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big-unibo / conversational-olap

Implementation of "COOL: A framework for conversational OLAP". Information Systems 104: 101752 (2022). DOI: https://doi.org/10.1016/j.is.2021.101752
Apache License 2.0
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chore(deps): update dependency numpy to v1.22.4 - autoclosed #361

Closed renovate[bot] closed 3 months ago

renovate[bot] commented 3 months ago

Mend Renovate

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
numpy (source, changelog) ==1.22.1 -> ==1.22.4 age adoption passing confidence

Release Notes

numpy/numpy (numpy) ### [`v1.22.4`](https://togithub.com/numpy/numpy/releases/tag/v1.22.4) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.22.3...v1.22.4) ### NumPy 1.22.4 Release Notes NumPy 1.22.4 is a maintenance release that fixes bugs discovered after the 1.22.3 release. In addition, the wheels for this release are built using the recently released Cython 0.29.30, which should fix the reported problems with [debugging](https://togithub.com/numpy/numpy/issues/21008). The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.15 rather than 10.6 that was used in previous NumPy release cycles. #### Contributors A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alexander Shadchin - Bas van Beek - Charles Harris - Hood Chatham - Jarrod Millman - John-Mark Gurney + - Junyan Ou + - Mariusz Felisiak + - Ross Barnowski - Sebastian Berg - Serge Guelton - Stefan van der Walt #### Pull requests merged A total of 22 pull requests were merged for this release. - [#​21191](https://togithub.com/numpy/numpy/pull/21191): TYP, BUG: Fix `np.lib.stride_tricks` re-exported under the... - [#​21192](https://togithub.com/numpy/numpy/pull/21192): TST: Bump mypy from 0.931 to 0.940 - [#​21243](https://togithub.com/numpy/numpy/pull/21243): MAINT: Explicitly re-export the types in `numpy._typing` - [#​21245](https://togithub.com/numpy/numpy/pull/21245): MAINT: Specify sphinx, numpydoc versions for CI doc builds - [#​21275](https://togithub.com/numpy/numpy/pull/21275): BUG: Fix typos - [#​21277](https://togithub.com/numpy/numpy/pull/21277): ENH, BLD: Fix math feature detection for wasm - [#​21350](https://togithub.com/numpy/numpy/pull/21350): MAINT: Fix failing simd and cygwin tests. - [#​21438](https://togithub.com/numpy/numpy/pull/21438): MAINT: Fix failing Python 3.8 32-bit Windows test. - [#​21444](https://togithub.com/numpy/numpy/pull/21444): BUG: add linux guard per [#​21386](https://togithub.com/numpy/numpy/issues/21386) - [#​21445](https://togithub.com/numpy/numpy/pull/21445): BUG: Allow legacy dtypes to cast to datetime again - [#​21446](https://togithub.com/numpy/numpy/pull/21446): BUG: Make mmap handling safer in frombuffer - [#​21447](https://togithub.com/numpy/numpy/pull/21447): BUG: Stop using PyBytesObject.ob_shash deprecated in Python 3.11. - [#​21448](https://togithub.com/numpy/numpy/pull/21448): ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS - [#​21472](https://togithub.com/numpy/numpy/pull/21472): BUG: Ensure compile errors are raised correclty - [#​21473](https://togithub.com/numpy/numpy/pull/21473): BUG: Fix segmentation fault - [#​21474](https://togithub.com/numpy/numpy/pull/21474): MAINT: Update doc requirements - [#​21475](https://togithub.com/numpy/numpy/pull/21475): MAINT: Mark `npy_memchr` with `no_sanitize("alignment")` on clang - [#​21512](https://togithub.com/numpy/numpy/pull/21512): DOC: Proposal - make the doc landing page cards