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  • [Python-announce] ANN: numexpr 2.8.8 released

    From Francesc Alted@faltet@gmail.com to comp.lang.python.announce on Mon Dec 11 14:22:04 2023
    From Newsgroup: comp.lang.python.announce

    ========================
    Announcing NumExpr 2.8.8
    ========================

    Hi everyone,

    NumExpr 2.8.8 is a release to deal mainly with issues appearing with
    upcoming `NumPy` 2.0. Also, some small fixes (support for simple complex expressions like `ne.evaluate('1.5j')`) and improvements are included.

    Project documentation is available at:

    http://numexpr.readthedocs.io/

    Changes from 2.8.7 to 2.8.8
    ---------------------------

    * Fix re_evaluate not taking global_dict as argument. Thanks to Teng Liu
    (@27rabbitlt).

    * Fix parsing of simple complex numbers. Now, `ne.evaluate('1.5j')` works.
    Thanks to Teng Liu (@27rabbitlt).

    * Fixes for upcoming NumPy 2.0:

    * Replace npy_cdouble with C++ complex. Thanks to Teng Liu (@27rabbitlt).
    * Add NE_MAXARGS for future numpy change NPY_MAXARGS. Now it is set to 64
    to match NumPy 2.0 value. Thanks to Teng Liu (@27rabbitlt).

    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!
    --
    Francesc Alted
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