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This PR contains the following updates:
1.24.4
->1.26.1
Release Notes
numpy/numpy (numpy)
v1.26.1
Compare Source
NumPy 1.26.1 Release Notes
NumPy 1.26.1 is a maintenance release that fixes bugs and regressions
discovered after the 1.26.0 release. In addition, it adds new
functionality for detecting BLAS and LAPACK when building from source.
Highlights are:
The 1.26.release series is the last planned minor release series before
NumPy 2.0. The Python versions supported by this release are 3.9-3.12.
Build system changes
Improved BLAS/LAPACK detection and control
Auto-detection for a number of BLAS and LAPACK is now implemented for
Meson. By default, the build system will try to detect MKL, Accelerate
(on macOS >=13.3), OpenBLAS, FlexiBLAS, BLIS and reference BLAS/LAPACK.
Support for MKL was significantly improved, and support for FlexiBLAS
was added.
New command-line flags are available to further control the selection of
the BLAS and LAPACK libraries to build against.
To select a specific library, use the config-settings interface via
pip
orpypa/build
. E.g., to selectlibblas
/liblapack
, use:This works not only for the libraries named above, but for any library
that Meson is able to detect with the given name through
pkg-config
orCMake.
Besides
-Dblas
and-Dlapack
, a number of other new flags areavailable to control BLAS/LAPACK selection and behavior:
-Dblas-order
and-Dlapack-order
: a list of library names tosearch for in order, overriding the default search order.
-Duse-ilp64
: if set totrue
, use ILP64 (64-bit integer) BLAS andLAPACK. Note that with this release, ILP64 support has been extended
to include MKL and FlexiBLAS. OpenBLAS and Accelerate were supported
in previous releases.
-Dallow-noblas
: if set totrue
, allow NumPy to build with itsinternal (very slow) fallback routines instead of linking against an
external BLAS/LAPACK library. The default for this flag may be
changed to ``true`` in a future 1.26.x release, however for
1.26.1 we'd prefer to keep it as ``false`` because if failures
to detect an installed library are happening, we'd like a bug
report for that, so we can quickly assess whether the new
auto-detection machinery needs further improvements.
-Dmkl-threading
: to select the threading layer for MKL. There arefour options:
seq
,iomp
,gomp
andtbb
. The default isauto
, which selects from those four as appropriate given theversion of MKL selected.
-Dblas-symbol-suffix
: manually select the symbol suffix to use forthe library - should only be needed for linking against libraries
built in a non-standard way.
New features
numpy._core
submodule stubsnumpy._core
submodule stubs were added to provide compatibility withpickled arrays created using NumPy 2.0 when running Numpy 1.26.
Contributors
A total of 13 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 20 pull requests were merged for this release.
-march=native
...use-compute-credits
for Cirrus.NumpyUnpickler
for backportingnumpy._core
stubs. RemoveNumpyUnpickler
Checksums
MD5
SHA256
v1.26.0
Compare Source
NumPy 1.26.0 Release Notes
The NumPy 1.26.0 release is a continuation of the 1.25.x release cycle
with the addition of Python 3.12.0 support. Python 3.12 dropped
distutils, consequently supporting it required finding a replacement for
the setup.py/distutils based build system NumPy was using. We have
chosen to use the Meson build system instead, and this is the first
NumPy release supporting it. This is also the first release that
supports Cython 3.0 in addition to retaining 0.29.X compatibility.
Supporting those two upgrades was a large project, over 100 files have
been touched in this release. The changelog doesn't capture the full
extent of the work, special thanks to Ralf Gommers, Sayed Adel, Stéfan
van der Walt, and Matti Picus who did much of the work in the main
development branch.
The highlights of this release are:
The Python versions supported in this release are 3.9-3.12.
New Features
Array API v2022.12 support in
numpy.array_api
numpy.array_api
now full supports thev2022.12 version of the array API standard. Note that this does not
yet include the optional
fft
extension in the standard.(gh-23789)
Support for the updated Accelerate BLAS/LAPACK library
Support for the updated Accelerate BLAS/LAPACK library, including ILP64
(64-bit integer) support, in macOS 13.3 has been added. This brings
arm64 support, and significant performance improvements of up to 10x for
commonly used linear algebra operations. When Accelerate is selected at
build time, the 13.3+ version will automatically be used if available.
