[Pytables-announce] [ANN] PyTables 1.2.3

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

Hi List,

A new version of PyTables has been released. The reason of this
release is to provide improved support for native HDF5 files, so that
ViTables, the soon-to-be-released teammate of PyTables, can recognize
a broader range of HDF5 files. Also, a problem with row iterators and
nested tables has been fixed.

See the official announcement below:

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 Announcing PyTables 1.2.3
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I'm glad to announce a new release of PyTables. This is a maintenance
version. Several bugs and small improvements (mainly related with
improved support of native HDF5 files) have been added.

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

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


Changes more in depth
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Improvements:

=2D One can read now native HDF5 attributes of type string with padded
  nulls or spaces (fortran style), instead of only the null terminated
  ones (C style). However, these types are not yet well supported in
  datasets.

Bug fixes:

=2D Fixed problem in ``Table.whereAppend()`` with nested tables.  The
  method copies the filtered rows into the destination table field by
  field.  However, ``Row`` does not support nested fields, so one must
  iterate over the flattened column names.

=2D Solved a problem that appeared when reading attributes of unsupported
  classes. Now, when an attribute is of unsupported class (like
  BITFIELD, OPAQUE, COMPOUND, REFERENCE or VLEN), an explicative string
  is returned instead of raising a TypeError. This will allow a better
  support of native HDF5 files, while keeping the user informed that
  this specific attribute cannot be read.


Important notes for Windows users
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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

Also, note that support for the UCL compressor has not been added in the
binary build of PyTables for Windows because of memory problems (perhaps
some bad interaction between UCL and something else). Eventually, UCL
support might be dropped in the future, so, please, refrain to create
datasets compressed with it.


What it is
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**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 and NumPy support is coming along) 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
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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
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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
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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
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Let us know of any bugs, suggestions, gripes, kudos, etc. you may
have.


=2D---

  **Enjoy data!**

  -- The PyTables Team

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V   V   C=E1rabos Coop. V. =A0=A0Enjoy Data
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