=========================== Announcing PyTables 2.2rc2 =========================== PyTables is a library for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data with support for full 64-bit file addressing. PyTables runs on top of the HDF5 library and NumPy package for achieving maximum throughput and convenient use. This is the second (and probably last) release candidate for PyTables 2.2, so please test it as much as you can before I declare the beast stable. The main new features in 2.2 series are: * A new compressor called Blosc, designed to read/write data to/from memory at speeds that can be faster than a system `memcpy()` call. With it, many internal PyTables operations that are currently bounded by CPU or I/O bandwith are speed-up. Some benchmarks: http://blosc.pytables.org/trac/wiki/SyntheticBenchmarks * A new `tables.Expr` module (based on Numexpr) that allows to do persistent, on-disk computations on many algebraic operations. For a brief look on its performance, see: http://pytables.org/moin/ComputingKernel * Support for HDF5 hard links, soft links and automatic external links (kind of mounting external filesystems). A new tutorial about its usage has been added to the 'Tutorials' chapter of User's Manual. * Suport for 'fancy' indexing (i.e., à la NumPy) in all the data containers in PyTables. Backported from the implementation in the h5py project. Thanks to Andrew Collette for his fine work on this! As always, a large amount of bugs have been addressed and squashed too. In case you want to know more in detail what has changed in this version, have a look at: http://www.pytables.org/moin/ReleaseNotes/Release_2.2rc2 You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://www.pytables.org/download/preliminary For an on-line version of the manual, visit: http://www.pytables.org/docs/manual-2.2rc2 Resources ========= About PyTables: http://www.pytables.org About the HDF5 library: http://hdfgroup.org/HDF5/ About NumPy: http://numpy.scipy.org/ Acknowledgments =============== Thanks to many 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. Most specially, a lot of kudos go to the HDF5 and NumPy (and numarray!) makers. Without them, PyTables simply would not exist. Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. ---- **Enjoy data!** -- The PyTables Team -- Francesc Alted