Changelog History
Page 1

v1.21.0.dev0
December 02, 2020 
v1.20.0.rc1 Changes
December 03, 2020๐ NumPy 1.20.0 Release Notes
๐ This NumPy release is the largest so made to date, some 654 PRs
๐ contributed by 182 people have been merged. See the list of highlights
๐ below for more details. The Python versions supported for this release
๐ are 3.73.9, support for Python 3.6 has been dropped. Highlights are Annotations for NumPy functions. This work is ongoing and
๐ 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.  ๐ 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
๐ readability and removes technical debt.  ๐ Preliminary support for the upcoming Cython 3.0.
๐ New functions
The random.Generator class has a new
permuted
function.The new function differs from
shuffle
andpermutation
in that the
subarrays indexed by an axis are permuted rather than the axis being
treated as a separate 1D array for every combination of the other
indexes. For example, it is now possible to permute the rows or columns
of a 2D array.(gh15121)
sliding_window_view
provides a sliding window view for numpy arrays[numpy.lib.stride_tricks.sliding_window_view]{.titleref} 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.(gh17394)
[numpy.broadcast_shapes]{.titleref} is a new userfacing function
[~numpy.broadcast_shapes]{.titleref} 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)
(gh17535)
๐ Deprecations
๐ Using the aliases of builtin types like
np.int
is deprecatedFor a long time,
np.int
has been an alias of the builtinint
. This
is repeatedly a cause of confusion for newcomers, and is also simply not
๐ useful.๐ These aliases have been deprecated. The table below shows the full list
๐ of deprecated aliases, along with their exact meaning. Replacing uses of
items in the first column with the contents of the second column will
๐ work identically and silence the deprecation warning.In many cases, it may have been intended to use the types from the third
column. Be aware that use of these types may result in subtle but
desirable behavior changes.๐ Deprecated name Identical to Possibly intended numpy type
numpy.bool
bool
[numpy.bool_]{.titleref}
0๏ธโฃnumpy.int
int
[numpy.int_]{.titleref} (default int dtype), [numpy.cint]{.titleref} (Cint
)
numpy.float
float
[numpy.float_]{.titleref}, [numpy.double]{.titleref} (equivalent)
numpy.complex
complex
[numpy.complex_]{.titleref}, [numpy.cdouble]{.titleref} (equivalent)
numpy.object
object
[numpy.object_]{.titleref}
numpy.str
str
[numpy.str_]{.titleref}
numpy.long
int
(long
on Python 2) [numpy.int_]{.titleref} (Clong
), [numpy.longlong]{.titleref} (largest integer type)
numpy.unicode
str
(unicode
on Python 2) [numpy.unicode_]{.titleref}๐ Note that for technical reasons these deprecation warnings will only be
emitted on Python 3.7 and above.(gh14882)
๐ Passing
shape=None
to functions with a nonoptional shape argument is deprecated๐ Previously, this was an alias for passing
shape=()
. This deprecation
is emitted by [PyArray_IntpConverter]{.titleref} in the C API. If your
๐ API is intended to support passingNone
, then you should check for
None
prior to invoking the converter, so as to be able to distinguish
None
and()
.(gh15886)
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 nonindexed dimension is of
๐ 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 nonempty index
[20]
was not checked for correctness.
๐ It will now be checked causing a deprecation warning which will be
turned into an error. This also applies to assignments.(gh15900)
๐ Inexact matches for
mode
andsearchside
are deprecatedInexact and case insensitive matches for
mode
andsearchside
were
๐ valid inputs earlier and will give a DeprecationWarning now. For
๐ example, below are some example usages which are now deprecated and will
๐ 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"
(gh16056)
๐ Deprecation of [numpy.dual]{.titleref}
๐ The module [numpy.dual]{.titleref} is deprecated. Instead of importing
functions from [numpy.dual]{.titleref}, the functions should be
imported directly from NumPy or SciPy.(gh16156)
๐
outer
andufunc.outer
deprecated for matrixnp.matrix
use with [~numpy.outer]{.titleref} or generic ufunc outer
calls such asnumpy.add.outer
. Previously, matrix was converted to an
array here. This will not be done in the future requiring a manual
conversion to arrays.(gh16232)
๐ Further Numeric Style types Deprecated
๐ The remaining numericstyle type codes
Bytes0
,Str0
,Uint32
,
๐Uint64
, andDatetime64
have been deprecated. The lowercase variants
should be used instead. For bytes and string"S"
and"U"
are further
alternatives.(gh16554)
๐ The
ndincr
method ofndindex
is deprecated๐ The documentation has warned against using this function since NumPy
1.8. Usenext(it)
instead ofit.ndincr()
.(gh17233)
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 whendtype
is a subarray
dtype such asnp.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 optin 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.(gh17596)
๐ Expired deprecations
๐ The deprecation of numeric style typecodes
np.dtype("Complex64")
(with upper case spelling), is expired."Complex64"
corresponded
to"complex128"
and"Complex32"
corresponded to"complex64"
.๐ The deprecation of
np.sctypeNA
andnp.typeNA
is expired. Both
๐ have been removed from the public API. Usenp.typeDict
instead.(gh16554)
The 14year deprecation of
np.ctypeslib.ctypes_load_library
is
expired. Use~numpy.ctypeslib.load_library
{.interpretedtext
role="func"} instead, which is identical.(gh17116)
๐ Financial functions removed
๐ In accordance with NEP 32, the financial functions are removed from
๐ NumPy 1.20. The functions that have been removed arefv
,ipmt
,
irr
,mirr
,nper
,npv
,pmt
,ppmt
,pv
, andrate
. These
functions are available in the
numpy_financial library.(gh17067)
Compatibility notes
Same kind casting in concatenate with
axis=None
When [~numpy.concatenate]{.titleref} is called with
axis=None
, the
flattened arrays were cast withunsafe
. Any other axis choice uses
๐ "same kind". That different default has been deprecated and "same
kind" casting will be used instead. The newcasting
keyword argument
can be used to retain the old behaviour.(gh16134)
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
๐ 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 Pythonfloat
, 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
๐datetime64
scalar to strings of too short length remains supported.
This means thatnp.asarray(np.datetime64("20201010"), dtype="S5")
succeeds now, when it failed before. In the long term this may be
๐ 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, ifdtype="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 providedtype="S"
when converting nonstrings to
strings.If
dtype="S"
is provided the results will be largely identical to
before, but NumPy scalars (not a Python float like1.0
), 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
๐ users. In extremely rare corner cases where arraylikes are nested:np.array([array_like1])
Things will now be more consistent with:
np.array([np.array(array_like1)])
This could potentially subtly change output for badly defined
arraylikes. We are not aware of any such case where the results were
not clearly incorrect previously.(gh16200)
Writing to the result of [numpy.broadcast_arrays]{.titleref} will export readonly buffers
โ In NumPy 1.17 [numpy.broadcast_arrays]{.titleref} started warning when
โ 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 readonly
โ buffers instead of giving a warning.(gh16350)
๐ Numericstyle type names have been removed from type dictionaries
๐ To stay in sync with the deprecation for
np.dtype("Complex64")
and
๐ other numericstyle (capital case) types. These were removed from
np.sctypeDict
andnp.typeDict
. You should use the lower case
๐ versions instead. Note that"Complex64"
corresponds to"complex128"
๐ and"Complex32"
corresponds to"complex64"
. The numpy style (new)
๐ versions, denote the full size and not the size of the real/imaginary
part.(gh16554)
The
operator.concat
function now raises TypeError for array argumentsThe previous behavior was to fall back to addition and add the two
arrays, which was thought to be unexpected behavior for a concatenation
function.(gh16570)
๐
nickname
attribute removed from ABCPolyBase๐ An abstract property
nickname
has been removed fromABCPolyBase
as
it was no longer used in the derived convenience classes. This may
affect users who have derived classes fromABCPolyBase
and overridden
the methods for representation and display, e.g.__str__
,__repr__
,
_repr_latex
, etc.(gh16589)
float>timedelta
anduint64>timedelta
promotion will raise a TypeErrorFloat 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.(gh16592)
numpy.genfromtxt
now correctly unpacks structured arraysPreviously, [numpy.genfromtxt]{.titleref} failed to unpack if it was
called withunpack=True
and a structured datatype was passed to the
dtype
argument (ordtype=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.])]
