Mailman 3 July 2010 - Python-announce-list

[ANN] Lupa 0.6 - Lua in Python
by Stefan Behnel 18 Jul '10

18 Jul '10

Hi all, I'm happy to announce the release of Lupa 0.6. http://pypi.python.org/pypi/lupa/0.6 What is Lupa? -------------- Lupa integrates the LuaJIT2 runtime [1] into CPython. It is a rewrite of LunaticPython in Cython. Features --------- * separate Lua runtime states through a LuaRuntime class * frees the GIL and supports threading in separate runtimes when calling into Lua * proper encoding and decoding of strings (configurable, UTF-8 by default) * supports Python 2.x and 3.x, potentially starting with Python 2.3 (currently untested) * written for LuaJIT2, as opposed to the Lua interpreter (tested with LuaJIT 2.0.0-beta4) * easy to hack on and extend as it is written in Cython, not C The major new feature in this release is iteration support for Lua objects, such as tables. Examples --------- >>> from lupa import LuaRuntime >>> lua = LuaRuntime() >>> lua.eval('1+1') 2 >>> lua_func = lua.eval('function(f, n) return f(n) end') >>> def py_add1(n): return n+1 >>> lua_func(py_add1, 2) 3 Why use it? ------------ It complements Python very well. Lua is a language as dynamic as Python, but LuaJIT compiles it to very fast machine code, sometimes faster than many other compiled languages [2]. The language runtime is extremely small and carefully designed for embedding. The complete binary module of Lupa, including a statically linked LuaJIT2 runtime, is only some 400KB on a 64 bit machine. However, Lua code is harder to write than Python code as the language lacks most of the batteries that Python includes. Writing large programs in Lua is rather futile, but it provides a perfect backup language when raw speed is more important than simplicity. Lupa is a very fast and thin wrapper around LuaJIT. It makes it easy to write dynamic Lua code that accompanies dynamic Python code by switching between the two languages at runtime, based on the tradeoff between simplicity and speed. [1] LuaJIT2: http://luajit.org/ [2] http://shootout.alioth.debian.org/u64/performance.php?test=mandelbrot

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[ANN] Struqtural: High level database interface library
by Nathan Rice 17 Jul '10

