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[Pytables-announce] PyTables 1.3 released
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From: Francesc A. <fa...@ca...> - 2006-04-01 18:55:18
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Hi List, After investing quite a lot of time in the set-up of the new web site for pytables: http://www.pytables.org which happens to be a wiki (so you are encouraged to contribute with recipes or other documents!), and setting up a new Trac site: http://www.pytables.org/trac/ that will allow people to better following the PyTables development, now it's time to announce PyTables 1.3. Enjoy it! =46rancesc =2D--------------------------------------------------------------------- =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Announcing PyTables 1.3 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D This is a new major release of PyTables. The most remarkable feature added in this version is a complete support (well, almost, because unicode arrays are not there yet) for NumPy objects. Improved support for native HDF5 is there as well. As an aside, I'm happy to inform you that the PyTables web site (http://www.pytables.org) has been converted into a wiki so that users can contribute to the project with recipes or any other document. Try it out! Go to the (new) 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 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Improvements: =2D Support for NumPy objects in all the objects of PyTables, namely: Array, CArray, EArray, VLArray and Table. All the numerical and character (except unicode arrays) flavors are supported as well as plain and nested heterogeneous NumPy arrays. PyTables leverages the adoption of the array interface (http://numeric.scipy.org/array_interface.html) for a very efficient conversion between all the numarray (which continues to be the native flavor for PyTables) object to/from NumPy/Numeric. =2D The FLAVOR schema in PyTables has been refined and simplified. Now, the only 'flavors' allowed for data objects are: "numarray", "numpy", "numeric" and "python". The changes has been made so that they are fully backward compatible with existing PyTables files. However, when users would try to use old flavors (like "Numeric" or "Tuple") in existing code, a ``DeprecationWarning`` will be issued in order to encourage them to migrate to the new flavors as soon as possible. =2D Nested fields can be specified in the "field" parameter of Table.read by using a '/' as a separator between fields (e.g. 'Info/value'). =2D The Table.Cols accessor has received a new ``__setitem__()`` method that allows doing things like: table.cols[4] =3D record table.cols.x[4:1000:2] =3D array # homogeneous column table.cols.Info[4:1000:2] =3D recarray # nested column =2D A clean-up function (using ``atexit``) has been registered so that remaining opened files are closed when a user hits a ^C, for example. That would help to avoid ending with corrupted files. =2D Native HDF5 compound datasets that are contiguous are supported now. Before, only chunked datasets were supported. =2D Updated (and much improved) sections about compression issues in the User's Guide. It includes new benchmarks made with PyTables 1.3 and a exhaustive comparison between Zlib, LZO and bzip2. =2D The HTML version of manual is made now from the docbook2html package for an improved look (IMO). Bug fixes: =2D Solved a problem when trying to save CharArrays with itemsize =3D 0 as attributes of nodes. Now, these objects are pickled in order to prevent HDF5 from crashing. =2D Fixed some alignment issues with nested record arrays under certain architectures (e.g. PowerPC). =2D Fixed automatic conversions when a VLArray is read in a platform with a byte ordering different from the file. Deprecated features: =2D Due to recurrent problems with the UCL compression library, it has been declared deprecated from this version on. You can still compile PyTables with UCL support (using the --force-ucl), but you are urged to not use it anymore and convert any existing datafiles with UCL to other supported library (zlib, lzo or bzip2) with the ``ptrepack`` utility. Backward-incompatible changes: =2D Please, see ``RELEASE-NOTES.txt`` file. Important note for Windows users =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D= =3D=3D=3D=3D=3D=3D=3D 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 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D **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 =3D=3D=3D=3D=3D=3D=3D=3D=3D 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 =3D=3D=3D=3D=3D=3D=3D=3D=3D 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 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D 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 =3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D=3D Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. =2D--- **Enjoy data!** -- The PyTables Team =2D-=20 >0,0< Francesc Altet =A0 =A0 http://www.carabos.com/ V V C=E1rabos Coop. V. =A0=A0Enjoy Data "-" |
