🌐 Mailman 3 August 2019 - Python-announce-list - python.org

PythonOnWheels now supports Dash 1.0.2
by Klaas Aug. 26, 2019

Aug. 26, 2019

Hi, PythonOnWheels now supports Dash 1.0.2 I made a short Howto article here that runs an embedded DASH-app demo here: https://www.pythononwheels.org/dash Currently supported Dash and tools versions dash==1.0.2 dash-core-components==1.0.0 dash-html-components==1.0.0 dash-renderer==1.0.0 dash-table==4.0.2 By integrating Dash in PythonOnWheels you get the additional benefit to easily use the features of the framework, like: Simple Database access SQL (MySQL, Postgres, Oracle and more sqlalchemy ) NoSQL DBs like MongoDB and tinyDB. Easy API creation with handlers and routing Automatic view scaffolding for models. Integrated CSS Frameworks bootstrap4 and semanticUI Data Model validation Authentication (oAuth Google, Twitter, Facebook ..) Session Management (Secure Cookies)

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pytest 5.1.1 has been released!
by Anthony Sottile Aug. 20, 2019

Aug. 20, 2019

pytest 5.1.1 has just been released to PyPI. This is a bug-fix release, being a drop-in replacement. To upgrade:: pip install --upgrade pytest The full changelog is available at https://docs.pytest.org/en/latest/changelog.html. Thanks to all who contributed to this release, among them: * Anthony Sottile * Bruno Oliveira * Daniel Hahler * Florian Bruhin * Hugo van Kemenade * Ran Benita * Ronny Pfannschmidt Happy testing, The pytest Development Team

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Pythran 0.9.3 - Hañv
by Serge Guelton Aug. 19, 2019

Aug. 19, 2019

Hi folks, I just released version 0.9.3 of the Pythran package, Short reminder: Pythran is an ahead-of-time compiler for scientific Python, with a focus on high-level numerical kernels, parallelism and vectorisation. Here is a simple kernel example, with a pythran annotation. Note that that kernel is still Python-compatible (from https://stackoverflow.com/questions/57199248/) : import numpy as np #pythran export col_sum(int[:,:] or float[:,:], int[:]) def col_sum(data, idx): return data.T[idx].sum(0) The Pythran package is available on PyPI, Github and Conda https://pypi.org/project/pythran/ https://anaconda.org/conda-forge/pythran https://github.com/serge-sans-paille/pythran The interested reader can have a look to the changelog for details https://pythran.readthedocs.io/en/latest/Changelog.html Long story short: bug fixes and better 32bit arch support. Plus (Thanks to Miro Hrončok), pythran is now available on Fedora \o/ Huge thanks to all contributors and bug reporters: Jean Laroche Yann Diorcet DWesl Miro Hrončok Piotr Bartmann Jochen Schröder Sylwester Arabas Marti Bosch rorroiga Pierre Augier Anubhab Haldar nbecker

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ANN: Lea 3.2.0 released
by pie.denis＠skynet.be Aug. 16, 2019

Aug. 16, 2019

Lea 3.2.0 is now released! ---> http://pypi.org/project/lea/3.2.0 What is Lea? ------------ Lea is a Python module aiming at working with discrete probability distributions in an intuitive way. It allows you modeling a broad range of random phenomena: gambling, weather, finance, etc. More generally, Lea may be used for any finite set of discrete values having known probability: numbers, booleans, date/times, symbols, ... Each probability distribution is modeled as a plain object, which can be named, displayed, queried or processed to produce new probability distributions. Lea also provides advanced functions, machine learning and Probabilistic Programming (PP) features; these include conditional probabilities, Bayesian networks, joint probability distributions, Markov chains, EM algorithm and symbolic computation. Lea can be used for AI, PP, gambling, education, etc. LGPL - Python 2.6+ / Python 3 supported What's new in Lea 3.2.0? ------------------------ The version 3.2.0 extends Lea with machine learning features. This includes the Expectation-Maximization algorithm (EM). It allows you to learn parameters of probabilistic models having hidden variables. The machine learning features are now described comprehensively in a dedicated page of the wiki: http://bitbucket.org/piedenis/lea/wiki/Lea3_Tutorial_4. To learn more... ---------------- Lea 3 on PyPI -> http://pypi.org/project/lea Lea project page -> http://bitbucket.org/piedenis/lea Documentation -> http://bitbucket.org/piedenis/lea/wiki/Home Statues algorithm -> http://arxiv.org/abs/1806.09997 With the hope that Lea can make this Universe less risky, Pierre Denis

