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 <[email protected]> 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 > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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