buckbanzai/seattlecitylight-mastodon-bot
1
0
Fork 0
Code Issues 7 Pull requests 3 Projects Releases Packages Wiki Activity

Update dependency numpy to v2.2.2 #77

Merged
renovate_bot merged 1 commit from renovate/numpy-2.x into main 2025-01-20 07:58:04 -08:00
Conversation 0 Commits 1 Files changed 1 +1 -1
renovate_bot commented 2025-01-20 07:57:25 -08:00
Collaborator
Copy link

This PR contains the following updates:

Package Update Change
numpy (changelog) patch ==2.2.1 -> ==2.2.2

Release Notes

numpy/numpy (numpy)

v2.2.2: 2.2.2 (Jan 18, 2025)

Compare Source

NumPy 2.2.2 Release Notes

NumPy 2.2.2 is a patch release that fixes bugs found after the 2.2.1
release. The number of typing fixes/updates is notable. This release
supports Python versions 3.10-3.13.

Contributors

A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

  • Alicia Boya García +
  • Charles Harris
  • Joren Hammudoglu
  • Kai Germaschewski +
  • Nathan Goldbaum
  • PTUsumit +
  • Rohit Goswami
  • Sebastian Berg

Pull requests merged

A total of 16 pull requests were merged for this release.

  • #​28050: MAINT: Prepare 2.2.x for further development
  • #​28055: TYP: fix void arrays not accepting str keys in __setitem__
  • #​28066: TYP: fix unnecessarily broad integer binop return types (#​28065)
  • #​28112: TYP: Better ndarray binop return types for float64 &...
  • #​28113: TYP: Return the correct bool from issubdtype
  • #​28114: TYP: Always accept date[time] in the datetime64 constructor
  • #​28120: BUG: Fix auxdata initialization in ufunc slow path
  • #​28131: BUG: move reduction initialization to ufunc initialization
  • #​28132: TYP: Fix interp to accept and return scalars
  • #​28137: BUG: call PyType_Ready in f2py to avoid data races
  • #​28145: BUG: remove unnecessary call to PyArray_UpdateFlags
  • #​28160: BUG: Avoid data race in PyArray_CheckFromAny_int
  • #​28175: BUG: Fix f2py directives and --lower casing
  • #​28176: TYP: Fix overlapping overloads issue in 2->1 ufuncs
  • #​28177: TYP: preserve shape-type in ndarray.astype()
  • #​28178: TYP: Fix missing and spurious top-level exports