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Twisted 19.7.0 Release Announcement
by Glyph Aug. 16, 2019

Aug. 16, 2019

On behalf of Twisted Matrix Laboratories and our long-suffering release manager Amber Brown, I am honored to announce[1] the release of Twisted 19.7.0! The highlights of this release include: A full description on the PyPI page! Check it out here: https://pypi.org/project/Twisted/19.7.0/ <https://pypi.org/project/Twisted/19.7.0/> (and compare to the slightly sad previous version, here: https://pypi.org/project/Twisted/19.2.1/ <https://pypi.org/project/Twisted/19.2.1/>) twisted.test.proto_helpers has been renamed to "twisted.internet.testing" This removes the gross special-case carve-out where it was the only "public" API in a test module, and now the rule is that all test modules are private once again. Conch's SSH server now supports hmac-sha2-512. The XMPP server in Twisted Words will now validate certificates! This is a very important security fix for any twisted.words / XMPP users. (See https://twistedmatrix.com/trac/ticket/9561 <https://twistedmatrix.com/trac/ticket/9561>) A nasty data-corruption bug in the IOCP reactor was fixed. If you're doing high-volume I/O on Windows you'll want to upgrade! Twisted Web no longer gives clients a traceback by default, both when you instantiate Site and when you use `twist web` on the command line. You can turn this behavior back on for local developemnt with `twist web --display-tracebacks`. Several bugfixes and documentation fixes resolving bytes/unicode type confusion in twisted.web. Python 3.4 is no longer supported. `pip install -U twisted[tls]` and enjoy all these enhancements today! Thanks for using Twisted, -glyph [1]: somewhat belatedly: it came out 10 days ago. Oops!

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pytest 5.1.0 released
by Bruno Oliveira Aug. 16, 2019

Aug. 16, 2019

The pytest team is proud to announce the 5.1.0 release! pytest is a mature Python testing tool with more than a 2000 tests against itself, passing on many different interpreters and platforms. This release contains a number of bugs fixes and improvements, so users are encouraged to take a look at the CHANGELOG: https://docs.pytest.org/en/latest/changelog.html For complete documentation, please visit: https://docs.pytest.org/en/latest/ As usual, you can upgrade from pypi via: pip install -U pytest Thanks to all who contributed to this release, among them: * Albert Tugushev * Alexey Zankevich * Anthony Sottile * Bruno Oliveira * Daniel Hahler * David Röthlisberger * Florian Bruhin * Ilya Stepin * Jon Dufresne * Kaiqi * Max R * Miro Hrončok * Oliver Bestwalter * Ran Benita * Ronny Pfannschmidt * Samuel Searles-Bryant * Semen Zhydenko * Steffen Schroeder * Thomas Grainger * Tim Hoffmann * William Woodall * Wojtek Erbetowski * Xixi Zhao * Yash Todi * boris * dmitry.dygalo * helloocc * martbln * mei-li Happy testing, The Pytest Development Team

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Trac 1.3.6 Released
by Ryan Ollos Aug. 14, 2019

