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==1.15->==1.19.5Release Notes
numpy/numpy
### [`v1.19.5`](https://togithub.com/numpy/numpy/releases/v1.19.5) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.19.4...v1.19.5) ### NumPy 1.19.5 Release Notes NumPy 1.19.5 is a short bugfix release. Apart from fixing several bugs, the main improvement is the update to OpenBLAS 0.3.13 that works around the windows 2004 bug while not breaking execution on other platforms. This release supports Python 3.6-3.9 and is planned to be the last release in the 1.19.x cycle. #### Contributors A total of 8 people contributed to this release. People with a \\"+\\" by their names contributed a patch for the first time. - Charles Harris - Christoph Gohlke - Matti Picus - Raghuveer Devulapalli - Sebastian Berg - Simon Graham + - Veniamin Petrenko + - Bernie Gray + #### Pull requests merged A total of 11 pull requests were merged for this release. - [#17756](https://togithub.com/numpy/numpy/pull/17756): BUG: Fix segfault due to out of bound pointer in floatstatus... - [#17774](https://togithub.com/numpy/numpy/pull/17774): BUG: fix np.timedelta64(\\'nat\\').\_\_format\_\_ throwing an exception - [#17775](https://togithub.com/numpy/numpy/pull/17775): BUG: Fixed file handle leak in array_tofile. - [#17786](https://togithub.com/numpy/numpy/pull/17786): BUG: Raise recursion error during dimension discovery - [#17917](https://togithub.com/numpy/numpy/pull/17917): BUG: Fix subarray dtype used with too large count in fromfile - [#17918](https://togithub.com/numpy/numpy/pull/17918): BUG: \\'bool\\' object has no attribute \\'ndim\\' - [#17919](https://togithub.com/numpy/numpy/pull/17919): BUG: ensure \_UFuncNoLoopError can be pickled - [#17924](https://togithub.com/numpy/numpy/pull/17924): BLD: use BUFFERSIZE=20 in OpenBLAS - [#18026](https://togithub.com/numpy/numpy/pull/18026): BLD: update to OpenBLAS 0.3.13 - [#18036](https://togithub.com/numpy/numpy/pull/18036): BUG: make a variable volatile to work around clang compiler bug - [#18114](https://togithub.com/numpy/numpy/pull/18114): REL: Prepare for the NumPy 1.19.5 release. #### Checksums ##### MD5 2651049b70d2ec07d8afd7637f198807 numpy-1.19.5-cp36-cp36m-macosx_10_9_x86_64.whl 71cc7869a54cf55df4699aebe27e9344 numpy-1.19.5-cp36-cp36m-manylinux1_i686.whl 28d23e25c6e6654b2f65218c6e9b3825 numpy-1.19.5-cp36-cp36m-manylinux1_x86_64.whl fb4128d719d72130cbf24baf308761c9 numpy-1.19.5-cp36-cp36m-manylinux2010_i686.whl 0c8edfbbb26823b7495b5371558b1ae5 numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl ad8e6247a175f3a9786eedb4baff7c06 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numpy-1.19.5.tar.gz a76f502430dd98d7546e1ea2250a7360c065a5fdea52b2dffe8ae7180909b6f4 numpy-1.19.5.zip ### [`v1.19.4`](https://togithub.com/numpy/numpy/releases/v1.19.4) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.19.3...v1.19.4) ##### NumPy 1.19.4 Release Notes NumPy 1.19.4 is a quick release to revert the OpenBLAS library version. It was hoped that the 0.3.12 OpenBLAS version used in 1.19.3 would work around the Microsoft fmod bug, but problems in some docker environments turned up. Instead, 1.19.4 will use the older library and run a sanity check on import, raising an error if the problem is detected. Microsoft is aware of the problem and has promised a fix, users should upgrade when it becomes available. This release supports Python 3.6-3.9 ##### Contributors A total of 1 people contributed to this release. 