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robertopreste / HmtNote

Human mitochondrial variants annotation using HmtVar.
https://hmtnote.readthedocs.io
MIT License
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Update numpy to 1.23.5 #135

Closed pyup-bot closed 1 year ago

pyup-bot commented 2 years ago

This PR updates numpy from 1.16.4 to 1.23.5.

Changelog ### 1.23.5 ``` the 1.23.4 release and keeps the build infrastructure current. The Python versions supported for this release are 3.8-3.11. Contributors A total of 7 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - \DWesl - Aayush Agrawal + - Adam Knapp + - Charles Harris - Navpreet Singh + - Sebastian Berg - Tania Allard Pull requests merged A total of 10 pull requests were merged for this release. - [22489](https://github.com/numpy/numpy/pull/22489): TST, MAINT: Replace most setup with setup_method (also teardown) - [22490](https://github.com/numpy/numpy/pull/22490): MAINT, CI: Switch to cygwin/cygwin-install-actionv2 - [22494](https://github.com/numpy/numpy/pull/22494): TST: Make test_partial_iteration_cleanup robust but require leak\... - [22592](https://github.com/numpy/numpy/pull/22592): MAINT: Ensure graceful handling of large header sizes - [22593](https://github.com/numpy/numpy/pull/22593): TYP: Spelling alignment for array flag literal - [22594](https://github.com/numpy/numpy/pull/22594): BUG: Fix bounds checking for `random.logseries` - [22595](https://github.com/numpy/numpy/pull/22595): DEV: Update GH actions and Dockerfile for Gitpod - [22596](https://github.com/numpy/numpy/pull/22596): CI: Only fetch in actions/checkout - [22597](https://github.com/numpy/numpy/pull/22597): BUG: Decrement ref count in gentype_reduce if allocated memory\... - [22625](https://github.com/numpy/numpy/pull/22625): BUG: Histogramdd breaks on big arrays in Windows Checksums MD5 8a412b79d975199cefadb465279fd569 numpy-1.23.5-cp310-cp310-macosx_10_9_x86_64.whl 1b56e8e6a0516c78473657abf0710538 numpy-1.23.5-cp310-cp310-macosx_11_0_arm64.whl c787f4763c9a5876e86a17f1651ba458 numpy-1.23.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl db07645022e56747ba3f00c2d742232e numpy-1.23.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl c63a6fb7cc16a13aabc82ec57ac6bb4d numpy-1.23.5-cp310-cp310-win32.whl 3fea9247e1d812600015641941fa273f numpy-1.23.5-cp310-cp310-win_amd64.whl 4222cfb36e5ac9aec348c81b075e2c05 numpy-1.23.5-cp311-cp311-macosx_10_9_x86_64.whl 6c7102f185b310ac70a62c13d46f04e6 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1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a numpy-1.23.5.tar.gz ``` ### 1.23.4 ``` the 1.23.3 release and keeps the build infrastructure current. The main improvements are fixes for some annotation corner cases, a fix for a long time `nested_iters` memory leak, and a fix of complex vector dot for very large arrays. The Python versions supported for this release are 3.8-3.11. Note that the mypy version needs to be 0.981+ if you test using Python 3.10.7, otherwise the typing tests will fail. Contributors A total of 8 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Matthew Barber - Matti Picus - Ralf Gommers - Ross Barnowski - Sebastian Berg - Sicheng Zeng + Pull requests merged A total of 13 pull requests were merged for this release. - [22368](https://github.com/numpy/numpy/pull/22368): BUG: Add `__array_api_version__` to `numpy.array_api` namespace - [22370](https://github.com/numpy/numpy/pull/22370): MAINT: update sde toolkit to 9.0, fix download link - [22382](https://github.com/numpy/numpy/pull/22382): BLD: use macos-11 image on azure, macos-1015 is deprecated - [22383](https://github.com/numpy/numpy/pull/22383): MAINT: random: remove `get_info` from \"extending with Cython\"\... - [22384](https://github.com/numpy/numpy/pull/22384): BUG: Fix complex vector dot with more than NPY_CBLAS_CHUNK elements - [22387](https://github.com/numpy/numpy/pull/22387): REV: Loosen `lookfor`\'s import try/except again - [22388](https://github.com/numpy/numpy/pull/22388): TYP,ENH: Mark `numpy.typing` protocols as runtime checkable - [22389](https://github.com/numpy/numpy/pull/22389): TYP,MAINT: Change more overloads to play nice with pyright - [22390](https://github.com/numpy/numpy/pull/22390): TST,TYP: Bump mypy to 0.981 - [22391](https://github.com/numpy/numpy/pull/22391): DOC: Update delimiter param description. - [22392](https://github.com/numpy/numpy/pull/22392): BUG: Memory leaks in numpy.nested_iters - [22413](https://github.com/numpy/numpy/pull/22413): REL: Prepare for the NumPy 1.23.4 release. - [22424](https://github.com/numpy/numpy/pull/22424): TST: Fix failing aarch64 wheel builds. 