ANN: python-blosc 1.10.0 released

[Francesc Alted]

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Announcing python-blosc 1.10.0

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What is new?

============

This is a maintenance release mainly to support Python wheels.  Also,

we have updated the internal C-Blosc sources to 1.21.0.

We are generating wheels for Intel (32 and 64 bits) and all major OS

(Win, Linux, Mac). In addition to extensions, we are distributing library

binaries in the wheels too.  This way, people willing to use the C-Blosc

library can make use of these wheels to install the necessary development

files.  For details, see:

https://github.com/Blosc/c-blosc/blob/master/COMPILING_WITH_WHEELS.rst

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 contain 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 command 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

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  **Enjoy data!**

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Francesc Alted