========================= 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: http://pytables.sourceforge.net/ 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: http://sourceforge.net/mailarchive/forum.php?thread_id=9253854&forum_id=13760 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: 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 (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 (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 have. ---- **Enjoy data!** -- The PyTables Team