deps: Update dependency numpy to >=2.3.1

[renovate]

This PR contains the following updates:

Package	Type	Update	Change
numpy (source)	feature-nogil	patch	>=2.3.0 -> >=2.3.1

---

Release Notes

numpy/numpy (numpy)

v2.3.1: (Jun 21, 2025)

Compare Source

NumPy 2.3.1 Release Notes

The NumPy 2.3.1 release is a patch release with several bug fixes,
annotation improvements, and better support for OpenBSD. Highlights are:

• Fix bug in matmul for non-contiguous out kwarg parameter
• Fix for Accelerate runtime warnings on M4 hardware
• Fix new in NumPy 2.3.0 np.vectorize casting errors
• Improved support of cpu features for FreeBSD and OpenBSD

This release supports Python versions 3.11-3.13, Python 3.14 will be
supported when it is released.

Contributors

A total of 9 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Brad Smith +
• Charles Harris
• Developer-Ecosystem-Engineering
• François Rozet
• Joren Hammudoglu
• Matti Picus
• Mugundan Selvanayagam
• Nathan Goldbaum
• Sebastian Berg

Pull requests merged

A total of 12 pull requests were merged for this release.

• #​29140: MAINT: Prepare 2.3.x for further development
• #​29191: BUG: fix matmul with transposed out arg (#​29179)
• #​29192: TYP: Backport typing fixes and improvements.
• #​29205: BUG: Revert np.vectorize casting to legacy behavior (#​29196)
• #​29222: TYP: Backport typing fixes
• #​29233: BUG: avoid negating unsigned integers in resize implementation...
• #​29234: TST: Fix test that uses uninitialized memory (#​29232)
• #​29235: BUG: Address interaction between SME and FPSR (#​29223)
• #​29237: BUG: Enforce integer limitation in concatenate (#​29231)
• #​29238: CI: Add support for building NumPy with LLVM for Win-ARM64
• #​29241: ENH: Detect CPU features on OpenBSD ARM and PowerPC64
• #​29242: ENH: Detect CPU features on FreeBSD / OpenBSD RISC-V64.