(gh16650)
0๏ธโฃ
mgrid
,r_
, etc. consistently return correct outputs for nondefault precision inputPreviously,
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
๐ bug potentially affects [~numpy.mgrid]{.titleref},
[~numpy.ogrid]{.titleref}, [~numpy.r_]{.titleref}, and
[~numpy.c_]{.titleref} when an input with dtype other than the
0๏ธโฃ defaultfloat64
andcomplex128
and equivalent Python types were
๐ used. The methods have been fixed to handle varying precision correctly.(gh16815)
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
.(gh17010)
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 theNpyIter
CAPI such cast errors
will now cause the [iternext()]{.titleref} function to return 0 and
thus abort iteration. Currently, there is no API to detect such an error
directly. It is necessary to checkPyErr_Occurred()
, which may be
problematic in combination withNpyIter_Reset
. These issues always
existed, but new API could be added if required by users.(gh17029)
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.(gh17068)
The first element of the
__array_interface__ ["data"]
tuple must be an integerThis has been the documented interface for many years, but there was
still code that would accept a byte string representation of the pointer
โ address. That code has been removed, passing the address as a byte
string will now raise an error.(gh17241)
poly1d respects the dtype of allzero argument
Previously, constructing an instance of
poly1d
with allzero
coefficients would cast the coefficients tonp.float64
. This affected
the output dtype of methods which constructpoly1d
instances
internally, such asnp.polymul
.(gh17577)
The numpy.i file for swig is Python 3 only.
โก๏ธ Uses of Python 2.7 CAPI functions have been updated to Python 3 only.
๐ Users who need the old version should take it from an older version of
NumPy.(gh17580)
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
representb"1"
faithfully.(gh17706)
C API changes
Size of
np.ndarray
andnp.void_
changedThe size of the
PyArrayObject
andPyVoidScalarObject
structures have
๐ 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
๐ change for different runtime versions of NumPy.The most likely relevant use are potential subclasses written in C which
โก๏ธ will have to be recompiled and should be updated. Please see the
๐ documentation for :cPyArrayObject
{.interpretedtext 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
๐ fixed structure size may have undefined behaviour and likely crash.(gh16938)
๐ New Features
where
keyword argument fornumpy.all
andnumpy.any
functionsThe keyword argument
where
is added and allows to only consider
specified elements or subaxes from an array in the Boolean evaluation of
all
andany
. This new keyword is available to the functionsall
andany
both vianumpy
directly or in the methods of
numpy.ndarray
.Any broadcastable Boolean array or a scalar can be set as
where
. It
0๏ธโฃ defaults toTrue
to evaluate the functions for all elements in an
array ifwhere
is not set by the user. Examples are given in the
๐ documentation of the functions.where
keyword argument fornumpy
functionsmean
,std
,var
The keyword argument
where
is added and allows to limit the scope in
the calculation ofmean
,std
andvar
to only a subset of elements.
It is available both vianumpy
directly or in the methods of
numpy.ndarray
.Any broadcastable Boolean array or a scalar can be set as
where
. It
0๏ธโฃ defaults toTrue
to evaluate the functions for all elements in an
array ifwhere
is not set by the user. Examples are given in the
๐ documentation of the functions.(gh15852)
norm=backward
,forward
keyword options fornumpy.fft
functionsThe keyword argument option
norm=backward
is added as an alias for
0๏ธโฃNone
and acts as the default option; using it has the direct
transforms unscaled and the inverse transforms scaled by1/n
.Using the new keyword argument option
norm=forward
has the direct
transforms scaled by1/n
and the inverse transforms unscaled (i.e.
0๏ธโฃ exactly opposite to the default optionnorm=backward
).(gh16476)
NumPy is now typed
Type annotations have been added for large parts of NumPy. There is also
a new [numpy.typing]{.titleref} module that contains useful types for
endusers. The currently available types areArrayLike
: for objects that can be coerced to an arrayDtypeLike
: for objects that can be coerced to a dtype
(gh16515)
numpy.typing
is accessible at runtimeThe 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]
(gh16558)
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.(gh16594)
โ
mypy
tests can be run via runtests.py๐ง Currently running mypy with the NumPy stubs configured requires either:
 Installing NumPy
 โ Adding the source directory to MYPYPATH and linking to the
mypy.ini
Both options are somewhat inconvenient, so add a
mypy
option to
โ runtests that handles setting things up for you. This will also be
๐ useful in the future for any typing codegen since it will ensure the
project is built before type checking.(gh17123)
Negation of user defined BLAS/LAPACK detection order
[~numpy.distutils]{.titleref} allows negation of libraries when
๐ 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.
(gh17219)
๐ Allow passing optimizations arguments to asv build
It is now possible to pass
j
,cpubaseline
,cpudispatch
and
๐disableoptimization
flags to ASV build when thebenchcompare
argument is used.(gh17284)
๐ The NVIDIA HPC SDK nvfortran compiler is now supported
๐ Support for the nvfortran compiler, a version of pgfortran, has been
โ added.(gh17344)
dtype
option forcov
andcorrcoef
The
dtype
option is now available for [numpy.cov]{.titleref} and
[numpy.corrcoef]{.titleref}. It specifies which datatype the returned
0๏ธโฃ result should have. By default the functions still return a
[numpy.float64]{.titleref} result.(gh17456)
๐ Improvements
Improved string representation for polynomials (
__str__
)The string representation (
__str__
) of all six polynomial types in
โก๏ธ [numpy.polynomial]{.titleref} has been updated to give the polynomial
as a mathematical expression instead of an array of coefficients. Two
๐ฆ packagewide formats for the polynomial expressions are available  one
using Unicode characters for superscripts and subscripts, and another
using only ASCII characters.(gh15666)
โ Remove the Accelerate library as a candidate LAPACK library
๐ Apple no longer supports Accelerate. Remove it.
(gh15759)
Object arrays containing multiline 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)
(gh15997)
๐ Concatenate supports providing an output dtype
๐ Support was added to [~numpy.concatenate]{.titleref} to provide an
outputdtype
andcasting
using keyword arguments. Thedtype
argument cannot be provided in conjunction with theout
one.(gh16134)
Thread safe f2py callback functions
Callback functions in f2py are now thread safe.
(gh16519)
๐ [numpy.core.records.fromfile]{.titleref} now supports filelike objects
[numpy.rec.fromfile]{.titleref} can now use filelike objects, for
instance :pyio.BytesIO
{.interpretedtext role="class"}(gh16675)
๐ RPATH support on AIX added to distutils
This allows SciPy to be built on AIX.
(gh16710)
๐ Use f90 compiler specified by the command line args
The compiler command selection for Fortran Portland Group Compiler is
๐ changed in [numpy.distutils.fcompiler]{.titleref}. This only affects
the linking command. This forces the use of the executable provided by
๐ป the command line option (if provided) instead of the pgfortran
๐ป executable. If no executable is provided to the command line option it
0๏ธโฃ defaults to the pgf90 executable, wich is an alias for pgfortran
๐ according to the PGI documentation.(gh16730)
โ Add NumPy declarations for Cython 3.0 and later
The pxd declarations for Cython 3.0 were improved to avoid using
๐ deprecated NumPy CAPI 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
๐ about deprecated API usage.(gh16986)
๐ Make the window functions exactly symmetric
๐ Make sure the window functions provided by NumPy are symmetric. There
were previously small deviations from symmetry due to numerical
๐ precision that are now avoided by better arrangement of the computation.(gh17195)
๐ Performance improvements and changes
Enable multiplatform SIMD compiler optimizations
A series of improvements for NumPy infrastructure to pave the way to
NEP38 , that can be summarized as follow:๐ New Build Arguments
cpubaseline
to specify the minimal set of required
0๏ธโฃ optimizations, default value ismin
which provides the minimum
CPU features that can safely run on a wide range of users
platforms.cpudispatch
to specify the dispatched set of additional
0๏ธโฃ optimizations, default value ismax xop fma4
which enables
all CPU features, except for AMD legacy features.disableoptimization
to explicitly disable the whole new
improvements, It also adds a new C compiler #definition
calledNPY_DISABLE_OPTIMIZATION
which it can be used as guard
for any SIMD code.
Advanced CPU dispatcher
A flexible crossarchitecture CPU dispatcher built on the top of
๐ 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 dispatchable C sources. These sources have the
ability to be compiled multiple times so that each compilation
๐จ process represents certain CPU features and provides differentdefinitions and flags that affect the code paths.
New autogenerated 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,
๐ง that had been configured through command arguments
'cpubaseline' and 'cpudispatch'.๐ 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.โ Add new attributes to NumPy umath module(Python level)
__cpu_baseline__
a list contains the minimal set of required
๐ optimizations that supported by the compiler and platform
according to the specified values to command argument
'cpubaseline'.__cpu_dispatch__
a list contains the dispatched set of
โ additional optimizations that supported by the compiler and
platform according to the specified values to command argument
'cpudispatch'.
โ Print the supported CPU features during the run of PytestTester
(gh13516)
๐ Changes
๐ Changed behavior of
divmod(1., 0.)
and related functionsThe 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, gcc5, gcc8,
or gcc9 now result in the same behavior. The changes are tabulated
below:๐ 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
(gh16161)
np.linspace
on integers now uses floorWhen using a
int
dtype in [numpy.linspace]{.titleref}, previously
float values would be rounded towards zero. Now
[numpy.floor]{.titleref} is used instead, which rounds towardinf
.
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])
(gh16841)
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 Annotations for NumPy functions. This work is ongoing and

v1.19.4 Changes
November 02, 2020๐ NumPy 1.19.4 Release Notes
๐ NumPy 1.19.4 is a quick release to revert the OpenBLAS library version.
It was hoped that the 0.3.12 OpenBLAS version used in 1.19.3 would work
๐ณ around the Microsoft fmod bug, but problems in some docker environments
turned up. Instead, 1.19.4 will use the older library and run a sanity
check on import, raising an error if the problem is detected. Microsoft
โฌ๏ธ is aware of the problem and has promised a fix, users should upgrade
when it becomes available.๐ This release supports Python 3.63.9
Contributors
๐ A total of 1 people contributed to this release. People with a "+" by
their names contributed a patch for the first time. Charles Harris
๐ Pull requests merged
๐ A total of 2 pull requests were merged for this release.
 ๐ #17679: MAINT: Add check for Windows 10 version 2004 bug.
 โช #17680: REV: Revert OpenBLAS to 1.19.2 version for 1.19.4
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v1.19.3 Changes
October 29, 2020๐ NumPy 1.19.3 Release Notes
๐ NumPy 1.19.3 is a small maintenace release with two major improvements:
 ๐ Python 3.9 binary wheels on all supported platforms.
 ๐ OpenBLAS fixes for Windows 10 version 2004 fmod bug.
๐ This release supports Python 3.63.9 and is linked with OpenBLAS 3.7 to
๐ avoid some of the fmod problems on Windows 10 version 2004. Microsoft is
โฌ๏ธ aware of the problem and users should upgrade when the fix becomes
available, the fix here is limited in scope.Contributors
๐ A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time. Charles Harris
 Chris Brown +
 Daniel Vanzo +
 E. Madison Bray +
 Hugo van Kemenade +
 Ralf Gommers
 Sebastian Berg
 @danbeibei +
๐ Pull requests merged
๐ A total of 10 pull requests were merged for this release.
 ๐ #17298: BLD: set upper versions for build dependencies
 ๐ #17336: BUG: Set deprecated fields to null in PyArray_InitArrFuncs
 ๐ #17446: ENH: Warn on unsupported Python 3.10+
 โก๏ธ #17450: MAINT: Update test_requirements.txt.
 ๐ #17522: ENH: Support for the NVIDIA HPC SDK nvfortran compiler
 โช #17568: BUG: Cygwin Workaround for #14787 on affected platforms
 #17647: BUG: Fix memory leak of bufferinfo cache due to relaxed strides
 ๐ #17652: MAINT: Backport openblas_support from master.
 ๐ #17653: TST: Add Python 3.9 to the CI testing on Windows, Mac.
 โ #17660: TST: Simplify source path names in test_extending.
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v1.19.2 Changes
September 10, 2020๐ NumPy 1.19.2 Release Notes
๐ NumPy 1.19.2 fixes several bugs, prepares for the upcoming Cython 3.x
๐ release. and pins setuptools to keep distutils working while upstream
โ modifications are ongoing. The aarch64 wheels are built with the latest
๐ manylinux2014 release that fixes the problem of differing page sizes
๐ง used by different linux distros.๐ This release supports Python 3.63.8. Cython >= 0.29.21 needs to be
๐ used when building with Python 3.9 for testing purposes.๐ There is a known problem with Windows 10 version=2004 and OpenBLAS svd
๐ that we are trying to debug. If you are running that Windows version you
should use a NumPy version that links to the MKL library, earlier
๐ Windows versions are fine.๐ Improvements
โ Add NumPy declarations for Cython 3.0 and later
The pxd declarations for Cython 3.0 were improved to avoid using
๐ deprecated NumPy CAPI 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
๐ about deprecated API usage.Contributors
๐ A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time. Charles Harris
 Matti Picus
 Pauli Virtanen
 Philippe Ombredanne +
 Sebastian Berg
 Stefan Behnel +
 Stephan Loyd +
 Zac HatfieldDodds
๐ Pull requests merged
๐ A total of 9 pull requests were merged for this release.
 #16959: TST: Change aarch64 to arm64 in travis.yml.
 ๐ง #16998: MAINT: Configure hypothesis in
np.test()
for determinism,...  ๐ #17000: BLD: pin setuptools < 49.2.0
 #17015: ENH: Add NumPy declarations to be used by Cython 3.0+
 ๐ #17125: BUG: Remove nonthreadsafe sigint handling from fft calculation
 #17243: BUG: core: fix ilp64 blas dot/vdot/... for strides > int32 max
 #17244: DOC: Use SPDX license expressions with correct license
 #17245: DOC: Fix the link to the quickstart in the old API functions
 #17272: BUG: fix pickling of arrays larger than 2GiB
Checksums
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SHA256
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v1.19.1 Changes
July 21, 2020๐ NumPy 1.19.1 Release Notes
๐ NumPy 1.19.1 fixes several bugs found in the 1.19.0 release, replaces
๐ several functions deprecated in the upcoming Python3.9 release, has
๐ improved support for AIX, and has a number of development related
โก๏ธ updates to keep CI working with recent upstream changes.๐ This release supports Python 3.63.8. Cython >= 0.29.21 needs to be
๐ used when building with Python 3.9 for testing purposes.Contributors
๐ A total of 15 people contributed to this release. People with a "+" by
their names contributed a patch for the first time. Abhinav Reddy +
 Anirudh Subramanian
 Antonio Larrosa +
 Charles Harris
 Chunlin Fang
 Eric Wieser
 Etienne Guesnet +
 Kevin Sheppard
 Matti Picus
 Raghuveer Devulapalli
 Roman Yurchak
 Ross Barnowski
 Sayed Adel
 Sebastian Berg
 Tyler Reddy
๐ Pull requests merged
๐ A total of 25 pull requests were merged for this release.
 #16649: MAINT, CI: disable Shippable cache
 #16652: MAINT: Replace PyUString_GET_SIZE with PyUnicode_GetLength.
 ๐ #16654: REL: Fix outdated docs link
 ๐ป #16656: BUG: raise IEEE exception on AIX
 #16672: BUG: Fix bug in AVX complex absolute while processing array of...
 #16693: TST: Add extra debugging information to CPU features detection
 #16703: BLD: Add CPU entry for Emscripten / WebAssembly
 โ #16705: TST: Disable Python 3.9dev testing.
 #16714: MAINT: Disable use_hugepages in case of ValueError
 #16724: BUG: Fix PyArray_SearchSorted signature.
 ๐ #16768: MAINT: Fixes for deprecated functions in scalartypes.c.src
 ๐ #16772: MAINT: Remove unneeded call to PyUnicode_READY
 ๐ #16776: MAINT: Fix deprecated functions in scalarapi.c
 ๐ #16779: BLD, ENH: Add RPATH support for AIX
 0๏ธโฃ #16780: BUG: Fix default fallback in genfromtxt
 #16784: BUG: Added missing return after raising error in methods.c
 โก๏ธ #16795: BLD: update cython to 0.29.21
 โ #16832: MAINT: setuptools 49.2.0 emits a warning, avoid it
 #16872: BUG: Validate output size in bin and multinomial
 ๐ #16875: BLD, MAINT: Pin setuptools
 โ #16904: DOC: Reconstruct Testing Guideline.
