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NumPy 1.26.4 is a maintenance release that fixes bugs and regressions
discovered after the 1.26.3 release. The Python versions supported by
this release are 3.9-3.12. This is the last planned release in the
1.26.x series.
Contributors
A total of 13 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Charles Harris
Elliott Sales de Andrade
Lucas Colley +
Mark Ryan +
Matti Picus
Nathan Goldbaum
Ola x Nilsson +
Pieter Eendebak
Ralf Gommers
Sayed Adel
Sebastian Berg
Stefan van der Walt
Stefano Rivera
Pull requests merged
A total of 19 pull requests were merged for this release.
NumPy 1.26.3 is a maintenance release that fixes bugs and regressions
discovered after the 1.26.2 release. The most notable changes are the
f2py bug fixes. The Python versions supported by this release are
3.9-3.12.
Compatibility
f2py will no longer accept ambiguous -m and .pyf CLI combinations.
When more than one .pyf file is passed, an error is raised. When both -m and a .pyf is passed, a warning is emitted and the -m provided
name is ignored.
Improvements
f2py now handles common blocks which have kind specifications from
modules. This further expands the usability of intrinsics like iso_fortran_env and iso_c_binding.
Contributors
A total of 18 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
NumPy 1.26.2 is a maintenance release that fixes bugs and regressions
discovered after the 1.26.1 release. 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.
Contributors
A total of 13 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
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:
Improved detection of BLAS and LAPACK libraries for meson builds
Pickle compatibility with the upcoming NumPy 2.0.
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 or pypa/build. E.g., to select libblas/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 or
CMake.
Besides -Dblas and -Dlapack, a number of other new flags are
available to control BLAS/LAPACK selection and behavior:
-Dblas-order and -Dlapack-order: a list of library names to
search for in order, overriding the default search order.
-Duse-ilp64: if set to true, use ILP64 (64-bit integer) BLAS and
LAPACK. 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 to true, allow NumPy to build with its
internal (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 are
four options: seq, iomp, gomp and tbb. The default is auto, which selects from those four as appropriate given the
version of MKL selected.
-Dblas-symbol-suffix: manually select the symbol suffix to use for
the library - should only be needed for linking against libraries
built in a non-standard way.
New features
numpy._core submodule stubs
numpy._core submodule stubs were added to provide compatibility with
pickled 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.
Andrew Nelson
Anton Prosekin +
Charles Harris
Chongyun Lee +
Ivan A. Melnikov +
Jake Lishman +
Mahder Gebremedhin +
Mateusz Sokół
Matti Picus
Munira Alduraibi +
Ralf Gommers
Rohit Goswami
Sayed Adel
Pull requests merged
A total of 20 pull requests were merged for this release.
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:
Python 3.12.0 support.
Cython 3.0.0 compatibility.
Use of the Meson build system
Updated SIMD support
f2py fixes, meson and bind(x) support
Support for the updated Accelerate BLAS/LAPACK library
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 the v2022.12 version of the array API standard. Note that this does not
yet include the optional fft extension in the standard.
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.
f2py in compile mode (i.e. f2py -c) now accepts the --backend meson option. This is the default option for Python 3.12
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 to dependency() calls in the meson backend, and does nothing in the distutils backend.
There are no changes for users of f2py only as a code generator, i.e.
without -c.
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') is
not honored by the f2py bindings by design, since bind(c) with the name is meant to guarantee only the same name in C and Fortran,
not in Python and Fortran.
Previously, users would have to define their own custom f2cmap file to
use type mappings defined by the Fortran2003 iso_c_binding intrinsic
module. These type maps are now natively supported by f2py
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 and pypa/build. The
following are supported:
Regular installs: pip install numpy or (in a cloned repo) pip install .
Building a wheel: python -m build (preferred), or pip wheel .
Development builds through the custom CLI implemented with spin: spin build.
All the regular pip and pypa/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 are
command-line flags that can be passed to the build via pip/build's
config-settings interface. These flags are all listed in the meson_options.txt file in the root of the repo. Detailed documented
will 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 temporary
workaround (on Python 3.9-3.11, not 3.12), by copying pyproject.toml.setuppy to pyproject.toml. However, please open an
issue with details on the NumPy issue tracker. We aim to phase out setup.py builds as soon as possible, and therefore would like to see
all 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.
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Updating dependencies
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Because thc-net depends on numpy (1.26.4) which requires Python >=3.9, version solving failed.
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This PR contains the following updates:
1.18.5→1.26.4Release Notes
numpy/numpy (numpy)
v1.26.4Compare Source
NumPy 1.26.4 Release Notes
NumPy 1.26.4 is a maintenance release that fixes bugs and regressions
discovered after the 1.26.3 release. The Python versions supported by
this release are 3.9-3.12. This is the last planned release in the
1.26.x series.
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.
numpy.array_api: fixlinalg.choleskyupper decomp...newaxisto__all__innumpy.array_api__config__.pyChecksums
MD5
SHA256
v1.26.3Compare Source
NumPy 1.26.3 Release Notes
NumPy 1.26.3 is a maintenance release that fixes bugs and regressions
discovered after the 1.26.2 release. The most notable changes are the
f2py bug fixes. The Python versions supported by this release are
3.9-3.12.
Compatibility
f2pywill no longer accept ambiguous-mand.pyfCLI combinations.When more than one
.pyffile is passed, an error is raised. When both-mand a.pyfis passed, a warning is emitted and the-mprovidedname is ignored.
Improvements
f2pynow handlescommonblocks which havekindspecifications frommodules. This further expands the usability of intrinsics like
iso_fortran_envandiso_c_binding.Contributors
A total of 18 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.
Pull requests merged
A total of 42 pull requests were merged for this release.
__getitem__innumpy.array_apinewaxisandlinalg.solveinnumpy.array_apilongtypebasein cpu_avx512_knf2pywrappers when modules and subroutines...iso_c_typemappings more consistentlyf2pyrewrite withmesondetailsnumpy/f2py/_backendsfrom main.f2py/*.pyfrom main.Checksums
MD5
SHA256
v1.26.2: 1.26.2 releaseCompare Source
NumPy 1.26.2 Release Notes
NumPy 1.26.2 is a maintenance release that fixes bugs and regressions
discovered after the 1.26.1 release. 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.
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 25 pull requests were merged for this release.
import_array()noexceptto shuffle helpersallow-noblasoption to true.np.dtypeto itself doesn't crashChecksums
MD5
SHA256
v1.26.1Compare 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
piporpypa/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-configorCMake.
Besides
-Dblasand-Dlapack, a number of other new flags areavailable to control BLAS/LAPACK selection and behavior:
-Dblas-orderand-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,gompandtbb. 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._coresubmodule stubsnumpy._coresubmodule 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-creditsfor Cirrus.NumpyUnpicklerfor backportingnumpy._corestubs. RemoveNumpyUnpicklerChecksums
MD5
SHA256
v1.26.0Compare 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_apinumpy.array_apinow full supports thev2022.12 version of the array API standard. Note that this does not
yet include the optional
fftextension 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)
mesonbackend forf2pyf2pyin compile mode (i.e.f2py -c) now accepts the--backend mesonoption. This is the default option for Python3.12on-wards. Older versions will still default to
--backend distutils.To support this in realistic use-cases, in compile mode
f2pytakes a--depflag one or many times which maps todependency()calls in themesonbackend, and does nothing in thedistutilsbackend.There are no changes for users of
f2pyonly as a code generator, i.e.without
-c.(gh-24532)
bind(c)support forf2pyBoth functions and subroutines can be annotated with
bind(c).f2pywill handle both the correct type mapping, and preserve the unique label
for other
Cinterfaces.Note:
bind(c, name = 'routine_name_other_than_fortran_routine')isnot honored by the
f2pybindings by design, sincebind(c)with thenameis meant to guarantee only the same name inCandFortran,not in
PythonandFortran.(gh-24555)
Improvements
iso_c_bindingsupport forf2pyPreviously, users would have to define their own custom
f2cmapfile touse type mappings defined by the Fortran2003
iso_c_bindingintrinsicmodule. 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
pipandpypa/build. Thefollowing are supported:
pip install numpyor (in a cloned repo)pip install .python -m build(preferred), orpip wheel .pip install -e . --no-build-isolationspin:
spin build.All the regular
pipandpypa/buildflags (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.cfgfile 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.txtfile 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.tomlfor 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.setuppytopyproject.toml. However, please open anissue with details on the NumPy issue tracker. We aim to phase out
setup.pybuilds 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__signatureConfiguration
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