🌐 Mailman 3 November 2015 - Python-announce-list - python.org

xlwings v0.5.0 ads support for Matplotlib in Excel
by Felix Zumstein Nov. 10, 2015

Nov. 10, 2015

I am pleased to announce the release of xlwings v0.5.0: Amongst other new features and bug fixes, this release allow to use Matplotlib in Excel in just 2 lines of code. Watch: https://twitter.com/ZoomerAnalytics/status/664159348822835200 Check the Release Notes for full details: http://docs.xlwings.org/whatsnew.html About xlwings: xlwings is a BSD-licensed python library that makes it easy to call python from Excel and vice versa: Interact with Excel from python using a syntax that is close to VBA yet pythonic. Replace your VBA macros with python code and still pass around your workbooks as easily as before. xlwings fully supports NumPy arrays and Pandas DataFrames. It works with Microsoft Excel on Windows and Mac. http://xlwings.org

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pytest-benchmark v3.0 released!
by Ionel Cristian Mărieș Nov. 8, 2015

Nov. 8, 2015

Hello, I've just released a new version of the pytest plugin for benchmarks. It integrates very well into your pytest suite and has loads of features and options. It works very well on all sorts of scenarios - check it out! Docs: http://pytest-benchmark.readthedocs.org/en/stable/ Repository and issues: https://github.com/ionelmc/pytest-benchmark Thanks, -- Ionel Cristian Mărieș, http://blog.ionelmc.ro

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[ANN] MicroPython 1.5
by Paul Sokolovsky Nov. 7, 2015

Nov. 7, 2015

Hello, MicroPython is a lean and efficient Python implementation for microcontrollers, embedded, and mobile systems (which also runs just as fine on desktops of course). https://github.com/micropython/micropython Recent 1.5 release is an important milestone for the project, major changes including: 1. Advanced REPL support with smart auto-indent and auto-completion for comfortable interactive typing pf Python code, easily switchable to paste mode to copy pre-made code snippets. 2. Support for client SSL connections. 3. upip, MicroPython builtin package manager, is now fully standalone thanks to SSL support. 4. There's new, elaborated API to access hardware features ("machine" module). 5. There're now 2 well-supported microcontroller boards for MicroPython, and dozen(s) community-supported ones. 6. MicroPython was selected as one of the languages supported for BBC micro:bit initiative: http://ntoll.org/article/story-micropython-on-microbit 7. There's work on native efficient JNI bridge, inspired by Kivy's PyJNIus module, included into growing Android port. More detailed changelog for this version is at https://github.com/micropython/micropython/releases/tag/v1.5 MicroPython supports growing subset of Python3 standard library (including simplified asyncio package) - not included by default with an executable, but easily installable per-module using builtin "upip" package manager: https://github.com/micropython/micropython-lib -- Best regards, Paul mailto:pmiscml@gmail.com

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New Berkeley DB bindings release: 6.1.1
by Jesus Cea Nov. 7, 2015

Nov. 7, 2015

This module provides a nearly complete wrapping of the Oracle/Sleepycat C API for the Database Environment, Database, Cursor, Log Cursor, Sequence and Transaction objects, and each of these is exposed as a Python type in the bsddb3.db module. The database objects can use various access methods: btree, hash, recno, and queue. Complete support of Berkeley DB distributed transactions. Complete support for Berkeley DB Replication Manager. Complete support for Berkeley DB Base Replication. Support for RPC. Homepage: https://www.jcea.es/programacion/pybsddb.htm 6.1.1 release: https://www.jcea.es/programacion/pybsddb.htm#bsddb3-6.1.1 PyPI: https://pypi.python.org/pypi/bsddb3/ ChangeLog: * Compatibility with Python 3.5. * Code cleanup after dropping Python 2.4/2.5 support. * PGP key changed. * Support for DB_FORCESYNCENV flag in "DB_ENV.close()". * Support for DB_LOG_NOSYNC flag in "DB_ENV.log_set_config()". * Fix tests under Windows. See https://bugs.python.org/issue22943 . * Solve an incorrect parameter verification with the "DB.compact()" method call. * Solve a compilation warning when compiling the bindings for Python 3.5 and Berkeley DB 4.8, 5.0 or 5.1. -- Jesús Cea Avión _/_/ _/_/_/ _/_/_/ jcea(a)jcea.es - http://www.jcea.es/ _/_/ _/_/ _/_/ _/_/ _/_/ Twitter: @jcea _/_/ _/_/ _/_/_/_/_/ jabber / xmpp:jcea@jabber.org _/_/ _/_/ _/_/ _/_/ _/_/ "Things are not so easy" _/_/ _/_/ _/_/ _/_/ _/_/ _/_/ "My name is Dump, Core Dump" _/_/_/ _/_/_/ _/_/ _/_/ "El amor es poner tu felicidad en la felicidad de otro" - Leibniz