 #16905: TST, BUG: Reraise MemoryError exception in test_large_zip's...
 #16906: BUG, DOC: Fix bad MPL kwarg.
 #16916: BUG: Fix string/bytes to complex assignment
 ๐ #16922: REL: Prepare for NumPy 1.19.1 release
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v1.19.0 Changes
June 20, 2020๐ NumPy 1.19.0 Release Notes
๐ This NumPy release is marked by the removal of much technical debt:
๐ support for Python 2 has been removed, many deprecations have been
๐ expired, and documentation has been improved. The polishing of the
๐ random module continues apace with bug fixes and better usability from
Cython.๐ The Python versions supported for this release are 3.63.8. Downstream
๐ developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.Highlights
Code compatibility with Python versions < 3.6 (including Python 2)
was dropped from both the python and C code. The shims in
๐ฆnumpy.compat
will remain to support thirdparty packages, but they
may be deprecated in a future release. Note that 1.19.x will not
compile with earlier versions of Python due to the use of fstrings.(gh15233)
๐ Expired deprecations
numpy.insert
andnumpy.delete
can no longer be passed an axis on 0d arrays๐ This concludes a deprecation from 1.9, where when an
axis
argument was
passed to a call to~numpy.insert
and~numpy.delete
on a 0d array,
theaxis
andobj
argument and indices would be completely ignored.
In these cases,insert(arr, "nonsense", 42, axis=0)
would actually
overwrite the entire array, whiledelete(arr, "nonsense", axis=0)
would bearr.copy()
Now passing
axis
on a 0d array raises~numpy.AxisError
.(gh15802)
numpy.delete
no longer ignores outofbounds indices๐ This concludes deprecations from 1.8 and 1.9, where
np.delete
would
ignore both negative and outofbounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.Now outofbounds items throw
IndexError
, and negative items index
from the end.(gh15804)
numpy.insert
andnumpy.delete
no longer accept nonintegral indices๐ This concludes a deprecation from 1.9, where sequences of nonintegers
indices were allowed and cast to integers. Now passing sequences of
nonintegral indices raisesIndexError
, just like it does when passing
a single nonintegral scalar.(gh15805)
numpy.delete
no longer casts boolean indices to integers๐ This concludes a deprecation from 1.8, where
np.delete
would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.(gh15815)
Compatibility notes
๐ Changed random variate stream from
numpy.random.Generator.dirichlet
A bug in the generation of random variates for the Dirichlet
๐ distribution with small 'alpha' values was fixed by using a different
algorithm whenmax(alpha) < 0.1
. Because of the change, the stream of
variates generated bydirichlet
in this case will be different from
๐ previous releases.(gh14924)
Scalar promotion in
PyArray_ConvertToCommonType
The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType
has been changed to adhere to those used
bynp.result_type
. This means that input such as
(1000, np.array([1], dtype=np.uint8)))
will now returnuint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
๐ this CAPI function is generally discouraged. This also fixes
np.choose
to behave the same way as the rest of NumPy in this respect.(gh14933)
๐ Fasttake and fastputmask slots are deprecated and NULL'ed
The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
๐ user dtype should set one of these a DeprecationWarning will be given.(gh14942)
np.ediff1d
casting behaviour withto_end
andto_begin
np.ediff1d
now uses the"same_kind"
casting rule for its additional
to_end
andto_begin
arguments. This ensures type safety except when
the input array has a smaller integer type thanto_begin
orto_end
.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.(gh14981)
Converting of empty arraylike objects to NumPy arrays
Objects with
len(obj) == 0
which implement an "arraylike"
interface, meaning an object implementingobj. __array__ ()
,
obj. __array_interface__
,obj. __array_struct__
, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of(0, 1)
previously, it could be converted into
an array of shape(0,)
(losing all dimensions after the first 0).(gh14995)
โ Removed
multiarray.int_asbuffer
As part of the continued removal of Python 2 compatibility,
๐multiarray.int_asbuffer
was removed. On Python 3, it threw a
NotImplementedError
and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.(gh15229)
๐
numpy.distutils.compat
has been removedThis module contained only the function
get_exception()
, which was
๐ used as:try: ... except Exception: e = get_exception()
Its purpose was to handle the change in syntax introduced in Python 2.6,
๐ป fromexcept Exception, e:
toexcept Exception as e:
, meaning it was
๐ only necessary for codebases supporting Python 2.5 and older.(gh15255)
issubdtype
no longer interpretsfloat
asnp.floating
โ
numpy.issubdtype
had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type likeint
orfloat
) will now be consistent with passing in
np.dtype(arg2).type
. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.(gh15773)
๐ Change output of
round
on scalars to be consistent with PythonOutput of the
__round__
dunder method and consequently the Python
builtinround
has been changed to be a Pythonint
to be consistent
with calling it on Pythonfloat
objects when called with no arguments.
Previously, it would return a scalar of thenp.dtype
that was passed
in.(gh15840)
The
numpy.ndarray
constructor no longer interpretsstrides=()
asstrides=None
The former has changed to have the expected meaning of setting
numpy.ndarray.strides
to()
, while the latter continues to result in
strides being chosen automatically.(gh15882)
CLevel string to datetime casts changed
๐ The Clevel casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts usingstring_arr.astype("M8")
while previously the cast
would behave likestring_arr.astype(np.int_).astype("M8")
. This only
affects code using lowlevel CAPI to do manual casts (not full array
casts) of single scalar values or using e.g.PyArray_GetCastFunc
, and
should thus not affect the vast majority of users.(gh16068)
๐
SeedSequence
with small seeds no longer conflicts with spawning๐ Small seeds (less than
2**96
) were previously implicitly 0padded out
to 128 bits, the size of the internal entropy pool. When spawned, the
spawn key was concatenated before the 0padding. Since the first spawn
๐ key is(0,)
, small seeds before the spawn created the same states as
๐ the first spawnedSeedSequence
. Now, the seed is explicitly 0padded
out to the internal pool size before concatenating the spawn key.
