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to python-ann...@python.org, Discussion of Numerical Python, pyd...@googlegroups.com
Please find here another maintenance release of NumExpr. Support for Python 3.6 has been dropped to enable support for NumPy 1.23 (and by extension Python 3.11 when it is released). Wheels for ARM64 multilinux should be available again after troubles with GitHub Actions and Apple Silicon wheels are also now available on PyPi for download.
Changes from 2.8.1 to 2.8.2 ---------------------------
* Support for Python 3.6 has been dropped due to the need to substitute the flag `NPY_ARRAY_WRITEBACKIFCOPY` for `NPY_ARRAY_UPDATEIFCOPY`. This flag change was initiated in NumPy 1.14 and finalized in 1.23. The only changes were made to cases where an unaligned constant was passed in with a pre-allocated output variable:
``` x = np.empty(5, dtype=np.uint8)[1:].view(np.int32) ne.evaluate('3', out=x) ```
We think the risk of issues is very low, but if you are using NumExpr as a expression evaluation tool you may want to write a test for this edge case. * Thanks to Matt Einhorn (@matham) for improvements to the GitHub Actions build process to add support for Apple Silicon and aarch64. * Thanks to Biswapriyo Nath (@biswa96) for a fix to allow `mingw` builds on Windows. * There have been some changes made to not import `platform.machine()` on `sparc` but it is highly advised to upgrade to Python 3.9+ to avoid this issue with the Python core package `platform`.
What's Numexpr? ---------------
Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python.
It has multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL:
Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies.
Where I can find Numexpr? -------------------------