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ANN lauda 1.2.0
by Andrea Stagi Nov. 4, 2015

Nov. 4, 2015

I've released lauda 1.2.0 https://github.com/astagi/lauda/releases/tag/1.2.0, a little python module to measure time. You can install it using pip install lauda The source code is on Github: https://github.com/astagi/lauda You can use lauda StopWatch to measure a portion of code from lauda import StopWatch watch = StopWatch() watch.start() for i in range(10000000): pass watch.stop() print ('Time spent in range {0}'.format(watch.elapsed_time)) In the new versionm you can also get the "elapsed_time" when the stopwatch is running, also you can call checkpoint function if you want to set a checkpoint and get the time spent between the last checkpoint: from lauda import StopWatch watch = StopWatch() watch.start() for i in range(10000000): pass check_time = watch.checkpoint() print ('Time spent in first range: {0} sec.'.format(check_time)) for i in range(10000000): pass check_time = watch.checkpoint() print ('Time spent in second range: {0} sec.'.format(check_time)) If you want to measure an entire function execution, you can decorate it using the stopwatch decorator from lauda import stopwatch @stopwatch def awesome_mul(a, b): return a * b By default stopwatch decorator will print the time spent inside the decorated function, if you want more control you can pass to your decorator a callback that will receive a StopWatch instance and the decorated function. from lauda import stopwatch def stopwatch_sum_cb(watch, function): print ('Time spent {0}'.format(watch.elapsed_time)) @stopwatch(callback=stopwatch_sum_cb) def awesome_sum(a, b): return a + b -- Andrea Stagi (@4stagi) - Develover @Nephila Job profile: http://linkedin.com/in/andreastagi Website: http://4spills.blogspot.it/ Github: http://github.com/astagi

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Tryton 3.8 released
by Cédric Krier Nov. 2, 2015

Nov. 2, 2015

We are proud to announce the 3.8 release of Tryton. Tryton is a three-tier high-level general purpose application platform under the license GPL-3 written in Python and using PostgreSQL as database engine. It is the core base of a complete business solution providing modularity, scalability and security. For the first time the release contains sao, the new web client of Tryton. It is the result of the Indiegogo campaign. It is developed using mainly jQuery and Bootstrap and its design is responsive. It requires a recent HTML5 compatible browser. Its usage doesn't require any modification on the server side, every modules are working out of the box with sao just like they do with the GTK client. A demo is available at http://demo.tryton.org using demo/demo as login/password. This brings to 3 the number of supported client for Tryton (tryton (GTK), proteus (Python script) and sao (Javascript). A lot of work has been done to improve the accessibility of the GTK and web clients. For the GTK client, we followed the GNOME Accessibility Developers Guide as much as possible and for the web client, we followed the Web Accessibility Initiative of the W3C. And of course, this release contains many bug fixes and performance improvements. As usual, migration from previous series is fully supported. For more information, here is the full news: http://www.tryton.org/posts/new-tryton-release-38.html

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[ANN] pyspread 1.0
by Martin Manns Nov. 2, 2015

Nov. 2, 2015

============== pyspread 1.0 ============== Pyspread 1.0 is released. After 7 years of development, the initially envisioned features are finally implemented. Besides source code changes, a Windows 32 bit installer is available (URL see below, 120MB download, requires Windows XP or later). Major changes to 0.4.3: * Full screen mode bug on Windows fixed * File handling for reading xls files improved * xlsx files can now be opened (cell content only) About pyspread ============== Pyspread is a non-traditional spreadsheet application that is based on and written in the programming language Python. The goal of pyspread is to be the most pythonic spreadsheet application. Pyspread is free software. It is released under the GPL v3. Project website: https://manns.github.io/pyspread/ Download page: https://pypi.python.org/pypi/pyspread Source code: https://github.com/manns/pyspread Windows installer: http://download.gna.org/pyspread/pyspread-1.0-win32-setup.exe Enjoy Martin