17 Jul '10

Struqtural makes it easy to get data into a database, and easy to work with it once it's there. Some of the big features include: * Automatically generate all tables and relations needed to represent XML in a database, including one to one, one to many, many to one and many to many relationships (JSON and YAML support is planned for a future release), returning mapped objects. * Automatically generate python objects for a specified table in a database, with support for discovery of all types of relationships (sort of like SQL Soup on 4 different kinds of steroids). * Automatically create persisted representations of python objects in a database, along with a persistent version of the object class. * Automatically infer SQL data types and create table representations for delimited text files (i.e. CSV/TSV), returning mapped objects. * Easily generate and query EAV/vertical attribute tables. * Easily generate and query graphs/trees/directed acyclic graphs. * Easily manage session configuration and creation for multiple databases. * Easily override almost all behavior with custom code or configuration variables * And much more... Once you're up and running, it's SQL Alchemy under the hood, with a few tune-ups that you are free to bypass if you don't like them. Home page: http://www.turnkey-analytics.com/struqtural/ PyPI: http://pypi.python.org/pypi/Struqtural Source: https://code.launchpad.net/pydatastep Bugs: https://bugs.launchpad.net/pydatastep To see Struqtural in action, take a look at the tutorial... ================================================= Struqtural has been designed to be as smart as possible. Because of that, most common use cases are retardedly simple. Let’s take a look, shall we? First, let’s just examine the most basic use case: >>> from struqtural.structures.structure import Structure >>> from struqtural.loaders import loader >>> data = [{"A":True, "B":1, "C":1.5, "D":"a"}, ... {"A":False, "B":2, "C":2.5, "D":"b"}, ... {"A":True, "B":3, "C":3.5, "D":"c"}, ... {"A":False, "B":4, "C":4.5, "D":"d"}] >>> example_structure = Structure(loader.CollectionLoader, data, "B", "SimpleInstance") >>> print "primary keys:", example_structure.primary_keys primary keys: ['B'] # Note that you could have also specified the primary key using an iterable >>> print "table name:", example_structure.table table name: SimpleInstances >>> print "table columns:", example_structure.columns table columns: ['SimpleInstances.A', 'SimpleInstances.C', 'SimpleInstances.B', 'SimpleInstances.D'] >>> for true_instance in example_structure.filter("B>2"): ... print true_instance SimpleInstance(A=True, C=3.5, B=3, D="c") SimpleInstance(A=False, C=4.5, B=4, D="d") >>> for small_true_instance in example_structure.query().filter("A", "C<3"): ... print small_true_instance SimpleInstance(A=True, C=1.5, B=1, D="a") Another nice feature is that Struqtural is pretty flexible about how you pass it data: >>> from struqtural.structures.structure import Structure >>> from struqtural.loaders import loader >>> more_data = [["T", "1", "1.0"], ... ["FALSE", "2", "2.0"], ... ["true", "3", "3.0"], ... ["False", "4", "4.0"]] # Note that more_data is strings, which isn't uncommon in the real world >>> data_headers = ["A", "B", "E"] >>> categories = Structure(loader.CollectionLoader, (data_headers, more_data), ["B", "E"], "Category") # Just a quick aside to demonstrate that Struqtural gets your table names right >>> print "table name:", categories.table table name: Categories >>> for category in categories: ... print category ... Category(A=True, B=1, E=1.0) Category(A=False, B=2, E=2.0) Category(A=True, B=3, E=3.0) Category(A=False, B=4, E=4.0) As you can see the strings have been handled elegantly. Type conversion is completely controllable and extensible. A small collection of useful type inference and conversion functions have been included. How about if we want to create a new structure out of multiple previously existing ones: >>> example_structure = Structure(loader.CollectionLoader, data, "B", "SimpleInstance") >>> connector = example_structure.connector >>> categories = Structure(loader.CollectionLoader, (data_headers, more_data), ["B", "E"], "Category", connector) >>> joint_structure = Structure(loader.JoinLoader, categories, example_structure) >>> for instance in joint_structure: ... print instance Category(A=True, B=1, E=1.0, C=1.5, D="a") Category(A=False, B=2, E=2.0, C=2.5, D="b") Category(A=True, B=3, E=3.0, C=3.5, D="c") Category(A=False, B=4, E=4.0, C=4.5, D="d") Note that we took advantage of some intelligence on the join we did there, since we have identically named, identically typed columns in the two tables. If you needed to be explicit about it you could just as easily have called structure like so (where the dictionary key is the column on the left table, in this case categories, and the value is the column on the joined table): >>> joined_with = (example_structure, {'A':'A', 'B':'B'}) >>> joint_structure = Structure(loader.JoinLoader, categories, joined_with) Next, let’s create persisted versions of pre-existing objects: >>> from struqtural.structures.structure import Structure >>> from struqtural.loaders import loader >>> class Mock(object): ... def __init__(self, a, b, c): ... self.a = a ... self.b = b ... self.c = c >>> mocks = [Mock(1, 2, 3), Mock(4, 5, 6), Mock(7, 8, 9)] >>> another_example_structure = Structure(loader.ObjectLoader, mocks, "a") >>> for mock in another_example_structure.query(): ... print mock Mock(a=1, c=3, b=2) Mock(a=4, c=6, b=5) Mock(a=7, c=9, b=8) Pretty easy, right? Notice that you got a nice __repr__ for free there, too. Any type which can be converted to a known SQL primitive will be mapped automatically by default, though this is easily configurable. If a variable cannot be directly mapped, or more than one type occurs on for a given variable name, by default it will be pickled so that what comes off the database is identical to what went in, though this can also be easily disabled. Note This does not directly instrument pre-existing objects. It populates the database with the data of the objects. Any changes made to the original Mock objects in the above example will not be persisted. I suggest replacing the original objects with the new objects from the session immediately. Also, rather than modify the original class, a modified subclass is created, so it is advisable to save the modified class as well. This was a design decision, however it would be trivial to switch, so the behavior may change depending on user feedback. Now on to something a little bit more complicated... Struqtural can automatically generate a schema from XML. As a basic example, let’s use the following document: <Sales> <Order> <Number>123</Number> <Date>10/29/00</Date> <Customer> <CustNum>007</CustNum> <Name>Bond, Inc.</Name> </Customer> <Line> <LineNum>1</LineNum> <Quantity>3</Quantity> <Part> <PartNum>ABC</PartNum> <Price>12.95</Price> </Part> </Line> </Order> </Sales> What happens when you give this document to Struqtural? Let’s take a look: >>> from struqtural.structures.structure import MultiStructure >>> from struqtural.loaders import loader, file as file_ >>> multi = MultiStructure(file_.XmlMultiLoader, fake_file_1) >>> order_structure = multi.properties["Order"] >>> for order in order_structure.query(): ... print "Order number:", order.number ... print "Order date:", order.date ... print "Order customer count:", len(order.customers) ... for customer in order.customers: ... print "Order customer:", customer ... print "Order line count:", len(order.lines) ... for line in order.lines: ... print "Order line:", line ... print "Order line part count:", len(line.parts) ... for part in line.parts: ... print "Order line part:", part ... Order number: 123 Order date: 2000-10-29 Order customer count: 1 Order customer: Customer(order_id=1, custnum=7, name="Bond, Inc.", id=1) Order line count: 1 Order line: Line(order_id=1, linenum=1, id=1, quantity=3) Order line part count: 1 Order line part: Part(line_id=1, price=12.95, id=1, partnum="ABC") Ok, Struqtural automatically figured out all the relations and properties from the XML file, that’s pretty neat. Because we didn’t set any tag primary keys or provide a SchemaFormatter object with a primary key identification engine, an id key is automatically added. Since this behavior is typically not desirable, it is very easy to override. Note You will probably notice that our customer has an order_id... In this case, that customer appears only on the one Order, so that is not too odd. Had the customer appeared on multiple orders, the order would have had a customer_id. Even better, if multiple customers appeared on any order, a secondary table would be automatically created to handle the relationship, as we’ll demonstrate in a minute. So Struqtural can get data into a database easily, you say. What if my data is already in the database? What can you do for me? No problem, check it out: >>> from struqtural.structures.structure import MultiStructure >>> from struqtural.loaders import loader, file as file_ >>> multi = MultiStructure(file_.XmlMultiLoader, fake_file_1) >>> order_structure = multi.properties["Order"] >>> connector = order_structure.connector # This isn't strictly needed, but gives us nice, informative names. # The key in names is the table name, the value is the name you would like to give # the instance you are creating to represent that table. >>> names = {"Customers":"Customer", "Lines":"Line", "Parts":"Part"} >>> multi = MultiStructure(loader.DatabaseMultiLoader, "Orders", connector, names) >>> order_structure = multi.properties["Orders"] >>> for order in order_structure.query(): ... print "Order number:", order.number ... print "Order date:", order.date ... print "Order customer count:", len(order.customers) ... for customer in order.customers: ... print "Order customer:", customer ... print "Order line count:", len(order.lines) ... for line in order.lines: ... print "Order line:", line ... print "Order line part count:", len(line.parts) ... for part in line.parts: ... print "Order line part:", part