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95de7dc7dc47a312f6feddd3da2500826defdccbc41608d0031276a24181a2c0 numpy-1.23.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef numpy-1.23.4-cp39-cp39-win32.whl f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e numpy-1.23.4-cp39-cp39-win_amd64.whl 61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911 numpy-1.23.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810 numpy-1.23.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962 numpy-1.23.4-pp38-pypy38_pp73-win_amd64.whl ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c numpy-1.23.4.tar.gz ``` ### 1.23.3 ``` 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 ``` ### 1.23.2 ``` the 1.23.1 release. 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://github.com/numpy/numpy/pull/22030): ENH: Add `__array_ufunc__` typing support to the `nin=1` ufuncs - [22031](https://github.com/numpy/numpy/pull/22031): MAINT, TYP: Fix `np.angle` dtype-overloads - [22032](https://github.com/numpy/numpy/pull/22032): MAINT: Do not let `_GenericAlias` wrap the underlying classes\'\... - [22033](https://github.com/numpy/numpy/pull/22033): TYP,MAINT: Allow `einsum` subscripts to be passed via integer\... - [22034](https://github.com/numpy/numpy/pull/22034): MAINT,TYP: Add object-overloads for the `np.generic` rich comparisons - [22035](https://github.com/numpy/numpy/pull/22035): MAINT,TYP: Allow the `squeeze` and `transpose` method to\... - [22036](https://github.com/numpy/numpy/pull/22036): BUG: Fix subarray to object cast ownership details - [22037](https://github.com/numpy/numpy/pull/22037): BUG: Use `Popen` to silently invoke f77 -v - [22038](https://github.com/numpy/numpy/pull/22038): BUG: Avoid errors on NULL during deepcopy - [22039](https://github.com/numpy/numpy/pull/22039): DOC: Add versionchanged for converter callable behavior. - [22057](https://github.com/numpy/numpy/pull/22057): MAINT: Quiet the anaconda uploads. - [22078](https://github.com/numpy/numpy/pull/22078): ENH: reorder includes for testing on top of system installations\... - [22106](https://github.com/numpy/numpy/pull/22106): TST: fix test_linear_interpolation_formula_symmetric - [22107](https://github.com/numpy/numpy/pull/22107): BUG: Fix skip condition for test_loss_of_precision\[complex256\] - [22115](https://github.com/numpy/numpy/pull/22115): BLD: Build python3.11.0rc1 wheels. 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c403c81bb8ffb1c993d0165a11493fd4bf1353d258f6997b3ee288b0a48fce77 numpy-1.23.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cf8c6aed12a935abf2e290860af8e77b26a042eb7f2582ff83dc7ed5f963340c numpy-1.23.2-cp39-cp39-win32.whl 5e28cd64624dc2354a349152599e55308eb6ca95a13ce6a7d5679ebff2962913 numpy-1.23.2-cp39-cp39-win_amd64.whl 806970e69106556d1dd200e26647e9bee5e2b3f1814f9da104a943e8d548ca38 numpy-1.23.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 2bd879d3ca4b6f39b7770829f73278b7c5e248c91d538aab1e506c628353e47f numpy-1.23.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl be6b350dfbc7f708d9d853663772a9310783ea58f6035eec649fb9c4371b5389 numpy-1.23.2-pp38-pypy38_pp73-win_amd64.whl b78d00e48261fbbd04aa0d7427cf78d18401ee0abd89c7559bbf422e5b1c7d01 numpy-1.23.2.tar.gz ``` ### 1.23.1 ``` The NumPy 1.23.1 is a 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://github.com/numpy/numpy/pull/21866): BUG: Fix discovered MachAr (still used within valgrind) - [21867](https://github.com/numpy/numpy/pull/21867): BUG: Handle NaNs correctly for float16 during sorting - [21868](https://github.com/numpy/numpy/pull/21868): BUG: Use `keepdims` during normalization in `np.average` and\... - [21869](https://github.com/numpy/numpy/pull/21869): DOC: mention changes to `max_rows` behaviour in `np.loadtxt` - [21870](https://github.com/numpy/numpy/pull/21870): BUG: Reject non integer array-likes with size 1 in delete - [21949](https://github.com/numpy/numpy/pull/21949): BLD: Make can_link_svml return False for 32bit builds on x86_64 - [21951](https://github.com/numpy/numpy/pull/21951): BUG: Reorder extern \"C\" to only apply to function declarations\... - 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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://github.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://github.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://github.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://github.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://github.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://github.com/numpy/numpy/pull/20875)) 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://github.com/numpy/numpy/pull/16830)) - The `alen` and `asscalar` functions have been removed. ([gh-20414](https://github.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://github.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://github.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://github.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://github.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://github.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://github.com/numpy/numpy/pull/15844)) New parameter `ndmin` added to `genfromtxt` This parameter behaves the same as `ndmin` from `numpy.loadtxt`. ([gh-20500](https://github.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://github.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://github.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://github.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://github.com/numpy/numpy/pull/21485)) 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://github.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', '<i4'), ('f1', '<f8')]) For promotion matching field names, order, and titles are enforced, however padding is ignored. Promotion involving structured dtypes now always ensures native byte-order for all fields (which may change the result of `np.concatenate`) and ensures that the result will be \"packed\", i.e. all fields are ordered contiguously and padding is removed. See `structured_dtype_comparison_and_promotion`{.interpreted-text role="ref"} for further details. The `repr` of aligned structures will now never print the long form including `offsets` and `itemsize` unless the structure includes padding not guaranteed by `align=True`. In alignment with the above changes to the promotion logic, the casting safety has been updated: - `"equiv"` enforces matching names and titles. The itemsize is allowed to differ due to padding. - `"safe"` allows mismatching field names and titles - The cast safety is limited by the cast safety of each included field. - The order of fields is used to decide cast safety of each individual field. Previously, the field names were used and only unsafe casts were possible when names mismatched. The main important change here is that name mismatches are now considered \"safe\" casts. ([gh-19226](https://github.com/numpy/numpy/pull/19226)) `NPY_RELAXED_STRIDES_CHECKING` has been removed NumPy cannot be compiled with `NPY_RELAXED_STRIDES_CHECKING=0` anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition. ([gh-20220](https://github.com/numpy/numpy/pull/20220)) `np.loadtxt` has recieved several changes The row counting of `numpy.loadtxt` was fixed. `loadtxt` ignores fully empty lines in the file, but counted them towards `max_rows`. When `max_rows` is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than `max_rows` rows even though more data was available to be read. If the old behaviour is required, `itertools.islice` may be used: import itertools lines = itertools.islice(open("file"), 0, max_rows) result = np.loadtxt(lines, ...) While generally much faster and improved, `numpy.loadtxt` may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are: - Parsing floating point values such as `1.0` into integers will now fail - Parsing hexadecimal floats such as `0x3p3` will fail - An `_` was previously accepted as a thousands delimiter `100_000`. This will now result in an error. If you experience these limitations, they can all be worked around by passing appropriate `converters=`. NumPy now supports passing a single converter to be used for all columns to make this more convenient. For example, `converters=float.fromhex` can read hexadecimal float numbers and `converters=int` will be able to read `100_000`. Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a `ValueError` is now always raised when parsing of a single entry fails. ([gh-20580](https://github.com/numpy/numpy/pull/20580)) Improvements `ndarray.__array_finalize__` is now callable This means subclasses can now use `super().__array_finalize__(obj)` without worrying whether `ndarray` is their superclass or not. The actual call remains a no-op. ([gh-20766](https://github.com/numpy/numpy/pull/20766)) Add support for VSX4/Power10 With VSX4/Power10 enablement, the new instructions available in Power ISA 3.1 can be used to accelerate some NumPy operations, e.g., floor_divide, modulo, etc. ([gh-20821](https://github.com/numpy/numpy/pull/20821)) `np.fromiter` now accepts objects and subarrays The `numpy.fromiter` function now supports object and subarray dtypes. Please see he function documentation for examples. ([gh-20993](https://github.com/numpy/numpy/pull/20993)) Math C library feature detection now uses correct signatures Compiling is preceded by a detection phase to determine whether the underlying libc supports certain math operations. Previously this code did not respect the proper signatures. Fixing this enables compilation for the `wasm-ld` backend (compilation for web assembly) and reduces the number of warnings. ([gh-21154](https://github.com/numpy/numpy/pull/21154)) `np.kron` now maintains subclass information `np.kron` maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs python >>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]]) >>> np.kron(x,x) masked_array( data=[[1, --, --, --], [--, 4, --, --], [--, --, 4, --], [--, --, --, 16]], mask=[[False, True, True, True], [ True, False, True, True], [ True, True, False, True], [ True, True, True, False]], fill_value=999999) :warning: Warning, `np.kron` output now follows `ufunc` ordering (`multiply`) to determine the output class type python >>> class myarr(np.ndarray): >>> __array_priority__ = -1 >>> a = np.ones([2, 2]) >>> ma = myarray(a.shape, a.dtype, a.data) >>> type(np.kron(a, ma)) == np.ndarray False Before it was True >>> type(np.kron(a, ma)) == myarr True ([gh-21262](https://github.com/numpy/numpy/pull/21262)) Performance improvements and changes Faster `np.loadtxt` `numpy.loadtxt` is now generally much faster than previously as most of it is now implemented in C. ([gh-20580](https://github.com/numpy/numpy/pull/20580)) Faster reduction operators Reduction operations like `numpy.sum`, `numpy.prod`, `numpy.add.reduce`, `numpy.logical_and.reduce` on contiguous integer-based arrays are now much faster. ([gh-21001](https://github.com/numpy/numpy/pull/21001)) Faster `np.where` `numpy.where` is now much faster than previously on unpredictable/random input data. ([gh-21130](https://github.com/numpy/numpy/pull/21130)) Faster operations on NumPy scalars Many operations on NumPy scalars are now significantly faster, although rare operations (e.g. with 0-D arrays rather than scalars) may be slower in some cases. However, even with these improvements users who want the best performance for their scalars, may want to convert a known NumPy scalar into a Python one using `scalar.item()`. ([gh-21188](https://github.com/numpy/numpy/pull/21188)) Faster `np.kron` `numpy.kron` is about 80% faster as the product is now computed using broadcasting. ([gh-21354](https://github.com/numpy/numpy/pull/21354)) Checksums MD5 c67b4cc1de8a0753bc65765a508aa0e3 numpy-1.23.0rc1-cp310-cp310-macosx_10_9_x86_64.whl 9daceb162c46298986b5fceb13f10e54 numpy-1.23.0rc1-cp310-cp310-macosx_11_0_arm64.whl ac07046b70001710d8d3243d9b5d0389 numpy-1.23.