Checksums

MD5

c353ac75ea083594a6cb674b5f943d83  numpy-2.3.1-cp311-cp311-macosx_10_9_x86_64.whl
fdb5454e372d399cf570868ea7e2b192  numpy-2.3.1-cp311-cp311-macosx_11_0_arm64.whl
dc0f17823bb1826519d6974c2b95fa90  numpy-2.3.1-cp311-cp311-macosx_14_0_arm64.whl
7e3118fe383af697a8868ba191b9eac0  numpy-2.3.1-cp311-cp311-macosx_14_0_x86_64.whl
705aafad1250aa3e41502c5710a26ed5  numpy-2.3.1-cp311-cp311-manylinux_2_28_aarch64.whl
003d6268344577b804205098e11cdaa0  numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl
7d0c0fd11c573c510a25dd7513e4ae0a  numpy-2.3.1-cp311-cp311-musllinux_1_2_aarch64.whl
d99f993ef05966ead99df736df18b521  numpy-2.3.1-cp311-cp311-musllinux_1_2_x86_64.whl
96933cac225fb8b60a9cc2c0efa14d36  numpy-2.3.1-cp311-cp311-win32.whl
f777712419f3dd586ac294ddce84b274  numpy-2.3.1-cp311-cp311-win_amd64.whl
1fe2615669de5c271a48b99356fa3528  numpy-2.3.1-cp311-cp311-win_arm64.whl
fccca48846d41d38966cc75395787f79  numpy-2.3.1-cp312-cp312-macosx_10_13_x86_64.whl
fa389e78db43f3c2841ce127c1205422  numpy-2.3.1-cp312-cp312-macosx_11_0_arm64.whl
2554944d786abd284db4a699d4edfe1e  numpy-2.3.1-cp312-cp312-macosx_14_0_arm64.whl
7fec491834803a8ffa3765ef3d03cea5  numpy-2.3.1-cp312-cp312-macosx_14_0_x86_64.whl
7c2d8b4412f12b9b02e98349fb5cd760  numpy-2.3.1-cp312-cp312-manylinux_2_28_aarch64.whl
94dcc636a2f2478666d820e21fc91682  numpy-2.3.1-cp312-cp312-manylinux_2_28_x86_64.whl
404128939d89d1ea26be105fb03b5028  numpy-2.3.1-cp312-cp312-musllinux_1_2_aarch64.whl
e89d8d460060e8315c3ba68b2b649db0  numpy-2.3.1-cp312-cp312-musllinux_1_2_x86_64.whl
a767bd10267ad6baef9655fb08db3fd3  numpy-2.3.1-cp312-cp312-win32.whl
f753b957fcb7f06f043cf9c6114f294c  numpy-2.3.1-cp312-cp312-win_amd64.whl
58ffa7c69587f9bf8f6025794fec7f63  numpy-2.3.1-cp312-cp312-win_arm64.whl
22a2a9a568dd0866b288ad8bd8bb3e90  numpy-2.3.1-cp313-cp313-macosx_10_13_x86_64.whl
5e1593fcc8bb3447e995622f2dca017b  numpy-2.3.1-cp313-cp313-macosx_11_0_arm64.whl
894d56072db9358e0096538710a1a8ce  numpy-2.3.1-cp313-cp313-macosx_14_0_arm64.whl
593cb311f5170cbcfcefb587cdcc70bb  numpy-2.3.1-cp313-cp313-macosx_14_0_x86_64.whl
22935447e75acda4075c57b332c0236a  numpy-2.3.1-cp313-cp313-manylinux_2_28_aarch64.whl
5aa2040f947204e15e95ec87461a7e91  numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl
6516337f0347974fada21a23a818be64  numpy-2.3.1-cp313-cp313-musllinux_1_2_aarch64.whl
ec956eb37b874b1ec52d6ffccda6ef65  numpy-2.3.1-cp313-cp313-musllinux_1_2_x86_64.whl
0aaed62cb1bae9c1b1a44d1a4eda2db7  numpy-2.3.1-cp313-cp313-win32.whl
57829996fc12f649547f0258443bbb20  numpy-2.3.1-cp313-cp313-win_amd64.whl
a0d0dd68bbf0ab378142b2daff0a8e06  numpy-2.3.1-cp313-cp313-win_arm64.whl
b22dc66970a8017e4d0ce83ef8c938af  numpy-2.3.1-cp313-cp313t-macosx_10_13_x86_64.whl
93c17afb38cf8fd876ca2bd9ea7e9612  numpy-2.3.1-cp313-cp313t-macosx_11_0_arm64.whl
283064dabb434f3dbc1a5e2514b9cb29  numpy-2.3.1-cp313-cp313t-macosx_14_0_arm64.whl
5b8c778033c98b4a0ce6e5bfc7625f05  numpy-2.3.1-cp313-cp313t-macosx_14_0_x86_64.whl
2340bd78962f194bcdbee6531d954acc  numpy-2.3.1-cp313-cp313t-manylinux_2_28_aarch64.whl
43a92ad37dc68d719bdeeeb65b3f4d2f  numpy-2.3.1-cp313-cp313t-manylinux_2_28_x86_64.whl
eb110c4aa0d73558187397ddfba179ad  numpy-2.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl
1f7f0076411ed4afa9c4553eb06564cb  numpy-2.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl
30f30dde6f806070b2164e48a632a350  numpy-2.3.1-cp313-cp313t-win32.whl
2375e2f2a5b75c5f5c908af6bb85d639  numpy-2.3.1-cp313-cp313t-win_amd64.whl
b421530a87bb8e9e3d4dc34c75d5d953  numpy-2.3.1-cp313-cp313t-win_arm64.whl
b1bc3cbf9cd407964b2bb25dfe86ca3d  numpy-2.3.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
4c2e234eb4f346f362d6e6c620fa7a56  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_arm64.whl
98ec3c19a365d0ae926113bb349e323b  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
e0c7bcd526cde46489d5a8f12e06cc77  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl
41f535aa1f1acaf3d8a32a462a4cd4c8  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl
2abf906a6688c98693045cbbc655d5b7  numpy-2.3.1-pp311-pypy311_pp73-win_amd64.whl
886559a4c541298b37245e389ce8bf10  numpy-2.3.1.tar.gz