Order number: 123 Order date: 2000-10-29 Order customer count: 1 Order customer: Customer(order_id=1, custnum=7, name="Bond, Inc.", id=1) Order line count: 1 Order line: Line(order_id=1, linenum=1, id=1, quantity=3) Order line part count: 1 Order line part: Part(line_id=1, price=12.95, partnum="ABC", id=1) You just got the “Orders” table and everything connected with it (including stuff that points to it as well as stuff it points to) loaded and mapped, and it took almost no work! You’re free to do interesting stuff! Pretty awesome, huh? Let’s push things to the edge now with a quick demo of many to many relationship support. For this example we’re going to be using the following XML: <Departments> <Department> <DeptNum>123</DeptNum> <DeptName>Sales</DeptName> <Employee> <Number>143</Number> <Name>Raul Lopez</Name> </Employee> <Employee> <Number>687</Number> <Name>John Smith</Name> </Employee> <Employee> <Number>947</Number> <Name>Ming Chu</Name> </Employee> </Department> <Department> <DeptNum>456</DeptNum> <DeptName>Marketing</DeptName> <Employee> <Number>157</Number> <Name>Jim Jones</Name> </Employee> <Employee> <Number>687</Number> <Name>John Smith</Name> </Employee> <Employee> <Number>947</Number> <Name>Ming Chu</Name> </Employee> </Department> </Departments> Note that we’re also going to be passing tag primary keys here, so that things work properly: >>> from struqtural.structures.structure import MultiStructure >>> from struqtural.loaders import loader, file as file_ >>> primary_keys = {"Department":"DeptNum", "Employee":"Number"} >>> multi = MultiStructure(file_.XmlMultiLoader, fake_file_2, ... primary_keys=primary_keys) >>> department_structure = multi.properties["Department"] >>> for department in department_structure.query(): ... print "Department number:", department.deptnum ... print "Employee count:", len(department.employees) ... for employee in department.employees: ... print "Employee name:", employee.name ... print "Employee number:", employee.number ... Department number: 123 Employee count: 3 Employee name: Raul Lopez Employee number: 143 Employee name: John Smith Employee number: 687 Employee name: Ming Chu Employee number: 947 Department number: 456 Employee count: 3 Employee name: Jim Jones Employee number: 157 Employee name: John Smith Employee number: 687 Employee name: Ming Chu Employee number: 947 If you already have a database with many to many relationships, that is no problem. As long as the secondary tables for the relationship don’t contain any non-foreign key columns or foreign keys for more than two tables, you’re in good shape. For example: >>> from struqtural.structures.structure import MultiStructure >>> from struqtural.loaders import loader, file as file_ >>> primary_keys = {"Department":"DeptNum", "Employee":"Number"} >>> multi = MultiStructure(file_.XmlMultiLoader, fake_file_2, ... primary_keys=primary_keys) >>> department_structure = multi.properties["Department"] >>> connector = department_structure.connector >>> names = {"Departments":"Department", "Employees":"Employee"} >>> multi = MultiStructure(loader.DatabaseMultiLoader, "Departments", connector, ... names, backref_by_default=True) >>> departments = multi.properties["Departments"] >>> for department in department_structure.query(): ... print "Department number:", department.deptnum ... print "Employee count:", len(department.employees) ... for employee in department.employees: ... print "Employee name:", employee.name ... print "Employee number:", employee.number ... Department number: 123 Employee count: 3 Employee name: Raul Lopez Employee number: 143 Employee name: John Smith Employee number: 687 Employee name: Ming Chu Employee number: 947 Department number: 456 Employee count: 3 Employee name: Jim Jones Employee number: 157 Employee name: John Smith Employee number: 687 Employee name: Ming Chu Employee number: 947 You will note that in all these examples we used a memory resident SQLite database. If you want to use a real database, you will need to pass a connector to your structure. You create one like so: >>> from struqtural.database import DatabaseConnector >>> connector = DatabaseConnector("my_database_config.cfg") That’s it! In fact, Struqtural will even automatically look in a few logical places for your configuration information, so you can set it up once and you’ll never have to pass it the location of a configuration file again. Just to give you an example of the format for the configuration file (this is included in the resources subdirectory): [current engine] engine=<db engine> [parameter values] user=<user> pass=<pass> host=<host> db=<database> [parameter lists] mysql=mysql://{user}:{pass}@{host}/{db} postgresql=postgresql://{user}:{pass}@{host}/{db} Note The configuration file shown here does not include driver, port or other information that you would probably want to be able to specify in a real case. Those parameters are supported, so if you modify the configuration to include them, the struqtural.database.connector.Databaseconnector will work as expected.

