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==1.20.3->==1.21.2Release Notes
numpy/numpy
### [`v1.21.2`](https://togithub.com/numpy/numpy/releases/v1.21.2) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.21.1...v1.21.2) # NumPy 1.21.2 Release Notes The NumPy 1.21.2 is maintenance release that fixes bugs discovered after 1.21.1. It also provides 64 bit manylinux Python 3.10.0rc1 wheels for downstream testing. Note that Python 3.10 is not yet final. There is also preliminary support for Windows on ARM64 builds, but there is no OpenBLAS for that platform and no wheels are available. The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with Python 3.10.0rc1 and Python 3.10 will be officially supported after it is released. The previous problems with gcc-11.1 have been fixed by gcc-11.2, check your version if you are using gcc-11. ## Contributors A total of 10 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Carl Johnsen + - Charles Harris - Gwyn Ciesla + - Matthieu Dartiailh - Matti Picus - Niyas Sait + - Ralf Gommers - Sayed Adel - Sebastian Berg ## Pull requests merged A total of 18 pull requests were merged for this release. - [#19497](https://togithub.com/numpy/numpy/pull/19497): MAINT: set Python version for 1.21.x to `<3.11` - [#19533](https://togithub.com/numpy/numpy/pull/19533): BUG: Fix an issue wherein importing `numpy.typing` could raise - [#19646](https://togithub.com/numpy/numpy/pull/19646): MAINT: Update Cython version for Python 3.10. - [#19648](https://togithub.com/numpy/numpy/pull/19648): TST: Bump the python 3.10 test version from beta4 to rc1 - [#19651](https://togithub.com/numpy/numpy/pull/19651): TST: avoid distutils.sysconfig in runtests.py - [#19652](https://togithub.com/numpy/numpy/pull/19652): MAINT: add missing dunder method to nditer type hints - [#19656](https://togithub.com/numpy/numpy/pull/19656): BLD, SIMD: Fix testing extra checks when `-Werror` isn't applicable... - [#19657](https://togithub.com/numpy/numpy/pull/19657): BUG: Remove logical object ufuncs with bool output - [#19658](https://togithub.com/numpy/numpy/pull/19658): MAINT: Include .coveragerc in source distributions to support... - [#19659](https://togithub.com/numpy/numpy/pull/19659): BUG: Fix bad write in masked iterator output copy paths - [#19660](https://togithub.com/numpy/numpy/pull/19660): ENH: Add support for windows on arm targets - [#19661](https://togithub.com/numpy/numpy/pull/19661): BUG: add base to templated arguments for platlib - [#19662](https://togithub.com/numpy/numpy/pull/19662): BUG,DEP: Non-default UFunc signature/dtype usage should be deprecated - [#19666](https://togithub.com/numpy/numpy/pull/19666): MAINT: Add Python 3.10 to supported versions. - [#19668](https://togithub.com/numpy/numpy/pull/19668): TST,BUG: Sanitize path-separators when running `runtest.py` - [#19671](https://togithub.com/numpy/numpy/pull/19671): BLD: load extra flags when checking for libflame - [#19676](https://togithub.com/numpy/numpy/pull/19676): BLD: update circleCI docker image - [#19677](https://togithub.com/numpy/numpy/pull/19677): REL: Prepare for 1.21.2 release. ## Checksums ##### MD5 c4d72c5f8aff59b5e48face558441e9f numpy-1.21.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl eb09d0bfc0bc39ce3e323182ae779fcb numpy-1.21.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e0bb19ea8cc13a5152085aa42d850077 numpy-1.21.2-cp37-cp37m-macosx_10_9_x86_64.whl af7d21992179dfa3669a2a238b94a980 numpy-1.21.2-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl 9acbaf0074af75d66ca8676b16cec03a numpy-1.21.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl 86b755c7ece248e5586a6a58259aa432 numpy-1.21.