Update dependency numpy to >=1.26.3,<1.27.0 (!197) · Merge requests · GitLab.org / ModelOps / AI Model Validation and Research / AI Evaluation / Prompt Library

Ghost User requested to merge renovate/numpy-1.x into main Jan 06, 2024

This MR contains the following updates:

Package	Type	Update	Change
numpy (source, changelog)	dependencies	minor	`>=1.24.4,<1.25.0` -> `>=1.26.3,<1.27.0`

Release Notes

numpy/numpy (numpy)

`v1.26.3`

Compare 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

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.

@DWesl
@Illviljan
Alexander Grund
Andrea Bianchi +
Charles Harris
Daniel Vanzo
Johann Rohwer +
Matti Picus
Nathan Goldbaum
Peter Hawkins
Raghuveer Devulapalli
Ralf Gommers
Rohit Goswami
Sayed Adel
Sebastian Berg
Stefano Rivera +
Thomas A Caswell
matoro

Pull requests merged

A total of 42 pull requests were merged for this release.

#25130: MAINT: prepare 1.26.x for further development
#25188: TYP: add None to __getitem__ in numpy.array_api
#25189: BLD,BUG: quadmath required where available [f2py]
#25190: BUG: alpha doesn't use REAL(10)
#25191: BUG: Fix FP overflow error in division when the divisor is scalar
#25192: MAINT: Pin scipy-openblas version.
#25201: BUG: Fix f2py to enable use of string optional inout argument
#25202: BUG: Fix -fsanitize=alignment issue in numpy/_core/src/multiarray/arraytypes.c.src
#25203: TST: Explicitly pass NumPy path to cython during tests (also...
#25204: BUG: fix issues with newaxis and linalg.solve in numpy.array_api
#25205: BUG: Disallow shadowed modulenames
#25217: BUG: Handle common blocks with kind specifications from modules
#25218: BUG: Fix moving compiled executable to root with f2py -c on Windows
#25219: BUG: Fix single to half-precision conversion on PPC64/VSX3
#25227: TST: f2py: fix issue in test skip condition
#25240: Revert "MAINT: Pin scipy-openblas version."
#25249: MAINT: do not use long type
#25377: TST: PyPy needs another gc.collect on latest versions
#25378: CI: Install Lapack runtime on Cygwin.
#25379: MAINT: Bump conda-incubator/setup-miniconda from 2.2.0 to 3.0.1
#25380: BLD: update vendored Meson for AIX shared library fix
#25419: MAINT: Init base in cpu_avx512_kn
#25420: BUG: Fix failing test_features on SapphireRapids
#25422: BUG: Fix non-contiguous memory load when ARM/Neon is enabled
#25428: MAINT,BUG: Never import distutils above 3.12 [f2py]
#25452: MAINT: make the import-time check for old Accelerate more specific
#25458: BUG: fix macOS version checks for Accelerate support
#25465: MAINT: Bump actions/setup-node and larsoner/circleci-artifacts-redirector-action
#25466: BUG: avoid seg fault from OOB access in RandomState.set_state()
#25467: BUG: Fix two errors related to not checking for failed allocations
#25468: BUG: Fix regression with f2py wrappers when modules and subroutines...
#25475: BUG: Fix build issues on SPR
#25478: BLD: fix uninitialized variable warnings from simd/neon/memory.h
#25480: BUG: Handle iso_c_type mappings more consistently
#25481: BUG: Fix module name bug in signature files [urgent] [f2py]
#25482: BUG: Handle .pyf.src and fix SciPy [urgent]
#25483: DOC: f2py rewrite with meson details
#25485: BUG: Add external library handling for meson [f2py]
#25486: MAINT: Run f2py's meson backend with the same python that ran...
#25489: MAINT: Update numpy/f2py/_backends from main.
#25490: MAINT: Easy updates of f2py/*.py from main.
#25491: MAINT: Update crackfortran.py and f2py2e.py from main

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`v1.26.2`: 1.26.2 release

Compare 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.

