[Numpy-discussion] ANN: numexpr 2.4.3 released

Neil Girdhar mistersheik at gmail.com
Mon Apr 27 16:44:19 EDT 2015


I've always wondered why numexpr accepts strings rather than looking a
function's source code, using ast to parse it, and then transforming the
AST.  I just looked at another project, pyautodiff, which does that.  And I
think numba does that for llvm code generation.  Wouldn't it be nicer to
just apply a decorator to a function than to write the function as a Python
string?


On Mon, Apr 27, 2015 at 11:50 AM, Francesc Alted <faltet at gmail.com> wrote:

>  Announcing Numexpr 2.4.3
> =========================
>
> 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 wears 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.
>
> What's new
> ==========
>
> This is a maintenance release to cope with an old bug affecting
> comparisons with empty strings.  Fixes #121 and PyTables #184.
>
> In case you want to know more in detail what has changed in this
> version, see:
>
> https://github.com/pydata/numexpr/wiki/Release-Notes
>
> or have a look at RELEASE_NOTES.txt in the tarball.
>
> 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
>
> Share your experience
> =====================
>
> Let us know of any bugs, suggestions, gripes, kudos, etc. you may
> have.
>
>
> Enjoy data!
>
> --
> Francesc Alted
>
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>
>
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