Hey Francesc,
Congrats, multidim support!
I had a few short questions about the carray;
- Are you planning to implement operator overloading for carrays?
For example, a<10 gives a boolean array for numpy, but gives False
in the case of a carray, but ca.eval('a<10') works also for carrays.
- Is there a reason why multidimensional slicing is not supported?
Stuff like a[:,2] is very nice imo.. [r[2] for r in a] builds a list,
and is a lot slower than the np variant..
- Is that also the reason why the shape information resides partly
in the dtype? Problem is that if you do:
b = ca.arange(9).reshape((3,3))
Then b.shape is (3,), which is not (3,3)..
The same issue with the dtype, which is not comparable to the original,
e.g. not atomic..
Gr, Han
> -----Original Message-----
> From: Francesc Alted [mailto:fal...@pytables.org]
> Sent: Tuesday, 18 January, 2011 17:30
> To: Discussion list for PyTables
> Subject: [Pytables-users] ANN: carray 0.3.1
>
> Hi List,
>
> I'm happy to announce the 0.3.1 version of carray, and in-memory
> compressed data container.
>
> carray comes with a ctable object, that from this version on, can
> emulate most of the features of the Table object in PyTables. However,
> as a ctable object is 1) resident in-memory and 2) column wise ordered,
> it is able to get much more faster speeds than a regular Table object.
> The drawback of a ctable object is that it is not persistent (but you
> can always use PyTables to serialize ctable objects).
>
> My plan is to give full support of carray objects in PyTables (be ready
> to see a new flavor named `carray` for PyTables 3), because it can be a
> good complement to it (e.g. think of compressed, extremely fast I/O
> buffers implemented with carray objects).
>
> carray is still in beta, but already comes with a pretty complete test
> suite, and there are no *known* bugs at the moment. So you may want to
> try it out and see if it can serve your purposes of dealing with your
> huge datasets faster.
>
> Now, the official announcement,
>
> ========================
> Announcing carray 0.3.1
> ========================
>
> What's new
> ==========
>
> This version implements multidimensional carrays and support for all
> NumPy dtypes, except 'object'. Some fixes have been included too.
>
> For more detailed info, see the release notes in:
> https://github.com/FrancescAlted/carray/wiki/Release-0.3.1
>
> What it is
> ==========
>
> carray is a container for numerical data that can be compressed
> in-memory. The compresion process is carried out internally by Blosc, a
> high-performance compressor that is optimized for binary data. Having
> data compressed in-memory can reduce the stress of the memory subsystem.
> The net result is that carray operations may be faster than using a
> traditional ndarray object from NumPy.
>
> carray also supports fully 64-bit addressing (both in UNIX and Windows).
>
> 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:
> car...@googlegroups.com
> http://groups.google.com/group/carray
>
> Share your experience
> =====================
>
> Let us know of any bugs, suggestions, gripes, kudos, etc. you may
> have.
>
> ----
>
> Enjoy!
>
> --
> Francesc Alted
>
> --------------------------------------------------------------------------
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