@stefan6419846
@thalassemia +
Andrew Nelson
Charles Bousseau +
Charles Harris
Marcel Bargull +
Mark Mentovai +
Matti Picus
Nathan Goldbaum
Ralf Gommers
Sayed Adel
Sebastian Berg
William Ayd +

Pull requests merged

A total of 25 pull requests were merged for this release.

#24814: MAINT: align test_dispatcher s390x targets with _umath_tests_mtargets
#24929: MAINT: prepare 1.26.x for further development
#24955: ENH: Add Cython enumeration for NPY_FR_GENERIC
#24962: REL: Remove Python upper version from the release branch
#24971: BLD: Use the correct Python interpreter when running tempita.py
#24972: MAINT: Remove unhelpful error replacements from import_array()
#24977: BLD: use classic linker on macOS, the new one in XCode 15 has...
#25003: BLD: musllinux_aarch64 [wheel build]
#25043: MAINT: Update mailmap
#25049: MAINT: Update meson build infrastructure.
#25071: MAINT: Split up .github/workflows to match main
#25083: BUG: Backport fix build on ppc64 when the baseline set to Power9...
#25093: BLD: Fix features.h detection for Meson builds [1.26.x Backport]
#25095: BUG: Avoid intp conversion regression in Cython 3 (backport)
#25107: CI: remove obsolete jobs, and move macOS and conda Azure jobs...
#25108: CI: Add linux_qemu action and remove travis testing.
#25112: MAINT: Update .spin/cmds.py from main.
#25113: DOC: Visually divide main license and bundled licenses in wheels
#25115: MAINT: Add missing noexcept to shuffle helpers
#25116: DOC: Fix license identifier for OpenBLAS
#25117: BLD: improve detection of Netlib libblas/libcblas/liblapack
#25118: MAINT: Make bitfield integers unsigned
#25119: BUG: Make n a long int for np.random.multinomial
#25120: BLD: change default of the allow-noblas option to true.
#25121: BUG: ensure passing np.dtype to itself doesn't crash

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`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:

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:

$ pip install numpy -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack
$ # OR
$ python -m build . -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack

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.

#24742: MAINT: Update cibuildwheel version
#24748: MAINT: fix version string in wheels built with setup.py
#24771: BLD, BUG: Fix build failure for host flags e.g. -march=native...
#24773: DOC: Updated the f2py docs to remove a note on -fimplicit-none
#24776: BUG: Fix SIMD f32 trunc test on s390x when baseline is none
#24785: BLD: add libquadmath to licences and other tweaks (#24753)
#24786: MAINT: Activate use-compute-credits for Cirrus.
#24803: BLD: updated vendored-meson/meson for mips64 fix
#24804: MAINT: fix licence path win
#24813: BUG: Fix order of Windows OS detection macros.
#24831: BUG, SIMD: use scalar cmul on bad Apple clang x86_64 (#24828)
#24840: BUG: Fix DATA statements for f2py
#24870: API: Add NumpyUnpickler for backporting
#24872: MAINT: Xfail test failing on PyPy.
#24879: BLD: fix math func feature checks, fix FreeBSD build, add CI...
#24899: ENH: meson: implement BLAS/LAPACK auto-detection and many CI...
#24902: DOC: add a 1.26.1 release notes section for BLAS/LAPACK build...
#24906: MAINT: Backport numpy._core stubs. Remove NumpyUnpickler
#24911: MAINT: Bump pypa/cibuildwheel from 2.16.1 to 2.16.2
#24912: BUG: loongarch doesn't use REAL(10)

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c8c6c72d4a9f831f328efb1312642a1cafafaa88981d9ab76368d50d07d93cbe  numpy-1.26.1.tar.gz

`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:

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.

(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 for `f2py`

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.

(gh-24532)

`bind(c)` support for `f2py`

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.

(gh-24555)

Improvements

`iso_c_binding` support for `f2py`

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

(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 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 .
Editable installs: pip install -e . --no-build-isolation
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.