Checksums

MD5
749cb2adf8043551aae22bbf0ed3130a  numpy-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl
bc79fa2e44316b7ce9bacb48a993ed91  numpy-2.2.2-cp310-cp310-macosx_11_0_arm64.whl
c6b2caa2bbb645b5950dccb77efb1dbb  numpy-2.2.2-cp310-cp310-macosx_14_0_arm64.whl
8c410efac169af880cacbbac8a731658  numpy-2.2.2-cp310-cp310-macosx_14_0_x86_64.whl
21d165669635a9b680d03b0b4e7f5b98  numpy-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
a34ef5e7c967136fdc59c822e99f87d6  numpy-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a81749effc5160ff8dde7eb2ebe868c4  numpy-2.2.2-cp310-cp310-musllinux_1_2_aarch64.whl
546612d82fae082697879aaf2b985b1b  numpy-2.2.2-cp310-cp310-musllinux_1_2_x86_64.whl
d874e626f58175ad603cb68fda2a4e28  numpy-2.2.2-cp310-cp310-win32.whl
20564a5caeb621061267f9d80c1e7ed0  numpy-2.2.2-cp310-cp310-win_amd64.whl
ef5336ddae73feef891844a205f89b15  numpy-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl
7a0c8804cb6ebca82b1cf3063b410687  numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whl
1682639d0420a532f8894c4a8685b23d  numpy-2.2.2-cp311-cp311-macosx_14_0_arm64.whl
d33d53efc5744b577cb8a6ac9971cfdb  numpy-2.2.2-cp311-cp311-macosx_14_0_x86_64.whl
c85b92e2ed7ef0eaeb15909ad73aea22  numpy-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
efa1a587f607a37336c477bed977ea64  numpy-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e0effe9902e262704a115c6f7095daf7  numpy-2.2.2-cp311-cp311-musllinux_1_2_aarch64.whl
425e0cebeb1c2c91bba42ae195836268  numpy-2.2.2-cp311-cp311-musllinux_1_2_x86_64.whl
57121319a2fbb76eed4b268282ed668e  numpy-2.2.2-cp311-cp311-win32.whl
fdb54e7345ff657d208fbb52469a5861  numpy-2.2.2-cp311-cp311-win_amd64.whl
bdf299e0abc45b5c5113a1cc5505636a  numpy-2.2.2-cp312-cp312-macosx_10_13_x86_64.whl
30c25784c07965592cf88104b6c02508  numpy-2.2.2-cp312-cp312-macosx_11_0_arm64.whl
65e630a0de5403c41a0083198bc14442  numpy-2.2.2-cp312-cp312-macosx_14_0_arm64.whl
6d9f50717e7b40f1ebdf139f83cc7504  numpy-2.2.2-cp312-cp312-macosx_14_0_x86_64.whl
6b092a9280ada70482d44f538752fc0b  numpy-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9c273da8438391eab30f6c1c4898be5d  numpy-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
d619047dcaf041b806a7b59ff0a798d5  numpy-2.2.2-cp312-cp312-musllinux_1_2_aarch64.whl
fa5d0d979104456d7c43a183223c8587  numpy-2.2.2-cp312-cp312-musllinux_1_2_x86_64.whl
3b8689aedff5037cad85b018e2d5e43a  numpy-2.2.2-cp312-cp312-win32.whl
a2340ff05cae7e09f63bfcfd4e75ea87  numpy-2.2.2-cp312-cp312-win_amd64.whl
044e86bd65492af34a59e4109fbeed16  numpy-2.2.2-cp313-cp313-macosx_10_13_x86_64.whl
7ca0f0e8c8d3d80ec473ec33929c2ae3  numpy-2.2.2-cp313-cp313-macosx_11_0_arm64.whl
4b866ad895e007005afe8a29837cf7d6  numpy-2.2.2-cp313-cp313-macosx_14_0_arm64.whl
2e6247faabf6d0ac0fafaca0bb405ff8  numpy-2.2.2-cp313-cp313-macosx_14_0_x86_64.whl
773982551185ae327cdefe416e73acfc  numpy-2.2.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
1c0ecc958a555a8a95c92c1dd7dc2358  numpy-2.2.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9f662eb58b8f711585550d6fdf8afa4f  numpy-2.2.2-cp313-cp313-musllinux_1_2_aarch64.whl
53471186fc990eb22e82a0512b310438  numpy-2.2.2-cp313-cp313-musllinux_1_2_x86_64.whl
6b4d65349c74dd91853a7cc6b5c5786e  numpy-2.2.2-cp313-cp313-win32.whl
33dc5bab2d3f752ef00f81021d68cb5a  numpy-2.2.2-cp313-cp313-win_amd64.whl
0acc5069c5ab4fe3ea7c35956636c462  numpy-2.2.2-cp313-cp313t-macosx_10_13_x86_64.whl
01e3f727594a12eee6d0677113525b96  numpy-2.2.2-cp313-cp313t-macosx_11_0_arm64.whl
7b1ddabcb187b18caa52055bb2b2dc67  numpy-2.2.2-cp313-cp313t-macosx_14_0_arm64.whl
a09f5c138ad8c87b9692eea99f344a98  numpy-2.2.2-cp313-cp313t-macosx_14_0_x86_64.whl
289ec3155aa21c5a161b2d61d2cf3c2d  numpy-2.2.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