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[`v1.19.3`](https://togithub.com/numpy/numpy/releases/v1.19.3) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.19.2...v1.19.3) # NumPy 1.19.3 Release Notes NumPy 1.19.3 is a small maintenace release with two major improvements: - Python 3.9 binary wheels on all supported platforms. - OpenBLAS fixes for Windows 10 version 2004 fmod bug. This release supports Python 3.6-3.9 and is linked with OpenBLAS 3.7 to avoid some of the fmod problems on Windows 10 version 2004. Microsoft is aware of the problem and users should upgrade when the fix becomes available, the fix here is limited in scope. ## Contributors A total of 8 people contributed to this release. People with a \\"+\\" by their names contributed a patch for the first time. - Charles Harris - Chris Brown + - Daniel Vanzo + - E. Madison Bray + - Hugo van Kemenade + - Ralf Gommers - Sebastian Berg - \\[@danbeibei](https://togithub.com/danbeibei) + ## Pull requests merged A total of 10 pull requests were merged for this release. - [#17298](https://togithub.com/numpy/numpy/pull/17298): BLD: set upper versions for build dependencies - [#17336](https://togithub.com/numpy/numpy/pull/17336): BUG: Set deprecated fields to null in PyArray_InitArrFuncs - [#17446](https://togithub.com/numpy/numpy/pull/17446): ENH: Warn on unsupported Python 3.10+ - [#17450](https://togithub.com/numpy/numpy/pull/17450): MAINT: Update test_requirements.txt. - [#17522](https://togithub.com/numpy/numpy/pull/17522): ENH: Support for the NVIDIA HPC SDK nvfortran compiler - [#17568](https://togithub.com/numpy/numpy/pull/17568): BUG: Cygwin Workaround for #14787 on affected platforms - [#17647](https://togithub.com/numpy/numpy/pull/17647): BUG: Fix memory leak of buffer-info cache due to relaxed strides - [#17652](https://togithub.com/numpy/numpy/pull/17652): MAINT: Backport openblas_support from master. - [#17653](https://togithub.com/numpy/numpy/pull/17653): TST: Add Python 3.9 to the CI testing on Windows, Mac. - [#17660](https://togithub.com/numpy/numpy/pull/17660): TST: Simplify source path names in test_extending. ## Checksums ##### MD5 e5c6c782b2f112c32dcc38242521ec83 numpy-1.19.3-cp36-cp36m-macosx_10_9_x86_64.whl 02323e4a20e14e6f7cded1c55f6a0afe numpy-1.19.3-cp36-cp36m-manylinux1_i686.whl 95f19f0b6c60a755a8454f22eb15f4d6 numpy-1.19.3-cp36-cp36m-manylinux1_x86_64.whl e66cf5ea007a9b567be2b1a901b3d2e0 numpy-1.19.3-cp36-cp36m-manylinux2010_i686.whl 8c7d422f147392bd31f9e5bfc41a170e numpy-1.19.3-cp36-cp36m-manylinux2010_x86_64.whl da02c95dcf0acf7688aebaba7ba2750d numpy-1.19.3-cp36-cp36m-manylinux2014_aarch64.whl 96e6ec05aca18516c8a5961c17a0cac6 numpy-1.19.3-cp36-cp36m-win32.whl 5aa36a829a7ce0a89e6fea502d4fa9ea numpy-1.19.3-cp36-cp36m-win_amd64.whl 9143b46601bc0457dd42795a71ccd2f1 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[`v1.19.2`](https://togithub.com/numpy/numpy/releases/v1.19.2) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.19.1...v1.19.2) # NumPy 1.19.2 Release Notes NumPy 1.19.2 fixes several bugs, prepares for the upcoming Cython 3.x release. and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be used when building with Python 3.9 for testing purposes. There is a known problem with Windows 10 version=2004 and OpenBLAS svd that we are trying to debug. If you are running that Windows version you should use a NumPy version that links to the MKL library, earlier Windows versions are fine. ## Improvements ##### Add NumPy declarations for Cython 3.0 and later The pxd declarations for Cython 3.0 were improved to avoid using deprecated NumPy C-API features. Extension modules built with Cython 3.0+ that use NumPy can now set the C macro `NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION` to avoid C compiler warnings about deprecated API usage. ## Contributors A total of 8 people contributed to this release. People with a \\"+\\" by their names contributed a patch for the first time. - Charles Harris - Matti Picus - Pauli Virtanen - Philippe Ombredanne + - Sebastian Berg - Stefan Behnel + - Stephan Loyd + - Zac Hatfield-Dodds ## Pull requests merged A total of 9 pull requests were merged for this release. - [#16959](https://togithub.com/numpy/numpy/pull/16959): TST: Change aarch64 to arm64 in travis.yml. - [#16998](https://togithub.com/numpy/numpy/pull/16998): MAINT: Configure hypothesis in `np.test()` for determinism,... - [#17000](https://togithub.com/numpy/numpy/pull/17000): BLD: pin setuptools \\< 49.2.0 - [#17015](https://togithub.com/numpy/numpy/pull/17015): ENH: Add NumPy declarations to be used by Cython 3.0+ - [#17125](https://togithub.com/numpy/numpy/pull/17125): BUG: Remove non-threadsafe sigint handling from fft calculation - [#17243](https://togithub.com/numpy/numpy/pull/17243): BUG: core: fix ilp64 blas dot/vdot/... for strides > int32 max - [#17244](https://togithub.com/numpy/numpy/pull/17244): DOC: Use SPDX license expressions with correct license - [#17245](https://togithub.com/numpy/numpy/pull/17245): DOC: Fix the link to the quick-start in the old API functions - [#17272](https://togithub.com/numpy/numpy/pull/17272): BUG: fix pickling of arrays larger than 2GiB ## Checksums ##### MD5 b74295cbb5b1c98f46f26e13c0fca0ea numpy-1.19.2-cp36-cp36m-macosx_10_9_x86_64.whl 3e307eca6c448bbe30e4c1dc99824642 numpy-1.19.2-cp36-cp36m-manylinux1_i686.whl bfe6c2053a7a792097df912d1175ef7e numpy-1.19.2-cp36-cp36m-manylinux1_x86_64.whl 3b61953b421460abc7d2ecb4df4060bc numpy-1.19.2-cp36-cp36m-manylinux2010_i686.whl 7c442b7c5af62bd5be669bf6c360e114 numpy-1.19.2-cp36-cp36m-manylinux2010_x86_64.whl f6eaf46804f0d66c123fa7ff728b178e numpy-1.19.2-cp36-cp36m-manylinux2014_aarch64.whl 30bbe0bcd774ab483c7494d1cf827199 numpy-1.19.2-cp36-cp36m-win32.whl cf54372ccde7de333d7b69cd16abfa70 numpy-1.19.2-cp36-cp36m-win_amd64.whl 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This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be used when building with Python 3.9 for testing purposes. ## Contributors A total of 15 people contributed to this release. People with a \\"+\\" by their names contributed a patch for the first time. - Abhinav Reddy + - Anirudh Subramanian - Antonio Larrosa + - Charles Harris - Chunlin Fang - Eric Wieser - Etienne Guesnet + - Kevin Sheppard - Matti Picus - Raghuveer Devulapalli - Roman Yurchak - Ross Barnowski - Sayed Adel - Sebastian Berg - Tyler Reddy ## Pull requests merged A total of 25 pull requests were merged for this release. - [#16649](https://togithub.com/numpy/numpy/pull/16649): MAINT, CI: disable Shippable cache - [#16652](https://togithub.com/numpy/numpy/pull/16652): MAINT: Replace PyUString_GET_SIZE with PyUnicode_GetLength. - [#16654](https://togithub.com/numpy/numpy/pull/16654): REL: Fix outdated docs link - [#16656](https://togithub.com/numpy/numpy/pull/16656): BUG: raise IEEE exception on AIX - [#16672](https://togithub.com/numpy/numpy/pull/16672): BUG: Fix bug in AVX complex absolute while processing array of... - [#16693](https://togithub.com/numpy/numpy/pull/16693): TST: Add extra debugging information to CPU features detection - [#16703](https://togithub.com/numpy/numpy/pull/16703): BLD: Add CPU entry for Emscripten / WebAssembly - [#16705](https://togithub.com/numpy/numpy/pull/16705): TST: Disable Python 3.9-dev testing. - [#16714](https://togithub.com/numpy/numpy/pull/16714): MAINT: Disable use_hugepages in case of ValueError - [#16724](https://togithub.com/numpy/numpy/pull/16724): BUG: Fix PyArray_SearchSorted signature. - [#16768](https://togithub.com/numpy/numpy/pull/16768): MAINT: Fixes for deprecated functions in scalartypes.c.src - [#16772](https://togithub.com/numpy/numpy/pull/16772): MAINT: Remove unneeded call to PyUnicode_READY - [#16776](https://togithub.com/numpy/numpy/pull/16776): MAINT: Fix deprecated functions in scalarapi.c - [#16779](https://togithub.com/numpy/numpy/pull/16779): BLD, ENH: Add RPATH support for AIX - [#16780](https://togithub.com/numpy/numpy/pull/16780): BUG: Fix default fallback in genfromtxt - [#16784](https://togithub.com/numpy/numpy/pull/16784): BUG: Added missing return after raising error in methods.c - [#16795](https://togithub.com/numpy/numpy/pull/16795): BLD: update cython to 0.29.21 - [#16832](https://togithub.com/numpy/numpy/pull/16832): MAINT: setuptools 49.2.0 emits a warning, avoid it - [#16872](https://togithub.com/numpy/numpy/pull/16872): BUG: Validate output size in bin- and multinomial - [#16875](https://togithub.com/numpy/numpy/pull/16875): BLD, MAINT: Pin setuptools - [#16904](https://togithub.com/numpy/numpy/pull/16904): DOC: Reconstruct Testing Guideline. - [#16905](https://togithub.com/numpy/numpy/pull/16905): TST, BUG: Re-raise MemoryError exception in test_large_zip\\'s... - [#16906](https://togithub.com/numpy/numpy/pull/16906): BUG, DOC: Fix bad MPL kwarg. - [#16916](https://togithub.com/numpy/numpy/pull/16916): BUG: Fix string/bytes to complex assignment - [#16922](https://togithub.com/numpy/numpy/pull/16922): REL: Prepare for NumPy 1.19.1 release ## 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b8456987b637232602ceb4d663cb34106f7eb780e247d51a260b84760fd8f491 numpy-1.19.1.zip ### [`v1.19.0`](https://togithub.com/numpy/numpy/releases/v1.19.