Python Programming

Robin, Give this a try: from IPython.Debugger import Tracer; debugger = Tracer() debugger() #add this line where you want to break barr

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])

Now that we have a dendrogram from which to inspect, let's find a suitable color threshold. One can see roughly three major clusters formed by using a distance threshold of 1.2. Now, let's plot another dendrogram using this as the color threshold. The legend displays the predictions of each flat cluster formed by cutting with this threshold

pywinauto is a set of python modules to automate the Microsoft Windows GUI. At it's simplest it allows you to send mouse and keyboard actions to windows dialogs and controls.

PyProtocols extends the PEP 246 adapt() function with a new "declaration API" that lets you easily define your own protocols and adapters, and declare what adapters should be used to adapt what types, objects, or protocols.  In addition to its own Interface type, PyProtocols can also use Twisted and Zope's Interface types too.  (Of course, since Twisted and Zope interfaces aren't as flexible, only a subset of the PyProtocols API works with them.  Specific limitations are listed in the documentation.)

If I have:     from sympy import Symbol, Integrate     x = Symbol('x')     f = x**2 + x     g = Integrate(f, x) how can I apply g to a numpy array? In other words, how can I "numpify" the g expression, injecting in it x = numpy.linspace(1, 9, 9)? What would be even nicer would be to be able to retrieve a lambda using numpy functions for g as a function of x (that way I don't have the overhead of numpifying it each time I want to apply it to an array).

What's the relation between these benefits and Stackless's implementation details? Here's a quick sketch: Continuations are the general-purpose concurrency construct. A continuation represents all the future computations of a particular program. Capturing all this control flow in a single conceptual object makes it programmable: It becomes possible to calculate or reason over the control flow. In particular, there's great scope for optimizing assignment of different calculations to different processes or threads or even hosts.

def comb(items, n=None): if n is None: n = len(items) for i in range(len(items)): v = items[i:i+1] if n == 1: yield v else: rest = items[i+1:] for c in comb(rest, n-1): yield v + c

def perm(items, n=None): if n is None: n = len(items) for i in range(len(items)): v = items[i:i+1] if n == 1: yield v else: rest = items[:i] + items[i+1:] for p in perm(rest, n-1): yield v + p

Permutation and Combination Enumerator

This is a project to develop a cross-platform pythonic GUI API.  The goals of this project are: