Path: csiph.com!fu-berlin.de!uni-berlin.de!not-for-mail From: Valentin Haenel Newsgroups: comp.lang.python.announce Subject: [ANN] python-blosc 1.6.1 Date: Sun, 21 Oct 2018 19:29:39 +0200 Lines: 57 Approved: python-announce-list@python.org Message-ID: References: <20181021172939.GA16581@kudu.in-berlin.de> Mime-Version: 1.0 Content-Type: text/plain; charset=us-ascii X-Trace: news.uni-berlin.de hE5zLbuvtBS1pU2WZD5IpQSBVp7n/2hRGRx1uUeg2Sdg== Return-Path: X-Original-To: python-announce-list@python.org Delivered-To: python-announce-list@mail.python.org X-Spam-Status: OK 0.009 X-Spam-Evidence: '*H*': 0.98; '*S*': 0.00; 'url:org)': 0.03; 'binary': 0.05; 'repository': 0.05; 'cache': 0.07; 'handy': 0.07; 'subject:ANN': 0.07; 'wrapper': 0.07; 'additionally': 0.09; 'compression': 0.09; 'fetch': 0.09; 'github': 0.09; 'url:rst': 0.09; '===========': 0.16; 'arrays,': 0.16; 'compiling': 0.16; 'compress': 0.16; 'machinery.': 0.16; 'numpy': 0.16; 'api': 0.16; 'python': 0.17; 'numerical': 0.20; 'skip:` 10': 0.20; 'url:)': 0.21; 'subject:python': 0.22; 'header:User-Agent:1': 0.25; 'arrays': 0.25; 'processor': 0.25; 'examples': 0.25; '----': 0.27; 'memory': 0.28; 'skip:= 10': 0.31; 'url-ip:192.30/16': 0.31; 'call.': 0.31; 'function.': 0.31; 'at:': 0.32; 'subject:] ': 0.32; 'buffers': 0.33; 'skip:= 20': 0.33; 'values,': 0.33; 'interface': 0.36; 'it?': 0.36; 'allows': 0.37; 'received:192': 0.37; 'data,': 0.37; 'to:addr:python-announce-list': 0.38; 'url- ip:192.30.253.112/32': 0.38; 'url-ip:192.30.253.113/32': 0.38; 'url-ip:192.30.253/24': 0.38; 'url:github': 0.38; 'tool': 0.38; 'charset:us-ascii': 0.38; 'there': 0.39; 'subject:[': 0.40; 'through': 0.60; 'more': 0.61; 'designed': 0.61; 'announcing': 0.61; 'efficiently': 0.62; 'optimized': 0.62; 'high': 0.63; 'skip:n 10': 0.66; 'services': 0.66; 'competitive': 0.68; 'direct': 0.71; 'url-ip:lookup error': 0.75; 'low': 0.75; 'site:': 0.76; '============': 0.84; 'compressor': 0.84; 'transmit': 0.84; 'url-ip:188.226/16': 0.84; 'url:master': 0.84; 'info,': 0.91 X-Authentication-Warning: kudu.in-berlin.de: esc set sender to valentin@haenel.co using -f Content-Disposition: inline User-Agent: Mutt/1.5.23 (2014-03-12) X-Mailman-Approved-At: Tue, 23 Oct 2018 03:40:43 -0400 X-BeenThere: python-announce-list@python.org X-Mailman-Version: 2.1.29 Precedence: list List-Id: Announcement-only list for the Python programming language List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-Mailman-Original-Message-ID: <20181021172939.GA16581@kudu.in-berlin.de> Xref: csiph.com comp.lang.python.announce:2998 ============================= Announcing python-blosc 1.6.1 ============================= What is new? ============ C-Blosc has been updated to 1.14.3. Additionally there have been a number of improvments such as not compiling snappy by default and the implementation of the `get_blocksize()` function. 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!**