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.18.5...v1.19.0) # NumPy 1.19.0 Release Notes This NumPy release is marked by the removal of much technical debt: support for Python 2 has been removed, many deprecations have been expired, and documentation has been improved. The polishing of the random module continues apace with bug fixes and better usability from Cython. The Python versions supported for this release are 3.6-3.8. Downstream developers should use Cython >= 0.29.16 for Python 3.8 support and OpenBLAS >= 3.7 to avoid problems on the Skylake architecture. ## Highlights - Code compatibility with Python versions \\< 3.6 (including Python 2) was dropped from both the python and C code. The shims in `numpy.compat` will remain to support third-party packages, but they may be deprecated in a future release. Note that 1.19.x will _not_ compile with earlier versions of Python due to the use of f-strings. ([gh-15233](https://togithub.com/numpy/numpy/pull/15233)) ## Expired deprecations ##### `numpy.insert` and `numpy.delete` can no longer be passed an axis on 0d arrays This concludes a deprecation from 1.9, where when an `axis` argument was passed to a call to `~numpy.insert` and `~numpy.delete` on a 0d array, the `axis` and `obj` argument and indices would be completely ignored. In these cases, `insert(arr, "nonsense", 42, axis=0)` would actually overwrite the entire array, while `delete(arr, "nonsense", axis=0)` would be `arr.copy()` Now passing `axis` on a 0d array raises `~numpy.AxisError`. ([gh-15802](https://togithub.com/numpy/numpy/pull/15802)) ##### `numpy.delete` no longer ignores out-of-bounds indices This concludes deprecations from 1.8 and 1.9, where `np.delete` would ignore both negative and out-of-bounds items in a sequence of indices. This was at odds with its behavior when passed a single index. Now out-of-bounds items throw `IndexError`, and negative items index from the end. ([gh-15804](https://togithub.com/numpy/numpy/pull/15804)) ##### `numpy.insert` and `numpy.delete` no longer accept non-integral indices This concludes a deprecation from 1.9, where sequences of non-integers indices were allowed and cast to integers. Now passing sequences of non-integral indices raises `IndexError`, just like it does when passing a single non-integral scalar. ([gh-15805](https://togithub.com/numpy/numpy/pull/15805)) ##### `numpy.delete` no longer casts boolean indices to integers This concludes a deprecation from 1.8, where `np.delete` would cast boolean arrays and scalars passed as an index argument into integer indices. The behavior now is to treat boolean arrays as a mask, and to raise an error on boolean scalars. ([gh-15815](https://togithub.com/numpy/numpy/pull/15815)) ## Compatibility notes ##### Changed random variate stream from `numpy.random.Generator.dirichlet` A bug in the generation of random variates for the Dirichlet distribution with small \\'alpha\\' values was fixed by using a different algorithm when `max(alpha) < 0.1`. Because of the change, the stream of variates generated by `dirichlet` in this case will be different from previous releases. ([gh-14924](https://togithub.com/numpy/numpy/pull/14924)) ##### Scalar promotion in `PyArray_ConvertToCommonType` The promotion of mixed scalars and arrays in `PyArray_ConvertToCommonType` has been changed to adhere to those used by `np.result_type`. This means that input such as `(1000, np.array([1], dtype=np.uint8)))` will now return `uint16` dtypes. In most cases the behaviour is unchanged. Note that the use of this C-API function is