=========================== Announcing PyTables 1.3.3 =========================== I'm happy to announce a new minor release of PyTables. In this one, we have focused on improving compatibility with latest beta versions of NumPy (0.9.8, 1.0b2, 1.0b3 and higher), adding some improvements and the typical bunch of fixes (some of them are important, like the possibility of re-using the same nested class in declaration of table records; see later). 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: - Added some workarounds on a couple of 'features' of recent versions of NumPy. Now, PyTables should work with a broad range of NumPy versions, ranging from 0.9.8 up to 1.0b3 (and hopefully beyond, but let's see). - When a loop for appending a table is not flushed before the node is unbounded (and hence, becomes ``killed`` in PyTables slang), like in:: import tables as T class Item(T.IsDescription): name = T.StringCol(length=16) vals = T.Float32Col(0.0) fileh = T.openFile("/tmp/test.h5", "w") table = fileh.createTable(fileh.root, 'table', Item) for i in range(100): table.row.append() #table.flush() # uncomment this prevent the warning table = None # Unbounding table node! a ``PerformanceWarning`` is issued telling the user that it is *much* recommended flushing the buffers in a table before unbounding it. Hopefully, this will also prevent other scary errors (like ``Illegal Instruction``, ``Malloc(): trying to call free() twice``, ``Bus Error`` or ``Segmentation fault`` ) that some people is seeing lately and which are most probably related with this issue. Bug fixes: - In situations where the same metaclass is used for declaring several columns in a table, like in:: class Nested(IsDescription): uid = IntCol() data = FloatCol() class B_Candidate(IsDescription): nested1 = Nested() nested2 = Nested() they were sharing the same column metadata behind the scenes, introducing several inconsistencies on it. This has been fixed. - More work on different padding conventions between NumPy/numarray. Now, all trailing spaces in chararrays are stripped-off during write/read operations. This means that when retrieving NumPy chararrays, it shouldn't appear spureous trailing spaces anymore (not even in the context of recarrays). The drawback is that you will loose *all* the trailing spaces, no matter if you want them in this place or not. This is not a very confortable situation to deal with, but hopefully, things will get better when NumPy would be at the core of PyTables. In the meanwhile, I hope that the current behaviour would be a minor evil for most of situations. This closes ticket #13 (again). - Solved a problem with conversions from numarray charrays to numpy objects. Before, when saving numpy chararrays with a declared length of N, but none of this components reached such a length, the dtype of the numpy chararray retrieved was the maximum length of the component strings. This has been corrected. - Fixed a minor glitch in detection of signedness in IntAtom classes. Thanks to Norbert Nemec for reporting this one and providing the fix. Known bugs: - Using ``Row.update()`` in tables with some columns marked as indexed gives a ``NotImplemented`` error although it should not. This is fixed in SVN trunk and the functionality will be available in the 1.4.x series. Meanwhile, a workaround would be refraining to declare columns as indexed and index them *after* the update process (with Col.createIndex() for example). Deprecated features: - None Backward-incompatible changes: - Please, see ``RELEASE-NOTES.txt`` file. Important note for 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 qsupport 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 (but NumPy and Numeric are 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 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 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 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