๐ SpawnedSeedSequences
will produce different results than in the
๐ previous release. UnspawnedSeedSequences
will still produce the same
results.(gh16551)
๐ Deprecations
๐ Deprecate automatic
dtype=object
for ragged input๐ Calling
np.array([[1, [1, 2, 3]])
will issue aDeprecationWarning
as
per NEP 34. Users should
โ explicitly usedtype=object
to avoid the warning.(gh15119)
๐ Passing
shape=0
to factory functions innumpy.rec
is deprecated0
is treated as a special case and is aliased toNone
in the
functions:numpy.core.records.fromarrays
numpy.core.records.fromrecords
numpy.core.records.fromstring
numpy.core.records.fromfile
In future,
0
will not be special cased, and will be treated as an
array length like any other integer.(gh15217)
๐ Deprecation of probably unused CAPI functions
The following CAPI functions are probably unused and have been
๐ deprecated:PyArray_GetArrayParamsFromObject
PyUFunc_GenericFunction
PyUFunc_SetUsesArraysAsData
In most cases
PyArray_GetArrayParamsFromObject
should be replaced by
converting to an array, whilePyUFunc_GenericFunction
can be replaced
๐ withPyObject_Call
(see documentation for details).(gh15427)
๐ Converting certain types to dtypes is Deprecated
The super classes of scalar types, such as
np.integer
,np.generic
,
๐ ornp.inexact
will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer
is converted tonp.int_
, while it would be expected to
represent any integer (e.g. alsoint8
,int16
, etc. For example,
dtype=np.floating
is currently identical todtype=np.float64
, even
though alsonp.float32
is a subclass ofnp.floating
.(gh15534)
๐ Deprecation of
round
fornp.complexfloating
scalarsOutput of the
__round__
dunder method and consequently the Python
๐ builtinround
has been deprecated on complex scalars. This does not
affectnp.round
.(gh15840)
๐
numpy.ndarray.tostring()
is deprecated in favor oftobytes()
๐
~numpy.ndarray.tobytes
has existed since the 1.9 release, but until
๐ this release~numpy.ndarray.tostring
emitted no warning. The change to
โ emit a warning brings NumPy in line with the builtinarray.array
methods of the same name.(gh15867)
C API changes
๐ Better support for
const
dimensions in API functionsThe following functions now accept a constant array of
npy_intp
:PyArray_BroadcastToShape
PyArray_IntTupleFromIntp
PyArray_OverflowMultiplyList
Previously the caller would have to cast away the constness to call
these functions.(gh15251)
Const qualify UFunc inner loops
UFuncGenericFunction
now expects pointers to constdimension
and
strides
as arguments. This means inner loops may no longer modify
eitherdimension
orstrides
. This change leads to an
โincompatiblepointertypes
warning forcing users to either ignore the
โ compiler warnings or to const qualify their own loop signatures.(gh15355)
๐ New Features
numpy.frompyfunc
now accepts an identity argumentThis allows the `
numpy.ufunc.identity
{.interpretedtext
role="attr"}[ attribute to be set on the resulting ufunc, meaning it can
be used for empty and multidimensional calls to
:meth:]{.titleref}[numpy.ufunc.reduce]{.titleref}`.(gh8255)
๐
np.str_
scalars now support the buffer protocolnp.str_
arrays are always stored as UCS4, so the corresponding scalars
now expose this through the buffer interface, meaning
โmemoryview(np.str_('test'))
now works.(gh15385)
subok
option fornumpy.copy
A new kwarg,
subok
, was added tonumpy.copy
to allow users to toggle
the behavior ofnumpy.copy
with respect to array subclasses. The
0๏ธโฃ default value isFalse
which is consistent with the behavior of
numpy.copy
for previous numpy versions. To create a copy that
preserves an array subclass withnumpy.copy
, call
๐np.copy(arr, subok=True)
. This addition better documents that the
0๏ธโฃ default behavior ofnumpy.copy
differs from thenumpy.ndarray.copy
0๏ธโฃ method which respects array subclasses by default.(gh15685)
numpy.linalg.multi_dot
now accepts anout
argumentout
can be used to avoid creating unnecessary copies of the final
product computed bynumpy.linalg.multidot
.(gh15715)
keepdims
parameter fornumpy.count_nonzero
The parameter
keepdims
was added tonumpy.count_nonzero
. The
parameter has the same meaning as it does in reduction functions such as
numpy.sum
ornumpy.mean
.(gh15870)
equal_nan
parameter fornumpy.array_equal
The keyword argument
equal_nan
was added tonumpy.array_equal
.
equal_nan
is a boolean value that toggles whether or notnan
values
0๏ธโฃ are considered equal in comparison (default isFalse
). This matches
API used in related functions such asnumpy.isclose
and
numpy.allclose
.(gh16128)
๐ Improvements
๐ Improve detection of CPU features
Replace
npy_cpu_supports
which was a gcc specific mechanism to test
support of AVX with more general functionsnpy_cpu_init
and
npy_cpu_have
, and expose the results via aNPY_CPU_HAVE
cmacro as
well as a pythonlevel__cpu_features__
dictionary.(gh13421)
๐ Use 64bit integer size on 64bit platforms in fallback lapack_lite
๐ Use 64bit integer size on 64bit platforms in the fallback LAPACK
library, which is used when the system has no LAPACK installed, allowing
it to deal with linear algebra for large arrays.(gh15218)
๐ Use AVX512 intrinsic to implement
np.exp
when input isnp.float64
๐ Use AVX512 intrinsic to implement
np.exp
when input isnp.float64
,
๐ which can improve the performance ofnp.exp
withnp.float64
input
57x faster than before. The_multiarray_umath.so
module has grown
๐ง about 63 KB on linux64.(gh15648)
Ability to disable madvise hugepages
๐ง On Linux NumPy has previously added support for madavise hugepages which
๐ can improve performance for very large arrays. Unfortunately, on older
0๏ธโฃ Kernel versions this led to peformance regressions, thus by default the
๐ support has been disabled on kernels before version 4.6. To override the
0๏ธโฃ default, you can use the environment variable:NUMPY_MADVISE_HUGEPAGE=0
๐ or set it to 1 to force enabling support. Note that this only makes a
difference if the operating system is set up to use madvise transparent
hugepage.(gh15769)
numpy.einsum
accepts NumPyint64
type in subscript listThere is no longer a type error thrown when
numpy.einsum
is passed a
NumPyint64
array as its subscript list.(gh16080)
np.logaddexp2.identity
changed toinf
The ufunc
~numpy.logaddexp2
now has an identity ofinf
, allowing it
to be called on empty sequences. This matches the identity of
~numpy.logaddexp
.(gh16102)
๐ Changes
Remove handling of extra argument to
__array__
โ A code path and test have been in the code since NumPy 0.4 for a
twoargument variant of__array__ (dtype=None, context=None)
. It was
activated when callingufunc(op)
orufunc.reduce(op)
if
op. __array__
existed. However that variant is not documented, and it
๐ is not clear what the intention was for its use. It has been removed.(gh15118)
numpy.random._bit_generator
moved tonumpy.random.bit_generator
In order to expose
numpy.random.BitGenerator
and
๐numpy.random.SeedSequence
to Cython, the_bitgenerator
module is now
public asnumpy.random.bit_generator
Cython access to the random distributions is provided via a
pxd
filec_distributions.pxd
provides access to the c functions behind many of
the random distributions from Cython, making it convenient to use and
extend them.(gh15463)
๐ Fixed
eigh
andcholesky
methods innumpy.random.multivariate_normal
Previously, when passing
method='eigh'
ormethod='cholesky'
,
numpy.random.multivariate_normal
produced samples from the wrong
๐ distribution. This is now fixed.(gh15872)
๐ Fixed the jumping implementation in
MT19937.jumped
This fix changes the stream produced from jumped MT19937 generators. It
does not affect the stream produced usingRandomState
orMT19937
๐ that are directly seeded.๐ The translation of the jumping code for the MT19937 contained a reversed
loop ordering.MT19937.jumped
matches the Makoto Matsumoto's original
implementation of the Horner and Sliding Window jump methods.(gh16153)
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v1.19.0.rc2 Changes
May 31, 2020๐ NumPy 1.19.0 Release Notes
๐ This NumPy release is marked by the removal of much technical debt:
๐ support for Python 2 has been removed, many deprecations have been
๐ expired, and documentation has been improved. The polishing of the
๐ random module continues apace with bug fixes and better usability from
Cython.๐ The Python versions supported for this release are 3.63.8. Downstream
๐ developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.Highlights
Code compatibility with Python versions < 3.6 (including Python 2)
was dropped from both the python and C code. The shims in
๐ฆnumpy.compat
will remain to support thirdparty packages, but they
may be deprecated in a future release. Note that 1.19.x will not
compile with earlier versions of Python due to the use of fstrings.(gh15233)
๐ Expired deprecations
numpy.insert
andnumpy.delete
can no longer be passed an axis on 0d arrays๐ This concludes a deprecation from 1.9, where when an
axis
argument was
passed to a call to~numpy.insert
and~numpy.delete
on a 0d array,
theaxis
andobj
argument and indices would be completely ignored.
