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sychotic is an innovative optimizing compiler for Python code. It has unique features, the most important of which is that it breaks through the ConstantTimeBarrier. It is very easy to use and has an interface similar to that of the popular Psyco project. Below, you can see a usage example. You can also learn about HowItWorks, the ProjectHistory and KnownIssues. You can also browse the easy-to-understand source especially the alysis.py analyzer module and the dingo.py runtime bootstrap. There is an introductory screencast available (less than 5 minutes long). Usage

Document options Document options requiring JavaScript are not displayed Rate this pageHelp us improve this contentLevel: IntroductoryDavid Mertz (mertz@gnosis.cx), Applied Metaphysician, Gnosis Software, Inc. 01 Mar 2001Although users usually think of Python as a procedural and object-oriented language, it actually contains everything you need for a completely functional approach to programming. This article discusses general concepts of functional programming, and illustrates ways of implementing functional techniques in Python. We'd better start with the hardest question: "What is functional programming (FP), anyway?" One answer would be to say that FP is what you do when you program in languages like Lisp, Scheme, Haskell, ML, OCAML, Clean, Mercury, or Erlang (or a few others). That is a safe answer, but not one that clarifies very much. Unfortunately, it is hard to get a consistent opinion on just what FP is, even from functional programmers themselves. A story about elephants and blind men seems apropos here. It is also safe to contrast FP with "imperative programming" (what you do in languages like C, Pascal, C++, Java, Perl, Awk, TCL, and most others, at least for the most part).

[IPython-user] ipython1 and farm tasking Brian Granger ellisonbg.net@gmail.... Wed Feb 27 16:29:03 CST 2008 Previous message: [IPython-user] ipython1 and farm tasking Next message: [IPython-user] yet another leopard/readline question Messages sorted by: [ date ] [ thread ] [ subject ] [ author ] Alex, First, I would suggest updating your ipython1 install from our svn repository. We are about to push out a major new version and the documentation is _much_ better. Also, there are many new features that will hopefully help you. Here is a simple example (using the latest svn of ipython1): In [1]: from ipython1.kernel import client In [2]: mec = client.MultiEngineClient(('127.0.0.1',10105)) In [3]: tc = client.TaskClient(('127.0.0.1',10113)) In [4]: def fold_package(x): ...: return 2.0*x ...: In [5]: mec.push_function(dict(fold_package=fold_package)) Out[5]: [None, None, None, None] In [6]: tasks = [client.Task("y=fold_package(x)",push={'x':x},pull=('y',)) for x in range(128)] In [7]: task_ids = [tc.run(t) for t in tasks] In [8]: tc.barrier(task_ids) In [9]: task_results = [tc.get_task_result(tid) for tid in task_ids] In [10]: results = [tr.ns.y for tr in task_results] In [11]: print results [0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0, 24.0, 26.0, 28.0, 30.0, 32.0, 34.0, 36.0, 38.0, 40.0, 42.0, 44.0, 46.0, 48.0, 50.0, 52.0, 54.0, 56.0, 58.0, 60.0, 62.0, 64.0, 66.0, 68.0, 70.0, 72.0, 74.0, 76.0, 78.0, 80.0, 82.0, 84.0, 86.0, 88.0, 90.0, 92.0, 94.0, 96.0, 98.0, 100.0, 102.0, 104.0, 106.0, 108.0, 110.0, 112.0, 114.0, 116.0, 118.0, 120.0, 122.0, 124.0, 126.0, 128.0, 130.0, 132.0, 134.0, 136.0, 138.0, 140.0, 142.0, 144.0, 146.0, 148.0, 150.0, 152.0, 154.0, 156.0, 158.0, 160.0, 162.0, 164.0, 166.0, 168.0, 170.0, 172.0, 174.0, 176.0, 178.0, 180.0, 182.0, 184.0, 186.0, 188.0, 190.0, 192.0, 194.0, 196.0, 198.0, 200.0, 202.0, 204.0, 206.0, 208.0, 210.0, 212.0, 214.0, 216.0, 218.0, 220.0, 222.0, 224.0, 226.0, 228.0, 230.0, 232.0, 234.0, 236.0, 238.0, 240.0, 242.0, 244.0, 246.0, 248.0, 250.0, 252.0, 254.0] Or if you don't need load balancing: # This sends the fold_package function for you! results = mec.map(fold_package, range(128)) Let us know if you run into other problems. Cheers, Brian

What idioms should I use to make my code easier to read? Read "The Python Cookbook", especially the first few chapters. It's a great source of well-written Python code examples.

Use function factories to create utility functions. Often, especially if you're using map and filter a lot, you need utility functions that convert other functions or methods to taking a single parameter. In particular, you often want to bind some data to the function once, and then apply it repeatedly to different objects. In the above example, we needed a function that multiplied a particular field of an object by 3, but what we really want is a factory that's able to return for any field name and amount a multiplier function in that family:

Use zip and dict to map fields to names. zip turns a pair of sequences into a list of tuples containing the first, second, etc. values from each sequence. For example, zip('abc', [1,2,3]) == [('a',1),('b',2),('c',3)]. You can use this to save a lot of typing when you have fields in a known order that you want to map to names:

Epydoc is a tool for generating API documentation for Python modules, based on their docstrings. For an example of epydoc's output, see the API documentation for epydoc itself (html, pdf). A lightweight markup language called epytext can be used to format docstrings, and to add information about specific fields, such as parameters and instance variables. Epydoc also understands docstrings written in reStructuredText, Javadoc, and plaintext. For a more extensive example of epydoc's output, see the API documentation for Python 2.5.