Aug. 14, 2019

Trac 1.3.6, the latest development release leading up to Trac 1.4, is available. You will find this release at the usual places: https://trac.edgewall.org/wiki/TracDownload#LatestDevRelease You can find the detailed release notes for 1.3.6 on the following pages: https://trac.edgewall.org/wiki/1.3/TracChangeLog https://trac.edgewall.org/wiki/TracDev/ReleaseNotes/1.3#MaintenanceReleases Now to the packages themselves: URLs: https://download.edgewall.org/trac/Trac-1.3.6.tar.gz https://download.edgewall.org/trac/Trac-1.3.6-py2-none-any.whl https://download.edgewall.org/trac/Trac-1.3.6.win32.exe https://download.edgewall.org/trac/Trac-1.3.6.win-amd64.exe MD5 sums: 1efa605c55fb9fb39df1cfa5d0ede95d Trac-1.3.6.tar.gz 283bfed3fd1f18ae4162ad29e2deff36 Trac-1.3.6-py2-none-any.whl cbd26a420d4ff987c15ef182d44dba63 Trac-1.3.6.win32.exe 89903a01dbd6f554d297040709a996d3 Trac-1.3.6.win-amd64.exe SHA256 sums: d3d35bf9861dcc8ae0e21fb46185ab828617a529834cfe83d295d284dcf17a6a Trac-1.3.6.tar.gz 6f048ddb1e922104858939705a870a1707e108a17bd6c2faecce5dc9b8a2edce Trac-1.3.6-py2-none-any.whl ed50b801080b1d2200d5921276b9427046c53d3ddffdd8566b3ba819b704cbb1 Trac-1.3.6.win32.exe 4b39eaf0afa4dc5d4b22bd11a761e8c123fc6bdc7ccaecfa97da40b7c0ad2e31 Trac-1.3.6.win-amd64.exe Acknowledgements ================ Many thanks to the growing number of people who have, and continue to, support the project. Also our thanks to all people providing feedback and bug reports that helps us make Trac better, easier to use and more effective. Without your invaluable help, Trac would not evolve. Thank you all. Finally, we hope that Trac will be useful to like-minded programmers around the world, and that this release will be an improvement over the last version. Please let us know. /The Trac Team https://trac.edgewall.org/

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ANN: NumExpr 2.7.0 Release
by Robert McLeod Aug. 14, 2019

Aug. 14, 2019

========================= Announcing Numexpr 2.7.0 ========================= Hi everyone, This is a minor version bump for NumExpr. We would like to highlight the changes made in 2.6.9 (which in retrospect should have been a minor version bump), where the maximum number of threads spawned can be limited by setting the environment variable "NUMEXPR_MAX_THREADS". If this variable is not set, in 2.7.0 the historical limit of 8 threads will be used. The lack of a check caused some problems on very large hosts in cluster environments in 2.6.9. In addition, we are officially dropping Python 2.6 support in this release as we cannot perform continuous integration for it. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.6.9 to 2.7.0 ---------------------------- - The default number of 'safe' threads has been restored to the historical limit of 8, if the environment variable "NUMEXPR_MAX_THREADS" has not been set. - Thanks to @eltoder who fixed a small memory leak. - Support for Python 2.6 has been dropped, as it is no longer available via TravisCI. - A typo in the test suite that had a less than rather than greater than symbol in the NumPy version check has been corrected thanks to dhomeier. - The file `site.cfg` was being accidentally included in the sdists on PyPi. It has now been excluded. What's Numexpr? --------------- 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 has 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. 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 Documentation is hosted at: http://numexpr.readthedocs.io/en/latest/ Share your experience --------------------- Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Robert McLeod robbmcleod(a)gmail.com robert.mcleod(a)hitachi-hhtc.ca

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cx_Oracle 7.2.2
by Anthony Tuininga Aug. 13, 2019

Aug. 13, 2019

What is cx_Oracle? cx_Oracle is a Python extension module that enables access to Oracle Database for Python 3.x and 2.x and conforms to the Python database API 2.0 specifications with a number of enhancements. Where do I get it? https://oracle.github.io/python-cx_Oracle The easiest method to install/upgrade cx_Oracle is via pip as in python -m pip install cx_Oracle --upgrade What's new? This release addresses a number of small issues. See the release notes for more information. https://cx-oracle.readthedocs.io/en/latest/release_notes.html#version-7-2-2… Please provide any feedback via GitHub issues ( https://github.com/oracle/python-cx_Oracle/issues).

