renovate[bot]
commented
1 year ago ⚠ Artifact update problem
Renovate failed to update an artifact related to this branch. You probably do not want to merge this PR as-is.
♻ Renovate will retry this branch, including artifacts, only when one of the following happens:
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The artifact failure details are included below:
File name: poetry.lock
installing v2 tool python v3.10.7
Using prebuild python
Requirement already satisfied: pip in /opt/buildpack/tools/python/3.10.7/lib/python3.10/site-packages (22.2.2)
Files removed: 1
linking tool python v3.10.7
Python 3.10.7
pip 22.2.2 from /opt/buildpack/tools/python/3.10.7/lib/python3.10/site-packages/pip (python 3.10)
Installed v2 /usr/local/buildpack/tools/v2/python.sh in 16 seconds
skip cleanup, not a docker build: d45b2e7a4ddd
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Collecting lockfile>=0.9
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Installing collected packages: webencodings, pylev, ptyprocess, msgpack, lockfile, distlib, urllib3, tomlkit, six, shellingham, pyrsistent, pyparsing, pycparser, poetry-core, platformdirs, pkginfo, pexpect, more-itertools, jeepney, idna, filelock, crashtest, charset-normalizer, certifi, cachy, attrs, virtualenv, requests, packaging, jsonschema, jaraco.classes, html5lib, dulwich, cleo, cffi, requests-toolbelt, cryptography, cachecontrol, SecretStorage, keyring, poetry-plugin-export, poetry
Successfully installed SecretStorage-3.3.3 attrs-22.1.0 cachecontrol-0.12.11 cachy-0.3.0 certifi-2022.9.24 cffi-1.15.1 charset-normalizer-2.1.1 cleo-1.0.0a5 crashtest-0.3.1 cryptography-38.0.1 distlib-0.3.6 dulwich-0.20.46 filelock-3.8.0 html5lib-1.1 idna-3.4 jaraco.classes-3.2.3 jeepney-0.8.0 jsonschema-4.16.0 keyring-23.9.3 lockfile-0.12.2 more-itertools-8.14.0 msgpack-1.0.4 packaging-21.3 pexpect-4.8.0 pkginfo-1.8.3 platformdirs-2.5.2 poetry-1.2.1 poetry-core-1.2.0 poetry-plugin-export-1.1.1 ptyprocess-0.7.0 pycparser-2.21 pylev-1.4.0 pyparsing-3.0.9 pyrsistent-0.18.1 requests-2.28.1 requests-toolbelt-0.9.1 shellingham-1.5.0 six-1.16.0 tomlkit-0.11.5 urllib3-1.26.12 virtualenv-20.16.5 webencodings-0.5.1
Creating virtualenv skqulacs in /mnt/renovate/gh/Qulacs-Osaka/scikit-qulacs/.venv
Updating dependencies
Resolving dependencies...
The current project's Python requirement (>=3.7.1,<3.11) is not compatible with some of the required packages Python requirement:
- numpy requires Python >=3.8, so it will not be satisfied for Python >=3.7.1,<3.8
- numpy requires Python >=3.8, so it will not be satisfied for Python >=3.7.1,<3.8
- numpy requires Python >=3.8, so it will not be satisfied for Python >=3.7.1,<3.8
- numpy requires Python >=3.8, so it will not be satisfied for Python >=3.7.1,<3.8
Because no versions of numpy match >1.23.0,<1.23.1 || >1.23.1,<1.23.2 || >1.23.2,<1.23.3 || >1.23.3,<1.24.0
and numpy (1.23.0) requires Python >=3.8, numpy is forbidden.
And because numpy (1.23.1) requires Python >=3.8
and numpy (1.23.2) requires Python >=3.8, numpy is forbidden.
So, because numpy (1.23.3) requires Python >=3.8
and skqulacs depends on numpy (~1.23.0), version solving failed.