more similar... - [#​21525](https://togithub.com/numpy/numpy/pull/21525): MAINT: Update Cython version to 0.29.30. - [#​21536](https://togithub.com/numpy/numpy/pull/21536): BUG: Fix GCC error during build configuration - [#​21541](https://togithub.com/numpy/numpy/pull/21541): REL: Prepare for the NumPy 1.22.4 release. - [#​21547](https://togithub.com/numpy/numpy/pull/21547): MAINT: Skip tests that fail on PyPy. #### Checksums ##### MD5 a19351fd3dc0b3bbc733495ed18b8f24 numpy-1.22.4-cp310-cp310-macosx_10_14_x86_64.whl 0730f9e196f70ad89f246bf95ccf05d5 numpy-1.22.4-cp310-cp310-macosx_10_15_x86_64.whl 63c74e5395a2b31d8adc5b1aa0c62471 numpy-1.22.4-cp310-cp310-macosx_11_0_arm64.whl 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The most noticeable fixes may be those for DLPack. One that may cause some problems is disallowing strings as inputs to logical ufuncs. It is still undecided how strings should be treated in those functions and it was thought best to simply disallow them until a decision was reached. That should not cause problems with older code. The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.14 rather than 10.9 that was used in previous NumPy release cycles. 10.14 is the oldest release supported by Apple. #### Contributors A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - [@​GalaxySnail](https://togithub.com/GalaxySnail) + - Alexandre de Siqueira - Bas van Beek - Charles Harris - Melissa Weber Mendonça - Ross Barnowski - Sebastian Berg - Tirth Patel - Matthieu Darbois #### Pull requests merged A total of 10 pull requests were merged for this release. - [#​21048](https://togithub.com/numpy/numpy/pull/21048): MAINT: Use "3.10" instead of "3.10-dev" on travis. - [#​21106](https://togithub.com/numpy/numpy/pull/21106): TYP,MAINT: Explicitly allow sequences of array-likes in `np.concatenate` - [#​21137](https://togithub.com/numpy/numpy/pull/21137): BLD,DOC: skip broken ipython 8.1.0 - [#​21138](https://togithub.com/numpy/numpy/pull/21138): BUG, ENH: np.\_from_dlpack: export correct device information - [#​21139](https://togithub.com/numpy/numpy/pull/21139): BUG: Fix numba DUFuncs added loops getting picked up - [#​21140](https://togithub.com/numpy/numpy/pull/21140): BUG: Fix unpickling an empty ndarray with a non-zero dimension... - [#​21141](https://togithub.com/numpy/numpy/pull/21141): BUG: use ThreadPoolExecutor instead of ThreadPool - [#​21142](https://togithub.com/numpy/numpy/pull/21142): API: Disallow strings in logical ufuncs - [#​21143](https://togithub.com/numpy/numpy/pull/21143): MAINT, DOC: Fix SciPy intersphinx link - [#​21148](https://togithub.com/numpy/numpy/pull/21148): BUG,ENH: np.\_from_dlpack: export arrays with any strided size-1... #### Checksums ##### MD5 14f1872bbab050b0579e5fcd8b341b81 numpy-1.22.3-cp310-cp310-macosx_10_14_x86_64.whl c673faa3ac8745ad10ed0428a21a77aa numpy-1.22.3-cp310-cp310-macosx_11_0_arm64.whl d925fff720561673fd7ee8ead0e94935 numpy-1.22.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 319f97f5ee26b9c3c06f7a2a3df412a3 numpy-1.22.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 866eae5dba934cad50eb38c8505c8449 numpy-1.22.3-cp310-cp310-win32.whl e4c512437a6d4eb4a384225861067ad8 numpy-1.22.3-cp310-cp310-win_amd64.whl a28052af37037f0d5c3b47f4a7040135 numpy-1.22.3-cp38-cp38-macosx_10_14_x86_64.whl d22dc074bde64f6e91a2d1990345f821 numpy-1.22.3-cp38-cp38-macosx_11_0_arm64.whl e8a01c2ca1474aff142366a0a2fe0812 numpy-1.22.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4fe6e71e7871cb31ffc4122aa5707be7 numpy-1.22.