Note: This project is currently being developed as a persistent layer for Blaze, with the BLZ codename. You can find the sources for BLZ over here: https://github.com/ContinuumIO/blaze/tree/master/blaze/io/blz
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.
By using compression, you can deal with more data using the same amount of memory. In case you wonder: which is the price to pay in terms of performance? you should know that nowadays memory access is the most common bottleneck in many computational scenarios, and CPUs spend most of its time waiting for data, and having data compressed in memory can reduce the stress of the memory subsystem.
In other words, the ultimate goal for carray is not only reducing the memory needs of large arrays, but also making carray operations to go faster than using a traditional ndarray object from NumPy. That is already the case for some special cases now, but will happen more generally in a short future, when carray will be able to take advantage of newer CPUs integrating more cores and wider vector units.
- Python >= 2.6
- NumPy >= 1.5
- Cython >= 0.16
Assuming that you have the requisites and a C compiler installed, do:
$ python setup.py build_ext --inplace
After compiling, you can quickly check that the package is sane by running:
$ PYTHONPATH=. (or "set PYTHONPATH=." on Windows) $ export PYTHONPATH (not needed on Windows) $ python carray/tests/test_all.py
Install it as a typical Python package:
$ python setup.py install
Please refer to the doc/ directory. The HTML manual is in doc/html/, and the PDF version is in doc/carray-manual.pdf. Of course, you can always access docstrings from the console (i.e. help(carray.ctable)).
Also, you may want to look at the bench/ directory for some examples of use.
Visit the main carray site repository at: http://github.com/FrancescAlted/carray
You can download a source package from: http://carray.pytables.org/download
Home of Blosc compressor: http://blosc.pytables.org
Please see CARRAY.txt in LICENSES/ directory.
Let us know of any bugs, suggestions, gripes, kudos, etc. you may have.