PyTables 3.1.1 released

PyTables 3.1.1

This is a bug-fix release that addresses a critical bug that make PyTables
unusable on some platforms.

What's new

• Fixed a critical bug that caused an exception at import time.
  The error was triggered when a bug in long-double detection is detected
  in the HDF5 library (see :issue:275) and numpy_ does not expose
  float96 or float128. Closes :issue:344.
• The internal Blosc_ library has been updated to version 1.3.5.
  This fixes a false buffer overrun condition that made c-blosc to fail,
  even if the problem was not real.

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/3.1.1

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.

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Enjoy data!

-- The PyTables Developers