{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Generators for some classic graphs.\n",
      "\n",
      "The typical graph generator is called as follows:\n",
      "\n",
      ">>> G=nx.complete_graph(100)\n",
      "\n",
      "returning the complete graph on n nodes labeled 0,..,99\n",
      "as a simple graph. Except for empty_graph, all the generators \n",
      "in this module return a Graph class (i.e. a simple, undirected graph).\n",
      "\n",
      "\"\"\"\n",
      "#    Copyright (C) 2004-2010 by \n",
      "#    Aric Hagberg <hagberg@lanl.gov>\n",
      "#    Dan Schult <dschult@colgate.edu>\n",
      "#    Pieter Swart <swart@lanl.gov>\n",
      "#    All rights reserved.\n",
      "#    BSD license.\n",
      "import itertools\n",
      "__author__ =\"\"\"Aric Hagberg (hagberg@lanl.gov)\\nPieter Swart (swart@lanl.gov)\"\"\"\n",
      "\n",
      "__all__ = [ 'balanced_tree',\n",
      "            'barbell_graph',\n",
      "            'complete_graph',\n",
      "            'complete_bipartite_graph',\n",
      "            'circular_ladder_graph',\n",
      "            'cycle_graph',\n",
      "            'dorogovtsev_goltsev_mendes_graph',\n",
      "            'empty_graph',\n",
      "            'full_rary_tree',\n",
      "            'grid_graph',\n",
      "            'grid_2d_graph',\n",
      "            'hypercube_graph',\n",
      "            'ladder_graph',\n",
      "            'lollipop_graph',\n",
      "            'null_graph',\n",
      "            'path_graph',\n",
      "            'star_graph',\n",
      "            'trivial_graph',\n",
      "            'wheel_graph']\n",
      "\n",
      "\n",
      "#-------------------------------------------------------------------\n",
      "#   Some Classic Graphs\n",
      "#-------------------------------------------------------------------\n",
      "import networkx as nx\n",
      "from networkx.utils import is_list_of_ints, flatten\n",
      "\n",
      "def _tree_edges(n,r):\n",
      "    # helper function for trees\n",
      "    # yields edges in rooted tree at 0 with n nodes and branching ratio r\n",
      "    nodes=iter(range(n))\n",
      "    parents=[next(nodes)] # stack of max length r\n",
      "    while parents:\n",
      "        source=parents.pop(0)\n",
      "        for i in range(r):\n",
      "            try:\n",
      "                target=next(nodes)\n",
      "                parents.append(target)\n",
      "                yield source,target\n",
      "            except StopIteration:\n",
      "                break\n",
      "\n",
      "def full_rary_tree(r, n, create_using=None):\n",
      "    \"\"\"Creates a full r-ary tree of n vertices. \n",
      "\n",
      "    Sometimes called a k-ary, n-ary, or m-ary tree.  \"... all non-leaf\n",
      "    vertices have exactly r children and all levels are full except\n",
      "    for some rightmost position of the bottom level (if a leaf at the\n",
      "    bottom level is missing, then so are all of the leaves to its\n",
      "    right.\" [1]_\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    r : int\n",
      "        branching factor of the tree\n",
      "    n : int\n",
      "        Number of nodes in the tree\n",
      "    create_using : NetworkX graph type, optional\n",
      "        Use specified type to construct graph (default = networkx.Graph)    \n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    G : networkx Graph\n",
      "        An r-ary tree with n nodes\n",
      "\n",
      "    References\n",
      "    ----------\n",
      "    .. [1] An introduction to data structures and algorithms,\n",
      "           James Andrew Storer,  Birkhauser Boston 2001, (page 225).\n",
      "    \"\"\"\n",
      "    G=nx.empty_graph(n,create_using)\n",
      "    G.add_edges_from(_tree_edges(n,r))\n",
      "    return G\n",
      "\n",
      "def balanced_tree(r, h, create_using=None):\n",
      "    \"\"\"Return the perfectly balanced r-tree of height h.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    r : int\n",
      "        Branching factor of the tree\n",
      "    h : int\n",
      "        Height of the tree\n",
      "    create_using : NetworkX graph type, optional\n",
      "        Use specified type to construct graph (default = networkx.Graph)    \n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    G : networkx Graph\n",
      "        A tree with n nodes\n",
      "\n",
      "    Notes\n",
      "    -----\n",
      "    This is the rooted tree where all leaves are at distance h from\n",
      "    the root. The root has degree r and all other internal nodes have\n",
      "    degree r+1.\n",
      "\n",
      "    Node labels are the integers 0 (the root) up to  number_of_nodes - 1.\n",
      "\n",
      "    Also refered to as a complete r-ary tree.\n",
      "    \"\"\"\n",
      "    # number of nodes is n=1+r+..+r^h\n",
      "    if r==1:\n",
      "        n=2\n",
      "    else:\n",
      "        n = int((1-r**(h+1))/(1-r)) # sum of geometric series r!=1\n",
      "    G=nx.empty_graph(n,create_using)\n",
      "    G.add_edges_from(_tree_edges(n,r))\n",
      "    return G\n",
      "\n",
      "    return nx.full_rary_tree(r,n,create_using)\n",
      "\n",
      "def barbell_graph(m1,m2,create_using=None):\n",
      "    \"\"\"Return the Barbell Graph: two complete graphs connected by a path.\n",
      "\n",
      "    For m1 > 1 and m2 >= 0.\n",
      "\n",
      "    Two identical complete graphs K_{m1} form the left and right bells,\n",
      "    and are connected by a path P_{m2}.\n",
      "\n",
      "    The 2*m1+m2  nodes are numbered\n",
      "        0,...,m1-1 for the left barbell,\n",
      "        m1,...,m1+m2-1 for the path,\n",
      "        and m1+m2,...,2*m1+m2-1 for the right barbell.\n",
      "\n",
      "    The 3 subgraphs are joined via the edges (m1-1,m1) and (m1+m2-1,m1+m2).\n",
      "    If m2=0, this is merely two complete graphs joined together.\n",
