On behalf of the numba team I am pleased to announce a new version of Numba, 0.6. The release includes faster numerical codes, better type inference support, faster autojit dispatch, python 2.6 support and more.
Download: http://pypi.python.org/pypi/numba/0.6.0 Documentation: http://numba.pydata.org/numba-doc/0.6/ Github: https://github.com/numba/numba Numba will be part of the next anaconda CE release 1.3.1, to be released tomorrow. Numba ====== Numba is an just-in-time specializing compiler for Python and NumPy code to LLVM for annotated functions (through decorators). It's goal is to seamlessly integrate with the Python scientific software stack and provide optimized native code and integration with native foreign languages. Dependencies: ============ * llvmpy 0.10.0 * meta (optional) * cython * numpy * LLVM 3.2 (3.1 might work but is not officially supported) Release notes: ============ * Python 2.6 support * Programmable typing * Allow users to add type inference for external code * Better NumPy type inference * outer, inner, dot, vdot, tensordot, nonzero, where, binary ufuncs + methods (reduce, accumulate, reduceat, outer) * Type based alias analysis * Support for strict aliasing * Much faster autojit dispatch when calling from Python * Faster numerical loops through data and stride pre-loading * Integral overflow and underflow checking for conversions from objects * Make Meta dependency optional Many thanks to everyone who contributed to this release! Dan Christensen Ilan Schnell Jon Riehl Lars Buitinck Mark Florisson Phillip Cloud Siu Kwan Lam Travis E. Oliphant Timo -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/