SHA256

6ea9e48336a402551f52cd8f593343699003d2353daa4b72ce8d34f66b722070  numpy-2.3.1-cp311-cp311-macosx_10_9_x86_64.whl
5ccb7336eaf0e77c1635b232c141846493a588ec9ea777a7c24d7166bb8533ae  numpy-2.3.1-cp311-cp311-macosx_11_0_arm64.whl
0bb3a4a61e1d327e035275d2a993c96fa786e4913aa089843e6a2d9dd205c66a  numpy-2.3.1-cp311-cp311-macosx_14_0_arm64.whl
e344eb79dab01f1e838ebb67aab09965fb271d6da6b00adda26328ac27d4a66e  numpy-2.3.1-cp311-cp311-macosx_14_0_x86_64.whl
467db865b392168ceb1ef1ffa6f5a86e62468c43e0cfb4ab6da667ede10e58db  numpy-2.3.1-cp311-cp311-manylinux_2_28_aarch64.whl
afed2ce4a84f6b0fc6c1ce734ff368cbf5a5e24e8954a338f3bdffa0718adffb  numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl
0025048b3c1557a20bc80d06fdeb8cc7fc193721484cca82b2cfa072fec71a93  numpy-2.3.1-cp311-cp311-musllinux_1_2_aarch64.whl
a5ee121b60aa509679b682819c602579e1df14a5b07fe95671c8849aad8f2115  numpy-2.3.1-cp311-cp311-musllinux_1_2_x86_64.whl
a8b740f5579ae4585831b3cf0e3b0425c667274f82a484866d2adf9570539369  numpy-2.3.1-cp311-cp311-win32.whl
d4580adadc53311b163444f877e0789f1c8861e2698f6b2a4ca852fda154f3ff  numpy-2.3.1-cp311-cp311-win_amd64.whl
ec0bdafa906f95adc9a0c6f26a4871fa753f25caaa0e032578a30457bff0af6a  numpy-2.3.1-cp311-cp311-win_arm64.whl
2959d8f268f3d8ee402b04a9ec4bb7604555aeacf78b360dc4ec27f1d508177d  numpy-2.3.1-cp312-cp312-macosx_10_13_x86_64.whl
762e0c0c6b56bdedfef9a8e1d4538556438288c4276901ea008ae44091954e29  numpy-2.3.1-cp312-cp312-macosx_11_0_arm64.whl
867ef172a0976aaa1f1d1b63cf2090de8b636a7674607d514505fb7276ab08fc  numpy-2.3.1-cp312-cp312-macosx_14_0_arm64.whl
4e602e1b8682c2b833af89ba641ad4176053aaa50f5cacda1a27004352dde943  numpy-2.3.1-cp312-cp312-macosx_14_0_x86_64.whl
8e333040d069eba1652fb08962ec5b76af7f2c7bce1df7e1418c8055cf776f25  numpy-2.3.1-cp312-cp312-manylinux_2_28_aarch64.whl
e7cbf5a5eafd8d230a3ce356d892512185230e4781a361229bd902ff403bc660  numpy-2.3.1-cp312-cp312-manylinux_2_28_x86_64.whl
5f1b8f26d1086835f442286c1d9b64bb3974b0b1e41bb105358fd07d20872952  numpy-2.3.1-cp312-cp312-musllinux_1_2_aarch64.whl
ee8340cb48c9b7a5899d1149eece41ca535513a9698098edbade2a8e7a84da77  numpy-2.3.1-cp312-cp312-musllinux_1_2_x86_64.whl
e772dda20a6002ef7061713dc1e2585bc1b534e7909b2030b5a46dae8ff077ab  numpy-2.3.1-cp312-cp312-win32.whl
cfecc7822543abdea6de08758091da655ea2210b8ffa1faf116b940693d3df76  numpy-2.3.1-cp312-cp312-win_amd64.whl
7be91b2239af2658653c5bb6f1b8bccafaf08226a258caf78ce44710a0160d30  numpy-2.3.1-cp312-cp312-win_arm64.whl
25a1992b0a3fdcdaec9f552ef10d8103186f5397ab45e2d25f8ac51b1a6b97e8  numpy-2.3.1-cp313-cp313-macosx_10_13_x86_64.whl
7dea630156d39b02a63c18f508f85010230409db5b2927ba59c8ba4ab3e8272e  numpy-2.3.1-cp313-cp313-macosx_11_0_arm64.whl
bada6058dd886061f10ea15f230ccf7dfff40572e99fef440a4a857c8728c9c0  numpy-2.3.1-cp313-cp313-macosx_14_0_arm64.whl
a894f3816eb17b29e4783e5873f92faf55b710c2519e5c351767c51f79d8526d  numpy-2.3.1-cp313-cp313-macosx_14_0_x86_64.whl
18703df6c4a4fee55fd3d6e5a253d01c5d33a295409b03fda0c86b3ca2ff41a1  numpy-2.3.1-cp313-cp313-manylinux_2_28_aarch64.whl