      "\n",
      "    This graph is an extremal example in David Aldous\n",
      "    and Jim Fill's etext on Random Walks on Graphs.\n",
      "\n",
      "    \"\"\"\n",
      "    if create_using is not None and create_using.is_directed():\n",
      "        raise nx.NetworkXError(\"Directed Graph not supported\")\n",
      "    if m1<2:\n",
      "        raise nx.NetworkXError(\\\n",
      "              \"Invalid graph description, m1 should be >=2\")\n",
      "    if m2<0:\n",
      "        raise nx.NetworkXError(\\\n",
      "              \"Invalid graph description, m2 should be >=0\")\n",
      "\n",
      "    # left barbell\n",
      "    G=complete_graph(m1,create_using)\n",
      "    G.name=\"barbell_graph(%d,%d)\"%(m1,m2)\n",
      "    \n",
      "    # connecting path\n",
      "    G.add_nodes_from([v for v in range(m1,m1+m2-1)])\n",
      "    if m2>1:\n",
      "        G.add_edges_from([(v,v+1) for v in range(m1,m1+m2-1)])\n",
      "    # right barbell\n",
      "    G.add_edges_from( (u,v) for u in range(m1+m2,2*m1+m2) for v in range(u+1,2*m1+m2))\n",
      "    # connect it up\n",
      "    G.add_edge(m1-1,m1)\n",
      "    if m2>0:\n",
      "        G.add_edge(m1+m2-1,m1+m2)\n",
      "    return G\n",
      "\n",
      "def complete_graph(n,create_using=None):\n",
      "    \"\"\" Return the complete graph K_n with n nodes. \n",
      "    \n",
      "    Node labels are the integers 0 to n-1.\n",
      "    \"\"\"\n",
      "    G=empty_graph(n,create_using)\n",
      "    G.name=\"complete_graph(%d)\"%(n)\n",
      "    if n>1:\n",
      "        if G.is_directed():\n",
      "            edges=itertools.permutations(range(n),2)\n",
      "        else:\n",
      "            edges=itertools.combinations(range(n),2)\n",
      "        G.add_edges_from(edges)\n",
      "    return G\n",
      "\n",
      "\n",
      "def complete_bipartite_graph(n1,n2,create_using=None):\n",
      "    \"\"\"Return the complete bipartite graph K_{n1_n2}.\n",
      "    \n",
      "    Composed of two partitions with n1 nodes in the first \n",
      "    and n2 nodes in the second. Each node in the first is \n",
      "    connected to each node in the second.\n",
      "\n",
      "    Node labels are the integers 0 to n1+n2-1\n",
      "    \n",
      "    \"\"\"\n",
      "    if create_using is not None and create_using.is_directed():\n",
      "        raise nx.NetworkXError(\"Directed Graph not supported\")\n",
      "    G=empty_graph(n1+n2,create_using)\n",
      "    G.name=\"complete_bipartite_graph(%d,%d)\"%(n1,n2)\n",
      "    for v1 in range(n1):\n",
      "        for v2 in range(n2):\n",
      "            G.add_edge(v1,n1+v2)\n",
      "    return G\n",
      "\n",
      "def circular_ladder_graph(n,create_using=None):\n",
      "    \"\"\"Return the circular ladder graph CL_n of length n.\n",
      "\n",
      "    CL_n consists of two concentric n-cycles in which\n",
      "    each of the n pairs of concentric nodes are joined by an edge.\n",
      "\n",
      "    Node labels are the integers 0 to n-1\n",
      "    \n",
      "    \"\"\"\n",
      "    G=ladder_graph(n,create_using)\n",
      "    G.name=\"circular_ladder_graph(%d)\"%n\n",
      "    G.add_edge(0,n-1)\n",
      "    G.add_edge(n,2*n-1)\n",
      "    return G\n",
      "\n",
      "def cycle_graph(n,create_using=None):\n",
      "    \"\"\"Return the cycle graph C_n over n nodes.\n",
      "   \n",
      "    C_n is the n-path with two end-nodes connected.\n",
      "\n",
      "    Node labels are the integers 0 to n-1\n",
      "    If create_using is a DiGraph, the direction is in increasing order.\n",
      "    \n",
      "    \"\"\"\n",
      "    G=path_graph(n,create_using)\n",
      "    G.name=\"cycle_graph(%d)\"%n\n",
      "    if n>1: G.add_edge(n-1,0)\n",
      "    return G\n",
      "\n",
      "def dorogovtsev_goltsev_mendes_graph(n,create_using=None):\n",
      "    \"\"\"Return the hierarchically constructed Dorogovtsev-Goltsev-Mendes graph.\n",
      "\n",
      "    n is the generation.\n",
      "    See: arXiv:/cond-mat/0112143 by Dorogovtsev, Goltsev and Mendes.\n",
      "    \n",
      "    \"\"\"\n",
      "    if create_using is not None:\n",
      "        if create_using.is_directed():\n",
      "            raise nx.NetworkXError(\"Directed Graph not supported\")\n",
      "        if create_using.is_multigraph():\n",
      "            raise nx.NetworkXError(\"Multigraph not supported\")\n",
      "    G=empty_graph(0,create_using)\n",
      "    G.name=\"Dorogovtsev-Goltsev-Mendes Graph\"\n",
      "    G.add_edge(0,1)\n",
      "    if n==0:\n",
      "        return G\n",
      "    new_node = 2         # next node to be added\n",
      "    for i in range(1,n+1): #iterate over number of generations.\n",
      "        last_generation_edges = G.edges()\n",
      "        number_of_edges_in_last_generation = len(last_generation_edges)\n",
      "        for j in range(0,number_of_edges_in_last_generation):\n",
      "            G.add_edge(new_node,last_generation_edges[j][0])\n",
      "            G.add_edge(new_node,last_generation_edges[j][1])\n",
      "            new_node += 1\n",
      "    return G\n",
      "\n",
      "def empty_graph(n=0,create_using=None):\n",
      "    \"\"\"Return the empty graph with n nodes and zero edges.\n",
      "\n",
      "    Node labels are the integers 0 to n-1\n",
      "\n",
      "    For example:\n",
      "    >>> G=nx.empty_graph(10)\n",
      "    >>> G.number_of_nodes()\n",
      "    10\n",
      "    >>> G.number_of_edges()\n",
      "    0\n",
      " \n",
      "    The variable create_using should point to a \"graph\"-like object that\n",
      "    will be cleaned (nodes and edges will be removed) and refitted as\n",
      "    an empty \"graph\" with n nodes with integer labels. This capability\n",
      "    is useful for specifying the class-nature of the resulting empty\n",
