numba 0.8

[mark florisson]

Numba 0.8 adds support for autojit classes and methods, building on
the perfect hash table ideas from SEP 201 [1]. This allows methods to
automatically specialize to argument types and instance attribute
types.
Many thanks to Dag Sverre Seljebotn for his work and ideas on the SEPs
and his ideas and implementation of the hash table and surrounding
facilities, and many thanks to Robert Bradshaw for his contributions
and ideas.

Further functionality includes Python 3 support for pycc, and
retrieving pointers from numba jit functions. A memory leak that
plagued users has also been fixed. Many thanks for all the bug reports
and feedback!

Over the coming months we hope to stabilize numba and allow
distribution of portable numba code and a well-defined and stable
runtime. We also hope to address some of the math portability and
numerical stability issues. We have a roadmap [2] which details what
we want to do, and also provides entry points for new potential
developers to contribute.

Stay tuned as we try to make numba more robust, portable,
interoperable and usable :)

Download
========
http://numba.pydata.org/download.html

Website
=======
http://numba.pydata.org/

Documentation
============
http://numba.pydata.org/numba-doc/0.8/index.html

Numba
======
Numba is an just-in-time specializing compiler which compiles
annotated Python and NumPy code to LLVM (through decorators). Its goal
is to seamlessly integrate with the Python scientific software stack
and produce optimized native code, as well as integrate with native
foreign languages.

References
=========

[1]: https://github.com/numfocus/sep/blob/master/sep201.rst
[2]: http://numba.pydata.org/numba-doc/dev/roadmap.html