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl b45fbbb0ffabcabcc6dc4cf957713d45 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1.21.1 Release Notes The NumPy 1.21.1 is maintenance release that fixes bugs discovered after the 1.21.0 release and updates OpenBLAS to v0.3.17 to deal with problems on arm64. The Python versions supported for this release are 3.7-3.9. The 1.21.x series is compatible with development Python 3.10. Python 3.10 will be officially supported after it is released. :warning: There are unresolved problems compiling NumPy 1.20.0 with gcc-11.1. - Optimization level -O3 results in many incorrect warnings when running the tests. - On some hardware NumPY will hang in an infinite loop. ## Contributors A total of 11 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Ganesh Kathiresan - Gregory R. Lee - Hugo Defois + - Kevin Sheppard - Matti Picus - Ralf Gommers - Sayed Adel - Sebastian Berg - Thomas J. Fan ## Pull requests merged A total of 26 pull requests were merged for this release. - [#19311](https://togithub.com/numpy/numpy/pull/19311): REV,BUG: Replace `NotImplemented` with `typing.Any` - [#19324](https://togithub.com/numpy/numpy/pull/19324): MAINT: Fixed the return-dtype of `ndarray.real` and `imag` - [#19330](https://togithub.com/numpy/numpy/pull/19330): MAINT: Replace `"dtype[Any]"` with `dtype` in the definiton of... - [#19342](https://togithub.com/numpy/numpy/pull/19342): DOC: Fix some docstrings that crash pdf generation. - [#19343](https://togithub.com/numpy/numpy/pull/19343): MAINT: bump scipy-mathjax - [#19347](https://togithub.com/numpy/numpy/pull/19347): BUG: Fix arr.flat.index for large arrays and big-endian machines - [#19348](https://togithub.com/numpy/numpy/pull/19348): ENH: add `numpy.f2py.get_include` function - [#19349](https://togithub.com/numpy/numpy/pull/19349): BUG: Fix reference count leak in ufunc dtype handling - [#19350](https://togithub.com/numpy/numpy/pull/19350): MAINT: Annotate missing attributes of `np.number` subclasses - [#19351](https://togithub.com/numpy/numpy/pull/19351): BUG: Fix cast safety and comparisons for zero sized voids - [#19352](https://togithub.com/numpy/numpy/pull/19352): BUG: Correct Cython declaration in random - [#19353](https://togithub.com/numpy/numpy/pull/19353): BUG: protect against accessing base attribute of a NULL subarray - [#19365](https://togithub.com/numpy/numpy/pull/19365): BUG, SIMD: Fix detecting AVX512 features on Darwin - [#19366](https://togithub.com/numpy/numpy/pull/19366): MAINT: remove `print()`'s in distutils template handling - [#19390](https://togithub.com/numpy/numpy/pull/19390): ENH: SIMD architectures to show_config - [#19391](https://togithub.com/numpy/numpy/pull/19391): BUG: Do not raise deprecation warning for all nans in unique... - [#19392](https://togithub.com/numpy/numpy/pull/19392): BUG: Fix NULL special case in object-to-any cast code - [#19430](https://togithub.com/numpy/numpy/pull/19430): MAINT: Use arm64-graviton2 for testing on travis - [#19495](https://togithub.com/numpy/numpy/pull/19495): BUILD: update OpenBLAS to v0.3.17 - [#19496](https://togithub.com/numpy/numpy/pull/19496): MAINT: Avoid unicode characters in division SIMD code comments - [#19499](https://togithub.com/numpy/numpy/pull/19499): BUG, SIMD: Fix infinite loop during count non-zero on GCC-11 - [#19500](https://togithub.com/numpy/numpy/pull/19500): BUG: fix a numpy.npiter leak in npyiter_multi_index_set - [#19501](https://togithub.com/numpy/numpy/pull/19501): TST: Fix a `GenericAlias` test failure for python 3.9.0 - [#19502](https://togithub.com/numpy/numpy/pull/19502): MAINT: Start testing with Python 3.10.0b3. - [#19503](https://togithub.com/numpy/numpy/pull/19503): MAINT: Add missing dtype overloads for object- and ctypes-based... - [#19510](https://togithub.com/numpy/numpy/pull/19510): REL: Prepare for NumPy 1.21.1 release. ## Checksums 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dff4af63638afcc57a3dfb9e4b26d434a7a602d225b42d746ea7fe2edf1342fd numpy-1.21.1.zip ### [`v1.21.0`](https://togithub.com/numpy/numpy/releases/v1.21.