      "    \"graph\" (i.e. Graph, DiGraph, MyWeirdGraphClass, etc.).\n",
      "    \n",
      "    The variable create_using has two main uses:\n",
      "    Firstly, the variable create_using can be used to create an\n",
      "    empty digraph, network,etc.  For example,\n",
      "\n",
      "    >>> n=10\n",
      "    >>> G=nx.empty_graph(n,create_using=nx.DiGraph())\n",
      "\n",
      "    will create an empty digraph on n nodes.\n",
      "\n",
      "    Secondly, one can pass an existing graph (digraph, pseudograph,\n",
      "    etc.) via create_using. For example, if G is an existing graph\n",
      "    (resp. digraph, pseudograph, etc.), then empty_graph(n,create_using=G)\n",
      "    will empty G (i.e. delete all nodes and edges using G.clear() in\n",
      "    base) and then add n nodes and zero edges, and return the modified\n",
      "    graph (resp. digraph, pseudograph, etc.).\n",
      "\n",
      "    See also create_empty_copy(G).\n",
      "    \n",
      "    \"\"\"\n",
      "    if create_using is None:\n",
      "        # default empty graph is a simple graph\n",
      "        G=nx.Graph()\n",
      "    else:\n",
      "        G=create_using\n",
      "        G.clear()\n",
      "\n",
      "    G.add_nodes_from(range(n))\n",
      "    G.name=\"empty_graph(%d)\"%n \n",
      "    return G\n",
      "\n",
      "def grid_2d_graph(m,n,periodic=False,create_using=None):\n",
      "    \"\"\" Return the 2d grid graph of mxn nodes,\n",
      "        each connected to its nearest neighbors.\n",
      "        Optional argument periodic=True will connect\n",
      "        boundary nodes via periodic boundary conditions.\n",
      "    \"\"\"\n",
      "    G=empty_graph(0,create_using)\n",
      "    G.name=\"grid_2d_graph\"\n",
      "    rows=range(m)\n",
      "    columns=range(n)\n",
      "    G.add_nodes_from( (i,j) for i in rows for j in columns )\n",
      "    G.add_edges_from( ((i,j),(i-1,j)) for i in rows for j in columns if i>0 )\n",
      "    G.add_edges_from( ((i,j),(i,j-1)) for i in rows for j in columns if j>0 )\n",
      "    if G.is_directed():\n",
      "        G.add_edges_from( ((i,j),(i+1,j)) for i in rows for j in columns if i<m-1 )\n",
      "        G.add_edges_from( ((i,j),(i,j+1)) for i in rows for j in columns if j<n-1 )\n",
      "    if periodic:\n",
      "        if n>2:\n",
      "            G.add_edges_from( ((i,0),(i,n-1)) for i in rows )\n",
      "            if G.is_directed():\n",
      "                G.add_edges_from( ((i,n-1),(i,0)) for i in rows )\n",
      "        if m>2:\n",
      "            G.add_edges_from( ((0,j),(m-1,j)) for j in columns )\n",
      "            if G.is_directed():\n",
      "                G.add_edges_from( ((m-1,j),(0,j)) for j in columns )\n",
      "        G.name=\"periodic_grid_2d_graph(%d,%d)\"%(m,n)\n",
      "    return G\n",
      "\n",
      "\n",
      "def grid_graph(dim,periodic=False):\n",
      "    \"\"\" Return the n-dimensional grid graph.\n",
      "\n",
      "    The dimension is the length of the list 'dim' and the\n",
      "    size in each dimension is the value of the list element.\n",
      "\n",
      "    E.g. G=grid_graph(dim=[2,3]) produces a 2x3 grid graph.\n",
      "\n",
      "    If periodic=True then join grid edges with periodic boundary conditions.\n",
      "\n",
      "    \"\"\"    \n",
      "    dlabel=\"%s\"%dim\n",
      "    if dim==[]:\n",
      "        G=empty_graph(0)\n",
      "        G.name=\"grid_graph(%s)\"%dim\n",
      "        return G\n",
      "    if not is_list_of_ints(dim):\n",
      "        raise nx.NetworkXError(\"dim is not a list of integers\")\n",
      "    if min(dim)<=0:\n",
      "        raise nx.NetworkXError(\\\n",
      "              \"dim is not a list of strictly positive integers\")      \n",
      "    if periodic:\n",
      "        func=cycle_graph\n",
      "    else:\n",
      "        func=path_graph\n",
      "\n",
      "    dim=list(dim)\n",
      "    current_dim=dim.pop()\n",
      "    G=func(current_dim)\n",
      "    while len(dim)>0:\n",
      "        current_dim=dim.pop() \n",
      "        # order matters: copy before it is cleared during the creation of Gnew\n",
      "        Gold=G.copy() \n",
      "        Gnew=func(current_dim)\n",
      "        # explicit: create_using=None \n",
      "        # This is so that we get a new graph of Gnew's class.\n",
      "        G=nx.cartesian_product(Gnew,Gold)\n",
      "    # graph G is done but has labels of the form (1,(2,(3,1)))\n",
      "    # so relabel\n",
      "    H=nx.relabel_nodes(G, flatten)\n",
      "    H.name=\"grid_graph(%s)\"%dlabel\n",
      "    return H\n",
      "\n",
      "def hypercube_graph(n):\n",
      "    \"\"\"Return the n-dimensional hypercube.\n",
      "\n",
      "    Node labels are the integers 0 to 2**n - 1.\n",
      "\n",
      "    \"\"\"\n",
      "    dim=n*[2]\n",
      "    G=grid_graph(dim)\n",
      "    G.name=\"hypercube_graph_(%d)\"%n\n",
      "    return G\n",
      "\n",
      "def ladder_graph(n,create_using=None):\n",
      "    \"\"\"Return the Ladder graph of length n.\n",
      "\n",
      "    This is two rows of n nodes, with\n",
      "    each pair connected by a single edge.\n",
      "\n",
      "    Node labels are the integers 0 to 2*n - 1.\n",
      "    \n",
      "    \"\"\"\n",
      "    if create_using is not None and create_using.is_directed():\n",
      "        raise nx.NetworkXError(\"Directed Graph not supported\")\n",
      "    G=empty_graph(2*n,create_using)\n",
      "    G.name=\"ladder_graph_(%d)\"%n \n",
      "    G.add_edges_from([(v,v+1) for v in range(n-1)])\n",
      "    G.add_edges_from([(v,v+1) for v in range(n,2*n-1)])\n",
      "    G.add_edges_from([(v,v+n) for v in range(n)])\n",
      "    return G\n",
      "\n",
      "def lollipop_graph(m,n,create_using=None):\n",
      "    \"\"\"Return the Lollipop Graph; K_m connected to P_n.\n",
      "\n",