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 14269d197cd6aac02655d43aa10ba108 numpy-1.23.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl ba5fc5cd776549afc353e0043f6c6f5a numpy-1.23.0rc1-cp310-cp310-win32.whl 0e1b00f156f32aabde1e29607c709a24 numpy-1.23.0rc1-cp310-cp310-win_amd64.whl 34b5a9f3abeb9f6e9c6fbd494305d53d numpy-1.23.0rc1-cp38-cp38-macosx_10_9_x86_64.whl bb825cf372822daad1e440577e324042 numpy-1.23.0rc1-cp38-cp38-macosx_11_0_arm64.whl 45296e6b41691c4265c90562c94254aa numpy-1.23.0rc1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl b10b131e5c0576629ab99829301d6fba numpy-1.23.0rc1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl bc6210254087b73715d8c6a79dafa3b8 numpy-1.23.0rc1-cp38-cp38-win32.whl d9b7fb5a539a738309a717051f13e41a numpy-1.23.0rc1-cp38-cp38-win_amd64.whl d2cec33489c96dfc489bb00353d351fe numpy-1.23.0rc1-cp39-cp39-macosx_10_9_x86_64.whl e4982f3bf3d4acab67cb61d3d0e2f85b numpy-1.23.0rc1-cp39-cp39-macosx_11_0_arm64.whl 781a79ea9f1683a579a1ef27d809a8e0 numpy-1.23.0rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 8c03ea50c2baa172e8252d10dea73498 numpy-1.23.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 61829dec0785cf72b8f5bc92dc44d828 numpy-1.23.0rc1-cp39-cp39-win32.whl 94c7c971ed5eedb1b61a09bcfc123617 numpy-1.23.0rc1-cp39-cp39-win_amd64.whl b5c6f674b468e7fc513882563391efec numpy-1.23.0rc1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 41ab4b757478c8e244018c37bcb52bb3 numpy-1.23.0rc1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f2082734772a6a7afbe3568e7b2ad458 numpy-1.23.0rc1-pp38-pypy38_pp73-win_amd64.whl faf6a08cda5696b96acb670c433495e5 numpy-1.23.0rc1.tar.gz SHA256 a72607e12891615a314a892f8d21301b930f211841f0084d269baa1eb31710b4 numpy-1.23.0rc1-cp310-cp310-macosx_10_9_x86_64.whl 4784a81089c75a941dcc013b09290dfb22768780a1f3525667328d09a3338116 numpy-1.23.0rc1-cp310-cp310-macosx_11_0_arm64.whl d4ebdeb0e2a57bbd28c3258a562ee011775127427eea833613744af1a66c3e11 numpy-1.23.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a2dfb54cb1c6470918a3c02da77706f28977cb7eac4b76cc40b14942c8634615 numpy-1.23.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1567c488f9ef97341c5937b4140a45ac37e0592c43ba2c59d3e49ff7d5da90b0 numpy-1.23.0rc1-cp310-cp310-win32.whl 78df1fc2ddf543508b5358dd24ac68ee693599e5df0d136062b9ec21ba7643cf numpy-1.23.0rc1-cp310-cp310-win_amd64.whl 020c6d8476fced48f42629f46996af8a07bc725cb821081205c4422eacaa2283 numpy-1.23.0rc1-cp38-cp38-macosx_10_9_x86_64.whl efd26eecd1ada0c8dadc5b221c52086ccc72e4cb0707e451889ef3b62c14163c numpy-1.23.0rc1-cp38-cp38-macosx_11_0_arm64.whl 8cf3f61984777a830eef452d8b04338795691949214e6cafc46c5236900cd1f5 numpy-1.23.0rc1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 96738ae39db010502564325ce2f4aff4f42b75adf64f3ccb2b19214e9be1c01c numpy-1.23.0rc1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 33b233f59d9430a27c2a58a056f32259eadf9584f41c6ec02c493c3aeb90f844 numpy-1.23.0rc1-cp38-cp38-win32.whl edf0a82d285e18418e3915fece2cf0f4f31e84fe62271331fbaafbbc7d57e9ee numpy-1.23.0rc1-cp38-cp38-win_amd64.whl a71f1602bf84d0a2fb5d586a2d8c31f29fbca9253ae1eecf46b7059fa265eb79 numpy-1.23.0rc1-cp39-cp39-macosx_10_9_x86_64.whl 05000d27fd135dd0aab90acaf96652991c070dda688739097ac2dea92189f9f0 numpy-1.23.0rc1-cp39-cp39-macosx_11_0_arm64.whl ebe07758ac3e7402290f43d379f6d79d81a247488561743490cf2e5b64351ba6 numpy-1.23.0rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3cd05784cdcd09114c2f6186bb99af7f5ee65ffd720dae9990722a94309b17ea numpy-1.23.