Skip to content
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

Update all dependencies (major) #12389

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Update all dependencies (major) #12389

wants to merge 1 commit into from
+29 −61

Conversation

renovate[bot]
Copy link
Contributor

@renovate renovate bot commented Jan 1, 2023 •
edited
Loading

This PR contains the following updates:

Package Change Age Adoption Passing Confidence Type Update
concurrently 8.2.0 -> 9.1.0 age adoption passing confidence devDependencies major
date-fns 3.6.0 -> 4.1.0 age adoption passing confidence dependencies major
minimatch 3.1.2 -> 10.0.1 age adoption passing confidence resolutions major
mistune ==0.8.4 -> ==3.0.2 age adoption passing confidence major
node 20 -> 23 age adoption passing confidence stage major
numpy (changelog) ==1.24.4 -> ==2.1.3 age adoption passing confidence major
pytz ==2022.7.1 -> ==2024.2 age adoption passing confidence major
tj-actions/changed-files v43 -> v45 age adoption passing confidence action major
ubuntu 22.04 -> 24.04 age adoption passing confidence github-runner major
ubuntu jammy -> noble age adoption passing confidence final major
ubuntu jammy -> noble age adoption passing confidence stage major

Release Notes

open-cli-tools/concurrently (concurrently)

v9.1.0

Compare Source

What's Changed

New Contributors

Full Changelog: open-cli-tools/concurrently@v9.0.1...v9.1.0

v9.0.1

Compare Source

What's Changed

Full Changelog: open-cli-tools/concurrently@v9.0.0...v9.0.1

v9.0.0

Compare Source

v8.2.2

Compare Source

What's Changed

New Contributors

Full Changelog: open-cli-tools/concurrently@v8.2.1...v8.2.2

v8.2.1

Compare Source

What's Changed

New Contributors

Full Changelog: open-cli-tools/concurrently@v8.2.0...v8.2.1

date-fns/date-fns (date-fns)

v4.1.0

Compare Source

This release adds time zone support to format functions (that I somehow missed when working on the feature) and fixes a few bugs.

Make sure also upgrade TZDate to v1.0.2 as it includes a bunch of critical bug fixes.

Fixed
  • Fixed internal constructFrom throwing an exception on null arguments. While null isn't allowed, the functions should rather return Invalid Date or NaN in such cases. See #​3885.
Added
  • Added missing time zone support to format, formatISO, formatISO9075, formatRelative and formatRFC3339. See #​3886.

v4.0.0

Compare Source

I have great news! First, ten years after its release, date-fns finally gets first-class time zone support.

Another great news is that there aren't many breaking changes in this release. All of them are type-related and will affect only those explicitly using internal date-fns types. Finally, it has been less than a year since the last major release, which is an improvement over the previous four years between v2 and v3. I plan on keeping the pace and minimizing breaking changes moving forward.

Read more about the release in the announcement blog post.

- Sasha @​kossnocorp

Added

  • Added time zones support via @date-fns/tz's TZDate class and tz helper function. See its README for the details about the API.

  • All relevant functions now accept the context in option, which allows to specify the time zone to make the calculations in. If the function also returns a date, it will be in the specified time zone:

    import { addDays, startOfDay } from "date-fns";
import { tz } from "@​date-fns/tz";

startOfDay(addDays(Date.now(), 5, { in: tz("Asia/Singapore") }));
//=> "2024-09-16T00:00:00.000+08:00"

    In the example, addDays will get the current date and time in Singapore and add 5 days to it. startOfDay will inherit the date type and return the start of the day in Singapore.

Changed

  • The function arguments, as well as Interval's start and end, now can be of different types, allowing you to mix UTCDate, TZDate, Date, and other extensions, as well as primitives (strings and numbers).

    The functions will normalize these values, make calculations, and return the result in the same type, preventing any bugs caused by the discrepancy. If passed, the type will be inferred from the context in option or the first encountered argument object type. The Interval's start and end will be considered separately, starting from start.

    In the given example, the result will be in the TZDate as the first argument is a number, and the start takes precedence over the end.

    clamp(Date.now(), {
  start: new TZDate(start, "Asia/Singapore"),
  end: new UTCDate(),
});
//=> TZDate

  • BREAKING: This release contains a bunch of types changes that should not affect the library's expected usage. The changes are primarily internal and nuanced, so rather than listing them here, I recommend you running the type checker after the upgrade. If there are unfixable problems, please open an issue.

  • BREAKING: The package now is ESM-first. The CommonJS is still support and It should not affect most users, but it might break in certains environments. If you encounter any issues, please report them.

Fixed
  • Fixed CDN build compatibility with jQuery and other tools that expose $ by properly wrapping the code in an IIFE.
isaacs/minimatch (minimatch)