• Check your dependencies Python requirement: The Python requirement can be specified via the `python` or `markers` properties
For numpy, a possible solution would be to set the `python` property to ">=3.8,<3.11"
For numpy, a possible solution would be to set the `python` property to ">=3.8,<3.11"
For numpy, a possible solution would be to set the `python` property to ">=3.8,<3.11"
For numpy, a possible solution would be to set the `python` property to ">=3.8,<3.11"
https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
https://python-poetry.org/docs/dependency-specification/#using-environment-markers
This PR contains the following updates:
~1.21.0->~1.23.0Release Notes
numpy/numpy
### [`v1.23.3`](https://togithub.com/numpy/numpy/releases/tag/v1.23.3) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.23.2...v1.23.3) ### NumPy 1.23.3 Release Notes NumPy 1.23.3 is a maintenance release that fixes bugs discovered after the 1.23.2 release. There is no major theme for this release, the main improvements are for some downstream builds and some annotation corner cases. The Python versions supported for this release are 3.8-3.11. Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the 10.15 version currently used will no longer be supported by our build infrastructure at that point. #### Contributors A total of 16 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Aaron Meurer - Bas van Beek - Charles Harris - Ganesh Kathiresan - Gavin Zhang + - Iantra Solari+ - Jyn Spring 琴春 + - Matti Picus - Rafael Cardoso Fernandes Sousa - Rafael Sousa + - Ralf Gommers - Rin Cat (鈴猫) + - Saransh Chopra + - Sayed Adel - Sebastian Berg - Serge Guelton #### Pull requests merged A total of 14 pull requests were merged for this release. - [#22136](https://togithub.com/numpy/numpy/pull/22136): BLD: Add Python 3.11 wheels to aarch64 build - [#22148](https://togithub.com/numpy/numpy/pull/22148): MAINT: Update setup.py for Python 3.11. - [#22155](https://togithub.com/numpy/numpy/pull/22155): CI: Test NumPy build against old versions of GCC(6, 7, 8) - [#22156](https://togithub.com/numpy/numpy/pull/22156): MAINT: support IBM i system - [#22195](https://togithub.com/numpy/numpy/pull/22195): BUG: Fix circleci build - [#22214](https://togithub.com/numpy/numpy/pull/22214): BUG: Expose heapsort algorithms in a shared header - [#22215](https://togithub.com/numpy/numpy/pull/22215): BUG: Support using libunwind for backtrack - [#22216](https://togithub.com/numpy/numpy/pull/22216): MAINT: fix an incorrect pointer type usage in f2py - [#22220](https://togithub.com/numpy/numpy/pull/22220): BUG: change overloads to play nice with pyright. - [#22221](https://togithub.com/numpy/numpy/pull/22221): TST,BUG: Use fork context to fix MacOS savez test - [#22222](https://togithub.com/numpy/numpy/pull/22222): TYP,BUG: Reduce argument validation in C-based `__class_getitem__` - [#22223](https://togithub.com/numpy/numpy/pull/22223): TST: ensure `np.equal.reduce` raises a `TypeError` - [#22224](https://togithub.com/numpy/numpy/pull/22224): BUG: Fix the implementation of numpy.array_api.vecdot - [#22230](https://togithub.com/numpy/numpy/pull/22230): BUG: Better report integer division overflow (backport) #### Checksums ##### MD5 a60bf0b1d440bf18d87c49409036d05a 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Notable features are: - Typing changes needed for Python 3.11 - Wheels for Python 3.11.0rc1 The Python versions supported for this release are 3.8-3.11. #### Contributors A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alexander Grund + - Bas van Beek - Charles Harris - Jon Cusick + - Matti Picus - Michael Osthege + - Pal Barta + - Ross Barnowski - Sebastian Berg #### Pull requests merged A total of 15 pull requests were merged for this release. - [#22030](https://togithub.com/numpy/numpy/pull/22030): ENH: Add `__array_ufunc__` typing support to the `nin=1` ufuncs - [#22031](https://togithub.com/numpy/numpy/pull/22031): MAINT, TYP: Fix `np.angle` dtype-overloads - [#22032](https://togithub.com/numpy/numpy/pull/22032): MAINT: Do not let `_GenericAlias` wrap the underlying classes'... - [#22033](https://togithub.com/numpy/numpy/pull/22033): TYP,MAINT: Allow `einsum` subscripts to be passed via integer... - [#22034](https://togithub.com/numpy/numpy/pull/22034): MAINT,TYP: Add object-overloads for the `np.generic` rich comparisons - [#22035](https://togithub.com/numpy/numpy/pull/22035): MAINT,TYP: Allow the `squeeze` and `transpose` method to... - [#22036](https://togithub.com/numpy/numpy/pull/22036): BUG: Fix subarray to object cast ownership details - [#22037](https://togithub.com/numpy/numpy/pull/22037): BUG: Use `Popen` to silently invoke f77 -v - [#22038](https://togithub.com/numpy/numpy/pull/22038): BUG: Avoid errors on NULL during deepcopy - [#22039](https://togithub.com/numpy/numpy/pull/22039): DOC: Add versionchanged for converter callable behavior. - [#22057](https://togithub.com/numpy/numpy/pull/22057): MAINT: Quiet the anaconda uploads. - [#22078](https://togithub.com/numpy/numpy/pull/22078): ENH: reorder includes for testing on top of system installations... - [#22106](https://togithub.com/numpy/numpy/pull/22106): TST: fix test_linear_interpolation_formula_symmetric - [#22107](https://togithub.com/numpy/numpy/pull/22107): BUG: Fix skip condition for test_loss_of_precision\[complex256] - [#22115](https://togithub.com/numpy/numpy/pull/22115): BLD: Build python3.11.0rc1 wheels. #### Checksums 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maintenance release that fixes bugs discovered after the 1.23.0 release. Notable fixes are: - Fix searchsorted for float16 NaNs - Fix compilation on Apple M1 - Fix KeyError in crackfortran operator support (Slycot) The Python version supported for this release are 3.8-3.10. #### 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 - Matthias Koeppe + - Pranab Das + - Rohit Goswami - Sebastian Berg - Serge Guelton - Srimukh Sripada + #### Pull requests merged A total of 8 pull requests were merged for this release. - [#21866](https://togithub.com/numpy/numpy/pull/21866): BUG: Fix discovered MachAr (still used within valgrind) - [#21867](https://togithub.com/numpy/numpy/pull/21867): BUG: Handle NaNs correctly for float16 during sorting - [#21868](https://togithub.com/numpy/numpy/pull/21868): BUG: Use `keepdims` during normalization in `np.average` and... - [#21869](https://togithub.com/numpy/numpy/pull/21869): DOC: mention changes to `max_rows` behaviour in `np.loadtxt` - [#21870](https://togithub.com/numpy/numpy/pull/21870): BUG: Reject non integer array-likes with size 1 in delete - [#21949](https://togithub.com/numpy/numpy/pull/21949): BLD: Make can_link_svml return False for 32bit builds on x86\_64 - [#21951](https://togithub.com/numpy/numpy/pull/21951): BUG: Reorder extern "C" to only apply to function declarations... - [#21952](https://togithub.com/numpy/numpy/pull/21952): BUG: Fix KeyError in crackfortran operator support #### Checksums ##### MD5 79f0d8c114f282b834b49209d6955f98 numpy-1.23.1-cp310-cp310-macosx_10_9_x86_64.whl 42a89a88ef26b768e8933ce46b1cc2bd numpy-1.23.1-cp310-cp310-macosx_11_0_arm64.whl 1c1d68b3483eaf99b9a3583c8ac8bf47 numpy-1.23.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9d3e9f7f9b3dce6cf15209e4f25f346e numpy-1.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a9afb7c34b48d08fc50427ae6516b42d numpy-1.23.1-cp310-cp310-win32.whl a0e02823883bdfcec49309e108f65e13 numpy-1.23.1-cp310-cp310-win_amd64.whl f40cdf4ec7bb0cf31a90a4fa294323c2 numpy-1.23.1-cp38-cp38-macosx_10_9_x86_64.whl 80115a959f0fe30d6c401b2650a61c70 numpy-1.23.1-cp38-cp38-macosx_11_0_arm64.whl 1cf199b3a93960c4f269853a56a8d8eb numpy-1.23.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl aa6f0f192312c79cd770c2c395e9982a numpy-1.23.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d07bee0ea3142a96cb5e4e16aca273ca numpy-1.23.1-cp38-cp38-win32.whl 02d0734ae8ad5e18a40c6c6de18486a0 numpy-1.23.1-cp38-cp38-win_amd64.whl e1ca14acd7d83bc74bdf6ab0bb4bd195 numpy-1.23.1-cp39-cp39-macosx_10_9_x86_64.whl c9152c62b2f31e742e24bfdc97b28666 numpy-1.23.1-cp39-cp39-macosx_11_0_arm64.whl 05b0b37c92f7a7e7c01afac0a5322b40 numpy-1.23.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl d9810bb71a0ef9837e87ea5c44fcab5e numpy-1.23.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4255577f857e838f7a94e3a614ddc5eb numpy-1.23.1-cp39-cp39-win32.whl 787486e3cd87b98024ffe1c969c4db7a numpy-1.23.1-cp39-cp39-win_amd64.whl 5c7b2d1471b1b9ec6ff1cb3fe1f8ac14 numpy-1.23.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 40d5b2ff869707b0d97325ce44631135 numpy-1.23.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 44ce1e07927cc09415df9898857792da numpy-1.23.1-pp38-pypy38_pp73-win_amd64.whl 4f8636a9c1a77ca0fb923ba55378891f numpy-1.23.1.tar.gz ##### SHA256 b15c3f1ed08df4980e02cc79ee058b788a3d0bef2fb3c9ca90bb8cbd5b8a3a04 numpy-1.23.1-cp310-cp310-macosx_10_9_x86_64.whl 9ce242162015b7e88092dccd0e854548c0926b75c7924a3495e02c6067aba1f5 numpy-1.23.1-cp310-cp310-macosx_11_0_arm64.whl e0d7447679ae9a7124385ccf0ea990bb85bb869cef217e2ea6c844b6a6855073 numpy-1.23.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3119daed207e9410eaf57dcf9591fdc68045f60483d94956bee0bfdcba790953 numpy-1.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3ab67966c8d45d55a2bdf40701536af6443763907086c0a6d1232688e27e5447 numpy-1.23.1-cp310-cp310-win32.whl 1865fdf51446839ca3fffaab172461f2b781163f6f395f1aed256b1ddc253622 numpy-1.23.1-cp310-cp310-win_amd64.whl aeba539285dcf0a1ba755945865ec61240ede5432df41d6e29fab305f4384db2 numpy-1.23.1-cp38-cp38-macosx_10_9_x86_64.whl 7e8229f3687cdadba2c4faef39204feb51ef7c1a9b669247d49a24f3e2e1617c numpy-1.23.1-cp38-cp38-macosx_11_0_arm64.whl 68b69f52e6545af010b76516f5daaef6173e73353e3295c5cb9f96c35d755641 numpy-1.23.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 1408c3527a74a0209c781ac82bde2182b0f0bf54dea6e6a363fe0cc4488a7ce7 numpy-1.23.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 47f10ab202fe4d8495ff484b5561c65dd59177949ca07975663f4494f7269e3e numpy-1.23.1-cp38-cp38-win32.whl 37e5ebebb0eb54c5b4a9b04e6f3018e16b8ef257d26c8945925ba8105008e645 numpy-1.23.1-cp38-cp38-win_amd64.whl 173f28921b15d341afadf6c3898a34f20a0569e4ad5435297ba262ee8941e77b numpy-1.23.1-cp39-cp39-macosx_10_9_x86_64.whl 876f60de09734fbcb4e27a97c9a286b51284df1326b1ac5f1bf0ad3678236b22 numpy-1.23.1-cp39-cp39-macosx_11_0_arm64.whl 35590b9c33c0f1c9732b3231bb6a72d1e4f77872390c47d50a615686ae7ed3fd numpy-1.23.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a35c4e64dfca659fe4d0f1421fc0f05b8ed1ca8c46fb73d9e5a7f175f85696bb numpy-1.23.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl c2f91f88230042a130ceb1b496932aa717dcbd665350beb821534c5c7e15881c numpy-1.23.1-cp39-cp39-win32.whl 37ece2bd095e9781a7156852e43d18044fd0d742934833335599c583618181b9 numpy-1.23.1-cp39-cp39-win_amd64.whl 8002574a6b46ac3b5739a003b5233376aeac5163e5dcd43dd7ad062f3e186129 numpy-1.23.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 5d732d17b8a9061540a10fda5bfeabca5785700ab5469a5e9b93aca5e2d3a5fb numpy-1.23.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 55df0f7483b822855af67e38fb3a526e787adf189383b4934305565d71c4b148 numpy-1.23.1-pp38-pypy38_pp73-win_amd64.whl d748ef349bfef2e1194b59da37ed5a29c19ea8d7e6342019921ba2ba4fd8b624 numpy-1.23.1.tar.gz ### [`v1.23.0`](https://togithub.com/numpy/numpy/releases/tag/v1.23.