(gh-24053)
meson
backend forf2py
f2py
in compile mode (i.e.f2py -c
) now accepts the--backend meson
option. This is the default option for Python3.12
on-wards. Older versions will still default to
--backend distutils
.To support this in realistic use-cases, in compile mode
f2py
takes a--dep
flag one or many times which maps todependency()
calls in themeson
backend, and does nothing in thedistutils
backend.There are no changes for users of
f2py
only as a code generator, i.e.without
-c
.(gh-24532)
bind(c)
support forf2py
Both functions and subroutines can be annotated with
bind(c)
.f2py
will handle both the correct type mapping, and preserve the unique label
for other
C
interfaces.Note:
bind(c, name = 'routine_name_other_than_fortran_routine')
isnot honored by the
f2py
bindings by design, sincebind(c)
with thename
is meant to guarantee only the same name inC
andFortran
,not in
Python
andFortran
.(gh-24555)
Improvements
iso_c_binding
support forf2py
Previously, users would have to define their own custom
f2cmap
file touse type mappings defined by the Fortran2003
iso_c_binding
intrinsicmodule. These type maps are now natively supported by
f2py
(gh-24555)
Build system changes
In this release, NumPy has switched to Meson as the build system and
meson-python as the build backend. Installing NumPy or building a wheel
can be done with standard tools like
pip
andpypa/build
. Thefollowing are supported:
pip install numpy
or (in a cloned repo)pip install .
python -m build
(preferred), orpip wheel .
pip install -e . --no-build-isolation
spin:
spin build
.All the regular
pip
andpypa/build
flags (e.g.,--no-build-isolation
) should work as expected.NumPy-specific build customization
Many of the NumPy-specific ways of customizing builds have changed. The
NPY_*
environment variables which control BLAS/LAPACK, SIMD,threading, and other such options are no longer supported, nor is a
site.cfg
file to select BLAS and LAPACK. Instead, there arecommand-line flags that can be passed to the build via
pip
/build
'sconfig-settings interface. These flags are all listed in the
meson_options.txt
file in the root of the repo. Detailed documentedwill be available before the final 1.26.0 release; for now please see
the SciPy "building from source" docs
since most build customization works in an almost identical way in SciPy as it
does in NumPy.
Build dependencies
While the runtime dependencies of NumPy have not changed, the build
dependencies have. Because we temporarily vendor Meson and meson-python,
there are several new dependencies - please see the
[build-system]
section of
pyproject.toml
for details.Troubleshooting
This build system change is quite large. In case of unexpected issues,
it is still possible to use a
setup.py
-based build as a temporaryworkaround (on Python 3.9-3.11, not 3.12), by copying
pyproject.toml.setuppy
topyproject.toml
. However, please open anissue with details on the NumPy issue tracker. We aim to phase out
setup.py
builds as soon as possible, and therefore would like to seeall potential blockers surfaced early on in the 1.26.0 release cycle.
Contributors
A total of 20 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 59 pull requests were merged for this release.
_NestedSequence.__getitem__
signatureextbuild.py
from main.asv dev
has been removed, useasv run
._umath_linalg
dependenciesbinding
directive to "False".casting
keyword tonp.clip
fromnumeric.pyi
iso_c_binding
type maps and fixbind(c)
...binary_repr
to accept any object implementing...dtype
andgeneric
hashabletyping.assert_type
meson
backend forf2py
spin docs
...Checksums
MD5
SHA256
v1.25.2
Compare Source
NumPy 1.25.2 Release Notes
NumPy 1.25.2 is a maintenance release that fixes bugs and regressions
discovered after the 1.25.1 release. This is the last planned release in
the 1.25.x series, the next release will be 1.26.0, which will use the
meson build system and support Python 3.12. The Python versions
supported by this release are 3.9-3.11.
Contributors
A total of 13 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 19 pull requests were merged for this release.
