Mailman 3 April 2016 - Python-announce-list

Sphinx 1.4.1 released
by Takayuki Shimizukawa 16 Apr '16

16 Apr '16

Hi all, I'm delighted to announce the release of Sphinx 1.4.1, now available on the Python package index at <http://pypi.python.org/pypi/Sphinx>. It includes about 13 bug fixes for the 1.4 release series, among them a incompatibility and a packaging error. For the full changelog, go to < http://www.sphinx-doc.org/en/stable/changes.html>. Thanks to all collaborators and contributers! What is it? =========== Sphinx is a tool that makes it easy to create intelligent and beautiful documentation for Python projects (or other documents consisting of multiple reStructuredText source files). Website: http://sphinx-doc.org/ IRC: #sphinx-doc on irc.freenode.net Enjoy! -- Takayuki SHIMIZUKAWA http://about.me/shimizukawa

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PyKMIP 0.5.0
by Peter Hamilton 14 Apr '16

14 Apr '16

I am pleased to announce the release of PyKMIP 0.5.0. PyKMIP is a Python implementation of the Key Management Interoperability Protocol (KMIP), a communications protocol for the storage and maintenance of keys, certificates, and other secret objects. PyKMIP provides clients for conducting key management operations against KMIP appliances and a software server application for testing and demonstration. The library is licensed under Apache 2.0 and supports Python 2.6, 2.7, 3.3, and 3.4. Changelog: * Add KmipServer server implementation * Add KmipSession to manage threaded client/server connections * Add KmipEngine for processing core server application logic * Add KmipEngine support for CRUD operations for managed objects * Add SQLAlchemy/SQLite support for KmipEngine data storage * Add CryptographyEngine component for cryptographic operations * Add pending deprecation warning for Python 2.6 support * Add pending deprecation warning for the KMIPServer implementation * Add support for building Sphinx documentation * Add support for SQLAlchemy tables to all Pie objects * Add Python magic methods to Attribute and Name objects * Add Attribute class unit tests * Add bin script to run the KmipServer * Add setup entry points to run the KmipServer * Update DiscoverVersions demo with optional versions argument * Update all demo scripts to setup their own logging infrastructure * Update README with information on the KmipServer implementation * Remove expired certificate files from the integration test suite * Remove default package log configuration and configuration file * Fix bug with Locate payload parsing optional values * Fix bug with DateTime string tests and move to UTC representation GitHub: https://github.com/OpenKMIP/PyKMIP PyPI: https://pypi.python.org/pypi/PyKMIP/0.5.0 IRC: #pykmip on freenode.net Thanks to all of the contributors for their time and effort. Cheers, Peter Hamilton

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[ANN] MicroPython 1.7
by Paul Sokolovsky 14 Apr '16

14 Apr '16

Hello, MicroPython is a lean and efficient Python implementation for microcontrollers, embedded, and mobile systems (which also runs just as fine on desktops, servers, and clouds). v1.7 adds the MicroPython cross-compiler that can generate .mpy files (pre-compiled bytecode) which can be executed within any MicroPython runtime/VM. The ESP8266 port is also vastly improved thanks to a hugely successful Kickstarter campaign. This port now has an alternative event loop implementation to allow proper Berkeley sockets using lwIP built from source, and a greatly improved network module. Many extension modules are now enabled (ujson, ubinascii, uctypes, uhashlib, urandom, uheapq, ure, uzlib) and the machine module includes Timer, Pin, PWM, ADC, I2C, SPI, and basic UART. Bignums are supported, along with 30-bit precision floating point, and normal error messages. There is FatFS support and uos.dupterm. Many other improvements and bug fixes have been made to the core and other ports. The basic test suite can now run within a 16k heap. Inline assembler functions now support 4 arguments. The Unix port has the -i option to start the REPL after a script is finished. The stmhal port now exposes the flash and SD card as proper objects with the block protocol. There is support for generic STM32F439 boards, the NUCLEO-F411RE board, and targets to deploy via ST-LINK and OpenOCD. MicroPython was packaged for the upcoming release of Fedora 24. Full changelog: https://github.com/micropython/micropython/releases/tag/v1.7 -- Best regards, Paul mailto:pmiscml@gmail.com

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Crochet 1.5.0: Use Twisted in Django, Flask, or anywhere else
by Itamar Turner-Trauring 14 Apr '16

14 Apr '16

Do you wish you could: * Use the power of Twisted's asynchronous networking in Django, Flask, or other threaded applications? * Provide a blocking API powered by Twisted underneath, without exposing it to the caller? * Write a library that provides APIs both for Twisted and non-Twisted applications? * Use threads more easily inside Twisted applications? Crochet lets you do all that, and more, by hiding and automatically starting the Twisted reactor and providing a blocking API for calling into Twisted. If you need to use your library from a normal Twisted application you can disable the auto-start functionality. Here's an example of using Twisted in a blocking manner: from __future__ import print_function from twisted.names import client from crochet import setup, wait_for setup() @wait_for(timeout=5.0) def gethostbyname(name): """Use the Twisted DNS library.""" d = client.lookupAddress(name) d.addCallback(lambda result: result[0][0].payload.dottedQuad()) return d if __name__ == '__main__': # Application code using the public API - notice it works in a normal # blocking manner, with no event loop visible: import sys name = sys.argv[1] ip = gethostbyname(name) print(name, "->", ip) New in 1.5.0 is official Python 3.5 support; Python 2.6, 3.3 and older versions of Twisted are no longer officially supported (but are likely to still work). If you need help using Crochet (or just general Twisted or Python help) I am currently available for short-term consulting. You can read the documentation at https://crochet.readthedocs.org/. --Itamar Turner-Trauring

