Skip to content

fix(deps): Update dependency numpy to v2.2.5 #295

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom
Merged
merged 1 commit into from
May 1, 2025
Merged

fix(deps): Update dependency numpy to v2.2.5 #295

merged 1 commit into from
May 1, 2025

Conversation

cq-bot
Copy link
Contributor

@cq-bot cq-bot commented May 1, 2025

This PR contains the following updates:

Package Update Change
numpy (changelog) patch ==2.2.4 -> ==2.2.5

Release Notes

numpy/numpy (numpy)

v2.2.5: (Apr 19, 2025)

Compare Source

NumPy 2.2.5 Release Notes

NumPy 2.2.5 is a patch release that fixes bugs found after the 2.2.4
release. It has a large number of typing fixes/improvements as well as
the normal bug fixes and some CI maintenance.

This release supports Python versions 3.10-3.13.

Contributors

A total of 7 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

  • Charles Harris
  • Joren Hammudoglu
  • Baskar Gopinath +
  • Nathan Goldbaum
  • Nicholas Christensen +
  • Sayed Adel
  • karl +

Pull requests merged

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

  • #​28545: MAINT: Prepare 2.2.x for further development
  • #​28582: BUG: Fix return type of NpyIter_GetIterNext in Cython declarations
  • #​28583: BUG: avoid deadlocks with C++ shared mutex in dispatch cache
  • #​28585: TYP: fix typing errors in _core.strings
  • #​28631: MAINT, CI: Update Ubuntu to 22.04 in azure-pipelines
  • #​28632: BUG: Set writeable flag for writeable dlpacks.
  • #​28633: BUG: Fix crackfortran parsing error when a division occurs within...
  • #​28650: TYP: fix ndarray.tolist() and .item() for unknown dtype
  • #​28654: BUG: fix deepcopying StringDType arrays (#​28643)
  • #​28661: TYP: Accept objects that write() to str in savetxt
  • #​28663: CI: Replace QEMU armhf with native (32-bit compatibility mode)
  • #​28682: SIMD: Resolve Highway QSort symbol linking error on aarch32/ASIMD
  • #​28683: TYP: add missing "b1" literals for dtype[bool]
  • #​28705: TYP: Fix false rejection of NDArray[object_].__abs__()
  • #​28706: TYP: Fix inconsistent NDArray[float64].__[r]truediv__ return...
  • #​28723: TYP: fix string-like ndarray rich comparison operators
  • #​28758: TYP: some [arg]partition fixes
  • #​28772: TYP: fix incorrect random.Generator.integers return type
  • #​28774: TYP: fix count_nonzero signature

