[Chicago] Fwd: ANN: PyTables 1.4 released

[michael bobak]

Begin forwarded message:

> From: Francesc Altet <faltet at carabos.com>
> Date: December 21, 2006 11:47:41 AM CST
> To: news at hdfgroup.org
> Subject: ANN: PyTables 1.4 released
>
> ===========================
>  Announcing PyTables 1.4
> ===========================
>
> PyTables is a Python library for managing hierarchical datasets and
> designed to efficiently cope with extremely large amounts of data with
> support for full 64-bit file addressing.  It is based on the HDF5
> library and leverages the numarray/NumPy/Numeric packages for
> providing convenient data containers.
>
> This is a new major release of PyTables, and probably the last  
> major one
> of the 1.x series (i.e. with numarray at the core). On it, we have
> implemented better code to deal with table buffers, enhanced the
> capability for reading native HDF5 files, enhanced support for 64-bit
> platforms (but not with Python 2.5: see ``Special Warning`` section
> below), better support for AIX, optional automatic parent creation and
> the traditional amount of bug fixes.
>
> 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:
>
> - Table buffers code refactored: now each Row read iterator has its  
> own
>   buffers, completely independent of their table (although write
>   iterators still share a single buffer in the same table). This
>   separation makes the logic of buffering much more clear and less  
> prone
>   to errors (in fact, some of them have been solved).  Performance and
>   memory consumption are more or less equal than before.
>
> - When flushing the complete file (i.e. when calling File.flush()),  
> only
>   the buffers of those nodes that are alive (i.e. referenced from user
>   code) are actually flushed. This brings much better efficiency (and
>   also stability) to situations where one has to flush (and hence,
>   close) files with many nodes on it.
>
> - Better support for AIX by renaming the internal LONLONG_MAX C  
> constant
>   (it was used internally by the xlc compiler). Thanks to Brian  
> Granger
>   for the report.
>
> - Added optional automatic parent creation support during node  
> creation,
>   copying and moving operations.  See the release notes for more
>   information.
>
> - Improved support for Python2.4 and 64-bit platforms (but beware,  
> there
>   are still known issues when using Python2.5 in combination with  
> 64-bit
>   platforms). Thanks to Gerard Vermeulen for his patches for Win64
>   platforms.
>
> - Implemented a workaround for a leak present in numarray --> Numeric
>   conversions when using the array protocol, as can be seen in:
>
>   http://comments.gmane.org/gmane.comp.python.numeric.general/12563
>
>   The workaround can potentially be far slower than the array protocol
>   (because a copy of the arrays is always made), but at least the new
>   code doesn't leak anymore.
>
> Bug fixes:
>
> - Previously, when the size for memory compounds type was less than  
> the
>   size of the type on disk (for example, when one have padding or
>   aligned fields), PyTables was unable to read info on them. This has
>   been fixed. This allows reading general compound types in HDF5 files
>   written with other tools than PyTables.
>
> - When many tables with indexed columns were created simultaneously, a
>   bug make PyTables to crash. This has been fixed (for more info, see
>   bug #26).
>
> - Fixed a typo in the code that prevented recognizing complex data in
>   non-PyTables files.
>
> - Table.createIndex() now refuses to index complex columns.
>
> - Now, it is possible to index several nested columns that hangs from
>   the same column parent. Fixes bug #24.
>
> - Fixed a typo in nctoh5 utility that prevented using filters
>   properly. Thanks to Lou Wicker for reporting this.
>
> - When setting/appending an array in-memory to an Array (or  
> descendant)
>   object and they have mismatched byteorders, the array was set/ 
> appended
>   without being byteswapped first. This has been fixed. Thanks to  
> Elias
>   Collas for the report.
>
> Deprecated features:
>
> - None
>
> Backward-incompatible changes:
>
> - Please, see ``RELEASE-NOTES.txt`` file.
>
>
> Special Warning for Python 2.5 and 64-bit platforms users
> =========================================================
>
> Unfortunately, and due to problems with the combination numarray  
> 1.5.2,
> Python2.5 and 64-bit platforms, PyTables cannot be safely used yet in
> such scenario.  This will be solved either when numarray can address
> this issue (hopefully with numarray 1.5.3), or when PyTables 2.x  
> series
> (with NumPy at its core) will be out.
>
>
> Important note for Windows users
> ================================
>
> If you are willing to use PyTables with Python 2.4 or 2.5 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
>
>
> 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
>
> About NumPy:
>
> http://numpy.scipy.org/
>
> 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 Acusim (http://www.acusim.com/) for sponsoring many of the job done
> for releasing this version of PyTables.
>
>
> Share your experience
> =====================
>
> Let us know of any bugs, suggestions, gripes, kudos, etc. you may
> have.
>
>
> ----
>
>   **Enjoy data!**
>
>   -- The PyTables Team
>
>
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