============================= Announcing python-blosc 1.2.8 ============================= What is new? ============ This is a maintenance release. Internal C-Blosc has been upgraded to 1.7.0 (although new bitshuffle support has not been made public, as it seems not ready for production yet). Also, there is support for bytes-like objects that support the buffer interface as input to ``compress`` and ``decompress``. On Python 2.x this includes unicode, on Python 3.x it doesn't. Thanks to Valentin Haenel. Finally, a memory leak in ``decompress```has been hunted and fixed. And new tests have been added to catch possible leaks in the future. Thanks to Santi Villalba. For more info, you can have a look at the release notes in: https://github.com/Blosc/python-blosc/blob/master/RELEASE_NOTES.rst More docs and examples are available in the documentation site: http://python-blosc.blosc.org What is it? =========== Blosc (http://www.blosc.org) is a high performance compressor optimized for binary data. It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() OS call. Blosc is the first compressor that is meant not only to reduce the size of large datasets on-disk or in-memory, but also to accelerate object manipulations that are memory-bound (http://www.blosc.org/docs/StarvingCPUs.pdf). See http://www.blosc.org/synthetic-benchmarks.html for some benchmarks on how much speed it can achieve in some datasets. Blosc works well for compressing numerical arrays that contains data with relatively low entropy, like sparse data, time series, grids with regular-spaced values, etc. python-blosc (http://python-blosc.blosc.org/) is the Python wrapper for the Blosc compression library. There is also a handy tool built on Blosc called Bloscpack (https://github.com/Blosc/bloscpack). It features a commmand line interface that allows you to compress large binary datafiles on-disk. It also comes with a Python API that has built-in support for serializing and deserializing Numpy arrays both on-disk and in-memory at speeds that are competitive with regular Pickle/cPickle machinery. Installing ========== python-blosc is in PyPI repository, so installing it is easy: $ pip install -U blosc # yes, you must omit the 'python-' prefix Download sources ================ The sources are managed through github services at: http://github.com/Blosc/python-blosc Documentation ============= There is Sphinx-based documentation site at: http://python-blosc.blosc.org/ Mailing list ============ There is an official mailing list for Blosc at: blosc@googlegroups.com http://groups.google.es/group/blosc Licenses ======== Both Blosc and its Python wrapper are distributed using the MIT license. See: https://github.com/Blosc/python-blosc/blob/master/LICENSES for more details. ---- **Enjoy data!** -- Francesc Alted