PyTables 2.2 released: entering the multi-core age

[Francesc Alted]

=================================
 Announcing PyTables 2.2 (final)
=================================

I'm happy to announce PyTables 2.2 (final).  After 18 months of
continuous development and testing, this is, by far, the most powerful
and well-tested release ever.  I hope you like it too.

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

The main new features in 2.2 series are:

  * A new compressor called Blosc, designed to read/write data to/from
    memory at speeds that can be faster than a system `memcpy()` call.
    With it, many internal PyTables operations that are currently
    bounded by CPU or I/O bandwith are speed-up.  Some benchmarks:
    http://blosc.pytables.org/trac/wiki/SyntheticBenchmarks

    And a demonstration on how Blosc can improve PyTables performance:
    http://www.pytables.org/docs/manual/ch05.html#chunksizeFineTune

  * Support for HDF5 hard links, soft links and external links (kind of
    mounting external filesystems).  A new tutorial about its usage has
    been added to the 'Tutorials' chapter of User's Manual.  See:
    http://www.pytables.org/docs/manual/ch03.html#LinksTutorial

  * A new `tables.Expr` module (based on Numexpr) that allows to do
    persistent, on-disk computations on many algebraic operations.
    For a brief look on its performance, see:
    http://pytables.org/moin/ComputingKernel

  * Suport for 'fancy' indexing (i.e., à la NumPy) in all the data
    containers in PyTables.  Backported from the implementation in the
    h5py project.  Thanks to Andrew Collette for his fine work on this!

  * Binaries for both Windows 32-bit and 64-bit are provided now.

As always, a large amount of bugs have been addressed and squashed too.

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_2.2

You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:
http://www.pytables.org/download/stable

For an on-line version of the manual, visit:
http://www.pytables.org/docs/manual-2.2

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.

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