ANN: PyTables 1.1.1 released

[Francesc Altet]

==========================
 Announcing PyTables 1.1.1
==========================

This is a maintenance release of PyTables. In it, several optimizations
and bug fixes have been made. As some of the fixed bugs were quite
important, it's strongly recommended for users to upgrade.

Go to the PyTables web site for downloading the beast:
http://pytables.sourceforge.net/

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


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

Improvements:

- Optimized the opening of files with a large number of objects. Now,
  files with table objects open a 50% faster, and files with arrays open
  more than twice as fast (up to 2000 objects/s on a Pentium
  4 at 2GHz). Hence, a file with a combination of both kinds of objects
  opens between a 50% and 100% faster than in 1.1.

- Optimized the creation of ``NestedRecArray`` objects using
  ``NumArray`` objects as columns, so that filling a table with the
  ``Table.append()`` method achieves a performance similar to PyTables
  pre-1.1 releases.

Bug fixes:

- ``Table.readCoordinates()`` now converts the coords parameter into ``Int64``
  indices automatically.

- Fixed a bug that prevented appending to tables (though
  ``Table.append()``) using a list of ``NumArray`` objects.

- ``Int32`` attributes are handled correctly in 64-bit platforms now.

- Correction for accepting lists of numarrays as input for
  ``NestedRecArrays``.

- Fixed a problem when creating rank 1 multi-dimensional string columns
  in ``Table`` objects. Closes SF bug #1269023.

- Avoid errors when unpickling objects stored in attributes.  See the
  section ``AttributeSet`` in the reference chapter of the User's
  Manual for more information. Closes SF bug #1254636.

- Assignment for ``*Array`` slices has been improved in order to solve
  some issues with shapes. Closes SF bug #1288792.

- The indexation properties were lost in case the table was closed
  before an index was created. Now, these properties are saved even in
  this case.

Known bugs:

- Classes inheriting from ``IsDescription`` subclasses do not inherit
  columns defined in the super-class. See SF bug #1207732 for more info.

- Time datatypes are non-portable between big-endian and little-endian
  architectures. This is ultimately a consequence of a HDF5
  limitation. See SF bug #1234709 for more info.

Backward-incompatible changes:

- None (that we are aware of).


Important note for MacOSX users
===============================

UCL compressor works badly on MacOSX platforms. Recent investigation
seems to point to a bug in the development tools in MacOSX (Panther).
Until the problem is isolated and eventually solved, UCL support will
not be compiled by default on MacOSX platforms, even if the installer
finds it in the system. However, if you still want to get UCL support
on MacOSX, you can use the ``--force-ucl`` flag in ``setup.py``.


Important note for Python 2.4 and 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/5-164-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/5-164-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 (Numeric is 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
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://pytables.sourceforge.net/

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 THANKS file in
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