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Jinja24Doc 1.3.1 released
by Ondrej Tuma 13 Apr '16

13 Apr '16

Hi everyone, I'm happy to announce that Jinja24Doc 1.3.1 has been released! As usual, you can install using pip: pip install Jinja24Doc The main changes in new release are: * separate functionality to extra files - modules (library usage) * reStructuredText support via docutils_tinyhtml writer as default format * new rst24doc and wiki24doc command tools * variable top and link for module (_reference template) * options system message, verbose, version * traceback and embed stylesheet support * new descriptor and dt styles * fix of regular expression for wiki * templates and skin update To see the full changelog, please visit: https://github.com/PoorHttp/Jinja24Doc/blob/master/doc/ChangeLog Documentation and reference api is at: http://poorhttp.zeropage.cz/jinja24doc.html Please make sure to report any regressions to the issues page at: https://github.com/PoorHttp/Jinja24Doc/issues -- Ondřej Tůma <mcbig(a)zeropage.cz> www: http://ipv6.mcbig.cz jabber: mcbig(a)jabber.cz twitter: mcbig_cz

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PoorWSGI 1.6.1 released
by Ondrej Tuma 13 Apr '16

13 Apr '16

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 Hi everyone, I'm happy to announce that PoorWSGI 1.6.1 has been released! Poor WSGI for Python is light WSGI connector with uri routing between WSGI server and your application. It have mod_python compatible request object, which is post to all uri or http state handler. As usual, you can install using pip: pip install PoorWSGI The main changes in new release are: * profiling rewrite * some deprecated elements are removed * new Application methods and properties * new Request methods and properties * better debug output * new hex filter * better json support * new log_info, log_debug and log_warning methods * runtime routing manipulation * some bug fixies and code refactoring To see the full changelog, please visit: https://github.com/PoorHttp/PoorWSGI/blob/master/doc/ChangeLog Documentation and reference api is at: http://poorhttp.zeropage.cz/poorwsgi.html Please make sure to report any regressions to the issues page at: https://github.com/PoorHttp/PoorWSGI/issues Ondřej Tůma - -- Ondřej Tůma <mcbig(a)zeropage.cz> www: http://ipv6.mcbig.cz jabber: mcbig(a)jabber.cz twitter: mcbig_cz -----BEGIN PGP SIGNATURE----- Version: GnuPG v2 iQIcBAEBCAAGBQJXDV2EAAoJEAXfp4slfOqa9XcQAJ7TWtSuabYWcTkFOzBufJtc yoNwkjE1ZTqZkqFvqH1fRa5qVF3zKHrV4RAh9JJ6lr8hadZ7BztkznWOP8y59+Gb piWlByVOnzpcGkFzDxPKpcveFM7Wx1cVpnvCsY28J/4DBDV3E6VEqsi09UISW8yM V/yEzUPD/Fn6IXT/UXRP23eS/1pAbKon1MZ8B3KyYZH4mlc5RbJE/tnNDFyQaqkN uDaG4J4eGglBTb2A0bdcOhtWbc/ifZEU0Irc/1Bqy3QAYqgcT9s9i3HYVj6cTSBS C1pDYosgwt/inXokej9hlgSlDS8+jFPJCzEDSE3NDy8VAh81XRHvE7uTK2MmdJAX HFj1KjcGihgDJwNxqbzIHg16dMnhYN0R3580zDzLG9Q5I//nY8xowFP9JxHrr0Ju rTFpTaIDyXFh52g5CKNxoH1xHOGDvgsPI2lF81//dhoCC/2PbheeU7MK/5W9p7ad 3QITBXQlu6KU2oXwXb2yUqS+GXEkYOXBTpVwaMzJtgbNa3hFL4BXDBExrQ6NhaBV vf/J4H4XRyXudmHvYoWhc4LnbQySTtB+lucjo+5EmimKHLAb2S5c/516YI6ARtww i6yz04ncbEhg19LrXYk3wsQAam7uTJB8ERINeWgWSG79qg0ilxXIb/9i7Z5B3539 Dlf0/cRuRgHaCHcOxEkR =yQe0 -----END PGP SIGNATURE-----

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Twisted 16.1.1 Release Announcement
by Amber "Hawkie" Brown 11 Apr '16