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ANN: numexpr 2.4.6 released
by Francesc Alted Nov. 2, 2015

Nov. 2, 2015

Hi, This is a quick release fixing some reported problems in the 2.4.5 version that I announced a few hours ago. Hope I have fixed the main issues now. Now, the official announcement: ========================= Announcing Numexpr 2.4.6 ========================= 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 quick maintenance version that offers better handling of MSVC symbols (#168, Francesc Alted), as well as fising some UserWarnings in Solaris (#189, Graham Jones). 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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ANN: np_utils 0.4
by David N. Mashburn Nov. 2, 2015

Nov. 2, 2015

Announcing np_utils version 0.4 Github: https://github.com/davidmashburn/np_utils Bitbucket: https://bitbucket.org/davidmashburn/np_utils PyPI: https://pypi.python.org/pypi/np_utils np_utils is a growing collection of succinct but powerful utility functions focused around extending numpy in important ways, but also includes a number of useful list, functional, and general purpose utilities. Small set of highlights (there are many, many more): * *NEW in 0.4* A succinct but fast and flexible groupby implementation (np_groupby) * A wrapper around np_groupby specifically tailored to common operations on record arrays (rec_groupby) * A function to partition an array into n-sized chunks (partitionNumpy) * A function to easily build a centered grid with a certain number of steps in each dimension (build_grid) * A simple n-dimensional interpolation system for arrays that lines up with standard indexing when integers are passed (interpNumpy) * A one-liner to make a new nd-array and paste in values from an existing nd-array with an offset (shapeShift) * A function to add a border to an array (addBorder) * A function to scale an nd-array by integer multiples, like an image up-scale (shape_multiply) * A utility to enable numpy-like fancy indexing to regular lists, including jagged nested lists (fancyIndexingList, fL for short) * A "flatten" function for nested lists (flatten) * A "deletecases" function for lists * A groupby for regular lists (groupByFunction) * Tools for analyzing connections and connection chains in lists (getElementConnections, getChainsFromConnections) * An interpolation function for lists (interp) * Functions for deep operations on nested lists (listAdd, listMul, etc) * A generic slice generator supporting every option imaginable: 0- and 1- based indexing, optional end point inclusion, strict/liberal bounds checking * Functional utilities like compose, identity, docAppend, conversion between splat and non-splat (convertToSingleArgFun & convertToMultiArgFun), and even an implementation of Jsoftware's "fork" * "Drawing" functions for ND-arrays: - Hyper-rectangles (NDRectangle) - ND Bresenham lines -lines to ND-arrays (BresenhamFunction) - Circles and Spheres (ImageCircle, ImageSphere) - A novel ND-Bresenham *triangular-plane*-drawing algorithm (BresenhamTriangle) supporting one of my other projects, SWS4D * Many others! Documentation is handled at the docstring level and is thorough with included examples, but there is little in the way of over-arching docs. The only dependency is numpy itself, and functions are meant to be relatively independent so that other projects can cherry-pick them as needed. The API is always subject to change, but has remained fairly stable -- mostly due to my own heavy dependence on the module A unit test suite is in progress, thoroughly covering a small but important set of the functions (notably groupby, build_grid, partitionNumpy). A number of functions have been inspired or taken verbatim from excellent examples on StackOverflow -- in these cases, links are provided. Suggestions are welcome and encouraged! (algorithms, style, code organization, documentation, projects that could benefit from some/ all of these functions, inclusions, mergers, etc) This project supports work that I did at Vanderbilt University and am doing at Pluralsight, LLC. Cheers! -David Mashburn <P><A HREF="https://github.com/davidmashburn/np_utils">np_utils 0.4</A> - a growing collection of succinct but powerful utility functions focused around extending numpy in important ways, but also includes a number of useful list, functional, and general purpose utilities. (02-Nov-15)

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ANN: numexpr 2.4.5 released
by Francesc Alted Nov. 2, 2015

Nov. 2, 2015

========================= Announcing Numexpr 2.4.5 ========================= 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 where an important bug in multithreading code has been fixed (#185 Benedikt Reinartz, Francesc Alted). Also, many harmless warnings (overflow/underflow, divide by zero and others) in the test suite have been silenced (#183, Francesc Alted). 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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