@DWesl
Albert Steppi +
Bas van Beek
Charles Harris
Developer-Ecosystem-Engineering
Filipe Laíns +
Jake Vanderplas
Liang Yan +
Marten van Kerkwijk
Matti Picus
Melissa Weber Mendonça
Namami Shanker
Nathan Goldbaum
Ralf Gommers
Rohit Goswami
Sayed Adel
Sebastian Berg
Stefan van der Walt
Tyler Reddy
Warren Weckesser

Pull requests merged

A total of 59 pull requests were merged for this release.

#24305: MAINT: Prepare 1.26.x branch for development
#24308: MAINT: Massive update of files from main for numpy 1.26
#24322: CI: fix wheel builds on the 1.26.x branch
#24326: BLD: update openblas to newer version
#24327: TYP: Trim down the _NestedSequence.__getitem__ signature
#24328: BUG: fix choose refcount leak
#24337: TST: fix running the test suite in builds without BLAS/LAPACK
#24338: BUG: random: Fix generation of nan by dirichlet.
#24340: MAINT: Dependabot updates from main
#24342: MAINT: Add back NPY_RUN_MYPY_IN_TESTSUITE=1
#24353: MAINT: Update extbuild.py from main.
#24356: TST: fix distutils tests for deprecations in recent setuptools...
#24375: MAINT: Update cibuildwheel to version 2.15.0
#24381: MAINT: Fix codespaces setup.sh script
#24403: ENH: Vendor meson for multi-target build support
#24404: BLD: vendor meson-python to make the Windows builds with SIMD...
#24405: BLD, SIMD: The meson CPU dispatcher implementation
#24406: MAINT: Remove versioneer
#24409: REL: Prepare for the NumPy 1.26.0b1 release.
#24453: MAINT: Pin upper version of sphinx.
#24455: ENH: Add prefix to _ALIGN Macro
#24456: BUG: cleanup warnings
#24460: MAINT: Upgrade to spin 0.5
#24495: BUG: asv dev has been removed, use asv run.
#24496: BUG: Fix meson build failure due to unchanged inplace auto-generated...
#24521: BUG: fix issue with git-version script, needs a shebang to run
#24522: BUG: Use a default assignment for git_hash
#24524: BUG: fix NPY_cast_info error handling in choose
#24526: BUG: Fix common block handling in f2py
#24541: CI,TYP: Bump mypy to 1.4.1
#24542: BUG: Fix assumed length f2py regression
#24544: MAINT: Harmonize fortranobject
#24545: TYP: add kind argument to numpy.isin type specification
#24561: BUG: fix comparisons between masked and unmasked structured arrays
#24590: CI: Exclude import libraries from list of DLLs on Cygwin.
#24591: BLD: fix _umath_linalg dependencies
#24594: MAINT: Stop testing on ppc64le.
#24602: BLD: meson-cpu: fix SIMD support on platforms with no features
#24606: BUG: Change Cython binding directive to "False".
#24613: ENH: Adopt new macOS Accelerate BLAS/LAPACK Interfaces, including...
#24614: DOC: Update building docs to use Meson
#24615: TYP: Add the missing casting keyword to np.clip
#24616: TST: convert cython test from setup.py to meson
#24617: MAINT: Fixup fromnumeric.pyi
#24622: BUG, ENH: Fix iso_c_binding type maps and fix bind(c)...
#24629: TYP: Allow binary_repr to accept any object implementing...
#24630: TYP: Explicitly declare dtype and generic hashable
#24637: ENH: Refactor the typing "reveal" tests using typing.assert_type
#24638: MAINT: Bump actions/checkout from 3.6.0 to 4.0.0
#24647: ENH: meson backend for f2py
#24648: MAINT: Refactor partial load Workaround for Clang
#24653: REL: Prepare for the NumPy 1.26.0rc1 release.
#24659: BLD: allow specifying the long double format to avoid the runtime...
#24665: BLD: fix bug in random.mtrand extension, don't link libnpyrandom
#24675: BLD: build wheels for 32-bit Python on Windows, using MSVC
#24700: BLD: fix issue with compiler selection during cross compilation
#24701: BUG: Fix data stmt handling for complex values in f2py
#24707: TYP: Add annotations for the py3.12 buffer protocol
#24718: DOC: fix a few doc build issues on 1.26.x and update spin docs...