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6ccb79435d4501b35ed3d807e1bf7345e42f68b25fbf720ade9c74c7196360f9 numpy-1.23.0rc1-cp39-cp39-win32.whl a8fbe61e09565fa2f7bca076627ea0efbf50ab689c35af5082c5d94fb24b30ee numpy-1.23.0rc1-cp39-cp39-win_amd64.whl 7a45352476e92c1958ce513fa84b508d59dd8e6ffe0e6f6cceebfc0f3c06d086 numpy-1.23.0rc1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 4f15768493ecf23c5d82e5542642a36764e551c7744781268c7c221f26c7ffd6 numpy-1.23.0rc1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl edf0b720c8ba3d35b23c71c0cd13df34290be87b42f0e10d0ec2f1639cda2692 numpy-1.23.0rc1-pp38-pypy38_pp73-win_amd64.whl 3a09d0f564f59b6da54f592909d3fdbd50b492ef9fbe6d699043c992538ba0e0 numpy-1.23.0rc1.tar.gz ``` ### 1.22.4 ``` 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://github.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://github.com/numpy/numpy/pull/21191): TYP, BUG: Fix `np.lib.stride_tricks` re-exported under the\... - [21192](https://github.com/numpy/numpy/pull/21192): TST: Bump mypy from 0.931 to 0.940 - [21243](https://github.com/numpy/numpy/pull/21243): MAINT: Explicitly re-export the types in `numpy._typing` - [21245](https://github.com/numpy/numpy/pull/21245): MAINT: Specify sphinx, numpydoc versions for CI doc builds - [21275](https://github.com/numpy/numpy/pull/21275): BUG: Fix typos - [21277](https://github.com/numpy/numpy/pull/21277): ENH, BLD: Fix math feature detection for wasm - [21350](https://github.com/numpy/numpy/pull/21350): MAINT: Fix failing simd and cygwin tests. - [21438](https://github.com/numpy/numpy/pull/21438): MAINT: Fix failing Python 3.8 32-bit Windows test. - [21444](https://github.com/numpy/numpy/pull/21444): BUG: add linux guard per #21386 - [21445](https://github.com/numpy/numpy/pull/21445): BUG: Allow legacy dtypes to cast to datetime again - [21446](https://github.com/numpy/numpy/pull/21446): BUG: Make mmap handling safer in frombuffer - [21447](https://github.com/numpy/numpy/pull/21447): BUG: Stop using PyBytesObject.ob_shash deprecated in Python 3.11. - [21448](https://github.com/numpy/numpy/pull/21448): ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS - [21472](https://github.com/numpy/numpy/pull/21472): BUG: Ensure compile errors are raised correclty - [21473](https://github.com/numpy/numpy/pull/21473): BUG: Fix segmentation fault - [21474](https://github.com/numpy/numpy/pull/21474): MAINT: Update doc requirements - [21475](https://github.com/numpy/numpy/pull/21475): MAINT: Mark `npy_memchr` with `no_sanitize("alignment")` on clang - [21512](https://github.com/numpy/numpy/pull/21512): DOC: Proposal - make the doc landing page cards more similar\... - [21525](https://github.com/numpy/numpy/pull/21525): MAINT: Update Cython version to 0.29.30. - [21536](https://github.com/numpy/numpy/pull/21536): BUG: Fix GCC error during build configuration - [21541](https://github.com/numpy/numpy/pull/21541): REL: Prepare for the NumPy 1.22.4 release. - [21547](https://github.com/numpy/numpy/pull/21547): MAINT: Skip tests that fail on PyPy. 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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 + - 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://github.com/numpy/numpy/pull/21048): MAINT: Use \"3.10\" instead of \"3.10-dev\" on travis. - [21106](https://github.com/numpy/numpy/pull/21106): TYP,MAINT: Explicitly allow sequences of array-likes in `np.concatenate` - [21137](https://github.com/numpy/numpy/pull/21137): BLD,DOC: skip broken ipython 8.1.0 - [21138](https://github.com/numpy/numpy/pull/21138): BUG, ENH: np.\_from_dlpack: export correct device information - [21139](https://github.com/numpy/numpy/pull/21139): BUG: Fix numba DUFuncs added loops getting picked up - [21140](https://github.com/numpy/numpy/pull/21140): BUG: Fix unpickling an empty ndarray with a non-zero dimension\... - [21141](https://github.com/numpy/numpy/pull/21141): BUG: use ThreadPoolExecutor instead of ThreadPool - [21142](https://github.com/numpy/numpy/pull/21142): API: Disallow strings in logical ufuncs - [21143](https://github.com/numpy/numpy/pull/21143): MAINT, DOC: Fix SciPy intersphinx link - [21148](https://github.com/numpy/numpy/pull/21148): BUG,ENH: np.\_from_dlpack: export arrays with any strided size-1\... 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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 - Mukuli
pyup-bot commented 1 year ago

Closing this in favor of #136