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===========================
Announcing PyTables @VERSION@
===========================
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
.. Local Variables:
.. mode: rst
.. coding: utf-8
.. fill-column: 72
.. End:
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