In these cases,insert(arr, "nonsense", 42, axis=0)
would actually
overwrite the entire array, whiledelete(arr, "nonsense", axis=0)
would bearr.copy()
Now passing
axis
on a 0d array raises~numpy.AxisError
.(gh15802)
numpy.delete
no longer ignores outofbounds indices๐ This concludes deprecations from 1.8 and 1.9, where
np.delete
would
ignore both negative and outofbounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.Now outofbounds items throw
IndexError
, and negative items index
from the end.(gh15804)
numpy.insert
andnumpy.delete
no longer accept nonintegral indices๐ This concludes a deprecation from 1.9, where sequences of nonintegers
indices were allowed and cast to integers. Now passing sequences of
nonintegral indices raisesIndexError
, just like it does when passing
a single nonintegral scalar.(gh15805)
numpy.delete
no longer casts boolean indices to integers๐ This concludes a deprecation from 1.8, where
np.delete
would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.(gh15815)
Compatibility notes
๐ Changed random variate stream from
numpy.random.Generator.dirichlet
A bug in the generation of random variates for the Dirichlet
๐ distribution with small 'alpha' values was fixed by using a different
algorithm whenmax(alpha) < 0.1
. Because of the change, the stream of
variates generated bydirichlet
in this case will be different from
๐ previous releases.(gh14924)
Scalar promotion in
PyArray_ConvertToCommonType
The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType
has been changed to adhere to those used
bynp.result_type
. This means that input such as
(1000, np.array([1], dtype=np.uint8)))
will now returnuint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
๐ this CAPI function is generally discouraged. This also fixes
np.choose
to behave the same way as the rest of NumPy in this respect.(gh14933)
๐ Fasttake and fastputmask slots are deprecated and NULL'ed
The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
๐ user dtype should set one of these a DeprecationWarning will be given.(gh14942)
np.ediff1d
casting behaviour withto_end
andto_begin
np.ediff1d
now uses the"same_kind"
casting rule for its additional
to_end
andto_begin
arguments. This ensures type safety except when
the input array has a smaller integer type thanto_begin
orto_end
.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.(gh14981)
Converting of empty arraylike objects to NumPy arrays
Objects with
len(obj) == 0
which implement an "arraylike"
interface, meaning an object implementingobj. __array__ ()
,
obj. __array_interface__
,obj. __array_struct__
, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of(0, 1)
previously, it could be converted into
an array of shape(0,)
(losing all dimensions after the first 0).(gh14995)
โ Removed
multiarray.int_asbuffer
As part of the continued removal of Python 2 compatibility,
๐multiarray.int_asbuffer
was removed. On Python 3, it threw a
NotImplementedError
and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.(gh15229)
๐
numpy.distutils.compat
has been removedThis module contained only the function
get_exception()
, which was
๐ used as:try: ... except Exception: e = get_exception()
Its purpose was to handle the change in syntax introduced in Python 2.6,
๐ป fromexcept Exception, e:
toexcept Exception as e:
, meaning it was
๐ only necessary for codebases supporting Python 2.5 and older.(gh15255)
issubdtype
no longer interpretsfloat
asnp.floating
โ
numpy.issubdtype
had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type likeint
orfloat
) will now be consistent with passing in
np.dtype(arg2).type
. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.(gh15773)
๐ Change output of
round
on scalars to be consistent with PythonOutput of the
__round__
dunder method and consequently the Python
builtinround
has been changed to be a Pythonint
to be consistent
with calling it on Pythonfloat
objects when called with no arguments.
Previously, it would return a scalar of thenp.dtype
that was passed
in.(gh15840)
The
numpy.ndarray
constructor no longer interpretsstrides=()
asstrides=None
The former has changed to have the expected meaning of setting
numpy.ndarray.strides
to()
, while the latter continues to result in
strides being chosen automatically.(gh15882)
CLevel string to datetime casts changed
๐ The Clevel casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts usingstring_arr.astype("M8")
while previously the cast
would behave likestring_arr.astype(np.int_).astype("M8")
. This only
affects code using lowlevel CAPI to do manual casts (not full array
casts) of single scalar values or using e.g.PyArray_GetCastFunc
, and
should thus not affect the vast majority of users.(gh16068)
๐ Deprecations
๐ Deprecate automatic
dtype=object
for ragged input๐ Calling
np.array([[1, [1, 2, 3]])
will issue aDeprecationWarning
as
per NEP 34. Users should
โ explicitly usedtype=object
to avoid the warning.(gh15119)
๐ Passing
shape=0
to factory functions innumpy.rec
is deprecated0
is treated as a special case and is aliased toNone
in the
functions:numpy.core.records.fromarrays
numpy.core.records.fromrecords
numpy.core.records.fromstring
numpy.core.records.fromfile
In future,
0
will not be special cased, and will be treated as an
array length like any other integer.(gh15217)
๐ Deprecation of probably unused CAPI functions
The following CAPI functions are probably unused and have been
๐ deprecated:PyArray_GetArrayParamsFromObject
PyUFunc_GenericFunction
PyUFunc_SetUsesArraysAsData
In most cases
PyArray_GetArrayParamsFromObject
should be replaced by
converting to an array, whilePyUFunc_GenericFunction
can be replaced
๐ withPyObject_Call
(see documentation for details).(gh15427)
๐ Converting certain types to dtypes is Deprecated
The super classes of scalar types, such as
np.integer
,np.generic
,
๐ ornp.inexact
will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer
is converted tonp.int_
, while it would be expected to
represent any integer (e.g. alsoint8
,int16
, etc. For example,
dtype=np.floating
is currently identical todtype=np.float64
, even
though alsonp.float32
is a subclass ofnp.floating
.(gh15534)
๐ Deprecation of
round
fornp.complexfloating
scalarsOutput of the
__round__
dunder method and consequently the Python
๐ builtinround
has been deprecated on complex scalars. This does not
affectnp.round
.(gh15840)
๐
numpy.ndarray.tostring()
is deprecated in favor oftobytes()
๐
~numpy.ndarray.tobytes
has existed since the 1.9 release, but until
๐ this release~numpy.ndarray.tostring
emitted no warning. The change to
โ emit a warning brings NumPy in line with the builtinarray.array
methods of the same name.(gh15867)
C API changes
๐ Better support for
const
dimensions in API functionsThe following functions now accept a constant array of
npy_intp
:PyArray_BroadcastToShape
PyArray_IntTupleFromIntp
PyArray_OverflowMultiplyList
Previously the caller would have to cast away the constness to call
these functions.(gh15251)
Const qualify UFunc inner loops
UFuncGenericFunction
now expects pointers to constdimension
and
strides
as arguments. This means inner loops may no longer modify
eitherdimension
orstrides
. This change leads to an
โincompatiblepointertypes
warning forcing users to either ignore the
โ compiler warnings or to const qualify their own loop signatures.(gh15355)
๐ New Features
numpy.frompyfunc
now accepts an identity argumentThis allows the `
numpy.ufunc.identity
{.interpretedtext
role="attr"}[ attribute to be set on the resulting ufunc, meaning it can
be used for empty and multidimensional calls to
:meth:]{.titleref}[numpy.ufunc.reduce]{.titleref}`.(gh8255)
๐
np.str_
scalars now support the buffer protocolnp.str_
arrays are always stored as UCS4, so the corresponding scalars
now expose this through the buffer interface, meaning
โmemoryview(np.str_('test'))
now works.(gh15385)
subok
option fornumpy.copy
A new kwarg,
subok
, was added tonumpy.copy
to allow users to toggle
the behavior ofnumpy.copy
with respect to array subclasses. The
0๏ธโฃ default value isFalse
which is consistent with the behavior of
numpy.copy
for previous numpy versions. To create a copy that
preserves an array subclass withnumpy.copy
, call
๐np.copy(arr, subok=True)
. This addition better documents that the
0๏ธโฃ default behavior ofnumpy.copy
differs from thenumpy.ndarray.copy
0๏ธโฃ method which respects array subclasses by default.(gh15685)
numpy.linalg.multi_dot
now accepts anout
argumentout
can be used to avoid creating unnecessary copies of the final
product computed bynumpy.linalg.multidot
.(gh15715)
keepdims
parameter fornumpy.count_nonzero
The parameter
keepdims
was added tonumpy.count_nonzero
. The
parameter has the same meaning as it does in reduction functions such as
numpy.sum
ornumpy.mean
.(gh15870)
equal_nan
parameter fornumpy.array_equal
The keyword argument
equal_nan
was added tonumpy.array_equal
.