11 Apr '16

On behalf of Twisted Matrix Laboratories, I am honoured to announce the release of Twisted 16.1.1! This is a small bugfix release in the 16.1 series, fixing a bug where `twistd web` would not output access logs. For more information, check the NEWS file (link provided below). You can find the downloads at <https://pypi.python.org/pypi/Twisted> (or alternatively <http://twistedmatrix.com/trac/wiki/Downloads>). The NEWS file is also available at <https://github.com/twisted/twisted/blob/twisted-16.1.1/NEWS>. Twisted Regards, Amber Brown

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ANN: bcolz 1.0.0 (final) released
by Francesc Alted 07 Apr '16

07 Apr '16

============================= Announcing bcolz 1.0.0 final ============================= What's new ========== Yeah, 1.0.0 is finally here. We are not introducing any exciting new feature (just some optimizations and bug fixes), but bcolz is already 6 years old and it implements most of the capabilities that it was designed for, so I decided to release a 1.0.0 meaning that the format is declared stable and that people can be assured that future bcolz releases will be able to read bcolz 1.0 data files (and probably much earlier ones too) for a long while. Such a format is fully described at: https://github.com/Blosc/bcolz/blob/master/DISK_FORMAT_v1.rst Also, a 1.0.0 release means that bcolz 1.x series will be based on C-Blosc 1.x series (https://github.com/Blosc/c-blosc). After C-Blosc 2.x (https://github.com/Blosc/c-blosc2) would be out, a new bcolz 2.x is expected taking advantage of shiny new features of C-Blosc2 (more compressors, more filters, native variable length support and the concept of super-chunks), which should be very beneficial for next bcolz generation. Important: this is a final release and there are no important known bugs there, so this is recommended to be used in production. Enjoy! For a more detailed change log, see: https://github.com/Blosc/bcolz/blob/master/RELEASE_NOTES.rst For some comparison between bcolz and other compressed data containers, see: https://github.com/FrancescAlted/DataContainersTutorials specially chapters 3 (in-memory containers) and 4 (on-disk containers). Also, if it happens that you are in Madrid during this weekend, you can drop by my tutorial and talk: http://pydata.org/madrid2016/schedule/ See you! What it is ========== *bcolz* provides columnar and compressed data containers that can live either on-disk or in-memory. Column storage allows for efficiently querying tables with a large number of columns. It also allows for cheap addition and removal of column. In addition, bcolz objects are compressed by default for reducing memory/disk I/O needs. The compression process is carried out internally by Blosc, an extremely fast meta-compressor that is optimized for binary data. Lastly, high-performance iterators (like ``iter()``, ``where()``) for querying the objects are provided. bcolz can use numexpr internally so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too). numexpr optimizes the memory usage and use several cores for doing the computations, so it is blazing fast. Moreover, since the carray/ctable containers can be disk-based, and it is possible to use them for seamlessly performing out-of-memory computations. bcolz has minimal dependencies (NumPy), comes with an exhaustive test suite and fully supports both 32-bit and 64-bit platforms. Also, it is typically tested on both UNIX and Windows operating systems. Together, bcolz and the Blosc compressor, are finally fulfilling the promise of accelerating memory I/O, at least for some real scenarios: http://nbviewer.ipython.org/github/Blosc/movielens-bench/blob/master/queryi… Other users of bcolz are Visualfabriq (http://www.visualfabriq.com/) , Quantopian (https://www.quantopian.com/) and Scikit-Allel ( https://github.com/cggh/scikit-allel) which you can read more about by pointing your browser at the links below. * Visualfabriq: * *bquery*, A query and aggregation framework for Bcolz: * https://github.com/visualfabriq/bquery * Quantopian: * Using compressed data containers for faster backtesting at scale: * https://quantopian.github.io/talks/NeedForSpeed/slides.html * Scikit-Allel * Provides an alternative backend to work with compressed arrays * https://scikit-allel.readthedocs.org/en/latest/model/bcolz.html Resources ========= Visit the main bcolz site repository at: http://github.com/Blosc/bcolz Manual: http://bcolz.blosc.org Home of Blosc compressor: http://blosc.org User's mail list: bcolz(a)googlegroups.com http://groups.google.com/group/bcolz License is the new BSD: https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt Release notes can be found in the Git repository: https://github.com/Blosc/bcolz/blob/master/RELEASE_NOTES.rst ---- **Enjoy data!** -- Francesc Alted

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ANN: python-blosc 1.3.1
by Francesc Alted 07 Apr '16

07 Apr '16

============================= 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-lib… )>`_. 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

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ANN: numexpr 2.5.2 released
by Francesc Alted 07 Apr '16

07 Apr '16

========================= Announcing Numexpr 2.5.2 ========================= Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It wears multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies. What's new ========== This is a maintenance release shaking some remaining problems with VML (it is nice to see how Anaconda VML's support helps raising hidden issues). Now conj() and abs() are actually added as VML-powered functions, preventing the same problems than log10() before (PR #212); thanks to Tom Kooij. Upgrading to this release is highly recommended. In case you want to know more in detail what has changed in this version, see: https://github.com/pydata/numexpr/blob/master/RELEASE_NOTES.rst Where I can find Numexpr? ========================= The project is hosted at GitHub in: https://github.com/pydata/numexpr You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted

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