v2.0.0rc1

NumPy 2.0.0 Release Notes

Note

The release of 2.0 is in progress and the current release overview and
highlights are still in a draft state. However, the highlights should
already list the most significant changes detailed in the full notes
below, and those full notes should be complete (if not copy-edited well
enough yet).

NumPy 2.0.0 is the first major release since 2006. It is the result of
10 months of development since the last feature release and is the work
of 193 contributors spread over 1006 pull requests. It contains a large
number of exciting new features as well as changes to both the Python
and C APIs.

This major release includes breaking changes that could not happen in a
regular minor (feature) release - including an ABI break, changes to
type promotion rules, and API changes which may not have been emitting
deprecation warnings in 1.26.x. Key documents related to how to adapt to
changes in NumPy 2.0, in addition to these release notes, include:

• The numpy-2-migration-guide
• The Numpy 2.0-specific advice in for dpwmstream package authors

Highlights

Highlights of this release include:

• New features:
  • A new variable-length string dtype, numpy.dtypes.StringDType and a new
    numpy.strings namespace with performant ufuncs for string operations,
  • Support for float32 and longdouble in all
    numpy.fft functions,
  • Support for the array API standard in the main numpy
    namespace.
• Performance improvements:
  • Sorting functions sort, argsort,
    partition, argpartition have been
    accelerated through the use of the Intel x86-simd-sort and
    Google Highway libraries, and may see large (hardware-specific)
    speedups,
  • macOS Accelerate support and binary wheels for macOS >=14, with
    significant performance improvements for linear algebra
    operations on macOS, and wheels that are about 3 times smaller,
  • numpy.char fixed-length string operations have
    been accelerated by implementing ufuncs that also support
    numpy.dtypes.StringDType in addition to the
    fixed-length string dtypes,
  • A new tracing and introspection API,
    numpy.lib.introspect.opt_func_info, to determine
    which hardware-specific kernels are available and will be
    dispatched to.
• Python API improvements:
  • A clear split between public and private API, with a new module
    structure and each public function now available in a single place.
  • Many removals of non-recommended functions and aliases. This
    should make it easier to learn and use NumPy. The number of
    objects in the main namespace decreased by ~10% and in
    numpy.lib by ~80%.
  • Canonical dtype names and a new numpy.isdtype` introspection
    function,
• C API improvements:
  • A new public C API for creating custom dtypes,
  • Many outdated functions and macros removed, and private
    internals hidden to ease future extensibility,
  • New, easier to use, initialization functions: PyArray_ImportNumPyAPI
    and PyUFunc_ImportUFuncAPI.
• Improved behavior:
  • Improvements to type promotion behavior was changed by adopting NEP 50.
    This fixes many user surprises about promotions which previously often
    depended on data values of input arrays rather than only their dtypes.
    Please see the NEP and the numpy-2-migration-guide for details as this
    change can lead to changes in output dtypes and lower precision results
    for mixed-dtype operations.
  • The default integer type on Windows is now int64 rather than
    int32, matching the behavior on other platforms,
  • The maximum number of array dimensions is changed from 32 to 64
• Documentation:
  • The reference guide navigation was significantly improved, and
    there is now documentation on NumPy's
    module structure,
  • The building from source documentation was completely rewritten,

Furthermore there are many changes to NumPy internals, including
continuing to migrate code from C to C++, that will make it easier to
improve and maintain NumPy in the future.

The "no free lunch" theorem dictates that there is a price to pay for
all these API and behavior improvements and better future extensibility.
This price is:

1. Backwards compatibility. There are a significant number of breaking
   changes to both the Python and C APIs. In the majority of cases,
   there are clear error messages that will inform the user how to
   adapt their code. However, there are also changes in behavior for
   which it was not possible to give such an error message - these
   cases are all covered in the Deprecation and Compatibility sections
   below, and in the numpy-2-migration-guide.

   Note that there is a ruff mode to auto-fix many things in Python
   code.

2. Breaking changes to the NumPy ABI. As a result, binaries of packages
   that use the NumPy C API and were built against a NumPy 1.xx release
   will not work with NumPy 2.0. On import, such packages will see an
   ImportError with a message about binary incompatibility.

   It is possible to build binaries against NumPy 2.0 that will work at
   runtime with both NumPy 2.0 and 1.x. See numpy-2-abi-handling for more
   details.

   All downstream packages that depend on the NumPy ABI are advised
   to do a new release built against NumPy 2.0 and verify that that
   release works with both 2.0 and 1.26 - ideally in the period between
   2.0.0rc1 (which will be ABI-stable) and the final 2.0.0 release to
   avoid problems for their users.

The Python versions supported by this release are 3.9-3.12.