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.20.3...v1.21.0) # NumPy 1.21.0 Release Notes The NumPy 1.21.0 release highlights are - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new `PCG64DXSM` bitgenerator for random numbers. In addition there are the usual large number of bug fixes and other improvements. The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released. :warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 . - Optimization level `-O3` results in many wrong warnings when running the tests. - On some hardware NumPy will hang in an infinite loop. ## New functions ##### Add PCG64DXSM BitGenerator Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by `default_rng` in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64. See `upgrading-pcg64` for more details. ([gh-18906](https://togithub.com/numpy/numpy/pull/18906)) ## Expired deprecations - The `shape` argument `numpy.unravel_index` cannot be passed as `dims` keyword argument anymore. (Was deprecated in NumPy 1.16.) ([gh-17900](https://togithub.com/numpy/numpy/pull/17900)) - The function `PyUFunc_GenericFunction` has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API. ([gh-18697](https://togithub.com/numpy/numpy/pull/18697)) - The function `PyUFunc_SetUsesArraysAsData` has been disabled. It was deprecated in NumPy 1.19. ([gh-18697](https://togithub.com/numpy/numpy/pull/18697)) - The class `PolyBase` has been removed (deprecated in numpy 1.9.0). Please use the abstract `ABCPolyBase` class instead. ([gh-18963](https://togithub.com/numpy/numpy/pull/18963)) - The unused `PolyError` and `PolyDomainError` exceptions are removed. ([gh-18963](https://togithub.com/numpy/numpy/pull/18963)) ## Deprecations ##### The `.dtype` attribute must return a `dtype` A `DeprecationWarning` is now given if the `.dtype` attribute of an object passed into `np.dtype` or as a `dtype=obj` argument is not a dtype. NumPy will stop attempting to recursively coerce the result of `.dtype`. ([gh-13578](https://togithub.com/numpy/numpy/pull/13578)) ##### Inexact matches for `numpy.convolve` and `numpy.correlate` are deprecated `numpy.convolve` and `numpy.correlate` now emit a warning when there are case insensitive and/or inexact matches found for `mode` argument in the functions. Pass full `"same"`, `"valid"`, `"full"` strings instead of `"s"`, `"v"`, `"f"` for the `mode` argument. ([gh-17492](https://togithub.com/numpy/numpy/pull/17492)) ##### `np.typeDict` has been formally deprecated `np.typeDict` is a deprecated alias for `np.sctypeDict` and has been so for over 14 years ([6689502](https://togithub.com/numpy/numpy/commit/668950285c407593a368336ff2e737c5da84af7d)). A deprecation warning will now be issued whenever getting `np.typeDict`. ([gh-17586](https://togithub.com/numpy/numpy/pull/17586)) ##### 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. A warning is now given when the exception is anything but AttributeError. To silence the warning, the type raising the exception has to be adapted to raise an `AttributeError`. ([gh-19001](https://togithub.com/numpy/numpy/pull/19001)) ##### Four `ndarray.ctypes` methods have been deprecated Four methods of the `ndarray.ctypes` object have been deprecated, as they are (undocumentated) implementation artifacts of their respective properties. The methods in question are: - `_ctypes.get_data` (use `_ctypes.data` instead) - `_ctypes.get_shape` (use `_ctypes.shape` instead) - `_ctypes.get_strides` (use `_ctypes.strides` instead) - `_ctypes.get_as_parameter` (use `_ctypes._as_parameter_` instead) ([gh-19031](https://togithub.com/numpy/numpy/pull/19031)) ## Expired deprecations - The `shape` argument `numpy.unravel_index`] cannot be passed as `dims` keyword argument anymore. (Was deprecated in NumPy 1.16.) ([gh-17900](https://togithub.com/numpy/numpy/pull/17900)) - The function `PyUFunc_GenericFunction` has been disabled. It was deprecated in NumPy 1.19. Users should call the ufunc directly using the Python API. ([gh-18697](https://togithub.com/numpy/numpy/pull/18697)) - The function `PyUFunc_SetUsesArraysAsData` has been disabled. It was deprecated in NumPy 1.19. ([gh-18697](https://togithub.com/numpy/numpy/pull/18697)) ##### Remove deprecated `PolyBase` and unused `PolyError` and `PolyDomainError` The