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`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.

Aaron Meurer
Andrew Nelson
Charles Harris
Kevin Sheppard
Matti Picus
Nathan Goldbaum
Peter Hawkins
Ralf Gommers
Randy Eckenrode +
Sam James +
Sebastian Berg
Tyler Reddy
dependabot[bot]

Pull requests merged

A total of 19 pull requests were merged for this release.

#24148: MAINT: prepare 1.25.x for further development
#24174: ENH: Improve clang-cl compliance
#24179: MAINT: Upgrade various build dependencies.
#24182: BLD: use -ftrapping-math with Clang on macOS
#24183: BUG: properly handle negative indexes in ufunc_at fast path
#24184: BUG: PyObject_IsTrue and PyObject_Not error handling in setflags
#24185: BUG: histogram small range robust
#24186: MAINT: Update meson.build files from main branch
#24234: MAINT: exclude min, max and round from np.__all__
#24241: MAINT: Dependabot updates
#24242: BUG: Fix the signature for np.array_api.take
#24243: BLD: update OpenBLAS to an intermeidate commit
#24244: BUG: Fix reference count leak in str(scalar).
#24245: BUG: fix invalid function pointer conversion error
#24255: BUG: Factor out slow getenv call used for memory policy warning
#24292: CI: correct URL in cirrus.star
#24293: BUG: Fix C types in scalartypes
#24294: BUG: do not modify the input to ufunc_at
#24295: BUG: Further fixes to indexing loop and added tests

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SHA256

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fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760  numpy-1.25.2.tar.gz

`v1.25.1`

Compare Source

NumPy 1.25.1 Release Notes

NumPy 1.25.1 is a maintenance release that fixes bugs and regressions discovered after the 1.25.0 release. The Python versions supported by this release are 3.9-3.11.

Contributors

A total of 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Andrew Nelson
Charles Harris
Developer-Ecosystem-Engineering
Hood Chatham
Nathan Goldbaum
Rohit Goswami
Sebastian Berg
Tim Paine +
dependabot[bot]
matoro +

Pull requests merged

A total of 14 pull requests were merged for this release.

#23968: MAINT: prepare 1.25.x for further development
#24036: BLD: Port long double identification to C for meson
#24037: BUG: Fix reduction return NULL to be goto fail
#24038: BUG: Avoid undefined behavior in array.astype()
#24039: BUG: Ensure __array_ufunc__ works without any kwargs passed
#24117: MAINT: Pin urllib3 to avoid anaconda-client bug.
#24118: TST: Pin pydantic<2 in Pyodide workflow
#24119: MAINT: Bump pypa/cibuildwheel from 2.13.0 to 2.13.1
#24120: MAINT: Bump actions/checkout from 3.5.2 to 3.5.3
#24122: BUG: Multiply or Divides using SIMD without a full vector can...
#24127: MAINT: testing for IS_MUSL closes #24074
#24128: BUG: Only replace dtype temporarily if dimensions changed
#24129: MAINT: Bump actions/setup-node from 3.6.0 to 3.7.0
#24134: BUG: Fix private procedures in f2py modules

Checksums

MD5

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SHA256

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9a3a9f3a61480cc086117b426a8bd86869c213fc4072e606f01c4e4b66eb92bf  numpy-1.25.1.tar.gz

`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:

Support for MUSL, there are now MUSL wheels.
Support the Fujitsu C/C++ compiler.
Object arrays are now supported in einsum
Support for inplace matrix multiplication (@=).

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 defined in np.core should generally be considered private.

(gh-22638)
np.finfo(None) is deprecated.

(gh-23011)
np.round_ is deprecated. Use np.round instead.

(gh-23302)
np.product is deprecated. Use np.prod instead.

(gh-23314)
np.cumproduct is deprecated. Use np.cumprod instead.

(gh-23314)
np.sometrue is deprecated. Use np.any instead.

(gh-23314)
np.alltrue is deprecated. Use np.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 the future, this will be limited to arrays of ndim 0 (e.g., np.array(3.14)). The following expressions will report a deprecation warning:
```
a = np.array([3.14])
float(a)  # better: a[0] to get the numpy.float or a.item()

b = np.array([[3.14]])
c = numpy.random.rand(10)
c[0] = b  # better: c[0] = b[0, 0]
```
(gh-10615)
numpy.find_common_type is now deprecated and its use should be replaced with either numpy.result_type or numpy.promote_types. Most users leave the second scalar_types argument to find_common_type as [] in which case np.result_type and np.promote_types are both faster and more robust. When not using scalar_types the main difference is that the replacement intentionally converts non-native byte-order to native byte order. Further, find_common_type returns object 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 more complicated. In most cases, using np.result_type and passing the Python values 0, 0.0, or 0j has the same result as using int, float, or complex in scalar_types.

When scalar_types is constructed, np.result_type is the correct replacement and it may be passed scalar values like np.float32(0.0). Passing values other than 0, may lead to value-inspecting behavior (which np.find_common_type never used and 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 or when 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 and np.finfo.machar have been removed.

(gh-22638)
+arr will now raise an error when the dtype is not numeric (and positive is undefined).

(gh-22998)
A sequence must now be passed into the stacking family of functions (stack, vstack, hstack, dstack and column_stack).

(gh-23019)
np.clip now defaults to same-kind casting. Falling back to unsafe casting was deprecated in NumPy 1.17.

(gh-23403)
np.clip will now propagate np.nan values passed as min or max. Previously, a scalar NaN was usually ignored. This was deprecated 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 in astype or the array creation functions such as asarray is now finalized. 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 all False when values are fundamentally not comparable (e.g. have different dtypes). An example is np.array(["a"]) == np.array([1]).
  
  This mimics the Python behavior of returning False and True when comparing incompatible types like "a" == 1 and "a" != 1. For a long time these gave DeprecationWarning or FutureWarning.
(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:
- raises
- slow
- setastest
- skipif
- knownfailif
- deprecated
- parametrize
- _needs_refcount
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:
- Tester
- import_nose
- run_module_suite
(gh-23041)
The numpy.testing.utils shim has been removed. Importing from the numpy.testing.utils shim has been deprecated since 2019, the shim has 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 environment variable has been removed. This variable disabled dispatching with __array_function__.

(gh-23376)
Support for y= as an alias of out= has been removed. The fix, isposinf and isneginf functions allowed using y= as a (deprecated) alias for out=. This is no longer supported.

(gh-23376)

Compatibility notes

The busday_count method now correctly handles cases where the begindates is later in time than the enddates. Previously, the enddates was included, even though the documentation states it is always excluded.

(gh-23229)
When comparing datetimes and timedelta using np.equal or np.not_equal numpy previously allowed the comparison with casting="unsafe". This operation now fails. Forcing the output dtype using the dtype kwarg can make the operation succeed, but we do not recommend it.

(gh-22707)
When loading data from a file handle using np.load, if the handle is at the end of file, as can happen when reading multiple arrays by calling np.load repeatedly, numpy previously raised ValueError if allow_pickle=False, and OSError if allow_pickle=True. Now it raises EOFError instead, in both cases.

(gh-23105)

`np.pad` with `mode=wrap` pads with strict multiples of original data

Code based on earlier version of pad that uses mode="wrap" will return different results when the padding size is larger than initial array.

np.pad with mode=wrap now always fills the space with strict multiples of original data even if the padding size is larger than the initial array.

