============================= Announcing python-blosc 1.3.1 =============================
What is new? ============ This is an important release in terms of stability. Now, the -O1 flag for compiling the included C-Blosc sources on Linux. This represents slower performance, but fixes the nasty issue #110. In case maximum speed is needed, please `compile python-blosc with an external C-Blosc library < https://github.com/Blosc/python-blosc#compiling-with-an-installed-blosc-library-recommended )>`_. Also, symbols like BLOSC_MAX_BUFFERSIZE have been replaced for allowing backward compatibility with python-blosc 1.2.x series. For whetting your appetite, look at some benchmarks here: https://github.com/Blosc/python-blosc#benchmarking 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 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, with added functions (`compress_ptr()` and `pack_array()`) for efficiently compressing NumPy arrays, minimizing the number of memory copies during the process. python-blosc can be used to compress in-memory data buffers for transmission to other machines, persistence or just as a compressed cache. There is also a handy tool built on top of python-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. Sources repository ============== The sources and documentation are managed through github services at: http://github.com/Blosc/python-blosc ---- **Enjoy data!** -- Francesc Alted -- https://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations/