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Announcing carray 0.4
=====================
What's new
----------
The most prominent feature in 0.4 is the support of multidimensional
carrays. Than means that, for example, you can do::
>>> a = ca.arange(6).reshape((2,3))
Now, you can access any element in any dimension::
>>> a[:]
array([[0, 1, 2],
[3, 4, 5]])
>>> a[1]
array([3, 4, 5])
>>> a[1,::2]
array([3, 5])
>>> a[1,1]
4
Also, all the iterators in carray have received a couple of new
parameters that allows to `limit` or `skip` selected elements in
queries.
Finally, many performance improvements have been implemented (mainly
related with efficient zero-detection code). This allows for improved
query times when using iterators.
See:
https://github.com/FrancescAlted/carray/wiki/query-compress
for an example on how fast the new iterators can do.
For more detailed info, see the release notes in:
https://github.com/FrancescAlted/carray/wiki/Release-0.3.2
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 needs. The compression process
is carried out internally by Blosc, a high-performance compressor that
is optimized for binary data.
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!
.. Local Variables:
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