generally discouraged. This also fixes `np.choose` to behave the same way as the rest of NumPy in this respect. ([gh-14933](https://togithub.com/numpy/numpy/pull/14933)) ##### Fasttake and fastputmask slots are deprecated and NULL\\'ed The fasttake and fastputmask slots are now never used and must always be set to NULL. This will result in no change in behaviour. However, if a user dtype should set one of these a DeprecationWarning will be given. ([gh-14942](https://togithub.com/numpy/numpy/pull/14942)) ##### `np.ediff1d` casting behaviour with `to_end` and `to_begin` `np.ediff1d` now uses the `"same_kind"` casting rule for its additional `to_end` and `to_begin` arguments. This ensures type safety except when the input array has a smaller integer type than `to_begin` or `to_end`. In rare cases, the behaviour will be more strict than it was previously in 1.16 and 1.17. This is necessary to solve issues with floating point NaN. ([gh-14981](https://togithub.com/numpy/numpy/pull/14981)) ##### Converting of empty array-like objects to NumPy arrays Objects with `len(obj) == 0` which implement an \\"array-like\\" interface, meaning an object implementing `obj.__array__()`, `obj.__array_interface__`, `obj.__array_struct__`, or the python buffer interface and which are also sequences (i.e. Pandas objects) will now always retain there shape correctly when converted to an array. If such an object has a shape of `(0, 1)` previously, it could be converted into an array of shape `(0,)` (losing all dimensions after the first 0). ([gh-14995](https://togithub.com/numpy/numpy/pull/14995)) ##### Removed `multiarray.int_asbuffer` As part of the continued removal of Python 2 compatibility, `multiarray.int_asbuffer` was removed. On Python 3, it threw a `NotImplementedError` and was unused internally. It is expected that there are no downstream use cases for this method with Python 3. ([gh-15229](https://togithub.com/numpy/numpy/pull/15229)) ##### `numpy.distutils.compat` has been removed This module contained only the function `get_exception()`, which was used as: try: ... except Exception: e = get_exception() Its purpose was to handle the change in syntax introduced in Python 2.6, from `except Exception, e:` to `except Exception as e:`, meaning it was only necessary for codebases supporting Python 2.5 and older. ([gh-15255](https://togithub.com/numpy/numpy/pull/15255)) ##### `issubdtype` no longer interprets `float` as `np.floating` `numpy.issubdtype` had a FutureWarning since NumPy 1.14 which has expired now. This means that certain input where the second argument was neither a datatype nor a NumPy scalar type (such as a string or a python type like `int` or `float`) will now be consistent with passing in `np.dtype(arg2).type`. This makes the result consistent with expectations and leads to a false result in some cases which previously returned true. ([gh-15773](https://togithub.com/numpy/numpy/pull/15773)) ##### Change output of `round` on scalars to be consistent with Python Output of the `__round__` dunder method and consequently the Python built-in `round` has been changed to be a Python `int` to be consistent with calling it on Python `float` objects when called with no arguments. Previously, it would return a scalar of the `np.dtype` that was passed in. ([gh-15840](https://togithub.com/numpy/numpy/pull/15840)) ##### The `numpy.ndarray` constructor no longer interprets `strides=()` as `strides=None` The former has changed to have the expected meaning of setting `numpy.ndarray.strides` to `()`, while the latter continues to result in strides being chosen automatically. ([gh-15882](https://togithub.com/numpy/numpy/pull/15882)) ##### C-Level string to datetime casts changed The C-level casts from strings were simplified. This changed also fixes string to datetime and timedelta casts to behave correctly (i.e. like Python casts using `string_arr.astype("M8")` while previously the cast would behave like `string_arr.astype(np.int_).astype("M8")`. This only affects code using low-level C-API to do manual casts (not full