Checksums

MD5
3a5d0889d6d7951f44bc6f7a03fa30c6  numpy-2.2.5-cp310-cp310-macosx_10_9_x86_64.whl
bcf9f4e768b070e17b2635f422a6e27d  numpy-2.2.5-cp310-cp310-macosx_11_0_arm64.whl
e82c8fa47a65bb5c2c83295f549dab12  numpy-2.2.5-cp310-cp310-macosx_14_0_arm64.whl
a5511a995c0f79a8b9a81f2b50e9f692  numpy-2.2.5-cp310-cp310-macosx_14_0_x86_64.whl
72bfc1f98238a8e4ba08999e61111e0e  numpy-2.2.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
146c83a5b8099d8d2607392b2ef7fedf  numpy-2.2.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
6ebdc80b54b008a10575e5d7bbb613f5  numpy-2.2.5-cp310-cp310-musllinux_1_2_aarch64.whl
97efde6443da8f9280a5fc2614a087e5  numpy-2.2.5-cp310-cp310-musllinux_1_2_x86_64.whl
c143f352206cec535b41b6b1d34c5898  numpy-2.2.5-cp310-cp310-win32.whl
0b17fbbf584785f675f1c5b24a00ff93  numpy-2.2.5-cp310-cp310-win_amd64.whl
58532622d7eff69a3c71c1ae89dea070  numpy-2.2.5-cp311-cp311-macosx_10_9_x86_64.whl
0d002c733bb02debe0b15de5ba872d1e  numpy-2.2.5-cp311-cp311-macosx_11_0_arm64.whl
ff0c736c60be96506806061ace2251a1  numpy-2.2.5-cp311-cp311-macosx_14_0_arm64.whl
4febdec973c4405fd08ef35e0c130de1  numpy-2.2.5-cp311-cp311-macosx_14_0_x86_64.whl
0bf4e457c612e565420e135458e70fe0  numpy-2.2.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a43b608ad15ebdc0960611497205d598  numpy-2.2.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7b4b1afd412149a9af7c25d7346fade8  numpy-2.2.5-cp311-cp311-musllinux_1_2_aarch64.whl
a1e70be013820f92dbfd4796fc4044bb  numpy-2.2.5-cp311-cp311-musllinux_1_2_x86_64.whl
73344e05a6fec0b38183363b4a026252  numpy-2.2.5-cp311-cp311-win32.whl
b7d5fdd23057c58d15c84eef6bfedb55  numpy-2.2.5-cp311-cp311-win_amd64.whl
801b11bb546aac2d92d7b3d5d6c90e86  numpy-2.2.5-cp312-cp312-macosx_10_13_x86_64.whl
68dc4298cad9405ad30cfb723be4ae48  numpy-2.2.5-cp312-cp312-macosx_11_0_arm64.whl
c31c872e0fa8df5ed7f91882621a925f  numpy-2.2.5-cp312-cp312-macosx_14_0_arm64.whl
179dfa545c32c44b77cf8db3b973785f  numpy-2.2.5-cp312-cp312-macosx_14_0_x86_64.whl
4562513ff2f1e3f31d66b8e435000141  numpy-2.2.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c80a2d8aab1a4d6a66f3fca2f0744744  numpy-2.2.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e363e0d8c116522d55b0ddd0cbf2de67  numpy-2.2.5-cp312-cp312-musllinux_1_2_aarch64.whl
d31d443270c76b7238ece2f87b048d21  numpy-2.2.5-cp312-cp312-musllinux_1_2_x86_64.whl
bf469fe048fa4ed75a5d8725297e283a  numpy-2.2.5-cp312-cp312-win32.whl
069b832aa15b6a815497135e7fa8cae8  numpy-2.2.5-cp312-cp312-win_amd64.whl
b2cf059c831cbcfdb4044613a1e5bc8d  numpy-2.2.5-cp313-cp313-macosx_10_13_x86_64.whl
70bcb93e55ff0f6602636602e0834607  numpy-2.2.5-cp313-cp313-macosx_11_0_arm64.whl
00c4938d67fd5b658ad92ac26fbe9cab  numpy-2.2.5-cp313-cp313-macosx_14_0_arm64.whl
0ca38aa51874b9252a2c9d85f81dcd07  numpy-2.2.5-cp313-cp313-macosx_14_0_x86_64.whl
6062cf707b8bc07a1600af0991a0a88e  numpy-2.2.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
62c1cf7de0327546f3a1e3852de640d3  numpy-2.2.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
ab3ad3390396552f76160139cc528784  numpy-2.2.5-cp313-cp313-musllinux_1_2_aarch64.whl
d258ba55c9a3936fa0c113cac8bbc0cc  numpy-2.2.5-cp313-cp313-musllinux_1_2_x86_64.whl
59bb7e1acb81fc4a02c3b791e110f01e  numpy-2.2.5-cp313-cp313-win32.whl
2e5728a9e5c6405d3a22138e4dd7019f  numpy-2.2.5-cp313-cp313-win_amd64.whl
d315521ec7275d0341787f2450e57e55  numpy-2.2.5-cp313-cp313t-macosx_10_13_x86_64.whl
17018c7c259ae81cf2ca4f58523d7d1c  numpy-2.2.5-cp313-cp313t-macosx_11_0_arm64.whl
ef6fd6a9c6a07db004a272b82f0ea710  numpy-2.2.5-cp313-cp313t-macosx_14_0_arm64.whl
07b2baf70b84b44ca6924794d9c7e431  numpy-2.2.5-cp313-cp313t-macosx_14_0_x86_64.whl
