======================== Announcing Numexpr 1.3 ======================== 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. On this release, and due to popular demand, support for single precision floating point types has been added. This allows for both improved performance and optimal usage of memory for the single precision computations. Of course, support for single precision in combination with Intel's VML is there too :) However, caveat emptor: the casting rules for floating point types slightly differs from those of NumPy. See the ``Casting rules`` section at: http://code.google.com/p/numexpr/wiki/Overview or the README.txt file for more info on this issue. In case you want to know more in detail what has changed in this version, see: http://code.google.com/p/numexpr/wiki/ReleaseNotes or have a look at RELEASE_NOTES.txt in the tarball. Where I can find Numexpr? ========================= The project is hosted at Google code in: http://code.google.com/p/numexpr/ And you can get the packages from PyPI as well: http://pypi.python.org/pypi How it works? ============= See: http://code.google.com/p/numexpr/wiki/Overview for a detailed description by the original author (David M. Cooke). Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy! -- Francesc Alted