-ftrapping-math
with Clang on macOSnp.__all__
getenv
call used for memory policy warningChecksums
MD5
SHA256
v1.25.1
Compare Source
v1.25.0
Compare Source
NumPy 1.25.0 Release Notes
The NumPy 1.25.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, and
clarify the documentation. There has also been work to prepare for the
future NumPy 2.0.0 release, resulting in a large number of new and
expired deprecation. Highlights are:
@=
).We will be releasing a NumPy 1.26 when Python 3.12 comes out. That is
needed because distutils has been dropped by Python 3.12 and we will be
switching to using meson for future builds. The next mainline release
will be NumPy 2.0.0. We plan that the 2.0 series will still support
downstream projects built against earlier versions of NumPy.
The Python versions supported in this release are 3.9-3.11.
Deprecations
np.core.MachAr
is deprecated. It is private API. In names definedin
np.core
should generally be considered private.(gh-22638)
np.finfo(None)
is deprecated.(gh-23011)
np.round_
is deprecated. Usenp.round
instead.(gh-23302)
np.product
is deprecated. Usenp.prod
instead.(gh-23314)
np.cumproduct
is deprecated. Usenp.cumprod
instead.(gh-23314)
np.sometrue
is deprecated. Usenp.any
instead.(gh-23314)
np.alltrue
is deprecated. Usenp.all
instead.(gh-23314)
Only ndim-0 arrays are treated as scalars. NumPy used to treat all
arrays of size 1 (e.g.,
np.array([3.14])
) as scalars. In thefuture, this will be limited to arrays of ndim 0 (e.g.,
np.array(3.14)
). The following expressions will report adeprecation warning:
(gh-10615)
numpy.find_common_type
is now deprecated and its useshould be replaced with either
numpy.result_type
ornumpy.promote_types
. Most users leave the secondscalar_types
argument tofind_common_type
as[]
in which casenp.result_type
andnp.promote_types
are both faster and morerobust. When not using
scalar_types
the main difference is thatthe replacement intentionally converts non-native byte-order to
native byte order. Further,
find_common_type
returnsobject
dtype rather than failing promotion. This leads to differences when
the inputs are not all numeric. Importantly, this also happens for
e.g. timedelta/datetime for which NumPy promotion rules are
currently sometimes surprising.
When the
scalar_types
argument is not[]
things are morecomplicated. In most cases, using
np.result_type
and passing thePython values
0
,0.0
, or0j
has the same result as usingint
,float
, orcomplex
inscalar_types
.When
scalar_types
is constructed,np.result_type
is the correctreplacement and it may be passed scalar values like
np.float32(0.0)
. Passing values other than 0, may lead tovalue-inspecting behavior (which
np.find_common_type
never usedand NEP 50 may change in the future). The main possible change in
behavior in this case, is when the array types are signed integers
and scalar types are unsigned.
If you are unsure about how to replace a use of
scalar_types
orwhen non-numeric dtypes are likely, please do not hesitate to open a
NumPy issue to ask for help.
(gh-22539)
Expired deprecations
np.core.machar
andnp.finfo.machar
have been removed.(gh-22638)
+arr
will now raise an error when the dtype is not numeric (andpositive is undefined).
(gh-22998)
A sequence must now be passed into the stacking family of functions
(
stack
,vstack
,hstack
,dstack
andcolumn_stack
).(gh-23019)
np.clip
now defaults to same-kind casting. Falling back to unsafecasting was deprecated in NumPy 1.17.
(gh-23403)
np.clip
will now propagatenp.nan
values passed asmin
ormax
. Previously, a scalar NaN was usually ignored. This wasdeprecated in NumPy 1.17.