a2fb1ed562d2b6da091d980c7486d113  numpy-2.2.5-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
22fa9137283f463436d7b20a220071cd  numpy-2.2.5-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b0ae924e4834155eb5ac159ae611c292  numpy-2.2.5-cp313-cp313t-musllinux_1_2_aarch64.whl
c7a8351484f2df9a499c68f1ac73121c  numpy-2.2.5-cp313-cp313t-musllinux_1_2_x86_64.whl
1da753e4127a0bdcdfbfa6639568057e  numpy-2.2.5-cp313-cp313t-win32.whl
a8c869efc0888f214239e5c4f0e6acfb  numpy-2.2.5-cp313-cp313t-win_amd64.whl
7255b93f38e7d54a59d6798182f24c6a  numpy-2.2.5-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
6743ce025de6c245b03ca8511b306503  numpy-2.2.5-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
5abbeec4ff2add1c46f8779f730c73fa  numpy-2.2.5-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8e2e01f02d05e111ef2b104d1b3afad1  numpy-2.2.5-pp310-pypy310_pp73-win_amd64.whl
df2e46b468f9fdf06b13b04eca9a723f  numpy-2.2.5.tar.gz
SHA256
1f4a922da1729f4c40932b2af4fe84909c7a6e167e6e99f71838ce3a29f3fe26  numpy-2.2.5-cp310-cp310-macosx_10_9_x86_64.whl
b6f91524d31b34f4a5fee24f5bc16dcd1491b668798b6d85585d836c1e633a6a  numpy-2.2.5-cp310-cp310-macosx_11_0_arm64.whl
19f4718c9012e3baea91a7dba661dcab2451cda2550678dc30d53acb91a7290f  numpy-2.2.5-cp310-cp310-macosx_14_0_arm64.whl
eb7fd5b184e5d277afa9ec0ad5e4eb562ecff541e7f60e69ee69c8d59e9aeaba  numpy-2.2.5-cp310-cp310-macosx_14_0_x86_64.whl
6413d48a9be53e183eb06495d8e3b006ef8f87c324af68241bbe7a39e8ff54c3  numpy-2.2.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
7451f92eddf8503c9b8aa4fe6aa7e87fd51a29c2cfc5f7dbd72efde6c65acf57  numpy-2.2.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0bcb1d057b7571334139129b7f941588f69ce7c4ed15a9d6162b2ea54ded700c  numpy-2.2.5-cp310-cp310-musllinux_1_2_aarch64.whl
36ab5b23915887543441efd0417e6a3baa08634308894316f446027611b53bf1  numpy-2.2.5-cp310-cp310-musllinux_1_2_x86_64.whl
422cc684f17bc963da5f59a31530b3936f57c95a29743056ef7a7903a5dbdf88  numpy-2.2.5-cp310-cp310-win32.whl
e4f0b035d9d0ed519c813ee23e0a733db81ec37d2e9503afbb6e54ccfdee0fa7  numpy-2.2.5-cp310-cp310-win_amd64.whl
c42365005c7a6c42436a54d28c43fe0e01ca11eb2ac3cefe796c25a5f98e5e9b  numpy-2.2.5-cp311-cp311-macosx_10_9_x86_64.whl
498815b96f67dc347e03b719ef49c772589fb74b8ee9ea2c37feae915ad6ebda  numpy-2.2.5-cp311-cp311-macosx_11_0_arm64.whl
6411f744f7f20081b1b4e7112e0f4c9c5b08f94b9f086e6f0adf3645f85d3a4d  numpy-2.2.5-cp311-cp311-macosx_14_0_arm64.whl
9de6832228f617c9ef45d948ec1cd8949c482238d68b2477e6f642c33a7b0a54  numpy-2.2.5-cp311-cp311-macosx_14_0_x86_64.whl
369e0d4647c17c9363244f3468f2227d557a74b6781cb62ce57cf3ef5cc7c610  numpy-2.2.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
262d23f383170f99cd9191a7c85b9a50970fe9069b2f8ab5d786eca8a675d60b  numpy-2.2.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
aa70fdbdc3b169d69e8c59e65c07a1c9351ceb438e627f0fdcd471015cd956be  numpy-2.2.5-cp311-cp311-musllinux_1_2_aarch64.whl
37e32e985f03c06206582a7323ef926b4e78bdaa6915095ef08070471865b906  numpy-2.2.5-cp311-cp311-musllinux_1_2_x86_64.whl
f5045039100ed58fa817a6227a356240ea1b9a1bc141018864c306c1a16d4175  numpy-2.2.5-cp311-cp311-win32.whl
b13f04968b46ad705f7c8a80122a42ae8f620536ea38cf4bdd374302926424dd  numpy-2.2.5-cp311-cp311-win_amd64.whl
ee461a4eaab4f165b68780a6a1af95fb23a29932be7569b9fab666c407969051  numpy-2.2.5-cp312-cp312-macosx_10_13_x86_64.whl
ec31367fd6a255dc8de4772bd1658c3e926d8e860a0b6e922b615e532d320ddc  numpy-2.2.5-cp312-cp312-macosx_11_0_arm64.whl
47834cde750d3c9f4e52c6ca28a7361859fcaf52695c7dc3cc1a720b8922683e  numpy-2.2.5-cp312-cp312-macosx_14_0_arm64.whl
2c1a1c6ccce4022383583a6ded7bbcda22fc635eb4eb1e0a053336425ed36dfa  numpy-2.2.5-cp312-cp312-macosx_14_0_x86_64.whl