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[ANN] Selenium Bindings - IE is here!
by Miki 16 Jul '10

16 Jul '10

Hello All, The latest version of the Selenium Python bindings (9341) includes the "native" IE bindings. This marks a "complete" release with remote, firefox, ie and chrome bindings. I consider this an "alpha" release (except for the 1.0 bindings which have been around for a while). Please report bugs to http://code.google.com/p/selenium/issues/list (don't forget to label Lang-Python). Happy testing, -- Miki

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Announcing IronPython 2.7 Alpha 1
by Dave Fugate 16 Jul '10

16 Jul '10

Hello Python Community, We're pleased to announce the Alpha release of IronPython 2.7 which can be downloaded at http://ironpython.codeplex.com/releases/view/42434. This is a major new version of IronPython with a number of significant updates. Because this is an Alpha release it is not yet feature complete nor fully compatible with CPython 2.7. Changes thus far include: * Updates the language to be compatible with CPython 2.7 * Adds integrated Visual Studio support (IronPython Tools for Visual Studio) * Extends CPython 2.7's documentation with useful information pertaining to IronPython * Adds the mmap and signal modules * Includes a number of performance updates and bug fixes * Switches the license to Apache License, Version 2.0 * Requires .NET 4.0 and Silverlight 4.0 Python 2.7 includes a number of features backported from the Python 3.0 series. This release implements the new builtin _io module, includes dictionary and set comprehensions, set literals, supports multiple context managers in the with statement, and adds several new functions to the itertools methods, and auto indexing for the new string formatting. There are also numerous updates to the standard library such as ordered dictionaries and the new argparse module. This release also includes a "IronPython Tools for Visual Studio" option within the IronPython installer. This enables one install to get both IronPython and IronPython Visual Studio support assuming you have an existing installation of Visual Studio 2010. This version of IronPython Tools includes a number of bug fixes as well as the start of improved WPF designer support. We discovered very late that the WPF designer support may crash VS when not running under the debugger. If you'd like to try the WPF designer support and give us feedback, just launch another Visual Studio instance and attach to the instance in which you are using the WPF designer support. We've also updated the IronPython installer to include documentation based upon the CPython documentation. This new .chm file includes documentation on the Python language and standard library. It's been extended from the normal Python documentation to include IronPython specific topics such as the DLR hosting APIs and extending IronPython from statically typed .NET languages. We flushed out more support for missing built-in modules which CPython includes. This release includes the mmap and signal modules bringing better support for interoperating with unmanaged code. As usual there are a number of bug fixes and performance improvements. This release includes major performance improvements in cPickle, the sum built-in function, and includes support for fast exceptions which do not use the .NET exception mechanism. There have also been improvements to significantly reduce memory usage of the IronPython ASTs. One of the end results of these numerous improvements is that IronPython's startup time has decreased by 10% when compared to IronPython 2.6.1. Finally, with this release we have changed the license from the Microsoft Public License to the Apache License, Version 2.0. We've made this change based upon continual feedback and questions from the community. The Apache License will be more familiar while remaining an open source license. - The IronPython Team

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[ANN] signedimp 0.1.3
by Ryan Kelly 16 Jul '10

16 Jul '10

Hi Everyone, I'm pleased to announce the latest release of "signedimp", an import wrapper and related machinery for verified loading of python modules. Details and downloads at the cheeseshop, or read on for more: http://pypi.python.org/pypi/signedimp/ http://github.com/cloudmatrix/signedimp/ Cheers, Ryan Description: ------------ The "signedimp" module implements an import hook for verifying Python modules before they are loaded, by means of cryptographically-signed hashes. It is compatible with PEP 302 and designed to complement the code-signing functionality of your host OS (e.g. Microsoft Authenticode, Apple OSX Code Signing) which may be able to verify the Python executable itself but not the code that is dynamically loaded at runtime. It will mostly be useful for frozen Python applications, or other situations where code is not expected to change. It will be almost useless with a standard Python interpreter. Example: -------- As a quick example, suppose you've got a private signing key stored in the file "mykey". You build a new frozen app with py2exe, then do: from signedimp.crypto.rsa import RSAKeyWithPSS key = RSAKeyWithPSS.load_from_file(open("mykey"),"password") import signedimp.tools signedimp.tools.sign_py2exe_app("/path/to/app/dir",key) This generates a signed manifest of hashes for all the modules included in the frozen app directory, and embeds the public key into the frozen executable. Your frozen application will now verify each module before it is loaded, and any tampering (malicious or otherwise) will cause the application to fail with an "IntegrityCheckError". Of course, you'll also need to sign the executable itself with something like Microsoft Authenticode or the whole scheme is worthless. Currently there is support for signing apps frozen with py2exe, py2app and cxfreeze. Why? ---- This is *not* a DRM-type solution. It's not designed to protect your code from your users. It would be trivial for a knowledgeable user to remove the signature checking from your frozen application. Rather, it's designed to protect your users from malicious tampering by a third party. You might find it especially useful if your app has a plugin system that means it may be running code from untrusted sources (and yes, it's possible to embed a signed manifest inside an egg). Are you a crypto expert? ------------------------ No. No no no. You have been warned. -- Ryan Kelly http://www.rfk.id.au | This message is digitally signed. Please visit ryan(a)rfk.id.au | http://www.rfk.id.au/ramblings/gpg/ for details -- http://mail.python.org/mailman/listinfo/python-announce-list Support the Python Software Foundation: http://www.python.org/psf/donations.html

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Toronto PyCamp 2010
by Chris Calloway 16 Jul '10

16 Jul '10

The University of Toronto Department of Physics brings PyCamp to Toronto on Monday, August 30 through Friday, September 3, 2010. Register today at http://trizpug.org/boot-camp/torpy10/ For beginners, this ultra-low-cost Python Boot Camp makes you productive so you can get your work done quickly. PyCamp emphasizes the features which make Python a simpler and more efficient language. Following along with example Python PushUps™ speeds your learning process in a modern high-tech classroom. Become a self-sufficient Python developer in just five days at PyCamp! Conducted on the campus of the University of Toronto, PyCamp comes with your own single OS/single developer copy of Wing Professional Python IDE. -- Sincerely, Chris Calloway office: 332 Chapman Hall phone: (919) 599-3530 mail: Campus Box #3300, UNC-CH, Chapel Hill, NC 27599

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New version of CodeInvestigator
by hans moleman 15 Jul '10

15 Jul '10

CodeInvestigator version 1.2.0 was released on July 15. Changes: Added a search by line function: Specify a line number in a program and you are shown where and when it is executed. Changes to how debug information is displayed and removed: You don't need to click the identifier or keyword again to remove the debug information. If you don't move your cursor when you click to open, the next click will close it for you. When you search: A match shows the identifier and the identifier's value. Cosmetic changes: Colored blocks of coding don't span the entire screen anymore. CodeInvestigator is a tracing tool for Python programs. Running a program through CodeInvestigator creates a recording. Program flow, function calls, variable values and conditions are all stored for every line the program executes. The recording is then viewed with an interface consisting of the code. The code can be clicked: A clicked variable displays its value, a clicked loop displays its iterations. You read code, and have at your disposal all the run time details of that code. A computerized desk check tool and another way to learn about your program. http://sourceforge.net/project/showfiles.php?group_id=183942

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Announcing Pushy 0.3
by axwalk 15 Jul '10

