Update dependency numpy to v2.3.1 #106

Open
renovate_bot wants to merge 1 commit from renovate/numpy-2.x into main
Collaborator

This PR contains the following updates:

Package Type Update Change
numpy (changelog) project.dependencies 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 has been generated by Renovate Bot.

This PR contains the following updates: | Package | Type | Update | Change | |---|---|---|---| | [numpy](https://github.com/numpy/numpy) ([changelog](https://numpy.org/doc/stable/release)) | project.dependencies | patch | `==2.3.0` -> `==2.3.1` | --- ### Release Notes <details> <summary>numpy/numpy (numpy)</summary> ### [`v2.3.1`](https://github.com/numpy/numpy/releases/tag/v2.3.1): (Jun 21, 2025) [Compare Source](https://github.com/numpy/numpy/compare/v2.3.0...v2.3.1) ### 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. - [#&#8203;29140](https://github.com/numpy/numpy/pull/29140): MAINT: Prepare 2.3.x for further development - [#&#8203;29191](https://github.com/numpy/numpy/pull/29191): BUG: fix matmul with transposed out arg ([#&#8203;29179](https://github.com/numpy/numpy/issues/29179)) - [#&#8203;29192](https://github.com/numpy/numpy/pull/29192): TYP: Backport typing fixes and improvements. - [#&#8203;29205](https://github.com/numpy/numpy/pull/29205): BUG: Revert `np.vectorize` casting to legacy behavior ([#&#8203;29196](https://github.com/numpy/numpy/issues/29196)) - [#&#8203;29222](https://github.com/numpy/numpy/pull/29222): TYP: Backport typing fixes - [#&#8203;29233](https://github.com/numpy/numpy/pull/29233): BUG: avoid negating unsigned integers in resize implementation... - [#&#8203;29234](https://github.com/numpy/numpy/pull/29234): TST: Fix test that uses uninitialized memory ([#&#8203;29232](https://github.com/numpy/numpy/issues/29232)) - [#&#8203;29235](https://github.com/numpy/numpy/pull/29235): BUG: Address interaction between SME and FPSR ([#&#8203;29223](https://github.com/numpy/numpy/issues/29223)) - [#&#8203;29237](https://github.com/numpy/numpy/pull/29237): BUG: Enforce integer limitation in concatenate ([#&#8203;29231](https://github.com/numpy/numpy/issues/29231)) - [#&#8203;29238](https://github.com/numpy/numpy/pull/29238): CI: Add support for building NumPy with LLVM for Win-ARM64 - [#&#8203;29241](https://github.com/numpy/numpy/pull/29241): ENH: Detect CPU features on OpenBSD ARM and PowerPC64 - [#&#8203;29242](https://github.com/numpy/numpy/pull/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 </details> --- ### 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. --- - [ ] <!-- rebase-check -->If you want to rebase/retry this PR, check this box --- This PR has been generated by [Renovate Bot](https://github.com/renovatebot/renovate). <!--renovate-debug:eyJjcmVhdGVkSW5WZXIiOiI0MC41NS4xIiwidXBkYXRlZEluVmVyIjoiNDAuNTUuMSIsInRhcmdldEJyYW5jaCI6Im1haW4iLCJsYWJlbHMiOltdfQ==-->
renovate_bot added 1 commit 2025-06-21 05:00:35 -07:00
Update dependency numpy to v2.3.1
Some checks failed
ci/woodpecker/pr/lint Pipeline failed
ci/woodpecker/pr/vulnerability-scan Pipeline was successful
e6492cde95
renovate_bot scheduled this pull request to auto merge when all checks succeed 2025-06-21 05:00:36 -07:00
Some checks failed
ci/woodpecker/pr/lint Pipeline failed
Required
Details
ci/woodpecker/pr/vulnerability-scan Pipeline was successful
Required
Details
Some required checks were not successful.
You are not authorized to merge this pull request.
View command line instructions

Checkout

From your project repository, check out a new branch and test the changes.
git fetch -u origin renovate/numpy-2.x:renovate/numpy-2.x
git checkout renovate/numpy-2.x
Sign in to join this conversation.
No reviewers
No labels
No milestone
No project
No assignees
1 participant
Notifications
Due date
The due date is invalid or out of range. Please use the format "yyyy-mm-dd".

No due date set.

Dependencies

No dependencies set.

Reference: buckbanzai/seattlecitylight-mastodon-bot#106
No description provided.