class `PolyBase` has been removed (deprecated in numpy 1.9.0). Please use the abstract `ABCPolyBase` class instead. Furthermore, the unused `PolyError` and `PolyDomainError` exceptions are removed from the `numpy.polynomial`. ([gh-18963](https://togithub.com/numpy/numpy/pull/18963)) ## Compatibility notes ##### Error type changes in universal functions The universal functions may now raise different errors on invalid input in some cases. The main changes should be that a `RuntimeError` was replaced with a more fitting `TypeError`. When multiple errors were present in the same call, NumPy may now raise a different one. ([gh-15271](https://togithub.com/numpy/numpy/pull/15271)) ##### `__array_ufunc__` argument validation NumPy will now partially validate arguments before calling `__array_ufunc__`. Previously, it was possible to pass on invalid arguments (such as a non-existing keyword argument) when dispatch was known to occur. ([gh-15271](https://togithub.com/numpy/numpy/pull/15271)) ##### `__array_ufunc__` and additional positional arguments Previously, all positionally passed arguments were checked for `__array_ufunc__` support. In the case of `reduce`, `accumulate`, and `reduceat` all arguments may be passed by position. This means that when they were passed by position, they could previously have been asked to handle the ufunc call via `__array_ufunc__`. Since this depended on the way the arguments were passed (by position or by keyword), NumPy will now only dispatch on the input and output array. For example, NumPy will never dispatch on the `where` array in a reduction such as `np.add.reduce`. ([gh-15271](https://togithub.com/numpy/numpy/pull/15271)) ##### Validate input values in `Generator.uniform` Checked that `high - low >= 0` in `np.random.Generator.uniform`. Raises `ValueError` if `low > high`. Previously out-of-order inputs were accepted and silently swapped, so that if `low > high`, the value generated was `high + (low - high) * random()`. ([gh-17921](https://togithub.com/numpy/numpy/pull/17921)) ##### `/usr/include` removed from default include paths The default include paths when building a package with `numpy.distutils` no longer include `/usr/include`. This path is normally added by the compiler, and hardcoding it can be problematic. In case this causes a problem, please open an issue. A workaround is documented in PR 18658. ([gh-18658](https://togithub.com/numpy/numpy/pull/18658)) ##### Changes to comparisons with `dtype=...` When the `dtype=` (or `signature`) arguments to comparison ufuncs (`equal`, `less`, etc.) is used, this will denote the desired output dtype in the future. This means that: > np.equal(2, 3, dtype=object) will give a `FutureWarning` that it will return an `object` array in the future, which currently happens for: > np.equal(None, None, dtype=object) due to the fact that `np.array(None)` is already an object array. (This also happens for some other dtypes.) Since comparisons normally only return boolean arrays, providing any other dtype will always raise an error in the future and give a `DeprecationWarning` now. ([gh-18718](https://togithub.com/numpy/numpy/pull/18718)) ##### Changes to `dtype` and `signature` arguments in ufuncs The universal function arguments `dtype` and `signature` which are also valid for reduction such as `np.add.reduce` (which is the implementation for `np.sum`) will now issue a warning when the `dtype` provided is not a "basic" dtype. NumPy almost always ignored metadata, byteorder or time units on these inputs. NumPy will now always ignore it and raise an error if byteorder or time unit changed. The following are the most important examples of changes which will give the error. In some cases previously the information stored was not ignored, in all of these an error is now raised: ### Previously ignored the byte-order (affect if non-native) np.add(3, 5, dtype=">i32") ### The biggest impact is for timedelta or datetimes: arr = np.arange(10, dtype="m8[s]") ### The examples always