15 Jul '10

Greetings, I am pleased to announce the release of Pushy 0.3. This is the first announcement for the Pushy project. Cheers, Andrew Wilkins. What is Pushy? ============================================================================= Pushy is a Python package for connecting Python interpreters, providing each one access to objects in the other. Pushy provides the novel ability to spawn and connect to Python interpreters not only on the local host, but also on remote hosts via SSH, requiring nothing but Python and a running SSH daemon on the remote host. Pushy also provides a simple Java API, which Java applications can use to spawn and connect to Python processes and access objects within them. Python support is currently limited to Python 2.4+, and not the 3.x series. The Pushy Java API supports Java 1.4+. License ============================================================================= Pushy is released exclusively under the MIT License. Resources ============================================================================= - Source Repository and Issue Tracker: http://launchpad.net/pushy - PyPI: http://pypi.python.org/pypi/pushy/0.3 - Homepage (work in progress): http://ii.net/~maxwalk/pushy - Blog: http://axwalk.blogspot.com

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[ANN] inflect.py: generate plurals, ordinals, numbers to words...
by Paul 15 Jul '10

15 Jul '10

I'm pleased to announce the release of inflect.py v0.1.8, a module that correctly generates: * the plural of singular nouns and verbs * the singular of plural nouns * ordinals * indefinite articles * present participles * and converts numbers to words Examples: >>> import inflect >>> p = inflect.engine() >>> p.pl('cat') 'cats' >>> p.pl('sheep') 'sheep' >>> p.pl('bacterium') 'bacteria' plural of singular verbs: it walks -> they walk >>> p.pl('walks') 'walk' singular of plural nouns: >>> p.sinoun('bacteria') 'bacterium' >>> p.sinoun('knives') 'knife' ordinals: >>> p.ordinal(31) '31st' >>> p.ordinal('twenty-five') 'twenty-fifth' indefinite articles: >>> p.a('horse') 'a horse' >>> p.a('ant') 'an ant' >>> p.a('hour') 'an hour' present participles: >>> p.prespart('drinks') 'drinking' >>> p.prespart('runs') 'running' >>> p.prespart('flies') 'flying' numbers to words: >>> p.numwords(1234567) 'one million, two hundred and thirty-four thousand, five hundred and sixty-seven' Installation: "pip install inflect" or "easy_install inflect" PyPi: http://pypi.python.org/pypi/inflect Bug Tracker: http://github.com/pwdyson/inflect.py/issues Source Code: http://github.com/pwdyson/inflect.py Cheers, Paul Dyson

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tox-0.7: fixes, python3-compat, site-packages
by holger krekel 14 Jul '10

14 Jul '10

Hi all, thanks to feedback and help from Michael Foord, Sridhar Ratnakumar, Kumar McMillan, Ronny Pfannschmidt and Ralf Schmitt i just released tox-0.7 which includes a couple of fixes, improved Python3 virtualenv support (on non-windows platforms), and a new sitepackages config option that let's you use globally installed packages. See the more detailed changelog below. The web page is here: http://codespeak.net/tox have fun, holger What is Tox? ---------------- TOX as is a virtualenv management and generic test command line tool you can use for: * checking your package installs correctly with different Python versions and interpreters * running your tests in each of the environments, configuring your test tool of choice * acting as a frontend to Continous Integration servers, greatly reducing boilerplate and merging CI and shell-based testing. Changes 0.7 ---------------------- - use virtualenv5 (my own fork of virtualenv3) for now to create python3 environments, fixes a couple of issues and makes tox more likely to work with Python3 (on non-windows environments) - add ``sitepackages`` option for testenv sections so that environments can be created with access to globals (default is not to have access, i.e. create environments with ``--no-site-packages``. - addressing issue4: always prepend venv-path to PATH variable when calling subprocesses - fix issue2: exit with proper non-zero return code if there were errors or test failures. - added unittest2 examples contributed by Michael Foord - only allow 'True' or 'False' for boolean config values (lowercase / uppercase is irrelevant) - recreate virtualenv on changed configurations 0.6 ----------------- - fix OSX related bugs that could cause the caller's environment to get screwed (sorry). tox was using the same file as virtualenv for tracking the Python executable dependency and there also was confusion wrt links. this should be fixed now. - fix long description, thanks Michael Foord 0.5 ----------------- - initial release

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