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1273fb3c77383ab28f2fb05192751340 numpy-1.22.3-cp38-cp38-win32.whl 001244a6bafa640d7509c85661a4e98e numpy-1.22.3-cp38-cp38-win_amd64.whl b8694b880a1a68d1716f60a9c9e82b38 numpy-1.22.3-cp39-cp39-macosx_10_14_x86_64.whl ba122eaa0988801e250f8674e3dd612e numpy-1.22.3-cp39-cp39-macosx_11_0_arm64.whl 3641825aca07cb26732425e52d034daf numpy-1.22.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl f92412e4273c2580abcc1b75c56e9651 numpy-1.22.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b38604778ffd0a17931c06738c3ce9ed numpy-1.22.3-cp39-cp39-win32.whl 644e0b141fa36a1baf0338032254cc9a numpy-1.22.3-cp39-cp39-win_amd64.whl 99d2dfb943327b108b2c3b923bd42000 numpy-1.22.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3305c27e5bdf7f19247a7eee00ac053e numpy-1.22.3.tar.gz b56530be068796a50bf5a09105c8011e numpy-1.22.3.zip ##### SHA256 92bfa69cfbdf7dfc3040978ad09a48091143cffb778ec3b03fa170c494118d75 numpy-1.22.3-cp310-cp310-macosx_10_14_x86_64.whl 8251ed96f38b47b4295b1ae51631de7ffa8260b5b087808ef09a39a9d66c97ab numpy-1.22.3-cp310-cp310-macosx_11_0_arm64.whl 48a3aecd3b997bf452a2dedb11f4e79bc5bfd21a1d4cc760e703c31d57c84b3e numpy-1.22.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a3bae1a2ed00e90b3ba5f7bd0a7c7999b55d609e0c54ceb2b076a25e345fa9f4 numpy-1.22.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f950f8845b480cffe522913d35567e29dd381b0dc7e4ce6a4a9f9156417d2430 numpy-1.22.3-cp310-cp310-win32.whl 08d9b008d0156c70dc392bb3ab3abb6e7a711383c3247b410b39962263576cd4 numpy-1.22.3-cp310-cp310-win_amd64.whl 201b4d0552831f7250a08d3b38de0d989d6f6e4658b709a02a73c524ccc6ffce numpy-1.22.3-cp38-cp38-macosx_10_14_x86_64.whl f8c1f39caad2c896bc0018f699882b345b2a63708008be29b1f355ebf6f933fe numpy-1.22.3-cp38-cp38-macosx_11_0_arm64.whl 568dfd16224abddafb1cbcce2ff14f522abe037268514dd7e42c6776a1c3f8e5 numpy-1.22.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3ca688e1b9b95d80250bca34b11a05e389b1420d00e87a0d12dc45f131f704a1 numpy-1.22.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e7927a589df200c5e23c57970bafbd0cd322459aa7b1ff73b7c2e84d6e3eae62 numpy-1.22.3-cp38-cp38-win32.whl 07a8c89a04997625236c5ecb7afe35a02af3896c8aa01890a849913a2309c676 numpy-1.22.3-cp38-cp38-win_amd64.whl 2c10a93606e0b4b95c9b04b77dc349b398fdfbda382d2a39ba5a822f669a0123 numpy-1.22.3-cp39-cp39-macosx_10_14_x86_64.whl fade0d4f4d292b6f39951b6836d7a3c7ef5b2347f3c420cd9820a1d90d794802 numpy-1.22.3-cp39-cp39-macosx_11_0_arm64.whl 5bfb1bb598e8229c2d5d48db1860bcf4311337864ea3efdbe1171fb0c5da515d numpy-1.22.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 97098b95aa4e418529099c26558eeb8486e66bd1e53a6b606d684d0c3616b168 numpy-1.22.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl fdf3c08bce27132395d3c3ba1503cac12e17282358cb4bddc25cc46b0aca07aa numpy-1.22.3-cp39-cp39-win32.whl 639b54cdf6aa4f82fe37ebf70401bbb74b8508fddcf4797f9fe59615b8c5813a numpy-1.22.3-cp39-cp39-win_amd64.whl c34ea7e9d13a70bf2ab64a2532fe149a9aced424cd05a2c4ba662fd989e3e45f numpy-1.22.