[Pytables-users] ANN: PyTables 1.3.2 is out

[Francesc A. <fa...@ca...>]

Hi List,

After some weeks of trying, I've finally found some time to release 1.3.2 :-)
I'm including below the official announcement. Enjoy!

--------------------------------------------------------------------------------------
===========================
 Announcing PyTables 1.3.2
===========================

This is a new minor release of PyTables.  There you will find, among
other things, improved support for NumPy strings and the ability to
create indexes of NumPy-flavored tables (this capability was broken in
earlier versions).

*Important note*: one of the fixes addresses an important bug that shows
when browsing files with lots of nodes, making PyTables to
crash. Because of this, an upgrade is encouraged.

Go to the PyTables web site for downloading the beast:
http://www.pytables.org/

or keep reading for more info about the new features and bugs fixed.


Changes more in depth
=====================

Improvements:

- Not many, really ;-)

Bug fixes:

- Changed the nodes in the lru cache heap from Pyrex to pure
  Python ones. This fixes a problem that can appear in certain situations
  (mainly, when navigating back and forth along lots of Node objects).
  While this fix is sub-optimal, at least it leads to well behaviour
  until the faster approach will eventually get back.

- Due to different conventions in padding chars, it has been added a
  special case when converting from numarray strings into numpy ones so
  that these different conventions are handled correctly.  Fixes ticket
  #13 and other strange numpy string quirks (thanks to Pepe Barbe).

- Solved an issue that appeared when indexing Table columns with flavor
  'numpy'. Now, tables that are 'numpy' flavored can be indexed as well.

- Solved an issue when saving string atoms with ``VLArray`` with a
  flavor different from "python".  The problem was that the item sizes
  of the original strings were not checked, so rubish was put on-disk.
  Now, if an item size of the input is different from the item size of
  the atom, a conversion is forced.  Added tests to check for these
  situations.

- Fixed a problem with removing a table with indexed columns under
  certain situations.  Thanks to Andrew Straw for reporting it.

- Fixed a small glitch in the ``ptdump`` utility that prevented dumping
  ``EArray`` data with an enlargeable dimension different from the first
  one.

- Make parent node unreference child node when creation fails.  Fixes
  ticket #12 (thanks to Eilif).

- Saving zero-length strings in Array objects used to raise a
  ZeroDivisionError. Now, it returns a more sensible NotImplementedError
  until this is supported.


Deprecated features:

- None

Backward-incompatible changes:

- Please, see ``RELEASE-NOTES.txt`` file.


Important note for Windows users
================================

If you are willing to use PyTables with Python 2.4 in Windows platforms,
you will need to get the HDF5 library compiled for MSVC 7.1, aka .NET
2003.  It can be found at:
ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-165-win-net.ZIP

Users of Python 2.3 on Windows will have to download the version of HDF5
compiled with MSVC 6.0 available in:
ftp://ftp.ncsa.uiuc.edu/HDF/HDF5/current/bin/windows/5-165-win.ZIP


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

**PyTables** is a package for managing hierarchical datasets and
designed to efficiently cope with extremely large amounts of data (with
support for full 64-bit file addressing).  It features an
object-oriented interface that, combined with C extensions for the
performance-critical parts of the code, makes it a very easy-to-use tool
for high performance data storage and retrieval.

PyTables runs on top of the HDF5 library and numarray (but NumPy and
Numeric are also supported) package for achieving maximum throughput and
convenient use.

Besides, PyTables I/O for table objects is buffered, implemented in C
and carefully tuned so that you can reach much better performance with
PyTables than with your own home-grown wrappings to the HDF5 library.
PyTables sports indexing capabilities as well, allowing doing selections
in tables exceeding one billion of rows in just seconds.


Platforms
=========

This version has been extensively checked on quite a few platforms, like
Linux on Intel32 (Pentium), Win on Intel32 (Pentium), Linux on Intel64
(Itanium2), FreeBSD on AMD64 (Opteron), Linux on PowerPC (and PowerPC64)
and MacOSX on PowerPC.  For other platforms, chances are that the code
can be easily compiled and run without further issues.  Please, contact
us in case you are experiencing problems.


Resources
=========

Go to the PyTables web site for more details:

http://www.pytables.org

About the HDF5 library:

http://hdf.ncsa.uiuc.edu/HDF5/

About numarray:

http://www.stsci.edu/resources/software_hardware/numarray

To know more about the company behind the PyTables development, see:

http://www.carabos.com/


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

Thanks to various the 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.  Many
thanks also to SourceForge who have helped to make and distribute this
package!  And last but not least, a big thank you to THG
(http://www.hdfgroup.org/) for sponsoring many of the new features
recently introduced in PyTables.


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

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


----

  **Enjoy data!**

  -- The PyTables Team