<commit_before>========================
 Announcing Numexpr 1.1
========================

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.

The expected speed-ups for Numexpr respect to NumPy are between 0.95x
and 15x, being 3x or 4x typical values.  The strided and unaligned
case has been optimized too, so if the expresion contains strided or
unaligned arrays, this speed-up can increase significantly.  Of
course, you will need to operate with large arrays (typically larger
than the cache size of your CPU) to see these improvements.

This release is mainly intended to put in sync many of the
improvements that has the Numexpr version integrated in PyTables.
With this, the standalone version of Numexpr will benefit of this
well-proven version in production for more than a year now.  Also, the
documentation has been improved and tests can be run programatically.

In case you want to know more in detail what has changed in this
version, have a look at ``RELEASE_NOTES.txt`` in the tarball.


Where I can find Numexpr?
=========================<commit_msg>Updated the announcement once more (hope it is the last one).<commit_after>========================
 Announcing Numexpr 1.1
========================

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.

The expected speed-ups for Numexpr respect to NumPy are between 0.95x
and 15x, being 3x or 4x typical values.  The strided and unaligned
case has been optimized too, so if the expresion contains such arrays,
the speed-up can increase significantly.  Of course, you will need to
operate with large arrays (typically larger than the cache size of
your CPU) to see these improvements in performance.

This release is mainly intended to put in sync some of the
improvements that had the Numexpr version integrated in PyTables.  So,
this standalone version of Numexpr will benefit of the well tested
PyTables' version that has been in production for more than a year
now.

In case you want to know more in detail what has changed in this
version, have a look at ``RELEASE_NOTES.txt`` in the tarball.


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