NumPy 2.0 Python API removals

• np.geterrobj, np.seterrobj and the related ufunc keyword
  argument extobj= have been removed. The preferred replacement for
  all of these is using the context manager with np.errstate():.

  (gh-23922)

• np.cast has been removed. The literal replacement for
  np.cast[dtype](arg) is np.asarray(arg, dtype=dtype).

• np.source has been removed. The preferred replacement is
  inspect.getsource.

• np.lookfor has been removed.

  (gh-24144)

• numpy.who has been removed. As an alternative for the removed
  functionality, one can use a variable explorer that is available in
  IDEs such as Spyder or Jupyter Notebook.

  (gh-24321)

• Multiple niche enums, expired members and functions have been
  removed from the main namespace, such as: ERR_*, SHIFT_*,
  np.fastCopyAndTranspose, np.kernel_version, np.numarray,
  np.oldnumeric and np.set_numeric_ops.

  (gh-24316)

• Replaced from ... import * in the numpy/__init__.py with
  explicit imports. As a result, these main namespace members got
  removed: np.FLOATING_POINT_SUPPORT, np.FPE_*, np.NINF,
  np.PINF, np.NZERO, np.PZERO, np.CLIP, np.WRAP, np.WRAP,
  np.RAISE, np.BUFSIZE, np.UFUNC_BUFSIZE_DEFAULT,
  np.UFUNC_PYVALS_NAME, np.ALLOW_THREADS, np.MAXDIMS,
  np.MAY_SHARE_EXACT, np.MAY_SHARE_BOUNDS, add_newdoc,
  np.add_docstring and np.add_newdoc_ufunc.

  (gh-24357)

• Alias np.float_ has been removed. Use np.float64 instead.

• Alias np.complex_ has been removed. Use np.complex128 instead.

• Alias np.longfloat has been removed. Use np.longdouble instead.

• Alias np.singlecomplex has been removed. Use np.complex64
  instead.

• Alias np.cfloat has been removed. Use np.complex128 instead.

• Alias np.longcomplex has been removed. Use np.clongdouble
  instead.

• Alias np.clongfloat has been removed. Use np.clongdouble
  instead.

• Alias np.string_ has been removed. Use np.bytes_ instead.

• Alias np.unicode_ has been removed. Use np.str_ instead.

• Alias np.Inf has been removed. Use np.inf instead.

• Alias np.Infinity has been removed. Use np.inf instead.

• Alias np.NaN has been removed. Use np.nan instead.

• Alias np.infty has been removed. Use np.inf instead.

• Alias np.mat has been removed. Use np.asmatrix instead.

• np.issubclass_ has been removed. Use the issubclass builtin
  instead.

• np.asfarray has been removed. Use np.asarray with a proper dtype
  instead.

• np.set_string_function has been removed. Use np.set_printoptions
  instead with a formatter for custom printing of NumPy objects.

• np.tracemalloc_domain is now only available from np.lib.

• np.recfromcsv and recfromtxt are now only available from
  np.lib.npyio.

• np.issctype, np.maximum_sctype, np.obj2sctype,
  np.sctype2char, np.sctypes, np.issubsctype were all removed
  from the main namespace without replacement, as they where niche
  members.

• Deprecated np.deprecate and np.deprecate_with_doc has been
  removed from the main namespace. Use DeprecationWarning instead.

• Deprecated np.safe_eval has been removed from the main namespace.
  Use ast.literal_eval instead.

  (gh-24376)

• np.find_common_type has been removed. Use numpy.promote_types or
  numpy.result_type instead. To achieve semantics for the
  scalar_types argument, use numpy.result_type and pass 0,
  0.0, or 0j as a Python scalar instead.

• np.round_ has been removed. ...

v2.0.0b1

NumPy 2.0.0 Release Notes

Note

The release of 2.0 is in progress and the current release overview and
highlights are still in a draft state. However, the highlights should
already list the most significant changes detailed in the full notes
below, and those full notes should be complete (if not copy-edited well
enough yet).

NumPy 2.0.0 is the first major release since 2006. It is the result of
10 months of development since the last feature release and is the work
of 190 contributors spread over 968 pull requests. It contains a large
number of exciting new features as well as changes to both the Python
and C APIs.

