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

Checksums

MD5

7660db27715df261948e7f0f13634f16  numpy-1.26.3-cp310-cp310-macosx_10_9_x86_64.whl
98d5b98c822de4bed0cf1b0b8f367192  numpy-1.26.3-cp310-cp310-macosx_11_0_arm64.whl
b71cd0710cec5460292a97a02fa349cd  numpy-1.26.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0f98a05c92598f849b1be2595f4a52a8  numpy-1.26.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b866c6aea8070c0753b776d2b521e875  numpy-1.26.3-cp310-cp310-musllinux_1_1_aarch64.whl
cfdde5868e469fb27655ea73b0b9593b  numpy-1.26.3-cp310-cp310-musllinux_1_1_x86_64.whl
2655440d61671b5e32b049d30397c58f  numpy-1.26.3-cp310-cp310-win32.whl
7718a5d33344784ca7821f3bdd467550  numpy-1.26.3-cp310-cp310-win_amd64.whl
28e4b2ed9192c392f792d88b3c246d1c  numpy-1.26.3-cp311-cp311-macosx_10_9_x86_64.whl
fb1ae72749463e2c82f0127699728364  numpy-1.26.3-cp311-cp311-macosx_11_0_arm64.whl
304dec822b508a1d495917610e7562bf  numpy-1.26.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2cc0d8b073dfd55946a60ba8ed4369f6  numpy-1.26.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
c99962375c599501820899c8ccab6960  numpy-1.26.3-cp311-cp311-musllinux_1_1_aarch64.whl
47ed42d067ce4863bbf1f40da61ba7d1  numpy-1.26.3-cp311-cp311-musllinux_1_1_x86_64.whl
3ab3757255feb54ca3793fb9db226586  numpy-1.26.3-cp311-cp311-win32.whl
c33f2a4518bae535645357a08a93be1a  numpy-1.26.3-cp311-cp311-win_amd64.whl
bea43600aaff3a4d9978611ccfa44198  numpy-1.26.3-cp312-cp312-macosx_10_9_x86_64.whl
c678d909ebe737fdabf215d8622ce2a3  numpy-1.26.3-cp312-cp312-macosx_11_0_arm64.whl
9f21f1875c92425cec1060564b3abb1c  numpy-1.26.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c44a1998965d45ec136078ee09d880f2  numpy-1.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9274f5c51fa4f3c8fac5efa3d78acd63  numpy-1.26.3-cp312-cp312-musllinux_1_1_aarch64.whl
07c9f8f86f45077febc46c87ebc0b644  numpy-1.26.3-cp312-cp312-musllinux_1_1_x86_64.whl
a4857b2f7b6a23bca41178bd344bb28a  numpy-1.26.3-cp312-cp312-win32.whl
495d9534961d7b10f16fec4515a3d72b  numpy-1.26.3-cp312-cp312-win_amd64.whl
6494f2d94fd1f184923a33e634692b5e  numpy-1.26.3-cp39-cp39-macosx_10_9_x86_64.whl
515a7314a0ff6aaba8d53a7a1aaa73ab  numpy-1.26.3-cp39-cp39-macosx_11_0_arm64.whl
c856adc6a6a78773c43e9c738d662ed5  numpy-1.26.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
09848456158a01feff28f88c6106aef1  numpy-1.26.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
adec00ea2bc98580a436f82e188c0e2f  numpy-1.26.3-cp39-cp39-musllinux_1_1_aarch64.whl
718bd35dd0431a6434bb30bf8d91d77d  numpy-1.26.3-cp39-cp39-musllinux_1_1_x86_64.whl
e813aa59cb807efb4a8fee52a6dd41ba  numpy-1.26.3-cp39-cp39-win32.whl
08e1b0973d0ae5976b38563eaec1253f  numpy-1.26.3-cp39-cp39-win_amd64.whl
e8887a14750161709636e9fb87df4f36  numpy-1.26.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
0bdb19040525451553fb5758b65caf4c  numpy-1.26.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b931c14d06cc37d85d63ed1ddd88e875  numpy-1.26.3-pp39-pypy39_pp73-win_amd64.whl
1c915dc6c36dd4c674d9379e9470ff8b  numpy-1.26.3.tar.gz