      "    This is the Barbell Graph without the right barbell.\n",
      "\n",
      "    For m>1 and n>=0, the complete graph K_m is connected to the \n",
      "    path P_n.  The resulting m+n nodes are labelled 0,...,m-1 for the\n",
      "    complete graph and m,...,m+n-1 for the path. The 2 subgraphs\n",
      "    are joined via the edge (m-1,m).  If n=0, this is merely a complete \n",
      "    graph.\n",
      "\n",
      "    Node labels are the integers 0 to number_of_nodes - 1.\n",
      "\n",
      "    (This graph is an extremal example in David Aldous and Jim\n",
      "    Fill's etext on Random Walks on Graphs.)\n",
      "    \n",
      "    \"\"\"\n",
      "    if create_using is not None and create_using.is_directed():\n",
      "        raise nx.NetworkXError(\"Directed Graph not supported\")\n",
      "    if m<2:\n",
      "        raise nx.NetworkXError(\\\n",
      "              \"Invalid graph description, m should be >=2\")\n",
      "    if n<0:\n",
      "        raise nx.NetworkXError(\\\n",
      "              \"Invalid graph description, n should be >=0\")\n",
      "    # the ball\n",
      "    G=complete_graph(m,create_using)\n",
      "    # the stick\n",
      "    G.add_nodes_from([v for v in range(m,m+n)])\n",
      "    if n>1:\n",
      "        G.add_edges_from([(v,v+1) for v in range(m,m+n-1)])\n",
      "    # connect ball to stick\n",
      "    if m>0: G.add_edge(m-1,m)\n",
      "    G.name=\"lollipop_graph(%d,%d)\"%(m,n)\n",
      "    return G\n",
      "\n",
      "def null_graph(create_using=None):\n",
      "    \"\"\" Return the Null graph with no nodes or edges. \n",
      "\n",
      "    See empty_graph for the use of create_using.\n",
      "\n",
      "    \"\"\"\n",
      "    G=empty_graph(0,create_using)\n",
      "    G.name=\"null_graph()\"\n",
      "    return G\n",
      "\n",
      "def path_graph(n,create_using=None):\n",
      "    \"\"\"Return the Path graph P_n of n nodes linearly connected by n-1 edges.\n",
      "\n",
      "    Node labels are the integers 0 to n - 1.\n",
      "    If create_using is a DiGraph then the edges are directed in \n",
      "    increasing order.\n",
      "\n",
      "    \"\"\"\n",
      "    G=empty_graph(n,create_using)\n",
      "    G.name=\"path_graph(%d)\"%n\n",
      "    G.add_edges_from([(v,v+1) for v in range(n-1)])\n",
      "    return G\n",
      "\n",
      "def star_graph(n,create_using=None):\n",
      "    \"\"\" Return the Star graph with n+1 nodes: one center node, connected to n outer nodes.\n",
      "\n",
      "   Node labels are the integers 0 to n.\n",
      "\n",
      "    \"\"\"\n",
      "    G=complete_bipartite_graph(1,n,create_using)\n",
      "    G.name=\"star_graph(%d)\"%n\n",
      "    return G\n",
      "\n",
      "def trivial_graph(create_using=None):\n",
      "    \"\"\" Return the Trivial graph with one node (with integer label 0) and no edges.\n",
      "\n",
      "    \"\"\"\n",
      "    G=empty_graph(1,create_using)\n",
      "    G.name=\"trivial_graph()\"\n",
      "    return G\n",
      "\n",
      "def wheel_graph(n,create_using=None):\n",
      "    \"\"\" Return the wheel graph: a single hub node connected to each node of the (n-1)-node cycle graph. \n",
      "\n",
      "   Node labels are the integers 0 to n - 1.\n",
      "\n",
      "    \"\"\"\n",
      "    G=star_graph(n-1,create_using)\n",
      "    G.name=\"wheel_graph(%d)\"%n\n",
      "    G.add_edges_from([(v,v+1) for v in range(1,n-1)])\n",
      "    if n>2:\n",
      "        G.add_edge(1,n-1)\n",
      "    return G"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "G=nx.complete_graph(100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['flatten']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(wheel_graph(10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFBCAYAAAA2bKVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiZT+YqIiJhM5SsiImIyla+IiIjJ/j8ILAOJMkx8MgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106f69090>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(star_graph(5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFBCAYAAAA2bKVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1070ac5d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(path_graph(5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAFBCAYAAABEo8fdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107094c90>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(lollipop_graph(10,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFBCAYAAAA2bKVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4k2Xbx/Fv0qYZHUCLQBktlFEoexSZMssQFFEQF4Ki\nIqjII0vR18ctqCgOhgriQMWJyFCWDBmyp2yUVSjdK6Ntkuv9I22fTijaJm05P8eRo+193wlnIvLr\nNW+NUkohhBBCCLfReroAIYQQ4noj4SuEEEK4mYSvEEII4WYSvkIIIYSbSfgKIYQQbibhK4QQQriZ\nhK8QQgjhZhK+QgghhJtJ+AohhBBuJuErhBBCuJmErxBCCOFmEr5CCCGEm0n4CiGEEG4m4SuEEEK4\nmYSvEEII4WYSvkIIIYSbSfgKIYQQbibhK4QQQriZhK8QQgjhZhK+QgghhJtJ+AohhBBuJuErhBBC\nuJmErxBCCOFmEr5CCCGEm0n4CiGEEG4m4SuEEEK4mYSvEEII4WYSvkIIIYSbSfgKIYQQbibhK4QQ\nQriZhK8QQgjhZhK+QgghhJtJ+AohhBBuJuErhBBCuJmErxBCCOFmEr5CCCGEm0n4CiGEEG4m4SuE\nEEK4mYSvEEII4WYSvkIIIYSbSfgKIYQQbibhK4QQQriZt6cLEEKUb6mpqVy8eJH09HT8/f2pW7cu\nvr6+ni5LiApNwlcIUYhSih07djD3rbdYtmIFtXx88NVoSHM6iXc4GHHnnYx/6ilatWrl6VKFqJA0\nSinl6SKEEOXHhQsXuHPQIGJPn2ac1cpop5OgPOcvAQu8vPhQr6dFu3Z8tWwZgYGBnipXiApJwlcI\nkevUqVP07tSJx5OTmexwXHFSiB14xseH5TVrsnHnTmrVquWuMoWo8CR8hRAAJCQk0KlVKybHxDDW\n6Szx817U6VjesCGb9+zBZDKVYYVCVB4y21kIAcB7s2ZxU0ICWU4nHQAD8ECe80eADkAgUBXoCmwB\nns/Kota5c3z+2Wdur1mIikpavkIIsrKyCL3hBtampHAC12/lqwErsCj7mhQgEaif/fMHwKtADPAb\nMLF+fQ789RcajcattQtREUnLVwjBTz/9RBOHg+bAUGAI5JtkBVAFaABoAAeufzyCs8/1AjLj4ti6\ndaubKhaiYpPwFUKweulS7kxPz3esuC6xqoAReAP4PvuYBhhhNvPrypVlVqMQlYmErxCChMuXqVng\nWHGdx8m4uqDvAobzv5CuASTGxJRNgaLElFJs3LiROwcNIrR6daoYjdTw96dNWBhvzpxJQkKCp0sU\nyJivEAK4o29f7lq/nuF5jj0HRPO/Md+CFOAPbANaAe8DL5hMhDRpQlBQUO6jevXq+X7OezwgIEDG\niEvR9999x/OTJqFJSmK82czNSlENsAGngAVGI8uU4rZbb2XWvHmyPtuDZIcrIa4zFouFw4cPc/Dg\nQQ4cOOB6/PEH3Qtcd7VIdABOIGdxUbRWy31jxjBq9GgSEhKIj48nISGBhIQETp8+zc6dOwsdt1qt\nBAYGljisg4KCCAwMxNtb/ukqaMbLLzN/xgw+sVjoReH/frWAblYrCcDLP/1El+3bWf3774SGhrq/\nWCEtXyEqK6UU58+fzxeyBw8e5OzZs4SHh9O6devcR1xcHG8+/DC709NxAlnAi7havh8DXsBGoDrQ\nEjDjahn/DuzDteFGmMnEz1u30qZNmxLXmJmZSWJiYqFQzvsoeDwpKQk/P78Sh3XOozKvQZ77/vu8\n+/TTbLBYqF3C58zSallQty5b9+2TFrAHSPgKUQlYrVb+/PPPfCF78OBB9Ho9rVq1yg3ZVq1a0bRp\nU3Q6Xb7nO51OGgUH83VsLL8ALxV4/ReACOD/gAuAH9AT16SresAyYGaLFmw7dKhs32h2rSkpKSUO\n65xjGo3mmsI6KCiIKlWqoNWW76kx586do214OE/YbKwADgN3U/RwwUu4/luuA3oDj/n4YB8xgg8/\n/9xt9QoXCV8hKhClFNHR0flC9sCBA5w5c4YmTZrkC9nWrVtTo0aNEr/2mzNnsu3FF/nRar1ql3Ne\ndqCnry9j581j5MiR1/ye3EEphcViKXFY5xw3m82FusWv1uIOCgoq9MtNWXpu2jTSZs+mZ2Zmkeuz\nc5zGtYwsEfgcV/heAiIMBs7ExFClShW31SwkfIUot2w2W25rNidkDx48iLe3d76Abd26NU2bNsXH\nx+df/Xlms5mekZEMOHmSl+z2EgWwE3hUr+dcZCTLf/vNraHjDllZWbnd4iUJ65xucZPJVOKwzjlu\nMpmuefJZZmYmITfcwMbUVJpmH8vpnSgYvgOBCcB4YCGu8AUY4etLt9de44kJE/75ByWumcxaEMLD\nlFJcvHgxX8geOHCAv//+m8aNG+cG7c0330yrVq3K7AYGvr6+rNiwgb6dO5N08SIzMzK40l17k4Fx\nRiPnw8P5ZdWqShe8ADqdjpo1a1KzZsGFWMVzOp2kpqYWG9Z//vlnkcedTudVw7rgsR07dtAIcoMX\nil6f/R2u7UIHFnFujNnMCx99JOHrZtLyFcKNbDYbR44cKTQJSqvVFtma1ev1bq8xJSWFsffdx9p1\n67jP6WRcZmbuP+4K2A/MNRj4Hhh+xx28t2ABBoPB7XVWNlar9ZomniUkJJCSksIQpVia53UKtnzT\ngPa4xnlDcO1SlrflexIYUKMGpy9fds8bFYC0fIUoE0opYmJiCo3Nnj59moYNG+YG7IABA3Jbs+Vl\nvWuVKlVYsnw558+f56M5c+g5bx4pFgt+3t6kZmURXK0aY598kuOPPHJNY8riyoxGI3Xr1qVu3bol\nfs78+fPZOWkSWCy5xwq2pl4ARuIK3qKu0QOZWVnXXK/4d6TlK8S/lJGRwdGjRwt1GyulCk2AioiI\n8Ehr9t9QSmG1WklLSyMgIACj0ejpkkS277//ni/HjGFpamrusYIt37bZP+e0tOJw7dP9NDAF11Kx\n+0NCOHT2rLvKFkjLV7hZQkICa9asIT4+HqfTSWBgIH369KF27ZKuTvSsolqzp06dIiwsLDdkJ02a\nROvWrQkODi43rdl/Q6PRYDKZKvU62YqqU6dOjM3MJB3XfttZuGafO4AMXOuz12cfA1eLNxJ4BxiQ\nfWy5tzdde/Vya91CWr7CTXbu3Mnct95i2fLl9NTpqJOZiQaI1elYa7fTp1cvxk+ZQs+ePctFYGVm\nZnL06NFCY7N2u73Q2GxERISMeQqPGRoVxcB167hI0euzny9wLO+YbxbQwGRi5bZttG7dusxrFf8j\n4SvKlN1uZ8Ijj7Dqm294zGbjAaeT6gWuSQUWazS8bzLRokcPPv/+e7d2bV6+fLnQcp4TJ07QoEGD\nQhtU1KlTp1z8ciBEjrVr1zLp9tvZl56O1zU+9zvg3Vat2HLgQFmUJq5AwleUGafTyT233Ubi+vV8\nb7EQcJXrM4DRRiOXW7bkl82bS31sNDMzk2PHjhVqzWZmZhYK2ebNm8vYpqgQnE4n/bp2pfnevczO\n7lEqiRPATUYjX61YQe/eva96vShdEr6izDw/bRobP/iANRYLJe2UdQDDjUYChw5lwZdf/uM/Oy4u\nrtDY7PHjxwkNDS00Capu3brSmhUVWnJyMj0iI7np/HlmZ2RctQW8D7jFaOSl997jwYceckeJogAJ\nX1EmUlJSqB8czGGrlaXApxTec/YPXDMz9+KaGNITeA/XvsGhBgN7jx276h1XsrKyOH78eKGZxjab\nLTdcc742b95cJg2JSis5OZk7Bw3i3IEDjLNYGKUUVfOcV8B2YK7JxCql+Pjzz7lj2DAPVSskfEWZ\n+OD999n8zDN8azazFIrcc/ZXXHfH6Y8rfB8HLgK/ABN9fPB98klefeON3NeMj48vFLLHjx+nXr16\nhSZB1atXT1qz4rqjlGLLli3MffNNfl27lg56PdWcTmwaDaeVItPPj/GTJjHqgQfkTkYeJuErykTz\n0FDmnDtHzzzHittzNsdeXK3fVOA40MVo5MHHHsu996zZbKZVq1b5QrZ58+b4+l5pE0Qhrk+XL19m\n//79JCcno9frCQ4OJjIystzfpel6Iet8Ramz2Wycio6mR4HjV/stbzPQIvv7cMAnMxO73c64ceNo\n1aoVoaGh0poVooRq1qxJ//79PV2GKIaEryh1KSkpVNHp0Dgc+Y5fKTYPAi8DP+c5VtPXl5EjR9Ku\nXbsyqFIIITxH+h9EqTMYDNiczkLHi2v5ngJuxjXZqmue4zalZLmPEKJSkvAVpS4gIAA0GqILHC+q\n5XsWiMK1C8+9eY6bgUuZmWV2+zwhhPAkCV9R6jQaDffefTcLvF2jGg7ARv49Zx1ANK4t7h4HHinw\nGl8Bvbp1o1q1au4qWwgh3EZmO4sycfDgQQZ26sQZq5VXKbzn7H9xtYRfgHw3bNcAKUA7Pz9m/vAD\n/fr1c0u9QgjhThK+osy0bdyYu06dYto1Pu9r4MW6dTly9qwsixBCVEryL5sodVlZWUyePJnLZjNv\n+/tzLZtErgGe9PXl25UrJXiFEJWW/OsmStXZs2fp3r07x44d49ChQ6zfto1ngoJ4xsuL2Cs8LxmY\nqdEw0t+fH3/9lVatWrmrZCGEcDsJX1Fqli1bRseOHRk+fDg///wzQUFBtGjRgu0HDnB5+HCaGAzc\nazLxK651vYdw3ej7EYOBBgYD+wYN4vfdu+nWrZtn34gQQpQxGfMV/1pmZiZTp07lp59+YsmSJXTq\n1KnI6xITE/ls0SKWLV5MQmIiTqeTwGrV6H/HHTw0dqwsKxJCXDckfMW/8tdffzFixAjq1KnDokWL\nZGmQEEKUgHQ7i3/shx9+oFOnTtx3330sXbpUglcIIUpI9nYW18xmszF58mRWrVrFypUriYyM9HRJ\nQghRoUjLV1yTU6dO0aVLF2JiYti7d68ErxBC/AMSvqLEvvnmGzp37syYMWP47rvvqFq1qqdLEkKI\nCkm6ncVVWa1W/vOf/7Bu3TpWr14tt/gTQoh/SVq+4oqOHz9Op06dSE5OZu/evRK8QghRCiR8RbG+\n/PJLunXrxvjx4/n6669dtwoUQgjxr0m3syjEYrEwYcIEfv/9d9atW0fr1q09XZIQQlQq0vIV+Rw9\nepQbb7wRm83G7t27JXiFEKIMSPiKXJ999hk33XQTEydO5IsvvsDf39/TJQkhRKUk3c4Cs9nMY489\nxo4dO9iwYQMtWrTwdElCCFGpScv3Onf48OHcjTJ2794twSuEEG4g4XudUkqxcOFCevXqxdSpU/n0\n00/x9fX1dFlCCHFdkG7n61B6ejqPPvoo+/fvZ9OmTURERHi6JCGEuK5Iy/c6c/DgQdq3b4/BYGDn\nzp0SvEII4QESvtcJpRQfffQRffr04fnnn2fBggWYTCZPlyWEENcl6Xa+DqSmpjJ27FiOHDnCli1b\nCA8P93RJQghxXZOWbyW3b98+2rdvT5UqVfjjjz8keIUQohyQ8K2klFLMmTOHfv368fLLLzN//nyM\nRqOnyxJCCIF0O1dKKSkpPPTQQ5w+fZpt27bRuHFjT5ckhBAiDwnfcsrhcLB+/Xr++usv0tPT8ff3\np1mzZnTv3h2NRlPs83bv3s2IESMYOHAgX3zxBQaDwY1VCyGEKAmNUkp5ugjxP7GxsSz86CM+fPdd\nbsjIoJ3djq/dTpq3N9u9vHBUrcq4yZO5f9Qoqlatmvs8pRTvvfcer776KnPnzmXYsGEefBdCCCGu\nRMK3HFmzZg333nEHtzocjLNa6VDgvAJ+B+aaTGz28WHZmjVERkaSlJTEgw8+yIULF/jmm28ICwvz\nQPVCCCFKSiZclRMrVqxg5G238WN6OguLCF4ADXATsMRiYV5yMjf36sXChQtp164doaGhbNmyRYJX\nCCEqAGn5lgNHjhyhR2QkKywWbryG560E7tRoeH/BAh588MGyKk8IIUQpk5ZvOTDrlVeYaLOxC+gA\nGIAH8pzPAoYBDXD9B9uUfXwQMFqn48zJk+4sVwghxL8kLV8PS0pKokHt2pyw2diKK1xXA1ZgUfY1\nWcA8oD0wHFiCq/sZ4AjQp0oVzsbG4uPj497ihRBC/CPS8vWwzxYtYpBGQw1gKDAECCpwjQ6YAHQF\nvAqciwCaOhz89NNPZV6rEEKI0iHh62F7f/+dKKs137Fr7Yrol57O3l27Sq8oIYQQZUrC18OSExOp\nVuBY8VtoFK0KkBwbW0oVCSGEKGsSvh5mNJmwFjh2rS1fK2D08yulioQQQpQ1CV8Pq9uoEce0+f8z\nXGvL97heT11Z3yuEEBWGhK+HjRwzhoUGA3bAAdgg9/uM7K9kf28r4vtU4HuNhrvvvtt9RQshhPhX\nJHw9rE2bNtQLC2MF8DJgAmYCiwEj8Gr2deHZ5y4C/QFf4BywWKOhT69e1K5d2+21CyGE+GdknW85\n8PXXXzPz4YfZYjZzLSO3sUBHk4nPVq2iR48eZVWeEEKIUiYt33Lgrrvuot0ttzDcZMJSwuckAoNN\nJkY+/rgErxBCVDASvuWARqPhw88/p8agQfTy9WUrxc94dgJrgC4mEz3GjOGlGTPcV6gQQohSId3O\n5YhSivlz5/LOa69hTE1lXHo6bQE/XBOrtms0zDOZ8KtViykvvsg9997r4YqFEEL8ExK+5ZDT6eS3\n335jwezZ/HXyJOcvXCDA35/ON93E2P/8h06dOqHRXOuCJCGEEOWFhG8FMHXqVIKCgpg2bZqnSxFC\nCFEKZMy3AvD39yctLc3TZQghhCglEr4VQEBAAKmpqZ4uQwghRCmR8K0ApOUrhBCVi4RvBSAtXyGE\nqFwkfCsAafkKIUTlIuFbAUjLVwghKhcJ3wpAWr5CCFG5SPhWABK+QghRuUj4VgDS7SyEEJWL7HBV\nAdjtdvR6PXa7XbaVFEKISkBavhWAt7c3er0ei6WkNxwUQghRnkn4VhAy7iuEEJWHhG8FIeO+QghR\neUj4VhDS8hVCiMpDwreCCAgIkPAVQohKQsK3gvD395duZyGEqCQkfCsIafkKIUTlIeFbQUjLVwgh\nKg8J3wpCJlwJIUTlIeFbQchSIyGEqDwkfCsIafkKIUTlIeFbQciEKyGEqDwkfCsImXAlhBCVh4Rv\nBSEtXyGEqDwkfCsIafkKIUTlIeFbQUjLVwghKg8J3wpCWr5CCFF5SPhWENLyFUKIykOjlFKeLkJc\nnd1ux2AwkJWVhUaj8XQ5Qggh/gVp+VYQ3t7e+Pj4YLVaPV2KEEKIf0nCt5y7fPkyr770Eh3Dw9HZ\nbDSrV4/2jRoxfcoUzp496+nyhBBC/APS7VxOnTlzhmcmTODXtWsZBtxns1EX0ACXgW98fPhCq6Vr\n58689t57tGjRwrMFCyGEKDEJ33Jo79693NKnD2NTU5ngdFK1mOvMwCfAy76+fL1sGX369HFjlUII\nIf4pCd9y5tSpU3Tv0IH3U1IYVsLnbAKGmUys2riRyMjIsixPCCFEKZAx33Jm9LBhTE9LIwboABiA\nBwpcsx5oCvgCvYEGwIcWC3fdeisOh8Ot9QohhLh2Er7lyL59+zh38iTjnE7qAP8HPFjgmnjgDuBV\nIAlXQI8AbgcCzWZWr17tzpKFEEL8AxK+5ci8d95hbEYG3sBQYAgQVOCaH4EWuALYB3gBOACcAMan\npTH3jTfcV7AQQoh/RMK3nHA6nXz1zTeMKdBtXHBA/k+gdZ6fTUAj4DCuFvDv27eTkJBQlqUKIYT4\nlyR8y4nU1FS0QK0CxwvuZWUGAgocCwDScQVxsF7P5cuXy6ZIIYQQpULCt5zIyMjAoC38n6Ngy9cP\nKHh7hRTAP/t7g0aDzWYr/QKFEEKUGgnfcqJKlSokZ2YWCtuCLd/muMZ4c5iB09nHAZIcDqpWLW5l\nsBBCiPJAwrecMBgMNAsNZX32zw7ABtizv8/I/joU1/juj9nnXwTaAE1wjQdnentTr149N1cvhBDi\nWkj4liPjpkxhrq8vAC/jGsOdCSwGjLiWF1UHfgCeBQKB3cCS7OfP0+t5eNw4dDqdmysXQghxLWSH\nq3IkPT2dkBo12G+1EnKNz00F6hsMHDx5krp165ZFeUIIIUqJtHzLET8/PyZNmcKdvr5YruF5dmCE\nXs+IESMkeIUQogKQ8C1npr/wAk0HDWKgyURiCa63AHfo9Wx3OOg3ZEhZlyeEEKIUSPiWMxqNhk++\n/prIBx+khdHIi97eXCziugTgLa2WFr6+BAwezMqNGxk3bhzfffedu0sWQghxjWTMtxw7dOgQ8955\nhyVLltBRp6O2w4FGKWK9vdmSmcmQW25h3KRJdOzYEY1Gw8GDBxkwYAAzZszg/vvv93T5QgghiiHh\nWwGkpaWxfv16EhIScDqdBAYG0qNHD6pXr17o2mPHjhEVFcVzzz3H2LFjPVCtEEKIq5HwrYROnz5N\n3759efLJJ5k4caKnyxFCCFGAt6cLEKWvYcOGbNq0iT59+mCxWJg+fbqnSxJCCJGHhG8lFRISwubN\nm+nbty8Wi4WXX34ZjabgZpVCCCE8QbqdK7m4uDj69etHr169mDVrlgSwEEKUAxK+14GkpCQGDhxI\n27ZtmTNnDtoi7p4khBDCfSR8rxNpaWkMHjyY+vXrs3DhQry9ZcRBCCE8RcL3OmKxWLjtttuoVq0a\nixcvlhswCCGEh0j/43XEZDLx888/Y7VaueOOO7DZbJ4uSQghrksSvtcZg8HADz/8gMFg4NZbb8Vi\nuZZbOAghhCgNEr7XIZ1Ox1dffUVwcDADBw4kLS3N0yUJIcR1RcL3OuXt7c2iRYuIiIggKiqKpKQk\nT5ckhBDXDQnf65hWq2Xu3Ll06dKF3r17ExcX5+mShBDiuiDhe53TaDTMmjWLQYMG0bNnTy5duuTp\nkoQQotKTxZ4CjUbDK6+8gslkokePHqxbt46QkBBPlyWEEJWWhK/INX369HwB3LBhQ0+XJIQQlZKE\nr8hn4sSJGI1Gevbsydq1a2natKmnSxJCiEpHwlcUMnbsWIxGI7179+aXX36hdevWni5JCCEqFQlf\nUaT7778fo9FI//79Wb58OZGRkZ4uSQghKg0JX1Gs4cOHYzAYGDRoED/++CPdunXzdElCCFEpyI0V\nxFWtXbuWe+65hyVLltCnTx9PlyOEEBWerPMVVxUVFcUPP/zA3XffzcqVKz1djhBCVHgSvqJEbrrp\nJpYvX86DDz7IDz/84OlyhBCiQpMxX1FiN954I6tXr2bgwIHYbDbuvfdeT5ckhBAVkoSvuCZt2rRh\n/fr19OvXD6vVykMPPeTpkoQQosKR8BXXLCIigg0bNtC3b18sFgsTJkzwdElCCFGhSPiKf6Rx48Zs\n3ryZPn36YLVamTZtmqdLEkKICkPCV/xjoaGhbNq0KbcF/MILL6DRaDxdlhBClHuyzlf8a7GxsURF\nRREVFcWbb74pASyEEFch4StKRWJiIgMGDCAyMpL3338frVZWsQkhRHEkfEWpSU1NZdCgQTRq1IgF\nCxbg5eXl6ZKEEKJckvAVpcpsNjNkyBCqV6/OF198gU6n83RJQghR7kjfoChVvr6+rFixgvT0dIYP\nH05GRoanSxJCiHJHwleUOoPBwI8//oi3tzdDhgzBYrF4uiQhhChXJHxFmfDx8WHJkiVUr16dQYMG\nkZaW5umShBCi3JDwFWXG29ubzz77jEaNGtG/f3+Sk5M9XZIQQpQLEr6iTHl5efHRRx8RGRlJnz59\niI+P93RJQgjhcRK+osxpNBpmz55Nv3796NmzJzExMZ4uSQghPEq2lxRuodFoeO211/D19eWmm25i\n/fr11KtXz9NlCSGER0j4CrfRaDQ899xzGI1GevTowbp16wgLC8t3zalTp1gwdy5H9+4lNTUVPz8/\nGjZvzpjx42nZsqWHKhdCiNIlm2wIj5g7dy4zZsxg7dq1hIeHs27dOt7673/Zu3cvox0OumRl4Q+Y\ngT1eXizw8aFheDj/ef55hg4d6unyhRDiX5HwFR7z6aefMn36dO65806++fhjXrNYGA4Yirg2C1gG\nTDeZGPrww7z+9tuyf7QQosKS8BUeNeq++9j45ZdsBeqW4PoEYJDJRK9HH+X1WbPKuDohhCgb0nQQ\nHrNr1y7WLl3KZgoH71GgN1AVaAz8lH08CFhhsfD9hx+yatUq9xUrhBClSMJXeMz7M2cyyWYjtMBx\nOzAEuBVIAj4C7gNOZp+vDrxgNvPuK6+4rVYhhChN0u0sPCI+Pp7G9epxymYjqMC5w0BnIO+GlP2B\nG4GXsn/OAEKMRrYcOEDjxo3LvmAhhChF0vIVHvHVV19xq0ZTKHiL48QVyjn0wGi7nUUffVT6xQkh\nRBmT8BUecebECVpZrUWeCwdqAG/imuW8BtgMFLy6VVYWZ44dK8syhRCiTEj4Co8wp6RgKuacDtcE\nq5VAMPAOcCeFJ2X5Ama5W5IQogKSHa6ERwQEBZF6hfMtgY15fu4CPFDgmlTg0JEjTJkyhaZNm+Y+\ngoJK2pldfl28eJFff/2V+Ph4lFIEBQXRr18/QkJCPF2aEKIUSPgKj2jRti3f+fkxLT29yPOHcC0x\ncgJzgcvA6ALXrNFoiE5M5IsvviAgIAC73U5cXBx6vZ6IiIh8gdy0aVNCQ0Px8vIqy7f1ryil2Lhx\nI3PeeIP1GzZws7c3tTMyAPjTx4dpTifdunRh/NSpREVFySYjQlRgMttZeITFYiGkRg12mc00KOL8\nVGABrjHfm4D3gby7QCcBIV5ebN+/H5vNxrZt29i6dStbt24lIyODpk2bcsMNN+Dt7U1cXBwHDhwg\nKzUVf5OJgKpVqd2gAbfdcQfdu3enSZMm+Pr6lv2bvoKMjAzG3HMPu1avZoLZzEggoMA1ZuAr4D1f\nXxp17cqXS5diMhXXeS+EKM8kfIXHPPX443h9+CFv2u3X/NxZwPOA02Bg1KhRTJs2jQYNGqCU4ty5\nc2zbto2VK1ey4ZdfSExMpI9Wy81OJwG4Jm4d8PLiC6UwGo0kZmYSHBycr5XcrFkzmjZtSs2aNdFo\nNKX8zvOz2+0MiYpCv2MHi63WYsfCc2QAYwwGolu25Nfff0ev15dpfUKI0ifhKzzC6XTyzDPPMOeN\nN1iDa0yTAEjwAAAgAElEQVS3pP4EehuNdOnfn5UrVwKg0+kYNGgQTz/9NO3atWPVqlWMGj6c8RkZ\nPOJwUKeI17EAS4CXDAYG3nMPN992GydOnODYsWO5j6ysrELd182aNSMsLAydTvevPweAp8aP5+hn\nn/GzxUJJX9EJ3Gk0UnXoUBZ8+WWp1CGEcB8JX+F20dHRjBo1ytXVOmYM08aP50erla4leO4h4Gaj\nkdfmzWPkqFGcOnWKhx9+mB07dqDVavHx8SEkJIToY8dYnpFBpxK8ZhyurSyz6tdn9NixdO3alQ4d\nOmA0GomPj+f48eMcPXo0XyhfuHCBBg0aFArmpk2bUqVKlRJ/FjmbjZy02aie53gmMA5YDyQCDYHX\ngQF5rkkHQg0G9p84IfdGFqKCkfAVbvXTTz/x6KOP8thjj/HMM8/g7e3N6tWruXvIEO7OyGACrnW+\nBZ0F5gALfHyYs2gRd99zT77z69ev55FHHiEuLg5HWhqrgW4FXiMRGAOsxbVF5evA3XnOddTrCe/T\nh9jYWI4cOUKrVq3o2rUrXbp0oWvXrtSsWTP3tWw2G6dOncoN45xwPn78OAEBAUWGct26dQtNknpz\n5kz+fPFFPi2w5tmCa53zA0AIrmVXd+P65SPvdpwT9HqqTJzIyzNmXPFzF0KULxK+wi3MZjNPPfUU\n69atY/HixXTu3Dn3XEpKCk2aNOHmqCh+Wb6cFkrROT2dAKVIB/b6+rLebObmW27h9x07OHnyJAEB\nBacjucZOh95yCzV+/ZWFRdSQE7QLgX3AIGAbEJF9fDnwakQEf/z5J2azmZ07d+ZO5Nq+fTtBQUF0\n7do1N5AjIiIKhanT6SQ6OjpfKzknnFNTUwkPD88N4/DwcKaMH893CQl0LMFn2Bp4Ach7N+OjQO8q\nVTgXF1dq3eBCiLIn4SvK3L59+7j77rvp2LEjH3zwQaHgnDBhAlarlY8//piMjAyWLVvG0SNHSE1M\nxL9aNcIaNuTbb7/l9ttvZ8uWLVSrVo1ZRdxOMCsri9AaNViTnEyLAufMQCCu8eJG2cdGAbVxtYAB\nHECYycSPmzfTvn37fM93Op0cOXKErVu35gZyQkICnTt3zg3kyMjIK86aTklJ4fjx47mBfOjQIdat\nWIEFuNqUrstAfeAA0KTAuTomE38cOyZdz0JUIBK+4qoyMzNZunQp637+mcTYWLy8vLihTh1uv+ce\nevfuXexsYKfTydtvv80bb7zBu+++y913313omn379jFgwACOHDlyxc0xfvjhBz744AO++eYbmjdv\nzsaNG2nevHm+a5YtW8ZbI0fyexG7Xu3D1Q1tznPsbVwbefyc59hrXl6cu/de5n/2WbG15IiJickN\n4m3btnHw4EEiIiK48cYbad++PS1atCAgIACz2YzZbMZiseT7Gh0dzYczZnA5K+uKf04WMBDXuud5\nRZyP8Pfn223baNGi4K8cQojySjbZEMWKi4vjvVmzWDB/Ps2cToalpVEDVwvxPDDx22/JqlKFcZMm\nMXbcOAwGQ+5zL168yKhRo7BarezcuZP69esXen2n08m4ceN47bXXrror1eDBg3nkkUew2Wz897//\n5YknnmD9+vX5gv/EiRN0tNmKfH46hdfN+pP/zkkAkQ4H327axLx584oMzKKO5XzVaDTs2bOHvXv3\notVqcTgcaLVa9Ho9BoMBvV6PVqvFbreTkZGB1WrF6yrB6wRGAgbgg2KusSol632FqGAkfEWRjh07\nxs09e9I3KYn1mZm546J5TUpPZ0t6Oq89+yzff/EFy9atIzAwkGXLljF27FjGjRvHs88+i7d30X/N\nFi5ciFar5YEHCm4cWZher2fYsGF89dVXTJ48mY8//phvv/2WESNG5F6TnpaGfzFh5geFtrNMwRXA\nefkDiYmJHDhwAF9fX0wmE9WqVaNOnTro9Xrsdjs2mw2LxUJ6ejppaWkkJyeTlJREfHw8cXFxxMTE\noNfrqVWrVu7MZ7PZTFxcHOnp6TRr1ox27dqhlGLxJ59wSSmCi6hZ4ZogFgesAoramysZSMjM5IYb\nbrjqZ1jQ/v37+XD2bA7s2kVKaiomo5HQsDAeeOIJBgwYUK53AxOiopNuZ1HI33//Tbf27XklOZkH\nSvDXwwlM9vFhU4MGtO3Wjd9++43FixfTpUvxq3fj4+Np3rw5q1evpk2bNiWqa8uWLYwdO5bDhw+z\nZcsW7rrrLj755BMuXLjAyZMnWblyJb0OH+a9Ip5b1JjvSKAe8Fqe69YCIzQajMHB+Pj4oNFoyMjI\nID09HYvFQo0aNahVqxbBwcFFfs15FGyJOhwONm/ezJw5c/jtt99ISkpCo9GgV4qpwItF1PworjHe\ndbhuIlGUd4Af27Vj3bZtJd5sY8WKFbw+fTrnT59mbEYGPR0OAnDNsD4MfOjvT5zBwOOTJzNx0iQJ\nYSHKgISvyEcpRfumTRl96hQTnM585+7Dte7UjGupzhjg2ZznAfdoNOwPCeGPAweuutb1oYcews/P\nj9mzZ1/xuoSEBE6ePMnJkyc5fvw4s2fPpl69ekRHR5ORkUH16tXp27cvjRs3JiUlha1z57LNYiny\nte7GNbFpAbAXGAxsB5rlueZl4B2jkUytlubNm1O/fn1sNhtxcXFER0cTExNDzZo1qV+/PvXr16dB\ngwb5vq9bt25uS//UqVN8/vnnLF26lGPHjuF0OqlTpw79+vVj7NixdOjQgV27dnHbTTdxLiMjXzfU\nWaABru7mvNH3Ef+bta2ARno91Vq04MKFC4wZM4axY8de8eYLM195hbmvv867FguDKb7raxcw1WSi\nSteufL1sGUajsdjXFEJcO+l2Fvls3LiRzIsXeaJA8AI8gyu4DMBxoAfQHtfGDxpgrlKEXb5MRvbN\nAIqzfft2fvnlF44cOQJAampqbsCePHmSEydO5H5vt9tp3Lhx7qNnz54opZg5cybR0dFMmTIFnU7H\niRMnuHjxIodsNg7huitSQXOBB3HdK7g6MJ/8wesA3gNqhoYyceJENm/ezOrVqxkzZgzz5s2jdu3a\nZGVlER0dzd9//82ZM2c4c+YMGzdu5O+//+b06dNcvnwZb29v7HY7DocDnU5Ho0aNmDx5Mvfffz9N\nmjTJ15KsWbMmTp2OtzIyeDpPLaG4ehSu5GONhoCQEHbt2sXx48eZP38+bdu2pXv37owfP56+ffvm\nWwr17ttv8+nrr7PNYilyx6+8IoHVFgsjf/+de4cO5buVK6UFLEQpkpavyGf4zTfT69dfGX+VvxbH\ngT64Zgq3y3P8IYOBhs8+yzPPPZfveovFwunTpzl69CgTJ06kSZMmOBwOTp48SUpKCg0aNCA4OJjA\nwED8/PxyAyw1NZVLly4RExNDTExMbjdw586dCQ4OJiYmhkuXLjFt2jSOHj3KJx99xB1mMwv+wV/r\n5cBIrRavqlVJTk7mhhtuYPTo0SQmJvL9998zdOhQJk+eTLNmrsjOyMhg69atLF68mDVr1hATEwNA\naGgokZGRtGjRApvNxtmzZzlx4gQnjh1DpaWh8/ZG7+OD1seHy2Yzffv2ZffmzbxhNjOqhHX/CDyo\n0zH9lVcYNmwYDRo0QKPRYDab+eqrr5gzZw4Wi4Vx48YxevRooqOj6dOxIzusVuoXeK2ewA7+95t4\nXVzrh8G1j3SUycSdM2bw+BNPXPNnKoQomoSvyBUfH0+junU5l5FRaGZwjvHAZ7j+Uf4A17hkXnuA\nW6tU4T/PPceRI0c4evQop0+fJikpicDAQJxOJxaLhdDQUDIyMkhJSSElJYXq1asXO4aa93s/Pz86\nduzIXXfdRXxMDJfPn+fnZcvI0moJCQvj+eefZ9zo0Sw1mwvtcHUlCUAXk4npc+dy7tw53n77bfR6\nPSkpKQD07duXwMBAVq5cSfXq1bHb7Zw9exaNRoO/vz89e/Zk1KhRREVF5euiPX78OK899xzLli9n\niFbLUKuVIMAO/A3M8/bmpEaDb7VqJMXG8jAwDdf646LEAu9ptSzw9eXehx/m3LlzbN26FaVU7k5c\nXbt2pU2bNuzZs4c5c+awatUq6t1wA7f/9RcvOByFXrMXrvHvB4v5M38HHq5Th6Pnz5f5TSaEuF5I\n+Ipce/fuZUyvXuxLvdJt7l1jjZuAYbhm4ebdnckB+AAaLy+8vLwICgqiTp06hIaGUqVKFZYsWcL0\n6dNp165dbqjecMMNJerSzMjI4PPPPuP1Z58lKzmZB+x2auPq8r4EfOvriyYoiF6DB/PNwoX8nJFB\n56u8JrhmEw82mbjpoYd48913AdeGGO+99x5vvvkmWq0Ws9mMw+FAKYVGo0Gn0xEcHMzTTz/NI488\nUuS9dTds2MCIW2/lPxYLjzidFLeY6hDwf15ebDcYiBo4kJUrVtAbuN9mIzj7/V3GtTPXr4C/nx/h\nbdoQERFB/fr1CQ0NxWAwcOnSJQ4fPsy2bds4ffo07dq1o2vXroSFhfHU+PEcz/68CuqFazx/TDH1\nKaCVnx/vLltG7969S/CJCiGuRsJX5Nq8eTPTb72VLdmtvasZh2v8950Cx/WAqWpV9Ho9Op0OHx8f\n9Ho9ly5dwmAwEBoaire3N97e3uh0utzvr/Sw2+2s+/ln6iYm8pzdThRQMO4Urk0zXtfp2OF04lCK\n/wBjnU7qFlG/BfgaeNlgoO/w4Tz+1FPY7Xb27t3Lhg0b2LBhA4mJiTgcDpxOJ3q9Hn9/f9q2bcuu\nXbsICQnBbDaj1WqZMmUKI0eOzF3rvGPHDgb37s23Fgu9SvBZKuAZnY41DRqwYsMGflq6lGVffEFC\nQgIJCQkYjUYemTSJ4SNGkJaWxpkzZ/KNO+f8nJaWRmhoKHXr1sVgMGCz2Th27BjNoqNZW8yf3QvX\nLHCFa1/tV3GN5+f1PrB72DA+++67ErwbIcTVSPiKXPv27WN0z54cuErLN8dDQC3glTzH7LjCt1GT\nJuj1ejIzM0lJSSEhIYGsrCyqVatGlSpV8Pf3z334+fnh6+ubu67WaDTmPvR6PRkZGbw/YwY3x8fz\nntNZKHQLUsB0YIHRSFCNGly4cIEewECHgyq4Qne/RsNiIMDfH68qVcjKyiI5ORmbzYZGo0Eplbs5\nhre3Nw6HA4vFQmZmZpF/Zk53rNFoxM/PD3N8PEucTgYXU+NJXJPChgNf5Kn7fq2WPSEhRN50U+4v\nHidPniQmJoaBAwde9ZcUh8NBSkoKycnJJCYm5q5ZHnH+PG8WU8tOoDmuHouvgceB/UBYnmvWAjPa\ntWP9nj1X+fSFECUhs51Frvr163M2I4NEXGti84rDtczoFlyt3XXAd9lf89oBBHh7c+nSJdLT0wEI\nCAhAq9USFRXF7bffTr169TAajSQnJ+e27BITE0lISODixYu53+cc1zsc3Op08j7/2wP5A+BTXOtS\n7wYW5alBg2u/5gyHg5NhYew6dIivvvySAzt2cDk6mt82baJ7VBTt09PZtWsXutRUbDYbtWvX5rbb\nbmPIkCF07twZHx+fQp9RSkoKs2fPZtasWWg0GsLDw5kwYQKHDh1i0aJF2O12kpKS6KTVMriIGeM5\nHsPVXZ93BFUDzHI6aRgdzWMdO2IwGLDb7Xh7exMdHU3t2rWx2+25j8zMTFJTU3N33srZbctqteY+\n0tLSSIyPL7SZSF55hw3uxxXAq3CFcA4jYC1m9zAhxLWTlq/IZ+Qdd9D2p594qkBwxOMa4z2Aq4XW\nBHgOuLXA828HLkZG8v6cOTRp0oQ9e/bw4osvcujQIbRaLUlJSXh5eaGUok6dOrRq1Yru3bvTrl07\nWrZsSY0aNXJfa+fOnUyaNIl9W7ZwmfwbTSzF1e28GrCSP3xzZAI1NRqCmzZFr9eTlpbGpUuXsFgs\nGI1GHA4Hbdu2pV+/fgwcOJCIiAgCAgJKNKkoJSWFd955h1mzZqGUIjIykrfeeovY2FgeuvNO5qSn\nc1sxz12SXX8EcIr/tXxz3OXjg7r1ViJvvJGkpCT++usvVq1aRdeuXXN308p5OBwO9Hp97mfqdDpz\nw9mZ/d9Qq9UyzuksdnvKggbiuuNT3vBdCczp3JlV27aV8FWEEFci4Svy+eOPP7ivb19OmM1X7d4t\nKBYI1+uZMG0aX3zxBVWrVmXYsGHMmjWL3bt306BBAywWC4cOHWLLli1s3LiRAwcOcPHixdytG318\nfKhXrx5Wq5WUlBQimjSh3b59vFdMd+//ARcoOnwBnsY1O1iffYODwMBA4uPj6d+/P35+frmt7Jyv\nNpuNwMBAAgMDCQoKIigo6Irfe3l58cknnzB//nycTifh4eFE//knMQ5Hkd1KqbjW0G7AtWHGaQqH\n7xZgmMlEZO/emM1mkpKSOHDgADVr1sRsNmO1WrHb7QC549BBQUEEBwdTv359mjRpQvPmzdHr9ezf\nv59vv/2WpL17OU3huyelAH/gGuP1Br4BxuLqdm6U57qJPj74Pvkkr77xRjGftBDiWkj4inyUUtzU\nvj19Dx/mv1fZ9B9cY7wbcAXgezodNbt35/3582nYsCFr167lgQceICkpiVGjRjF27Fjatm1b6DUy\nMjL4888/Wb58OZ988gnR0dGAa/zUkZ7OHvJvhpHXc0A0xYfveaCZlxfvfvght9xyCzVq1KBp06Ys\nXbo033rd5ORkkpOTuXz5MhcuXCA6OppLly4RGxtLfHw8SUlJpKSkkJaWhsViwWazkZWVhSN76U7O\nODG41j0XNzL6JK51tFNwbSlZVMs3GagJaPIE68mTJ7nrrrto0aIFLVu2zJ0tnreVfuHCBdauXcva\ntWtZt24d1apVIyoqiqioKKaOH8/CixcLLb+KB24GjuHaSasZrl2++uS5xgyEGAzsPXaM0NDQYt6Z\nEOJayJivyEej0fD9L7/QuXVrfOPjmeRwFHmv2UvAx7hab3WBEKCl3Y511y66tmpFmzZtiOzdG19f\n39yNKIYMGUJwcDBjx45lxIgRufe+zVlXu3btWiZPnsz48ePR6/Xs2LGDgT170iy7lVdkvVd5P/UA\nX42G77//niVLlpCamsq5c+fo0aMHWVlZWCwWHA5HvglePj4++Pj45M7E9speNpXT6lVK5T5yxl5j\nYmKIi4vDx8cH32Ja6ftxjZvvy/65uN96TYBDq8WeZ4y1efPmPPPMM/luG5iens6mTZtYu3Yta9as\nITY2lj59+hAVFcXrr7+eLygPHzrErBdeoFuBdb7VcU24upKvgG5dukjwClGKpOUrinT+/Hlu7tGD\nJjEx/MdqpSv/C7ovgSdwzdR9DGhV4LkZwPfATI2GrLp1+W3HDoKDg3E4HPz666/Mnz+fbdu2MXjw\nYNLS0ti8eTMTJ07kySefxN//f1ODzp07R9eICM6bzRTnai1fcP1ikFmzJlWrVsVgMHD+/Hlq165N\nWFgYfn5+mEwmDAZD7pKonK95v9fpdKSnpxMdHc2FCxeIiYkhNjaWuLg4YmNjcTqduS3fVrjGxgt6\nF9de2DnvMB3XuugIYHee6+KApiYTCXned//+/Xn00UepWbMmGzZsYO3atezZs4cOHTrQr18/oqKi\naNu2baH10kopvv76ayZOnIgzPZ3ZNhv3XcP/8keBnkYjP61fT+fOJVk1LYQoCWn5iiLVq1ePbQcO\nsGjhQh566y18UlIYlp7OcVxraTcDxd26XQ/cC9yjFK/ExNC1bVu27ttHcHAwgwYNok2bNjz99NN8\n++236HQ6mjVrRkhISL5bD9rtdo4ePUrKVWbYlmS/JSuQkpiIUoqWLVui0Who06YNTz31FOHh4eh0\nOi5dusSePXs4fPgwx48f58yZM1y6dImEhATS0tJylxj5+Pjg5+eXO96bmJhIz549GT9+PB06dMDH\nx4eIBg1ItdkK7RL2CPlvivAWcAbXHtN5bQRaNW2Kw+Hg008/Zc6MGRw+dYp1a9YA4KvT0aNHD9av\nX0/Hjh0pzvnz5xk3bhznzp1j1apVGAwG+nTpgkpPZ2QJAng/cIvJxBsffCDBK0RpU0JchdPpVOvW\nrVMjhg9XgV5e6hQodQ2Pl7y9VbvwcHX+/Hk1ceJEFRgYqKZMmaLi4uJUVlaWWrp0qRowYICqWrWq\n