ANN: python-blosc 1.9.0 released

[Francesc Alted]

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Announcing python-blosc 1.9.0

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What is new?

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In this release we got rid of the support for Python 2.7 and 3.5.

Also, we fixed the copy of the leftovers of a chunk when its size is not a

multiple of the typesize.  Although this is a very unusual situation,

it can certainly happen (e.g. https://github.com/Blosc/python-blosc/issues/220).

Finally, sources for C-Blosc v1.18.1 have been included.

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?

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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

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The sources and documentation are managed through github services at:

http://github.com/Blosc/python-blosc

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  **Enjoy data!**