ignored the time unit "ns": np.add(arr, arr, dtype="m8[ns]") np.maximum.reduce(arr, dtype="m8[ns]") ### The following previously did use "ns" (as opposed to `arr.dtype`) np.add(3, 5, dtype="m8[ns]") # Now return generic time units np.maximum(arr, arr, dtype="m8[ns]") # Now returns "s" (from `arr`) The same applies for functions like `np.sum` which use these internally. This change is necessary to achieve consistent handling within NumPy. If you run into these, in most cases pass for example `dtype=np.timedelta64` which clearly denotes a general `timedelta64` without any unit or byte-order defined. If you need to specify the output dtype precisely, you may do so by either casting the inputs or providing an output array using `out=`. NumPy may choose to allow providing an exact output `dtype` here in the future, which would be preceded by a `FutureWarning`. ([gh-18718](https://togithub.com/numpy/numpy/pull/18718)) ##### Ufunc `signature=...` and `dtype=` generalization and `casting` The behaviour for `np.ufunc(1.0, 1.0, signature=...)` or `np.ufunc(1.0, 1.0, dtype=...)` can now yield different loops in 1.21 compared to 1.20 because of changes in promotion. When `signature` was previously used, the casting check on inputs was relaxed, which could lead to downcasting inputs unsafely especially if combined with `casting="unsafe"`. Casting is now guaranteed to be safe. If a signature is only partially provided, for example using `signature=("float64", None, None)`, this could lead to no loop being found (an error). In that case, it is necessary to provide the complete signature to enforce casting the inputs. If `dtype="float64"` is used or only outputs are set (e.g. `signature=(None, None, "float64")` the is unchanged. We expect that very few users are affected by this change. Further, the meaning of `dtype="float64"` has been slightly modified and now strictly enforces only the correct output (and not input) DTypes. This means it is now always equivalent to: signature=(None, None, "float64") (If the ufunc has two inputs and one output). Since this could lead to no loop being found in some cases, NumPy will normally also search for the loop: signature=("float64", "float64", "float64") if the first search failed. In the future, this behaviour may be customized to achieve the expected results for more complex ufuncs. (For some universal functions such as `np.ldexp` inputs can have different DTypes.) ([gh-18880](https://togithub.com/numpy/numpy/pull/18880)) ##### Distutils forces strict floating point model on clang NumPy distutils will now always add the `-ffp-exception-behavior=strict` compiler flag when compiling with clang. Clang defaults to a non-strict version, which allows the compiler to generate code that does not set floating point warnings/errors correctly. ([gh-19049](https://togithub.com/numpy/numpy/pull/19049)) ## C API changes ##### Use of `ufunc->type_resolver` and "type tuple" NumPy now normalizes the "type tuple" argument to the type resolver functions before calling it. Note that in the use of this type resolver is legacy behaviour and NumPy will not do so when possible. Calling `ufunc->type_resolver` or `PyUFunc_DefaultTypeResolver` is strongly discouraged and will now enforce a normalized type tuple if done. Note that this does not affect providing a type resolver, which is expected to keep working in most circumstances. If you have an unexpected use-case for calling the type resolver, please inform the NumPy developers so that a solution can be found. ([gh-18718](https://togithub.com/numpy/numpy/pull/18718)) ## New Features ##### Added a mypy plugin for handling platform-specific `numpy.number` precisions A [mypy](http://mypy-lang.org/) plugin is now available for automatically assigning the (platform-dependent) precisions of certain `numpy.number` subclasses, including the likes of `numpy.int_`, `numpy.intp` and `numpy.longlong`. See the documentation on `scalar typesConfiguration
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