chore(deps): update dependency numpy to ==1.26.* - abandoned #295
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This PR contains the following updates:
==1.23.*->==1.26.*Release Notes
numpy/numpy (numpy)
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
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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
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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__signatureextbuild.pyfrom main.asv devhas been removed, useasv run._umath_linalgdependenciesbindingdirective to "False".castingkeyword tonp.clipfromnumeric.pyiiso_c_bindingtype maps and fixbind(c)...binary_reprto accept any object implementing...dtypeandgenerichashabletyping.assert_typemesonbackend forf2pyspin docs...Checksums
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v1.25.2Compare 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-mathwith Clang on macOSnp.__all__getenvcall used for memory policy warningChecksums
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v1.25.1Compare Source
v1.25.0Compare 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.MachAris deprecated. It is private API. In names definedin
np.coreshould generally be considered private.(gh-22638)
np.finfo(None)is deprecated.(gh-23011)
np.round_is deprecated. Usenp.roundinstead.(gh-23302)
np.productis deprecated. Usenp.prodinstead.(gh-23314)
np.cumproductis deprecated. Usenp.cumprodinstead.(gh-23314)
np.sometrueis deprecated. Usenp.anyinstead.(gh-23314)
np.alltrueis deprecated. Usenp.allinstead.(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_typeis now deprecated and its useshould be replaced with either
numpy.result_typeornumpy.promote_types. Most users leave the secondscalar_typesargument tofind_common_typeas[]in which casenp.result_typeandnp.promote_typesare both faster and morerobust. When not using
scalar_typesthe main difference is thatthe replacement intentionally converts non-native byte-order to
native byte order. Further,
find_common_typereturnsobjectdtype 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_typesargument is not[]things are morecomplicated. In most cases, using
np.result_typeand passing thePython values
0,0.0, or0jhas the same result as usingint,float, orcomplexinscalar_types.When
scalar_typesis constructed,np.result_typeis 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_typenever 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_typesorwhen non-numeric dtypes are likely, please do not hesitate to open a
NumPy issue to ask for help.
(gh-22539)
Expired deprecations
np.core.macharandnp.finfo.macharhave been removed.(gh-22638)
+arrwill 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,dstackandcolumn_stack).(gh-23019)
np.clipnow defaults to same-kind casting. Falling back to unsafecasting was deprecated in NumPy 1.17.
(gh-23403)
np.clipwill now propagatenp.nanvalues passed asminormax. Previously, a scalConfiguration
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