equal_nan
is a boolean value that toggles whether or notnan
values
0๏ธโฃ are considered equal in comparison (default isFalse
). This matches
API used in related functions such asnumpy.isclose
and
numpy.allclose
.(gh16128)
๐ Improvements
๐ Improve detection of CPU features
Replace
npy_cpu_supports
which was a gcc specific mechanism to test
support of AVX with more general functionsnpy_cpu_init
and
npy_cpu_have
, and expose the results via aNPY_CPU_HAVE
cmacro as
well as a pythonlevel__cpu_features__
dictionary.(gh13421)
๐ Use 64bit integer size on 64bit platforms in fallback lapack_lite
๐ Use 64bit integer size on 64bit platforms in the fallback LAPACK
library, which is used when the system has no LAPACK installed, allowing
it to deal with linear algebra for large arrays.(gh15218)
๐ Use AVX512 intrinsic to implement
np.exp
when input isnp.float64
๐ Use AVX512 intrinsic to implement
np.exp
when input isnp.float64
,
๐ which can improve the performance ofnp.exp
withnp.float64
input
57x faster than before. The_multiarray_umath.so
module has grown
๐ง about 63 KB on linux64.(gh15648)
Ability to disable madvise hugepages
๐ง On Linux NumPy has previously added support for madavise hugepages which
๐ can improve performance for very large arrays. Unfortunately, on older
0๏ธโฃ Kernel versions this led to peformance regressions, thus by default the
๐ support has been disabled on kernels before version 4.6. To override the
0๏ธโฃ default, you can use the environment variable:NUMPY_MADVISE_HUGEPAGE=0
๐ or set it to 1 to force enabling support. Note that this only makes a
difference if the operating system is set up to use madvise transparent
hugepage.(gh15769)
numpy.einsum
accepts NumPyint64
type in subscript listThere is no longer a type error thrown when
numpy.einsum
is passed a
NumPyint64
array as its subscript list.(gh16080)
np.logaddexp2.identity
changed toinf
The ufunc
~numpy.logaddexp2
now has an identity ofinf
, allowing it
to be called on empty sequences. This matches the identity of
~numpy.logaddexp
.(gh16102)
๐ Changes
Remove handling of extra argument to
__array__
โ A code path and test have been in the code since NumPy 0.4 for a
twoargument variant of__array__ (dtype=None, context=None)
. It was
activated when callingufunc(op)
orufunc.reduce(op)
if
op. __array__
existed. However that variant is not documented, and it
๐ is not clear what the intention was for its use. It has been removed.(gh15118)
numpy.random._bit_generator
moved tonumpy.random.bit_generator
In order to expose
numpy.random.BitGenerator
and
๐numpy.random.SeedSequence
to Cython, the_bitgenerator
module is now
public asnumpy.random.bit_generator
Cython access to the random distributions is provided via a
pxd
filec_distributions.pxd
provides access to the c functions behind many of
the random distributions from Cython, making it convenient to use and
extend them.(gh15463)
๐ Fixed
eigh
andcholesky
methods innumpy.random.multivariate_normal
Previously, when passing
method='eigh'
ormethod='cholesky'
,
numpy.random.multivariate_normal
produced samples from the wrong
๐ distribution. This is now fixed.(gh15872)
๐ Fixed the jumping implementation in
MT19937.jumped
This fix changes the stream produced from jumped MT19937 generators. It
does not affect the stream produced usingRandomState
orMT19937
๐ that are directly seeded.๐ The translation of the jumping code for the MT19937 contained a reversed
loop ordering.MT19937.jumped
matches the Makoto Matsumoto's original
implementation of the Horner and Sliding Window jump methods.(gh16153)
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v1.19.0.rc1 Changes
May 18, 2020๐ NumPy 1.19.0 Release Notes
๐ This NumPy release is marked by the removal of much technical debt:
๐ support for Python 2 has been removed, many deprecations have been
๐ expired, and documentation has been improved. The polishing of the
๐ random module continues apace with bug fixes and better usability from
Cython.๐ The Python versions supported for this release are 3.63.8. Downstream
๐ developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.Highlights
Code compatibility with Python versions < 3.5 (including Python 2)
was dropped from both the python and C code. The shims in
๐ฆnumpy.compat
will remain to support thirdparty packages, but they
๐ may be deprecated in a future release.(gh15233)
๐ Expired deprecations
numpy.insert
andnumpy.delete
can no longer be passed an axis on 0d arrays๐ This concludes a deprecation from 1.9, where when an
axis
argument was
passed to a call to~numpy.insert
and~numpy.delete
on a 0d array,
theaxis
andobj
argument and indices would be completely ignored.
In these cases,insert(arr, "nonsense", 42, axis=0)
would actually
overwrite the entire array, whiledelete(arr, "nonsense", axis=0)
would bearr.copy()
Now passing
axis
on a 0d array raises~numpy.AxisError
.(gh15802)
numpy.delete
no longer ignores outofbounds indices๐ This concludes deprecations from 1.8 and 1.9, where
np.delete
would
ignore both negative and outofbounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.Now outofbounds items throw
IndexError
, and negative items index
from the end.(gh15804)
numpy.insert
andnumpy.delete
no longer accept nonintegral indices๐ This concludes a deprecation from 1.9, where sequences of nonintegers
indices were allowed and cast to integers. Now passing sequences of
nonintegral indices raisesIndexError
, just like it does when passing
a single nonintegral scalar.(gh15805)
numpy.delete
no longer casts boolean indices to integers๐ This concludes a deprecation from 1.8, where
np.delete
would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.(gh15815)
Compatibility notes
๐ Changed random variate stream from
numpy.random.Generator.dirichlet
A bug in the generation of random variates for the Dirichlet
๐ distribution with small 'alpha' values was fixed by using a different
algorithm whenmax(alpha) < 0.1
. Because of the change, the stream of
variates generated bydirichlet
in this case will be different from
๐ previous releases.(gh14924)
Scalar promotion in
PyArray_ConvertToCommonType
The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType
has been changed to adhere to those used
bynp.result_type
. This means that input such as
(1000, np.array([1], dtype=np.uint8)))
will now returnuint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
๐ this CAPI function is generally discouraged. This also fixes
np.choose
to behave the same way as the rest of NumPy in this respect.(gh14933)
๐ Fasttake and fastputmask slots are deprecated and NULL'ed
The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
๐ user dtype should set one of these a DeprecationWarning will be given.(gh14942)
np.ediff1d
casting behaviour withto_end
andto_begin
np.ediff1d
now uses the"same_kind"
casting rule for its additional
to_end
andto_begin
arguments. This ensures type safety except when
the input array has a smaller integer type thanto_begin
orto_end
.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.(gh14981)
Converting of empty arraylike objects to NumPy arrays
Objects with
len(obj) == 0
which implement an "arraylike"
interface, meaning an object implementingobj. __array__ ()
,
obj. __array_interface__
,obj. __array_struct__
, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of(0, 1)
previously, it could be converted into
an array of shape(0,)
(losing all dimensions after the first 0).(gh14995)
โ Removed
multiarray.int_asbuffer
As part of the continued removal of Python 2 compatibility,
๐multiarray.int_asbuffer
was removed. On Python 3, it threw a
NotImplementedError
and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.(gh15229)
๐
numpy.distutils.compat
has been removedThis module contained only the function
get_exception()
, which was
๐ used as:try: ... except Exception: e = get_exception()
Its purpose was to handle the change in syntax introduced in Python 2.6,
๐ป fromexcept Exception, e:
toexcept Exception as e:
, meaning it was
๐ only necessary for codebases supporting Python 2.5 and older.(gh15255)
issubdtype
no longer interpretsfloat
asnp.floating
โ
numpy.issubdtype
had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type likeint
orfloat
) will now be consistent with passing in
np.dtype(arg2).type
. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.(gh15773)
๐ Change output of
round
on scalars to be consistent with PythonOutput of the
__round__
dunder method and consequently the Python
builtinround
has been changed to be a Pythonint
to be consistent
with calling it on Pythonfloat
objects when called with no arguments.
