File: ANNOUNCE.rst

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========================
 Announcing Numexpr 2.4
========================

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
MKL (Math Kernel Library), 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.  Look here for a some benchmarks of numexpr using MKL:

https://github.com/pydata/numexpr/wiki/NumexprMKL

Its only dependency is NumPy (MKL is optional), so it works well as an
easy-to-deploy, easy-to-use, computational engine for projects that
don't want to adopt other solutions requiring more heavy dependencies.

What's new
==========

A new `contains()` function has been added for detecting substrings in
strings.  Only plain strings (bytes) are supported for now (see ticket
#142).  Thanks to Marcin Krol.

You can have a glimpse on how `contains()` work in this notebook:

http://nbviewer.ipython.org/gist/FrancescAlted/10595974

where it can be seen that this can make queries using numexpr more
than 10x faster than with regular substring searches.

You can find the source for the notbook here:

https://github.com/FrancescAlted/ngrams

Also, there is a new version of setup.py that allows better management
of the NumPy dependency during pip installs.  Thanks to Aleks Bunin.

Windows related bugs have been addressed and (hopefully) squashed.
Thanks to Christoph Gohlke.

In case you want to know more in detail what has changed in this
version, see:

https://github.com/pydata/numexpr/wiki/Release-Notes

or have a look at RELEASE_NOTES.txt in the tarball.

Where I can find Numexpr?
=========================

The project is hosted at GitHub in:

https://github.com/pydata/numexpr

You can get the packages from PyPI as well (but not for RC releases):

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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