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    OutputArea.prototype.append_svg = function (svg, element) {
        var toinsert = $("<div/>").addClass("box-flex1 output_subarea output_svg");
        toinsert.append(svg);
        element.append(toinsert);
        // The <svg> tag cannot be made resizable so we wrap it in a resizable <div>.
        // The problem with this is that we need to 1) set the initial size of the
        // <div> based on the size of the <svg> and 2) we need to tie the size of the
        // <div> and the <svg>.
        var img = $('<div/>');
        img.html(svg);

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import networkx as nx
import matplotlib as mat

# Builds a graphene sheet with the given number of rows and columns.
# The sheet has a defected column running through the optionally passed
# arg. Otherwise it defaults to the center.
def build_defected_graph(rows, columns, defect = 0):
    g = nx.Graph()
    
    if( columns <= 2 ):

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============================
Configuring IPython.parallel
============================

This guide describes the steps needed to run IPython engines on Microsoft Azure to perform a parallel
computation in the Microsoft cloud. We assume (see above guide) that Python, IPython and PyZMQ have
been installed on a set of Azure compute nodes.

1. Install Python, IPython and PyZMQ on a system having open access to the internet. We used an
   Ubuntu 11.04 system for this purpose. This system will run the IPython controller and the IPython 

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"""Solve the 1D diffusion equation using CN and finite differences."""
from time import sleep

import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# The total number of nodes
nodx = 3
nody = 3

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"""Solve the 1D diffusion equation using CN and finite differences."""
from time import sleep

import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# The total number of nodes
numx = 5
numy = 5

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from numpy import *
from matplotlib.pyplot import *

from time import sleep

import numpy as np
import networkx as nx

dim = [3,2]