=========================== Announcing PyTables 1.3.1 =========================== This is a new minor release of PyTables. On it, you will find support for NumPy integers as indexes of datasets and many bug fixes. *Important note*: one of the fixes adresses a bug in the flushing of I/O buffers that was introduced back in PyTables 1.1. So, for those of you that want to improve the integrity of the PyTables files during unexpected crashes, an upgrade is strongly encouraged. 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: - NumPy integer scalars are supported as indexes for ``__getitem__()`` and ``__setitem__()`` methods in ``Leaf`` objects. In addition, any object that exposes the __index__() method (see PEP 357) is supported as well. This later feature will be more useful for those of you that start using Python 2.5. Meanwhile, PyTables will use its own guessing (quite trivial, in fact) in order to convert indexes to 64-bit integers (you know, PyTables does support 64-bit indexes even in 32-bit platforms). Bug fixes: - ``Leaf.flush()`` didn't force an actual flush on-disk at HDF5 level, raising the chances of letting the file in an inconsistent state during an unexpected shutdown of the program. Now, it works as it should. This bug was around from PyTables 1.1 on. Thanks to Andrew Straw for complaining about this persistently enough. ;-) - The code for recognizing a leaf class in a native HDF5 file was not aware of the ``trMap`` option if ``openFile()``, thus giving potentially scary error messages. This has been fixed. - When an iterator was used on an unbound table, PyTables crashed. A workaround has been implemented so as to avoid this. In addition, a better solution has been devised, but as it requires an in-deep refactorisation, it will be delivered with PyTables 1.4 series. - Added ``Enum.__ne__()`` method to avoid equal enumerations comparing to both True and False. Closes ticket #8 (thanks to Ashley Walsh). - Work around Python bug when storing floats in attributes under some locales. See ticket #9 (thanks to Fabio Zadrozny). 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 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 (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