v10.0.1

Compare Source

v10.0.0

Compare Source

v9.0.5

Compare Source

v9.0.4

Compare Source

v9.0.3

Compare Source

v9.0.2

Compare Source

v9.0.1

Compare Source

v9.0.0

Compare Source

v8.0.4

Compare Source

v8.0.3

Compare Source

v8.0.2

Compare Source

v8.0.1

Compare Source

v8.0.0

Compare Source

v7.4.6

Compare Source

v7.4.5

Compare Source

v7.4.4

Compare Source

v7.4.3

Compare Source

v7.4.2

Compare Source

v7.4.1

Compare Source

v7.4.0

Compare Source

v7.3.0

Compare Source

v7.2.0

Compare Source

v7.1.4

Compare Source

v7.1.3

Compare Source

v7.1.2

Compare Source

v7.1.1

Compare Source

v7.1.0

Compare Source

v7.0.1

Compare Source

v7.0.0

Compare Source

v6.2.0

Compare Source

v6.1.10

Compare Source

v6.1.9

Compare Source

v6.1.8

Compare Source

v6.1.7

Compare Source

v6.1.6

Compare Source

v6.1.5

Compare Source

v6.1.4

Compare Source

v6.1.3

Compare Source

v6.1.2

Compare Source

v6.1.1

Compare Source

v6.1.0

Compare Source

v6.0.4

Compare Source

v6.0.3

Compare Source

v6.0.2

Compare Source

v6.0.1

Compare Source

v6.0.0

Compare Source

v5.1.6

Compare Source

v5.1.5

Compare Source

v5.1.4

Compare Source

v5.1.3

Compare Source

v5.1.2

Compare Source

v5.1.1

Compare Source

v5.1.0

Compare Source

v5.0.1

Compare Source

v5.0.0

Compare Source

v4.2.3

Compare Source

v4.2.2

Compare Source

v4.2.1

Compare Source

v4.2.0

Compare Source

v4.1.1

Compare Source

v4.1.0

Compare Source

v4.0.0

Compare Source

lepture/mistune (mistune)

v3.0.2

Compare Source

   🐞 Bug Fixes
    View changes on GitHub

v3.0.1

Compare Source

   🚀 Features
   🐞 Bug Fixes
    View changes on GitHub

v3.0.0

Compare Source

   🚀 Features
   🐞 Bug Fixes
  • Ensure new line at end of the text  -  by @​lepture
  • Do not strip leading unicode spaces like emsp (full-width space)  -  by @​alphatownsman
    View changes on GitHub

v2.0.5: Version 2.0.5

Compare Source

Improve on parsing list. Make it possible to customize list regex. ref https://github.com/lepture/mistune/issues/331

v2.0.4: Version 2.0.4

Compare Source

  • Fix url plugin in <a> tag
  • Fix * formatting

v2.0.3: Version 2.0.3

Compare Source

v2.0.2: Version 2.0.2

Compare Source

Fix escape_url via https://github.com/lepture/mistune/pull/295

v2.0.1: Version 2.0.1

Compare Source

Fix XSS for image link syntax.

v2.0.0: Version 2.0.0

Compare Source

First release of Mistune v2.

nodejs/node (node)

v23

Moved to doc/changelogs/CHANGELOG_V6.md#6.1.0.

v22

Moved to doc/changelogs/CHANGELOG_V4.md#4.4.1.

v21

Moved to doc/changelogs/CHANGELOG_V4.md#4.2.6.

numpy/numpy (numpy)

v2.1.3

Compare Source

v2.1.2

Compare Source

v2.1.1: 2.1.1 (Sep 3, 2024)

Compare Source

NumPy 2.1.1 Release Notes

NumPy 2.1.1 is a maintenance release that fixes bugs and regressions
discovered after the 2.1.0 release.

The Python versions supported by this release are 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.

  • Andrew Nelson
  • Charles Harris
  • Mateusz Sokół
  • Maximilian Weigand +
  • Nathan Goldbaum
  • Pieter Eendebak
  • Sebastian Berg
Pull requests merged

A total of 10 pull requests were merged for this release.

