Hi List, This is to announce the release of PyTables 2.2 final.
I've finally declared Blosc stable and I'm very happy to say that Blosc is showing very good results on every benchmark that I'm doing with PyTables. I'd like to thank all of those that have collaborated on its intensive testing. Well, I hope you will like the new release too! Francesc ================================= Announcing PyTables 2.2 (final) ================================= I'm happy to announce PyTables 2.2 (final). After 18 months of continuous development and testing, this is, by far, the most powerful and well-tested release ever. What's new ========== 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 And a demonstration on how Blosc can improve PyTables performance: http://www.pytables.org/docs/manual/ch05.html#chunksizeFineTune * Support for HDF5 hard links, soft links and external links (kind of mounting external filesystems). A new tutorial about its usage has been added to the 'Tutorials' chapter of User's Manual. See: http://www.pytables.org/docs/manual/ch03.html#LinksTutorial * 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 * 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! * Binaries for both Windows 32-bit and 64-bit are provided now. 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_2.2 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-2.2 What it is? =========== 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. 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 ------------------------------------------------------------------------------ This SF.net email is sponsored by Sprint What will you do first with EVO, the first 4G phone? Visit sprint.com/first -- http://p.sf.net/sfu/sprint-com-first _______________________________________________ Pytables-users mailing list Pytables-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/pytables-users