[Numpy-discussion] [ANN] carray 0.5 released

Francesc Alted francesc@continuum...
Tue Aug 21 13:32:16 CDT 2012


Announcing carray 0.5
=====================

What's new
----------

carray 0.5 supports completely transparent storage on-disk in addition
to memory.  That means that everything that can be done with an
in-memory container can be done using the disk instead.

The advantages of a disk-based container is that your addressable space
is much larger than just your available memory.  Also, as carray is
based on a chunked and compressed data layout based on the super-fast
Blosc compression library, and the different cache levels existing in
both modern operating systems and the internal carray machinery, the
data access speed is very good.

The format chosen for the persistence layer is based on the 'bloscpack'
library (thanks to Valentin Haenel for his inspiration) and described in
'persistence.rst', although not everything has been implemented yet.
You may want to contribute by proposing enhancements to it.  See:
https://github.com/FrancescAlted/carray/wiki/PersistenceProposal

CAVEAT: The bloscpack format is still evolving, so don't trust on
forward compatibility of the format, at least until 1.0, where the
internal format will be declared frozen.

For more detailed info, see the release notes in:
https://github.com/FrancescAlted/carray/wiki/Release-0.5


What it is
----------

carray is a chunked container for numerical data.  Chunking allows for
efficient enlarging/shrinking of data container.  In addition, it can
also be compressed for reducing memory/disk needs.  The compression
process is carried out internally by Blosc, a high-performance
compressor that is optimized for binary data.

carray can use numexpr internally so as to accelerate many vector and
query operations (although it can use pure NumPy for doing so too).
numexpr can use optimize the memory usage and use several cores for
doing the computations, so it is blazing fast.  Moreover, with the
introduction of a carray/ctable disk-based container (in version 0.5),
it can be used for seamlessly performing out-of-core computations.

carray comes with an exhaustive test suite and fully supports both
32-bit and 64-bit platforms.  Also, it is typically tested on both UNIX
and Windows operating systems.

Resources
---------

Visit the main carray site repository at:
http://github.com/FrancescAlted/carray

You can download a source package from:
http://carray.pytables.org/download

Manual:
http://carray.pytables.org/docs/manual

Home of Blosc compressor:
http://blosc.pytables.org

User's mail list:
carray@googlegroups.com
http://groups.google.com/group/carray

----

    Enjoy!

-- Francesc Alted


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