[hdf-forum] ANN: PyTables 2.2b1 ready for testing

[Francesc Alted]

===========================
 Announcing PyTables 2.2b1
===========================

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 first beta of the PyTables 2.2 series.  Here, you will
find support for NumPy's extended slicing in all `Leaf` objects as
well as an updated Numexpr module (to 1.3.1), which can lead to up a
25% improvement of the time for both in-kernel and indexed queries for
unaligned columns in tables (which can be a quite common situation).

But perhaps the most interesting feature is the introduction of the
`Expr` class, which allows evaluating expressions containing general
array-like objects.  It can evaluate expressions (like '3*a+4*b') that
operate on *arbitrary large* arrays while optimizing the resources
(basically main memory and CPU cache memory) required to perform them.
It works similarly to the Numexpr package, but in addition to NumPy
objects, it also accepts disk-based homogeneous arrays, like the
`Array`, `CArray`, `EArray` and `Column` PyTables objects.

You can find the documentation about the new `Expr` class at:
http://www.pytables.org/docs/manual-2.2b1/ch04.html#ExprClass

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_2.2b1

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-2.2b1


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

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