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  • [Python-announce] ANN: NumExpr 2.8.4 Release

    From Robert McLeod@robbmcleod@gmail.com to comp.lang.python.announce on Tue Oct 25 20:52:00 2022
    From Newsgroup: comp.lang.python.announce

    ========================
    Announcing NumExpr 2.8.4
    ========================

    Hi everyone,

    This is a maintenance and bug-fix release for NumExpr. In particular, now
    we have
    added Python 3.11 support.

    Project documentation is available at:

    http://numexpr.readthedocs.io/

    Changes from 2.8.3 to 2.8.4
    ---------------------------

    * Support for Python 3.11 has been added.
    * Thanks to Tobias Hangleiter for an improved accuracy complex `expm1` function.
    While it is 25 % slower, it is significantly more accurate for the real component
    over a range of values and matches NumPy outputs much more closely.
    * Thanks to Kirill Kouzoubov for a range of fixes to constants parsing that
    was
    resulting in duplicated constants of the same value.
    * Thanks to Mark Harfouche for noticing that we no longer need `numpy`
    version
    checks. `packaging` is no longer a requirement as a result.


    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:

    https://github.com/pydata/numexpr/wiki/NumexprMKL

    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?
    -------------------------

    The project is hosted at GitHub in:

    https://github.com/pydata/numexpr

    You can get the packages from PyPI as well (but not for RC releases):

    http://pypi.python.org/pypi/numexpr

    Documentation is hosted at:

    http://numexpr.readthedocs.io/en/latest/

    Share your experience
    ---------------------

    Let us know of any bugs, suggestions, gripes, kudos, etc. you may
    have.

    Enjoy data!
    --
    Robert McLeod
    robbmcleod@gmail.com
    robert.mcleod@hitachi-hightech.com
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