Following my thoughts yesterday, here are some VIM python scripts to add python breakpoint and debugging features to VIM. With this set up the F7 key will set a breakpoint on a line of code, Shift-F7 will remove all breakpoints and Shift-F12 will execute a script in the python debugger. This only runs on windows as far as I know, because it uses the 'start' command to launch the debugger in a seperate process without VIM waiting for it to finish. This allows you to look through the source code (and fix it) while the debugging is still in progress.

Python in Maya Done RightPymel makes python scripting with Maya work the way it should. Maya's command module is a direct translation of mel commands into python commands. The result is a very awkward and unpythonic syntax which does not take advantage of python's strengths -- particulary, a flexible, object-oriented design. Pymel builds on the cmds module by organizing many of its commands into a class hierarchy, and by customizing them to operate in a more succinct and intuitive way. Powerful new classes

Neat script I wrote for a friend to annotate a Python script with the types observed during a run of the program

One of the main difficulties I've had using parser generators has been the difficulty of figuring out why a grammar didn't work. Fixing shift-reduce and reduce-reduce conflicts seemed like voodoo to me, and though I slightly understand better how to fix such things now it's still a different mode of thinking that I don't want to try to get into when I just want to parse something simple. PyMeta uses a variation on the Parsing Expression Grammar (PEG) approach to parsing. The chief consequence of this is there's no possibility of ambiguity in a parse: a successful parse will yield exactly one result, and you can trace the control flow through the grammar to figure out how it got there.

Easily create Google charts using python syntax and datasets. Python wrapper for the Google Chart API. The wrapper can render the URL of the Google chart, based on your parameters, or it can render an HTML img tag to insert into webpages on the fly. Made for dynamic python websites (Django,Zope,CGI,etc.) that need on the fly chart generation without any extra modules. The wrapper SHOULD also work with Eastwood the Google Chart API workalike. G = GChart('p3', [5,10])

Pypar does not require the Python interpreter to be modified or recompiled: Parallel python programs use the standard Python and need merely import the pypar module. This means for example that you can upgrade Python independently of your parallel codes.

Pypar is an efficient but easy-to-use module that allows programs/scripts written in the Python programming language to run in parallel on multiple processors and communicate using message passing. Pypar provides bindings to an important subset of the message passing interface standard MPI. Other Python MPI bindings available from other developers include: PyMPI, Scientific Python and pythonMPI.

What is Py++? Definition: Py++ is an object-oriented framework for creating a code generator for Boost.Python library. Py++ uses few different programming paradigms to help you to expose C++ declarations to Python. This code generator will not stand on your way. It will guide you through the whole process. It will raise warnings in the case you are doing something wrong with a link to the explanation. And the most important it will save your time - you will not have to update code generator script every time source code is changed.

IPython on resizeable Win32 Console If you are on win32, you might want to use Console, an open source replacement for that inflexible old win32 terminal. Here's how. Install IPython using the .exe installer. Download stable version of Console (1.5 at this time). Create a shortcut to console.exe, right-click -> properties. Set target to e.g. C:\opt\Console\console.exe -c "/k python c:\python25\scripts\ipython.py -p sh" you can also create a tab dedicated to Ipython (and have other dedicated to other shells or command-line app). go to edit->settings->tabs : click on the add button and fill the following fields : * title : ipython ico : C:\Python24\py.ico shell : cmd.exe /k "ipython" start-up dir : whatever is good for you. note that python.exe need to be in the path Using IPython with Version 2 of Console use the -r option for creating the shortcut, e.g. C:\opt\Console2\console.exe -r "/k python c:\python25\scripts\ipython.py -p sh" Some advantages of Console2 over normal console (for IPython use) Distinctive taskbar icon Can reside in system tray There are many other benefits, so it's well worth setting up Example config for Console-2.00b120-Beta (you need to edit the xml file): http://vvtools.googlecode.com/svn/trunk/config/console.xml If you have problems with ctrl+C killing Console2 immediately, launch ipython from a normal Console2 session manually.

To attempt to explain it in the simplest terms, a continuation is a representation, at a particular point in a program, of everything the program is capable of doing subsequently. A continuation is a potential that depends on initial conditions. Rather than loop in a traditional way, it is possible to invoke the same continuation recursively with different initial conditions. One broad claim I have read is that continuations, in a theoretical sense, are more fundamental and underlie every other control structure. Don't worry if these ideas cause your brain to melt; that is a normal reaction.

Louie provides Python programmers with a straightforward way to dispatch signals between objects in a wide variety of contexts. It is based on PyDispatcher, which in turn was based on a highly-rated recipe in the Python Cookbook. Louie is licensed under The BSD License.

Pycha is a very simple Python package for drawing charts using the great Cairo library. Its goals are: Lightweight Simple to use Nice looking with default values Customization It won't try to draw any possible chart on earth but draw the most common ones nicely. There are some other options you may want to look at like pyCairoChart Pycha is based on Plotr which is based on PlotKit. Both libraries are written in JavaScript and are great for client web programming. I needed the same for the server side so that's the reason I ported Plotr to Python. Now we can deliver charts to people with JavaScript disabled or embed them in PDF reports.