github numpy/numpy v1.20.0

latest releases: v1.23.0rc1, v1.24.0.dev0, v1.22.4...
16 months ago

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
  • 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
  • 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.


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.


[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)



Using the aliases of builtin types like is deprecated

For a long time, 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 nameIdentical toNumPy scalar type names
numpy.boolboolnumpy.bool\_\_ (default), numpy.int64, or numpy.int32
numpy.floatfloatnumpy.float64, numpy.float\_, numpy.double (equivalent)
numpy.complexcomplexnumpy.complex128, numpy.complex\_, numpy.cdouble (equivalent)
numpy.strstrnumpy.str\_\_ (C long), numpy.longlong (largest integer type)

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 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
  • 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:


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).


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 ().


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))
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.


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"


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.


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.


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


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().


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:


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

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.


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.


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.


  • 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.


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.


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.


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

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:


Things will now be more consistent with:


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


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.


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


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


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.


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.


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.])]


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

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.


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

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.


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.


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


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.


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.


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


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.


C API changes

The PyArray_DescrCheck macro is modified

The PyArray_DescrCheck macro has been updated since NumPy 1.16.6 to

#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.


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

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

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.


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).


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


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]


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.


mypy tests can be run via

Currently running mypy with the NumPy stubs configured requires either:

  • Installing NumPy
  • Adding the source directory to MYPYPATH and linking to the

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.


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 build

That will use any of the accelerated libraries instead.


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.


The NVIDIA HPC SDK nvfortran compiler is now supported

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


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.



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.


Remove the Accelerate library as a candidate LAPACK library

Apple no longer supports Accelerate. Remove it.


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)


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.


Thread safe f2py callback functions

Callback functions in f2py are now thread safe.


[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"}


RPATH support on AIX added to distutils

This allows SciPy to be built on AIX.


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.


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.


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.


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
    • --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

    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_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
  • Print the supported CPU features during the run of PytestTester



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

OperatorOld WarningNew WarningOld ResultNew ResultWorks on MacOS
np.divmod(1.0, 0.0)InvalidInvalid and Dividebyzeronan, naninf, nanYes
np.fmod(1.0, 0.0)InvalidInvalidnannanNo? Yes
np.floor_divide(1.0, 0.0)InvalidDividebyzeronaninfYes
np.remainder(1.0, 0.0)InvalidInvalidnannanYes

: Summary of New Behavior


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])




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