PyTables 2.2rc2 ready to test
faltet at pytables.org
Thu Jun 17 15:22:52 CEST 2010
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:
* 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:
* 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:
You can download a source package with generated PDF and HTML docs, as
well as binaries for Windows, from:
For an on-line version of the manual, visit:
About the HDF5 library:
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
-- The PyTables Team
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