This major release includes breaking changes that could not happen in a
regular minor (feature) release - including an ABI break, changes to
type promotion rules, and API changes which may not have been emitting
deprecation warnings in 1.26.x. Key documents related to how to adapt to
changes in NumPy 2.0, in addition to these release notes, include:

• The numpy-2-migration-guide
• The Numpy 2.0-specific advice in for dpwmstream package authors

Highlights

Highlights of this release include:

• New features:
  • A new variable-length string dtype, numpy.dtypes.StringDType and a new
    numpy.strings namespace with performant ufuncs for string operations,
  • Support for float32 and longdouble in all
    numpy.fft functions,
  • Support for the array API standard in the main numpy
    namespace.
• Performance improvements:
  • Sorting functions sort, argsort,
    partition, argpartition have been
    accelerated through the use of the Intel x86-simd-sort and
    Google Highway libraries, and may see large (hardware-specific)
    speedups,
  • macOS Accelerate support and binary wheels for macOS >=14, with
    significant performance improvements for linear algebra
    operations on macOS, and wheels that are about 3 times smaller,
  • numpy.char fixed-length string operations have
    been accelerated by implementing ufuncs that also support
    numpy.dtypes.StringDType in addition to the
    fixed-length string dtypes,
  • A new tracing and introspection API,
    numpy.lib.introspect.opt_func_info, to determine
    which hardware-specific kernels are available and will be
    dispatched to.
• Python API improvements:
  • A clear split between public and private API, with a new module
    structure and each public function now available in a single place.
  • Many removals of non-recommended functions and aliases. This
    should make it easier to learn and use NumPy. The number of
    objects in the main namespace decreased by ~10% and in
    numpy.lib by ~80%.
  • Canonical dtype names and a new numpy.isdtype` introspection
    function,
• C API improvements:
  • A new public C API for creating custom dtypes,
  • Many outdated functions and macros removed, and private
    internals hidden to ease future extensibility,
  • New, easier to use, initialization functions: PyArray_ImportNumPyAPI
    and PyUFunc_ImportUFuncAPI.
• Improved behavior:
  • Improvements to type promotion behavior was changed by adopting NEP 50.
    This fixes many user surprises about promotions which previously often
    depended on data values of input arrays rather than only their dtypes.
    Please see the NEP and the numpy-2-migration-guide for details as this
    change can lead to changes in output dtypes and lower precision results
    for mixed-dtype operations.
  • The default integer type on Windows is now int64 rather than
    int32, matching the behavior on other platforms,
  • The maximum number of array dimensions is changed from 32 to 64
• Documentation:
  • The reference guide navigation was significantly improved, and
    there is now documentation on NumPy's
    module structure,
  • The building from source documentation was completely rewritten,

Furthermore there are many changes to NumPy internals, including
continuing to migrate code from C to C++, that will make it easier to
improve and maintain NumPy in the future.

The "no free lunch" theorem dictates that there is a price to pay for
all these API and behavior improvements and better future extensibility.
This price is:

1. Backwards compatibility. There are a significant number of breaking
   changes to both the Python and C APIs. In the majority of cases,
   there are clear error messages that will inform the user how to
   adapt their code. However, there are also changes in behavior for
   which it was not possible to give such an error message - these
   cases are all covered in the Deprecation and Compatibility sections
   below, and in the numpy-2-migration-guide.

   Note that there is a ruff mode to auto-fix many things in Python
   code.

2. Breaking changes to the NumPy ABI. As a result, binaries of packages
   that use the NumPy C API and were built against a NumPy 1.xx release
   will not work with NumPy 2.0. On import, such packages will see an
   ImportError with a message about binary incompatibility.

   It is possible to build binaries against NumPy 2.0 that will work at
   runtime with both NumPy 2.0 and 1.x. See numpy-2-abi-handling for more
   details.

   All downstream packages that depend on the NumPy ABI are advised
   to do a new release built against NumPy 2.0 and verify that that
   release works with both 2.0 and 1.26 - ideally in the period between
   2.0.0rc1 (which will be ABI-stable) and the final 2.0.0 release to
   avoid problems for their users.

The Python versions supported by this release are 3.9-3.12.

NumPy 2.0 Python API removals

• np.geterrobj, np.seterrobj and the related ufunc keyword
  argument extobj= have been removed. The preferred replacement for
  all of these is using the context manager with np.errstate():.

  (gh-23922)

• np.cast has been removed. The literal replacement for
  np.cast[dtype](arg) is np.asarray(arg, dtype=dtype).

• np.source has been removed. The preferred replacement is
  inspect.getsource.

• np.lookfor has been removed.

  (gh-24144)

• numpy.who has been removed. As an alternative for the removed
  functionality, one can use a variable explorer that is available in
  IDEs such as Spyder or Jupyter Notebook.

  (gh-24321)

• Multiple niche enums, expired members and functions have been
  removed from the main namespace, such as: ERR_*, SHIFT_*,
  np.fastCopyAndTranspose, np.kernel_version, np.numarray,
  np.oldnumeric and np.set_numeric_ops.