6bb3eb03d400ad708942afbfebd07abc  numpy-2.2.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
62f8ef2a5c9e76b0e43851a7bb9c0379  numpy-2.2.2-cp313-cp313t-musllinux_1_2_aarch64.whl
59b4b77118f958dd07484686e82b1e7a  numpy-2.2.2-cp313-cp313t-musllinux_1_2_x86_64.whl
726b58ec542581c5e46adfd4c5c0fed0  numpy-2.2.2-cp313-cp313t-win32.whl
f2b4eab55a963e8cd4c6c1e573c9a59f  numpy-2.2.2-cp313-cp313t-win_amd64.whl
f6a93eaebee6f9890a4922571141ecb5  numpy-2.2.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
fb457bbe2d231e836d2230b06d4706ca  numpy-2.2.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
df4c07a48a24621167c12704ba5ac0de  numpy-2.2.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
0d1108b9060469eb28bb4a4cffa7b98f  numpy-2.2.2-pp310-pypy310_pp73-win_amd64.whl
ac108586d3aeab9e2d0134b744763eb9  numpy-2.2.2.tar.gz
SHA256
7079129b64cb78bdc8d611d1fd7e8002c0a2565da6a47c4df8062349fee90e3e  numpy-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl
2ec6c689c61df613b783aeb21f945c4cbe6c51c28cb70aae8430577ab39f163e  numpy-2.2.2-cp310-cp310-macosx_11_0_arm64.whl
40c7ff5da22cd391944a28c6a9c638a5eef77fcf71d6e3a79e1d9d9e82752715  numpy-2.2.2-cp310-cp310-macosx_14_0_arm64.whl
995f9e8181723852ca458e22de5d9b7d3ba4da3f11cc1cb113f093b271d7965a  numpy-2.2.2-cp310-cp310-macosx_14_0_x86_64.whl
b78ea78450fd96a498f50ee096f69c75379af5138f7881a51355ab0e11286c97  numpy-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
3fbe72d347fbc59f94124125e73fc4976a06927ebc503ec5afbfb35f193cd957  numpy-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
8e6da5cffbbe571f93588f562ed130ea63ee206d12851b60819512dd3e1ba50d  numpy-2.2.2-cp310-cp310-musllinux_1_2_aarch64.whl
09d6a2032faf25e8d0cadde7fd6145118ac55d2740132c1d845f98721b5ebcfd  numpy-2.2.2-cp310-cp310-musllinux_1_2_x86_64.whl
159ff6ee4c4a36a23fe01b7c3d07bd8c14cc433d9720f977fcd52c13c0098160  numpy-2.2.2-cp310-cp310-win32.whl
64bd6e1762cd7f0986a740fee4dff927b9ec2c5e4d9a28d056eb17d332158014  numpy-2.2.2-cp310-cp310-win_amd64.whl
642199e98af1bd2b6aeb8ecf726972d238c9877b0f6e8221ee5ab945ec8a2189  numpy-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl
6d9fc9d812c81e6168b6d405bf00b8d6739a7f72ef22a9214c4241e0dc70b323  numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whl
c7d1fd447e33ee20c1f33f2c8e6634211124a9aabde3c617687d8b739aa69eac  numpy-2.2.2-cp311-cp311-macosx_14_0_arm64.whl
451e854cfae0febe723077bd0cf0a4302a5d84ff25f0bfece8f29206c7bed02e  numpy-2.2.2-cp311-cp311-macosx_14_0_x86_64.whl
bd249bc894af67cbd8bad2c22e7cbcd46cf87ddfca1f1289d1e7e54868cc785c  numpy-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
02935e2c3c0c6cbe9c7955a8efa8908dd4221d7755644c59d1bba28b94fd334f  numpy-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a972cec723e0563aa0823ee2ab1df0cb196ed0778f173b381c871a03719d4826  numpy-2.2.2-cp311-cp311-musllinux_1_2_aarch64.whl
d6d6a0910c3b4368d89dde073e630882cdb266755565155bc33520283b2d9df8  numpy-2.2.2-cp311-cp311-musllinux_1_2_x86_64.whl
860fd59990c37c3ef913c3ae390b3929d005243acca1a86facb0773e2d8d9e50  numpy-2.2.2-cp311-cp311-win32.whl
da1eeb460ecce8d5b8608826595c777728cdf28ce7b5a5a8c8ac8d949beadcf2  numpy-2.2.2-cp311-cp311-win_amd64.whl
ac9bea18d6d58a995fac1b2cb4488e17eceeac413af014b1dd26170b766d8467  numpy-2.2.2-cp312-cp312-macosx_10_13_x86_64.whl
23ae9f0c2d889b7b2d88a3791f6c09e2ef827c2446f1c4a3e3e76328ee4afd9a  numpy-2.2.2-cp312-cp312-macosx_11_0_arm64.whl
3074634ea4d6df66be04f6728ee1d173cfded75d002c75fac79503a880bf3825  numpy-2.2.2-cp312-cp312-macosx_14_0_arm64.whl
8ec0636d3f7d68520afc6ac2dc4b8341ddb725039de042faf0e311599f54eb37  numpy-2.2.2-cp312-cp312-macosx_14_0_x86_64.whl
