ANN: PyTables 2.2b2 released

[Francesc Alted]

===========================
 Announcing PyTables 2.2b2
===========================

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.

This is the second beta version of 2.2 release.  The main addition is
the support for links.  All HDF5 kind of links are supported: hard, soft
and external.  Hard and soft links are similar to hard and symbolic
links in regular UNIX filesystems, while external links are more like
mounting external filesystems (in this case, HDF5 files) on top of
existing ones.  This allows for a considerable degree of flexibility
when defining your object tree.  See the new tutorial at:

http://www.pytables.org/docs/manual-2.2b2/ch03.html#LinksTutorial

Also, some other new features (like complete control of HDF5 chunk cache
parameters and native compound types in attributes), bug fixes and a
couple of (small) API changes happened.

In case you want to know more in detail what has changed in this
version, have a look at:

http://www.pytables.org/moin/ReleaseNotes/Release_2.2b2

You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:

http://www.pytables.org/download/preliminary

For an on-line version of the manual, visit:

http://www.pytables.org/docs/manual-2.2b2


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

-- 
Francesc Alted