  • #​27236: REL: Prepare for the NumPy 2.1.0 release [wheel build]
  • #​27252: MAINT: prepare 2.1.x for further development
  • #​27259: BUG: revert unintended change in the return value of set_printoptions
  • #​27266: BUG: fix reference counting bug in __array_interface__ implementation...
  • #​27267: TST: Add regression test for missing descr in array-interface
  • #​27276: BUG: Fix #​27256 and #​27257
  • #​27278: BUG: Fix array_equal for numeric and non-numeric scalar types
  • #​27287: MAINT: Update maintenance/2.1.x after the 2.0.2 release
  • #​27303: BLD: cp311- macosx_arm64 wheels [wheel build]
  • #​27304: BUG: f2py: better handle filtering of public/private subroutines
Checksums
MD5
3053a97400db800b7377749e691eb39e  numpy-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl
84b752a2220dce7c96ff89eef4f4aec3  numpy-2.1.1-cp310-cp310-macosx_11_0_arm64.whl
47ed4f704a64261f07ca24ef2e674524  numpy-2.1.1-cp310-cp310-macosx_14_0_arm64.whl
b8a45caa870aee980c298053cf064d28  numpy-2.1.1-cp310-cp310-macosx_14_0_x86_64.whl
e097ad5eee572b791b4a25eedad6df4a  numpy-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
ae502c99315884cda7f0236a07c035c4  numpy-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
841a859d975c55090c0b60b72aab93a3  numpy-2.1.1-cp310-cp310-musllinux_1_1_x86_64.whl
d51be2b17f5b87aac64ab80fdfafc85e  numpy-2.1.1-cp310-cp310-musllinux_1_2_aarch64.whl
1f8249bd725397c6233fe6a0e8ad18b1  numpy-2.1.1-cp310-cp310-win32.whl
d38d6f06589c1ec104a6a31ff6035781  numpy-2.1.1-cp310-cp310-win_amd64.whl
6a18fe3029aae00986975250313bf16f  numpy-2.1.1-cp311-cp311-macosx_10_9_x86_64.whl
5b0b3aa01fbd0b5a8b0f354bb878351e  numpy-2.1.1-cp311-cp311-macosx_11_0_arm64.whl
1c492dad399abe7b97274b4c6c12ae53  numpy-2.1.1-cp311-cp311-macosx_14_0_arm64.whl
4d55d91e71b62eb5fa6561c606524f60  numpy-2.1.1-cp311-cp311-macosx_14_0_x86_64.whl
88e99ecd063c178f25bc08d20792a9bf  numpy-2.1.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f3c8b0e4fb059b9219e8ec86d9fda861  numpy-2.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
df632b5fed7eb78d39e7194d2475c19b  numpy-2.1.1-cp311-cp311-musllinux_1_1_x86_64.whl
65499daccdb178d26e322d9f359cf146  numpy-2.1.1-cp311-cp311-musllinux_1_2_aarch64.whl
eb97327fd7aa6027e2409d0dcca1129a  numpy-2.1.1-cp311-cp311-win32.whl
9e4b05b38cbff22c2bdfead528b9d2bc  numpy-2.1.1-cp311-cp311-win_amd64.whl
6b8a359bb865b5c624fd9ffc848393e1  numpy-2.1.1-cp312-cp312-macosx_10_9_x86_64.whl
eaf8dce312efa2b0f17ad46612fb1681  numpy-2.1.1-cp312-cp312-macosx_11_0_arm64.whl
c861ff048b336284fe7c0791b1a6b0b4  numpy-2.1.1-cp312-cp312-macosx_14_0_arm64.whl
7e1befccfe729dc5d6c450a5fb6b801c  numpy-2.1.1-cp312-cp312-macosx_14_0_x86_64.whl
ea0a401ef653a167221987a10cbef260  numpy-2.1.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
97326ac792d26f2e536a519c82f2d6bc  numpy-2.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
fdd2a82232c03d11bbc7cec0a8e01ab0  numpy-2.1.1-cp312-cp312-musllinux_1_1_x86_64.whl
0d6716e9a7b2c0d6e5ace9c01b9bca01  numpy-2.1.1-cp312-cp312-musllinux_1_2_aarch64.whl
ba589ed2a79c88187c3b8574ae72a1c7  numpy-2.1.1-cp312-cp312-win32.whl
806ca7c1e2a2013b786edbb619f6da47  numpy-2.1.1-cp312-cp312-win_amd64.whl
647665353e5af5884df4e51610990c22  numpy-2.1.1-cp313-cp313-macosx_10_13_x86_64.whl
bfd3b3c5c4616ef99d917bd94d39114a  numpy-2.1.1-cp313-cp313-macosx_11_0_arm64.whl
cb989095f9c74e3b32250a984390faeb  numpy-2.1.1-cp313-cp313-macosx_14_0_arm64.whl
55ad7548e58f61b9a4f91749e36d237f  numpy-2.1.1-cp313-cp313-macosx_14_0_x86_64.whl
5bc73d67dd1032524bfd36ef877b09e4  numpy-2.1.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
c7dfb09db8284cb75296f708c3f77ea3  numpy-2.1.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
7cf90ce1b844a97aeea1a5b8c71fb49b  numpy-2.1.1-cp313-cp313-musllinux_1_1_x86_64.whl
