=========================== Announcing PyTables 3.1.0 =========================== We are happy to announce PyTables 3.1.0. This is a feature release. The upgrading is recommended for users that are running PyTables in production environments. What's new ========== Probably the most relevant changes in this release are internal improvements like the new node cache that is now compatible with the upcoming Python 3.4 and the registry for open files has been deeply reworked. The caching feature of file handlers has been completely dropped so now PyTables is a little bit more "thread friendly". New, user visible, features include: - a new lossy filter for HDF5 datasets (EArray, CArray, VLArray and Table objects). The *quantization* filter truncates floating point data to a specified precision before writing to disk. This can significantly improve the performance of compressors (many thanks to Andreas Hilboll). - support for the H5FD_SPLIT HDF5 driver (thanks to simleo) - all new features introduced in the Blosc_ 1.3.x series, and in particular the ability to leverage different compressors within Blosc_ are now available in PyTables via the blosc filter (a big thank you to Francesc) - the ability to save/restore the default value of :class:`EnumAtom` types Also, installations of the HDF5 library that have a broken support for the *long double* data type (see the `Issues with H5T_NATIVE_LDOUBLE`_ thread on the HFG5 forum) are detected by PyTables 3.1.0 and the corresponding features are automatically disabled. Users that need support for the *long double* data type should ensure to build PyTables against an installation of the HDF5 library that is not affected by the bug. .. _`Issues with H5T_NATIVE_LDOUBLE`: http://hdf-forum.184993.n3.nabble.com/Issues-with-H5T-NATIVE-LDOUBLE-tt40264... As always, a large amount of bugs have been addressed and squashed as well. In case you want to know more in detail what has changed in this version, please refer to: http://pytables.github.io/release_notes.html You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://sourceforge.net/projects/pytables/files/pytables/3.1.0 For an online version of the manual, visit: http://pytables.github.io/usersguide/index.html 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. PyTables includes OPSI, a new indexing technology, allowing to perform data lookups in tables exceeding 10 gigarows (10**10 rows) in less than a tenth of a second. 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 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 Developers