9d75f338f5f79ee23548b03d801d28a505198297534f62416391857ea0479571  numpy-2.2.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3a801fef99668f309b88640e28d261991bfad9617c27beda4a3aec4f217ea073  numpy-2.2.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
abe38cd8381245a7f49967a6010e77dbf3680bd3627c0fe4362dd693b404c7f8  numpy-2.2.5-cp312-cp312-musllinux_1_2_aarch64.whl
5a0ac90e46fdb5649ab6369d1ab6104bfe5854ab19b645bf5cda0127a13034ae  numpy-2.2.5-cp312-cp312-musllinux_1_2_x86_64.whl
0cd48122a6b7eab8f06404805b1bd5856200e3ed6f8a1b9a194f9d9054631beb  numpy-2.2.5-cp312-cp312-win32.whl
ced69262a8278547e63409b2653b372bf4baff0870c57efa76c5703fd6543282  numpy-2.2.5-cp312-cp312-win_amd64.whl
059b51b658f4414fff78c6d7b1b4e18283ab5fa56d270ff212d5ba0c561846f4  numpy-2.2.5-cp313-cp313-macosx_10_13_x86_64.whl
47f9ed103af0bc63182609044b0490747e03bd20a67e391192dde119bf43d52f  numpy-2.2.5-cp313-cp313-macosx_11_0_arm64.whl
261a1ef047751bb02f29dfe337230b5882b54521ca121fc7f62668133cb119c9  numpy-2.2.5-cp313-cp313-macosx_14_0_arm64.whl
4520caa3807c1ceb005d125a75e715567806fed67e315cea619d5ec6e75a4191  numpy-2.2.5-cp313-cp313-macosx_14_0_x86_64.whl
3d14b17b9be5f9c9301f43d2e2a4886a33b53f4e6fdf9ca2f4cc60aeeee76372  numpy-2.2.5-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2ba321813a00e508d5421104464510cc962a6f791aa2fca1c97b1e65027da80d  numpy-2.2.5-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a4cbdef3ddf777423060c6f81b5694bad2dc9675f110c4b2a60dc0181543fac7  numpy-2.2.5-cp313-cp313-musllinux_1_2_aarch64.whl
54088a5a147ab71a8e7fdfd8c3601972751ded0739c6b696ad9cb0343e21ab73  numpy-2.2.5-cp313-cp313-musllinux_1_2_x86_64.whl
c8b82a55ef86a2d8e81b63da85e55f5537d2157165be1cb2ce7cfa57b6aef38b  numpy-2.2.5-cp313-cp313-win32.whl
d8882a829fd779f0f43998e931c466802a77ca1ee0fe25a3abe50278616b1471  numpy-2.2.5-cp313-cp313-win_amd64.whl
e8b025c351b9f0e8b5436cf28a07fa4ac0204d67b38f01433ac7f9b870fa38c6  numpy-2.2.5-cp313-cp313t-macosx_10_13_x86_64.whl
8dfa94b6a4374e7851bbb6f35e6ded2120b752b063e6acdd3157e4d2bb922eba  numpy-2.2.5-cp313-cp313t-macosx_11_0_arm64.whl
97c8425d4e26437e65e1d189d22dff4a079b747ff9c2788057bfb8114ce1e133  numpy-2.2.5-cp313-cp313t-macosx_14_0_arm64.whl
352d330048c055ea6db701130abc48a21bec690a8d38f8284e00fab256dc1376  numpy-2.2.5-cp313-cp313t-macosx_14_0_x86_64.whl
8b4c0773b6ada798f51f0f8e30c054d32304ccc6e9c5d93d46cb26f3d385ab19  numpy-2.2.5-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
55f09e00d4dccd76b179c0f18a44f041e5332fd0e022886ba1c0bbf3ea4a18d0  numpy-2.2.5-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
02f226baeefa68f7d579e213d0f3493496397d8f1cff5e2b222af274c86a552a  numpy-2.2.5-cp313-cp313t-musllinux_1_2_aarch64.whl
c26843fd58f65da9491165072da2cccc372530681de481ef670dcc8e27cfb066  numpy-2.2.5-cp313-cp313t-musllinux_1_2_x86_64.whl
1a161c2c79ab30fe4501d5a2bbfe8b162490757cf90b7f05be8b80bc02f7bb8e  numpy-2.2.5-cp313-cp313t-win32.whl
d403c84991b5ad291d3809bace5e85f4bbf44a04bdc9a88ed2bb1807b3360bb8  numpy-2.2.5-cp313-cp313t-win_amd64.whl
b4ea7e1cff6784e58fe281ce7e7f05036b3e1c89c6f922a6bfbc0a7e8768adbe  numpy-2.2.5-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
d7543263084a85fbc09c704b515395398d31d6395518446237eac219eab9e55e  numpy-2.2.5-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
0255732338c4fdd00996c0421884ea8a3651eea555c3a56b84892b66f696eb70  numpy-2.2.5-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d2e3bdadaba0e040d1e7ab39db73e0afe2c74ae277f5614dad53eadbecbbb169  numpy-2.2.5-pp310-pypy310_pp73-win_amd64.whl
a9c0d994680cd991b1cb772e8b297340085466a6fe964bc9d4e80f5e2f43c291  numpy-2.2.5.tar.gz