(gh-23403)
The
np.dual
submodule has been removed.(gh-23480)
NumPy now always ignores sequence behavior for an array-like
(defining one of the array protocols). (Deprecation started NumPy
1.20)
(gh-23660)
The niche
FutureWarning
when casting to a subarray dtype inastype
or the array creation functions such asasarray
is nowfinalized. The behavior is now always the same as if the subarray
dtype was wrapped into a single field (which was the workaround,
previously). (FutureWarning since NumPy 1.20)
(gh-23666)
==
and!=
warnings have been finalized. The==
and!=
operators on arrays now always:
raise errors that occur during comparisons such as when the
arrays have incompatible shapes
(
np.array([1, 2]) == np.array([1, 2, 3])
).return an array of all
True
or allFalse
when values arefundamentally not comparable (e.g. have different dtypes). An
example is
np.array(["a"]) == np.array([1])
.This mimics the Python behavior of returning
False
andTrue
when comparing incompatible types like
"a" == 1
and"a" != 1
. For a long time these gaveDeprecationWarning
orFutureWarning
.(gh-22707)
Nose support has been removed. NumPy switched to using pytest in
2018 and nose has been unmaintained for many years. We have kept
NumPy's nose support to avoid breaking downstream projects who
might have been using it and not yet switched to pytest or some
other testing framework. With the arrival of Python 3.12, unpatched
nose will raise an error. It is time to move on.
Decorators removed:
These are not to be confused with pytest versions with similar
names, e.g., pytest.mark.slow, pytest.mark.skipif,
pytest.mark.parametrize.
Functions removed:
(gh-23041)
The
numpy.testing.utils
shim has been removed. Importing from thenumpy.testing.utils
shim has been deprecated since 2019, the shimhas now been removed. All imports should be made directly from
numpy.testing
.(gh-23060)
The environment variable to disable dispatching has been removed.
Support for the
NUMPY_EXPERIMENTAL_ARRAY_FUNCTION
environmentvariable has been removed. This variable disabled dispatching with
__array_function__
.(gh-23376)
Support for
y=
as an alias ofout=
has been removed. Thefix
,isposinf
andisneginf
functions allowed usingy=
as a(deprecated) alias for
out=
. This is no longer supported.(gh-23376)
Compatibility notes
The
busday_count
method now correctly handles cases where thebegindates
is later in time than theenddates
. Previously, theenddates
was included, even though the documentation states it isalways excluded.
(gh-23229)
When comparing datetimes and timedelta using
np.equal
ornp.not_equal
numpy previously allowed the comparison withcasting="unsafe"
. This operation now fails. Forcing the outputdtype using the
dtype
kwarg can make the operation succeed, but wedo not recommend it.
(gh-22707)
When loading data from a file handle using
np.load
, if the handleis at the end of file, as can happen when reading multiple arrays by
calling
np.load
repeatedly, numpy previously raisedValueError
if
allow_pickle=False
, andOSError
ifallow_pickle=True
. Nowit raises
EOFError
instead, in both cases.(gh-23105)
np.pad
withmode=wrap
pads with strict multiples of original dataCode based on earlier version of
pad
that usesmode="wrap"
willreturn different results when the padding size is larger than initial
array.
np.pad
withmode=wrap
now always fills the space with strictmultiples of original data even if the padding size is larger than the
initial array.
(gh-22575)
Cython
long_t
andulong_t
removedlong_t
andulong_t
were aliases forlonglong_t
andulonglong_t
and confusing (a remainder from of Python 2). This change may lead to
the errors:
We recommend use of bit-sized types such as
cnp.int64_t
or the use ofcnp.intp_t
which is 32 bits on 32 bit systems and 64 bits on 64 bitsystems (this is most compatible with indexing). If C
long
is desired,use plain
long
ornpy_long
.cnp.int_t
is alsolong
(NumPy'sdefault integer). However,
long
is 32 bit on 64 bit windows and we maywish to adjust this even in NumPy. (Please do not hesitate to contact
NumPy developers if you are curious about this.)
(gh-22637)
Changed error message and type for bad
axes
argument toufunc
The error message and type when a wrong
axes
value is passed toufunc(..., axes=[...])
has changed. The message is now moreindicative of the problem, and if the value is mismatched an
AxisError
will be raised. ATypeError
will still be raised forinvalidinput types.