2ffbb1acd69fdf8e89dd60ef6182ca90a743620957afb7066385a7bbe88dc748  numpy-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0349b025e15ea9d05c3d63f9657707a4e1d471128a3b1d876c095f328f8ff7f0  numpy-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
463247edcee4a5537841d5350bc87fe8e92d7dd0e8c71c995d2c6eecb8208278  numpy-2.2.2-cp312-cp312-musllinux_1_2_aarch64.whl
9dd47ff0cb2a656ad69c38da850df3454da88ee9a6fde0ba79acceee0e79daba  numpy-2.2.2-cp312-cp312-musllinux_1_2_x86_64.whl
4525b88c11906d5ab1b0ec1f290996c0020dd318af8b49acaa46f198b1ffc283  numpy-2.2.2-cp312-cp312-win32.whl
5acea83b801e98541619af398cc0109ff48016955cc0818f478ee9ef1c5c3dcb  numpy-2.2.2-cp312-cp312-win_amd64.whl
b208cfd4f5fe34e1535c08983a1a6803fdbc7a1e86cf13dd0c61de0b51a0aadc  numpy-2.2.2-cp313-cp313-macosx_10_13_x86_64.whl
d0bbe7dd86dca64854f4b6ce2ea5c60b51e36dfd597300057cf473d3615f2369  numpy-2.2.2-cp313-cp313-macosx_11_0_arm64.whl
22ea3bb552ade325530e72a0c557cdf2dea8914d3a5e1fecf58fa5dbcc6f43cd  numpy-2.2.2-cp313-cp313-macosx_14_0_arm64.whl
128c41c085cab8a85dc29e66ed88c05613dccf6bc28b3866cd16050a2f5448be  numpy-2.2.2-cp313-cp313-macosx_14_0_x86_64.whl
250c16b277e3b809ac20d1f590716597481061b514223c7badb7a0f9993c7f84  numpy-2.2.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e0c8854b09bc4de7b041148d8550d3bd712b5c21ff6a8ed308085f190235d7ff  numpy-2.2.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b6fb9c32a91ec32a689ec6410def76443e3c750e7cfc3fb2206b985ffb2b85f0  numpy-2.2.2-cp313-cp313-musllinux_1_2_aarch64.whl
57b4012e04cc12b78590a334907e01b3a85efb2107df2b8733ff1ed05fce71de  numpy-2.2.2-cp313-cp313-musllinux_1_2_x86_64.whl
4dbd80e453bd34bd003b16bd802fac70ad76bd463f81f0c518d1245b1c55e3d9  numpy-2.2.2-cp313-cp313-win32.whl
5a8c863ceacae696aff37d1fd636121f1a512117652e5dfb86031c8d84836369  numpy-2.2.2-cp313-cp313-win_amd64.whl
b3482cb7b3325faa5f6bc179649406058253d91ceda359c104dac0ad320e1391  numpy-2.2.2-cp313-cp313t-macosx_10_13_x86_64.whl
9491100aba630910489c1d0158034e1c9a6546f0b1340f716d522dc103788e39  numpy-2.2.2-cp313-cp313t-macosx_11_0_arm64.whl
41184c416143defa34cc8eb9d070b0a5ba4f13a0fa96a709e20584638254b317  numpy-2.2.2-cp313-cp313t-macosx_14_0_arm64.whl
7dca87ca328f5ea7dafc907c5ec100d187911f94825f8700caac0b3f4c384b49  numpy-2.2.2-cp313-cp313t-macosx_14_0_x86_64.whl
0bc61b307655d1a7f9f4b043628b9f2b721e80839914ede634e3d485913e1fb2  numpy-2.2.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9fad446ad0bc886855ddf5909cbf8cb5d0faa637aaa6277fb4b19ade134ab3c7  numpy-2.2.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
149d1113ac15005652e8d0d3f6fd599360e1a708a4f98e43c9c77834a28238cb  numpy-2.2.2-cp313-cp313t-musllinux_1_2_aarch64.whl
106397dbbb1896f99e044efc90360d098b3335060375c26aa89c0d8a97c5f648  numpy-2.2.2-cp313-cp313t-musllinux_1_2_x86_64.whl
0eec19f8af947a61e968d5429f0bd92fec46d92b0008d0a6685b40d6adf8a4f4  numpy-2.2.2-cp313-cp313t-win32.whl
97b974d3ba0fb4612b77ed35d7627490e8e3dff56ab41454d9e8b23448940576  numpy-2.2.2-cp313-cp313t-win_amd64.whl
b0531f0b0e07643eb089df4c509d30d72c9ef40defa53e41363eca8a8cc61495  numpy-2.2.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
e9e82dcb3f2ebbc8cb5ce1102d5f1c5ed236bf8a11730fb45ba82e2841ec21df  numpy-2.2.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
e0d4142eb40ca6f94539e4db929410f2a46052a0fe7a2c1c59f6179c39938d2a  numpy-2.2.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
356ca982c188acbfa6af0d694284d8cf20e95b1c3d0aefa8929376fea9146f60  numpy-2.2.2-pp310-pypy310_pp73-win_amd64.whl
