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ANN: python-blosc 1.9.2 released!

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  ANN: python-blosc 1.9.2 released! Francesc Alted <faltet@gmail.com> - 2020-09-09 13:27 +0200

#3482 — ANN: python-blosc 1.9.2 released!

=============================
Announcing python-blosc 1.9.2
=============================

What is new?
============

This is a maintenance release to better support recent versions of Python
(3.8 and 3.9).  Also, and due to the evolution of modern CPUs,
the number of default threads has been raised to 8 (from 4).
Finally, zero-copy decompression is now supported by allowing bytes-like
input.  Thanks to Lehman Garrison.

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 data files 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!**

-- 
The Blosc Development Team

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