========================== 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@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. 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 problems. 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 thanks 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