ed6906f61834d687738d25988ae117683705636936cc605be0bb208b23df4d8f  numpy-2.2.2.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 | Update | Change | |---|---|---| | [numpy](https://github.com/numpy/numpy) ([changelog](https://numpy.org/doc/stable/release)) | patch | `==2.2.1` -> `==2.2.2` | --- ### Release Notes <details> <summary>numpy/numpy (numpy)</summary> ### [`v2.2.2`](https://github.com/numpy/numpy/releases/tag/v2.2.2): 2.2.2 (Jan 18, 2025) [Compare Source](https://github.com/numpy/numpy/compare/v2.2.1...v2.2.2) ### NumPy 2.2.2 Release Notes NumPy 2.2.2 is a patch release that fixes bugs found after the 2.2.1 release. The number of typing fixes/updates is notable. This release supports Python versions 3.10-3.13. #### Contributors A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alicia Boya García + - Charles Harris - Joren Hammudoglu - Kai Germaschewski + - Nathan Goldbaum - PTUsumit + - Rohit Goswami - Sebastian Berg #### Pull requests merged A total of 16 pull requests were merged for this release. - [#&#8203;28050](https://github.com/numpy/numpy/pull/28050): MAINT: Prepare 2.2.x for further development - [#&#8203;28055](https://github.com/numpy/numpy/pull/28055): TYP: fix `void` arrays not accepting `str` keys in `__setitem__` - [#&#8203;28066](https://github.com/numpy/numpy/pull/28066): TYP: fix unnecessarily broad `integer` binop return types ([#&#8203;28065](https://github.com/numpy/numpy/issues/28065)) - [#&#8203;28112](https://github.com/numpy/numpy/pull/28112): TYP: Better `ndarray` binop return types for `float64` &... - [#&#8203;28113](https://github.com/numpy/numpy/pull/28113): TYP: Return the correct `bool` from `issubdtype` - [#&#8203;28114](https://github.com/numpy/numpy/pull/28114): TYP: Always accept `date[time]` in the `datetime64` constructor - [#&#8203;28120](https://github.com/numpy/numpy/pull/28120): BUG: Fix auxdata initialization in ufunc slow path - [#&#8203;28131](https://github.com/numpy/numpy/pull/28131): BUG: move reduction initialization to ufunc initialization - [#&#8203;28132](https://github.com/numpy/numpy/pull/28132): TYP: Fix `interp` to accept and return scalars - [#&#8203;28137](https://github.com/numpy/numpy/pull/28137): BUG: call PyType_Ready in f2py to avoid data races - [#&#8203;28145](https://github.com/numpy/numpy/pull/28145): BUG: remove unnecessary call to PyArray_UpdateFlags - [#&#8203;28160](https://github.com/numpy/numpy/pull/28160): BUG: Avoid data race in PyArray_CheckFromAny_int - [#&#8203;28175](https://github.com/numpy/numpy/pull/28175): BUG: Fix f2py directives and --lower casing - [#&#8203;28176](https://github.com/numpy/numpy/pull/28176): TYP: Fix overlapping overloads issue in 2->1 ufuncs - [#&#8203;28177](https://github.com/numpy/numpy/pull/28177): TYP: preserve shape-type in ndarray.astype() - [#&#8203;28178](https://github.com/numpy/numpy/pull/28178): TYP: Fix missing and spurious top-level exports #### Checksums ##### MD5 749cb2adf8043551aae22bbf0ed3130a numpy-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl bc79fa2e44316b7ce9bacb48a993ed91 numpy-2.2.2-cp310-cp310-macosx_11_0_arm64.whl c6b2caa2bbb645b5950dccb77efb1dbb numpy-2.2.2-cp310-cp310-macosx_14_0_arm64.whl 8c410efac169af880cacbbac8a731658 numpy-2.2.2-cp310-cp310-macosx_14_0_x86_64.whl 21d165669635a9b680d03b0b4e7f5b98 numpy-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a34ef5e7c967136fdc59c822e99f87d6 numpy-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a81749effc5160ff8dde7eb2ebe868c4 numpy-2.2.2-cp310-cp310-musllinux_1_2_aarch64.whl 546612d82fae082697879aaf2b985b1b numpy-2.2.2-cp310-cp310-musllinux_1_2_x86_64.whl d874e626f58175ad603cb68fda2a4e28 numpy-2.2.2-cp310-cp310-win32.whl 20564a5caeb621061267f9d80c1e7ed0 numpy-2.2.2-cp310-cp310-win_amd64.whl ef5336ddae73feef891844a205f89b15 numpy-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl 7a0c8804cb6ebca82b1cf3063b410687 numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whl 1682639d0420a532f8894c4a8685b23d