(gh-22575)

Cython `long_t` and `ulong_t` removed

long_t and ulong_t were aliases for longlong_t and ulonglong_t and confusing (a remainder from of Python 2). This change may lead to the errors:

'long_t' is not a type identifier
'ulong_t' is not a type identifier

We recommend use of bit-sized types such as cnp.int64_t or the use of cnp.intp_t which is 32 bits on 32 bit systems and 64 bits on 64 bit systems (this is most compatible with indexing). If C long is desired, use plain long or npy_long. cnp.int_t is also long (NumPy's default integer). However, long is 32 bit on 64 bit windows and we may wish 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 to `ufunc`

The error message and type when a wrong axes value is passed to ufunc(..., axes=[...]) has changed. The message is now more indicative of the problem, and if the value is mismatched an AxisError will be raised. A TypeError will still be raised for invalidinput types.

(gh-22675)

Array-likes that define `__array_ufunc__` can now override ufuncs if used as `where`

If the where keyword argument of a numpy.ufunc{.interpreted-text role="class"} is a subclass of numpy.ndarray{.interpreted-text role="class"} or is a duck type that defines numpy.class.__array_ufunc__{.interpreted-text role="func"} it can override 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 the where argument to pass it into the default implementation of the ufunc or, for numpy.ndarray{.interpreted-text role="class"} subclasses before using 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:

#define NPY_TARGET_VERSION NPY_1_22_API_VERSION

before including NumPy or by passing the equivalent -D option to the compiler. The NumPy 1.25 default is NPY_1_19_API_VERSION. Because the NumPy 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 with `object` dtype

The code path will call python operators on object dtype arrays, much like np.dot and np.matmul.

(gh-18053)

Add support for inplace matrix multiplication

It is now possible to perform inplace matrix multiplication via the @= operator.

>>> import numpy as np

>>> a = np.arange(6).reshape(3, 2)
>>> print(a)
[[0 1]
 [2 3]
 [4 5]]

>>> b = np.ones((2, 2), dtype=int)
>>> a @&#8203;= b
>>> print(a)
[[1 1]
 [5 5]
 [9 9]]

(gh-21120)

Added `NPY_ENABLE_CPU_FEATURES` environment variable

Users 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` namespace

NumPy 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 NamedTuples

np.linalg functions that return tuples now return namedtuples. These functions are eig(), eigh(), qr(), slogdet(), and svd(). The return 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 dtypes

Custom 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 to np.dtype('U8'). This feature is most useful when writing generic code dealing 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:

python setup.py build -c fujitsu

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 feature in Python.

(gh-23113)

Fix power of complex zero

np.power now returns a different result for 0^{non-zero} for complex numbers. 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 or 0-0j.

(gh-18535)

New `DTypePromotionError`

NumPy now has a new DTypePromotionError which is used when two dtypes cannot be promoted to a common one, for example:

np.result_type("M8[s]", np.complex128)

raises this new exception.

(gh-22707)

`np.show_config` uses information from Meson

Build and system information now contains information from Meson. np.show_config now has a new optional parameter mode to help customize the output.

(gh-22769)

Fix `np.ma.diff` not preserving the mask when called with arguments prepend/append.

Calling np.ma.diff with arguments prepend and/or append now returns a MaskedArray with the input mask preserved.

Previously, a MaskedArray without the mask was returned.

(gh-22776)

Corrected error handling for NumPy C-API in Cython

Many NumPy C functions defined for use in Cython were lacking the correct error indicator like except -1 or except *. These have now been added.

(gh-22997)

Ability to directly spawn random number generators

numpy.random.Generator.spawn now allows to directly spawn new independent child generators via the numpy.random.SeedSequence.spawn mechanism. numpy.random.BitGenerator.spawn does the same for the underlying bit generator.

Additionally, numpy.random.BitGenerator.seed_seq now gives direct access to the seed sequence used for initializing the bit generator. This allows for example:

seed = 0x2e09b90939db40c400f8f22dae617151
rng = np.random.default_rng(seed)
child_rng1, child_rng2 = rng.spawn(2)

safely use rng, child_rng1, and child_rng2

Previously, this was hard to do without passing the SeedSequence explicitly. Please see numpy.random.SeedSequence for more information.

(gh-23195)

`numpy.logspace` now supports a non-scalar `base` argument

The base argument of numpy.logspace can now be array-like if it is broadcastable against the start and stop arguments.