6ec8baeac5f979a3b98017679d457bbc  numpy-2.1.1-cp313-cp313-musllinux_1_2_aarch64.whl
1f198cb5210c76faae81359a83d58230  numpy-2.1.1-cp313-cp313-win32.whl
1766258213ad41f7e36f2209ee6d2a30  numpy-2.1.1-cp313-cp313-win_amd64.whl
f0a7a0456308dbeb739ad886f1632f16  numpy-2.1.1-cp313-cp313t-macosx_10_13_x86_64.whl
302c9cf7b4aa695974500ee1935a92c9  numpy-2.1.1-cp313-cp313t-macosx_11_0_arm64.whl
f4aa7d784992abb9bd9fe9db09c01c06  numpy-2.1.1-cp313-cp313t-macosx_14_0_arm64.whl
3bb4ae9906499609769f1774438149a5  numpy-2.1.1-cp313-cp313t-macosx_14_0_x86_64.whl
ff6b9e1993d3d540074736014b1d13af  numpy-2.1.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
749489c091ee9c00abf1ad1ef822c3ca  numpy-2.1.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
32d2daf4064031f365ced5036757ad8b  numpy-2.1.1-cp313-cp313t-musllinux_1_1_x86_64.whl
603dfe4ef56c01e1fc0dcc9d5e3090ed  numpy-2.1.1-cp313-cp313t-musllinux_1_2_aarch64.whl
70fa2d3b78633bb6061c90e17364f27f  numpy-2.1.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
9a430be5d14b689ed051eccc540dfbdc  numpy-2.1.1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
7291ff124e471d32c03464da18ff108d  numpy-2.1.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e56ce141724af119c7c647a8705827a5  numpy-2.1.1-pp310-pypy310_pp73-win_amd64.whl
f63b4750618bfa5490f10cae37fde998  numpy-2.1.1.tar.gz
SHA256
c8a0e34993b510fc19b9a2ce7f31cb8e94ecf6e924a40c0c9dd4f62d0aac47d9  numpy-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl
7dd86dfaf7c900c0bbdcb8b16e2f6ddf1eb1fe39c6c8cca6e94844ed3152a8fd  numpy-2.1.1-cp310-cp310-macosx_11_0_arm64.whl
5889dd24f03ca5a5b1e8a90a33b5a0846d8977565e4ae003a63d22ecddf6782f  numpy-2.1.1-cp310-cp310-macosx_14_0_arm64.whl
59ca673ad11d4b84ceb385290ed0ebe60266e356641428c845b39cd9df6713ab  numpy-2.1.1-cp310-cp310-macosx_14_0_x86_64.whl
13ce49a34c44b6de5241f0b38b07e44c1b2dcacd9e36c30f9c2fcb1bb5135db7  numpy-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
913cc1d311060b1d409e609947fa1b9753701dac96e6581b58afc36b7ee35af6  numpy-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
caf5d284ddea7462c32b8d4a6b8af030b6c9fd5332afb70e7414d7fdded4bfd0  numpy-2.1.1-cp310-cp310-musllinux_1_1_x86_64.whl
57eb525e7c2a8fdee02d731f647146ff54ea8c973364f3b850069ffb42799647  numpy-2.1.1-cp310-cp310-musllinux_1_2_aarch64.whl
9a8e06c7a980869ea67bbf551283bbed2856915f0a792dc32dd0f9dd2fb56728  numpy-2.1.1-cp310-cp310-win32.whl
d10c39947a2d351d6d466b4ae83dad4c37cd6c3cdd6d5d0fa797da56f710a6ae  numpy-2.1.1-cp310-cp310-win_amd64.whl
0d07841fd284718feffe7dd17a63a2e6c78679b2d386d3e82f44f0108c905550  numpy-2.1.1-cp311-cp311-macosx_10_9_x86_64.whl
b5613cfeb1adfe791e8e681128f5f49f22f3fcaa942255a6124d58ca59d9528f  numpy-2.1.1-cp311-cp311-macosx_11_0_arm64.whl
0b8cc2715a84b7c3b161f9ebbd942740aaed913584cae9cdc7f8ad5ad41943d0  numpy-2.1.1-cp311-cp311-macosx_14_0_arm64.whl
b49742cdb85f1f81e4dc1b39dcf328244f4d8d1ded95dea725b316bd2cf18c95  numpy-2.1.1-cp311-cp311-macosx_14_0_x86_64.whl
e8d5f8a8e3bc87334f025194c6193e408903d21ebaeb10952264943a985066ca  numpy-2.1.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d51fc141ddbe3f919e91a096ec739f49d686df8af254b2053ba21a910ae518bf  numpy-2.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
98ce7fb5b8063cfdd86596b9c762bf2b5e35a2cdd7e967494ab78a1fa7f8b86e  numpy-2.1.1-cp311-cp311-musllinux_1_1_x86_64.whl
24c2ad697bd8593887b019817ddd9974a7f429c14a5469d7fad413f28340a6d2  numpy-2.1.1-cp311-cp311-musllinux_1_2_aarch64.whl
397bc5ce62d3fb73f304bec332171535c187e0643e176a6e9421a6e3eacef06d  numpy-2.1.1-cp311-cp311-win32.whl
ae8ce252404cdd4de56dcfce8b11eac3c594a9c16c231d081fb705cf23bd4d9e  numpy-2.1.1-cp311-cp311-win_amd64.whl
7c803b7934a7f59563db459292e6aa078bb38b7ab1446ca38dd138646a38203e  numpy-2.1.1-cp312-cp312-macosx_10_9_x86_64.whl