numpy-2.2.2-cp311-cp311-macosx_14_0_arm64.whl d33d53efc5744b577cb8a6ac9971cfdb numpy-2.2.2-cp311-cp311-macosx_14_0_x86_64.whl c85b92e2ed7ef0eaeb15909ad73aea22 numpy-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl efa1a587f607a37336c477bed977ea64 numpy-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e0effe9902e262704a115c6f7095daf7 numpy-2.2.2-cp311-cp311-musllinux_1_2_aarch64.whl 425e0cebeb1c2c91bba42ae195836268 numpy-2.2.2-cp311-cp311-musllinux_1_2_x86_64.whl 57121319a2fbb76eed4b268282ed668e numpy-2.2.2-cp311-cp311-win32.whl fdb54e7345ff657d208fbb52469a5861 numpy-2.2.2-cp311-cp311-win_amd64.whl bdf299e0abc45b5c5113a1cc5505636a numpy-2.2.2-cp312-cp312-macosx_10_13_x86_64.whl 30c25784c07965592cf88104b6c02508 numpy-2.2.2-cp312-cp312-macosx_11_0_arm64.whl 65e630a0de5403c41a0083198bc14442 numpy-2.2.2-cp312-cp312-macosx_14_0_arm64.whl 6d9f50717e7b40f1ebdf139f83cc7504 numpy-2.2.2-cp312-cp312-macosx_14_0_x86_64.whl 6b092a9280ada70482d44f538752fc0b numpy-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9c273da8438391eab30f6c1c4898be5d numpy-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d619047dcaf041b806a7b59ff0a798d5 numpy-2.2.2-cp312-cp312-musllinux_1_2_aarch64.whl fa5d0d979104456d7c43a183223c8587 numpy-2.2.2-cp312-cp312-musllinux_1_2_x86_64.whl 3b8689aedff5037cad85b018e2d5e43a numpy-2.2.2-cp312-cp312-win32.whl a2340ff05cae7e09f63bfcfd4e75ea87 numpy-2.2.2-cp312-cp312-win_amd64.whl 044e86bd65492af34a59e4109fbeed16 numpy-2.2.2-cp313-cp313-macosx_10_13_x86_64.whl 7ca0f0e8c8d3d80ec473ec33929c2ae3 numpy-2.2.2-cp313-cp313-macosx_11_0_arm64.whl 4b866ad895e007005afe8a29837cf7d6 numpy-2.2.2-cp313-cp313-macosx_14_0_arm64.whl 2e6247faabf6d0ac0fafaca0bb405ff8 numpy-2.2.2-cp313-cp313-macosx_14_0_x86_64.whl 773982551185ae327cdefe416e73acfc numpy-2.2.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 1c0ecc958a555a8a95c92c1dd7dc2358 numpy-2.2.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 9f662eb58b8f711585550d6fdf8afa4f numpy-2.2.2-cp313-cp313-musllinux_1_2_aarch64.whl 53471186fc990eb22e82a0512b310438 numpy-2.2.2-cp313-cp313-musllinux_1_2_x86_64.whl 6b4d65349c74dd91853a7cc6b5c5786e numpy-2.2.2-cp313-cp313-win32.whl 33dc5bab2d3f752ef00f81021d68cb5a numpy-2.2.2-cp313-cp313-win_amd64.whl 0acc5069c5ab4fe3ea7c35956636c462 numpy-2.2.2-cp313-cp313t-macosx_10_13_x86_64.whl 01e3f727594a12eee6d0677113525b96 numpy-2.2.2-cp313-cp313t-macosx_11_0_arm64.whl 7b1ddabcb187b18caa52055bb2b2dc67 numpy-2.2.2-cp313-cp313t-macosx_14_0_arm64.whl a09f5c138ad8c87b9692eea99f344a98 numpy-2.2.2-cp313-cp313t-macosx_14_0_x86_64.whl 289ec3155aa21c5a161b2d61d2cf3c2d numpy-2.2.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 6bb3eb03d400ad708942afbfebd07abc numpy-2.2.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 62f8ef2a5c9e76b0e43851a7bb9c0379 numpy-2.2.2-cp313-cp313t-musllinux_1_2_aarch64.whl 59b4b77118f958dd07484686e82b1e7a numpy-2.2.2-cp313-cp313t-musllinux_1_2_x86_64.whl 726b58ec542581c5e46adfd4c5c0fed0 numpy-2.2.2-cp313-cp313t-win32.whl f2b4eab55a963e8cd4c6c1e573c9a59f numpy-2.2.2-cp313-cp313t-win_amd64.whl f6a93eaebee6f9890a4922571141ecb5 numpy-2.2.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl fb457bbe2d231e836d2230b06d4706ca numpy-2.2.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl df4c07a48a24621167c12704ba5ac0de numpy-2.2.