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.22.4...v1.23.0) ### NumPy 1.23.0 Release Notes The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are: - Implementation of `loadtxt` in C, greatly improving its performance. - Exposing DLPack at the Python level for easy data exchange. - Changes to the promotion and comparisons of structured dtypes. - Improvements to f2py. See below for the details, #### New functions - A masked array specialization of `ndenumerate` is now available as `numpy.ma.ndenumerate`. It provides an alternative to `numpy.ndenumerate` and skips masked values by default. ([gh-20020](https://togithub.com/numpy/numpy/pull/20020)) - `numpy.from_dlpack` has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the `__dlpack__` and `__dlpack_device__` methods and returns a ndarray object which is generally the view of the data of the input object. ([gh-21145](https://togithub.com/numpy/numpy/pull/21145)) #### Deprecations - Setting `__array_finalize__` to `None` is deprecated. It must now be a method and may wish to call `super().__array_finalize__(obj)` after checking for `None` or if the NumPy version is sufficiently new. ([gh-20766](https://togithub.com/numpy/numpy/pull/20766)) - Using `axis=32` (`axis=np.MAXDIMS`) in many cases had the same meaning as `axis=None`. This is deprecated and `axis=None` must be used instead. ([gh-20920](https://togithub.com/numpy/numpy/pull/20920)) - The hook function `PyDataMem_SetEventHook` has been deprecated and the demonstration of its use in tool/allocation_tracking has been removed. The ability to track allocations is now built-in to python via `tracemalloc`. ([gh-20394](https://togithub.com/numpy/numpy/pull/20394)) - `numpy.distutils` has been deprecated, as a result of `distutils` itself being deprecated. It will not be present in NumPy for Python >= 3.12, and will be removed completely 2 years after the release of Python 3.12 For more details, see `distutils-status-migration`{.interpreted-text role="ref"}. ([gh-20875](https://togithub.com/numpy/numpy/pull/20875)) - `numpy.loadtxt` will now give a `DeprecationWarning` when an integer `dtype` is requested but the value is formatted as a floating point number. ([gh-21663](https://togithub.com/numpy/numpy/pull/21663)) #### Expired deprecations - The `NpzFile.iteritems()` and `NpzFile.iterkeys()` methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15. ([gh-16830](https://togithub.com/numpy/numpy/pull/16830)) - The `alen` and `asscalar` functions have been removed. ([gh-20414](https://togithub.com/numpy/numpy/pull/20414)) - The `UPDATEIFCOPY` array flag has been removed together with the enum `NPY_ARRAY_UPDATEIFCOPY`. The associated (and deprecated) `PyArray_XDECREF_ERR` was also removed. These were all deprecated in 1.14. They are replaced by `WRITEBACKIFCOPY`, that requires calling `PyArray_ResoveWritebackIfCopy` before the array is deallocated. ([gh-20589](https://togithub.com/numpy/numpy/pull/20589)) - Exceptions will be raised during array-like creation. When an object raised an exception during access of the special attributes `__array__` or `__array_interface__`, this exception was usually ignored. This behaviour was deprecated in 1.21, and the exception will now be raised. ([gh-20835](https://togithub.com/numpy/numpy/pull/20835)) - Multidimensional indexing with non-tuple values is not allowed. Previously, code such as `arr[ind]` where `ind = [[0, 1], [0, 1]]` produced a `FutureWarning` and was interpreted as a multidimensional index (i.e., `arr[tuple(ind)]`). Now this example is treated like an array index over a single dimension (`arr[array(ind)]`). Multidimensional indexing with anything but a tuple was deprecated in NumPy 1.15. ([gh-21029](https://togithub.com/numpy/numpy/pull/21029)) - Changing to a dtype of different size in F-contiguous arrays is no longer permitted. Deprecated since Numpy 1.11.0. See below for an extended explanation of the effects of this change. ([gh-20722](https://togithub.com/numpy/numpy/pull/20722)) #### New Features ##### crackfortran has support for operator and assignment overloading `crackfortran` parser now understands operator and assignment