)

which is compatible with all NumPy versions.

Size of np.ndarray and np.void_ changed

The size of the PyArrayObject and PyVoidScalarObject structures have changed. The following header definition has been removed:

#define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields))

since the size must not be considered a compile time constant: it will change for different runtime versions of NumPy.

The most likely relevant use are potential subclasses written in C which will have to be recompiled and should be updated. Please see the documentation for :cPyArrayObject{.interpreted-text role="type"} for more details and contact the NumPy developers if you are affected by this change.

NumPy will attempt to give a graceful error but a program expecting a fixed structure size may have undefined behaviour and likely crash.

(gh-16938)

New Features

where keyword argument for numpy.all and numpy.any functions

The keyword argument where is added and allows to only consider specified elements or subaxes from an array in the Boolean evaluation of all and any. This new keyword is available to the functions all and any both via numpy directly or in the methods of numpy.ndarray.

Any broadcastable Boolean array or a scalar can be set as where. It defaults to True to evaluate the functions for all elements in an array if where is not set by the user. Examples are given in the documentation of the functions.

where keyword argument for numpy functions mean, std, var

The keyword argument where is added and allows to limit the scope in the calculation of mean, std and var to only a subset of elements. It is available both via numpy directly or in the methods of numpy.ndarray.

Any broadcastable Boolean array or a scalar can be set as where. It defaults to True to evaluate the functions for all elements in an array if where is not set by the user. Examples are given in the documentation of the functions.

(gh-15852)

norm=backward, forward keyword options for numpy.fft functions

The keyword argument option norm=backward is added as an alias for None and acts as the default option; using it has the direct transforms unscaled and the inverse transforms scaled by 1/n.

Using the new keyword argument option norm=forward has the direct transforms scaled by 1/n and the inverse transforms unscaled (i.e. exactly opposite to the default option norm=backward).

(gh-16476)

NumPy is now typed

Type annotations have been added for large parts of NumPy. There is also a new [numpy.typing]{.title-ref} module that contains useful types for end-users. The currently available types are

• ArrayLike: for objects that can be coerced to an array
• DtypeLike: for objects that can be coerced to a dtype

(gh-16515)

numpy.typing is accessible at runtime

The types in numpy.typing can now be imported at runtime. Code like the following will now work:

from numpy.typing import ArrayLike
x: ArrayLike = [1, 2, 3, 4]

(gh-16558)

New __f2py_numpy_version__ attribute for f2py generated modules.

Because f2py is released together with NumPy, __f2py_numpy_version__ provides a way to track the version f2py used to generate the module.

(gh-16594)

mypy tests can be run via runtests.py

Currently running mypy with the NumPy stubs configured requires either:

• Installing NumPy
• Adding the source directory to MYPYPATH and linking to the mypy.ini

Both options are somewhat inconvenient, so add a --mypy option to runtests that handles setting things up for you. This will also be useful in the future for any typing codegen since it will ensure the project is built before type checking.

(gh-17123)

Negation of user defined BLAS/LAPACK detection order

[~numpy.distutils]{.title-ref} allows negation of libraries when determining BLAS/LAPACK libraries. This may be used to remove an item from the library resolution phase, i.e. to disallow NetLIB libraries one could do:

NPY_BLAS_ORDER='^blas' NPY_LAPACK_ORDER='^lapack' python setup.py build

That will use any of the accelerated libraries instead.

(gh-17219)

Allow passing optimizations arguments to asv build

It is now possible to pass -j, --cpu-baseline, --cpu-dispatch and --disable-optimization flags to ASV build when the --bench-compare argument is used.

(gh-17284)

The NVIDIA HPC SDK nvfortran compiler is now supported

Support for the nvfortran compiler, a version of pgfortran, has been added.

(gh-17344)

dtype option for cov and corrcoef

The dtype option is now available for [numpy.cov]{.title-ref} and [numpy.corrcoef]{.title-ref}. It specifies which data-type the returned result should have. By default the functions still return a [numpy.float64]{.title-ref} result.

(gh-17456)

Improvements

Improved string representation for polynomials (__str__)

The string representation (__str__) of all six polynomial types in [numpy.polynomial]{.title-ref} has been updated to give the polynomial as a mathematical expression instead of an array of coefficients. Two package-wide formats for the polynomial expressions are available - one using Unicode characters for superscripts and subscripts, and another using only ASCII characters.

(gh-15666)

Remove the Accelerate library as a candidate LAPACK library

Apple no longer supports Accelerate. Remove it.

(gh-15759)

Object arrays containing multi-line objects have a more readable repr

If elements of an object array have a repr containing new lines, then the wrapped lines will be aligned by column. Notably, this improves the repr of nested arrays:

>>> np.array([np.eye(2), np.eye(3)], dtype=object)
array([array([[1., 0.],
              [0., 1.]]),
       array([[1., 0., 0.],
              [0., 1., 0.],
              [0., 0., 1.]])], dtype=object)

(gh-15997)

Concatenate supports providing an output dtype

Support was added to [~numpy.concatenate]{.title-ref} to provide an output dtype and casting using keyword arguments. The dtype argument cannot be provided in conjunction with the out one.

