[Let's hope that the message is complete this time :)] 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-0.4 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! -- Francesc Alted