(gh-22675)
Array-likes that define
__array_ufunc__
can now override ufuncs if used aswhere
If the
where
keyword argument of anumpy.ufunc
{.interpreted-textrole="class"} is a subclass of
numpy.ndarray
{.interpreted-textrole="class"} or is a duck type that defines
numpy.class.__array_ufunc__
{.interpreted-text role="func"} it canoverride the behavior of the ufunc using the same mechanism as the input
and output arguments. Note that for this to work properly, the
where.__array_ufunc__
implementation will have to unwrap thewhere
argument to pass it into the default implementation of the
ufunc
or,for
numpy.ndarray
{.interpreted-text role="class"} subclasses beforeusing
super().__array_ufunc__
.(gh-23240)
Compiling against the NumPy C API is now backwards compatible by default
NumPy now defaults to exposing a backwards compatible subset of the
C-API. This makes the use of
oldest-supported-numpy
unnecessary.Libraries can override the default minimal version to be compatible with
using:
before including NumPy or by passing the equivalent
-D
option to thecompiler. The NumPy 1.25 default is
NPY_1_19_API_VERSION
. Because theNumPy 1.19 C API was identical to the NumPy 1.16 one resulting programs
will be compatible with NumPy 1.16 (from a C-API perspective). This
default will be increased in future non-bugfix releases. You can still
compile against an older NumPy version and run on a newer one.
For more details please see
for-downstream-package-authors
{.interpreted-text role="ref"}.(gh-23528)
New Features
np.einsum
now accepts arrays withobject
dtypeThe code path will call python operators on object dtype arrays, much
like
np.dot
andnp.matmul
.(gh-18053)
Add support for inplace matrix multiplication
It is now possible to perform inplace matrix multiplication via the
@=
operator.
(gh-21120)
Added
NPY_ENABLE_CPU_FEATURES
environment variableUsers may now choose to enable only a subset of the built CPU features
at runtime by specifying the
NPY_ENABLE_CPU_FEATURES
environment variable. Note that these specified features must be outside
the baseline, since those are always assumed. Errors will be raised if
attempting to enable a feature that is either not supported by your CPU,
or that NumPy was not built with.
(gh-22137)
NumPy now has an
np.exceptions
namespaceNumPy now has a dedicated namespace making most exceptions and warnings
available. All of these remain available in the main namespace, although
some may be moved slowly in the future. The main reason for this is to
increase discoverability and add future exceptions.
(gh-22644)
np.linalg
functions return NamedTuplesnp.linalg
functions that return tuples now return namedtuples. Thesefunctions are
eig()
,eigh()
,qr()
,slogdet()
, andsvd()
. Thereturn type is unchanged in instances where these functions return
non-tuples with certain keyword arguments (like
svd(compute_uv=False)
).(gh-22786)
String functions in
np.char
are compatible with NEP 42 custom dtypesCustom dtypes that represent unicode strings or byte strings can now be
passed to the string functions in
np.char
.(gh-22863)
String dtype instances can be created from the string abstract dtype classes
It is now possible to create a string dtype instance with a size without
using the string name of the dtype. For example,
type(np.dtype('U'))(8)
will create a dtype that is equivalent tonp.dtype('U8')
. This feature is most useful when writing generic codedealing with string dtype classes.
(gh-22963)
Fujitsu C/C++ compiler is now supported
Support for Fujitsu compiler has been added. To build with Fujitsu
compiler, run:
SSL2 is now supported
Support for SSL2 has been added. SSL2 is a library that provides
OpenBLAS compatible GEMM functions. To enable SSL2, it need to edit
site.cfg and build with Fujitsu compiler. See site.cfg.example.
(gh-22982)
Improvements
NDArrayOperatorsMixin
specifies that it has no__slots__
The
NDArrayOperatorsMixin
class now specifies that it contains no__slots__
, ensuring that subclasses can now make use of this featurein Python.
(gh-23113)
Fix power of complex zero
np.power
now returns a different result for0^{non-zero}
for complexnumbers. Note that the value is only defined when the real part of the
exponent is larger than zero. Previously, NaN was returned unless the
imaginary part was strictly zero. The return value is either
0+0j
or0-0j
.(gh-18535)
New
DTypePromotionError
NumPy now has a new
DTypePromotionError
which is used when two dtypescannot be promoted
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