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a906c0b4301a3d62ccf66d058fe779a65c1c34f6719ef2058f96e1856f48bca5 numpy-1.22.3.tar.gz dbc7601a3b7472d559dc7b933b18b4b66f9aa7452c120e87dfb33d02008c8a18 numpy-1.22.3.zip ### [`v1.22.2`](https://togithub.com/numpy/numpy/releases/tag/v1.22.2) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.22.1...v1.22.2) ### NumPy 1.22.2 Release Notes The NumPy 1.22.2 is maintenance release that fixes bugs discovered after the 1.22.1 release. Notable fixes are: - Several build related fixes for downstream projects and other platforms. - Various Annotation fixes/additions. - Numpy wheels for Windows will use the 1.41 tool chain, fixing downstream link problems for projects using NumPy provided libraries on Windows. - Deal with CVE-2021-41495 complaint. The Python versions supported for this release are 3.8-3.10. #### Contributors A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Andrew J. Hesford + - Bas van Beek - Brénainn Woodsend + - Charles Harris - Hood Chatham - Janus Heide + - Leo Singer - Matti Picus - Mukulika Pahari - Niyas Sait - Pearu Peterson - Ralf Gommers - Sebastian Berg - Serge Guelton #### Pull requests merged A total of 21 pull requests were merged for this release. - [#​20842](https://togithub.com/numpy/numpy/pull/20842): BLD: Add NPY_DISABLE_SVML env var to opt out of SVML - [#​20843](https://togithub.com/numpy/numpy/pull/20843): BUG: Fix build of third party extensions with Py_LIMITED_API - [#​20844](https://togithub.com/numpy/numpy/pull/20844): TYP: Fix pyright being unable to infer the `real` and `imag`... - [#​20845](https://togithub.com/numpy/numpy/pull/20845): BUG: Fix comparator function signatures - [#​20906](https://togithub.com/numpy/numpy/pull/20906): BUG: Avoid importing `numpy.distutils` on import numpy.testing - [#​20907](https://togithub.com/numpy/numpy/pull/20907): MAINT: remove outdated mingw32 fseek support - [#​20908](https://togithub.com/numpy/numpy/pull/20908): TYP: Relax the return type of `np.vectorize` - [#​20909](https://togithub.com/numpy/numpy/pull/20909): BUG: fix f2py's define for threading when building with Mingw - [#​20910](https://togithub.com/numpy/numpy/pull/20910): BUG: distutils: fix building mixed C/Fortran extensions - [#​20912](https://togithub.com/numpy/numpy/pull/20912): DOC,TST: Fix Pandas code example as per new release - [#​20935](https://togithub.com/numpy/numpy/pull/20935): TYP, MAINT: Add annotations for `flatiter.__setitem__` - [#​20936](https://togithub.com/numpy/numpy/pull/20936): MAINT, TYP: Added missing where typehints in `fromnumeric.pyi` - [#​20937](https://togithub.com/numpy/numpy/pull/20937): BUG: Fix build_ext interaction with non numpy extensions - [#​20938](https://togithub.com/numpy/numpy/pull/20938): BUG: Fix missing intrinsics for windows/arm64 target - [#​20945](https://togithub.com/numpy/numpy/pull/20945): REL: Prepare for the NumPy 1.22.2 release. - [#​20982](https://togithub.com/numpy/numpy/pull/20982): MAINT: f2py: don't generate code that triggers `-Wsometimes-uninitialized`. - [#​20983](https://togithub.com/numpy/numpy/pull/20983): BUG: Fix incorrect return type in reduce without initial value - [#​20984](https://togithub.com/numpy/numpy/pull/20984): ENH: review return values for PyArray_DescrNew - [#​20985](https://togithub.com/numpy/numpy/pull/20985): MAINT: be more tolerant of setuptools >= 60 - [#​20986](https://togithub.com/numpy/numpy/pull/20986): BUG: Fix misplaced return. - [#​20992](https://togithub.com/numpy/numpy/pull/20992): MAINT: Further small return value validation fixes #### Checksums ##### MD5 2319f8d7c629d0ba3d3d3b1d5605d494 numpy-1.22.2-cp310-cp310-macosx_10_14_x86_64.whl 023c01a6d3aa528f8e88b0837dcab7ed numpy-1.22.2-cp310-cp310-macosx_11_0_arm64.whl 84b36e8893b811d17a19404c68db7ce6 numpy-1.22.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 744da9614e8272a384b542d129cd17a9 numpy-1.22.