========================== Announcing Numexpr 2.2.2 ========================== What's new ========== This is mainly a release for compatibility with PyTables < 3.0, which I decided to continue to support. For details, see: https://code.google.com/p/numexpr/issues/detail?id=115 Also, added a fix so that s390 architecture compiles. Fixes #116. Thanks to Antonio Valentino for reporting and for the patch. 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. What it is? =========== 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 VML library (included in Intel MKL), 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. Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational kernel for projects that don't want to adopt other solutions that require more heavy dependencies. Where I can find Numexpr? ========================= The project is hosted at Google code in: http://code.google.com/p/numexpr/ You can get the packages from PyPI as well: http://pypi.python.org/pypi/numexpr Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! .. Local Variables: .. mode: rst .. coding: utf-8 .. fill-column: 70 .. End:
