[Numpy-discussion] ANN: PyTables 1.2.1

Francesc Altet faltet at carabos.com
Thu Dec 22 01:11:04 EST 2005

Announcing PyTables 1.2.1

I'm happy to announce a new version of PyTables. This is a maintenance
version and only bugs has been fixed in it. In particular, the
documentation has not change at all, so it is not necessary that you
look at it for changes.

Go to the PyTables web site for downloading the beast:

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

I take the opportunity to announce as well that PyTables is adopting
the bazaar-ng (http://www.bazaar-ng.org/) distributed version control,
in order to easy the contributions from developers throughout the
world. See more info about this new facility (currently in beta) in:


Changes more in depth

Bug fixes:

- Table.flush() is called automatically before disposing a table object
  from the user space. This avoids a problem that appears when the user
  does not explicitely do the flush and the table is unbounded and
  rebounded after on (using h5file.getNode() for example).

- A small typo has been fixed in the ptrepack utility. This
  prevented ptrepack from working correctly when asking for statistics
  on operations done (-v flag).

Known issues:

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

Backward-incompatible changes:

- None.

Important note for MacOSX users

From PyTables 1.2 on, the UCL compressor seems to work well again on
MacOSX platforms. We don't know exactly why, but the fact is that all
the test suite passes (using UCL) executes flawlessly. So, from now on,
support for UCL in MacOSX is enabled again by default (i.e. you don't
need to use the flag ``--force-ucl``, which has disappeared).

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:

Users of Python 2.3 on Windows will have to download the version of
HDF5 compiled with MSVC 6.0 available in:

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.


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.


Go to the PyTables web site for more details:


About the HDF5 library:


About numarray:


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



Thanks to various the users who provided feature improvements,
patches, bug reports, support and suggestions. See THANKS file in
distribution package for a (necessarily 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


  **Enjoy data!**

  -- The PyTables Team

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