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 0d1108b9060469eb28bb4a4cffa7b98f numpy-2.2.2-pp310-pypy310_pp73-win_amd64.whl ac108586d3aeab9e2d0134b744763eb9 numpy-2.2.2.tar.gz ##### SHA256 7079129b64cb78bdc8d611d1fd7e8002c0a2565da6a47c4df8062349fee90e3e numpy-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl 2ec6c689c61df613b783aeb21f945c4cbe6c51c28cb70aae8430577ab39f163e numpy-2.2.2-cp310-cp310-macosx_11_0_arm64.whl 40c7ff5da22cd391944a28c6a9c638a5eef77fcf71d6e3a79e1d9d9e82752715 numpy-2.2.2-cp310-cp310-macosx_14_0_arm64.whl 995f9e8181723852ca458e22de5d9b7d3ba4da3f11cc1cb113f093b271d7965a numpy-2.2.2-cp310-cp310-macosx_14_0_x86_64.whl b78ea78450fd96a498f50ee096f69c75379af5138f7881a51355ab0e11286c97 numpy-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3fbe72d347fbc59f94124125e73fc4976a06927ebc503ec5afbfb35f193cd957 numpy-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 8e6da5cffbbe571f93588f562ed130ea63ee206d12851b60819512dd3e1ba50d numpy-2.2.2-cp310-cp310-musllinux_1_2_aarch64.whl 09d6a2032faf25e8d0cadde7fd6145118ac55d2740132c1d845f98721b5ebcfd numpy-2.2.2-cp310-cp310-musllinux_1_2_x86_64.whl 159ff6ee4c4a36a23fe01b7c3d07bd8c14cc433d9720f977fcd52c13c0098160 numpy-2.2.2-cp310-cp310-win32.whl 64bd6e1762cd7f0986a740fee4dff927b9ec2c5e4d9a28d056eb17d332158014 numpy-2.2.2-cp310-cp310-win_amd64.whl 642199e98af1bd2b6aeb8ecf726972d238c9877b0f6e8221ee5ab945ec8a2189 numpy-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl 6d9fc9d812c81e6168b6d405bf00b8d6739a7f72ef22a9214c4241e0dc70b323 numpy-2.2.2-cp311-cp311-macosx_11_0_arm64.whl c7d1fd447e33ee20c1f33f2c8e6634211124a9aabde3c617687d8b739aa69eac numpy-2.2.2-cp311-cp311-macosx_14_0_arm64.whl 451e854cfae0febe723077bd0cf0a4302a5d84ff25f0bfece8f29206c7bed02e numpy-2.2.2-cp311-cp311-macosx_14_0_x86_64.whl bd249bc894af67cbd8bad2c22e7cbcd46cf87ddfca1f1289d1e7e54868cc785c numpy-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 02935e2c3c0c6cbe9c7955a8efa8908dd4221d7755644c59d1bba28b94fd334f numpy-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a972cec723e0563aa0823ee2ab1df0cb196ed0778f173b381c871a03719d4826 numpy-2.2.2-cp311-cp311-musllinux_1_2_aarch64.whl d6d6a0910c3b4368d89dde073e630882cdb266755565155bc33520283b2d9df8 numpy-2.2.2-cp311-cp311-musllinux_1_2_x86_64.whl 860fd59990c37c3ef913c3ae390b3929d005243acca1a86facb0773e2d8d9e50 numpy-2.2.2-cp311-cp311-win32.whl da1eeb460ecce8d5b8608826595c777728cdf28ce7b5a5a8c8ac8d949beadcf2 numpy-2.2.2-cp311-cp311-win_amd64.whl ac9bea18d6d58a995fac1b2cb4488e17eceeac413af014b1dd26170b766d8467 numpy-2.2.2-cp312-cp312-macosx_10_13_x86_64.whl 23ae9f0c2d889b7b2d88a3791f6c09e2ef827c2446f1c4a3e3e76328ee4afd9a numpy-2.2.2-cp312-cp312-macosx_11_0_arm64.whl 3074634ea4d6df66be04f6728ee1d173cfded75d002c75fac79503a880bf3825 numpy-2.2.2-cp312-cp312-macosx_14_0_arm64.whl 8ec0636d3f7d68520afc6ac2dc4b8341ddb725039de042faf0e311599f54eb37 numpy-2.2.2-cp312-cp312-macosx_14_0_x86_64.whl 2ffbb1acd69fdf8e89dd60ef6182ca90a743620957afb7066385a7bbe88dc748 numpy-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 0349b025e15ea9d05c3d63f9657707a4e1d471128a3b1d876c095f328f8ff7f0 numpy-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 463247edcee4a5537841d5350bc87fe8e92d7dd0e8c71c995d2c6eecb8208278 numpy-2.2.2-cp312-cp312-musllinux_1_2_aarch64.whl 9dd47ff0cb2a656ad69c38da850df3454da88ee9a6fde0ba79acceee0e79daba numpy-2.2.2-cp312-cp312-musllinux_1_2_x86_64.whl 4525b88c11906d5ab1b0ec1f290996c0020dd318af8b49acaa46f198b1ffc283 numpy-2.2.2-cp312-cp312-win32.whl 5acea83b801e98541619af398cc0109ff48016955cc0818f478ee9ef1c5c3dcb numpy-2.2.2-cp312-cp312-win_amd64.whl b208cfd4f5fe34e1535c08983a1a6803fdbc7a1e86cf13dd0c61de0b51a0aadc numpy-2.2.2-cp313-cp313-macosx_10_13_x86_64.whl d0bbe7dd86dca64854f4b6ce2ea5c60b51e36dfd597300057cf473d3615f2369 numpy-2.2.2-cp313-cp313-macosx_11_0_arm64.whl 22ea3bb552ade325530e72a0c557cdf2dea8914d3a5e1fecf58fa5dbcc6f43cd numpy-2.2.2-cp313-cp313-macosx_14_0_arm64.whl 128c41c085cab8a85dc29e66ed88c05613dccf6bc28b3866cd16050a2f5448be numpy-2.2.2-cp313-cp313-macosx_14_0_x86_64.whl 250c16b277e3b809ac20d1f590716597481061b514223c7badb7a0f9993c7f84 numpy-2.2.