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ANN: A new version (0.4.5) of python-gnupg has been released.
by Vinay Sajip Aug. 12, 2019

Aug. 12, 2019

A new version of the Python module which wraps GnuPG has been released. What Changed?============= This is an enhancement and bug-fix release, and all users are encouraged toupgrade. Brief summary: * Fixed #107: Improved documentation. * Fixed #112: Raised a ValueError if a gnupghome is specified which is not an existing directory. * Fixed #113: Corrected stale link in the documentation. * Fixed #116: Updated documentation to clarify when spurious key-expired/ signature-expired messages might be seen. * Fixed #119: Added --yes to avoid pinentry when deleting secret keys with GnuPG >= 2.1. * A warning is logged if gpg returns a non-zero return code. * Added ``extra_args`` to ``import_keys``. * Added support for CI using AppVeyor. This release [2] has been signed with my code signing key: Vinay Sajip (CODE SIGNING KEY) <vinay_sajip at yahoo.co.uk>Fingerprint: CA74 9061 914E AC13 8E66 EADB 9147 B477 339A 9B86 Recent changes to PyPI don't show the GPG signature with the download links.An alternative download source where the signatures are available is the project'sown downloads page [5]. What Does It Do?================The gnupg module allows Python programs to make use of thefunctionality provided by the Gnu Privacy Guard (abbreviated GPG orGnuPG). Using this module, Python programs can encrypt and decryptdata, digitally sign documents and verify digital signatures, manage(generate, list and delete) encryption keys, using proven Public KeyInfrastructure (PKI) encryption technology based on OpenPGP. This module is expected to be used with Python versions >= 2.4, as itmakes use of the subprocess module which appeared in that version ofPython. This module is a newer version derived from earlier work byAndrew Kuchling, Richard Jones and Steve Traugott. A test suite using unittest is included with the source distribution. Simple usage: >>> import gnupg>>> gpg = gnupg.GPG(gnupghome='/path/to/keyring/directory')>>> gpg.list_keys() [{...'fingerprint': 'F819EE7705497D73E3CCEE65197D5DAC68F1AAB2','keyid': '197D5DAC68F1AAB2','length': '1024','type': 'pub','uids': ['', 'Gary Gross (A test user) <gary.gr... at gamma.com>']},{...'fingerprint': '37F24DD4B918CC264D4F31D60C5FEFA7A921FC4A','keyid': '0C5FEFA7A921FC4A','length': '1024',...'uids': ['', 'Danny Davis (A test user) <danny.da... at delta.com>']}]>>> encrypted = gpg.encrypt("Hello, world!", ['0C5FEFA7A921FC4A'])>>> str(encrypted) '-----BEGIN PGP MESSAGE-----\nVersion: GnuPG v1.4.9 (GNU/Linux)\n\nhQIOA/6NHMDTXUwcEAf.-----END PGP MESSAGE-----\n'>>> decrypted = gpg.decrypt(str(encrypted), passphrase='secret')>>> str(decrypted) 'Hello, world!'>>> signed = gpg.sign("Goodbye, world!", passphrase='secret')>>> verified = gpg.verify(str(signed))>>> print "Verified" if verified else "Not verified" 'Verified' As always, your feedback is most welcome (especially bug reports [3],patches and suggestions for improvement, or any other points via themailing list/discussion group [4]). Enjoy! Cheers Vinay SajipRed Dove Consultants Ltd. [1] https://bitbucket.org/vinay.sajip/python-gnupg[2] https://pypi.org/project/python-gnupg/0.4.5[3] https://bitbucket.org/vinay.sajip/python-gnupg/issues[4] https://groups.google.com/forum/#!forum/python-gnupg[5] https://bitbucket.org/vinay.sajip/python-gnupg/downloads/------------------…

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