6435c48250c12f001920f0751fe50c0348f5f240852cfddc5e2f97e007544cbe  numpy-2.1.1-cp312-cp312-macosx_11_0_arm64.whl
3269c9eb8745e8d975980b3a7411a98976824e1fdef11f0aacf76147f662b15f  numpy-2.1.1-cp312-cp312-macosx_14_0_arm64.whl
fac6e277a41163d27dfab5f4ec1f7a83fac94e170665a4a50191b545721c6521  numpy-2.1.1-cp312-cp312-macosx_14_0_x86_64.whl
fcd8f556cdc8cfe35e70efb92463082b7f43dd7e547eb071ffc36abc0ca4699b  numpy-2.1.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
d2b9cd92c8f8e7b313b80e93cedc12c0112088541dcedd9197b5dee3738c1201  numpy-2.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
afd9c680df4de71cd58582b51e88a61feed4abcc7530bcd3d48483f20fc76f2a  numpy-2.1.1-cp312-cp312-musllinux_1_1_x86_64.whl
8661c94e3aad18e1ea17a11f60f843a4933ccaf1a25a7c6a9182af70610b2313  numpy-2.1.1-cp312-cp312-musllinux_1_2_aarch64.whl
950802d17a33c07cba7fd7c3dcfa7d64705509206be1606f196d179e539111ed  numpy-2.1.1-cp312-cp312-win32.whl
3fc5eabfc720db95d68e6646e88f8b399bfedd235994016351b1d9e062c4b270  numpy-2.1.1-cp312-cp312-win_amd64.whl
046356b19d7ad1890c751b99acad5e82dc4a02232013bd9a9a712fddf8eb60f5  numpy-2.1.1-cp313-cp313-macosx_10_13_x86_64.whl
6e5a9cb2be39350ae6c8f79410744e80154df658d5bea06e06e0ac5bb75480d5  numpy-2.1.1-cp313-cp313-macosx_11_0_arm64.whl
d4c57b68c8ef5e1ebf47238e99bf27657511ec3f071c465f6b1bccbef12d4136  numpy-2.1.1-cp313-cp313-macosx_14_0_arm64.whl
8ae0fd135e0b157365ac7cc31fff27f07a5572bdfc38f9c2d43b2aff416cc8b0  numpy-2.1.1-cp313-cp313-macosx_14_0_x86_64.whl
981707f6b31b59c0c24bcda52e5605f9701cb46da4b86c2e8023656ad3e833cb  numpy-2.1.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2ca4b53e1e0b279142113b8c5eb7d7a877e967c306edc34f3b58e9be12fda8df  numpy-2.1.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e097507396c0be4e547ff15b13dc3866f45f3680f789c1a1301b07dadd3fbc78  numpy-2.1.1-cp313-cp313-musllinux_1_1_x86_64.whl
f7506387e191fe8cdb267f912469a3cccc538ab108471291636a96a54e599556  numpy-2.1.1-cp313-cp313-musllinux_1_2_aarch64.whl
251105b7c42abe40e3a689881e1793370cc9724ad50d64b30b358bbb3a97553b  numpy-2.1.1-cp313-cp313-win32.whl
f212d4f46b67ff604d11fff7cc62d36b3e8714edf68e44e9760e19be38c03eb0  numpy-2.1.1-cp313-cp313-win_amd64.whl
920b0911bb2e4414c50e55bd658baeb78281a47feeb064ab40c2b66ecba85553  numpy-2.1.1-cp313-cp313t-macosx_10_13_x86_64.whl
bab7c09454460a487e631ffc0c42057e3d8f2a9ddccd1e60c7bb8ed774992480  numpy-2.1.1-cp313-cp313t-macosx_11_0_arm64.whl
cea427d1350f3fd0d2818ce7350095c1a2ee33e30961d2f0fef48576ddbbe90f  numpy-2.1.1-cp313-cp313t-macosx_14_0_arm64.whl
e30356d530528a42eeba51420ae8bf6c6c09559051887196599d96ee5f536468  numpy-2.1.1-cp313-cp313t-macosx_14_0_x86_64.whl
e8dfa9e94fc127c40979c3eacbae1e61fda4fe71d84869cc129e2721973231ef  numpy-2.1.1-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
910b47a6d0635ec1bd53b88f86120a52bf56dcc27b51f18c7b4a2e2224c29f0f  numpy-2.1.1-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
13cc11c00000848702322af4de0147ced365c81d66053a67c2e962a485b3717c  numpy-2.1.1-cp313-cp313t-musllinux_1_1_x86_64.whl
53e27293b3a2b661c03f79aa51c3987492bd4641ef933e366e0f9f6c9bf257ec  numpy-2.1.1-cp313-cp313t-musllinux_1_2_aarch64.whl
7be6a07520b88214ea85d8ac8b7d6d8a1839b0b5cb87412ac9f49fa934eb15d5  numpy-2.1.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl
52ac2e48f5ad847cd43c4755520a2317f3380213493b9d8a4c5e37f3b87df504  numpy-2.1.1-pp310-pypy310_pp73-macosx_14_0_x86_64.whl
50a95ca3560a6058d6ea91d4629a83a897ee27c00630aed9d933dff191f170cd  numpy-2.1.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
99f4a9ee60eed1385a86e82288971a51e71df052ed0b2900ed30bc840c0f2e39  numpy-2.1.1-pp310-pypy310_pp73-win_amd64.whl
d0cf7d55b1051387807405b3898efafa862997b4cba8aa5dbe657be794afeafd  numpy-2.1.1.tar.gz