definitions in a module. They are added in the `body` list of the module which contains a new key `implementedby` listing the names of the subroutines or functions implementing the operator or assignment. ([gh-15006](https://togithub.com/numpy/numpy/pull/15006)) ##### f2py supports reading access type attributes from derived type statements As a result, one does not need to use `public` or `private` statements to specify derived type access properties. ([gh-15844](https://togithub.com/numpy/numpy/pull/15844)) ##### New parameter `ndmin` added to `genfromtxt` This parameter behaves the same as `ndmin` from `numpy.loadtxt`. ([gh-20500](https://togithub.com/numpy/numpy/pull/20500)) ##### `np.loadtxt` now supports quote character and single converter function `numpy.loadtxt` now supports an additional `quotechar` keyword argument which is not set by default. Using `quotechar='"'` will read quoted fields as used by the Excel CSV dialect. Further, it is now possible to pass a single callable rather than a dictionary for the `converters` argument. ([gh-20580](https://togithub.com/numpy/numpy/pull/20580)) ##### Changing to dtype of a different size now requires contiguity of only the last axis Previously, viewing an array with a dtype of a different item size required that the entire array be C-contiguous. This limitation would unnecessarily force the user to make contiguous copies of non-contiguous arrays before being able to change the dtype. This change affects not only `ndarray.view`, but other construction mechanisms, including the discouraged direct assignment to `ndarray.dtype`. This change expires the deprecation regarding the viewing of F-contiguous arrays, described elsewhere in the release notes. ([gh-20722](https://togithub.com/numpy/numpy/pull/20722)) ##### Deterministic output files for F2PY For F77 inputs, `f2py` will generate `modname-f2pywrappers.f` unconditionally, though these may be empty. For free-form inputs, `modname-f2pywrappers.f`, `modname-f2pywrappers2.f90` will both be generated unconditionally, and may be empty. This allows writing generic output rules in `cmake` or `meson` and other build systems. Older behavior can be restored by passing `--skip-empty-wrappers` to `f2py`. `f2py-meson`{.interpreted-text role="ref"} details usage. ([gh-21187](https://togithub.com/numpy/numpy/pull/21187)) ##### `keepdims` parameter for `average` The parameter `keepdims` was added to the functions `numpy.average` and `numpy.ma.average`. The parameter has the same meaning as it does in reduction functions such as `numpy.sum` or `numpy.mean`. ([gh-21485](https://togithub.com/numpy/numpy/pull/21485)) ##### New parameter `equal_nan` added to `np.unique` `np.unique` was changed in 1.21 to treat all `NaN` values as equal and return a single `NaN`. Setting `equal_nan=False` will restore pre-1.21 behavior to treat `NaNs` as unique. Defaults to `True`. ([gh-21623](https://togithub.com/numpy/numpy/pull/21623)) #### Compatibility notes ##### 1D `np.linalg.norm` preserves float input types, even for scalar results Previously, this would promote to `float64` when the `ord` argument was not one of the explicitly listed values, e.g. `ord=3`: >>> f32 = np.float32([1, 2]) >>> np.linalg.norm(f32, 2).dtype dtype('float32') >>> np.linalg.norm(f32, 3) dtype('float64') # numpy 1.22 dtype('float32') # numpy 1.23 This change affects only `float32` and `float16` vectors with `ord` other than `-Inf`, `0`, `1`, `2`, and `Inf`. ([gh-17709](https://togithub.com/numpy/numpy/pull/17709)) ##### Changes to structured (void) dtype promotion and comparisons In general, NumPy now defines correct, but slightly limited, promotion for structured dtypes by promoting the subtypes of each field instead of raising an exception: >>> np.result_type(np.dtype("i,i"), np.dtype("i,d")) dtype([('f0', 'Configuration
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