(gh-23275)

`np.ma.dot()` now supports for non-2d arrays

Previously np.ma.dot() only worked if a and b were both 2d. Now it works for non-2d arrays as well as np.dot().

(gh-23322)

Explicitly show keys of .npz file in repr

NpzFile shows keys of loaded .npz file when printed.

>>> npzfile = np.load('arr.npz')
>>> npzfile
NpzFile 'arr.npz' with keys arr_0, arr_1, arr_2, arr_3, arr_4...

(gh-23357)

NumPy now exposes DType classes in `np.dtypes`

The new numpy.dtypes module now exposes DType classes and will contain future dtype related functionality. Most users should have no need to use these classes directly.

(gh-23358)

Drop dtype metadata before saving in .npy or .npz files

Currently, a *.npy file containing a table with a dtype with metadata cannot be read back. Now, np.save and np.savez drop metadata before saving.

(gh-23371)

`numpy.lib.recfunctions.structured_to_unstructured` returns views in more cases

structured_to_unstructured now returns a view, if the stride between the fields is constant. Prior, padding between the fields or a reversed field would lead to a copy. This change only applies to ndarray, memmap and recarray. For all other array subclasses, the behavior remains unchanged.

(gh-23652)

Signed and unsigned integers always compare correctly

When uint64 and int64 are mixed in NumPy, NumPy typically promotes both to float64. This behavior may be argued about but is confusing for comparisons ==, <=, since the results returned can be incorrect but the conversion is hidden since the result is a boolean. NumPy will now return the correct results for these by avoiding the cast to float.

(gh-23713)

Performance improvements and changes

Faster `np.argsort` on AVX-512 enabled processors

32-bit and 64-bit quicksort algorithm for np.argsort gain up to 6x speed up on processors that support AVX-512 instruction set.

Thanks to Intel corporation for sponsoring this work.

(gh-23707)

Faster `np.sort` on AVX-512 enabled processors

Quicksort for 16-bit and 64-bit dtypes gain up to 15x and 9x speed up on processors that support AVX-512 instruction set.

Thanks to Intel corporation for sponsoring this work.

(gh-22315)

`__array_function__` machinery is now much faster

The overhead of the majority of functions in NumPy is now smaller especially when keyword arguments are used. This change significantly speeds up many simple function calls.

(gh-23020)

`ufunc.at` can be much faster

Generic ufunc.at can be up to 9x faster. The conditions for this speedup:

operands are aligned
no casting

If ufuncs with appropriate indexed loops on 1d arguments with the above conditions, ufunc.at can be up to 60x faster (an additional 7x speedup). Appropriate indexed loops have been added to add, subtract, multiply, floor_divide, maximum, minimum, fmax, and fmin.

The internal logic is similar to the logic used for regular ufuncs, which also have fast paths.

Thanks to the D. E. Shaw group for sponsoring this work.

(gh-23136)

Faster membership test on `NpzFile`

Membership test on NpzFile will no longer decompress the archive if it is successful.

(gh-23661)

Changes

`np.r_[]` and `np.c_[]` with certain scalar values

In rare cases, using mainly np.r_ with scalars can lead to different results. The main potential changes are highlighted by the following:

>>> np.r_[np.arange(5, dtype=np.uint8), -1].dtype
int16  # rather than the default integer (int64 or int32)
>>> np.r_[np.arange(5, dtype=np.int8), 255]
array([  0,   1,   2,   3,   4, 255], dtype=int16)

Where the second example returned:

array([ 0,  1,  2,  3,  4, -1], dtype=int8)

The first one is due to a signed integer scalar with an unsigned integer array, while the second is due to 255 not fitting into int8 and NumPy currently inspecting values to make this work. (Note that the second example is expected to change in the future due to NEP 50 <NEP50>{.interpreted-text role="ref"}; it will then raise an error.)