v2.1.0

Compare Source

v2.0.2: NumPy 2.0.2 release (Aug 26, 2024)

Compare Source

NumPy 2.0.2 Release Notes

NumPy 2.0.2 is a maintenance release that fixes bugs and regressions
discovered after the 2.0.1 release.

The Python versions supported by this release are 3.9-3.12.

Contributors

A total of 13 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

  • Bruno Oliveira +
  • Charles Harris
  • Chris Sidebottom
  • Christian Heimes +
  • Christopher Sidebottom
  • Mateusz Sokół
  • Matti Picus
  • Nathan Goldbaum
  • Pieter Eendebak
  • Raghuveer Devulapalli
  • Ralf Gommers
  • Sebastian Berg
  • Yair Chuchem +
Pull requests merged

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

  • #​27000: REL: Prepare for the NumPy 2.0.1 release [wheel build]
  • #​27001: MAINT: prepare 2.0.x for further development
  • #​27021: BUG: cfuncs.py: fix crash when sys.stderr is not available
  • #​27022: DOC: Fix migration note for alltrue and sometrue
  • #​27061: BUG: use proper input and output descriptor in array_assign_subscript...
  • #​27073: BUG: Mirror VQSORT_ENABLED logic in Quicksort
  • #​27074: BUG: Bump Highway to latest master
  • #​27077: BUG: Off by one in memory overlap check
  • #​27122: BUG: Use the new npyv_loadable_stride_ functions for ldexp and...
  • #​27126: BUG: Bump Highway to latest
  • #​27128: BUG: add missing error handling in public_dtype_api.c
  • #​27129: BUG: fix another cast setup in array_assign_subscript
  • #​27130: BUG: Fix building NumPy in FIPS mode
  • #​27131: BLD: update vendored Meson for cross-compilation patches
  • #​27146: MAINT: Scipy openblas 0.3.27.44.4
  • #​27151: BUG: Do not accidentally store dtype metadata in np.save
  • #​27195: REV: Revert undef I and document it
  • #​27213: BUG: Fix NPY_RAVEL_AXIS on backwards compatible NumPy 2 builds
  • #​27279: BUG: Fix array_equal for numeric and non-numeric scalar types
Checksums
MD5
ae4bc199b56d20305984b7465d6fbdf1  numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl
ecce0a682c2ccaaa14500b87ffb69f63  numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl
a94f34bec8a62dab95ce9883a87a82a6  numpy-2.0.2-cp310-cp310-macosx_14_0_arm64.whl
a0a26dadf73264d31b7a6952b816d7c8  numpy-2.0.2-cp310-cp310-macosx_14_0_x86_64.whl
972f4366651a1a2ef00f630595104d15  numpy-2.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
6cffef937fe67a3879abefd3d2c40fb8  numpy-2.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
3717a5deda20f465720717a1a7a293a6  numpy-2.0.2-cp310-cp310-musllinux_1_1_x86_64.whl
e31136ecc97bb76b3cb7e86bfc9471ac  numpy-2.0.2-cp310-cp310-musllinux_1_2_aarch64.whl
9703a02ca6b63ca53f83660d089f4294  numpy-2.0.2-cp310-cp310-win32.whl
12c097ef2c7492282a5514b5c4b68784  numpy-2.0.2-cp310-cp310-win_amd64.whl
f11d11bfa3aaf371d2e7fa0160e3208b  numpy-2.0.2-cp311-cp311-macosx_10_9_x86_64.whl
86fc67666fc6e27740fde7dacb19c484  numpy-2.0.2-cp311-cp311-macosx_11_0_arm64.whl
5fd12e0dd7162ea9599c49bbb6e6730e  numpy-2.0.2-cp311-cp311-macosx_14_0_arm64.whl
a40f473db729ea10ae401ce71899120a  numpy-2.0.2-cp311-cp311-macosx_14_0_x86_64.whl
36ea96e0be954896597543d726157eda  numpy-2.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
cfa726b6d5445687020fc4d4f7191e42  numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
dfb9a7b7fe218e931b0dfb885a8250d6  numpy-2.0.2-cp311-cp311-musllinux_1_1_x86_64.whl
d8bf100186e6cd1b2f27eb617ba9e581  numpy-2.0.2-cp311-cp311-musllinux_1_2_aarch64.whl
4fe937eba0fc4d28a65c0ba571c809fc  numpy-2.0.2-cp311-cp311-win32.whl
a9a0f8e1bc4d825272514896e3b17f15  numpy-2.0.2-cp311-cp311-win_amd64.whl
5ef80ec3b2db487d89c590eb301a7aa4  numpy-2.0.2-cp312-cp312-macosx_10_9_x86_64.whl
1bb398d93422bb9baf63c958ed1aa492  numpy-2.0.2-cp312-cp312-macosx_11_0_arm64.whl
cc8d990a1ad3f4d66d0143ea709ccc99  numpy-2.0.2-cp312-cp312-macosx_14_0_arm64.whl
4fee57e854bc3e9a267e865740438d53  numpy-2.0.2-cp312-cp312-macosx_14_0_x86_64.whl
c2c18eef5118607c0b023f6267ee9774  numpy-2.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
2928ed26d7153a488bfb126424d86c8f  numpy-2.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
e32167073981b0a1a419aaaec741773e  numpy-2.0.2-cp312-cp312-musllinux_1_1_x86_64.whl
80a10803a3122472c1bf6c4617d0d1c5  numpy-2.0.2-cp312-cp312-musllinux_1_2_aarch64.whl
39724e27a003b6ce9b1bcbf251e50b4b  numpy-2.0.2-cp312-cp312-win32.whl
8319d0b3d23285d4698cbece73b23fde  numpy-2.0.2-cp312-cp312-win_amd64.whl
da0f655880bbcb53094816b77cd493d1  numpy-2.0.2-cp39-cp39-macosx_10_9_x86_64.whl
47347c028f6ccf47d6a22724111fc96f  numpy-2.0.2-cp39-cp39-macosx_11_0_arm64.whl
26a5c8dec993258522fcef84ef0c040e  numpy-2.0.2-cp39-cp39-macosx_14_0_arm64.whl
fe447af86983ef2262e605a941bd46af  numpy-2.0.2-cp39-cp39-macosx_14_0_x86_64.whl
96477b8563e6d4e2db710f4915a4c5e0  numpy-2.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4e8255cdff60de62944aed1f4235ff68  numpy-2.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
05d8465b87ca983eee044b66bc725391  numpy-2.0.2-cp39-cp39-musllinux_1_1_x86_64.whl
dcf448ef80720bae7de6724f92499754  numpy-2.0.2-cp39-cp39-musllinux_1_2_aarch64.whl
71557f67f24d39db709cc4ccb85ae5b5  numpy-2.0.2-cp39-cp39-win32.whl
f5dc31c5530037c4d1d990696b1d041c  numpy-2.0.2-cp39-cp39-win_amd64.whl
a8f814da1a4509724346c14cd838b5dc  numpy-2.0.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
918f072481d014229dd5f0f5ba75306f  numpy-2.0.2-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