Previously, it would return a scalar of thenp.dtype
that was passed
in.(gh15840)
The
numpy.ndarray
constructor no longer interpretsstrides=()
asstrides=None
The former has changed to have the expected meaning of setting
numpy.ndarray.strides
to()
, while the latter continues to result in
strides being chosen automatically.(gh15882)
CLevel string to datetime casts changed
๐ The Clevel casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts usingstring_arr.astype("M8")
while previously the cast
would behave likestring_arr.astype(np.int_).astype("M8")
. This only
affects code using lowlevel CAPI to do manual casts (not full array
casts) of single scalar values or using e.g.PyArray_GetCastFunc
, and
should thus not affect the vast majority of users.(gh16068)
๐ Deprecations
๐ Deprecate automatic
dtype=object
for ragged input๐ Calling
np.array([[1, [1, 2, 3]])
will issue aDeprecationWarning
as
per NEP 34. Users should
โ explicitly usedtype=object
to avoid the warning.(gh15119)
๐ Passing
shape=0
to factory functions innumpy.rec
is deprecated0
is treated as a special case and is aliased toNone
in the
functions:numpy.core.records.fromarrays
numpy.core.records.fromrecords
numpy.core.records.fromstring
numpy.core.records.fromfile
In future,
0
will not be special cased, and will be treated as an
array length like any other integer.(gh15217)
๐ Deprecation of probably unused CAPI functions
The following CAPI functions are probably unused and have been
๐ deprecated:PyArray_GetArrayParamsFromObject
PyUFunc_GenericFunction
PyUFunc_SetUsesArraysAsData
In most cases
PyArray_GetArrayParamsFromObject
should be replaced by
converting to an array, whilePyUFunc_GenericFunction
can be replaced
๐ withPyObject_Call
(see documentation for details).(gh15427)
๐ Converting certain types to dtypes is Deprecated
The super classes of scalar types, such as
np.integer
,np.generic
,
๐ ornp.inexact
will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer
is converted tonp.int_
, while it would be expected to
represent any integer (e.g. alsoint8
,int16
, etc. For example,
dtype=np.floating
is currently identical todtype=np.float64
, even
though alsonp.float32
is a subclass ofnp.floating
.(gh15534)
๐ Deprecation of
round
fornp.complexfloating
scalarsOutput of the
__round__
dunder method and consequently the Python
๐ builtinround
has been deprecated on complex scalars. This does not
affectnp.round
.(gh15840)
๐
numpy.ndarray.tostring()
is deprecated in favor oftobytes()
๐
~numpy.ndarray.tobytes
has existed since the 1.9 release, but until
๐ this release~numpy.ndarray.tostring
emitted no warning. The change to
โ emit a warning brings NumPy in line with the builtinarray.array
methods of the same name.(gh15867)
C API changes
๐ Better support for
const
dimensions in API functionsThe following functions now accept a constant array of
npy_intp
:PyArray_BroadcastToShape
PyArray_IntTupleFromIntp
PyArray_OverflowMultiplyList
Previously the caller would have to cast away the constness to call
these functions.(gh15251)
Const qualify UFunc inner loops
UFuncGenericFunction
now expects pointers to constdimension
and
strides
as arguments. This means inner loops may no longer modify
eitherdimension
orstrides
. This change leads to an
โincompatiblepointertypes
warning forcing users to either ignore the
โ compiler warnings or to const qualify their own loop signatures.(gh15355)
๐ New Features
numpy.frompyfunc
now accepts an identity argumentThis allows the `
numpy.ufunc.identity
{.interpretedtext
role="attr"}[ attribute to be set on the resulting ufunc, meaning it can
be used for empty and multidimensional calls to
:meth:]{.titleref}[numpy.ufunc.reduce]{.titleref}`.(gh8255)
๐
np.str_
scalars now support the buffer protocolnp.str_
arrays are always stored as UCS4, so the corresponding scalars
now expose this through the buffer interface, meaning
โmemoryview(np.str_('test'))
now works.(gh15385)
subok
option fornumpy.copy
A new kwarg,
subok
, was added tonumpy.copy
to allow users to toggle
the behavior ofnumpy.copy
with respect to array subclasses. The
0๏ธโฃ default value isFalse
which is consistent with the behavior of
numpy.copy
for previous numpy versions. To create a copy that
preserves an array subclass withnumpy.copy
, call
๐np.copy(arr, subok=True)
. This addition better documents that the
0๏ธโฃ default behavior ofnumpy.copy
differs from thenumpy.ndarray.copy
0๏ธโฃ method which respects array subclasses by default.(gh15685)
numpy.linalg.multi_dot
now accepts anout
argumentout
can be used to avoid creating unnecessary copies of the final
product computed bynumpy.linalg.multidot
.(gh15715)
keepdims
parameter fornumpy.count_nonzero
The parameter
keepdims
was added tonumpy.count_nonzero
. The
parameter has the same meaning as it does in reduction functions such as
numpy.sum
ornumpy.mean
.(gh15870)
equal_nan
parameter fornumpy.array_equal
The keyword argument
equal_nan
was added tonumpy.array_equal
.
equal_nan
is a boolean value that toggles whether or notnan
values
0๏ธโฃ are considered equal in comparison (default isFalse
). This matches
API used in related functions such asnumpy.isclose
and
numpy.allclose
.(gh16128)
๐ Improvements
๐ Improve detection of CPU features
Replace
npy_cpu_supports
which was a gcc specific mechanism to test
support of AVX with more general functionsnpy_cpu_init
and
npy_cpu_have
, and expose the results via aNPY_CPU_HAVE
cmacro as
well as a pythonlevel__cpu_features__
dictionary.(gh13421)
๐ Use 64bit integer size on 64bit platforms in fallback lapack_lite
๐ Use 64bit integer size on 64bit platforms in the fallback LAPACK
library, which is used when the system has no LAPACK installed, allowing
it to deal with linear algebra for large arrays.(gh15218)
๐ Use AVX512 intrinsic to implement
np.exp
when input isnp.float64
๐ Use AVX512 intrinsic to implement
np.exp
when input isnp.float64
,
๐ which can improve the performance ofnp.exp
withnp.float64
input
57x faster than before. The_multiarray_umath.so
module has grown
๐ง about 63 KB on linux64.(gh15648)
Ability to disable madvise hugepages
๐ง On Linux NumPy has previously added support for madavise hugepages which
๐ can improve performance for very large arrays. Unfortunately, on older
0๏ธโฃ Kernel versions this led to peformance regressions, thus by default the
๐ support has been disabled on kernels before version 4.6. To override the
0๏ธโฃ default, you can use the environment variable:NUMPY_MADVISE_HUGEPAGE=0
๐ or set it to 1 to force enabling support. Note that this only makes a
difference if the operating system is set up to use madvise transparent
hugepage.(gh15769)
numpy.einsum
accepts NumPyint64
type in subscript listThere is no longer a type error thrown when
numpy.einsum
is passed a
NumPyint64
array as its subscript list.(gh16080)
np.logaddexp2.identity
changed toinf
The ufunc
~numpy.logaddexp2
now has an identity ofinf
, allowing it
to be called on empty sequences. This matches the identity of
~numpy.logaddexp
.(gh16102)
๐ Changes
Remove handling of extra argument to
__array__
โ A code path and test have been in the code since NumPy 0.4 for a
twoargument variant of__array__ (dtype=None, context=None)
. It was
activated when callingufunc(op)
orufunc.reduce(op)
if
op. __array__
existed. However that variant is not documented, and it
๐ is not clear what the intention was for its use. It has been removed.(gh15118)
numpy.random._bit_generator
moved tonumpy.random.bit_generator
In order to expose
numpy.random.BitGenerator
and
๐numpy.random.SeedSequence
to Cython, the_bitgenerator
module is now
public asnumpy.random.bit_generator
Cython access to the random distributions is provided via a
pxd
filec_distributions.pxd
provides access to the c functions behind many of
the random distributions from Cython, making it convenient to use and
extend them.(gh15463)
๐ Fixed
eigh
andcholesky
methods innumpy.random.multivariate_normal
Previously, when passing
method='eigh'
ormethod='cholesky'
,
numpy.random.multivariate_normal
produced samples from the wrong
๐ distribution. This is now fixed.(gh15872)
๐ Fixed the jumping implementation in
MT19937.jumped
This fix changes the stream produced from jumped MT19937 generators. It
does not affect the stream produced usingRandomState
orMT19937
๐ that are directly seeded.๐ The translation of the jumping code for the MT19937 contained a reversed
loop ordering.MT19937.jumped
matches the Makoto Matsumoto's original
implementation of the Horner and Sliding Window jump methods.(gh16153)
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v1.18.5 Changes
June 04, 2020๐ NumPy 1.18.5 Release Notes
๐ This is a short release to allow pickle
protocol=5
to be used in
Python3.5. It is motivated by the recent backport of pickle5 to
Python3.5.๐ The Python versions supported in this release are 3.53.8. Downstream
๐ developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.Contributors
๐ A total of 3 people contributed to this release. People with a "+" by
their names contributed a patch for the first time. Charles Harris
 Matti Picus
 Siyuan Zhuang +
๐ Pull requests merged
๐ A total of 2 pull requests were merged for this release.
 ๐ #16439: ENH: enable pickle protocol 5 support for python3.5
 ๐ #16441: BUG: relpath fails for different drives on windows
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