  (gh-24316)

• Replaced from ... import * in the numpy/__init__.py with
  explicit imports. As a result, these main namespace members got
  removed: np.FLOATING_POINT_SUPPORT, np.FPE_*, np.NINF,
  np.PINF, np.NZERO, np.PZERO, np.CLIP, np.WRAP, np.WRAP,
  np.RAISE, np.BUFSIZE, np.UFUNC_BUFSIZE_DEFAULT,
  np.UFUNC_PYVALS_NAME, np.ALLOW_THREADS, np.MAXDIMS,
  np.MAY_SHARE_EXACT, np.MAY_SHARE_BOUNDS, add_newdoc,
  np.add_docstring and np.add_newdoc_ufunc.

  (gh-24357)

• Alias np.float_ has been removed. Use np.float64 instead.

• Alias np.complex_ has been removed. Use np.complex128 instead.

• Alias np.longfloat has been removed. Use np.longdouble instead.

• Alias np.singlecomplex has been removed. Use np.complex64
  instead.

• Alias np.cfloat has been removed. Use np.complex128 instead.

• Alias np.longcomplex has been removed. Use np.clongdouble
  instead.

• Alias np.clongfloat has been removed. Use np.clongdouble
  instead.

• Alias np.string_ has been removed. Use np.bytes_ instead.

• Alias np.unicode_ has been removed. Use np.str_ instead.

• Alias np.Inf has been removed. Use np.inf instead.

• Alias np.Infinity has been removed. Use np.inf instead.

• Alias np.NaN has been removed. Use np.nan instead.

• Alias np.infty has been removed. Use np.inf instead.

• Alias np.mat has been removed. Use np.asmatrix instead.

• np.issubclass_ has been removed. Use the issubclass builtin
  instead.

• np.asfarray has been removed. Use np.asarray with a proper dtype
  instead.

• np.set_string_function has been removed. Use np.set_printoptions
  instead with a formatter for custom printing of NumPy objects.

• np.tracemalloc_domain is now only available from np.lib.

• np.recfromcsv and recfromtxt are now only available from
  np.lib.npyio.

• np.issctype, np.maximum_sctype, np.obj2sctype,
  np.sctype2char, np.sctypes, np.issubsctype were all removed
  from the main namespace without replacement, as they where niche
  members.

• Deprecated np.deprecate and np.deprecate_with_doc has been
  removed from the main namespace. Use DeprecationWarning instead.

• Deprecated np.safe_eval has been removed from the main namespace.
  Use ast.literal_eval instead.

  (gh-24376)

• np.find_common_type has been removed. Use numpy.promote_types or
  numpy.result_type instead. To achieve semantics for the
  scalar_types argument, use numpy.result_type and pass 0,
  0.0, or 0j as a Python scalar instead.

• np.round_ has been removed. U...

v1.26.4

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.

• 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.

• #25323: BUG: Restore missing asstr import
• #25523: MAINT: prepare 1.26.x for further development
• #25539: BUG: numpy.array_api: fix linalg.cholesky upper decomp...
• #25584: CI: Bump azure pipeline timeout to 120 minutes
• #25585: MAINT, BLD: Fix unused inline functions warnings on clang
• #25599: BLD: include fix for MinGW platform detection
• #25618: TST: Fix test_numeric on riscv64
• #25619: BLD: fix building for windows ARM64
• #25620: MAINT: add newaxis to __all__ in numpy.array_api
• #25630: BUG: Use large file fallocate on 32 bit linux platforms
• #25643: TST: Fix test_warning_calls on Python 3.12
• #25645: TST: Bump pytz to 2023.3.post1
• #25658: BUG: Fix AVX512 build flags on Intel Classic Compiler
• #25670: BLD: fix potential issue with escape sequences in __config__.py
• #25718: CI: pin cygwin python to 3.9.16-1 and fix typing tests [skip...
• #25720: MAINT: Bump cibuildwheel to v2.16.4
• #25748: BLD: unvendor meson-python on 1.26.x and upgrade to meson-python...
• #25755: MAINT: Include header defining backtrace
• #25756: BUG: Fix np.quantile([Fraction(2,1)], 0.5) (#24711)

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v1.26.3

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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b1240f767f69d7c4c8a29adde2310...

1.26.2 release

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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1a13860fdcd95de7cf58bd6f8bc5a5ef81c0b0625eb2c9a783948847abb...

v1.26.1

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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v1.26.0

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 Cygwi...

v1.26.0rc1

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

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 18 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
• Jake Vanderplas
• 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 51 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 [skip azp][skip circle][skip travis][skip...
• #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 [skip ci]
• #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](https://gith...

v1.26.0b1

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

The Python versions supported in this release are 3.9-3.12.

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 11 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Bas van Beek
• Charles Harris
• Matti Picus
• Melissa Weber Mendonça
• Ralf Gommers
• Sayed Adel
• Sebastian Berg
• Stefan van der Walt
• Tyler Reddy
• Warren Weckesser

Pull requests merged

A total of 18 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

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v1.25.2

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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