fcbe2e38506fbbbeda509a89063563d3  numpy-2.0.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
b99eff795ca26f8a513aace76a45a356  numpy-2.0.2-pp39-pypy39_pp73-win_amd64.whl
d517a3be706295c4a4c8f75f5ee7b261  numpy-2.0.2.tar.gz
SHA256
51129a29dbe56f9ca83438b706e2e69a39892b5eda6cedcb6b0c9fdc9b0d3ece  numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl
f15975dfec0cf2239224d80e32c3170b1d168335eaedee69da84fbe9f1f9cd04  numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl
8c5713284ce4e282544c68d1c3b2c7161d38c256d2eefc93c1d683cf47683e66  numpy-2.0.2-cp310-cp310-macosx_14_0_arm64.whl
becfae3ddd30736fe1889a37f1f580e245ba79a5855bff5f2a29cb3ccc22dd7b  numpy-2.0.2-cp310-cp310-macosx_14_0_x86_64.whl
2da5960c3cf0df7eafefd806d4e612c5e19358de82cb3c343631188991566ccd  numpy-2.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
496f71341824ed9f3d2fd36cf3ac57ae2e0165c143b55c3a035ee219413f3318  numpy-2.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a61ec659f68ae254e4d237816e33171497e978140353c0c2038d46e63282d0c8  numpy-2.0.2-cp310-cp310-musllinux_1_1_x86_64.whl
d731a1c6116ba289c1e9ee714b08a8ff882944d4ad631fd411106a30f083c326  numpy-2.0.2-cp310-cp310-musllinux_1_2_aarch64.whl
984d96121c9f9616cd33fbd0618b7f08e0cfc9600a7ee1d6fd9b239186d19d97  numpy-2.0.2-cp310-cp310-win32.whl
c7b0be4ef08607dd04da4092faee0b86607f111d5ae68036f16cc787e250a131  numpy-2.0.2-cp310-cp310-win_amd64.whl
49ca4decb342d66018b01932139c0961a8f9ddc7589611158cb3c27cbcf76448  numpy-2.0.2-cp311-cp311-macosx_10_9_x86_64.whl
11a76c372d1d37437857280aa142086476136a8c0f373b2e648ab2c8f18fb195  numpy-2.0.2-cp311-cp311-macosx_11_0_arm64.whl
807ec44583fd708a21d4a11d94aedf2f4f3c3719035c76a2bbe1fe8e217bdc57  numpy-2.0.2-cp311-cp311-macosx_14_0_arm64.whl
8cafab480740e22f8d833acefed5cc87ce276f4ece12fdaa2e8903db2f82897a  numpy-2.0.2-cp311-cp311-macosx_14_0_x86_64.whl
a15f476a45e6e5a3a79d8a14e62161d27ad897381fecfa4a09ed5322f2085669  numpy-2.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
13e689d772146140a252c3a28501da66dfecd77490b498b168b501835041f951  numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
9ea91dfb7c3d1c56a0e55657c0afb38cf1eeae4544c208dc465c3c9f3a7c09f9  numpy-2.0.2-cp311-cp311-musllinux_1_1_x86_64.whl
c1c9307701fec8f3f7a1e6711f9089c06e6284b3afbbcd259f7791282d660a15  numpy-2.0.2-cp311-cp311-musllinux_1_2_aarch64.whl
a392a68bd329eafac5817e5aefeb39038c48b671afd242710b451e76090e81f4  numpy-2.0.2-cp311-cp311-win32.whl
286cd40ce2b7d652a6f22efdfc6d1edf879440e53e76a75955bc0c826c7e64dc  numpy-2.0.2-cp311-cp311-win_amd64.whl
df55d490dea7934f330006d0f81e8551ba6010a5bf035a249ef61a94f21c500b  numpy-2.0.2-cp312-cp312-macosx_10_9_x86_64.whl
8df823f570d9adf0978347d1f926b2a867d5608f434a7cff7f7908c6570dcf5e  numpy-2.0.2-cp312-cp312-macosx_11_0_arm64.whl
9a92ae5c14811e390f3767053ff54eaee3bf84576d99a2456391401323f4ec2c  numpy-2.0.2-cp312-cp312-macosx_14_0_arm64.whl
a842d573724391493a97a62ebbb8e731f8a5dcc5d285dfc99141ca15a3302d0c  numpy-2.0.2-cp312-cp312-macosx_14_0_x86_64.whl
c05e238064fc0610c840d1cf6a13bf63d7e391717d247f1bf0318172e759e692  numpy-2.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
0123ffdaa88fa4ab64835dcbde75dcdf89c453c922f18dced6e27c90d1d0ec5a  numpy-2.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
96a55f64139912d61de9137f11bf39a55ec8faec288c75a54f93dfd39f7eb40c  numpy-2.0.2-cp312-cp312-musllinux_1_1_x86_64.whl
ec9852fb39354b5a45a80bdab5ac02dd02b15f44b3804e9f00c556bf24b4bded  numpy-2.0.2-cp312-cp312-musllinux_1_2_aarch64.whl
671bec6496f83202ed2d3c8fdc486a8fc86942f2e69ff0e986140339a63bcbe5  numpy-2.0.2-cp312-cp312-win32.whl
cfd41e13fdc257aa5778496b8caa5e856dc4896d4ccf01841daee1d96465467a  numpy-2.0.2-cp312-cp312-win_amd64.whl
9059e10581ce4093f735ed23f3b9d283b9d517ff46009ddd485f1747eb22653c  numpy-2.0.2-cp39-cp39-macosx_10_9_x86_64.whl
423e89b23490805d2a5a96fe40ec507407b8ee786d66f7328be214f9679df6dd  numpy-2.0.2-cp39-cp39-macosx_11_0_arm64.whl
2b2955fa6f11907cf7a70dab0d0755159bca87755e831e47932367fc8f2f2d0b  numpy-2.0.2-cp39-cp39-macosx_14_0_arm64.whl
97032a27bd9d8988b9a97a8c4d2c9f2c15a81f61e2f21404d7e8ef00cb5be729  numpy-2.0.2-cp39-cp39-macosx_14_0_x86_64.whl
1e795a8be3ddbac43274f18588329c72939870a16cae810c2b73461c40718ab1  numpy-2.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
f26b258c385842546006213344c50655ff1555a9338e2e5e02a0756dc3e803dd  numpy-2.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
5fec9451a7789926bcf7c2b8d187292c9f93ea30284802a0ab3f5be8ab36865d  numpy-2.0.2-cp39-cp39-musllinux_1_1_x86_64.whl
9189427407d88ff25ecf8f12469d4d39d35bee1db5d39fc5c168c6f088a6956d  numpy-2.0.2-cp39-cp39-musllinux_1_2_aarch64.whl
905d16e0c60200656500c95b6b8dca5d109e23cb24abc701d41c02d74c6b3afa  numpy-2.0.2-cp39-cp39-win32.whl
a3f4ab0caa7f053f6797fcd4e1e25caee367db3112ef2b6ef82d749530768c73  numpy-2.0.2-cp39-cp39-win_amd64.whl
7f0a0c6f12e07fa94133c8a67404322845220c06a9e80e85999afe727f7438b8  numpy-2.0.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
312950fdd060354350ed123c0e25a71327d3711584beaef30cdaa93320c392d4  numpy-2.0.2-pp39-pypy39_pp73-macosx_14_0_x86_64.whl
26df23238872200f63518dd2aa984cfca675d82469535dc7162dc2ee52d9dd5c  numpy-2.0.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
a46288ec55ebbd58947d31d72be2c63cbf839f0a63b49cb755022310792a3385  numpy-2.0.2-pp39-pypy39_pp73-win_amd64.whl
883c987dee1880e2a864ab0dc9892292582510604156762362d9326444636e78  numpy-2.0.2.tar.gz