(gh-16134)

Thread safe f2py callback functions

Callback functions in f2py are now thread safe.

(gh-16519)

[numpy.core.records.fromfile]{.title-ref} now supports file-like objects

[numpy.rec.fromfile]{.title-ref} can now use file-like objects, for instance :pyio.BytesIO{.interpreted-text role="class"}

(gh-16675)

RPATH support on AIX added to distutils

This allows SciPy to be built on AIX.

(gh-16710)

Use f90 compiler specified by the command line args

The compiler command selection for Fortran Portland Group Compiler is changed in [numpy.distutils.fcompiler]{.title-ref}. This only affects the linking command. This forces the use of the executable provided by the command line option (if provided) instead of the pgfortran executable. If no executable is provided to the command line option it defaults to the pgf90 executable, wich is an alias for pgfortran according to the PGI documentation.

(gh-16730)

Add NumPy declarations for Cython 3.0 and later

The pxd declarations for Cython 3.0 were improved to avoid using deprecated NumPy C-API features. Extension modules built with Cython 3.0+ that use NumPy can now set the C macro NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION to avoid C compiler warnings about deprecated API usage.

(gh-16986)

Make the window functions exactly symmetric

Make sure the window functions provided by NumPy are symmetric. There were previously small deviations from symmetry due to numerical precision that are now avoided by better arrangement of the computation.

(gh-17195)

Performance improvements and changes

Enable multi-platform SIMD compiler optimizations

A series of improvements for NumPy infrastructure to pave the way to NEP-38, that can be summarized as follow:

• New Build Arguments

  • --cpu-baseline to specify the minimal set of required optimizations, default value is min which provides the minimum CPU features that can safely run on a wide range of users platforms.
  • --cpu-dispatch to specify the dispatched set of additional optimizations, default value is max -xop -fma4 which enables all CPU features, except for AMD legacy features.
  • --disable-optimization to explicitly disable the whole new improvements, It also adds a new C compiler #definition called NPY_DISABLE_OPTIMIZATION which it can be used as guard for any SIMD code.

• Advanced CPU dispatcher

  A flexible cross-architecture CPU dispatcher built on the top of Python/Numpy distutils, support all common compilers with a wide range of CPU features.

  The new dispatcher requires a special file extension *.dispatch.c to mark the dispatch-able C sources. These sources have the ability to be compiled multiple times so that each compilation process represents certain CPU features and provides different #definitions and flags that affect the code paths.

• New auto-generated C header ``core/src/common/_cpu_dispatch.h``

  This header is generated by the distutils module ccompiler_opt, and contains all the #definitions and headers of instruction sets, that had been configured through command arguments '--cpu-baseline' and '--cpu-dispatch'.

• New C header ``core/src/common/npy_cpu_dispatch.h``

  This header contains all utilities that required for the whole CPU dispatching process, it also can be considered as a bridge linking the new infrastructure work with NumPy CPU runtime detection.

• Add new attributes to NumPy umath module(Python level)

  • __cpu_baseline__ a list contains the minimal set of required optimizations that supported by the compiler and platform according to the specified values to command argument '--cpu-baseline'.
  • __cpu_dispatch__ a list contains the dispatched set of additional optimizations that supported by the compiler and platform according to the specified values to command argument '--cpu-dispatch'.

• Print the supported CPU features during the run of PytestTester

(gh-13516)

Changes

Changed behavior of divmod(1., 0.) and related functions

The changes also assure that different compiler versions have the same behavior for nan or inf usages in these operations. This was previously compiler dependent, we now force the invalid and divide by zero flags, making the results the same across compilers. For example, gcc-5, gcc-8, or gcc-9 now result in the same behavior. The changes are tabulated below:

Operator	Old Warning	New Warning	Old Result	New Result	Works on MacOS
np.divmod(1.0, 0.0)	Invalid	Invalid and Dividebyzero	nan, nan	inf, nan	Yes
np.fmod(1.0, 0.0)	Invalid	Invalid	nan	nan	No? Yes
np.floor_divide(1.0, 0.0)	Invalid	Dividebyzero	nan	inf	Yes
np.remainder(1.0, 0.0)	Invalid	Invalid	nan	nan	Yes

: Summary of New Behavior

(gh-16161)

np.linspace on integers now uses floor

When using a int dtype in [numpy.linspace]{.title-ref}, previously float values would be rounded towards zero. Now [numpy.floor]{.title-ref} is used instead, which rounds toward -inf. This changes the results for negative values. For example, the following would previously give:

>>> np.linspace(-3, 1, 8, dtype=int)
array([-3, -2, -1, -1,  0,  0,  0,  1])

and now results in:

>>> np.linspace(-3, 1, 8, dtype=int)
array([-3, -3, -2, -2, -1, -1,  0,  1])

The former result can still be obtained with:

>>> np.linspace(-3, 1, 8).astype(int)
array([-3, -2, -1, -1,  0,  0,  0,  1])

(gh-16841)

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