5902660491bd7a48b2ec16c23ccb9124b8abfd9583c5fdfa123fe6b421e03de1  numpy-2.3.1-cp313-cp313-manylinux_2_28_x86_64.whl
36890eb9e9d2081137bd78d29050ba63b8dab95dff7912eadf1185e80074b2a0  numpy-2.3.1-cp313-cp313-musllinux_1_2_aarch64.whl
a780033466159c2270531e2b8ac063704592a0bc62ec4a1b991c7c40705eb0e8  numpy-2.3.1-cp313-cp313-musllinux_1_2_x86_64.whl
39bff12c076812595c3a306f22bfe49919c5513aa1e0e70fac756a0be7c2a2b8  numpy-2.3.1-cp313-cp313-win32.whl
8d5ee6eec45f08ce507a6570e06f2f879b374a552087a4179ea7838edbcbfa42  numpy-2.3.1-cp313-cp313-win_amd64.whl
0c4d9e0a8368db90f93bd192bfa771ace63137c3488d198ee21dfb8e7771916e  numpy-2.3.1-cp313-cp313-win_arm64.whl
b0b5397374f32ec0649dd98c652a1798192042e715df918c20672c62fb52d4b8  numpy-2.3.1-cp313-cp313t-macosx_10_13_x86_64.whl
c5bdf2015ccfcee8253fb8be695516ac4457c743473a43290fd36eba6a1777eb  numpy-2.3.1-cp313-cp313t-macosx_11_0_arm64.whl
d70f20df7f08b90a2062c1f07737dd340adccf2068d0f1b9b3d56e2038979fee  numpy-2.3.1-cp313-cp313t-macosx_14_0_arm64.whl
2fb86b7e58f9ac50e1e9dd1290154107e47d1eef23a0ae9145ded06ea606f992  numpy-2.3.1-cp313-cp313t-macosx_14_0_x86_64.whl
23ab05b2d241f76cb883ce8b9a93a680752fbfcbd51c50eff0b88b979e471d8c  numpy-2.3.1-cp313-cp313t-manylinux_2_28_aarch64.whl
ce2ce9e5de4703a673e705183f64fd5da5bf36e7beddcb63a25ee2286e71ca48  numpy-2.3.1-cp313-cp313t-manylinux_2_28_x86_64.whl
c4913079974eeb5c16ccfd2b1f09354b8fed7e0d6f2cab933104a09a6419b1ee  numpy-2.3.1-cp313-cp313t-musllinux_1_2_aarch64.whl
010ce9b4f00d5c036053ca684c77441f2f2c934fd23bee058b4d6f196efd8280  numpy-2.3.1-cp313-cp313t-musllinux_1_2_x86_64.whl
6269b9edfe32912584ec496d91b00b6d34282ca1d07eb10e82dfc780907d6c2e  numpy-2.3.1-cp313-cp313t-win32.whl
2a809637460e88a113e186e87f228d74ae2852a2e0c44de275263376f17b5bdc  numpy-2.3.1-cp313-cp313t-win_amd64.whl
eccb9a159db9aed60800187bc47a6d3451553f0e1b08b068d8b277ddfbb9b244  numpy-2.3.1-cp313-cp313t-win_arm64.whl
ad506d4b09e684394c42c966ec1527f6ebc25da7f4da4b1b056606ffe446b8a3  numpy-2.3.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl
ebb8603d45bc86bbd5edb0d63e52c5fd9e7945d3a503b77e486bd88dde67a19b  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_arm64.whl
15aa4c392ac396e2ad3d0a2680c0f0dee420f9fed14eef09bdb9450ee6dcb7b7  numpy-2.3.1-pp311-pypy311_pp73-macosx_14_0_x86_64.whl
c6e0bf9d1a2f50d2b65a7cf56db37c095af17b59f6c132396f7c6d5dd76484df  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl
eabd7e8740d494ce2b4ea0ff05afa1b7b291e978c0ae075487c51e8bd93c0c68  numpy-2.3.1-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl
e610832418a2bc09d974cc9fecebfa51e9532d6190223bc5ef6a7402ebf3b5cb  numpy-2.3.1-pp311-pypy311_pp73-win_amd64.whl
1ec9ae20a4226da374362cca3c62cd753faf2f951440b0e3b98e93c235441d2b  numpy-2.3.1.tar.gz

---

Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Enabled.

♻ Rebasing: Whenever PR is behind base branch, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

---

• If you want to rebase/retry this PR, check this box

---

This PR was generated by Mend Renovate. View the repository job log.