ANN: PyTables 2.3.1 released

[Antonio Valentino]

===========================
 Announcing PyTables 2.3.1
===========================

We are happy to announce PyTables 2.3.1.
This is a bugfix release. Upgrading is recommended for users that are
running PyTables in production environments.


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

This release includes a small number of changes.  It only fixes a couple of
bugs that are considered serious even if they should not impact a large
number of users:

- :issue:`113` caused installation of PyTables 2.3 to fail on hosts with
  multiple python versions installed.
- :issue:`111` prevented to read scalar datasets of UnImplemented types.

In case you want to know more in detail what has changed in this
version, have a look at:
http://pytables.github.com/release_notes.html

You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:
http://sourceforge.net/projects/pytables/files/pytables/@VERSION@

For an on-line version of the manual, visit:
http://pytables.github.com/usersguide/index.html


What it is?
===========

PyTables is a library for managing hierarchical datasets and
designed to efficiently cope with extremely large amounts of data with
support for full 64-bit file addressing.  PyTables runs on top of
the HDF5 library and NumPy package for achieving maximum throughput and
convenient use.  PyTables includes OPSI, a new indexing technology,
allowing to perform data lookups in tables exceeding 10 gigarows
(10**10 rows) in less than 1 tenth of a second.


Resources
=========

About PyTables:

http://www.pytables.org

About the HDF5 library:

http://hdfgroup.org/HDF5/

About NumPy:

http://numpy.scipy.org/


Acknowledgments
===============

Thanks to many users who provided feature improvements, patches, bug
reports, support and suggestions.  See the ``THANKS`` file in the
distribution package for a (incomplete) list of contributors.  Most
specially, a lot of kudos go to the HDF5 and NumPy (and numarray!)
makers.  Without them, PyTables simply would not exist.


Share your experience
=====================

Let us know of any bugs, suggestions, gripes, kudos, etc. you may
have.


----

  **Enjoy data!**

--
The PyTables Team