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl e0c8854b09bc4de7b041148d8550d3bd712b5c21ff6a8ed308085f190235d7ff numpy-2.2.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b6fb9c32a91ec32a689ec6410def76443e3c750e7cfc3fb2206b985ffb2b85f0 numpy-2.2.2-cp313-cp313-musllinux_1_2_aarch64.whl 57b4012e04cc12b78590a334907e01b3a85efb2107df2b8733ff1ed05fce71de numpy-2.2.2-cp313-cp313-musllinux_1_2_x86_64.whl 4dbd80e453bd34bd003b16bd802fac70ad76bd463f81f0c518d1245b1c55e3d9 numpy-2.2.2-cp313-cp313-win32.whl 5a8c863ceacae696aff37d1fd636121f1a512117652e5dfb86031c8d84836369 numpy-2.2.2-cp313-cp313-win_amd64.whl b3482cb7b3325faa5f6bc179649406058253d91ceda359c104dac0ad320e1391 numpy-2.2.2-cp313-cp313t-macosx_10_13_x86_64.whl 9491100aba630910489c1d0158034e1c9a6546f0b1340f716d522dc103788e39 numpy-2.2.2-cp313-cp313t-macosx_11_0_arm64.whl 41184c416143defa34cc8eb9d070b0a5ba4f13a0fa96a709e20584638254b317 numpy-2.2.2-cp313-cp313t-macosx_14_0_arm64.whl 7dca87ca328f5ea7dafc907c5ec100d187911f94825f8700caac0b3f4c384b49 numpy-2.2.2-cp313-cp313t-macosx_14_0_x86_64.whl 0bc61b307655d1a7f9f4b043628b9f2b721e80839914ede634e3d485913e1fb2 numpy-2.2.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9fad446ad0bc886855ddf5909cbf8cb5d0faa637aaa6277fb4b19ade134ab3c7 numpy-2.2.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 149d1113ac15005652e8d0d3f6fd599360e1a708a4f98e43c9c77834a28238cb numpy-2.2.2-cp313-cp313t-musllinux_1_2_aarch64.whl 106397dbbb1896f99e044efc90360d098b3335060375c26aa89c0d8a97c5f648 numpy-2.2.2-cp313-cp313t-musllinux_1_2_x86_64.whl 0eec19f8af947a61e968d5429f0bd92fec46d92b0008d0a6685b40d6adf8a4f4 numpy-2.2.2-cp313-cp313t-win32.whl 97b974d3ba0fb4612b77ed35d7627490e8e3dff56ab41454d9e8b23448940576 numpy-2.2.2-cp313-cp313t-win_amd64.whl b0531f0b0e07643eb089df4c509d30d72c9ef40defa53e41363eca8a8cc61495 numpy-2.2.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl e9e82dcb3f2ebbc8cb5ce1102d5f1c5ed236bf8a11730fb45ba82e2841ec21df numpy-2.2.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl e0d4142eb40ca6f94539e4db929410f2a46052a0fe7a2c1c59f6179c39938d2a numpy-2.2.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 356ca982c188acbfa6af0d694284d8cf20e95b1c3d0aefa8929376fea9146f60 numpy-2.2.2-pp310-pypy310_pp73-win_amd64.whl ed6906f61834d687738d25988ae117683705636936cc605be0bb208b23df4d8f numpy-2.2.2.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:eyJjcmVhdGVkSW5WZXIiOiIzOS4zMS40IiwidXBkYXRlZEluVmVyIjoiMzkuMzEuNCIsInRhcmdldEJyYW5jaCI6Im1haW4iLCJsYWJlbHMiOltdfQ==-->
renovate_bot added 1 commit 2025-01-20 07:57:26 -08:00
Update dependency numpy to v2.2.2
Some checks failed
ci/woodpecker/pr/docker-buildx Pipeline failed
Details
ci/woodpecker/pr/lint Pipeline was successful
Details
ci/woodpecker/pr/vulnerability-scan Pipeline was successful
Details
ci/woodpecker/pull_request_closed/lint Pipeline was successful
Details
ci/woodpecker/pull_request_closed/vulnerability-scan Pipeline was successful
Details
ci/woodpecker/push/docker-buildx Pipeline failed
Details
ci/woodpecker/push/lint Pipeline was successful
Details
ci/woodpecker/push/vulnerability-scan Pipeline was successful
Details
1096de431f
renovate_bot scheduled this pull request to auto merge when all checks succeed 2025-01-20 07:57:26 -08:00
renovate_bot merged commit 1096de431f into main 2025-01-20 07:58:04 -08:00
Sign in to join this conversation.
Reviewers
No reviewers
Labels
Clear labels
No items
No labels
Milestone
Clear milestone
No items
No milestone
Projects
Clear projects
No items
No project
Assignees
Clear assignees
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#77
No description provided.
Delete branch "renovate/numpy-2.x"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?