Configuration

📅 Schedule: Branch creation - Between 12:00 AM and 03:59 AM, on day 1 of the month ( * 0-3 1 * * ) (UTC), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR becomes conflicted, 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.

@cq-bot cq-bot added the automerge Add to automerge PRs once requirements are met label May 1, 2025
@kodiakhq kodiakhq bot merged commit 21a5637 into main May 1, 2025
8 checks passed
@kodiakhq kodiakhq bot deleted the renovate/numpy-2.x branch May 1, 2025 02:27
@cq-bot cq-bot mentioned this pull request May 1, 2025
Merged
kodiakhq bot pushed a commit that referenced this pull request May 9, 2025
@cq-bot
chore(main): Release v0.1.44 (#297)
46d9a29 
🤖 I have created a release *beep* *boop*
---


## [0.1.44](v0.1.43...v0.1.44) (2025-05-09)


### Bug Fixes

* **deps:** Update dependency cloudquery-plugin-pb to v0.0.43 ([#300](#300)) ([addbf20](addbf20))
* **deps:** Update dependency grpcio to v1.71.0 ([#299](#299)) ([ffce9eb](ffce9eb))
* **deps:** Update dependency numpy to v2.2.5 ([#295](#295)) ([21a5637](21a5637))
* **deps:** Update dependency pytest to v8.3.5 ([#296](#296)) ([5cc3e5e](5cc3e5e))

---
This PR was generated with [Release Please](https://github.com/googleapis/release-please). See [documentation](https://github.com/googleapis/release-please#release-please).
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Reviewers

@kodiakhq kodiakhq[bot] kodiakhq[bot] approved these changes

Assignees
No one assigned
Labels
automerge Add to automerge PRs once requirements are met
Projects
None yet
Milestone
No milestone
Development

Successfully merging this pull request may close these issues.
1 participant
@cq-bot