[v2.0.1](https://redirect.gith

Configuration

📅 Schedule: Branch creation - "on the first day of january" (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.

👻 Immortal: This PR will be recreated if closed unmerged. Get config help if that's undesired.

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

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

@webteam-app
Copy link

Demo starting at https://ubuntu-com-12389.demos.haus

@renovate renovate bot force-pushed the renovate/major-all branch 9 times, most recently from 96b79e6 to 53b1c11 Compare January 9, 2023 18:15
@renovate renovate bot force-pushed the renovate/major-all branch 13 times, most recently from 6b0341a to 5dc1c56 Compare January 17, 2023 10:26
@renovate renovate bot force-pushed the renovate/major-all branch 3 times, most recently from 3164e7c to 7637395 Compare January 18, 2023 01:04
@renovate renovate bot changed the title Update all dependencies (major) chore(deps): update all dependencies (major) Jan 18, 2023
@renovate renovate bot force-pushed the renovate/major-all branch 3 times, most recently from cae4361 to 4bf55b8 Compare January 23, 2023 11:29
@renovate renovate bot changed the title chore(deps): update all dependencies (major) Update all dependencies (major) Aug 23, 2024
@renovate renovate bot force-pushed the renovate/major-all branch 7 times, most recently from d72c3f6 to ab0ef97 Compare August 27, 2024 06:50
@fasih-mehmood fasih-mehmood mentioned this pull request Aug 28, 2024
Merged
@renovate renovate bot changed the title Update all dependencies (major) chore(deps): update all dependencies (major) Aug 28, 2024
@renovate renovate bot changed the title chore(deps): update all dependencies (major) Update all dependencies (major) Sep 4, 2024
@renovate renovate bot changed the title Update all dependencies (major) chore(deps): update all dependencies (major) Sep 5, 2024
@renovate renovate bot force-pushed the renovate/major-all branch 3 times, most recently from bcf4ccb to 8f6fe3b Compare September 12, 2024 03:19
@renovate renovate bot changed the title chore(deps): update all dependencies (major) Update all dependencies (major) Sep 13, 2024
@renovate renovate bot force-pushed the renovate/major-all branch 2 times, most recently from c1abb46 to cf9764e Compare September 17, 2024 05:28
@renovate renovate bot changed the title Update all dependencies (major) chore(deps): update all dependencies (major) Sep 18, 2024
@renovate renovate bot changed the title chore(deps): update all dependencies (major) Update all dependencies (major) Sep 30, 2024
@renovate renovate bot changed the title Update all dependencies (major) chore(deps): update all dependencies (major) Oct 7, 2024
@renovate renovate bot changed the title chore(deps): update all dependencies (major) Update all dependencies (major) Nov 7, 2024
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Reviewers
No reviews
Assignees

@abhigyanghosh30 abhigyanghosh30

@fasih-mehmood fasih-mehmood

Labels
None yet
Projects
None yet
Milestone
No milestone
Development

Successfully merging this pull request may close these issues.
5 participants
@webteam-app @anthonydillon @tbille @abhigyanghosh30 @fasih-mehmood