6tmzp+rYsaMyGo3K399fGUCdLOI17aCsoJ4GNRKULftYwesugvLz9lYDBgxQ9erVUzqdTgFKo9Eo\nXBmY+9BqtcrPz0+Fhoaqbt26qXvvvVe9+OKL6ocfflB//fWXcjqdSiml4uLi1LBhw1RERITas2dP\nvs/Kbrer7u3aqfdK8Lm8kF17wePdjEbVqmVL5a/VquYajfoMVAooJygHqLOgXvD2VrWNRtW9bVt1\n5MiRfDU4HA41f/58Vb16dfXSSy+pjIyM3HOHDx9WoTfcoIb4+qo12a9X8M8/DOoxvV4FGo3q22++\nKfu/YEJchyR8RYl1b9NGfVPgH2pfUH55Hl6gnihwjRNUe51O+fn5qQkTJqhLly4ppZSKj49XX375\npbrllltU1apVlY+Pj9JqtUqr1aqaNWuqxx57TD08apSarNMVCoj/gtIUeLxYRJA8D0oPSqfTqapV\nq6qwsDAVGhqq6tSpowYNGqQGDBig2rRpo6pVq6a8vb2Vv7+/MhqNSq/Xq/DwcHXnnXeq2bNnq40b\nN6qEhAS1YsUKVbt2bfXUU08pq9Wa+9lYrVY1f/581ahRIxUREaGaGAzKeY2/pChQf4Ly1WhUbZ1O\nbb/KtZmg5mk0qoa/v9q6datSSqkTJ06oHj16qBtvvFEdPny4yP+OaWlp6sP581WrBg1UY19f9ZDR\nqCZ5e6txBoPq7u+valWpov7v6afVuXPnyv4vlRDXKRnzFSVy6NAhBtx4I2esVnTFXGPGtePVL1Bo\nVu3XwActWzL99ddZvHgxmzZtIjY2FoBatWrRs2dPhg4dSvfu3alatSrLli3jgw8+YN++fWjMZmKc\nTq71jrJ2IESvx2IwcPDgQUJCQjh9+jQzZsxg+/bt3HvvvVSvXp2bb76ZOnXqkJaWxrFjxzhy5Ah7\n9+5l165dnDx5kqSkJHx8fLBld4GHhYXRrVs3unXrRqNGjdi8eTPz5s2jQ4cOTJ06la5du3JT+/b0\n/vNPXirBjPG8n18TQOvlxU6Hg+ASPu8XYLSfH/c/+iiLFi3iueee44knnrjqftlKKf744w8OHTpE\ncnIyvr6+hISE0L9//yI3GBFClB4JX1Eij40ZQ43PP+e/V5h5/BmuZSqnijiXges+umkaDbVr16ZT\np060bt0anU7H2bNn+fvvv4mOjiYuLo7U7B2nlFJ4eXlhdDq5Ryk+vMaan/P25o/27enSrx/r168n\nQKNh95493KwUNTMy0Hp5cUGvZ6XDQZ9evRg/ZQq9evUqtMnG+vXrGT16NGFhYTRt2pTDhw9z7Ngx\nEhMTc68JCAggPDycdu3a0aNHD8LCwrh36FBGx8XxrN1+1bHpZKA3cEyjYb9SNClw/gKuvbS34doG\nchgwG9fyIHDdh3hmQAC/79tHWFgYQohyzpPNblFxtGvYUO24Sjdor2K6fnMet2Z3/+aMt3p5eSl/\nf38VEhKiIiMj1R133KGmTp2qPv30U3Xw4EGVlZWllFIqKSlJNQsJUc96eZWoK9cJaqZWq/y0WrVi\nxQrVv3t31USjUZ+CshRxfQqoORqNauLrq+4fPjx3jNRms6mpU6eqWrVqqZ9++kkppdShQ4fU/fff\nr6pVq6aefPJJtW7dOvX111+rxx57THXq1EnVqFFDaTQapdVqFdnd8p1ALafoMekkULM1GlXfZFK1\nAgPVHcW8p6GgRoPKABUDqiXkG1e2gaphMKhjx4557O+IEKLkpOUrSqRRzZr8EhtL42LOnwUa4tqx\nqbjVoA9rNGxr1owePXpQr149TCZToaU9BR8559LS0nh05EjqX7jANJuNThQ903kP8JbBwKFatXh4\n4kSenzSJYV5ezM/MLLa7PIcFuMdoxNmlC/+dOZMHHniAhg0bMn/+fE6cOMHMmTPZvXs3EyZMYNy4\ncVSrVg1wdd8ePXo0d4OLzZs3U79+fRo2bEhWVhZHjx4l8exZvB0ObgeCcfUEnNJq+VWj4cbISKa8\n8ALjR43iy8uX6VREbeG4lioNyP55Kq7dsvLOlJ7u7Y314Yd5Z+7cq7xTIYSnSfiKEmlerx5LLlyg\nZTHnX8G1gcSGK7zGvRoNuxo1Ijw8HD8/P4xGIzqdjszMTDIzM8nIyCj2kZmZ6bpRQHIyWK3UVIqx\nkHs/38u4Nvy4oNVirFaN6jVrknzxIl1SUvhWqRItSQLIAvp4ebFPq+WuUaMICAhgxYoVpKWlcddd\ndzF48GACsreq3LNnD9u3b2fLli14e3vTp08fBgwYQFRUFEFBhe/eu2vXLn7++WcOHTzIhejo3GVM\nycnJOBwOauPqXi6q1gm4uqY/BBJxhfArwJA815wA+gQGcj4hoYTvVgjhKRK+okT6derEozt2cHsx\n55vguo3f6Cu8RhejkbDbb8fLy4u//vqL06dPk5SURP369Qn7//buPraq8gDA+NPbFui9bay7vSRg\ntkFbI1lClpFSwhIm6EpMnMIYiUZGAtrhJKgTP7a5P8ZmNAGXsI/gBzFT1Bg6s81oxnQjbjpSRQGH\nLimoIMXZht3KcRRu6ff+OLSW9gLFwctgzy85SXPvuembpsnTc87b962spKqqiqqqqsGvJ0+eTEnJ\nyGlWfX19bN68mecaGmhraYk3uS8s5FB3N2+++SbTpk1j1qxZrFu9mn92deWdqLWReHnHD4kniT3B\np5PEDgCTCwooTKUoLi6mqqqK8vJystks2WyWKIro7OykpKSEMWPGUFhYSE9Pz+AfCsCIK/cTXdEP\nHC0tLfRv3UrjsBWoBhwEvg68Q7wwxxLg18PO6QAuLiri6GlM8pJ0bhhfjcqGDRtoWLGCTce2CRyq\nEZgLI3YeGmoXcHlZGfuzWcaOHTv4ei6X44MPPmDPnj2Dx0CYm5ubSafTI6I8cKTT6RGTo44cOUJD\nQ31CoRgAAAYRSURBVAOr7r2XBQcO8PM8Y/kz8B3gN8Tb6bUC/cRX0QMWJBKMXbiQaTU1bN68mcbG\nRqZOnUpdXR1z586ltraW4uL8N7IHlpwceuXe3t7OgQMHBlfFamtrG9zQIYoi9u3bR8V777ElzzaE\n/cfGuQC4C2gHbiS+Fb16yHldQCqRoKunZ1Q7M0k6d4yvRqWjo4MvjB/P64cPUzXsve8SX3VtOMnn\nvzdmDMnbbuOBB0+0pftIvb29fPTRRyOiPHD09vbmDfMll1zC12pr844V4KvE8V16ku+9BfhGYSE3\nLFtGXV0dc+bMoby8HIivvKMooq2tjba2tsGYDj2Gv5bL5Uin02QyGSoqKo47MpkMURTx+zVr+Hsu\nN2IsWeKNFv7Np0tTPke8o9M7Q85rAb5cWkq2vX3UP2NJ54bx1ah9/4472P/wwzzT2TnqZ6gQP4uc\nOW4c25uamDRp0hkbTxRFecO8e/duelpb+Veez/QSb1zwU+Ax4CgwH3gQGDfkvH5gbEEB316yhE8+\n+eS4kEZRRFlZ2YiA5ovqwNcXXXTRSa9GOzo6+Hwmw9YjR0b8wdBPvHnF7cCdxFe+S4nvMjw95Lxf\nFhTQePXVbHzhhdP5MUo6B4yvRi2XyzGntpbL332X1d3dowrwh8CcZJIfrF1L/bJlZ3uIAGzfvp36\nK67grUOHRrzXQhyyGuAF4sXN5wGziScwDfW5oiJ++MADVFZWHhfVdDp93DrUZ8pdt95K4tFHWZPn\nme1W4lvO/zg25iuBXwGZY+/3A18qLWX9pk3MmjXrjI9N0pllfHVastksV8+eTfXevdx/9CiTT3Be\nL7AJWJ5MsnLVKu64++5gY2xqauKbM2awK8/t1whIE98iX3zstd8Rh3fHsHNLi4tp/fjj43ZaOpve\nf/99Zk6dyp48mzKcyovA3ZMm8fbevT7vlc4DiXM9AJ1fMpkMf3njDSbW1zM9leKa0lKeJ55QtY94\nJ5zViQTVqRT3TZnCww0NQcMLMGHCBFq7usj35PNi4ivfU9kPFBUVUVpaemYHdxLV1dVct2gRC5NJ\n8u8InN8e4MZkkjUPPWR4pfOEV776zHK5HBs3buSpdetoaW2lo7OT8rIyambO5JaVK5k+ffo5G9vC\nq67iypde4pY87/2YeD3kPxDfwr2WeGnHnww550dFRRy+6SZ+8cjwDf/Orp6eHq675hoOvfoqz+Zy\nlJ/i/O3AvJISVq1dS/3NN4cYoqQzwPjqgvTyyy9z+7x5vH348Ihn0z3Ek5eeIZ5kdR2whnjNZIhX\nn/piSQl/3bGDKVOmBBvzgN7eXlYuX87TTz7J4r4+bunq4rIh7/cR/7vUQ6kUW4D1TzzBtxYuDD5O\nSZ+d8dUFqb+/n6mVldzZ3MzS0/wVv6+oiL9Nn86fGhvP0uhGp7m5mfXr1vHYI49QAVQkEnQD+7u7\nyUyYwPJ77uGGRYtIpU7039WS/lcZX12wmpqamD1jBo+2tzN/lJ9ZX1DA/ek0r+3cycSJE0/9gQA6\nOzvZtWsXURRRXFzM+PHjqa6u9vmudB4zvrqgbdu2jWvr6qg/dIgVfX2MP8F5+4GfFRezqaKCP77y\nCpdeeqItJCTpv+dsZ13QampqaHzrLVquv57Lxo1jUTLJ88DrwGvAb4H5qRRfSSZh6VJe27nT8Eo6\n67zy1f+NKIrY8PjjvPjssxw8eJBEIkG6ooL5ixf77FRSUMZXkqTAvO0sSVJgxleSpMCMryRJgRlf\nSZICM76SJAVmfCVJCsz4SpIUmPGVJCkw4ytJUmDGV5KkwIyvJEmBGV9JkgIzvpIkBWZ8JUkKzPhK\nkhSY8ZUkKTDjK0lSYMZXkqTAjK8kSYEZX0mSAjO+kiQFZnwlSQrM+EqSFJjxlSQpMOMrSVJgxleS\npMCMryRJgRlfSZICM76SJAVmfCVJCsz4SpIUmPGVJCkw4ytJUmDGV5KkwIyvJEmBGV9JkgIzvpIk\nBWZ8JUkKzPhKkhSY8ZUkKTDjK0lSYMZXkqTAjK8kSYEZX0mSAjO+kiQFZnwlSQrM+EqSFJjxlSQp\nMOMrSVJgxleSpMCMryRJgRlfSZIC+w/TOP61HRIPagAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107100dd0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(ladder_graph(10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFBCAYAAAA2bKVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYG0kHXfiiwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10712bb90>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(hypercube_graph(3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFBCAYAAAA2bKVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w+8mZOhenKXUwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1072047d0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.draw(grid_graph(dim=[2,3]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAFBCAYAAAA2bKVrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3/IAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106f3b250>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}