==========================
 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!


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