(gh-22539)

Most NumPy functions are wrapped into a C-callable

To speed up the __array_function__ dispatching, most NumPy functions are now wrapped into C-callables and are not proper Python functions or C methods. They still look and feel the same as before (like a Python function), and this should only improve performance and user experience (cleaner tracebacks). However, please inform the NumPy developers if this change confuses your program for some reason.

(gh-23020)

C++ standard library usage

NumPy builds now depend on the C++ standard library, because the numpy.core._multiarray_umath extension is linked with the C++ linker.

(gh-23601)

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Edited Jan 11, 2024 by Tan Le

Update dependency numpy to >=1.26.3,<1.27.0

Release Notes

v1.26.3

NumPy 1.26.3 Release Notes

Compatibility

Improvements

Contributors

Pull requests merged

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v1.26.2: 1.26.2 release

NumPy 1.26.2 Release Notes

Contributors

Pull requests merged

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v1.26.1

NumPy 1.26.1 Release Notes

Build system changes

Improved BLAS/LAPACK detection and control

New features

numpy._core submodule stubs

Contributors

Pull requests merged

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v1.26.0

NumPy 1.26.0 Release Notes

New Features

Array API v2022.12 support in numpy.array_api

Support for the updated Accelerate BLAS/LAPACK library

meson backend for f2py

bind(c) support for f2py

Improvements

iso_c_binding support for f2py

Build system changes

NumPy-specific build customization

Build dependencies

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Pull requests merged

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v1.25.2

NumPy 1.25.2 Release Notes

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Pull requests merged

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v1.25.1

NumPy 1.25.1 Release Notes

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Pull requests merged

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v1.25.0

NumPy 1.25.0 Release Notes

Deprecations

Expired deprecations

Compatibility notes

np.pad with mode=wrap pads with strict multiples of original data

Cython long_t and ulong_t removed

Changed error message and type for bad axes argument to ufunc

Array-likes that define __array_ufunc__ can now override ufuncs if used as where

Compiling against the NumPy C API is now backwards compatible by default

New Features

np.einsum now accepts arrays with object dtype

Add support for inplace matrix multiplication

Added NPY_ENABLE_CPU_FEATURES environment variable

NumPy now has an np.exceptions namespace

np.linalg functions return NamedTuples

String functions in np.char are compatible with NEP 42 custom dtypes

String dtype instances can be created from the string abstract dtype classes

Fujitsu C/C++ compiler is now supported

`v1.26.3`

`v1.26.2`: 1.26.2 release

`v1.26.1`

`numpy._core` submodule stubs

`v1.26.0`

Array API v2022.12 support in `numpy.array_api`

`meson` backend for `f2py`

`bind(c)` support for `f2py`

`iso_c_binding` support for `f2py`

`v1.25.2`

`v1.25.1`

`v1.25.0`

`np.pad` with `mode=wrap` pads with strict multiples of original data

Cython `long_t` and `ulong_t` removed

Changed error message and type for bad `axes` argument to `ufunc`

Array-likes that define `__array_ufunc__` can now override ufuncs if used as `where`

`np.einsum` now accepts arrays with `object` dtype

Added `NPY_ENABLE_CPU_FEATURES` environment variable

NumPy now has an `np.exceptions` namespace

`np.linalg` functions return NamedTuples

String functions in `np.char` are compatible with NEP 42 custom dtypes

`NDArrayOperatorsMixin` specifies that it has no `slots`

New `DTypePromotionError`

`np.show_config` uses information from Meson

Fix `np.ma.diff` not preserving the mask when called with arguments prepend/append.

`numpy.logspace` now supports a non-scalar `base` argument

`np.ma.dot()` now supports for non-2d arrays

NumPy now exposes DType classes in `np.dtypes`

`numpy.lib.recfunctions.structured_to_unstructured` returns views in more cases

Faster `np.argsort` on AVX-512 enabled processors

Faster `np.sort` on AVX-512 enabled processors

`__array_function__` machinery is now much faster

`ufunc.at` can be much faster

Faster membership test on `NpzFile`

`np.r_[]` and `np.c_[]` with certain scalar values