array casts) of single scalar values or using e.g. `PyArray_GetCastFunc`, and should thus not affect the vast majority of users. ([gh-16068](https://togithub.com/numpy/numpy/pull/16068)) ##### `SeedSequence` with small seeds no longer conflicts with spawning Small seeds (less than `2**96`) were previously implicitly 0-padded out to 128 bits, the size of the internal entropy pool. When spawned, the spawn key was concatenated before the 0-padding. Since the first spawn key is `(0,)`, small seeds before the spawn created the same states as the first spawned `SeedSequence`. Now, the seed is explicitly 0-padded out to the internal pool size before concatenating the spawn key. Spawned `SeedSequences` will produce different results than in the previous release. Unspawned `SeedSequences` will still produce the same results. ([gh-16551](https://togithub.com/numpy/numpy/pull/16551)) ## Deprecations ##### Deprecate automatic `dtype=object` for ragged input Calling `np.array([[1, [1, 2, 3]])` will issue a `DeprecationWarning` as per [NEP 34](https://numpy.org/neps/nep-0034.html). Users should explicitly use `dtype=object` to avoid the warning. ([gh-15119](https://togithub.com/numpy/numpy/pull/15119)) ##### Passing `shape=0` to factory functions in `numpy.rec` is deprecated `0` is treated as a special case and is aliased to `None` in the functions: - `numpy.core.records.fromarrays` - `numpy.core.records.fromrecords` - `numpy.core.records.fromstring` - `numpy.core.records.fromfile` In future, `0` will not be special cased, and will be treated as an array length like any other integer. ([gh-15217](https://togithub.com/numpy/numpy/pull/15217)) ##### Deprecation of probably unused C-API functions The following C-API functions are probably unused and have been deprecated: - `PyArray_GetArrayParamsFromObject` - `PyUFunc_GenericFunction` - `PyUFunc_SetUsesArraysAsData` In most cases `PyArray_GetArrayParamsFromObject` should be replaced by converting to an array, while `PyUFunc_GenericFunction` can be replaced with `PyObject_Call` (see documentation for details). ([gh-15427](https://togithub.com/numpy/numpy/pull/15427)) ##### Converting certain types to dtypes is Deprecated The super classes of scalar types, such as `np.integer`, `np.generic`, or `np.inexact` will now give a deprecation warning when converted to a dtype (or used in a dtype keyword argument). The reason for this is that `np.integer` is converted to `np.int_`, while it would be expected to represent _any_ integer (e.g. also `int8`, `int16`, etc. For example, `dtype=np.floating` is currently identical to `dtype=np.float64`, even though also `np.float32` is a subclass of `np.floating`. ([gh-15534](https://togithub.com/numpy/numpy/pull/15534)) ##### Deprecation of `round` for `np.complexfloating` scalars Output of the `__round__` dunder method and consequently the Python built-in `round` has been deprecated on complex scalars. This does not affect `np.round`. ([gh-15840](https://togithub.com/numpy/numpy/pull/15840)) ##### `numpy.ndarray.tostring()` is deprecated in favor of `tobytes()` `~numpy.ndarray.tobytes` has existed since the 1.9 release, but until this release `~numpy.ndarray.tostring` emitted no warning. The change to emit a warning brings NumPy in line with the builtin `array.array` methods of the same name. ([gh-15867](https://togithub.com/numpy/numpy/pull/15867)) ## C API changes ##### Better support for `const` dimensions in API functions The following functions now accept a constant array of `npy_intp`: - `PyArray_BroadcastToShape` - `PyArray_IntTupleFromIntp` - `PyArray_OverflowMultiplyList` Previously the caller would have to cast away the const-ness to call these functions. ([gh-15251](https://togithub.com/numpy/numpy/pull/15251)) ##### Const qualify UFunc inner loops `UFuncGenericFunction` now expects pointers to const `dimension` and `strides` as arguments. This means inner loops may no longer modify either `dimension` or `strides`. This change leads to an `incompatible-pointer-types` warning forcing users to either ignore the compiler