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl ee012ed5e7c98c6f48026dfa818b2274 numpy-1.22.2-cp310-cp310-win_amd64.whl 73e4fdcf398327bc4241dc38b6d10211 numpy-1.22.2-cp38-cp38-macosx_10_14_x86_64.whl 9fcbca2a614af3b9a37456643ab1c99d numpy-1.22.2-cp38-cp38-macosx_11_0_arm64.whl b7e0d4a19867d33765c7187d1390eef4 numpy-1.22.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl dc8d79d75588737ea77fe85a4f05365a numpy-1.22.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 05906141c095148c53c043c381e6fabe numpy-1.22.2-cp38-cp38-win32.whl 05d3b6d34c0fa031e69ec0476e8d4c9c numpy-1.22.2-cp38-cp38-win_amd64.whl 1449889d856de0e88437fa76d3284e00 numpy-1.22.2-cp39-cp39-macosx_10_14_x86_64.whl e25666ab6ec0692368f328b7b98c27a3 numpy-1.22.2-cp39-cp39-macosx_11_0_arm64.whl 59e3013894bcc6267054c746d9339cf8 numpy-1.22.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 7606b9898c20d2b2aa7fc7018bc9c5cd numpy-1.22.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2686a1495c620e85842967bf8a5f1b2f numpy-1.22.2-cp39-cp39-win32.whl 54432a84807ab69ac3432e6090d5a169 numpy-1.22.2-cp39-cp39-win_amd64.whl 4dbecace42595742485b854b213341b6 numpy-1.22.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 5b506b01ef454f39272ca75de1c7f61c numpy-1.22.2.tar.gz a903008d992b77cb68129173c0f61f60 numpy-1.22.2.zip ##### SHA256 515a8b6edbb904594685da6e176ac9fbea8f73a5ebae947281de6613e27f1956 numpy-1.22.2-cp310-cp310-macosx_10_14_x86_64.whl 76a4f9bce0278becc2da7da3b8ef854bed41a991f4226911a24a9711baad672c numpy-1.22.2-cp310-cp310-macosx_11_0_arm64.whl 168259b1b184aa83a514f307352c25c56af111c269ffc109d9704e81f72e764b numpy-1.22.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3556c5550de40027d3121ebbb170f61bbe19eb639c7ad0c7b482cd9b560cd23b numpy-1.22.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl aafa46b5a39a27aca566198d3312fb3bde95ce9677085efd02c86f7ef6be4ec7 numpy-1.22.2-cp310-cp310-win_amd64.whl 55535c7c2f61e2b2fc817c5cbe1af7cb907c7f011e46ae0a52caa4be1f19afe2 numpy-1.22.2-cp38-cp38-macosx_10_14_x86_64.whl 60cb8e5933193a3cc2912ee29ca331e9c15b2da034f76159b7abc520b3d1233a numpy-1.22.2-cp38-cp38-macosx_11_0_arm64.whl 0b536b6840e84c1c6a410f3a5aa727821e6108f3454d81a5cd5900999ef04f89 numpy-1.22.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 2638389562bda1635b564490d76713695ff497242a83d9b684d27bb4a6cc9d7a numpy-1.22.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6767ad399e9327bfdbaa40871be4254d1995f4a3ca3806127f10cec778bd9896 numpy-1.22.2-cp38-cp38-win32.whl 03ae5850619abb34a879d5f2d4bb4dcd025d6d8fb72f5e461dae84edccfe129f numpy-1.22.2-cp38-cp38-win_amd64.whl d76a26c5118c4d96e264acc9e3242d72e1a2b92e739807b3b69d8d47684b6677 numpy-1.22.2-cp39-cp39-macosx_10_14_x86_64.whl 15efb7b93806d438e3bc590ca8ef2f953b0ce4f86f337ef4559d31ec6cf9d7dd numpy-1.22.2-cp39-cp39-macosx_11_0_arm64.whl badca914580eb46385e7f7e4e426fea6de0a37b9e06bec252e481ae7ec287082 numpy-1.22.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 94dd11d9f13ea1be17bac39c1942f527cbf7065f94953cf62dfe805653da2f8f numpy-1.22.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 8cf33634b60c9cef346663a222d9841d3bbbc0a2f00221d6bcfd0d993d5543f6 numpy-1.22.2-cp39-cp39-win32.whl 59153979d60f5bfe9e4c00e401e24dfe0469ef8da6d68247439d3278f30a180f numpy-1.22.2-cp39-cp39-win_amd64.whl 4a176959b6e7e00b5a0d6f549a479f869829bfd8150282c590deee6d099bbb6e numpy-1.22.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 093d513a460fd94f94c16193c3ef29b2d69a33e482071e3d6d6e561a700587a6 numpy-1.22.2.tar.gz 076aee5a3763d41da6bef9565fdf3cb987606f567cd8b104aded2b38b7b47abf numpy-1.22.2.zip

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