[Numpy-discussion] ANN: PyTables 1.2

[Francesc A. <fa...@ca...>]

PyTables is a library to deal with very large datasets. It leverages
the excellent HDF5 and numarray libraries to allow doing that in a
very efficient way using the Python language. More info in:
http://pytables.sourceforge.net/

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 Announcing PyTables 1.2
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The PyTables development team is happy to announce the availability of
a new major version of PyTables package.

This version sports a completely new in-memory tree implementation
based around a *node cache system*.  This system loads nodes only when
needed and unloads them when they are rarely used.  The new feature
allows the opening and creation of HDF5 files with large hierarchies
very quickly and with a low memory consumption (the object tree is no
longer completely loaded in-memory), while retaining all the powerful
browsing capabilities of the previous implementation of the object
tree.

You can read more about the dings and bells of the new cache system in:

http://www.carabos.com/downloads/pytables/NewObjectTreeCache.pdf

Also, Jeff Whitaker has kindly contributed a new module called
tables.NetCDF. It is designed to be used as a drop-in replacement for
Scientific.IO.NetCDF, with only minor actions to existing code. Also, if
you have the Scientific.IO.NetCDF module installed, it allows to do
conversions between HDF5 <--> NetCDF3 formats.

Go to the PyTables web site for downloading the beast:
http://pytables.sourceforge.net/

If you want more info about this release, please check out the more
comprehensive announcement message available in:

http://www.carabos.com/downloads/pytables/ANNOUNCE-1.2.html


Acknowledgments
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Thanks to the users who provided feature improvements, patches, bug
reports, support and suggestions. See THANKS file in distribution
package for a (incomplete) list of contributors. Many thanks also to
SourceForge who have helped to make and distribute this package! And
last but not least, a big thank you to THG (http://www.hdfgroup.org/)
for sponsoring many of the new features recently introduced in
PyTables.

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  **Enjoy data!**

  -- The PyTables Team

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