warnings or to const qualify their own loop signatures. ([gh-15355](https://togithub.com/numpy/numpy/pull/15355)) ## New Features ##### `numpy.frompyfunc` now accepts an identity argument This allows the `` `numpy.ufunc.identity``{.interpreted-text role="attr"}[ attribute to be set on the resulting ufunc, meaning it can be used for empty and multi-dimensional calls to :meth:]{.title-ref}[numpy.ufunc.reduce]{.title-ref}\`. ([gh-8255](https://togithub.com/numpy/numpy/pull/8255)) ##### `np.str_` scalars now support the buffer protocol `np.str_` arrays are always stored as UCS4, so the corresponding scalars now expose this through the buffer interface, meaning `memoryview(np.str_('test'))` now works. ([gh-15385](https://togithub.com/numpy/numpy/pull/15385)) ##### `subok` option for `numpy.copy` A new kwarg, `subok`, was added to `numpy.copy` to allow users to toggle the behavior of `numpy.copy` with respect to array subclasses. The default value is `False` which is consistent with the behavior of `numpy.copy` for previous numpy versions. To create a copy that preserves an array subclass with `numpy.copy`, call `np.copy(arr, subok=True)`. This addition better documents that the default behavior of `numpy.copy` differs from the `numpy.ndarray.copy` method which respects array subclasses by default. ([gh-15685](https://togithub.com/numpy/numpy/pull/15685)) ##### `numpy.linalg.multi_dot` now accepts an `out` argument `out` can be used to avoid creating unnecessary copies of the final product computed by `numpy.linalg.multidot`. ([gh-15715](https://togithub.com/numpy/numpy/pull/15715)) ##### `keepdims` parameter for `numpy.count_nonzero` The parameter `keepdims` was added to `numpy.count_nonzero`. The parameter has the same meaning as it does in reduction functions such as `numpy.sum` or `numpy.mean`. ([gh-15870](https://togithub.com/numpy/numpy/pull/15870)) ##### `equal_nan` parameter for `numpy.array_equal` The keyword argument `equal_nan` was added to `numpy.array_equal`. `equal_nan` is a boolean value that toggles whether or not `nan` values are considered equal in comparison (default is `False`). This matches API used in related functions such as `numpy.isclose` and `numpy.allclose`. ([gh-16128](https://togithub.com/numpy/numpy/pull/16128)) ## Improvements ## Improve detection of CPU features Replace `npy_cpu_supports` which was a gcc specific mechanism to test support of AVX with more general functions `npy_cpu_init` and `npy_cpu_have`, and expose the results via a `NPY_CPU_HAVE` c-macro as well as a python-level `__cpu_features__` dictionary. ([gh-13421](https://togithub.com/numpy/numpy/pull/13421)) ##### Use 64-bit integer size on 64-bit platforms in fallback lapack_lite Use 64-bit integer size on 64-bit platforms in the fallback LAPACK library, which is used when the system has no LAPACK installed, allowing it to deal with linear algebra for large arrays. ([gh-15218](https://togithub.com/numpy/numpy/pull/15218)) ##### Use AVX512 intrinsic to implement `np.exp` when input is `np.float64` Use AVX512 intrinsic to implement `np.exp` when input is `np.float64`, which can improve the performance of `np.exp` with `np.float64` input 5-7x faster than before. The `_multiarray_umath.so` module has grown about 63 KB on linux64. ([gh-15648](https://togithub.com/numpy/numpy/pull/15648)) ##### Ability to disable madvise hugepages On Linux NumPy has previously added support for madavise hugepages which can improve performance for very large arrays. Unfortunately, on older Kernel versions this led to peformance regressions, thus by default the support has been disabled on kernels before version 4.6. To override the default, you can use the environment variable: NUMPY_MADVISE_HUGEPAGE=0 or set it to 1 to force enabling support. Note that this only makes a difference if the operating system is set up to use madvise transparent hugepage. ([gh-15769](https://togithub.com/numpy/numpy/pull/15769)) ##### `numpy.einsum` accepts NumPy `int64` type in sRenovate configuration
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