SHA256

806dd64230dbbfaca8a27faa64e2f414bf1c6622ab78cc4264f7f5f028fee3bf  numpy-1.26.3-cp310-cp310-macosx_10_9_x86_64.whl
02f98011ba4ab17f46f80f7f8f1c291ee7d855fcef0a5a98db80767a468c85cd  numpy-1.26.3-cp310-cp310-macosx_11_0_arm64.whl
6d45b3ec2faed4baca41c76617fcdcfa4f684ff7a151ce6fc78ad3b6e85af0a6  numpy-1.26.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
bdd2b45bf079d9ad90377048e2747a0c82351989a2165821f0c96831b4a2a54b  numpy-1.26.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
211ddd1e94817ed2d175b60b6374120244a4dd2287f4ece45d49228b4d529178  numpy-1.26.3-cp310-cp310-musllinux_1_1_aarch64.whl
b1240f767f69d7c4c8a29adde2310b871153df9b26b5cb2b54a561ac85146485  numpy-1.26.3-cp310-cp310-musllinux_1_1_x86_64.whl
21a9484e75ad018974a2fdaa216524d64ed4212e418e0a551a2d83403b0531d3  numpy-1.26.3-cp310-cp310-win32.whl
9e1591f6ae98bcfac2a4bbf9221c0b92ab49762228f38287f6eeb5f3f55905ce  numpy-1.26.3-cp310-cp310-win_amd64.whl
b831295e5472954104ecb46cd98c08b98b49c69fdb7040483aff799a755a7374  numpy-1.26.3-cp311-cp311-macosx_10_9_x86_64.whl
9e87562b91f68dd8b1c39149d0323b42e0082db7ddb8e934ab4c292094d575d6  numpy-1.26.3-cp311-cp311-macosx_11_0_arm64.whl
8c66d6fec467e8c0f975818c1796d25c53521124b7cfb760114be0abad53a0a2  numpy-1.26.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f25e2811a9c932e43943a2615e65fc487a0b6b49218899e62e426e7f0a57eeda  numpy-1.26.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
af36e0aa45e25c9f57bf684b1175e59ea05d9a7d3e8e87b7ae1a1da246f2767e  numpy-1.26.3-cp311-cp311-musllinux_1_1_aarch64.whl
51c7f1b344f302067b02e0f5b5d2daa9ed4a721cf49f070280ac202738ea7f00  numpy-1.26.3-cp311-cp311-musllinux_1_1_x86_64.whl
7ca4f24341df071877849eb2034948459ce3a07915c2734f1abb4018d9c49d7b  numpy-1.26.3-cp311-cp311-win32.whl
39763aee6dfdd4878032361b30b2b12593fb445ddb66bbac802e2113eb8a6ac4  numpy-1.26.3-cp311-cp311-win_amd64.whl
a7081fd19a6d573e1a05e600c82a1c421011db7935ed0d5c483e9dd96b99cf13  numpy-1.26.3-cp312-cp312-macosx_10_9_x86_64.whl
12c70ac274b32bc00c7f61b515126c9205323703abb99cd41836e8125ea0043e  numpy-1.26.3-cp312-cp312-macosx_11_0_arm64.whl
7f784e13e598e9594750b2ef6729bcd5a47f6cfe4a12cca13def35e06d8163e3  numpy-1.26.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
5f24750ef94d56ce6e33e4019a8a4d68cfdb1ef661a52cdaee628a56d2437419  numpy-1.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
77810ef29e0fb1d289d225cabb9ee6cf4d11978a00bb99f7f8ec2132a84e0166  numpy-1.26.3-cp312-cp312-musllinux_1_1_aarch64.whl
8ed07a90f5450d99dad60d3799f9c03c6566709bd53b497eb9ccad9a55867f36  numpy-1.26.3-cp312-cp312-musllinux_1_1_x86_64.whl
f73497e8c38295aaa4741bdfa4fda1a5aedda5473074369eca10626835445511  numpy-1.26.3-cp312-cp312-win32.whl
da4b0c6c699a0ad73c810736303f7fbae483bcb012e38d7eb06a5e3b432c981b  numpy-1.26.3-cp312-cp312-win_amd64.whl
1666f634cb3c80ccbd77ec97bc17337718f56d6658acf5d3b906ca03e90ce87f  numpy-1.26.3-cp39-cp39-macosx_10_9_x86_64.whl
18c3319a7d39b2c6a9e3bb75aab2304ab79a811ac0168a671a62e6346c29b03f  numpy-1.26.3-cp39-cp39-macosx_11_0_arm64.whl
0b7e807d6888da0db6e7e75838444d62495e2b588b99e90dd80c3459594e857b  numpy-1.26.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
b4d362e17bcb0011738c2d83e0a65ea8ce627057b2fdda37678f4374a382a137  numpy-1.26.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b8c275f0ae90069496068c714387b4a0eba5d531aace269559ff2b43655edd58  numpy-1.26.3-cp39-cp39-musllinux_1_1_aarch64.whl
cc0743f0302b94f397a4a65a660d4cd24267439eb16493fb3caad2e4389bccbb  numpy-1.26.3-cp39-cp39-musllinux_1_1_x86_64.whl
9bc6d1a7f8cedd519c4b7b1156d98e051b726bf160715b769106661d567b3f03  numpy-1.26.3-cp39-cp39-win32.whl
867e3644e208c8922a3be26fc6bbf112a035f50f0a86497f98f228c50c607bb2  numpy-1.26.3-cp39-cp39-win_amd64.whl
3c67423b3703f8fbd90f5adaa37f85b5794d3366948efe9a5190a5f3a83fc34e  numpy-1.26.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
46f47ee566d98849323f01b349d58f2557f02167ee301e5e28809a8c0e27a2d0  numpy-1.26.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a8474703bffc65ca15853d5fd4d06b18138ae90c17c8d12169968e998e448bb5  numpy-1.26.3-pp39-pypy39_pp73-win_amd64.whl
697df43e2b6310ecc9d95f05d5ef20eacc09c7c4ecc9da3f235d39e71b7da1e4  numpy-1.26.3.tar.gz